diff --git a/.gitignore b/.gitignore index b51654f..54f7533 100644 --- a/.gitignore +++ b/.gitignore @@ -123,3 +123,5 @@ dmypy.json .pyre/ *~ +*.gif +.DS_Store diff --git a/01_Week1/00_ReadingPython/README.md b/01_Week1/00_ReadingPython/README.md new file mode 100644 index 0000000..55d4a5d --- /dev/null +++ b/01_Week1/00_ReadingPython/README.md @@ -0,0 +1,14 @@ +# Welcome to the First Day of the Python for STEM Beginner Week! + +# Day 1 Schedule - Introduction to Python +* 8:30 Introductions, Overview of the week +* 8:50 Getting repl.it set up (accessing the resources) +* 9:30 Understanding Python Vocabulary +* 10:45 Reading Python Programs +* 11:45 Wrap up + +# Below are links to the resources for the day + +## [Introductory Presentation](https://docs.google.com/presentation/d/1Ax2IB1lcuJM9RdKISdMi-7wqaAQw2k2Mx8cbfvd9AcU/edit?usp=sharing) +## [Vocabulary Breakout Groups Document](https://docs.google.com/presentation/d/1WZp9upfId1k8lAF4t4pGZvaM-YnZtBShKLLWFexOTwQ/edit?usp=sharing) +## See Presentation for additional activities \ No newline at end of file diff --git a/01_Week1/01_Documentation/01_A_Documentation_Activity.ipynb b/01_Week1/01_Documentation/01_A_Documentation_Activity.ipynb new file mode 100644 index 0000000..820f90a --- /dev/null +++ b/01_Week1/01_Documentation/01_A_Documentation_Activity.ipynb @@ -0,0 +1,104 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here are some examples where directly Googling the header of each problem will give you a solution. For example, for the first problem you should Google \"python print every item in a dictionary stack overflow\". " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Print every item in a dictionary" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "myDictionary = {'apples':2,'oranges':3,'bananas':6,'limes':2}\n", + "# print every key,value pair in this dictionary\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Combine two lists" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "myList1 = [1,2,3,4]\n", + "myList2 = [5,6,7,8]\n", + "# combine these two lists here\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Create a scatter plot with two lists " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "myList1 = [1,2,3,4]\n", + "myList2 = [5,6,7,8]\n", + "\n", + "# Create a scatter plot here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Multiply every item in a list by 2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "myList1 = [1,2,3,4]\n", + "\n", + "# multiply every item in a list by 2 here" + ] + } + ], + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/01_Week1/01_Documentation/README.md b/01_Week1/01_Documentation/README.md new file mode 100644 index 0000000..6fb4410 --- /dev/null +++ b/01_Week1/01_Documentation/README.md @@ -0,0 +1,14 @@ +# Welcome to Day 2 of Python for STEM! + +## Day 2 - Documentation and Understanding Errors +* 8:30 Introducing documentation/Stack Overflow +* 9:30 Identifying and fixing errors +* 10:30 Writing a working program +* 11:45 Wrap up + +This module will introduce you to the world of Python documentation and other online resources for getting quick solutions to errors and code questions. There is a powerpoint presentation below that will go through some necessary information, then there will be a series of examples to get used to this process in the `01_A_Documentation_Activity.ipynb` notebook. + +## [Python Documentation Presentation](https://docs.google.com/presentation/d/1-0mnmeNHwSPoogBafsuYW4VzN-3L2RffOeSJ-6J4lA0/edit?usp=sharing) + +## 01_A_Documentation_Activity +* Some live examples of using Stack Overflow to solve some programming questions. \ No newline at end of file diff --git a/01_Week1/02_FixMe/02_A_FixMe.ipynb b/01_Week1/02_FixMe/02_A_FixMe.ipynb new file mode 100644 index 0000000..1aa630d --- /dev/null +++ b/01_Week1/02_FixMe/02_A_FixMe.ipynb @@ -0,0 +1,548 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "#" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fixing Broken Code\n", + "\n", + "The following code snipets don't work. Follow along with the [flow chart](https://pythonforbiologists.com/29-common-beginner-errors-on-one-page) and use google and stackoverflow to get these pieces of code working properly." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello\n", + "world\n", + "Wow\n" + ] + } + ], + "source": [ + "# This cell should print Hello on one line, and world on the next\n", + "print(\"Hello\")\n", + "print(\"world\")\n", + "print(\"Wow\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### This cell should do nothing, just make sure there is no error. You can call the function `Hello` if you want, and then it should print Hello World." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello World\n" + ] + } + ], + "source": [ + "def Hello():\n", + " print(\"Hello World\")\n", + " \n", + "Hello()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### This cell should print each of the elements of the list `science`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "science = ['biology', 'physics', 'chemistry']\n", + "for i in range(science):\n", + " print(science[i])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### `science` is the list defined in the cell above, and this cell should just print the 3rd element (index 2) of the list" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(science(2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Here we want to print one letter in the string called `spam`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "spam = 'My favorite food is Spam!'\n", + "print(spam[40])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### This cell should print: `My favorite number is 4!`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"My favorite number is \" + 4 + \"!\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### This cell should print: `My name is Monty Python. How are you?`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "myName = 'Monty Python'\n", + "\n", + "print('My name is ' + myName + . How are you?')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### This cell should create a list of the three school subjects below. You can call it whatever you want..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class = ['biology', 'physics', 'chemistry']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### This cell should print `Wow you got the answer!` when you give the `answer` function an argument of `42`, and `Wrong Answer` otherwise." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def answer(MyAnswer):\n", + " if MyAnswer = 42:\n", + " print(\"Wow you got the answer!\")\n", + " else:\n", + " print(\"Wrong Answer\")\n", + " \n", + "answer(42)\n", + "answer()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### This cell should print a list that has elements [`Bye`,`Bye`,`Bye`]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def goodBye():\n", + " for i in range(0,3):\n", + " output.append(\"Bye\")\n", + " return output\n", + " \n", + "goodBye()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### This cell should print a random number between 0 and 100000" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def my_random_number():\n", + " return np.random.randint(0, 1E5)\n", + "\n", + "my_random_number()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### This cell should print multiples of 10 between 0 and 100" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for i in range(0,100):\n", + " if i % 10 == 0:\n", + "print(i)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### This cell should print each of the subjects in the list `my_class` separated by a dash (e.g. `biology-physics`, `physics-chemistry`,...)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "my_class = ['biology', 'physics', 'chemistry', 'python']\n", + "\n", + "for i in range(len(my_class)):\n", + " print(my_class[i]+\"-\"+my_class[i+1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Handling Errors\n", + "\n", + "Sometime we will run into errors that we need to check for and handle.\n", + "\n", + "Try fixing these errors with if statements or [try except](https://docs.python.org/3/tutorial/errors.html) statements." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### This cell should simply not error, meaning you need to handle the case of dividing by zero. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def super_complex_calculation(number):\n", + " import numpy as np\n", + " return 1/np.random.randint(0, 10)\n", + "\n", + "for number in range(0,20):\n", + " x = super_complex_calculation(number)\n", + " print(x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### This is just the syntax for a `try:` `except:` statement" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " pass\n", + "except:\n", + " pass" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Library Errors\n", + "\n", + "Errors from libraries can be long and complicatied.\n", + "\n", + "Tips for reading long errors:\n", + "\n", + "1) The top lines will show where in your code there is a problem\n", + "\n", + "2) At the bottom will be the error type and a small description\n", + "\n", + "3) Everything inbetween is the libraries code. Unless you wrote the library don't worry too much about it.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ===============================================================\n", + "\n", + "1) The top lines will show where in your code there is a problem:\n", + "\n", + "``` python\n", + "---------------------------------------------------------------------------\n", + "NameError Traceback (most recent call last)\n", + " in \n", + " 4 return x+y\n", + " 5 \n", + "----> 6 z = add()\n", + "```\n", + "2) At the bottom will be the error type and a small description\n", + "\n", + "```python\n", + "NameError: name 'np' is not defined\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### This cell will print the sum of the `x` and `y` arrays" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def add():\n", + " x = np.array([1, 2, 3])\n", + " y = np.array([4, 5, 6])\n", + " return x+y\n", + "\n", + "z = add()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ===============================================================\n", + "\n", + "1) The top lines will show where in your code there is a problem:\n", + "\n", + "``` python\n", + "ValueError Traceback (most recent call last)\n", + " in \n", + " 4 return y-x\n", + " 5 \n", + "----> 6 subtract()\n", + "```\n", + "2) At the bottom will be the error type and a small description\n", + "\n", + "```python\n", + "ValueError: operands could not be broadcast together with shapes (4,) (3,) \n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### This cell will print the difference of the `y` and `x` arrays" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def subtract():\n", + " x = np.array([1, 2, 3])\n", + " y = np.array([4, 5, 6, 7])\n", + " return y-x\n", + "\n", + "subtract()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ===============================================================\n", + "\n", + "1) The top lines will show where in your code there is a problem:\n", + "\n", + "``` python\n", + "---------------------------------------------------------------------------\n", + "UFuncTypeError Traceback (most recent call last)\n", + " in \n", + " 4 return x*2\n", + " 5 \n", + "----> 6 z = add_2()\n", + "```\n", + "2) At the bottom will be the error type and a small description\n", + "\n", + "```python\n", + "UFuncTypeError: ufunc 'add' did not contain a loop with signature matching types (dtype(' dtype(' in \n", + " 1 hist = np.random.randint(0, 10, 1000)\n", + "----> 2 plt.hist(hist, bins=3, range=4)\n", + " 3 plt.show()\n", + "```\n", + "\n", + "2) At the bottom will be the error type and a small description\n", + "\n", + "```python\n", + "TypeError: cannot unpack non-iterable int object\n", + "```\n", + "\n", + "Python is telling you that one of the input varialbe types in this function is wrong. It's useful to look at the documentation for the function to see what each of the types should be. (https://matplotlib.org/api/_as_gen/matplotlib.pyplot.hist.html)\n", + "\n", + "3) Everything inbetween is the libraries code. Unless you wrote the library don't worry too much about it." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### This should just make a histogram of some random numbers." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "random_hist = np.random.randint(0, 10, 1000)\n", + "plt.hist(random_hist, bins=10, range=4)\n", + "plt.show()" + ] + }, + { + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/01_Week1/02_FixMe/02_A_FixMe_solutions.ipynb b/01_Week1/02_FixMe/02_A_FixMe_solutions.ipynb new file mode 100644 index 0000000..4f06943 --- /dev/null +++ b/01_Week1/02_FixMe/02_A_FixMe_solutions.ipynb @@ -0,0 +1,537 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "# https://inventwithpython.com/blog/2012/07/09/16-common-python-runtime-errors-beginners-find/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fixing Broken Code\n", + "\n", + "The following code snipets don't work. Follow along with the [flow chart](https://pythonforbiologists.com/29-common-beginner-errors-on-one-page) and use google and stackoverflow to get these pieces of code working properly." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# This cell should print Hello on one line, and world on the next\n", + "print(\"Hello\")\n", + "print(\"world\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### This cell should do nothing, just make sure there is no error. You can call the function `Hello` if you want, and then it should print Hello World." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "def Hello():\n", + " print(\"Hello World\")\n", + "Hello()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### This cell should print each of the elements of the list `science`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "science = ['biology', 'physics', 'chemistry']\n", + "for i in range(len(science)):\n", + " print(science[i])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### `science` is the list defined in the cell above, and this cell should just print the 4th element (index 3) of the list" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(science[3])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Here we want to print one letter in the string called `spam`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "spam = 'My favorite food is Spam!'\n", + "print(spam[20]) # Just make sure the index is less than the length of the string" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### This cell should print: `My favorite number is 4!`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"My favorite number is \" + '4' + \"!\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### This cell should print: `My name is Monty Python. How are you?`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "myName = 'Monty Python'\n", + "\n", + "print('My name is ' + myName + '. How are you?')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### This cell should create a list of the three school subjects below. You can call it whatever you want..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "myList = ['biology', 'physics', 'chemistry']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### This cell should print `Wow you got the answer!` when you give the `answer` function an argument of `42`, and `Wrong Answer` otherwise." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def answer(MyAnswer):\n", + " if MyAnswer == 42:\n", + " print(\"Wow you got the answer!\")\n", + " else:\n", + " print(\"Wrong Answer\")\n", + " \n", + "answer(42)\n", + "answer(40)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### This cell should print a list that has elements [`Bye`,`Bye`,`Bye`]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def goodBye():\n", + " output=[]\n", + " for i in range(0,3):\n", + " output.append(\"Bye\")\n", + " return output\n", + " \n", + "goodBye()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### This cell should print a random number between 0 and 100000" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "def my_random_number():\n", + " return np.random.randint(0, 1E5)\n", + "\n", + "my_random_number()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### This cell should print multiples of 10 between 0 and 100" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for i in range(0,100):\n", + " if i % 10 == 0:\n", + " print(i)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### This cell should print each of the subjects in the list `my_class` separated by a dash (e.g. `biology-physics`, `physics-chemistry`,...)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "my_class = ['biology', 'physics', 'chemistry', 'python']\n", + "\n", + "for i in range(len(my_class)-1):\n", + " print(my_class[i]+\"-\"+my_class[i+1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Handling Errors\n", + "\n", + "Sometime we will run into errors that we need to check for and handle.\n", + "\n", + "Try fixing these errors with if statements or [try except](https://docs.python.org/3/tutorial/errors.html) statements." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### This cell should simply not error, meaning you need to handle the case of dividing by zero. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def super_complex_calculation(number):\n", + " import numpy as np\n", + " try:\n", + " return 1/np.random.randint(0, 10)\n", + " except:\n", + " print(\"You must have tried to divide by zero.\")\n", + "\n", + "for number in range(0,20):\n", + " x = super_complex_calculation(number)\n", + " print(x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### This is just the syntax for a `try:` `except:` statement" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " pass\n", + "except:\n", + " pass" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Library Errors\n", + "\n", + "Errors from libraries can be long and complicatied.\n", + "\n", + "Tips for reading long errors:\n", + "\n", + "1) The top lines will show where in your code there is a problem\n", + "\n", + "2) At the bottom will be the error type and a small description\n", + "\n", + "3) Everything inbetween is the libraries code. Unless you wrote the library don't worry too much about it.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ===============================================================\n", + "\n", + "1) The top lines will show where in your code there is a problem:\n", + "\n", + "``` python\n", + "---------------------------------------------------------------------------\n", + "NameError Traceback (most recent call last)\n", + " in \n", + " 4 return x+y\n", + " 5 \n", + "----> 6 z = add()\n", + "```\n", + "2) At the bottom will be the error type and a small description\n", + "\n", + "```python\n", + "NameError: name 'np' is not defined\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### This cell will print the sum of the `x` and `y` arrays" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "def add():\n", + " x = np.array([1, 2, 3])\n", + " y = np.array([4, 5, 6])\n", + " return x+y\n", + "\n", + "z = add()\n", + "print(z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ===============================================================\n", + "\n", + "1) The top lines will show where in your code there is a problem:\n", + "\n", + "``` python\n", + "ValueError Traceback (most recent call last)\n", + " in \n", + " 4 return y-x\n", + " 5 \n", + "----> 6 subtract()\n", + "```\n", + "2) At the bottom will be the error type and a small description\n", + "\n", + "```python\n", + "ValueError: operands could not be broadcast together with shapes (4,) (3,) \n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### This cell will print the difference of the `y` and `x` arrays" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def subtract():\n", + " x = np.array([1, 2, 3,4])\n", + " y = np.array([4, 5, 6, 7])\n", + " return y-x\n", + "\n", + "subtract()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ===============================================================\n", + "\n", + "1) The top lines will show where in your code there is a problem:\n", + "\n", + "``` python\n", + "---------------------------------------------------------------------------\n", + "UFuncTypeError Traceback (most recent call last)\n", + " in \n", + " 4 return x*2\n", + " 5 \n", + "----> 6 z = add_2()\n", + "```\n", + "2) At the bottom will be the error type and a small description\n", + "\n", + "```python\n", + "UFuncTypeError: ufunc 'add' did not contain a loop with signature matching types (dtype(' dtype(' in \n", + " 1 hist = np.random.randint(0, 10, 1000)\n", + "----> 2 plt.hist(hist, bins=3, range=4)\n", + " 3 plt.show()\n", + "```\n", + "\n", + "2) At the bottom will be the error type and a small description\n", + "\n", + "```python\n", + "TypeError: cannot unpack non-iterable int object\n", + "```\n", + "\n", + "Python is telling you that one of the input varialbe types in this function is wrong. It's useful to look at the documentation for the function to see what each of the types should be. (https://matplotlib.org/api/_as_gen/matplotlib.pyplot.hist.html)\n", + "\n", + "3) Everything inbetween is the libraries code. Unless you wrote the library don't worry too much about it." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### This should just make a histogram of some random numbers." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "random_hist = np.random.randint(0, 10, 1000)\n", + "plt.hist(random_hist, bins=10, range=[4,10])\n", + "plt.show()" + ] + }, + { + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/01_Week1/02_FixMe/README.md b/01_Week1/02_FixMe/README.md new file mode 100644 index 0000000..7f772f6 --- /dev/null +++ b/01_Week1/02_FixMe/README.md @@ -0,0 +1,10 @@ +# Welcome to Day 2 of Python for STEM! + +## Day 2 - Documentation and Understanding Errors +* 8:30 Introducing documentation/Stack Overflow +* 9:30 Identifying and fixing errors +* 10:30 Writing a working program +* 11:45 Wrap up + +### 02_FixMe +* Activities aimed at having participants fix broken and/or misbehaving code to learn error handling and useful documentation \ No newline at end of file diff --git a/01_Week1/02_FixMe/drawing.pdf b/01_Week1/02_FixMe/drawing.pdf new file mode 100644 index 0000000..59a95a3 Binary files /dev/null and b/01_Week1/02_FixMe/drawing.pdf differ diff --git a/01_Week1/03_WorkingProgram/03_A_simple_program_activity.ipynb b/01_Week1/03_WorkingProgram/03_A_simple_program_activity.ipynb new file mode 100644 index 0000000..003a510 --- /dev/null +++ b/01_Week1/03_WorkingProgram/03_A_simple_program_activity.ipynb @@ -0,0 +1,377 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This activity will guide you through a series of steps necessary to create your first working program. You should use the references you were introduced to yesterday, as well as the information you learned about reading documentation and Stack Overflow threads to complete this activity. If you complete the activity quickly, you may add extra features to this program (some suggestions at the bottom), or move on to a more complicated activity in this same folder." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1a \n", + "You're going to the grocery store to shop for food. Create some items for your list, setting each variable equal to the number of that item that you need to buy. Print the items in your grocery list." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# A\n", + "apples = 4\n", + "cookies = 12\n", + "chips = 2\n", + "print('apples: ',apples)\n", + "print('cookies: ',cookies)\n", + "print('chips: ',chips)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# B\n", + "apples = 4, cookies = 12, chips = 2\n", + "print('apples: ',apples)\n", + "print('cookies: ',cookies)\n", + "print('chips: ',chips)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# C\n", + "apples = 4\n", + "cookies = 12\n", + "chips = 2\n", + "print 'apples: ',apples\n", + "print 'cookies: ',cookies\n", + "print 'chips: ',chips" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1b\n", + "You've forgotten how many apples you need to buy and don't want to look at your list to find out. Add a second variable called ``guessApples``, and set it to be a guess at the number of apples you needed. Then add an if/else statement that checks if ``guessApples`` and ``apples`` are the same number. If they are, print \"Right, I needed that many!\", otherwise print \"Hmm, not the right number.\" You can remove the print statement from the previous problem." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "####PASTE YOUR ANSWER FROM PROBLEM 1a HERE (and run) ######\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# A\n", + "guessApples = 23\n", + "for number in [7,4,2,8]:\n", + " if number == guessApples:\n", + " print('You got it!')\n", + " else:\n", + " print('Try again.')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# B\n", + "guessApples = 8\n", + "if guessApples == apples:\n", + " print('You got it!')\n", + "else:\n", + " print('Try again.')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# C\n", + "guessApples = 18\n", + "if guessApples != apples:\n", + " print('You got it!')\n", + "else:\n", + " print('Try again.')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1c\n", + "Okay it's too hard for you to guess the number of apples you need from scratch. Ammend your if/else statement to have some extra conditions. Instead of just checking to see if the numbers are equal, check if the guess is less than, greater than, or equal to your number. Print whichever case it is. For example if the number of apples you need is 10 and the guess is 4, it should print something like \"Too low!\"." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "####Paste your grocery list and \"guessApples\" variable declarations here (and run). \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# A\n", + "if guessApples < apples:\n", + " print('Too low!')\n", + "elif guessApples > apples:\n", + " print('Too high!')\n", + "else:\n", + " print('You got it!')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# B\n", + "if guessApples == apples:\n", + " print('Too low!')\n", + "elif guessApples > apples:\n", + " print('Too high!')\n", + "else:\n", + " print('You got it!')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# C\n", + "if guessApples != apples:\n", + " print('You got it!')\n", + "elif guessApples > apples:\n", + " print('Too high!')\n", + "else:\n", + " print('Too low!') " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1d\n", + "You don't want to have to physically change your guess every time, wouldn't it be nice if you were prompted for a guess and you could type it in? Replace the \"guessApples\" variable from problem 1b with an input number that you can type in to guess. You can input a number using the \"input\" function. Have the input function print something like \"What is your guess?\". Note that any input will arrive as a \"string\" type, whereas the variable you'll be comparing against will be of \"integer\" type. You can convert from string to integer using the \"int()\" function. Other than this, leave everything the same." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "####PASTE YOUR FINAL CODE FROM PROBLEM 1c HERE. Replace the \"guess\" variable with #######\n", + "# one of the following (and run)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# A \n", + "print('What is your guess?')\n", + "guessApples = 17" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# B\n", + "guessApples = input('What is your guess?')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# C\n", + "guessApples = int(input('What is your guess?'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You should now have a working program that allows you create a grocery list, then to guess how many apples you said you needed in your grocery list. It tells you if it was too low, too high, or just right compared to the number of apples you set in your grocery list. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2a\n", + "You decide it would be easier to remember how many apples you needed for your trip to the store if you printed the number of apples multiple times, instead of just once. Use a for loop to print the number of apples you need to buy, ``number_of_prints`` number of times. So for example if your grocery list said you needed 5 apples, and \"number_of_prints\" was set to 3, running your cell should produce the following:\n", + "\n", + "apples: 5\n", + "\n", + "apples: 5\n", + "\n", + "apples: 5" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# A\n", + "apples = 4\n", + "number_of_prints = 8.3\n", + "for i in range(number_of_prints):\n", + " print('apples: ', apples)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# B\n", + "apples = 4\n", + "number_of_prints = 5\n", + "for i in range(number_of_prints):\n", + " print('apples: ', apples)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# C\n", + "apples = 4\n", + "number_of_prints = 5\n", + "for i in number_of_prints:\n", + " print('apples: ', apples)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2b\n", + "Okay it didn't work, you're still having a lot of trouble remembering how many apples you need so you'll have to go back to guessing the number. Instead of just asking for and checking a single guess at a time though (which is wasting your valuable shopping time), you realize you can use your code from 2a and problem 1d to ask for a new guess on every loop iteration, then print it. Don't worry about checking your guess against your grocery list at the moment, just print your guess on each iteration. You should be able to copy and paste the line of code containing the \"input\" function from 1d into your code from 2a, inside of the for loop (replacing the print statement). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "####COMBINE 1d and 2a BELOW####\n", + "# When run, you should be asked to input a guess \"number_of_prints\" times.\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3 \n", + "Okay you're ready to have your cool looping guess program check each guess against your grocery list. Take the rest of your code from problem 1 (the if/else statement) and put it inside of the for loop as well. The code should now ask for a guess (called ``guessApples``) on every loop iteration, and compare it to the ``apples`` variable. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "####ADD YOUR SOLUTION TO PART 1d to your solution to 2b\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# That's it!\n", + "You wrote a simple guessing game! Some optional stuff to add:\n", + "\n", + "1. Have the loop stop if you get the answer right. This can be done with the \"break\" statement.\n", + "\n", + "2. Keep track of the number of guesses on each loop iteration. When the guess is eventually correct, print the number of tries in addition to the statement that says the guess was correct. " + ] + }, + { + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/01_Week1/03_WorkingProgram/03_B_simple_program_activity.ipynb b/01_Week1/03_WorkingProgram/03_B_simple_program_activity.ipynb new file mode 100644 index 0000000..b52bd7a --- /dev/null +++ b/01_Week1/03_WorkingProgram/03_B_simple_program_activity.ipynb @@ -0,0 +1,212 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is a more complicated program, and it also provides much less in the way of guidelines. There is a solution notebook for this activity since there is no code here, which you can use if you need a hint or to check your answers. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1a\n", + "I need some code that lets a user guess a letter that I have chosen from the alphabet. \n", + "The code should ask for a guess from the user, and then print back the guess with\n", + "some phrase like, `You guessed: x`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1b\n", + "Take your code from problem 1a, and put it inside of a function called `ask_user_for_next_letter`. You don't have to change anything, just make it so that instead of simply running the cell and having it ask for input, a user has to call the function to get the prompt to give it a guess." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2a\n", + "Now I need some code that prints the letters in a word one-by-one. Start by defining some word as a string (e.g. `myWord='python'`). Then use a `for` loop to go through each letter in the word, and print it. The letters will (probably) appear on separate lines." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2b\n", + "Now ammend your code so that you store a word and a letter (in your word) at the top of the cell e.g.: \n", + "\n", + "`myWord='python'`\n", + "\n", + "`myLetter='p'`\n", + "\n", + "Add an `if/else` statement to your loop that checks if each letter is equal to your saved letter (myLetter) before it prints. If it is the same letter, it prints the letter. Otherwise it just prints an dash ('-')." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2c\n", + "Make another ammendment to your code that checks if each letter is in a *list* of letters that you define, instead of just a single letter (Hint: `==` checks if two things are exactly equal, while `in` checks if one variable is part of another.)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2d\n", + "Make one more change to your code. Instead of printing each letter or underscore inside of the loop, add it to a string. Strings are really lists, so something like this will work in Python:\n", + "\n", + "`myString='p'`\n", + "\n", + "`myString=myString+'y'`\n", + "\n", + "Now myString will be \"py\". At the end of your loop, print the final string." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2e\n", + "Take your code from problem 2 and place it inside of a function called `generate_word_string`. The function should take two arguments: The word that you're looping through, and a list of letters that you're checking. Make sure that the names of the arguments that you use match the variables that you set at the top of your cell in part 2c if you're going to copy/paste your code. This function should return the string (`myString`) that you printed at the end of part 2d." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3a\n", + "Use a \"while\" loop to continuously ask for letters to guess by calling your function in problem 1 on every iteration. Every while loop needs a condition to be met, or else it will just continue forever (an infinite loop). Create a variable at the top of the cell that is equal to a letter. If the user's guess is equal to that letter, stop the loop and tell them they guessed correctly. \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3b\n", + "Combine your work in problems 1 and 2. Set a word at the top of the cell, then on every loop iteration ask for a new letter from the user. Pass the word you set at the top of the cell and the letter guessed by the user to your function from problem 2, and have it print the result. If the letter is in the word, have the while loop exit. If not, have it ask for another letter. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3c \n", + "Instead of exiting the loop when a single letter is guessed correctly, have it exit only when all letters have been guessed. You can do this in a couple of ways: Check if the output of your function from problem 2 is identical to the word you've set at the top of the cell for problem 3, keep track of each guess and check if all of the (*unique*) letters in the word you've chosen have been guessed, etc. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# That's it!\n", + "Congratulations, you made a simple hangman game. Some optional things you could add:\n", + "\n", + "1. Handle the case of a user sending you upper or lower case letters\n", + "\n", + "2. Have the program print that a letter has already been guessed, instead of simply accepting the guess\n", + "\n", + "3. Implement an allowed total number of guesses, and have the program exit if either the word is guessed OR the number of guesses reaches your limit\n", + "\n", + "4. Anything else you want!" + ] + } + ], + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/01_Week1/03_WorkingProgram/03_B_simple_program_activity_solution.ipynb b/01_Week1/03_WorkingProgram/03_B_simple_program_activity_solution.ipynb new file mode 100644 index 0000000..4ddbc7b --- /dev/null +++ b/01_Week1/03_WorkingProgram/03_B_simple_program_activity_solution.ipynb @@ -0,0 +1,293 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1a\n", + "I need some code that lets a user guess a letter that I have chosen from the alphabet. \n", + "The code should ask for a guess from the user, and then print back the guess with\n", + "some phrase like, `You guessed: x`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "letter = input(\"Guess your letter: \")\n", + "print(\"You guessed: \",letter)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1b\n", + "Take your code from problem 1a, and put it inside of a function called `ask_user_for_next_letter`. You don't have to change anything, just make it so that instead of simply running the cell and having it ask for input, a user has to call the function to get the prompt to give it a guess." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def ask_user_for_next_letter():\n", + " letter = input(\"Guess your letter: \")\n", + " return letter.strip().upper() # You probably won't have the .strip() or the .upper()\n", + " # But they help with issues that may arise with inputs\n", + " # from users. the \"strip\" function removes excess whitespace,\n", + " # and the \"upper\" function just makes everything uppercase\n", + " # so you don't need to worry about case.\n", + "letter = ask_user_for_next_letter()\n", + "print(\"You guessed: \",letter)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2a\n", + "Now I need some code that prints the letters in a word one-by-one. Start by defining some word as a string (e.g. `myWord='python'`). Then use a `for` loop to go through each letter in the word, and print it. The letters will (probably) appear on separate lines." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "myWord = 'python'\n", + "for letter in myWord: # You could index myWord as though it's a list (myWord[i]),\n", + " print(letter) # and have the loop run over the length of the word if you want" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2b\n", + "Now ammend your code so that you store a word and a letter (in your word) at the top of the cell e.g.: \n", + "\n", + "`myWord='python'`\n", + "\n", + "`myLetter='p'`\n", + "\n", + "Add an `if/else` statement to your loop that checks if each letter is equal to your saved letter (myLetter) before it prints. If it is the same letter, it prints the letter. Otherwise it just prints an dash ('-')." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "myWord = 'python'\n", + "myLetter = 'p'\n", + "for letter in myWord:\n", + " if letter == myLetter: # note that == means the case and whitespace have to match\n", + " print(letter)\n", + " else:\n", + " print('_')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2c\n", + "Make another ammendment to your code that checks if each letter is in a *list* of letters that you define, instead of just a single letter (Hint: `==` checks if two things are exactly equal, while `in` checks if one variable is part of another.)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "myWord = 'python'\n", + "myLetterList = ['p','q','y']\n", + "for letter in myWord:\n", + " if letter in myLetterList: # here case and whitespace still need to match to work\n", + " print(letter)\n", + " else:\n", + " print('_')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2d\n", + "Make one more change to your code. Instead of printing each letter or underscore inside of the loop, add it to a string. Strings are really lists, so something like this will work in Python:\n", + "\n", + "`myString='p'`\n", + "\n", + "`myString=myString+'y'`\n", + "\n", + "Now myString will be \"py\". At the end of your loop, print the final string." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "myWord = 'python'\n", + "myLetterList = ['p','q','y']\n", + "myString = ''\n", + "for letter in myWord:\n", + " if letter in myLetterList:\n", + " myString = myString + letter # myString += letter will do the same thing, try it!\n", + " else:\n", + " myString = myString + '_'\n", + "print(myString)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2e\n", + "Take your code from problem 2 and place it inside of a function called `generate_word_string`. The function should take two arguments: The word that you're looping through, and a list of letters that you're checking. Make sure that the names of the arguments that you use match the variables that you set at the top of your cell in part 2c if you're going to copy/paste your code. This function should return the string (`myString`) that you printed at the end of part 2d." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def generate_word_string(myWord,myLetterList): # note that if you have another variable set called\n", + " myString = '' # myWord or myLetterList, inside the function they\n", + " for letter in myWord: # will match the value of the function arguments,\n", + " if letter.upper() in myLetterList: # and outside the function they will match the value\n", + " myString = myString + letter.upper() # of your variables\n", + " else:\n", + " myString = myString + '_'\n", + " return myString\n", + "finalString = generate_word_string('python',['P','Q','Y'])\n", + "print(finalString)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3a\n", + "Use a \"while\" loop to continuously ask for letters to guess by calling your function in problem 1 on every iteration. Every while loop needs a condition to be met, or else it will just continue forever (an infinite loop). Create a variable at the top of the cell that is equal to a letter. If the user's guess is equal to that letter, stop the loop and tell them they guessed correctly. \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "myLetter = 'A'\n", + "guessed_letter = ask_user_for_next_letter()\n", + "while guessed_letter != myLetter: # You could also do something like while True:, which will never\n", + " guessed_letter = ask_user_for_next_letter() # be false, and then use a \"break\" statement if \n", + " # the condition is met in an if statement\n", + "print('You got it!')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3b\n", + "Combine your work in problems 1 and 2. Set a variable equal to a word at the top of the cell, then on every loop iteration ask for a new letter from the user. Pass the word you set at the top of the cell and the letter guessed by the user to your function from problem 2, and have it print the result. If the letter is in the word, have the while loop exit. If not, have it ask for another letter. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "myWord = 'python'\n", + "guessed_letter = ask_user_for_next_letter()\n", + "finalString = generate_word_string(myWord,[guessed_letter]) # The way this was done, you need\n", + "print(finalString) # to create the variable before the\n", + "while guessed_letter not in myWord.upper(): # loop, but there are ways to do it\n", + " guessed_letter = ask_user_for_next_letter() # where this isn't necessary.\n", + " finalString = generate_word_string(myWord,[guessed_letter])\n", + " print(finalString)\n", + " \n", + "print('You got it!')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3c \n", + "Instead of exiting the loop when a single letter is guessed correctly, have it exit only when all letters have been guessed. You can do this in a couple of ways: Check if the output of your function from problem 2 is identical to the word you've set at the top of the cell for problem 3, keep track of each guess and check if all of the (*unique*) letters in the word you've chosen have been guessed, etc. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "myWord = 'python'\n", + "all_guessed_letters = [] # Now you need to keep track of guessed letters by appending to a list\n", + "guessed_letter = ask_user_for_next_letter()\n", + "all_guessed_letters.append(guessed_letter)\n", + "finalString = generate_word_string(myWord,all_guessed_letters)\n", + "print(finalString)\n", + "while finalString != myWord.upper():\n", + " guessed_letter = ask_user_for_next_letter()\n", + " all_guessed_letters.append(guessed_letter)\n", + " finalString = generate_word_string(myWord,all_guessed_letters)\n", + " print(finalString)\n", + " \n", + "print('You got it!')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# That's it!\n", + "Congratulations, you made a simple hangman game. Some optional things you could add:\n", + "\n", + "1. Handle the case of a user sending you upper or lower case letters\n", + "\n", + "2. Have the program print that a letter has already been guessed, instead of simply accepting the guess\n", + "\n", + "3. Implement an allowed total number of guesses, and have the program exit if either the word is guessed OR the number of guesses reaches your limit\n", + "\n", + "4. Anything else you want!" + ] + } + ], + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/01_Week1/03_WorkingProgram/README.md b/01_Week1/03_WorkingProgram/README.md new file mode 100644 index 0000000..649243b --- /dev/null +++ b/01_Week1/03_WorkingProgram/README.md @@ -0,0 +1,10 @@ +# Welcome to Day 2 of Python for STEM! + +## Day 2 - Documentation and Understanding Errors +* 8:30 Introducing documentation/Stack Overflow +* 9:30 Identifying and fixing errors +* 10:30 Writing a working program +* 11:45 Wrap up + +### 03_WorkingProgram +* Activities that have participants create their first fully operational program from scratch \ No newline at end of file diff --git a/01_Week1/04_Loops/04_A_broken_loops_activity.ipynb b/01_Week1/04_Loops/04_A_broken_loops_activity.ipynb new file mode 100644 index 0000000..5fa1320 --- /dev/null +++ b/01_Week1/04_Loops/04_A_broken_loops_activity.ipynb @@ -0,0 +1,245 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You will use the reference documents, your experience fixing errors from the `00_FixMe` activities, and your ability to read documentation/Stack Overflow threads to complete this activity. It is simply another set of cells to fix, now specifically for `for` and `while` loops. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# For loops" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's break your old grocery list back out. This cell should define a list of grocery items, then loop through it and print the list. Fix this cell:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "myGroceryList = ['apples','fish','chips','cookies'] # This line creates a list called \"myGroceryList\"\n", + " # containing strings ('apples','fish',...)\n", + "# Defining a for loop. Check the error and compare with working loops\n", + "for groceryItem in myGroceryList\n", + " print(groceryItem)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the same cell as above with a different issue. Look at the comments above and see if you can fix this cell. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "myGroceryList = ['apples','fish','chips','cookies']\n", + "for in myGroceryList:\n", + " print(groceryItem)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the same cell as above with a different issue. Look at the comments above and see if you can fix this cell. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "myGroceryList = ['apples','fish','chips','cookies']\n", + "for groceryItem in myGroceryList:\n", + "print(groceryItem)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Okay now you want this loop to do the same thing as before: Print out each element of your grocery list. This time though, instead of printing each element itself you'll index the list and print each element in this new way. You'll notice not all elements are printed though, figure out why that is and fix it. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "myGroceryList = ['apples','fish','chips','cookies']\n", + "\n", + "# the \"range\" function basically creates a list you don't see that runs from the first argument\n", + "# to the second, incrementing by 1. Importantly, the second argument is not inclusive, so currently\n", + "# the \"range\" function below will run over the integers 1,2,3\n", + "for index in range(1,4): \n", + " print('Index: ',index,' Item: ',myGroceryList[index]) # This is indexing a list. \n", + " # If index is equal to 1,\n", + " # which element of this list is being accessed? You\n", + " # can check by seeing which element of the list is \n", + " # printed next to and index of 1. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The list `myGroceryListAmounts` is the number of each item in your grocery list that you need to buy at the store. You remember you had one extra person coming for dinner, so you need to add one to each of the items in your list. Make sure that the printed result successfully increases the number of each element in the list by 1. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "myGroceryListAmounts = [2,2,1,4]\n", + "print('The length of my list is: ',len(myGroceryListAmounts)) # Just printing the length of the list, note\n", + " # that this is then used in the loop declaration\n", + " \n", + "# You can use the range function to run up to the length of the list instead of a specific\n", + "# number in case the list changes. Note that the length of this list is 4, so the range function\n", + "# runs over the integers 0,1,2,3. Since list indexing starts at 0, this is all of the numbers\n", + "# in the list.\n", + "for index in range(0,len(myGroceryListAmounts)): \n", + " myGroceryListAmounts[index] = 1\n", + "print(myGroceryListAmounts)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# While loops\n", + "`while` loops are alternatives to `for` loops. You must set the number of loops during the declaration when using a `for` loop, but not a `while` loop. A `while` loop runs indefinitely until some condition is met.\n", + "\n", + "If you accidentally create an infinite loop (i.e. a cell doesn't complete its task immediately), go up to the top of the notebook and hit \"Kernel\", and then \"Interrupt\"." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following cells are very similar to the first section, just now using `while` loops instead of `for` loops. Fix each of them using what you know about syntax, and online documentation if necessary. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "myGroceryList = ['apples','fish','chips','cookies']\n", + "myGroceryListIndex = 0 # This is the same thing as doing\n", + " # for myGroceryListIndex in range(5):\n", + "while myGroceryListIndex < 3\n", + " print(myGroceryList[myGroceryListIndex])\n", + " myGroceryListIndex = myGroceryListIndex + 1 # Always make sure to update your condition\n", + " # variable on each iteration" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "myGroceryList = ['apples','fish','chips','cookies']\n", + "myGroceryListIndex = 0\n", + "while:\n", + " print(myGroceryList[myGroceryListIndex])\n", + " myGroceryListIndex = myGroceryListIndex + 1 # Always make sure to update your condition\n", + " # variable on each iteration" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "myNumber = 0\n", + "while myNumber < 5:\n", + "print(myNumber)\n", + "myNumber = myNumber + 1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "myGroceryList = ['apples','fish','chips','cookies']\n", + "myGroceryListIndex = 0\n", + "while myGroceryListIndex < 3:\n", + " print(myGroceryList[myGroceryListIndex])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This cell will run, but won't print all of the items in your list. Change it so that it prints the entire list." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "myGroceryList = ['apples','fish','chips','cookies']\n", + "print('The length of my grocery list is: ',len(myGroceryList))\n", + "myGroceryListIndex = 0\n", + "while myGroceryListIndex < 2:\n", + " print(myGroceryList[myGroceryListIndex])\n", + " myGroceryListIndex = myGroceryListIndex + 1\n" + ] + }, + { + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/01_Week1/04_Loops/04_A_broken_loops_activity_solutions.ipynb b/01_Week1/04_Loops/04_A_broken_loops_activity_solutions.ipynb new file mode 100644 index 0000000..8b55d6e --- /dev/null +++ b/01_Week1/04_Loops/04_A_broken_loops_activity_solutions.ipynb @@ -0,0 +1,246 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You will use the reference documents, your experience fixing errors from the `00_FixMe` activities, and your ability to read documentation/Stack Overflow threads to complete this activity. It is simply another set of cells to fix, now specifically for `for` and `while` loops. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# For loops" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's break your old grocery list back out. This cell should define a list of grocery items, then loop through it and print the list. Fix this cell:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "myGroceryList = ['apples','fish','chips','cookies'] # This line creates a list called \"myGroceryList\"\n", + " # containing strings ('apples','fish',...)\n", + "# Defining a for loop. Check the error and compare with working loops\n", + "for groceryItem in myGroceryList:\n", + " print(groceryItem)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the same cell as above with a different issue. Look at the comments above and see if you can fix this cell. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "myGroceryList = ['apples','fish','chips','cookies']\n", + "for groceryItem in myGroceryList:\n", + " print(groceryItem)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the same cell as above with a different issue. Look at the comments above and see if you can fix this cell. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "myGroceryList = ['apples','fish','chips','cookies']\n", + "for groceryItem in myGroceryList:\n", + " print(groceryItem)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Okay now you want this loop to do the same thing as before: Print out each element of your grocery list. This time though, instead of printing each element itself you'll index the list and print each element in this new way. You'll notice not all elements are printed though, figure out why that is and fix it. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "myGroceryList = ['apples','fish','chips','cookies']\n", + "\n", + "# the \"range\" function basically creates a list you don't see that runs from the first argument\n", + "# to the second, incrementing by 1. Importantly, the second argument is not inclusive, so currently\n", + "# the \"range\" function below will run over the integers 1,2,3\n", + "for index in range(0,4): \n", + " print('Index: ',index,' Item: ',myGroceryList[index]) # This is indexing a list. \n", + " # If index is equal to 1,\n", + " # which element of this list is being accessed? You\n", + " # can check by seeing which element of the list is \n", + " # printed next to and index of 1. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The list `myGroceryListAmounts` is the number of each item in your grocery list that you need to buy at the store. You remember you had one extra person coming for dinner, so you need to add one to each of the items in your list. Make sure that the printed result successfully increases the number of each element in the list by 1. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "myGroceryListAmounts = [2,2,1,4]\n", + "print('The length of my list is: ',len(myGroceryListAmounts)) # Just printing the length of the list, note\n", + " # that this is then used in the loop declaration\n", + " \n", + "# You can use the range function to run up to the length of the list instead of a specific\n", + "# number in case the list changes. Note that the length of this list is 4, so the range function\n", + "# runs over the integers 0,1,2,3. Since list indexing starts at 0, this is all of the numbers\n", + "# in the list.\n", + "for index in range(0,len(myGroceryListAmounts)): \n", + " myGroceryListAmounts[index] += 1\n", + "print(myGroceryListAmounts)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# While loops\n", + "`while` loops are alternatives to `for` loops. You must set the number of loops during the declaration when using a `for` loop, but not a `while` loop. A `while` loop runs indefinitely until some condition is met.\n", + "\n", + "If you accidentally create an infinite loop (i.e. a cell doesn't complete its task immediately), go up to the top of the notebook and hit \"Kernel\", and then \"Interrupt\"." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following cells are very similar to the first section, just now using `while` loops instead of `for` loops. Fix each of them using what you know about syntax, and online documentation if necessary. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "myGroceryList = ['apples','fish','chips','cookies']\n", + "myGroceryListIndex = 0 # This is the same thing as doing\n", + " # for myGroceryListIndex in range(5):\n", + "while myGroceryListIndex < 4:\n", + " print(myGroceryList[myGroceryListIndex])\n", + " myGroceryListIndex = myGroceryListIndex + 1 # Always make sure to update your condition\n", + " # variable on each iteration" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "myGroceryList = ['apples','fish','chips','cookies']\n", + "myGroceryListIndex = 0\n", + "while myGroceryListIndex<4:\n", + " print(myGroceryList[myGroceryListIndex])\n", + " myGroceryListIndex = myGroceryListIndex + 1 # Always make sure to update your condition\n", + " # variable on each iteration" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "myNumber = 0\n", + "while myNumber < 5:\n", + " print(myNumber)\n", + " myNumber = myNumber + 1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "myGroceryList = ['apples','fish','chips','cookies']\n", + "myGroceryListIndex = 0\n", + "while myGroceryListIndex < 4:\n", + " print(myGroceryList[myGroceryListIndex])\n", + " myGroceryListIndex+=1\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This cell will run, but won't print all of the items in your list. Change it so that it prints the entire list." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "myGroceryList = ['apples','fish','chips','cookies']\n", + "print('The length of my grocery list is: ',len(myGroceryList))\n", + "myGroceryListIndex = 0\n", + "while myGroceryListIndex < len(myGroceryList):\n", + " print(myGroceryList[myGroceryListIndex])\n", + " myGroceryListIndex = myGroceryListIndex + 1\n" + ] + }, + { + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/01_Week1/04_Loops/04_B_for_loop_activity.ipynb b/01_Week1/04_Loops/04_B_for_loop_activity.ipynb new file mode 100644 index 0000000..fb2b90c --- /dev/null +++ b/01_Week1/04_Loops/04_B_for_loop_activity.ipynb @@ -0,0 +1,137 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is a list of extra practice creating for loops with much less guidance. There is a solution notebook that you can use as a reference and to check your answers." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1\n", + "Create a list of numbers (e.g. `myList = [1,2,3,4]`). Write a `for` loop that loops through the numbers in your list, and print each number by adding a print statement inside the loop. The variable that you use to do the looping (whatever is between `for` and `in`), can be named anything that you want. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2\n", + "Take your code from problem 1, and instead of simply printing the number on each loop iteration, add 1 to the value and then print it. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3\n", + "Replace your list of numbers from problem 1 (no adding, just printing) with a list of strings (e.g. `myList = ['hello','how','are','you']`) and run the same code to verify that you understand what the loop is doing. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 4\n", + "Take your code from problem 1 (printing not adding) and replace the your explicit list of numbers with the `range` function. The first argument of the function is the number you want to start with, and the second argument is 1 number higher than the number you want to end with (i.e. if you want to loop through the numbers 1-->5, you use `range(1,6)`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 5\n", + "Ignoring loops for a moment, play around with an `index` variable and your list of strings. For example, `print(myList[0])` would print the first element of your list of strings. If you had a variable called index and set it to 0 (`index = 0`), then what should `print(myList[index])` do?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 6\n", + "Put your list of strings from problem 3 back at the top of the cell, and below it paste your code from problem 4. Now set the indexing variable in the for loop (the variable between `for` and `in`) to be called `index`. Replace your print statement from problem 4 (printing each number created by the range function) with your print statement from problem 5. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 7\n", + "Add a second list called `myList2` at the top of the cell, and then paste your code from problem 6 below it. Then instead of just printing `myList[index]`, print each element of both `myList` and `myList2` by adding a second print statement. This is one major use of for loops: doing some operation on each element of multiple lists." + ] + }, + { + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/01_Week1/04_Loops/04_B_for_loop_activity_solutions.ipynb b/01_Week1/04_Loops/04_B_for_loop_activity_solutions.ipynb new file mode 100644 index 0000000..a7d9d34 --- /dev/null +++ b/01_Week1/04_Loops/04_B_for_loop_activity_solutions.ipynb @@ -0,0 +1,171 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1\n", + "Create a list of numbers (e.g. `myList = [1,2,3,4]`). Write a `for` loop that loops through the numbers in your list, and print each number by adding a print statement inside the loop. The variable that you use to do the looping (whatever is between `for` and `in`), can be named anything that you want. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "myList = [1,2,3,4]\n", + "for num in myList: # this loops through every number in the list\n", + " print(num)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2\n", + "Take your code from problem 1, and instead of simply printing the number on each loop iteration, add 1 to the value and then print it. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "myList = [1,2,3,4]\n", + "for num in myList:\n", + " num = num + 1 \n", + " print(num)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3\n", + "Replace your list of numbers from problem 1 (no adding, just printing) with a list of strings (e.g. `myList = ['hello','how','are','you']`) and run the same code to verify that you understand what the loop is doing. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "myList = ['hello','how','are','you']\n", + "for num in myList: # note that we still have the variable name \"num\", but it's printing words\n", + " print(num)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 4\n", + "Take your code from problem 1 (printing not adding) and replace the your explicit list of numbers with the `range` function. The first argument of the function is the number you want to start with, and the second argument is 1 number higher than the number you want to end with (i.e. if you want to loop through the numbers 1-->5, you use `range(1,6)`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for num in range(0,4): # you can also just use range(4), 0 is the first argument by default\n", + " print(num)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 5\n", + "Ignoring loops for a moment, play around with an `index` variable and your list of strings. For example, `print(myList[0])` would print the first element of your list of strings. If you had a variable called index and set it to 0 (`index = 0`), then what should `print(myList[index])` do?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "myList = ['hello','how','are','you']\n", + "print(myList[0])\n", + "index = 0\n", + "print(myList[index])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 6\n", + "Put your list of strings from problem 3 back at the top of the cell, and below it paste your code from problem 4. Now set the indexing variable in the for loop (the variable between `for` and `in`) to be called `index`. Replace your print statement from problem 4 (printing each number created by the range function) with your print statement from problem 5. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "myList = ['hello','how','are','you']\n", + "for index in range(0,4): #Make sure you don't let this loop run beyond the length of the list\n", + " print(myList[index])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 7\n", + "Add a second list called `myList2` (the same length as `myList`) at the top of the cell, and then paste your code from problem 6 below it. Then instead of just printing `myList[index]`, print each element of both `myList` and `myList2` by adding a second print statement. This is one major use of for loops: doing some operation on each element of multiple lists." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "myList = ['hello','how','are','you']\n", + "myList2 = ['I','am','doing','well']\n", + "# The two lists have to be the same length to have this work (or\n", + "# the indexing variable needs to run to a number smaller than\n", + "# the length of either list)\n", + "for index in range(0,4): \n", + " print(myList[index])\n", + " print(myList2[index])" + ] + }, + { + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/01_Week1/04_Loops/04_C_while_loop_activity.ipynb b/01_Week1/04_Loops/04_C_while_loop_activity.ipynb new file mode 100644 index 0000000..b0b4156 --- /dev/null +++ b/01_Week1/04_Loops/04_C_while_loop_activity.ipynb @@ -0,0 +1,92 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the same activity as with for loops with some additions. You can always write a for loop in the form of a while loop, but not vice versa. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1\n", + "Create a list of numbers (e.g. `myList = [1,2,3,4]`). Write a `while` loop that loops through the numbers in your list, and print each number by adding a print statement inside the loop. The variable that you use to do the looping (whatever is between `for` and `in`), can be named anything that you want. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2\n", + "Take your code from problem 1, and instead of simply printing the number on each loop iteration, add 1 to the value and then print it. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3\n", + "Replace your list of numbers from problem 1 (no adding, just printing) with a list of strings (e.g. `myList = ['hello','how','are','you']`) and run the same code to verify that you understand what the loop is doing. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 4\n", + "Start a `while` loop with `while True:`, this will always be the case, so the loop will run forever. However, you can still check conditions inside of the loop and use a `break` statement to leave it. Use the same loop from problem 1, use this different form of `while True:` and a `break` statement. " + ] + }, + { + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/01_Week1/04_Loops/04_C_while_loop_activity_solutions.ipynb b/01_Week1/04_Loops/04_C_while_loop_activity_solutions.ipynb new file mode 100644 index 0000000..d860506 --- /dev/null +++ b/01_Week1/04_Loops/04_C_while_loop_activity_solutions.ipynb @@ -0,0 +1,115 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1\n", + "Create a list of numbers (e.g. `myList = [1,2,3,4]`). Write a `while` loop that loops through the numbers in your list, and print each number by adding a print statement inside the loop. The variable that you use to do the looping (whatever is between `for` and `in`), can be named anything that you want. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "myList = [1,2,3,4]\n", + "num = 0\n", + "while num < len(myList):\n", + " print(myList[num])\n", + " num = num + 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2\n", + "Take your code from problem 1, and instead of simply printing the number on each loop iteration, add 1 to the value and then print it. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "myList = [1,2,3,4]\n", + "num = 0\n", + "while num < len(myList):\n", + " myList[num] = myList[num] + 1\n", + " print(myList[num])\n", + " num = num + 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3\n", + "Replace your list of numbers from problem 1 (no adding, just printing) with a list of strings (e.g. `myList = ['hello','how','are','you']`) and run the same code to verify that you understand what the loop is doing. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "myList = ['hello','how','are','you']\n", + "num = 0\n", + "while num < len(myList):\n", + " print(myList[num])\n", + " num = num + 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 4\n", + "Start a `while` loop with `while True:`, this will always be the case, so the loop will run forever. However, you can still check conditions inside of the loop and use a `break` statement to leave it. Use the same loop from problem 1, use this different form of `while True:` and a `break` statement. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "myList = [1,2,3,4]\n", + "num = 0\n", + "while True:\n", + " print(myList[num])\n", + " num = num + 1\n", + " \n", + " # note that before we had num < len(myList) as the condition\n", + " # but this time we need to check the opposte condition. \n", + " if num >= len(myList): \n", + " break" + ] + } + ], + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/01_Week1/04_Loops/README.md b/01_Week1/04_Loops/README.md new file mode 100644 index 0000000..91ba8af --- /dev/null +++ b/01_Week1/04_Loops/README.md @@ -0,0 +1,11 @@ +# Welcome to Day 3 of Python for STEM! + +## Day 3 - Loops and Packages +* 8:30 Introduction to for/while loops +* 9:00 Fixing/writing loops +* 10:00 Introduction to Python packages +* 10:30 Simple Numpy, Pandas, Matplotlib usage +* 11:45 Wrap up + +### 04_Loops +* Introduction to `for` and `while` loops \ No newline at end of file diff --git a/01_Week1/05_PackagesIntro/05_A_packages_activity.ipynb b/01_Week1/05_PackagesIntro/05_A_packages_activity.ipynb new file mode 100644 index 0000000..727fa1d --- /dev/null +++ b/01_Week1/05_PackagesIntro/05_A_packages_activity.ipynb @@ -0,0 +1,323 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In Python, there is often something that you want to be able to do that someone else has already figured out. For example, you may want to read an excel file, or plot some data, or calculate some statistic. You don't need to start from scratch and write an entire script that accomplishes tasks like this. Instead you can do what's called an ``import``, which basically gives you access to code that someone else has written. To do this, you just need to know the name of the ``package``, and the name of the ``function`` you want to use. In this activity, you'll use what you learned about reading documentation to implement code written by someone else, that you can use for your own purposes. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# NumPy\n", + "You will use what you learned about finding the correct documentation and code help to implement functions inside of the NumPy package. Note while searching online for information about Numpy that it is often abbreviated as ``np``. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Consider your grocery list from previous activities. This time, suppose you're keeping track of the cost of apples over time. You go to the store 5 times, and each time you record the price of apples:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "apples_price = [1.34,2.01,1.75,1.50,2.50]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Turn this list into a Numpy array. First figure out how to ``import`` Numpy correctly, then how to turn this into a Numpy array. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#add import here\n", + "\n", + "#then create your array\n", + "apples_price_array = " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Whoops, you made a mistake and wrote down the price for 2 apples each time instead of 1! Multiply your array by 0.5 (or divide by 2) to correct for this issue. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# fix the mistake here\n", + "\n", + "print(apples_price_array)\n", + "# did the numbers in the array halve?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You also recorded the price of cookies while shopping, and made a new list of prices for cookies. Turn it into an array, and then add it to your apples array to get your total cost of shopping per trip." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# paste your apples_price_array declaration here\n", + "\n", + "cookies_price = [0.53,1.21,0.75,1.23,0.95]\n", + "#create your array\n", + "cookies_price_array = \n", + "\n", + "#add together the apples_price_array and the cookies_price_array here\n", + "total_shopping_cost_per_trip = \n", + "print('My cost per trip was: ',total_shopping_cost_per_trip)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Your final array (``total_shopping_cost_per_trip``) tells you the cost of each of your trips to the grocery store over 5 trips. How much did you spend combined for all trips? Try Numpy's ``sum`` function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "total_cost_of_groceries = \n", + "print('In total I spent: ',total_cost_of_groceries)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Matplotlib\n", + "You will use what you learned about finding the correct documentation and code help to implement functions inside of the Matplotlib package. This one is a little more complicated so we'll give you the first line for free. Matplotlib has a variety of components built into it, so that instead of simply importing the Matplotlib package and using its valuable functions, you need to call the ``pyplot`` module inside of Matplotlib. This won't really matter to you in terms of usage, except that when we say something like \"use the Matplotlib ``plot`` function\", you will run ``matplotlib.pyplot.plot`` instead of ``matplotlib.plot``." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use the Matplotlib ``hist`` function to plot a histogram of the price data for apples that you implemented above." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot #your matplotlib import, note the added \".pyplot\"\n", + "import numpy #your numpy import\n", + "# replace your definition of the apples_price_array here:\n", + "apples_price_array = \n", + "\n", + "# create your histogram here:\n", + "\n", + "\n", + "matplotlib.pyplot.show() # This just shows your plot. You could also save it with \"savefig\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now use the Matplotlib ``scatter`` function to plot the price of cookies vs. the price of apples per trip to the grocery store (critical information to have!)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# insert your implementation of the \"scatter\" function here\n", + "\n", + "\n", + "matplotlib.pyplot.show() # This just shows your plot. You could also save it with \"savefig\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Spend as much time as you want looking at matplotlib documentation and other sources to make your plots look better. Some ideas: Add axis labels, change the color of your data points, add a legend, etc. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Pandas\n", + "You will use what you learned about finding the correct documentation and code help to implement functions inside of the Pandas package. Pandas is a very powerful package that makes it much simpler to interact with large datasets, data files, and excel files (plus much more). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We're going to use pandas to read/write excel files" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas\n", + "survey_responses = pandas.read_excel('https://github.com/uofscphysics/STEM_Python_Course/blob/Summer2020/01_Week1/05_PackagesIntro/participant_survey.xlsx?raw=true')\n", + "print(survey_responses)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now go through each of the survey question responses and see what we can do with pandas, numpy, and matplotlib for each one. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Which of these foods do you prefer?\n", + "Try making a histogram using matplotlib. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# How big of a Harry Potter Fan are you?\n", + "Try making a histogram using pandas and/or matplotlib. Also take a look at things like `mean`, `median`, `std`, `max`, and `min` using numpy." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# What is your favorite Ice Cream Flavor?\n", + "Try making a histogram using matplotlib." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# What is your age?\n", + "Try making a histogram using pandas and/or matplotlib. Also take a look at things like `mean`, `median`, `std`, `max`, and `min` using numpy." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Which state are you from? \n", + "Try making a histogram using matplotlib. Also take a look at `unique` from numpy." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Which country are you from?\n", + "Try making a histogram using matplotlib. Also take a look at `unique` from numpy." + ] + }, + { + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/01_Week1/05_PackagesIntro/05_A_packages_activity_completed.ipynb b/01_Week1/05_PackagesIntro/05_A_packages_activity_completed.ipynb new file mode 100644 index 0000000..c55e864 --- /dev/null +++ b/01_Week1/05_PackagesIntro/05_A_packages_activity_completed.ipynb @@ -0,0 +1,357 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In Python, there is often something that you want to be able to do that someone else has already figured out. For example, you may want to read an excel file, or plot some data, or calculate some statistic. You don't need to start from scratch and write an entire script that accomplishes tasks like this. Instead you can do what's called an ``import``, which basically gives you access to code that someone else has written. To do this, you just need to know the name of the ``package``, and the name of the ``function`` you want to use. In this activity, you'll use what you learned about reading documentation to implement code written by someone else, that you can use for your own purposes. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# NumPy\n", + "You will use what you learned about finding the correct documentation and code help to implement functions inside of the NumPy package. Note while searching online for information about Numpy that it is often abbreviated as ``np``. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Consider your grocery list from previous activities. This time, suppose you're keeping track of the cost of apples over time. You go to the store 5 times, and each time you record the price of apples:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "apples_price = [1.34,2.01,1.75,1.50,2.50]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Turn this list into a Numpy array. First figure out how to ``import`` Numpy correctly, then how to turn this into a Numpy array. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#add import here\n", + "import numpy as np\n", + "#then create your array\n", + "apples_price_array = np.array(apples_price)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Whoops, you made a mistake and wrote down the price for 2 apples each time instead of 1! Multiply your array by 0.5 (or divide by 2) to correct for this issue. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# fix the mistake here\n", + "apples_price_array/=2\n", + "print(apples_price_array)\n", + "# did the numbers in the array halve?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You also recorded the price of cookies while shopping, and made a new list of prices for cookies. Turn it into an array, and then add it to your apples array to get your total cost of shopping per trip." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# paste your apples_price_array declaration here\n", + "apples_price_array = np.array(apples_price)\n", + "cookies_price = [0.53,1.21,0.75,1.23,0.95]\n", + "#create your array\n", + "cookies_price_array = np.array(cookies_price)\n", + "\n", + "#add together the apples_price_array and the cookies_price_array here\n", + "total_shopping_cost_per_trip = apples_price_array+cookies_price_array\n", + "print('My cost per trip was: ',total_shopping_cost_per_trip)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Your final array (``total_shopping_cost_per_trip``) tells you the cost of each of your trips to the grocery store over 5 trips. How much did you spend combined for all trips? Try Numpy's ``sum`` function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "total_cost_of_groceries = np.sum(total_shopping_cost_per_trip)\n", + "print('In total I spent: ',total_cost_of_groceries)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Matplotlib\n", + "You will use what you learned about finding the correct documentation and code help to implement functions inside of the Matplotlib package. This one is a little more complicated so we'll give you the first line for free. Matplotlib has a variety of components built into it, so that instead of simply importing the Matplotlib package and using its valuable functions, you need to call the ``pyplot`` module inside of Matplotlib. This won't really matter to you in terms of usage, except that when we say something like \"use the Matplotlib ``plot`` function\", you will run ``matplotlib.pyplot.plot`` instead of ``matplotlib.plot``." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use the Matplotlib ``hist`` function to plot a histogram of the price data for apples that you implemented above." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt #your matplotlib import, note the added \".pyplot\"\n", + "import numpy #your numpy import\n", + "# replace your definition of the apples_price_array here:\n", + "\n", + "hist = plt.hist(apples_price_array)\n", + "\n", + "# create your histogram here:\n", + "\n", + "\n", + "plt.show() # This just shows your plot. You could also save it with \"savefig\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now use the Matplotlib ``scatter`` function to plot the price of cookies vs. the price of apples per trip to the grocery store (critical information to have!)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# insert your implementation of the \"scatter\" function here\n", + "\n", + "plt.scatter(apples_price_array,cookies_price_array)\n", + "plt.show() # This just shows your plot. You could also save it with \"savefig\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Spend as much time as you want looking at matplotlib documentation and other sources to make your plots look better. Some ideas: Add axis labels, change the color of your data points, add a legend, etc. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Pandas\n", + "You will use what you learned about finding the correct documentation and code help to implement functions inside of the Pandas package. Pandas is a very powerful package that makes it much simpler to interact with large datasets, data files, and excel files (plus much more). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We're going to use pandas to read/write excel files" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas\n", + "survey_responses = pandas.read_excel('https://github.com/uofscphysics/STEM_Python_Course/blob/Summer2020/01_Week1/05_PackagesIntro/participant_survey.xlsx?raw=true')\n", + "print(survey_responses)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now go through each of the survey question responses and see what we can do with pandas, numpy, and matplotlib for each one. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Which of these foods do you prefer?\n", + "Try making a histogram using matplotlib. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.hist(survey_responses['Which of these foods do you prefer?'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# How big of a Harry Potter fan are you?\n", + "Try making a histogram using pandas and/or matplotlib. Also take a look at things like `mean`, `median`, `std`, `max`, and `min` using numpy." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "survey_responses['How big of a Harry Potter fan are you?'].hist()\n", + "print(np.median(survey_responses['How big of a Harry Potter fan are you?']))\n", + "print(np.std(survey_responses['How big of a Harry Potter fan are you?']))\n", + "print(np.max(survey_responses['How big of a Harry Potter fan are you?']))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# What is your favorite Ice Cream Flavor?\n", + "Try making a histogram using matplotlib." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.hist(survey_responses['What is your favorite Ice Cream flavor?'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# What is your age?\n", + "Try making a histogram using pandas and/or matplotlib. Also take a look at things like `mean`, `median`, `std`, `max`, and `min` using numpy." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "survey_responses['What is your age?'].hist()\n", + "print(np.mean(survey_responses['What is your age?']))\n", + "print(np.median(survey_responses['What is your age?']))\n", + "print(np.std(survey_responses['What is your age?']))\n", + "print(np.max(survey_responses['What is your age?']))\n", + "print(np.min(survey_responses['What is your age?']))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Which state are you from? \n", + "Try making a histogram using matplotlib. Also take a look at `unique` from numpy." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "survey_responses['Which state are you from? (Leave blank if not applicable)']=\\\n", + " survey_responses['Which state are you from? (Leave blank if not applicable)'].fillna('N/A')\n", + "plt.hist(survey_responses['Which state are you from? (Leave blank if not applicable)'])\n", + "print(np.unique(survey_responses['Which state are you from? (Leave blank if not applicable)']))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Which country are you from?\n", + "Try making a histogram using matplotlib. Also take a look at `unique` from numpy." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "survey_responses['Which country are you from?']=\\\n", + " survey_responses['Which country are you from?'].fillna('N/A')\n", + "plt.hist(survey_responses['Which country are you from?'])\n", + "print(np.unique(survey_responses['Which country are you from?']))" + ] + }, + { + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/01_Week1/05_PackagesIntro/README.md b/01_Week1/05_PackagesIntro/README.md new file mode 100644 index 0000000..6038d13 --- /dev/null +++ b/01_Week1/05_PackagesIntro/README.md @@ -0,0 +1,11 @@ +# Welcome to Day 3 of Python for STEM! + +## Day 3 - Loops and Packages +* 8:30 Introduction to for/while loops +* 9:00 Fixing/writing loops +* 10:00 Introduction to Python packages +* 10:30 Simple Numpy, Pandas, Matplotlib usage +* 11:45 Wrap up + +### 05_PackagesIntro +* Introduction to Python packages and some specific uses \ No newline at end of file diff --git a/01_Week1/05_PackagesIntro/participant_survey.xlsx b/01_Week1/05_PackagesIntro/participant_survey.xlsx new file mode 100644 index 0000000..9272112 Binary files /dev/null and b/01_Week1/05_PackagesIntro/participant_survey.xlsx differ diff --git a/01_Week1/06_PandasIMDB/06_A_Movies_Pandas.ipynb b/01_Week1/06_PandasIMDB/06_A_Movies_Pandas.ipynb new file mode 100644 index 0000000..113ef79 --- /dev/null +++ b/01_Week1/06_PandasIMDB/06_A_Movies_Pandas.ipynb @@ -0,0 +1,322 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import numpy as np\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Lets get some data to look at\n", + "\n", + "Movies! IMDB has a lot of information about movies and tv shows and it's easy to download and get.\n", + "\n", + "\n", + "The documentioan is here:\n", + "\n", + "https://www.imdb.com/interfaces/\n", + "\n", + "Let's look at movie ratings!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ratings = pd.read_csv(\"https://datasets.imdbws.com/title.ratings.tsv.gz\", sep='\\t')\n", + "ratings.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Ok great we have these ratings but what movie are they for?\n", + "\n", + "Looking at the documentation:\n", + "\n", + "\n", + "#### title.ratings.tsv.gz – Contains the IMDb rating and votes information for titles\n", + "* tconst (string) - alphanumeric unique identifier of the title\n", + "* averageRating – weighted average of all the individual user ratings\n", + "* numVotes - number of votes the title has received\n", + "\n", + "We'll have to get the name from somewhere else using \"tconst\" varialbe to match between dataframes.\n", + "\n", + "#### title.basics.tsv.gz - Contains the following information for titles:\n", + "* tconst (string) - alphanumeric unique identifier of the title\n", + "* titleType (string) – the type/format of the title (e.g. movie, short, tvseries, tvepisode, video, etc)\n", + "* primaryTitle (string) – the more popular title / the title used by the filmmakers on promotional materials at the point of release\n", + "* originalTitle (string) - original title, in the original language\n", + "* isAdult (boolean) - 0: non-adult title; 1: adult title\n", + "* startYear (YYYY) – represents the release year of a title. In the case of TV Series, it is the series start year\n", + "* endYear (YYYY) – TV Series end year. ‘\\N’ for all other title types\n", + "* runtimeMinutes – primary runtime of the title, in minutes\n", + "* genres (string array) – includes up to three genres associated with the title\n", + "\n", + "#### Lets load the titles" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "titles = pd.read_csv(\"https://datasets.imdbws.com/title.basics.tsv.gz\", sep='\\t')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "titles.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's cleanup some of the data by selecting a type (Movies) and drop a few of the columns we're not going to use.\n", + "\n", + "#### 1) Use titleType to get only movies \n", + "* What are the titleType's?\n", + "* How do we get rid of the ones we don't want?\n", + "\n", + "#### 2) Let's drop some things we're not using\n", + "* Once we know titleType is Movies we can drop it\n", + "* It seems primaryTitle\tand originalTitle are the same for most, let's only keep 1\n", + "* endYear seems to be only used for tv shows, lets drop endYear and rename startYear to just year." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Let's select 'movie' and 'tvMovie' and get rid of all the rest." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Let's drop some of our unused columns\n", + "\n", + "Use the [drop](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.drop.html) method built into pandas." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Let's match up the movies to their rating now. \n", + "\n", + "[Merge](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.merge.html)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Let's plot something to see how the data looks" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Ooops looks like something is wrong\n", + "\n", + "```\n", + "ValueError: could not convert string to float: '\\\\N'\n", + "```\n", + "\n", + "#### A ‘\\N’ is used to denote that a particular field is missing or null for that title/name.\n", + "\n", + "Let's take out all the numbered values that are null\n", + "\n", + "First let's make them all into numbers and anything that's not get changed to NaN (Not a number)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Now that we have some clean data let's make some cool plots!\n", + "\n", + "Look at some of the examples on [seaborn](https://seaborn.pydata.org/examples/index.html) and [matplotlib](https://matplotlib.org/3.1.1/gallery/index.html) to get some ideas and make some plots with the data we have." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.7.5 64-bit", + "language": "python", + "name": "python37564bit97448d587f3e42bf9dd368801ee71c4b" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} + diff --git a/01_Week1/06_PandasIMDB/06_B_Movies_Pandas_Finished.ipynb b/01_Week1/06_PandasIMDB/06_B_Movies_Pandas_Finished.ipynb new file mode 100644 index 0000000..a453968 --- /dev/null +++ b/01_Week1/06_PandasIMDB/06_B_Movies_Pandas_Finished.ipynb @@ -0,0 +1,386 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import numpy as np\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Lets get some data to look at\n", + "\n", + "Movies! IMDB has a lot of information about movies and tv shows and it's easy to download and get.\n", + "\n", + "\n", + "The documentioan is here:\n", + "\n", + "https://www.imdb.com/interfaces/\n", + "\n", + "Let's look at movie ratings!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ratings = pd.read_csv(\"https://datasets.imdbws.com/title.ratings.tsv.gz\", sep='\\t')\n", + "ratings.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Ok great we have these ratings but what movie are they for?\n", + "\n", + "Looking at the documentation:\n", + "\n", + "\n", + "#### title.ratings.tsv.gz – Contains the IMDb rating and votes information for titles\n", + "* tconst (string) - alphanumeric unique identifier of the title\n", + "* averageRating – weighted average of all the individual user ratings\n", + "* numVotes - number of votes the title has received\n", + "\n", + "We'll have to get the name from somewhere else using \"tconst\" varialbe to match between dataframes.\n", + "\n", + "#### title.basics.tsv.gz - Contains the following information for titles:\n", + "* tconst (string) - alphanumeric unique identifier of the title\n", + "* titleType (string) – the type/format of the title (e.g. movie, short, tvseries, tvepisode, video, etc)\n", + "* primaryTitle (string) – the more popular title / the title used by the filmmakers on promotional materials at the point of release\n", + "* originalTitle (string) - original title, in the original language\n", + "* isAdult (boolean) - 0: non-adult title; 1: adult title\n", + "* startYear (YYYY) – represents the release year of a title. In the case of TV Series, it is the series start year\n", + "* endYear (YYYY) – TV Series end year. ‘\\N’ for all other title types\n", + "* runtimeMinutes – primary runtime of the title, in minutes\n", + "* genres (string array) – includes up to three genres associated with the title\n", + "\n", + "#### Lets load the titles" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "titles = pd.read_csv(\"https://datasets.imdbws.com/title.basics.tsv.gz\", sep='\\t')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "titles.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's cleanup some of the data by selecting a type (Movies) and drop a few of the columns we're not going to use.\n", + "\n", + "#### 1) Use titleType to get only movies \n", + "* What are the titleType's?\n", + "* How do we get rid of the ones we don't want?\n", + "\n", + "#### 2) Let's drop some things we're not using\n", + "* Once we know titleType is Movies we can drop it\n", + "* It seems primaryTitle\tand originalTitle are the same for most, let's only keep 1\n", + "* endYear seems to be only used for tv shows, lets drop endYear and rename startYear to just year." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "titles.titleType.unique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Let's select 'movie' and 'tvMovie' and get rid of all the rest." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "movies = titles[(titles.titleType == 'movie') | (titles.titleType == 'tvMovie')]\n", + "\n", + "# Also select only non-adult movies to be safe for work\n", + "movies = movies[movies.isAdult == 0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "movies.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Let's drop some of our unused columns\n", + "\n", + "Use the [drop](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.drop.html) method built into pandas." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "movies = movies.drop(columns=['isAdult', 'originalTitle', 'titleType', 'endYear', 'genres'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Let's match up the movies to their rating now. \n", + "\n", + "[Merge](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.merge.html)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "movies = movies.merge(ratings, left_on='tconst', right_on='tconst')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "movies.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "movies.rename(columns={'startYear': 'Year', 'averageRating': 'Rating'}, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Let's plot something to see how the data looks" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "movies.plot.scatter('Year', 'Rating');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Ooops looks like something is wrong\n", + "\n", + "```\n", + "ValueError: could not convert string to float: '\\\\N'\n", + "```\n", + "\n", + "#### A ‘\\N’ is used to denote that a particular field is missing or null for that title/name.\n", + "\n", + "Let's take out all the numbered values that are null\n", + "\n", + "First let's make them all into numbers and anything that's not get changed to NaN (Not a number)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "movies.runtimeMinutes = pd.to_numeric(movies.runtimeMinutes, errors='coerce')\n", + "movies.Rating = pd.to_numeric(movies.Rating, errors='coerce')\n", + "movies.Year = pd.to_numeric(movies.Year, errors='coerce')\n", + "movies.numVotes = pd.to_numeric(movies.numVotes, errors='coerce')\n", + "movies.dropna(axis=0, how='any', inplace=True)\n", + "\n", + "movies.plot.scatter('Year', 'Rating');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Now that we have some clean data let's make some cool plots!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=(16,9))\n", + "plt.hist2d(movies.Year, movies.Rating, bins=(100,30), range=((1920,2020),None), cmap='Spectral_r')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "from scipy.stats import norm\n", + "\n", + "fig = plt.figure(figsize=(16,9))\n", + "plt.hist(movies.runtimeMinutes, bins=180,range=(0, 180), density=True)\n", + "mean,std=norm.fit(movies.runtimeMinutes[movies.runtimeMinutes < 180])\n", + "xmin, xmax = plt.xlim()\n", + "x = np.linspace(xmin, xmax, 100)\n", + "y = norm.pdf(x, mean, std)\n", + "plt.plot(x, y)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "top_movies = pd.read_csv(\"https://raw.githubusercontent.com/thechaudharysab/imdb-data-pandas-visualization/master/data/imdb_1000.csv\")\n", + "top_movies.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "movies = movies[movies.Rating >= np.min(top_movies.star_rating)]\n", + "movies.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "movies = movies.merge(top_movies, left_on='primaryTitle', right_on='title')\n", + "\n", + "movies.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=(16,9))\n", + "\n", + "for content_rating in movies.content_rating.unique():\n", + " movie_content_rating = movies[movies.content_rating == content_rating]\n", + " plt.hist(movie_content_rating.star_rating, range=(7,10),label=content_rating)\n", + " \n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=(16,9))\n", + "sns.jointplot(x=movies.Year, y=movies.duration, kind='kde' , color=\"blue\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=(16,9))\n", + "sns.boxplot(y=movies.duration, x=movies.star_rating)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=(16,9))\n", + "sns.violinplot(y=movies.duration, x=movies.star_rating)\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.7.5 64-bit", + "language": "python", + "name": "python37564bit97448d587f3e42bf9dd368801ee71c4b" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/01_Week1/06_PandasIMDB/README.md b/01_Week1/06_PandasIMDB/README.md new file mode 100644 index 0000000..6fcc7b7 --- /dev/null +++ b/01_Week1/06_PandasIMDB/README.md @@ -0,0 +1,9 @@ +# Welcome to Day 4 of Python for STEM! + +## Day 4 - Useful Pandas Applications +* 8:30 Examples of Pandas usages +* 9:00 Activities implementing Pandas +* 11:45 Wrap up + +### 06_PandasIMDB +* More in-depth use of the Pandas package for data analysis \ No newline at end of file diff --git a/01_Week1/07_PandasCovid19/07_A_SouthCarolina.ipynb b/01_Week1/07_PandasCovid19/07_A_SouthCarolina.ipynb new file mode 100644 index 0000000..2b0af24 --- /dev/null +++ b/01_Week1/07_PandasCovid19/07_A_SouthCarolina.ipynb @@ -0,0 +1,2069 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "# Load in some common libraries to do our analysis\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.dates import DateFormatter, date2num\n", + "import geopandas\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Lets go get some data\n", + "\n", + "The new york times has a dataset which they share on their github page. We will look at the [historical data](https://github.com/nytimes/covid-19-data#historical-data) and we're going to select the [county level data](https://github.com/nytimes/covid-19-data#county-level-data) as it will let us breakdown based on seperate states and counties. They have links for the raw data which will let us easily download the data through pandas.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Load data from nytimes\n", + "# We also tell pandas to treat the column names date as a special varialbe type \n", + "# called a datetime which will make analysis easier\n", + "\n", + "df = pd.read_csv('https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-counties.csv', \n", + " parse_dates=['date'])" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "my_dict = {'cases':[1,2,3] , 'county': ['Richland', 'Lexington']}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now lets look at the data using the [head](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.head.html) command which shows off the first few rows of the dataframe." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
datecountystatefipscasesdeaths
02020-01-21SnohomishWashington53061.010
12020-01-22SnohomishWashington53061.010
22020-01-23SnohomishWashington53061.010
32020-01-24CookIllinois17031.010
42020-01-24SnohomishWashington53061.010
\n", + "
" + ], + "text/plain": [ + " date county state fips cases deaths\n", + "0 2020-01-21 Snohomish Washington 53061.0 1 0\n", + "1 2020-01-22 Snohomish Washington 53061.0 1 0\n", + "2 2020-01-23 Snohomish Washington 53061.0 1 0\n", + "3 2020-01-24 Cook Illinois 17031.0 1 0\n", + "4 2020-01-24 Snohomish Washington 53061.0 1 0" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looks great except we're in South Carolina so it might be nice to just look at the data from here.\n", + "\n", + "We can select items from a dataframe by using boolean operators (`>, <, >=, <=, ==, !=`) for a specific column. It looks like we should be able to select on the state column and see which ones are equal to (`==`) \"South Carolina\"" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 Washington\n", + "1 Washington\n", + "2 Washington\n", + "3 Illinois\n", + "4 Washington\n", + "Name: state, dtype: object" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.state.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 False\n", + "1 False\n", + "2 False\n", + "3 False\n", + "4 False\n", + " ... \n", + "359973 False\n", + "359974 False\n", + "359975 False\n", + "359976 False\n", + "359977 False\n", + "Length: 359978, dtype: bool" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cut1 = (df.state == \"South Carolina\") & (df.county == \"Richland\")\n", + "cut2 = (df.county == \"Richland\") & (df.state == \"South Carolina\")\n", + "\n", + "(df.state == \"South Carolina\") | (df.state == \"North Carolina\")\n", + "\n", + "cut = (df.state == \"South Carolina\") & (df.county == \"Richland\")\n", + "cut" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
datecountystatefipscasesdeaths
3527822020-07-20PickensSouth Carolina45077.0143114
3527832020-07-20RichlandSouth Carolina45079.06216109
3527842020-07-20SaludaSouth Carolina45081.03504
3527852020-07-20SpartanburgSouth Carolina45083.0311764
3527862020-07-20SumterSouth Carolina45085.0171534
.....................
3591942020-07-22SpartanburgSouth Carolina45083.0325268
3591952020-07-22SumterSouth Carolina45085.0187735
3591962020-07-22UnionSouth Carolina45087.02380
3591972020-07-22WilliamsburgSouth Carolina45089.067118
3591982020-07-22YorkSouth Carolina45091.0257117
\n", + "

100 rows × 6 columns

\n", + "
" + ], + "text/plain": [ + " date county state fips cases deaths\n", + "352782 2020-07-20 Pickens South Carolina 45077.0 1431 14\n", + "352783 2020-07-20 Richland South Carolina 45079.0 6216 109\n", + "352784 2020-07-20 Saluda South Carolina 45081.0 350 4\n", + "352785 2020-07-20 Spartanburg South Carolina 45083.0 3117 64\n", + "352786 2020-07-20 Sumter South Carolina 45085.0 1715 34\n", + "... ... ... ... ... ... ...\n", + "359194 2020-07-22 Spartanburg South Carolina 45083.0 3252 68\n", + "359195 2020-07-22 Sumter South Carolina 45085.0 1877 35\n", + "359196 2020-07-22 Union South Carolina 45087.0 238 0\n", + "359197 2020-07-22 Williamsburg South Carolina 45089.0 671 18\n", + "359198 2020-07-22 York South Carolina 45091.0 2571 17\n", + "\n", + "[100 rows x 6 columns]" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Select South Carolina\n", + "cut = (df.state == \"South Carolina\")\n", + "\n", + "state = df[cut]\n", + "# Tail works similar to head but for the bottom of the list\n", + "state.tail(100)" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
fipscasesdeaths
count5728.0000005728.0000005728.000000
mean45046.155028394.3615579.591306
std26.511030918.41063516.657481
min45001.0000001.0000000.000000
25%45023.00000030.0000000.000000
50%45047.000000116.0000003.000000
75%45069.000000339.25000012.000000
max45091.0000009968.000000137.000000
\n", + "
" + ], + "text/plain": [ + " fips cases deaths\n", + "count 5728.000000 5728.000000 5728.000000\n", + "mean 45046.155028 394.361557 9.591306\n", + "std 26.511030 918.410635 16.657481\n", + "min 45001.000000 1.000000 0.000000\n", + "25% 45023.000000 30.000000 0.000000\n", + "50% 45047.000000 116.000000 3.000000\n", + "75% 45069.000000 339.250000 12.000000\n", + "max 45091.000000 9968.000000 137.000000" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "state = df[(df.state == \"South Carolina\")]\n", + "\n", + "state.cases.std()\n", + "\n", + "state.describe()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[,\n", + " ],\n", + " [,\n", + " ]],\n", + " dtype=object)" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we have data for South Carolina but it's still not in the best format. We have each row as values for each date in each county. It might be more useful to use a [pivot](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.pivot.html) function similar to pivots in excel in order to sum our cases per-day, per-county. We'll use our date as an index and have our county information as the columns so that we can easily plot them later." + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
countyAbbevilleAikenAllendaleAndersonBambergBarnwellBeaufortBerkeleyCalhounCharleston...OconeeOrangeburgPickensRichlandSaludaSpartanburgSumterUnionWilliamsburgYork
date
2020-07-13157.0735.088.01067.0199.0135.02020.02290.0166.07835.0...448.01226.01197.04995.0290.02562.01449.0162.0507.01949.0
2020-07-14162.0800.092.01134.0206.0153.02098.02450.0170.08250.0...460.01271.01227.05229.0300.02648.01489.0163.0523.01989.0
2020-07-15165.0873.092.01177.0223.0156.02176.02512.0176.08466.0...475.01301.01254.05413.0303.02745.01582.0170.0538.02062.0
2020-07-16170.0903.0101.01247.0240.0169.02285.02618.0188.08677.0...492.01383.01282.05550.0307.02826.01610.0183.0563.02138.0
2020-07-17176.0924.0112.01299.0247.0176.02338.02752.0201.08971.0...511.01437.01329.05756.0322.02905.01635.0186.0572.02203.0
2020-07-18185.0992.0114.01351.0261.0183.02397.02828.0203.09098.0...545.01472.01372.05930.0332.02950.01686.0190.0582.02232.0
2020-07-19190.01027.0122.01383.0303.0207.02488.02954.0218.09437.0...561.01565.01403.06050.0343.03041.01707.0198.0627.02390.0
2020-07-20242.01049.0125.01424.0311.0214.02521.02989.0226.09623.0...570.01595.01431.06216.0350.03117.01715.0203.0650.02438.0
2020-07-21246.01061.0129.01487.0320.0225.02584.03092.0231.09785.0...577.01638.01470.06408.0361.03166.01810.0224.0659.02514.0
2020-07-22249.01109.0132.01519.0328.0231.02663.03177.0233.09968.0...585.01697.01484.06581.0366.03252.01877.0238.0671.02571.0
\n", + "

10 rows × 46 columns

\n", + "
" + ], + "text/plain": [ + "county Abbeville Aiken Allendale Anderson Bamberg Barnwell \\\n", + "date \n", + "2020-07-13 157.0 735.0 88.0 1067.0 199.0 135.0 \n", + "2020-07-14 162.0 800.0 92.0 1134.0 206.0 153.0 \n", + "2020-07-15 165.0 873.0 92.0 1177.0 223.0 156.0 \n", + "2020-07-16 170.0 903.0 101.0 1247.0 240.0 169.0 \n", + "2020-07-17 176.0 924.0 112.0 1299.0 247.0 176.0 \n", + "2020-07-18 185.0 992.0 114.0 1351.0 261.0 183.0 \n", + "2020-07-19 190.0 1027.0 122.0 1383.0 303.0 207.0 \n", + "2020-07-20 242.0 1049.0 125.0 1424.0 311.0 214.0 \n", + "2020-07-21 246.0 1061.0 129.0 1487.0 320.0 225.0 \n", + "2020-07-22 249.0 1109.0 132.0 1519.0 328.0 231.0 \n", + "\n", + "county Beaufort Berkeley Calhoun Charleston ... Oconee Orangeburg \\\n", + "date ... \n", + "2020-07-13 2020.0 2290.0 166.0 7835.0 ... 448.0 1226.0 \n", + "2020-07-14 2098.0 2450.0 170.0 8250.0 ... 460.0 1271.0 \n", + "2020-07-15 2176.0 2512.0 176.0 8466.0 ... 475.0 1301.0 \n", + "2020-07-16 2285.0 2618.0 188.0 8677.0 ... 492.0 1383.0 \n", + "2020-07-17 2338.0 2752.0 201.0 8971.0 ... 511.0 1437.0 \n", + "2020-07-18 2397.0 2828.0 203.0 9098.0 ... 545.0 1472.0 \n", + "2020-07-19 2488.0 2954.0 218.0 9437.0 ... 561.0 1565.0 \n", + "2020-07-20 2521.0 2989.0 226.0 9623.0 ... 570.0 1595.0 \n", + "2020-07-21 2584.0 3092.0 231.0 9785.0 ... 577.0 1638.0 \n", + "2020-07-22 2663.0 3177.0 233.0 9968.0 ... 585.0 1697.0 \n", + "\n", + "county Pickens Richland Saluda Spartanburg Sumter Union \\\n", + "date \n", + "2020-07-13 1197.0 4995.0 290.0 2562.0 1449.0 162.0 \n", + "2020-07-14 1227.0 5229.0 300.0 2648.0 1489.0 163.0 \n", + "2020-07-15 1254.0 5413.0 303.0 2745.0 1582.0 170.0 \n", + "2020-07-16 1282.0 5550.0 307.0 2826.0 1610.0 183.0 \n", + "2020-07-17 1329.0 5756.0 322.0 2905.0 1635.0 186.0 \n", + "2020-07-18 1372.0 5930.0 332.0 2950.0 1686.0 190.0 \n", + "2020-07-19 1403.0 6050.0 343.0 3041.0 1707.0 198.0 \n", + "2020-07-20 1431.0 6216.0 350.0 3117.0 1715.0 203.0 \n", + "2020-07-21 1470.0 6408.0 361.0 3166.0 1810.0 224.0 \n", + "2020-07-22 1484.0 6581.0 366.0 3252.0 1877.0 238.0 \n", + "\n", + "county Williamsburg York \n", + "date \n", + "2020-07-13 507.0 1949.0 \n", + "2020-07-14 523.0 1989.0 \n", + "2020-07-15 538.0 2062.0 \n", + "2020-07-16 563.0 2138.0 \n", + "2020-07-17 572.0 2203.0 \n", + "2020-07-18 582.0 2232.0 \n", + "2020-07-19 627.0 2390.0 \n", + "2020-07-20 650.0 2438.0 \n", + "2020-07-21 659.0 2514.0 \n", + "2020-07-22 671.0 2571.0 \n", + "\n", + "[10 rows x 46 columns]" + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Group cases by county for each date\n", + "cases_sc = state.pivot(index='date', columns='county', values='cases')\n", + "cases_sc = cases_sc.fillna(0.0)\n", + "\n", + "# Lets look at the last 10 days\n", + "cases_sc.tail(10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plotting\n", + "\n", + "Let's see if we can get some more information from the data from plotting the data. Pandas has a built in plot method which calls matplotlib to plot the data. Let's see what we get when we just try to plot the data." + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot = cases_sc.plot()\n", + "plot.legend(ncol=2, bbox_to_anchor=(1, 1), loc='upper left')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot = cases_sc.plot(figsize=(12,8), linewidth=5, cmap='PuBu')\n", + "plot.legend(ncol=2, bbox_to_anchor=(1, 1), loc='upper left')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Show plotting inside of pandas\n", + "plot = cases_sc.Richland.plot(figsize=(12,8), linewidth=5, legend=True)\n", + "\n", + "# You can also access a column by a string name\n", + "plot = cases_sc[\"Lexington\"].plot(linewidth=5, legend=True)\n", + "\n", + "### TODO: Add a few more counties to this graph" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Now let's also look at deaths instead of cases.\n", + "TODO: re-do the last few steps from the pivot but this time for deaths. Label the new dataframe \"deaths_sc\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# deaths_sc = ..." + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['Abbeville', 'Aiken', 'Allendale', 'Anderson', 'Bamberg', 'Barnwell',\n", + " 'Beaufort', 'Berkeley', 'Calhoun', 'Charleston', 'Cherokee', 'Chester',\n", + " 'Chesterfield', 'Clarendon', 'Colleton', 'Darlington', 'Dillon',\n", + " 'Dorchester', 'Edgefield', 'Fairfield', 'Florence', 'Georgetown',\n", + " 'Greenville', 'Greenwood', 'Hampton', 'Horry', 'Jasper', 'Kershaw',\n", + " 'Lancaster', 'Laurens', 'Lee', 'Lexington', 'Marion', 'Marlboro',\n", + " 'McCormick', 'Newberry', 'Oconee', 'Orangeburg', 'Pickens', 'Richland',\n", + " 'Saluda', 'Spartanburg', 'Sumter', 'Union', 'Williamsburg', 'York'],\n", + " dtype='object', name='county')" + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cases_sc.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Abbeville\n", + "Aiken\n", + "Allendale\n", + "Anderson\n", + "Bamberg\n", + "Barnwell\n", + "Beaufort\n", + "Berkeley\n", + "Calhoun\n", + "Charleston\n", + "Cherokee\n", + "Chester\n", + "Chesterfield\n", + "Clarendon\n", + "Colleton\n", + "Darlington\n", + "Dillon\n", + "Dorchester\n", + "Edgefield\n", + "Fairfield\n", + "Florence\n", + "Georgetown\n", + "Greenville\n", + "Greenwood\n", + "Hampton\n", + "Horry\n", + "Jasper\n", + "Kershaw\n", + "Lancaster\n", + "Laurens\n", + "Lee\n", + "Lexington\n", + "Marion\n", + "Marlboro\n", + "McCormick\n", + "Newberry\n", + "Oconee\n", + "Orangeburg\n", + "Pickens\n", + "Richland\n", + "Saluda\n", + "Spartanburg\n", + "Sumter\n", + "Union\n", + "Williamsburg\n", + "York\n" + ] + } + ], + "source": [ + "\n", + "\n", + "for county in cases_sc.columns:\n", + " cases_sc[county]" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
countyAbbevilleAikenAllendaleAndersonBambergBarnwellBeaufortBerkeleyCalhounCharleston...OconeeOrangeburgPickensRichlandSaludaSpartanburgSumterUnionWilliamsburgYork
date
2020-07-189.068.02.052.014.07.059.076.02.0127.0...34.035.043.0174.010.045.051.04.010.029.0
2020-07-195.035.08.032.042.024.091.0126.015.0339.0...16.093.031.0120.011.091.021.08.045.0158.0
2020-07-2052.022.03.041.08.07.033.035.08.0186.0...9.030.028.0166.07.076.08.05.023.048.0
2020-07-214.012.04.063.09.011.063.0103.05.0162.0...7.043.039.0192.011.049.095.021.09.076.0
2020-07-223.048.03.032.08.06.079.085.02.0183.0...8.059.014.0173.05.086.067.014.012.057.0
\n", + "

5 rows × 46 columns

\n", + "
" + ], + "text/plain": [ + "county Abbeville Aiken Allendale Anderson Bamberg Barnwell \\\n", + "date \n", + "2020-07-18 9.0 68.0 2.0 52.0 14.0 7.0 \n", + "2020-07-19 5.0 35.0 8.0 32.0 42.0 24.0 \n", + "2020-07-20 52.0 22.0 3.0 41.0 8.0 7.0 \n", + "2020-07-21 4.0 12.0 4.0 63.0 9.0 11.0 \n", + "2020-07-22 3.0 48.0 3.0 32.0 8.0 6.0 \n", + "\n", + "county Beaufort Berkeley Calhoun Charleston ... Oconee Orangeburg \\\n", + "date ... \n", + "2020-07-18 59.0 76.0 2.0 127.0 ... 34.0 35.0 \n", + "2020-07-19 91.0 126.0 15.0 339.0 ... 16.0 93.0 \n", + "2020-07-20 33.0 35.0 8.0 186.0 ... 9.0 30.0 \n", + "2020-07-21 63.0 103.0 5.0 162.0 ... 7.0 43.0 \n", + "2020-07-22 79.0 85.0 2.0 183.0 ... 8.0 59.0 \n", + "\n", + "county Pickens Richland Saluda Spartanburg Sumter Union \\\n", + "date \n", + "2020-07-18 43.0 174.0 10.0 45.0 51.0 4.0 \n", + "2020-07-19 31.0 120.0 11.0 91.0 21.0 8.0 \n", + "2020-07-20 28.0 166.0 7.0 76.0 8.0 5.0 \n", + "2020-07-21 39.0 192.0 11.0 49.0 95.0 21.0 \n", + "2020-07-22 14.0 173.0 5.0 86.0 67.0 14.0 \n", + "\n", + "county Williamsburg York \n", + "date \n", + "2020-07-18 10.0 29.0 \n", + "2020-07-19 45.0 158.0 \n", + "2020-07-20 23.0 48.0 \n", + "2020-07-21 9.0 76.0 \n", + "2020-07-22 12.0 57.0 \n", + "\n", + "[5 rows x 46 columns]" + ] + }, + "execution_count": 103, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Copy data\n", + "new_per_day = cases_sc.copy(deep=True)\n", + "\n", + "# Get difference per day instead of total cases\n", + "for county in cases_sc.columns:\n", + " new_per_day[county] = new_per_day[county].diff()\n", + " \n", + "new_per_day = new_per_day.fillna(0.0)\n", + "new_per_day.tail(5) \n" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot difference\n", + "plot = new_per_day.Richland.plot(figsize=(12,8), linewidth=3, legend=True)\n", + "# Looks jumpy...." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Let's make this look better\n", + "\n", + "We're going to use [matplotlib](https://matplotlib.org/3.1.1/index.html) to plot these graphs instead of the builtin plotting function. Sometimes when we want to add more features to our plots it is easier to use matplotlib instead of pandas built in plotting." + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Setup Figure (fig) and Axes (ax)\n", + "fig, ax = plt.subplots(figsize=(12,8))\n", + "\n", + "# This function converts our dates into numbers so matplotlib can plot them\n", + "dates = date2num(cases_sc.index)\n", + "# We setup how the date format and put them onto the axes\n", + "date_form = DateFormatter(\"%b-%d\")\n", + "ax.xaxis.set_major_formatter(date_form)\n", + "\n", + "# Lets get the new cases per day for Richland county\n", + "npd_richland = new_per_day.Richland\n", + "\n", + "# Then we'll plot it with plot_dates functon\n", + "ax.plot_date(dates, npd_richland, linewidth=3, fmt='-')\n", + "\n", + "# Show our plot at the end\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We've repoduced our plot from before let's customize it some more. To get rid of the large jumps in our data we can do a rolling mean over the data we have." + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Setup Figure (fig) and Axes (ax)\n", + "fig, ax = plt.subplots(figsize=(10,6))\n", + "\n", + "# This function converts our dates into numbers so matplotlib can plot them\n", + "dates = date2num(cases_sc.index)\n", + "# We setup how the date format and put them onto the axes\n", + "date_form = DateFormatter(\"%b-%d\")\n", + "ax.xaxis.set_major_formatter(date_form)\n", + "\n", + "# Lets get the new cases per day for Richland county\n", + "npd_richland = new_per_day.Richland\n", + "\n", + "#### Let's average over the last two weeks and plot it\n", + "rolling_days = 14\n", + "npd_richland_avg = new_per_day.Richland.rolling(rolling_days).mean()\n", + "####\n", + "\n", + "\n", + "# Then we'll plot it with plot_dates functon\n", + "ax.plot_date(dates, npd_richland_avg, fmt='-', linewidth=3)\n", + "ax.set_title('New Cases per Day in Richland')\n", + "ax.set_xlabel('Date')\n", + "\n", + "# Show our plot at the end\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Great our plot is looking a bit better now\n", + "\n", + "Now let's add a bar graph to show the original new per day data." + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Setup Figure (fig) and Axes (ax)\n", + "fig, ax = plt.subplots(figsize=(10,6))\n", + "\n", + "# This function converts our dates into numbers so matplotlib can plot them\n", + "dates = date2num(cases_sc.index)\n", + "# We setup how the date format and put them onto the axes\n", + "date_form = DateFormatter(\"%b-%d\")\n", + "ax.xaxis.set_major_formatter(date_form)\n", + "\n", + "# Lets get the new cases per day for Richland county\n", + "npd_richland = new_per_day.Richland\n", + "\n", + "#### We'll add a bar graph to our axes now that is a little lighter in color (alpha)\n", + "ax.bar(dates, npd_richland, alpha=0.2)\n", + "###\n", + "\n", + "# Let's average over the last two weeks and plot it\n", + "rolling_days = 14\n", + "npd_richland_avg = new_per_day.Richland.rolling(rolling_days).mean()\n", + "\n", + "# Then we'll plot it with plot_dates functon\n", + "ax.plot_date(dates, npd_richland_avg, fmt='-', linewidth=3)\n", + "\n", + "# Show our plot at the end\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig2, ax2 = plt.subplots(ncols=2, figsize=(10,6))\n", + "\n", + "\n", + "# # This function converts our dates into numbers so matplotlib can plot them\n", + "dates = date2num(cases_sc.index)\n", + "# # We setup how the date format and put them onto the axes\n", + "date_form = DateFormatter(\"%b-%d\")\n", + "ax2[0].xaxis.set_major_formatter(date_form)\n", + "ax2[1].xaxis.set_major_formatter(date_form)\n", + "\n", + "# # Lets get the new cases per day for Richland county\n", + "npd_richland = new_per_day.Richland\n", + "\n", + "# #### We'll add a bar graph to our axes now that is a little lighter in color (alpha)\n", + "ax2[0].bar(dates, npd_richland, alpha=0.2)\n", + "# ###\n", + "\n", + "ax2[1].bar(dates, new_per_day.Lexington, alpha=0.2)\n", + "\n", + "\n", + "# # Let's average over the last two weeks and plot it\n", + "# rolling_days = 14\n", + "# npd_richland_avg = new_per_day.Richland.rolling(rolling_days).mean()\n", + "\n", + "# # Then we'll plot it with plot_dates functon\n", + "# ax.plot_date(dates, npd_richland_avg, fmt='-', linewidth=3)\n", + "\n", + "# # Show our plot at the end\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Let's finish by putting a trendline for the last two weeks" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Setup Figure (fig) and Axes (ax)\n", + "fig, ax = plt.subplots(figsize=(12,8))\n", + "\n", + "# This function converts our dates into numbers so matplotlib can plot them\n", + "dates = date2num(cases_sc.index)\n", + "# We setup how the date format and put them onto the axes\n", + "date_form = DateFormatter(\"%b-%d\")\n", + "ax.xaxis.set_major_formatter(date_form)\n", + "\n", + "# Lets get the new cases per day for Richland county\n", + "npd_richland = new_per_day.Richland\n", + "\n", + "# We'll add a bar graph to our axes now that is a little lighter in color (alpha)\n", + "ax.bar(dates, npd_richland, alpha=0.2)\n", + "\n", + "# Let's average over the last two weeks and plot it\n", + "rolling_days = 14\n", + "npd_richland_avg = new_per_day.Richland.rolling(rolling_days).mean()\n", + "\n", + "# Then we'll plot it with plot_dates functon\n", + "ax.plot_date(dates, npd_richland_avg, fmt='-', linewidth=3)\n", + "\n", + "### Plot trend line for the last set of days averaged over\n", + "z = np.polyfit(dates[-rolling_days:], npd_richland_avg[-rolling_days:], 1)\n", + "p = np.poly1d(z)\n", + "ax.plot(dates[-rolling_days:],p(dates[-rolling_days:]),'-', linewidth=4, label=f'Trend for last {rolling_days} days')\n", + "###\n", + "\n", + "\n", + "# Show our plot at the end\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Let's make a Map\n", + "\n", + "We're going to use the geopandas to plot our data. There are a ton of different maps availible that work with geopandas on this [github page](https://github.com/deldersveld/topojson)." + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
idSTATEFPCOUNTYFPCOUNTYNSAFFGEOIDGEOIDNAMELSADALANDAWATERgeometry
0None45041012480010500000US4504145041Florence0620718947149743913POLYGON ((-80.07250 34.08662, -79.80741 34.252...
1None45081012480160500000US4508145081Saluda06117269062423419265POLYGON ((-81.87158 34.13522, -81.86098 34.180...
2None45007012479810500000US4500745007Anderson061853026075108724190POLYGON ((-82.84005 34.62251, -82.78880 34.671...
3None45023012479880500000US4502345023Chester06150389331314261786POLYGON ((-81.47925 34.82066, -80.89782 34.820...
4None45043012480020500000US4504345043Georgetown062107094125572624603POLYGON ((-79.31788 33.78004, -79.24719 33.695...
\n", + "
" + ], + "text/plain": [ + " id STATEFP COUNTYFP COUNTYNS AFFGEOID GEOID NAME LSAD \\\n", + "0 None 45 041 01248001 0500000US45041 45041 Florence 06 \n", + "1 None 45 081 01248016 0500000US45081 45081 Saluda 06 \n", + "2 None 45 007 01247981 0500000US45007 45007 Anderson 06 \n", + "3 None 45 023 01247988 0500000US45023 45023 Chester 06 \n", + "4 None 45 043 01248002 0500000US45043 45043 Georgetown 06 \n", + "\n", + " ALAND AWATER geometry \n", + "0 2071894714 9743913 POLYGON ((-80.07250 34.08662, -79.80741 34.252... \n", + "1 1172690624 23419265 POLYGON ((-81.87158 34.13522, -81.86098 34.180... \n", + "2 1853026075 108724190 POLYGON ((-82.84005 34.62251, -82.78880 34.671... \n", + "3 1503893313 14261786 POLYGON ((-81.47925 34.82066, -80.89782 34.820... \n", + "4 2107094125 572624603 POLYGON ((-79.31788 33.78004, -79.24719 33.695... " + ] + }, + "execution_count": 126, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Get geometry data\n", + "# https://github.com/deldersveld/topojson\n", + "\n", + "JSON_url = \"https://raw.githubusercontent.com/deldersveld/topojson/master/countries/us-states\"\n", + "SC_json = \"SC-45-south-carolina-counties\"\n", + "\n", + "geodata = geopandas.read_file(JSON_url+\"/\"+SC_json+\".json\")\n", + "\n", + "geodata.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Make a new figure and axis\n", + "fig, ax = plt.subplots(figsize=(16,9))\n", + "# Plot the outlines of SC\n", + "geodata.plot(color=\"white\", edgecolor='black', ax=ax)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## To add color we can add a new column based on our current cases\n", + "\n", + "Let's look at our cases dataframe and see how we'll match it to the plot." + ] + }, + { + "cell_type": "code", + "execution_count": 157, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(46,)" + ] + }, + "execution_count": 157, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#cases_sc.tail(3)\n", + "\n", + "geodata[\"color\"] = np.ones_like(geodata.NAME) \n", + "\n", + "\n", + "\n", + "#geodata[\"total_area\"] = geodata.ALAND + geodata.AWATER\n", + "\n", + "geodata.head()\n", + "\n", + "np.zeros_like(geodata.NAME).shape\n", + "\n", + "#geodata[\"color\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 169, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Make a new figure and axis\n", + "fig, ax_geo = plt.subplots(figsize=(16,9))\n", + "# Plot the outlines of SC\n", + "geodata.plot(color=\"white\", edgecolor='black', ax=ax)\n", + "\n", + "#### First we add a new column for our colors\n", + "geodata[\"color\"] = np.zeros_like(geodata.NAME)\n", + "\n", + "#### Now we can match our data from our cases_sc dataframe, \n", + "#### to our plotting dataframe using the name of the county\n", + "\n", + "# iteritems will return the and the column name (In this case our county names)\n", + "# And the entire column of data (Our numbers)\n", + "for county_name, county_nums in cases_sc.iteritems():\n", + " # To get the most recent we can get the -1 index\n", + " most_recent_num = county_nums[-1]\n", + " # We want to put the value for our most recent cases in the color part\n", + " # Only for the location of the row of the geodata that is the same as our county name\n", + " geodata.loc[geodata.NAME == county_name, 'color'] = most_recent_num\n", + "\n", + "# And plot based on the new color cloumn\n", + "geodata.plot(column='color', ax=ax_geo, cmap='PuBu')\n", + "####\n", + "\n", + "ax.set_title(\"Current Cases in SC\")\n", + "ax.set_yticklabels([])\n", + "ax.set_xticklabels([])\n", + "ax.get_yaxis().set_visible(False)\n", + "ax.get_xaxis().set_visible(False)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Try to make some more plots on your own" + ] + }, + { + "cell_type": "code", + "execution_count": 160, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'cases')" + ] + }, + "execution_count": 160, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(new_per_day.Richland)\n", + "plt.xlabel(\"cases\")" + ] + }, + { + "cell_type": "code", + "execution_count": 165, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 165, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist2d(state.cases,state.deaths);\n", + "plt.colorbar()" + ] + }, + { + "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": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/01_Week1/07_PandasCovid19/07_B_usaMap.ipynb b/01_Week1/07_PandasCovid19/07_B_usaMap.ipynb new file mode 100644 index 0000000..eecd9f1 --- /dev/null +++ b/01_Week1/07_PandasCovid19/07_B_usaMap.ipynb @@ -0,0 +1,225 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.dates import DateFormatter, date2num\n", + "import matplotlib.ticker as ticker\n", + "import geopandas\n", + "from shapely.geometry import shape\n", + "from PIL import Image\n", + "import glob\n", + "from IPython.display import HTML\n", + "\n", + "# Load data from nytimes\n", + "# Has data for each county in each state\n", + "df = pd.read_csv('https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-counties.csv', \n", + " parse_dates=['date'])\n", + "\n", + "\n", + "# https://gist.github.com/rogerallen/1583593\n", + "us_state_abbrev = {\n", + " 'Alabama': 'AL',\n", + " 'Alaska': 'AK',\n", + " 'American Samoa': 'AS',\n", + " 'Arizona': 'AZ',\n", + " 'Arkansas': 'AR',\n", + " 'California': 'CA',\n", + " 'Colorado': 'CO',\n", + " 'Connecticut': 'CT',\n", + " 'Delaware': 'DE',\n", + " 'District of Columbia': 'DC',\n", + " 'Florida': 'FL',\n", + " 'Georgia': 'GA',\n", + " 'Guam': 'GU',\n", + " 'Hawaii': 'HI',\n", + " 'Idaho': 'ID',\n", + " 'Illinois': 'IL',\n", + " 'Indiana': 'IN',\n", + " 'Iowa': 'IA',\n", + " 'Kansas': 'KS',\n", + " 'Kentucky': 'KY',\n", + " 'Louisiana': 'LA',\n", + " 'Maine': 'ME',\n", + " 'Maryland': 'MD',\n", + " 'Massachusetts': 'MA',\n", + " 'Michigan': 'MI',\n", + " 'Minnesota': 'MN',\n", + " 'Mississippi': 'MS',\n", + " 'Missouri': 'MO',\n", + " 'Montana': 'MT',\n", + " 'Nebraska': 'NE',\n", + " 'Nevada': 'NV',\n", + " 'New Hampshire': 'NH',\n", + " 'New Jersey': 'NJ',\n", + " 'New Mexico': 'NM',\n", + " 'New York': 'NY',\n", + " 'North Carolina': 'NC',\n", + " 'North Dakota': 'ND',\n", + " 'Northern Mariana Islands':'MP',\n", + " 'Ohio': 'OH',\n", + " 'Oklahoma': 'OK',\n", + " 'Oregon': 'OR',\n", + " 'Pennsylvania': 'PA',\n", + " 'Puerto Rico': 'PR',\n", + " 'Rhode Island': 'RI',\n", + " 'South Carolina': 'SC',\n", + " 'South Dakota': 'SD',\n", + " 'Tennessee': 'TN',\n", + " 'Texas': 'TX',\n", + " 'Utah': 'UT',\n", + " 'Vermont': 'VT',\n", + " 'Virgin Islands': 'VI',\n", + " 'Virginia': 'VA',\n", + " 'Washington': 'WA',\n", + " 'West Virginia': 'WV',\n", + " 'Wisconsin': 'WI',\n", + " 'Wyoming': 'WY'\n", + "}\n", + "\n", + "@np.vectorize\n", + "def state_abbrev(state):\n", + " return us_state_abbrev[state]\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Print the data\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Select South Carolina\n", + "state = df.copy(deep=True)\n", + "state[\"name\"] = state.county + \" ,\" + state_abbrev(state.state)\n", + "# Group by county for each date\n", + "state = state.pivot(index='date', columns='name', values='cases')\n", + "state = state.fillna(0.0)\n", + "state.tail(5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#plot = state.plot(figsize=(12,8), linewidth=5, cmap='PuBu')\n", + "#plot.legend(ncol=2, bbox_to_anchor=(1, 1), loc='upper left')\n", + "#plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Copy data\n", + "new_per_day = state.copy(deep=True)\n", + "\n", + "# Get difference per day instead of total cases\n", + "for county in state.columns:\n", + " new_per_day[county] = new_per_day[county].sum()\n", + "new_per_day = new_per_day.fillna(0.0)\n", + "new_per_day.tail(5) \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# Get geometry data\n", + "# https://github.com/deldersveld/topojson\n", + "geodata = geopandas.read_file(f\"https://github.com/deldersveld/topojson/blob/master/countries/united-states/us-albers-counties.json?raw=true\")\n", + "\n", + "geodata" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "geodata['full_name'] = geodata.name + \" ,\" + state_abbrev(geodata.state)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(16,16))\n", + "geodata.plot(color=\"white\", edgecolor='black', ax=ax)\n", + "max_count = np.max(state.max(axis=0))\n", + "\n", + "geodata[\"color\"] = np.zeros_like(geodata.full_name)\n", + "\n", + "for c in state.iteritems():\n", + " geodata.loc[geodata.full_name == c[0], 'color'] = c[1][-1]\n", + "\n", + "geodata.plot(column='color', ax=ax, cmap='hot_r')\n", + "ax.set_title(f\"Current Cases\")\n", + "ax.set_yticklabels([])\n", + "ax.set_xticklabels([])\n", + "ax.get_yaxis().set_visible(False)\n", + "ax.get_xaxis().set_visible(False)\n", + "plt.show()\n", + "plt.close()" + ] + }, + { + "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": "3.7.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/01_Week1/07_PandasCovid19/07_C_WorldData.ipynb b/01_Week1/07_PandasCovid19/07_C_WorldData.ipynb new file mode 100644 index 0000000..9ccb003 --- /dev/null +++ b/01_Week1/07_PandasCovid19/07_C_WorldData.ipynb @@ -0,0 +1,127 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## https://towardsdatascience.com/visualizing-covid-19-data-beautifully-in-python-in-5-minutes-or-less-affc361b2c6a" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "# Section 1 - Loading our Libraries\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.dates import DateFormatter\n", + "import matplotlib.ticker as ticker\n", + "\n", + "# Section 2 - Loading and Selecting Data\n", + "df = pd.read_csv('https://raw.githubusercontent.com/datasets/covid-19/master/data/countries-aggregated.csv', parse_dates=['Date'])\n", + "countries = ['Canada', 'Germany', 'United Kingdom', 'US', 'France', 'China']\n", + "df = df[df['Country'].isin(countries)]\n", + "\n", + "# Section 3 - Creating a Summary Column\n", + "df['Cases'] = df[['Confirmed', 'Recovered', 'Deaths']].sum(axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Section 4 - Restructuring our Data\n", + "df = df.pivot(index='Date', columns='Country', values='Cases')\n", + "countries = list(df.columns)\n", + "covid = df.reset_index('Date')\n", + "covid.set_index(['Date'], inplace=True)\n", + "covid.columns = countries\n", + "\n", + "# Section 5 - Calculating Rates per 100,000\n", + "populations = {'Canada':37664517, 'Germany': 83721496 , 'United Kingdom': 67802690 , 'US': 330548815, 'France': 65239883, 'China':1438027228}\n", + "percapita = covid.copy()\n", + "for country in list(percapita.columns):\n", + " percapita[country] = percapita[country]/populations[country]*100000" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Section 6 - Generating Colours and Style\n", + "colors = {'Canada':'#045275', 'China':'#089099', 'France':'#7CCBA2', 'Germany':'#FCDE9C', 'US':'#DC3977', 'United Kingdom':'#7C1D6F'}\n", + "plt.style.use('fivethirtyeight')\n", + "\n", + "# Section 7 - Creating the Visualization\n", + "plot = covid.plot(figsize=(12,8), color=list(colors.values()), linewidth=5, legend=False)\n", + "plot.yaxis.set_major_formatter(ticker.StrMethodFormatter('{x:,.0f}'))\n", + "plot.grid(color='#d4d4d4')\n", + "plot.set_xlabel('Date')\n", + "plot.set_ylabel('# of Cases')\n", + "\n", + "# Section 8 - Assigning Colour\n", + "for country in list(colors.keys()):\n", + " plot.text(x = covid.index[-1], y = covid[country].max(), color = colors[country], s = country, weight = 'bold')\n", + "\n", + "# Section 9 - Adding Labels\n", + "plot.text(x = covid.index[1], y = int(covid.max().max())+350000, s = \"COVID-19 Cases by Country\", fontsize = 23, weight = 'bold', alpha = .75)\n", + "plot.text(x = covid.index[1], y = int(covid.max().max())+15000, s = \"For the USA, China, Germany, France, United Kingdom, and Canada\\nIncludes Current Cases, Recoveries, and Deaths\", fontsize = 16, alpha = .75)\n", + "plot.text(x = percapita.index[1], y = -100000,s = 'datagy.io Source: https://github.com/datasets/covid-19/blob/master/data/countries-aggregated.csv', fontsize = 10);" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "percapitaplot = percapita.plot(figsize=(12,8), color=list(colors.values()), linewidth=5, legend=False)\n", + "percapitaplot.grid(color='#d4d4d4')\n", + "percapitaplot.set_xlabel('Date')\n", + "percapitaplot.set_ylabel('# of Cases per 100,000 People')\n", + "for country in list(colors.keys()):\n", + " percapitaplot.text(x = percapita.index[-1], y = percapita[country].max(), color = colors[country], s = country, weight = 'bold')\n", + "percapitaplot.text(x = percapita.index[1], y = percapita.max().max()+100, s = \"Per Capita COVID-19 Cases by Country\", fontsize = 23, weight = 'bold', alpha = .75)\n", + "percapitaplot.text(x = percapita.index[1], y = percapita.max().max(), s = \"For the USA, China, Germany, France, United Kingdom, and Canada\\nIncludes Current Cases, Recoveries, and Deaths\", fontsize = 16, alpha = .75)\n", + "percapitaplot.text(x = percapita.index[1], y = -30,s = 'datagy.io Source: https://github.com/datasets/covid-19/blob/master/data/countries-aggregated.csv', fontsize = 10);" + ] + }, + { + "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": "3.7.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/01_Week1/07_PandasCovid19/README.md b/01_Week1/07_PandasCovid19/README.md new file mode 100644 index 0000000..41ffdde --- /dev/null +++ b/01_Week1/07_PandasCovid19/README.md @@ -0,0 +1,9 @@ +# Welcome to Day 4 of Python for STEM! + +## Day 4 - Useful Pandas Applications +* 8:30 Examples of Pandas usages +* 9:00 Activities implementing Pandas +* 11:45 Wrap up + +### 06_PandasCovid19 +* More in-depth use of the Pandas package for data analysis \ No newline at end of file diff --git a/01_Week1/README.md b/01_Week1/README.md new file mode 100644 index 0000000..8e0fadf --- /dev/null +++ b/01_Week1/README.md @@ -0,0 +1,84 @@ +# STEM Python Course 2020 + +University of South Carolina STEM Python Course Summer 2020 + +### [Set up your repl.it](https://docs.google.com/presentation/d/16pSbnx0jwZfSlloKJGbU4lP8OR7l1Ou77B-CzGDTN6I/edit?usp=sharing) + + + +### (or) Run with Binder + +[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/uofscphysics/STEM_Python_Course/Summer2020) + +# Day 1 - Introduction to Python +* 8:30 Introductions, Overview of the week +* 8:50 Getting [repl.it](repl.it) set up (accessing the resources) [Password: STEM_Python] +* 9:30 Understanding Python Vocabulary +* 10:45 Reading Python Programs +* 11:45 Wrap up + +[Slides for this day](https://docs.google.com/presentation/d/1Ax2IB1lcuJM9RdKISdMi-7wqaAQw2k2Mx8cbfvd9AcU/edit?usp=sharing) | +[Recording for this day](https://www.youtube.com/watch?v=OozJzWEEg2Y) + +____ +# Day 2 - Documentation and Understanding Errors +* 8:30 Introducing documentation/Stack Overflow +* 9:30 Identifying and fixing errors +* 10:30 Writing a working program +* 11:45 Wrap up + +[Slides for this day](https://docs.google.com/presentation/d/1-0mnmeNHwSPoogBafsuYW4VzN-3L2RffOeSJ-6J4lA0/edit?usp=sharing) | +[Recording for this day](https://www.youtube.com/watch?v=HcO2DsU95Ak) + +____ +# Day 3 - Loops and Packages +* 8:30 Introduction to for/while loops +* 9:00 Fixing/writing loops +* 10:00 Introduction to Python packages +* 10:30 Simple Numpy, Pandas, Matplotlib usage +* 11:45 Wrap up + +[Slides for this day](https://docs.google.com/presentation/d/1Aewdkzqhup3znR1ociduYPhapbNPmxdD5oeyvJMRyJA/edit?usp=sharing) | +[Recording for this day](https://www.youtube.com/watch?v=2e_g_2F-POI) + +____ +# Day 4 - Useful Pandas Applications +* 8:30 Examples of Pandas usages +* 9:00 Activities implementing Pandas +* 11:45 Wrap up + +[Slides for this day](https://docs.google.com/presentation/d/153839mni4snNC_CszEN9V2dnsFY-njy1f_wc_7oTuTI/edit?usp=sharing) | +[Recording for this day](https://www.youtube.com/watch?v=Ym-LwsQAOZk) + +____ +# Day 5 - Instruction Based on Feedback + +This day will be optional. We will solicit feedback during the week on topics you would like to learn more about, and then +spend Friday breaking up participants into groups to investigate the module they are most interested in. This will +run during the same time period as days 1-4. + +[Recording for this day](https://www.youtube.com/watch?v=7cy5WntCj0k) + + +# Repository Table of Contents + +## 00_ReadingPython +* Powerpoint and links to games to learn basic Python vocabulary and understanding of simple programs + +## 01_Documentation +* Powerpoint introducing participants to documentation and other resources for Python + +## 02_FixMe +* Activities aimed at having participants fix broken and/or misbehaving code to learn error handling and useful documentation + +## 03_WorkingProgram +* Activities that have participants create their first fully operational program from scratch + +## 04_Loops +* Introduction to `for` and `while` loops + +## 05_PackagesIntro +* Introduction to Python packages and some specific uses + +## 06_PandasIMDB & 07_PandasCovid19 +* More in-depth use of the Pandas package for data analysis diff --git a/01_Week1/References/README.md b/01_Week1/References/README.md new file mode 100644 index 0000000..3ef6f22 --- /dev/null +++ b/01_Week1/References/README.md @@ -0,0 +1,3 @@ +# References + +Here you'll find a reference for using `git`, as well as a code reference that should give lots of useful code and definitions for using Python. If you would like to watch some videos to help learn more about any specific part of Python, [here is a compilation of useful videos](https://docs.google.com/document/d/1XMUe2EIJVfO8lMc_Lagh3TkGVLGJoNrBQ2Qp3qSpai0/edit?usp=sharing). \ No newline at end of file diff --git a/Day_1/Introduction_git/README.md b/01_Week1/References/git_reference/README.md similarity index 100% rename from Day_1/Introduction_git/README.md rename to 01_Week1/References/git_reference/README.md diff --git a/Day_1/Introduction_git/img/git_add.png b/01_Week1/References/git_reference/img/git_add.png similarity index 100% rename from Day_1/Introduction_git/img/git_add.png rename to 01_Week1/References/git_reference/img/git_add.png diff --git a/Day_1/Introduction_git/img/git_commit.png b/01_Week1/References/git_reference/img/git_commit.png similarity index 100% rename from Day_1/Introduction_git/img/git_commit.png rename to 01_Week1/References/git_reference/img/git_commit.png diff --git a/Day_1/Introduction_git/img/git_desktop_changes.png b/01_Week1/References/git_reference/img/git_desktop_changes.png similarity index 100% rename from Day_1/Introduction_git/img/git_desktop_changes.png rename to 01_Week1/References/git_reference/img/git_desktop_changes.png diff --git a/Day_1/Introduction_git/img/git_desktop_commit.png b/01_Week1/References/git_reference/img/git_desktop_commit.png similarity index 100% rename from Day_1/Introduction_git/img/git_desktop_commit.png rename to 01_Week1/References/git_reference/img/git_desktop_commit.png diff --git a/Day_1/Introduction_git/img/git_desktop_fork.png b/01_Week1/References/git_reference/img/git_desktop_fork.png similarity index 100% rename from Day_1/Introduction_git/img/git_desktop_fork.png rename to 01_Week1/References/git_reference/img/git_desktop_fork.png diff --git a/Day_1/Introduction_git/img/git_desktop_push.png b/01_Week1/References/git_reference/img/git_desktop_push.png similarity index 100% rename from Day_1/Introduction_git/img/git_desktop_push.png rename to 01_Week1/References/git_reference/img/git_desktop_push.png diff --git a/Day_1/Introduction_git/img/git_diff.png b/01_Week1/References/git_reference/img/git_diff.png similarity index 100% rename from Day_1/Introduction_git/img/git_diff.png rename to 01_Week1/References/git_reference/img/git_diff.png diff --git a/Day_1/Introduction_git/img/git_push.png b/01_Week1/References/git_reference/img/git_push.png similarity index 100% rename from Day_1/Introduction_git/img/git_push.png rename to 01_Week1/References/git_reference/img/git_push.png diff --git a/Day_1/Introduction_git/img/git_status.png b/01_Week1/References/git_reference/img/git_status.png similarity index 100% rename from Day_1/Introduction_git/img/git_status.png rename to 01_Week1/References/git_reference/img/git_status.png diff --git a/Day_1/Introduction_git/img/htop_example.png b/01_Week1/References/git_reference/img/htop_example.png similarity index 100% rename from Day_1/Introduction_git/img/htop_example.png rename to 01_Week1/References/git_reference/img/htop_example.png diff --git a/Day_1/Introduction_git/img/top_example.png b/01_Week1/References/git_reference/img/top_example.png similarity index 100% rename from Day_1/Introduction_git/img/top_example.png rename to 01_Week1/References/git_reference/img/top_example.png diff --git a/Day_1/Introduction_tutorial/Day1_tutorial.ipynb b/01_Week1/References/python_reference/code_reference.ipynb similarity index 52% rename from Day_1/Introduction_tutorial/Day1_tutorial.ipynb rename to 01_Week1/References/python_reference/code_reference.ipynb index c0afd97..4bb2525 100644 --- a/Day_1/Introduction_tutorial/Day1_tutorial.ipynb +++ b/01_Week1/References/python_reference/code_reference.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Introduction to Python - Day 1\n", + "# Introduction to Python - Code Reference\n", "## Topics:\n", "- Installation\n", "- Using the Jupyter notebook\n", @@ -60,20 +60,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "95" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Cell example 2: This is a code cell. Any text after a # is a comment.\n", "# Click once to edit it and re-assign two random whole numbers to a and b. \n", @@ -165,7 +154,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -181,20 +170,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "7" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Addition\n", "num1 + num2" @@ -202,20 +180,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "-10.41" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Subtraction\n", "num2 - num3" @@ -223,20 +190,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "-4.446" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Multiplication\n", "num3 * num4" @@ -244,20 +200,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "-0.08571428571428572" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Division\n", "num4 / num5" @@ -265,20 +210,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "343" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Exponent\n", "num5 ** num6" @@ -286,20 +220,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "15.11" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Increment existing variable\n", "num7 += 4\n", @@ -308,20 +231,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Decrement existing variable\n", "num6 -= 2\n", @@ -330,20 +242,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "37.05" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Multiply & re-assign\n", "num3 *= 5\n", @@ -352,20 +253,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "-101.14999999999999" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Assign the value of an expression to a variable\n", "num8 = num1 + num2 * num3\n", @@ -374,20 +264,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Are these two expressions equal to each other?\n", "num1 + num2 == num5" @@ -395,20 +274,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Are these two expressions not equal to each other?\n", "num3 != num4" @@ -416,20 +284,9 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "False" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Is the first expression less than the second expression?\n", "num5 < num6" @@ -437,20 +294,9 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Is this expression True?\n", "5 > 3 > 1" @@ -458,20 +304,9 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "False" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Is this expression True?\n", "5 > 3 < 4 == 3 + 1\n", @@ -480,7 +315,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -491,20 +326,9 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'an example of using the + operator'" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Addition\n", "simple_string1 + ' of using the + operator'" @@ -512,20 +336,9 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'an example'" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Notice that the string was not modified\n", "simple_string1" @@ -533,20 +346,9 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'oranges oranges oranges oranges '" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Multiplication\n", "simple_string2 * 4" @@ -554,20 +356,9 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'oranges '" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# This string wasn't modified either\n", "simple_string2" @@ -575,20 +366,9 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "False" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Are these two expressions equal to each other?\n", "simple_string1 == simple_string2" @@ -596,20 +376,9 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Are these two expressions equal to each other?\n", "simple_string1 == 'an example'" @@ -617,20 +386,9 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'an example that re-assigned the original string'" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Add and re-assign\n", "simple_string1 += ' that re-assigned the original string'\n", @@ -639,20 +397,9 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'oranges oranges oranges '" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Multiply and re-assign\n", "simple_string2 *= 3\n", @@ -695,7 +442,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -708,20 +455,9 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[3, 5, 6, 3, 'dog', 'cat', False]" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Items in the list object are stored in the order they were added\n", "list1" @@ -729,20 +465,9 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(3, 5, 6, 3, 'dog', 'cat', False)" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Items in the tuple object are stored in the order they were added\n", "tuple1" @@ -750,20 +475,9 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{3, 5, 6, False, 'cat', 'dog'}" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Items in the set object are not stored in the order they were added\n", "# Also, notice that the value 3 only appears once in this set object\n", @@ -772,20 +486,9 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'name': 'Jane', 'age': 23, 'fav_foods': ['pizza', 'fruit', 'fish']}" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Items in the dict object are not stored in the order they were added\n", "dict1" @@ -793,20 +496,9 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[3, 5, 6, 3, 'dog', 'cat', False, 5, 'grapes']" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Add and re-assign\n", "list1 += [5, 'grapes']\n", @@ -815,20 +507,9 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(3, 5, 6, 3, 'dog', 'cat', False, 5, 'grapes')" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Add and re-assign\n", "tuple1 += (5, 'grapes')\n", @@ -837,20 +518,9 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[1, 2, 3, 4, 1, 2, 3, 4]" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Multiply\n", "[1, 2, 3, 4] * 2" @@ -858,20 +528,9 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4)" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Multiply\n", "(1, 2, 3, 4) * 3" @@ -901,20 +560,9 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "3" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Access the first item in a sequence\n", "list1[0]" @@ -922,20 +570,9 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'grapes'" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Access the last item in a sequence\n", "tuple1[-1]" @@ -943,20 +580,9 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'examp'" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Access a range of items in a sequence\n", "simple_string1[3:8] # prints characters 3 up to but not including 8" @@ -964,20 +590,9 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(3, 5, 6, 3, 'dog', 'cat')" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Access a range of items in a sequence\n", "tuple1[:-3] # prints everything less than item -3, but not -3" @@ -985,20 +600,9 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['dog', 'cat', False, 5, 'grapes']" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Access a range of items in a sequence\n", "list1[4:] # prints 4th item and greater" @@ -1006,20 +610,9 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'Jane'" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Access an item or value in a dictionary belonging to a key\n", "dict1['name']" @@ -1027,20 +620,9 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'fish'" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Access a specific value of a key with multiple values in a dictionary\n", "dict1['fav_foods'][2] # pizza is 0, fruit is 1, fish is 2" @@ -1073,20 +655,9 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "str" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Use the type() function to determine the type of an object\n", "type(simple_string1)" @@ -1094,20 +665,9 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "3" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Use the len() function to determine how many items are in a container\n", "len(dict1)" @@ -1115,20 +675,9 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "24" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Use the len() function to determine how many items are in a container\n", "len(simple_string2)" @@ -1136,20 +685,9 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Use the callable() function to determine if an object is callable\n", "callable(len)" @@ -1157,20 +695,9 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "False" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Use the callable() function to determine if an object is callable\n", "callable(dict1)" @@ -1178,20 +705,9 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[-3, 1, 2, 3.6, 5, 7, 10]" - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Use the sorted() function to return a new list from a container, with the items sorted\n", "sorted([10, 1, 3.6, 7, 5, 2, -3])" @@ -1199,20 +715,9 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['California', 'Chicago', 'ants', 'cats', 'dogs', 'mice', 'zebras']" - ] - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Use the sorted() function to return a new list from a container, with the items sorted\n", "# - notice that capitalized strings come first\n", @@ -1221,20 +726,9 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "25.6" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Use the sum() function to compute the sum of a container of numbers\n", "sum([10, 1, 3.6, 7, 5, 2, -3])" @@ -1242,20 +736,9 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "-3" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Use the min() function to determine the smallest item in a container\n", "min([10, 1, 3.6, 7, 5, 2, -3])" @@ -1263,20 +746,9 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'a'" - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Use the min() function to determine the smallest item in a container\n", "min(['g', 'z', 'a', 'y'])" @@ -1284,20 +756,9 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "10" - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Use the max() function to determine the largest item in a container\n", "max([10, 1, 3.6, 7, 5, 2, -3])" @@ -1305,20 +766,9 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'s'" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Use the max() function to determine the largest item in a container\n", "max('gibberish')" @@ -1326,20 +776,9 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "10" - ] - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Use the abs() function to determine the absolute value of a number\n", "abs(10)" @@ -1347,20 +786,9 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "12" - ] - }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Use the abs() function to determine the absolute value of a number\n", "abs(-12)" @@ -1368,20 +796,9 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"[3, 5, 6, 3, 'dog', 'cat', False, 5, 'grapes']\"" - ] - }, - "execution_count": 61, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Use the repr() function to return a string representation of an object\n", "repr(set1)\n", @@ -1420,7 +837,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1430,20 +847,9 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'This is a string'" - ] - }, - "execution_count": 63, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Return a capitalized version of the string\n", "a_string.capitalize()" @@ -1451,20 +857,9 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'THIS IS A STRING'" - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Return an uppercase version of the string\n", "a_string.upper()" @@ -1472,20 +867,9 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'this is a string'" - ] - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Return a lowercase version of the string\n", "a_string.lower()" @@ -1493,20 +877,9 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'tHis is a sTriNg'" - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Notice that the methods called have not actually modified the string\n", "a_string" @@ -1514,20 +887,9 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "3" - ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Count number of occurences of a substring in the string\n", "a_string.count('i')" @@ -1535,20 +897,9 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1" - ] - }, - "execution_count": 70, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Count number of occurrences of a substring in the string after a certain position\n", "a_string.count('i', 7)" @@ -1556,20 +907,9 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "2" - ] - }, - "execution_count": 71, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Count number of occurences of a substring in the string\n", "a_string.count('is')" @@ -1577,20 +917,9 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "False" - ] - }, - "execution_count": 72, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Does the string start with 'this'?\n", "a_string.startswith('this')" @@ -1598,20 +927,9 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 73, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Does the lowercase string start with 'this'?\n", "a_string.lower().startswith('this')" @@ -1619,20 +937,9 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Does the string end with 'Ng'?\n", "a_string.endswith('Ng')" @@ -1640,20 +947,9 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'tHXYZ XYZ a sTriNg'" - ] - }, - "execution_count": 75, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Return a version of the string with a substring replaced with something else\n", "a_string.replace('is', 'XYZ')" @@ -1661,20 +957,9 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'tH!s !s a sTr!Ng'" - ] - }, - "execution_count": 76, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Return a version of the string with a substring replaced with something else\n", "a_string.replace('i', '!')" @@ -1682,20 +967,9 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'tH!s !s a sTriNg'" - ] - }, - "execution_count": 77, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Return a version of the string with the first 2 occurences a substring replaced with something else\n", "a_string.replace('i', '!', 2)" @@ -1703,20 +977,9 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['tHis', 'is', 'a', 'sTriNg']" - ] - }, - "execution_count": 78, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Split the string into individual strings (delimited by spaces)\n", "a_string.split()" @@ -1737,7 +1000,7 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1747,20 +1010,9 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[2, 'grilled', 'relish', 'chili', 'mustard']" - ] - }, - "execution_count": 80, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Add a single item to the list\n", "hd.append('mustard')\n", @@ -1769,20 +1021,9 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[2, 'grilled', 'relish', 'chili', 'mustard', 'ketchup', 'mayo', 'onions']" - ] - }, - "execution_count": 81, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Add multiple items to the list\n", "hd.extend(['ketchup', 'mayo', 'onions'])\n", @@ -1791,20 +1032,9 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[2, 'grilled', 'relish', 'chili', 'mustard', 'ketchup', 'onions']" - ] - }, - "execution_count": 82, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Remove a single item from the list\n", "hd.remove('mayo')\n", @@ -1813,20 +1043,9 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[2, 'grilled', 'relish', 'chili', 'mustard']" - ] - }, - "execution_count": 84, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Remove and return the last item in the list\n", "hd.pop()\n", @@ -1835,20 +1054,9 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[2, 'grilled', 'relish', 'chili']" - ] - }, - "execution_count": 86, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Remove and return an item at an index\n", "hd.pop(4)\n", @@ -1872,20 +1080,9 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'name': 'Francie', 'age': 43, 'fav_foods': ['veggies', 'rice', 'pasta']}" - ] - }, - "execution_count": 88, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Assign keywords and their values to a dictionary called dict2\n", "dict2 = {'name': 'Francie', 'age': 43, 'fav_foods': ['veggies', 'rice', 'pasta']}\n", @@ -1894,23 +1091,9 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'name': 'Francie',\n", - " 'age': 43,\n", - " 'fav_foods': ['veggies', 'rice', 'pasta'],\n", - " 'fav_color': 'purple'}" - ] - }, - "execution_count": 89, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Add a single key-value pair fav_color purple to the dictionary dict2\n", "dict2['fav_color'] = 'purple'\n", @@ -1919,25 +1102,9 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'name': 'Francie',\n", - " 'age': 43,\n", - " 'fav_foods': ['veggies', 'rice', 'pasta'],\n", - " 'fav_color': 'purple',\n", - " 'eye_color': 'blue',\n", - " 'hobbies': ['running', 'knitting']}" - ] - }, - "execution_count": 90, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Add multiple key-value pairs to dict2\n", "dict2.update([('eye_color', 'blue'),('hobbies', ['running', 'knitting'])])\n", @@ -1946,27 +1113,9 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'name': 'Francie',\n", - " 'age': 43,\n", - " 'fav_foods': ['veggies', 'rice', 'pasta'],\n", - " 'fav_color': 'purple',\n", - " 'eye_color': 'blue',\n", - " 'hobbies': ['running', 'knitting'],\n", - " 'fav_animal': 'cat',\n", - " 'phone': 'iPhone'}" - ] - }, - "execution_count": 91, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Add all keys and values from another dict to dict2\n", "dict3 = {'fav_animal': 'cat', 'phone': 'iPhone'}\n", @@ -1976,20 +1125,9 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "dict_keys(['name', 'age', 'fav_foods', 'fav_color', 'eye_color', 'hobbies', 'fav_animal', 'phone'])" - ] - }, - "execution_count": 92, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Return a list of keys in the dict\n", "dict2.keys()" @@ -1997,20 +1135,9 @@ }, { "cell_type": "code", - "execution_count": 93, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "dict_values(['Francie', 43, ['veggies', 'rice', 'pasta'], 'purple', 'blue', ['running', 'knitting'], 'cat', 'iPhone'])" - ] - }, - "execution_count": 93, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Return a list of values in the dict\n", "dict2.values()" @@ -2018,22 +1145,11 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [ - { - "data": { - "text/plain": [ - "dict_items([('name', 'Francie'), ('age', 43), ('fav_foods', ['veggies', 'rice', 'pasta']), ('fav_color', 'purple'), ('eye_color', 'blue'), ('hobbies', ['running', 'knitting']), ('fav_animal', 'cat'), ('phone', 'iPhone')])" - ] - }, - "execution_count": 94, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Return a list of key-value pairs (tuples) in the dict \n", "dict2.items()" @@ -2041,20 +1157,9 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'pasta'" - ] - }, - "execution_count": 95, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Return a specific value of a key in dict2 that has multiple values\n", "dict2['fav_foods'][2] # veggies is 0, rice is 1, pasta is 2" @@ -2062,20 +1167,9 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['running', 'knitting']" - ] - }, - "execution_count": 96, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Return value(s) for a specific key in dict2 (or None if key not found)\n", "dict2.get('hobbies')" @@ -2083,26 +1177,9 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'name': 'Francie',\n", - " 'age': 43,\n", - " 'fav_foods': ['veggies', 'rice', 'pasta'],\n", - " 'fav_color': 'purple',\n", - " 'eye_color': 'blue',\n", - " 'hobbies': ['running', 'knitting'],\n", - " 'fav_animal': 'cat'}" - ] - }, - "execution_count": 97, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Remove key and return its value from dict2 (error if key not found)\n", "dict2.pop('phone')\n", @@ -2135,28 +1212,9 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Please enter an integer: a\n" - ] - }, - { - "ename": "ValueError", - "evalue": "invalid literal for int() with base 10: 'a'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Please enter an integer: \"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'You entered a negative integer'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'You entered 0'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: 'a'" - ] - } - ], + "outputs": [], "source": [ "x = int(input(\"Please enter an integer: \"))\n", "if x < 0:\n", @@ -2178,23 +1236,9 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0\n", - "1\n", - "1\n", - "2\n", - "3\n", - "5\n", - "8\n" - ] - } - ], + "outputs": [], "source": [ "# Fibonacci series: the sum of two elements defines the next\n", "a, b = 0, 1 # a multiple assignment: the variables a and b simultaneously get values 0 and 1\n", @@ -2223,19 +1267,9 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "cat 3\n", - "Keanu 5\n", - "abracadabra 11\n" - ] - } - ], + "outputs": [], "source": [ "# Measure some strings:\n", "words = ['cat', 'Keanu', 'abracadabra'] # a container (list) named words consisting of 3 strings\n", @@ -2252,21 +1286,9 @@ }, { "cell_type": "code", - "execution_count": 104, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0\n", - "1\n", - "2\n", - "3\n", - "4\n" - ] - } - ], + "outputs": [], "source": [ "for i in range(5): # from index 0 to 4\n", " print(i)" @@ -2281,24 +1303,9 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['Point', 'Break', 'is', 'a', 'great', 'movie']\n", - "6\n", - "0 Point\n", - "1 Break\n", - "2 is\n", - "3 a\n", - "4 great\n", - "5 movie\n" - ] - } - ], + "outputs": [], "source": [ "b_string = 'Point Break is a great movie' # an example string\n", "c = b_string.split() # create a container (list) made of strings from splitting b_string\n", @@ -2317,7 +1324,7 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -2333,21 +1340,9 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'temp' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtemp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mstring_loop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mNameError\u001b[0m: name 'temp' is not defined" - ] - } - ], + "outputs": [], "source": [ "print(temp)\n", "string_loop()" @@ -2371,20 +1366,9 @@ }, { "cell_type": "code", - "execution_count": 110, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]" - ] - }, - "execution_count": 110, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "squares = [] # creates an empty container (list) named \"squares\"\n", "for x in range(10): # for every number in the range from 0 to 9\n", @@ -2403,20 +1387,9 @@ }, { "cell_type": "code", - "execution_count": 111, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]" - ] - }, - "execution_count": 111, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "squares = [x**2 for x in range(10)] # define a list whose entries are the numbers 0 to 9 squared\n", "squares # print the list" @@ -2434,20 +1407,9 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'d', 'r'}" - ] - }, - "execution_count": 112, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "a = {x for x in 'abracadabra' if x not in 'abc'}\n", "a" @@ -2465,20 +1427,9 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{2: 4, 4: 16, 6: 36}" - ] - }, - "execution_count": 113, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "{x: x**2 for x in (2, 4, 6)}" ] @@ -2508,7 +1459,7 @@ }, { "cell_type": "code", - "execution_count": 114, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -2521,36 +1472,16 @@ }, { "cell_type": "code", - "execution_count": 115, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Enter a Fahrenheit temperature: 212\n" - ] - }, - { - "ename": "TypeError", - "evalue": "unsupported operand type(s) for -: 'str' and 'int'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfahr_to_cels_1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m\u001b[0m in \u001b[0;36mfahr_to_cels_1\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mfahr_to_cels_1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# defines a function called fahr_to_cels_1 that converts Fahrenheit to Celsius\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mtemp_str\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Enter a Fahrenheit temperature: '\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# prints a statement whose input becomes object temp_str\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mnewTemp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtemp_str\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m9\u001b[0m \u001b[0;31m# equation to convert the input temp to Celsius\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'The Fahrenheit temperature'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtemp\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'is equivalent to '\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m''\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# prints the result without a line break\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnewTemp\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'degrees Celsius.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for -: 'str' and 'int'" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "fahr_to_cels_1()" ] }, { "cell_type": "code", - "execution_count": 116, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -2564,36 +1495,16 @@ }, { "cell_type": "code", - "execution_count": 118, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Enter a Fahrenheit temperature: a\n" - ] - }, - { - "ename": "ValueError", - "evalue": "invalid literal for int() with base 10: 'a'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfahr_to_cels_2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m\u001b[0m in \u001b[0;36mfahr_to_cels_2\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mfahr_to_cels_2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mtemp_str\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Enter a Fahrenheit temperature: '\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mtemp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtemp_str\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# anything typed in from an input statement is a string, convert to integer!\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mnewTemp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtemp\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m9\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'The Fahrenheit temperature'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtemp\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'is equivalent to '\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m''\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: 'a'" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "fahr_to_cels_2()" ] }, { "cell_type": "code", - "execution_count": 119, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -2613,18 +1524,9 @@ }, { "cell_type": "code", - "execution_count": 122, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Enter a Fahrenheit temperature: \n", - "You didn't enter a number! Try again.\n" - ] - } - ], + "outputs": [], "source": [ "fahr_to_cels_3()" ] @@ -2641,18 +1543,9 @@ }, { "cell_type": "code", - "execution_count": 123, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "x = 5 \n", "y = 'string'\n", @@ -2663,295 +1556,27 @@ }, { "cell_type": "code", - "execution_count": 124, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'STRING'" - ] - }, - "execution_count": 124, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "y.upper() # attribute that acts on the string y to capitalize each letter" ] }, { "cell_type": "code", - "execution_count": 125, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'int' object has no attribute 'upper'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# what happens if you try to use the .upper method on an object belonging to the integer class?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m: 'int' object has no attribute 'upper'" - ] - } - ], + "outputs": [], "source": [ "x.upper() # what happens if you try to use the .upper method on an object belonging to the integer class?" ] }, { "cell_type": "code", - "execution_count": 126, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on class int in module builtins:\n", - "\n", - "class int(object)\n", - " | int(x=0) -> integer\n", - " | int(x, base=10) -> integer\n", - " | \n", - " | Convert a number or string to an integer, or return 0 if no arguments\n", - " | are given. If x is a number, return x.__int__(). For floating point\n", - " | numbers, this truncates towards zero.\n", - " | \n", - " | If x is not a number or if base is given, then x must be a string,\n", - " | bytes, or bytearray instance representing an integer literal in the\n", - " | given base. The literal can be preceded by '+' or '-' and be surrounded\n", - " | by whitespace. The base defaults to 10. Valid bases are 0 and 2-36.\n", - " | Base 0 means to interpret the base from the string as an integer literal.\n", - " | >>> int('0b100', base=0)\n", - " | 4\n", - " | \n", - " | Methods defined here:\n", - " | \n", - " | __abs__(self, /)\n", - " | abs(self)\n", - " | \n", - " | __add__(self, value, /)\n", - " | Return self+value.\n", - " | \n", - " | __and__(self, value, /)\n", - " | Return self&value.\n", - " | \n", - " | __bool__(self, /)\n", - " | self != 0\n", - " | \n", - " | __ceil__(...)\n", - " | Ceiling of an Integral returns itself.\n", - " | \n", - " | __divmod__(self, value, /)\n", - " | Return divmod(self, value).\n", - " | \n", - " | __eq__(self, value, /)\n", - " | Return self==value.\n", - " | \n", - " | __float__(self, /)\n", - " | float(self)\n", - " | \n", - " | __floor__(...)\n", - " | Flooring an Integral returns itself.\n", - " | \n", - " | __floordiv__(self, value, /)\n", - " | Return self//value.\n", - " | \n", - " | __format__(...)\n", - " | default object formatter\n", - " | \n", - " | __ge__(self, value, /)\n", - " | Return self>=value.\n", - " | \n", - " | __getattribute__(self, name, /)\n", - " | Return getattr(self, name).\n", - " | \n", - " | __getnewargs__(...)\n", - " | \n", - " | __gt__(self, value, /)\n", - " | Return self>value.\n", - " | \n", - " | __hash__(self, /)\n", - " | Return hash(self).\n", - " | \n", - " | __index__(self, /)\n", - " | Return self converted to an integer, if self is suitable for use as an index into a list.\n", - " | \n", - " | __int__(self, /)\n", - " | int(self)\n", - " | \n", - " | __invert__(self, /)\n", - " | ~self\n", - " | \n", - " | __le__(self, value, /)\n", - " | Return self<=value.\n", - " | \n", - " | __lshift__(self, value, /)\n", - " | Return self<>self.\n", - " | \n", - " | __rshift__(self, value, /)\n", - " | Return self>>value.\n", - " | \n", - " | __rsub__(self, value, /)\n", - " | Return value-self.\n", - " | \n", - " | __rtruediv__(self, value, /)\n", - " | Return value/self.\n", - " | \n", - " | __rxor__(self, value, /)\n", - " | Return value^self.\n", - " | \n", - " | __sizeof__(...)\n", - " | Returns size in memory, in bytes\n", - " | \n", - " | __str__(self, /)\n", - " | Return str(self).\n", - " | \n", - " | __sub__(self, value, /)\n", - " | Return self-value.\n", - " | \n", - " | __truediv__(self, value, /)\n", - " | Return self/value.\n", - " | \n", - " | __trunc__(...)\n", - " | Truncating an Integral returns itself.\n", - " | \n", - " | __xor__(self, value, /)\n", - " | Return self^value.\n", - " | \n", - " | bit_length(...)\n", - " | int.bit_length() -> int\n", - " | \n", - " | Number of bits necessary to represent self in binary.\n", - " | >>> bin(37)\n", - " | '0b100101'\n", - " | >>> (37).bit_length()\n", - " | 6\n", - " | \n", - " | conjugate(...)\n", - " | Returns self, the complex conjugate of any int.\n", - " | \n", - " | from_bytes(...) from builtins.type\n", - " | int.from_bytes(bytes, byteorder, *, signed=False) -> int\n", - " | \n", - " | Return the integer represented by the given array of bytes.\n", - " | \n", - " | The bytes argument must be a bytes-like object (e.g. bytes or bytearray).\n", - " | \n", - " | The byteorder argument determines the byte order used to represent the\n", - " | integer. If byteorder is 'big', the most significant byte is at the\n", - " | beginning of the byte array. If byteorder is 'little', the most\n", - " | significant byte is at the end of the byte array. To request the native\n", - " | byte order of the host system, use `sys.byteorder' as the byte order value.\n", - " | \n", - " | The signed keyword-only argument indicates whether two's complement is\n", - " | used to represent the integer.\n", - " | \n", - " | to_bytes(...)\n", - " | int.to_bytes(length, byteorder, *, signed=False) -> bytes\n", - " | \n", - " | Return an array of bytes representing an integer.\n", - " | \n", - " | The integer is represented using length bytes. An OverflowError is\n", - " | raised if the integer is not representable with the given number of\n", - " | bytes.\n", - " | \n", - " | The byteorder argument determines the byte order used to represent the\n", - " | integer. If byteorder is 'big', the most significant byte is at the\n", - " | beginning of the byte array. If byteorder is 'little', the most\n", - " | significant byte is at the end of the byte array. To request the native\n", - " | byte order of the host system, use `sys.byteorder' as the byte order value.\n", - " | \n", - " | The signed keyword-only argument determines whether two's complement is\n", - " | used to represent the integer. If signed is False and a negative integer\n", - " | is given, an OverflowError is raised.\n", - " | \n", - " | ----------------------------------------------------------------------\n", - " | Data descriptors defined here:\n", - " | \n", - " | denominator\n", - " | the denominator of a rational number in lowest terms\n", - " | \n", - " | imag\n", - " | the imaginary part of a complex number\n", - " | \n", - " | numerator\n", - " | the numerator of a rational number in lowest terms\n", - " | \n", - " | real\n", - " | the real part of a complex number\n", - "\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "help(int) # indeed there is no method upper in the int class!" ] @@ -2966,7 +1591,7 @@ }, { "cell_type": "code", - "execution_count": 128, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -2979,57 +1604,27 @@ }, { "cell_type": "code", - "execution_count": 129, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'Francie'" - ] - }, - "execution_count": 129, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "MyClass.name # returns a string" ] }, { "cell_type": "code", - "execution_count": 130, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 130, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "MyClass.hello # returns a function object" ] }, { "cell_type": "code", - "execution_count": 131, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Hello world\n" - ] - } - ], + "outputs": [], "source": [ "MyClass.hello() # method hello " ] @@ -3044,7 +1639,7 @@ }, { "cell_type": "code", - "execution_count": 132, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -3060,34 +1655,18 @@ }, { "cell_type": "code", - "execution_count": 133, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Hello world, my name is Francie\n" - ] - } - ], + "outputs": [], "source": [ "me.hello()" ] }, { "cell_type": "code", - "execution_count": 135, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Hello world, my name is Fluffers\n" - ] - } - ], + "outputs": [], "source": [ "# Create a new instance \"you\" that passes your name into MyClass and call the hello method.\n", "me2=MyClass('Fluffers')\n", @@ -3104,7 +1683,7 @@ }, { "cell_type": "code", - "execution_count": 136, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -3125,34 +1704,18 @@ }, { "cell_type": "code", - "execution_count": 137, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "I drive a 2015 Honda Civic that is silver with 78000 miles.\n" - ] - } - ], + "outputs": [], "source": [ "mycar.describe()" ] }, { "cell_type": "code", - "execution_count": 138, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "I drive a 2018 Hyundai Sonata that is black with 3000 miles.\n" - ] - } - ], + "outputs": [], "source": [ "yourcar = Car('Hyundai','Sonata', 2018, 3000, 'black') # Fill in your car's attributes\n", "yourcar.describe()" @@ -3160,20 +1723,9 @@ }, { "cell_type": "code", - "execution_count": 139, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'silver'" - ] - }, - "execution_count": 139, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# We can also access any attribute of the instance\n", "mycar.color" @@ -3188,7 +1740,7 @@ }, { "cell_type": "code", - "execution_count": 140, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -3212,27 +1764,9 @@ }, { "cell_type": "code", - "execution_count": 141, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "I drive a 2015 Honda Civic that is black with 78000 miles.\n" - ] - }, - { - "data": { - "text/plain": [ - "'black'" - ] - }, - "execution_count": 141, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "mycar.change_color('black') # updating my car color \n", "mycar.describe()\n", @@ -3263,22 +1797,11 @@ }, { "cell_type": "code", - "execution_count": 142, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Barber, barber, shave a pig.\n", - "How many hairs will make a wig?\n", - "Four and twenty; that's enough.\n", - "Give the barber a pinch of snuff." - ] - } - ], + "outputs": [], "source": [ - "f = open('day1.txt', 'r') # opens the file day1.txt for reading\n", + "f = open('data/day1.txt', 'r') # opens the file day1.txt for reading\n", "for line in f: # for loop to print each line in the file\n", " print(line, end='') # the file has a new line (\\n) at the end of each line already\n", "f.close() # don't forget to close the file!" @@ -3286,79 +1809,9 @@ }, { "cell_type": "code", - "execution_count": 145, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['_CHUNK_SIZE',\n", - " '__class__',\n", - " '__del__',\n", - " '__delattr__',\n", - " '__dict__',\n", - " '__dir__',\n", - " '__doc__',\n", - " '__enter__',\n", - " '__eq__',\n", - " '__exit__',\n", - " '__format__',\n", - " '__ge__',\n", - " '__getattribute__',\n", - " '__getstate__',\n", - " '__gt__',\n", - " '__hash__',\n", - " '__init__',\n", - " '__init_subclass__',\n", - " '__iter__',\n", - " '__le__',\n", - " '__lt__',\n", - " '__ne__',\n", - " '__new__',\n", - " '__next__',\n", - " '__reduce__',\n", - " '__reduce_ex__',\n", - " '__repr__',\n", - " '__setattr__',\n", - " '__sizeof__',\n", - " '__str__',\n", - " '__subclasshook__',\n", - " '_checkClosed',\n", - " '_checkReadable',\n", - " '_checkSeekable',\n", - " '_checkWritable',\n", - " '_finalizing',\n", - " 'buffer',\n", - " 'close',\n", - " 'closed',\n", - " 'detach',\n", - " 'encoding',\n", - " 'errors',\n", - " 'fileno',\n", - " 'flush',\n", - " 'isatty',\n", - " 'line_buffering',\n", - " 'mode',\n", - " 'name',\n", - " 'newlines',\n", - " 'read',\n", - " 'readable',\n", - " 'readline',\n", - " 'readlines',\n", - " 'seek',\n", - " 'seekable',\n", - " 'tell',\n", - " 'truncate',\n", - " 'writable',\n", - " 'write',\n", - " 'writelines']" - ] - }, - "execution_count": 145, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "dir(f)" ] @@ -3372,22 +1825,11 @@ }, { "cell_type": "code", - "execution_count": 143, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"Barber, barber, shave a pig.\\nHow many hairs will make a wig?\\nFour and twenty; that's enough.\\nGive the barber a pinch of snuff.\"" - ] - }, - "execution_count": 143, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "with open('day1.txt') as f: \n", + "with open('data/day1.txt') as f: \n", " read_file = f.read()\n", "\n", "read_file" @@ -3395,20 +1837,9 @@ }, { "cell_type": "code", - "execution_count": 144, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 144, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "f.closed # Is the file closed?" ] @@ -3434,194 +1865,100 @@ }, { "cell_type": "code", - "execution_count": 146, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Examples of Methods of File Objects\n", - "f = open('day1.txt', 'r')" + "f = open('data/day1.txt', 'r')" ] }, { "cell_type": "code", - "execution_count": 147, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"Barber, barber, shave a pig.\\nHow many hairs will make a wig?\\nFour and twenty; that's enough.\\nGive the barber a pinch of snuff.\"" - ] - }, - "execution_count": 147, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "f = open('day1.txt', 'r')\n", + "f = open('data/day1.txt', 'r')\n", "f.read()" ] }, { "cell_type": "code", - "execution_count": 148, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'Barber, barber, shave a pig.\\n'" - ] - }, - "execution_count": 148, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "f = open('day1.txt', 'r')\n", + "f = open('data/day1.txt', 'r')\n", "f.readline()" ] }, { "cell_type": "code", - "execution_count": 149, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'How many hairs will make a wig?\\n'" - ] - }, - "execution_count": 149, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "f.readline()" ] }, { "cell_type": "code", - "execution_count": 150, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"Four and twenty; that's enough.\\n\"" - ] - }, - "execution_count": 150, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "f.readline()" ] }, { "cell_type": "code", - "execution_count": 151, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'Give the barber a pinch of snuff.'" - ] - }, - "execution_count": 151, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "f.readline()" ] }, { "cell_type": "code", - "execution_count": 152, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [ - { - "data": { - "text/plain": [ - "''" - ] - }, - "execution_count": 152, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "f.readline()" ] }, { "cell_type": "code", - "execution_count": 153, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['Barber, barber, shave a pig.\\n',\n", - " 'How many hairs will make a wig?\\n',\n", - " \"Four and twenty; that's enough.\\n\",\n", - " 'Give the barber a pinch of snuff.']" - ] - }, - "execution_count": 153, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "f = open('day1.txt', 'r')\n", + "f = open('data/day1.txt', 'r')\n", "list(f)" ] }, { "cell_type": "code", - "execution_count": 154, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['Barber, barber, shave a pig.\\n',\n", - " 'How many hairs will make a wig?\\n',\n", - " \"Four and twenty; that's enough.\\n\",\n", - " 'Give the barber a pinch of snuff.']" - ] - }, - "execution_count": 154, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "f = open('day1.txt', 'r')\n", + "f = open('data/day1.txt', 'r')\n", "f.readlines()" ] }, { "cell_type": "code", - "execution_count": 155, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "f = open('day1.txt', 'r')\n", - "w = open('day1_copy.txt', 'w')\n", + "f = open('data/day1.txt', 'r')\n", + "w = open('data/day1_copy.txt', 'w')\n", "\n", "for line in f:\n", " w.write(line)\n", @@ -3632,7 +1969,7 @@ }, { "cell_type": "code", - "execution_count": 157, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -3649,11 +1986,11 @@ }, { "cell_type": "code", - "execution_count": 158, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "about_me('day1_aboutme.txt', 'Francie', 43, 'green')" + "about_me('data/day1_aboutme.txt', 'Francie', 43, 'green')" ] }, { @@ -3683,7 +2020,7 @@ }, { "cell_type": "code", - "execution_count": 160, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -3700,52 +2037,18 @@ }, { "cell_type": "code", - "execution_count": 161, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['Date', 'high', 'low', 'avg_high', 'avg_low']\n", - "['5/1/2019', '88', '64', '80', '56']\n", - "['5/2/2019', '86', '62', '80', '56']\n", - "['5/3/2019', '83', '66', '81', '56']\n", - "['5/4/2019', '91', '67', '81', '57']\n", - "['5/5/2019', '86', '67', '81', '57']\n", - "['5/6/2019', '84', '65', '82', '57']\n", - "['5/7/2019', '84', '65', '82', '58']\n", - "['5/8/2019', '86', '64', '82', '58']\n", - "['5/9/2019', '78', '61', '82', '58']\n", - "['5/10/2019', '84', '66', '82', '58']\n", - "['5/11/2019', '85', '65', '83', '59']\n", - "['5/12/2019', '77', '67', '83', '59']\n", - "['5/13/2019', '83', '65', '83', '59']\n", - "['5/14/2019', '75', '53', '84', '60']\n", - "['5/15/2019', '77', '50', '84', '60']\n", - "['5/16/2019', '85', '58', '84', '60']\n", - "['5/17/2019', '91', '64', '84', '61']\n", - "['5/18/2019', '94', '66', '84', '61']\n", - "['5/19/2019', '91', '68', '85', '61']\n", - "['5/20/2019', '93', '72', '85', '62']\n", - "['5/21/2019', '94', '67', '85', '62']\n", - "['5/22/2019', '93', '71', '85', '62']\n", - "['5/23/2019', '92', '70', '86', '63']\n", - "['5/24/2019', '96', '71', '86', '63']\n", - "['5/25/2019', '101', '72', '86', '63']\n", - "['5/26/2019', '99', '73', '86', '64']\n" - ] - } - ], - "source": [ - "read_csv_file('may2019_temps.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 162, + "outputs": [], + "source": [ + "read_csv_file('data/may2019_temps.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -3762,24 +2065,16 @@ }, { "cell_type": "code", - "execution_count": 163, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[['Date', 'high', 'low', 'avg_high', 'avg_low'], ['5/1/2019', '88', '64', '80', '56'], ['5/2/2019', '86', '62', '80', '56'], ['5/3/2019', '83', '66', '81', '56'], ['5/4/2019', '91', '67', '81', '57'], ['5/5/2019', '86', '67', '81', '57'], ['5/6/2019', '84', '65', '82', '57'], ['5/7/2019', '84', '65', '82', '58'], ['5/8/2019', '86', '64', '82', '58'], ['5/9/2019', '78', '61', '82', '58'], ['5/10/2019', '84', '66', '82', '58'], ['5/11/2019', '85', '65', '83', '59'], ['5/12/2019', '77', '67', '83', '59'], ['5/13/2019', '83', '65', '83', '59'], ['5/14/2019', '75', '53', '84', '60'], ['5/15/2019', '77', '50', '84', '60'], ['5/16/2019', '85', '58', '84', '60'], ['5/17/2019', '91', '64', '84', '61'], ['5/18/2019', '94', '66', '84', '61'], ['5/19/2019', '91', '68', '85', '61'], ['5/20/2019', '93', '72', '85', '62'], ['5/21/2019', '94', '67', '85', '62'], ['5/22/2019', '93', '71', '85', '62'], ['5/23/2019', '92', '70', '86', '63'], ['5/24/2019', '96', '71', '86', '63'], ['5/25/2019', '101', '72', '86', '63'], ['5/26/2019', '99', '73', '86', '64']]\n" - ] - } - ], + "outputs": [], "source": [ - "read_csv_file1('may2019_temps.csv')" + "read_csv_file1('data/may2019_temps.csv')" ] }, { "cell_type": "code", - "execution_count": 165, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -3798,52 +2093,18 @@ }, { "cell_type": "code", - "execution_count": 166, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Date high low avg_high avg_low\n", - "5/1/2019 88 64 80 56\n", - "5/2/2019 86 62 80 56\n", - "5/3/2019 83 66 81 56\n", - "5/4/2019 91 67 81 57\n", - "5/5/2019 86 67 81 57\n", - "5/6/2019 84 65 82 57\n", - "5/7/2019 84 65 82 58\n", - "5/8/2019 86 64 82 58\n", - "5/9/2019 78 61 82 58\n", - "5/10/2019 84 66 82 58\n", - "5/11/2019 85 65 83 59\n", - "5/12/2019 77 67 83 59\n", - "5/13/2019 83 65 83 59\n", - "5/14/2019 75 53 84 60\n", - "5/15/2019 77 50 84 60\n", - "5/16/2019 85 58 84 60\n", - "5/17/2019 91 64 84 61\n", - "5/18/2019 94 66 84 61\n", - "5/19/2019 91 68 85 61\n", - "5/20/2019 93 72 85 62\n", - "5/21/2019 94 67 85 62\n", - "5/22/2019 93 71 85 62\n", - "5/23/2019 92 70 86 63\n", - "5/24/2019 96 71 86 63\n", - "5/25/2019 101 72 86 63\n", - "5/26/2019 99 73 86 64\n" - ] - } - ], - "source": [ - "read_csv_file2('may2019_temps.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 167, + "outputs": [], + "source": [ + "read_csv_file2('data/may2019_temps.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -3866,26 +2127,18 @@ }, { "cell_type": "code", - "execution_count": 168, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'5/1/2019': ('88', '64', '80', '56'), '5/2/2019': ('86', '62', '80', '56'), '5/3/2019': ('83', '66', '81', '56'), '5/4/2019': ('91', '67', '81', '57'), '5/5/2019': ('86', '67', '81', '57'), '5/6/2019': ('84', '65', '82', '57'), '5/7/2019': ('84', '65', '82', '58'), '5/8/2019': ('86', '64', '82', '58'), '5/9/2019': ('78', '61', '82', '58'), '5/10/2019': ('84', '66', '82', '58'), '5/11/2019': ('85', '65', '83', '59'), '5/12/2019': ('77', '67', '83', '59'), '5/13/2019': ('83', '65', '83', '59'), '5/14/2019': ('75', '53', '84', '60'), '5/15/2019': ('77', '50', '84', '60'), '5/16/2019': ('85', '58', '84', '60'), '5/17/2019': ('91', '64', '84', '61'), '5/18/2019': ('94', '66', '84', '61'), '5/19/2019': ('91', '68', '85', '61'), '5/20/2019': ('93', '72', '85', '62'), '5/21/2019': ('94', '67', '85', '62'), '5/22/2019': ('93', '71', '85', '62'), '5/23/2019': ('92', '70', '86', '63'), '5/24/2019': ('96', '71', '86', '63'), '5/25/2019': ('101', '72', '86', '63'), '5/26/2019': ('99', '73', '86', '64')}\n" - ] - } - ], + "outputs": [], "source": [ - "read_csv_file3('may2019_temps.csv')" + "read_csv_file3('data/may2019_temps.csv')" ] }, { "cell_type": "code", - "execution_count": 169, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -3913,11 +2166,11 @@ }, { "cell_type": "code", - "execution_count": 170, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "update_csv('mass.csv', 'mass_avg.csv')" + "update_csv('data/mass.csv', 'data/mass_avg.csv')" ] }, { @@ -3941,19 +2194,9 @@ }, { "cell_type": "code", - "execution_count": 175, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1\n", - "2\n", - "3\n" - ] - } - ], + "outputs": [], "source": [ "testArray = [1,2,3]\n", "for value in testArray:\n", @@ -4030,21 +2273,9 @@ }, { "cell_type": "code", - "execution_count": 176, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "ZeroDivisionError", - "evalue": "division by zero", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mZeroDivisionError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero" - ] - } - ], + "outputs": [], "source": [ "a = 10/0\n", "print(a)" @@ -4062,21 +2293,9 @@ }, { "cell_type": "code", - "execution_count": 177, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "IndexError", - "evalue": "list index out of range", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mabc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mabc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mIndexError\u001b[0m: list index out of range" - ] - } - ], + "outputs": [], "source": [ "abc=[10,20,20]\n", "print(abc[3])" @@ -4098,29 +2317,9 @@ }, { "cell_type": "code", - "execution_count": 182, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Arithmetic exception raised.\n" - ] - }, - { - "ename": "ZeroDivisionError", - "evalue": "division by zero", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mZeroDivisionError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Arithmetic exception raised.\"\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0;32mraise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mExceptionObject\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 9\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Success. You are a rock star.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mdenominator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mdenominator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mArithmeticError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mExceptionObject\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "denominator=0\n", "\n", @@ -4163,7 +2362,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.2" + "version": "3.6.8" } }, "nbformat": 4, diff --git a/Day_1/Introduction_tutorial/day1.txt b/01_Week1/References/python_reference/data/day1.txt similarity index 100% rename from Day_1/Introduction_tutorial/day1.txt rename to 01_Week1/References/python_reference/data/day1.txt diff --git a/Day_1/Introduction_tutorial/day1_aboutme.txt b/01_Week1/References/python_reference/data/day1_aboutme.txt similarity index 100% rename from Day_1/Introduction_tutorial/day1_aboutme.txt rename to 01_Week1/References/python_reference/data/day1_aboutme.txt diff --git a/Day_1/Introduction_tutorial/day1_copy.txt b/01_Week1/References/python_reference/data/day1_copy.txt similarity index 100% rename from Day_1/Introduction_tutorial/day1_copy.txt rename to 01_Week1/References/python_reference/data/day1_copy.txt diff --git a/Day_1/Introduction_tutorial/mass.csv b/01_Week1/References/python_reference/data/mass.csv similarity index 100% rename from Day_1/Introduction_tutorial/mass.csv rename to 01_Week1/References/python_reference/data/mass.csv diff --git a/Day_1/Introduction_tutorial/mass_avg.csv b/01_Week1/References/python_reference/data/mass_avg.csv similarity index 100% rename from Day_1/Introduction_tutorial/mass_avg.csv rename to 01_Week1/References/python_reference/data/mass_avg.csv diff --git a/Day_1/Introduction_tutorial/may2019_temps.csv b/01_Week1/References/python_reference/data/may2019_temps.csv similarity index 100% rename from Day_1/Introduction_tutorial/may2019_temps.csv rename to 01_Week1/References/python_reference/data/may2019_temps.csv diff --git a/Day_1/Introduction_tutorial/periodictable.csv b/01_Week1/References/python_reference/data/periodictable.csv similarity index 100% rename from Day_1/Introduction_tutorial/periodictable.csv rename to 01_Week1/References/python_reference/data/periodictable.csv diff --git a/Day_1/Introduction_tutorial/readme.md b/01_Week1/References/python_reference/data/readme.md similarity index 100% rename from Day_1/Introduction_tutorial/readme.md rename to 01_Week1/References/python_reference/data/readme.md diff --git a/Day_1/Introduction_tutorial/Day1_exercises.ipynb b/01_Week1/References/python_reference/extra_practice.ipynb similarity index 99% rename from Day_1/Introduction_tutorial/Day1_exercises.ipynb rename to 01_Week1/References/python_reference/extra_practice.ipynb index fed287a..aecf480 100644 --- a/Day_1/Introduction_tutorial/Day1_exercises.ipynb +++ b/01_Week1/References/python_reference/extra_practice.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Introduction to Python - Day 1\n", + "# Introduction to Python - Extra Practice\n", "\n", "## Exercises\n", "\n", @@ -238,7 +238,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.2" + "version": "3.6.8" } }, "nbformat": 4, diff --git a/Day_1/Introduction_tutorial/Day1_solutions.ipynb b/01_Week1/References/python_reference/extra_practice_solutions.ipynb similarity index 96% rename from Day_1/Introduction_tutorial/Day1_solutions.ipynb rename to 01_Week1/References/python_reference/extra_practice_solutions.ipynb index de02326..85b1147 100644 --- a/Day_1/Introduction_tutorial/Day1_solutions.ipynb +++ b/01_Week1/References/python_reference/extra_practice_solutions.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Introduction to Python - Day 1\n", + "# Introduction to Python - Extra Practice Solutions\n", "\n", "## Exercises Solutions\n", "\n", @@ -23,24 +23,14 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, - "outputs": [ - { - "ename": "SyntaxError", - "evalue": "invalid syntax (, line 5)", - "output_type": "error", - "traceback": [ - "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m5\u001b[0m\n\u001b[0;31m 1.type()\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" - ] - } - ], + "outputs": [], "source": [ "def exercise1(x,y):\n", " print(x+y)\n", " print(x*y)\n", - " \n", - "type()" + " " ] }, { @@ -536,7 +526,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.2" + "version": "3.6.8" } }, "nbformat": 4, diff --git a/01_Week1/Workshop_Surveys/SurveyClean.ipynb b/01_Week1/Workshop_Surveys/SurveyClean.ipynb new file mode 100644 index 0000000..88acd68 --- /dev/null +++ b/01_Week1/Workshop_Surveys/SurveyClean.ipynb @@ -0,0 +1,307 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# pip install textblob" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from astropy.table import Table, vstack, unique #this package is similar to pandas but personally, I like it more for survey data\n", + "import pandas\n", + "import numpy as np\n", + "import pycountry\n", + "import matplotlib.pyplot as plt\n", + "from textblob import TextBlob" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "##Read in your data. os.path.join makes it so that you can type in your path without slashes because windows hates slashes\n", + "##If you have an excel file or csv file, you can use that with pandas\n", + "##read_csv from pandas reads in the csv file\n", + "##Then we turn the data into an astropy Table\n", + "\n", + "# #An example to deal with headers in the reading stage\n", + "# dat=pandas.read_excel(os.path.join('C:','\\\\','Users','elean','Documents','School','Python4STEM','SurveyData.xlsx'), header=0, skiprows=1)\n", + "# dataA=Table.from_pandas(dat)\n", + "\n", + "#An example where you would want the first line to be your column names\n", + "dat=pandas.read_excel(os.path.join('C:','\\\\','Users','elean','Documents','School','Python4STEM','SurveyData.xlsx'))\n", + "dataA=Table.from_pandas(dat)\n", + "\n", + "# #If you have a header but you don't want spaces in the questions (this is pretty unusual but qualtrics can be annoying with the double header)\n", + "for i in range(len(dataA.colnames)):\n", + " dataA.rename_column(dataA.colnames[i],dataA.colnames[i].replace(\" \",\"\"))\n", + "\n", + "data=pandas.read_csv(os.path.join('C:','\\\\','Users','elean','Documents','School','Python4STEM','SurveyData2.csv'))\n", + "dataB=Table.from_pandas(data)\n", + "\n", + "for i in range(len(dataB.colnames)):\n", + " dataB.rename_column(dataB.colnames[i],dataB.colnames[i].replace(\" \",\"\"))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Header problems\n", + "#We didn't want the data in row 0 (row after the header) so by doing [1:] we tell it to take the data after the 0th row\n", + "#But before that, we want to store the row 0 for reference because it has the actual questions\n", + "\n", + "#By doing the data1[0:0], we created a new table with the same header and the same width as the data1 table\n", + "qRef=Table(dataA[0:0])\n", + "#This adds the row with the questions in them to the question reference table\n", + "qRef.add_row(dataB[0])\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data1=dataA[1:]\n", + "data2=dataB[1:]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Demonstrating stacking multiple datasets\n", + "#vstack stacks data vertically so you want to do this when your data have the same columns\n", + "result=vstack([data1,data2])\n", + "print(result)\n", + "result.to_pandas().to_csv(os.path.join('C:','\\\\','Users','elean','Documents','School',\n", + " 'SurveyDataAll.csv'))\n", + "\n", + "#oh no, the data types are different what do we do???" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#First we might try printing the data types of the two tables to visually see the difference\n", + "print(data1.dtype)\n", + "print(data2.dtype)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Wow there are a lot of differences\n", + "#We are just going to change the data types of all columns\n", + "#Here we are changing everything to a 256 bit string\n", + "data1=Table(np.array(data1),dtype=['" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# We want to make a 2D Gaussian defined by the sum of 2 2D Gaussian distributions.\n", + "# As we saw, each 2D Gaussian needs a mean in each dimension and a covariance matrix.\n", + "Gaussian1Mean = [1,1]\n", + "Gaussian2Mean = [2,4]\n", + "\n", + "Gaussian1Covariance = np.identity(2)*1\n", + "Gaussian2Covariance = np.identity(2)*1\n", + "\n", + "# Like in the introduction, we now have a function for each Gaussian that accepts a point (A,B),\n", + "# and returns the result of the Gaussian defined by the parameters above. \n", + "def Gaussian1(ABpoint):\n", + " return scipy.stats.multivariate_normal.pdf( ABpoint, mean = Gaussian1Mean, \n", + " cov = Gaussian1Covariance )\n", + "\n", + "def Gaussian2(ABpoint):\n", + " return scipy.stats.multivariate_normal.pdf( ABpoint, mean = Gaussian2Mean, \n", + " cov = Gaussian2Covariance )\n", + "\n", + "# And finally we define our final distribution, which is the sum of our other two Gaussian\n", + "# distributions\n", + "def GaussianMultiModal(ABpoint):\n", + " return Gaussian1(ABpoint) + Gaussian2(ABpoint)\n", + "\n", + "# Use our helper to plot what we get.\n", + "SimpleColorPlotFromFunc( \n", + " Func2D = GaussianMultiModal,\n", + " xmin = -1,\n", + " xmax = 5, \n", + " ymin = -1,\n", + " ymax = 5, \n", + " )\n", + "plt.title(\"Probability Density of A & B\", fontsize=30)\n", + "plt.ylabel('B',fontsize=40)\n", + "plt.xlabel('A',fontsize=40)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that what we get now is a 2D distribution with 2 clear peaks. Consider a case where this is the probability density you observe for some measurement you're making. This means there are two regions of parameter space that have a high probability of occurring. Or if we pretend this is a measured \"posterior\" distribution after fitting for two parameters, we can see that there are two regions of parameter space that have an equal probability of being the \"truth\". It's important when fitting for parameters or measuring underlying distributions that we don't simply attempt to find the most probable solution, but also know what this full picture looks like. We will discuss this while learning fitting with MCMC. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Assume that the samples generated below are representative of the distribution" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Combined shape: (20000, 2)\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# We can create 10k samples from these underlying distributions by simply asking for 10k samples\n", + "# using the same parameters that define the Gaussian distributions.\n", + "DataSamples1 = np.random.multivariate_normal(Gaussian1Mean,Gaussian1Covariance,size=10000)\n", + "DataSamples2 = np.random.multivariate_normal(Gaussian2Mean,Gaussian2Covariance,size=10000)\n", + "\n", + "CombinedSamples = np.vstack([DataSamples1,DataSamples2]) # We don't need a transpose here\n", + " # because of the shape of the array\n", + " # returned by the multivariate_normal\n", + " # function (see the print statement)\n", + "print('Combined shape:',CombinedSamples.shape)\n", + "np.random.shuffle(CombinedSamples) # This just shuffles the samples around \n", + "# Let's plot this, and note that it looks like a pixelated version\n", + "# of the 2D plot above. This shows that it's a dataset from the same\n", + "# underlying distribution, but just a smaller number of datapoints. \n", + "_=plt.hist2d(CombinedSamples[:,0],CombinedSamples[:,1],bins=30) \n", + "\n", + "# We can define these same Adata and Bdata as we did in the introduction notebook\n", + "Adata = CombinedSamples[:,0]\n", + "Bdata = CombinedSamples[:,1]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Find & plot the conditional probability P(B | A = 1,1.5,2)\n", + "Using the probability theory reference notebook, find and plot each conditional probability of A, given B=1, then 1.5, then 2. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Find & plot the marginal probability of A\n", + "Using the samples provided above (`CombinedSamples`), find and plot a histogram showing the marginal probability of A. Then add a best-fit Gaussian on top of your histogram." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Find & plot the marginal probability of B\n", + "Using the samples provided above (`CombinedSamples`), find and plot a histogram showing the marginal probability of A. Then add a best-fit Gaussian on top of your histogram." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/02_Week2/00_Probability_Theory/00_B_probability_theory_exercise_completed.ipynb b/02_Week2/00_Probability_Theory/00_B_probability_theory_exercise_completed.ipynb new file mode 100644 index 0000000..3461e95 --- /dev/null +++ b/02_Week2/00_Probability_Theory/00_B_probability_theory_exercise_completed.ipynb @@ -0,0 +1,282 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import scipy\n", + "import scipy.stats\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(3) # This just makes sure everyone has the same \"random\" samples" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# This is the same helper plotting function for 2D distributions used before\n", + "def SimpleColorPlotFromFunc( \n", + " Func2D = None,\n", + " xmin = None,\n", + " xmax = None, \n", + " ymin = None,\n", + " ymax = None, \n", + " ):\n", + "\n", + " #Make the list of poitns to plug in from the boundaries:\n", + " x = np.linspace(xmin, xmax, 100)\n", + " y = np.linspace(ymin, ymax, 100)\n", + " X, Y = np.meshgrid(x, y)\n", + " PointsToPlugIn = np.vstack([X.ravel(), Y.ravel()])\n", + " PointsToPlugInDataset = PointsToPlugIn.T\n", + "\n", + "\n", + " #plug in the list of points:\n", + " FunctionResultValuesForGrid = []\n", + " for Point in PointsToPlugInDataset:\n", + " Value = Func2D(Point)\n", + " FunctionResultValuesForGrid.append(Value)\n", + "\n", + " #reshape stuff in a confusing way so matplotlib can think of the data like a matrix\n", + " Z = np.reshape(FunctionResultValuesForGrid, X.shape).T\n", + "\n", + "\n", + " #Actually construct the figure...\n", + " plt.figure()\n", + " heatmap = plt.imshow( \n", + " np.rot90(Z), \n", + " extent=[xmin, xmax, ymin, ymax] ,\n", + " aspect = 'auto' ,\n", + " interpolation = None,\n", + " ) \n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Defining our distribution\n", + "We define an underlying model (in this case a 2D Gaussian distribution) and then take a large number of samples from it to represent an \"observed\" vs. \"model\" distribution. First we Define the underlying distribution:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# We want to make a 2D Gaussian defined by the sum of 2 2D Gaussian distributions.\n", + "# As we saw, each 2D Gaussian needs a mean in each dimension and a covariance matrix.\n", + "Gaussian1Mean = [1,1]\n", + "Gaussian2Mean = [2,4]\n", + "\n", + "Gaussian1Covariance = np.identity(2)*1\n", + "Gaussian2Covariance = np.identity(2)*1\n", + "\n", + "# Like in the introduction, we now have a function for each Gaussian that accepts a point (A,B),\n", + "# and returns the result of the Gaussian defined by the parameters above. \n", + "def Gaussian1(ABpoint):\n", + " return scipy.stats.multivariate_normal.pdf( ABpoint, mean = Gaussian1Mean, \n", + " cov = Gaussian1Covariance )\n", + "\n", + "def Gaussian2(ABpoint):\n", + " return scipy.stats.multivariate_normal.pdf( ABpoint, mean = Gaussian2Mean, \n", + " cov = Gaussian2Covariance )\n", + "\n", + "# And finally we define our final distribution, which is the sum of our other two Gaussian\n", + "# distributions\n", + "def GaussianMultiModal(ABpoint):\n", + " return Gaussian1(ABpoint) + Gaussian2(ABpoint)\n", + "\n", + "# Use our helper to plot what we get.\n", + "SimpleColorPlotFromFunc( \n", + " Func2D = GaussianMultiModal,\n", + " xmin = -1,\n", + " xmax = 5, \n", + " ymin = -1,\n", + " ymax = 5, \n", + " )\n", + "plt.title(\"Probability Density of A & B\", fontsize=30)\n", + "plt.ylabel('B',fontsize=40)\n", + "plt.xlabel('A',fontsize=40)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that what we get now is a 2D distribution with 2 clear peaks. Consider a case where this is the probability density you observe for some measurement you're making. This means there are two regions of parameter space that have a high probability of occurring. Or if we pretend this is a measured \"posterior\" distribution after fitting for two parameters, we can see that there are two regions of parameter space that have an equal probability of being the \"truth\". It's important when fitting for parameters or measuring underlying distributions that we don't simply attempt to find the most probable solution, but also know what this full picture looks like. We will discuss this while learning fitting with MCMC. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Assume that the samples generated below are representative of the distribution" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# We can create 10k samples from these underlying distributions by simply asking for 10k samples\n", + "# using the same parameters that define the Gaussian distributions.\n", + "DataSamples1 = np.random.multivariate_normal(Gaussian1Mean,Gaussian1Covariance,size=10000)\n", + "DataSamples2 = np.random.multivariate_normal(Gaussian2Mean,Gaussian2Covariance,size=10000)\n", + "\n", + "CombinedSamples = np.vstack([DataSamples1,DataSamples2]) # We don't need a transpose here\n", + " # because of the shape of the array\n", + " # returned by the multivariate_normal\n", + " # function (see the print statement)\n", + "print('Combined shape:',CombinedSamples.shape)\n", + "np.random.shuffle(CombinedSamples) # This just shuffles the samples around \n", + "# Let's plot this, and note that it looks like a pixelated version\n", + "# of the 2D plot above. This shows that it's a dataset from the same\n", + "# underlying distribution, but just a smaller number of datapoints. \n", + "_=plt.hist2d(CombinedSamples[:,0],CombinedSamples[:,1],bins=30) \n", + "\n", + "# We can define these same Adata and Bdata as we did in the introduction notebook\n", + "Adata = CombinedSamples[:,0]\n", + "Bdata = CombinedSamples[:,1]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Find & plot the conditional probability P(B | A = 1,1.5,2)\n", + "Using the probability theory reference notebook, find and plot each conditional probability of A, given B=1, then 1.5, then 2. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "A_fixed_values=[1,1.5,2]\n", + "\n", + "testB_Values=np.linspace(np.min(Bdata),np.max(Bdata),1000)\n", + "\n", + "# We can loop through the A_fixed_values list above,\n", + "# and essentially create the same list and plot as the \n", + "# previous cell for each iteration. Therefore on the \n", + "# first iteration for example, we will be finding the \n", + "# conditional probability density of B given that A=1.\n", + "for Afixed in A_fixed_values:\n", + " def BdensityConditionalOnA_unormalized(Bpoint):\n", + " return GaussianMultiModal( [Afixed, Bpoint] )\n", + " BdensityConditionalOnA_Values=[]\n", + " for B_value in testB_Values:\n", + " BdensityConditionalOnA_Values.append(BdensityConditionalOnA_unormalized(B_value))\n", + " plt.plot(testB_Values,BdensityConditionalOnA_Values,label='A='+str(Afixed))\n", + "\n", + "plt.legend(fontsize=15)\n", + "plt.ylabel('P(B|A)',fontsize=30)\n", + "plt.xlabel('B',fontsize=30)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Find & plot the marginal probability of A\n", + "Using the samples provided above (`CombinedSamples`), find and plot a histogram showing the marginal probability of A. Then add a best-fit Gaussian on top of your histogram." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "A_standard_deviation = np.std( Adata )\n", + "A_mean = np.mean(Adata)\n", + "\n", + "ATrialPoints = np.linspace(-3, 5, 100)\n", + "AValuePoints = scipy.stats.norm.pdf(ATrialPoints, loc = A_mean, scale = A_standard_deviation )\n", + "plt.plot(ATrialPoints, AValuePoints,label='Gaussian A')\n", + "\n", + "plt.hist(Adata, density=True,bins=50,label='A')\n", + "plt.xlabel('A')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Find & plot the marginal probability of B\n", + "Using the samples provided above (`CombinedSamples`), find and plot a histogram showing the marginal probability of A. Then add a best-fit Gaussian on top of your histogram." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "B_standard_deviation = np.std( Bdata )\n", + "B_mean = np.mean(Bdata)\n", + "\n", + "BTrialPoints = np.linspace(-5, 8, 100)\n", + "BValuePoints = scipy.stats.norm.pdf(BTrialPoints, loc = B_mean, scale = B_standard_deviation )\n", + "\n", + "plt.hist(Bdata, density=True,bins=50,label='B')\n", + "plt.plot(BTrialPoints, BValuePoints,label='Gaussian B')\n", + "\n", + "plt.xlabel('B')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/02_Week2/00_Probability_Theory/README.md b/02_Week2/00_Probability_Theory/README.md new file mode 100644 index 0000000..aca8865 --- /dev/null +++ b/02_Week2/00_Probability_Theory/README.md @@ -0,0 +1,18 @@ +# Table of Contents: + +## 00_A_probability_theory_introduction.ipynb +A notebook meant as a lecture to introduce you to some important topics in probability theory using Python. + +[![*Run this notebook in Colab:*](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/uofscphysics/STEM_Python_Course/blob/Summer2020/02_Week2/00_Probability_Theory/00_A_probability_theory_introduction.ipynb) + +___ +## 00_B_probability_theory_exercise.ipynb +A (mostly) empty notebook with an exercise aimed at helping you learn some probability theory with Python. + +[![*Run this notebook in Colab:*](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/uofscphysics/STEM_Python_Course/blob/Summer2020/02_Week2/00_Probability_Theory/00_B_probability_theory_exercise.ipynb) + +___ +## 00_B_probability_theory_exercise_completed.ipynb +The completed notebook for the exercise above. + +[![*Run this notebook in Colab:*](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/uofscphysics/STEM_Python_Course/blob/Summer2020/02_Week2/00_Probability_Theory/00_B_probability_theory_exercise_completed.ipynb) \ No newline at end of file diff --git a/02_Week2/01_Simple_Fitting/01_A_generate_dataset.ipynb b/02_Week2/01_Simple_Fitting/01_A_generate_dataset.ipynb new file mode 100644 index 0000000..f934042 --- /dev/null +++ b/02_Week2/01_Simple_Fitting/01_A_generate_dataset.ipynb @@ -0,0 +1,69 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#The data were generated with a simple quadratic equation:\n", + "#ax^2+bx+c. \n", + "a,b,c=1.4,.4,2.5 #true parameters\n", + "def my_model(x,a,b,c): #We define the model described above\n", + " return(a*x**2+b*x+c)\n", + "x_values= np.linspace(0,10,15)\n", + "y_values=my_model(x_values,a,b,c)\n", + "error=.1*y_values+5\n", + "final_y=np.random.normal(loc=y_values,scale=error)\n", + "pandas.DataFrame(data=np.array([x_values,final_y,error]).T,columns=['x','y','error']).to_csv('../Data/1D_intro_examples.dat')" + ] + }, + { + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/02_Week2/01_Simple_Fitting/01_A_simple_fitting_introduction.ipynb b/02_Week2/01_Simple_Fitting/01_A_simple_fitting_introduction.ipynb new file mode 100644 index 0000000..655361e --- /dev/null +++ b/02_Week2/01_Simple_Fitting/01_A_simple_fitting_introduction.ipynb @@ -0,0 +1,133 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# import all packages\n", + "import scipy\n", + "import pandas\n", + "import scipy.optimize\n", + "import scipy.stats\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# read in some data we created for this example (.dat is a generic filename, it's just a text file)\n", + "data_filename='https://raw.githubusercontent.com/uofscphysics/STEM_Python_Course/Summer2020/02_Week2/Data/1D_intro_examples.dat'\n", + "example_data_1D = pandas.read_csv(data_filename)#this file is separated by spaces and its first line contains the names of the columns (header) \n", + "print(example_data_1D.head())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Let's plot the data, with error bars, that we read from file (See Day 2)\n", + "plt.errorbar(example_data_1D['x'], #x,y,and error are the column names\n", + " example_data_1D['y'], \n", + " yerr=example_data_1D['error'],#yerr denotes an error in the y-direction for plotting\n", + " fmt='.') #fmt is \"format\", saying that I want data marked by \"points\"\n", + "plt.xlabel('x') #set the x-axis label \n", + "plt.ylabel('y') #set the y-axis label\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#The data were generated with a simple quadratic equation:\n", + "#ax^2+bx+c. \n", + "def my_model(x,a,b,c): #We define the model described above\n", + " return(a*x**2+b*x+c)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Write a $\\chi$-square function that we can then minimize. The $\\chi$-square statistic is defined as the sum of the square of the ratio of the difference between the model and data (observed-model) and the uncertainty. Here's some pseudo-code:\n", + "\n", + "`chisq = sum( ((obs-model)/err)^2)`\n", + "\n", + "### Make sure your function matches the format required by the `scipy.optimize.minimize` function (It accepts a single argument, which is a tuple of items passed by the call to minimize). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Your chi-square function here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### For this exercise we are assuming that the best-fit parameters for the data are found where the $\\chi$-square is at a minimum, which is essentially the definition of the statistic. So now use the `scipy.optimize.minimize` function to \"minimize\" your $\\chi$-square function. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Your scipy.optimize.minimize function here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot the data with your best-fit result as a line through it. How does it look? The best-fit parameters from the result of a `scipy.optimize.minimize` function can be found in the following way:\n", + "\n", + "`result=scipy.optimize.minimize(...)`\n", + "`bestFitParameters=result.x`" + ] + }, + { + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/02_Week2/01_Simple_Fitting/01_A_simple_fitting_introduction_complete.ipynb b/02_Week2/01_Simple_Fitting/01_A_simple_fitting_introduction_complete.ipynb new file mode 100644 index 0000000..6b47e64 --- /dev/null +++ b/02_Week2/01_Simple_Fitting/01_A_simple_fitting_introduction_complete.ipynb @@ -0,0 +1,188 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# import all packages\n", + "import scipy\n", + "import pandas\n", + "import scipy.optimize\n", + "import scipy.stats\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# read in some data we created for this example (.dat is a generic filename, it's just a text file)\n", + "data_filename='https://raw.githubusercontent.com/uofscphysics/STEM_Python_Course/Summer2020/02_Week2/Data/1D_intro_examples.dat'\n", + "example_data_1D = pandas.read_csv(data_filename,sep=',',header=0)#this file is separated by spaces and its first line contains the names of the columns (header) \n", + "print(example_data_1D.head())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Let's plot the data, with error bars, that we read from file (See Day 2)\n", + "plt.errorbar(example_data_1D['x'], #x,y,and error are the column names\n", + " example_data_1D['y'], \n", + " yerr=example_data_1D['error'],#yerr denotes an error in the y-direction for plotting\n", + " fmt='.') #fmt is \"format\", saying that I want data marked by \"points\"\n", + "plt.xlabel('x') #set the x-axis label \n", + "plt.ylabel('y') #set the y-axis label\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "a_true=1.4\n", + "b_true=.4\n", + "c_true=2.5\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#The data were generated with a simple quadratic equation:\n", + "#ax^2+bx+c. \n", + "def my_model(x,a,b,c): #We define the model described above\n", + " return(a*x**2+b*x+c)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Write a $\\chi$-square function that we can then minimize. The $\\chi$-square statistic is defined as the sum of the square of the ratio of the difference between the model and data (observed-model) and the uncertainty. Here's some pseudo-code:\n", + "\n", + "`chisq = sum( ((obs-model)/err)^2)`\n", + "\n", + "### Make sure your function matches the format required by the `scipy.optimize.minimize` function (It accepts a single argument, which is a tuple of items passed by the call to minimize). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Let's try and fit the data with a chi-square minimization\n", + "def chisq_likelihood(theta, args):\n", + " #This function accepts an argument \"theta\", which is \n", + " #a list of model parameters a, b and c. It then calculates\n", + " #a chi-square statistic that it returns, which compares\n", + " #the observations, errors, and model provided in args.\n", + " \n", + " x, y, yerr,mod = args #args is a list, so this is the same as x=args[0],y=args[1],yerr=args[2]. x,y, and yerr are numpy arrays, mod is a function.\n", + " a,b,c = theta #theta is also a list, so it follows the same as args above\n", + " model_observations = mod(x,a,b,c) #mod (a model) is the 4th element of args, and it accepts x values, and the three model parameters a,b,c. Now model_observations contains the model values at every point in x (and is a numpy array)\n", + " inv_sigma2 = 1./yerr**2 #The chi-square statistic contains an inverse-square error, which we calculate here\n", + " chisquare = np.sum((y-model_observations)**2*inv_sigma2 )#calculate the chi-square statistic. \n", + " return chisquare\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### For this exercise we are assuming that the best-fit parameters for the data are found where the $\\chi$-square is at a minimum, which is essentially the definition of the statistic. So now use the `scipy.optimize.minimize` function to \"minimize\" your $\\chi$-square function. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Use scipy.optimize.minimize to minimize the chi-square, which\n", + "#is the same as maximizing the likelihood.\n", + "result = scipy.optimize.minimize(chisq_likelihood, #the first argument is the function to minimize, which must accept a list of parameters as its first argument, and any other necessary agruments as its second.\n", + " x0=[1,1,1], #x0 is a first guess for each of your parameters (depends on your model/data)\n", + " bounds=[(-100,100),(-100,100),(-100,100)], #optional bounds for each of your parameters\n", + " args=[example_data_1D['x'],#the args passed to chisq_likelihood above\n", + " example_data_1D['y'],\n", + " example_data_1D['error'],\n", + " my_model])#my_model is defined in a previous cell\n", + "\n", + "print(result)#scipy.optimize.minimize returns a dictionary which I've called result. It has other information, the \"x\" element has the best fit values\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot the data with your best-fit result as a line through it. How does it look? The best-fit parameters from the result of a `scipy.optimize.minimize` function can be found in the following way:\n", + "\n", + "`result=scipy.optimize.minimize(...)`\n", + "`bestFitParameters=result.x`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "a_ml,b_ml,c_ml = result[\"x\"] #get our best fit values from result\n", + "\n", + "#set up plotting the model over the data\n", + "plt.errorbar(example_data_1D['x'],\n", + " example_data_1D['y'],\n", + " yerr=example_data_1D['error'],\n", + " fmt='.',\n", + " label='Data')\n", + "\n", + "plt.plot(example_data_1D['x'],\n", + " my_model(example_data_1D['x'],a_ml,b_ml,c_ml),\n", + " 'g--',#make the line green and dashed\n", + " label='$\\chi^2$ Fit')\n", + "\n", + "plt.xlabel('x')\n", + "plt.ylabel('y')\n", + "plt.legend()\n", + "print('Chi-square fit:',a_ml, b_ml,c_ml)\n", + "print('True:',a_true,b_true,c_true)\n", + "plt.show()" + ] + } + ], + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/02_Week2/01_Simple_Fitting/01_B_second_model_fit_exercise.ipynb b/02_Week2/01_Simple_Fitting/01_B_second_model_fit_exercise.ipynb new file mode 100644 index 0000000..b5b84c4 --- /dev/null +++ b/02_Week2/01_Simple_Fitting/01_B_second_model_fit_exercise.ipynb @@ -0,0 +1,92 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# import all packages\n", + "import scipy\n", + "import pandas\n", + "import scipy.optimize\n", + "import scipy.stats\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# read in some data we created for this example (.dat is a generic filename, it's just a text file)\n", + "data_filename='https://raw.githubusercontent.com/uofscphysics/STEM_Python_Course/Summer2020/02_Week2/Data/1D_intro_examples.dat'\n", + "example_data_1D = pandas.read_csv(data_filename,sep=',',header=0)#this file is separated by spaces and its first line contains the names of the columns (header) \n", + "print(example_data_1D.head())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Let's plot the data, with error bars, that we read from file (See Day 2)\n", + "plt.errorbar(example_data_1D['x'], #x,y,and error are the column names\n", + " example_data_1D['y'], \n", + " yerr=example_data_1D['error'],#yerr denotes an error in the y-direction for plotting\n", + " fmt='.') #fmt is \"format\", saying that I want data marked by \"points\"\n", + "plt.xlabel('x') #set the x-axis label \n", + "plt.ylabel('y') #set the y-axis label\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def my_model(x,a,b,c):\n", + " return a + b*np.exp(c*x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Repeat the same process as for the quadratic equation introductory exercise with this new model, seeing how the fit is. Keep things in mind like what you would do if there was a different number of parameters, etc. Note that based on the data we'll want an increasing exponential function. Put a bound on the `c` variable to make it positive. Sometimes we need to apply our own knowledge of a situation like this to make sure model fitting doesn't go awry. " + ] + }, + { + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/02_Week2/01_Simple_Fitting/01_B_second_model_fit_exercise_complete.ipynb b/02_Week2/01_Simple_Fitting/01_B_second_model_fit_exercise_complete.ipynb new file mode 100644 index 0000000..ee63735 --- /dev/null +++ b/02_Week2/01_Simple_Fitting/01_B_second_model_fit_exercise_complete.ipynb @@ -0,0 +1,143 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# import all packages\n", + "import scipy\n", + "import pandas\n", + "import scipy.optimize\n", + "import scipy.stats\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# read in some data we created for this example (.dat is a generic filename, it's just a text file)\n", + "data_filename='https://raw.githubusercontent.com/uofscphysics/STEM_Python_Course/Summer2020/02_Week2/Data/1D_intro_examples.dat'\n", + "example_data_1D = pandas.read_csv(data_filename,sep=',',header=0)#this file is separated by spaces and its first line contains the names of the columns (header) \n", + "print(example_data_1D.head())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Let's plot the data, with error bars, that we read from file (See Day 2)\n", + "plt.errorbar(example_data_1D['x'], #x,y,and error are the column names\n", + " example_data_1D['y'], \n", + " yerr=example_data_1D['error'],#yerr denotes an error in the y-direction for plotting\n", + " fmt='.') #fmt is \"format\", saying that I want data marked by \"points\"\n", + "plt.xlabel('x') #set the x-axis label \n", + "plt.ylabel('y') #set the y-axis label\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def my_model(x,a,b,c):\n", + " return a + b*np.exp(c*x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Repeat the same process as for the quadratic equation introductory exercise with this new model, seeing how the fit is. Keep things in mind like what you would do if there was a different number of parameters, etc. Note that based on the data we'll want an increasing exponential function. Put a bound on the `c` variable to make it positive. Sometimes we need to apply our own knowledge of a situation like this to make sure model fitting doesn't go awry. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Let's try and fit the data with a chi-square minimization\n", + "def chisq_likelihood(theta, args):\n", + " #This function accepts an argument \"theta\", which is \n", + " #a list of model parameters a, b and c. It then calculates\n", + " #a chi-square statistic that it returns, which compares\n", + " #the observations, errors, and model provided in args.\n", + " \n", + " x, y, yerr = args #args is a list, so this is the same as x=args[0],y=args[1],yerr=args[2]. x,y, and yerr are numpy arrays, mod is a function.\n", + " a,b,c = theta #theta is also a list, so it follows the same as args above\n", + " model_observations = my_model(x,a,b,c) #mod (a model) is the 4th element of args, and it accepts x values, and the three model parameters a,b,c. Now model_observations contains the model values at every point in x (and is a numpy array)\n", + " inv_sigma2 = 1./yerr**2 #The chi-square statistic contains an inverse-square error, which we calculate here\n", + " chisquare = np.sum((y-model_observations)**2*inv_sigma2 )#calculate the chi-square statistic. \n", + " return chisquare\n", + "\n", + "#Use scipy.optimize.minimize to minimize the chi-square, which\n", + "#is the same as maximizing the likelihood.\n", + "result = scipy.optimize.minimize(chisq_likelihood, #the first argument is the function to minimize, which must accept a list of parameters as its first argument, and any other necessary agruments as its second.\n", + " x0=[-10,200,.1], #x0 is a first guess for each of your parameters (depends on your model/data)\n", + " bounds=[(-100,100),(-100,100),(0,100)], #optional bounds for each of your parameters\n", + " args=[example_data_1D['x'],#the args passed to chisq_likelihood above\n", + " example_data_1D['y'],\n", + " example_data_1D['error']])#my_model is defined in a previous cell\n", + "\n", + "print(result)#scipy.optimize.minimize returns a dictionary which I've called result. It has other information, the \"x\" element has the best fit values\n", + "a_ml,b_ml,c_ml = result[\"x\"] #get our best fit values from result\n", + "\n", + "#set up plotting the model over the data\n", + "plt.errorbar(example_data_1D['x'],\n", + " example_data_1D['y'],\n", + " yerr=example_data_1D['error'],\n", + " fmt='.',\n", + " label='Data')\n", + "\n", + "plt.plot(example_data_1D['x'],\n", + " my_model(example_data_1D['x'],a_ml,b_ml,c_ml),\n", + " 'g--',#make the line green and dashed\n", + " label='$\\chi^2$ Fit')\n", + "\n", + "plt.xlabel('x')\n", + "plt.ylabel('y')\n", + "plt.legend()\n", + "print('Chi-square fit:',a_ml, b_ml,c_ml)\n", + "plt.show()" + ] + }, + { + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/02_Week2/01_Simple_Fitting/README.md b/02_Week2/01_Simple_Fitting/README.md new file mode 100644 index 0000000..e3e3970 --- /dev/null +++ b/02_Week2/01_Simple_Fitting/README.md @@ -0,0 +1,30 @@ +# Table of Contents: + +## 01_A_generate_dataset.ipynb +Was used to create the data for this set of activites, you can use it to see how that was done. + +[![*Run this notebook in Colab:*](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/uofscphysics/STEM_Python_Course/blob/Summer2020/02_Week2/01_Simple_Fitting/01_A_generate_dataset.ipynb) + +____ +## 01_A_simple_fitting_introduction.ipynb +A notebook that introduces chi-square minimization as a fitting technique, which is mostly blank. + +[![*Run this notebook in Colab:*](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/uofscphysics/STEM_Python_Course/blob/Summer2020/02_Week2/01_Simple_Fitting/01_A_simple_fitting_introduction.ipynb) + +____ +## 01_A_simple_fitting_introduction_complete.ipynb +The completed notebook that introduces chi-square minimization as a fitting technique. + +[![*Run this notebook in Colab:*](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/uofscphysics/STEM_Python_Course/blob/Summer2020/02_Week2/01_Simple_Fitting/01_A_simple_fitting_introduction_complete.ipynb) + +____ +## 01_B_second_model_fit_exercise.ipynb +A (mostly) empty notebook that uses the same example as above to introduce chi-square minimization, with a different model + +[![*Run this notebook in Colab:*](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/uofscphysics/STEM_Python_Course/blob/Summer2020/02_Week2/01_Simple_Fitting/01_B_second_model_fit_exercise.ipynb) + +____ +## 01_B_second_model_fit_exercise_complete.ipynb +The completed notebook that uses a new model to introduce chi-square minimization. + +[![*Run this notebook in Colab:*](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/uofscphysics/STEM_Python_Course/blob/Summer2020/02_Week2/01_Simple_Fitting/01_B_second_model_fit_exercise_complete.ipynb) \ No newline at end of file diff --git a/02_Week2/02_MCMC_Fitting/02_A_generate_dataset.ipynb b/02_Week2/02_MCMC_Fitting/02_A_generate_dataset.ipynb new file mode 100644 index 0000000..9e91b43 --- /dev/null +++ b/02_Week2/02_MCMC_Fitting/02_A_generate_dataset.ipynb @@ -0,0 +1,71 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "#The data were generated with a simple quadratic equation:\n", + "#ax^2+bx+c. \n", + "a_true=5.4\n", + "b_true=2.4\n", + "c_true=0.5\n", + "def my_model(x,a,b,c): #We define the model described above\n", + " return((a*x)**2+b*x+c)\n", + "x_values= np.linspace(0,10,15)\n", + "y_values=my_model(x_values,a_true,b_true,c_true)\n", + "error=.1*y_values+5\n", + "final_y=np.random.normal(loc=y_values,scale=error)\n", + "pandas.DataFrame(data=np.array([x_values,final_y,error]).T,columns=['x','y','error']).to_csv('../Data/1D_intro_examples.dat')" + ] + }, + { + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/02_Week2/02_MCMC_Fitting/02_A_mcmc_chisq_likelihood_fitting_exercise.ipynb b/02_Week2/02_MCMC_Fitting/02_A_mcmc_chisq_likelihood_fitting_exercise.ipynb new file mode 100644 index 0000000..b920890 --- /dev/null +++ b/02_Week2/02_MCMC_Fitting/02_A_mcmc_chisq_likelihood_fitting_exercise.ipynb @@ -0,0 +1,299 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install corner emcee" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# import all packages\n", + "import corner,emcee\n", + "import pandas\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# read in some data we created for this example (.dat is a generic filename, it's just a text file)\n", + "data_filename='https://raw.githubusercontent.com/uofscphysics/STEM_Python_Course/Summer2020/02_Week2/Data/1D_intro_examples.dat'\n", + "example_data_1D = pandas.read_csv(data_filename,sep=',',header=0)#this file is separated by spaces and its first line contains the names of the columns (header) \n", + "print(example_data_1D.head())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Let's plot the data, with error bars, that we read from file (See Day 2)\n", + "plt.errorbar(example_data_1D['x'], #x,y,and error are the column names\n", + " example_data_1D['y'], \n", + " yerr=example_data_1D['error'],#yerr denotes an error in the y-direction for plotting\n", + " fmt='.') #fmt is \"format\", saying that I want data marked by \"points\"\n", + "plt.xlabel('x') #set the x-axis label \n", + "plt.ylabel('y') #set the y-axis label\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#The data were generated with a simple quadratic equation:\n", + "#ax^2+bx+c. \n", + "def my_model(x,a,b,c): #We define the model described above\n", + " return((a*x)**2+b*x+c)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### We can obtain best-fit parameters using a Markov Chain Monte Carlo algorithm (MCMC). The premise here is that we have a way of representing the \"likelihood\" of a given set of parameters being the \"truth\". This is done by having a function that compares your model (given a choice of parameters) to your data, returning a \"likelihood\" that increases for more likely choices of parameters (parameters that cause a better fit to the data). Once we have this function, the MCMC algorithm essentially just tries a set number of different combinations of your model parameters in a more efficient manner than simply random choices. Based on the final distribution of samples and resulting likelihoods, we obtain estimates of the best-fit parameters and posterior distributions. \n", + "\n", + "#### Now create such a likelihood function. Remember that before we used a $\\chi^2$ function to estimate best-fit parameters. In that case, we wanted to minimize the $\\chi^2$ statistic to find best-fit parameters. Now we want a likelihood function maximize instead of minimize, but the principle is the same (calculating how well a given choice of parameters reproduces the observed data given your model). Therefore, you can just use the opposite (negative) of your $\\chi^2$ function as your likelihood function. For now, just leave your function as-is (we can add the negative in later)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Your chi-square function here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### The prior function holds any information you know beforehand about the model parameters (e.g. bounds). The simplest form of prior just returns a probability of 1 if you are within specified bounds, or 0 if not. It is computationally more efficient to add numbers than multiply, and a $\\chi^2$ is already the log of a probability, so we usually use a \"log\" prior instead of simply a prior so that it can be added directly to our likelihood. Your prior function should therefore return 0 if all parameters are within their bounds ($\\ln(1)=0$), and `-np.inf` otherwise ($\\ln(0)\\rightarrow-\\infty$). You can set your bounds however you want, maybe start with (-20,20) for all parameters." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a log-prior function. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Okay that's it. Now we just need a function that combines the two functions written above, that should be maximized by the MCMC sampler. It should accept an array of parameters, as well as the data and uncertainty (`x,y,yerr`)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Set up your final log-likelihood function here\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Now we can finally set up an MCMC sampler. We need to define the number of dimensions (number of model parameters) and the number of \"walkers\" we want. Think of a large room, which contains all of the possible combinations of parameters for our model. Imagine you walk this room blindfolded, and every step you take you just ask someone else what the likelihood is that your current space is the \"truth\", before moving on. The more people walking around your room the better coverage you'll get, meaning you'll be able to try more independent regions of your parameter space. This is what `nwalkers` does below, it sets the number of blindfolded people walking around the room. The higher this number, the slower the code will run." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Set up the MCMC to sample the full parameter space\n", + "ndim, nwalkers = 3, 50 #number of parameters to fit (3); number of individual \"walkers\" that randomly sample the space. Choose any number, the higher the slower.\n", + "best_guess_parameters = np.array([1,1,1])\n", + "\n", + "# This sets the starting position of each walker. It's just creating a list\n", + "# of length nwalkers, and number of columns equal to ndim. Each row contains\n", + "# a random location for a walker to start, based on a Gaussian\n", + "# centered on your best guess. You can tighten or loosen this constraint by\n", + "# changing the \"starting_location_width\" parameter\n", + "starting_location_width = 5\n", + "starting_positions = []\n", + "for i in range(nwalkers):\n", + " starting_positions.append(best_guess_parameters + starting_location_width*np.random.randn(ndim))\n", + "\n", + "#Let's just look at where walkers are starting in the a-b-c parameter space\n", + "#(Doesn't need to be included in your own code)\n", + "#Notice we get a random sampling near our best guess (blue lines)\n", + "corner.corner(starting_positions,\n", + " labels=['a','b','c'],\n", + " truths=best_guess_parameters,\n", + " plot_contours=False,plot_density=False,plot_datapoints=True)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Okay this actually starts the sampler. The necessary parameters are the number of walkers, the number of dimensions, your function to maximize (the last one you wrote above), and an argument called `args` that is equal to a list or tuple of any arguments that need to be passed to your (log)-likelihood function apart from the array of parameter guesses." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#set up the MCMC sampler\n", + "sampler = emcee.EnsembleSampler(nwalkers, \n", + " ndim, \n", + " lnprob, #the likelihood function to maximize \n", + " args=(example_data_1D['x'], #the arguments passed to the likelihood functions (other than the model parameters)\n", + " example_data_1D['y'], \n", + " example_data_1D['error']))\n", + "#run the MCMC with the starting positions we defined and 100 sampling points per walker.\n", + "#this is the equivalent of setting the number of steps each blindfolded person can \n", + "#take in the room, using the example above.\n", + "n_samples_per_walker=100\n", + "output = sampler.run_mcmc(starting_positions, n_samples_per_walker) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Okay let's see what we got! " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#sampler.chain contains all of the samples from the MCMC.\n", + "print(sampler.chain.shape)\n", + "#It currently holds the samples separately for each walker.\n", + "#We don't care about what each walker does, so let's flatten it:\n", + "#The -1 here means we don't care how many rows it takes, \n", + "#give us the same number of columns as we have parameters\n", + "samples = sampler.chain.reshape((-1, ndim)) \n", + "print(samples.shape)\n", + "#So we tried 5000 total model realizations for 3 parameters" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#So what did we actually get from this? Let's use another\n", + "#python package to see the output of the MCMC sampling.\n", + "#The dashed line is one way of estimating the best-fit parameters\n", + "#from an MCMC sampler (the median of the samples). The blue line\n", + "#shows the true values.\n", + "fig = corner.corner(samples, #samples is defined above\n", + " labels=[\"$a$\", \"$b$\",\"$c$\"],#parameter labels\n", + " quantiles=[.5],\n", + " plot_contours=False,plot_density=False,\n", + " plot_datapoints=True)\n", + "\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#But how close did we get to the true values?\n", + "#We can take the 50th percentile of the distributions you see above\n", + "#As our result. Incidentally we could calculate the uncertainty as \n", + "#well, perhaps as the differences between the 84th and 50th for\n", + "#the upper uncertainty, and 50th and 16th for the lower uncertainty\n", + "\n", + "#axis=0 means we want to calculate percentiles along columns, not rows\n", + "a_mcmc, b_mcmc,c_mcmc = np.percentile(samples, 50,axis=0)\n", + "a_mcmc_lower, b_mcmc_lower,c_mcmc_lower = np.percentile(samples, 16,axis=0)\n", + "a_mcmc_upper, b_mcmc_upper,c_mcmc_upper = np.percentile(samples, 84,axis=0)\n", + "\n", + "print('a:',a_mcmc,'±(',a_mcmc_upper-a_mcmc,a_mcmc-a_mcmc_lower,')')\n", + "print('b:',a_mcmc,'±(',b_mcmc_upper-b_mcmc,b_mcmc-b_mcmc_lower,')')\n", + "print('c:',a_mcmc,'±(',c_mcmc_upper-c_mcmc,c_mcmc-c_mcmc_lower,')')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Plot the result. Show the data (with error bars), with your best-fit line going through. How does it look? You could compare it to your $\\chi^2$ result if you want." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#set up plotting the model over the data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Not bad! Play with the `nwalkers` and `n_samples_per_walker` to see what it does to computation time and accuracy. What extra information do we get from this approach, vs. the simple $\\chi$-square minimization?" + ] + }, + { + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/02_Week2/02_MCMC_Fitting/02_A_mcmc_chisq_likelihood_fitting_exercise_completed.ipynb b/02_Week2/02_MCMC_Fitting/02_A_mcmc_chisq_likelihood_fitting_exercise_completed.ipynb new file mode 100644 index 0000000..0808517 --- /dev/null +++ b/02_Week2/02_MCMC_Fitting/02_A_mcmc_chisq_likelihood_fitting_exercise_completed.ipynb @@ -0,0 +1,377 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install corner emcee" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# import all packages\n", + "import corner,emcee\n", + "import pandas\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# read in some data we created for this example (.dat is a generic filename, it's just a text file)\n", + "data_filename='https://raw.githubusercontent.com/uofscphysics/STEM_Python_Course/Summer2020/02_Week2/Data/1D_intro_examples.dat'\n", + "example_data_1D = pandas.read_csv(data_filename,sep=',',header=0)#this file is separated by spaces and its first line contains the names of the columns (header) \n", + "print(example_data_1D.head())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Let's plot the data, with error bars, that we read from file (See Day 2)\n", + "plt.errorbar(example_data_1D['x'], #x,y,and error are the column names\n", + " example_data_1D['y'], \n", + " yerr=example_data_1D['error'],#yerr denotes an error in the y-direction for plotting\n", + " fmt='.') #fmt is \"format\", saying that I want data marked by \"points\"\n", + "plt.xlabel('x') #set the x-axis label \n", + "plt.ylabel('y') #set the y-axis label\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#The data were generated with a simple quadratic equation:\n", + "#ax^2+bx+c. \n", + "def my_model(x,a,b,c): #We define the model described above\n", + " return((a*x)**2+b*x+c)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### We can obtain best-fit parameters using a Markov Chain Monte Carlo algorithm (MCMC). The premise here is that we have a way of representing the \"likelihood\" of a given set of parameters being the \"truth\". This is done by having a function that compares your model (given a choice of parameters) to your data, returning a \"likelihood\" that increases for more likely choices of parameters (parameters that cause a better fit to the data). Once we have this function, the MCMC algorithm essentially just tries a set number of different combinations of your model parameters in a more efficient manner than simply random choices. Based on the final distribution of samples and resulting likelihoods, we obtain estimates of the best-fit parameters and posterior distributions. \n", + "\n", + "#### Now create such a likelihood function. Remember that before we used a $\\chi^2$ function to estimate best-fit parameters. In that case, we wanted to minimize the $\\chi^2$ statistic to find best-fit parameters. Now we want a likelihood function maximize instead of minimize, but the principle is the same (calculating how well a given choice of parameters reproduces the observed data given your model). Therefore, you can just use the opposite (negative) of your $\\chi^2$ function as your likelihood function. For now, just leave your function as-is (we can add the negative in later)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "def chisq_likelihood(theta, args):\n", + " #This function accepts an argument \"theta\", which is \n", + " #a list of model parameters a, b and c. It then calculates\n", + " #a chi-square statistic that it returns, which compares\n", + " #the observations, errors, and model provided in args.\n", + " \n", + " x, y, yerr = args #args is a list, so this is the same as x=args[0],y=args[1],yerr=args[2]. x,y, and yerr are numpy arrays, mod is a function.\n", + " a,b,c = theta #theta is also a list, so it follows the same as args above\n", + " model_observations = my_model(x,a,b,c) #mod (a model) is the 4th element of args, and it accepts x values, and the three model parameters a,b,c. Now model_observations contains the model values at every point in x (and is a numpy array)\n", + " inv_sigma2 = 1./yerr**2 #The chi-square statistic contains an inverse-square error, which we calculate here\n", + " chisquare = np.sum((y-model_observations)**2*inv_sigma2 )#calculate the chi-square statistic. \n", + " return chisquare\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### The prior function holds any information you know beforehand about the model parameters (e.g. bounds). The simplest form of prior just returns a probability of 1 if you are within specified bounds, or 0 if not. It is computationally more efficient to add numbers than multiply, and a $\\chi^2$ is already the log of a probability, so we usually use a \"log\" prior instead of simply a prior so that it can be added directly to our likelihood. Your prior function should therefore return 0 if all parameters are within their bounds ($\\ln(1)=0$), and `-np.inf` otherwise ($\\ln(0)\\rightarrow-\\infty$)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a log-prior function. \n", + "def lnprior(theta):#accepts the model parameters (theta)\n", + " a,b,c = theta #set a,b,c (see above)\n", + " \n", + " #give the following bounds: a=(0,5),b=(-5,5),c=(0,10)\n", + " if -20 < a < 20. and -20 < b < 20 and -20 < c < 20: #we are assuming a \"uniform prior\" on all parameters, which is the same as just giving each parameter bounds.\n", + " return 0.0 #if you try parameters inside the bounds, return a probability of 1 (log(1)=0)\n", + " return -np.inf #if you try parameters outside the bounds, return 0 (log(0)=-inf)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Okay that's it. Now we just need a function that combines the two functions written above, that should be maximized by the MCMC sampler. It should accept an array of parameters, as well as the data and uncertainty (`x,y,yerr`). So that the code below works, call your function `lnprob`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# This is now a log-likelihood function, which is commonly used.\n", + "def lnprob(theta, x, y, yerr): #accepts theta (the model parameters), and the same x,y,yerr, and mod from above\n", + " lp = lnprior(theta) #get the probability from the prior function\n", + " if not np.isfinite(lp): \n", + " return -np.inf #return a probability of negative infinity if the prior is negative infinity\n", + "\n", + " #the chisq_likelihood function returns a chi-square, \n", + " #which you want to be as small as possible. We are \n", + " #maximizing the likelihood here, so we take\n", + " #the negative of the chi-square function. The 0.5 here comes from the definition\n", + " #of a Gaussian distribution.\n", + " \n", + " #the total likelihood is the product of the prior and the likelihood \n", + " #(or the sum of the log-prior and log-likelihood)\n", + " return lp - 0.5*chisq_likelihood(theta, [x, y, yerr]) \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Now we can finally set up an MCMC sampler. We need to define the number of dimensions (number of model parameters) and the number of \"walkers\" we want. Think of a large room, which contains all of the possible combinations of parameters for our model. Imagine you walk this room blindfolded, and every step you take you just ask someone else what the likelihood is that your current space is the \"truth\", before moving on. The more people walking around your room the better coverage you'll get, meaning you'll be able to try more independent regions of your parameter space. This is what `nwalkers` does below, it sets the number of blindfolded people walking around the room. The higher this number, the slower the code will run." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Set up the MCMC to sample the full parameter space\n", + "ndim, nwalkers = 3, 50 #number of parameters to fit (3); number of individual \"walkers\" that randomly sample the space. Choose any number, the higher the slower.\n", + "best_guess_parameters = np.array([1,1,1])\n", + "\n", + "# This sets the starting position of each walker. It's just creating a list\n", + "# of length nwalkers, and number of columns equal to ndim. Each row contains\n", + "# a random location for a walker to start, based on a Gaussian\n", + "# centered on your best guess. You can tighten or loosen this constraint by\n", + "# changing the \"starting_location_width\" parameter\n", + "starting_location_width = 5\n", + "starting_positions = []\n", + "for i in range(nwalkers):\n", + " starting_positions.append(best_guess_parameters + starting_location_width*np.random.randn(ndim))\n", + "\n", + "#Let's just look at where walkers are starting in the a-b-c parameter space\n", + "#(Doesn't need to be included in your own code)\n", + "#Notice we get a random sampling near our best guess (blue lines)\n", + "corner.corner(starting_positions,\n", + " labels=['a','b','c'],\n", + " truths=best_guess_parameters,\n", + " plot_contours=False,plot_density=False,plot_datapoints=True)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Okay this actually starts the sampler. The necessary parameters are the number of walkers, the number of dimensions, your function to maximize (the last one you wrote above), and an argument called `args` that is equal to a list or tuple of any arguments that need to be passed to your (log)-likelihood function apart from the array of parameter guesses." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#set up the MCMC sampler\n", + "sampler = emcee.EnsembleSampler(nwalkers, \n", + " ndim, \n", + " lnprob, #the likelihood function to maximize \n", + " args=(example_data_1D['x'], #the arguments passed to the likelihood functions (other than the model parameters)\n", + " example_data_1D['y'], \n", + " example_data_1D['error']))\n", + "#run the MCMC with the starting positions we defined and 100 sampling points per walker.\n", + "#this is the equivalent of setting the number of steps each blindfolded person can \n", + "#take in the room, using the example above.\n", + "n_samples_per_walker=100\n", + "output = sampler.run_mcmc(starting_positions, n_samples_per_walker) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Okay let's see what we got! " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#sampler.chain contains all of the samples from the MCMC.\n", + "print(sampler.chain.shape)\n", + "#It currently holds the samples separately for each walker.\n", + "#We don't care about what each walker does, so let's flatten it:\n", + "#The -1 here means we don't care how many rows it takes, \n", + "#give us the same number of columns as we have parameters\n", + "samples = sampler.chain.reshape((-1, ndim)) \n", + "print(samples.shape)\n", + "#So we tried 5000 total model realizations for 3 parameters" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#So what did we actually get from this? Let's use another\n", + "#python package to see the output of the MCMC sampling.\n", + "#The dashed line is one way of estimating the best-fit parameters\n", + "#from an MCMC sampler (the median of the samples). The blue line\n", + "#shows the true values.\n", + "fig = corner.corner(samples, #samples is defined above\n", + " labels=[\"$a$\", \"$b$\",\"$c$\"],#parameter labels\n", + " quantiles=[.5],\n", + " plot_contours=False,plot_density=False,\n", + " plot_datapoints=True)\n", + "\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#But how close did we get to the true values?\n", + "#We can take the 50th percentile of the distributions you see above\n", + "#As our result. Incidentally we could calculate the uncertainty as \n", + "#well, perhaps as the differences between the 84th and 50th for\n", + "#the upper uncertainty, and 50th and 16th for the lower uncertainty\n", + "\n", + "#axis=0 means we want to calculate percentiles along columns, not rows\n", + "a_mcmc, b_mcmc,c_mcmc = np.percentile(samples, 50,axis=0)\n", + "a_mcmc_lower, b_mcmc_lower,c_mcmc_lower = np.percentile(samples, 16,axis=0)\n", + "a_mcmc_upper, b_mcmc_upper,c_mcmc_upper = np.percentile(samples, 84,axis=0)\n", + "\n", + "print('a:',a_mcmc,'±(',a_mcmc_upper-a_mcmc,a_mcmc-a_mcmc_lower,')')\n", + "print('b:',a_mcmc,'±(',b_mcmc_upper-b_mcmc,b_mcmc-b_mcmc_lower,')')\n", + "print('c:',a_mcmc,'±(',c_mcmc_upper-c_mcmc,c_mcmc-c_mcmc_lower,')')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# For a double-peaked posterior, the median of the samples may not be \n", + "# a great measure of the best-fit parameters\n", + "LogProbabilities = sampler.lnprobability\n", + "LogProbabilities = LogProbabilities.reshape((-1,1)).flatten()\n", + "maximum_likelihood_estimate_index=LogProbabilities.argmax()\n", + "maximum_likelihood_estimate = LogProbabilities[maximum_likelihood_estimate_index]\n", + "best_fit_parameters = samples[maximum_likelihood_estimate_index]\n", + "print(maximum_likelihood_estimate_index)\n", + "print(maximum_likelihood_estimate)\n", + "print(best_fit_parameters)\n", + "print(lnprob(best_fit_parameters,example_data_1D['x'], #the arguments passed to the likelihood functions (other than the model parameters)\n", + " example_data_1D['y'], \n", + " example_data_1D['error']))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Plot the result. Show the data (with error bars), with your best-fit line going through. How does it look? You could compare it to your $\\chi^2$ result if you want." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#set up plotting the model over the data\n", + "plt.errorbar(example_data_1D['x'],\n", + " example_data_1D['y'],\n", + " yerr=example_data_1D['error'],\n", + " fmt='.',\n", + " label='Data')\n", + "\n", + "a_mle=best_fit_parameters[0]\n", + "b_mle=best_fit_parameters[1]\n", + "c_mle=best_fit_parameters[2]\n", + "plt.plot(example_data_1D['x'],\n", + " my_model(example_data_1D['x'],a_mcmc, b_mcmc,c_mcmc),\n", + " 'r--',label='MCMC Fit')\n", + "plt.plot(example_data_1D['x'],\n", + " my_model(example_data_1D['x'],a_mle, b_mle,c_mle),\n", + " 'g--',label='MCMC Fit-MLE')\n", + "\n", + "plt.xlabel('x')\n", + "plt.ylabel('y')\n", + "plt.legend()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Not bad! Play with the `nwalkers` and `n_samples_per_walker` to see what it does to computation time and accuracy. What extra information do we get from this approach, vs. the simple $\\chi$-square minimization?" + ] + }, + { + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/02_Week2/02_MCMC_Fitting/02_B_nested_sampling_chisq_likelihood_fitting_exercise.ipynb b/02_Week2/02_MCMC_Fitting/02_B_nested_sampling_chisq_likelihood_fitting_exercise.ipynb new file mode 100644 index 0000000..364c829 --- /dev/null +++ b/02_Week2/02_MCMC_Fitting/02_B_nested_sampling_chisq_likelihood_fitting_exercise.ipynb @@ -0,0 +1,246 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install corner nestle" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# import all packages\n", + "import corner,nestle\n", + "import pandas\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# read in some data we created for this example (.dat is a generic filename, it's just a text file)\n", + "data_filename='https://raw.githubusercontent.com/uofscphysics/STEM_Python_Course/Summer2020/02_Week2/Data/1D_intro_examples.dat'\n", + "example_data_1D = pandas.read_csv(data_filename,sep=',',header=0)#this file is separated by spaces and its first line contains the names of the columns (header) \n", + "print(example_data_1D.head())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Let's plot the data, with error bars, that we read from file (See Day 2)\n", + "plt.errorbar(example_data_1D['x'], #x,y,and error are the column names\n", + " example_data_1D['y'], \n", + " yerr=example_data_1D['error'],#yerr denotes an error in the y-direction for plotting\n", + " fmt='.') #fmt is \"format\", saying that I want data marked by \"points\"\n", + "plt.xlabel('x') #set the x-axis label \n", + "plt.ylabel('y') #set the y-axis label\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#The data were generated with a simple quadratic equation:\n", + "#ax^2+bx+c. \n", + "def my_model(x,a,b,c): #We define the model described above\n", + " return((a*x)**2+b*x+c)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### An alternative to MCMC is Bayesian Nested Sampling. It is extremely similar but randomly samples the full parameter space instead of sending out individual walkers. MCMC is often better for a many-dimensional model.\n", + "\n", + "#### Now create such a likelihood function. Remember that before we used a $\\chi^2$ function to estimate best-fit parameters. In that case, we wanted to minimize the $\\chi^2$ statistic to find best-fit parameters. Now we want a likelihood function maximize instead of minimize, but the principle is the same (calculating how well a given choice of parameters reproduces the observed data given your model). Therefore, you can just use the opposite (negative) of your $\\chi^2$ function as your likelihood function. For now, just leave your function as-is (we can add the negative in later)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Your chi-square function here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Nestle requires a function to maximize with only 1 argument. Create a `loglike` function that accepts one argument (the array of parameter guesses), then returns the $\\chi^2$ result using your $\\chi^2$-likelihood function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define a likelihood function. Make sure to call it \"loglike\" so the \n", + "# code below works as well." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Now we want to apply bounds as we did for an MCMC. This can be a bit difficult to understand. We are using the same bounds on our parameters as before here `[a=(-20,20),b=(-20,20),c=(-20,20)]`. However, nested sampling always reduces the problem to the range (0,1) for every parameter. Then we always need to provide a function prior_transform, which applies our prior to the model and simultaneously tranforms from this unit space, to our actual parameter space. This essentially means rescaling the parameter from the range (0,1) to the range (-20,20).\n", + "\n", + "#### For example, we want a uniform prior on b, meaning bounds from -20 to 20. The algorithm will only try values for b from 0 to 1. Suppose it tries b=0.5. That's halfway between 0 and 1, which means it is halfway between our bounds (-20,20), which is 0, not 0.5. So we need to find a way to change 0.5-->0. Take a second choice of this (guess b=1$\\rightarrow$20 for example), allowing you to solve this system of equations for the necessary parameters. I'll fill this function in for you, then see if you can work out how to write your own for another scenario. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def prior_transform(parameters):\n", + " return np.array([40, 40, 40]) * parameters + np.array([-20, -20, -20])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Now all we need is to run the nested sampler. We give it the our likelihood function, our prior transformation function, the number of dimensions (model parameters), and the number of \"points\". The number of points is analagous to the walker/sample number for MCMC (i.e. increasing it should result in a slower but more accurate fit). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Run nested sampling.\n", + "result_nest = nestle.sample(loglike, prior_transform, ndim=3,npoints=500)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Okay let's see what we got! " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#So what did we actually get from this? Let's use another\n", + "#python package to see the output of the nested sampling.\n", + "#The dashed line is one way of estimating the best-fit parameters\n", + "#(the median of the samples). \n", + "fig = corner.corner(result_nest.samples, #samples is defined above\n", + " weights=result_nest.weights,\n", + " labels=[\"$a$\", \"$b$\",\"$c$\"],#parameter labels\n", + " quantiles=[.5],\n", + " plot_contours=False,plot_density=False,\n", + " plot_datapoints=True)\n", + "# Since we can see there is a double peak here, let's also print the \"most likely\" as opposed\n", + "# to simply the 50th percentile. \n", + "best_ind=np.where(result_nest.weights==np.max(result_nest.weights))[0][0]\n", + "fig = corner.corner(result_nest.samples, #samples is defined above\n", + " weights=result_nest.weights,\n", + " labels=[\"$a$\", \"$b$\",\"$c$\"],#parameter labels\n", + " quantiles=[.5],fig=fig,\n", + " truths=result_nest.samples[best_ind],\n", + " plot_contours=False,plot_density=False,\n", + " plot_datapoints=False)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#But how close did we get to the true values?\n", + "#We can take the 50th percentile of the distributions you see above\n", + "#As our result. Incidentally we could calculate the uncertainty as \n", + "#well, perhaps as the differences between the 84th and 50th for\n", + "#the upper uncertainty, and 50th and 16th for the lower uncertainty\n", + "\n", + "#axis=0 means we want to calculate percentiles along columns, not rows\n", + "a_nest, b_nest,c_nest = np.percentile(result_nest.samples, 50,axis=0)\n", + "a_nest_lower, b_nest_lower,c_nest_lower = np.percentile(result_nest.samples, 16,axis=0)\n", + "a_nest_upper, b_nest_upper,c_nest_upper = np.percentile(result_nest.samples, 84,axis=0)\n", + "\n", + "print('a:',a_nest,'±(',a_nest_upper-a_nest,a_nest-a_nest_lower,')')\n", + "print('b:',b_nest,'±(',b_nest_upper-b_nest,b_nest-b_nest_lower,')')\n", + "print('c:',c_nest,'±(',c_nest_upper-c_nest,c_nest-c_nest_lower,')')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Plot the result. Show the data (with error bars), with your best-fit line going through. How does it look? You could compare it to your $\\chi^2$ result if you want." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#set up plotting the model over the data\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Play with the `npoints` to see what it does to computation time and accuracy. What extra information do we get from this approach, vs. the simple $\\chi$-square minimization?" + ] + }, + { + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/02_Week2/02_MCMC_Fitting/02_B_nested_sampling_chisq_likelihood_fitting_exercise_completed.ipynb b/02_Week2/02_MCMC_Fitting/02_B_nested_sampling_chisq_likelihood_fitting_exercise_completed.ipynb new file mode 100644 index 0000000..042d00c --- /dev/null +++ b/02_Week2/02_MCMC_Fitting/02_B_nested_sampling_chisq_likelihood_fitting_exercise_completed.ipynb @@ -0,0 +1,286 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install corner nestle" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# import all packages\n", + "import corner,nestle\n", + "import pandas\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# read in some data we created for this example (.dat is a generic filename, it's just a text file)\n", + "data_filename='https://raw.githubusercontent.com/uofscphysics/STEM_Python_Course/Summer2020/02_Week2/Data/1D_intro_examples.dat'\n", + "example_data_1D = pandas.read_csv(data_filename,sep=',',header=0)#this file is separated by spaces and its first line contains the names of the columns (header) \n", + "print(example_data_1D.head())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Let's plot the data, with error bars, that we read from file (See Day 2)\n", + "plt.errorbar(example_data_1D['x'], #x,y,and error are the column names\n", + " example_data_1D['y'], \n", + " yerr=example_data_1D['error'],#yerr denotes an error in the y-direction for plotting\n", + " fmt='.') #fmt is \"format\", saying that I want data marked by \"points\"\n", + "plt.xlabel('x') #set the x-axis label \n", + "plt.ylabel('y') #set the y-axis label\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#The data were generated with a simple quadratic equation:\n", + "#ax^2+bx+c. \n", + "def my_model(x,a,b,c): #We define the model described above\n", + " return((a*x)**2+b*x+c)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### An alternative to MCMC is Bayesian Nested Sampling. It is extremely similar but randomly samples the full parameter space instead of sending out individual walkers. MCMC is often better for a many-dimensional model.\n", + "\n", + "#### Now create such a likelihood function. Remember that before we used a $\\chi^2$ function to estimate best-fit parameters. In that case, we wanted to minimize the $\\chi^2$ statistic to find best-fit parameters. Now we want a likelihood function maximize instead of minimize, but the principle is the same (calculating how well a given choice of parameters reproduces the observed data given your model). Therefore, you can just use the opposite (negative) of your $\\chi^2$ function as your likelihood function. For now, just leave your function as-is (we can add the negative in later)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "def chisq_likelihood(theta, args):\n", + " #This function accepts an argument \"theta\", which is \n", + " #a list of model parameters a, b and c. It then calculates\n", + " #a chi-square statistic that it returns, which compares\n", + " #the observations, errors, and model provided in args.\n", + " \n", + " x, y, yerr = args #args is a list, so this is the same as x=args[0],y=args[1],yerr=args[2]. x,y, and yerr are numpy arrays\n", + " a,b,c = theta #theta is also a list, so it follows the same as args above\n", + " model_observations = my_model(x,a,b,c) \n", + " inv_sigma2 = 1./yerr**2 #The chi-square statistic contains an inverse-square error, which we calculate here\n", + " chisquare = np.sum((y-model_observations)**2*inv_sigma2 )#calculate the chi-square statistic. \n", + " return chisquare\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Nestle requires a function to maximize with only 1 argument. Create a `loglike` function that accepts one argument (the array of parameter guesses), then returns the $\\chi^2$ result using your $\\chi^2$-likelihood function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define a likelihood function. Let's use a chi-square again.\n", + "# The package doing the fitting needs a likelihood function\n", + "# that only accepts the parameters, so we can create this \n", + "# small wrapper function that calls our original chisq_likelihood\n", + "# function with the necessary arguments.\n", + "def loglike(theta):\n", + " args=(np.array(example_data_1D['x']),\n", + " np.array(example_data_1D['y']),\n", + " np.array(example_data_1D['error']))\n", + " chisq = chisq_likelihood(theta,args)\n", + " #again we need to take the negative of the chi-square because we need a function to maximize\n", + " #and the 0.5 comes from the definition of a Gaussian distribution\n", + " return -0.5*chisq " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Now we want to apply bounds as we did for an MCMC. This can be a bit difficult to understand. We are using the same bounds on our parameters as before here `[a=(-20,20),b=(-20,20),c=(-20,20)]`. However, nested sampling always reduces the problem to the range (0,1) for every parameter. Then we always need to provide a function prior_transform, which applies our prior to the model and simultaneously tranforms from this unit space, to our actual parameter space. This essentially means rescaling the parameter from the range (0,1) to the range (-20,20).\n", + "\n", + "#### For example, we want a uniform prior on b, meaning bounds from -20 to 20. The algorithm will only try values for b from 0 to 1. Suppose it tries b=0.5. That's halfway between 0 and 1, which means it is halfway between our bounds (-20,20), which is 0, not 0.5. So we need to find a way to change 0.5-->0. Take a second choice of this (guess b=1$\\rightarrow$20 for example), allowing you to solve this system of equations for the necessary parameters. I'll fill this function in for you, then see if you can work out how to write your own for another scenario. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def prior_transform(parameters):\n", + " return np.array([40, 40, 40]) * parameters + np.array([-20, -20, -20])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Now all we need is to run the nested sampler. We give it the our likelihood function, our prior transformation function, the number of dimensions (model parameters), and the number of \"points\". The number of points is analagous to the walker/sample number for MCMC (i.e. increasing it should result in a slower but more accurate fit). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Run nested sampling.\n", + "result_nest = nestle.sample(loglike, prior_transform, ndim=3,npoints=100)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Okay let's see what we got! " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#So what did we actually get from this? Let's use another\n", + "#python package to see the output of the nested sampling.\n", + "#The dashed line is one way of estimating the best-fit parameters\n", + "#(the median of the samples). \n", + "fig = corner.corner(result_nest.samples, #samples is defined above\n", + " weights=result_nest.weights,\n", + " labels=[\"$a$\", \"$b$\",\"$c$\"],#parameter labels\n", + " quantiles=[.5],\n", + " plot_contours=False,plot_density=False,\n", + " plot_datapoints=True)\n", + "\n", + "best_ind=np.where(result_nest.weights==np.max(result_nest.weights))[0][0]\n", + "fig = corner.corner(result_nest.samples, #samples is defined above\n", + " weights=result_nest.weights,\n", + " labels=[\"$a$\", \"$b$\",\"$c$\"],#parameter labels\n", + " quantiles=[.5],fig=fig,\n", + " truths=result_nest.samples[best_ind],\n", + " plot_contours=False,plot_density=False,\n", + " plot_datapoints=False)\n", + "\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#But how close did we get to the true values?\n", + "#We can take the 50th percentile of the distributions you see above\n", + "#As our result. Incidentally we could calculate the uncertainty as \n", + "#well, perhaps as the differences between the 84th and 50th for\n", + "#the upper uncertainty, and 50th and 16th for the lower uncertainty\n", + "\n", + "#axis=0 means we want to calculate percentiles along columns, not rows\n", + "a_nest, b_nest,c_nest = result_nest.samples[best_ind]\n", + "\n", + "print('a:',a_nest)\n", + "print('b:',b_nest)\n", + "print('c:',c_nest)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Plot the result. Show the data (with error bars), with your best-fit line going through. How does it look? You could compare it to your $\\chi^2$ result if you want." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#set up plotting the model over the data\n", + "plt.errorbar(example_data_1D['x'],\n", + " example_data_1D['y'],\n", + " yerr=example_data_1D['error'],\n", + " fmt='.',\n", + " label='Data')\n", + "\n", + "plt.plot(example_data_1D['x'],\n", + " my_model(example_data_1D['x'],a_nest,b_nest,c_nest),\n", + " 'r--',#make the line green and dashed\n", + " label='Nestle Fit')\n", + "\n", + "plt.xlabel('x')\n", + "plt.ylabel('y')\n", + "plt.legend()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Not bad! Play with the `npoints` to see what it does to computation time and accuracy. What extra information do we get from this approach, vs. the simple $\\chi$-square minimization?" + ] + }, + { + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/02_Week2/02_MCMC_Fitting/02_C_global_optimization_activity.ipynb b/02_Week2/02_MCMC_Fitting/02_C_global_optimization_activity.ipynb new file mode 100644 index 0000000..9c0720a --- /dev/null +++ b/02_Week2/02_MCMC_Fitting/02_C_global_optimization_activity.ipynb @@ -0,0 +1,257 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install emcee corner" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import corner\n", + "import emcee\n", + "import numpy as np\n", + "import scipy\n", + "import scipy.stats\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def SimpleColorPlotFromFunc( \n", + " Func2D = None,\n", + " xmin = None,\n", + " xmax = None, \n", + " ymin = None,\n", + " ymax = None, \n", + " ):\n", + "\n", + " #Make the list of poitns to plug in from the boundaries:\n", + " x = np.linspace(xmin, xmax, 100)\n", + " y = np.linspace(ymin, ymax, 100)\n", + " X, Y = np.meshgrid(x, y)\n", + " PointsToPlugIn = np.vstack([X.ravel(), Y.ravel()])\n", + " PointsToPlugInDataset = PointsToPlugIn.T\n", + "\n", + "\n", + " #plug in the list of points:\n", + " FunctionResultValuesForGrid = []\n", + " for Point in PointsToPlugInDataset:\n", + " Value = Func2D(Point)\n", + " FunctionResultValuesForGrid.append(Value)\n", + "\n", + " #reshape stuff in a confusing way so matplotlib can think of the data like a matrix\n", + " Z = np.reshape(FunctionResultValuesForGrid, X.shape).T\n", + "\n", + "\n", + " #Actually construct the figure...\n", + " plt.figure()\n", + " heatmap = plt.imshow( \n", + " np.rot90(Z), \n", + " extent=[xmin, xmax, ymin, ymax] ,\n", + " aspect = 'auto' ,\n", + " interpolation = None,\n", + " ) \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "Gaussian1Mean = [1,1]\n", + "Gaussian2Mean = [2,4]\n", + "\n", + "Gaussian1Covariance = np.identity(2)*1\n", + "Gaussian2Covariance = np.identity(2)*.5\n", + "\n", + "\n", + "def Gaussian1(ABpoint):\n", + " return scipy.stats.multivariate_normal.pdf( ABpoint, mean = Gaussian1Mean, \n", + " cov = Gaussian1Covariance )\n", + "\n", + "def Gaussian2(ABpoint):\n", + " return scipy.stats.multivariate_normal.pdf( ABpoint, mean = Gaussian2Mean, \n", + " cov = Gaussian2Covariance )\n", + "\n", + "def GaussianMultiModal(ABpoint):\n", + " return Gaussian1(ABpoint) + Gaussian2(ABpoint)\n", + "\n", + "SimpleColorPlotFromFunc(GaussianMultiModal,\n", + " xmin=-1,\n", + " xmax=6,\n", + " ymin=-1,\n", + " ymax=6)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Use the emcee package and what you learned in the MCMC exercise to find the global peak of the joint probability density function `GaussianMultiModal`. Start with the initialized sampler below (with only 4 walkers and 125 steps per walker), but fill in a `lnprob` function that calculates the loglikelihood for a given choice of A and B. Note that in this case, the `GaussianMultiModal` function is exactly the likelihood." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Fill in your lnprob function here, it could also call a lnprior function that imposes bounds" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Set up the MCMC to sample the full parameter space\n", + "ndim, nwalkers = 2, 4 #number of parameters to fit (3); number of individual \"walkers\" that randomly sample the space. Choose any number, the higher the slower.\n", + "best_guess_parameters = np.array([1,1])\n", + "\n", + "# This sets the starting position of each walker. It's just creating a list\n", + "# of length nwalkers, and number of columns equal to ndim. Each row contains\n", + "# a random location for a walker to start, based on a Gaussian\n", + "# centered on your best guess. You can tighten or loosen this constraint by\n", + "# changing the \"starting_location_width\" parameter. Setting it to be a very small number\n", + "# like this is basically starting all walkers in the same location, which is the point of this \n", + "# exercise.\n", + "\n", + "starting_location_width = .0001\n", + "starting_positions = []\n", + "for i in range(nwalkers):\n", + " starting_positions.append(best_guess_parameters + starting_location_width*np.random.randn(ndim))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#set up the MCMC sampler\n", + "sampler = emcee.EnsembleSampler(nwalkers, \n", + " ndim, \n", + " lnprob) #the likelihood function to maximize\n", + "#run the MCMC with the starting positions we defined and 100 sampling points per walker.\n", + "#this is the equivalent of setting the number of steps each blindfolded person can \n", + "#take in the room, using the example above.\n", + "n_samples_per_walker=125\n", + "output = sampler.run_mcmc(starting_positions, n_samples_per_walker) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#sampler.chain contains all of the samples from the MCMC.\n", + "print(sampler.chain.shape)\n", + "#It currently holds the samples separately for each walker.\n", + "#We don't care about what each walker does, so let's flatten it:\n", + "#The -1 here means we don't care how many rows it takes, \n", + "#give us the same number of columns as we have parameters\n", + "samples = sampler.chain.reshape((-1, ndim)) \n", + "print(samples.shape)\n", + "#So we tried 500 total model realizations for 3 parameters\n", + "LogProbabilities = sampler.lnprobability.reshape((-1, 1)).flatten()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#So what did we actually get from this? Let's use another\n", + "#python package to see the output of the MCMC sampling.\n", + "#The dashed line is one way of estimating the best-fit parameters\n", + "#from an MCMC sampler (the median of the samples). The blue line\n", + "#shows the true values.\n", + "\n", + "DomainBoxMinPoint=[0,-2]\n", + "DomainBoxMaxPoint=[4,5]\n", + "\n", + "RangeObject = np.vstack([DomainBoxMinPoint, DomainBoxMaxPoint]).T\n", + "fig = corner.corner(samples, #samples is defined above\n", + " labels=[\"$A$\", \"$B$\"],#parameter labels\n", + " truths=Gaussian2Mean,\n", + " plot_contours=False,plot_density=False,\n", + " plot_datapoints=True,range=RangeObject)\n", + "\n", + "fig = corner.corner(samples, #samples is defined above\n", + " labels=[\"$A$\", \"$B$\"],#parameter labels\n", + " truths=samples[LogProbabilities.argmax()],fig=fig,\n", + " plot_contours=False,plot_density=False,truth_color='red',\n", + " plot_datapoints=False,range=RangeObject)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def cross_hair(x, y, ax=None, **kwargs):\n", + " if ax is None:\n", + " ax = plt.gca()\n", + " horiz = ax.axhline(y, **kwargs)\n", + " vert = ax.axvline(x, **kwargs)\n", + " return horiz, vert\n", + "best_guess_peak=samples[LogProbabilities.argmax()]\n", + "\n", + "SimpleColorPlotFromFunc(GaussianMultiModal,\n", + " xmin=-1,\n", + " xmax=6,\n", + " ymin=-1,\n", + " ymax=6)\n", + "cross_hair(best_guess_peak[0],best_guess_peak[1],color='red')\n", + "print('Guess:',best_guess_peak,'Truth:',Gaussian2Mean)" + ] + }, + { + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/02_Week2/02_MCMC_Fitting/02_C_global_optimization_activity_completed.ipynb b/02_Week2/02_MCMC_Fitting/02_C_global_optimization_activity_completed.ipynb new file mode 100644 index 0000000..e3d5006 --- /dev/null +++ b/02_Week2/02_MCMC_Fitting/02_C_global_optimization_activity_completed.ipynb @@ -0,0 +1,270 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install emcee corner" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import corner\n", + "import emcee\n", + "import numpy as np\n", + "import scipy\n", + "import scipy.stats\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def SimpleColorPlotFromFunc( \n", + " Func2D = None,\n", + " xmin = None,\n", + " xmax = None, \n", + " ymin = None,\n", + " ymax = None, \n", + " ):\n", + "\n", + " #Make the list of poitns to plug in from the boundaries:\n", + " x = np.linspace(xmin, xmax, 100)\n", + " y = np.linspace(ymin, ymax, 100)\n", + " X, Y = np.meshgrid(x, y)\n", + " PointsToPlugIn = np.vstack([X.ravel(), Y.ravel()])\n", + " PointsToPlugInDataset = PointsToPlugIn.T\n", + "\n", + "\n", + " #plug in the list of points:\n", + " FunctionResultValuesForGrid = []\n", + " for Point in PointsToPlugInDataset:\n", + " Value = Func2D(Point)\n", + " FunctionResultValuesForGrid.append(Value)\n", + "\n", + " #reshape stuff in a confusing way so matplotlib can think of the data like a matrix\n", + " Z = np.reshape(FunctionResultValuesForGrid, X.shape).T\n", + "\n", + "\n", + " #Actually construct the figure...\n", + " plt.figure()\n", + " heatmap = plt.imshow( \n", + " np.rot90(Z), \n", + " extent=[xmin, xmax, ymin, ymax] ,\n", + " aspect = 'auto' ,\n", + " interpolation = None,\n", + " ) \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "Gaussian1Mean = [1,1]\n", + "Gaussian2Mean = [2,4]\n", + "\n", + "Gaussian1Covariance = np.identity(2)*1\n", + "Gaussian2Covariance = np.identity(2)*.5\n", + "\n", + "\n", + "def Gaussian1(ABpoint):\n", + " return scipy.stats.multivariate_normal.pdf( ABpoint, mean = Gaussian1Mean, \n", + " cov = Gaussian1Covariance )\n", + "\n", + "def Gaussian2(ABpoint):\n", + " return scipy.stats.multivariate_normal.pdf( ABpoint, mean = Gaussian2Mean, \n", + " cov = Gaussian2Covariance )\n", + "\n", + "def GaussianMultiModal(ABpoint):\n", + " return Gaussian1(ABpoint) + Gaussian2(ABpoint)\n", + "\n", + "SimpleColorPlotFromFunc(GaussianMultiModal,\n", + " xmin=-1,\n", + " xmax=6,\n", + " ymin=-1,\n", + " ymax=6)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def lnprior(theta):#accepts the model parameters (theta)\n", + " a,b = theta #set a,b,c (see above)\n", + " \n", + " #give the following bounds: a=(0,5),b=(-5,5),c=(0,10)\n", + " if -20 < a < 20. and -20 < b < 20: #we are assuming a \"uniform prior\" on all parameters, which is the same as just giving each parameter bounds.\n", + " return 0.0 #if you try parameters inside the bounds, return a probability of 1 (log(1)=0)\n", + " return -np.inf #if you try parameters outside the bounds, return 0 (log(0)=-inf)\n", + "\n", + "# This is now a log-likelihood function, which is commonly used.\n", + "def lnprob(theta): #accepts theta (the model parameters), and the same x,y,yerr, and mod from above\n", + " lp = lnprior(theta) #get the probability from the prior function\n", + " if not np.isfinite(lp): \n", + " return -np.inf #return a probability of negative infinity if the prior is negative infinity\n", + " \n", + " #the total likelihood is the product of the prior and the likelihood \n", + " #(or the sum of the log-prior and log-likelihood)\n", + " return lp + np.log(GaussianMultiModal(theta))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Create your own algorithm that attempts to find the global peak of the 2D distribution. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Set up the MCMC to sample the full parameter space\n", + "ndim, nwalkers = 2, 4 #number of parameters to fit (3); number of individual \"walkers\" that randomly sample the space. Choose any number, the higher the slower.\n", + "best_guess_parameters = np.array([1,1])\n", + "\n", + "# This sets the starting position of each walker. It's just creating a list\n", + "# of length nwalkers, and number of columns equal to ndim. Each row contains\n", + "# a random location for a walker to start, based on a Gaussian\n", + "# centered on your best guess. You can tighten or loosen this constraint by\n", + "# changing the \"starting_location_width\" parameter\n", + "starting_location_width = .0001\n", + "starting_positions = []\n", + "for i in range(nwalkers):\n", + " starting_positions.append(best_guess_parameters + starting_location_width*np.random.randn(ndim))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#set up the MCMC sampler\n", + "sampler = emcee.EnsembleSampler(nwalkers, \n", + " ndim, \n", + " lnprob) #the likelihood function to maximize\n", + "#run the MCMC with the starting positions we defined and 100 sampling points per walker.\n", + "#this is the equivalent of setting the number of steps each blindfolded person can \n", + "#take in the room, using the example above.\n", + "n_samples_per_walker=125\n", + "output = sampler.run_mcmc(starting_positions, n_samples_per_walker) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#sampler.chain contains all of the samples from the MCMC.\n", + "print(sampler.chain.shape)\n", + "#It currently holds the samples separately for each walker.\n", + "#We don't care about what each walker does, so let's flatten it:\n", + "#The -1 here means we don't care how many rows it takes, \n", + "#give us the same number of columns as we have parameters\n", + "samples = sampler.chain.reshape((-1, ndim)) \n", + "print(samples.shape)\n", + "#So we tried 500 total model realizations for 3 parameters\n", + "LogProbabilities = sampler.lnprobability.reshape((-1, 1)).flatten()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#So what did we actually get from this? Let's use another\n", + "#python package to see the output of the MCMC sampling.\n", + "#The dashed line is one way of estimating the best-fit parameters\n", + "#from an MCMC sampler (the median of the samples). The blue line\n", + "#shows the true values.\n", + "\n", + "DomainBoxMinPoint=[0,-2]\n", + "DomainBoxMaxPoint=[4,5]\n", + "\n", + "RangeObject = np.vstack([DomainBoxMinPoint, DomainBoxMaxPoint]).T\n", + "fig = corner.corner(samples, #samples is defined above\n", + " labels=[\"$A$\", \"$B$\"],#parameter labels\n", + " truths=Gaussian2Mean,\n", + " plot_contours=False,plot_density=False,\n", + " plot_datapoints=True,range=RangeObject)\n", + "\n", + "fig = corner.corner(samples, #samples is defined above\n", + " labels=[\"$A$\", \"$B$\"],#parameter labels\n", + " truths=samples[LogProbabilities.argmax()],fig=fig,\n", + " plot_contours=False,plot_density=False,truth_color='red',\n", + " plot_datapoints=False,range=RangeObject)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def cross_hair(x, y, ax=None, **kwargs):\n", + " if ax is None:\n", + " ax = plt.gca()\n", + " horiz = ax.axhline(y, **kwargs)\n", + " vert = ax.axvline(x, **kwargs)\n", + " return horiz, vert\n", + "best_guess_peak=samples[LogProbabilities.argmax()]\n", + "\n", + "SimpleColorPlotFromFunc(GaussianMultiModal,\n", + " xmin=-1,\n", + " xmax=6,\n", + " ymin=-1,\n", + " ymax=6)\n", + "cross_hair(best_guess_peak[0],best_guess_peak[1],color='red')\n", + "print('Guess:',best_guess_peak,'Truth:',Gaussian2Mean)" + ] + }, + { + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/02_Week2/02_MCMC_Fitting/02_C_global_optimization_comparison.ipynb b/02_Week2/02_MCMC_Fitting/02_C_global_optimization_comparison.ipynb new file mode 100644 index 0000000..4dc92bd --- /dev/null +++ b/02_Week2/02_MCMC_Fitting/02_C_global_optimization_comparison.ipynb @@ -0,0 +1,435 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install corner" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import corner\n", + "import numpy\n", + "import numpy as np\n", + "import scipy\n", + "import scipy.stats\n", + "import pandas\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def SimpleColorPlotFromFunc( \n", + " Func2D = None,\n", + " xmin = None,\n", + " xmax = None, \n", + " ymin = None,\n", + " ymax = None, \n", + " ):\n", + "\n", + " #Make the list of poitns to plug in from the boundaries:\n", + " x = np.linspace(xmin, xmax, 100)\n", + " y = np.linspace(ymin, ymax, 100)\n", + " X, Y = np.meshgrid(x, y)\n", + " PointsToPlugIn = numpy.vstack([X.ravel(), Y.ravel()])\n", + " PointsToPlugInDataset = PointsToPlugIn.T\n", + "\n", + "\n", + " #plug in the list of points:\n", + " FunctionResultValuesForGrid = []\n", + " for Point in PointsToPlugInDataset:\n", + " Value = Func2D(Point)\n", + " FunctionResultValuesForGrid.append(Value)\n", + "\n", + " #reshape stuff in a confusing way so matplotlib can think of the data like a matrix\n", + " Z = numpy.reshape(FunctionResultValuesForGrid, X.shape).T\n", + "\n", + "\n", + " #Actually construct the figure...\n", + " plt.figure()\n", + " heatmap = plt.imshow( \n", + " numpy.rot90(Z), \n", + " extent=[xmin, xmax, ymin, ymax] ,\n", + " aspect = 'auto' ,\n", + " interpolation = None,\n", + " ) \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def Library_NumpyTwoDimensionalDatasetRandomUniform(\n", + " DomainBoxMinPoint= None,\n", + " DomainBoxMaxPoint= None,\n", + " SampleSize= None,\n", + " PrintExtra = False,\n", + " ):\n", + "\n", + " Result = None\n", + "\n", + " ResultTranspose = []\n", + " for ColumnIndex in range(len(DomainBoxMinPoint)):\n", + " Column = numpy.random.uniform( \n", + " low = DomainBoxMinPoint[ColumnIndex],\n", + " high = DomainBoxMaxPoint[ColumnIndex],\n", + " size = SampleSize\n", + " )\n", + " #print 'Column', Column\n", + " ResultTranspose.append(Column)\n", + " ResultTranspose = numpy.array(ResultTranspose)\n", + "\n", + " Result = ResultTranspose.T\n", + " return Result" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def Library_CheckIfCoordinateInsideDomainBox(\n", + " Coordinate= None,\n", + " DomainBoxMinimum= None,\n", + " DomainBoxMaximum= None,\n", + " PrintExtra = False,\n", + " ):\n", + "\n", + " Result = None\n", + "\n", + "\n", + " if DomainBoxMinimum is None:\n", + " raise Exception('DomainBoxMinimum is None')\n", + " if DomainBoxMaximum is None:\n", + " raise Exception('DomainBoxMaximum is None')\n", + " \n", + "\n", + " DomainBoxDimensionCount = len(DomainBoxMinimum)\n", + " assert(DomainBoxDimensionCount == len(DomainBoxMaximum))\n", + "\n", + " Result = True\n", + " for Dimension in range( DomainBoxDimensionCount ):\n", + "\n", + " if not ( DomainBoxMinimum[Dimension] <= Coordinate[Dimension] <= DomainBoxMaximum[Dimension] ):\n", + " Result = False\n", + " break\n", + "\n", + " return Result" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### This is our code from the probability theory activity, which makes a double-peaked joint probablility density function. Then we apply a simulated annealing algorithm (one choice of global optimizer) to find the global peak of this distribution. This is akin to having your MCMC algorithm running with a single walker and 500 steps. Compare the results. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "Gaussian1Mean = [1,1]\n", + "Gaussian2Mean = [2,4]\n", + "\n", + "Gaussian1Covariance = numpy.identity(2)*1\n", + "Gaussian2Covariance = numpy.identity(2)*.5\n", + "\n", + "\n", + "def Gaussian1(ABpoint):\n", + " return scipy.stats.multivariate_normal.pdf( ABpoint, mean = Gaussian1Mean, \n", + " cov = Gaussian1Covariance )\n", + "\n", + "def Gaussian2(ABpoint):\n", + " return scipy.stats.multivariate_normal.pdf( ABpoint, mean = Gaussian2Mean, \n", + " cov = Gaussian2Covariance )\n", + "\n", + "def GaussianMultiModal(ABpoint):\n", + " return Gaussian1(ABpoint) + Gaussian2(ABpoint)\n", + "\n", + "SimpleColorPlotFromFunc( \n", + " Func2D = GaussianMultiModal,\n", + " xmin = -1,\n", + " xmax = 5, \n", + " ymin = -1,\n", + " ymax = 5, \n", + " )\n", + "plt.title(\"Probability Density of A & B\", fontsize=30)\n", + "plt.ylabel('B',fontsize=40)\n", + "plt.xlabel('A',fontsize=40)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Simulated Annealing wants to minimize, so we take the negative of our likelihood\n", + "def negativeGaussianMultiModal(ABpoint):\n", + " return -1*np.log(GaussianMultiModal(ABpoint))\n", + "ScalarFunctionPython= negativeGaussianMultiModal\n", + "DomainMinimumPoint= [-5, -5]\n", + "DomainMaximumPoint= [5, 5]\n", + "# DomainMinimumPoint= [0, 0]\n", + "# DomainMaximumPoint= [3, 5]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "DomainMinimumPoint = numpy.array(DomainMinimumPoint)\n", + "DomainMaximumPoint = numpy.array(DomainMaximumPoint)\n", + "\n", + "\n", + "\n", + "DomainRange = DomainMaximumPoint - DomainMinimumPoint\n", + "\n", + "#Define an affine dependent neighbor range box:\n", + "NeighborRange = DomainRange / 2\n", + "\n", + "BoxCenter = DomainMinimumPoint + DomainRange / 2.\n", + "\n", + "\n", + "TotalTrialCount = 5000 # Change this to increase the max number of trials\n", + "SampleCount = 500 # Change this to increase number of accepted samples\n", + "Beta = .0005 # Change this to change the \"cooling rate\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def CoolingScheduleFunction( Step ):\n", + " Temp = numpy.exp( - Beta*Step )\n", + " return Temp" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "RejectionCount = 0\n", + "\n", + "#Start us off with a single sample from the middle of the box:\n", + "StepNumbers = [0]\n", + "Samples = [BoxCenter]\n", + "FunctionValues = [ScalarFunctionPython(BoxCenter)]\n", + "AcceptanceProbabilities = [1]\n", + "Temperatures = [Beta]\n", + "\n", + "\n", + "\n", + "#Now iterate and get smaller values to minimize the function:\n", + "CurrentStep = 1\n", + "#while CurrentStep < TotalTrialCount:\n", + "while (len(Samples) < SampleCount and CurrentStep < TotalTrialCount) :\n", + "\n", + "\n", + " #Retain the information on the previously accepted sample:\n", + " PreviousSample = Samples[-1]\n", + " PreviousFunctionValue = FunctionValues[-1]\n", + "\n", + "\n", + " #First choose a random point in the neighborhood (possible new sample):\n", + " RandomNeighborOnBox = None\n", + " while (\n", + " (RandomNeighborOnBox is None) \\\n", + " or Library_CheckIfCoordinateInsideDomainBox( \n", + " Coordinate = RandomNeighborOnBox,\n", + " DomainBoxMinimum = DomainMinimumPoint,\n", + " DomainBoxMaximum = DomainMaximumPoint,\n", + " ) == False \n", + " ):\n", + " RandomNeighborOnBox = Library_NumpyTwoDimensionalDatasetRandomUniform(\n", + " DomainBoxMinPoint= PreviousSample - NeighborRange/2,\n", + " DomainBoxMaxPoint= PreviousSample + NeighborRange/2,\n", + " SampleSize= 1,\n", + " )[0]\n", + " #What is the function value at the newly randomly selected point in the box?\n", + " FunctionValue = ScalarFunctionPython(RandomNeighborOnBox)\n", + " #print ('f(',RandomNeighborOnBox,')=', FunctionValue)\n", + "\n", + " #What temperature are we at now?\n", + " Temp = CoolingScheduleFunction(CurrentStep )\n", + "\n", + "\n", + "\n", + "\n", + "\n", + " #Calculate the probability to accept the new sample:\n", + " ProbabilityToAccept = 0.0\n", + " if FunctionValue < PreviousFunctionValue: #Sample got better\n", + " ProbabilityToAccept = 1.0\n", + " else: #Sample got worse\n", + " ValueDiff = -numpy.abs(FunctionValue - PreviousFunctionValue ) \n", + " ProbabilityToAccept = numpy.exp( ValueDiff / Temp )\n", + "\n", + " #Decide if we accept the new sample:\n", + " AcceptSample = False\n", + " ZeroOneUniformValue = numpy.random.uniform(0,1,1)[0]\n", + " if ZeroOneUniformValue < ProbabilityToAccept:\n", + " AcceptSample = True\n", + " else:\n", + " AcceptSample = False\n", + "\n", + "\n", + " #If we accept the new sample - keep track of all its information\n", + " if AcceptSample:\n", + " StepNumbers.append(CurrentStep)\n", + " Samples.append(RandomNeighborOnBox)\n", + " AcceptanceProbabilities.append(ProbabilityToAccept)\n", + " Temperatures.append(Temp)\n", + " FunctionValues.append(FunctionValue)\n", + " else:\n", + " RejectionCount += 1\n", + "\n", + "\n", + " if CurrentStep %300 == 0: \n", + " print ('CurrentStep:', CurrentStep , '| CurrentSampleCount:', len(Samples), '|Temp:', Temp, '|Probability', ProbabilityToAccept)\n", + " print (' ValueDiff', ValueDiff)\n", + " print (' PreviousSample', PreviousSample)\n", + " print (' RandomNeighborOnBox', RandomNeighborOnBox)\n", + "\n", + "\n", + " CurrentStep+= 1\n", + "\n", + "\n", + "print ('TotalTrialCount', TotalTrialCount)\n", + "print ('AcceptanceCount', len(Samples))\n", + "print ('RejectionCount', RejectionCount)\n", + "\n", + "Result = {\n", + " 'Samples': numpy.array(Samples),\n", + " 'FunctionValues': numpy.array(FunctionValues),\n", + " 'AcceptanceProbabilities': numpy.array(AcceptanceProbabilities),\n", + " 'Temperatures':numpy.array(Temperatures),\n", + " 'StepNumbers':numpy.array(StepNumbers), \n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Lifted right from stack overflow:\n", + "def cross_hair(x, y, ax=None, **kwargs):\n", + " if ax is None:\n", + " ax = plt.gca()\n", + " horiz = ax.axhline(y, **kwargs)\n", + " vert = ax.axvline(x, **kwargs)\n", + " return horiz, vert\n", + "SimpleColorPlotFromFunc( \n", + " Func2D = GaussianMultiModal, \n", + " xmin =DomainMinimumPoint[0] ,\n", + " xmax=DomainMaximumPoint[0],\n", + " ymin = DomainMinimumPoint[1] ,\n", + " ymax=DomainMaximumPoint[1],\n", + ") \n", + "best_guess_peak=np.array(Result['Samples'])[np.array(Result['FunctionValues']).argmin()]\n", + "print('Guess:',best_guess_peak,'Truth:',Gaussian2Mean)\n", + "fig=plt.gcf()\n", + "ax=fig.gca()\n", + "ax.scatter(Result['Samples'][:,0],Result['Samples'][:,1])\n", + "cross_hair(best_guess_peak[0],best_guess_peak[1],color='red')\n", + "plt.xlabel('A',fontsize=20)\n", + "plt.ylabel('B',fontsize=20)\n", + "plt.draw()\n", + "\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for i in range(len(Result['Temperatures'])):\n", + " plt.scatter(i,Result['Temperatures'][i],color='k')\n", + "plt.xlabel('Trial Number',fontsize=20)\n", + "plt.ylabel('Temperatures',fontsize=20)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for i in range(len(Result['AcceptanceProbabilities'])):\n", + " plt.scatter(i,Result['AcceptanceProbabilities'][i],color='k')\n", + "plt.xlabel('Trial Number',fontsize=20)\n", + "plt.ylabel('Acceptance Probabilities',fontsize=20)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "corner.corner(Result['Samples'],\n", + " truths=Gaussian2Mean,\n", + " labels=['A','B'],\n", + " plot_contours=False,plot_density=False,\n", + " plot_datapoints=True)\n", + "plt.show()" + ] + }, + { + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/02_Week2/02_MCMC_Fitting/README.md b/02_Week2/02_MCMC_Fitting/README.md new file mode 100644 index 0000000..7aef041 --- /dev/null +++ b/02_Week2/02_MCMC_Fitting/README.md @@ -0,0 +1,48 @@ +# Table of Contents: + +## 02_A_generate_dataset.ipynb +Was used to create the data for this set of activites, you can use it to see how that was done. + +[![*Run this notebook in Colab:*](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/uofscphysics/STEM_Python_Course/blob/Summer2020/02_Week2/02_MCMC_Fitting/02_A_generate_dataset.ipynb) + +_____ +## 02_A_mcmc_chisq_likelihood_fitting_exercise.ipynb +A (mostly) empty notebook used for an exercise to learn about setting up and running an MCMC sampler. + +[![*Run this notebook in Colab:*](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/uofscphysics/STEM_Python_Course/blob/Summer2020/02_Week2/02_MCMC_Fitting/02_A_mcmc_chisq_likelihood_fitting_exercise.ipynb) + +_____ +## 02_A_mcmc_chisq_likelihood_fitting_exercise_completed.ipynb +The completed notebook for the above MCMC exercise. + +[![*Run this notebook in Colab:*](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/uofscphysics/STEM_Python_Course/blob/Summer2020/02_Week2/02_MCMC_Fitting/02_A_mcmc_chisq_likelihood_fitting_exercise_completed.ipynb) + +_____ +## 02_B_nested_sampling_chisq_likelihood_fitting_exercise.ipynb +A (mostly) empty notebook used for an exercise to learn about setting up and running a Bayesian Nested Sampling algorithm. + +[![*Run this notebook in Colab:*](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/uofscphysics/STEM_Python_Course/blob/Summer2020/02_Week2/02_MCMC_Fitting/02_B_nested_sampling_chisq_likelihood_fitting_exercise.ipynb) + +_____ +## 02_B_nested_sampling_chisq_likelihood_fitting_exercise_completed.ipynb +The completed notebook for the above nested sampling exercise. + +[![*Run this notebook in Colab:*](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/uofscphysics/STEM_Python_Course/blob/Summer2020/02_Week2/02_MCMC_Fitting/02_B_nested_sampling_chisq_likelihood_fitting_exercise_completed.ipynb) + +_____ +## 02_C_global_optimization_activity.ipynb +The partially empty notebook you'll use to complete an exercise aimed at learning about global optimization via MCMC. + +[![*Run this notebook in Colab:*](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/uofscphysics/STEM_Python_Course/blob/Summer2020/02_Week2/02_MCMC_Fitting/02_C_global_optimization_activity.ipynb) + +_____ +## 02_C_global_optimization_activity_completed.ipynb +The completed version of the global optimization activity. + +[![*Run this notebook in Colab:*](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/uofscphysics/STEM_Python_Course/blob/Summer2020/02_Week2/02_MCMC_Fitting/02_C_global_optimization_activity_completed.ipynb) + +_____ +## 02_C_global_optimization_comparison.ipynb +A notebook that implements one global optimization algorithm (simulated annealing) for comparison. + +[![*Run this notebook in Colab:*](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/uofscphysics/STEM_Python_Course/blob/Summer2020/02_Week2/02_MCMC_Fitting/02_C_global_optimization_comparison.ipynb) \ No newline at end of file diff --git a/02_Week2/03_ML_classification/03_Pt1_ML_clustering_complete.ipynb b/02_Week2/03_ML_classification/03_Pt1_ML_clustering_complete.ipynb new file mode 100644 index 0000000..b1bb8ca --- /dev/null +++ b/02_Week2/03_ML_classification/03_Pt1_ML_clustering_complete.ipynb @@ -0,0 +1,397 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "03_Pt1_ML_clustering_complete.ipynb", + "provenance": [], + "collapsed_sections": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "2ZnjFtKPqhcz", + "colab_type": "text" + }, + "source": [ + "# Python for STEM - Week 2 (Advanced) \n", + "\n", + "## Day 3 - Part 1: Unsupervised learning - clustering\n", + "\n", + "In this notebook, we will focus on examples of unsupervised machine learning. More specifically, we will be doing clustering using Scikit-learn, one of the machine learning packages in Python. Before we start, here we first import all the packages that we need for this notebook. \n", + "\n", + "All the machine learning functions we will use in Day 3 and Day 4 all comes from [scikit-learn](https://scikit-learn.org/stable/index.html). You can find very detailed descriptions on many machine learning models included in the package user guide and various examples. This would be a good place to start when you want to adopt machine learning for your own research/work. \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "gkWCpoG4qasM", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## In this cell, we import all the packages needed for this notebook\n", + "import numpy as np ## packages for data handling\n", + "import pandas as pd \n", + "from scipy.spatial.distance import cdist \n", + "import matplotlib.pyplot as plt ## packages for visualization \n", + "import seaborn as sbn " + ], + "execution_count": 16, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1J9Lh3hnqdHQ", + "colab_type": "text" + }, + "source": [ + "## Data ingest \n", + "In this notebook, we will use a dataset from geography/remote sensing. The data includes 1875 data points (locations) in the western North Carolina (Asheville region). Each row of the data contains the information of a point with its latitude/longitude, land cover type (forest, crop, urban, or water), and the surface reflectance data of six channels from the OLI sensor onboard USGS/NASA Land resource satellite Landsat-8. The reflectance data provide unique feature of the land surface as seen by the satellite sensor, which allows geographer understand how the surface is changing through time. To find more information about the data and satellite, you can visit USGS website about [Landsat-8 OLI data.](https://www.usgs.gov/land-resources/nli/landsat/landsat-8?qt-science_support_page_related_con=0#qt-science_support_page_related_con)" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "c948Q9kIsH_G", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## First we use pandas to read in the comma separated values (CSV) file\n", + "datafile = 'https://raw.githubusercontent.com/uofscphysics/STEM_Python_Course/Summer2020/02_Week2/Data/03_land_use_land_cover_asheville.csv'\n", + "AVLData = pd.read_csv(datafile, index_col=None)\n", + "\n", + "## We will check the first five rows of the data to have initial understadning of our data\n", + "print( AVLData.head() )" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "twKa9vtGvC_H", + "colab_type": "text" + }, + "source": [ + "In this dataset, **Class** refers to the land cover type, **B1** ~ **B6** are the surface reflectances of the sixe OLI channels. The table below explains what the class code represents.\n", + "\n", + "| Class No. | Land Cover Type |\n", + "|-:|-:|\n", + "|0|Forest| \n", + "|1|Crop|\n", + "|2|Development/Urban|\n", + "|3|Water|\n", + "\n", + "Additionally, the following table gives us a quick explaination of what are the six OLI channels. \n", + "\n", + "| Channel No. | Channel Name | Wavelength |\n", + "|-:|-:|:-:|\n", + "|B1|Coastal/Areasol|0.433 – 0.453 μm| \n", + "|B2|Blue|0.450 – 0.515 μm|\n", + "|B3|Green|0.525 – 0.600 μm|\n", + "|B4|Red|0.630 – 0.680 μm|\n", + "|B5|Near Infrared|0.845 – 0.885 μm|\n", + "|B6|Short Wavelength Infrared|1.560 – 1.660 μm| \n", + "\n", + "Typically, refletance value is between 0 and 1, describing the percentage of light reflected by the surface. The reflectance value in our data is the scaled value between 0 and 10000. You can simply convert it back to regular reflectance by multiplying 0.0001. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Wimfgdw5ySx1", + "colab_type": "text" + }, + "source": [ + "In this notebook, we are doing clustering, so the **Class** information is not relevant because we are trying to guess how many clusters that we have based on this dataset. So let's assume that we do not have the land cover information." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "QusheEMHuqPq", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## Now we have a simple matrix with six columns (attributes)\n", + "## X is the new pandas data.frame with only the six channel reflectance\n", + "X = AVLData.iloc[:,3:]\n", + "print( X.head() )\n", + "## We are now looking at the pairwise scatter plots between these six channels\n", + "## using seaborn.pairplot function to look at them in one bix plot.\n", + "## We are using \"alpha\" key word to change the transparency for the dots since\n", + "## there are many overlapping amongst the data.\n", + "sbn.pairplot(X, diag_kind = 'kde',\n", + " plot_kws = {'alpha': 0.5, 's': 30, 'edgecolor': 'k'},\n", + " height = 2)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HY55QlVSVJ-0", + "colab_type": "text" + }, + "source": [ + "## Feature transformation/extraction\n", + "\n", + "As we can see from the pairwise scatter plots, the current six channels share some strong correlation amongst them. Can we find more useful features based on these six original channels? \n", + "\n", + "Feature transformation/extraction is the process to reduce the dimensionality of the data to explain the most of the variances in the data. Principle Component Analysis (PCA) is one of these techniques." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "RJsDigGOypOc", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## Using PCA function from scikit-learn to perform feature transformation\n", + "from sklearn.decomposition import PCA\n", + "pca_AVL = PCA(n_components=6)\n", + "principalComponents_X = pca_AVL.fit_transform(X)\n", + "print( principalComponents_X )\n", + "## The outcome of PCA is a ndarry here" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "l5KKAs1CX6rF", + "colab_type": "code", + "colab": {} + }, + "source": [ + "\n", + "## we convert the ndarry to pandas data.frame with specified column names\n", + "## for PCA\n", + "PCA_df_X = pd.DataFrame(principalComponents_X, \n", + " columns=['PC1', 'PC2', 'PC3', 'PC4', 'PC5', 'PC6'])\n", + "print( PCA_df_X )\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "82G6JqPO3PaB", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## We now want to know how much variance of the data is explained by each\n", + "## of the principle components.\n", + "with np.printoptions(precision=4, suppress=True):\n", + " print('Explained variation per principal component: {}'.\\\n", + " format(pca_AVL.explained_variance_ratio_) )" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "8As6B9BCXm1F", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## We can now visualize the first three components via the pairwise scatter \n", + "## plot that we have done earlier.\n", + "sbn.pairplot(PCA_df_X.iloc[:,:3], diag_kind = 'kde',\n", + " plot_kws = {'alpha': 0.5, 's': 30, 'edgecolor': 'k', 'color': 'darkgreen'},\n", + " height = 3)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YD4iMwXNpmoT", + "colab_type": "text" + }, + "source": [ + "## k-Means clustering\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "hf2z0Je74SNN", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## Generate cluster results using KMeans in scikit-learn\n", + "from sklearn.cluster import KMeans ## package for k-means clustering\n", + "from sklearn import metrics \n", + "kmeanModel = KMeans(n_clusters=8, init='k-means++', n_init=10,\n", + " random_state=42) # using random_state to ensure reproducible\n", + "kmeanModel.fit(PCA_df_X)\n", + "## Now we have a cluster model and the cluster label for each data points\n", + "clusterLabel = kmeanModel.labels_\n", + "## Print out the unique values of our cluster label\n", + "print ( np.unique(clusterLabel) )" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jArpYHlw6CBA", + "colab_type": "text" + }, + "source": [ + "How do we know what k value is the best value? Let's try different k values and use the \"Elbow rule\" to find a reasonable k value for our clustering task." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "s0xOL2Oapt42", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## Average of the squared distances from the cluster centers of the respective clusters\n", + "distortions = [] \n", + "## Sum of squared distances of samples to their closest cluster center\n", + "inertias = [] \n", + "## Candidate number of clusters (k)\n", + "Kval = range(1,10) \n", + " \n", + "for k in Kval: \n", + " #Building and fitting the model \n", + " kmeanModel = KMeans(n_clusters=k, random_state=42) \n", + " kmeanModel.fit(PCA_df_X) \n", + "\n", + " ## Calculate distortions using Euclidean distance with PCA tranformed data \n", + " distortions.append(sum(np.min(cdist(PCA_df_X, \n", + " kmeanModel.cluster_centers_, \n", + " 'euclidean'),axis=1)) / PCA_df_X.shape[0]) \n", + " ## Calculate inertias (part of the KMeans output)\n", + " inertias.append(kmeanModel.inertia_) \n", + " " + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "ym8tFvXMqty9", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## Create the Elbow plot between distortions and different K values\n", + "plt.plot(Kval, distortions, 'bx-') \n", + "plt.xlabel('Values of K') \n", + "plt.ylabel('Distortion') \n", + "plt.title('The Elbow Method using Distortion') \n", + "plt.show() " + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "vyT_X4ieq7ui", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## Create the Elbow plot between inertias and different K values\n", + "plt.plot(Kval, inertias, 'bx-') \n", + "plt.xlabel('Values of K') \n", + "plt.ylabel('Inertia') \n", + "plt.title('The Elbow Method using Inertia') \n", + "plt.show() " + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "QZA-v331rokQ", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## Fit final K-means clustering model with k = 4\n", + "kmeans = KMeans(init='k-means++', n_clusters=4, n_init=10, random_state=42)\n", + "kmeans.fit(PCA_df_X)\n", + "## Create the final cluter labels for the data\n", + "AVLcluster = kmeans.predict(PCA_df_X)\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "-PWGcfBY1Txe", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# We are now visualizing our cluster result in our feature space\n", + "# Each cluster is represented using different colors\n", + "# Define color for each class lables\n", + "colormap = np.array([\"darkgreen\", \"orange\", \"grey\", \"royalblue\"])\n", + "cpt = colormap[np.int_(AVLcluster)]\n", + "\n", + "# Initialize a new matplotlib figure and its axes object.\n", + "fig, ax = plt.subplots()\n", + "\n", + "# Creating the scatter plot with multiple clusters\n", + "scatter = ax.scatter(PCA_df_X[[\"PC1\"]], PCA_df_X[[\"PC2\"]], \n", + " alpha=0.5, c=cpt)\n", + "ax.set_xlabel(\"Principle Component 1\")\n", + "ax.set_ylabel(\"Principle Component 2\")\n", + "\n", + "fig.show()" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "8x2-ZZPKzfgM", + "colab_type": "code", + "colab": {} + }, + "source": [ + "" + ], + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/02_Week2/03_ML_classification/03_Pt1_ML_clustering_exercise.ipynb b/02_Week2/03_ML_classification/03_Pt1_ML_clustering_exercise.ipynb new file mode 100644 index 0000000..5b0e368 --- /dev/null +++ b/02_Week2/03_ML_classification/03_Pt1_ML_clustering_exercise.ipynb @@ -0,0 +1,363 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "03_Pt1_ML_clustering_exercise.ipynb", + "provenance": [], + "collapsed_sections": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "2ZnjFtKPqhcz", + "colab_type": "text" + }, + "source": [ + "# Python for STEM - Week 2 (Advanced) \n", + "\n", + "## Day 3 - Part 1: Unsupervised learning - clustering\n", + "\n", + "In this notebook, we will focus on examples of unsupervised machine learning. More specifically, we will be doing clustering using Scikit-learn, one of the machine learning packages in Python. Before we start, here we first import all the packages that we need for this notebook. \n", + "\n", + "All the machine learning functions we will use in Day 3 and Day 4 all comes from [scikit-learn](https://scikit-learn.org/stable/index.html). You can find very detailed descriptions on many machine learning models included in the package user guide and various examples. This would be a good place to start when you want to adopt machine learning for your own research/work. \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "gkWCpoG4qasM", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## In this cell, we import all the packages needed for this notebook\n", + "import numpy as np ## packages for data handling\n", + "import pandas as pd \n", + "from scipy.spatial.distance import cdist \n", + "import matplotlib.pyplot as plt ## packages for visualization \n", + "import seaborn as sbn \n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1J9Lh3hnqdHQ", + "colab_type": "text" + }, + "source": [ + "## Data ingest \n", + "In this notebook, we will use a dataset from geography/remote sensing. The data includes 1875 data points (locations) in the western North Carolina (Asheville region). Each row of the data contains the information of a point with its latitude/longitude, land cover type (forest, crop, urban, or water), and the surface reflectance data of six channels from the OLI sensor onboard USGS/NASA Land resource satellite Landsat-8. The reflectance data provide unique feature of the land surface as seen by the satellite sensor, which allows geographer understand how the surface is changing through time. To find more information about the data and satellite, you can visit USGS website about [Landsat-8 OLI data.](https://www.usgs.gov/land-resources/nli/landsat/landsat-8?qt-science_support_page_related_con=0#qt-science_support_page_related_con)" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "c948Q9kIsH_G", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## First we use pandas to read in the comma separated values (CSV) file\n", + "datafile = 'https://raw.githubusercontent.com/uofscphysics/STEM_Python_Course/Summer2020/02_Week2/Data/03_land_use_land_cover_asheville.csv'\n", + "AVLData = pd.read_csv(datafile, index_col=None)\n", + "\n", + "## We will check the first five rows of the data to have initial understadning of our data\n", + "print( AVLData.head() )" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "twKa9vtGvC_H", + "colab_type": "text" + }, + "source": [ + "In this dataset, **Class** refers to the land cover type, **B1** ~ **B6** are the surface reflectances of the sixe OLI channels. The table below explains what the class code represents.\n", + "\n", + "| Class No. | Land Cover Type |\n", + "|-:|-:|\n", + "|0|Forest| \n", + "|1|Crop|\n", + "|2|Development/Urban|\n", + "|3|Water|\n", + "\n", + "Additionally, the following table gives us a quick explaination of what are the six OLI channels. \n", + "\n", + "| Channel No. | Channel Name | Wavelength |\n", + "|-:|-:|:-:|\n", + "|B1|Coastal/Areasol|0.433 – 0.453 μm| \n", + "|B2|Blue|0.450 – 0.515 μm|\n", + "|B3|Green|0.525 – 0.600 μm|\n", + "|B4|Red|0.630 – 0.680 μm|\n", + "|B5|Near Infrared|0.845 – 0.885 μm|\n", + "|B6|Short Wavelength Infrared|1.560 – 1.660 μm| \n", + "\n", + "Typically, refletance value is between 0 and 1, describing the percentage of light reflected by the surface. The reflectance value in our data is the scaled value between 0 and 10000. You can simply convert it back to regular reflectance by multiplying 0.0001. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Wimfgdw5ySx1", + "colab_type": "text" + }, + "source": [ + "In this notebook, we are doing clustering, so the **Class** information is not relevant because we are trying to guess how many clusters that we have based on this dataset. So let's assume that we do not have the land cover information." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "QusheEMHuqPq", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## Now we have a simple matrix with six columns (attributes)\n", + "## X is the new pandas data.frame with only the six channel reflectance\n", + "X = AVLData.iloc[:,3:]\n", + "print( X.head() )\n", + "## We are now looking at the pairwise scatter plots between these six channels\n", + "## using seaborn.pairplot function to look at them in one bix plot.\n", + "## We are using \"alpha\" key word to change the transparency for the dots since\n", + "## there are many overlapping amongst the data.\n", + "sbn.pairplot(X, diag_kind = 'kde',\n", + " plot_kws = {'alpha': 0.5, 's': 30, 'edgecolor': 'k'},\n", + " height = 2)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HY55QlVSVJ-0", + "colab_type": "text" + }, + "source": [ + "## Feature transformation/extraction\n", + "\n", + "As we can see from the pairwise scatter plots, the current six channels share some strong correlation amongst them. Can we find more useful features based on these six original channels? \n", + "\n", + "Feature transformation/extraction is the process to reduce the dimensionality of the data to explain the most of the variances in the data. Principle Component Analysis (PCA) is one of these techniques." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "RJsDigGOypOc", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## Using PCA function from scikit-learn to perform feature transformation\n", + "\n", + "## The outcome of PCA is a ndarry here" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "l5KKAs1CX6rF", + "colab_type": "code", + "colab": {} + }, + "source": [ + "\n", + "## we convert the ndarry to pandas data.frame with specified column names\n", + "## for PCA\n", + "\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "82G6JqPO3PaB", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## We now want to know how much variance of the data is explained by each\n", + "## of the principle components.\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "8As6B9BCXm1F", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## We can now visualize the first three components via the pairwise scatter \n", + "## plot that we have done earlier.\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YD4iMwXNpmoT", + "colab_type": "text" + }, + "source": [ + "## k-Means clustering\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "hf2z0Je74SNN", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## Generate cluster results using KMeans in scikit-learn\n", + "from sklearn.cluster import KMeans ## package for k-means clustering\n", + "from sklearn import metrics \n", + "kmeanModel = KMeans(n_clusters=8, init='k-means++', n_init=10,\n", + " random_state=42) # using random_state to ensure reproducible\n", + "kmeanModel.fit(PCA_df_X)\n", + "## Now we have a cluster model and the cluster label for each data points\n", + "clusterLabel = kmeanModel.labels_\n", + "## Print out the unique values of our cluster label\n", + "print ( np.unique(clusterLabel) )" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jArpYHlw6CBA", + "colab_type": "text" + }, + "source": [ + "How do we know what k value is the best value? Let's try different k values and use the \"Elbow rule\" to find a reasonable k value for our clustering task. Basically, we are repeating the clustering process for each k-value of our choice, and evaluate it based on the distance among the generated clusters." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "s0xOL2Oapt42", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## Average of the squared distances from the cluster centers of the respective clusters\n", + "distortions = [] \n", + "## Sum of squared distances of samples to their closest cluster center\n", + "inertias = [] \n", + "## Candidate number of clusters (k)\n", + "Kval = range(1,10) \n", + " \n", + "for k in Kval: \n", + " #Building and fitting the model \n", + " \n", + " ## Calculate distortions using Euclidean distance with PCA tranformed data \n", + " \n", + " ## Calculate inertias (part of the KMeans output)\n", + " " + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "ym8tFvXMqty9", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## Create the Elbow plot between distortions and different K values\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "vyT_X4ieq7ui", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## Create the Elbow plot between inertias and different K values\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "QZA-v331rokQ", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## Fit final K-means clustering model with k = 4\n", + "\n", + "## Create the final cluter labels for the data\n", + "\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "-PWGcfBY1Txe", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# We are now visualizing our cluster result in our feature space\n", + "# Each cluster is represented using different colors\n", + "# Define color for each class lables\n", + "\n", + "\n", + "# Initialize a new matplotlib figure and its axes object.\n", + "\n", + "\n", + "# Creating the scatter plot with multiple clusters\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "8x2-ZZPKzfgM", + "colab_type": "code", + "colab": {} + }, + "source": [ + "" + ], + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/02_Week2/03_ML_classification/03_Pt2_ML_classification_complete.ipynb b/02_Week2/03_ML_classification/03_Pt2_ML_classification_complete.ipynb new file mode 100644 index 0000000..4af318f --- /dev/null +++ b/02_Week2/03_ML_classification/03_Pt2_ML_classification_complete.ipynb @@ -0,0 +1,817 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "03_Pt2_ML_classification_complete.ipynb", + "provenance": [], + "collapsed_sections": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "2ZnjFtKPqhcz", + "colab_type": "text" + }, + "source": [ + "# Python for STEM - Week 2 (Advanced) \n", + "\n", + "## Day 3 - Part 2: Supervised learning - classification\n", + "\n", + "In this notebook, we will focus on examples of supervised machine learning. More specifically, we will be doing classification using Scikit-learn, one of the machine learning packages in Python. Before we start, here we first import all the packages that we need for this notebook. \n", + "\n", + "All the machine learning functions we will use in Day 3 and Day 4 all comes from [scikit-learn](https://scikit-learn.org/stable/index.html). You can find very detailed descriptions on many machine learning models included in the package user guide and various examples. This would be a good place to start when you want to adopt machine learning for your own research/work. \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "gkWCpoG4qasM", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## In this cell, we import all the packages needed for this notebook\n", + "import numpy as np ## packages for data handling\n", + "import pandas as pd \n", + "from scipy.spatial.distance import cdist \n", + "import matplotlib.pyplot as plt ## packages for visualization \n", + "import seaborn as sbn \n" + ], + "execution_count": 11, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1J9Lh3hnqdHQ", + "colab_type": "text" + }, + "source": [ + "## Data ingest \n", + "In this notebook, we will use a dataset from geography/remote sensing. The data includes 1875 data points (locations) in the western North Carolina (Asheville region). Each row of the data contains the information of a point with its latitude/longitude, land cover type (forest, crop, urban, or water), and the surface reflectance data of six channels from the OLI sensor onboard USGS/NASA Land resource satellite Landsat-8. The reflectance data provide unique feature of the land surface as seen by the satellite sensor, which allows geographer understand how the surface is changing through time. To find more information about the data and satellite, you can visit USGS website about [Landsat-8 OLI data.](https://www.usgs.gov/land-resources/nli/landsat/landsat-8?qt-science_support_page_related_con=0#qt-science_support_page_related_con)" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "c948Q9kIsH_G", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 119 + }, + "outputId": "92a1bb5c-3661-43ec-87f8-bb1460f44138" + }, + "source": [ + "## First we use pandas to read in the comma separated values (CSV) file\n", + "datafile = 'https://raw.githubusercontent.com/uofscphysics/STEM_Python_Course/Summer2020/02_Week2/Data/03_land_use_land_cover_asheville.csv'\n", + "AVLData = pd.read_csv(datafile, index_col=None)\n", + "\n", + "## We will check the first five rows of the data to have initial understadning of our data\n", + "print( AVLData.head() )" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "text": [ + " Latitude Longitude Class B1 B2 B3 B4 B5 B6\n", + "0 35.514769 -82.680451 0 127 150 550 226 3609 1441\n", + "1 35.753979 -82.520432 0 81 115 426 170 2913 1110\n", + "2 35.710635 -82.305661 0 156 220 538 477 2492 2077\n", + "3 35.512814 -82.413861 0 245 280 663 507 2732 1531\n", + "4 35.520636 -82.853181 0 148 181 534 265 3320 1457\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "twKa9vtGvC_H", + "colab_type": "text" + }, + "source": [ + "In this dataset, **Class** refers to the land cover type, **B1** ~ **B6** are the surface reflectances of the sixe OLI channels. The table below explains what the class code represents.\n", + "\n", + "| Class No. | Land Cover Type |\n", + "|-:|-:|\n", + "|0|Forest| \n", + "|1|Crop|\n", + "|2|Development/Urban|\n", + "|3|Water|\n", + "\n", + "Additionally, the following table gives us a quick explaination of what are the six OLI channels. \n", + "\n", + "| Channel No. | Channel Name | Wavelength |\n", + "|-:|-:|:-:|\n", + "|B1|Coastal/Areasol|0.433 – 0.453 μm| \n", + "|B2|Blue|0.450 – 0.515 μm|\n", + "|B3|Green|0.525 – 0.600 μm|\n", + "|B4|Red|0.630 – 0.680 μm|\n", + "|B5|Near Infrared|0.845 – 0.885 μm|\n", + "|B6|Short Wavelength Infrared|1.560 – 1.660 μm| \n", + "\n", + "Typically, refletance value is between 0 and 1, describing the percentage of light reflected by the surface. The reflectance value in our data is the scaled value between 0 and 10000. You can simply convert it back to regular reflectance by multiplying 0.0001. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Wimfgdw5ySx1", + "colab_type": "text" + }, + "source": [ + "In this notebook, we are doing clustering, so the **Class** information is not relevant because we are trying to guess how many clusters that we have based on this dataset. So let's assume that we do not have the land cover information." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "QusheEMHuqPq", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## Now we have a simple matrix with six columns (attributes)\n", + "## X is the new pandas data.frame with only the six channel reflectance\n", + "X = AVLData.iloc[:,3:]\n", + "print( X.head() )\n", + "## We are now looking at the pairwise scatter plots between these six channels\n", + "## using seaborn.pairplot function to look at them in one bix plot.\n", + "## We are using \"alpha\" key word to change the transparency for the dots since\n", + "## there are many overlapping amongst the data.\n", + "sbn.pairplot(X, diag_kind = 'kde',\n", + " plot_kws = {'alpha': 0.5, 's': 30, 'edgecolor': 'k'},\n", + " height = 2)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HY55QlVSVJ-0", + "colab_type": "text" + }, + "source": [ + "## Feature transformation/extraction\n", + "\n", + "As we can see from the pairwise scatter plots, the current six channels share some strong correlation amongst them. Can we find more useful features based on these six original channels? \n", + "\n", + "Feature transformation/extraction is the process to reduce the dimensionality of the data to explain the most of the variances in the data. Principle Component Analysis (PCA) is one of these techniques." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "RJsDigGOypOc", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## Using PCA function from scikit-learn to perform feature transformation\n", + "from sklearn.decomposition import PCA\n", + "pca_AVL = PCA(n_components=6)\n", + "principalComponents_X = pca_AVL.fit_transform(X)\n", + "print( principalComponents_X )\n", + "## The outcome of PCA is a ndarry here" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "l5KKAs1CX6rF", + "colab_type": "code", + "colab": {} + }, + "source": [ + "\n", + "## we convert the ndarry to pandas data.frame with specified column names\n", + "## for PCA\n", + "PCA_df_X = pd.DataFrame(principalComponents_X, \n", + " columns=['PC1', 'PC2', 'PC3', 'PC4', 'PC5', 'PC6'])\n", + "print( PCA_df_X )\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "82G6JqPO3PaB", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## We now want to know how much variance of the data is explained by each\n", + "## of the principle components.\n", + "with np.printoptions(precision=4, suppress=True):\n", + " print('Explained variation per principal component: {}'.\\\n", + " format(pca_AVL.explained_variance_ratio_) )" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "8As6B9BCXm1F", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## We can now visualize the first three components via the pairwise scatter \n", + "## plot that we have done earlier.\n", + "sbn.pairplot(PCA_df_X.iloc[:,:3], diag_kind = 'kde',\n", + " plot_kws = {'alpha': 0.5, 's': 80, 'edgecolor': 'k', 'color': 'darkgreen'},\n", + " height = 3)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BnQT1kl3dKPw", + "colab_type": "text" + }, + "source": [ + "## Data spliting\n", + "\n", + "For supervised learning, data spliting is the first step in order to develop and evaluation your model. \n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "3COORCzddaZ4", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## Import function for data spliting\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "## getting the class label from the original data and assign it as the target outcome\n", + "y = AVLData.values[:,2]\n", + "\n", + "## define the class label list\n", + "class_labels_list = ['Forest', 'Crop', 'Urban', 'Water']\n", + "\n", + "## We are spliting our data set into training and testing sets based on a 80:20 ratio\n", + "X_train, X_test, y_train, y_test = train_test_split(principalComponents_X, y, test_size=0.2, random_state=42)\n", + "\n", + "print( X_train.shape, X_test.shape )" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZGId8GFaek_A", + "colab_type": "text" + }, + "source": [ + "When we are spliting the data, we want to make sure the training and testing dataset can resemble the distribution of these four classes in order to make sure our data can represent the reality. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YD4iMwXNpmoT", + "colab_type": "text" + }, + "source": [ + "## k-Nearest neighbors (kNN)\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "hf2z0Je74SNN", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## Import packages for k-nearest neighbor classifier\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "\n", + "# Initialize a KNN classifier object. Later, we'll train it and then have it predict the\n", + "# classes of withheld testing samples.\n", + "knn_classifier = KNeighborsClassifier(n_neighbors=3)\n", + "\n", + "# let's train the model with k=3\n", + "knn_classifier.fit(X_train, y_train.ravel())\n", + "\n", + "# Have the newly trained classifier predict the classes of the withheld testing data.\n", + "knn_predicted = knn_classifier.predict(X_test)" + ], + "execution_count": 28, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xwfV4cecgIDN", + "colab_type": "text" + }, + "source": [ + "Now we have a kNN classifier generated. We need to evaluate how the model performs based on our data. We can tabulate the predicted classes and the reference classes into a confusion matrix." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "s0xOL2Oapt42", + "colab_type": "code", + "colab": {} + }, + "source": [ + "from sklearn.metrics import confusion_matrix\n", + "from sklearn.metrics import classification_report\n", + "\n", + "## Calculate the confusion matrix and normalized confusion matrix\n", + "knnMatrix = confusion_matrix(y_test.ravel(), knn_predicted.ravel())\n", + "knnMatrix_normalized = knnMatrix/knnMatrix.sum()\n", + "\n", + "# Initialize figure, axes for the two confusion matrices.\n", + "fig, ax = plt.subplots(1, 2, figsize=(14, 5))\n", + "\n", + "# Plot the raw counts' confusion matrix.\n", + "sbn.heatmap(\n", + " knnMatrix, cmap=\"Greens\", annot=knnMatrix, square=True, cbar=True,\n", + " xticklabels=class_labels_list, yticklabels=class_labels_list,\n", + " ax=ax[0]\n", + ")\n", + "\n", + "# Add labels to the x-axis and the y-axis.\n", + "ax[0].set_xlabel(\"Predicted\", fontsize=16)\n", + "ax[0].set_ylabel(\"Reference\", fontsize=16)\n", + "\n", + "# Plot the percentages' confusion matrix.\n", + "sbn.heatmap(\n", + " knnMatrix_normalized, cmap=\"Greens\", annot=True, square=True,\n", + " xticklabels=class_labels_list, yticklabels=class_labels_list,\n", + " ax=ax[1]\n", + ")\n", + "\n", + "# Add a label to the x-axis.\n", + "ax[1].set_xlabel(\"Predicted\", fontsize=16)\n", + "\n", + "# Add a title to the figure.\n", + "fig.suptitle(\"Confusion matrices (k-NN): raw counts and normalized frequencies\", fontsize=16)\n", + "\n", + "# Display the figure.\n", + "fig.show()\n", + "\n", + "\n", + "\n", + " " + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "5LWBThgFhzF4", + "colab_type": "code", + "colab": {} + }, + "source": [ + "print(\n", + " classification_report(y_test.ravel(), knn_predicted.ravel(),\n", + " target_names=class_labels_list)\n", + ")" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_upVzF9uhJLw", + "colab_type": "text" + }, + "source": [ + "## Cross validation\n", + "\n", + "If you have noticed, we fix the k-value for this k-Nearest neighbor classifier (k=9). \n", + "\n", + "1. Can we improve our overall accuracy by changing our k value in the model trianing? \n", + "\n", + "2. How can we decide which is the best k value for the model? \n", + "\n", + "To answer these questions, we need to use cross validation, a process used for finding the best model hyperparameters for a machine learning model (e.g., k for k-nearest neighbors).\n", + "\n", + "To explore the optimum combination of the model hyperparameters, we typically uses the cross validation (CV) strategy. There are different type of cross validation strategy, such as, k-fold cross validation, leave-one-out cross validation, repeated cross validation. More information can be found in this [well written article about the importance of CV for machine learning](https://www.digitalvidya.com/blog/cross-validation-in-machine-learning/). \n", + "\n", + "The most commonly used CV strategy is usually [k-fold CV](https://machinelearningmastery.com/k-fold-cross-validation/). But it still depends on the data that have for your problem. If you are using time series data, your cross validation strategy will be different since you want to account for the tempooral autocorrelation within your data. Same idea applies when your data have strong spatial auto correlation. But today, let's assume that k-fold CV can solve our problem. \n", + "\n", + "The most commonly used CV strategy is usually k-fold CV. But it still depends on the data that have for your problem. If you are using time series data, your cross validation strategy will be different since you want to account for the tempooral autocorrelation within your data. Same idea applies when your data have strong spatial auto correlation. But today, let's assume that k-fold CV can solve our problem.\n", + "\n", + "We can use a stratified 10-fold CV to find the optimum hyperparameters for our two classifiers (i.e., k-NN and random forest) to see if the model performance could be further improved. Let's start with k-NN to find out the best number of neighbors (k) for the model.\n", + "\n", + "The idea is to repeat the model traininig 10 times using a subset (9/10) of the training data set with different value of k. The average value of the model performance metrices from these 10 iteration of model training is used to choose which hyperparameter (i.e., k) performs best." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "8x2-ZZPKzfgM", + "colab_type": "code", + "colab": {} + }, + "source": [ + "from sklearn.model_selection import StratifiedKFold\n", + "from sklearn.metrics import accuracy_score, make_scorer\n", + "from sklearn.model_selection import GridSearchCV\n", + "\n", + "# Set up the parameter grid. In this case, it's just a list of all the\n", + "# candidate number of neighbors we'll consider.\n", + "n_neighbors_list = [1, 3, 5, 7, 9, 11, 13, 15]\n", + "param_grid = {\"n_neighbors\" : n_neighbors_list}\n", + "\n", + "# Initialize a KNN classifier object. \n", + "knn_classifier = KNeighborsClassifier()\n", + "\n", + "# Initialize a stratified 10-fold generator for the cross-validation.\n", + "stratified_kfold_generator = StratifiedKFold(n_splits=10)\n", + "\n", + "# Create the grid search object. This object will take the kNN classifier\n", + "# and run stratified 10-fold cross-validation for each of the potential\n", + "# candidates for k. It will record the averaged accuracy for each k so\n", + "# that afterwards we can view how the classifier's accuracy improves or\n", + "# worsens with respect to k.\n", + "gridsearch_cv_obj = GridSearchCV(\n", + " knn_classifier, \n", + " param_grid, \n", + " scoring=make_scorer(accuracy_score), \n", + " cv=stratified_kfold_generator,\n", + " n_jobs=-1,\n", + " )\n", + "\n", + "# Run the grid search for the optimal number of neighbors, k.\n", + "gridsearch_cv_obj.fit(X_train, y_train.ravel());" + ], + "execution_count": 31, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bOPm2v0qji-P", + "colab_type": "text" + }, + "source": [ + "We now have a set of models and its performance based on this stratified 10-fold cross validation. Now, we can plot the model accuracy as a function of *k*. This will assist us selecting the optimum solution." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "edERAaT-jluG", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## Retrieving the mean and standard deviation of the accuracy for the model test scores\n", + "mean_test_accuracy = gridsearch_cv_obj.cv_results_[\"mean_test_score\"]\n", + "std_test_accuracy = gridsearch_cv_obj.cv_results_[\"std_test_score\"]\n", + "\n", + "## Plotting out the accuracy agiand k\n", + "fig, ax = plt.subplots()\n", + "\n", + "ax.errorbar(\n", + " x=n_neighbors_list,\n", + " y=mean_test_accuracy,\n", + " yerr=std_test_accuracy,\n", + " solid_capstyle=\"round\"\n", + ")\n", + "ax.set(\n", + " xlabel=\"# of neighbors (k)\", \n", + " ylabel=\"Mean accuracy\", \n", + " title=\"k-NN classification accuracy\"\n", + ");" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "c5-DpbAnj7aN", + "colab_type": "text" + }, + "source": [ + "Now we know that the kNN model performs best when we use k value of 9 based on our 10-fold cross validation strategy. Now we can safely train out final kNN model with k of 9 and apply it to our independent testing data that we set aside at the begining of this process." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "08SG-FRnkPKY", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# Initialize a KNN classifier object. Later, we'll train it and then have it predict the\n", + "# classes of withheld testing samples.\n", + "knn_classifier = KNeighborsClassifier(n_neighbors=9)\n", + "knn_classifier.fit(X_train, y_train.ravel())\n", + "\n", + "# Have the final classifier predict the classes of the withheld testing data.\n", + "knn_predicted = knn_classifier.predict(X_test)\n", + "\n", + "# Let's print out the accuracy indicators\n", + "print(\n", + " classification_report(y_test.ravel(), knn_predicted.ravel(),\n", + " target_names=class_labels_list)\n", + ")" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "HbJrI8YCk8_9", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## Now, we can also visualize the testing confusion matrix\n", + "## Calculate the confusion matrix and normalized confusion matrix\n", + "knnMatrix = confusion_matrix(y_test.ravel(), knn_predicted.ravel())\n", + "knnMatrix_normalized = knnMatrix/knnMatrix.sum()\n", + "\n", + "# Initialize figure, axes for the two confusion matrices.\n", + "fig, ax = plt.subplots(1, 2, figsize=(14, 5))\n", + "\n", + "# Plot the raw counts' confusion matrix.\n", + "sbn.heatmap(\n", + " knnMatrix, cmap=\"Greens\", annot=knnMatrix, square=True, cbar=True,\n", + " xticklabels=class_labels_list, yticklabels=class_labels_list,\n", + " ax=ax[0]\n", + ")\n", + "\n", + "# Add labels to the x-axis and the y-axis.\n", + "ax[0].set_xlabel(\"Predicted\", fontsize=16)\n", + "ax[0].set_ylabel(\"Reference\", fontsize=16)\n", + "\n", + "# Plot the percentages' confusion matrix.\n", + "sbn.heatmap(\n", + " knnMatrix_normalized, cmap=\"Greens\", annot=True, square=True,\n", + " xticklabels=class_labels_list, yticklabels=class_labels_list,\n", + " ax=ax[1]\n", + ")\n", + "\n", + "# Add a label to the x-axis.\n", + "ax[1].set_xlabel(\"Predicted\", fontsize=16)\n", + "\n", + "# Add a title to the figure.\n", + "fig.suptitle(\"Confusion matrices (k-NN): raw counts and normalized frequencies\", fontsize=16)\n", + "\n", + "# Display the figure.\n", + "fig.show()" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "k_S-UOoQlloN", + "colab_type": "text" + }, + "source": [ + "## Random forest \n", + "\n", + "### 2.3 - Random forest \n", + "\n", + "There are more sophiscated models that we can use to address more sophisicated classification problems. Here, we choose to introduce [random forest](https://en.wikipedia.org/wiki/Random_forest) model because of its robust performance in many applications. The random forest is an ensemble learning model by creating a suite of decision trees at training time and outputting the class that is the majority of the classes from each individual tree for classification problem. The fundamental idea of random forest model is that the **collective power of multiple \"weak\" models can outperform any individual \"strong\" model**. It can address overfitting issue comparing to the regular decision tree model. \n", + "\n", + "Similar with k-NN, there are different flavors of random forest model. We are using the classic random forest model with *scikit-learn* package in Python (i.e., [_RandomForestClassifier_](https://scikit-learn.org/stable/modules/ensemble.html#forests-of-randomized-trees). As the model gets more complicated, we have more to consider about the model structure, such as, how many variables we want to have as input for each tree (*max_features*), number of trees (*n_estimator*), the depth of a decision tree (*max_depth*, *min_sample_split*, *min_sample_leaf*). These could all have potential impact on our final model outcomes. \n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "2H7Hrwxylwvh", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# Import the random forest classifier from sklearn.ensemble\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "\n", + "# Set up the parameter grid. In this case, it's just a list of all the\n", + "# candidate number of neighbors we'll consider.\n", + "tuned_parameters = {'n_estimators': [50], \n", + " 'max_depth': [None],\n", + " 'min_samples_split': [2],\n", + " 'random_state': [42],\n", + " 'max_features': [2, 3, 4, 5, 6], \n", + " 'min_samples_leaf': [1, 2, 3, 4, 5, 6, 7]}\n", + "\n", + "# Initialize a ra classifier object. \n", + "rf_classifier = RandomForestClassifier()\n", + "\n", + "# Create the grid search object. This object will take the kNN classifier\n", + "# and run stratified 10-fold cross-validation for each of the potential\n", + "# candidates for k. It will record the averaged accuracy for each k so\n", + "# that afterwards we can view how the classifier's accuracy improves or\n", + "# worsens with respect to k.\n", + "gridsearch_cv_obj = GridSearchCV(\n", + " rf_classifier, \n", + " tuned_parameters, \n", + " scoring=make_scorer(accuracy_score), \n", + " cv=stratified_kfold_generator,\n", + " n_jobs=-1,\n", + " )\n", + "\n", + "# Run the grid search for the optimal number of neighbors, k.\n", + "gridsearch_cv_obj.fit(X_train, y_train.ravel());\n" + ], + "execution_count": 35, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "ptD1txhymaP6", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## Print best parameter combination\n", + "print(\"Best parameters set found on development set:\")\n", + "print()\n", + "print(gridsearch_cv_obj.best_params_)\n", + "print()\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "FH2LNi3emf6e", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## Calculating the mean and standard deviation of the accuracy for the model test scores\n", + "mean_test_accuracy = gridsearch_cv_obj.cv_results_[\"mean_test_score\"]\n", + "stds_test_accuracy = gridsearch_cv_obj.cv_results_[\"std_test_score\"]\n", + "\n", + "max_features = gridsearch_cv_obj.cv_results_[\"param_max_features\"]\n", + "min_samples_leaf = gridsearch_cv_obj.cv_results_[\"param_min_samples_leaf\"]\n", + "\n", + "## rearrange the accuracy data to a 2d array so we can have a heatmap\n", + "mean_accuracy_2d = mean_test_accuracy.reshape(5, 7)\n", + "stds_accuracy_2d = stds_test_accuracy.reshape(5, 7)\n", + "# 5 rows for max_features & 7 columns for min_samples_leaf\n", + "\n", + "## Creating the heatmap for both mean accuracy and standard deviation\n", + "# Initialize figure, axes for the accuracy heatmap.\n", + "fig, ax = plt.subplots(1, 2, figsize=(14, 5))\n", + "\n", + "# Plot the raw counts' confusion matrix.\n", + "sbn.heatmap(\n", + " mean_accuracy_2d, cmap=\"Blues\", annot=True, square=False, cbar=True,\n", + " xticklabels=[1,2,3,4,5,6,7], yticklabels=[2,3,4,5,6],\n", + " ax=ax[0]\n", + ")\n", + "\n", + "# Add labels to the x-axis and the y-axis.\n", + "ax[0].set_xlabel(\"Minimum # of samples\", fontsize=16)\n", + "ax[0].set_ylabel(\"Maximum # of features\", fontsize=16)\n", + "\n", + "# Plot the percentages' confusion matrix.\n", + "sbn.heatmap(\n", + " stds_accuracy_2d, cmap=\"Reds\", annot=True, square=False,\n", + " xticklabels=[1,2,3,4,5,6,7], yticklabels=[2,3,4,5,6],\n", + " ax=ax[1]\n", + ")\n", + "\n", + "# Add labels to the x-axis and the y-axis.\n", + "ax[1].set_xlabel(\"Minimum # of samples\", fontsize=16)\n", + "\n", + "# Add a title to the figure.\n", + "fig.suptitle(\"Random forest testing accuracy for 10-fold startified Cross Validation\", fontsize=16)\n", + "\n", + "# Display the figure.\n", + "fig.show()\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "SAE9xoWXnKEe", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## Train our final model based on our 10-fold cross validation results with the \n", + "## best model hyperparameters\n", + "rf_Classifier = RandomForestClassifier(n_estimators=50, max_depth=None,\n", + " max_features=2, min_samples_leaf=3,\n", + " min_samples_split=2, random_state=0)\n", + "\n", + "# Using the training set, we can fit our model now\n", + "rf_Classifier.fit(X_train, y_train.ravel())\n", + "\n", + "# Have the newly trained classifier predict the classes of the withheld testing data.\n", + "rf_predicted = rf_Classifier.predict(X_test)\n", + "\n", + "## reporting the indicators for the random forest classifier\n", + "print(\n", + " classification_report(y_test.ravel(), rf_predicted.ravel(),\n", + " target_names=class_labels_list)\n", + ")\n", + "\n", + "\n", + "\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "s_37UfVNn2u0", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## Calculate the confusion matrix and normalized confusion matrix\n", + "rfMatrix = confusion_matrix(y_test.ravel(), rf_predicted.ravel())\n", + "rfMatrix_normalized = rfMatrix/rfMatrix.sum()\n", + "\n", + "# Initialize figure, axes for the two confusion matrices.\n", + "fig, ax = plt.subplots(1, 2, figsize=(14, 5))\n", + "\n", + "# Plot the raw counts' confusion matrix.\n", + "sbn.heatmap(\n", + " rfMatrix, cmap=\"Greens\", annot=rfMatrix, square=True, cbar=True,\n", + " xticklabels=class_labels_list, yticklabels=class_labels_list,\n", + " ax=ax[0]\n", + ")\n", + "\n", + "# Add labels to the x-axis and the y-axis.\n", + "ax[0].set_xlabel(\"Predicted\", fontsize=16)\n", + "ax[0].set_ylabel(\"Reference\", fontsize=16)\n", + "\n", + "# Plot the percentages' confusion matrix.\n", + "sbn.heatmap(\n", + " rfMatrix_normalized, cmap=\"Greens\", annot=True, square=True,\n", + " xticklabels=class_labels_list, yticklabels=class_labels_list,\n", + " ax=ax[1]\n", + ")\n", + "\n", + "# Add a label to the x-axis.\n", + "ax[1].set_xlabel(\"Predicted\", fontsize=16)\n", + "\n", + "# Add a title to the figure.\n", + "fig.suptitle(\"Confusion matrices (Random Forest): raw counts and normalized frequencies\", fontsize=16)\n", + "\n", + "# Display the figure.\n", + "fig.show()" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "3G1m3znFoGST", + "colab_type": "code", + "colab": {} + }, + "source": [ + "" + ], + "execution_count": 39, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/02_Week2/03_ML_classification/03_Pt2_ML_classification_exercise.ipynb b/02_Week2/03_ML_classification/03_Pt2_ML_classification_exercise.ipynb new file mode 100644 index 0000000..b9af410 --- /dev/null +++ b/02_Week2/03_ML_classification/03_Pt2_ML_classification_exercise.ipynb @@ -0,0 +1,699 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "03_Pt2_ML_classification_exercise.ipynb", + "provenance": [], + "collapsed_sections": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "2ZnjFtKPqhcz", + "colab_type": "text" + }, + "source": [ + "# Python for STEM - Week 2 (Advanced) \n", + "\n", + "## Day 3 - Part 2: Supervised learning - classification\n", + "\n", + "In this notebook, we will focus on examples of supervised machine learning. More specifically, we will be doing classification using Scikit-learn, one of the machine learning packages in Python. Before we start, here we first import all the packages that we need for this notebook. \n", + "\n", + "All the machine learning functions we will use in Day 3 and Day 4 all comes from [scikit-learn](https://scikit-learn.org/stable/index.html). You can find very detailed descriptions on many machine learning models included in the package user guide and various examples. This would be a good place to start when you want to adopt machine learning for your own research/work. \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "gkWCpoG4qasM", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## In this cell, we import all the packages needed for this notebook\n", + "import numpy as np ## packages for data handling\n", + "import pandas as pd \n", + "from scipy.spatial.distance import cdist \n", + "import matplotlib.pyplot as plt ## packages for visualization \n", + "import seaborn as sbn " + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1J9Lh3hnqdHQ", + "colab_type": "text" + }, + "source": [ + "## Data ingest \n", + "In this notebook, we will use a dataset from geography/remote sensing. The data includes 1875 data points (locations) in the western North Carolina (Asheville region). Each row of the data contains the information of a point with its latitude/longitude, land cover type (forest, crop, urban, or water), and the surface reflectance data of six channels from the OLI sensor onboard USGS/NASA Land resource satellite Landsat-8. The reflectance data provide unique feature of the land surface as seen by the satellite sensor, which allows geographer understand how the surface is changing through time. To find more information about the data and satellite, you can visit USGS website about [Landsat-8 OLI data.](https://www.usgs.gov/land-resources/nli/landsat/landsat-8?qt-science_support_page_related_con=0#qt-science_support_page_related_con)" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "c948Q9kIsH_G", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## First we use pandas to read in the comma separated values (CSV) file\n", + "datafile = 'https://raw.githubusercontent.com/uofscphysics/STEM_Python_Course/Summer2020/02_Week2/Data/03_land_use_land_cover_asheville.csv'\n", + "AVLData = pd.read_csv(datafile, index_col=None)\n", + "\n", + "## We will check the first five rows of the data to have initial understadning of our data\n", + "print( AVLData.head() )" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "twKa9vtGvC_H", + "colab_type": "text" + }, + "source": [ + "In this dataset, **Class** refers to the land cover type, **B1** ~ **B6** are the surface reflectances of the sixe OLI channels. The table below explains what the class code represents.\n", + "\n", + "| Class No. | Land Cover Type |\n", + "|-:|-:|\n", + "|0|Forest| \n", + "|1|Crop|\n", + "|2|Development/Urban|\n", + "|3|Water|\n", + "\n", + "Additionally, the following table gives us a quick explaination of what are the six OLI channels. \n", + "\n", + "| Channel No. | Channel Name | Wavelength |\n", + "|-:|-:|:-:|\n", + "|B1|Coastal/Areasol|0.433 – 0.453 μm| \n", + "|B2|Blue|0.450 – 0.515 μm|\n", + "|B3|Green|0.525 – 0.600 μm|\n", + "|B4|Red|0.630 – 0.680 μm|\n", + "|B5|Near Infrared|0.845 – 0.885 μm|\n", + "|B6|Short Wavelength Infrared|1.560 – 1.660 μm| \n", + "\n", + "Typically, refletance value is between 0 and 1, describing the percentage of light reflected by the surface. The reflectance value in our data is the scaled value between 0 and 10000. You can simply convert it back to regular reflectance by multiplying 0.0001. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Wimfgdw5ySx1", + "colab_type": "text" + }, + "source": [ + "In this notebook, we are doing clustering, so the **Class** information is not relevant because we are trying to guess how many clusters that we have based on this dataset. So let's assume that we do not have the land cover information." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "QusheEMHuqPq", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## Now we have a simple matrix with six columns (attributes)\n", + "## X is the new pandas data.frame with only the six channel reflectance\n", + "X = AVLData.iloc[:,3:]\n", + "print( X.head() )\n", + "## We are now looking at the pairwise scatter plots between these six channels\n", + "## using seaborn.pairplot function to look at them in one bix plot.\n", + "## We are using \"alpha\" key word to change the transparency for the dots since\n", + "## there are many overlapping amongst the data.\n", + "sbn.pairplot(X, diag_kind = 'kde',\n", + " plot_kws = {'alpha': 0.5, 's': 30, 'edgecolor': 'k'},\n", + " height = 2)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HY55QlVSVJ-0", + "colab_type": "text" + }, + "source": [ + "## Feature transformation/extraction\n", + "\n", + "As we can see from the pairwise scatter plots, the current six channels share some strong correlation amongst them. Can we find more useful features based on these six original channels? \n", + "\n", + "Feature transformation/extraction is the process to reduce the dimensionality of the data to explain the most of the variances in the data. Principle Component Analysis (PCA) is one of these techniques." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "RJsDigGOypOc", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## Using PCA function from scikit-learn to perform feature transformation\n", + "from sklearn.decomposition import PCA\n", + "pca_AVL = PCA(n_components=6)\n", + "principalComponents_X = pca_AVL.fit_transform(X)\n", + "print( principalComponents_X )\n", + "## The outcome of PCA is a ndarry here" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "l5KKAs1CX6rF", + "colab_type": "code", + "colab": {} + }, + "source": [ + "\n", + "## we convert the ndarry to pandas data.frame with specified column names\n", + "## for PCA\n", + "PCA_df_X = pd.DataFrame(principalComponents_X, \n", + " columns=['PC1', 'PC2', 'PC3', 'PC4', 'PC5', 'PC6'])\n", + "print( PCA_df_X )\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "82G6JqPO3PaB", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## We now want to know how much variance of the data is explained by each\n", + "## of the principle components.\n", + "with np.printoptions(precision=4, suppress=True):\n", + " print('Explained variation per principal component: {}'.\\\n", + " format(pca_AVL.explained_variance_ratio_) )" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "8As6B9BCXm1F", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## We can now visualize the first three components via the pairwise scatter \n", + "## plot that we have done earlier.\n", + "sbn.pairplot(PCA_df_X.iloc[:,:3], diag_kind = 'kde',\n", + " plot_kws = {'alpha': 0.5, 's': 80, 'edgecolor': 'k', 'color': 'darkgreen'},\n", + " height = 3)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BnQT1kl3dKPw", + "colab_type": "text" + }, + "source": [ + "## Data spliting\n", + "\n", + "For supervised learning, data spliting is the first step in order to develop and evaluation your model. \n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "3COORCzddaZ4", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## Import function for data spliting\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "## We are spliting our data set into training and testing sets based on a 80:20 ratio\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZGId8GFaek_A", + "colab_type": "text" + }, + "source": [ + "When we are spliting the data, we want to make sure the training and testing dataset can resemble the distribution of these four classes in order to make sure our data can represent the reality. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YD4iMwXNpmoT", + "colab_type": "text" + }, + "source": [ + "## k-Nearest neighbors (kNN)\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "hf2z0Je74SNN", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## Import packages for k-nearest neighbor classifier\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "\n", + "# Initialize a KNN classifier object. Later, we'll train it and then have it predict the\n", + "# classes of withheld testing samples.\n", + "\n", + "\n", + "# let's train the model with k=5\n", + "\n", + "\n", + "\n", + "# Have the newly trained classifier predict the classes of the withheld testing data.\n", + "\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xwfV4cecgIDN", + "colab_type": "text" + }, + "source": [ + "Now we have a kNN classifier generated. We need to evaluate how the model performs based on our data. We can tabulate the predicted classes and the reference classes into a confusion matrix." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "s0xOL2Oapt42", + "colab_type": "code", + "colab": {} + }, + "source": [ + "from sklearn.metrics import confusion_matrix\n", + "from sklearn.metrics import classification_report\n", + "\n", + "## Calculate the confusion matrix and normalized confusion matrix\n", + "\n", + "\n", + "# Initialize figure, axes for the two confusion matrices.\n", + "\n", + "\n", + "# Plot the raw counts' confusion matrix.\n", + "\n", + "\n", + "# Add labels to the x-axis and the y-axis.\n", + "\n", + "\n", + "# Plot the percentages' confusion matrix.\n", + "\n", + "\n", + "# Add a label to the x-axis.\n", + "\n", + "# Add a title to the figure.\n", + "\n", + "# Display the figure.\n", + "\n", + "\n", + "\n", + " " + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "5LWBThgFhzF4", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## Print the accuracy matrix for the classifier\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_upVzF9uhJLw", + "colab_type": "text" + }, + "source": [ + "## Cross validation\n", + "\n", + "If you have noticed, we fix the k-value for this k-Nearest neighbor classifier (k=9). \n", + "\n", + "1. Can we improve our overall accuracy by changing our k value in the model trianing? \n", + "\n", + "2. How can we decide which is the best k value for the model? \n", + "\n", + "To answer these questions, we need to use cross validation, a process used for finding the best model hyperparameters for a machine learning model (e.g., k for k-nearest neighbors).\n", + "\n", + "To explore the optimum combination of the model hyperparameters, we typically uses the cross validation (CV) strategy. There are different type of cross validation strategy, such as, k-fold cross validation, leave-one-out cross validation, repeated cross validation. More information can be found in this [well written article about the importance of CV for machine learning](https://www.digitalvidya.com/blog/cross-validation-in-machine-learning/). \n", + "\n", + "The most commonly used CV strategy is usually [k-fold CV](https://machinelearningmastery.com/k-fold-cross-validation/). But it still depends on the data that have for your problem. If you are using time series data, your cross validation strategy will be different since you want to account for the tempooral autocorrelation within your data. Same idea applies when your data have strong spatial auto correlation. But today, let's assume that k-fold CV can solve our problem. \n", + "\n", + "The most commonly used CV strategy is usually k-fold CV. But it still depends on the data that have for your problem. If you are using time series data, your cross validation strategy will be different since you want to account for the tempooral autocorrelation within your data. Same idea applies when your data have strong spatial auto correlation. But today, let's assume that k-fold CV can solve our problem.\n", + "\n", + "We can use a stratified 10-fold CV to find the optimum hyperparameters for our two classifiers (i.e., k-NN and random forest) to see if the model performance could be further improved. Let's start with k-NN to find out the best number of neighbors (k) for the model.\n", + "\n", + "The idea is to repeat the model traininig 10 times using a subset (9/10) of the training data set with different value of k. The average value of the model performance metrices from these 10 iteration of model training is used to choose which hyperparameter (i.e., k) performs best." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "8x2-ZZPKzfgM", + "colab_type": "code", + "colab": {} + }, + "source": [ + "from sklearn.model_selection import StratifiedKFold\n", + "from sklearn.metrics import accuracy_score, make_scorer\n", + "from sklearn.model_selection import GridSearchCV\n", + "\n", + "# Set up the parameter grid. In this case, it's just a list of all the\n", + "# candidate number of neighbors we'll consider.\n", + "\n", + "# Initialize a KNN classifier object. \n", + "\n", + "\n", + "# Initialize a stratified 10-fold generator for the cross-validation.\n", + "\n", + "# Create the grid search object. This object will take the kNN classifier\n", + "# and run stratified 10-fold cross-validation for each of the potential\n", + "# candidates for k. It will record the averaged accuracy for each k so\n", + "# that afterwards we can view how the classifier's accuracy improves or\n", + "# worsens with respect to k.\n", + "\n", + "# Run the grid search for the optimal number of neighbors, k.\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bOPm2v0qji-P", + "colab_type": "text" + }, + "source": [ + "We now have a set of models and its performance based on this stratified 10-fold cross validation. Now, we can plot the model accuracy as a function of *k*. This will assist us selecting the optimum solution." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "edERAaT-jluG", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## Retrieving the mean and standard deviation of the accuracy for the model test scores\n", + "\n", + "## Plotting out the accuracy agiand k\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "c5-DpbAnj7aN", + "colab_type": "text" + }, + "source": [ + "Now we know that the kNN model performs best when we use k value of 9 based on our 10-fold cross validation strategy. Now we can safely train out final kNN model with k of 9 and apply it to our independent testing data that we set aside at the begining of this process." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "08SG-FRnkPKY", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# Initialize a KNN classifier object. Later, we'll train it and then have it predict the\n", + "# classes of withheld testing samples.\n", + "\n", + "# Have the final classifier predict the classes of the withheld testing data.\n", + "\n", + "# Let's print out the accuracy indicators\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "HbJrI8YCk8_9", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## Now, we can also visualize the testing confusion matrix\n", + "## Calculate the confusion matrix and normalized confusion matrix\n", + "\n", + "# Initialize figure, axes for the two confusion matrices.\n", + "\n", + "# Plot the raw counts' confusion matrix.\n", + "\n", + "# Add labels to the x-axis and the y-axis.\n", + "\n", + "# Plot the percentages' confusion matrix.\n", + "\n", + "# Add a label to the x-axis.\n", + "\n", + "\n", + "# Add a title to the figure.\n", + "\n", + "\n", + "# Display the figure.\n", + "\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "k_S-UOoQlloN", + "colab_type": "text" + }, + "source": [ + "## Random forest \n", + "\n", + "### 2.3 - Random forest \n", + "\n", + "There are more sophiscated models that we can use to address more sophisicated classification problems. Here, we choose to introduce [random forest](https://en.wikipedia.org/wiki/Random_forest) model because of its robust performance in many applications. The random forest is an ensemble learning model by creating a suite of decision trees at training time and outputting the class that is the majority of the classes from each individual tree for classification problem. The fundamental idea of random forest model is that the **collective power of multiple \"weak\" models can outperform any individual \"strong\" model**. It can address overfitting issue comparing to the regular decision tree model. \n", + "\n", + "Similar with k-NN, there are different flavors of random forest model. We are using the classic random forest model with *scikit-learn* package in Python (i.e., [_RandomForestClassifier_](https://scikit-learn.org/stable/modules/ensemble.html#forests-of-randomized-trees). As the model gets more complicated, we have more to consider about the model structure, such as, how many variables we want to have as input for each tree (*max_features*), number of trees (*n_estimator*), the depth of a decision tree (*max_depth*, *min_sample_split*, *min_sample_leaf*). These could all have potential impact on our final model outcomes. \n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "2H7Hrwxylwvh", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# Import the random forest classifier from sklearn.ensemble\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "\n", + "# Set up the parameter grid. In this case, it's just a list of all the\n", + "# candidate number of neighbors we'll consider.\n", + "\n", + "\n", + "# Initialize a ra classifier object. \n", + "\n", + "\n", + "# Create the grid search object. This object will take the kNN classifier\n", + "# and run stratified 10-fold cross-validation for each of the potential\n", + "# candidates for k. It will record the averaged accuracy for each k so\n", + "# that afterwards we can view how the classifier's accuracy improves or\n", + "# worsens with respect to k.\n", + "\n", + "\n", + "# Run the grid search for the optimal number of neighbors, k.\n", + "\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "ptD1txhymaP6", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## Print best parameter combination\n", + "\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "FH2LNi3emf6e", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## Calculating the mean and standard deviation of the accuracy for the model test scores\n", + "\n", + "\n", + "## rearrange the accuracy data to a 2d array so we can have a heatmap\n", + "\n", + "## 5 rows for max_features & 7 columns for min_samples_leaf\n", + "\n", + "## Creating the heatmap for both mean accuracy and standard deviation\n", + "# Initialize figure, axes for the accuracy heatmap.\n", + "\n", + "\n", + "# Plot the raw counts' confusion matrix.\n", + "\n", + "\n", + "\n", + "# Add labels to the x-axis and the y-axis.\n", + "\n", + "\n", + "# Plot the percentages' confusion matrix.\n", + "\n", + "\n", + "\n", + "# Add labels to the x-axis and the y-axis.\n", + "\n", + "\n", + "# Add a title to the figure.\n", + "\n", + "\n", + "# Display the figure.\n", + "\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_QisdYFcnKya", + "colab_type": "text" + }, + "source": [ + "" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "SAE9xoWXnKEe", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## Train our final model based on our 10-fold cross validation results with the \n", + "## best model hyperparameters\n", + "\n", + "\n", + "# Using the training set, we can fit our model now\n", + "\n", + "\n", + "# Have the newly trained classifier predict the classes of the withheld testing data.\n", + "\n", + "\n", + "## reporting the indicators for the random forest classifier\n", + "\n", + "\n", + "\n", + "\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "s_37UfVNn2u0", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## Calculate the confusion matrix and normalized confusion matrix\n", + "\n", + "\n", + "# Initialize figure, axes for the two confusion matrices.\n", + "\n", + "\n", + "# Plot the raw counts' confusion matrix.\n", + "\n", + "\n", + "\n", + "# Add labels to the x-axis and the y-axis.\n", + "\n", + "\n", + "# Plot the percentages' confusion matrix.\n", + "\n", + "\n", + "\n", + "# Add a label to the x-axis.\n", + "\n", + "\n", + "# Add a title to the figure.\n", + "\n", + "\n", + "# Display the figure.\n", + "\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "3G1m3znFoGST", + "colab_type": "code", + "colab": {} + }, + "source": [ + "" + ], + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/02_Week2/03_ML_classification/README.md b/02_Week2/03_ML_classification/README.md new file mode 100644 index 0000000..b5d8f7d --- /dev/null +++ b/02_Week2/03_ML_classification/README.md @@ -0,0 +1,28 @@ +# Table of Contents: + +## 03_A_k_means_clustering_exercise.ipynb + +This is the in-class exercise notebook to practice clustering using k-means method. + +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/uofscphysics/STEM_Python_Course/blob/Summer2020/02_Week2/03_ML_classification/03_Pt1_ML_clustering_exercise.ipynb) + +_____ +## 03_A_k_means_clustering_complete.ipynb + +This is the complete notebook to implement k-means method for clustering. + +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/uofscphysics/STEM_Python_Course/blob/Summer2020/02_Week2/03_ML_classification/03_Pt1_ML_clustering_complete.ipynb) + +_____ +## 03_B_classification_exercise.ipynb + +This is the in-class exercise notebook to implement k-nearest neighbor and random forest for classification examples. + +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/uofscphysics/STEM_Python_Course/blob/Summer2020/02_Week2/03_ML_classification/03_Pt2_ML_classification_exercise.ipynb) + +_____ +## 03_B_classification_complete.ipynb + +This is the complete notebook to Timplement k-nearest neighbor and random forest for classification examples. + +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/uofscphysics/STEM_Python_Course/blob/Summer2020/02_Week2/03_ML_classification/03_Pt2_ML_classification_complete.ipynb) diff --git a/02_Week2/04_ML_regression/04_Pt1_ML_regression_random_forest_complete.ipynb b/02_Week2/04_ML_regression/04_Pt1_ML_regression_random_forest_complete.ipynb new file mode 100644 index 0000000..a64990c --- /dev/null +++ b/02_Week2/04_ML_regression/04_Pt1_ML_regression_random_forest_complete.ipynb @@ -0,0 +1,900 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "04_Pt1_ML_regression_random_forest_complete.ipynb", + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "2ZnjFtKPqhcz", + "colab_type": "text" + }, + "source": [ + "# Python for STEM - Week 2 (Advanced) \n", + "\n", + "## Day 4 - Part 1: Supervised learning - Regression\n", + "\n", + "In this notebook, we will focus on examples of supervised machine learning. More specifically, we will be doing regression using Scikit-learn, one of the machine learning packages in Python. Before we start, here we first import all the packages that we need for this notebook. \n", + "\n", + "All the machine learning functions we will use in Day 3 and Day 4 all comes from [scikit-learn](https://scikit-learn.org/stable/index.html). You can find very detailed descriptions on many machine learning models included in the package user guide and various examples. This would be a good place to start when you want to adopt machine learning for your own research/work. \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "gkWCpoG4qasM", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 51 + }, + "outputId": "dbc66de6-e165-482e-9343-74640584451f" + }, + "source": [ + "## In this cell, we import all the packages needed for this notebook\n", + "import numpy as np ## packages for data handling\n", + "import pandas as pd \n", + "import matplotlib.pyplot as plt ## packages for visualization \n", + "import seaborn as sbn " + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n", + " import pandas.util.testing as tm\n" + ], + "name": "stderr" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "fOu7zI-RoIuV", + "colab_type": "text" + }, + "source": [ + "## Data Ingest\n", + "\n", + "In this notebook, we will be using another real world problem of predictive modeling of the concentration of PM2.5 in Beijing using hourly weather information. PM2.5 refers to atmospheric particulate matter (PM) that have a diameter less than 2.5 $\\mu m$. The data is between 2010 and 2014. The data is stored as a comma separated value (CSV) data file, with 11 columns. The table below shows the meaning of each column. \n", + "\n", + "| Variable name | Full name |\n", + "|-:|-:|\n", + "|*year*|Year of observation| \n", + "|*month*|Month of observation|\n", + "|*day*|Day of observation|\n", + "|*hour*|Hour of observation|\n", + "|*pm2.5*|Concentration of PM 2.5 ($\\mu g/m^3$)|\n", + "|*DEWP*|Dew Point ($^\\circ C$)|\n", + "|*TEMP*|Temperature ($^\\circ C$)|\n", + "|*PRES*|Pressure (hPa)|\n", + "|*WIND*|Wind speed (m/s)|\n", + "|*TSNOW*|Continuos hours of snow|\n", + "|*TRAIN*|Continuos hours of rain|" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "xLqniMTknb5P", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 238 + }, + "outputId": "451b0e33-d9d6-406e-bf08-d9c7b5fb4f65" + }, + "source": [ + "filename = \"https://raw.githubusercontent.com/uofscphysics/STEM_Python_Course/Summer2020/02_Week2/Data/04_Beijing_PM2.5_hourly_20100101-20141231.csv\"\n", + "PMdata = pd.read_csv(filename, index_col=None)\n", + "print( PMdata.head() )\n", + "print( PMdata.shape )\n", + "\n", + "## We see there are mission values at the begining so we drop these missing \n", + "## values in the dataset\n", + "PMdata = PMdata.dropna()\n", + "print( PMdata.head() )" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + " year month day hour pm2.5 DEWP TEMP PRES WIND TSNOW TRAIN\n", + "0 2010 1 1 0 NaN -21 -11.0 1021.0 1.79 0 0\n", + "1 2010 1 1 1 NaN -21 -12.0 1020.0 4.92 0 0\n", + "2 2010 1 1 2 NaN -21 -11.0 1019.0 6.71 0 0\n", + "3 2010 1 1 3 NaN -21 -14.0 1019.0 9.84 0 0\n", + "4 2010 1 1 4 NaN -20 -12.0 1018.0 12.97 0 0\n", + "(43824, 11)\n", + " year month day hour pm2.5 DEWP TEMP PRES WIND TSNOW TRAIN\n", + "24 2010 1 2 0 129.0 -16 -4.0 1020.0 1.79 0 0\n", + "25 2010 1 2 1 148.0 -15 -4.0 1020.0 2.68 0 0\n", + "26 2010 1 2 2 159.0 -11 -5.0 1021.0 3.57 0 0\n", + "27 2010 1 2 3 181.0 -7 -5.0 1022.0 5.36 1 0\n", + "28 2010 1 2 4 138.0 -7 -5.0 1022.0 6.25 2 0\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "u1rVvBok5ekQ", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 119 + }, + "outputId": "b6bb6c75-0567-499b-abe7-1883fb9faf8e" + }, + "source": [ + "## Since we will not use the time information for modeling, we here only keep\n", + "## pm2.5 and weather information\n", + "PMdata = PMdata.iloc[:,4:]\n", + "print ( PMdata.head() )" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + " pm2.5 DEWP TEMP PRES WIND TSNOW TRAIN\n", + "24 129.0 -16 -4.0 1020.0 1.79 0 0\n", + "25 148.0 -15 -4.0 1020.0 2.68 0 0\n", + "26 159.0 -11 -5.0 1021.0 3.57 0 0\n", + "27 181.0 -7 -5.0 1022.0 5.36 1 0\n", + "28 138.0 -7 -5.0 1022.0 6.25 2 0\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Bb5Iqk4C2yOd", + "colab_type": "text" + }, + "source": [ + "Now we can look at the time-series of our data." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "cO_INv6b29T7", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 701 + }, + "outputId": "364347a3-1bdb-407d-cb33-d37ed2463612" + }, + "source": [ + "# specify columns to plot\n", + "index = [0, 1, 2, 3, 4, 5, 6]\n", + "i = 1\n", + "# plotting each column\n", + "plt.figure(figsize=(11, 12))\n", + "for ind in index:\n", + "\tplt.subplot(len(index), 1, i)\n", + "\tplt.plot(PMdata.iloc[:, ind].values)\n", + "\tplt.title(PMdata.columns[ind], y=0.5, loc='right')\n", + "\ti += 1\n", + "plt.show()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OB2dLHRh8EL1", + "colab_type": "text" + }, + "source": [ + "## Data Scaling \n", + "\n", + "From the data, we see there are large differences of the magnitude of different variables, which can cause issues when we directly build our model with current data. Data with notably larger magnitude could dominate the model performance. To avoid that, we usually scale our data to [0, 1], or [-1, 1]. In other cases, we can also scale all variables to a distribution with 0 mean and standard deviation of 1. " + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "gzcOfEE39RS3", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 153 + }, + "outputId": "fc093195-4cfe-47d0-8cd1-e248f57b7a46" + }, + "source": [ + "## import function to perform data scaling\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "\n", + "scaler = MinMaxScaler(feature_range=(0, 1))\n", + "scaled = scaler.fit_transform(PMdata)\n" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "MinMaxScaler(copy=True, feature_range=(0, 1))\n", + "[[0.12977867 0.35294118 0.24590164 ... 0.00237151 0. 0. ]\n", + " [0.14889336 0.36764706 0.24590164 ... 0.00394662 0. 0. ]\n", + " [0.15995976 0.42647059 0.2295082 ... 0.00552173 0. 0. ]\n", + " ...\n", + " [0.01006036 0.26470588 0.26229508 ... 0.42873071 0. 0. ]\n", + " [0.00804829 0.26470588 0.24590164 ... 0.43584525 0. 0. ]\n", + " [0.01207243 0.27941176 0.26229508 ... 0.44138468 0. 0. ]]\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "XP8uCr3--OG_", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "0150fa0d-57b8-4ef2-9bcb-d9d5e805a143" + }, + "source": [ + "## After scaling our data, we can separate the data into training and testing data\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "## Independent variables for our model (weather data) \n", + "X = scaled[:,1:]\n", + "## Target output for our model (pm2.5)\n", + "y = scaled[:,0]\n", + "## We are spliting our data set into training and testing sets based on a 60:40 ratio\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=42)\n", + "print( X_train.shape, y_train.shape)\n" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "(25054, 6) (25054,)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wsqZvOR3_BVM", + "colab_type": "text" + }, + "source": [ + "## Decision tree regression\n", + "\n", + "As the basis of the random forest, we will try to fit a decision tree model for regression first and visualize the model to have a better understanding of the model structure, and why it is called a \"decision tree\"." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Tmg94PMeAbHY", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 119 + }, + "outputId": "1bbe8d4f-d721-4ee6-81fd-cba11bccfabc" + }, + "source": [ + "## Import the decision tree model\n", + "from sklearn.tree import DecisionTreeRegressor\n", + "\n", + "## define our model\n", + "dtModel = DecisionTreeRegressor(min_samples_leaf=15)\n", + "\n", + "## fit our model with .fit and our training data\n", + "dtModel.fit(X_train, y_train)\n" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=None,\n", + " max_features=None, max_leaf_nodes=None,\n", + " min_impurity_decrease=0.0, min_impurity_split=None,\n", + " min_samples_leaf=15, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, presort='deprecated',\n", + " random_state=None, splitter='best')" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 48 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "pBV209syBBKf", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 816 + }, + "outputId": "f2d9116d-a100-4550-a52f-1d90cf05d907" + }, + "source": [ + "## We can now visualize what a decision tree looks like\n", + "## Because we have a really big tree, so let's try text representation first\n", + "from sklearn import tree\n", + "text_representation = tree.export_text(dtModel,max_depth=3)\n", + "print(text_representation)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "|--- feature_0 <= 0.39\n", + "| |--- feature_3 <= 0.02\n", + "| | |--- feature_1 <= 0.20\n", + "| | | |--- feature_2 <= 0.75\n", + "| | | | |--- truncated branch of depth 8\n", + "| | | |--- feature_2 > 0.75\n", + "| | | | |--- truncated branch of depth 7\n", + "| | |--- feature_1 > 0.20\n", + "| | | |--- feature_0 <= 0.35\n", + "| | | | |--- truncated branch of depth 11\n", + "| | | |--- feature_0 > 0.35\n", + "| | | | |--- truncated branch of depth 10\n", + "| |--- feature_3 > 0.02\n", + "| | |--- feature_3 <= 0.08\n", + "| | | |--- feature_0 <= 0.32\n", + "| | | | |--- truncated branch of depth 10\n", + "| | | |--- feature_0 > 0.32\n", + "| | | | |--- truncated branch of depth 9\n", + "| | |--- feature_3 > 0.08\n", + "| | | |--- feature_2 <= 0.61\n", + "| | | | |--- truncated branch of depth 9\n", + "| | | |--- feature_2 > 0.61\n", + "| | | | |--- truncated branch of depth 15\n", + "|--- feature_0 > 0.39\n", + "| |--- feature_1 <= 0.39\n", + "| | |--- feature_0 <= 0.48\n", + "| | | |--- feature_1 <= 0.27\n", + "| | | | |--- truncated branch of depth 12\n", + "| | | |--- feature_1 > 0.27\n", + "| | | | |--- truncated branch of depth 13\n", + "| | |--- feature_0 > 0.48\n", + "| | | |--- feature_3 <= 0.05\n", + "| | | | |--- truncated branch of depth 13\n", + "| | | |--- feature_3 > 0.05\n", + "| | | | |--- truncated branch of depth 6\n", + "| |--- feature_1 > 0.39\n", + "| | |--- feature_3 <= 0.00\n", + "| | | |--- feature_1 <= 0.58\n", + "| | | | |--- truncated branch of depth 14\n", + "| | | |--- feature_1 > 0.58\n", + "| | | | |--- truncated branch of depth 23\n", + "| | |--- feature_3 > 0.00\n", + "| | | |--- feature_0 <= 0.88\n", + "| | | | |--- truncated branch of depth 22\n", + "| | | |--- feature_0 > 0.88\n", + "| | | | |--- truncated branch of depth 14\n", + "\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "h3TTrpcECSvF", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "3b797269-2427-4cae-cdc9-ea3c4910a6c2" + }, + "source": [ + "## We can further visualize the tree structure using plot_tree function\n", + "fig = plt.figure(figsize=(25,20))\n", + "_ = tree.plot_tree(dtModel, \n", + " feature_names=PMdata.columns[1:], \n", + " max_depth=2,\n", + " filled=True)\n", + "fig.show()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "oMs63A3dFft9", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "5d1d9ddf-4d3d-4d4b-8ac4-3636c8024adc" + }, + "source": [ + "## How does our decision tree model perform on modeling the PM2.5 data?\n", + "## We can check the root mean squre error for our model\n", + "y_predicted = dtModel.predict(X_test)\n", + "\n", + "## import metric for regression model performance\n", + "from sklearn.metrics import mean_squared_error, r2_score\n", + "RMSE_test = np.sqrt(mean_squared_error(y_test, y_predicted))\n", + "print(RMSE_test)\n" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "0.07537165988057153\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "yJSux2tRHpAF", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 472 + }, + "outputId": "1f8e2e06-c457-406d-f1f5-1fc20b482add" + }, + "source": [ + "## To show the prediction results, we can also plot a kernal density plot between \n", + "## predicted value and testing value\n", + "plt.figure()\n", + "fig = sbn.jointplot(y_test, y_predicted, xlim=[0,1], ylim=[0,1],\n", + " kind=\"kde\", space=0, color=\"g\")\n", + "fig.set_axis_labels(xlabel='True', ylabel='Predicted')\n", + "plt.show()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "display_data", + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LUW_QwEAKI2Z", + "colab_type": "text" + }, + "source": [ + "It appears that the decision tree model can be improved. How about let's try the random forest model and see if the random forest can improve the performance." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mwxiY6-xMZcA", + "colab_type": "text" + }, + "source": [ + "## Random Forest\n", + "\n", + "This time, we are fitting a random forest regression model instead of classification. The concept is similar with the classifier, but we are using the regressor function instead." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "uVvG9BRpMlP6", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 357 + }, + "outputId": "df03cada-4227-489b-9077-838649b3b6bc" + }, + "source": [ + "## import randomforest regressor from sklearn\n", + "from sklearn.ensemble import RandomForestRegressor\n", + "from sklearn.metrics import make_scorer\n", + "from sklearn.model_selection import KFold\n", + "from sklearn.model_selection import GridSearchCV\n", + "\n", + "## Let's first define our model parameter grids\n", + "param_grid = {\n", + " 'n_estimators': [10, 25, 50, 100],\n", + " 'max_features': [3, 4]\n", + "}\n", + "\n", + "# Initialize a stratified 5-fold generator for the cross-validation.\n", + "kfold_generator = KFold(n_splits=5)\n", + "\n", + "## Define the model and use min_samples_leaf=15 same as the decision tree\n", + "rfModel = RandomForestRegressor(min_samples_leaf=10) \n", + "\n", + "## Define the grid search object\n", + "gridsearch_cv_obj = GridSearchCV(\n", + " rfModel, \n", + " param_grid, \n", + " scoring=make_scorer(r2_score), \n", + " cv=kfold_generator,\n", + " n_jobs=-1,\n", + " )\n", + "\n", + "gridsearch_cv_obj.fit(X_train, y_train.ravel())\n" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "GridSearchCV(cv=KFold(n_splits=5, random_state=None, shuffle=False),\n", + " error_score=nan,\n", + " estimator=RandomForestRegressor(bootstrap=True, ccp_alpha=0.0,\n", + " criterion='mse', max_depth=None,\n", + " max_features='auto',\n", + " max_leaf_nodes=None,\n", + " max_samples=None,\n", + " min_impurity_decrease=0.0,\n", + " min_impurity_split=None,\n", + " min_samples_leaf=10,\n", + " min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0,\n", + " n_estimators=100, n_jobs=None,\n", + " oob_score=False, random_state=None,\n", + " verbose=0, warm_start=False),\n", + " iid='deprecated', n_jobs=-1,\n", + " param_grid={'max_features': [3, 4],\n", + " 'n_estimators': [10, 25, 50, 100]},\n", + " pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n", + " scoring=make_scorer(r2_score), verbose=0)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 94 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "330BnyNcP9Z4", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 68 + }, + "outputId": "db74bdb9-52d6-4359-9ed8-da7cd67bd77b" + }, + "source": [ + "print( 'Best model hyperparameter for the random forest regressor is:' )\n", + "print( gridsearch_cv_obj.best_params_)\n", + "\n", + "## Now we can fit the optimum model for the random forest\n", + "rfModel = RandomForestRegressor(n_estimators=100, random_state=42, \n", + " max_features=4, min_samples_leaf=10)\n", + "rfModel.fit(X_train, y_train)\n", + "\n", + "## predict the testing data\n", + "rf_predicted = rfModel.predict(X_test)\n", + "\n", + "## Get the root mean square error of the prediction\n", + "rfRMSE = np.sqrt(mean_squared_error(y_test, rf_predicted))\n", + "print(rfRMSE)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Best model hyperparameter for the random forest regressor is:\n", + "{'max_features': 4, 'n_estimators': 100}\n", + "0.07150493142412419\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "EIZRz3qjRnPc", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 472 + }, + "outputId": "0849cb78-acf7-4a23-dc79-13e94c034c33" + }, + "source": [ + "## To show the prediction results, we can also plot a kernal density plot between \n", + "## predicted value and testing value\n", + "plt.figure()\n", + "fig = sbn.jointplot(y_test, rf_predicted, xlim=[0,1], ylim=[0,1],\n", + " kind=\"kde\", space=0, color=\"g\")\n", + "fig.set_axis_labels(xlabel='True', ylabel='Predicted (Random Forest)')\n", + "plt.show()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "display_data", + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "uYeNiHSiR4ix", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 472 + }, + "outputId": "699c0af2-5055-4bb1-9239-664c948ab51d" + }, + "source": [ + "## We can also compare the model error between decision tree and random forest\n", + "## and see the performance differences.\n", + "plt.figure()\n", + "fig = sbn.jointplot(y_test - y_predicted, \n", + " y_test - rf_predicted, \n", + " xlim=[-0.3,0.3], ylim=[-0.3,0.3],\n", + " kind=\"kde\", space=0, color=\"g\")\n", + "fig.set_axis_labels(xlabel='Decision Tree Error', ylabel='Random Forest Error')\n", + "plt.show()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "display_data", + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAbYAAAG2CAYAAAATCaNwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3deZwU5bn28d89+8Y6sgwgCAooIuvIorij4oqKRoyixuSgOWJizEleE3djEj0mHo9LTDAeFXGLJkaiKCqCiBEBUURc2HcQhAEGmH2e94/uwaanZ6aHme7q6rm+fPozVdXVVXcNMNc8T1U9Zc45REREkkWK1wWIiIg0JwWbiIgkFQWbiIgkFQWbiIgkFQWbiIgklTSvC4gBXeYpIi2FeV1AIlKLTUREkoqCTeQgbNy9karqKq/LEJEIFGwijTR/43y6P9idC164gL3le70uR0TCKNhEGsE5xy/f/iVZaVlMXzGdk58+ma17t3pdloiEULCJNMIbK97gvbXv8aMhP+Kuk+9iyTdLOO6J41i+fbnXpYlIkCXhWJFJd0CSGKqqqxj0l0HsLNnJkxc8SVpKGl9s+4Jb3r2FNEvjX9//FyO6jfC6TGlZdFVkBGqxiUTp2SXP8vnWz7lmyDWkpQTulOnXoR8Pn/UwmWmZnPr0qcxYMcPjKkVELTaRKJRWltL34b7kpOfw6DmPkmIH/k5YVFLEjW/eSH5OPouuXeRRldICqcUWgVpsIlF4dP6jrNu9jv8Y+h+1Qg2gXXY7xhwxhk+2fMLG3Rs9qFBEaijYRBqws3Qnv33/txzb5ViGFAypc72a82vTl0+PV2kiEoGCTaQB9829j6LSIiYOnVjveoe1PYyCvAJeW/5anCoTkUgUbCL12Lh7Iw9+9CCje43miPZH1LuumTGi2wjeWfUOJRUlcapQRMIp2ETq8ct3fklVdRXXDLomqvVHdBvBvop9zF4zO7aFiUidFGwidZi1ehbPLXmO8f3HU9CqIKrPDOo8iKy0LF5bpu5IEa8o2EQiKK8q5/rp19MlrwuXH3N51J/LSM1gaMFQXlv2Gkl4K42ILyjYRCJ4cN6DfPntl0waNonMtMxGfXZkt5Gs272Oz7d+HqPqRKQ+CjaRMOt3reeu9+7i+EOPZ+ShIxv9+eHdhgOoO1LEIwo2kTA/m/EzqqqrmDRs0kF9/pCcQ+ib35fXl7/ezJWJSDQUbCIh3lzxJn//8u9cMeAKOud1PujtjOg2gg83fMi3+75txupEJBoKNpGg0spSJk2fxKGtD+V7R3+vSdsa2W0k1a6aN1e82UzViUi0FGwiQffOvZeVRSv56fCfkpGa0aRt9c7vTfvs9jrPJuIBBZsIMHfdXO6Zcw+je41maJehTd5eiqUwvOtw3lzxJhVVFc1QoYhES8EmLd72fdsZ//J4Oud15sbhNzbbdkd2G8musl18sP6DZtumiDRMwSYtmnOOq/95NVv3buX2k24nNyO32bY9tMtQ0lPS1R0pEmcKNmnRHvjwAV5b/ho/LvwxffL7NOu2c9JzGNR5ENO+nqZRSETiSMEmLda8DfO4eebNnNj9RC448oKY7OPEHieyfMdyZq2ZFZPti0htCjZpkYpKihj/8ng65nTkF8f/AjOLyX7OOPwM8rPz+f37v4/J9kWkNgWbtDjVrpofvPoDNhZv5LaTbiMvIy9m+8pIzWBcv3G8s/odFm5aGLP9iMh3FGzS4vx65q959etXuXbotRx5yJEx39/5fc6nVUYrfj9XrTaReFCwSYvy2ILHuO+D+zi/7/mMO2pcXPaZm5HL2L5jeeXLV/jq26/isk+RlkzBJi3Ga8teY9IbkxjZbSQ/GfaTmJ1Xi2Rcv3FkpGZw3wf3xW2fIi2Vgk1ahAUbF3Dpy5fSp30fbjvxNlJTUuO6/7ZZbTm799lM/Wwq63ati+u+RVoaBZskvdVFqzn3+XNpk9mG3572W7LTsz2p49KjLwXgj//+oyf7F2kpFGyS1L7d9y1jnh1DaWUp946+l/bZ7T2rpVNeJ07reRqPL3qcbXu3eVaHSLJTsEnSWrtzLcf/3/Gs2bmG35zyG7q36e51SVzW/zJKK0t56KOHvC5FJGkp2CQpfb71c4574jg2F2/m/tPvZ0CnAV6XBECPtj0Y1X0Ujyx4hF2lu7wuRyQpKdgk6cxdN5cTnjyB8upyHhzzYMKEWo3LB1zO7rLdXPDCBewt3+t1OSJJR8EmSWXa19M4/ZnTaZ3ZmofPephe7Xp5XVItffP78qtRv2LOujmc/ezZ7Cnf43VJIklFwSZJ48lPnuTCFy+kZ9uePDTmITrndfa6pDqN7jWaW064hbnr53L2s2dTXFbsdUkiSUPBJknhTwv+xDXTrmFowVD+eMYfaZPVxuuSGnRqz1O55YRb+Pf6f3PWs2cp3ESaiYJNfO+BDx/g+unXc/yhx3PPqfd4dp/awTi156nceuKtzNswjzFTx7C7bLfXJYn4noJNfO137/+On7/1c07qcRJ3nnwnGakZXpfUaCcfdjK3nXQb8zfNZ9jjw/h408delyTiawo28SXnHLfPup1b3r2F03udzm0n3kZaSprXZR20k3qcxH2j72NHyQ5GPDGCe+feS1V1lddlifiSJeEj65PugORAzjl++fYv+cOHf+Ds3mdz04ib4j72Y6zsLtvNAx8+wHtr3+OE7icw5cIpHNb2MK/LksQVv5G8fUTBJr5SUVXBf/zrP3h68dOM7TuWnwz/CSmWXB0PzjneWvUWD3/0MKkpqTw05iGuHHhlXJ9GIL6hfxQRKNjEN/aW7+WSly7hjRVvcPWgq7lyQHL/sN9cvJnfz/09S7YuYdSho3jorIcYXDDY67IksSTvf4AmULCJL2zbu41znjuHjzd/zI0jbuS8Pud5XVJcVFVX8eaKN/nrJ39lV+kuJg6dyD2n3sMhOYd4XZokBgVbBAo2SXiri1ZzxtQzWL9rPbeeeCujuo/yuqS4Ky4r5unFT/PPr/5Jq8xW3H3y3fxwyA/JSc/xujTxloItAgWbJCznHK8te42J/5rIvsp93HPqPRzT8Rivy/LU6qLVPLLgERZtXkTrzNZcfszl/GjIjxhSMMTr0sQbCrYIFGySkJZtX8aNb97IGyveoEebHtxx0h30bNfT67ISgnOOxd8sZvry6cxZO4eyqjIGdx7MDwf/kMuOuczTZ85J3CnYIlCwSULZU76He+bcwwMfPkBGagZXDrySi466yNf3qMXSnvI9vLPqHaYvn87yHctJS0njzMPPZHz/8YztO5ZWma28LlFiS8EWgYJNEoJzjuc/f55fvP0LNhVv4szDz2Ti0IlqfTTC8u3Lmbl6JrPXzOabvd+QlZbFuX3OZcKACZzT+5ykuddPDqBgi0DBJp5bvGUxN7xxA++ve5+++X25YdgNHN3xaK/L8q1qV83SbUuZtXoWs9fMpqi0iB5tenDDsBu4ZvA1tMtu53WJ0nwUbBEo2MQzO0p2cPus23ls4WO0ymjFj4b8iLOOOEsti2ZUVV3FB+s/4JUvX+HTbz4lJz2HCQMmMGnYJPp37O91edJ0CrYIFGwSd0UlRUxZPIXfzPkNRaVFjO07lh8M+oHOB8XYih0reOWrV5i5aiZlVWUM6zKMqwddzfj+49WK8y8FWwQKNomLyupK3lr5Fk99+hSvfv0q5VXlDOw0kEnDJnFE+yO8Lq9F2VW6ixkrZzBj5QxWFa0iIzWDsX3HctXAqzil5ym6N85fFGwRKNgkJpxzrN21lkWbFzF33VyeX/I8W/ZuoU1mG07rdRpjDh/DEe2PSOohsRKdc44VO1bw5so3eXf1u+ws3UmqpdKvQz+GdR3GsV2O5diux9K/Y39fPg6ohdB/oAgUbNIsisuKmbN2DnPXzeXjzR+zaPMitpdsByDN0hjebThjjhjD8K7DSU9N97haCVdRVcHHmz/mi21f8NW3X/H19q/3P/Q0PSWdIw85koGdBzKw00AGdBrAwE4D6ZTXyeOqBQVbRAo2OShllWX8e/2/eXf1u8xcPZP5G+dT5apIS0mjZ9ue9M7vTZ/2feiT34de7XqRmZbpdcnSCM45Nu/ZzFfffsXKHStZWbSSVUWr2LZv2/51OuR0YECnAQzqPIgBnQYwoNMAjjrkKP1dx5eCLQIFm0RtddFq3ljxBm+seIN3V7/Lvop9pFgKR+YfyeCCwQwuGEz/Dv31gy2J7SrdxaqiVfuDbmXRStbuXEtZVRkAaSlp9Mnvw6DOgw5o3XXO66xu59jQNzUCBZvUUlVdxbpd61ixYwXLdyzny21f8vaqt/l6+9cAdMnrwrCuwyjsUsjAzgPJy8jzuGLxUlV1FRuLN7Jix4oDQm/r3q3718nPzmdgp4EM7BwIu55te1LtqqlyVVRVV1FZXYnDkZueS6vMVrTKaLX/a25GbtI9c68ZKdgiULC1EFXVVewo2cG2fdvYuncr2/ZuO2B6677A103Fm1izcw0V1RX7P5uVlsUxHY9hWNdhDOs6jENbH6rfvqVBxWXF+0OuJvBWF63e37qLlmHkZuSSl5FHq4xWtM5szSE5h3BY28Po2bYnPdv15LC2h9GjTQ/yc/Jb2vBr+o8YgafBZmZjgP8FUoG/OufuDXv/OuB6oArYA0x0zn3RwGZbXLBVVVfx7b5v2bJny/5gWrtrLWt2rtn/2rZvG9WuOuLn22S2oW1W2/2vrq260rV11/1f87PzFWTSLKqqq9hUvImt+7aSaqmkWMr+l2GUVpayr2If+yr3UVJREpiuCE5Xfje9s3QnW/ZsYVfZrlr7aJ3ZmrZZbcnPzqdddjuy07JJS0kjLSWN9NR00lLSyEzNJCc9p9YrNz33gPns9GwsmB0OR83Py7yMPDrndSY/J9/r1qT+Y0bgWbCZWSqwDDgd2AAsAC4LDS4za+2c2x2cPh/4T+fcmAY2HbcDqnbVVFRVUFFdsf9rZXXlQS+rqAouD1tWUV1BSUUJxeXFgVdZMXvK97CjZAeb92xm295tVLmqA2pLS0mjc15nOuV2olNeJ/Kz8w8Ir5pXm8w2GulDfGtv+V6+2fsNm4s3s3XvVnaX76a47Lv/J8XlxVRWVVLpKvd3eVa5KiqqKiitLKW0svSA3onGSrVUOuZ2pCCvgA65HcjLyAu0LtMDX3PTA92oZoZh+7+mp6aTlZZFZmomWWlZgem0kOng8prbLByOaleNc25/wDocgzoPUrBF4GWbfRiwwjm3CsDMXgDGAvuDrSbUgnKJIrQOf+jwms8GvgY/Uu+8C1ke/n7wa5WrqhU4Lo6Nw4zUjO9+i0zLJjs9m7yMPIYWDKV9dnvys/MDX3Py6ZTbKRF+kxSJuVaZgXNxTbnJv7K6kvKqckorSymrLAsEXtV306FqWm/7Kvexo2THAa91u9btD8uSyhJKKkoa3e3aWO6OFtdBFRUvg60rsD5kfgMwPHwlM7seuAnIAE6NtCEzmwhMBMjIzODI/kc2e7Fe2r5tO/kd8mst3xf8s/6Ab6M/1HVMfpVsxwM6psZKD/5pRWyHhtu+bTs7vt0BgD1j7oFnHuBnI38W030msIgt1oQ/y+qcexR41My+D9wKXBVhncnAZICc3Bw3Y+GM+BYZY2cWnomOKbEl2/GAjskPCroU6Px3BF72VW0EDg2Z7xZcVpcXgAtiWpGIiM8k4ZXtTeZlsC0AeptZTzPLAMYD00JXMLPeIbPnAMvjWJ+ISMILv3BMPOyKdM5VmtkkYAaBy/3/zzm31MzuBhY656YBk8xsNFABFBGhGzJc+0OS74nLV0y8wusSml2yHVOyHQ/omPyivKrc6xISTtLdoD2wcGDSnWMTEYmkoEsBdzx3B3eefKfXpXgl4glGXQ8uIuJTZqYWWwQKNhERnzKMiqqDv8E8WSnYRER8Si22yBRsIiI+ZSjYIlGwiYj4lFpskSnYRER8yjDKqxVs4RRsIiI+pRZbZAo2ERGf0lWRkSnYRET8yjTySCQKNhERn9JVkZEp2EREfErn2CJTsImI+JRabJEp2EREfMrMqKjWxSPhFGwiIj6lFltkCjYREZ8yM8orFWzhFGwiIj6lkUciU7CJiPiUWmyRKdhERHzK0MUjkSjYRER8SvexRaZgExHxKV0VGZmCTUTEp9Rii0zBJiLiUzXn2JxzXpeSUBRsIiI+ZWYAVFZXelxJYlGwiYj4lBEINnVHHkjBJiLiUzUtNgXbgRRsIiI+pRZbZAo2ERGfUostMgWbiIhP1bTYNPrIgRRsIiKSVBRsIiI+l2L6UR5K3w0REZ9yBG7MrumSlAAFm4iIz6nFdiB9N0REJKko2EREfCol+CN8X8U+jytJLAo2ERGfSkkJ/AgvLi/2uJLEomATEfGpmnNrxWUKtlAKNhERn0q1VEAttnAKNhERn1KLLTIFm4iIT+0PNrXYDqBgExHxKbXYIlOwiYj4VIqlYJhabGEUbCIiPpaTnqMWWxgFm4iIj+Wk56jFFkbBJiLiY1lpWRp5JIyCTUTEx9JT0imtLPW6jITiabCZ2Rgz+9rMVpjZzRHev8nMvjCzz8xsppn18KJOEZFElZGaQVlVmddlJBTPgs3MUoFHgbOAfsBlZtYvbLVPgELn3ADgZeC/41uliEhiS0tNU4stjJcttmHACufcKudcOfACMDZ0BefcLOdcTefxPKBbnGsUEUlo6SnplFWqxRbKy2DrCqwPmd8QXFaXHwJvxLQiERGfyU7LZk/5Hq/LSChpXhcQDTO7AigETqrj/YnARICu3evLRhERf3tm8jNMnTwVgKLtRXTN7MrG4o0eV5VYvGyxbQQODZnvFlx2ADMbDdwCnO+ci9jeds5Nds4VOucK8zvkx6RYEZFEMGHiBGYsnMGMhTNol9+OvIw8dpbu9LqshOJlsC0AeptZTzPLAMYD00JXMLPBwF8IhNpWD2oUEUloeRl57CrbRVV1ldelJAzPgs05VwlMAmYAXwJ/c84tNbO7zez84Gr3A3nAS2b2qZlNq2NzIiItUquMVgDsLtvtcSWJw9NzbM656cD0sGW3h0yPjntRIiI+kpeRB0BRaRHtstt5XE1i0MgjIiI+VhNsOs/2HQWbiIiP7W+xlRR5XEniULCJiPhYzTk2tdi+o2ATEfExdUXWpmATEfGxVpmBFltRqboiayjYRER8LDstmxRLUYsthIJNRMTHzIy8jDxdPBJCwSYi4nOtMlqxs0wtthoKNhERn1OL7UAKNhERn1OwHUjBJiLic22y2rB1n8aJr6FgExHxuXZZ7di2d5vXZSQMBZuIiM+1zWpLcXkxpZWlXpeSEBRsIiI+1y4rMKq/Wm0BCjYREZ9rk9UGgK17dZ4NFGwiIr5X02JTsAUo2EREfK5tVltAwVZDwSYi4nM1T85WsAUo2EREfC47LZvM1EwFW5CCTUTE58yMtlltdZN2kIJNRCQJ5KTnUFxW7HUZCUHBJiKSBDLTMimpLPG6jISgYBMRSQIZqRmUVCjYQMEmIpIUstKy2Fexz+syEoKCTUQkCWSmZirYghRsIiJJICM1Q+fYghRsIiJJQF2R31GwiYgkgYzUDD22JkjBJiKSBLLSsnRVZJCCTUQkCWSmZlJWVUZVdZXXpXhOwSYikgSy07MB2FO+x+NKvKdgExFJAq0zWwOwvWS7x5V4T8EmIpIE2mQGnqK9fZ+CTcEmIpIE1GL7joJNRCQJ1ATbt/u+9bgS7ynYRESSQPvs9gCs37Xe40q8p2ATEUkCuRm5dMrtxJKtS7wuxXMKNhGRJNGrXS8Wf7PY6zI8p2ATEUkSh7c7nK+//brFD62lYBMRSRK92veiylXx5bYvvS7FUwo2EZEkcUS7IwBYsGmBx5V4S8EmIpIkurXuRn52PrPXzPa6FE8p2EREkoSZMbDTQGatnoVzzutyPKNgExFJIoMKBrFl7xaWbV/mdSmeUbCJiCSRwZ0HAzBrzSyPK/GOgk1EJIl0bdWVDjkdWvR5Nk+DzczGmNnXZrbCzG6O8P6JZrbIzCrN7GIvahQR8RMzo1+Hfny44UOvS/GMZ8FmZqnAo8BZQD/gMjPrF7baOuBq4Ln4Vici4l9HdziadbvWsbl4s9eleMLLFtswYIVzbpVzrhx4ARgbuoJzbo1z7jOg2osCRUT8qF+HQBvho40feVyJN7wMtq5A6DDUG4LLGs3MJprZQjNbuH2bnkUkIsnrmcnPcGbhmZxZeCZF24sirtM7vzfpKel8uL5ldkcmxcUjzrnJzrlC51xhfod8r8sREYmZCRMnMGPhDGYsnEG7/HYR18lIzaB3+97MXTc3ztUlBi+DbSNwaMh8t+AyERFposKuhczbOI9te7d5XUrceRlsC4DeZtbTzDKA8cA0D+sREUkaxx96PNWumteWveZ1KXHnWbA55yqBScAM4Evgb865pWZ2t5mdD2Bmx5rZBuAS4C9mttSrekVE/KR3+950yu3Eq1+/6nUpcZdW35tmZkA351xMnjXunJsOTA9bdnvI9AICXZQiItIIZsZxhx7HmyveZF/FPnLSc7wuKW7qbbG5wCia0+tbR0REEtPx3Y+npLKEd1a943UpcRVNV+QiMzs25pWIiEizOqbjMaSlpLW4y/7r7YoMGg5cbmZrgb2AEWjMDYhpZSIi0iQZqRn0ateLhZsWel1KXEUTbGfGvAoRiZuFBH7IFVLocSUSD33y+zB33VyccwQum0h+DXZFOufWAm2B84KvtsFlIuIDC8P+hC6X5Nc3vy87S3eyqmiV16XETYPBZmY/BZ4FOgZfU83shlgXJiIHL1KQScvUN78vAPM3zve4kviJ5uKRHwLDnXO3By/FHwH8R2zLEpGDoTCTcL3a9aJ1ZmteX/6616XETTTn2AyoCpmvCi4TkQShMJO6pKakMqr7KKZ9PY3SylKy0rK8LinmommxPQl8ZGZ3mtmdwDzgiZhWJSJRUQtNonFSj5MoLi/mrZVveV1KXNQbbGaWQiDIfgDsCL5+4Jx7MA61iUg9FGgSrSEFQ2id2ZqXvnjJ61Liot6uSOdctZk96pwbDCyKU00iUg8FmjRWWkpai+qOjKYrcqaZjbOWcgOESAJrzlDTfWwtywndT2B32W7eW/Oe16XEXDTBdi3wElBmZrvNrNjMdse4LhEJo5aaNMXgzoPJTM1k+vLkH/43mnNsY5xzKc65DOdca+dcK+dc6zjVJ9Li6QIRaQ6ZaZkMLhjMa8tfIzC+ffJqaHT/auCRONUiImFiFWjqhmyZhncdzqqiVSzfsdzrUmJK59hEEpRaadLcRnQbAcDry5L7Zm2dYxNJQLEMNbXWWq7OeZ3p1a4XLyx9wetSYiqaQZBb6RybSPyopSaxNObwMczfOJ+lW5d6XUrM1BlsZnZFyPTxYe9NimVRIi1VrENNrTU54/AzSEtJ44lPkncAqfpabDeFTD8c9t41MahFpMXSlY8SL22y2nD8occzZfEUyirLvC4nJuoLNqtjOtK8iBykeAWaWmtS4+zeZ7O9ZDvTvp7mdSkxUV+wuTqmI82LSCOplSZeGVowlM55nXngwweS8p62+oLtSDP7zMyWhEzXzPeNU30iSSnegabWmoRKTUnl+8d8n3kb5/HGije8LqfZ1TcI8lFxq0KkhfCihaZQk0jOOuIsXvj8BW5991bOOuIskulW5TpbbM65tfW94lmkSDJQt6MkkrSUNK4ceCWfbPmEV756xetymlU0N2iLSBN5FWpqrUl9RvccTY82Pbh91u1UVVd5XU6zUbCJxJhaapKoUlNSuWrQVSzdtpTnljzndTnNpsFgM7OfRrNMRA7UmKseY3GFpFprEo2TepxE3/y+3PLuLZRWlnpdTrOIpsV2VYRlVzdzHSJJ5WADbWEdfxpLoSbRSrEUrh16Let3r+eR+cnxMJc6r4o0s8uA7wM9zSz0Lr7WwI5YFybiV9EEkQY5lkQyuGAwI7qO4Lfv/5ZrBl9D++z2XpfUJPW12P4N/BH4Kvi15nUTcGbsSxPxH4Wa+NXEoRPZXbab373/O69LabKGLvefDYwG3nfOvQdsBrqhIbVEamkosGI90ohCTZqiZ7uenHn4mTw8/2Hmb5zvdTlNEs05tjlAlpl1Bd4CJgBPxbIoET9pKLCaI9B0ZaXEw7VDryU/O5+xz49l/a71Xpdz0KIJNnPO7QMuAv7knLsEODq2ZYn4Q6wDrUZDrTG11qQ5tMlqw29P/S3F5cWc+/y57Cnf43VJByWqYDOzkcDlQM3zxFNjV5JI4qsvtOI9uLFCTZpTz3Y9uf2k2/l86+dc/vfLfXnjdjTBdiPwK+AV59xSM+sFzIptWSKJq6FWWnOrL7gUahILw7oO4/pjr2fasmn8auavvC6n0eobBBmA4EUj75lZTnB+FfCTWBcmkogONtQWseiA+SEMabaaRGLhwiMvZO2utdz/7/spLivmN6f+hkNyDvG6rKhEM/LISDP7gsBl/5jZQDP7U8wrE0kwB9P1uCj4p67lkd4LpdaaeMXMuGHYDVx45IU8vuhxej/cmwfnPUhFVYXXpTUomq7IBwnct7YdwDm3GDgxlkWJJJKGzqeFiza0QtdvLIWaxENaSho/Gf4THj/vcY5odwQ/m/Ez+j/Wn9eXvZ7QDyiNahBk51z4dZ/+O5sochCibaU1NsxE/KRnu5789+n/ze9O+x0lFSWc+/y5jJk6hi+2feF1aRE1eI4NWG9mxwHOzNKBnwJfxrYsEe9FCrXwZaFBFmn9prSs6vqsWmviBTNjZLeRFBYU8urXrzJl8RQGPDaAHxf+mDtPvpP8nHyvS9wvmmC7DvhfoCuwkcBN2tfHsigRLzXU7RjeKgtdf/PmzQAUFBRE3Fa0oaRQk0SVnprOxf0u5vRep/PUp0/x2MLHmLpkKhMGTGDcUeMY1X0UqSne3hFm9fWTmlkqMMU5d3n8SmqagYUD3YyFM7wuQ3yqMaEWvm5NqNWoCbdw4eEU6QrJSAGmUJNwoweN5pF/ejsi/+qi1Ty1+Ck+2vARZVVldMzpyIVHXci4o8Zx8mEnk56aHsvdRxzesd4Wm3Ouysx6mFmGc648NnWJJIZoQ63eQKt5q7B26y1aCjDxk57tenLXyXdRUlHCvI3zmLN2DlMWT+EvHxcQ4hQAAB1YSURBVP+F9tntGdt3LOOOGsfoXqPJTMuMS03RdEWuAj4IPrpmb81C59wDMatKJM4OJtQiBlr4ZoIBFxpuC1nY6PBS2Emiy07P5pTDTuGUw06hrLKMBZsWMGftHF764iWe/PRJWme25tw+5zLuqHGMOWIMOek5MaslmmBbGXylAK1iVomIRxobalEFWuj7EcKtLuqClGSQmZbJqO6jGNV9FBVVFSzavIg56+bw+rLXeW7Jc+Sk53B277MZd9Q4zul9Dq0ymzdaohl55C4AM8sLzjfbqJhmNobAhSmpwF+dc/eGvZ8JTAGGEriP7lLn3Jrm2r/IQYdaQ4FWj9CgCj2/pgCTZJSems7wbsMZ3m04N424iU+/+ZQ5a+cwa/UsXv7iZTJTMxlzxBh+cdwvOL778c2yz2hGHulvZp8AS4GlZvaxmTV5dP/ghSmPAmcB/YDLzKxf2Go/BIqcc0cA/wPc19T9itRoUqjVvOreeLNQ2EkySU1JZWjBUH424me8ePGLPDTmIc7rcx5z1s5h1JOjOPOZM5m3YV6T9xPNDdqTgZuccz2ccz2AnwOPN3nPMAxY4ZxbFbww5QVgbNg6Y4Gng9MvA6eZmR5yKk1W1z1qUYdauEVhrzo0prWmUJNklpqSyjGdjuH6Ydfz7EXPct3Q65i/aT4jnxjJOc+ew8JNB//bYTTn2HKdc/tH83fOzTaz3IPe43e6AqEjmmwAhte1jnOu0sx2AfnAt6ErmdlEYCJA1+5dm6E0SWYN3Xgdfp9arVA7iMFFws+vNTQIskJN6vLM5GeYOnkqAEXbizyupnlkp2dzaf9LOb/v+bzy1Su8uPRFjn38WM7rcx53nXwXgwsGN2p70bTYVpnZbWZ2WPB1K4ErJROGc26yc67QOVeY3yFx7n6XxFLXmI91hdpCFtYdag11RTZCeIgp1KQ+EyZOYMbCGcxYOIN2+e28LqdZZadn8/1jvs9zFz3HDwf/kNlrZjNk8hAuevEiPvvms6i3E02wXQN0AP4B/B04JLisqTYCh4bMdwsui7iOmaUBbQgOxizSGNEMYhza/dhgqMGB70UKurD5msDSBSMi9cvNyOWKAVfw3LjnuHrg1by18i0G/nkg33vpe1E91bvOYDOziwCcc0XAHc65Ic65oc65G4PLmmoB0NvMeppZBjAemBa2zjTgquD0xcC7LpGHlJaE1JhQi7BS9MIzKkJmqQtSJHp5GXlcNegqpl40lfzsfF764iVW7FjR4Ofqa7HdGjI9s8kVhnHOVQKTgBkEBlX+W/AJ3Xeb2fnB1Z4A8s1sBXATcHNz1yHJrbGhVu8TsOvLpNA8GnLg8oKCgqguDlGoidRW7ap5bOFjbC/ZzkNjHmJQ50ENfqa+i0esjulm45ybDkwPW3Z7yHQpcEks9i3JrzHPUAtfHj7uY53Cs2hIyPLCAy8aqa8LUqEmUptzjkfmP8JbK9/i7pPv5obhN0T1ufqCLdvMBhNo1WUFp/cHnHNOD56ShBVtqEXqgqx1E3a4ujIoLNS+W73+EFOoiUT21OKneOWrV7hpxE3ceuKtDX8gqL5g2wzUjAe5JWQawAGnNrpKkTiotzsxRKQuyIgttdDNDSHy5f6hoRYU2gVZ01pTiIk0zDnHi0tfZMriKVwz6Br+cMYfaMwtzHUGm3PulGapUCSO6gu1Rp1Xi/aikfBAC36NdkR/BZ3IgTYVb+LBeQ+yYNMCLj7qYiafN7lRoQbR3aAt4gsHE2qhoj6vVtNqayDUGmqtKdREvlNZXcnflv6NKYunkJ6azkNjHuI/j/3Pg3poqYJNkkK0odaY9/aryZ/wLsnQ9xRqIgdt6dal/PHDP7J652ouPPJCHjrrIbq17nbQ21Owie81JrjqvF8tGuEBp1ATaZI95Xt4/OPH+deyf9GlVRf+eek/GXtk+JDBjRdVsJnZAOCw0PWdc/9o8t5FYqihUKu3tVZIdFdFNhBqtT+qUBMBmL1mNo/Mf4Si0iJ+Ovyn3H3K3c32XLYGg83M/g8YQOCxNdXBxY7AEFsinmrsvWqNEkUGhV4koiGzRBrmnGPyosm88PkLDOk8hBlXzGBol6HNuo9oWmwjnHPhz0kT8VxjQq2h1lrUF46ECG+lgW7CFqlPZXUlf/j3H5ixcgY/LvwxD531EGkpzX9GLJotfmhm/ZxzXzT73kUOUnOGWmNFaqWBQk2kPqWVpdz13l3M2zCPu06+i9tOvK3Rl/FHK5pgm0Ig3LYAZQRGH3HOuQExqUikAc3SzdhIke5La8xDQ0Vasl2lu/j1u7/mq2+/4s/n/JlrC6+N6f6iCbYngAnAEr47xybiicZeuh/tVZChwRXeLdnUUFPQSUu2de9Wfvn2L9myZwsvXfISFx11Ucz3GU2wbXPOhT9ORiShNKYVV0hhvevXN2pIeEgp1ETq9umWT7lnzj1UVFcw44oZnHTYSXHZbzTB9omZPQf8i0BXJKDL/SX+GtsF2aR71sJECiiFmkhk1a6a55Y8x5OfPsnh7Q7nH5f+g/4d+8dt/9EEWzaBQDsjZJku95e4avLoIU1QXyst0vt1LRNpCXaV7uJ3c3/H/I3zGX/0eCafN7nZ7k+LVoPB5pz7QTwKEanLwYRaQ621hroja9YJp1ATqdvnWz/nN3N+w87Snfzp7D9xXeF1MbvysT7R3KDdDXgYOD646H3gp865DbEsTATiewVkfYEUaSQRhZpIgHOOl754iccXPU631t2Y/v3pzX7TdWNE0xX5JPAc3z3J+orgstNjVZRINJoaetGGUDSttMZsTySZlFWWce8H9zJ7zWwu6HsBT17wJG2z2npaU0oU63Rwzj3pnKsMvp4COsS4LpEmBVddYzU2dhsKNZG67SrdxX+9/V/MXjObe0+7l39c+g/PQw2ia7FtN7MrgOeD85cB22NXkkjDoRbLLsrGDmCsUJOWaOPujdw882a27d3G3y7+G5ccfUnDH4qTaFps1wDfA7YAm4GLAV1QIgmvsa22SC20Ggo1ke8s3bqUSW9MoqSihJlXzkyoUIPoropcC5wfh1pEgPgPmVVfANYXXAo1aYneW/sev3//93Rr3Y03Ln+D3vm9vS6pljqDzcweJnC/WkTOuZ/EpCJp0aIJtcYE3xCG1Lr0P9qWnEJN5DvOOf629G/85eO/MKLbCF4d/yodchPzcov6Wmw1Pz2OB/oBLwbnLwE00r/4RmO7JBVoIgcqryrnjx/+kbdWvsUl/S7h6QueJjs92+uy6lRnsDnnngYwsx8Do5xzlcH5PxO4l02kWUXbEovm5uqD0VBoKdSkJdpRsoPbZt3GF9u+iPnjZppLNFdFtgNaAzuC83nBZSJJQ6EmUtuy7cu4bdZt7Cnfw8uXvMy4fuO8Likq0QTbvQQGQp5F4FlsJwJ3xrIoaXka2wJrrlZbNIGlUJOWaPaa2dw791465Hbgg2s+YFDnQV6XFLVorop80szeAIYHF/0/59yW2JYlElvRhpVCTVqi55Y8x+OLHue4bsfxj0v/Qae8Tl6X1CjRtNgAUoFtwfX7mFkf59yc2JUl0rDGttoaE1IKNGmp3lr5Fo8vepzx/cfz1NinyEzL9LqkRotmEOT7gEuBpXz3BG0HKNikWcTyvrWDCSiFmrRUS7Yu4Q///gMn9ziZpy94mozUDK9LOijRtNguAPo658oaXFMkziK12g42mBRo0pJt2bOFO2bdQY82Pfj7pX/3bahBdMG2Ckgn5OnZIomkOQJJoSYt2d7yvfx65q9xOF77/mu0z27vdUlNEk2w7QM+NbOZhISbRh6RZKFQk5asqrqKe+bcw7pd63jzijfpe0hfr0tqsmiCbVrwJZJUFGgi8PznzzNv4zz+dPafGN1rtNflNItoLvd/Oh6FiMSTQk0EPt/6OU99+hTjjx7PdYXXeV1Os4nmqsjewO8JjBeZVbPcOdcrhnVJCxHvkfwVaCIBxWXF3DPnHrq36c6fz/1zwg+T1RjRdEU+CdwB/A9wCoFnsUXzHDeRhKFAE/mOc477/30/20u2M+2yabTJauN1Sc0qmoDKds7NBMw5t9Y5dydwTmzLkpYi1oFTGPwjIt/517J/8f669/n9ab9nWNdhXpfT7KIJtjIzSwGWm9kkM7uQwEDIIs0iVsGjQBOpbVXRKh5d8ChnHH4GN428yetyYiKaYPspkAP8BBgKTACujGVR0vI0ZwiplSYSWUlFCXe/dzftstrxzIXPkGLJeVapwaNyzi1wzu1xzm1wzv2AwINGj4h9adLS6EZrkdh6dMGjrNu1jqkXTaVjbkevy4mZOi8eMbPWwPVAVwL3sb0dnP858BnwbDwKlJYllgMbi7Rk765+l9eXv86vRv0qae5Xq0t9V0U+AxQBHwI/An5N4HlsFzrnPo1DbdJCKaxEmtfm4s088OEDjOg2grtOvsvrcmKuvmDr5Zw7BsDM/gpsBro750rjUpmIiDSL/5n3P6SlpPH8uOdJT033upyYqy/YKmomnHNVZrZBoSYi4i+LtyxmwaYF3H/6/RzW9jCvy4mL+i4eGWhmu4OvYmBAzbSZ7W7KTs2svZm9bWbLg1/b1bHem2a208xea8r+RERaIuccT3zyBF3yunD9sdd7XU7c1BlszrlU51zr4KuVcy4tZLp1E/d7MzDTOdcbmBmcj+R+ArcXiIhII83fOJ8lW5dw20m3kZ2e7XU5cePVTQxjgZrBlZ8m8DDTWoIjnhTHqygRkWRR7ap54pMn6Nm2J9cMvsbrcuIqmrEiY6GTc25zcHoL0MmjOkREktKCjQtYvmM5Uy6Y4uunYR+MmAWbmb0DdI7w1i2hM845Z2auifuaCEwE6Nq9a1M2JSKS0J6Z/AxTJ08FoGh7UZ3rfbD+A/Iy8ri0/6XxKi1hxCzYnHN13gFoZt+YWYFzbrOZFQBbm7ivycBkgIGFA5sUkiIiiWzCxAlMmBi49GD0oMg/Zp1zfLTxI07vdXqLa62Bd+fYpgFXBaevAl71qA4RkaSzqmgVW/du5dw+53pdiie8CrZ7gdPNbDkwOjiPmRUGbwYnOP8+8BJwmpltMLMzPalWRMRHPtzwIQBn9z7b40q84cnFI8657cBpEZYvJDB8V838CfGsS0QkGczfOJ+hBUPpnBfpMofkl5zPLBARaaF2le5i6balnNO75T4PWsEmIpJE5m+aT7Wr5pw+CjYREUkC8zbMo2NuRwq7tNynZCjYRESSRFV1FQs3LeSsI85K2qdjR6PlHrmISJL5YtsX7C7b3aLPr4GCTUQkaczbMI+0lDTOOPwMr0vxlIJNRCRJzNs4j1GHjqJNVhuvS/GUgk1EJAl8s+cbVhWtatFXQ9ZQsImIJIGPNn4E0OLPr4GCTUQkKczbMI/D2h7GkYcc6XUpnlOwiYj4nHOOz7d+zum9TsfMvC7Hcwo2ERGf27JnC8XlxS36puxQCjYREZ9btn0ZAEMKhnhcSWJQsImI+NyyHctIS0mjf8f+XpeSEBRsIiI+t2z7Mo7ucDRZaVlel5IQFGwiIj63YdcGtdZCKNhERHzMOce3Jd/SrXU3r0tJGAo2EREf2122m8rqSrq06uJ1KQlDwSYi4mPf7vsWQMEWQsEmIuJj20u2Awq2UAo2EREf274vEGyd8zp7XEniULCJiPjYztKdAHTK7eRxJYlDwSYi4mNFpUXkpueSm5HrdSkJQ8EmIuJjRaVFdMjt4HUZCUXBJiLiYztLdqobMoyCTUTEx3aW7aRTnoItlIJNRMTHikqK6JjT0esyEoqCTUTEx3aW7qRjroItlIJNRMSnqqqrqHJV6ooMo2ATEfGpSlcJoBZbGAWbiIhPVVVXAdAhR5f7h1KwiYj4VJULBFvrzNYeV5JYFGwiIj7lnAMgJz3H40oSi4JNRMSnql01oGALp2ATEfEpBVtkCjYREZ9SV2RkCjYREZ+qJtBiy07P9riSxKJgExHxqWpXTUZqBmkpaV6XklAUbCIiPlXtqtUNGYGCTUTEp5xzZKepGzKcgk1ExKfUYotMwSYi4lPVrprcjFyvy0g4CjYREZ+qdtXkpivYwinYRER8yjmnrsgIFGwiIj5Vjc6xRaJgExHxKV08EpknwWZm7c3sbTNbHvzaLsI6g8zsQzNbamafmdmlXtQqIpKo1BUZmVcttpuBmc653sDM4Hy4fcCVzrmjgTHAg2bWNo41iogkNLXYIvMq2MYCTwennwYuCF/BObfMObc8OL0J2AroMbEiIkEKtsi8GmCsk3Nuc3B6C9CpvpXNbBiQAays4/2JwESArt27NmOZIiKJ5ZnJzzB18lRAXZF1iVmwmdk7QOcIb90SOuOcc2bm6tlOAfAMcJVzwYcPhXHOTQYmAwwsHFjntkRE/G7CxAlMmDgBgIIuBQq2CGIWbM650XW9Z2bfmFmBc25zMLi21rFea+B14Bbn3LwYlSoi4luZqZlel5BwvDrHNg24Kjh9FfBq+ApmlgG8Akxxzr0cx9pERHwjPTXd6xISjlfBdi9wupktB0YH5zGzQjP7a3Cd7wEnAleb2afB1yBvyhURSUx6FlttnnxHnHPbgdMiLF8I/Cg4PRWYGufSRER8RcFWm0YeERHxsVRL9bqEhKNgExHxsdQUBVs4BZuIiI+pxVabgk1ExMdSTD/Gw+k7IiLiY+qKrE3BJiLiY2qx1abviIiIjynYatN3RETEx3TxSG0KNhERH1OLrTZ9R0REfEzBVpu+IyIiPqarImtTsImI+JhhXpeQcBRsIiI+pq7I2vQdERGRpKJgExHxMTN1RYZTsImI+JjOsdWmYBMRkaSiYBMR8TF1RdamYBMR8TF1RdamYBMRkaSiYBMR8TF1RdamYBMR8TF1RdamYBMRkaSiYBMR8TF1RdamYBMR8TF1RdamYBMRkaSiYBMR8TF1RdamYBMR8TF1RdamYBMRkaSiYBMR8TF1RdamYBMR8TF1RdamYBMRkaSiYBMR8TF1RdamYBMR8TF1RdamYBMRkaSiYBMR8TF1RdamYBMR8TF1RdamYBMRkaSiYBMR8TF1RdamYBMR8TF1RdamYBMRkaSiYBMR8TF1RdamYBMR8TF1RdamYBMRkaSiYBMR8TF1RdamYBMR8TF1RdbmSbCZWXsze9vMlge/touwTg8zW2Rmn5rZUjO7zotaRUTEX7xqsd0MzHTO9QZmBufDbQZGOucGAcOBm82sSxxrFBFJeOqKrM2rYBsLPB2cfhq4IHwF51y5c64sOJuJuk1FRGpRV2Rt5pyL/07Ndjrn2ganDSiqmQ9b71DgdeAI4BfOuUfr2N5EYGJwtj/weUwK984hwLdeF9HMku2Yku14QMeUqA4BOgSnU51zWV4Wk4hiFmxm9g7QOcJbtwBPhwaZmRU552qdZwt5vwvwT+A859w3Dex3oXOu8CDLTkg6psSXbMcDOiY/SLbjaS5psdqwc250Xe+Z2TdmVuCc22xmBcDWBra1ycw+B04AXm7mUkVEJIl4dd5qGnBVcPoq4NXwFcysm5llB6fbAaOAr+NWoYiI+JJXwXYvcLqZLQdGB+cxs0Iz+2twnaOAj8xsMfAe8Afn3JIotj05FgV7TMeU+JLteEDH5AfJdjzNwpOLR0RERGJFl9CLiEhSUbCJiEhS8X2wJePwXFEe0yAz+zB4PJ+Z2aVe1BqtaI4puN6bZrbTzF6Ld43RMLMxZva1ma0ws1oj5phZppm9GHz/IzM7LP5VNk4Ux3Ri8P9PpZld7EWNjRHF8dxkZl8E/9/MNLMeXtTZGFEc03VmtiT4M26umfXzos6E4Zzz9Qv4b+Dm4PTNwH0R1skAMoPTecAaoIvXtTfxmPoAvYPTXQgMQdbW69qbckzB904DzgNe87rmCLWlAiuBXsF/U4uBfmHr/Cfw5+D0eOBFr+tuhmM6DBgATAEu9rrmZjieU4Cc4PSPk+TvqHXI9PnAm17X7eXL9y02knN4rmiOaZlzbnlwehOBewE7hK+XQBo8JgDn3EygOF5FNdIwYIVzbpVzrhx4gcBxhQo9zpeB0yyxB/Nr8Jicc2ucc58B1V4U2EjRHM8s59y+4Ow8oFuca2ysaI5pd8hsLtCirwpM9B/w0ejknNscnN4CdIq0kpkdamafAesJtBY2xavAgxDVMdUws2EEfpNbGevCmqBRx5SguhL491NjQ3BZxHWcc5XALiA/LtUdnGiOyU8aezw/BN6IaUVNF9Uxmdn1ZraSQO/IT+JUW0KK2cgjzamB4bn2c845M4v4m4pzbj0woGZ4LjN72TUwPFcsNccxBbdTADwDXOWc8/Q36uY6JpF4MLMrgELgJK9raQ4uMJbuo2b2feBWvhsEo8XxRbC5JByeqzmOycxaExgk+hbn3LwYlRq15vx7SlAbgUND5rsFl0VaZ4OZpQFtgO3xKe+gRHNMfhLV8ZjZaAK/cJ0UcpoiUTX27+gF4LGYVpTgkqErMhmH54rmmDKAV4Apzjk/jJ/Z4DH5wAKgt5n1DH7/xxM4rlChx3kx8K4LntFPUNEck580eDxmNhj4C3C+c84Pv2BFc0y9Q2bPAZbHsb7E4/XVK019ETh/MZPAX+Q7QPvg8kLgr8Hp04HPCFxN9Bkw0eu6m+GYrgAqgE9DXoO8rr0pxxScfx/YBpQQOJdwpte1hx3H2cAyAuczbwkuu5vAD0mALOAlYAUwH+jldc3NcEzHBv8u9hJofS71uuYmHs87wDch/2+meV1zMxzT/wJLg8czCzja65q9fGlILRERSSrJ0BUpIiKyn4JNRESSioJNRESSioJNRESSioJNRESSioJNEoqZVYU8hWGxmf3czA7q36mZ3R28Ebeu968zsysPvlows2OC9X5qZjvMbHVw+p2mbDdsH1eb2baQ/Xza4kdvF6mHLveXhGJme5xzecHpjsBzwAfOuTu8raxhZvYUgacSvBy2PM0Fxo082O1eDRQ65ybVs84B+4h2n02tTSQRqcUmCcsFRoWYCEyygFQzu9/MFgSfpXVtzbpm9v+Cz6NabGb3Bpc9VfP8MDO7N+QZXH8ILrvTzP4rOD3IzOYF338lOEINZjbbzO4zs/lmtszMToim9uDnHjSzhcBPzWyomb1nZh+b2YzgsGKY2eEWeAbdx2b2vpkdGe33x8xODn5mGvBFhPksM3sy+H35xMxOCX7uajObZmbvErhpXiSp+GKsSGm5nHOrzCwV6EjgUR27nHPHmlkm8IGZvQUcGXxvuHNun5m1D92GmeUDFwJHOuecmbWNsKspwA3OuffM7G7gDuDG4HtpzrlhZnZ2cHmd3ZthMpxzhWaWDrwHjHXObbPAQ2F/C1wDTAauc84tN7PhwJ+AUyNs61IzGxUyPzL4dQjQ3zm32sxODpv/eeBb6I4JBuZbZtYn5HMDnHM7ojwWEd9QsImfnEHgCQ01T3FuA/QmEDRPuuAztiL8sN4FlAJPWODJ3Ac8ndvM2hB4SOt7wUVPExgWq8Y/gl8/JvDQzWi9GPzaF+gPvG2BR7OlApvNLA84DnjJvntkW2Zd2wrvigx+Zr5zbnXI4tD5UcDDAM65r8xsLYEH1AK8rVCTZKVgk4RmZr2AKgJPAzACraoZYeucWd82nHOVFnhm3WkEBiaeRORWUV1qRn+vonH/Z/bWlEhgfMWRoW9a4OkMO51zgxqxzbr2Udd8tJ8TSRo6xyYJy8w6AH8GHnGBq5xmAD8Odu1hZn3MLBd4G/iBmeUEl4d3ReYBbZxz04GfAQND33fO7QKKQs6fTSDQddhcvgY6mNnIYD3pZna0Czz1eLWZXRJcbmY2sL4NNdL7wOXBbfcBupPYT7UQaRZqsUmiyTazT4F0oJLAQ1QfCL73VwJdgYss0A+3DbjAOfemmQ0CFppZOTAd+HXINlsBr5pZFoHW000R9nsV8OdgOK4CftBcB+ScKw92nz4U7PZMAx4kMBr75cBjZnZr8JhfIPAUinDh59j+M4pd/ym47SUEvpdXO+fKQro9RZKSLvcXEZGkoq5IERFJKgo2ERFJKgo2ERFJKgo2ERFJKgo2ERFJKgo2ERFJKgo2ERFJKv8fOUdSXwtgnXgAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WbihUuSIQUri", + "colab_type": "text" + }, + "source": [ + "Right now, we are spliting the data randomly into training and testing. There is another possibility of data spliting, which is based on time. Since we want to make sure the training data capture the seasonal cycle within our data, we can use the first three years of data (2010-2012) to build up our model and use the remaining two years of data (2013-2014) to evaluate the model performance.\n", + "\n", + "This will be our group exercise to do so." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "EHpfvgiRXAFa", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "efc52563-736d-4815-987e-97a8371c9be7" + }, + "source": [ + "index = 365*24*3 ## total data poitns within three years.\n", + "\n", + "X_train_time = scaled[:index, 1:]\n", + "X_test_time = scaled[index:, 1:]\n", + "y_train_time = scaled[:index, 0]\n", + "y_test_time = scaled[index:, 0]\n", + "\n", + "print( X_train_time.shape, y_train_time.shape )" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "(26280, 6) (26280,)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "0NLRJrtKX09d", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 357 + }, + "outputId": "9be7e921-a3b3-43d7-c873-6750076f600d" + }, + "source": [ + "## Here we will be repeating the same random forest procedure\n", + "## Let's first define our model parameter grids\n", + "param_grid = {\n", + " 'n_estimators': [10, 25, 50, 100],\n", + " 'max_features': [3, 4],\n", + " 'min_samples_leaf': [5, 10]\n", + "}\n", + "\n", + "# Initialize a stratified 5-fold generator for the cross-validation.\n", + "kfold_generator = KFold(n_splits=5)\n", + "\n", + "## Define the model \n", + "rfModel_time = RandomForestRegressor() \n", + "\n", + "## Define the grid search object\n", + "gridsearch_cv_obj = GridSearchCV(\n", + " rfModel_time, \n", + " param_grid, \n", + " scoring=make_scorer(r2_score), \n", + " cv=kfold_generator,\n", + " n_jobs=-1,\n", + " )\n", + "\n", + "gridsearch_cv_obj.fit(X_train_time, y_train_time)\n" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "GridSearchCV(cv=KFold(n_splits=5, random_state=None, shuffle=False),\n", + " error_score=nan,\n", + " estimator=RandomForestRegressor(bootstrap=True, ccp_alpha=0.0,\n", + " criterion='mse', max_depth=None,\n", + " max_features='auto',\n", + " max_leaf_nodes=None,\n", + " max_samples=None,\n", + " min_impurity_decrease=0.0,\n", + " min_impurity_split=None,\n", + " min_samples_leaf=1,\n", + " min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0,\n", + " n_estimators=100, n_jobs=None,\n", + " oob_score=False, random_state=None,\n", + " verbose=0, warm_start=False),\n", + " iid='deprecated', n_jobs=-1,\n", + " param_grid={'max_features': [3, 4], 'min_samples_leaf': [5, 10],\n", + " 'n_estimators': [10, 25, 50, 100]},\n", + " pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n", + " scoring=make_scorer(r2_score), verbose=0)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 99 + } + ] + } + ] +} \ No newline at end of file diff --git a/02_Week2/04_ML_regression/04_Pt1_ML_regression_random_forest_exercise.ipynb b/02_Week2/04_ML_regression/04_Pt1_ML_regression_random_forest_exercise.ipynb new file mode 100644 index 0000000..2ceef1d --- /dev/null +++ b/02_Week2/04_ML_regression/04_Pt1_ML_regression_random_forest_exercise.ipynb @@ -0,0 +1,508 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "04_Pt1_ML_regression_random_forest_exercise.ipynb", + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "2ZnjFtKPqhcz", + "colab_type": "text" + }, + "source": [ + "# Python for STEM - Week 2 (Advanced) \n", + "\n", + "## Day 4 - Part 1: Supervised learning - Regression\n", + "\n", + "In this notebook, we will focus on examples of supervised machine learning. More specifically, we will be doing regression using Scikit-learn, one of the machine learning packages in Python. Before we start, here we first import all the packages that we need for this notebook. \n", + "\n", + "All the machine learning functions we will use in Day 3 and Day 4 all comes from [scikit-learn](https://scikit-learn.org/stable/index.html). You can find very detailed descriptions on many machine learning models included in the package user guide and various examples. This would be a good place to start when you want to adopt machine learning for your own research/work. \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "gkWCpoG4qasM", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 51 + }, + "outputId": "dbc66de6-e165-482e-9343-74640584451f" + }, + "source": [ + "## In this cell, we import all the packages needed for this notebook\n", + "import numpy as np ## packages for data handling\n", + "import pandas as pd \n", + "import matplotlib.pyplot as plt ## packages for visualization \n", + "import seaborn as sbn " + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n", + " import pandas.util.testing as tm\n" + ], + "name": "stderr" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "fOu7zI-RoIuV", + "colab_type": "text" + }, + "source": [ + "## Data Ingest\n", + "\n", + "In this notebook, we will be using another real world problem of predictive modeling of the concentration of PM2.5 in Beijing using hourly weather information. PM2.5 refers to atmospheric particulate matter (PM) that have a diameter less than 2.5 $\\mu m$. The data is between 2010 and 2014. The data is stored as a comma separated value (CSV) data file, with 11 columns. The table below shows the meaning of each column. \n", + "\n", + "| Variable name | Full name |\n", + "|-:|-:|\n", + "|*year*|Year of observation| \n", + "|*month*|Month of observation|\n", + "|*day*|Day of observation|\n", + "|*hour*|Hour of observation|\n", + "|*pm2.5*|Concentration of PM 2.5 ($\\mu g/m^3$)|\n", + "|*DEWP*|Dew Point ($^\\circ C$)|\n", + "|*TEMP*|Temperature ($^\\circ C$)|\n", + "|*PRES*|Pressure (hPa)|\n", + "|*WIND*|Wind speed (m/s)|\n", + "|*TSNOW*|Continuos hours of snow|\n", + "|*TRAIN*|Continuos hours of rain|" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "xLqniMTknb5P", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 238 + }, + "outputId": "451b0e33-d9d6-406e-bf08-d9c7b5fb4f65" + }, + "source": [ + "filename = \"https://raw.githubusercontent.com/uofscphysics/STEM_Python_Course/Summer2020/02_Week2/Data/04_Beijing_PM2.5_hourly_20100101-20141231.csv\"\n", + "PMdata = pd.read_csv(filename, index_col=None)\n", + "print( PMdata.head() )\n", + "print( PMdata.shape )\n", + "\n", + "## We see there are mission values at the begining so we drop these missing \n", + "## values in the dataset\n", + "PMdata = PMdata.dropna()\n", + "print( PMdata.head() )" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + " year month day hour pm2.5 DEWP TEMP PRES WIND TSNOW TRAIN\n", + "0 2010 1 1 0 NaN -21 -11.0 1021.0 1.79 0 0\n", + "1 2010 1 1 1 NaN -21 -12.0 1020.0 4.92 0 0\n", + "2 2010 1 1 2 NaN -21 -11.0 1019.0 6.71 0 0\n", + "3 2010 1 1 3 NaN -21 -14.0 1019.0 9.84 0 0\n", + "4 2010 1 1 4 NaN -20 -12.0 1018.0 12.97 0 0\n", + "(43824, 11)\n", + " year month day hour pm2.5 DEWP TEMP PRES WIND TSNOW TRAIN\n", + "24 2010 1 2 0 129.0 -16 -4.0 1020.0 1.79 0 0\n", + "25 2010 1 2 1 148.0 -15 -4.0 1020.0 2.68 0 0\n", + "26 2010 1 2 2 159.0 -11 -5.0 1021.0 3.57 0 0\n", + "27 2010 1 2 3 181.0 -7 -5.0 1022.0 5.36 1 0\n", + "28 2010 1 2 4 138.0 -7 -5.0 1022.0 6.25 2 0\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "u1rVvBok5ekQ", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 119 + }, + "outputId": "b6bb6c75-0567-499b-abe7-1883fb9faf8e" + }, + "source": [ + "## Since we will not use the time information for modeling, we here only keep\n", + "## pm2.5 and weather information\n", + "PMdata = PMdata.iloc[:,4:]\n", + "print ( PMdata.head() )" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + " pm2.5 DEWP TEMP PRES WIND TSNOW TRAIN\n", + "24 129.0 -16 -4.0 1020.0 1.79 0 0\n", + "25 148.0 -15 -4.0 1020.0 2.68 0 0\n", + "26 159.0 -11 -5.0 1021.0 3.57 0 0\n", + "27 181.0 -7 -5.0 1022.0 5.36 1 0\n", + "28 138.0 -7 -5.0 1022.0 6.25 2 0\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Bb5Iqk4C2yOd", + "colab_type": "text" + }, + "source": [ + "Now we can look at the time-series of our data." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "cO_INv6b29T7", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 701 + }, + "outputId": "364347a3-1bdb-407d-cb33-d37ed2463612" + }, + "source": [ + "# specify columns to plot\n", + "index = [0, 1, 2, 3, 4, 5, 6]\n", + "i = 1\n", + "# plotting each column\n", + "plt.figure(figsize=(11, 12))\n", + "for ind in index:\n", + "\tplt.subplot(len(index), 1, i)\n", + "\tplt.plot(PMdata.iloc[:, ind].values)\n", + "\tplt.title(PMdata.columns[ind], y=0.5, loc='right')\n", + "\ti += 1\n", + "plt.show()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OB2dLHRh8EL1", + "colab_type": "text" + }, + "source": [ + "## Data Scaling \n", + "\n", + "From the data, we see there are large differences of the magnitude of different variables, which can cause issues when we directly build our model with current data. Data with notably larger magnitude could dominate the model performance. To avoid that, we usually scale our data to [0, 1], or [-1, 1]. In other cases, we can also scale all variables to a distribution with 0 mean and standard deviation of 1. " + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "gzcOfEE39RS3", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## import function to perform data scaling\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "\n", + "## scale the data\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "XP8uCr3--OG_", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## After scaling our data, we can separate the data into training and testing data\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "## Independent variables for our model (weather data) \n", + "\n", + "## Target output for our model (pm2.5)\n", + "\n", + "## We are spliting our data set into training and testing sets based on a 60:40 ratio\n", + "\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wsqZvOR3_BVM", + "colab_type": "text" + }, + "source": [ + "## Decision tree regression\n", + "\n", + "As the basis of the random forest, we will try to fit a decision tree model for regression first and visualize the model to have a better understanding of the model structure, and why it is called a \"decision tree\"." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Tmg94PMeAbHY", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## Import the decision tree model\n", + "from sklearn.tree import DecisionTreeRegressor\n", + "\n", + "## define our model\n", + "\n", + "## fit our model with .fit and our training data\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "pBV209syBBKf", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## We can now visualize what a decision tree looks like\n", + "## Because we have a really big tree, so let's try text representation first\n", + "from sklearn import tree\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "h3TTrpcECSvF", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## We can further visualize the tree structure using plot_tree function\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "oMs63A3dFft9", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## How does our decision tree model perform on modeling the PM2.5 data?\n", + "## We can check the root mean squre error for our model\n", + "\n", + "## import metric for regression model performance\n", + "\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "yJSux2tRHpAF", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## To show the prediction results, we can also plot a kernal density plot between \n", + "## predicted value and testing value\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LUW_QwEAKI2Z", + "colab_type": "text" + }, + "source": [ + "It appears that the decision tree model can be improved. How about let's try the random forest model and see if the random forest can improve the performance." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mwxiY6-xMZcA", + "colab_type": "text" + }, + "source": [ + "## Random Forest\n", + "\n", + "This time, we are fitting a random forest regression model instead of classification. The concept is similar with the classifier, but we are using the regressor function instead." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "uVvG9BRpMlP6", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## import randomforest regressor from sklearn\n", + "from sklearn.ensemble import RandomForestRegressor\n", + "from sklearn.metrics import make_scorer\n", + "from sklearn.model_selection import KFold\n", + "from sklearn.model_selection import GridSearchCV\n", + "\n", + "## Let's first define our model parameter grids\n", + "\n", + "\n", + "# Initialize a stratified 5-fold generator for the cross-validation.\n", + "\n", + "## Define the model and use min_samples_leaf=15 same as the decision tree\n", + "\n", + "## Define the grid search object\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "330BnyNcP9Z4", + "colab_type": "code", + "colab": {} + }, + "source": [ + "\n", + "## Now we can fit the optimum model for the random forest\n", + "\n", + "## predict the testing data\n", + "\n", + "\n", + "## Get the root mean square error of the prediction\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "EIZRz3qjRnPc", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## To show the prediction results, we can also plot a kernal density plot between \n", + "## predicted value and testing value\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "uYeNiHSiR4ix", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## We can also compare the model error between decision tree and random forest\n", + "## and see the performance differences.\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WbihUuSIQUri", + "colab_type": "text" + }, + "source": [ + "Right now, we are spliting the data randomly into training and testing. There is another possibility of data spliting, which is based on time. Since we want to make sure the training data capture the seasonal cycle within our data, we can use the first three years of data (2010-2012) to build up our model and use the remaining two years of data (2013-2014) to evaluate the model performance.\n", + "\n", + "This will be our group exercise to do so." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "EHpfvgiRXAFa", + "colab_type": "code", + "colab": {} + }, + "source": [ + "index = 365*24*3 ## total data poitns within three years.\n", + "\n", + "X_train_time = scaled[:index, 1:]\n", + "X_test_time = scaled[index:, 1:]\n", + "y_train_time = scaled[:index, 0]\n", + "y_test_time = scaled[index:, 0]\n", + "\n", + "print( X_train_time.shape, y_train_time.shape )" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "0NLRJrtKX09d", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## Here we will be repeating the same random forest procedure\n", + "## Let's first define our model parameter grids\n", + "\n", + "\n", + "# Initialize a stratified 5-fold generator for the cross-validation.\n", + "\n", + "## Define the model \n", + "\n", + "## Define the grid search object\n", + "\n", + "## calculate the model performance with RMSE\n" + ], + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/02_Week2/04_ML_regression/04_Pt2_ML_regression_neural_network_complete.ipynb b/02_Week2/04_ML_regression/04_Pt2_ML_regression_neural_network_complete.ipynb new file mode 100644 index 0000000..06c5f2f --- /dev/null +++ b/02_Week2/04_ML_regression/04_Pt2_ML_regression_neural_network_complete.ipynb @@ -0,0 +1,779 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "04_Pt2_ML_regression_neural_network_complete.ipynb", + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "2ZnjFtKPqhcz", + "colab_type": "text" + }, + "source": [ + "# Python for STEM - Week 2 (Advanced) \n", + "\n", + "## Day 4 - Part 1: Supervised learning - Regression\n", + "\n", + "In this notebook, we will focus on examples of supervised machine learning. More specifically, we will be doing regression using Scikit-learn, one of the machine learning packages in Python. Before we start, here we first import all the packages that we need for this notebook. \n", + "\n", + "All the machine learning functions we will use in Day 3 and Day 4 all comes from [scikit-learn](https://scikit-learn.org/stable/index.html). You can find very detailed descriptions on many machine learning models included in the package user guide and various examples. This would be a good place to start when you want to adopt machine learning for your own research/work. \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "gkWCpoG4qasM", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 51 + }, + "outputId": "9da3091c-4c61-43b7-ca98-0563099f6446" + }, + "source": [ + "## In this cell, we import all the packages needed for this notebook\n", + "import numpy as np ## packages for data handling\n", + "import pandas as pd \n", + "import matplotlib.pyplot as plt ## packages for visualization \n", + "import seaborn as sbn " + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n", + " import pandas.util.testing as tm\n" + ], + "name": "stderr" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "fOu7zI-RoIuV", + "colab_type": "text" + }, + "source": [ + "## Data Ingest\n", + "\n", + "In this notebook, we will be using another real world problem of predictive modeling of the concentration of PM2.5 in Beijing using hourly weather information. PM2.5 refers to atmospheric particulate matter (PM) that have a diameter less than 2.5 $\\mu m$. The data is between 2010 and 2014. The data is stored as a comma separated value (CSV) data file, with 11 columns. The table below shows the meaning of each column. \n", + "\n", + "| Variable name | Full name |\n", + "|-:|-:|\n", + "|*year*|Year of observation| \n", + "|*month*|Month of observation|\n", + "|*day*|Day of observation|\n", + "|*hour*|Hour of observation|\n", + "|*pm2.5*|Concentration of PM 2.5 ($\\mu g/m^3$)|\n", + "|*DEWP*|Dew Point ($^\\circ C$)|\n", + "|*TEMP*|Temperature ($^\\circ C$)|\n", + "|*PRES*|Pressure (hPa)|\n", + "|*WIND*|Wind speed (m/s)|\n", + "|*TSNOW*|Continuos hours of snow|\n", + "|*TRAIN*|Continuos hours of rain|" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "xLqniMTknb5P", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 238 + }, + "outputId": "f72962e7-3b97-409b-ad40-a32810b51363" + }, + "source": [ + "filename = \"https://raw.githubusercontent.com/uofscphysics/STEM_Python_Course/Summer2020/02_Week2/Data/04_Beijing_PM2.5_hourly_20100101-20141231.csv\"\n", + "PMdata = pd.read_csv(filename, index_col=None)\n", + "print( PMdata.head() )\n", + "print( PMdata.shape )\n", + "\n", + "## We see there are mission values at the begining so we drop these missing \n", + "## values in the dataset\n", + "PMdata = PMdata.dropna()\n", + "print( PMdata.head() )" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + " year month day hour pm2.5 DEWP TEMP PRES WIND TSNOW TRAIN\n", + "0 2010 1 1 0 NaN -21 -11.0 1021.0 1.79 0 0\n", + "1 2010 1 1 1 NaN -21 -12.0 1020.0 4.92 0 0\n", + "2 2010 1 1 2 NaN -21 -11.0 1019.0 6.71 0 0\n", + "3 2010 1 1 3 NaN -21 -14.0 1019.0 9.84 0 0\n", + "4 2010 1 1 4 NaN -20 -12.0 1018.0 12.97 0 0\n", + "(43824, 11)\n", + " year month day hour pm2.5 DEWP TEMP PRES WIND TSNOW TRAIN\n", + "24 2010 1 2 0 129.0 -16 -4.0 1020.0 1.79 0 0\n", + "25 2010 1 2 1 148.0 -15 -4.0 1020.0 2.68 0 0\n", + "26 2010 1 2 2 159.0 -11 -5.0 1021.0 3.57 0 0\n", + "27 2010 1 2 3 181.0 -7 -5.0 1022.0 5.36 1 0\n", + "28 2010 1 2 4 138.0 -7 -5.0 1022.0 6.25 2 0\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "u1rVvBok5ekQ", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 119 + }, + "outputId": "258fbc59-63fb-4a29-8398-32785686b01a" + }, + "source": [ + "## Since we will not use the time information for modeling, we here only keep\n", + "## pm2.5 and weather information\n", + "PMdata = PMdata.iloc[:,4:]\n", + "print ( PMdata.head() )" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + " pm2.5 DEWP TEMP PRES WIND TSNOW TRAIN\n", + "24 129.0 -16 -4.0 1020.0 1.79 0 0\n", + "25 148.0 -15 -4.0 1020.0 2.68 0 0\n", + "26 159.0 -11 -5.0 1021.0 3.57 0 0\n", + "27 181.0 -7 -5.0 1022.0 5.36 1 0\n", + "28 138.0 -7 -5.0 1022.0 6.25 2 0\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Bb5Iqk4C2yOd", + "colab_type": "text" + }, + "source": [ + "Now we can look at the time-series of our data." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "cO_INv6b29T7", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 701 + }, + "outputId": "364347a3-1bdb-407d-cb33-d37ed2463612" + }, + "source": [ + "# specify columns to plot\n", + "index = [0, 1, 2, 3, 4, 5, 6]\n", + "i = 1\n", + "# plotting each column\n", + "plt.figure(figsize=(11, 12))\n", + "for ind in index:\n", + "\tplt.subplot(len(index), 1, i)\n", + "\tplt.plot(PMdata.iloc[:, ind].values)\n", + "\tplt.title(PMdata.columns[ind], y=0.5, loc='right')\n", + "\ti += 1\n", + "plt.show()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OB2dLHRh8EL1", + "colab_type": "text" + }, + "source": [ + "## Data Scaling \n", + "\n", + "From the data, we see there are large differences of the magnitude of different variables, which can cause issues when we directly build our model with current data. Data with notably larger magnitude could dominate the model performance. To avoid that, we usually scale our data to [0, 1], or [-1, 1]. In other cases, we can also scale all variables to a distribution with 0 mean and standard deviation of 1. " + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "gzcOfEE39RS3", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## import function to perform data scaling\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "\n", + "scaler = MinMaxScaler(feature_range=(0, 1))\n", + "scaled = scaler.fit_transform(PMdata)\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "XP8uCr3--OG_", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "8d8b0220-43b2-4446-a3fb-c30e949aa6e7" + }, + "source": [ + "## After scaling our data, we can separate the data into training and testing data\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "## Independent variables for our model (weather data) \n", + "X = scaled[:,1:]\n", + "## Target output for our model (pm2.5)\n", + "y = scaled[:,0]\n", + "## We are spliting our data set into training and testing sets based on a 60:40 ratio\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=42)\n", + "print( X_train.shape, y_train.shape)\n" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "(25054, 6) (25054,)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "yqEyxELYY1DD", + "colab_type": "code", + "colab": {} + }, + "source": [ + "" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wsqZvOR3_BVM", + "colab_type": "text" + }, + "source": [ + "## Neural Network \n", + "\n", + "Neural network is the backbone of the recent booming of the deep learning application in many discplines. The basis of neural network is using multiple linear equation to mimic possible nonlinear patterns in the data. The main hyperparameters that could affect the performance includes the number of layers and neurons in each layer. Additionally, the regularization penalty as well as the activation function could also change the results notably. So it usually requires an intensive traning process to find the best combination of model hyperparameters.\n", + "\n", + "Now, let's first try to see the impact of number of layers and number of neurons in each layer for the model performance.\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Tmg94PMeAbHY", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 391 + }, + "outputId": "29ae0f19-844b-4190-e49c-42f6df0a315d" + }, + "source": [ + "## Import the decision tree model\n", + "from sklearn.neural_network import MLPRegressor\n", + "from sklearn.metrics import make_scorer\n", + "from sklearn.metrics import mean_squared_error, r2_score\n", + "from sklearn.model_selection import KFold\n", + "from sklearn.model_selection import GridSearchCV\n", + "\n", + "## This time, let's try to minimizing the sample size and accelerating our training process\n", + "## We vwill just use one year worth of data for this training process\n", + "hour_index = 365*24*2\n", + "X_train_2yr = scaled[:hour_index, 1:]\n", + "X_test_2yr = scaled[hour_index:, 1:]\n", + "y_train_2yr = scaled[:hour_index, 0]\n", + "y_test_2yr = scaled[hour_index:, 0]\n", + "\n", + "## define our model\n", + "nnModel = MLPRegressor(random_state=42)\n", + "\n", + "## Define parameter grid for tunning, which is different list of the neurons\n", + "## 1-hidden layer, 2-hidden layer, 3-hidden layer\n", + "param_grid = {\n", + " 'hidden_layer_sizes': [(10,), (20,), (50,),\n", + " (10,10), (20,10), (50,10),\n", + " (10,10,10), (20,20,10), (50, 20, 10)]\n", + "}\n", + "\n", + "## define grid search process\n", + "gridsearch_cv_obj = GridSearchCV(\n", + " nnModel, \n", + " param_grid, \n", + " scoring=make_scorer(r2_score), \n", + " cv=KFold(n_splits=5),\n", + " n_jobs=-1,\n", + " )\n", + "\n", + "gridsearch_cv_obj.fit(X_train_2yr, y_train_2yr)\n", + "\n" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "GridSearchCV(cv=KFold(n_splits=5, random_state=None, shuffle=False),\n", + " error_score=nan,\n", + " estimator=MLPRegressor(activation='relu', alpha=0.0001,\n", + " batch_size='auto', beta_1=0.9, beta_2=0.999,\n", + " early_stopping=False, epsilon=1e-08,\n", + " hidden_layer_sizes=(100,),\n", + " learning_rate='constant',\n", + " learning_rate_init=0.001, max_fun=15000,\n", + " max_iter=200, momentum=0.9,\n", + " n_iter_no_change=10,\n", + " n...5,\n", + " random_state=42, shuffle=True,\n", + " solver='adam', tol=0.0001,\n", + " validation_fraction=0.1, verbose=False,\n", + " warm_start=False),\n", + " iid='deprecated', n_jobs=-1,\n", + " param_grid={'hidden_layer_sizes': [(10,), (20,), (50,), (10, 10),\n", + " (20, 10), (50, 10),\n", + " (10, 10, 10), (20, 20, 10),\n", + " (50, 20, 10)]},\n", + " pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n", + " scoring=make_scorer(r2_score), verbose=0)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 32 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "330BnyNcP9Z4", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 51 + }, + "outputId": "d74a596e-0266-41d9-8d82-d8d2c1567de1" + }, + "source": [ + "print( 'Best model hyperparameter for the neural network regressor is:' )\n", + "print( gridsearch_cv_obj.best_params_)\n" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Best model hyperparameter for the neural network regressor is:\n", + "{'hidden_layer_sizes': (50, 20, 10)}\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "5NgBe2pceICG", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 279 + }, + "outputId": "b269237b-7e86-4ec7-f86b-2033f5bae365" + }, + "source": [ + "## Let's look at how the model performance change with the number of layers\n", + "mean_r2 = gridsearch_cv_obj.cv_results_['mean_test_score']\n", + "plt.figure()\n", + "plt.plot(mean_r2, 'bx-')\n", + "plt.xlabel('Parameter Grid')\n", + "plt.ylabel('R2')\n", + "plt.show()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "2pZ_1MqBfWNp", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "c8a234ac-fa23-41c8-90de-b6c1c00fc40a" + }, + "source": [ + "## Now, we can define a best neural network model based on our grid search\n", + "nnModel = MLPRegressor(hidden_layer_sizes=(50,20,10))\n", + "\n", + "nnModel.fit(X_train_2yr, y_train_2yr)\n", + "\n", + "nn_predicted = nnModel.predict(X_test_1yr)\n", + "\n", + "nnRMSE = np.sqrt(mean_squared_error(y_test_1yr, nn_predicted))\n", + "print( nnRMSE )" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "0.07992455772418675\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mwxiY6-xMZcA", + "colab_type": "text" + }, + "source": [ + "## Activation Function & Regularization\n", + "\n", + "Assume that we fix the structure of the model in terms of the number of layers, and number of neurons in each layer. We now want to examine the impact of the activation function and regularization penalty for our model. \n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "uVvG9BRpMlP6", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 357 + }, + "outputId": "b75ca04f-c464-41a4-ce09-2774888614a5" + }, + "source": [ + "## Let's first define our model parameter grids\n", + "param_grid = {\n", + " 'activation': ['relu', 'tanh'],\n", + " 'alpha': [0.00001, 0.0001, 0.001, 0.01]\n", + "}\n", + "\n", + "## deinfe the number of layers and neurons in the model\n", + "nnModel = MLPRegressor(hidden_layer_sizes= (50,20,10), random_state=42)\n", + "\n", + "## define grid search process\n", + "gridsearch_cv_obj = GridSearchCV(\n", + " nnModel, \n", + " param_grid, \n", + " scoring=make_scorer(r2_score), \n", + " cv=KFold(n_splits=5),\n", + " n_jobs=-1,\n", + " )\n", + "\n", + "gridsearch_cv_obj.fit(X_train_2yr, y_train_2yr)\n", + "\n" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "GridSearchCV(cv=KFold(n_splits=5, random_state=None, shuffle=False),\n", + " error_score=nan,\n", + " estimator=MLPRegressor(activation='relu', alpha=0.0001,\n", + " batch_size='auto', beta_1=0.9, beta_2=0.999,\n", + " early_stopping=False, epsilon=1e-08,\n", + " hidden_layer_sizes=(50, 20, 10),\n", + " learning_rate='constant',\n", + " learning_rate_init=0.001, max_fun=15000,\n", + " max_iter=200, momentum=0.9,\n", + " n_iter_no_change=10,\n", + " nesterovs_momentum=True, power_t=0.5,\n", + " random_state=42, shuffle=True,\n", + " solver='adam', tol=0.0001,\n", + " validation_fraction=0.1, verbose=False,\n", + " warm_start=False),\n", + " iid='deprecated', n_jobs=-1,\n", + " param_grid={'activation': ['relu', 'tanh'],\n", + " 'alpha': [1e-05, 0.0001, 0.001, 0.01]},\n", + " pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n", + " scoring=make_scorer(r2_score), verbose=0)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 39 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "jr7pN9rFiLHb", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 299 + }, + "outputId": "e89fb474-43e9-4ec8-a826-87d901c08d3e" + }, + "source": [ + "## Let's print out the best model hyperparameter combination\n", + "print(gridsearch_cv_obj.best_params_)\n", + "\n", + "## Calculating the mean and standard deviation of the accuracy for the model test scores\n", + "mean_test_r2 = gridsearch_cv_obj.cv_results_[\"mean_test_score\"]\n", + "stds_test_r2 = gridsearch_cv_obj.cv_results_[\"std_test_score\"]\n", + "\n", + "plt.plot(mean_test_r2, 'bx-')" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "{'activation': 'relu', 'alpha': 0.0001}\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 40 + }, + { + "output_type": "display_data", + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "0ucvCWthjQNi", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "2c0803ab-eff9-4327-a890-32155f021b31" + }, + "source": [ + "## Now we can create a final prediction with the optimized model hyperparameters\n", + "nnModel = MLPRegressor(hidden_layer_sizes=(50,20,10),\n", + " activation='relu',\n", + " alpha = 0.0001)\n", + "\n", + "nnModel.fit(X_train_2yr, y_train_2yr)\n", + "\n", + "nn_predicted = nnModel.predict(X_test_2yr)\n", + "\n", + "nnRMSE = np.sqrt(mean_squared_error(y_test_2yr, nn_predicted))\n", + "print(nnRMSE)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "0.07846505175217186\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "EIZRz3qjRnPc", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 472 + }, + "outputId": "2514871b-e800-45bb-cd50-86f73e269e2e" + }, + "source": [ + "## To show the prediction results, we can also plot a kernal density plot between \n", + "## predicted value and testing value\n", + "plt.figure()\n", + "fig = sbn.jointplot(y_test, nn_predicted, xlim=[0,1], ylim=[0,1],\n", + " kind=\"kde\", space=0, color=\"g\")\n", + "fig.set_axis_labels(xlabel='True', ylabel='Predicted (Random Forest)')\n", + "plt.show()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "display_data", + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAbYAAAG2CAYAAAATCaNwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3deXxV9Z3/8dcnCQlLSBChguwoiChYICLqaF0rouLYWjek1o1qXbrN/GynztSf/XVsa9U644qC1o77OrSCOy51KyDKpq3siyxhJyxJSD6/P+4NhpjknoSce+49eT993EfOds/5nCvkzfec7/0ec3dERETiIifqAkRERFqSgk1ERGJFwSYiIrGiYBMRkVhRsImISKzkRV1AM6gbp4gIWNQFZCq12EREJFZiFWyrt62OugQREYlYbILtjvfv4MDbD+T5T5+PuhQREYmQZeHII18peNrn0zjz8TOp9mqGdxvOzAkzMdPlZxGJNf2Sa0DWt9g+W/8ZFzx7Af079eeaI6/hozUf8eriV6MuS0REIpL1wXb+M+eTa7n86qRfMfaQsXRp34Xf/PU3UZclIiIRyepg27RzE3PWzuHcwefSrbAb+bn5fGfwd5i+dDofrvww6vJERCQCWR1s80vnA9B/v/57lp058EyKCoq45a+3RFWWiIhEKLuDbV0i2Pp26rtnWfs27Rk7cCxT/j6FpZuXRlOYiIhEJquDbd66ebRv054DOhyw1/IzB54JwKSPJkVRloiIRCjrg61vp75f6dp/QOEBjOwxksmzJ7O7endE1YmISBSyO9hK5+11GbK2MweeyRdlXzD186npLUpERCKVtcG2bvs61u9YT79O/epdP6rnKLq068LEWRPTXJmIiEQpa4Nt3rp5AA0GW15OHqMPHs20hdNYsWVFOksTEZEIZW2w1dcjsq4xA8bg7mq1iYi0IlkbbPPWzaOooIjO7To3uE33jt05rs9x3P7B7SzfsjyN1YmISFSyOtj6Fn+1R2RdV5dcTVV1FT966UdpqkxERKKUlcHm7swvnU/f/fqm3LZbYTfGHzGe5z97nmmfTwu/OBERiVRWBtuqbavYUr6lwY4jdZ03+Dx6F/fmumnXsbNyZ8jViYhIlLIy2Gp6RDbWcaS2NrltuP6o61m0aRHjnx+vL22LiMRYVgZbTY/IoC02gBHdR3DNkdfw7KfPcskLl1BVXRVWeSIiEqG8qAtojgWlC9iv7X4Uty1u0vvOHXwu5VXlPPjRg7TNbcv9Z91PXk5WfgQiItKArPytvmjTInoU9WjWe8cNGUdFVQWTP57Msi3LePLcJ9m//f4tXKGIiEQlKy9FLtq0iO6F3Zv9/ku/fin/55j/w9vL3qbkgRI+WfNJC1YnIiJRyrpgq6iqYNXWVRzY8cB92s/pA07nztF3sr1iO6MmjeKBWQ/g7i1UpYiIRCXrgm3Z5mU4TrfCbvu8r0O7Hsr9Z97P4V0PZ8JfJjDuuXFsK9/WAlWKiEhUsi7YFm9aDLDPLbYa+7Xbj9+e+lsuH3Y5T85/kqMnHc3CjQtbZN8iIpJ+WRdsSzYvAdine2x15VgOFw+9mN+d+jtWbF3BkQ8cySuLXmmx/YuISPpkXbAt3rSY/Nz8UHoyjug+gnvPuJfO7Tpz+qOnM+mjSS1+DBERCVdWBlu3wm7kWDilH9jxQO46/S5GdB/BFX++glveuUWdSkREskjWBduSTUta9DJkfdq1acevT/o1p/Q/hX9749/48cs/VriJiGSJrPuC9uLNi/lGn2+Efpw2uW34+T/9nOKCYu788E7Kd5dz9xl3h9ZSFBGRlpF1wbZ51+YW6xGZSo7lcM2R15Cfm899s+6jsrqSiWdNVLiJiGSwrAs2aNkekamYGVcOv5K8nDwmzZ5EZXUlk8dOJjcnN201iIhIcNkZbB3TF2yQCLfLhl1GXk4eD338EJVVlTxyziMaQFlEJANl5W/mdLbYavvuEd+lTU4bJn40kcrqSh791qPk5+ZHUouIiNQv64KtuKCYDvkdIjv+hUMuJC8nj3tm3sPmnZt57vzn6FjQMbJ6RERkb1nXCyLdlyHr853DvsMNx97A9KXTOeHhE1hbtjbqkkREJCnrgq0lBj9uCaMPHs3/O+n/sWD9Ao568Cg+XvNx1CWJiAhZGGwHFqanq38Qo3qO4o7T7mBH5Q6OmXQMj819LOqSRERavawLtky4FFnboC6DuO/M+xi4/0DGPTeOa168hh2VO6IuS0Sk1cq+YIuoR2RjOrfrzO+/+XvOG3we98y8hxH3j+Cj1R9FXZaISKuUdcHWq7hX1CXUKy8nj6uPvJrff/P3bNi5gaMePIrf/PU3VFVXRV2aiEirYtk2uO/0JdPdzKIuo1Fby7dyx/t38OayNzmu93E8cs4j9O3UN+qyRCReMvsXYYSyLtjeXPpmVhTs7ryy+BX++8P/xsy49dRbmTBigsaZFJGWomBrgH7LhsTMOO2g03hw7IMM3H8gV794Nac8cgqLNi6KujQRkVhTiy0N3J0XP3+R+2beh+P850n/ybUjr9VAyiKyL9Ria4CCLY1Kt5dy+/u388GqDzi659FMPnsyg7oMirosEclOCrYGKNjSzN15bfFr3DXjLsp3l3PTCTfxL8f8i54UICJNpWBrgIItIht3buTOD+/k7WVvM6zbMO4acxfH9Dom6rJEJHso2BqgYIvYW0vf4u4Zd1O6o5SLhlzELSffQu/i3lGXJSKZT8HWAAVbBthZuZPH5z3Ok/OfpNqrOf+w8/np0T9lWPdhUZcmIplLwdYABVsGWVu2lmc+fYapn09lR+UODut6GOcMOoexh4xlePfh6kUpIrUp2BqgYMtAZRVlvLTwJf66/K/MXTeXaq+mqKCI43ofxyn9T+GsgWdxUOeDoi5TRKKlYGuAgi3Dbdm1hZlfzOSTtZ/w8ZqPWbF1BQCH7H8IFw25iEuOuIQ+nfpEXKWIREDB1gAFW5b5YtsXvL/yfd5d/i6z18zGME7qdxKXDbuMcwadQ7s27aIuUUTSQ8HWAAVbFltTtoaXF77My4teZnXZaooLirng8Au49OuXMrLHSDJ9sGgR2Sf6C94ABVsMVHs1n6z5hGkLp/HO8nfYtXsXg7sM5uxBZ3NSv5M4ptcxtG/TPuoyRaRlKdgaoGCLmbKKMt5c+iavLHqFBaULqPIqci2X3sW9Gbj/QA7ufDADOg/g4M4H06dTH3oV9aKooEitO5Hso7+0DVCwxdiOyh3MWzePeevmsWrrKlZtW8XKrSvZXrl9r+0K8wvp2bEnPYt7cmDHA+navitd23elZ1FPDup8EAd3Ppgu7btEdBYi0gAFWwMUbK2Mu7OlfAurtq5i7fa1rN+xntLtpZTuKKV0eykbd25k065NlFeV7/W+bh26MfzA4QzrNizx6j6Mfp36qaUnEh395WuAgk3qtbNyJ2u3r2X1ttWs2LqCRZsWsXDDQpZtWUaVVwHQLq8dfTv1pf9+/dm//f4UFxRTXFBMUUERxW2LKcwvpG1eW9rltaNtXlva5rWlfZv27N9+f7q070LbvLYRn6VIVlOwNSC0YDOzycCZwDp3P7ye9QbcCYwBdgDfc/ePUu1XwRat8t3lLNm8hH9s+Acrt65k9bbVrNm+hrLyMsoqy9hesR0n2P+i4oLiPcHYr1M/+u3Xjz7FfejSvgud23WmML+QHMvZ62Vme6bzc/MpyC1Qq1FaK/3Bb0CYwXY8UAY80kCwjQGuIxFsRwF3uvtRqfarYMts1V7Nrt272F6xnZ27d1JRVbHXa+funWwt38qWXVvYsGMDq8tWs6ZsDWvK1nzl8mcQbXLaUFRQRMeCjhQVFFGUX0RBXgH5ufnk5+bTJrfNl9M5bfb62dD6muVtctrs+ZmXkxd4Oi8nb897a6Y1HJqEQMHWgNAeAubub5tZ30Y2OZtE6DnwgZl1MrPu7r66sf3mWE4LViktLcdyKMwvpDC/sEnvc3c27drE2rK1bC3fytaKreys3AkkwtLdqSbxs2a6sqqSHZU72FG5g7KKssTPyjI2l29md/VuqqqrqKyuZHf1bnZX76ayqtZ0dSWVVZWBW5f7yrCvBl9OG7DEOjNL+TOHxJ/9vdYlW6u1t627Tc365krXZ1SjqbU2p8Ueh2PMuHJGk7ZvTaJ8umUPYEWt+ZXJZV8JNjObAEwAKCgo4PDDv9IAbFVKS0vp2rVr1GVEprHzz03+V0BBmqtKL/0ZiN/5B/kHRGlpKevXrwfgsD8c5vPnzw+7rExX778GsuKxze4+EZgI0KFDB585c2bEFUWrpKSE1vwZtPbzB30Grf38IfEZSP2ivK63CuhVa75ncpmIiEizRRlsU4DvWsIoYEuq+2siIiKphHYp0sweB04AupjZSuCXQBsAd78PmEqiR+RCEt39Lw2y3y5dNALGhAkToi4hUq39/EGfQWs/f2lc1n1Bu6SkpNXfYxMR0X1GoIHOI+o7LyIisaJgExGRWFGwiYhIrCjYREQkVhRsIiISKwo2ERGJFQWbiIjEioJNRERiRcEmIiKxomATEZFYUbCJiEisKNhERCRWFGwiIhIrCjYREYkVBZuIiMSKgk1ERGJFwSYiIrGiYBMRkVhRsImISKwo2EREJFYUbCIiEisKNhERiRUFm4iIxIqCTUREYkXBJiIisaJgExGRWFGwiYhIrCjYREQkVhRsIiISKwo2ERGJFQWbiIjEioJNRERiRcEmIiKxomATEZFYUbCJiEisKNhERCRWFGwiIhIrCjYREYkVBZuIiMSKgk1ERGJFwSYiIrGiYBMRkVhRsImISKwo2EREJFYUbCIiEisKNhERiRUFm4iIxIqCTUREYkXBJiIisaJgExGRWFGwiYhIrCjYREQkVhRsIiISKwo2ERGJFQWbiIjEioJNRERiJdRgM7PRZvZ3M1toZj+rZ31vM5tuZrPNbI6ZjQmzHhERib/Qgs3McoG7gdOBwcCFZja4zmY3Ak+5+zDgAuCesOoREZHWIcwW20hgobsvdvcK4Ang7DrbOFCUnC4GvgixHhERaQXyQtx3D2BFrfmVwFF1trkJeMXMrgM6AKeEWI+IiLQCUXceuRB42N17AmOAP5nZV2oyswlmNtPMZpaWlqa9SBGRTDBx4kRKSkooKSlBvwsbFmawrQJ61ZrvmVxW2+XAUwDu/j7QFuhSd0fuPtHdS9y9pGvXriGVKyKS2SZMmMDMmTOZOXMm+l3YsDCDbQYwwMz6mVk+ic4hU+pssxw4GcDMDiURbPpniIiINFtowebuu4FrgZeBT0n0fpxvZjeb2djkZj8FrjSzT4DHge+5u4dVk4iIxF+YnUdw96nA1DrL/qPW9ALg2DBrEBGR1iXqziMiIiItSsEmIiKxomATEZFYUbCJiEisKNhERCRWFGwiIhIrCjYREYkVBZuIiMSKgk1ERGJFwSYiIrGiYBMRkVhRsImISKwo2EREJFYUbCIiEisKNhERiRUFm4iIxIqCTUREYkXBJiIisaJgExGRWFGwiYhIrCjYREQkVhRsIiISKwo2ERGJFQWbiIjEioJNRERiRcEmIiKxomATEZFYUbCJiEisKNhERCRW8lJtYGY9gQuA44ADgZ3APOBFYJq7V4daoYiISBM0Gmxm9hDQA/gL8FtgHdAWGAiMBn5hZj9z97fDLlRERCSIVC2229x9Xj3L5wHPmVk+0LvlyxIREWmeRu+x1YSamf2w7joz+6G7V7j7wrCKExERaaqgnUcuqWfZ91qwDhERkRaR6h7bhcBFQD8zm1JrVRGwMczCREREmiPVPbb3gNVAF+C2Wsu3AXPCKkpERKS5Gg02d18GLDOzU4Cd7l5tZgOBQcDcdBQoIiLSFEHvsb0NtDWzHsArwHjg4bCKEhERaa6gwWbuvgP4FnCPu38HOCy8skRERJoncLCZ2dHAOBIjjgDkhlOSiIhI8wUNth8BPweed/f5ZtYfmB5eWSIiIs2TcqxIAHd/C3jLzNon5xcD14dZmIiISHMEarGZ2dFmtgD4LDl/hJndE2plIiIizRD0UuQfgNOADQDu/glwfFhFiYiINFfg57G5+4o6i6pauBYREZF9FugeG7DCzI4B3MzaAD8EPg2vLBERkeYJ2mK7CriGxLPZVgFfT86LiIhklCBP0M4F7nT3cWmoR0REZJ+kbLG5exXQJ/lQURERkYwW9B7bYuDd5KNrttcsdPfbQ6lKRESkmYIG26LkKwfoGF45IiIi+yboyCP/F8DMCpPzZWEWJSIi0lxBRx453MxmA/OB+WY2y8w0ur+IiGScoN39JwI/cfc+7t4H+CnwQHhliYiINE/QYOvg7ntG83f3N4EOoVQkIiKyDwL3ijSzfwf+lJy/mERPSRERkYwStMV2GdAVeA54FuiSXCYiIpJRGg02M/sWgLtvAn7p7sPdfYS7/yi5rFFmNtrM/m5mC83sZw1sc56ZLTCz+Wb2WLPOQkREJClVi+3GWtOvN2XHyaG47gZOBwYDF5rZ4DrbDCDxZO5j3f0wEk/qFhERabZUwWYNTAcxEljo7ovdvQJ4Aji7zjZXAnfXtP7cfV0TjyEiIrKXVJ1H2pnZMBIB2DY5vSfg3P2jRt7bA6j9DLeVwFF1thkIYGbvArnATe7+Ut0dmdkEYAJA7969U5QsIhJPEydOZOLEiQCUlpZGXE3mMndveKXZ9AZXgrv7SY2891xgtLtfkZwfDxzl7tfW2uYvQCVwHtATeBsY4u6bG9pvSUmJz5w5s5GyRETir6SkBP0urP9KYqMtNnc/cR8OuAroVWu+Z3JZbSuBD929ElhiZv8ABgAz9uG4IiLSigXt7t8cM4ABZtYv+cibC4ApdbZ5ATgBwMy6kLg0qe/HiYhIs4UWbO6+G7gWeBn4FHjK3eeb2c1mNja52cvABjNbAEwH/tXdN4RVk4iIxF/QkUeaxd2nAlPrLPuPWtMO/CT5EhER2WeBg83MhgJ9a7/H3Z8LoSYREZFmCxRsZjYZGErisTXVycVOYogtERGRjBG0xTbK3Qen3kxERCRaQTuPvF93OCwREZFMFLTF9giJcFsDlJP4Upy7+9DQKhMREWmGoME2CRgPzOXLe2wiIiIZJ2iwlbp73S9Xi4iIZJygwTY7+ay0P5O4FAmou7+IiGSeoMHWjkSgfbPWMnX3FxGRjBMo2Nz90rALERERaQmBuvubWU8ze97M1iVfz5pZz7CLExERaaqg32N7iMTI/AcmX39OLhMREckoQYOtq7s/5O67k6+Hga4h1iUiItIsQYNtg5ldbGa5ydfFgB4vIyIiGSdosF0GnAesAVYD5wLqUCIiIhknaK/IZcDYlBuKiIhErNFgM7P/JvF9tXq5+/UtXpGIiMg+SHUpciYwC2gLDAc+T76+DuSHW5qIiEjTNdpic/c/ApjZ1cA/ufvu5Px9wDvhlyciItI0QTuP7AcU1ZovTC4TERHJKEHHivwNiYGQp5N4FtvxwE1hFSUiItJcQXtFPmRm04CjkotucPc14ZUlIiLSPEEvRQLkAqXAJmCgmR0fTkkiIiLNF6jFZma/Bc4H5vPlE7QdeDukukRERJol6D22fwYOcffylFuKiIhEKOilyMVAmzALERERaQlBW2w7gI/N7HUST9IGNPKIiIhknqDBNiX5EhERyWhBu/v/MexCREREWkLQXpEDgFuAwSTGjQTA3fuHVJeIiEizBO088hBwL7AbOBF4BPifsIoSERFprqDB1s7dXwfM3Ze5+03AGeGVJSIi0jxBO4+Um1kO8LmZXQusIjEQsoiISEYJ2mL7IdAeuB4YAYwHvhtWUSIiIs0VtFfkjORkGXCpmeUCFwAfhlWYiIhIczTaYjOzIjP7uZndZWbftIRrgYXAeekpUUREJLhULbY/kRjN/33gCuDfSDyP7Rx3/zjk2kRERJosVbD1d/chAGb2ILAa6O3uu0KvTEREpBlSdR6prJlw9ypgpUJNREQyWaoW2xFmtjU5bUC75LwB7u5FoVYnIiLSRI0Gm7vnpqsQERGRlpCqV2TKL2EH2UZERCRdUt1j+18zu83MjjezDjULzay/mV1uZi8Do8MtUUREJLhUlyJPNrMxwPeBY81sPxIDIf8deBG4xN3XhF+miIhIMClHHnH3qcDUNNQiIiKyz4KOFSkiIpIVFGwiIhIrCjYREYmVRu+xmVnnxta7+8aWLUdERGTfpOo8MgtwEiON9CYxILIBnYDlQL9QqxMREWmiRi9Funs/d+8PvAac5e5d3H1/4EzglXQUKCIi0hRB77GNSnb7B8DdpwHHhFOSiIhI8wV6gjbwhZndCPxPcn4c8EU4JYmIiDRf0BbbhUBX4HngueT0hWEVJSIi0lyBWmzJ3o8/NLMO7r495JpERESaLVCLzcyOMbMFwKfJ+SPM7J5QKxMREWmGoPfY7gBOA6YAuPsnZnZ8qjeZ2WjgTiAXeNDdf9PAdt8GngGOdPeZje2zggpWsjJg2SIi8bRsy7KoS8hYgUcecfcVdRZVNba9meUCdwOnA4OBC81scD3bdQR+CHwYtBYRkdZuZ+XOqEvIWEGDbYWZHQO4mbUxs38heVmyESOBhe6+2N0rgCeAs+vZ7lfAb4FdQYsWERFpSNBguwq4BugBrAK+DvwgxXt6ALVbeSuTy/Yws+FAL3d/MWAdIiIijQp6j+0Qdx9Xe4GZHQu829wDm1kOcDvwvQDbTgAmAPTo3SPF1iIi8fToxEd5dOKjAFRWVkZcTeYyd0+9kdlH7j481bI6648GbnL305LzPwdw91uS88XAIqAs+ZZuwEZgbGMdSIaWDPWpM/XcUxFp3Qb1HUTZ0rLUG8ab1bcw1ej+R5MYOqurmf2k1qoiEj0dGzMDGGBm/UhcvrwAuKhmpbtvAbrUOtabwL+k6hUpIiLSmFT32PKBQhIB2LHWaytwbmNvdPfdwLXAyyQ6mjzl7vPN7GYzG7uvhYuIiNQn6KXIPu6eEV+a0KVIERFdikyq91Jk0F6RD5pZpz17MtvPzF5ukbJERERaUNBg6+Lum2tm3H0T8LVwShIREWm+oMFWbWa9a2bMrA+JJ2uLiIhklKDfY/sF8Fcze4vENc3jSH6vTEREJJMEfWzNS8lRQkYlF/3I3deHV5aIiEjzNHop0swGJX8OB3qTeGr2F0Dv5DIREZGMkqrF9lPgSuC2etY5cFKLVyQiIrIPGg02d78y+fPE9JQjIiKyb1INqfWtxta7+3MtW46IiAQRZHCN1irVpcizkj+/RmLMyDeS8ycC7wEKNhGRCFR7ddQlZKxUlyIvBTCzV4DB7r46Od8deDj06kREpF5VXhV1CRkr6Be0e9WEWtJaEr0kRUQkAmqxNSzoF7RfT44N+Xhy/nzgtXBKEhGRVKq8CnfHrN5xgFu1oF/QvtbMzgGOTy6a6O7Ph1eWiIg0yqG8qpy2eW2jriTjBG2xAXwEbHP318ysvZl1dPdtYRUmIiKN21a+TcFWj0D32MzsSuAZ4P7koh7AC2EVJSIiqZVVtPrnsdUraOeRa4BjSTw5G3f/HD22RkQkUgq2+gUNtnJ3r6iZMbM89NgaEZFIbavQ3aD6BA22t8zs34B2ZnYq8DTw5/DKEhGRVNRiq1/QYLsBKAXmAt8HpgI3hlWUiIikpmCrX8pekWaWC8x390HAA+GXJCIiQWwr16XI+qRssbl7FfB3M9NIIyIiGUQttvoF/R7bfsB8M/sbsL1mobuPDaUqERFJScFWv6DB9u+hViEiIk1j6hXZkFTPY2sLXAUcTKLjyCR3352OwkREpGE5lqMWWwNS3WP7I1BCItROB24LvSIREUkpBwVbQ1Jdihzs7kMAzGwS8LfwSxIRkVRyLEeXIhuQqsVWWTOhS5AiIpnDzNRia0CqFtsRZrY1OW0kRh7Zmpx2dy8KtToREalXjuVQVq5gq0+jwebuuekqREREgsuxHLZWbE29YSsUdEgtERHJIDmWw6adm6IuIyMp2EREslBeTh5rytbgrget1KVgExHJQnk5eZRXlbN51+aoS8k4CjYRkSyUl5PoIrFi64qIK8k8CjYRkSyUn5sPwILSBRFXknkUbCIiWSg/N59cy2XeunlRl5JxFGwiIlnIzOhV1Iu56+ZGXUrGUbCJiGSpQ7ocwnvL31PPyDoUbCIiWWroAUNZv3O97rPVoWATEclSRx54JIbx7KfPRl1KRlGwiYhkqa4dunLEAUfw6JxHdTmyFgWbiEgWO7n/yfxj4z+YtXpW1KVkDAWbiEgW+0afb5Cfm8/k2ZOjLiVjKNhERLJYx4KOnND3BP40509sK9eDR0HBJiKS9c4+5GzKKsp4dO6jUZeSERRsIiJZ7tAuhzKg8wD+8MEf2F29O+pyIqdgExHJcmbGxUMv5u8b/s7/zPmfqMuJnIJNRCQGjut9HIP2H8Qvp/+S8t3lUZcTKQWbiEgMmBmXD7+c5VuXM3HWxKjLiZSCTUQkJkZ0H8ERBxzBLX+9hV27d0VdTmQUbCIiMWFmjD9iPKvLVvPQ7IeiLicyCjYRkRgZ3m04h+x/CPfPuj/qUiKjYBMRiREz47SDTuOTtZ8wZ+2cqMuJhIJNRCRmTup3Enk5efzx4z9GXUokFGwiIjFT3LaYUT1G8djcx6iqroq6nLRTsImIxNBJ/U9izfY1vLP8nahLSTsFm4hIDI3qMYp2ee14Yt4TUZeSdqEGm5mNNrO/m9lCM/tZPet/YmYLzGyOmb1uZn3CrEdEpLVo16YdR/c8mmcWPENlVWXU5aRVaMFmZrnA3cDpwGDgQjMbXGez2UCJuw8FngF+F1Y9IiKtzYn9TmTDzg28vuT1qEtJqzBbbCOBhe6+2N0rgCeAs2tv4O7T3X1HcvYDoGeI9YiItCoje4ykML+w1T3OJsxg6wGsqDW/MrmsIZcD0+pbYWYTzGymmc3cWLqxBUsUEckej058lDElYxhTMoYtG7ak3D4/N5+T+53MU/OfYt32dWmoMDNkROcRM7sYKAFurW+9u0909xJ3L+nctXN6ixMRyRDjJoxj6sypTJ05leL9iwO955xB51BRVcGDHz0YcnWZI8xgWwX0qjXfM7lsL2Z2CvALYKy7t+5nLYiItLA+nfowovsI7plxTxH/mG4AABDUSURBVKt5CGmYwTYDGGBm/cwsH7gAmFJ7AzMbBtxPItRaTztZRCSNzhl0Dqu2reKFz16IupS0CC3Y3H03cC3wMvAp8JS7zzezm81sbHKzW4FC4Gkz+9jMpjSwOxERaaZRPUfRvbA7//Xhf0VdSlrkhblzd58KTK2z7D9qTZ8S5vFFRARyc3IZe8hY7p91P3PWzmHoAUOjLilUGdF5REREwjVmwBgKcgu46293RV1K6BRsIiKtQFFBEacedCqPfPIISzYtibqcUCnYRERaie8O/S5mxmX/e1mse0gq2EREWomuHbryw6N+yJvL3uSqv1xFtVdHXVIoQu08IiIimWX0waP5YtsXTJo9ifKqch4860EK8gqiLqtFKdhERFqZS79+Kfm5+UyaPYnlm5fz/AXP07ldfEZ10qVIEZFWxsy4eOjF3Hjcjby/8n2OfvBoFm5cGHVZLUbBJiLSSp3c/2Ru++ZtrN2+llEPjmL6kulRl9QiFGwiIq3YkAOGcPeYuynML+TUP53KHe/fgbtHXdY+UbCJiLRyPYp6cM8Z93Bsr2P5ySs/4aJnL2LTzk1Rl9Vsrb7zyAd80KTtRzEqpEpERKLTvk17bjrhJh6b9xgPzX6IN5e9yZ2j7+Q7g7+DmUVdXpO02mBraqAFfZ+CT0SylZkxbsg4jjzwSG5991bOf+Z8butxG78+6dec3O/krAm4Vncp8oPkf2HvP8xjiIiEaeD+A7nvzPv412P+lcWbFnPqn06lZGIJj819jJ2VO6MuL6VW02KLImhqH1MtORHJJrk5uYwZMIZT+p/CK4te4ekFTzPuuXEUFRRx7qHnMv6I8Rzf53hyLPPaR7EPtkxpOdWtQ0EnItkgPzefMweeyZgBY/h4zce8uuhVnpj/BJM/nkyvol6MGzKO8UeMZ3DXwVGXuodlW7fOoSVDferMqSm3y5RAaw6FnoikcvLQk7l/yv2RHHvX7l28t+I9Xl38KjNWzaDKqxjefTjjh47ngsMvoFtht3SVUu9Nv9gFWzYHWl0KOBFpSJTBVtvGnRt5Y8kbvL7kdT5b/xm5lsup/U9l/BHj+edB/0z7Nu3DPHy9wRabS5H7Gmjv8V6g7Y7hmH06TlPUnJMCTkQyVed2nTl38LmcO/hclm1exquLX+W1xa/x0qKXKMwv5NuHfpuLh17MiX1PJDcnNy01xaLF1txQCxpmQYUZego3EaktU1ps9an2auauncuri1/lrWVvUVZRRo+OPbh82OVcMfwKehX3aqlDxfNSZFNCraWDrDEtHXIKNhGpLZODrbaKqgreW/EeLy18ib+t+htmxukHn85VJVdx+sGn72srLt6XIhuSzjCre9yWDLcP+EDhJiJZJz83nxP6nsAJfU9gTdka/vKPvzBt4TRe/PxFehb15LqR13FVyVUUFRS12DEz7wsITdBYa+295H8iIpIZuhV244rhV/DkuU9y0zduomv7rtzw2g30+UMfbnzjRkq3l7bIcbI62OqTSYHWknWotSYicZGXk8c3+n6D20+7nXvPuJchXxvCf77zn/T5Qx+un3Y9a8vW7tv+W6jOtKuvtdaUINHgxyIi0RvUZRA3n3gzy7cs5/F5j3PvzHt56OOH+NmxP+PHR/+4WV8XiE2LLVWofVDnv6aKcvxHhaqIxF3v4t7ccOwNTB47mWHdhnHj9BsZ8N8DeOSTR5r8fLisDLa6AdNYqLV0IKU74BRqItKa9Cruxc0n3sydo++kqKCIS164hLMeP6tJlyezMtiCyIQR9ve1V6RCTURaq6EHDOXuMXdz3cjreG3xa4yaNIplm5cFem/WBdt2tu81X19rLepAawkKNRFp7XIsh28d+i3uOO0ONuzYwLef+nagy5JZF2ypZEqo7UtrTaEmIvKlQ7seyhXDr2DW6lnMXjM75fZZHWyZ0q2/JSnURES+asuuLQB0atsp5bZZHWx1pau1lip80jlQsohI3L2x5A0emfMIYweOpV+nfim3z9rvsdUVh1BTa01E5EsVVRU8+NGDPL3gaY7tdSyPnPMIZvUOD7mX2ARbOijURETSY9nmZfz6nV/z+cbP+UHJD7hj9B3k5+YHem/WBlvt+2tBW2tzyuYAMLRwaJOPp1ATEQmfu/Pnf/yZe2fcS2F+If97wf8y9pCxTdpH1gZbU9QEWnOEfT9NoSYikrB512ZuffdW3lv5Ht/s/00e/ueH6d6xe5P3k/XB1lBrbV/CrIZaaSIi6TFj1Qx+++5v2VaxjTtOu4Prj7qeHGte/8asDLbGuvm3RKBB48GjVpqISMuoqKrggVkP8MynzzC462De+PYbDD2g6beLasvKYKtRt7UWJNSC3F8LI9QUZiIie1uyaQm/fufXLNq0iGuPvJbfnfo72rVpt8/7zbpgK6PsK8syuZWmQBMR2Zu788JnL3D/rPspLijmxYteZMyAMS22/6wLthrN+d5aY621lgw1hZmISP3Kd5fzu3d/xxtL32DMwWOYfPZkDig8oEWPkbXBViPsTiJNCTUFmohIw3ZX7+YXb/yCWatnccvJt3DDsTcE+sJ1U2VlsNW01poSag211loi1BRoIiKpPTT7IWatnsWksZO4bNhloR0nK4MN9g617XO3f2V9hyEdUu6joUBSK01EpGVt2bWFZz99louHXhxqqEEWBlvd57HVF2o1yxsLt30NNQWaiEhwM7+YSXlVOdeNvC70Y2VdsMGXrbU9oVbTj2QfsyZIqCnQRESabuYXM9mv7X6M6D4i9GPF6rE1dTtKNtSaqy+cUoXaqOR/IiLSNDsrd/LO8nc4Y+AZ5Obkhn68rA22r7TWaqT4FkBzQ01ERJrn4Y8fZnvldq498tq0HC/rgm1n9c4vZ2pCrO4IW034iltjoaZWmojIvvnbqr/x9IKn+f6I73NUz6PScsysvMdW7yXG96ChjGqoq39DoaYwExHZd6XbS7nlr7dwWNfDuP2029N23KwMNqD+1lqdcKvbK7J2YNUXago0EZGWUVVdxa/e/hWVVZU8fd7TtG/TPm3Hzt5gg69eggxIoSYiEq5Jsycxd91cHv3WowzqMiitx866YKveWZ1ordUOtUa6+9dchqwJrrqhpkATEWk5u3bv4oGPHuC5T5/j+yO+z0VDLkp7DVkXbM3RUHgp1ERE9l21VzO/dD5vLH6Dt5a9xaZdm7hu5HXceuqtkdQTn2Abtfd0faOO1G6tKdRERJpne8V2PtvwGZ+t/4xPSz/l0/WfsnHnRtrmteXMgWdy3cjrOL7P8ZHVl73BVpNR7/FlqB3DXgE3tHDoVy5BKtBERILZXrGdFVtXsHzLcpZtWcaKLSv2/HQcgIM7H8zpB5/O6INHc/YhZ9OxoGPEVWdhsOW0y6F6VPWX99Vq3zJLZlaHIR3qDTUREdmbu7N+x3qWbVnG8i3L97xWbF3B+h3r92yXl5PHQfsdxLBuw7hi2BWM7DGSI3scSed2nSOsvn5ZF2ztctrBEGDIV7/PVhNo8NXOImqpiUhrVu3VrNu+jiWblrBkc+K1YkuiNbZz95cDXxQVFDGoyyDOGHAGg7oM2vM6aL+DaJPbJsIzCC7UYDOz0cCdQC7woLv/ps76AuARYASwATjf3Zc2ts8OdOCgwoMSM0d/dX19rTSFmoi0JtVezbLNy5i7bi6frf+MpZuXsnTz0r0CrEfHHhzW9TBOO+g0Du166J4AO6DDAaE8/DOdQgs2M8sF7gZOBVYCM8xsirsvqLXZ5cAmdz/YzC4Afguc39h+CykM/HBQBZqItBZfbPuCN5e+yZy1c1hQuoBtFdsA6NK+C0cccASn9j+Vw792OId/7XAGdx1McdviiCsOT5gttpHAQndfDGBmTwBnA7WD7WzgpuT0M8BdZmbu7o3tWIMWi4gkRvd4dfGrTFs4jTlrE4/zGtx1MBcefiHH9j6WY3sdS//9+md9C6ypLEWGNH/HZucCo939iuT8eOAod7+21jbzktusTM4vSm6zvs6+JgATkrOHA/NCKTp7dAHWp9wqvlr7+YM+g9Z6/l2ArsnpXHdvG2UxmSorOo+4+0RgIoCZzXT3kohLilRr/wxa+/mDPoPWfv6Q+AyiriFThfnYmlVAr1rzPZPL6t3GzPKAYhKdSERERJolzGCbAQwws35mlg9cAEyps80U4JLk9LnAG6nur4mIiDQmtEuR7r7bzK4FXibR3X+yu883s5uBme4+BZgE/MnMFgIbSYRfKhPDqjmLtPbPoLWfP+gzaO3nD/oMGhRa5xEREZEohHkpUkREJO0UbCIiEisZG2xmNtrM/m5mC83sZ/WsLzCzJ5PrPzSzvumvMjwBzv8nZrbAzOaY2etm1ieKOsOU6jOotd23zczNLHbdv4N8BmZ2XvLPwnwzeyzdNYYpwN+D3mY23cxmJ/8ujImizrCY2WQzW5f8zm99683M/iv5+cwxs+HprjEjuXvGvUh0NlkE9AfygU+AwXW2+QFwX3L6AuDJqOtO8/mfCLRPTl8dp/MP+hkkt+sIvE3ieQ8lUdcdwZ+DAcBsYL/k/NeirjvN5z8RuDo5PRhYGnXdLfwZHA8MB+Y1sH4MMA0wEs83+TDqmjPhlakttj3Dcbl7BVAzHFdtZwN/TE4/A5xs8Rk3JuX5u/t0d9+RnP2AxPcE4yTInwGAX5EYY3RXOotLkyCfwZXA3e6+CcDd16W5xjAFOX8HipLTxcAXaawvdO7+Noke4w05G3jEEz4AOplZ9/RUl7kyNdh6ACtqza9MLqt3G3ffDWwB9k9LdeELcv61XU7iX21xkvIzSF526eXuL6azsDQK8udgIDDQzN41sw+ST9SIiyDnfxNwsZmtBKYC16WntIzR1N8VrUJWDKklDTOzi4ES4BtR15JOZpYD3A58L+JSopZH4nLkCSRa7W+b2RB33xxpVelzIfCwu99mZkeT+F7s4e5eHXVhEp1MbbG19uG4gpw/ZnYK8AtgrLuXp6m2dEn1GXQkMSD2m2a2lMT9hSkx60AS5M/BSmCKu1e6+xLgHySCLg6CnP/lwFMA7v4+0JbEQMGtRaDfFa1NpgZbax+OK+X5m9kw4H4SoRan+yo1Gv0M3H2Lu3dx977u3pfEfcax7h6ngWGD/D14gURrDTPrQuLS5OJ0FhmiIOe/HDgZwMwOJRFspWmtMlpTgO8me0eOAra4++qoi4paRl6K9PCG48oKAc//VqAQeDrZZ2a5u4+NrOgWFvAziLWAn8HLwDfNbAFQBfyru8fiykXA8/8p8ICZ/ZhER5LvxegfuJjZ4yT+4dIleR/xl0AbAHe/j8R9xTHAQmAHcGk0lWYWDaklIiKxkqmXIkVERJpFwSYiIrGiYBMRkVhRsImISKwo2EREJFYysru/SBTMbH/g9eRsNxLd52u+EzUyOV6hiGQ4dfcXqYeZ3QSUufvvay3LS45LKiIZTC02kUaY2cMknhwwDHjXzLZSK/CSz8k6092XJsftvJ7EI1Y+BH7g7lXRVC7Seukem0hqPYFj3P0nDW2QHM7pfOBYd/86icuY49JUn4jUohabSGpPB2h5nQyMAGYkhzhrB8RxDE+RjKdgE0lte63p3ex9paNt8qcBf3T3n6etKhGply5FijTNUmA47HnQab/k8teBc83sa8l1nc2sTyQVirRyCjaRpnkW6Gxm84FrSTz/DHdfANwIvGJmc4BXge6RVSnSiqm7v4iIxIpabCIiEisKNhERiRUFm4iIxIqCTUREYkXBJiIisaJgExGRWFGwiYhIrPx/zTZ2F0b0zdUAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "uYeNiHSiR4ix", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 472 + }, + "outputId": "8a3fe8ec-b9d0-4a35-ca81-e1deede3c5f8" + }, + "source": [ + "## We can also compare the model error between decision tree and random forest\n", + "## and see the performance differences.\n", + "plt.figure()\n", + "fig = sbn.jointplot(y_test_2yr, \n", + " y_test_2yr - nn_predicted, \n", + " xlim=[0,1], ylim=[-0.2,0.2],\n", + " kind=\"kde\", space=0, color=\"g\")\n", + "fig.set_axis_labels(xlabel='True value', ylabel='Neural Network Error')\n", + "plt.show()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "display_data", + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "sXLYwFBnkSOv", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## Earlier, we optimize the hyperparameter separately, which might not give us the best results.\n", + "## Can you try to do the optimization together for both number of neurons, activation function, as well as regularization?\n", + "\n" + ], + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/02_Week2/04_ML_regression/04_Pt2_ML_regression_neural_network_exercise.ipynb b/02_Week2/04_ML_regression/04_Pt2_ML_regression_neural_network_exercise.ipynb new file mode 100644 index 0000000..dab9d6c --- /dev/null +++ b/02_Week2/04_ML_regression/04_Pt2_ML_regression_neural_network_exercise.ipynb @@ -0,0 +1,489 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "04_Pt2_ML_regression_neural_network_exercise.ipynb", + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "2ZnjFtKPqhcz", + "colab_type": "text" + }, + "source": [ + "# Python for STEM - Week 2 (Advanced) \n", + "\n", + "## Day 4 - Part 1: Supervised learning - Regression\n", + "\n", + "In this notebook, we will focus on examples of supervised machine learning. More specifically, we will be doing regression using Scikit-learn, one of the machine learning packages in Python. Before we start, here we first import all the packages that we need for this notebook. \n", + "\n", + "All the machine learning functions we will use in Day 3 and Day 4 all comes from [scikit-learn](https://scikit-learn.org/stable/index.html). You can find very detailed descriptions on many machine learning models included in the package user guide and various examples. This would be a good place to start when you want to adopt machine learning for your own research/work. \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "gkWCpoG4qasM", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 51 + }, + "outputId": "9da3091c-4c61-43b7-ca98-0563099f6446" + }, + "source": [ + "## In this cell, we import all the packages needed for this notebook\n", + "import numpy as np ## packages for data handling\n", + "import pandas as pd \n", + "import matplotlib.pyplot as plt ## packages for visualization \n", + "import seaborn as sbn " + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n", + " import pandas.util.testing as tm\n" + ], + "name": "stderr" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "fOu7zI-RoIuV", + "colab_type": "text" + }, + "source": [ + "## Data Ingest\n", + "\n", + "In this notebook, we will be using another real world problem of predictive modeling of the concentration of PM2.5 in Beijing using hourly weather information. PM2.5 refers to atmospheric particulate matter (PM) that have a diameter less than 2.5 $\\mu m$. The data is between 2010 and 2014. The data is stored as a comma separated value (CSV) data file, with 11 columns. The table below shows the meaning of each column. \n", + "\n", + "| Variable name | Full name |\n", + "|-:|-:|\n", + "|*year*|Year of observation| \n", + "|*month*|Month of observation|\n", + "|*day*|Day of observation|\n", + "|*hour*|Hour of observation|\n", + "|*pm2.5*|Concentration of PM 2.5 ($\\mu g/m^3$)|\n", + "|*DEWP*|Dew Point ($^\\circ C$)|\n", + "|*TEMP*|Temperature ($^\\circ C$)|\n", + "|*PRES*|Pressure (hPa)|\n", + "|*WIND*|Wind speed (m/s)|\n", + "|*TSNOW*|Continuos hours of snow|\n", + "|*TRAIN*|Continuos hours of rain|" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "xLqniMTknb5P", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 238 + }, + "outputId": "f72962e7-3b97-409b-ad40-a32810b51363" + }, + "source": [ + "filename = \"https://raw.githubusercontent.com/uofscphysics/STEM_Python_Course/Summer2020/02_Week2/Data/04_Beijing_PM2.5_hourly_20100101-20141231.csv\"\n", + "PMdata = pd.read_csv(filename, index_col=None)\n", + "print( PMdata.head() )\n", + "print( PMdata.shape )\n", + "\n", + "## We see there are mission values at the begining so we drop these missing \n", + "## values in the dataset\n", + "PMdata = PMdata.dropna()\n", + "print( PMdata.head() )" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + " year month day hour pm2.5 DEWP TEMP PRES WIND TSNOW TRAIN\n", + "0 2010 1 1 0 NaN -21 -11.0 1021.0 1.79 0 0\n", + "1 2010 1 1 1 NaN -21 -12.0 1020.0 4.92 0 0\n", + "2 2010 1 1 2 NaN -21 -11.0 1019.0 6.71 0 0\n", + "3 2010 1 1 3 NaN -21 -14.0 1019.0 9.84 0 0\n", + "4 2010 1 1 4 NaN -20 -12.0 1018.0 12.97 0 0\n", + "(43824, 11)\n", + " year month day hour pm2.5 DEWP TEMP PRES WIND TSNOW TRAIN\n", + "24 2010 1 2 0 129.0 -16 -4.0 1020.0 1.79 0 0\n", + "25 2010 1 2 1 148.0 -15 -4.0 1020.0 2.68 0 0\n", + "26 2010 1 2 2 159.0 -11 -5.0 1021.0 3.57 0 0\n", + "27 2010 1 2 3 181.0 -7 -5.0 1022.0 5.36 1 0\n", + "28 2010 1 2 4 138.0 -7 -5.0 1022.0 6.25 2 0\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "u1rVvBok5ekQ", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 119 + }, + "outputId": "258fbc59-63fb-4a29-8398-32785686b01a" + }, + "source": [ + "## Since we will not use the time information for modeling, we here only keep\n", + "## pm2.5 and weather information\n", + "PMdata = PMdata.iloc[:,4:]\n", + "print ( PMdata.head() )" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + " pm2.5 DEWP TEMP PRES WIND TSNOW TRAIN\n", + "24 129.0 -16 -4.0 1020.0 1.79 0 0\n", + "25 148.0 -15 -4.0 1020.0 2.68 0 0\n", + "26 159.0 -11 -5.0 1021.0 3.57 0 0\n", + "27 181.0 -7 -5.0 1022.0 5.36 1 0\n", + "28 138.0 -7 -5.0 1022.0 6.25 2 0\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Bb5Iqk4C2yOd", + "colab_type": "text" + }, + "source": [ + "Now we can look at the time-series of our data." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "cO_INv6b29T7", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 701 + }, + "outputId": "364347a3-1bdb-407d-cb33-d37ed2463612" + }, + "source": [ + "# specify columns to plot\n", + "index = [0, 1, 2, 3, 4, 5, 6]\n", + "i = 1\n", + "# plotting each column\n", + "plt.figure(figsize=(11, 12))\n", + "for ind in index:\n", + "\tplt.subplot(len(index), 1, i)\n", + "\tplt.plot(PMdata.iloc[:, ind].values)\n", + "\tplt.title(PMdata.columns[ind], y=0.5, loc='right')\n", + "\ti += 1\n", + "plt.show()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OB2dLHRh8EL1", + "colab_type": "text" + }, + "source": [ + "## Data Scaling \n", + "\n", + "From the data, we see there are large differences of the magnitude of different variables, which can cause issues when we directly build our model with current data. Data with notably larger magnitude could dominate the model performance. To avoid that, we usually scale our data to [0, 1], or [-1, 1]. In other cases, we can also scale all variables to a distribution with 0 mean and standard deviation of 1. " + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "gzcOfEE39RS3", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## import function to perform data scaling\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "\n", + "scaler = MinMaxScaler(feature_range=(0, 1))\n", + "scaled = scaler.fit_transform(PMdata)\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "XP8uCr3--OG_", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "8d8b0220-43b2-4446-a3fb-c30e949aa6e7" + }, + "source": [ + "## After scaling our data, we can separate the data into training and testing data\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "## Independent variables for our model (weather data) \n", + "X = scaled[:,1:]\n", + "## Target output for our model (pm2.5)\n", + "y = scaled[:,0]\n", + "## We are spliting our data set into training and testing sets based on a 60:40 ratio\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=42)\n", + "print( X_train.shape, y_train.shape)\n" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "(25054, 6) (25054,)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "yqEyxELYY1DD", + "colab_type": "code", + "colab": {} + }, + "source": [ + "" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wsqZvOR3_BVM", + "colab_type": "text" + }, + "source": [ + "## Neural Network \n", + "\n", + "Neural network is the backbone of the recent booming of the deep learning application in many discplines. The basis of neural network is using multiple linear equation to mimic possible nonlinear patterns in the data. The main hyperparameters that could affect the performance includes the number of layers and neurons in each layer. Additionally, the regularization penalty as well as the activation function could also change the results notably. So it usually requires an intensive traning process to find the best combination of model hyperparameters.\n", + "\n", + "Now, let's first try to see the impact of number of layers and number of neurons in each layer for the model performance.\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Tmg94PMeAbHY", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## Import the decision tree model\n", + "from sklearn.neural_network import MLPRegressor\n", + "from sklearn.metrics import make_scorer\n", + "from sklearn.metrics import mean_squared_error, r2_score\n", + "from sklearn.model_selection import KFold\n", + "from sklearn.model_selection import GridSearchCV\n", + "\n", + "## This time, let's try to minimizing the sample size and accelerating our training process\n", + "## We vwill just use two year worth of data for this training process\n", + "\n", + "\n", + "## define our model\n", + "\n", + "## Define parameter grid for tunning, which is different list of the neurons\n", + "## 1-hidden layer, 2-hidden layer, 3-hidden layer\n", + "param_grid = {\n", + " 'hidden_layer_sizes': [(10,), (20,), (50,),\n", + " (10,10), (20,10), (50,10),\n", + " (10,10,10), (20,20,10), (50, 20, 10)]\n", + "}\n", + "\n", + "## define grid search process& train the model\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "330BnyNcP9Z4", + "colab_type": "code", + "colab": {} + }, + "source": [ + "\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "5NgBe2pceICG", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## Let's look at how the model performance change with the number of layers\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "2pZ_1MqBfWNp", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## Now, we can define a best neural network model based on our grid search\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mwxiY6-xMZcA", + "colab_type": "text" + }, + "source": [ + "## Activation Function & Regularization\n", + "\n", + "Assume that we fix the structure of the model in terms of the number of layers, and number of neurons in each layer. We now want to examine the impact of the activation function and regularization penalty for our model. \n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "uVvG9BRpMlP6", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## Let's first define our model parameter grids\n", + "param_grid = {\n", + " 'activation': ['relu', 'tanh'],\n", + " 'alpha': [0.00001, 0.0001, 0.001, 0.01]\n", + "}\n", + "\n", + "## deinfe the number of layers and neurons in the model\n", + "\n", + "\n", + "## define grid search process\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "jr7pN9rFiLHb", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## Let's print out the best model hyperparameter combination\n", + "\n", + "\n", + "## Calculating the mean and standard deviation of the accuracy for the model test scores\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "0ucvCWthjQNi", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## Now we can create a final prediction with the optimized model hyperparameters\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "EIZRz3qjRnPc", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## To show the prediction results, we can also plot a kernal density plot between \n", + "## predicted value and testing value\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "uYeNiHSiR4ix", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## We can also compare the model error between decision tree and random forest\n", + "## and see the performance differences.\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "sXLYwFBnkSOv", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## Earlier, we optimize the hyperparameter separately, which might not give us the best results.\n", + "## Can you try to do the optimization together for both number of neurons, activation function, as well as regularization?\n", + "\n" + ], + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/02_Week2/04_ML_regression/README.md b/02_Week2/04_ML_regression/README.md new file mode 100644 index 0000000..4c89f58 --- /dev/null +++ b/02_Week2/04_ML_regression/README.md @@ -0,0 +1,27 @@ +# Table of Contents: + +## 04_Pt1_ML_regression_random_forest_exercise.ipynb + +This is the in-class exercise notebook for implementing random forest for regression. + +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/uofscphysics/STEM_Python_Course/blob/Summer2020/02_Week2/04_ML_regression/04_Pt1_ML_regression_random_forest_exercise.ipynb) +_____ +## 04_Pt1_ML_regression_random_forest_complete.ipynb + +This is the complete notebook for implementing random forest for regression. + +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/uofscphysics/STEM_Python_Course/blob/Summer2020/02_Week2/04_ML_regression/04_Pt1_ML_regression_random_forest_complete.ipynb) + +_____ +## 04_Pt2_ML_regression_neural_network_exercise.ipynb + +This is the in-class exercise notebook for implementing neural network for regression. + +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/uofscphysics/STEM_Python_Course/blob/Summer2020/02_Week2/04_ML_regression/04_Pt2_ML_regression_neural_network_exercise.ipynb) + +_____ +## 04_Pt2_ML_regression_neural_network_complete.ipynb + +This is the complete notebook for implementing neural network for regression. + +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/uofscphysics/STEM_Python_Course/blob/Summer2020/02_Week2/04_ML_regression/04_Pt2_ML_regression_neural_network_complete.ipynb) diff --git a/02_Week2/Data/03_land_use_land_cover_asheville.csv b/02_Week2/Data/03_land_use_land_cover_asheville.csv new file mode 100644 index 0000000..e87a420 --- /dev/null +++ b/02_Week2/Data/03_land_use_land_cover_asheville.csv @@ -0,0 +1,1876 @@ +Latitude,Longitude,Class,B1,B2,B3,B4,B5,B6 +35.5147693572276,-82.6804514604663,0,127,150,550,226,3609,1441 +35.7539792870272,-82.5204322997573,0,81,115,426,170,2913,1110 +35.7106347084667,-82.3056611655266,0,156,220,538,477,2492,2077 +35.512813962706,-82.4138614941527,0,245,280,663,507,2732,1531 +35.5206355407921,-82.8531809007429,0,148,181,534,265,3320,1457 +35.6190570650421,-82.8078801607458,0,202,289,651,522,3047,2251 +35.5542031467449,-82.5047888787511,0,208,250,521,408,2905,1738 +35.5603952293964,-82.3007725964622,0,60,91,486,184,3431,1342 +35.545729770485,-82.5051147833554,0,122,155,463,219,3385,1264 +35.6493656801257,-82.4796942242204,0,207,260,603,441,3205,1801 +35.750720296158,-82.8359079567152,0,94,140,561,235,3222,1404 +35.5193319444444,-82.3838782705575,0,133,163,433,241,3162,1348 +35.7171526902052,-82.4021289283981,0,80,116,452,191,3105,1253 +35.6105836887822,-82.3972403593336,0,94,123,452,180,3821,1364 +35.5014074946638,-82.3343407707046,0,164,243,490,408,2722,1586 +35.6904289650777,-82.6208109178803,0,148,176,524,238,3637,1502 +35.4991262010554,-82.8528549961386,0,124,162,585,248,3659,1470 +35.4697952832325,-82.8414483349882,0,97,148,497,286,3043,1591 +35.6125390833037,-82.8225458679391,0,98,150,443,256,2501,1234 +35.4961931092731,-82.5347721023463,0,99,125,411,181,3530,1284 +35.5441002750504,-82.8557881375772,0,237,298,643,602,2652,1928 +35.6822814879047,-82.6807773650706,0,260,309,666,557,2934,2111 +35.5522477522234,-82.6957689768682,0,136,160,510,219,3240,1253 +35.5353009997036,-82.3737752278244,0,94,125,544,238,3863,1519 +35.6203606613898,-82.3714938955943,0,141,185,503,339,3302,1466 +35.6637052399502,-82.3301040108488,0,74,126,466,198,3298,1208 +35.5708240001779,-82.4177723494042,0,97,137,505,222,3366,1434 +35.5467074677458,-82.7863704568623,0,77,127,493,229,3073,1356 +35.5792973764378,-82.3737752278244,0,101,152,497,307,2687,1536 +35.7477872043757,-82.445148336165,0,74,122,471,210,2777,1322 +35.680977891557,-82.4161428263828,0,198,273,571,596,2401,2222 +35.6992282404245,-82.8678466079362,0,163,231,549,473,2402,1785 +35.619382964129,-82.8479664270741,0,106,160,499,277,2651,1436 +35.7282332591604,-82.8564399467858,0,105,138,487,225,3342,1468 +35.4789204576663,-82.7827855062151,0,126,173,585,336,2829,1552 +35.6232937531721,-82.4816496518461,0,163,214,496,335,2862,1442 +35.6855404787739,-82.8326489106722,0,153,200,561,358,3199,1722 +35.7269296628128,-82.6677411808988,0,144,174,476,227,2872,1375 +35.5551808440057,-82.3871373166005,0,105,154,548,327,2668,1439 +35.7344253418119,-82.7919108351353,0,109,143,492,222,2837,1197 +35.7334476445512,-82.6341730066564,0,152,178,526,231,3142,1298 +35.7034649285545,-82.385507793579,0,120,176,372,351,2166,1403 +35.6653347353848,-82.5100033524198,0,82,116,511,200,3190,1278 +35.6907548641646,-82.457206806524,0,113,180,378,360,1556,1250 +35.5066218800546,-82.5673625627758,0,126,161,440,221,2796,1244 +35.6177534686944,-82.774963795712,0,129,169,628,288,3695,1686 +35.5512700549627,-82.5865909344292,0,116,160,486,227,3456,1385 +35.668593726254,-82.7736601772948,0,105,143,537,255,3546,1667 +35.6760894052532,-82.4989225958738,0,105,157,551,277,3396,1670 +35.7119383048144,-82.522387727383,0,89,128,511,213,4061,1588 +35.7363807363335,-82.3102238299868,0,209,265,425,428,1808,922 +35.5017333937507,-82.7563872332671,0,86,108,395,154,3299,1233 +35.4867420357524,-82.4686134676743,0,116,140,491,220,3092,1223 +35.5763642846555,-82.8336266244851,0,156,199,613,321,3761,1825 +35.4971708065339,-82.5452010496837,0,107,147,484,222,3588,1392 +35.5926592390015,-82.8684984171447,0,158,218,525,403,2382,1663 +35.6578390563856,-82.4311344381804,0,86,130,460,221,2677,1209 +35.6141685787383,-82.6791478420491,0,124,149,462,200,3501,1305 +35.5356268987905,-82.3551986653795,0,143,179,559,323,3893,1707 +35.4873938339262,-82.7981030226169,0,192,257,615,433,3361,2004 +35.6532764691687,-82.7847409338408,0,124,174,536,290,3177,1695 +35.7210634792482,-82.3542209515666,0,1397,1484,1713,1743,2645,534 +35.5268276234436,-82.7071756380185,0,271,355,791,656,3539,2663 +35.6584908545595,-82.6090783521256,0,232,285,588,410,2196,1177 +35.6268786431282,-82.3056611655266,0,175,203,500,344,2987,1396 +35.4994521001423,-82.3349925799132,0,157,235,479,384,2508,1542 +35.7165008920313,-82.6097301613342,0,157,195,590,280,3512,1604 +35.5010815955769,-82.8130946344145,0,104,143,539,231,3951,1638 +35.5248722289221,-82.4966412636437,0,90,133,523,225,3599,1478 +35.7354030390727,-82.6670893716902,0,120,143,393,187,2397,1078 +35.6770671025139,-82.6191813948588,0,157,189,517,254,3332,1416 +35.6461066892565,-82.3894186488305,0,127,170,466,335,2113,1354 +35.607324697913,-82.8430778580097,0,175,232,568,422,2785,1992 +35.6353520193881,-82.3252154417844,0,77,109,536,196,3924,1518 +35.7490908007234,-82.6107078751471,0,292,308,586,387,2685,1587 +35.6350261203012,-82.7612758023316,0,101,125,492,184,4006,1507 +35.6708750198625,-82.7690975128346,0,108,143,518,206,4206,1589 +35.7474613052888,-82.8303675784422,0,118,159,427,252,3114,1236 +35.6536023682557,-82.6752369867976,0,228,259,595,360,3536,1886 +35.6148203769121,-82.760623993123,0,132,169,478,237,2762,1373 +35.7191080847267,-82.8541586145557,0,108,157,526,233,3301,1472 +35.6467584874303,-82.7035906873713,0,305,417,805,691,3247,2208 +35.4769650631448,-82.3666053265299,0,99,151,497,306,2862,1677 +35.4873938339262,-82.3969144547293,0,136,158,516,235,3309,1386 +35.6920584605123,-82.7971253088041,0,132,176,556,283,3434,1627 +35.5669132111348,-82.3255413463886,0,115,161,548,331,2424,1647 +35.5528995503972,-82.7778969371506,0,106,166,592,332,3379,1773 +35.6861922769477,-82.491426789975,0,89,126,472,205,3505,1354 +35.7158490938575,-82.3545468561709,0,170,246,493,429,2630,1637 +35.7086793139452,-82.6175518718373,0,167,199,467,275,2415,1339 +35.7275814609866,-82.6067970198956,0,158,190,553,275,2929,1320 +35.4828312467094,-82.8254790093778,0,113,171,556,275,3827,1613 +35.713567800249,-82.6221145362974,0,189,220,543,326,2899,1567 +35.5359527978774,-82.6781701282362,0,136,161,519,230,3549,1425 +35.7357289381596,-82.6807773650706,0,232,285,695,457,3470,1959 +35.5489887613542,-82.2978394550235,0,69,108,517,221,3250,1425 +35.5216132380529,-82.7355293385922,0,103,138,456,210,3307,1428 +35.5173765499229,-82.5096774478155,0,84,118,453,179,3356,1226 +35.6676160289932,-82.8613285158502,0,133,183,625,313,3819,1693 +35.5441002750504,-82.6110337797514,0,144,178,511,300,3652,1712 +35.6477361846911,-82.331407629266,0,73,113,467,188,3329,1364 +35.5577880367011,-82.3851818889747,0,132,184,584,339,3365,1634 +35.6871699742085,-82.3701902771771,0,172,242,571,565,2662,2189 +35.660446249081,-82.3741011324286,0,131,175,484,336,2579,1618 +35.5610470275703,-82.8248272001692,0,141,184,475,329,2587,1314 +35.7516979934187,-82.4167946355913,0,97,142,454,226,3025,1303 +35.7474613052888,-82.5334684839291,0,115,144,420,195,3727,1353 +35.4929341184039,-82.3252154417844,0,87,139,557,296,2991,1673 +35.4707729804933,-82.7593203747058,0,97,137,537,210,3949,1637 +35.5075995773153,-82.6432983355766,0,103,134,450,196,2960,1260 +35.6027621106961,-82.7013093551412,0,171,215,663,469,3459,1928 +35.7363807363335,-82.5080479247941,0,83,134,446,195,3130,1181 +35.664682937211,-82.5442233358708,0,143,167,379,201,2557,1079 +35.6881476714692,-82.4695911814872,0,145,203,555,384,3181,1579 +35.5463815686588,-82.6087524475213,0,179,223,581,339,4147,1759 +35.5571362385272,-82.3695384679685,0,84,132,558,266,3231,1534 +35.5525736513103,-82.3578059022139,0,97,139,543,268,3107,1400 +35.6487138819519,-82.3017503102751,0,128,166,561,307,2870,1503 +35.6777189006878,-82.379967415306,0,134,195,355,357,1571,1367 +35.7233447728566,-82.7616017069359,0,127,171,571,265,3875,1710 +35.676741203427,-82.3128310668211,0,79,129,509,277,2627,1363 +35.5056441827938,-82.6550309013313,0,134,164,533,236,3286,1351 +35.4818535494486,-82.7847409338408,0,120,167,664,343,3564,1802 +35.7204116810744,-82.3294522016402,0,130,184,532,360,2753,1622 +35.5929851380885,-82.3288003924316,0,91,116,465,201,3353,1296 +35.6907548641646,-82.6967466906811,0,164,199,456,305,2490,1364 +35.5284571188782,-82.6563345197485,0,213,300,580,578,3039,1579 +35.7308404518558,-82.5869168390335,0,180,207,439,267,2147,1267 +35.7569123788094,-82.5898499804722,0,175,204,511,266,2746,1283 +35.7142195984229,-82.6592676611871,0,157,192,453,276,2478,1395 +35.648062083778,-82.4633989940056,0,99,141,419,237,2521,1221 +35.6079764960868,-82.3310817246617,0,134,159,400,230,2905,1167 +35.5219391371398,-82.5914795034937,0,149,179,446,249,2844,1245 +35.7246483692043,-82.5504155233525,0,94,136,398,216,2449,1079 +35.6956433504684,-82.6592676611871,0,235,285,585,436,2461,1761 +35.7077016166845,-82.5986494047882,0,153,179,451,265,2799,1222 +35.6288340376497,-82.4330898658061,0,96,130,401,207,2427,1176 +35.7367066354204,-82.6455796678067,0,176,200,466,247,3048,1335 +35.7207375801613,-82.6934876446381,0,127,151,367,191,2567,1195 +35.6457807901696,-82.7221672498161,0,431,501,788,808,2950,2307 +35.4838089439701,-82.4477555729994,0,95,126,484,219,3168,1276 +35.5144434581406,-82.7290112465063,0,186,227,570,419,2881,1611 +35.5734311928732,-82.6980503090983,0,203,264,529,387,2700,1308 +35.672830414384,-82.5556299970212,0,142,179,446,230,2964,1242 +35.7373584335942,-82.3255413463886,0,138,221,415,404,1640,669 +35.6184052668683,-82.5849614114078,0,138,175,447,228,3083,1247 +35.6959692495553,-82.671326131546,0,157,192,492,250,2801,1337 +35.5268276234436,-82.3806192245146,0,151,204,479,373,2551,1373 +35.4785945585794,-82.482627365659,0,238,372,603,524,3105,1630 +35.5001038983161,-82.6188554902545,0,144,173,428,245,2501,1207 +35.6813037906439,-82.5931090265152,0,167,197,469,265,2724,1369 +35.7533274888533,-82.6599194703957,0,198,238,550,316,2827,1551 +35.5095549718368,-82.6960948814725,0,142,179,537,275,3269,1632 +35.4789204576663,-82.4171205401956,0,119,145,421,218,2669,1184 +35.5059700818807,-82.6530754737055,0,142,181,477,274,2492,1221 +35.4942377147516,-82.7303148649235,0,240,290,618,436,3312,1698 +35.6845627815131,-82.6915322170124,0,156,185,493,235,2989,1222 +35.6610980472548,-82.6517718552883,0,154,178,475,231,2884,1230 +35.5300866143128,-82.5846355068035,0,134,163,424,208,2807,1173 +35.6897771669038,-82.5073961155855,0,143,178,457,242,2788,1256 +35.6705491207755,-82.5872427436378,0,149,183,427,240,2649,1220 +35.7145454975098,-82.6794737466534,0,176,197,504,254,2616,1345 +35.5160729535752,-82.5618221845028,0,108,145,404,186,2843,1152 +35.7181303874659,-82.6230922501103,0,178,207,475,318,2357,1413 +35.50075569649,-82.6185295856502,0,148,187,419,259,2437,1254 +35.7432246171588,-82.5696438950059,0,139,176,444,244,2600,1249 +35.6581649554726,-82.5592149476684,0,95,136,412,193,2779,1120 +35.7129160020752,-82.651445950684,0,167,191,458,252,2421,1178 +35.7331217454643,-82.3066388793395,0,896,978,1170,1188,2381,519 +35.7533274888533,-82.3310817246617,0,70,118,255,241,1367,621 +35.6826073869916,-82.7681197990218,0,135,162,477,225,2654,1235 +35.7373584335942,-82.3239118233672,0,236,318,523,488,1598,553 +35.4994521001423,-82.6126633027729,0,125,167,466,247,2758,1260 +35.4766391640579,-82.4487332868123,0,107,133,456,219,3005,1289 +35.5933110371754,-82.4643767078185,0,143,175,412,253,2509,1277 +35.7481131034626,-82.5667107535672,0,155,193,502,286,2715,1380 +35.7370325345073,-82.2864327938732,0,503,556,709,651,1995,334 +35.6992282404245,-82.6749110821933,0,178,201,496,265,2968,1437 +35.7393138281157,-82.328148583223,0,-91,46,336,313,1647,551 +35.7285591582474,-82.2926249813548,0,55,159,400,338,1957,570 +35.729862754595,-82.3046834517137,0,-141,10,313,303,1709,635 +35.7116124057275,-82.5748583686746,0,213,249,530,388,2671,1398 +35.545729770485,-82.5667107535672,0,126,152,391,187,2777,1127 +35.472076576841,-82.5233654411959,0,170,215,495,302,2835,1404 +35.6819555888177,-82.594086740328,0,178,221,446,322,2332,1410 +35.5160729535752,-82.5549781878126,0,129,166,431,215,2831,1182 +35.5098808709238,-82.5627998983157,0,135,167,425,246,2569,1240 +35.7184562865528,-82.5836577929906,0,166,184,399,250,2134,1110 +35.5401894860074,-82.5921313127023,0,143,174,405,209,2733,1177 +35.5033628891853,-82.5422679082451,0,273,322,606,664,2304,1939 +35.5281312197913,-82.5559559016255,0,130,158,395,195,2608,1109 +35.7171526902052,-82.5481341911224,0,127,164,395,202,2573,1016 +35.7197598829005,-82.6781701282362,0,191,215,509,306,2369,1362 +35.6757635061663,-82.6462314770153,0,165,178,532,264,3080,1287 +35.6105836887822,-82.5647553259414,0,218,260,536,383,3183,1548 +35.6650088362979,-82.6191813948588,0,158,193,484,284,2863,1372 +35.5255240270959,-82.3633462804869,0,125,162,512,261,3045,1357 +35.6079764960868,-82.5820282699691,0,127,160,419,195,3658,1285 +35.5610470275703,-82.4757833689688,0,182,236,592,433,3466,1755 +35.7266037637258,-82.2926249813548,0,-29,56,262,226,1456,648 +35.611235486956,-82.4506887144381,0,110,145,393,204,2939,1288 +35.6663124326456,-82.6364543388864,0,164,196,423,268,2220,1258 +35.7207375801613,-82.663504421043,0,159,196,449,266,2539,1369 +35.7461577089411,-82.6579640427699,0,186,213,531,333,2944,1597 +35.5261758252698,-82.3995216915637,0,142,173,459,248,2907,1276 +35.7516979934187,-82.6465573816196,0,126,165,522,293,3258,1502 +35.7569123788094,-82.6165741580244,0,99,138,354,214,1865,1172 +35.5630024220918,-82.6592676611871,0,133,157,421,214,2970,1155 +35.7171526902052,-82.6830586973007,0,159,187,415,230,2087,1088 +35.7350771399858,-82.290669553729,0,145,243,495,416,2028,303 +35.6594685518202,-82.6432983355766,0,243,289,656,462,3006,1416 +35.6884735705562,-82.7674679898132,0,161,199,497,290,2779,1403 +35.6679419280802,-82.6142928257944,0,163,202,472,270,2543,1296 +35.6682678271671,-82.645905572411,0,181,221,522,285,3054,1400 +35.680977891557,-82.5540004739997,0,151,194,466,269,2724,1415 +35.6350261203012,-82.6087524475213,0,150,176,457,238,2763,1193 +35.4948895129254,-82.6155964442115,0,168,201,451,306,2330,1326 +35.5216132380529,-82.5517191417696,0,136,162,393,214,2557,1153 +35.7334476445512,-82.2880623168947,0,-67,12,270,182,2314,630 +35.7184562865528,-82.7413956214696,0,152,189,508,288,2714,1314 +35.7148713965967,-82.6648080394601,0,145,173,476,240,2840,1283 +35.4792463567532,-82.5702957042145,0,158,198,535,289,2879,1359 +35.6552318636903,-82.7084792564357,0,374,443,808,651,3191,2123 +35.7363807363335,-82.5412901944322,0,317,426,891,1061,2279,2150 +35.6219901568244,-82.6120114935643,0,203,241,593,380,3810,1668 +35.6294858358236,-82.7247744866505,0,150,175,462,241,2870,1294 +35.5121621645322,-82.5605185660856,0,125,147,382,197,2609,1062 +35.7288850573343,-82.5497637141439,0,287,372,742,704,2894,2163 +35.5571362385272,-82.5706216088188,0,135,166,406,202,2985,1250 +35.5049923846199,-82.5680143719844,0,140,166,415,217,2573,1118 +35.5665873120479,-82.6863177433436,0,542,682,1222,1647,2864,2478 +35.717804488379,-82.677844223632,0,200,243,587,341,2844,1645 +35.6894512678169,-82.7136937301044,0,186,202,407,224,2742,1081 +35.5522477522234,-82.5429197174537,0,153,184,372,228,2436,1146 +35.750394397071,-82.6540531875184,0,157,184,411,262,2140,1294 +35.7311663509427,-82.5849614114078,0,218,244,495,348,2434,1490 +35.5001038983161,-82.4249422506987,0,111,190,547,514,2376,1271 +35.729862754595,-82.6296103421962,0,166,195,446,272,2483,1270 +35.6741340107317,-82.5305353424904,0,138,176,452,254,2678,1212 +35.5027110910115,-82.6129892073772,0,130,170,412,242,2447,1206 +35.7279073600735,-82.3121792576125,0,5,80,253,209,1437,487 +35.5196578435314,-82.5722511318402,0,124,156,419,215,2869,1220 +35.4697952832325,-82.4891454577449,0,144,166,394,215,2298,1065 +35.4789204576663,-82.4852346024934,0,166,204,450,352,2657,1426 +35.4779427604056,-82.4246163460944,0,170,216,506,312,2854,1459 +35.7292109564212,-82.2913213629376,0,80,157,395,310,2049,446 +35.6539282673426,-82.6426465263681,0,184,213,480,290,2622,1375 +35.7575641769833,-82.3278226786187,0,89,140,293,258,1602,645 +35.5362786969643,-82.5680143719844,0,138,166,386,209,2655,1168 +35.5261758252698,-82.5882204574507,0,138,172,454,246,2828,1319 +35.484460742144,-82.4526441420638,0,301,365,802,746,3103,2116 +35.5186801462706,-82.4070174974625,0,142,183,438,259,2609,1310 +35.7494166998103,-82.3796415107017,0,116,152,411,217,2832,1097 +35.5092290727499,-82.5540004739997,0,143,178,453,256,2631,1191 +35.6747858089055,-82.7335739109665,0,167,207,530,300,2609,1498 +35.7204116810744,-82.6331952928435,0,184,219,544,337,3068,1576 +35.4701211823195,-82.4705688953001,0,203,245,585,492,2686,1784 +35.5594175321357,-82.868824321749,0,100,134,451,214,2977,1265 +35.6226419549982,-82.4790424150118,0,161,209,531,330,3392,1559 +35.5939628353492,-82.6344989112607,0,143,167,432,250,2881,1432 +35.5232427334875,-82.6899026939909,0,115,168,546,351,3290,1531 +35.615472175086,-82.6374320526993,0,254,309,540,376,2774,1426 +35.7389879290288,-82.548786000331,0,182,240,465,374,2458,1340 +35.6281822394759,-82.4637248986099,0,96,172,417,378,1970,1238 +35.7005318367722,-82.7075015426228,0,156,189,521,257,3455,1500 +35.6157980741729,-82.6123373981686,0,172,209,556,269,3963,1600 +35.7526756906795,-82.5526968555825,0,162,203,542,317,3004,1412 +35.5242204307483,-82.8440555718226,0,102,159,404,284,2073,1177 +35.4913046229693,-82.7420474306781,0,189,246,585,447,2555,1772 +35.4913046229693,-82.5689920857973,0,238,310,565,496,1502,1003 +35.7213893783351,-82.5536745693954,0,145,181,469,259,2814,1281 +35.5216132380529,-82.8150500620403,0,214,271,622,436,3011,1599 +35.6852145796869,-82.5276022010518,0,129,161,463,224,3009,1251 +35.7174785892921,-82.7707270358561,0,121,151,498,208,3113,1247 +35.5079254764022,-82.5491119049353,0,141,183,456,263,2570,1198 +35.7174785892921,-82.7117383024787,0,158,190,490,260,2799,1318 +35.5027110910115,-82.4458001453736,0,88,136,452,270,2898,1524 +35.4779427604056,-82.4862123163063,0,348,413,765,731,3115,1883 +35.5183542471837,-82.5836577929906,0,420,534,945,1077,3338,3303 +35.6268786431282,-82.4783906058032,0,160,196,470,296,2736,1280 +35.513139861793,-82.6169000626287,0,182,235,546,426,2918,2022 +35.6614239463418,-82.5999530232054,0,249,302,687,486,3794,2035 +35.5450779723112,-82.5660589443586,0,135,167,417,215,2953,1232 +35.7112865066406,-82.6494905230582,0,199,246,549,373,2937,1631 +35.7034649285545,-82.7521504734113,0,127,160,465,208,3158,1373 +35.5561585412665,-82.6133151119815,0,124,146,385,183,3026,1226 +35.525198128009,-82.5566077108341,0,123,164,453,284,2887,1155 +35.6760894052532,-82.5996271186011,0,139,160,405,200,2814,1170 +35.6901030659908,-82.5895240758679,0,163,197,464,279,2603,1433 +35.5339974033559,-82.3848559843704,0,133,166,485,237,2926,1290 +35.521287338966,-82.8130946344145,0,161,217,498,363,2369,1377 +35.7520238925056,-82.5393347668064,0,94,143,479,205,3287,1364 +35.6920584605123,-82.6837105065093,0,142,168,523,232,3180,1252 +35.537582293312,-82.4034325468152,0,151,206,610,486,2904,1470 +35.5695204038302,-82.6918581216166,0,131,154,500,221,3276,1271 +35.574408890134,-82.7938662627611,0,147,197,457,301,2488,1190 +35.5486628622673,-82.6872954571565,0,285,392,784,683,2915,1938 +35.6995541395114,-82.7576908516843,0,127,161,478,226,2826,1227 +35.4906528247954,-82.8209163449176,0,95,137,379,271,1874,1150 +35.713567800249,-82.6191813948588,0,171,208,469,286,2496,1307 +35.5287830179651,-82.4034325468152,0,125,157,479,237,2808,1274 +35.5206355407921,-82.3806192245146,0,164,212,539,343,2834,1420 +35.7295368555081,-82.6406910987423,0,187,219,463,300,2384,1408 +35.5157470544883,-82.3923517902692,0,157,178,489,270,2832,1437 +35.5626765230049,-82.4989225958738,0,117,146,399,197,2621,1020 +35.6751117079924,-82.5292317240733,0,122,151,463,218,2771,1186 +35.7451800116803,-82.5803987469476,0,208,259,606,460,2750,1761 +35.7142195984229,-82.5862650298249,0,237,295,585,414,2879,1571 +35.7435505162457,-82.302728024088,0,-75,63,303,375,1685,916 +35.5072736782284,-82.5650812305457,0,129,157,418,228,2783,1249 +35.5496405595281,-82.8541586145557,0,134,174,478,255,2775,1360 +35.4860902375786,-82.5891981712636,0,126,156,476,217,3059,1228 +35.7285591582474,-82.5438974312665,0,99,126,423,174,2964,1085 +35.7376843326811,-82.6638303256472,0,133,154,437,187,3270,1449 +35.5075995773153,-82.6807773650706,0,172,212,535,276,2952,1597 +35.697272845903,-82.6758887960062,0,161,192,509,249,2948,1461 +35.6245973495197,-82.3372739121433,0,242,339,641,705,1819,1683 +35.680651992470104,-82.7925626443439,0,143,178,462,250,2599,1297 +35.6347002212143,-82.4601399479626,0,111,156,463,267,3219,1326 +35.6790224970355,-82.5373793391806,0,108,141,432,204,2996,1262 +35.5326938070082,-82.640365194138,0,236,299,618,497,2720,1632 +35.6897771669038,-82.7205377267946,0,136,160,408,191,2585,1155 +35.676741203427,-82.5191286813401,0,103,143,443,210,2884,1148 +35.5310643115736,-82.5553040924169,0,116,152,454,207,3126,1334 +35.5528995503972,-82.594086740328,0,144,173,439,222,3239,1324 +35.648062083778,-82.7413956214696,0,181,225,555,392,2939,1489 +35.5196578435314,-82.5839836975949,0,396,510,940,1004,3249,3084 +35.7119383048144,-82.7889776936967,0,124,150,434,207,2778,1200 +35.5600693303095,-82.5966939771624,0,133,162,407,198,3174,1152 +35.5441002750504,-82.4614435663798,0,248,320,624,493,2995,1712 +35.5196578435314,-82.4298308197632,0,132,165,473,259,2655,1229 +35.5522477522234,-82.5937608357237,0,131,160,400,194,2934,1138 +35.5222650362267,-82.814724157436,0,153,201,483,306,2317,1252 +35.6558836618641,-82.7648607529788,0,175,232,565,353,3265,1554 +35.7187821856398,-82.5468305727052,0,129,166,432,216,2784,1175 +35.7047685249022,-82.51684734911,0,142,175,451,271,3025,1454 +35.5545290458319,-82.4774128919903,0,157,223,589,468,2885,1475 +35.7018354331199,-82.568666181193,0,250,321,596,521,2790,1571 +35.6708750198625,-82.7397660984481,0,206,296,696,513,1375,1347 +35.7077016166845,-82.4911008853707,0,126,236,574,491,2664,1561 +35.7142195984229,-82.7306407695278,0,178,213,500,343,2453,1569 +35.4994521001423,-82.6837105065093,0,46,96,491,196,3205,1286 +35.7236706719436,-82.7384624800309,0,115,143,458,193,2990,1308 +35.7409433235503,-82.6710002269417,0,142,162,468,252,3170,1505 +35.701183634946,-82.7932144535525,0,211,254,641,381,3919,1829 +35.5030369900984,-82.608426542917,0,140,172,458,231,2923,1289 +35.701183634946,-82.5957162633495,0,148,172,475,229,2956,1222 +35.6845627815131,-82.5784433193219,0,265,358,727,636,3164,2166 +35.7344253418119,-82.694465358451,0,154,216,535,396,2139,1352 +35.6004808170876,-82.437326625662,0,391,542,1148,1230,3143,2091 +35.6532764691687,-82.7140196347087,0,199,225,483,307,3042,1356 +35.5470333668327,-82.5817023653648,0,122,147,397,171,3059,1129 +35.6536023682557,-82.5592149476684,0,131,171,469,279,2566,1256 +35.521287338966,-82.4458001453736,0,187,222,470,347,2817,1595 +35.5222650362267,-82.5448751450794,0,105,138,441,196,3092,1194 +35.7367066354204,-82.5979975955796,0,271,352,747,561,3333,2100 +35.5271535225305,-82.3930035994778,0,125,167,480,293,3037,1370 +35.5802750736986,-82.6752369867976,0,146,215,566,374,3559,1772 +35.7285591582474,-82.6165741580244,0,512,701,1259,1574,1895,2532 +35.4704470814064,-82.7501950457855,0,115,153,438,235,2547,1272 +35.6887994696431,-82.6015825462268,0,153,178,464,230,3179,1441 +35.6294858358236,-82.4226609184687,0,86,116,473,189,3330,1226 +35.6047175052176,-82.3415106719991,0,209,268,543,374,3162,1668 +35.7468095071149,-82.6097301613342,0,170,195,483,270,2835,1481 +35.6177534686944,-82.5934349311194,0,149,181,495,244,2947,1344 +35.750720296158,-82.5898499804722,0,240,297,630,457,3304,1811 +35.6597944509072,-82.6155964442115,0,132,153,436,200,2995,1243 +35.680977891557,-82.5350980069506,0,131,166,463,225,2903,1175 +35.5199837426183,-82.568666181193,0,125,154,447,208,2970,1221 +35.7266037637258,-82.3095720207782,0,85,148,348,324,1962,1077 +35.5105326690976,-82.6814291742792,0,86,136,442,226,3366,1442 +35.7161749929444,-82.6674152762945,0,199,244,565,422,2825,1661 +35.6650088362979,-82.6048415922698,0,682,954,1439,1552,2304,2412 +35.7549569842879,-82.554326378604,0,332,427,680,620,2732,1847 +35.5092290727499,-82.5572595200427,0,129,172,502,267,2875,1262 +35.5320420088343,-82.5777915101133,1,226,290,680,485,4168,2225 +35.5075995773153,-82.3806192245146,1,391,571,1030,1182,3468,3124 +35.5656096147871,-82.5657330397543,1,415,504,833,962,2758,2737 +35.6549059646034,-82.7322702925493,1,411,500,862,1106,1983,2077 +35.5841858627416,-82.7312925787364,1,342,453,959,896,2928,2201 +35.5062959809676,-82.6899026939909,1,334,433,764,892,2383,2407 +35.7451800116803,-82.7149973485216,1,225,290,642,490,3569,2147 +35.7458318098542,-82.5354239115549,1,289,376,842,700,3967,2695 +35.7034649285545,-82.5008780234996,1,221,301,732,551,4034,2292 +35.6923843595992,-82.7084792564357,1,212,337,875,621,3837,2024 +35.6164498723467,-82.5696438950059,1,708,894,973,837,3622,2492 +35.6659865335586,-82.7149973485216,1,362,481,811,1075,1926,2363 +35.606998798826,-82.3437920042292,1,358,458,801,978,1844,1573 +35.4847866412309,-82.4800201288246,1,226,289,675,485,3998,1963 +35.5124880636191,-82.3868114119962,1,540,722,1357,1739,3083,2975 +35.6454548910827,-82.4787165104075,1,139,187,564,317,3995,1710 +35.6092800924345,-82.7299889603192,1,374,459,826,978,2753,2406 +35.7223670755959,-82.737484766218,1,394,502,811,707,3173,1929 +35.6676160289932,-82.7153232531259,1,350,413,654,762,1609,1812 +35.6008067161746,-82.6159223488158,1,245,289,721,460,4569,1988 +35.5395376878335,-82.671326131546,1,392,483,728,789,2212,2629 +35.4789204576663,-82.4347193888276,1,151,271,677,616,2781,1657 +35.5626765230049,-82.5911535988894,1,367,479,865,972,2844,2672 +35.475987365884,-82.4363489118491,1,319,451,876,1017,2520,2178 +35.4935859165777,-82.496967168248,1,165,213,616,348,4104,2097 +35.7119383048144,-82.5328166747205,1,110,186,618,353,4346,1814 +35.4867420357524,-82.462747184797,1,163,195,577,301,4256,2008 +35.5998290189138,-82.7322702925493,1,431,509,987,919,3748,2767 +35.697272845903,-82.5891981712636,1,418,556,959,923,2742,2115 +35.7572382778964,-82.5396606714107,1,299,376,637,683,2147,2296 +35.6174275696075,-82.7215154406075,1,194,235,593,358,3550,1880 +35.5072736782284,-82.6859918387393,1,189,234,626,386,3757,1850 +35.5059700818807,-82.3780119876802,1,398,596,1079,1271,3794,3255 +35.6141685787383,-82.6622008026258,1,388,512,943,987,3396,3234 +35.6741340107317,-82.669044799316,1,288,344,720,754,3291,2068 +35.5607211284833,-82.5660589443586,1,473,569,851,1043,2113,2781 +35.6109095878691,-82.6067970198956,1,212,277,658,439,3701,2217 +35.5398635869204,-82.6706743223374,1,281,340,595,572,2126,2190 +35.6962951486422,-82.7215154406075,1,286,409,810,829,3150,2863 +35.5274794216175,-82.4497110006252,1,204,255,518,505,2553,1730 +35.4981485037946,-82.404736165232407,1,430,611,1084,1562,3227,2962 +35.5300866143128,-82.622766345506,1,299,375,755,670,3745,2487 +35.6304635330843,-82.645905572411,1,545,567,971,971,3550,2161 +35.5062959809676,-82.3724716094072,1,182,265,619,576,2792,1744 +35.5111844672714,-82.3829005567446,1,329,445,828,954,2969,2868 +35.5424707796158,-82.6335211974478,1,223,246,569,368,3114,1861 +35.6347002212143,-82.6775183190277,1,381,521,1031,996,3358,1842 +35.5587657339618,-82.5627998983157,1,459,581,993,1145,2624,2891 +35.607324697913,-82.4353711980362,1,418,501,798,1054,1749,2432 +35.6578390563856,-82.7671420852089,1,304,457,876,1020,2343,2583 +35.4900010266216,-82.4630730894013,1,124,165,460,267,3345,1356 +35.5069477791415,-82.4956635498308,1,237,297,745,517,4275,2379 +35.4730542741017,-82.4500369052295,1,306,479,865,1033,3597,1599 +35.5183542471837,-82.6859918387393,1,162,275,542,666,1186,1219 +35.5102067700107,-82.37116799099,1,208,272,638,523,3168,1880 +35.676741203427,-82.6309139606134,1,394,511,835,831,2608,1843 +35.5959182298707,-82.8026656870771,1,272,374,770,722,3459,3080 +35.7513720943318,-82.408647020484,1,271,361,641,753,2067,2068 +35.5010815955769,-82.7127160162916,1,226,283,655,474,3557,2139 +35.5027110910115,-82.4959894544351,1,247,307,698,503,4034,2092 +35.5059700818807,-82.4132096849441,1,376,552,944,1338,2614,2602 +35.7226929746828,-82.7368329570094,1,180,321,745,637,3181,2226 +35.5955923307838,-82.8023397824728,1,210,285,643,548,3315,2783 +35.5920074408277,-82.5204322997573,1,122,170,500,240,4266,1672 +35.5525736513103,-82.8274344370035,1,-10,219,752,756,2732,2155 +35.5147693572276,-82.6700225131289,1,348,413,818,742,3439,2227 +35.5496405595281,-82.7019611643498,1,209,255,632,458,3867,1950 +35.6366556157358,-82.4213573000515,1,175,224,625,360,4631,1898 +35.4984744028815,-82.4073434020668,1,386,541,992,1477,3163,2794 +35.6672901299063,-82.8359079567152,1,208,238,562,296,4853,1709 +35.7024872312937,-82.5689920857973,1,331,413,707,693,2646,2148 +35.5388858896597,-82.7185822991689,1,416,506,875,1157,1763,1803 +35.4753355677102,-82.4506887144381,1,185,289,699,564,3751,1821 +35.7243224701174,-82.6380838619079,1,536,678,1106,1099,2900,2578 +35.7083534148583,-82.5015298327081,1,259,321,704,576,3478,2388 +35.488371531187,-82.4301567243675,1,317,490,900,1168,1706,2004 +35.4929341184039,-82.4604658525669,1,279,317,680,531,3805,2135 +35.480224054014,-82.4376525302662,1,486,618,1044,1391,2293,2885 +35.5467074677458,-82.6270031053619,1,298,352,740,620,3459,1912 +35.6624016436025,-82.6406910987423,1,127,200,580,373,3171,1554 +35.7005318367722,-82.8033174962857,1,268,434,907,973,2958,2444 +35.4697952832325,-82.4471037637908,1,220,346,717,905,3075,2155 +35.7575641769833,-82.5474823819138,1,269,347,783,626,3694,2426 +35.4968449074469,-82.473827941343,1,243,355,795,681,3384,2738 +35.4707729804933,-82.5559559016255,1,812,917,1211,1168,3264,2128 +35.7376843326811,-82.5526968555825,1,132,218,646,447,3932,1952 +35.6083023951737,-82.728685341902,1,365,459,831,959,2368,2023 +35.6555577627772,-82.5080479247941,1,233,307,691,527,3630,2113 +35.5512700549627,-82.6436242401809,1,432,526,904,916,3606,3425 +35.6682678271671,-82.8342784336937,1,231,267,687,360,5248,1888 +35.504666485533,-82.3773601784716,1,335,418,858,655,4200,2513 +35.6682678271671,-82.8355820521109,1,173,210,510,257,5294,1591 +35.6477361846911,-82.6882731709694,1,351,424,756,876,2994,2304 +35.5040146873592,-82.4154910171742,1,307,460,809,1170,2344,2274 +35.6702232216886,-82.8456850948441,1,396,502,943,1322,2139,2239 +35.6793483961224,-82.7247744866505,1,361,469,885,881,2786,2198 +35.6653347353848,-82.6846882203221,1,171,228,688,337,6213,2170 +35.6516469737341,-82.5931090265152,1,297,366,654,641,2514,2186 +35.4740319713625,-82.4500369052295,1,274,415,716,850,3246,1482 +35.6943397541207,-82.7149973485216,1,172,204,483,270,3090,1387 +35.7572382778964,-82.5409642898279,1,266,327,552,583,1992,1926 +35.6509951755603,-82.5165214445057,1,283,350,747,594,3911,1867 +35.6669642308194,-82.837863384341,1,505,739,1408,1645,3788,2873 +35.6320930285189,-82.7355293385922,1,213,255,716,398,4686,1969 +35.4913046229693,-82.491426789975,1,199,258,597,354,4133,1978 +35.5023851919246,-82.502507546521,1,230,297,775,528,4178,2143 +35.4707729804933,-82.445148336165,1,316,463,801,1229,2644,2524 +35.6773930016009,-82.7091310656443,1,437,556,948,935,3267,2859 +35.5154211554014,-82.6987021183069,1,276,347,713,579,3686,1956 +35.5672391102217,-82.8381892889453,1,238,289,610,457,3226,2132 +35.541493082355,-82.6338471020521,1,317,460,850,862,2537,2118 +35.6594685518202,-82.5999530232054,1,366,433,846,837,2962,2104 +35.5349751006166,-82.7058720196013,1,344,418,803,799,3246,2275 +35.492608219317,-82.4607917571712,1,536,673,1057,957,3319,2271 +35.6272045422151,-82.7097828748529,1,300,366,774,647,3289,2404 +35.5062959809676,-82.6811032696749,1,297,366,734,622,3407,2372 +35.6822814879047,-82.6775183190277,1,456,530,806,794,2672,2460 +35.6526246709949,-82.7384624800309,1,178,271,696,699,2597,1770 +35.5196578435314,-82.5986494047882,1,460,610,861,768,3588,2006 +35.4870679348393,-82.4630730894013,1,139,172,513,248,4277,1698 +35.5408412841812,-82.654704996727,1,354,416,831,627,3643,2256 +35.524546329835204,-82.6022343554354,1,105,206,541,478,2308,1745 +35.6702232216886,-82.5377052437849,1,209,273,667,442,3820,2018 +35.5056441827938,-82.3724716094072,1,272,349,750,612,3576,2278 +35.7513720943318,-82.4089729250883,1,283,372,683,825,2166,2247 +35.7226929746828,-82.6397133849294,1,520,642,1051,1220,2059,2266 +35.6829332860785,-82.568666181193,1,-64,104,593,537,2922,2085 +35.6477361846911,-82.8271085323992,1,226,314,784,655,4180,2569 +35.4981485037946,-82.4731761321345,1,241,343,737,687,3813,2206 +35.5248722289221,-82.6654598486687,1,188,243,596,459,3920,1842 +35.5137916599668,-82.3890927442262,1,174,212,567,350,3865,1643 +35.5401894860074,-82.6683929901074,1,365,565,1050,1063,2073,2120 +35.623619652259,-82.7237967728376,1,166,205,569,282,3881,1704 +35.4870679348393,-82.4255940599073,1,192,264,585,472,2225,1350 +35.68912536873,-82.7094569702486,1,302,415,1014,865,4178,2394 +35.5118362654453,-82.46828756307,1,225,290,674,537,3479,2347 +35.7044426258153,-82.496967168248,1,175,224,597,373,3534,1773 +35.4737060722756,-82.4503628098338,1,282,427,764,920,3141,1530 +35.5072736782284,-82.6853400295307,1,177,226,608,386,3641,1926 +35.549966458615,-82.5777915101133,1,213,256,595,313,5088,1330 +35.5121621645322,-82.6843623157179,1,188,232,617,381,4115,1727 +35.5141175590537,-82.496967168248,1,228,281,588,411,3896,1930 +35.6989023413376,-82.537053434576407,1,148,181,576,271,4075,1758 +35.5062959809676,-82.3802933199103,1,469,653,1110,1298,3156,2941 +35.6245973495197,-82.7065238288099,1,242,293,696,471,3718,2258 +35.713567800249,-82.5465046681009,1,294,356,707,564,3010,1899 +35.6379592120835,-82.8280862462121,1,114,223,684,424,4253,1426 +35.7396397272027,-82.5285799148647,1,482,678,1147,1489,3479,3228 +35.512813962706,-82.3868114119962,1,663,787,1328,1606,3126,3417 +35.566261412961,-82.5680143719844,1,289,374,754,674,3436,2444 +35.5281312197913,-82.5715993226317,1,128,173,454,258,3271,1379 +35.7282332591604,-82.6351507204693,1,595,709,1178,1251,2726,2104 +35.5427966787027,-82.6279808191748,1,236,282,606,436,2682,1290 +35.6477361846911,-82.8261308185863,1,147,210,518,357,3600,1376 +35.6826073869916,-82.7221672498161,1,314,416,905,877,3088,2281 +35.480224054014,-82.4656803262357,1,329,412,811,808,3145,2658 +35.5001038983161,-82.8724092723963,1,524,716,1101,1426,2376,3147 +35.672504515297,-82.6830586973007,1,208,250,618,333,4594,1834 +35.6294858358236,-82.7560613286628,1,76,208,798,703,3880,2077 +35.6307894321712,-82.6452537632024,1,454,497,796,765,2911,1981 +35.4766391640579,-82.4360230072448,1,265,417,928,1021,2404,2105 +35.525198128009,-82.6934876446381,1,183,230,596,339,4313,2009 +35.7543051861141,-82.7736601772948,1,105,193,528,366,3652,1483 +35.5656096147871,-82.5660589443586,1,354,445,785,866,2871,2443 +35.5304125133997,-82.622766345506,1,299,377,727,585,3961,2189 +35.5069477791415,-82.3721457048029,1,247,342,719,646,3009,2182 +35.5323679079213,-82.7146714439173,1,240,296,705,472,3772,2355 +35.4851125403178,-82.4790424150118,1,295,346,784,614,4877,2017 +35.5643060184395,-82.5712734180274,1,370,463,770,854,2871,2551 +35.4838089439701,-82.4311344381804,1,167,199,525,293,3849,1505 +35.5623506239179,-82.5627998983157,1,275,348,756,615,4123,1949 +35.5441002750504,-82.651445950684,1,502,627,1034,1108,2915,2556 +35.701183634946,-82.5781174147176,1,295,410,746,800,2860,2483 +35.492608219317,-82.4950117406222,1,199,249,643,394,4175,1912 +35.4746837695363,-82.4392820532877,1,148,192,505,339,3032,1402 +35.5437743759635,-82.6331952928435,1,227,260,563,458,2392,1269 +35.5437743759635,-82.606145210687,1,133,216,556,389,2884,1299 +35.6076505969999,-82.3460733364593,1,261,406,751,938,1247,1429 +35.5405153850943,-82.4402597671006,1,909,1187,1667,1850,2218,3457 +35.6708750198625,-82.7136937301044,1,361,408,781,682,2865,2572 +35.6096059915214,-82.4412374809135,1,372,521,857,812,2760,1780 +35.5392117887466,-82.7192341083775,1,360,426,832,920,2715,1979 +35.7575641769833,-82.5409642898279,1,220,289,549,547,1937,1707 +35.5952664316969,-82.6292844375919,1,368,419,772,744,3362,2621 +35.5411671832681,-82.6380838619079,1,407,535,908,904,2449,2162 +35.6079764960868,-82.7426992398867,1,167,224,607,320,5465,1781 +35.475987365884,-82.4376525302662,1,447,589,975,1354,3119,3507 +35.566261412961,-82.5673625627758,1,410,518,872,981,2982,2928 +35.7233447728566,-82.6413429079509,1,377,443,795,826,2544,2257 +35.6949915522946,-82.571925227236,1,445,562,914,969,2725,1954 +35.6682678271671,-82.8349302429023,1,227,262,663,339,5248,1845 +35.5444261741373,-82.6651339440644,1,215,303,587,657,1901,1525 +35.6373074139096,-82.8303675784422,1,70,157,523,287,4338,1247 +35.5626765230049,-82.5650812305457,1,263,344,677,600,3442,1785 +35.5822304682201,-82.6980503090983,1,224,280,694,428,4688,2069 +35.5177024490098,-82.6866436479479,1,351,425,744,784,2148,1773 +35.6793483961224,-82.6622008026258,1,224,264,630,342,4883,1770 +35.7448541125934,-82.5566077108341,1,192,241,628,358,4569,1861 +35.6624016436025,-82.820264535709,1,452,712,1230,1588,3218,3101 +35.6800001942962,-82.7322702925493,1,267,348,849,612,4237,2426 +35.4893492284478,-82.7358552431965,1,203,262,653,524,3141,1627 +35.6083023951737,-82.4158169217785,1,366,466,850,1077,2114,2187 +35.7210634792482,-82.6931617400338,1,244,309,675,546,3254,2296 +35.6796742952093,-82.6446019539938,1,309,373,715,1029,2138,2114 +35.652298771908,-82.7387883846352,1,181,246,669,539,3192,1745 +35.5001038983161,-82.8730610816049,1,568,790,1174,1547,2450,3235 +35.5623506239179,-82.5634517075243,1,271,362,730,618,3330,1894 +35.6627275426894,-82.7163009669388,1,398,500,821,976,2084,1982 +35.5480110640934,-82.5758360824875,1,248,308,644,466,4309,1538 +35.4717506777541,-82.5605185660856,1,322,433,778,771,2475,2167 +35.4808758521878,-82.4321121519932,1,414,564,1062,1233,2422,2487 +35.7249742682912,-82.6351507204693,1,347,431,799,753,2805,2597 +35.5607211284833,-82.5627998983157,1,459,564,865,1038,2181,2811 +35.6692455244279,-82.8430778580097,1,343,453,889,1148,2264,1974 +35.5496405595281,-82.6429724309723,1,343,406,718,667,3233,2853 +35.5150952563145,-82.8476405224698,1,370,504,783,1043,1757,2741 +35.5450779723112,-82.6328693882392,1,320,389,768,720,2966,1997 +35.5049923846199,-82.4132096849441,1,287,412,684,1003,2153,1739 +35.5952664316969,-82.5191286813401,1,177,237,552,380,3579,1682 +35.6148203769121,-82.6677411808988,1,191,223,604,340,4304,1674 +35.656209560951,-82.70880516104,1,132,232,713,535,3727,2020 +35.5903779453931,-82.4559031881068,1,282,338,740,548,4040,2369 +35.5079254764022,-82.410928352714,1,435,540,805,1062,1916,2907 +35.6099318906083,-82.8730610816049,1,254,336,729,609,3886,2126 +35.4974967056208,-82.4060397836496,1,449,629,1106,1570,3193,2817 +35.5281312197913,-82.577465605509,1,222,282,761,485,4556,1974 +35.5056441827938,-82.4956635498308,1,189,229,607,364,4081,1871 +35.5196578435314,-82.6071229244998,1,267,334,716,648,3216,2120 +35.6698973226017,-82.8316711968594,1,136,199,583,345,4195,1724 +35.6871699742085,-82.5706216088188,1,903,1090,1314,1322,3058,2165 +35.5441002750504,-82.6299362468005,1,206,243,579,388,3311,1497 +35.5959182298707,-82.7423733352824,1,252,327,760,654,3821,2038 +35.6301376339974,-82.6837105065093,1,250,286,743,546,3485,1684 +35.7451800116803,-82.6328693882392,1,340,434,791,751,2768,2169 +35.5600693303095,-82.5647553259414,1,494,590,860,1057,2129,3014 +35.4753355677102,-82.4669839446528,1,222,277,623,455,3764,2067 +35.6669642308194,-82.7153232531259,1,376,433,850,722,3198,2017 +35.5626765230049,-82.7104346840615,1,256,284,563,459,2986,1713 +35.6943397541207,-82.7081533518314,1,380,475,858,895,3034,2597 +35.4766391640579,-82.4324380565975,1,166,269,786,668,3447,1996 +35.6679419280802,-82.840144716571,1,297,401,738,943,1852,2269 +35.5095549718368,-82.3708420863857,1,236,319,664,623,2888,2150 +35.4961931092731,-82.4050620698367,1,383,530,1027,1367,3137,2840 +35.5613729266572,-82.5608444706899,1,310,411,893,774,3793,1872 +35.5427966787027,-82.6328693882392,1,303,359,704,698,2655,2138 +35.603413908869904,-82.7352034339879,1,392,484,906,1198,2528,2838 +35.6418700011265,-82.3499841917108,1,221,270,676,435,4180,2007 +35.6461066892565,-82.3610649482568,1,327,603,1076,1246,1436,1754 +35.5985254225661,-82.4132096849441,1,402,584,992,774,3715,2398 +35.668593726254,-82.8437296672183,1,253,326,691,710,2683,1724 +35.5141175590537,-82.385507793579,1,402,624,1085,1356,2826,2630 +35.4766391640579,-82.4399338624963,1,313,464,940,1015,2790,2384 +35.4997779992292,-82.41972777703,1,301,415,811,989,2617,2159 +35.6389369093442,-82.3548727607752,1,202,252,672,404,4184,2057 +35.6444771938219,-82.8264567231906,1,759,857,1265,1153,3845,1819 +35.4997779992292,-82.4194018724257,1,367,465,822,981,2513,2053 +35.4730542741017,-82.4422151947264,1,441,564,981,1168,2152,2328 +35.6421959002135,-82.3551986653795,1,183,229,619,351,4206,1773 +35.4955413110993,-82.428527201346,1,284,404,828,844,2821,1982 +35.5066218800546,-82.379967415306,1,473,696,1195,1554,3009,3102 +35.5062959809676,-82.3796415107017,1,462,667,1218,1557,3214,3162 +35.5633283211787,-82.5901758850765,1,423,530,915,1050,2647,2726 +35.50075569649,-82.8675207033319,1,302,410,643,782,1207,1780 +35.4733801731887,-82.4386302440791,1,286,423,842,946,2971,2347 +35.4733801731887,-82.4431929085393,1,247,337,762,715,2870,2055 +35.5995031198269,-82.3160901128641,1,213,291,711,582,3652,2009 +35.4724024759279,-82.439607957892,1,362,463,757,990,2728,2243 +35.6096059915214,-82.4158169217785,1,295,405,822,986,2173,1885 +35.4831571457963,-82.4255940599073,1,198,260,616,450,3806,1960 +35.4994521001423,-82.4200536816343,1,242,334,741,681,3383,2028 +35.5649578166133,-82.5852873160121,1,500,630,958,1173,2543,3238 +35.4831571457963,-82.4317862473889,1,264,338,784,562,4265,2529 +35.5255240270959,-82.705546114997,1,319,428,801,786,3154,3087 +35.4779427604056,-82.4415633855178,1,399,481,1006,1005,3143,2173 +35.4890233293608,-82.4096247342969,1,273,429,780,1094,2845,2617 +35.6405664047789,-82.3548727607752,1,199,271,723,561,3578,1939 +35.5066218800546,-82.3793156060974,1,539,751,1231,1582,3078,3513 +35.4873938339262,-82.428527201346,1,363,509,896,1100,1845,1698 +35.6425217993004,-82.3551986653795,1,187,231,632,353,4221,1805 +35.5646319175264,-82.5849614114078,1,489,612,939,1186,2521,3321 +35.6389369093442,-82.3522655239409,1,313,389,765,752,3150,2316 +35.4834830448832,-82.434067579619,1,78,247,741,602,3710,2153 +35.4753355677102,-82.4386302440791,1,435,584,989,1278,3293,3379 +35.4710988795802,-82.4379784348705,1,971,1125,1391,1426,3239,2789 +35.5196578435314,-82.6087524475213,1,421,534,956,1165,2914,2583 +35.4746837695363,-82.4425410993307,1,97,142,526,259,3989,1626 +35.6451289919957,-82.3597613298397,1,342,576,1006,1170,1379,1649 +35.6692455244279,-82.8349302429023,1,173,252,573,535,2783,1753 +35.4808758521878,-82.439607957892,1,165,222,613,354,3708,1758 +35.4763132649709,-82.4399338624963,1,128,248,718,618,3231,2056 +35.5405153850943,-82.4409115763092,1,-52,130,773,620,3582,2547 +35.6405664047789,-82.3555245699838,1,172,239,697,438,4376,2023 +35.5190060453575,-82.6077747337084,1,567,692,1135,1589,2805,3422 +35.6063470006522,-82.351287810128,1,4314,4808,5658,6309,7012,7051 +35.6314412303451,-82.7352034339879,1,226,310,720,552,3882,2117 +35.5203096417052,-82.6100560659385,1,492,617,1047,1379,2781,3001 +35.4828312467094,-82.4347193888276,1,214,302,687,558,3972,2104 +35.4737060722756,-82.451666428251,1,205,244,606,319,5229,1770 +35.6089541933476,-82.3372739121433,1,229,290,701,587,4086,2084 +35.701183634946,-82.5790951285305,1,1287,1664,2303,2313,3701,3203 +35.6171016705206,-82.3727975140115,1,425,563,981,947,3191,2698 +35.6682678271671,-82.840144716571,1,245,378,727,949,1565,2062 +35.4955413110993,-82.4288531059503,1,361,474,883,1002,2333,2153 +35.4701211823195,-82.4458001453736,1,167,216,591,326,4761,1575 +35.6428476983873,-82.3493323825022,1,186,234,661,436,4112,2109 +35.5180283480968,-82.6120114935643,1,358,457,885,1017,3080,2525 +35.4818535494486,-82.4425410993307,1,479,593,1057,973,3345,2175 +35.4831571457963,-82.4262458691159,1,175,251,638,545,3478,1722 +35.4733801731887,-82.4487332868123,1,152,225,649,433,4074,1894 +35.6392628084312,-82.3538950469623,1,88,162,593,352,3865,1687 +35.5972218262184,-82.3200009681156,1,301,466,1128,1034,2948,2196 +35.6457807901696,-82.3604131390482,1,376,620,1001,1108,1286,1566 +35.5222650362267,-82.4096247342969,1,128,298,834,763,2977,2504 +35.4733801731887,-82.4435188131436,1,131,194,565,315,3853,1781 +35.4834830448832,-82.4321121519932,1,296,376,833,669,4184,2574 +35.4750096686233,-82.442867003935,1,172,217,597,380,3877,1694 +35.5001038983161,-82.4194018724257,1,443,528,867,951,2018,1759 +35.4740319713625,-82.4519923328553,1,145,186,574,281,5422,1751 +35.5150952563145,-82.3881150304134,1,289,425,836,774,3963,2383 +35.6689196253409,-82.8430778580097,1,377,481,858,1203,1791,1942 +35.6389369093442,-82.3519396193366,1,260,334,777,578,3910,2358 +35.5623506239179,-82.5914795034937,1,454,571,914,1046,2820,3090 +35.4727283750148,-82.4409115763092,1,435,556,980,1107,3242,3112 +35.4873938339262,-82.4278753921374,1,198,333,780,830,2870,1869 +35.5004297974031,-82.4194018724257,1,325,447,796,936,2484,2066 +35.472076576841,-82.4409115763092,1,371,489,835,1140,3038,2630 +35.6076505969999,-82.4360230072448,1,438,564,935,1201,2254,2670 +35.6415441020396,-82.3548727607752,1,184,235,638,374,4171,1847 +35.4724024759279,-82.4415633855178,1,331,413,890,846,3697,2657 +35.5066218800546,-82.3796415107017,1,492,698,1182,1511,3032,3203 +35.4737060722756,-82.4399338624963,1,287,364,837,624,3609,2092 +35.6682678271671,-82.8398188119668,1,208,345,669,907,1660,2172 +35.49651900836,-82.4027807376066,1,176,229,558,528,3641,1847 +35.6389369093442,-82.3542209515666,1,212,259,654,418,3884,2059 +35.4812017512748,-82.439607957892,1,524,644,1140,1149,3130,2230 +35.639914606605,-82.3522655239409,1,211,254,666,432,4182,2015 +35.4782686594925,-82.4422151947264,1,235,303,737,510,3484,2024 +35.49651900836,-82.4024548330024,1,226,302,636,698,3499,2074 +35.4948895129254,-82.4096247342969,1,324,489,903,1228,2939,2488 +35.4834830448832,-82.4435188131436,1,573,700,1004,1032,3488,2262 +35.4860902375786,-82.4343934842233,1,254,363,803,701,3145,1822 +35.6451289919957,-82.8261308185863,1,940,1063,1443,1429,3133,1940 +35.5646319175264,-82.5872427436378,1,396,502,815,1012,2363,2393 +35.4795722558402,-82.4888195531406,1,324,399,686,738,2359,2416 +35.5626765230049,-82.5908276942851,1,350,467,962,1020,2828,2437 +35.5199837426183,-82.6090783521256,1,462,577,958,1123,3043,2662 +35.4994521001423,-82.4203795862386,1,257,336,790,783,3690,2111 +35.6454548910827,-82.3607390436525,1,375,590,1016,1167,1354,1625 +35.5001038983161,-82.4190759678214,1,320,372,749,687,3087,1876 +35.4740319713625,-82.4409115763092,1,352,529,1034,1262,2188,2331 +35.4922823202301,-82.6902285985952,1,255,408,923,975,2930,2198 +35.615472175086,-82.7172786807517,1,265,330,725,568,4220,2132 +35.4743578704494,-82.4389561486834,1,442,581,974,1238,3510,3737 +35.5183542471837,-82.6126633027729,1,402,505,921,1154,2832,2691 +35.4834830448832,-82.425268155303,1,380,488,1014,1241,2481,2405 +35.4779427604056,-82.437326625662,1,277,381,724,850,2390,2031 +35.5183542471837,-82.6133151119815,1,364,455,850,928,3232,2457 +35.6314412303451,-82.7355293385922,1,161,220,609,431,3957,1799 +35.6079764960868,-82.336948007539,1,299,363,722,596,3287,1836 +35.6412182029527,-82.3542209515666,1,180,239,659,399,4384,1967 +35.5190060453575,-82.608426542917,1,425,550,972,1171,2966,2702 +35.6689196253409,-82.8440555718226,1,373,491,884,1136,2084,1972 +35.639914606605,-82.3538950469623,1,169,229,655,481,3603,1791 +35.5427966787027,-82.583005983782,1,293,375,790,661,3775,2292 +35.6786965979485,-82.6419947171595,1,409,494,960,1455,2005,2469 +35.484460742144,-82.4334157704104,1,624,761,1103,1251,1857,1637 +35.5115103663584,-82.382248747536,1,328,421,817,891,3013,2520 +35.6454548910827,-82.360087234444,1,369,622,1023,1144,1307,1471 +35.668593726254,-82.843403762614,1,304,408,788,996,2148,1882 +35.6395887075181,-82.3542209515666,1,173,231,672,434,3883,1699 +35.6076505969999,-82.445148336165,1,172,212,591,313,4542,1658 +35.6043916061307,-82.4106024481097,1,339,561,937,826,3047,1811 +35.4821794485355,-82.4392820532877,1,63,227,766,743,3399,2356 +35.4847866412309,-82.4265717737202,1,77,172,598,453,3772,1983 +35.4952154120123,-82.4295049151589,1,307,400,685,836,1785,1948 +35.5630024220918,-82.5908276942851,1,374,484,872,924,2855,2691 +35.4769650631448,-82.4405856717049,1,182,309,820,691,2859,1945 +35.4834830448832,-82.4259199645116,1,337,495,1056,1111,2907,2279 +35.4974967056208,-82.4076693066711,1,377,554,1049,1571,3306,2998 +35.4792463567532,-82.4418892901221,1,306,325,663,539,3454,1891 +35.6415441020396,-82.3555245699838,1,211,272,691,479,3875,1977 +35.6066728997391,-82.3486805732936,1,4383,4913,5729,6241,6934,7131 +35.5219391371398,-82.4099506389012,1,-218,-32,587,535,3737,2157 +35.4766391640579,-82.4389561486834,1,183,282,883,633,4044,2020 +35.6425217993004,-82.3503100963151,1,287,357,767,588,3697,2011 +35.4838089439701,-82.4324380565975,1,260,325,760,494,4574,2234 +35.5219391371398,-82.4089729250883,1,413,544,1004,1034,3281,2315 +35.639914606605,-82.3545468561709,1,159,227,672,433,3830,1733 +35.7409433235503,-82.4138614941527,1,203,322,834,661,3742,1771 +35.5633283211787,-82.5898499804722,1,432,550,938,1080,2660,2732 +35.6047175052176,-82.4122319711312,1,39,192,668,484,4224,2050 +35.4795722558402,-82.4311344381804,1,170,278,756,572,2886,1848 +35.4697952832325,-82.4464519545822,1,174,215,633,328,5187,1803 +35.6063470006522,-82.3525914285452,1,3894,4416,5313,6067,6480,6466 +35.6474102856042,-82.3617167574654,1,391,631,1031,1171,1380,1788 +35.4740319713625,-82.4392820532877,1,434,569,951,1204,3565,3244 +35.6477361846911,-82.3620426620697,1,509,764,1167,1321,1517,1810 +35.6721786162101,-82.8108133021845,1,107,187,611,345,4953,1885 +35.6050434043045,-82.4115801619226,1,26,336,684,726,2813,2128 +35.4763132649709,-82.4392820532877,1,351,510,1016,1220,2607,2526 +35.5646319175264,-82.5859391252206,1,408,512,848,1053,2557,2793 +35.6402405056919,-82.3532432377537,1,224,311,756,551,4080,2146 +35.4890233293608,-82.4102765435054,1,234,340,724,835,3279,2260 +35.5372563942251,-82.3907222672477,1,282,354,787,648,3896,2556 +35.6245973495197,-82.714345539313,1,210,247,617,359,4087,1758 +35.4831571457963,-82.4265717737202,1,170,246,611,442,3452,1831 +35.5832081654809,-82.4699170860915,1,280,346,670,503,3413,2013 +35.6076505969999,-82.4366748164534,1,109,149,390,203,4834,928 +35.4740319713625,-82.437326625662,1,196,329,755,885,2873,1995 +35.5199837426183,-82.6077747337084,1,569,704,1177,1633,2688,3187 +35.4779427604056,-82.4422151947264,1,149,204,591,440,3281,1808 +35.6063470006522,-82.3486805732936,1,4559,5178,6135,6763,7416,7414 +35.5066218800546,-82.3806192245146,1,421,670,1200,1531,3015,2916 +35.5646319175264,-82.5862650298249,1,436,547,900,1118,2597,2762 +35.5030369900984,-82.3695384679685,1,148,202,559,326,3697,1507 +35.4776168613186,-82.4418892901221,1,133,231,607,439,3481,1712 +35.5199837426183,-82.6094042567299,1,424,528,925,982,3116,2431 +35.6747858089055,-82.7547577102457,1,263,389,907,1062,3084,2264 +35.4805499531009,-82.4418892901221,1,167,224,593,409,4103,1722 +35.4753355677102,-82.4458001453736,1,266,353,914,672,4363,2195 +35.5379081923989,-82.3907222672477,1,273,342,783,618,3982,2562 +35.6464325883434,-82.3610649482568,1,426,660,1069,1223,1438,1893 +35.5995031198269,-82.3157642082598,1,193,275,719,554,3480,1979 +35.6712009189494,-82.809183779163,1,215,303,749,584,3761,2161 +35.4886974302739,-82.4099506389012,1,281,376,726,857,3162,2500 +35.6718527171232,-82.75736494708,1,273,392,926,1007,2805,2176 +35.4743578704494,-82.4425410993307,1,139,192,603,356,4217,1671 +35.4873938339262,-82.4282012967417,1,198,333,780,830,2870,1869 +35.4890233293608,-82.4262458691159,1,415,504,981,1291,2393,2346 +35.4864161366655,-82.4477555729994,1,400,510,820,747,3258,2024 +35.6177534686944,-82.3744270370329,1,246,405,861,719,3719,2322 +35.6461066892565,-82.3607390436525,1,365,615,1047,1215,1425,1790 +35.7018354331199,-82.579746937739,1,219,368,724,692,3126,2262 +35.6695714235148,-82.8450332856355,1,328,446,870,1220,1798,1821 +35.6408923038658,-82.3542209515666,1,153,243,712,510,3635,1823 +35.6454548910827,-82.3604131390482,1,327,592,1061,1198,1406,1661 +35.6744599098186,-82.7547577102457,1,311,420,949,1089,2727,2233 +35.4737060722756,-82.4487332868123,1,156,208,658,315,4651,1976 +35.4737060722756,-82.4474296683951,1,224,275,691,417,4308,2134 +35.4828312467094,-82.4334157704104,1,244,320,745,557,4169,2231 +35.5649578166133,-82.5856132206164,1,418,536,876,1024,2682,2887 +35.6434994965611,-82.3551986653795,1,178,222,616,356,4267,1796 +35.484460742144,-82.4249422506987,1,271,360,814,693,3645,2133 +35.5056441827938,-82.4200536816343,1,282,389,801,758,3811,2215 +35.5626765230049,-82.5905017896808,1,435,557,871,1087,2457,2610 +35.6047175052176,-82.4119060665269,1,39,192,668,484,4224,2050 +35.4737060722756,-82.4392820532877,1,482,630,1035,1409,3298,3738 +35.6109095878691,-82.414187398757,1,293,427,913,1133,2384,2059 +35.5646319175264,-82.5869168390335,1,403,512,840,1074,2226,2575 +35.4733801731887,-82.4392820532877,1,344,440,841,857,2997,2601 +35.6408923038658,-82.3548727607752,1,145,231,690,461,3774,1816 +35.4838089439701,-82.4327639612018,1,177,256,709,463,4350,2236 +35.4952154120123,-82.4298308197632,1,251,318,666,612,2669,1872 +35.6122131842168,-82.3525914285452,1,154,266,683,639,3072,2126 +35.5144434581406,-82.3864855073919,1,482,753,1348,1762,3421,3136 +35.6171016705206,-82.3734493232201,1,367,489,929,802,3750,2561 +35.4785945585794,-82.4422151947264,1,75,176,672,369,3844,1954 +35.4733801731887,-82.439607957892,1,308,409,843,918,2967,2512 +35.4831571457963,-82.4324380565975,1,438,556,1034,1149,3660,2861 +35.6252491476936,-82.7136937301044,1,210,255,617,388,4130,1639 +35.6060211015653,-82.3483546686893,1,3235,3596,4289,4796,5375,5812 +35.6063470006522,-82.3509619055237,1,4352,4836,5614,6135,6822,7049 +35.4750096686233,-82.4386302440791,1,514,675,1073,1493,3327,3897 +35.606998798826,-82.4366748164534,1,105,146,384,247,3586,1127 +35.5186801462706,-82.6120114935643,1,372,473,928,1113,3111,2512 +35.5186801462706,-82.6081006383127,1,393,516,941,1251,2864,2879 +35.5030369900984,-82.4200536816343,1,14,137,523,371,3290,1721 +35.4753355677102,-82.4435188131436,1,172,215,609,345,4273,1738 +35.6412182029527,-82.3555245699838,1,211,266,671,420,4117,2073 +35.4971708065339,-82.7368329570094,1,198,285,699,760,3228,2010 +35.4828312467094,-82.434067579619,1,458,584,964,925,3293,2225 +35.5203096417052,-82.6081006383127,1,582,713,1176,1613,2758,3189 +35.6444771938219,-82.8261308185863,1,759,857,1265,1153,3845,1819 +35.5147693572276,-82.3881150304134,1,364,506,986,1165,3346,2678 +35.5633283211787,-82.5905017896808,1,376,475,817,895,3169,2710 +35.475987365884,-82.4431929085393,1,157,198,608,357,3789,1629 +35.4834830448832,-82.4337416750147,1,136,269,796,660,3719,2137 +35.5639801193525,-82.5882204574507,1,484,598,985,1213,2585,2894 +35.5193319444444,-82.5843096021992,1,411,532,953,1017,3390,3160 +35.472076576841,-82.4415633855178,1,319,399,804,791,3346,2370 +35.4776168613186,-82.4422151947264,1,127,185,524,345,3324,1735 +35.504666485533,-82.3845300797661,1,621,691,1166,1128,4949,4111 +35.4740319713625,-82.4490591914166,1,205,258,662,438,3974,1960 +35.472076576841,-82.4412374809135,1,394,505,880,1108,3167,2986 +35.5382340914858,-82.7166268715431,1,310,403,820,931,2514,1849 +35.4743578704494,-82.4386302440791,1,550,719,1169,1663,3416,4295 +35.6731563134709,-82.7567131378714,1,139,226,675,517,3422,1853 +35.4740319713625,-82.4412374809135,1,395,562,1085,1351,2282,2537 +35.4750096686233,-82.4379784348705,1,412,551,918,1264,2952,3035 +35.4743578704494,-82.4471037637908,1,199,248,617,391,3725,1680 +35.6786965979485,-82.6416688125552,1,400,476,937,1303,2367,2348 +35.4756614667971,-82.4379784348705,1,471,633,1012,1420,3206,3681 +35.5030369900984,-82.3698643725728,1,163,240,668,540,4096,1834 +35.4730542741017,-82.439607957892,1,381,507,902,1041,2963,2757 +35.4798981549271,-82.4308085335761,1,249,284,451,463,1418,1834 +35.4733801731887,-82.4389561486834,1,360,472,874,891,3091,2489 +35.4838089439701,-82.4288531059503,1,384,468,929,894,2890,2380 +35.475987365884,-82.4356971026405,1,200,293,699,546,3131,1705 +35.5639801193525,-82.5878945528464,1,478,590,973,1218,2473,2844 +35.4942377147516,-82.4004994053766,1,440,507,761,870,3205,2115 +35.6252491476936,-82.3274967740144,2,143,179,519,314,2940,1485 +35.7484390025495,-82.5452010496837,2,308,492,907,939,2308,1891 +35.7080275157714,-82.4011512145852,2,187,254,510,548,2074,1909 +35.521287338966,-82.7101087794572,2,155,203,543,306,3245,1461 +35.50075569649,-82.6103819705428,2,215,258,582,364,2603,1395 +35.5656096147871,-82.5474823819138,2,182,214,439,280,2163,955 +35.6887994696431,-82.7778969371506,2,200,241,623,364,3586,1897 +35.6184052668683,-82.3415106719991,2,183,238,490,397,2718,1529 +35.5362786969643,-82.5214100135702,2,262,306,591,460,2823,1701 +35.6229678540851,-82.5585631384598,2,239,321,668,588,2934,1641 +35.549966458615,-82.7280335326934,2,191,236,609,414,3958,1832 +35.4769650631448,-82.4920785991836,2,249,311,660,504,2988,1541 +35.549966458615,-82.6452537632024,2,208,238,524,324,3182,1561 +35.5307384124867,-82.6224404409017,2,301,357,721,544,3959,2121 +35.6076505969999,-82.5911535988894,2,261,302,540,395,3109,1580 +35.6718527171232,-82.5536745693954,2,199,222,516,323,2752,1260 +35.6887994696431,-82.4676357538614,2,74,127,460,213,3068,1266 +35.6542541664295,-82.6716520361503,2,341,395,873,767,3489,2157 +35.7282332591604,-82.6475350954325,2,203,304,692,532,2809,1762 +35.6425217993004,-82.5781174147176,2,297,349,669,579,2630,1599 +35.5134657608799,-82.4487332868123,2,133,162,539,231,3993,1597 +35.5248722289221,-82.5341202931377,2,185,221,539,319,3080,1298 +35.6265527440413,-82.5377052437849,2,91,126,426,174,3444,1207 +35.5845117618285,-82.6058193060827,2,173,230,542,371,3086,1534 +35.5362786969643,-82.7459582859297,2,393,463,817,718,3692,2379 +35.664682937211,-82.5957162633495,2,318,364,657,557,2613,1631 +35.4854384394047,-82.8711056539791,2,183,233,606,399,3411,1669 +35.5522477522234,-82.7211895360032,2,184,270,738,553,4228,2104 +35.5568103394403,-82.5298835332818,2,175,220,459,282,2961,1259 +35.6148203769121,-82.4992485004781,2,160,197,469,291,2996,1269 +35.5346492015297,-82.7035906873713,2,376,420,787,641,3075,1840 +35.6852145796869,-82.5530227601868,2,157,196,467,282,3004,1441 +35.5307384124867,-82.6162482534201,2,117,150,439,202,3320,1321 +35.606998798826,-82.6048415922698,2,324,358,672,539,3020,1697 +35.4854384394047,-82.726404009672,2,471,503,792,550,3675,2038 +35.6128649823906,-82.448407382208,2,247,326,713,510,4043,1839 +35.6698973226017,-82.4418892901221,2,296,374,655,647,2256,2094 +35.6477361846911,-82.5680143719844,2,288,374,735,619,3185,1956 +35.4769650631448,-82.5689920857973,2,153,189,492,278,3268,1388 +35.6011326152615,-82.4200536816343,2,110,143,485,186,3811,1423 +35.603413908869904,-82.5416160990365,2,138,175,443,230,3442,1295 +35.562024724831,-82.85448451916,2,115,222,611,407,2941,1481 +35.6151462759991,-82.3317335338702,2,210,289,599,413,3401,1717 +35.6692455244279,-82.6022343554354,2,280,341,731,576,3818,2132 +35.7513720943318,-82.4819755564504,2,176,248,657,424,4037,2013 +35.6268786431282,-82.3258672509929,2,213,261,578,307,3308,1445 +35.6431735974742,-82.3089202115696,2,102,146,523,288,3435,1539 +35.7122642039013,-82.5553040924169,2,155,199,483,253,3234,1300 +35.5871189545239,-82.6612230888129,2,441,571,990,1080,2914,2509 +35.6138426796514,-82.485886411702,2,89,119,463,176,3836,1374 +35.5587657339618,-82.8476405224698,2,225,274,565,421,2776,1653 +35.5395376878335,-82.4819755564504,2,160,202,435,283,2912,1241 +35.5427966787027,-82.6879472663651,2,546,630,976,823,3535,2180 +35.6040657070438,-82.5412901944322,2,293,350,558,441,2599,1233 +35.492608219317,-82.3610649482568,2,89,128,512,222,3263,1434 +35.517050650836,-82.2981653596278,2,187,310,816,617,3587,2066 +35.5515959540496,-82.4666580400485,2,313,394,819,694,3606,2191 +35.4763132649709,-82.5546522832083,2,63,118,450,195,3645,1406 +35.5004297974031,-82.5784433193219,2,173,210,523,297,2980,1284 +35.5682168074825,-82.5148919214843,2,163,219,485,311,3059,1294 +35.4978226047077,-82.3447697180421,2,141,191,521,369,3035,1865 +35.6783706988616,-82.7270558188805,2,226,304,783,601,3477,1698 +35.6245973495197,-82.3229341095543,2,129,189,572,294,3335,1412 +35.5235686325744,-82.7104346840615,2,170,207,507,276,3533,1521 +35.4828312467094,-82.3040316425052,2,90,129,521,226,3346,1400 +35.5372563942251,-82.5324907701162,2,254,306,574,434,2811,1396 +35.7262778646389,-82.459488138754,2,64,110,428,195,2747,1083 +35.6053693033914,-82.6045156876655,2,266,330,760,604,3398,1868 +35.6340484230404,-82.3548727607752,2,119,156,433,237,3089,1348 +35.6021103125222,-82.5601926614813,2,357,419,678,566,2669,1610 +35.5760383855686,-82.6162482534201,2,140,191,439,241,3285,1310 +35.6676160289932,-82.8427519534054,2,79,203,650,431,3584,1982 +35.6549059646034,-82.8365597659238,2,241,309,728,548,3924,2369 +35.5942887344361,-82.5142401122757,2,106,142,413,240,2944,1217 +35.5874448536108,-82.6390615757208,2,297,337,629,390,3359,1786 +35.5085772745761,-82.6067970198956,2,50,139,461,243,3700,1454 +35.5118362654453,-82.717604585356,2,307,379,805,591,4167,2312 +35.5098808709238,-82.8694761309576,2,188,225,552,286,5299,1626 +35.5408412841812,-82.7987548318255,2,270,370,681,657,2590,2038 +35.5926592390015,-82.4920785991836,2,182,214,537,331,3727,1551 +35.5978736243923,-82.3597613298397,2,342,421,723,752,2266,1800 +35.5281312197913,-82.8280862462121,2,340,431,806,705,3340,2116 +35.6969469468161,-82.5305353424904,2,125,263,775,606,4223,1877 +35.6995541395114,-82.5764878916961,2,291,325,670,553,3079,1954 +35.6369815148227,-82.671326131546,2,383,486,933,898,3117,2366 +35.5920074408277,-82.462747184797,2,202,264,629,463,3295,2161 +35.6350261203012,-82.4764351781774,2,98,156,501,246,4870,1486 +35.5630024220918,-82.6393874803251,2,341,409,759,654,3203,2164 +35.5799491746116,-82.637106148095,2,642,811,1244,1221,3560,2824 +35.6089541933476,-82.6019084508311,2,115,144,403,191,2887,1132 +35.5610470275703,-82.6804514604663,2,339,412,751,674,3142,2533 +35.6770671025139,-82.7576908516843,2,333,428,819,747,3152,2106 +35.5525736513103,-82.6361284342821,2,196,233,567,896,3571,2274 +35.5590916330487,-82.6703484177332,2,283,357,691,704,2972,2034 +35.5519218531365,-82.7127160162916,2,368,542,960,953,3076,2276 +35.4707729804933,-82.531187151699,2,307,408,718,596,3093,1922 +35.6461066892565,-82.5129364938585,2,316,412,725,563,3185,1702 +35.5343233024428,-82.5282540102604,2,320,366,605,492,2326,1465 +35.6268786431282,-82.299468978045,2,118,155,501,245,2842,1286 +35.7324699472904,-82.5732288456531,2,193,238,589,384,3506,1791 +35.5613729266572,-82.5295576286776,2,154,184,483,247,3175,1278 +35.5678909083956,-82.5836577929906,2,186,224,473,276,3400,1471 +35.5467074677458,-82.7378106708223,2,237,329,809,768,3594,2250 +35.4785945585794,-82.5116328754413,2,122,178,444,239,2899,1116 +35.6852145796869,-82.6142928257944,2,215,237,648,369,3576,1651 +35.5669132111348,-82.640365194138,2,117,143,460,188,3771,1400 +35.5401894860074,-82.8251531047735,2,226,290,681,476,3367,1915 +35.6105836887822,-82.5614962798985,2,154,183,489,241,3425,1279 +35.6102577896952,-82.4966412636437,2,183,244,578,436,3141,1826 +35.6063470006522,-82.3447697180421,2,181,269,723,618,3075,1537 +35.4896751275347,-82.7446546675125,2,236,311,728,498,4156,2140 +35.606998798826,-82.4425410993307,2,226,290,714,514,3873,1976 +35.513139861793,-82.4458001453736,2,156,229,514,364,2778,1412 +35.6131908814775,-82.451666428251,2,195,218,542,327,2654,1290 +35.5594175321357,-82.7886517890924,2,104,148,503,241,3081,1306 +35.7256260664651,-82.6045156876655,2,152,187,454,268,2391,1237 +35.4753355677102,-82.6397133849294,2,145,180,444,239,2908,1286 +35.5845117618285,-82.5308612470947,2,165,220,613,397,3847,2017 +35.480224054014,-82.3499841917108,2,136,151,512,248,3367,1513 +35.4988003019685,-82.8228717725434,2,77,130,495,219,3784,1682 +35.4779427604056,-82.5269503918432,2,156,185,456,267,2836,1230 +35.6343743221274,-82.6012566416225,2,213,277,557,325,3443,1529 +35.6040657070438,-82.4184241586128,2,115,144,439,201,3153,1275 +35.5229168344006,-82.5191286813401,2,164,204,495,286,3343,1386 +35.6392628084312,-82.3118533530082,2,-215,-110,367,157,3683,1758 +35.6040657070438,-82.5285799148647,2,413,489,751,713,2615,1660 +35.6268786431282,-82.557585424647,2,265,311,547,397,2780,1357 +35.6262268449544,-82.7101087794572,2,686,842,1080,1147,2174,2476 +35.7041167267283,-82.5641035167329,2,133,240,451,417,2339,1450 +35.5450779723112,-82.7303148649235,2,255,370,746,688,3112,1923 +35.5480110640934,-82.5331425793248,2,348,428,739,612,2751,2016 +35.6288340376497,-82.5513932371653,2,85,152,461,295,2698,1319 +35.5981995234792,-82.5324907701162,2,232,338,710,618,2855,1795 +35.5427966787027,-82.6967466906811,2,248,336,729,670,3562,2366 +35.6060211015653,-82.3565022837967,2,321,443,779,900,1879,1790 +35.553877247658,-82.6970725952854,2,603,656,887,917,1967,1751 +35.5284571188782,-82.6837105065093,2,79,221,625,440,3560,1938 +35.5594175321357,-82.5106551616284,2,379,451,731,649,2901,1697 +35.5571362385272,-82.5249949642174,2,274,336,663,580,2983,1812 +35.6265527440413,-82.5660589443586,2,329,373,618,496,2665,1554 +35.4906528247954,-82.4871900301192,2,386,418,724,654,3374,1811 +35.5861412572631,-82.5380311483892,2,460,540,870,787,3326,2111 +35.5636542202656,-82.5054406879597,2,185,242,532,331,3512,1488 +35.4984744028815,-82.5165214445057,2,325,414,775,732,3202,1890 +35.6353520193881,-82.5862650298249,2,307,366,675,521,3105,1694 +35.5522477522234,-82.5993012139968,2,523,628,926,987,2655,2407 +35.7125901029883,-82.5947385495366,2,275,361,658,572,2933,1791 +35.5864671563501,-82.6041897830612,2,388,443,832,671,3278,1960 +35.5551808440057,-82.5810505561562,2,780,892,1160,1303,2317,3453 +35.4896751275347,-82.5028334511253,2,241,276,584,407,3224,1586 +35.5568103394403,-82.6527495691012,2,83,201,699,687,3043,1938 +35.6434994965611,-82.5983235001839,2,960,1245,1991,2044,3320,2710 +35.6226419549982,-82.548786000331,2,332,394,621,514,2501,1438 +35.5981995234792,-82.4842568886805,2,146,264,707,618,3108,1994 +35.6705491207755,-82.583005983782,2,-308,-64,529,552,1987,1390 +35.4955413110993,-82.8704538447705,2,332,488,950,868,3305,1992 +35.5382340914858,-82.8407965257796,2,110,266,490,344,585,471 +35.4900010266216,-82.543245622058,2,264,314,601,439,2942,1371 +35.7125901029883,-82.5611703752942,2,116,162,545,271,3733,1661 +35.6920584605123,-82.5601926614813,2,241,333,635,561,2377,1612 +35.6115613860429,-82.5553040924169,2,1491,1514,2014,1944,2863,2491 +35.7138936993359,-82.5618221845028,2,270,384,573,555,1841,1213 +35.6004808170876,-82.5817023653648,2,372,503,921,897,2832,1959 +35.5323679079213,-82.8600248974331,2,375,523,875,917,2046,1797 +35.5968959271315,-82.6188554902545,2,101,127,384,151,3003,1175 +35.5900520463062,-82.4529700466681,2,267,389,804,675,3619,2056 +35.5467074677458,-82.5126105892542,2,245,296,560,449,2703,1782 +35.5568103394403,-82.5064184017726,2,416,503,741,744,1959,1618 +35.6190570650421,-82.583005983782,2,439,497,715,621,2898,1572 +35.5636542202656,-82.5337943885334,2,388,463,856,656,3603,2160 +35.5920074408277,-82.588546362055,2,287,325,532,414,2682,1360 +35.566261412961,-82.5817023653648,2,496,609,990,948,2712,2053 +35.5437743759635,-82.6038638784569,2,621,842,1159,1311,1928,1916 +35.5887484499585,-82.5269503918432,2,420,553,882,914,2334,1791 +35.7523497915926,-82.5452010496837,2,222,320,641,454,3310,1817 +35.6545800655164,-82.579746937739,2,149,322,829,740,2757,1787 +35.6395887075181,-82.568666181193,2,209,273,642,469,3608,1811 +35.508903173663,-82.5188027767358,2,284,389,643,649,1911,1331 +35.5718016974386,-82.5849614114078,2,199,265,527,366,2764,1389 +35.5450779723112,-82.817983203479,2,296,372,715,587,3313,1991 +35.6076505969999,-82.548786000331,2,126,216,574,433,2840,1614 +35.5643060184395,-82.5204322997573,2,308,376,667,553,3010,1784 +35.5698463029171,-82.5745324640703,2,414,514,822,784,3400,2140 +35.6200347623029,-82.7671420852089,2,203,262,649,437,3886,1968 +35.5437743759635,-82.8492700454913,2,431,517,891,865,3074,2311 +35.5379081923989,-82.617225967233,2,396,516,952,954,3135,2560 +35.5542031467449,-82.6996798321197,2,273,473,858,929,1923,1837 +35.6076505969999,-82.5520450463739,2,340,425,753,785,2914,1916 +35.5532254494842,-82.7136937301044,2,210,282,616,493,4015,2076 +35.7093311121191,-82.5735547502574,2,262,352,643,555,2919,1808 +35.5470333668327,-82.602886164644,2,196,244,553,339,2745,1292 +35.4857643384916,-82.520106395153,2,278,346,573,493,2303,1152 +35.5359527978774,-82.8580694698073,2,345,463,826,773,3199,2179 +35.5102067700107,-82.5315130563033,2,223,273,559,427,2521,1341 +35.6167757714337,-82.428527201346,2,414,520,785,771,2427,1675 +35.4890233293608,-82.5715993226317,2,328,448,717,738,2553,1663 +35.606998798826,-82.5305353424904,2,651,773,1071,997,3121,1971 +35.6353520193881,-82.6263512961533,2,304,398,760,602,3748,1989 +35.5480110640934,-82.5119587800456,2,320,379,676,552,3410,2065 +35.6122131842168,-82.4330898658061,2,234,347,651,570,2937,1757 +35.5822304682201,-82.656986328957,2,540,622,959,838,3297,2232 +35.7399656262896,-82.5452010496837,2,185,320,711,686,2155,1639 +35.5610470275703,-82.5194545859444,2,589,811,1230,1357,3396,2228 +35.5894002481323,-82.5308612470947,2,372,421,679,627,2479,1610 +35.5379081923989,-82.7006575459326,2,172,358,792,703,2936,1897 +35.5968959271315,-82.6110337797514,2,184,222,505,279,3485,1391 +35.4909787238824,-82.5729029410488,2,328,384,702,554,3442,1961 +35.5437743759635,-82.7560613286628,2,266,517,1079,1129,2498,1904 +35.5408412841812,-82.8319971014637,2,462,580,896,906,2395,1822 +35.5102067700107,-82.5171732537143,2,529,608,982,908,3141,1868 +35.7533274888533,-82.5461787634966,2,367,431,761,713,2394,1787 +35.6575131572987,-82.7704011312518,2,320,456,870,788,3404,2222 +35.545729770485,-82.4497110006252,2,413,413,671,596,2155,1438 +35.6017844134353,-82.5836577929906,2,136,177,470,230,3546,1334 +35.504666485533,-82.8528549961386,2,253,388,773,707,3156,2013 +35.5968959271315,-82.4950117406222,2,147,183,470,275,3028,1366 +35.6232937531721,-82.3529173331494,2,300,387,668,508,2803,1707 +35.5177024490098,-82.6045156876655,2,200,326,806,709,3242,2194 +35.570172202004,-82.5090256386069,2,475,559,841,875,2367,1576 +35.553877247658,-82.5227136319873,2,517,677,992,1000,2428,2037 +35.6405664047789,-82.5794210331348,2,167,312,751,781,2381,2005 +35.6470843865173,-82.579746937739,2,481,551,802,763,2392,1572 +35.6428476983873,-82.5667107535672,2,389,482,830,753,3281,1917 +35.484460742144,-82.5422679082451,2,232,276,582,432,2978,1429 +35.5066218800546,-82.4171205401956,2,359,447,791,724,3238,2072 +35.5408412841812,-82.8359079567152,2,406,501,798,761,2647,1902 +35.5545290458319,-82.6429724309723,2,181,204,569,319,3894,1775 +35.611235486956,-82.6374320526993,2,216,285,523,520,2083,1333 +35.6953174513815,-82.5497637141439,2,306,405,704,562,3415,1793 +35.4880456321001,-82.5188027767358,2,278,338,613,508,2691,1486 +35.5757124864817,-82.5843096021992,2,765,880,1149,1121,3079,2129 +35.705420323076,-82.5807246515519,2,528,627,902,919,2606,1934 +35.5854894590893,-82.7935403581568,2,172,238,577,423,2995,1724 +35.6138426796514,-82.5833318883863,2,311,418,688,604,2455,1394 +35.566261412961,-82.6318916744263,2,495,586,882,864,2653,2086 +35.6004808170876,-82.4034325468152,2,572,699,1116,1186,2438,1787 +35.6109095878691,-82.6361284342821,2,372,540,934,930,2620,1814 +35.746483608028,-82.545526954288,2,217,338,735,653,2554,1787 +35.6327448266928,-82.6882731709694,2,181,236,523,338,3173,1536 +35.578319679177,-82.511306970837,2,384,507,762,757,1743,1276 +35.7008577358591,-82.559866756877,2,201,301,585,421,3246,1681 +35.545729770485,-82.5328166747205,2,576,691,946,942,2329,1728 +35.5913556426539,-82.5429197174537,2,289,347,613,454,3228,1359 +35.701183634946,-82.5784433193219,2,465,582,931,1017,2636,2497 +35.611235486956,-82.3209786819285,2,31,126,604,442,3042,1728 +35.7086793139452,-82.5823541745734,2,487,586,948,1307,1993,2616 +35.5845117618285,-82.6149446350029,2,389,468,873,792,2993,1824 +35.619382964129,-82.2867586984775,2,405,533,1140,1134,2445,2101 +35.5613729266572,-82.697724404494,2,142,240,651,558,3174,1649 +35.5760383855686,-82.6097301613342,2,281,359,605,492,2802,1544 +35.5317161097474,-82.8430778580097,2,3322,3870,4639,5059,5614,4665 +35.4919564211431,-82.5015298327081,2,94,251,730,573,3591,2014 +35.6102577896952,-82.360087234444,2,5076,5620,6477,6953,7322,4918 +35.4974967056208,-82.5197804905487,2,481,612,898,971,1992,1836 +35.5441002750504,-82.6987021183069,2,689,880,1408,1653,2656,2826 +35.615472175086,-82.3219563957414,2,647,795,1120,1168,1765,1746 +35.5929851380885,-82.5572595200427,2,506,683,1018,1090,2042,1775 +35.6190570650421,-82.290669553729,2,586,689,974,1034,1870,1716 +35.6008067161746,-82.385507793579,2,334,503,921,1060,2371,2438 +35.5020592928377,-82.5227136319873,2,723,955,1294,1394,2158,1637 +35.6438253956481,-82.5976716909753,2,836,951,1415,1471,2760,2205 +35.5910297435669,-82.531187151699,2,339,539,931,987,1957,1776 +35.5372563942251,-82.7505209503898,2,819,1005,1280,1389,2007,2197 +35.635677918475,-82.5898499804722,2,1119,1204,1427,1533,2086,2251 +35.5796232755247,-82.6002789278097,2,404,503,690,708,1364,1079 +35.6001549180007,-82.3864855073919,2,120,257,617,437,3146,1472 +35.6184052668683,-82.6250476777361,2,1106,1364,2197,2107,4255,3446 +35.5470333668327,-82.6374320526993,2,497,591,962,998,3017,2062 +35.6138426796514,-82.3646498989041,2,391,595,955,1077,1418,1719 +35.6040657070438,-82.5530227601868,2,580,701,990,1029,1980,1748 +35.5864671563501,-82.5478082865181,2,231,276,404,292,751,691 +35.5763642846555,-82.5135883030671,2,582,766,1128,1216,2170,2012 +35.5920074408277,-82.5569336154384,2,822,1027,1279,1379,1732,1649 +35.5183542471837,-82.5970198817667,2,812,978,1265,1435,1666,2108 +35.5854894590893,-82.6009307370182,2,249,365,719,641,2057,1383 +35.5564844403534,-82.5116328754413,2,488,556,817,812,1956,1766 +35.6174275696075,-82.3633462804869,2,347,492,915,945,1958,1818 +35.5913556426539,-82.5699697996102,2,465,603,945,956,2850,1778 +35.5946146335231,-82.5706216088188,2,238,336,669,561,2166,1593 +35.5861412572631,-82.5266244872389,2,126,156,417,272,2342,1362 +35.5864671563501,-82.6270031053619,2,227,378,761,715,2601,2009 +35.6402405056919,-82.5764878916961,2,2976,3525,3395,3759,4216,3133 +35.4994521001423,-82.5214100135702,2,676,823,1104,1195,1650,1586 +35.4860902375786,-82.5663848489629,2,289,408,769,660,2478,1622 +35.5815786700462,-82.5214100135702,2,1183,1302,1513,1590,1682,1759 +35.4756614667971,-82.4839309840762,2,218,276,588,483,2873,1470 +35.4935859165777,-82.8724092723963,2,425,646,1027,1150,2154,2029 +35.6060211015653,-82.3613908528611,2,316,480,819,938,1341,1618 +35.5962441289577,-82.4063656882539,2,267,363,735,620,2820,1843 +35.5946146335231,-82.5800728423433,2,642,731,1084,1103,2429,3369 +35.5864671563501,-82.6032120692483,2,593,706,1033,1040,2748,2298 +35.586793055437,-82.4842568886805,2,545,745,1068,1142,2050,1724 +35.5972218262184,-82.5461787634966,2,1674,1869,2112,2272,2539,2507 +35.606998798826,-82.626025391549,2,515,700,1051,1093,2106,1627 +35.5382340914858,-82.6683929901074,2,815,1044,1598,1971,2268,2765 +35.6708750198625,-82.5836577929906,2,193,476,1293,1427,2768,3181 +35.5763642846555,-82.5083738293984,2,515,610,830,880,1848,1791 +35.5167247517491,-82.5249949642174,2,436,599,994,1042,2457,1813 +35.5972218262184,-82.4207054908429,2,693,813,1072,992,2384,2368 +35.5447520732242,-82.7006575459326,2,696,906,1193,1282,2018,2519 +35.5339974033559,-82.6045156876655,2,428,589,809,774,2087,1623 +35.5864671563501,-82.5670366581715,2,941,1125,1686,1931,2354,2584 +35.5339974033559,-82.8564399467858,2,1163,1376,1794,2028,2611,3092 +35.5724534956125,-82.5034852603339,2,345,499,793,843,1656,1520 +35.5773419819163,-82.5142401122757,2,621,834,1169,1273,1688,1728 +35.598851321653,-82.5468305727052,2,797,1045,1492,1672,2739,2831 +35.6037398079568,-82.5536745693954,2,2001,2265,2882,3086,3437,2952 +35.586793055437,-82.4845827932848,2,1188,1427,1846,1989,3136,2701 +35.578645578264,-82.5657330397543,2,708,858,1141,1290,1716,2099 +35.5434484768766,-82.8479664270741,2,982,1155,1449,1613,2268,2329 +35.6151462759991,-82.3483546686893,2,459,570,1071,1578,2114,2137 +35.7077016166845,-82.5807246515519,2,543,685,1080,1331,1761,2224 +35.5258499261829,-82.6041897830612,2,414,536,869,844,2794,1749 +35.6272045422151,-82.4519923328553,2,258,382,739,771,2378,2172 +35.4909787238824,-82.5230395365916,2,587,657,922,907,2224,1458 +35.6063470006522,-82.3203268727199,2,1115,1323,1945,1914,3436,2696 +35.5858153581762,-82.5611703752942,2,2670,3099,3775,4209,4753,4057 +35.6865181760346,-82.577465605509,2,3407,3723,4416,4759,5629,6095 +35.7119383048144,-82.5618221845028,2,502,609,855,886,2094,1987 +35.598851321653,-82.5533486647911,2,490,614,913,957,2203,1535 +35.5242204307483,-82.6064711152913,2,305,491,931,1037,2487,2212 +35.6141685787383,-82.6534013783098,2,542,639,905,945,1873,1572 +35.5659355138741,-82.5334684839291,2,760,902,1243,1358,2552,2509 +35.6122131842168,-82.3653017081127,2,139,309,685,644,2244,1495 +35.5102067700107,-82.5996271186011,2,318,623,1217,1345,2358,2119 +35.5838599636547,-82.5526968555825,2,416,644,1047,1167,2127,2189 +35.5926592390015,-82.4334157704104,2,211,288,647,414,3328,1594 +35.6601203499941,-82.669044799316,2,470,611,1051,1213,2799,2789 +35.5326938070082,-82.8642616572889,2,541,713,1200,1219,3092,2644 +35.6043916061307,-82.4337416750147,2,584,715,1002,1015,3209,2205 +35.5903779453931,-82.6045156876655,2,534,638,890,910,2341,1747 +35.5434484768766,-82.5331425793248,2,572,686,984,1023,2687,2049 +35.6242714504328,-82.6625267072301,2,758,868,1184,1298,2419,2504 +35.5978736243923,-82.5738806548617,2,721,914,1246,1431,1823,2145 +35.5890743490454,-82.4510146190424,2,268,447,755,805,1993,1845 +35.574408890134,-82.5018557373124,2,687,778,1123,1121,2288,1861 +35.578319679177,-82.5070702109812,2,223,291,607,443,3005,1376 +35.5349751006166,-82.8368856705281,2,515,663,997,1012,2344,1746 +35.6099318906083,-82.6351507204693,2,488,614,920,987,1899,1649 +35.5535513485711,-82.85220318693,2,1935,2137,2431,2564,3175,2615 +35.4991262010554,-82.5210841089659,2,1176,1323,1621,1693,2550,1810 +35.6288340376497,-82.6270031053619,2,900,1081,1492,1481,3059,2113 +35.4776168613186,-82.4839309840762,2,275,325,658,522,3332,1772 +35.578319679177,-82.5927831219109,2,943,1064,1267,1274,2645,1723 +35.6001549180007,-82.4112542573183,2,863,1117,1469,1572,1761,2959 +35.5923333399146,-82.4275494875331,2,-386,135,1068,1203,2141,2909 +35.5874448536108,-82.5038111649382,2,492,634,860,936,1756,1412 +35.6454548910827,-82.5761619870918,2,497,601,873,933,2425,1949 +35.5486628622673,-82.5341202931377,2,335,461,858,788,2790,2028 +35.5760383855686,-82.5070702109812,2,1200,1524,2073,2260,2658,3350 +35.5574621376141,-82.5979975955796,2,220,330,758,677,3013,2007 +35.5779937800901,-82.5103292570241,2,914,1190,1697,1891,3034,3038 +35.4798981549271,-82.5272762964475,2,1813,2042,2741,3073,3705,3989 +35.6464325883434,-82.5803987469476,2,603,723,957,1005,1813,1628 +35.5460556695719,-82.6625267072301,2,767,958,1548,1852,3285,3442 +35.59917722074,-82.5569336154384,2,238,368,796,763,2442,1811 +35.5203096417052,-82.6032120692483,2,897,1015,1365,1452,1739,2012 +35.619382964129,-82.5416160990365,2,912,992,1305,1533,2215,2017 +35.4838089439701,-82.5500896187482,2,462,545,922,904,2768,1794 +35.5447520732242,-82.6273290099662,2,148,201,553,300,3960,1634 +35.5933110371754,-82.4282012967417,2,726,911,1264,1442,2086,2151 +35.488371531187,-82.545526954288,2,434,543,897,874,2895,1861 +35.5232427334875,-82.4070174974625,2,218,438,912,991,2295,1659 +35.5926592390015,-82.5318389609076,2,529,664,920,968,1785,1564 +35.6037398079568,-82.3763824646587,2,662,795,1107,1165,2498,2331 +35.5405153850943,-82.5191286813401,2,786,1026,1309,1390,2354,2432 +35.5232427334875,-82.6067970198956,2,128,300,754,725,2464,1684 +35.6350261203012,-82.5768137963004,2,406,540,876,901,2349,1650 +35.570498101091,-82.5367275299721,2,494,652,983,1046,1992,1557 +35.5708240001779,-82.5067443063769,2,517,583,754,781,1583,1386 +35.5310643115736,-82.8469887132613,2,336,576,1037,1127,2024,1812 +35.7031390294676,-82.5689920857973,2,402,487,779,720,2580,1795 +35.5985254225661,-82.5478082865181,2,801,929,1290,1398,2676,2208 +35.6017844134353,-82.5285799148647,2,257,408,758,744,2430,1537 +35.6131908814775,-82.5745324640703,2,1183,1325,1574,1642,4045,2548 +35.5838599636547,-82.5249949642174,2,560,733,971,1084,1297,1494 +35.5841858627416,-82.5272762964475,2,312,510,815,855,1334,1189 +35.603088009783,-82.5569336154384,2,592,699,958,1057,1671,1507 +35.5812527709593,-82.5269503918432,2,3670,3980,4595,4971,5361,5464 +35.5933110371754,-82.5588890430641,2,480,696,974,1065,1663,1344 +35.7037908276414,-82.5764878916961,2,854,1047,1299,1446,1792,1829 +35.5877707526977,-82.5517191417696,2,787,895,1183,1242,2341,1949 +35.5799491746116,-82.5262985826346,2,913,1074,1427,1638,2247,3045 +35.607324697913,-82.3750788462415,2,-291,61,794,774,2583,2225 +35.5441002750504,-82.7544318056414,2,829,931,1291,1340,2800,1970 +35.5727793946994,-82.5249949642174,2,1199,1440,1754,1951,2300,3113 +35.4968449074469,-82.5210841089659,2,745,867,1289,1440,1996,1992 +35.6187311659552,-82.3209786819285,2,1386,1758,2162,2451,2912,2399 +35.6180793677813,-82.3630203758826,2,165,207,364,266,379,249 +35.6190570650421,-82.628306723779,2,126,303,747,719,1345,1248 +35.5864671563501,-82.4884936485363,2,335,518,846,923,1475,1570 +35.5496405595281,-82.5357498161592,2,476,594,885,901,2037,1554 +35.6174275696075,-82.5683402765887,2,1467,1762,2178,2474,3781,4302 +35.4776168613186,-82.5790951285305,2,411,492,773,764,2194,1819 +35.4727283750148,-82.5409642898279,2,647,799,1172,1224,2515,2332 +35.5105326690976,-82.5324907701162,2,584,687,892,890,1744,1373 +35.4906528247954,-82.522387727383,2,2049,2161,2737,2808,3344,3304 +35.6066728997391,-82.5530227601868,2,662,837,1188,1305,1838,1792 +35.5929851380885,-82.6058193060827,2,325,471,740,749,2735,2013 +35.512813962706,-82.5230395365916,2,2929,3315,4057,4363,4900,4378 +35.5757124864817,-82.5663848489629,2,705,987,1683,1853,2928,2757 +35.5695204038302,-82.5064184017726,2,791,934,1255,1328,2092,1886 +35.5607211284833,-82.5220618227788,2,968,1116,1394,1414,2393,2181 +35.5323679079213,-82.8375374797367,2,313,457,678,720,942,946 +35.4968449074469,-82.5207582043616,2,618,765,1073,1185,1795,1977 +35.5877707526977,-82.5869168390335,2,1976,2243,2539,2788,3062,2268 +35.5248722289221,-82.6038638784569,2,567,667,904,1037,1415,1784 +35.5877707526977,-82.5305353424904,2,486,611,863,902,1961,1543 +35.5665873120479,-82.5438974312665,2,643,802,1071,1192,1567,1574 +35.5336715042689,-82.5204322997573,2,1108,1310,1669,1646,3815,2440 +35.6832591851654,-82.5680143719844,2,647,810,1068,1146,3284,2638 +35.5854894590893,-82.5276022010518,2,625,799,1087,1202,1760,1663 +35.5815786700462,-82.5266244872389,2,4563,5071,5736,6220,6685,6340 +35.5287830179651,-82.5259726780303,2,577,698,926,986,2016,1665 +35.5476851650065,-82.6579640427699,2,471,549,733,798,1483,1681 +35.6184052668683,-82.434067579619,2,216,296,604,450,3380,1819 +35.603413908869904,-82.3617167574654,2,1038,1126,1472,1534,2731,2042 +35.4867420357524,-82.5553040924169,2,1633,1843,2306,2389,2980,2359 +35.5665873120479,-82.5715993226317,2,323,398,750,643,3198,2273 +35.5832081654809,-82.5526968555825,2,795,930,1166,1264,1738,2070 +35.5033628891853,-82.5220618227788,2,2843,3177,4060,4296,4667,4261 +35.607324697913,-82.3643239942998,2,191,271,453,461,1107,1184 +35.582556367307,-82.5279281056561,2,1747,2018,2446,2696,2842,2295 +35.5766901837424,-82.5494378095396,2,1477,1605,2003,2067,2399,2333 +35.6966210477292,-82.5601926614813,2,304,700,993,1185,1728,1737 +35.5978736243923,-82.405387974441,2,3571,4185,4985,5072,5181,4310 +35.5225909353137,-82.6048415922698,2,2096,2528,3212,3596,3943,3846 +35.5561585412665,-82.539986576015,2,262,414,611,669,849,812 +35.5773419819163,-82.5927831219109,2,758,954,1273,1391,1961,1797 +35.5698463029171,-82.5452010496837,2,876,1010,1310,1444,2161,2031 +35.6118872851299,-82.365627612717,2,686,811,1074,1210,1424,1534 +35.6865181760346,-82.5699697996102,2,4723,5396,6524,7027,7467,6221 +35.513139861793,-82.5233654411959,2,4817,5357,6273,6787,7191,6004 +35.5349751006166,-82.8411224303839,2,313,466,771,956,1131,1094 +35.6832591851654,-82.5696438950059,2,3056,3342,4139,4322,4449,5505 +35.5985254225661,-82.3939813132907,2,708,907,1381,1651,2152,2453 +35.5708240001779,-82.502507546521,2,433,552,818,881,1781,1977 +35.5242204307483,-82.6038638784569,2,796,1014,1263,1439,1686,2055 +35.5317161097474,-82.8411224303839,2,279,489,1009,1008,2981,2191 +35.5372563942251,-82.6677411808988,2,525,727,1177,1443,1636,2208 +35.5933110371754,-82.5546522832083,2,2998,3275,3773,3994,4040,3702 +35.5506182567888,-82.4741538459473,2,231,342,583,531,2142,1505 +35.5727793946994,-82.5324907701162,2,2726,3264,4128,4555,5454,3504 +35.6281822394759,-82.5533486647911,2,438,682,959,1038,1600,1796 +35.5688686056564,-82.6263512961533,2,344,523,779,863,1087,1055 +35.5894002481323,-82.4819755564504,2,261,470,847,935,1858,1739 +35.5828822663939,-82.5279281056561,2,992,1133,1359,1485,1635,1704 +35.5809268718724,-82.522387727383,2,327,548,1006,1084,2444,2001 +35.5779937800901,-82.5155437306929,2,747,961,1244,1362,1770,1729 +35.5952664316969,-82.5562818062298,2,1699,1979,2569,2926,3139,2515 +35.5450779723112,-82.5158696352972,2,3333,3748,4339,4718,5602,5583 +35.5981995234792,-82.4063656882539,2,580,749,959,851,967,1108 +35.6272045422151,-82.5526968555825,2,456,668,1031,1151,1577,1553 +35.4714247786671,-82.5435715266622,2,594,887,1210,1374,1601,2079 +35.5528995503972,-82.4787165104075,2,599,761,1059,1071,2159,1756 +35.705420323076,-82.5670366581715,2,3325,3660,4175,4423,4642,3480 +35.4974967056208,-82.5191286813401,2,648,840,1126,1302,1749,2100 +35.5916815417408,-82.6045156876655,2,404,593,987,1086,1597,1471 +35.5874448536108,-82.5546522832083,2,807,957,1328,1386,2643,2765 +35.607324697913,-82.3200009681156,2,683,747,926,1025,1421,1734 +35.611235486956,-82.3594354252354,2,210,332,715,766,2495,2111 +35.5613729266572,-82.5243431550088,2,730,1033,1553,1590,3390,2834 +35.607324697913,-82.3646498989041,2,337,450,692,785,1190,1512 +35.5356268987905,-82.6703484177332,2,689,844,1187,1381,1640,2157 +35.5353009997036,-82.843403762614,2,2134,2378,2641,2684,3096,2383 +35.6845627815131,-82.5670366581715,2,2756,3204,4042,4491,5454,4742 +35.59494053261,-82.5523709509782,2,650,821,1026,1140,1530,1421 +35.5864671563501,-82.596368072558107,2,271,414,776,688,1963,1333 +35.5675650093087,-82.5605185660856,2,556,669,901,1070,1334,1607 +35.5049923846199,-82.5240172504045,2,545,744,1044,1153,1750,1529 +35.5555067430926,-82.5950644541409,2,1276,1413,1688,1813,1999,1960 +35.5809268718724,-82.5214100135702,2,1102,1270,1548,1636,2223,1876 +35.5287830179651,-82.6045156876655,2,850,1031,1420,1646,2071,2189 +35.5502923577019,-82.8727351770006,2,213,294,666,519,3432,1999 +35.6839109833393,-82.5673625627758,2,3198,3600,4650,5362,5696,5557 +35.5939628353492,-82.554326378604,2,1092,1242,1531,1690,2193,2146 +35.525198128009,-82.410928352714,2,840,1027,1351,1516,2263,3039 +35.5444261741373,-82.6563345197485,2,2439,2901,3490,3536,4264,3378 +35.7041167267283,-82.5748583686746,2,787,933,1325,1454,2173,2288 +35.5310643115736,-82.8427519534054,2,413,728,1214,1350,1970,1980 +35.5441002750504,-82.5318389609076,2,524,624,842,918,1057,1139 +35.5343233024428,-82.6638303256472,2,742,950,1031,1044,2954,2248 +35.5346492015297,-82.6644821348558,2,872,1196,1029,989,3373,2136 +35.6203606613898,-82.4334157704104,2,385,626,1172,1192,2576,2851 +35.5897261472193,-82.6312398652177,2,755,868,1038,1092,1506,1888 +35.5310643115736,-82.6019084508311,2,369,430,573,559,1095,941 +35.5506182567888,-82.8737128908135,2,369,465,790,736,2665,2093 +35.4847866412309,-82.4337416750147,2,475,677,1096,1248,1804,1878 +35.5747347892209,-82.5230395365916,2,2469,2702,3085,3300,3624,3146 +35.603413908869904,-82.3587836160268,2,258,364,721,572,2617,1704 +35.5336715042689,-82.6051674968741,2,531,725,890,990,1690,1461 +35.6229678540851,-82.5764878916961,2,532,681,948,907,1736,1530 +35.5623506239179,-82.6318916744263,2,409,660,977,1154,1329,1459 +35.549966458615,-82.8733869862092,2,228,288,605,490,3271,2202 +35.5737570919602,-82.5266244872389,2,902,1062,1304,1415,2060,2277 +35.603413908869904,-82.5533486647911,2,1311,1545,1910,2129,2781,2677 +35.5577880367011,-82.6276549145705,2,566,803,1216,1317,1990,1928 +35.4707729804933,-82.5409642898279,2,528,675,874,976,1162,1246 +35.5238945316613,-82.4122319711312,2,389,619,1106,1236,2499,2033 +35.4779427604056,-82.574206559466,2,1494,1817,1893,2098,2666,2988 +35.6474102856042,-82.5993012139968,3,345,406,577,588,397,262 +35.5092290727499,-82.571925227236,3,184,242,417,374,483,232 +35.6923843595992,-82.6162482534201,3,410,473,646,679,425,304 +35.7187821856398,-82.645905572411,3,265,326,631,535,1578,943 +35.4740319713625,-82.545526954288,3,516,587,734,658,633,616 +35.6232937531721,-82.5803987469476,3,187,267,528,424,1479,675 +35.5362786969643,-82.6556827105399,3,179,217,345,239,126,74 +35.4701211823195,-82.4640508032142,3,270,343,666,563,1843,1026 +35.6327448266928,-82.3610649482568,3,107,122,261,190,1068,513 +35.570498101091,-82.3773601784716,3,151,197,293,253,235,166 +35.4932600174908,-82.5872427436378,3,217,280,521,427,1149,601 +35.4854384394047,-82.5641035167329,3,63,185,474,384,1212,711 +35.5936369362623,-82.5732288456531,3,340,407,621,574,1171,650 +35.6405664047789,-82.5993012139968,3,369,454,651,638,716,388 +35.5981995234792,-82.5755101778832,3,468,598,1134,1260,2926,2089 +35.639914606605,-82.5986494047882,3,378,454,636,631,541,378 +35.5643060184395,-82.5901758850765,3,219,271,445,418,289,247 +35.6047175052176,-82.5794210331348,3,311,387,541,555,239,160 +35.717804488379,-82.6436242401809,3,416,489,661,680,425,273 +35.717804488379,-82.6325434836349,3,350,421,595,606,536,219 +35.6571872582118,-82.6110337797514,3,285,354,565,558,878,445 +35.6689196253409,-82.6208109178803,3,433,507,683,699,438,277 +35.672830414384,-82.3372739121433,3,110,151,473,247,2878,1194 +35.4834830448832,-82.626025391549,3,134,169,394,248,1570,830 +35.4922823202301,-82.5784433193219,3,197,254,480,406,828,478 +35.4740319713625,-82.5546522832083,3,257,330,476,462,119,87 +35.5121621645322,-82.5758360824875,3,165,231,424,375,510,281 +35.664357038124,-82.3402070535819,3,260,306,353,284,313,237 +35.7396397272027,-82.6706743223374,3,286,336,513,533,269,233 +35.5388858896597,-82.6661116578773,3,666,814,898,367,227,192 +35.6989023413376,-82.6152705396072,3,339,400,588,590,381,302 +35.6620757445156,-82.3385775305605,3,283,329,368,306,343,269 +35.5108585681845,-82.5878945528464,3,356,433,608,581,592,372 +35.4769650631448,-82.545526954288,3,139,179,326,157,271,201 +35.475987365884,-82.5276022010518,3,147,180,295,149,109,50 +35.6141685787383,-82.5764878916961,3,293,350,517,484,244,88 +35.6249232486067,-82.5839836975949,3,222,282,440,448,139,107 +35.5297607152259,-82.656986328957,3,207,239,392,280,304,214 +35.513139861793,-82.5781174147176,3,118,181,390,328,811,412 +35.6969469468161,-82.6136410165858,3,314,365,567,513,1007,636 +35.5320420088343,-82.5605185660856,3,260,326,496,484,385,196 +35.6757635061663,-82.3340148661003,3,181,219,274,206,243,175 +35.4750096686233,-82.545526954288,3,383,457,590,446,411,347 +35.6350261203012,-82.5673625627758,3,368,457,668,601,378,278 +35.6712009189494,-82.3340148661003,3,256,302,346,286,329,264 +35.7236706719436,-82.6488387138497,3,194,229,454,313,1946,871 +35.4818535494486,-82.5328166747205,3,317,366,515,367,339,262 +35.672830414384,-82.3340148661003,3,266,301,353,291,337,260 +35.4737060722756,-82.5403124806193,3,360,431,644,553,750,483 +35.6568613591249,-82.6051674968741,3,199,221,478,301,2093,1049 +35.4733801731887,-82.5373793391806,3,315,373,510,370,304,242 +35.541493082355,-82.5663848489629,3,378,448,615,616,405,284 +35.5649578166133,-82.5878945528464,3,232,277,503,423,1504,1015 +35.5304125133997,-82.656986328957,3,157,212,426,288,1441,783 +35.6340484230404,-82.5611703752942,3,288,358,539,449,206,170 +35.5017333937507,-82.5924572173066,3,297,357,519,517,234,124 +35.6232937531721,-82.5976716909753,3,355,417,597,597,396,312 +35.4782686594925,-82.5406383852236,3,254,317,458,309,290,181 +35.5460556695719,-82.5761619870918,3,213,268,470,405,954,545 +35.7161749929444,-82.6279808191748,3,421,490,668,692,447,289 +35.6330707257797,-82.5605185660856,3,339,409,574,482,338,282 +35.6392628084312,-82.5986494047882,3,326,393,576,572,196,259 +35.6597944509072,-82.6221145362974,3,306,376,595,513,1267,519 +35.6008067161746,-82.5764878916961,3,77,154,422,266,2232,957 +35.668593726254,-82.3398811489776,3,242,279,321,262,291,217 +35.6575131572987,-82.6067970198956,3,389,445,615,630,375,253 +35.6232937531721,-82.5934349311194,3,137,231,547,419,2046,919 +35.6412182029527,-82.5993012139968,3,654,810,901,977,609,300 +35.5649578166133,-82.5745324640703,3,202,258,419,419,249,122 +35.6333966248666,-82.559866756877,3,293,349,479,387,270,187 +35.5137916599668,-82.5803987469476,3,190,242,406,362,494,189 +35.6689196253409,-82.333688961496,3,285,322,399,336,368,300 +35.475987365884,-82.539986576015,3,282,348,480,338,263,207 +35.5750606883078,-82.5676884673801,3,222,278,504,434,1078,536 +35.5770160828294,-82.568666181193,3,209,254,441,412,415,279 +35.6832591851654,-82.6182036810459,3,363,427,598,620,330,265 +35.4795722558402,-82.5566077108341,3,425,493,634,627,447,324 +35.7363807363335,-82.6638303256472,3,233,309,519,492,632,478 +35.7406174244634,-82.6775183190277,3,185,250,460,433,748,438 +35.6656606344717,-82.3356443891218,3,334,375,427,369,408,340 +35.7373584335942,-82.6661116578773,3,218,270,444,463,309,63 +35.6666383317325,-82.3362961983304,3,374,420,476,415,455,376 +35.6617498454287,-82.3398811489776,3,265,306,354,285,330,255 +35.6695714235148,-82.3375998167476,3,217,253,298,233,265,199 +35.7406174244634,-82.677844223632,3,182,247,456,438,847,295 +35.5923333399146,-82.5729029410488,3,373,438,616,632,370,295 +35.7406174244634,-82.6732815591718,3,281,333,515,534,458,190 +35.4763132649709,-82.5377052437849,3,326,376,517,378,308,242 +35.4906528247954,-82.5833318883863,3,194,262,405,392,147,90 +35.5431225777896,-82.5680143719844,3,207,270,467,422,748,406 +35.5871189545239,-82.5699697996102,3,319,367,550,532,515,285 +35.6249232486067,-82.5878945528464,3,250,314,475,444,536,249 +35.586793055437,-82.5696438950059,3,172,267,490,478,626,251 +35.6350261203012,-82.5650812305457,3,286,357,556,467,282,194 +35.656209560951,-82.602886164644,3,367,426,609,605,450,244 +35.6470843865173,-82.4037584514195,3,127,156,513,247,2877,1385 +35.6650088362979,-82.3415106719991,3,141,168,205,131,159,104 +35.717804488379,-82.6465573816196,3,387,450,623,639,428,241 +35.668593726254,-82.3421624812077,3,157,175,212,138,190,125 +35.578645578264,-82.531187151699,3,209,254,550,383,2819,1455 +35.4756614667971,-82.5253208688217,3,108,143,373,186,2396,1029 +35.4789204576663,-82.537053434576407,3,140,208,486,389,882,588 +35.6718527171232,-82.3349925799132,3,269,308,352,290,329,269 +35.6444771938219,-82.4001735007723,3,268,308,345,299,373,293 +35.6708750198625,-82.3333630568917,3,301,336,394,331,386,310 +35.7288850573343,-82.6488387138497,3,345,408,588,607,348,216 +35.6698973226017,-82.3356443891218,3,240,276,327,266,298,228 +35.5098808709238,-82.5732288456531,3,266,325,506,484,479,208 +35.6757635061663,-82.3356443891218,3,137,163,219,148,193,124 +35.4753355677102,-82.537053434576407,3,270,326,462,322,252,193 +35.7067239194237,-82.6191813948588,3,222,319,564,562,769,380 +35.6568613591249,-82.6032120692483,3,376,455,624,643,382,251 +35.5336715042689,-82.6543790921227,3,291,344,490,389,282,197 +35.5392117887466,-82.5852873160121,3,174,211,329,201,338,265 +35.4929341184039,-82.577465605509,3,145,215,510,356,1759,865 +35.6519728728211,-82.6012566416225,3,421,490,653,657,433,278 +35.5542031467449,-82.5895240758679,3,182,234,439,389,740,412 +35.6206865604767,-82.5787692239262,3,203,305,546,534,697,428 +35.4906528247954,-82.5810505561562,3,198,256,431,393,444,261 +35.6024362116092,-82.5771397009047,3,391,475,775,725,1932,1438 +35.4906528247954,-82.5663848489629,3,357,429,582,573,323,274 +35.6584908545595,-82.6182036810459,3,389,444,610,608,422,323 +35.6330707257797,-82.559866756877,3,322,389,532,435,288,229 +35.5473592659196,-82.5856132206164,3,230,292,611,422,2694,1524 +35.7067239194237,-82.6182036810459,3,128,251,565,550,1040,694 +35.5372563942251,-82.5618221845028,3,181,230,480,341,1923,856 +35.7191080847267,-82.6498164276625,3,362,418,615,579,872,383 +35.6219901568244,-82.5794210331348,3,212,282,539,451,1444,664 +35.660446249081,-82.622766345506,3,382,442,608,627,384,239 +35.5300866143128,-82.6556827105399,3,92,159,375,260,1454,880 +35.5887484499585,-82.5709475134231,3,196,233,532,359,2453,1227 +35.6614239463418,-82.3415106719991,3,295,335,387,323,365,279 +35.4818535494486,-82.5337943885334,3,231,290,420,269,211,151 +35.5238945316613,-82.4334157704104,3,228,293,548,425,2497,1673 +35.6718527171232,-82.3343407707046,3,244,273,322,260,306,233 +35.5102067700107,-82.5738806548617,3,103,193,498,362,1968,922 +35.4789204576663,-82.5350980069506,3,244,300,441,296,205,155 +35.4753355677102,-82.5393347668064,3,326,381,517,382,310,243 +35.6242714504328,-82.6009307370182,3,317,385,556,570,272,225 +35.5297607152259,-82.6556827105399,3,195,223,385,260,852,379 +35.5359527978774,-82.6739333683804,3,240,311,575,499,1547,1480 +35.5366045960512,-82.5611703752942,3,236,297,450,449,236,79 +35.7181303874659,-82.6442760493895,3,2,168,529,497,1032,576 +35.6506692764734,-82.6006048324139,3,374,445,613,623,400,241 +35.6144944778252,-82.5771397009047,3,223,265,592,443,2968,1505 +35.6764153043401,-82.6129892073772,3,414,480,654,673,446,289 +35.6764153043401,-82.3346666753089,3,113,143,216,131,152,94 +35.5864671563501,-82.5693179904016,3,89,208,533,471,1297,837 +35.5229168344006,-82.5585631384598,3,321,384,548,552,551,286 +35.729862754595,-82.6507941414754,3,414,490,675,708,400,248 +35.4740319713625,-82.5259726780303,3,276,329,454,313,292,257 +35.6340484230404,-82.559866756877,3,214,266,407,318,138,121 +35.5656096147871,-82.5856132206164,3,194,248,436,377,808,281 +35.6637052399502,-82.3372739121433,3,308,346,399,346,381,300 +35.6917325614254,-82.6175518718373,3,448,522,709,733,492,304 +35.656535460038,-82.6035379738526,3,341,408,598,616,373,261 +35.5506182567888,-82.4607917571712,3,412,500,741,764,1762,1374 +35.5463815686588,-82.5764878916961,3,128,198,391,364,445,193 +35.5238945316613,-82.5582372338556,3,283,355,557,579,1126,791 +35.4779427604056,-82.5386829575978,3,314,369,505,362,296,233 +35.4922823202301,-82.571925227236,3,235,304,488,463,670,478 +35.4772909622317,-82.5380311483892,3,333,394,537,391,322,258 +35.5646319175264,-82.5895240758679,3,168,231,443,374,678,345 +35.5427966787027,-82.5086997340026,3,110,135,272,165,1105,628 +35.7393138281157,-82.6706743223374,3,289,346,522,542,297,135 +35.6692455244279,-82.6169000626287,3,450,527,711,745,458,331 +35.6347002212143,-82.4790424150118,3,193,342,581,514,1276,797 +35.738336130855,-82.6687188947117,3,61,138,466,270,2744,894 +35.4922823202301,-82.5722511318402,3,214,290,492,468,670,689 +35.6601203499941,-82.6217886316931,3,394,469,647,658,372,276 +35.5105326690976,-82.5735547502574,3,54,144,419,302,1518,652 +35.7386620299419,-82.5585631384598,3,-272,-36,506,327,950,1184 +35.6698973226017,-82.3366221029347,3,213,244,289,227,259,190 +35.6229678540851,-82.594086740328,3,394,477,646,661,386,294 +35.4763132649709,-82.5292317240733,3,269,324,449,338,508,352 +35.6340484230404,-82.5999530232054,3,325,391,552,574,437,256 +35.6715268180363,-82.3359702937261,3,267,302,346,285,324,253 +35.6764153043401,-82.6126633027729,3,335,403,574,545,638,351 +35.7233447728566,-82.6501423322668,3,241,334,634,585,1125,722 +35.6731563134709,-82.3366221029347,3,163,189,236,175,223,151 +35.717804488379,-82.6455796678067,3,386,463,637,659,402,248 +35.5349751006166,-82.6566604243528,3,245,286,419,320,205,136 +35.615472175086,-82.5768137963004,3,261,334,517,514,429,279 +35.5955923307838,-82.574206559466,3,335,414,577,585,345,251 +35.4955413110993,-82.5895240758679,3,317,390,554,542,323,188 +35.4929341184039,-82.5735547502574,3,341,411,577,554,474,344 +35.4763132649709,-82.5390088622021,3,331,395,535,398,327,259 +35.6741340107317,-82.6129892073772,3,289,344,535,520,516,272 +35.4766391640579,-82.5422679082451,3,170,218,390,236,658,422 +35.6043916061307,-82.5787692239262,3,185,233,457,368,1679,924 +35.4798981549271,-82.534446197742,3,224,279,420,265,197,140 +35.4756614667971,-82.543245622058,3,401,462,602,460,409,340 +35.7158490938575,-82.6273290099662,3,344,407,589,615,378,237 +35.4710988795802,-82.5350980069506,3,364,424,566,421,340,318 +35.7543051861141,-82.6710002269417,3,449,520,778,735,1364,860 +35.5441002750504,-82.5699697996102,3,249,300,465,451,312,142 +35.6640311390371,-82.3437920042292,3,259,289,330,257,293,224 +35.6301376339974,-82.3610649482568,3,84,116,281,165,905,394 +35.5799491746116,-82.688924980178,3,255,300,502,363,-48,118 +35.4756614667971,-82.5448751450794,3,268,325,444,296,248,200 +35.5343233024428,-82.6550309013313,3,300,343,483,389,295,218 +35.5434484768766,-82.5875686482421,3,164,208,337,203,333,145 +35.7445282135065,-82.6784960328405,3,112,251,622,525,1851,1157 +35.5672391102217,-82.4591622341497,3,514,561,606,674,1409,826 +35.7530015897664,-82.6719779407546,3,160,213,505,361,1872,975 +35.5809268718724,-82.5318389609076,3,147,180,496,248,3316,1242 +35.4785945585794,-82.5393347668064,3,257,307,437,285,230,169 +35.4782686594925,-82.5373793391806,3,211,256,394,241,173,127 +35.6627275426894,-82.3408588627905,3,284,326,374,308,344,269 +35.4916305220562,-82.5683402765887,3,373,439,593,593,402,253 +35.6698973226017,-82.3353184845175,3,291,329,378,318,354,284 +35.7324699472904,-82.6582899473742,3,334,376,647,553,1544,880 +35.6027621106961,-82.5784433193219,3,233,295,497,434,825,331 +35.7034649285545,-82.6175518718373,3,307,361,538,548,330,207 +35.7181303874659,-82.6446019539938,3,156,264,551,544,807,509 +35.5330197060951,-82.654704996727,3,340,401,539,441,329,252 +35.5388858896597,-82.6667634670859,3,626,766,849,317,181,118 +35.6343743221274,-82.5621480891071,3,338,402,601,505,315,230 +35.5405153850943,-82.4334157704104,3,155,256,658,444,1589,818 +35.4776168613186,-82.539986576015,3,261,315,445,293,233,169 +35.6705491207755,-82.3349925799132,3,270,308,356,293,328,262 +35.5062959809676,-82.5921313127023,3,204,241,466,353,1375,701 +35.6252491476936,-82.3620426620697,3,335,476,935,898,1035,589 +35.5372563942251,-82.6566604243528,3,167,212,336,225,33,100 +35.6434994965611,-82.4014771191895,3,146,172,212,165,227,158 +35.4763132649709,-82.5360757207635,3,310,364,504,361,297,226 +35.664357038124,-82.3395552443733,3,237,272,319,242,284,217 +35.7347512408989,-82.6615489934172,3,394,461,633,628,610,319 +35.541493082355,-82.5650812305457,3,199,267,531,438,2239,1410 +35.5665873120479,-82.5696438950059,3,123,199,493,375,1890,821 +35.6203606613898,-82.5787692239262,3,373,447,607,605,404,248 +35.7347512408989,-82.6622008026258,3,340,402,575,593,347,189 +35.5349751006166,-82.6579640427699,3,158,193,595,281,3105,1409 +35.6581649554726,-82.6074488291041,3,247,325,591,537,1104,692 +35.6298117349105,-82.3604131390482,3,90,135,290,225,690,578 +35.6689196253409,-82.3379257213519,3,250,285,330,266,297,232 +35.6575131572987,-82.6071229244998,3,405,460,626,641,408,280 +35.5313902106605,-82.6563345197485,3,203,250,380,279,190,115 +35.7445282135065,-82.6775183190277,3,429,507,691,715,471,288 +35.7187821856398,-82.6367802434907,3,326,389,559,588,326,135 +35.6294858358236,-82.3636721850912,3,331,371,502,480,748,931 +35.545729770485,-82.5751842732789,3,229,294,468,441,473,356 +35.4821794485355,-82.5588890430641,3,90,196,458,382,1083,581 +35.5463815686588,-82.3346666753089,3,115,153,252,138,131,67 +35.607324697913,-82.5803987469476,3,264,330,583,485,1685,904 +35.7077016166845,-82.6195072994631,3,426,496,677,706,458,266 +35.6252491476936,-82.3617167574654,3,196,377,922,850,1164,712 +35.6656606344717,-82.3372739121433,3,373,420,473,418,457,386 +35.6617498454287,-82.6234181547146,3,398,473,677,677,497,333 +35.6989023413376,-82.6149446350029,3,310,375,554,570,289,199 +35.6633793408633,-82.3385775305605,3,291,324,379,315,353,278 +35.6698973226017,-82.3415106719991,3,132,169,297,158,189,131 +35.5105326690976,-82.5895240758679,3,265,325,548,445,1443,690 +35.4805499531009,-82.5341202931377,3,256,314,447,296,234,173 +35.6747858089055,-82.3349925799132,3,181,211,239,185,231,170 +35.4798981549271,-82.5341202931377,3,251,311,441,295,267,188 +35.6640311390371,-82.3402070535819,3,272,313,366,286,325,255 +35.7559346815487,-82.669044799316,3,241,291,482,475,345,203 +35.6637052399502,-82.3402070535819,3,247,288,335,276,296,227 +35.4704470814064,-82.5377052437849,3,296,353,483,333,274,217 +35.5317161097474,-82.559866756877,3,271,332,559,505,1065,711 +35.6640311390371,-82.3405329581862,3,251,288,339,273,295,221 +35.5343233024428,-82.5611703752942,3,205,270,432,415,370,217 +35.4772909622317,-82.5562818062298,3,387,456,597,606,387,288 +35.475987365884,-82.537053434576407,3,287,340,477,339,263,201 +35.6421959002135,-82.5983235001839,3,581,652,1031,1039,686,542 +35.6718527171232,-82.6142928257944,3,256,352,648,561,1513,755 +35.6232937531721,-82.5960421679538,3,458,530,903,798,2623,1750 +35.4847866412309,-82.5627998983157,3,327,396,555,548,324,248 +35.6907548641646,-82.6185295856502,3,430,499,681,701,461,293 +35.7253001673782,-82.6478610000368,3,166,228,424,387,708,259 +35.4821794485355,-82.5582372338556,3,291,354,510,496,351,225 +35.7200857819874,-82.6507941414754,3,454,522,701,724,464,250 +35.4795722558402,-82.5354239115549,3,230,285,418,266,198,134 +35.5434484768766,-82.5878945528464,3,167,208,340,206,420,320 +35.6102577896952,-82.4220091092601,3,332,429,769,666,943,527 +35.6379592120835,-82.5989753093925,3,381,455,636,646,352,300 +35.6024362116092,-82.3757306554501,3,93,128,230,155,313,202 +35.6343743221274,-82.5670366581715,3,154,215,462,342,1899,873 +35.4974967056208,-82.5921313127023,3,325,393,556,552,311,206 +35.6741340107317,-82.333688961496,3,222,260,298,255,304,227 +35.6682678271671,-82.3395552443733,3,213,246,300,226,261,196 +35.529434816139,-82.656986328957,3,179,213,346,249,142,78 +35.7174785892921,-82.6318916744263,3,405,480,657,682,387,339 +35.5343233024428,-82.656986328957,3,198,236,414,270,829,334 +35.4766391640579,-82.5461787634966,3,134,183,291,148,87,81 +35.5095549718368,-82.7612758023316,3,199,242,575,390,3657,1763 +35.6529505700818,-82.6006048324139,3,357,423,588,605,397,247 +35.6731563134709,-82.3362961983304,3,235,262,308,253,303,231 +35.6405664047789,-82.5989753093925,3,408,487,657,679,424,297 +35.6946656532076,-82.6142928257944,3,440,511,696,719,471,295 +35.668593726254,-82.3389034351648,3,219,253,295,232,263,195 +35.5320420088343,-82.6563345197485,3,61,148,389,256,942,522 +35.6203606613898,-82.5784433193219,3,206,293,536,478,1656,896 +35.6672901299063,-82.3372739121433,3,367,419,464,403,448,371 +35.4750096686233,-82.5386829575978,3,367,430,571,430,368,291 +35.4795722558402,-82.5347721023463,3,222,274,406,258,189,140 +35.6575131572987,-82.6100560659385,3,422,498,678,688,415,251 +35.4769650631448,-82.5341202931377,3,203,252,374,230,165,114 +35.6617498454287,-82.3437920042292,3,254,295,361,266,261,235 +35.7428987180719,-82.6804514604663,3,251,311,557,502,1291,756 +35.7445282135065,-82.6755628914019,3,4,149,505,434,1192,678 +35.6470843865173,-82.4034325468152,3,210,235,384,288,1213,710 +35.5447520732242,-82.5856132206164,3,344,399,535,413,343,273 +35.4896751275347,-82.56540713515,3,367,429,597,594,365,264 +35.6021103125222,-82.3754047508458,3,189,214,283,208,162,112 +35.6620757445156,-82.6247217731318,3,402,462,654,656,635,396 +35.5343233024428,-82.6543790921227,3,261,338,550,486,1282,781 +35.6089541933476,-82.428527201346,3,181,204,293,228,586,267 +35.4812017512748,-82.5347721023463,3,158,206,378,200,595,376 +35.6483879828649,-82.4040843560238,3,285,327,435,361,1000,545 +35.6011326152615,-82.5764878916961,3,314,378,563,552,498,272 +35.4792463567532,-82.5566077108341,3,411,474,615,605,426,308 +35.5307384124867,-82.6566604243528,3,189,231,389,274,515,244 +35.5551808440057,-82.5898499804722,3,92,175,392,341,680,364 +35.5792973764378,-82.5683402765887,3,227,291,493,439,865,407 +35.607324697913,-82.5800728423433,3,154,213,384,308,687,297 +35.5098808709238,-82.5901758850765,3,331,395,550,547,332,229 +35.5411671832681,-82.56540713515,3,194,291,523,489,775,530 +35.6907548641646,-82.6113596843557,3,280,405,778,860,1288,1352 +35.5467074677458,-82.333688961496,3,59,99,215,104,198,90 +35.5427966787027,-82.5667107535672,3,111,189,478,370,1814,934 +35.5708240001779,-82.3780119876802,3,90,134,311,268,931,632 +35.7080275157714,-82.6208109178803,3,394,461,642,660,493,275 +35.4763132649709,-82.5298835332818,3,401,462,622,494,416,367 +35.738336130855,-82.6674152762945,3,208,255,460,433,539,302 +35.4753355677102,-82.5367275299721,3,289,355,493,357,279,220 +35.4717506777541,-82.5360757207635,3,329,394,530,386,319,252 +35.7357289381596,-82.6628526118344,3,335,399,577,579,474,250 +35.6324189276058,-82.3610649482568,3,103,115,203,121,179,121 +35.6715268180363,-82.3349925799132,3,266,305,353,288,328,264 +35.6327448266928,-82.3607390436525,3,96,114,222,158,700,345 +35.541818981442,-82.5670366581715,3,166,208,480,279,2722,1161 +35.7331217454643,-82.6592676611871,3,406,481,663,691,448,237 +35.5382340914858,-82.5618221845028,3,210,272,423,419,495,207 +35.512813962706,-82.5771397009047,3,76,150,433,304,1999,942 +35.5812527709593,-82.5676884673801,3,319,384,572,553,438,286 +35.5313902106605,-82.559866756877,3,402,474,662,650,608,398 +35.4769650631448,-82.537053434576407,3,278,334,467,327,250,191 +35.6578390563856,-82.6097301613342,3,381,443,610,625,376,225 +35.475987365884,-82.5435715266622,3,387,453,593,452,403,329 +35.6350261203012,-82.5647553259414,3,348,439,628,543,348,244 +35.6653347353848,-82.3418365766034,3,213,259,303,227,252,188 +35.6731563134709,-82.3323853430788,3,129,168,339,198,1404,615 +35.6558836618641,-82.602886164644,3,240,313,570,468,1596,703 +35.6340484230404,-82.360087234444,3,68,95,180,94,144,86 +35.6764153043401,-82.3359702937261,3,119,146,237,144,166,92 +35.4831571457963,-82.5331425793248,3,87,197,422,296,897,648 +35.6663124326456,-82.3362961983304,3,374,424,483,425,463,383 +35.5444261741373,-82.5856132206164,3,350,399,531,408,339,261 +35.6607721481679,-82.336948007539,3,146,191,390,280,1313,673 +35.6597944509072,-82.620485013276,3,344,401,571,590,328,191 +35.7073757175975,-82.6201591086717,3,387,462,654,662,618,317 +35.7350771399858,-82.6628526118344,3,300,363,558,550,447,284 +35.6536023682557,-82.6009307370182,3,400,465,638,632,503,346 +35.7468095071149,-82.6742592729847,3,303,359,630,552,1748,1178 +35.5392117887466,-82.6680670855031,3,646,839,1299,1363,2530,2352 +35.6718527171232,-82.3330371522874,3,321,359,407,353,395,306 +35.5463815686588,-82.577465605509,3,210,261,504,399,1476,667 +35.6493656801257,-82.5989753093925,3,383,449,644,648,603,362 +35.4740319713625,-82.5262985826346,3,256,302,423,281,237,166 +35.7409433235503,-82.6788219374448,3,300,355,538,531,541,200 +35.4710988795802,-82.5383570529935,3,316,375,510,360,308,252 +35.5291089170521,-82.656986328957,3,123,169,314,199,260,235 +35.5098808709238,-82.5905017896808,3,353,423,591,572,521,350 +35.6597944509072,-82.6211368224846,3,369,442,616,633,374,207 +35.6089541933476,-82.4288531059503,3,123,138,206,139,259,178 +35.7354030390727,-82.6622008026258,3,150,265,539,491,931,571 +35.7331217454643,-82.6573122335613,3,217,268,597,448,1910,1013 +35.6245973495197,-82.5891981712636,3,330,397,570,589,365,179 +35.4750096686233,-82.5556299970212,3,175,236,506,357,1842,974 +35.4812017512748,-82.5341202931377,3,241,301,428,277,223,159 +35.6796742952093,-82.6146187303986,3,138,271,652,575,1941,1106 +35.5206355407921,-82.837863384341,3,159,296,622,579,2133,1541 +35.750394397071,-82.6739333683804,3,165,264,608,468,1920,1001 +35.4776168613186,-82.531187151699,3,167,211,333,185,103,89 +35.5349751006166,-82.6576381381656,3,200,243,446,292,868,398 +35.6578390563856,-82.6074488291041,3,406,472,638,647,440,234 +35.480224054014,-82.5562818062298,3,74,196,493,423,1082,628 +35.636003817562,-82.5996271186011,3,378,455,628,651,294,265 +35.4697952832325,-82.5383570529935,3,273,330,521,384,931,714 +35.4808758521878,-82.5572595200427,3,238,322,539,513,619,400 +35.6871699742085,-82.6191813948588,3,370,439,630,633,497,319 +35.6698973226017,-82.3346666753089,3,292,335,381,326,363,294 +35.6601203499941,-82.3389034351648,3,384,427,491,420,473,384 +35.6659865335586,-82.3372739121433,3,388,431,499,439,478,392 +35.6676160289932,-82.3362961983304,3,307,346,394,336,373,299 +35.6333966248666,-82.5608444706899,3,348,420,603,508,333,249 +35.4766391640579,-82.5386829575978,3,341,403,539,394,330,262 +35.6826073869916,-82.617225967233,3,325,377,637,572,1070,670 +35.4776168613186,-82.5562818062298,3,405,479,620,615,431,300 +35.6676160289932,-82.3398811489776,3,203,237,290,218,247,186 +35.5349751006166,-82.656986328957,3,269,320,451,345,227,173 +35.7275814609866,-82.6478610000368,3,339,401,571,575,414,250 +35.717804488379,-82.6318916744263,3,232,337,622,604,1580,827 +35.6249232486067,-82.5820282699691,3,264,302,590,434,2025,946 +35.4714247786671,-82.5386829575978,3,274,327,456,312,269,206 +35.488371531187,-82.5650812305457,3,258,333,638,531,2527,1566 +35.6588167536464,-82.6178777764416,3,370,436,602,621,375,228 +35.6653347353848,-82.3382516259562,3,335,381,443,377,404,326 +35.6718527171232,-82.333688961496,3,256,285,333,276,314,244 +35.4909787238824,-82.5663848489629,3,201,289,497,465,657,324 +35.7373584335942,-82.6664375624816,3,197,239,450,424,800,247 +35.660446249081,-82.3398811489776,3,326,369,413,357,398,319 +35.5806009727855,-82.5680143719844,3,272,334,583,482,1607,816 +35.623619652259,-82.5983235001839,3,272,337,556,517,1058,792 +35.603088009783,-82.3754047508458,3,148,177,281,190,463,212 +35.6086282942606,-82.5787692239262,3,80,190,423,373,993,384 +35.5388858896597,-82.6654598486687,3,161,182,283,143,917,555 +35.4805499531009,-82.5331425793248,3,268,329,469,318,286,206 +35.5118362654453,-82.5856132206164,3,263,336,492,494,271,149 +35.6229678540851,-82.5953903587452,3,393,473,635,654,313,278 +35.7008577358591,-82.6162482534201,3,411,475,668,676,464,319 +35.672504515297,-82.336948007539,3,266,301,397,316,340,251 +35.4756614667971,-82.5409642898279,3,303,361,492,351,279,221 +35.6663124326456,-82.3405329581862,3,174,207,246,168,195,138 +35.6252491476936,-82.5846355068035,3,219,272,431,436,111,84 +35.6353520193881,-82.5673625627758,3,263,332,528,454,212,157 +35.713567800249,-82.6247217731318,3,387,457,648,659,493,285 +35.6532764691687,-82.6002789278097,3,100,198,423,358,1187,563 +35.6118872851299,-82.3268449648058,3,345,459,767,826,2262,1786 +35.5369304951382,-82.5614962798985,3,208,273,452,415,604,297 +35.4782686594925,-82.5367275299721,3,195,246,377,218,151,102 +35.6021103125222,-82.3760565600544,3,62,117,249,162,480,165 +35.5431225777896,-82.5073961155855,3,102,123,209,149,229,149 +35.4779427604056,-82.5556299970212,3,305,371,605,525,1596,1005 +35.6588167536464,-82.6191813948588,3,370,448,630,630,599,278 +35.5952664316969,-82.574206559466,3,365,436,613,621,335,318 +35.4769650631448,-82.534446197742,3,217,267,400,258,190,134 +35.6369815148227,-82.3578059022139,3,229,297,575,442,2604,1452 +35.6914066623384,-82.6113596843557,3,267,387,740,715,1584,1453 +35.4857643384916,-82.5634517075243,3,137,225,429,400,558,276 +35.6301376339974,-82.3607390436525,3,135,156,219,175,58,46 +35.6692455244279,-82.6182036810459,3,475,537,730,759,454,367 +35.5362786969643,-82.6576381381656,3,255,289,425,326,47,163 +35.6637052399502,-82.6250476777361,3,154,257,550,488,1495,701 +35.537582293312,-82.6700225131289,3,371,536,816,596,867,811 +35.6656606344717,-82.3428142904163,3,109,170,237,169,210,155 +35.4929341184039,-82.5777915101133,3,116,180,513,305,2601,1160 +35.4740319713625,-82.5390088622021,3,350,415,550,412,345,284 +35.5470333668327,-82.5862650298249,3,203,260,373,257,244,174 +35.4743578704494,-82.5403124806193,3,336,399,541,399,338,280 +35.6907548641646,-82.6191813948588,3,296,361,622,537,1573,926 +35.6627275426894,-82.3421624812077,3,334,367,415,347,378,313 +35.6343743221274,-82.3587836160268,3,36,71,173,127,351,141 +35.5379081923989,-82.5618221845028,3,193,257,439,408,476,272 +35.6653347353848,-82.3431401950206,3,166,218,281,209,255,194 +35.4756614667971,-82.5364016253678,3,326,383,525,388,313,248 +35.6747858089055,-82.3366221029347,3,139,173,353,230,1231,733 +35.5502923577019,-82.4607917571712,3,464,643,933,979,1857,1548 +35.6614239463418,-82.3490064778979,3,130,203,463,220,1249,639 +35.6249232486067,-82.5833318883863,3,272,335,495,501,211,109 +35.6490397810388,-82.5993012139968,3,340,394,653,565,1272,682 +35.7510461952449,-82.6739333683804,3,329,384,733,620,2609,1794 +35.4753355677102,-82.5412901944322,3,312,379,522,370,301,256 +35.4766391640579,-82.5357498161592,3,277,335,467,328,253,197 +35.4756614667971,-82.5403124806193,3,303,354,485,345,275,215 +35.6672901299063,-82.3431401950206,3,78,105,135,56,112,54 +35.652298771908,-82.6012566416225,3,382,444,609,626,388,243 +35.4968449074469,-82.7254262958591,3,124,162,282,254,509,411 +35.6242714504328,-82.3617167574654,3,731,851,1198,1318,1807,1905 +35.6633793408633,-82.3418365766034,3,350,401,459,391,419,334 +35.6588167536464,-82.617225967233,3,364,433,589,604,364,208 +35.537582293312,-82.4386302440791,3,254,335,597,503,1772,855 +35.504666485533,-82.6879472663651,3,220,317,680,494,2817,1627 +35.6474102856042,-82.3597613298397,3,169,354,743,881,1082,1201 +35.508903173663,-82.5911535988894,3,252,307,497,431,884,456 +35.4746837695363,-82.5403124806193,3,336,395,532,393,328,271 +35.6467584874303,-82.5996271186011,3,403,471,660,664,419,274 +35.6539282673426,-82.6022343554354,3,310,370,561,538,645,501 +35.6490397810388,-82.6002789278097,3,315,377,576,549,747,469 +35.6004808170876,-82.4871900301192,3,153,198,373,259,1376,528 +35.4919564211431,-82.5865909344292,3,259,320,479,462,331,134 +35.5255240270959,-82.5585631384598,3,200,255,491,383,1481,707 +35.6578390563856,-82.6090783521256,3,389,461,626,647,392,243 +35.7445282135065,-82.6768665098191,3,426,496,669,708,404,298 +35.4795722558402,-82.5396606714107,3,200,237,367,211,173,108 +35.680977891557,-82.4816496518461,3,117,182,457,330,1863,1111 +35.7311663509427,-82.6527495691012,3,92,167,540,386,2131,1222 +35.4740319713625,-82.5412901944322,3,326,382,549,442,263,235 +35.7543051861141,-82.6706743223374,3,569,619,832,818,748,502 +35.6232937531721,-82.6002789278097,3,290,364,573,542,806,510 +35.6347002212143,-82.5605185660856,3,202,287,643,474,3457,1894 +35.4952154120123,-82.5898499804722,3,222,295,500,452,657,413 +35.6180793677813,-82.5781174147176,3,303,366,558,533,921,417 +35.6630534417764,-82.3395552443733,3,249,288,340,267,296,236 +35.6311153312582,-82.6015825462268,3,270,338,515,513,369,307 +35.6347002212143,-82.5670366581715,3,397,479,673,599,461,387 +35.7409433235503,-82.6768665098191,3,277,348,524,555,226,148 +35.5036887882723,-82.5924572173066,3,265,324,480,473,159,84 +35.6483879828649,-82.4044102606281,3,266,312,373,322,430,345 +35.5646319175264,-82.5817023653648,3,186,231,454,354,1306,441 +35.4831571457963,-82.626025391549,3,150,194,261,195,212,147 +35.5118362654453,-82.5673625627758,3,204,258,450,392,706,265 +35.6839109833393,-82.6185295856502,3,389,451,653,654,644,379 +35.664357038124,-82.3389034351648,3,247,283,326,255,292,226 +35.4795722558402,-82.5360757207635,3,200,256,382,230,169,107 +35.4815276503617,-82.534446197742,3,204,254,423,242,641,300 +35.6633793408633,-82.3389034351648,3,268,308,364,298,326,252 +35.4916305220562,-82.5856132206164,3,246,301,476,442,365,277 diff --git a/02_Week2/Data/04_Beijing_PM2.5_hourly_20100101-20141231.csv b/02_Week2/Data/04_Beijing_PM2.5_hourly_20100101-20141231.csv new file mode 100644 index 0000000..8f4656f --- /dev/null +++ b/02_Week2/Data/04_Beijing_PM2.5_hourly_20100101-20141231.csv @@ -0,0 +1,43825 @@ +year,month,day,hour,pm2.5,DEWP,TEMP,PRES,WIND,TSNOW,TRAIN +2010,1,1,0,NA,-21,-11,1021,1.79,0,0 +2010,1,1,1,NA,-21,-12,1020,4.92,0,0 +2010,1,1,2,NA,-21,-11,1019,6.71,0,0 +2010,1,1,3,NA,-21,-14,1019,9.84,0,0 +2010,1,1,4,NA,-20,-12,1018,12.97,0,0 +2010,1,1,5,NA,-19,-10,1017,16.1,0,0 +2010,1,1,6,NA,-19,-9,1017,19.23,0,0 +2010,1,1,7,NA,-19,-9,1017,21.02,0,0 +2010,1,1,8,NA,-19,-9,1017,24.15,0,0 +2010,1,1,9,NA,-20,-8,1017,27.28,0,0 +2010,1,1,10,NA,-19,-7,1017,31.3,0,0 +2010,1,1,11,NA,-18,-5,1017,34.43,0,0 +2010,1,1,12,NA,-19,-5,1015,37.56,0,0 +2010,1,1,13,NA,-18,-3,1015,40.69,0,0 +2010,1,1,14,NA,-18,-2,1014,43.82,0,0 +2010,1,1,15,NA,-18,-1,1014,0.89,0,0 +2010,1,1,16,NA,-19,-2,1015,1.79,0,0 +2010,1,1,17,NA,-18,-3,1015,2.68,0,0 +2010,1,1,18,NA,-18,-5,1016,1.79,0,0 +2010,1,1,19,NA,-17,-4,1017,1.79,0,0 +2010,1,1,20,NA,-17,-5,1017,0.89,0,0 +2010,1,1,21,NA,-17,-5,1018,1.79,0,0 +2010,1,1,22,NA,-17,-5,1018,2.68,0,0 +2010,1,1,23,NA,-17,-5,1020,0.89,0,0 +2010,1,2,0,129,-16,-4,1020,1.79,0,0 +2010,1,2,1,148,-15,-4,1020,2.68,0,0 +2010,1,2,2,159,-11,-5,1021,3.57,0,0 +2010,1,2,3,181,-7,-5,1022,5.36,1,0 +2010,1,2,4,138,-7,-5,1022,6.25,2,0 +2010,1,2,5,109,-7,-6,1022,7.14,3,0 +2010,1,2,6,105,-7,-6,1023,8.93,4,0 +2010,1,2,7,124,-7,-5,1024,10.72,0,0 +2010,1,2,8,120,-8,-6,1024,12.51,0,0 +2010,1,2,9,132,-7,-5,1025,14.3,0,0 +2010,1,2,10,140,-7,-5,1026,17.43,1,0 +2010,1,2,11,152,-8,-5,1026,20.56,0,0 +2010,1,2,12,148,-8,-5,1026,23.69,0,0 +2010,1,2,13,164,-8,-5,1025,27.71,0,0 +2010,1,2,14,158,-9,-5,1025,31.73,0,0 +2010,1,2,15,154,-9,-5,1025,35.75,0,0 +2010,1,2,16,159,-9,-5,1026,37.54,0,0 +2010,1,2,17,164,-8,-5,1027,39.33,0,0 +2010,1,2,18,170,-8,-5,1027,42.46,0,0 +2010,1,2,19,149,-8,-5,1028,44.25,0,0 +2010,1,2,20,154,-7,-5,1028,46.04,0,0 +2010,1,2,21,164,-7,-5,1027,49.17,1,0 +2010,1,2,22,156,-8,-6,1028,52.3,2,0 +2010,1,2,23,126,-8,-6,1027,55.43,3,0 +2010,1,3,0,90,-7,-6,1027,58.56,4,0 +2010,1,3,1,63,-8,-6,1026,61.69,5,0 +2010,1,3,2,65,-8,-7,1026,65.71,6,0 +2010,1,3,3,55,-8,-7,1025,68.84,7,0 +2010,1,3,4,65,-8,-7,1024,72.86,8,0 +2010,1,3,5,83,-9,-8,1024,76.88,9,0 +2010,1,3,6,91,-10,-8,1024,80.9,10,0 +2010,1,3,7,86,-10,-9,1024,84.92,11,0 +2010,1,3,8,82,-10,-9,1024,89.84,12,0 +2010,1,3,9,86,-11,-9,1023,93.86,13,0 +2010,1,3,10,78,-11,-9,1023,97.88,15,0 +2010,1,3,11,98,-11,-9,1022,102.8,16,0 +2010,1,3,12,107,-11,-9,1021,105.93,17,0 +2010,1,3,13,90,-11,-9,1020,111.74,19,0 +2010,1,3,14,96,-11,-9,1020,116.66,20,0 +2010,1,3,15,95,-11,-9,1020,121.58,21,0 +2010,1,3,16,86,-11,-9,1020,124.71,22,0 +2010,1,3,17,70,-11,-9,1020,127.84,23,0 +2010,1,3,18,61,-11,-9,1021,0.89,24,0 +2010,1,3,19,53,-11,-9,1022,1.78,25,0 +2010,1,3,20,71,-10,-9,1022,4.02,26,0 +2010,1,3,21,72,-11,-10,1023,7.15,27,0 +2010,1,3,22,76,-11,-9,1023,11.17,0,0 +2010,1,3,23,73,-12,-11,1023,14.3,0,0 +2010,1,4,0,79,-14,-12,1023,16.09,0,0 +2010,1,4,1,58,-16,-9,1023,21.9,0,0 +2010,1,4,2,25,-17,-10,1024,29.95,0,0 +2010,1,4,3,26,-18,-11,1024,39.78,0,0 +2010,1,4,4,28,-19,-11,1025,48.72,0,0 +2010,1,4,5,26,-20,-12,1026,55.87,0,0 +2010,1,4,6,20,-21,-12,1027,64.81,0,0 +2010,1,4,7,29,-21,-13,1027,73.75,0,0 +2010,1,4,8,26,-22,-13,1028,80.9,0,0 +2010,1,4,9,27,-22,-13,1029,90.73,0,0 +2010,1,4,10,27,-22,-12,1030,100.56,0,0 +2010,1,4,11,25,-23,-12,1031,108.61,0,0 +2010,1,4,12,29,-21,-11,1030,117.55,0,0 +2010,1,4,13,32,-20,-10,1030,127.38,0,0 +2010,1,4,14,28,-21,-10,1030,136.32,0,0 +2010,1,4,15,29,-21,-9,1030,145.26,0,0 +2010,1,4,16,30,-21,-9,1031,152.41,0,0 +2010,1,4,17,30,-20,-11,1032,159.56,0,0 +2010,1,4,18,28,-23,-11,1032,165.37,0,0 +2010,1,4,19,26,-21,-12,1033,171.18,0,0 +2010,1,4,20,31,-24,-12,1034,180.12,0,0 +2010,1,4,21,33,-24,-13,1034,187.27,0,0 +2010,1,4,22,29,-24,-13,1035,195.32,0,0 +2010,1,4,23,31,-26,-15,1035,198.45,0,0 +2010,1,5,0,30,-26,-17,1035,201.58,0,0 +2010,1,5,1,34,-26,-18,1035,205.6,0,0 +2010,1,5,2,27,-26,-19,1035,208.73,0,0 +2010,1,5,3,25,-27,-18,1035,213.65,0,0 +2010,1,5,4,28,-27,-19,1035,218.57,0,0 +2010,1,5,5,28,-27,-16,1034,4.92,0,0 +2010,1,5,6,27,-26,-16,1035,8.05,0,0 +2010,1,5,7,27,-27,-16,1034,13.86,0,0 +2010,1,5,8,27,-26,-16,1035,18.78,0,0 +2010,1,5,9,29,-26,-15,1035,24.59,0,0 +2010,1,5,10,36,-25,-14,1035,29.51,0,0 +2010,1,5,11,30,-25,-13,1035,34.43,0,0 +2010,1,5,12,27,-25,-12,1034,39.35,0,0 +2010,1,5,13,39,-24,-11,1032,41.14,0,0 +2010,1,5,14,41,-22,-11,1032,0.89,0,0 +2010,1,5,15,33,-23,-11,1031,1.79,0,0 +2010,1,5,16,50,-24,-11,1031,3.58,0,0 +2010,1,5,17,56,-23,-11,1031,5.37,0,0 +2010,1,5,18,59,-23,-11,1032,7.16,0,0 +2010,1,5,19,60,-22,-13,1033,10.29,0,0 +2010,1,5,20,84,-22,-12,1033,13.42,0,0 +2010,1,5,21,106,-24,-18,1033,16.55,0,0 +2010,1,5,22,66,-22,-13,1034,20.57,0,0 +2010,1,5,23,50,-22,-16,1033,23.7,0,0 +2010,1,6,0,56,-25,-17,1033,26.83,0,0 +2010,1,6,1,77,-25,-14,1033,4.02,0,0 +2010,1,6,2,50,-26,-14,1034,8.04,0,0 +2010,1,6,3,44,-26,-14,1033,13.85,0,0 +2010,1,6,4,27,-26,-14,1033,17.87,0,0 +2010,1,6,5,28,-26,-14,1033,23.68,0,0 +2010,1,6,6,21,-26,-14,1033,28.6,0,0 +2010,1,6,7,25,-26,-15,1034,33.52,0,0 +2010,1,6,8,20,-26,-14,1034,39.33,0,0 +2010,1,6,9,29,-25,-13,1035,44.25,0,0 +2010,1,6,10,34,-25,-12,1035,50.06,0,0 +2010,1,6,11,42,-24,-11,1035,54.98,0,0 +2010,1,6,12,28,-24,-10,1034,59,0,0 +2010,1,6,13,36,-22,-10,1033,4.02,0,0 +2010,1,6,14,48,-22,-9,1033,5.81,0,0 +2010,1,6,15,49,-22,-8,1033,0.89,0,0 +2010,1,6,16,52,-22,-8,1033,1.79,0,0 +2010,1,6,17,56,-21,-9,1033,3.58,0,0 +2010,1,6,18,96,-22,-9,1033,5.37,0,0 +2010,1,6,19,75,-22,-14,1034,0.89,0,0 +2010,1,6,20,105,-22,-12,1035,1.79,0,0 +2010,1,6,21,132,-21,-14,1034,3.58,0,0 +2010,1,6,22,93,-22,-16,1035,5.37,0,0 +2010,1,6,23,131,-21,-16,1035,7.16,0,0 +2010,1,7,0,127,-21,-16,1035,8.95,0,0 +2010,1,7,1,130,-21,-16,1035,0.45,0,0 +2010,1,7,2,43,-22,-18,1036,1.34,0,0 +2010,1,7,3,37,-23,-15,1036,4.02,0,0 +2010,1,7,4,30,-24,-16,1035,7.15,0,0 +2010,1,7,5,28,-25,-15,1035,10.28,0,0 +2010,1,7,6,24,-24,-15,1035,14.3,0,0 +2010,1,7,7,23,-25,-13,1036,4.92,0,0 +2010,1,7,8,24,-24,-14,1036,4.02,0,0 +2010,1,7,9,27,-23,-12,1036,8.94,0,0 +2010,1,7,10,40,-22,-11,1036,12.96,0,0 +2010,1,7,11,42,-20,-10,1036,16.09,0,0 +2010,1,7,12,42,-21,-9,1035,19.22,0,0 +2010,1,7,13,55,-21,-8,1034,21.01,0,0 +2010,1,7,14,52,-20,-7,1033,22.8,0,0 +2010,1,7,15,51,-20,-7,1032,24.59,0,0 +2010,1,7,16,57,-18,-8,1032,26.38,0,0 +2010,1,7,17,50,-19,-9,1032,28.17,0,0 +2010,1,7,18,54,-19,-11,1032,0.89,0,0 +2010,1,7,19,67,-18,-11,1033,1.79,0,0 +2010,1,7,20,106,-19,-15,1032,1.79,0,0 +2010,1,7,21,159,-19,-14,1032,0.45,0,0 +2010,1,7,22,198,-21,-14,1032,1.34,0,0 +2010,1,7,23,190,-21,-16,1032,2.23,0,0 +2010,1,8,0,210,-21,-17,1031,1.79,0,0 +2010,1,8,1,195,-19,-16,1031,3.58,0,0 +2010,1,8,2,275,-20,-16,1031,0.89,0,0 +2010,1,8,3,164,-19,-15,1030,0.89,0,0 +2010,1,8,4,110,-18,-15,1030,0.45,0,0 +2010,1,8,5,100,-18,-15,1029,1.34,0,0 +2010,1,8,6,81,-18,-15,1029,1.79,0,0 +2010,1,8,7,71,-16,-13,1029,0.89,0,0 +2010,1,8,8,66,-16,-12,1029,0.89,0,0 +2010,1,8,9,92,-16,-12,1030,1.78,0,0 +2010,1,8,10,135,-17,-10,1030,0.89,0,0 +2010,1,8,11,155,-16,-10,1029,0.89,0,0 +2010,1,8,12,198,-16,-9,1028,3.13,0,0 +2010,1,8,13,250,-15,-8,1026,4.92,0,0 +2010,1,8,14,200,-16,-8,1026,0.89,0,0 +2010,1,8,15,231,-16,-8,1025,1.78,0,0 +2010,1,8,16,250,-16,-8,1025,2.67,0,0 +2010,1,8,17,212,-17,-8,1025,0.89,0,0 +2010,1,8,18,219,-17,-9,1026,0.45,0,0 +2010,1,8,19,227,-17,-11,1026,1.34,0,0 +2010,1,8,20,226,-17,-12,1026,1.79,0,0 +2010,1,8,21,225,-18,-14,1027,1.79,0,0 +2010,1,8,22,168,-16,-9,1027,4.92,0,0 +2010,1,8,23,169,-16,-11,1027,6.71,0,0 +2010,1,9,0,165,-17,-13,1027,0.89,0,0 +2010,1,9,1,159,-17,-13,1027,3.13,0,0 +2010,1,9,2,167,-17,-14,1027,4.92,0,0 +2010,1,9,3,196,-17,-15,1027,8.05,0,0 +2010,1,9,4,169,-17,-13,1027,9.84,0,0 +2010,1,9,5,155,-19,-16,1027,13.86,0,0 +2010,1,9,6,119,-18,-15,1027,17.88,0,0 +2010,1,9,7,106,-19,-15,1027,21.9,0,0 +2010,1,9,8,93,-18,-14,1028,25.92,0,0 +2010,1,9,9,84,-18,-9,1029,5.81,0,0 +2010,1,9,10,73,-17,-7,1029,11.62,0,0 +2010,1,9,11,66,-17,-6,1030,17.43,0,0 +2010,1,9,12,40,-16,-6,1029,23.24,0,0 +2010,1,9,13,49,-16,-5,1029,28.16,0,0 +2010,1,9,14,50,-15,-4,1029,33.08,0,0 +2010,1,9,15,49,-14,-4,1028,37.1,0,0 +2010,1,9,16,41,-15,-4,1029,38.89,0,0 +2010,1,9,17,37,-15,-4,1030,1.79,0,0 +2010,1,9,18,45,-15,-7,1030,3.13,0,0 +2010,1,9,19,44,-15,-6,1031,3.13,0,0 +2010,1,9,20,54,-15,-7,1032,4.92,0,0 +2010,1,9,21,50,-15,-6,1032,3.13,0,0 +2010,1,9,22,47,-15,-8,1033,0.45,0,0 +2010,1,9,23,66,-15,-8,1033,0.9,0,0 +2010,1,10,0,75,-15,-11,1033,0.89,0,0 +2010,1,10,1,82,-17,-13,1033,1.78,0,0 +2010,1,10,2,66,-17,-12,1033,4.91,0,0 +2010,1,10,3,83,-17,-13,1033,8.04,0,0 +2010,1,10,4,62,-19,-14,1032,9.83,0,0 +2010,1,10,5,40,-18,-13,1032,12.96,0,0 +2010,1,10,6,23,-19,-13,1032,16.09,0,0 +2010,1,10,7,25,-19,-14,1033,19.22,0,0 +2010,1,10,8,27,-19,-14,1034,22.35,0,0 +2010,1,10,9,35,-17,-12,1033,24.14,0,0 +2010,1,10,10,50,-16,-7,1033,4.02,0,0 +2010,1,10,11,70,-15,-7,1033,3.13,0,0 +2010,1,10,12,75,-15,-7,1032,6.26,0,0 +2010,1,10,13,58,-14,-6,1031,9.39,0,0 +2010,1,10,14,70,-14,-6,1031,12.52,0,0 +2010,1,10,15,68,-13,-5,1030,14.31,0,0 +2010,1,10,16,71,-14,-5,1031,16.1,0,0 +2010,1,10,17,88,-14,-6,1031,19.23,0,0 +2010,1,10,18,84,-13,-7,1032,23.25,0,0 +2010,1,10,19,83,-15,-6,1032,27.27,0,0 +2010,1,10,20,66,-15,-6,1033,30.4,0,0 +2010,1,10,21,27,-15,-4,1034,35.32,0,0 +2010,1,10,22,24,-16,-4,1034,44.26,0,0 +2010,1,10,23,22,-17,-5,1035,52.31,0,0 +2010,1,11,0,23,-17,-6,1035,60.36,0,0 +2010,1,11,1,27,-17,-8,1035,0.89,0,0 +2010,1,11,2,23,-19,-11,1036,4.02,0,0 +2010,1,11,3,17,-20,-11,1036,8.94,0,0 +2010,1,11,4,17,-21,-11,1035,4.02,0,0 +2010,1,11,5,16,-22,-12,1035,3.13,0,0 +2010,1,11,6,16,-22,-12,1035,4.92,0,0 +2010,1,11,7,20,-22,-13,1036,5.81,0,0 +2010,1,11,8,20,-22,-9,1036,13.86,0,0 +2010,1,11,9,18,-22,-10,1036,21.91,0,0 +2010,1,11,10,25,-22,-9,1036,29.06,0,0 +2010,1,11,11,26,-21,-9,1036,33.08,0,0 +2010,1,11,12,27,-20,-6,1035,42.02,0,0 +2010,1,11,13,28,-19,-5,1034,50.96,0,0 +2010,1,11,14,15,-19,-5,1033,59.01,0,0 +2010,1,11,15,24,-20,-5,1033,66.16,0,0 +2010,1,11,16,13,-20,-5,1032,71.97,0,0 +2010,1,11,17,13,-20,-5,1033,76.89,0,0 +2010,1,11,18,13,-20,-7,1033,81.81,0,0 +2010,1,11,19,17,-22,-7,1033,85.83,0,0 +2010,1,11,20,20,-22,-8,1033,89.85,0,0 +2010,1,11,21,27,-23,-10,1033,91.64,0,0 +2010,1,11,22,20,-23,-13,1033,93.43,0,0 +2010,1,11,23,15,-22,-12,1032,0.89,0,0 +2010,1,12,0,21,-22,-15,1031,1.78,0,0 +2010,1,12,1,21,-22,-14,1031,1.79,0,0 +2010,1,12,2,37,-22,-14,1031,0.89,0,0 +2010,1,12,3,26,-24,-18,1030,1.79,0,0 +2010,1,12,4,15,-24,-13,1030,3.58,0,0 +2010,1,12,5,9,-25,-14,1030,3.13,0,0 +2010,1,12,6,11,-24,-14,1030,7.15,0,0 +2010,1,12,7,11,-25,-12,1030,12.96,0,0 +2010,1,12,8,16,-26,-14,1030,16.98,0,0 +2010,1,12,9,13,-25,-12,1031,21,0,0 +2010,1,12,10,15,-25,-11,1031,26.81,0,0 +2010,1,12,11,21,-24,-11,1030,35.75,0,0 +2010,1,12,12,24,-23,-11,1029,47.82,0,0 +2010,1,12,13,22,-22,-11,1029,63.02,0,0 +2010,1,12,14,22,-23,-11,1029,75.98,0,0 +2010,1,12,15,22,-22,-11,1029,88.05,0,0 +2010,1,12,16,19,-23,-11,1030,101.01,0,0 +2010,1,12,17,23,-24,-11,1030,109.06,0,0 +2010,1,12,18,25,-24,-11,1032,118,0,0 +2010,1,12,19,25,-23,-11,1032,126.05,0,0 +2010,1,12,20,31,-23,-12,1032,134.1,0,0 +2010,1,12,21,25,-24,-12,1033,142.15,0,0 +2010,1,12,22,22,-23,-12,1033,150.2,0,0 +2010,1,12,23,22,-23,-12,1034,160.03,0,0 +2010,1,13,0,15,-24,-12,1034,169.86,0,0 +2010,1,13,1,16,-24,-12,1033,178.8,0,0 +2010,1,13,2,11,-25,-12,1033,189.98,0,0 +2010,1,13,3,11,-24,-13,1033,198.03,0,0 +2010,1,13,4,12,-25,-12,1032,205.18,0,0 +2010,1,13,5,11,-25,-12,1033,215.01,0,0 +2010,1,13,6,12,-25,-12,1032,226.19,0,0 +2010,1,13,7,27,-25,-12,1032,235.13,0,0 +2010,1,13,8,28,-25,-12,1032,243.18,0,0 +2010,1,13,9,34,-23,-11,1033,252.12,0,0 +2010,1,13,10,33,-23,-10,1033,257.93,0,0 +2010,1,13,11,36,-22,-9,1033,263.74,0,0 +2010,1,13,12,26,-21,-8,1031,270.89,0,0 +2010,1,13,13,49,-21,-6,1030,278.94,0,0 +2010,1,13,14,52,-20,-5,1029,286.99,0,0 +2010,1,13,15,47,-22,-5,1028,294.14,0,0 +2010,1,13,16,47,-21,-5,1028,299.06,0,0 +2010,1,13,17,39,-19,-7,1028,1.79,0,0 +2010,1,13,18,59,-19,-8,1028,0.45,0,0 +2010,1,13,19,76,-20,-9,1028,1.34,0,0 +2010,1,13,20,96,-19,-10,1027,1.79,0,0 +2010,1,13,21,95,-19,-13,1027,0.89,0,0 +2010,1,13,22,68,-18,-12,1027,1.78,0,0 +2010,1,13,23,65,-18,-13,1027,0.89,0,0 +2010,1,14,0,75,-18,-14,1026,1.78,0,0 +2010,1,14,1,257,-20,-15,1025,2.67,0,0 +2010,1,14,2,174,-20,-15,1025,0.89,0,0 +2010,1,14,3,164,-20,-16,1025,1.78,0,0 +2010,1,14,4,161,-20,-16,1024,3.57,0,0 +2010,1,14,5,137,-21,-17,1024,6.7,0,0 +2010,1,14,6,92,-19,-14,1023,10.72,0,0 +2010,1,14,7,64,-21,-17,1023,13.85,0,0 +2010,1,14,8,87,-19,-15,1024,15.64,0,0 +2010,1,14,9,87,-19,-14,1024,18.77,0,0 +2010,1,14,10,84,-18,-10,1024,22.79,0,0 +2010,1,14,11,95,-17,-9,1023,24.58,0,0 +2010,1,14,12,79,-16,-6,1023,26.37,0,0 +2010,1,14,13,75,-17,-5,1022,28.16,0,0 +2010,1,14,14,79,-16,-5,1022,29.95,0,0 +2010,1,14,15,86,-15,-4,1022,33.08,0,0 +2010,1,14,16,72,-16,-4,1023,0.89,0,0 +2010,1,14,17,89,-15,-5,1025,1.79,0,0 +2010,1,14,18,94,-14,-3,1026,5.81,0,0 +2010,1,14,19,69,-16,-3,1027,11.62,0,0 +2010,1,14,20,49,-15,-4,1029,20.56,0,0 +2010,1,14,21,33,-16,-5,1031,24.58,0,0 +2010,1,14,22,27,-17,-6,1032,3.13,0,0 +2010,1,14,23,20,-18,-6,1033,8.05,0,0 +2010,1,15,0,36,-19,-7,1034,13.86,0,0 +2010,1,15,1,37,-17,-8,1035,5.81,0,0 +2010,1,15,2,30,-18,-10,1035,11.62,0,0 +2010,1,15,3,18,-19,-10,1036,14.75,0,0 +2010,1,15,4,21,-19,-9,1036,19.67,0,0 +2010,1,15,5,13,-19,-9,1036,7.15,0,0 +2010,1,15,6,23,-20,-9,1037,12.96,0,0 +2010,1,15,7,26,-19,-9,1037,18.77,0,0 +2010,1,15,8,27,-19,-8,1038,22.79,0,0 +2010,1,15,9,30,-19,-8,1039,28.6,0,0 +2010,1,15,10,28,-19,-7,1039,35.75,0,0 +2010,1,15,11,26,-18,-6,1039,41.56,0,0 +2010,1,15,12,26,-18,-5,1039,45.58,0,0 +2010,1,15,13,31,-17,-4,1038,1.79,0,0 +2010,1,15,14,35,-16,-3,1037,0.89,0,0 +2010,1,15,15,36,-17,-3,1037,1.78,0,0 +2010,1,15,16,37,-17,-2,1037,2.23,0,0 +2010,1,15,17,64,-15,-5,1036,0.89,0,0 +2010,1,15,18,83,-15,-6,1036,0.89,0,0 +2010,1,15,19,91,-15,-6,1037,1.78,0,0 +2010,1,15,20,90,-14,-6,1037,1.79,0,0 +2010,1,15,21,102,-14,-8,1036,3.58,0,0 +2010,1,15,22,94,-14,-10,1037,0.89,0,0 +2010,1,15,23,87,-15,-11,1036,1.78,0,0 +2010,1,16,0,141,-15,-11,1036,2.67,0,0 +2010,1,16,1,211,-16,-13,1036,0.89,0,0 +2010,1,16,2,242,-16,-12,1036,0.89,0,0 +2010,1,16,3,271,-16,-13,1035,0.89,0,0 +2010,1,16,4,249,-15,-12,1035,1.78,0,0 +2010,1,16,5,200,-14,-10,1034,1.79,0,0 +2010,1,16,6,147,-15,-10,1034,2.68,0,0 +2010,1,16,7,114,-15,-11,1034,4.47,0,0 +2010,1,16,8,102,-15,-11,1034,6.26,0,0 +2010,1,16,9,108,-14,-9,1034,8.05,0,0 +2010,1,16,10,135,-14,-9,1034,9.84,0,0 +2010,1,16,11,120,-13,-6,1034,0.89,0,0 +2010,1,16,12,148,-13,-5,1033,1.79,0,0 +2010,1,16,13,127,-12,-4,1031,0.89,0,0 +2010,1,16,14,128,-12,-3,1031,1.78,0,0 +2010,1,16,15,140,-12,-3,1030,2.67,0,0 +2010,1,16,16,148,-11,-3,1030,3.56,0,0 +2010,1,16,17,150,-11,-4,1031,0.89,0,0 +2010,1,16,18,154,-12,-7,1031,0.89,0,0 +2010,1,16,19,197,-12,-7,1032,1.78,0,0 +2010,1,16,20,235,-12,-8,1032,2.23,0,0 +2010,1,16,21,261,-12,-9,1032,3.12,0,0 +2010,1,16,22,269,-13,-11,1032,0.89,0,0 +2010,1,16,23,266,-14,-11,1032,2.68,0,0 +2010,1,17,0,263,-15,-12,1032,5.81,0,0 +2010,1,17,1,241,-14,-11,1032,0.45,0,0 +2010,1,17,2,205,-14,-11,1031,1.34,0,0 +2010,1,17,3,160,-14,-12,1031,1.79,0,0 +2010,1,17,4,150,-14,-12,1031,3.58,0,0 +2010,1,17,5,148,-14,-10,1030,6.71,0,0 +2010,1,17,6,208,-14,-11,1030,9.84,0,0 +2010,1,17,7,209,-15,-10,1030,11.63,0,0 +2010,1,17,8,183,-13,-10,1030,14.76,0,0 +2010,1,17,9,161,-13,-8,1030,17.89,0,0 +2010,1,17,10,182,-12,-6,1030,0.45,0,0 +2010,1,17,11,230,-11,-5,1030,1.34,0,0 +2010,1,17,12,170,-11,-4,1029,2.23,0,0 +2010,1,17,13,172,-11,-3,1028,3.12,0,0 +2010,1,17,14,154,-10,-1,1027,4.01,0,0 +2010,1,17,15,166,-11,-1,1027,4.46,0,0 +2010,1,17,16,177,-10,-1,1026,0.89,0,0 +2010,1,17,17,191,-10,-3,1026,1.78,0,0 +2010,1,17,18,212,-10,-5,1027,2.67,0,0 +2010,1,17,19,250,-10,-6,1027,0.89,0,0 +2010,1,17,20,268,-11,-7,1027,0.45,0,0 +2010,1,17,21,317,-10,-7,1027,0.9,0,0 +2010,1,17,22,291,-11,-9,1027,1.35,0,0 +2010,1,17,23,313,-12,-9,1027,0.89,0,0 +2010,1,18,0,282,-12,-10,1028,0.89,0,0 +2010,1,18,1,282,-13,-11,1028,3.13,0,0 +2010,1,18,2,303,-11,-9,1028,4.92,0,0 +2010,1,18,3,349,-13,-10,1028,6.71,0,0 +2010,1,18,4,407,-13,-11,1028,7.6,0,0 +2010,1,18,5,361,-13,-11,1027,1.79,0,0 +2010,1,18,6,234,-14,-11,1028,0.89,0,0 +2010,1,18,7,149,-14,-11,1028,4.02,0,0 +2010,1,18,8,147,-14,-11,1028,5.81,0,0 +2010,1,18,9,184,-11,-6,1028,9.83,0,0 +2010,1,18,10,161,-10,-5,1028,11.62,0,0 +2010,1,18,11,137,-9,-3,1028,13.41,0,0 +2010,1,18,12,146,-8,-1,1027,15.2,0,0 +2010,1,18,13,144,-9,0,1026,16.99,0,0 +2010,1,18,14,154,-9,1,1025,1.79,0,0 +2010,1,18,15,173,-9,1,1025,0.89,0,0 +2010,1,18,16,188,-8,1,1025,0.89,0,0 +2010,1,18,17,203,-7,-1,1025,2.68,0,0 +2010,1,18,18,233,-7,-2,1025,0.89,0,0 +2010,1,18,19,408,-7,-3,1025,0.89,0,0 +2010,1,18,20,435,-5,-2,1026,0.89,0,0 +2010,1,18,21,403,-5,-1,1026,1.79,0,0 +2010,1,18,22,360,-5,-1,1026,3.58,0,0 +2010,1,18,23,402,-6,-1,1026,0.89,0,0 +2010,1,19,0,358,-4,-1,1025,1.79,0,0 +2010,1,19,1,383,-5,-1,1024,4.92,0,0 +2010,1,19,2,332,-5,-1,1024,6.71,0,0 +2010,1,19,3,274,-5,-1,1023,8.5,0,0 +2010,1,19,4,256,-4,-1,1022,10.29,0,0 +2010,1,19,5,302,-4,-1,1021,11.18,0,0 +2010,1,19,6,330,-4,-1,1021,12.07,0,0 +2010,1,19,7,367,-4,-1,1021,13.86,0,0 +2010,1,19,8,297,-4,0,1021,0.89,0,0 +2010,1,19,9,331,-4,0,1021,1.79,0,0 +2010,1,19,10,323,-4,0,1021,0.89,0,0 +2010,1,19,11,339,-5,1,1021,1.79,0,0 +2010,1,19,12,301,-5,1,1020,0.89,0,0 +2010,1,19,13,310,-4,1,1019,1.34,0,0 +2010,1,19,14,305,-4,1,1018,0.89,0,0 +2010,1,19,15,313,-3,2,1018,0.45,0,0 +2010,1,19,16,336,-3,2,1018,1.34,0,0 +2010,1,19,17,357,-2,2,1018,0.89,0,0 +2010,1,19,18,485,-3,1,1018,0.89,0,0 +2010,1,19,19,426,-3,2,1020,1.79,0,0 +2010,1,19,20,403,-3,1,1020,4.92,0,0 +2010,1,19,21,374,-3,0,1021,6.71,0,0 +2010,1,19,22,333,-3,0,1021,8.5,0,0 +2010,1,19,23,343,-3,1,1020,11.63,0,0 +2010,1,20,0,364,-2,0,1021,14.76,0,0 +2010,1,20,1,389,-2,-1,1022,16.55,0,0 +2010,1,20,2,368,-4,-2,1022,19.68,0,0 +2010,1,20,3,267,-2,-1,1023,25.49,0,0 +2010,1,20,4,71,-3,1,1023,28.62,0,0 +2010,1,20,5,81,-3,0,1024,31.75,0,0 +2010,1,20,6,51,-5,2,1026,36.67,0,0 +2010,1,20,7,49,-9,1,1027,41.59,0,0 +2010,1,20,8,44,-10,0,1028,48.74,0,0 +2010,1,20,9,45,-10,0,1030,55.89,0,0 +2010,1,20,10,35,-11,1,1032,63.04,0,0 +2010,1,20,11,29,-13,0,1033,71.09,0,0 +2010,1,20,12,27,-13,0,1033,78.24,0,0 +2010,1,20,13,29,-13,-1,1033,8.05,0,0 +2010,1,20,14,16,-13,-1,1032,7.15,0,0 +2010,1,20,15,23,-15,-1,1032,5.81,0,0 +2010,1,20,16,28,-14,-1,1033,5.81,0,0 +2010,1,20,17,22,-15,-2,1033,12.96,0,0 +2010,1,20,18,11,-16,-3,1034,21.01,0,0 +2010,1,20,19,25,-17,-4,1035,28.16,0,0 +2010,1,20,20,16,-19,-3,1036,37.99,0,0 +2010,1,20,21,20,-18,-4,1037,46.93,0,0 +2010,1,20,22,20,-19,-4,1038,55.87,0,0 +2010,1,20,23,18,-19,-5,1038,65.7,0,0 +2010,1,21,0,18,-20,-6,1038,75.53,0,0 +2010,1,21,1,17,-20,-6,1038,85.36,0,0 +2010,1,21,2,17,-20,-7,1038,95.19,0,0 +2010,1,21,3,17,-22,-8,1038,103.24,0,0 +2010,1,21,4,15,-23,-8,1038,111.29,0,0 +2010,1,21,5,19,-24,-9,1038,118.44,0,0 +2010,1,21,6,16,-24,-9,1038,128.27,0,0 +2010,1,21,7,18,-25,-9,1038,138.1,0,0 +2010,1,21,8,18,-25,-8,1039,147.04,0,0 +2010,1,21,9,18,-25,-8,1039,155.98,0,0 +2010,1,21,10,26,-22,-7,1039,160.9,0,0 +2010,1,21,11,26,-22,-7,1038,3.13,0,0 +2010,1,21,12,25,-22,-5,1037,4.02,0,0 +2010,1,21,13,24,-21,-5,1035,3.13,0,0 +2010,1,21,14,15,-21,-4,1034,1.79,0,0 +2010,1,21,15,17,-20,-3,1034,1.79,0,0 +2010,1,21,16,15,-21,-2,1033,4.92,0,0 +2010,1,21,17,21,-20,-2,1033,10.73,0,0 +2010,1,21,18,40,-20,-2,1034,15.65,0,0 +2010,1,21,19,51,-19,-3,1034,21.46,0,0 +2010,1,21,20,45,-19,-3,1035,25.48,0,0 +2010,1,21,21,61,-17,-8,1035,0.89,0,0 +2010,1,21,22,72,-18,-9,1035,1.79,0,0 +2010,1,21,23,38,-18,-9,1035,3.58,0,0 +2010,1,22,0,35,-18,-6,1036,7.6,0,0 +2010,1,22,1,29,-20,-4,1037,17.43,0,0 +2010,1,22,2,26,-21,-5,1037,27.26,0,0 +2010,1,22,3,18,-21,-6,1037,35.31,0,0 +2010,1,22,4,15,-20,-7,1036,42.46,0,0 +2010,1,22,5,15,-20,-7,1035,48.27,0,0 +2010,1,22,6,22,-20,-7,1035,54.08,0,0 +2010,1,22,7,21,-20,-7,1036,59,0,0 +2010,1,22,8,19,-19,-7,1036,63.92,0,0 +2010,1,22,9,22,-20,-5,1036,67.94,0,0 +2010,1,22,10,25,-19,-4,1036,75.09,0,0 +2010,1,22,11,22,-19,-4,1036,84.03,0,0 +2010,1,22,12,26,-19,-3,1035,95.21,0,0 +2010,1,22,13,32,-18,-3,1034,105.04,0,0 +2010,1,22,14,30,-19,-2,1033,114.87,0,0 +2010,1,22,15,32,-18,-2,1032,124.7,0,0 +2010,1,22,16,30,-18,-2,1032,133.64,0,0 +2010,1,22,17,32,-18,-2,1032,139.45,0,0 +2010,1,22,18,37,-18,-3,1032,144.37,0,0 +2010,1,22,19,49,-17,-5,1033,149.29,0,0 +2010,1,22,20,47,-18,-4,1033,153.31,0,0 +2010,1,22,21,39,-17,-4,1032,159.12,0,0 +2010,1,22,22,35,-17,-4,1032,164.04,0,0 +2010,1,22,23,48,-17,-7,1032,168.06,0,0 +2010,1,23,0,49,-17,-4,1033,172.08,0,0 +2010,1,23,1,24,-17,-8,1033,0.89,0,0 +2010,1,23,2,23,-17,-5,1032,4.92,0,0 +2010,1,23,3,20,-18,-6,1032,8.05,0,0 +2010,1,23,4,19,-18,-5,1031,12.97,0,0 +2010,1,23,5,16,-18,-6,1031,16.1,0,0 +2010,1,23,6,22,-18,-7,1031,20.12,0,0 +2010,1,23,7,24,-18,-7,1031,24.14,0,0 +2010,1,23,8,24,-18,-6,1031,25.93,0,0 +2010,1,23,9,41,-18,-2,1031,30.85,0,0 +2010,1,23,10,23,-17,-1,1031,38.9,0,0 +2010,1,23,11,19,-17,0,1030,46.95,0,0 +2010,1,23,12,15,-19,1,1029,55,0,0 +2010,1,23,13,19,-20,2,1027,63.05,0,0 +2010,1,23,14,17,-19,2,1025,71.1,0,0 +2010,1,23,15,17,-19,3,1024,79.15,0,0 +2010,1,23,16,22,-19,3,1024,86.3,0,0 +2010,1,23,17,NA,-18,2,1024,91.22,0,0 +2010,1,23,18,NA,-18,1,1024,96.14,0,0 +2010,1,23,19,NA,-17,0,1024,100.16,0,0 +2010,1,23,20,NA,-18,0,1024,1.79,0,0 +2010,1,23,21,NA,-15,-3,1024,0.89,0,0 +2010,1,23,22,NA,-16,0,1023,1.79,0,0 +2010,1,23,23,NA,-16,0,1023,4.92,0,0 +2010,1,24,0,NA,-15,-2,1022,6.71,0,0 +2010,1,24,1,NA,-14,-7,1022,0.45,0,0 +2010,1,24,2,NA,-14,-8,1021,1.79,0,0 +2010,1,24,3,NA,-16,-11,1021,3.58,0,0 +2010,1,24,4,NA,-14,-9,1019,4.47,0,0 +2010,1,24,5,NA,-15,-10,1019,0.89,0,0 +2010,1,24,6,NA,-16,-12,1019,0.89,0,0 +2010,1,24,7,NA,-15,-12,1019,0.89,0,0 +2010,1,24,8,NA,-16,-13,1019,1.79,0,0 +2010,1,24,9,NA,-13,-7,1020,0.89,0,0 +2010,1,24,10,NA,-12,-4,1019,1.78,0,0 +2010,1,24,11,NA,-13,0,1019,2.67,0,0 +2010,1,24,12,NA,-16,4,1018,4.02,0,0 +2010,1,24,13,NA,-19,5,1017,13.85,0,0 +2010,1,24,14,NA,-18,5,1017,22.79,0,0 +2010,1,24,15,NA,-19,5,1017,32.62,0,0 +2010,1,24,16,NA,-20,5,1018,40.67,0,0 +2010,1,24,17,NA,-19,3,1020,49.61,0,0 +2010,1,24,18,NA,-18,1,1023,61.68,0,0 +2010,1,24,19,NA,-20,-1,1027,76.88,0,0 +2010,1,24,20,NA,-20,-2,1028,89.84,0,0 +2010,1,24,21,NA,-21,-3,1029,98.78,0,0 +2010,1,24,22,NA,-25,-4,1030,113.98,0,0 +2010,1,24,23,NA,-26,-5,1031,129.18,0,0 +2010,1,25,0,NA,-25,-5,1031,141.25,0,0 +2010,1,25,1,NA,-25,-6,1031,150.19,0,0 +2010,1,25,2,NA,-24,-6,1032,162.26,0,0 +2010,1,25,3,NA,-24,-6,1033,172.09,0,0 +2010,1,25,4,NA,-23,-6,1033,181.92,0,0 +2010,1,25,5,NA,-22,-7,1033,187.73,0,0 +2010,1,25,6,NA,-21,-7,1033,192.65,0,0 +2010,1,25,7,NA,-21,-7,1034,196.67,0,0 +2010,1,25,8,NA,-21,-6,1035,201.59,0,0 +2010,1,25,9,NA,-21,-4,1036,207.4,0,0 +2010,1,25,10,NA,-22,-3,1036,216.34,0,0 +2010,1,25,11,NA,-22,-2,1036,222.15,0,0 +2010,1,25,12,NA,-23,-1,1035,230.2,0,0 +2010,1,25,13,NA,-26,1,1033,238.25,0,0 +2010,1,25,14,NA,-25,2,1032,245.4,0,0 +2010,1,25,15,NA,-25,2,1031,252.55,0,0 +2010,1,25,16,NA,-24,2,1031,257.47,0,0 +2010,1,25,17,NA,-22,1,1031,4.02,0,0 +2010,1,25,18,NA,-19,-1,1031,7.15,0,0 +2010,1,25,19,NA,-19,-1,1031,10.28,0,0 +2010,1,25,20,NA,-17,-4,1031,12.07,0,0 +2010,1,25,21,NA,-18,-1,1031,15.2,0,0 +2010,1,25,22,NA,-18,-5,1030,0.89,0,0 +2010,1,25,23,NA,-18,-3,1030,3.13,0,0 +2010,1,26,0,NA,-16,-9,1030,0.89,0,0 +2010,1,26,1,NA,-16,-8,1029,1.78,0,0 +2010,1,26,2,NA,-16,-11,1029,2.23,0,0 +2010,1,26,3,NA,-16,-12,1029,3.12,0,0 +2010,1,26,4,NA,-16,-10,1028,3.13,0,0 +2010,1,26,5,NA,-17,-10,1028,4.92,0,0 +2010,1,26,6,NA,-17,-12,1027,6.71,0,0 +2010,1,26,7,NA,-17,-10,1027,8.5,0,0 +2010,1,26,8,NA,-18,-13,1028,1.79,0,0 +2010,1,26,9,NA,-16,-8,1028,4.02,0,0 +2010,1,26,10,NA,-16,-6,1027,5.81,0,0 +2010,1,26,11,NA,-15,-3,1027,0.89,0,0 +2010,1,26,12,88,-15,-1,1025,1.79,0,0 +2010,1,26,13,85,-18,0,1024,3.58,0,0 +2010,1,26,14,90,-19,1,1023,0.89,0,0 +2010,1,26,15,127,-18,2,1021,1.79,0,0 +2010,1,26,16,144,-17,1,1021,4.92,0,0 +2010,1,26,17,203,-14,-1,1021,5.81,0,0 +2010,1,26,18,199,-13,-3,1021,6.7,0,0 +2010,1,26,19,231,-13,-3,1021,0.89,0,0 +2010,1,26,20,274,-11,-5,1020,0.89,0,0 +2010,1,26,21,340,-13,-6,1020,1.79,0,0 +2010,1,26,22,298,-11,-5,1020,0.89,0,0 +2010,1,26,23,268,-11,-4,1019,1.79,0,0 +2010,1,27,0,299,-11,-4,1019,3.58,0,0 +2010,1,27,1,300,-12,-3,1018,5.37,0,0 +2010,1,27,2,240,-11,-4,1017,7.16,0,0 +2010,1,27,3,213,-11,-4,1016,10.29,0,0 +2010,1,27,4,171,-12,-3,1016,12.08,0,0 +2010,1,27,5,153,-11,-4,1016,15.21,0,0 +2010,1,27,6,153,-11,-5,1015,17,0,0 +2010,1,27,7,140,-11,-4,1015,18.79,0,0 +2010,1,27,8,147,-11,-3,1015,3.13,0,0 +2010,1,27,9,158,-10,-1,1015,6.26,0,0 +2010,1,27,10,153,-9,1,1015,9.39,0,0 +2010,1,27,11,154,-9,3,1015,1.79,0,0 +2010,1,27,12,143,-9,4,1014,4.92,0,0 +2010,1,27,13,90,-9,6,1013,8.05,0,0 +2010,1,27,14,55,-13,8,1012,16.1,0,0 +2010,1,27,15,52,-14,8,1013,24.15,0,0 +2010,1,27,16,41,-14,7,1014,32.2,0,0 +2010,1,27,17,29,-13,7,1015,39.35,0,0 +2010,1,27,18,37,-13,5,1016,47.4,0,0 +2010,1,27,19,49,-12,4,1018,52.32,0,0 +2010,1,27,20,56,-13,3,1019,56.34,0,0 +2010,1,27,21,38,-14,3,1020,62.15,0,0 +2010,1,27,22,37,-14,3,1021,67.96,0,0 +2010,1,27,23,28,-14,0,1022,71.98,0,0 +2010,1,28,0,20,-13,-1,1023,76.9,0,0 +2010,1,28,1,22,-15,-1,1023,7.15,0,0 +2010,1,28,2,27,-15,-1,1024,12.07,0,0 +2010,1,28,3,34,-15,-2,1025,16.09,0,0 +2010,1,28,4,27,-15,-2,1025,21.01,0,0 +2010,1,28,5,32,-15,-3,1025,25.03,0,0 +2010,1,28,6,28,-15,-3,1025,3.13,0,0 +2010,1,28,7,28,-15,-3,1026,8.05,0,0 +2010,1,28,8,18,-16,-3,1027,12.97,0,0 +2010,1,28,9,17,-17,-2,1028,20.12,0,0 +2010,1,28,10,16,-18,-2,1029,25.93,0,0 +2010,1,28,11,18,-19,-1,1028,35.76,0,0 +2010,1,28,12,16,-21,1,1027,44.7,0,0 +2010,1,28,13,12,-24,1,1026,53.64,0,0 +2010,1,28,14,13,-25,1,1026,62.58,0,0 +2010,1,28,15,23,-25,1,1026,72.41,0,0 +2010,1,28,16,21,-24,1,1026,82.24,0,0 +2010,1,28,17,13,-25,0,1026,91.18,0,0 +2010,1,28,18,14,-22,0,1027,99.23,0,0 +2010,1,28,19,23,-20,-1,1027,103.25,0,0 +2010,1,28,20,16,-21,-1,1027,105.04,0,0 +2010,1,28,21,25,-21,-4,1028,0.89,0,0 +2010,1,28,22,20,-21,-2,1027,4.02,0,0 +2010,1,28,23,25,-22,-2,1027,7.15,0,0 +2010,1,29,0,19,-22,-1,1027,12.07,0,0 +2010,1,29,1,24,-21,-4,1027,15.2,0,0 +2010,1,29,2,25,-21,-4,1026,0.89,0,0 +2010,1,29,3,20,-19,-8,1026,1.79,0,0 +2010,1,29,4,24,-20,-7,1025,0.89,0,0 +2010,1,29,5,17,-20,-8,1024,1.79,0,0 +2010,1,29,6,10,-18,-10,1024,0.89,0,0 +2010,1,29,7,11,-21,-2,1024,4.02,0,0 +2010,1,29,8,16,-21,-1,1025,9.83,0,0 +2010,1,29,9,20,-24,1,1025,17.88,0,0 +2010,1,29,10,13,-25,2,1025,26.82,0,0 +2010,1,29,11,12,-24,3,1024,33.97,0,0 +2010,1,29,12,12,-25,4,1023,42.02,0,0 +2010,1,29,13,12,-24,5,1022,50.07,0,0 +2010,1,29,14,12,-25,6,1020,57.22,0,0 +2010,1,29,15,31,-25,6,1020,65.27,0,0 +2010,1,29,16,36,-23,6,1020,72.42,0,0 +2010,1,29,17,42,-22,5,1020,78.23,0,0 +2010,1,29,18,52,-21,5,1020,84.04,0,0 +2010,1,29,19,26,-20,5,1020,89.85,0,0 +2010,1,29,20,24,-20,3,1020,93.87,0,0 +2010,1,29,21,25,-20,3,1020,97.89,0,0 +2010,1,29,22,43,-19,3,1021,101.02,0,0 +2010,1,29,23,47,-19,3,1020,105.94,0,0 +2010,1,30,0,61,-18,0,1020,109.96,0,0 +2010,1,30,1,66,-18,0,1020,114.88,0,0 +2010,1,30,2,65,-15,-1,1020,1.79,0,0 +2010,1,30,3,70,-15,-2,1020,0.89,0,0 +2010,1,30,4,36,-16,-1,1020,4.02,0,0 +2010,1,30,5,37,-16,-4,1019,8.94,0,0 +2010,1,30,6,22,-15,-5,1020,0.89,0,0 +2010,1,30,7,24,-16,-5,1020,3.13,0,0 +2010,1,30,8,29,-15,-6,1021,0.89,0,0 +2010,1,30,9,35,-15,0,1021,4.02,0,0 +2010,1,30,10,35,-18,3,1021,8.04,0,0 +2010,1,30,11,33,-21,5,1020,12.96,0,0 +2010,1,30,12,32,-22,6,1020,18.77,0,0 +2010,1,30,13,25,-22,7,1019,25.92,0,0 +2010,1,30,14,16,-22,7,1018,35.75,0,0 +2010,1,30,15,26,-23,7,1018,45.58,0,0 +2010,1,30,16,32,-23,7,1017,52.73,0,0 +2010,1,30,17,36,-22,6,1018,58.54,0,0 +2010,1,30,18,37,-21,5,1018,64.35,0,0 +2010,1,30,19,45,-21,5,1019,70.16,0,0 +2010,1,30,20,71,-19,4,1019,74.18,0,0 +2010,1,30,21,67,-19,5,1020,78.2,0,0 +2010,1,30,22,69,-19,2,1020,81.33,0,0 +2010,1,30,23,94,-18,2,1021,84.46,0,0 +2010,1,31,0,85,-18,1,1021,87.59,0,0 +2010,1,31,1,35,-18,1,1021,90.72,0,0 +2010,1,31,2,26,-16,-3,1022,93.85,0,0 +2010,1,31,3,24,-15,-2,1022,95.64,0,0 +2010,1,31,4,20,-15,0,1022,99.66,0,0 +2010,1,31,5,16,-17,1,1023,105.47,0,0 +2010,1,31,6,18,-15,0,1023,109.49,0,0 +2010,1,31,7,19,-18,0,1025,113.51,0,0 +2010,1,31,8,22,-17,0,1025,116.64,0,0 +2010,1,31,9,17,-17,2,1026,120.66,0,0 +2010,1,31,10,18,-19,3,1026,129.6,0,0 +2010,1,31,11,11,-19,4,1026,138.54,0,0 +2010,1,31,12,11,-20,5,1025,145.69,0,0 +2010,1,31,13,16,-17,5,1024,4.02,0,0 +2010,1,31,14,18,-18,6,1024,7.15,0,0 +2010,1,31,15,17,-18,6,1024,0.89,0,0 +2010,1,31,16,19,-18,5,1024,1.79,0,0 +2010,1,31,17,26,-17,4,1024,3.58,0,0 +2010,1,31,18,24,-17,1,1024,1.79,0,0 +2010,1,31,19,54,-15,-1,1025,2.68,0,0 +2010,1,31,20,71,-13,-1,1025,0.89,0,0 +2010,1,31,21,129,-8,-1,1026,5.81,0,0 +2010,1,31,22,145,-7,-2,1027,10.73,0,0 +2010,1,31,23,101,-7,-2,1027,13.86,0,0 +2010,2,1,0,65,-8,-3,1027,15.65,0,0 +2010,2,1,1,70,-9,-4,1027,0.89,0,0 +2010,2,1,2,67,-9,-4,1027,1.78,0,0 +2010,2,1,3,76,-9,-4,1027,0.89,0,0 +2010,2,1,4,76,-9,-4,1026,0.89,0,0 +2010,2,1,5,68,-9,-5,1027,0.89,0,0 +2010,2,1,6,73,-10,-4,1027,0.89,0,0 +2010,2,1,7,73,-10,-4,1027,0.89,0,0 +2010,2,1,8,64,-10,-6,1028,0.89,0,0 +2010,2,1,9,53,-9,-3,1028,1.79,0,0 +2010,2,1,10,72,-9,-2,1028,0.89,0,0 +2010,2,1,11,78,-9,-1,1028,1.79,0,0 +2010,2,1,12,72,-10,0,1027,6.71,0,0 +2010,2,1,13,56,-12,0,1026,10.73,0,0 +2010,2,1,14,54,-14,1,1026,14.75,0,0 +2010,2,1,15,40,-14,0,1025,16.54,0,0 +2010,2,1,16,49,-12,0,1026,19.67,0,0 +2010,2,1,17,63,-10,-1,1025,21.46,0,0 +2010,2,1,18,66,-9,-2,1026,23.25,0,0 +2010,2,1,19,64,-10,-2,1026,26.38,0,0 +2010,2,1,20,63,-10,-4,1027,30.4,0,0 +2010,2,1,21,65,-10,-5,1027,34.42,0,0 +2010,2,1,22,64,-10,-5,1027,39.34,0,0 +2010,2,1,23,64,-11,-7,1027,41.13,0,0 +2010,2,2,0,62,-10,-7,1028,42.92,0,0 +2010,2,2,1,56,-11,-8,1028,0.89,0,0 +2010,2,2,2,87,-11,-9,1028,1.34,0,0 +2010,2,2,3,72,-11,-10,1028,1.79,0,0 +2010,2,2,4,104,-12,-10,1028,0.89,0,0 +2010,2,2,5,97,-13,-11,1028,0.89,0,0 +2010,2,2,6,96,-11,-9,1028,0.89,0,0 +2010,2,2,7,86,-11,-8,1028,0.89,0,0 +2010,2,2,8,85,-10,-7,1029,1.79,0,0 +2010,2,2,9,92,-14,-6,1029,4.92,0,0 +2010,2,2,10,55,-18,-5,1029,10.73,0,0 +2010,2,2,11,36,-20,-3,1029,14.75,0,0 +2010,2,2,12,27,-20,-2,1028,16.54,0,0 +2010,2,2,13,25,-20,-1,1028,18.33,0,0 +2010,2,2,14,28,-20,0,1027,1.79,0,0 +2010,2,2,15,32,-22,0,1027,2.68,0,0 +2010,2,2,16,35,-22,1,1027,3.57,0,0 +2010,2,2,17,43,-19,-2,1027,1.79,0,0 +2010,2,2,18,70,-20,-4,1028,3.13,0,0 +2010,2,2,19,83,-21,-3,1029,4.02,0,0 +2010,2,2,20,66,-19,-6,1029,0.89,0,0 +2010,2,2,21,70,-16,-7,1029,0.89,0,0 +2010,2,2,22,80,-21,-5,1030,1.79,0,0 +2010,2,2,23,88,-19,-8,1030,4.92,0,0 +2010,2,3,0,69,-20,-8,1030,8.05,0,0 +2010,2,3,1,71,-21,-8,1030,0.89,0,0 +2010,2,3,2,62,-18,-10,1030,0.89,0,0 +2010,2,3,3,73,-17,-10,1030,0.89,0,0 +2010,2,3,4,107,-17,-12,1030,0.89,0,0 +2010,2,3,5,94,-17,-13,1030,1.79,0,0 +2010,2,3,6,121,-17,-13,1030,4.92,0,0 +2010,2,3,7,132,-19,-11,1030,0.89,0,0 +2010,2,3,8,161,-18,-11,1030,1.34,0,0 +2010,2,3,9,120,-18,-8,1030,1.79,0,0 +2010,2,3,10,72,-19,-4,1030,3.13,0,0 +2010,2,3,11,58,-20,-2,1030,4.92,0,0 +2010,2,3,12,34,-22,0,1029,12.07,0,0 +2010,2,3,13,14,-23,1,1027,19.22,0,0 +2010,2,3,14,17,-24,1,1026,28.16,0,0 +2010,2,3,15,15,-25,2,1026,37.1,0,0 +2010,2,3,16,23,-22,2,1026,46.93,0,0 +2010,2,3,17,34,-21,1,1026,54.08,0,0 +2010,2,3,18,96,-22,1,1026,59,0,0 +2010,2,3,19,87,-17,0,1027,63.02,0,0 +2010,2,3,20,87,-20,-4,1027,0.89,0,0 +2010,2,3,21,95,-20,-5,1028,1.79,0,0 +2010,2,3,22,105,-21,-6,1028,4.92,0,0 +2010,2,3,23,114,-21,-7,1028,0.45,0,0 +2010,2,4,0,103,-21,-7,1028,1.79,0,0 +2010,2,4,1,99,-21,-8,1028,4.92,0,0 +2010,2,4,2,102,-21,-9,1028,0.89,0,0 +2010,2,4,3,104,-19,-10,1028,1.34,0,0 +2010,2,4,4,78,-19,-10,1028,1.79,0,0 +2010,2,4,5,33,-19,-12,1028,0.89,0,0 +2010,2,4,6,29,-18,-12,1028,0.89,0,0 +2010,2,4,7,27,-20,-15,1028,4.02,0,0 +2010,2,4,8,27,-17,-10,1028,8.04,0,0 +2010,2,4,9,26,-19,-5,1028,12.06,0,0 +2010,2,4,10,33,-20,-3,1028,15.19,0,0 +2010,2,4,11,29,-20,-2,1028,4.02,0,0 +2010,2,4,12,31,-22,0,1027,3.13,0,0 +2010,2,4,13,28,-22,1,1025,7.15,0,0 +2010,2,4,14,28,-22,1,1025,1.79,0,0 +2010,2,4,15,61,-23,1,1024,4.92,0,0 +2010,2,4,16,64,-23,1,1024,6.71,0,0 +2010,2,4,17,80,-22,0,1024,8.5,0,0 +2010,2,4,18,91,-20,-4,1025,11.63,0,0 +2010,2,4,19,79,-22,-3,1026,14.76,0,0 +2010,2,4,20,48,-22,-3,1026,15.65,0,0 +2010,2,4,21,59,-20,-5,1027,1.79,0,0 +2010,2,4,22,82,-21,-3,1027,0.89,0,0 +2010,2,4,23,63,-19,-4,1027,1.79,0,0 +2010,2,5,0,48,-19,-4,1027,4.92,0,0 +2010,2,5,1,54,-19,-4,1027,8.05,0,0 +2010,2,5,2,42,-19,-4,1028,0.89,0,0 +2010,2,5,3,47,-19,-5,1028,1.79,0,0 +2010,2,5,4,87,-15,-5,1028,1.79,0,0 +2010,2,5,5,105,-13,-6,1028,0.89,0,0 +2010,2,5,6,90,-13,-7,1029,0.89,0,0 +2010,2,5,7,101,-12,-7,1030,1.78,0,0 +2010,2,5,8,126,-19,-6,1030,3.13,0,0 +2010,2,5,9,114,-18,-5,1031,4.92,0,0 +2010,2,5,10,103,-14,-4,1031,1.79,0,0 +2010,2,5,11,114,-13,-3,1031,1.79,0,0 +2010,2,5,12,100,-14,-2,1031,3.58,0,0 +2010,2,5,13,72,-13,-1,1030,6.71,0,0 +2010,2,5,14,68,-14,-1,1029,1.79,0,0 +2010,2,5,15,72,-13,-1,1029,1.79,0,0 +2010,2,5,16,71,-12,-1,1029,3.58,0,0 +2010,2,5,17,80,-12,-2,1030,5.37,0,0 +2010,2,5,18,70,-12,-4,1030,6.26,0,0 +2010,2,5,19,63,-13,-4,1031,8.05,0,0 +2010,2,5,20,67,-13,-5,1031,8.94,0,0 +2010,2,5,21,62,-12,-5,1032,9.83,0,0 +2010,2,5,22,69,-12,-6,1033,11.62,0,0 +2010,2,5,23,58,-11,-6,1033,13.41,0,0 +2010,2,6,0,51,-11,-7,1033,15.2,0,0 +2010,2,6,1,51,-11,-8,1033,16.99,0,0 +2010,2,6,2,54,-11,-8,1033,18.78,0,0 +2010,2,6,3,45,-11,-8,1033,20.57,0,0 +2010,2,6,4,47,-11,-7,1032,23.7,0,0 +2010,2,6,5,85,-11,-7,1032,25.49,0,0 +2010,2,6,6,78,-12,-7,1032,27.28,0,0 +2010,2,6,7,68,-12,-7,1032,29.07,0,0 +2010,2,6,8,64,-13,-7,1033,0.89,0,0 +2010,2,6,9,65,-12,-6,1033,1.79,1,0 +2010,2,6,10,80,-11,-5,1032,4.92,0,0 +2010,2,6,11,74,-11,-4,1032,8.05,0,0 +2010,2,6,12,93,-11,-3,1031,11.18,0,0 +2010,2,6,13,77,-11,-3,1030,15.2,0,0 +2010,2,6,14,67,-12,-2,1029,19.22,0,0 +2010,2,6,15,84,-12,-2,1029,22.35,0,0 +2010,2,6,16,77,-12,-2,1028,25.48,0,0 +2010,2,6,17,83,-12,-2,1027,29.5,0,0 +2010,2,6,18,82,-10,-3,1028,33.52,0,0 +2010,2,6,19,100,-9,-3,1029,35.31,1,0 +2010,2,6,20,189,-7,-4,1029,37.1,2,0 +2010,2,6,21,99,-6,-5,1028,38.89,3,0 +2010,2,6,22,71,-6,-5,1028,42.02,4,0 +2010,2,6,23,43,-6,-5,1027,45.15,5,0 +2010,2,7,0,64,-6,-5,1027,46.94,6,0 +2010,2,7,1,68,-7,-5,1026,48.73,7,0 +2010,2,7,2,69,-7,-5,1025,0.89,8,0 +2010,2,7,3,65,-7,-5,1025,1.78,9,0 +2010,2,7,4,71,-7,-6,1024,0.89,10,0 +2010,2,7,5,77,-7,-6,1024,0.89,0,0 +2010,2,7,6,81,-7,-6,1024,0.89,0,0 +2010,2,7,7,88,-7,-6,1023,1.78,0,0 +2010,2,7,8,90,-6,-5,1024,1.79,0,0 +2010,2,7,9,92,-6,-5,1024,1.79,1,0 +2010,2,7,10,88,-6,-4,1023,3.58,0,0 +2010,2,7,11,80,-6,-4,1023,0.89,0,0 +2010,2,7,12,100,-6,-3,1023,1.78,0,0 +2010,2,7,13,105,-7,-3,1022,2.67,0,0 +2010,2,7,14,118,-7,-3,1021,3.56,0,0 +2010,2,7,15,125,-6,-2,1020,1.79,0,0 +2010,2,7,16,141,-6,-2,1020,3.58,0,0 +2010,2,7,17,152,-6,-3,1020,5.37,0,0 +2010,2,7,18,164,-7,-4,1020,0.89,0,0 +2010,2,7,19,147,-7,-5,1021,0.89,0,0 +2010,2,7,20,162,-7,-5,1021,1.78,0,0 +2010,2,7,21,157,-6,-4,1022,2.67,0,0 +2010,2,7,22,156,-5,-3,1022,0.89,0,0 +2010,2,7,23,160,-7,-5,1020,1.79,0,0 +2010,2,8,0,178,-6,-4,1021,0.89,0,0 +2010,2,8,1,187,-6,-5,1020,1.34,0,0 +2010,2,8,2,181,-7,-6,1020,0.89,0,0 +2010,2,8,3,185,-8,-6,1020,0.89,0,0 +2010,2,8,4,197,-6,-5,1019,0.89,0,0 +2010,2,8,5,180,-6,-4,1019,1.79,0,0 +2010,2,8,6,172,-6,-4,1019,1.79,0,0 +2010,2,8,7,166,-6,-4,1020,4.92,0,0 +2010,2,8,8,136,-6,-4,1019,8.05,0,0 +2010,2,8,9,133,-5,-3,1020,12.07,0,0 +2010,2,8,10,140,-5,-2,1020,15.2,0,0 +2010,2,8,11,159,-5,-1,1019,0.89,0,0 +2010,2,8,12,169,-5,0,1019,2.68,0,0 +2010,2,8,13,170,-5,2,1017,1.79,0,0 +2010,2,8,14,184,-5,4,1017,0.89,0,0 +2010,2,8,15,163,-5,5,1017,1.79,0,0 +2010,2,8,16,140,-4,4,1017,4.92,0,0 +2010,2,8,17,144,-4,3,1017,6.71,0,0 +2010,2,8,18,166,-5,1,1019,0.89,0,0 +2010,2,8,19,196,-4,0,1020,0.89,0,0 +2010,2,8,20,227,-4,-1,1020,0.89,0,0 +2010,2,8,21,210,-5,-2,1020,1.78,0,0 +2010,2,8,22,233,-4,-2,1021,2.23,0,0 +2010,2,8,23,258,-4,-3,1021,3.12,0,0 +2010,2,9,0,247,-5,-3,1021,1.79,0,0 +2010,2,9,1,230,-5,-3,1022,0.89,0,0 +2010,2,9,2,273,-6,-4,1022,0.89,0,0 +2010,2,9,3,253,-5,-3,1022,0.89,0,0 +2010,2,9,4,254,-6,-5,1022,2.68,0,0 +2010,2,9,5,241,-6,-5,1022,5.81,0,0 +2010,2,9,6,229,-5,-4,1023,0.89,0,0 +2010,2,9,7,199,-5,-4,1023,1.34,0,0 +2010,2,9,8,193,-4,-3,1024,2.23,0,0 +2010,2,9,9,207,-9,1,1025,4.02,0,0 +2010,2,9,10,176,-15,2,1025,8.04,0,0 +2010,2,9,11,91,-17,3,1025,3.13,0,0 +2010,2,9,12,58,-18,4,1025,4.92,0,0 +2010,2,9,13,39,-16,4,1024,0.89,0,0 +2010,2,9,14,50,-14,5,1023,1.78,0,0 +2010,2,9,15,51,-13,4,1023,1.79,0,0 +2010,2,9,16,49,-12,4,1023,0.89,0,0 +2010,2,9,17,54,-11,3,1024,1.79,0,0 +2010,2,9,18,153,-10,2,1024,3.58,0,0 +2010,2,9,19,181,-7,2,1025,6.71,0,0 +2010,2,9,20,181,-6,0,1025,0.89,0,0 +2010,2,9,21,182,-8,-1,1026,3.13,0,0 +2010,2,9,22,156,-15,1,1026,8.05,0,0 +2010,2,9,23,27,-18,1,1027,12.07,0,0 +2010,2,10,0,16,-20,0,1028,16.09,0,0 +2010,2,10,1,22,-19,0,1028,21.01,0,0 +2010,2,10,2,21,-21,-1,1028,25.03,0,0 +2010,2,10,3,6,-21,-1,1029,29.95,0,0 +2010,2,10,4,8,-22,-2,1029,34.87,0,0 +2010,2,10,5,8,-22,-2,1030,39.79,0,0 +2010,2,10,6,8,-22,-3,1030,45.6,0,0 +2010,2,10,7,10,-22,-3,1031,51.41,0,0 +2010,2,10,8,9,-23,-4,1032,58.56,0,0 +2010,2,10,9,20,-23,-4,1033,65.71,0,0 +2010,2,10,10,14,-22,-3,1033,71.52,0,0 +2010,2,10,11,16,-24,-2,1032,77.33,0,0 +2010,2,10,12,14,-24,-1,1032,83.14,0,0 +2010,2,10,13,10,-26,0,1031,5.81,0,0 +2010,2,10,14,7,-26,0,1030,5.81,0,0 +2010,2,10,15,8,-22,0,1030,11.62,0,0 +2010,2,10,16,12,-22,0,1030,17.43,0,0 +2010,2,10,17,15,-23,-1,1030,24.58,0,0 +2010,2,10,18,8,-23,-1,1031,31.73,0,0 +2010,2,10,19,11,-22,-1,1032,38.88,0,0 +2010,2,10,20,18,-22,-2,1032,44.69,0,0 +2010,2,10,21,16,-21,-2,1033,50.5,0,0 +2010,2,10,22,11,-22,-2,1034,56.31,0,0 +2010,2,10,23,10,-21,-3,1035,61.23,0,0 +2010,2,11,0,11,-22,-3,1035,67.04,0,0 +2010,2,11,1,10,-22,-3,1035,72.85,0,0 +2010,2,11,2,10,-22,-3,1035,77.77,0,0 +2010,2,11,3,12,-22,-4,1035,82.69,0,0 +2010,2,11,4,12,-23,-4,1034,89.84,0,0 +2010,2,11,5,10,-23,-5,1034,95.65,0,0 +2010,2,11,6,10,-23,-5,1035,5.81,0,0 +2010,2,11,7,9,-22,-6,1036,9.83,0,0 +2010,2,11,8,9,-22,-5,1037,14.75,0,0 +2010,2,11,9,11,-22,-4,1037,19.67,0,0 +2010,2,11,10,12,-22,-4,1037,25.48,0,0 +2010,2,11,11,12,-22,-3,1037,5.81,0,0 +2010,2,11,12,12,-22,-3,1036,11.62,0,0 +2010,2,11,13,21,-24,-2,1036,4.92,0,0 +2010,2,11,14,18,-23,-2,1035,4.02,0,0 +2010,2,11,15,13,-23,-2,1035,7.15,0,0 +2010,2,11,16,18,-23,-2,1035,4.02,0,0 +2010,2,11,17,17,-23,-2,1035,4.92,0,0 +2010,2,11,18,19,-22,-3,1035,8.05,0,0 +2010,2,11,19,22,-21,-3,1036,11.18,0,0 +2010,2,11,20,20,-21,-5,1036,3.13,0,0 +2010,2,11,21,24,-21,-5,1036,8.05,0,0 +2010,2,11,22,19,-20,-5,1037,11.18,0,0 +2010,2,11,23,13,-21,-5,1038,4.02,0,0 +2010,2,12,0,17,-22,-5,1038,9.83,0,0 +2010,2,12,1,22,-22,-6,1038,14.75,0,0 +2010,2,12,2,16,-22,-6,1038,18.77,0,0 +2010,2,12,3,16,-22,-7,1037,22.79,0,0 +2010,2,12,4,11,-23,-8,1037,3.13,0,0 +2010,2,12,5,10,-22,-8,1037,7.15,0,0 +2010,2,12,6,9,-22,-9,1038,11.17,0,0 +2010,2,12,7,11,-23,-9,1038,14.3,0,0 +2010,2,12,8,14,-21,-9,1038,17.43,0,0 +2010,2,12,9,21,-22,-6,1038,4.02,0,0 +2010,2,12,10,26,-22,-5,1037,7.15,0,0 +2010,2,12,11,21,-22,-4,1036,3.13,0,0 +2010,2,12,12,19,-23,-3,1035,3.13,0,0 +2010,2,12,13,13,-23,-2,1034,3.13,0,0 +2010,2,12,14,13,-23,-1,1033,6.26,0,0 +2010,2,12,15,13,-23,-1,1032,1.79,0,0 +2010,2,12,16,13,-23,-1,1032,1.79,0,0 +2010,2,12,17,11,-23,-1,1031,3.58,0,0 +2010,2,12,18,19,-22,-3,1031,5.37,0,0 +2010,2,12,19,22,-21,-4,1032,0.89,0,0 +2010,2,12,20,50,-19,-5,1032,0.89,0,0 +2010,2,12,21,58,-18,-6,1032,1.34,0,0 +2010,2,12,22,60,-16,-8,1032,1.79,0,0 +2010,2,12,23,69,-18,-8,1032,0.89,0,0 +2010,2,13,0,108,-18,-8,1031,3.13,0,0 +2010,2,13,1,51,-18,-11,1031,6.26,0,0 +2010,2,13,2,54,-19,-8,1031,8.05,0,0 +2010,2,13,3,38,-19,-11,1030,11.18,0,0 +2010,2,13,4,27,-19,-10,1030,14.31,0,0 +2010,2,13,5,23,-19,-11,1030,16.1,0,0 +2010,2,13,6,27,-19,-11,1030,19.23,0,0 +2010,2,13,7,28,-19,-11,1030,21.02,0,0 +2010,2,13,8,24,-17,-10,1031,24.15,0,0 +2010,2,13,9,26,-17,-7,1031,0.89,0,0 +2010,2,13,10,23,-18,-5,1031,1.78,0,0 +2010,2,13,11,37,-18,-3,1030,1.79,0,0 +2010,2,13,12,46,-19,0,1029,0.89,0,0 +2010,2,13,13,49,-20,1,1028,1.79,0,0 +2010,2,13,14,53,-20,3,1027,1.79,0,0 +2010,2,13,15,57,-20,3,1026,3.13,0,0 +2010,2,13,16,51,-20,3,1026,7.15,0,0 +2010,2,13,17,64,-20,2,1026,11.17,0,0 +2010,2,13,18,100,-18,0,1026,15.19,0,0 +2010,2,13,19,113,-15,-1,1027,18.32,0,0 +2010,2,13,20,136,-15,0,1027,22.34,0,0 +2010,2,13,21,213,-15,-4,1027,0.89,0,0 +2010,2,13,22,259,-13,-5,1028,1.78,0,0 +2010,2,13,23,267,-13,-6,1028,2.67,0,0 +2010,2,14,0,351,-13,-7,1028,0.89,0,0 +2010,2,14,1,980,-14,-7,1029,0.89,0,0 +2010,2,14,2,NA,-14,-5,1029,3.13,0,0 +2010,2,14,3,599,-14,-6,1030,6.26,0,0 +2010,2,14,4,39,-14,-5,1030,10.28,0,0 +2010,2,14,5,7,-17,-3,1030,15.2,0,0 +2010,2,14,6,8,-17,-3,1031,4.02,0,0 +2010,2,14,7,7,-18,-4,1032,4.02,0,0 +2010,2,14,8,7,-20,-3,1033,8.94,0,0 +2010,2,14,9,7,-21,-3,1034,14.75,0,0 +2010,2,14,10,16,-22,-3,1035,20.56,0,0 +2010,2,14,11,19,-22,-3,1035,5.81,0,0 +2010,2,14,12,15,-23,-2,1035,4.92,0,0 +2010,2,14,13,21,-24,-2,1033,4.92,0,0 +2010,2,14,14,18,-23,-2,1033,9.84,0,0 +2010,2,14,15,16,-24,-2,1032,13.86,0,0 +2010,2,14,16,15,-24,-2,1032,17.88,0,0 +2010,2,14,17,16,-23,-2,1032,21.9,0,0 +2010,2,14,18,16,-23,-4,1033,23.69,0,0 +2010,2,14,19,21,-22,-5,1033,0.89,0,0 +2010,2,14,20,36,-23,-5,1035,1.79,0,0 +2010,2,14,21,51,-22,-8,1035,4.92,0,0 +2010,2,14,22,67,-21,-7,1036,8.05,0,0 +2010,2,14,23,46,-22,-8,1036,11.18,0,0 +2010,2,15,0,45,-21,-7,1036,4.02,0,0 +2010,2,15,1,27,-22,-7,1037,8.04,0,0 +2010,2,15,2,24,-22,-7,1036,12.96,0,0 +2010,2,15,3,18,-22,-8,1037,0.89,0,0 +2010,2,15,4,15,-22,-8,1036,1.78,0,0 +2010,2,15,5,17,-22,-9,1036,3.13,0,0 +2010,2,15,6,11,-22,-9,1036,7.15,0,0 +2010,2,15,7,16,-22,-10,1037,4.02,0,0 +2010,2,15,8,16,-21,-9,1037,7.15,0,0 +2010,2,15,9,39,-22,-6,1038,10.28,0,0 +2010,2,15,10,25,-23,-5,1038,12.07,0,0 +2010,2,15,11,17,-23,-4,1037,15.2,0,0 +2010,2,15,12,8,-23,-3,1036,16.99,0,0 +2010,2,15,13,11,-23,-1,1035,18.78,0,0 +2010,2,15,14,14,-23,0,1033,4.02,0,0 +2010,2,15,15,15,-26,0,1032,1.79,0,0 +2010,2,15,16,15,-26,0,1032,2.68,0,0 +2010,2,15,17,13,-26,0,1032,3.57,0,0 +2010,2,15,18,13,-23,-3,1033,1.79,0,0 +2010,2,15,19,26,-22,-4,1033,2.68,0,0 +2010,2,15,20,50,-21,-6,1034,0.89,0,0 +2010,2,15,21,73,-20,-9,1034,0.89,0,0 +2010,2,15,22,96,-20,-8,1034,0.89,0,0 +2010,2,15,23,116,-18,-10,1034,1.79,0,0 +2010,2,16,0,146,-17,-11,1034,4.92,0,0 +2010,2,16,1,113,-19,-10,1034,8.05,0,0 +2010,2,16,2,91,-19,-11,1034,11.18,0,0 +2010,2,16,3,70,-20,-12,1034,12.07,0,0 +2010,2,16,4,65,-18,-12,1033,0.45,0,0 +2010,2,16,5,51,-17,-13,1033,0.89,0,0 +2010,2,16,6,29,-20,-12,1033,4.02,0,0 +2010,2,16,7,30,-20,-12,1033,8.04,0,0 +2010,2,16,8,26,-18,-11,1033,9.83,0,0 +2010,2,16,9,26,-19,-7,1033,12.96,0,0 +2010,2,16,10,24,-20,-4,1032,0.89,0,0 +2010,2,16,11,21,-20,-3,1032,1.78,0,0 +2010,2,16,12,24,-21,-1,1031,1.79,0,0 +2010,2,16,13,39,-21,1,1030,0.89,0,0 +2010,2,16,14,79,-19,2,1028,4.02,0,0 +2010,2,16,15,99,-16,3,1027,8.04,0,0 +2010,2,16,16,118,-15,3,1027,12.06,0,0 +2010,2,16,17,140,-15,1,1026,15.19,0,0 +2010,2,16,18,159,-13,-1,1026,18.32,0,0 +2010,2,16,19,161,-12,-1,1027,20.11,0,0 +2010,2,16,20,167,-12,-3,1027,21,0,0 +2010,2,16,21,178,-12,-5,1026,0.89,0,0 +2010,2,16,22,192,-12,-6,1026,1.78,0,0 +2010,2,16,23,198,-12,-4,1026,0.89,0,0 +2010,2,17,0,207,-13,-6,1025,0.89,0,0 +2010,2,17,1,237,-13,-5,1025,1.78,0,0 +2010,2,17,2,247,-12,-6,1025,3.13,0,0 +2010,2,17,3,227,-12,-5,1025,7.15,0,0 +2010,2,17,4,176,-12,-3,1024,11.17,0,0 +2010,2,17,5,113,-8,-1,1024,19.22,0,0 +2010,2,17,6,46,-15,-1,1025,28.16,0,0 +2010,2,17,7,18,-15,-2,1026,35.31,0,0 +2010,2,17,8,13,-19,-3,1027,42.46,0,0 +2010,2,17,9,22,-22,-2,1028,54.53,0,0 +2010,2,17,10,15,-22,-1,1029,63.47,0,0 +2010,2,17,11,18,-23,-1,1028,74.65,0,0 +2010,2,17,12,18,-25,0,1028,86.72,0,0 +2010,2,17,13,12,-25,1,1027,97.9,0,0 +2010,2,17,14,11,-25,1,1027,107.73,0,0 +2010,2,17,15,11,-26,2,1026,118.91,0,0 +2010,2,17,16,12,-27,1,1026,130.09,0,0 +2010,2,17,17,9,-25,1,1026,139.03,0,0 +2010,2,17,18,9,-22,-3,1026,1.79,0,0 +2010,2,17,19,27,-22,-2,1026,0.89,0,0 +2010,2,17,20,31,-19,-4,1026,2.68,0,0 +2010,2,17,21,39,-18,-4,1027,4.47,0,0 +2010,2,17,22,49,-20,-4,1027,6.26,0,0 +2010,2,17,23,48,-18,-8,1027,9.39,0,0 +2010,2,18,0,50,-19,-6,1027,0.45,0,0 +2010,2,18,1,38,-19,-7,1027,0.89,0,0 +2010,2,18,2,36,-17,-9,1026,0.89,0,0 +2010,2,18,3,41,-18,-9,1026,0.89,0,0 +2010,2,18,4,80,-16,-10,1025,0.89,0,0 +2010,2,18,5,70,-18,-9,1025,0.89,0,0 +2010,2,18,6,74,-18,-11,1026,1.79,0,0 +2010,2,18,7,77,-18,-11,1026,0.89,0,0 +2010,2,18,8,87,-17,-8,1026,1.78,0,0 +2010,2,18,9,107,-18,-4,1027,3.13,0,0 +2010,2,18,10,54,-17,0,1026,0.89,0,0 +2010,2,18,11,36,-21,2,1026,1.78,0,0 +2010,2,18,12,34,-21,3,1024,2.67,0,0 +2010,2,18,13,19,-21,5,1022,5.81,0,0 +2010,2,18,14,26,-21,5,1021,11.62,0,0 +2010,2,18,15,27,-21,6,1021,13.41,0,0 +2010,2,18,16,27,-21,4,1021,5.81,0,0 +2010,2,18,17,29,-21,4,1021,9.83,0,0 +2010,2,18,18,41,-21,3,1021,4.02,0,0 +2010,2,18,19,47,-20,2,1021,4.92,0,0 +2010,2,18,20,115,-20,3,1021,8.94,0,0 +2010,2,18,21,265,-20,2,1021,12.96,0,0 +2010,2,18,22,236,-17,-1,1021,14.75,0,0 +2010,2,18,23,296,-16,-3,1021,0.89,0,0 +2010,2,19,0,159,-15,-5,1020,1.78,0,0 +2010,2,19,1,119,-15,-6,1020,2.67,0,0 +2010,2,19,2,115,-14,-7,1019,3.56,0,0 +2010,2,19,3,168,-15,-8,1019,4.45,0,0 +2010,2,19,4,177,-15,-8,1018,4.9,0,0 +2010,2,19,5,191,-15,-8,1018,5.79,0,0 +2010,2,19,6,185,-15,-9,1018,1.79,0,0 +2010,2,19,7,157,-15,-9,1018,3.58,0,0 +2010,2,19,8,114,-13,-6,1018,0.89,0,0 +2010,2,19,9,117,-14,-2,1018,2.68,0,0 +2010,2,19,10,112,-15,1,1018,4.47,0,0 +2010,2,19,11,107,-17,4,1017,0.89,0,0 +2010,2,19,12,96,-16,7,1017,1.78,0,0 +2010,2,19,13,99,-16,9,1016,4.02,0,0 +2010,2,19,14,82,-17,10,1015,5.81,0,0 +2010,2,19,15,58,-18,11,1014,8.94,0,0 +2010,2,19,16,40,-18,10,1014,3.13,0,0 +2010,2,19,17,45,-18,9,1014,3.13,0,0 +2010,2,19,18,44,-19,7,1014,6.26,0,0 +2010,2,19,19,49,-18,7,1014,10.28,0,0 +2010,2,19,20,65,-16,1,1015,0.89,0,0 +2010,2,19,21,101,-14,-1,1015,1.78,0,0 +2010,2,19,22,146,-13,-2,1015,2.67,0,0 +2010,2,19,23,165,-13,-3,1015,3.56,0,0 +2010,2,20,0,205,-12,-4,1015,4.45,0,0 +2010,2,20,1,191,-14,-1,1015,5.34,0,0 +2010,2,20,2,204,-14,-3,1015,0.89,0,0 +2010,2,20,3,190,-12,-5,1014,1.78,0,0 +2010,2,20,4,174,-12,-6,1014,3.57,0,0 +2010,2,20,5,144,-12,-6,1014,5.36,0,0 +2010,2,20,6,120,-12,-7,1014,0.89,0,0 +2010,2,20,7,122,-13,-8,1014,1.79,0,0 +2010,2,20,8,152,-11,-6,1014,0.89,0,0 +2010,2,20,9,158,-12,0,1015,1.79,0,0 +2010,2,20,10,178,-12,3,1014,1.79,0,0 +2010,2,20,11,184,-11,5,1014,3.58,0,0 +2010,2,20,12,184,-10,7,1013,5.37,0,0 +2010,2,20,13,185,-9,8,1012,8.5,0,0 +2010,2,20,14,175,-9,9,1011,11.63,0,0 +2010,2,20,15,165,-8,9,1010,14.76,0,0 +2010,2,20,16,150,-8,9,1011,0.89,0,0 +2010,2,20,17,163,-8,8,1010,1.78,0,0 +2010,2,20,18,172,-8,7,1010,3.13,0,0 +2010,2,20,19,184,-8,5,1011,4.92,0,0 +2010,2,20,20,178,-9,2,1011,1.79,0,0 +2010,2,20,21,211,-7,-1,1011,0.89,0,0 +2010,2,20,22,213,-8,-2,1012,0.45,0,0 +2010,2,20,23,282,-8,-2,1011,1.34,0,0 +2010,2,21,0,261,-7,-2,1011,2.23,0,0 +2010,2,21,1,215,-6,-1,1011,2.68,0,0 +2010,2,21,2,233,-6,-2,1011,3.57,0,0 +2010,2,21,3,235,-6,-2,1011,0.89,0,0 +2010,2,21,4,214,-7,-2,1011,2.68,0,0 +2010,2,21,5,205,-8,-3,1011,4.47,0,0 +2010,2,21,6,206,-8,-5,1011,7.6,0,0 +2010,2,21,7,220,-7,-3,1012,9.39,0,0 +2010,2,21,8,191,-6,-1,1013,12.52,0,0 +2010,2,21,9,189,-6,3,1014,17.44,0,0 +2010,2,21,10,109,-8,6,1014,22.36,0,0 +2010,2,21,11,65,-8,10,1014,25.49,0,0 +2010,2,21,12,35,-13,12,1014,31.3,0,0 +2010,2,21,13,35,-15,13,1013,36.22,0,0 +2010,2,21,14,27,-15,14,1012,42.03,0,0 +2010,2,21,15,36,-15,14,1012,47.84,0,0 +2010,2,21,16,30,-15,14,1012,51.86,0,0 +2010,2,21,17,23,-15,12,1013,3.13,0,0 +2010,2,21,18,20,-15,9,1014,0.89,0,0 +2010,2,21,19,30,-13,4,1015,1.78,0,0 +2010,2,21,20,69,-13,4,1016,2.67,0,0 +2010,2,21,21,99,-12,4,1017,1.79,0,0 +2010,2,21,22,117,-3,2,1018,7.6,0,0 +2010,2,21,23,154,-6,1,1019,11.62,0,0 +2010,2,22,0,154,-6,-1,1020,0.89,0,0 +2010,2,22,1,136,-5,0,1021,1.34,0,0 +2010,2,22,2,143,-6,-2,1021,1.79,0,0 +2010,2,22,3,132,-7,-1,1021,4.92,0,0 +2010,2,22,4,100,-8,-3,1021,6.71,0,0 +2010,2,22,5,89,-8,-3,1021,8.5,0,0 +2010,2,22,6,90,-7,-3,1021,10.29,0,0 +2010,2,22,7,90,-8,-4,1021,12.08,0,0 +2010,2,22,8,99,-7,-2,1022,15.21,0,0 +2010,2,22,9,92,-7,-1,1022,19.23,0,0 +2010,2,22,10,80,-8,0,1022,23.25,0,0 +2010,2,22,11,56,-9,2,1022,26.38,0,0 +2010,2,22,12,60,-10,3,1020,31.3,0,0 +2010,2,22,13,72,-10,3,1019,34.43,0,0 +2010,2,22,14,76,-10,4,1018,38.45,0,0 +2010,2,22,15,79,-10,5,1017,42.47,0,0 +2010,2,22,16,84,-10,4,1016,45.6,0,0 +2010,2,22,17,92,-9,3,1015,48.73,0,0 +2010,2,22,18,100,-8,2,1015,49.62,0,0 +2010,2,22,19,95,-7,0,1015,51.41,0,0 +2010,2,22,20,81,-7,-1,1014,0.89,0,0 +2010,2,22,21,95,-7,0,1014,3.13,0,0 +2010,2,22,22,87,-6,-1,1014,4.92,0,0 +2010,2,22,23,86,-7,-2,1014,0.89,0,0 +2010,2,23,0,90,-7,-4,1013,0.89,0,0 +2010,2,23,1,111,-6,-4,1013,0.89,0,0 +2010,2,23,2,104,-7,-4,1012,0.89,0,0 +2010,2,23,3,129,-8,-5,1012,1.78,0,0 +2010,2,23,4,117,-7,-5,1011,2.23,0,0 +2010,2,23,5,105,-8,-6,1011,0.89,0,0 +2010,2,23,6,112,-7,-6,1010,1.79,0,0 +2010,2,23,7,130,-8,-6,1010,3.58,0,0 +2010,2,23,8,134,-6,-4,1010,0.89,0,0 +2010,2,23,9,136,-5,-1,1010,1.78,0,0 +2010,2,23,10,142,-6,0,1009,1.79,0,0 +2010,2,23,11,150,-7,2,1008,0.89,0,0 +2010,2,23,12,148,-7,4,1007,1.78,0,0 +2010,2,23,13,160,-7,5,1006,1.79,0,0 +2010,2,23,14,179,-7,6,1004,0.89,0,0 +2010,2,23,15,183,-6,7,1003,1.78,0,0 +2010,2,23,16,191,-6,7,1003,1.79,0,0 +2010,2,23,17,195,-6,7,1003,3.58,0,0 +2010,2,23,18,196,-6,5,1003,4.47,0,0 +2010,2,23,19,218,-7,3,1003,0.89,0,0 +2010,2,23,20,246,-6,2,1003,1.79,0,0 +2010,2,23,21,264,-6,0,1004,0.89,0,0 +2010,2,23,22,274,-5,0,1004,0.45,0,0 +2010,2,23,23,282,-6,-1,1004,1.34,0,0 +2010,2,24,0,283,-6,-1,1003,1.79,0,0 +2010,2,24,1,242,-6,0,1003,3.58,0,0 +2010,2,24,2,227,-5,-1,1003,0.89,0,0 +2010,2,24,3,223,-6,1,1003,1.78,0,0 +2010,2,24,4,227,-6,-1,1002,1.79,0,0 +2010,2,24,5,227,-5,-2,1003,3.58,0,0 +2010,2,24,6,237,-5,-2,1003,6.71,0,0 +2010,2,24,7,243,-5,-1,1003,9.84,0,0 +2010,2,24,8,262,-5,0,1004,12.97,0,0 +2010,2,24,9,296,-5,3,1004,16.99,0,0 +2010,2,24,10,301,-4,5,1004,21.01,0,0 +2010,2,24,11,302,-4,6,1004,25.03,0,0 +2010,2,24,12,311,-4,7,1004,29.05,0,0 +2010,2,24,13,307,-2,10,1003,33.97,0,0 +2010,2,24,14,312,-2,11,1002,37.1,0,0 +2010,2,24,15,298,-2,11,1002,0.89,0,0 +2010,2,24,16,292,-1,11,1003,1.78,0,0 +2010,2,24,17,299,0,10,1003,1.79,0,0 +2010,2,24,18,353,0,8,1005,4.92,0,0 +2010,2,24,19,368,2,6,1006,0.89,0,0 +2010,2,24,20,309,3,7,1007,1.79,0,0 +2010,2,24,21,284,3,6,1008,3.58,0,0 +2010,2,24,22,244,2,6,1009,6.71,0,0 +2010,2,24,23,218,3,5,1010,0.89,0,0 +2010,2,25,0,164,2,4,1010,1.78,0,0 +2010,2,25,1,153,1,3,1011,2.67,0,0 +2010,2,25,2,135,1,3,1012,3.12,0,0 +2010,2,25,3,134,1,3,1012,4.01,0,0 +2010,2,25,4,139,0,2,1012,0.89,0,0 +2010,2,25,5,148,0,1,1013,0.89,0,0 +2010,2,25,6,210,-1,0,1014,2.68,0,0 +2010,2,25,7,218,-3,-2,1014,4.47,0,0 +2010,2,25,8,253,-1,0,1016,1.79,0,0 +2010,2,25,9,266,1,2,1017,2.68,0,0 +2010,2,25,10,241,0,5,1017,6.7,0,0 +2010,2,25,11,235,0,7,1017,11.62,0,0 +2010,2,25,12,237,-1,7,1017,16.54,0,0 +2010,2,25,13,248,-2,8,1017,21.46,0,0 +2010,2,25,14,180,-2,8,1016,26.38,0,0 +2010,2,25,15,176,-1,6,1016,30.4,0,0 +2010,2,25,16,165,-1,6,1016,35.32,0,0 +2010,2,25,17,138,-1,6,1017,39.34,0,0 +2010,2,25,18,136,-2,6,1017,42.47,0,0 +2010,2,25,19,136,-2,5,1018,45.6,0,0 +2010,2,25,20,130,-2,5,1018,47.39,0,0 +2010,2,25,21,118,-6,4,1019,54.54,0,0 +2010,2,25,22,56,-7,3,1019,59.46,0,0 +2010,2,25,23,55,-6,2,1020,63.48,0,0 +2010,2,26,0,52,-7,1,1020,66.61,0,0 +2010,2,26,1,46,-7,0,1020,69.74,0,0 +2010,2,26,2,46,-6,0,1021,71.53,0,0 +2010,2,26,3,45,-6,0,1021,73.32,0,0 +2010,2,26,4,51,-5,0,1020,75.11,0,0 +2010,2,26,5,53,-5,0,1021,76.9,0,0 +2010,2,26,6,50,-5,0,1021,78.69,0,0 +2010,2,26,7,46,-5,0,1021,79.58,0,0 +2010,2,26,8,58,-6,0,1022,81.37,0,0 +2010,2,26,9,75,-6,1,1022,0.89,0,0 +2010,2,26,10,72,-6,2,1022,1.79,0,0 +2010,2,26,11,82,-6,3,1022,0.89,0,0 +2010,2,26,12,81,-6,4,1021,1.78,0,0 +2010,2,26,13,91,-6,5,1021,3.57,0,0 +2010,2,26,14,93,-6,5,1020,4.02,0,0 +2010,2,26,15,90,-6,5,1020,8.04,0,0 +2010,2,26,16,79,-7,4,1020,12.06,0,0 +2010,2,26,17,79,-7,3,1020,15.19,0,0 +2010,2,26,18,70,-7,3,1020,16.08,0,0 +2010,2,26,19,70,-7,3,1021,16.97,0,0 +2010,2,26,20,84,-8,2,1021,17.86,0,0 +2010,2,26,21,103,-10,2,1021,21.88,0,0 +2010,2,26,22,95,-11,1,1022,25.9,0,0 +2010,2,26,23,66,-12,0,1021,29.92,0,0 +2010,2,27,0,38,-11,-1,1021,33.05,0,0 +2010,2,27,1,35,-13,-1,1021,37.07,0,0 +2010,2,27,2,30,-13,-1,1021,40.2,0,0 +2010,2,27,3,29,-13,-1,1021,44.22,0,0 +2010,2,27,4,31,-12,-1,1020,47.35,0,0 +2010,2,27,5,29,-6,-3,1021,51.37,1,0 +2010,2,27,6,31,-5,-4,1021,53.16,2,0 +2010,2,27,7,34,-5,-4,1021,54.95,3,0 +2010,2,27,8,52,-5,-4,1021,0.89,4,0 +2010,2,27,9,65,-5,-3,1021,1.79,0,0 +2010,2,27,10,89,-4,-2,1021,0.89,0,0 +2010,2,27,11,138,-4,-2,1021,1.78,0,0 +2010,2,27,12,77,-4,-1,1020,2.67,0,0 +2010,2,27,13,88,-4,-1,1019,1.79,0,0 +2010,2,27,14,95,-4,0,1018,3.58,0,0 +2010,2,27,15,105,-5,0,1017,6.71,0,0 +2010,2,27,16,101,-5,0,1017,9.84,0,0 +2010,2,27,17,102,-5,0,1017,12.97,0,0 +2010,2,27,18,103,-5,0,1017,14.76,0,0 +2010,2,27,19,105,-5,0,1017,16.55,0,0 +2010,2,27,20,98,-5,0,1018,0.89,0,0 +2010,2,27,21,124,-5,0,1018,1.79,1,0 +2010,2,27,22,112,-5,0,1019,0.45,0,0 +2010,2,27,23,123,-5,-1,1020,0.89,0,0 +2010,2,28,0,126,-4,-1,1020,0.89,0,0 +2010,2,28,1,114,-4,-1,1019,0.45,0,0 +2010,2,28,2,115,-4,-1,1019,1.34,0,0 +2010,2,28,3,128,-4,-2,1019,0.89,0,0 +2010,2,28,4,138,-6,-3,1019,0.89,0,0 +2010,2,28,5,145,-5,-2,1020,1.79,0,0 +2010,2,28,6,139,-4,-2,1020,5.81,0,0 +2010,2,28,7,119,-5,-1,1020,9.83,0,0 +2010,2,28,8,114,-4,-1,1021,13.85,0,0 +2010,2,28,9,78,-7,2,1023,5.81,0,0 +2010,2,28,10,80,-11,3,1023,12.96,0,0 +2010,2,28,11,37,-15,3,1023,21.01,0,0 +2010,2,28,12,20,-16,4,1024,26.82,0,0 +2010,2,28,13,20,-18,4,1023,33.97,0,0 +2010,2,28,14,14,-18,4,1023,42.02,0,0 +2010,2,28,15,13,-14,3,1022,7.15,0,0 +2010,2,28,16,16,-8,1,1023,16.09,0,0 +2010,2,28,17,15,-6,0,1023,21.9,0,0 +2010,2,28,18,21,-3,-2,1024,26.82,1,0 +2010,2,28,19,72,-3,-2,1025,29.95,2,0 +2010,2,28,20,85,-3,-2,1026,33.08,3,0 +2010,2,28,21,108,-3,-2,1026,34.87,4,0 +2010,2,28,22,128,-3,-2,1026,0.89,5,0 +2010,2,28,23,161,-3,-3,1027,3.13,6,0 +2010,3,1,0,69,-4,-3,1026,7.15,7,0 +2010,3,1,1,59,-5,-4,1026,12.07,8,0 +2010,3,1,2,42,-5,-4,1025,15.2,9,0 +2010,3,1,3,35,-5,-4,1025,18.33,0,0 +2010,3,1,4,35,-5,-5,1025,20.12,0,0 +2010,3,1,5,29,-5,-4,1025,23.25,1,0 +2010,3,1,6,25,-5,-4,1026,25.04,2,0 +2010,3,1,7,23,-5,-4,1025,28.17,0,0 +2010,3,1,8,17,-5,-4,1026,29.96,0,0 +2010,3,1,9,25,-5,-3,1026,33.09,1,0 +2010,3,1,10,39,-5,-2,1025,36.22,0,0 +2010,3,1,11,44,-5,-1,1024,38.01,0,0 +2010,3,1,12,51,-6,-2,1023,39.8,0,0 +2010,3,1,13,61,-6,-1,1022,42.93,0,0 +2010,3,1,14,70,-5,-1,1021,46.06,0,0 +2010,3,1,15,70,-5,-1,1021,50.08,0,0 +2010,3,1,16,65,-5,-1,1020,53.21,0,0 +2010,3,1,17,75,-5,-1,1020,58.13,0,0 +2010,3,1,18,86,-5,-2,1020,63.05,1,0 +2010,3,1,19,91,-5,-2,1020,67.07,0,0 +2010,3,1,20,95,-5,-2,1020,70.2,0,0 +2010,3,1,21,89,-5,-2,1019,73.33,0,0 +2010,3,1,22,96,-5,-2,1019,77.35,0,0 +2010,3,1,23,99,-5,-2,1019,80.48,0,0 +2010,3,2,0,101,-5,-2,1019,82.27,0,0 +2010,3,2,1,101,-5,-2,1019,0.89,0,0 +2010,3,2,2,108,-5,-2,1019,0.89,0,0 +2010,3,2,3,108,-5,-3,1018,0.89,0,0 +2010,3,2,4,115,-6,-5,1018,1.78,0,0 +2010,3,2,5,111,-7,-6,1018,1.79,0,0 +2010,3,2,6,120,-7,-6,1019,0.89,0,0 +2010,3,2,7,125,-8,-6,1020,1.78,0,0 +2010,3,2,8,138,-6,-5,1021,1.79,0,0 +2010,3,2,9,164,-4,0,1022,4.92,0,0 +2010,3,2,10,169,-7,2,1023,12.07,0,0 +2010,3,2,11,82,-9,4,1022,20.12,0,0 +2010,3,2,12,63,-9,6,1022,28.17,0,0 +2010,3,2,13,37,-11,6,1022,36.22,0,0 +2010,3,2,14,37,-12,7,1021,44.27,0,0 +2010,3,2,15,36,-12,8,1021,52.32,0,0 +2010,3,2,16,30,-13,8,1021,60.37,0,0 +2010,3,2,17,27,-12,8,1021,65.29,0,0 +2010,3,2,18,28,-13,7,1022,69.31,0,0 +2010,3,2,19,34,-12,7,1023,72.44,0,0 +2010,3,2,20,44,-12,5,1024,0.89,0,0 +2010,3,2,21,116,-11,5,1024,1.79,0,0 +2010,3,2,22,139,-6,3,1024,3.13,0,0 +2010,3,2,23,129,-6,0,1025,0.89,0,0 +2010,3,3,0,109,-6,3,1024,1.79,0,0 +2010,3,3,1,97,-5,0,1025,0.89,0,0 +2010,3,3,2,92,-5,-2,1025,0.89,0,0 +2010,3,3,3,158,-7,-1,1025,4.02,0,0 +2010,3,3,4,178,-8,-1,1024,8.94,0,0 +2010,3,3,5,135,-8,-1,1024,12.96,0,0 +2010,3,3,6,111,-8,-1,1024,16.98,0,0 +2010,3,3,7,114,-9,-2,1025,0.89,0,0 +2010,3,3,8,121,-7,-1,1025,0.89,0,0 +2010,3,3,9,126,-8,0,1026,0.89,0,0 +2010,3,3,10,133,-7,1,1026,3.13,0,0 +2010,3,3,11,146,-7,2,1025,6.26,0,0 +2010,3,3,12,165,-5,3,1025,9.39,0,0 +2010,3,3,13,202,-5,4,1023,12.52,0,0 +2010,3,3,14,230,-5,5,1022,15.65,0,0 +2010,3,3,15,247,-6,5,1021,18.78,0,0 +2010,3,3,16,236,-6,6,1021,20.57,0,0 +2010,3,3,17,227,-6,5,1020,24.59,0,0 +2010,3,3,18,224,-6,4,1019,0.89,0,0 +2010,3,3,19,187,-5,3,1020,1.79,0,0 +2010,3,3,20,150,-5,3,1020,3.58,0,0 +2010,3,3,21,144,-5,2,1020,0.89,0,0 +2010,3,3,22,123,-4,1,1020,1.78,0,0 +2010,3,3,23,134,-6,2,1020,1.79,0,0 +2010,3,4,0,98,-5,2,1019,4.92,0,0 +2010,3,4,1,101,-5,1,1019,6.71,0,0 +2010,3,4,2,108,-5,1,1018,9.84,0,0 +2010,3,4,3,126,-5,1,1017,12.97,0,0 +2010,3,4,4,125,-4,0,1016,0.89,0,0 +2010,3,4,5,121,-3,0,1015,1.79,0,0 +2010,3,4,6,123,-4,-1,1015,1.79,0,0 +2010,3,4,7,126,-4,-1,1015,3.58,0,0 +2010,3,4,8,131,-3,0,1016,4.47,0,0 +2010,3,4,9,126,-4,1,1016,7.6,0,0 +2010,3,4,10,156,-4,3,1016,9.39,0,0 +2010,3,4,11,187,-4,3,1015,12.52,0,0 +2010,3,4,12,220,-4,4,1015,14.31,0,0 +2010,3,4,13,237,-4,6,1014,17.44,0,0 +2010,3,4,14,252,-4,7,1013,19.23,0,0 +2010,3,4,15,226,-4,7,1014,22.36,0,0 +2010,3,4,16,231,-3,5,1013,3.13,0,1 +2010,3,4,17,222,-2,3,1013,8.05,0,0 +2010,3,4,18,246,-2,3,1013,0.89,0,0 +2010,3,4,19,261,-2,3,1014,1.78,0,0 +2010,3,4,20,256,-2,3,1015,1.79,0,0 +2010,3,4,21,255,-2,3,1016,4.92,0,0 +2010,3,4,22,263,-2,2,1017,8.05,0,0 +2010,3,4,23,269,-2,2,1018,12.07,0,0 +2010,3,5,0,248,-2,1,1019,16.99,0,0 +2010,3,5,1,89,-10,3,1020,22.8,0,0 +2010,3,5,2,15,-13,2,1021,26.82,0,0 +2010,3,5,3,19,-17,1,1022,35.76,0,0 +2010,3,5,4,15,-16,0,1022,45.59,0,0 +2010,3,5,5,13,-16,-1,1024,53.64,0,0 +2010,3,5,6,16,-16,-2,1026,62.58,0,0 +2010,3,5,7,19,-18,-3,1027,71.52,0,0 +2010,3,5,8,19,-19,-2,1028,81.35,0,0 +2010,3,5,9,24,-23,-1,1027,95.21,0,0 +2010,3,5,10,24,-25,0,1027,105.04,0,0 +2010,3,5,11,17,-25,2,1029,110.85,0,0 +2010,3,5,12,11,-24,2,1029,116.66,0,0 +2010,3,5,13,17,-25,3,1028,123.81,0,0 +2010,3,5,14,14,-25,3,1028,130.96,0,0 +2010,3,5,15,16,-25,2,1028,4.92,0,0 +2010,3,5,16,16,-24,3,1028,1.79,0,0 +2010,3,5,17,18,-25,2,1029,3.13,0,0 +2010,3,5,18,22,-24,1,1029,6.26,0,0 +2010,3,5,19,30,-24,0,1030,9.39,0,0 +2010,3,5,20,27,-25,0,1030,12.52,0,0 +2010,3,5,21,29,-23,0,1031,17.44,0,0 +2010,3,5,22,20,-22,-1,1032,23.25,0,0 +2010,3,5,23,14,-22,-2,1033,29.06,0,0 +2010,3,6,0,19,-22,-2,1033,34.87,0,0 +2010,3,6,1,16,-21,-3,1034,40.68,0,0 +2010,3,6,2,12,-21,-3,1034,44.7,0,0 +2010,3,6,3,13,-22,-4,1035,49.62,0,0 +2010,3,6,4,15,-22,-4,1034,55.43,0,0 +2010,3,6,5,11,-23,-5,1034,61.24,0,0 +2010,3,6,6,19,-23,-5,1035,66.16,0,0 +2010,3,6,7,16,-23,-5,1035,71.97,0,0 +2010,3,6,8,27,-22,-5,1035,76.89,0,0 +2010,3,6,9,23,-22,-3,1036,80.91,0,0 +2010,3,6,10,29,-23,-2,1036,82.7,0,0 +2010,3,6,11,26,-22,-1,1035,1.79,0,0 +2010,3,6,12,39,-22,0,1034,1.79,0,0 +2010,3,6,13,52,-20,1,1033,3.13,0,0 +2010,3,6,14,65,-18,1,1032,4.92,0,0 +2010,3,6,15,91,-13,2,1031,4.02,0,0 +2010,3,6,16,103,-13,2,1031,1.79,0,0 +2010,3,6,17,99,-10,1,1031,3.13,0,0 +2010,3,6,18,99,-9,-1,1032,6.26,0,0 +2010,3,6,19,103,-9,-1,1032,9.39,0,0 +2010,3,6,20,96,-9,-1,1033,11.18,0,0 +2010,3,6,21,76,-9,-3,1034,12.07,0,0 +2010,3,6,22,69,-9,-4,1034,13.86,0,0 +2010,3,6,23,83,-9,-3,1035,18.78,0,0 +2010,3,7,0,62,-9,-3,1035,21.91,0,0 +2010,3,7,1,51,-9,-4,1035,25.04,0,0 +2010,3,7,2,53,-9,-5,1035,28.17,0,0 +2010,3,7,3,68,-10,-6,1035,31.3,0,0 +2010,3,7,4,71,-9,-5,1035,34.43,0,0 +2010,3,7,5,62,-10,-5,1035,37.56,0,0 +2010,3,7,6,58,-10,-6,1035,39.35,0,0 +2010,3,7,7,69,-11,-7,1036,0.45,0,0 +2010,3,7,8,74,-9,-5,1037,0.9,1,0 +2010,3,7,9,67,-10,-3,1037,1.79,0,0 +2010,3,7,10,57,-10,-2,1035,4.92,0,0 +2010,3,7,11,69,-10,-1,1036,8.05,0,0 +2010,3,7,12,83,-11,0,1036,11.18,0,0 +2010,3,7,13,92,-11,1,1035,1.79,0,0 +2010,3,7,14,100,-10,0,1034,3.58,0,0 +2010,3,7,15,108,-11,0,1034,3.13,0,0 +2010,3,7,16,132,-11,0,1033,6.26,0,0 +2010,3,7,17,132,-12,0,1034,8.05,0,0 +2010,3,7,18,134,-12,-1,1034,0.89,0,0 +2010,3,7,19,128,-11,-1,1035,1.79,0,0 +2010,3,7,20,124,-10,-1,1036,0.89,0,0 +2010,3,7,21,116,-10,-1,1036,1.78,0,0 +2010,3,7,22,114,-10,-1,1036,2.67,0,0 +2010,3,7,23,70,-11,-3,1037,3.56,0,0 +2010,3,8,0,76,-5,-4,1038,1.79,0,0 +2010,3,8,1,68,-5,-4,1038,3.58,0,0 +2010,3,8,2,74,-5,-4,1038,5.37,1,0 +2010,3,8,3,75,-5,-4,1038,7.16,2,0 +2010,3,8,4,63,-5,-4,1038,10.29,3,0 +2010,3,8,5,73,-5,-4,1038,12.08,4,0 +2010,3,8,6,61,-5,-4,1038,13.87,5,0 +2010,3,8,7,63,-5,-4,1038,15.66,6,0 +2010,3,8,8,65,-6,-5,1040,18.79,7,0 +2010,3,8,9,43,-7,-5,1040,22.81,8,0 +2010,3,8,10,29,-6,-4,1041,24.6,9,0 +2010,3,8,11,28,-6,-3,1041,26.39,10,0 +2010,3,8,12,26,-6,-2,1041,28.18,11,0 +2010,3,8,13,29,-6,-2,1040,0.45,0,0 +2010,3,8,14,32,-8,-2,1039,1.34,0,0 +2010,3,8,15,33,-7,-2,1039,2.23,0,0 +2010,3,8,16,43,-7,-2,1039,1.79,0,0 +2010,3,8,17,50,-7,-2,1039,4.92,0,0 +2010,3,8,18,56,-8,-3,1039,8.05,0,0 +2010,3,8,19,63,-8,-2,1040,12.97,0,0 +2010,3,8,20,47,-8,-3,1041,17.89,0,0 +2010,3,8,21,27,-10,-3,1041,25.04,0,0 +2010,3,8,22,20,-12,-4,1042,33.09,0,0 +2010,3,8,23,16,-13,-4,1042,40.24,0,0 +2010,3,9,0,5,-15,-5,1042,46.05,0,0 +2010,3,9,1,7,-16,-5,1042,53.2,0,0 +2010,3,9,2,8,-17,-6,1042,60.35,0,0 +2010,3,9,3,10,-18,-6,1041,67.5,0,0 +2010,3,9,4,13,-19,-7,1040,73.31,0,0 +2010,3,9,5,11,-19,-7,1040,80.46,0,0 +2010,3,9,6,13,-20,-8,1039,84.48,0,0 +2010,3,9,7,20,-18,-8,1039,88.5,0,0 +2010,3,9,8,18,-18,-6,1039,92.52,0,0 +2010,3,9,9,18,-19,-5,1039,98.33,0,0 +2010,3,9,10,14,-18,-4,1038,105.48,0,0 +2010,3,9,11,20,-17,-3,1037,112.63,0,0 +2010,3,9,12,19,-15,-2,1035,119.78,0,0 +2010,3,9,13,20,-16,-1,1034,127.83,0,0 +2010,3,9,14,20,-19,0,1032,136.77,0,0 +2010,3,9,15,23,-20,1,1031,145.71,0,0 +2010,3,9,16,21,-23,1,1031,154.65,0,0 +2010,3,9,17,14,-22,1,1030,164.48,0,0 +2010,3,9,18,16,-20,0,1030,170.29,0,0 +2010,3,9,19,16,-18,0,1030,175.21,0,0 +2010,3,9,20,26,-17,-2,1030,179.23,0,0 +2010,3,9,21,22,-18,-2,1030,0.89,0,0 +2010,3,9,22,27,-13,-5,1029,1.78,0,0 +2010,3,9,23,51,-16,-2,1029,1.79,0,0 +2010,3,10,0,46,-11,-5,1028,2.68,0,0 +2010,3,10,1,51,-13,-6,1027,1.79,0,0 +2010,3,10,2,48,-12,-6,1027,2.68,0,0 +2010,3,10,3,61,-13,-7,1026,3.57,0,0 +2010,3,10,4,58,-14,-8,1026,5.36,0,0 +2010,3,10,5,85,-14,-7,1025,7.15,0,0 +2010,3,10,6,84,-14,-9,1025,10.28,0,0 +2010,3,10,7,87,-13,-8,1025,0.89,0,0 +2010,3,10,8,111,-11,-4,1025,1.78,0,0 +2010,3,10,9,105,-13,-1,1025,2.67,0,0 +2010,3,10,10,70,-15,2,1024,3.13,0,0 +2010,3,10,11,53,-16,5,1023,6.26,0,0 +2010,3,10,12,48,-16,7,1021,3.13,0,0 +2010,3,10,13,44,-18,8,1020,3.13,0,0 +2010,3,10,14,33,-19,9,1018,7.15,0,0 +2010,3,10,15,37,-19,9,1017,10.28,0,0 +2010,3,10,16,40,-15,8,1016,14.3,0,0 +2010,3,10,17,34,-15,8,1016,17.43,0,0 +2010,3,10,18,32,-14,6,1016,21.45,0,0 +2010,3,10,19,33,-12,5,1016,24.58,0,0 +2010,3,10,20,51,-11,3,1017,27.71,0,0 +2010,3,10,21,62,-10,2,1017,29.5,0,0 +2010,3,10,22,68,-11,4,1017,32.63,0,0 +2010,3,10,23,89,-10,3,1016,35.76,0,0 +2010,3,11,0,101,-8,-1,1015,0.89,0,0 +2010,3,11,1,106,-8,-1,1014,0.89,0,0 +2010,3,11,2,144,-8,-4,1014,0.45,0,0 +2010,3,11,3,142,-8,-4,1014,1.79,0,0 +2010,3,11,4,140,-7,-3,1012,0.89,0,0 +2010,3,11,5,158,-8,-4,1013,1.79,0,0 +2010,3,11,6,135,-7,-4,1013,0.89,0,0 +2010,3,11,7,145,-7,-4,1012,1.78,0,0 +2010,3,11,8,161,-6,-1,1013,2.67,0,0 +2010,3,11,9,183,-9,1,1013,0.89,0,0 +2010,3,11,10,199,-10,3,1012,0.89,0,0 +2010,3,11,11,224,-10,5,1011,3.13,0,0 +2010,3,11,12,193,-10,6,1010,6.26,0,0 +2010,3,11,13,167,-10,7,1009,9.39,0,0 +2010,3,11,14,162,-9,8,1007,12.52,0,0 +2010,3,11,15,163,-9,8,1005,16.54,0,0 +2010,3,11,16,163,-8,9,1005,1.79,0,0 +2010,3,11,17,154,-9,8,1005,2.68,0,0 +2010,3,11,18,189,-6,6,1004,1.79,0,0 +2010,3,11,19,210,-6,5,1005,4.02,0,0 +2010,3,11,20,222,-6,4,1005,5.81,0,0 +2010,3,11,21,216,-6,3,1005,7.6,0,0 +2010,3,11,22,219,-6,4,1005,10.73,0,0 +2010,3,11,23,223,-6,2,1005,11.62,0,0 +2010,3,12,0,213,-6,5,1006,16.54,0,0 +2010,3,12,1,314,-9,10,1007,31.74,0,0 +2010,3,12,2,59,-10,8,1009,39.79,0,0 +2010,3,12,3,38,-10,6,1011,47.84,0,0 +2010,3,12,4,29,-13,5,1012,57.67,0,0 +2010,3,12,5,27,-13,4,1013,64.82,0,0 +2010,3,12,6,20,-13,3,1015,73.76,0,0 +2010,3,12,7,18,-14,3,1016,82.7,0,0 +2010,3,12,8,19,-15,4,1018,88.51,0,0 +2010,3,12,9,14,-16,5,1019,3.13,0,0 +2010,3,12,10,19,-18,6,1020,8.05,0,0 +2010,3,12,11,23,-20,7,1020,12.97,0,0 +2010,3,12,12,14,-20,8,1020,20.12,0,0 +2010,3,12,13,12,-18,9,1019,25.93,0,0 +2010,3,12,14,12,-19,9,1019,30.85,0,0 +2010,3,12,15,16,-18,9,1019,3.13,0,0 +2010,3,12,16,16,-19,8,1019,3.13,0,0 +2010,3,12,17,17,-18,8,1020,3.13,0,0 +2010,3,12,18,19,-19,7,1020,1.79,0,0 +2010,3,12,19,32,-17,4,1021,2.68,0,0 +2010,3,12,20,53,-13,2,1023,0.89,0,0 +2010,3,12,21,54,-8,0,1023,0.89,0,0 +2010,3,12,22,52,-12,2,1024,4.02,0,0 +2010,3,12,23,91,-5,1,1025,8.04,0,0 +2010,3,13,0,79,-5,0,1026,12.06,0,0 +2010,3,13,1,71,-6,0,1027,16.08,0,0 +2010,3,13,2,47,-7,-1,1026,19.21,0,0 +2010,3,13,3,40,-7,-1,1028,0.89,0,0 +2010,3,13,4,41,-7,0,1028,3.13,0,0 +2010,3,13,5,41,-8,0,1028,6.26,0,0 +2010,3,13,6,38,-10,0,1028,8.05,0,0 +2010,3,13,7,50,-10,-1,1028,0.89,0,0 +2010,3,13,8,69,-12,0,1029,1.79,0,0 +2010,3,13,9,82,-13,1,1029,5.81,0,0 +2010,3,13,10,79,-13,2,1029,9.83,0,0 +2010,3,13,11,76,-12,4,1029,12.96,0,0 +2010,3,13,12,78,-12,5,1028,16.98,0,0 +2010,3,13,13,71,-11,7,1027,21,0,0 +2010,3,13,14,73,-10,7,1025,25.02,0,0 +2010,3,13,15,68,-10,7,1024,29.04,0,0 +2010,3,13,16,58,-9,8,1024,33.96,0,0 +2010,3,13,17,61,-10,7,1023,37.98,0,0 +2010,3,13,18,58,-11,6,1024,42,0,0 +2010,3,13,19,75,-10,5,1024,47.81,0,0 +2010,3,13,20,78,-10,4,1024,51.83,0,0 +2010,3,13,21,64,-10,3,1025,56.75,0,0 +2010,3,13,22,53,-11,3,1025,60.77,0,0 +2010,3,13,23,41,-10,3,1025,0.89,0,0 +2010,3,14,0,37,-11,2,1025,3.13,0,0 +2010,3,14,1,44,-10,2,1025,4.92,0,0 +2010,3,14,2,65,-9,2,1024,6.71,0,0 +2010,3,14,3,68,-8,2,1023,0.89,0,0 +2010,3,14,4,75,-9,2,1023,0.89,0,1 +2010,3,14,5,80,-8,1,1023,0.45,0,2 +2010,3,14,6,80,-6,1,1023,3.13,0,3 +2010,3,14,7,105,-3,-1,1022,6.26,1,0 +2010,3,14,8,86,-2,-1,1022,9.39,2,0 +2010,3,14,9,93,-2,-1,1022,11.18,3,0 +2010,3,14,10,90,-2,-1,1021,14.31,4,0 +2010,3,14,11,73,-1,-1,1021,16.1,6,0 +2010,3,14,12,68,-1,0,1021,20.12,8,0 +2010,3,14,13,68,-1,0,1020,23.25,9,0 +2010,3,14,14,78,-1,0,1019,26.38,10,0 +2010,3,14,15,83,-1,0,1019,28.17,11,0 +2010,3,14,16,86,-1,0,1019,29.96,13,0 +2010,3,14,17,94,-1,0,1018,0.45,14,0 +2010,3,14,18,112,-1,0,1018,0.89,0,0 +2010,3,14,19,108,-2,-1,1018,1.79,0,0 +2010,3,14,20,116,-1,0,1019,4.92,0,0 +2010,3,14,21,105,-1,0,1019,8.94,0,0 +2010,3,14,22,112,-1,1,1019,12.07,0,0 +2010,3,14,23,87,-2,-1,1019,13.86,0,0 +2010,3,15,0,103,-1,0,1019,17.88,0,0 +2010,3,15,1,129,-1,0,1020,21.01,0,0 +2010,3,15,2,153,-2,2,1020,26.82,0,0 +2010,3,15,3,220,-3,1,1020,1.79,0,0 +2010,3,15,4,175,-8,2,1020,4.02,0,0 +2010,3,15,5,132,-10,2,1020,11.17,0,0 +2010,3,15,6,69,-13,1,1021,16.98,0,0 +2010,3,15,7,60,-12,0,1022,22.79,0,0 +2010,3,15,8,50,-13,1,1023,26.81,0,0 +2010,3,15,9,43,-13,1,1024,33.96,0,0 +2010,3,15,10,36,-11,1,1025,41.11,0,0 +2010,3,15,11,26,-12,2,1025,50.05,0,0 +2010,3,15,12,21,-12,2,1025,62.12,0,0 +2010,3,15,13,27,-11,2,1024,73.3,0,0 +2010,3,15,14,21,-10,3,1024,83.13,0,0 +2010,3,15,15,22,-11,3,1024,94.31,0,0 +2010,3,15,16,22,-11,4,1024,106.38,0,0 +2010,3,15,17,17,-11,4,1024,116.21,0,0 +2010,3,15,18,25,-11,3,1024,124.26,0,0 +2010,3,15,19,52,-12,3,1025,129.18,0,0 +2010,3,15,20,30,-11,1,1026,132.31,0,0 +2010,3,15,21,33,-13,2,1026,134.1,0,0 +2010,3,15,22,38,-11,-1,1026,135.89,0,0 +2010,3,15,23,44,-11,-1,1026,0.89,0,0 +2010,3,16,0,61,-10,-3,1026,0.89,0,0 +2010,3,16,1,63,-9,-3,1025,0.89,0,0 +2010,3,16,2,72,-9,-4,1025,1.78,0,0 +2010,3,16,3,67,-9,-4,1024,2.67,0,0 +2010,3,16,4,55,-9,-6,1023,0.89,0,0 +2010,3,16,5,50,-10,-7,1023,0.89,0,0 +2010,3,16,6,55,-10,-7,1023,1.78,0,0 +2010,3,16,7,71,-10,-7,1024,0.89,0,0 +2010,3,16,8,79,-9,-3,1024,1.79,0,0 +2010,3,16,9,72,-9,-1,1025,3.58,0,0 +2010,3,16,10,73,-12,1,1024,6.71,0,0 +2010,3,16,11,73,-15,2,1024,8.5,0,0 +2010,3,16,12,53,-13,4,1023,1.79,0,0 +2010,3,16,13,56,-13,5,1022,3.58,0,0 +2010,3,16,14,60,-14,6,1022,5.37,0,0 +2010,3,16,15,64,-13,6,1021,1.79,0,0 +2010,3,16,16,53,-14,7,1021,3.58,0,0 +2010,3,16,17,45,-12,7,1021,6.71,0,0 +2010,3,16,18,54,-11,4,1021,8.5,0,0 +2010,3,16,19,62,-10,3,1022,11.63,0,0 +2010,3,16,20,63,-7,2,1023,14.76,0,0 +2010,3,16,21,67,-9,2,1023,1.79,0,0 +2010,3,16,22,85,-11,4,1024,3.13,0,0 +2010,3,16,23,89,-11,4,1024,3.13,0,0 +2010,3,17,0,76,-9,3,1025,3.13,0,0 +2010,3,17,1,41,-7,2,1026,1.79,0,0 +2010,3,17,2,83,-7,1,1026,1.79,0,0 +2010,3,17,3,85,-11,2,1025,6.71,0,0 +2010,3,17,4,41,-11,2,1026,9.84,0,0 +2010,3,17,5,18,-12,2,1026,13.86,0,0 +2010,3,17,6,27,-13,1,1027,19.67,0,0 +2010,3,17,7,33,-15,1,1027,26.82,0,0 +2010,3,17,8,34,-15,1,1028,33.97,0,0 +2010,3,17,9,28,-16,2,1029,39.78,0,0 +2010,3,17,10,27,-15,3,1029,44.7,0,0 +2010,3,17,11,23,-15,5,1028,49.62,0,0 +2010,3,17,12,22,-14,5,1027,54.54,0,0 +2010,3,17,13,26,-17,6,1027,58.56,0,0 +2010,3,17,14,20,-17,6,1026,3.13,0,0 +2010,3,17,15,21,-17,7,1025,3.13,0,0 +2010,3,17,16,24,-18,7,1025,1.79,0,0 +2010,3,17,17,23,-17,7,1025,2.68,0,0 +2010,3,17,18,28,-14,5,1025,0.89,0,0 +2010,3,17,19,39,-12,3,1025,0.89,0,0 +2010,3,17,20,51,-12,2,1026,0.89,0,0 +2010,3,17,21,67,-11,1,1026,0.45,0,0 +2010,3,17,22,66,-7,-1,1026,0.89,0,0 +2010,3,17,23,76,-6,-1,1027,4.92,0,0 +2010,3,18,0,71,-5,-1,1027,9.84,0,0 +2010,3,18,1,89,-5,-2,1027,13.86,0,0 +2010,3,18,2,96,-5,-2,1026,17.88,0,0 +2010,3,18,3,91,-5,-2,1026,19.67,0,0 +2010,3,18,4,86,-5,-2,1025,0.89,0,0 +2010,3,18,5,83,-4,-2,1025,1.78,0,0 +2010,3,18,6,80,-4,-1,1025,2.67,0,0 +2010,3,18,7,90,-4,-1,1024,3.56,0,0 +2010,3,18,8,110,-4,-1,1024,1.79,0,0 +2010,3,18,9,135,-4,-1,1024,3.58,0,0 +2010,3,18,10,144,-3,0,1023,5.37,1,0 +2010,3,18,11,166,-3,1,1022,0.89,0,0 +2010,3,18,12,179,-3,2,1021,1.79,0,0 +2010,3,18,13,201,-3,3,1020,3.58,0,0 +2010,3,18,14,227,-2,4,1018,0.89,0,0 +2010,3,18,15,219,-2,3,1016,3.13,0,0 +2010,3,18,16,208,-2,2,1015,3.13,0,0 +2010,3,18,17,202,-3,2,1015,4.02,0,0 +2010,3,18,18,205,-2,2,1014,5.81,0,0 +2010,3,18,19,206,-2,2,1014,7.6,0,0 +2010,3,18,20,209,-3,2,1014,0.89,0,0 +2010,3,18,21,226,-2,2,1013,0.89,0,0 +2010,3,18,22,222,-2,2,1012,1.78,0,0 +2010,3,18,23,239,-2,2,1011,0.89,0,0 +2010,3,19,0,241,-1,2,1011,1.78,0,0 +2010,3,19,1,249,-1,3,1009,2.67,0,0 +2010,3,19,2,248,0,3,1008,3.56,0,0 +2010,3,19,3,247,2,3,1007,1.79,0,1 +2010,3,19,4,228,2,3,1006,0.89,0,2 +2010,3,19,5,225,2,3,1005,0.89,0,3 +2010,3,19,6,222,2,3,1005,1.78,0,0 +2010,3,19,7,221,2,2,1004,3.57,0,0 +2010,3,19,8,214,2,3,1004,5.36,0,0 +2010,3,19,9,217,2,3,1004,8.49,0,0 +2010,3,19,10,231,2,3,1003,10.28,0,0 +2010,3,19,11,249,3,4,1002,0.89,0,0 +2010,3,19,12,245,3,5,1002,1.79,0,0 +2010,3,19,13,250,3,5,1001,0.89,0,0 +2010,3,19,14,232,2,7,1001,1.78,0,0 +2010,3,19,15,243,2,7,1000,1.79,0,0 +2010,3,19,16,239,2,7,1001,0.89,0,0 +2010,3,19,17,234,2,7,1001,1.78,0,0 +2010,3,19,18,240,2,7,1001,2.67,0,0 +2010,3,19,19,224,2,6,1001,1.79,0,0 +2010,3,19,20,206,2,6,1002,5.81,0,0 +2010,3,19,21,217,2,5,1002,8.94,0,0 +2010,3,19,22,180,2,4,1002,0.89,0,0 +2010,3,19,23,180,2,4,1002,1.79,0,0 +2010,3,20,0,183,3,4,1002,0.89,0,0 +2010,3,20,1,180,3,5,1003,1.79,0,0 +2010,3,20,2,216,3,4,1001,1.79,0,0 +2010,3,20,3,215,3,4,1001,1.79,0,0 +2010,3,20,4,700,2,6,1000,4.92,0,0 +2010,3,20,5,NA,2,5,999,1.79,0,0 +2010,3,20,6,NA,-6,9,1001,12.07,0,0 +2010,3,20,7,473,-2,7,1002,23.25,0,0 +2010,3,20,8,201,-5,7,1003,35.32,0,0 +2010,3,20,9,274,-5,7,1004,46.5,0,0 +2010,3,20,10,381,-5,7,1006,58.57,0,0 +2010,3,20,11,343,-7,8,1008,69.75,0,0 +2010,3,20,12,156,-10,8,1010,84.95,0,0 +2010,3,20,13,64,-14,7,1012,98.81,0,0 +2010,3,20,14,51,-19,10,1012,114.01,0,0 +2010,3,20,15,47,-18,10,1012,129.21,0,0 +2010,3,20,16,37,-17,10,1013,140.39,0,0 +2010,3,20,17,26,-15,9,1014,149.33,0,0 +2010,3,20,18,22,-13,8,1015,158.27,0,0 +2010,3,20,19,19,-12,7,1016,163.19,0,0 +2010,3,20,20,23,-12,7,1016,166.32,0,0 +2010,3,20,21,21,-10,7,1017,171.24,0,0 +2010,3,20,22,27,-7,6,1017,177.05,0,0 +2010,3,20,23,41,-5,5,1017,181.97,0,0 +2010,3,21,0,46,-6,3,1017,185.1,0,0 +2010,3,21,1,55,-5,3,1017,0.89,0,0 +2010,3,21,2,46,-5,2,1018,1.78,0,0 +2010,3,21,3,60,-6,2,1017,1.79,0,0 +2010,3,21,4,47,-6,0,1016,3.58,0,0 +2010,3,21,5,50,-5,1,1016,5.37,0,0 +2010,3,21,6,51,-5,1,1017,0.89,0,0 +2010,3,21,7,41,-5,0,1017,1.78,0,0 +2010,3,21,8,48,-5,4,1017,1.79,0,0 +2010,3,21,9,50,-8,7,1017,4.92,0,0 +2010,3,21,10,55,-10,10,1016,8.94,0,0 +2010,3,21,11,56,-12,11,1016,12.96,0,0 +2010,3,21,12,52,-14,12,1013,18.77,0,0 +2010,3,21,13,49,-14,13,1012,3.13,0,0 +2010,3,21,14,53,-14,14,1011,4.92,0,0 +2010,3,21,15,73,-13,14,1010,10.73,0,0 +2010,3,21,16,82,-12,14,1010,13.86,0,0 +2010,3,21,17,90,-12,14,1009,17.88,0,0 +2010,3,21,18,106,-11,13,1009,21.9,0,0 +2010,3,21,19,113,-5,10,1010,25.92,0,0 +2010,3,21,20,121,-5,9,1010,30.84,0,0 +2010,3,21,21,116,-5,8,1011,34.86,0,0 +2010,3,21,22,111,-5,7,1012,37.99,0,0 +2010,3,21,23,83,-5,7,1012,3.13,0,0 +2010,3,22,0,85,-5,6,1012,1.79,0,0 +2010,3,22,1,87,-3,5,1011,3.13,0,0 +2010,3,22,2,90,-3,6,1011,0.89,0,0 +2010,3,22,3,90,-3,5,1011,0.89,0,0 +2010,3,22,4,100,-4,5,1010,1.78,0,0 +2010,3,22,5,95,-4,4,1010,0.89,0,0 +2010,3,22,6,96,-3,3,1010,1.79,0,0 +2010,3,22,7,102,-2,4,1011,2.68,0,0 +2010,3,22,8,112,-2,5,1012,0.89,0,0 +2010,3,22,9,315,-2,6,1012,3.13,0,0 +2010,3,22,10,784,-8,11,1013,11.18,0,0 +2010,3,22,11,761,-10,12,1013,18.33,0,0 +2010,3,22,12,204,-11,12,1013,28.16,0,0 +2010,3,22,13,122,-12,13,1014,37.99,0,0 +2010,3,22,14,158,-10,13,1014,47.82,0,0 +2010,3,22,15,226,-14,14,1014,57.65,0,0 +2010,3,22,16,128,-13,14,1015,67.48,0,0 +2010,3,22,17,49,-14,13,1015,77.31,0,0 +2010,3,22,18,47,-12,12,1016,84.46,0,0 +2010,3,22,19,27,-12,11,1018,92.51,0,0 +2010,3,22,20,25,-12,9,1019,97.43,0,0 +2010,3,22,21,27,-12,9,1020,103.24,0,0 +2010,3,22,22,18,-11,8,1021,110.39,0,0 +2010,3,22,23,NA,-10,7,1022,4.02,0,0 +2010,3,23,0,28,-10,7,1022,4.02,0,0 +2010,3,23,1,16,-9,5,1022,1.79,0,0 +2010,3,23,2,24,-10,6,1022,7.6,0,0 +2010,3,23,3,24,-9,1,1021,4.02,0,0 +2010,3,23,4,25,-8,2,1022,0.89,0,0 +2010,3,23,5,46,-6,0,1022,0.89,0,0 +2010,3,23,6,54,-8,0,1022,0.89,0,0 +2010,3,23,7,60,-7,1,1023,0.89,0,0 +2010,3,23,8,86,-6,4,1023,0.89,0,0 +2010,3,23,9,58,-6,6,1024,3.13,0,0 +2010,3,23,10,63,-7,10,1024,8.94,0,0 +2010,3,23,11,59,-7,11,1023,14.75,0,0 +2010,3,23,12,59,-8,12,1022,21.9,0,0 +2010,3,23,13,54,-8,13,1021,29.05,0,0 +2010,3,23,14,53,-8,13,1019,34.86,0,0 +2010,3,23,15,55,-8,13,1018,40.67,0,0 +2010,3,23,16,60,-8,13,1018,46.48,0,0 +2010,3,23,17,55,-9,12,1018,52.29,0,0 +2010,3,23,18,64,-8,11,1018,59.44,0,0 +2010,3,23,19,64,-8,11,1019,64.36,0,0 +2010,3,23,20,55,-7,9,1020,67.49,0,0 +2010,3,23,21,57,-8,10,1020,71.51,0,0 +2010,3,23,22,63,-6,7,1020,75.53,0,0 +2010,3,23,23,69,-7,6,1020,79.55,0,0 +2010,3,24,0,70,-6,6,1020,83.57,0,0 +2010,3,24,1,72,-6,6,1020,86.7,0,0 +2010,3,24,2,71,-5,5,1019,89.83,0,0 +2010,3,24,3,73,-6,4,1020,3.13,0,0 +2010,3,24,4,71,-6,5,1020,1.79,0,0 +2010,3,24,5,70,-6,4,1020,1.79,0,0 +2010,3,24,6,68,-5,4,1021,1.79,0,0 +2010,3,24,7,63,-4,4,1022,4.92,0,0 +2010,3,24,8,67,-6,7,1022,10.73,0,0 +2010,3,24,9,76,-8,9,1022,16.54,0,0 +2010,3,24,10,65,-11,10,1023,7.15,0,0 +2010,3,24,11,44,-14,11,1023,14.3,0,0 +2010,3,24,12,36,-15,11,1022,19.22,0,0 +2010,3,24,13,29,-16,11,1022,24.14,0,0 +2010,3,24,14,20,-15,11,1021,4.02,0,0 +2010,3,24,15,53,-9,10,1022,8.04,0,0 +2010,3,24,16,92,-8,10,1021,12.96,0,0 +2010,3,24,17,134,-5,9,1022,18.77,0,0 +2010,3,24,18,97,-4,7,1023,23.69,0,0 +2010,3,24,19,64,-3,6,1024,27.71,0,0 +2010,3,24,20,52,-1,6,1026,4.92,0,1 +2010,3,24,21,63,1,3,1027,3.13,0,2 +2010,3,24,22,36,1,2,1027,4.02,0,3 +2010,3,24,23,22,0,1,1027,7.15,0,0 +2010,3,25,0,48,0,2,1027,10.28,0,0 +2010,3,25,1,52,0,1,1028,14.3,0,0 +2010,3,25,2,43,-1,1,1028,16.09,0,0 +2010,3,25,3,44,-2,1,1028,17.88,0,0 +2010,3,25,4,51,-2,1,1027,19.67,0,0 +2010,3,25,5,69,-2,1,1028,1.79,0,0 +2010,3,25,6,54,-2,1,1029,5.81,0,0 +2010,3,25,7,10,-13,1,1029,12.96,0,0 +2010,3,25,8,12,-16,3,1030,21.01,0,0 +2010,3,25,9,10,-15,4,1030,28.16,0,0 +2010,3,25,10,9,-17,5,1030,37.99,0,0 +2010,3,25,11,12,-20,6,1029,46.93,0,0 +2010,3,25,12,12,-20,7,1028,51.85,0,0 +2010,3,25,13,18,-20,6,1027,1.79,0,0 +2010,3,25,14,16,-19,8,1025,4.02,0,0 +2010,3,25,15,20,-19,8,1023,9.83,0,0 +2010,3,25,16,20,-18,9,1022,13.85,0,0 +2010,3,25,17,18,-16,8,1022,3.13,0,0 +2010,3,25,18,30,-14,7,1021,4.92,0,0 +2010,3,25,19,27,-12,6,1022,0.89,0,0 +2010,3,25,20,29,-10,5,1023,1.79,0,0 +2010,3,25,21,33,-10,6,1023,0.89,0,0 +2010,3,25,22,34,-10,5,1023,1.78,0,0 +2010,3,25,23,59,-6,3,1023,2.67,0,0 +2010,3,26,0,63,-7,2,1023,0.89,0,0 +2010,3,26,1,67,-6,1,1023,0.89,0,0 +2010,3,26,2,62,-9,1,1023,3.13,0,0 +2010,3,26,3,72,-11,-1,1022,0.45,0,0 +2010,3,26,4,48,-7,-1,1022,0.9,0,0 +2010,3,26,5,70,-9,-1,1022,0.89,0,0 +2010,3,26,6,57,-8,-2,1021,0.89,0,0 +2010,3,26,7,61,-6,-1,1021,0.89,0,0 +2010,3,26,8,38,-7,3,1021,0.89,0,0 +2010,3,26,9,44,-14,6,1021,1.78,0,0 +2010,3,26,10,37,-17,8,1021,3.13,0,0 +2010,3,26,11,43,-18,10,1020,7.15,0,0 +2010,3,26,12,50,-17,11,1019,12.96,0,0 +2010,3,26,13,64,-15,12,1018,17.88,0,0 +2010,3,26,14,64,-14,14,1016,22.8,0,0 +2010,3,26,15,72,-14,15,1015,26.82,0,0 +2010,3,26,16,70,-12,15,1014,31.74,0,0 +2010,3,26,17,65,-13,15,1014,36.66,0,0 +2010,3,26,18,72,-11,13,1014,39.79,0,0 +2010,3,26,19,84,-8,11,1015,0.89,0,0 +2010,3,26,20,85,-8,8,1016,1.79,0,0 +2010,3,26,21,98,-8,7,1018,3.13,0,0 +2010,3,26,22,142,-9,6,1019,4.02,0,0 +2010,3,26,23,97,-10,4,1019,7.15,0,0 +2010,3,27,0,106,-8,3,1020,10.28,0,0 +2010,3,27,1,89,-10,7,1020,4.02,0,0 +2010,3,27,2,61,-11,8,1021,8.94,0,0 +2010,3,27,3,46,-11,7,1023,10.73,0,0 +2010,3,27,4,127,-4,4,1024,4.92,0,0 +2010,3,27,5,134,-4,2,1025,8.94,0,0 +2010,3,27,6,99,-7,1,1025,12.07,0,0 +2010,3,27,7,85,-8,2,1026,13.86,0,0 +2010,3,27,8,75,-9,3,1027,16.99,0,0 +2010,3,27,9,79,-10,4,1028,21.01,0,0 +2010,3,27,10,81,-9,6,1028,24.14,0,0 +2010,3,27,11,74,-10,8,1028,28.16,0,0 +2010,3,27,12,62,-10,8,1027,31.29,0,0 +2010,3,27,13,71,-10,9,1027,35.31,0,0 +2010,3,27,14,58,-10,9,1026,39.33,0,0 +2010,3,27,15,48,-9,11,1025,3.13,0,0 +2010,3,27,16,50,-10,10,1024,3.13,0,0 +2010,3,27,17,54,-9,10,1024,6.26,0,0 +2010,3,27,18,61,-8,8,1025,9.39,0,0 +2010,3,27,19,59,-7,7,1025,12.52,0,0 +2010,3,27,20,62,-7,5,1026,17.44,0,0 +2010,3,27,21,54,-8,4,1027,20.57,0,0 +2010,3,27,22,57,-8,3,1027,24.59,0,0 +2010,3,27,23,44,-8,2,1028,28.61,0,0 +2010,3,28,0,41,-8,2,1028,30.4,0,0 +2010,3,28,1,48,-7,1,1028,32.19,0,0 +2010,3,28,2,44,-7,0,1028,33.98,0,0 +2010,3,28,3,56,-7,-1,1028,0.89,0,0 +2010,3,28,4,59,-7,-2,1028,0.89,0,0 +2010,3,28,5,67,-6,-2,1028,0.89,0,0 +2010,3,28,6,68,-6,-3,1028,0.89,0,0 +2010,3,28,7,99,-5,-1,1029,0.45,0,0 +2010,3,28,8,88,-6,2,1029,1.79,0,0 +2010,3,28,9,74,-6,4,1030,1.79,0,0 +2010,3,28,10,86,-6,5,1030,3.58,0,0 +2010,3,28,11,93,-7,6,1029,5.37,0,0 +2010,3,28,12,96,-7,10,1027,1.79,0,0 +2010,3,28,13,120,-6,13,1026,4.92,0,0 +2010,3,28,14,99,-6,15,1025,4.02,0,0 +2010,3,28,15,100,-6,16,1024,8.94,0,0 +2010,3,28,16,63,-9,16,1023,13.86,0,0 +2010,3,28,17,57,-11,16,1023,18.78,0,0 +2010,3,28,18,46,-9,14,1023,22.8,0,0 +2010,3,28,19,48,-8,10,1023,25.93,0,0 +2010,3,28,20,60,-9,12,1024,30.85,0,0 +2010,3,28,21,57,-8,9,1024,32.64,0,0 +2010,3,28,22,72,-8,8,1025,34.43,0,0 +2010,3,28,23,82,-7,7,1025,35.32,0,0 +2010,3,29,0,92,-4,6,1025,37.11,0,0 +2010,3,29,1,100,-6,6,1025,38.9,0,0 +2010,3,29,2,117,-6,5,1025,40.69,0,0 +2010,3,29,3,129,-5,5,1024,42.48,0,0 +2010,3,29,4,132,-4,3,1024,43.37,0,0 +2010,3,29,5,142,-4,2,1024,0.89,0,0 +2010,3,29,6,147,-4,3,1025,1.78,0,0 +2010,3,29,7,169,-3,3,1025,0.89,0,0 +2010,3,29,8,208,-1,7,1025,1.79,0,0 +2010,3,29,9,198,-2,9,1025,4.92,0,0 +2010,3,29,10,203,-2,11,1025,8.05,0,0 +2010,3,29,11,218,-3,11,1025,11.18,0,0 +2010,3,29,12,220,-4,12,1024,14.31,0,0 +2010,3,29,13,222,-3,12,1024,0.89,0,0 +2010,3,29,14,237,-1,11,1023,1.78,0,0 +2010,3,29,15,245,3,10,1022,4.02,0,1 +2010,3,29,16,260,5,9,1022,5.81,0,0 +2010,3,29,17,259,5,8,1021,0.89,0,0 +2010,3,29,18,275,5,8,1021,0.89,0,0 +2010,3,29,19,280,5,8,1021,1.79,0,0 +2010,3,29,20,296,6,8,1022,3.58,0,1 +2010,3,29,21,366,6,8,1022,4.47,0,2 +2010,3,29,22,317,6,8,1022,0.89,0,3 +2010,3,29,23,297,6,8,1022,0.89,0,4 +2010,3,30,0,288,6,7,1021,0.89,0,5 +2010,3,30,1,288,6,7,1020,2.68,0,6 +2010,3,30,2,254,6,7,1020,4.47,0,7 +2010,3,30,3,254,6,7,1019,6.26,0,8 +2010,3,30,4,248,6,7,1018,8.05,0,9 +2010,3,30,5,271,6,7,1018,9.84,0,10 +2010,3,30,6,NA,6,8,1018,11.63,0,11 +2010,3,30,7,NA,6,8,1018,13.42,0,0 +2010,3,30,8,NA,6,8,1019,14.31,0,0 +2010,3,30,9,NA,6,9,1018,17.44,0,0 +2010,3,30,10,NA,6,10,1018,20.57,0,0 +2010,3,30,11,NA,6,11,1018,24.59,0,1 +2010,3,30,12,NA,6,10,1018,26.38,0,2 +2010,3,30,13,195,7,10,1017,28.17,0,3 +2010,3,30,14,NA,7,10,1016,29.06,0,4 +2010,3,30,15,NA,7,10,1016,1.79,0,5 +2010,3,30,16,NA,7,9,1015,2.68,0,6 +2010,3,30,17,NA,7,9,1014,0.89,0,7 +2010,3,30,18,NA,7,9,1014,1.78,0,8 +2010,3,30,19,NA,7,8,1015,0.89,0,9 +2010,3,30,20,NA,7,8,1015,0.89,0,10 +2010,3,30,21,NA,7,8,1015,1.78,0,11 +2010,3,30,22,NA,7,8,1016,0.89,0,0 +2010,3,30,23,NA,8,9,1015,3.13,0,0 +2010,3,31,0,NA,7,8,1015,4.92,0,1 +2010,3,31,1,NA,7,8,1015,0.45,0,0 +2010,3,31,2,NA,6,7,1015,1.34,0,0 +2010,3,31,3,NA,6,7,1014,2.23,0,0 +2010,3,31,4,NA,6,7,1014,0.89,0,0 +2010,3,31,5,NA,5,7,1014,0.45,0,0 +2010,3,31,6,NA,5,7,1014,1.34,0,0 +2010,3,31,7,124,6,8,1014,1.79,0,0 +2010,3,31,8,129,5,9,1015,0.89,0,0 +2010,3,31,9,NA,7,10,1015,1.78,0,0 +2010,3,31,10,NA,7,12,1015,2.67,0,0 +2010,3,31,11,NA,7,12,1014,1.79,0,0 +2010,3,31,12,NA,7,13,1013,0.89,0,0 +2010,3,31,13,NA,6,15,1013,1.79,0,0 +2010,3,31,14,NA,6,16,1012,0.89,0,0 +2010,3,31,15,NA,4,16,1012,1.78,0,0 +2010,3,31,16,NA,1,17,1012,4.92,0,0 +2010,3,31,17,200,-5,16,1012,12.07,0,0 +2010,3,31,18,187,-7,15,1013,19.22,0,0 +2010,3,31,19,149,-7,14,1014,26.37,0,0 +2010,3,31,20,113,-8,12,1016,33.52,0,0 +2010,3,31,21,85,-8,11,1017,39.33,0,0 +2010,3,31,22,71,-10,10,1018,46.48,0,0 +2010,3,31,23,42,-13,10,1020,53.63,0,0 +2010,4,1,0,31,-14,9,1021,63.46,0,0 +2010,4,1,1,39,-14,8,1021,73.29,0,0 +2010,4,1,2,49,-14,7,1021,81.34,0,0 +2010,4,1,3,35,-13,5,1021,85.36,0,0 +2010,4,1,4,31,-16,6,1021,89.38,0,0 +2010,4,1,5,26,-18,6,1021,92.51,0,0 +2010,4,1,6,24,-18,5,1022,95.64,0,0 +2010,4,1,7,22,-19,6,1023,99.66,0,0 +2010,4,1,8,30,-19,7,1023,104.58,0,0 +2010,4,1,9,25,-20,9,1023,111.73,0,0 +2010,4,1,10,21,-18,10,1022,120.67,0,0 +2010,4,1,11,NA,-18,11,1021,128.72,0,0 +2010,4,1,12,NA,-17,12,1020,135.87,0,0 +2010,4,1,13,35,-17,12,1020,147.94,0,0 +2010,4,1,14,31,-18,12,1019,159.12,0,0 +2010,4,1,15,15,-18,12,1018,171.19,0,0 +2010,4,1,16,16,-17,12,1018,183.26,0,0 +2010,4,1,17,18,-18,11,1018,193.09,0,0 +2010,4,1,18,15,-19,11,1017,201.14,0,0 +2010,4,1,19,17,-19,10,1018,208.29,0,0 +2010,4,1,20,24,-17,9,1019,211.42,0,0 +2010,4,1,21,20,-17,9,1019,213.21,0,0 +2010,4,1,22,54,-11,4,1019,0.89,0,0 +2010,4,1,23,39,-11,5,1018,1.78,0,0 +2010,4,2,0,27,-7,2,1018,0.89,0,0 +2010,4,2,1,52,-8,3,1018,0.45,0,0 +2010,4,2,2,41,-9,4,1018,0.89,0,0 +2010,4,2,3,35,-12,4,1019,4.02,0,0 +2010,4,2,4,44,-10,2,1019,7.15,0,0 +2010,4,2,5,45,-12,3,1019,10.28,0,0 +2010,4,2,6,40,-15,4,1019,13.41,0,0 +2010,4,2,7,27,-14,5,1020,17.43,0,0 +2010,4,2,8,27,-16,7,1021,23.24,0,0 +2010,4,2,9,21,-18,7,1022,35.31,0,0 +2010,4,2,10,14,-18,8,1022,44.25,0,0 +2010,4,2,11,14,-19,9,1022,53.19,0,0 +2010,4,2,12,17,-21,9,1022,62.13,0,0 +2010,4,2,13,13,-20,11,1021,69.28,0,0 +2010,4,2,14,17,-20,11,1021,76.43,0,0 +2010,4,2,15,16,-20,12,1020,83.58,0,0 +2010,4,2,16,18,-22,12,1020,92.52,0,0 +2010,4,2,17,12,-22,11,1020,101.46,0,0 +2010,4,2,18,12,-21,11,1020,108.61,0,0 +2010,4,2,19,21,-19,7,1021,110.4,0,0 +2010,4,2,20,30,-12,5,1021,0.89,0,0 +2010,4,2,21,69,-10,4,1022,1.78,0,0 +2010,4,2,22,77,-11,3,1022,3.57,0,0 +2010,4,2,23,49,-10,3,1022,5.36,0,0 +2010,4,3,0,48,-8,0,1022,1.79,0,0 +2010,4,3,1,53,-9,0,1022,0.89,0,0 +2010,4,3,2,52,-7,-1,1022,1.78,0,0 +2010,4,3,3,53,-12,1,1022,2.67,0,0 +2010,4,3,4,58,-9,0,1022,3.12,0,0 +2010,4,3,5,56,-11,0,1022,4.01,0,0 +2010,4,3,6,60,-10,-1,1022,4.46,0,0 +2010,4,3,7,100,-7,0,1022,5.35,0,0 +2010,4,3,8,133,-9,4,1022,1.79,0,0 +2010,4,3,9,138,-9,8,1021,4.92,0,0 +2010,4,3,10,67,-11,10,1021,8.05,0,0 +2010,4,3,11,59,-11,11,1019,12.07,0,0 +2010,4,3,12,82,-12,13,1018,16.99,0,0 +2010,4,3,13,89,-11,13,1016,21.91,0,0 +2010,4,3,14,110,-8,13,1015,25.93,0,0 +2010,4,3,15,128,-7,15,1013,29.06,0,0 +2010,4,3,16,132,-5,16,1012,33.98,0,0 +2010,4,3,17,136,-4,15,1011,38.9,0,0 +2010,4,3,18,132,-4,13,1011,42.03,0,0 +2010,4,3,19,119,-4,12,1010,45.16,0,0 +2010,4,3,20,131,-4,13,1010,49.18,0,0 +2010,4,3,21,161,-2,12,1010,53.2,0,0 +2010,4,3,22,168,-2,12,1011,57.22,0,0 +2010,4,3,23,164,-2,11,1010,62.14,0,0 +2010,4,4,0,184,-1,10,1010,0.89,0,0 +2010,4,4,1,178,-1,9,1010,1.79,0,0 +2010,4,4,2,194,-1,7,1010,0.89,0,0 +2010,4,4,3,206,-1,8,1010,0.89,0,0 +2010,4,4,4,191,-1,7,1010,0.89,0,0 +2010,4,4,5,180,-1,6,1010,1.78,0,0 +2010,4,4,6,200,0,5,1010,3.13,0,0 +2010,4,4,7,170,0,7,1011,4.92,0,0 +2010,4,4,8,197,1,11,1011,1.79,0,0 +2010,4,4,9,184,0,13,1012,4.92,0,0 +2010,4,4,10,167,-3,16,1011,8.94,0,0 +2010,4,4,11,111,-8,17,1011,1.79,0,0 +2010,4,4,12,92,-8,19,1010,4.02,0,0 +2010,4,4,13,44,-11,20,1009,7.15,0,0 +2010,4,4,14,41,-13,21,1009,1.79,0,0 +2010,4,4,15,34,-11,21,1008,1.79,0,0 +2010,4,4,16,27,-11,22,1008,3.58,0,0 +2010,4,4,17,32,-13,21,1007,6.71,0,0 +2010,4,4,18,43,-10,20,1008,10.73,0,0 +2010,4,4,19,61,-11,16,1008,14.75,0,0 +2010,4,4,20,70,-8,14,1009,16.54,0,0 +2010,4,4,21,87,-7,15,1009,19.67,0,0 +2010,4,4,22,98,2,14,1010,24.59,0,0 +2010,4,4,23,173,2,13,1010,28.61,0,0 +2010,4,5,0,199,2,12,1010,31.74,0,0 +2010,4,5,1,177,1,11,1011,34.87,0,0 +2010,4,5,2,169,1,11,1011,39.79,0,0 +2010,4,5,3,187,0,10,1012,0.89,0,0 +2010,4,5,4,166,0,8,1011,1.78,0,0 +2010,4,5,5,161,0,9,1011,1.79,0,0 +2010,4,5,6,169,1,7,1012,3.58,0,0 +2010,4,5,7,180,1,7,1013,0.89,0,0 +2010,4,5,8,166,2,10,1013,1.78,0,0 +2010,4,5,9,185,1,13,1013,1.79,0,0 +2010,4,5,10,166,0,14,1013,1.79,0,0 +2010,4,5,11,171,0,15,1012,1.79,0,0 +2010,4,5,12,176,1,16,1012,5.81,0,0 +2010,4,5,13,186,1,16,1011,10.73,0,0 +2010,4,5,14,142,1,16,1011,15.65,0,0 +2010,4,5,15,121,2,15,1011,20.57,0,0 +2010,4,5,16,130,2,14,1012,26.38,0,0 +2010,4,5,17,132,2,13,1014,29.51,0,0 +2010,4,5,18,126,4,8,1016,8.05,0,1 +2010,4,5,19,47,4,8,1018,3.13,0,2 +2010,4,5,20,25,4,8,1020,3.13,0,0 +2010,4,5,21,23,4,8,1022,3.13,0,0 +2010,4,5,22,20,4,6,1023,0.89,0,0 +2010,4,5,23,18,3,7,1024,1.79,0,0 +2010,4,6,0,30,2,6,1024,0.89,0,0 +2010,4,6,1,14,-3,7,1024,3.13,0,0 +2010,4,6,2,12,-7,7,1025,7.15,0,0 +2010,4,6,3,18,-7,6,1025,10.28,0,0 +2010,4,6,4,19,-8,6,1026,14.3,0,0 +2010,4,6,5,33,-11,6,1026,19.22,0,0 +2010,4,6,6,26,-12,5,1027,24.14,0,0 +2010,4,6,7,24,-13,7,1027,29.95,0,0 +2010,4,6,8,28,-14,9,1028,37.1,0,0 +2010,4,6,9,27,-16,11,1028,45.15,0,0 +2010,4,6,10,19,-16,12,1028,54.98,0,0 +2010,4,6,11,16,-17,13,1027,62.13,0,0 +2010,4,6,12,15,-14,13,1026,1.79,0,0 +2010,4,6,13,16,-15,14,1025,4.92,0,0 +2010,4,6,14,16,-17,16,1024,5.81,0,0 +2010,4,6,15,15,-17,16,1024,12.96,0,0 +2010,4,6,16,17,-17,16,1023,18.77,0,0 +2010,4,6,17,12,-17,15,1024,22.79,0,0 +2010,4,6,18,15,-15,14,1023,25.92,0,0 +2010,4,6,19,17,-17,13,1024,0.45,0,0 +2010,4,6,20,23,-15,13,1025,3.13,0,0 +2010,4,6,21,19,-11,11,1025,1.79,0,0 +2010,4,6,22,24,-8,8,1025,0.89,0,0 +2010,4,6,23,34,-10,8,1025,1.78,0,0 +2010,4,7,0,52,-5,5,1025,0.89,0,0 +2010,4,7,1,55,-9,6,1025,0.45,0,0 +2010,4,7,2,69,-9,4,1024,1.34,0,0 +2010,4,7,3,87,-8,3,1024,0.89,0,0 +2010,4,7,4,102,-7,4,1024,0.89,0,0 +2010,4,7,5,79,-7,3,1024,0.89,0,0 +2010,4,7,6,75,-7,1,1024,1.79,0,0 +2010,4,7,7,68,-3,3,1025,0.89,0,0 +2010,4,7,8,78,-5,6,1025,0.89,0,0 +2010,4,7,9,101,-5,10,1024,1.79,0,0 +2010,4,7,10,114,-5,12,1024,1.79,0,0 +2010,4,7,11,135,-4,15,1022,3.13,0,0 +2010,4,7,12,140,-4,17,1021,8.05,0,0 +2010,4,7,13,118,-5,19,1019,15.2,0,0 +2010,4,7,14,108,-7,20,1018,24.14,0,0 +2010,4,7,15,103,-7,20,1017,33.97,0,0 +2010,4,7,16,94,-7,20,1015,42.91,0,0 +2010,4,7,17,90,-8,20,1014,50.96,0,0 +2010,4,7,18,87,-7,19,1014,59.01,0,0 +2010,4,7,19,80,-8,18,1014,63.93,0,0 +2010,4,7,20,84,-6,16,1014,68.85,0,0 +2010,4,7,21,96,-4,16,1015,74.66,0,0 +2010,4,7,22,110,-3,15,1015,81.81,0,0 +2010,4,7,23,104,-2,14,1015,88.96,0,0 +2010,4,8,0,97,-1,13,1014,94.77,0,0 +2010,4,8,1,84,-1,12,1013,98.79,0,0 +2010,4,8,2,74,-1,11,1012,102.81,0,0 +2010,4,8,3,74,-1,10,1012,105.94,0,0 +2010,4,8,4,67,-1,10,1011,109.96,0,0 +2010,4,8,5,69,-1,9,1011,113.09,0,0 +2010,4,8,6,69,0,8,1011,116.22,0,0 +2010,4,8,7,75,0,9,1011,119.35,0,0 +2010,4,8,8,78,1,10,1011,123.37,0,0 +2010,4,8,9,91,1,12,1010,126.5,0,0 +2010,4,8,10,107,1,13,1010,129.63,0,0 +2010,4,8,11,107,1,14,1009,134.55,0,0 +2010,4,8,12,127,1,17,1008,138.57,0,0 +2010,4,8,13,132,0,18,1007,143.49,0,0 +2010,4,8,14,128,1,19,1006,148.41,0,0 +2010,4,8,15,122,1,20,1005,152.43,0,0 +2010,4,8,16,113,1,21,1003,159.58,0,0 +2010,4,8,17,105,1,21,1003,165.39,0,0 +2010,4,8,18,127,1,20,1003,168.52,0,0 +2010,4,8,19,144,2,19,1003,170.31,0,0 +2010,4,8,20,158,3,17,1005,0.89,0,0 +2010,4,8,21,141,4,16,1006,1.79,0,0 +2010,4,8,22,124,4,15,1006,1.79,0,0 +2010,4,8,23,131,4,16,1008,1.79,0,0 +2010,4,9,0,150,3,15,1007,3.13,0,0 +2010,4,9,1,137,4,15,1008,7.15,0,0 +2010,4,9,2,128,3,15,1009,10.28,0,0 +2010,4,9,3,103,0,16,1009,16.09,0,0 +2010,4,9,4,60,2,14,1011,4.92,0,0 +2010,4,9,5,50,2,13,1011,4.92,0,0 +2010,4,9,6,63,1,13,1011,10.73,0,0 +2010,4,9,7,77,2,12,1011,16.54,0,0 +2010,4,9,8,62,0,13,1012,22.35,0,0 +2010,4,9,9,58,-1,14,1012,26.37,0,0 +2010,4,9,10,77,6,13,1013,8.05,0,0 +2010,4,9,11,131,3,11,1013,15.2,0,0 +2010,4,9,12,121,3,10,1012,24.14,0,0 +2010,4,9,13,81,3,10,1012,29.06,0,0 +2010,4,9,14,77,3,10,1011,33.98,0,0 +2010,4,9,15,67,3,9,1011,38,0,0 +2010,4,9,16,66,3,9,1011,42.02,0,0 +2010,4,9,17,60,2,8,1011,45.15,0,0 +2010,4,9,18,71,2,8,1011,48.28,0,0 +2010,4,9,19,94,2,8,1012,50.07,0,0 +2010,4,9,20,90,2,8,1013,1.79,0,0 +2010,4,9,21,85,2,8,1014,0.89,0,0 +2010,4,9,22,75,2,8,1015,0.89,0,0 +2010,4,9,23,79,2,8,1015,0.89,0,0 +2010,4,10,0,91,2,8,1016,2.68,0,0 +2010,4,10,1,89,3,7,1017,5.81,0,0 +2010,4,10,2,102,3,8,1017,7.6,0,0 +2010,4,10,3,49,0,8,1017,11.62,0,0 +2010,4,10,4,14,-8,8,1018,5.81,0,0 +2010,4,10,5,14,-10,7,1019,10.73,0,0 +2010,4,10,6,16,-10,7,1020,15.65,0,0 +2010,4,10,7,14,-11,7,1020,21.46,0,0 +2010,4,10,8,22,-12,8,1021,26.38,0,0 +2010,4,10,9,17,-11,10,1021,3.13,0,0 +2010,4,10,10,16,-13,11,1020,1.79,0,0 +2010,4,10,11,19,-13,13,1019,4.02,0,0 +2010,4,10,12,19,-13,13,1018,5.81,0,0 +2010,4,10,13,24,-13,14,1017,7.6,0,0 +2010,4,10,14,22,-13,16,1016,3.13,0,0 +2010,4,10,15,22,-13,16,1015,7.15,0,0 +2010,4,10,16,32,-13,15,1015,14.3,0,0 +2010,4,10,17,30,-14,15,1015,20.11,0,0 +2010,4,10,18,24,-14,14,1015,25.92,0,0 +2010,4,10,19,29,-8,12,1016,30.84,0,0 +2010,4,10,20,56,-4,11,1016,37.99,0,0 +2010,4,10,21,58,-5,10,1018,46.04,0,0 +2010,4,10,22,53,-6,8,1019,51.85,0,0 +2010,4,10,23,37,-6,8,1018,55.87,0,0 +2010,4,11,0,39,-5,7,1018,59.89,0,0 +2010,4,11,1,40,-4,7,1018,63.91,0,0 +2010,4,11,2,44,-3,6,1018,67.93,0,0 +2010,4,11,3,48,-3,6,1017,71.06,0,0 +2010,4,11,4,54,-3,6,1017,75.98,0,0 +2010,4,11,5,73,-2,6,1018,79.11,0,0 +2010,4,11,6,89,-1,6,1018,82.24,0,1 +2010,4,11,7,80,0,5,1018,85.37,0,2 +2010,4,11,8,84,1,5,1019,89.39,0,3 +2010,4,11,9,91,1,5,1019,91.18,0,4 +2010,4,11,10,68,2,5,1019,92.97,0,5 +2010,4,11,11,67,2,6,1019,96.99,0,6 +2010,4,11,12,75,1,5,1019,100.12,0,0 +2010,4,11,13,68,1,6,1017,104.14,0,0 +2010,4,11,14,63,1,7,1017,0.89,0,0 +2010,4,11,15,57,1,7,1016,1.78,0,0 +2010,4,11,16,50,1,8,1015,1.79,0,0 +2010,4,11,17,63,2,7,1015,1.79,0,0 +2010,4,11,18,81,2,7,1015,3.58,0,0 +2010,4,11,19,78,3,7,1015,1.79,0,0 +2010,4,11,20,79,2,7,1016,3.58,0,0 +2010,4,11,21,87,2,6,1015,1.79,0,0 +2010,4,11,22,86,2,7,1015,1.79,0,0 +2010,4,11,23,86,3,6,1015,3.58,0,0 +2010,4,12,0,87,2,6,1014,0.45,0,0 +2010,4,12,1,84,2,6,1014,0.9,0,0 +2010,4,12,2,86,3,7,1014,1.79,0,0 +2010,4,12,3,108,3,6,1015,3.58,0,0 +2010,4,12,4,116,4,7,1015,5.37,0,1 +2010,4,12,5,105,4,6,1014,7.16,0,0 +2010,4,12,6,95,4,6,1015,8.95,0,0 +2010,4,12,7,44,4,6,1016,12.08,0,0 +2010,4,12,8,33,3,7,1017,17,0,0 +2010,4,12,9,23,-2,7,1017,21.02,0,0 +2010,4,12,10,22,-3,8,1017,29.07,0,0 +2010,4,12,11,42,-8,8,1018,34.88,0,0 +2010,4,12,12,88,-13,8,1017,44.71,0,0 +2010,4,12,13,40,-15,11,1018,57.67,0,0 +2010,4,12,14,25,-12,9,1018,70.63,0,0 +2010,4,12,15,29,-14,9,1018,84.49,0,0 +2010,4,12,16,31,-16,10,1018,98.35,0,0 +2010,4,12,17,23,-15,9,1018,109.53,0,0 +2010,4,12,18,14,-12,8,1020,118.47,0,0 +2010,4,12,19,14,-13,6,1022,128.3,0,0 +2010,4,12,20,12,-13,6,1023,136.35,0,0 +2010,4,12,21,11,-14,5,1024,143.5,0,0 +2010,4,12,22,8,-14,5,1025,150.65,0,0 +2010,4,12,23,7,-14,4,1025,156.46,0,0 +2010,4,13,0,8,-14,4,1026,162.27,0,0 +2010,4,13,1,11,-13,4,1026,170.32,0,0 +2010,4,13,2,14,-13,3,1026,175.24,0,0 +2010,4,13,3,13,-14,3,1026,180.16,0,0 +2010,4,13,4,13,-14,2,1026,183.29,0,0 +2010,4,13,5,16,-15,2,1027,187.31,0,0 +2010,4,13,6,14,-15,2,1027,190.44,0,0 +2010,4,13,7,14,-14,4,1028,194.46,0,0 +2010,4,13,8,19,-17,6,1028,199.38,0,0 +2010,4,13,9,12,-20,7,1028,209.21,0,0 +2010,4,13,10,13,-21,8,1028,218.15,0,0 +2010,4,13,11,15,-22,9,1028,229.33,0,0 +2010,4,13,12,14,-22,10,1027,239.16,0,0 +2010,4,13,13,13,-22,10,1027,251.23,0,0 +2010,4,13,14,12,-20,11,1026,262.41,0,0 +2010,4,13,15,11,-20,11,1026,272.24,0,0 +2010,4,13,16,13,-19,11,1025,280.29,0,0 +2010,4,13,17,13,-19,10,1025,285.21,0,0 +2010,4,13,18,13,-18,9,1025,288.34,0,0 +2010,4,13,19,17,-16,8,1026,1.79,0,0 +2010,4,13,20,31,-11,5,1026,1.79,0,0 +2010,4,13,21,47,-11,4,1028,3.58,0,0 +2010,4,13,22,68,-15,6,1029,5.37,0,0 +2010,4,13,23,69,-16,4,1029,0.89,0,0 +2010,4,14,0,100,-8,1,1029,1.78,0,0 +2010,4,14,1,81,-12,1,1029,1.79,0,0 +2010,4,14,2,55,-7,0,1029,0.89,0,0 +2010,4,14,3,48,-8,1,1029,0.89,0,0 +2010,4,14,4,42,-7,1,1029,0.89,0,0 +2010,4,14,5,39,-11,2,1029,0.89,0,0 +2010,4,14,6,26,-8,1,1029,2.68,0,0 +2010,4,14,7,24,-7,3,1029,4.47,0,0 +2010,4,14,8,25,-10,5,1030,3.13,0,0 +2010,4,14,9,30,-10,6,1030,4.92,0,0 +2010,4,14,10,43,-16,8,1030,6.71,0,0 +2010,4,14,11,42,-16,9,1029,8.5,0,0 +2010,4,14,12,57,-17,11,1027,1.79,0,0 +2010,4,14,13,38,-18,12,1026,1.79,0,0 +2010,4,14,14,41,-17,13,1025,3.58,0,0 +2010,4,14,15,54,-17,12,1023,3.13,0,0 +2010,4,14,16,50,-16,13,1022,3.13,0,0 +2010,4,14,17,81,-10,13,1022,4.92,0,0 +2010,4,14,18,96,-6,12,1022,9.84,0,0 +2010,4,14,19,94,-4,11,1022,15.65,0,0 +2010,4,14,20,84,-3,10,1023,22.8,0,0 +2010,4,14,21,67,-3,10,1023,28.61,0,0 +2010,4,14,22,70,-2,9,1023,34.42,0,0 +2010,4,14,23,76,-1,8,1023,39.34,0,0 +2010,4,15,0,79,-1,7,1023,47.39,0,0 +2010,4,15,1,81,-1,6,1023,53.2,0,0 +2010,4,15,2,84,-1,6,1023,59.01,0,0 +2010,4,15,3,97,-1,5,1022,63.03,0,0 +2010,4,15,4,106,0,4,1022,66.16,0,0 +2010,4,15,5,121,0,4,1022,69.29,0,0 +2010,4,15,6,130,0,3,1022,72.42,0,0 +2010,4,15,7,142,1,4,1022,76.44,0,0 +2010,4,15,8,171,1,5,1022,79.57,0,0 +2010,4,15,9,196,1,6,1022,82.7,0,0 +2010,4,15,10,198,1,6,1022,84.49,0,0 +2010,4,15,11,206,1,6,1022,87.62,0,0 +2010,4,15,12,203,2,6,1021,90.75,0,0 +2010,4,15,13,204,1,7,1020,93.88,0,0 +2010,4,15,14,199,1,7,1020,97.01,0,0 +2010,4,15,15,193,1,8,1019,98.8,0,0 +2010,4,15,16,194,2,9,1018,0.89,0,0 +2010,4,15,17,202,1,9,1018,1.78,0,0 +2010,4,15,18,200,2,8,1018,1.79,0,0 +2010,4,15,19,206,1,7,1018,1.79,0,0 +2010,4,15,20,212,2,7,1019,1.79,0,0 +2010,4,15,21,220,1,6,1019,2.68,0,0 +2010,4,15,22,237,1,5,1019,3.57,0,0 +2010,4,15,23,225,1,5,1018,0.89,0,0 +2010,4,16,0,235,1,4,1018,1.34,0,0 +2010,4,16,1,239,0,3,1018,1.79,0,0 +2010,4,16,2,244,0,2,1017,3.58,0,0 +2010,4,16,3,255,0,3,1017,5.37,0,0 +2010,4,16,4,247,1,3,1017,6.26,0,0 +2010,4,16,5,253,1,3,1017,7.15,0,0 +2010,4,16,6,242,1,3,1017,8.94,0,0 +2010,4,16,7,244,2,4,1018,9.83,0,0 +2010,4,16,8,235,3,6,1018,12.96,0,0 +2010,4,16,9,249,3,7,1019,16.09,0,0 +2010,4,16,10,254,4,10,1019,3.13,0,0 +2010,4,16,11,266,4,11,1018,6.26,0,0 +2010,4,16,12,286,3,13,1018,1.79,0,0 +2010,4,16,13,289,3,14,1017,5.81,0,0 +2010,4,16,14,285,3,15,1017,9.83,0,0 +2010,4,16,15,215,3,15,1017,13.85,0,0 +2010,4,16,16,185,3,15,1016,17.87,0,0 +2010,4,16,17,151,3,15,1017,21.89,0,0 +2010,4,16,18,134,4,13,1017,26.81,0,0 +2010,4,16,19,143,5,11,1018,32.62,0,0 +2010,4,16,20,123,5,10,1020,38.43,0,0 +2010,4,16,21,102,5,9,1021,42.45,0,0 +2010,4,16,22,96,4,9,1022,47.37,0,0 +2010,4,16,23,92,5,8,1023,52.29,0,0 +2010,4,17,0,91,4,8,1023,56.31,0,0 +2010,4,17,1,107,4,7,1023,59.44,0,0 +2010,4,17,2,103,3,6,1023,61.23,0,0 +2010,4,17,3,129,3,6,1023,63.02,0,0 +2010,4,17,4,130,2,5,1023,64.81,0,0 +2010,4,17,5,116,2,5,1023,66.6,0,0 +2010,4,17,6,128,2,5,1024,68.39,0,0 +2010,4,17,7,152,2,5,1024,71.52,0,0 +2010,4,17,8,154,2,5,1024,75.54,0,0 +2010,4,17,9,151,2,6,1025,78.67,0,0 +2010,4,17,10,165,2,7,1024,81.8,0,0 +2010,4,17,11,149,2,9,1024,84.93,0,0 +2010,4,17,12,162,2,11,1023,88.06,0,0 +2010,4,17,13,170,3,11,1022,91.19,0,0 +2010,4,17,14,143,3,12,1021,95.21,0,0 +2010,4,17,15,144,4,12,1020,98.34,0,0 +2010,4,17,16,156,4,11,1019,101.47,0,0 +2010,4,17,17,161,5,11,1018,103.26,0,0 +2010,4,17,18,140,5,11,1018,105.05,0,0 +2010,4,17,19,134,5,11,1018,108.18,0,0 +2010,4,17,20,138,5,11,1018,109.97,0,0 +2010,4,17,21,184,5,11,1018,0.89,0,0 +2010,4,17,22,223,5,10,1018,1.79,0,0 +2010,4,17,23,212,5,10,1018,3.58,0,0 +2010,4,18,0,206,5,10,1018,0.89,0,0 +2010,4,18,1,212,6,10,1016,1.79,0,0 +2010,4,18,2,209,6,9,1016,0.89,0,0 +2010,4,18,3,198,7,9,1016,2.68,0,0 +2010,4,18,4,192,7,9,1015,4.47,0,0 +2010,4,18,5,184,6,8,1015,6.26,0,0 +2010,4,18,6,177,7,8,1014,0.89,0,0 +2010,4,18,7,183,7,9,1015,0.45,0,0 +2010,4,18,8,184,6,10,1015,0.9,0,0 +2010,4,18,9,195,6,11,1014,1.79,0,0 +2010,4,18,10,209,6,13,1014,1.79,0,0 +2010,4,18,11,200,6,14,1013,1.79,0,0 +2010,4,18,12,207,6,16,1012,2.68,0,0 +2010,4,18,13,215,6,18,1011,1.79,0,0 +2010,4,18,14,229,6,19,1011,1.79,0,0 +2010,4,18,15,248,6,19,1010,3.13,0,0 +2010,4,18,16,255,6,20,1009,6.26,0,0 +2010,4,18,17,268,6,20,1009,9.39,0,0 +2010,4,18,18,264,7,19,1010,12.52,0,0 +2010,4,18,19,257,7,17,1011,0.45,0,0 +2010,4,18,20,300,7,16,1011,0.89,0,0 +2010,4,18,21,309,7,14,1012,2.68,0,0 +2010,4,18,22,331,8,13,1012,4.47,0,0 +2010,4,18,23,351,8,14,1011,6.26,0,0 +2010,4,19,0,389,8,14,1011,7.15,0,0 +2010,4,19,1,385,8,14,1011,0.89,0,0 +2010,4,19,2,382,9,12,1011,2.68,0,0 +2010,4,19,3,382,9,14,1011,1.79,0,0 +2010,4,19,4,369,8,13,1011,3.58,0,0 +2010,4,19,5,343,8,13,1012,5.37,0,0 +2010,4,19,6,342,8,14,1012,8.5,0,0 +2010,4,19,7,324,8,14,1014,10.29,0,1 +2010,4,19,8,307,9,14,1014,4.92,0,2 +2010,4,19,9,272,8,14,1015,10.73,0,3 +2010,4,19,10,131,8,14,1015,17.88,0,0 +2010,4,19,11,36,6,15,1016,23.69,0,0 +2010,4,19,12,22,6,16,1015,29.5,0,0 +2010,4,19,13,17,6,17,1015,33.52,0,0 +2010,4,19,14,17,6,17,1014,36.65,0,0 +2010,4,19,15,27,8,18,1014,4.02,0,0 +2010,4,19,16,170,9,17,1014,8.94,0,0 +2010,4,19,17,175,9,16,1014,14.75,0,0 +2010,4,19,18,121,7,15,1015,19.67,0,0 +2010,4,19,19,198,9,13,1016,23.69,0,0 +2010,4,19,20,184,9,12,1017,26.82,0,0 +2010,4,19,21,167,9,12,1018,29.95,0,0 +2010,4,19,22,155,8,12,1018,33.97,0,0 +2010,4,19,23,152,8,11,1018,35.76,0,0 +2010,4,20,0,110,8,11,1018,37.55,0,0 +2010,4,20,1,94,8,10,1018,38.44,0,0 +2010,4,20,2,100,8,9,1019,0.89,0,0 +2010,4,20,3,100,7,9,1018,1.79,0,0 +2010,4,20,4,94,7,9,1018,3.58,0,0 +2010,4,20,5,87,7,8,1018,4.47,0,0 +2010,4,20,6,82,7,8,1019,5.36,0,0 +2010,4,20,7,83,8,10,1019,8.49,0,0 +2010,4,20,8,94,8,12,1020,0.89,0,0 +2010,4,20,9,92,8,13,1020,1.79,0,0 +2010,4,20,10,84,8,15,1020,0.89,0,0 +2010,4,20,11,69,7,16,1020,2.68,0,0 +2010,4,20,12,83,2,17,1019,4.47,0,0 +2010,4,20,13,90,2,19,1019,6.26,0,0 +2010,4,20,14,95,2,19,1018,3.13,0,0 +2010,4,20,15,73,1,19,1018,1.79,0,0 +2010,4,20,16,66,1,19,1017,4.92,0,0 +2010,4,20,17,77,1,18,1017,4.02,0,0 +2010,4,20,18,80,1,17,1019,7.15,0,0 +2010,4,20,19,76,0,16,1019,10.28,0,0 +2010,4,20,20,80,2,15,1020,0.89,0,0 +2010,4,20,21,71,-2,15,1021,4.92,0,0 +2010,4,20,22,55,-2,15,1022,9.84,0,0 +2010,4,20,23,53,-3,14,1022,13.86,0,0 +2010,4,21,0,48,-2,13,1022,16.99,0,0 +2010,4,21,1,52,0,12,1022,17.88,0,0 +2010,4,21,2,46,1,12,1022,19.67,0,0 +2010,4,21,3,38,1,12,1022,0.89,0,0 +2010,4,21,4,30,-1,12,1022,0.89,0,0 +2010,4,21,5,26,-2,12,1022,4.91,0,0 +2010,4,21,6,27,0,12,1023,1.79,0,0 +2010,4,21,7,47,2,12,1023,4.02,0,1 +2010,4,21,8,50,4,12,1023,9.83,0,2 +2010,4,21,9,51,4,12,1023,16.98,0,0 +2010,4,21,10,48,4,12,1024,22.79,0,1 +2010,4,21,11,51,6,11,1024,28.6,0,2 +2010,4,21,12,49,5,13,1023,35.75,0,0 +2010,4,21,13,51,3,13,1022,42.9,0,0 +2010,4,21,14,39,2,13,1022,50.95,0,0 +2010,4,21,15,23,2,13,1021,4.92,0,0 +2010,4,21,16,16,2,13,1021,5.81,0,0 +2010,4,21,17,26,2,13,1020,11.62,0,0 +2010,4,21,18,32,2,13,1021,15.64,0,0 +2010,4,21,19,35,2,12,1021,1.79,0,1 +2010,4,21,20,34,3,12,1022,4.92,0,2 +2010,4,21,21,46,3,11,1023,8.05,0,0 +2010,4,21,22,44,4,10,1023,11.18,0,1 +2010,4,21,23,41,6,9,1022,14.31,0,2 +2010,4,22,0,34,6,9,1022,3.13,0,3 +2010,4,22,1,31,6,9,1022,1.79,0,4 +2010,4,22,2,49,6,8,1022,3.58,0,5 +2010,4,22,3,37,6,8,1021,3.13,0,6 +2010,4,22,4,27,6,8,1021,4.92,0,7 +2010,4,22,5,37,6,8,1021,8.05,0,8 +2010,4,22,6,45,6,8,1021,11.18,0,9 +2010,4,22,7,44,6,8,1021,14.31,0,10 +2010,4,22,8,53,6,9,1021,18.33,0,11 +2010,4,22,9,48,6,9,1021,21.46,0,12 +2010,4,22,10,47,7,9,1021,23.25,0,13 +2010,4,22,11,46,6,10,1021,25.04,0,14 +2010,4,22,12,42,7,10,1021,29.06,0,15 +2010,4,22,13,40,6,10,1021,33.08,0,0 +2010,4,22,14,38,6,11,1020,38,0,1 +2010,4,22,15,30,5,10,1020,42.92,0,2 +2010,4,22,16,34,5,11,1020,0.89,0,3 +2010,4,22,17,35,4,12,1020,3.13,0,0 +2010,4,22,18,35,4,12,1020,6.26,0,0 +2010,4,22,19,26,4,11,1020,3.13,0,0 +2010,4,22,20,33,4,10,1021,4.92,0,0 +2010,4,22,21,47,4,11,1021,3.13,0,0 +2010,4,22,22,41,3,10,1022,7.15,0,0 +2010,4,22,23,47,3,6,1022,10.28,0,0 +2010,4,23,0,37,4,6,1022,0.45,0,0 +2010,4,23,1,41,3,5,1022,0.89,0,0 +2010,4,23,2,60,4,5,1021,0.89,0,0 +2010,4,23,3,62,2,3,1021,3.13,0,0 +2010,4,23,4,36,1,3,1021,6.26,0,0 +2010,4,23,5,31,2,4,1021,8.05,0,0 +2010,4,23,6,24,2,4,1021,9.84,0,0 +2010,4,23,7,65,4,7,1022,11.63,0,0 +2010,4,23,8,51,2,10,1023,14.76,0,0 +2010,4,23,9,41,-1,13,1023,17.89,0,0 +2010,4,23,10,39,-2,15,1022,1.79,0,0 +2010,4,23,11,31,-4,17,1022,3.13,0,0 +2010,4,23,12,19,-10,19,1021,7.15,0,0 +2010,4,23,13,19,-10,20,1020,3.13,0,0 +2010,4,23,14,18,-11,21,1019,5.81,0,0 +2010,4,23,15,10,-12,21,1018,12.96,0,0 +2010,4,23,16,16,-13,21,1018,21.01,0,0 +2010,4,23,17,23,-9,20,1018,25.03,0,0 +2010,4,23,18,21,-10,19,1018,28.16,0,0 +2010,4,23,19,20,-10,18,1018,31.29,0,0 +2010,4,23,20,23,-8,18,1019,4.92,0,0 +2010,4,23,21,32,-8,17,1019,8.94,0,0 +2010,4,23,22,39,-7,16,1020,13.86,0,0 +2010,4,23,23,38,-6,15,1019,17.88,0,0 +2010,4,24,0,45,-6,15,1019,22.8,0,0 +2010,4,24,1,53,-5,12,1019,24.59,0,0 +2010,4,24,2,56,-4,11,1018,27.72,0,0 +2010,4,24,3,54,-3,11,1018,30.85,0,0 +2010,4,24,4,65,-2,9,1018,32.64,0,0 +2010,4,24,5,73,-2,7,1019,35.77,0,0 +2010,4,24,6,70,0,8,1019,37.56,0,0 +2010,4,24,7,81,1,10,1019,0.45,0,0 +2010,4,24,8,90,0,13,1019,1.79,0,0 +2010,4,24,9,86,0,16,1019,3.58,0,0 +2010,4,24,10,107,-1,18,1019,6.71,0,0 +2010,4,24,11,122,2,20,1018,11.63,0,0 +2010,4,24,12,114,3,23,1017,18.78,0,0 +2010,4,24,13,111,3,24,1015,26.83,0,0 +2010,4,24,14,112,3,24,1015,32.64,0,0 +2010,4,24,15,111,3,23,1014,40.69,0,0 +2010,4,24,16,117,4,23,1013,48.74,0,0 +2010,4,24,17,103,4,23,1013,54.55,0,0 +2010,4,24,18,85,5,21,1013,59.47,0,0 +2010,4,24,19,79,5,20,1013,62.6,0,0 +2010,4,24,20,78,5,19,1013,65.73,0,0 +2010,4,24,21,83,5,19,1014,70.65,0,0 +2010,4,24,22,89,6,17,1014,75.57,0,0 +2010,4,24,23,93,6,17,1014,81.38,0,0 +2010,4,25,0,98,6,16,1015,86.3,0,0 +2010,4,25,1,93,6,14,1015,90.32,0,0 +2010,4,25,2,96,6,15,1015,94.34,0,0 +2010,4,25,3,92,6,14,1015,98.36,0,0 +2010,4,25,4,106,7,13,1015,101.49,0,0 +2010,4,25,5,98,6,12,1015,104.62,0,0 +2010,4,25,6,110,7,12,1015,108.64,0,0 +2010,4,25,7,129,7,13,1015,111.77,0,0 +2010,4,25,8,141,7,13,1015,115.79,0,0 +2010,4,25,9,140,7,14,1015,119.81,0,0 +2010,4,25,10,142,7,15,1015,124.73,0,0 +2010,4,25,11,134,7,18,1015,131.88,0,0 +2010,4,25,12,133,6,19,1014,139.03,0,0 +2010,4,25,13,128,5,20,1012,146.18,0,0 +2010,4,25,14,110,5,21,1012,153.33,0,0 +2010,4,25,15,120,6,20,1012,159.14,0,0 +2010,4,25,16,135,8,18,1012,7.15,0,0 +2010,4,25,17,22,7,11,1013,16.98,0,1 +2010,4,25,18,15,8,10,1014,25.92,0,2 +2010,4,25,19,19,8,10,1015,29.94,0,3 +2010,4,25,20,16,8,9,1015,0.89,0,4 +2010,4,25,21,9,7,9,1017,4.92,0,5 +2010,4,25,22,7,6,8,1017,9.84,0,0 +2010,4,25,23,7,4,8,1017,14.76,0,0 +2010,4,26,0,10,4,8,1017,4.02,0,0 +2010,4,26,1,8,3,7,1017,3.13,0,0 +2010,4,26,2,5,0,7,1016,8.94,0,0 +2010,4,26,3,9,-3,8,1016,14.75,0,0 +2010,4,26,4,20,-5,7,1016,19.67,0,0 +2010,4,26,5,8,-7,6,1016,25.48,0,0 +2010,4,26,6,7,-7,6,1016,31.29,0,0 +2010,4,26,7,6,-8,8,1015,37.1,0,0 +2010,4,26,8,16,-7,10,1015,42.02,0,0 +2010,4,26,9,10,-8,10,1015,46.94,0,0 +2010,4,26,10,11,-10,11,1014,50.07,0,0 +2010,4,26,11,11,-7,12,1013,1.79,0,0 +2010,4,26,12,18,-7,12,1011,3.13,0,0 +2010,4,26,13,26,-7,11,1011,6.26,0,0 +2010,4,26,14,25,-8,12,1010,11.18,0,0 +2010,4,26,15,20,-7,13,1010,16.1,0,0 +2010,4,26,16,24,-4,12,1011,24.15,0,0 +2010,4,26,17,15,-1,7,1012,11.18,0,1 +2010,4,26,18,10,-2,8,1011,19.23,0,0 +2010,4,26,19,10,-4,8,1012,27.28,0,0 +2010,4,26,20,14,-5,8,1012,34.43,0,0 +2010,4,26,21,11,-9,8,1013,42.48,0,0 +2010,4,26,22,11,-11,7,1013,51.42,0,0 +2010,4,26,23,16,-11,7,1013,61.25,0,0 +2010,4,27,0,13,-12,6,1013,71.08,0,0 +2010,4,27,1,16,-15,6,1012,82.26,0,0 +2010,4,27,2,15,-14,5,1011,90.31,0,0 +2010,4,27,3,17,-14,5,1011,95.23,0,0 +2010,4,27,4,18,-13,4,1011,98.36,0,0 +2010,4,27,5,22,-12,3,1010,100.15,0,0 +2010,4,27,6,19,-13,4,1010,104.17,0,0 +2010,4,27,7,22,-12,6,1010,109.98,0,0 +2010,4,27,8,27,-12,7,1010,114.9,0,0 +2010,4,27,9,31,-11,8,1009,119.82,0,0 +2010,4,27,10,42,-9,10,1009,123.84,0,0 +2010,4,27,11,46,-9,11,1008,129.65,0,0 +2010,4,27,12,50,-8,12,1008,136.8,0,0 +2010,4,27,13,45,-8,12,1007,140.82,0,0 +2010,4,27,14,47,-6,13,1006,146.63,0,0 +2010,4,27,15,30,-7,13,1007,155.57,0,0 +2010,4,27,16,31,-9,13,1006,164.51,0,0 +2010,4,27,17,16,-9,12,1007,172.56,0,0 +2010,4,27,18,14,-11,12,1007,180.61,0,0 +2010,4,27,19,17,-12,11,1008,187.76,0,0 +2010,4,27,20,15,-10,10,1010,193.57,0,0 +2010,4,27,21,14,-10,9,1011,199.38,0,0 +2010,4,27,22,14,-10,9,1011,204.3,0,0 +2010,4,27,23,11,-11,8,1011,211.45,0,0 +2010,4,28,0,24,-11,8,1012,217.26,0,0 +2010,4,28,1,27,-8,7,1012,225.31,0,0 +2010,4,28,2,15,-6,6,1011,231.12,0,0 +2010,4,28,3,13,-8,6,1011,239.17,0,0 +2010,4,28,4,11,-10,6,1011,246.32,0,0 +2010,4,28,5,11,-10,6,1011,254.37,0,0 +2010,4,28,6,13,-10,6,1012,259.29,0,0 +2010,4,28,7,13,-11,7,1013,266.44,0,0 +2010,4,28,8,14,-11,9,1014,273.59,0,0 +2010,4,28,9,15,-11,10,1014,281.64,0,0 +2010,4,28,10,14,-11,11,1014,292.82,0,0 +2010,4,28,11,19,-11,13,1014,304,0,0 +2010,4,28,12,14,-11,13,1014,315.18,0,0 +2010,4,28,13,13,-10,14,1013,328.14,0,0 +2010,4,28,14,15,-11,15,1012,342,0,0 +2010,4,28,15,11,-11,15,1012,355.86,0,0 +2010,4,28,16,20,-10,15,1012,368.82,0,0 +2010,4,28,17,16,-10,14,1012,380,0,0 +2010,4,28,18,14,-10,14,1012,389.83,0,0 +2010,4,28,19,17,-10,13,1012,398.77,0,0 +2010,4,28,20,16,-8,12,1013,410.84,0,0 +2010,4,28,21,15,-7,11,1014,417.99,0,0 +2010,4,28,22,38,-7,11,1014,426.04,0,0 +2010,4,28,23,15,-7,10,1013,431.85,0,0 +2010,4,29,0,18,-7,10,1013,434.98,0,0 +2010,4,29,1,16,-4,7,1012,1.79,0,0 +2010,4,29,2,19,-5,7,1012,1.79,0,0 +2010,4,29,3,18,-7,9,1012,0.89,0,0 +2010,4,29,4,20,-6,9,1012,4.92,0,0 +2010,4,29,5,19,-7,10,1012,9.84,0,0 +2010,4,29,6,17,-6,9,1012,12.97,0,0 +2010,4,29,7,21,-6,11,1013,20.12,0,0 +2010,4,29,8,20,-6,13,1013,28.17,0,0 +2010,4,29,9,19,-6,14,1013,38,0,0 +2010,4,29,10,17,-8,15,1013,47.83,0,0 +2010,4,29,11,16,-8,17,1013,57.66,0,0 +2010,4,29,12,15,-9,17,1013,67.49,0,0 +2010,4,29,13,17,-9,19,1013,79.56,0,0 +2010,4,29,14,22,-9,19,1012,93.42,0,0 +2010,4,29,15,19,-9,19,1012,107.28,0,0 +2010,4,29,16,17,-8,19,1012,117.11,0,0 +2010,4,29,17,18,-8,19,1013,128.29,0,0 +2010,4,29,18,27,-9,18,1013,139.47,0,0 +2010,4,29,19,22,-7,17,1014,147.52,0,0 +2010,4,29,20,18,-6,16,1015,153.33,0,0 +2010,4,29,21,19,-5,15,1016,156.46,0,0 +2010,4,29,22,15,-4,14,1017,161.38,0,0 +2010,4,29,23,19,-3,14,1017,166.3,0,0 +2010,4,30,0,27,-3,14,1017,171.22,0,0 +2010,4,30,1,21,-4,13,1017,174.35,0,0 +2010,4,30,2,24,-4,12,1017,178.37,0,0 +2010,4,30,3,32,-5,12,1017,182.39,0,0 +2010,4,30,4,40,-5,12,1017,187.31,0,0 +2010,4,30,5,36,-5,11,1016,191.33,0,0 +2010,4,30,6,20,-5,12,1017,196.25,0,0 +2010,4,30,7,32,-6,14,1017,201.17,0,0 +2010,4,30,8,28,-6,16,1017,206.09,0,0 +2010,4,30,9,21,-8,18,1017,211.9,0,0 +2010,4,30,10,20,-8,19,1017,216.82,0,0 +2010,4,30,11,19,-8,21,1016,221.74,0,0 +2010,4,30,12,19,-10,22,1015,227.55,0,0 +2010,4,30,13,16,-8,23,1014,234.7,0,0 +2010,4,30,14,20,-7,24,1012,241.85,0,0 +2010,4,30,15,15,-6,25,1011,249.9,0,0 +2010,4,30,16,20,-7,25,1010,257.95,0,0 +2010,4,30,17,20,-7,25,1010,266,0,0 +2010,4,30,18,23,-7,25,1009,4.92,0,0 +2010,4,30,19,37,-5,22,1009,12.07,0,0 +2010,4,30,20,34,-4,20,1009,20.12,0,0 +2010,4,30,21,39,-3,19,1010,28.17,0,0 +2010,4,30,22,45,-3,19,1010,36.22,0,0 +2010,4,30,23,54,-3,18,1010,39.35,0,0 +2010,5,1,0,62,-2,16,1010,0.89,0,0 +2010,5,1,1,70,0,13,1009,0.89,0,0 +2010,5,1,2,66,1,9,1009,0.89,0,0 +2010,5,1,3,70,1,8,1009,1.78,0,0 +2010,5,1,4,82,1,8,1009,0.89,0,0 +2010,5,1,5,97,2,8,1008,1.78,0,0 +2010,5,1,6,86,2,8,1009,3.57,0,0 +2010,5,1,7,94,2,13,1009,5.36,0,0 +2010,5,1,8,89,0,16,1009,0.89,0,0 +2010,5,1,9,90,0,19,1009,1.79,0,0 +2010,5,1,10,86,0,22,1009,3.58,0,0 +2010,5,1,11,70,-1,25,1008,0.89,0,0 +2010,5,1,12,79,-1,28,1007,2.68,0,0 +2010,5,1,13,80,-1,31,1005,4.02,0,0 +2010,5,1,14,69,0,31,1005,9.83,0,0 +2010,5,1,15,52,-1,32,1004,15.64,0,0 +2010,5,1,16,48,-2,32,1003,22.79,0,0 +2010,5,1,17,35,-3,31,1002,29.94,0,0 +2010,5,1,18,31,-2,29,1002,37.09,0,0 +2010,5,1,19,37,-2,27,1002,42.9,0,0 +2010,5,1,20,63,-2,26,1002,48.71,0,0 +2010,5,1,21,61,1,24,1003,53.63,0,0 +2010,5,1,22,74,2,22,1002,57.65,0,0 +2010,5,1,23,74,3,22,1002,62.57,0,0 +2010,5,2,0,75,3,21,1001,66.59,0,0 +2010,5,2,1,68,3,20,1001,68.38,0,0 +2010,5,2,2,77,4,18,1000,71.51,0,0 +2010,5,2,3,93,6,18,1001,73.3,0,0 +2010,5,2,4,102,7,17,1000,1.79,0,0 +2010,5,2,5,111,7,16,1000,0.89,0,0 +2010,5,2,6,127,9,16,1001,0.89,0,0 +2010,5,2,7,132,10,17,1002,1.79,0,0 +2010,5,2,8,146,10,19,1003,1.79,0,0 +2010,5,2,9,151,10,22,1003,3.58,0,0 +2010,5,2,10,142,8,25,1003,3.13,0,0 +2010,5,2,11,115,7,26,1003,1.79,0,0 +2010,5,2,12,59,-1,30,1002,0.89,0,0 +2010,5,2,13,63,-5,32,1002,4.92,0,0 +2010,5,2,14,65,-5,32,1002,4.02,0,0 +2010,5,2,15,57,-4,32,1001,7.15,0,0 +2010,5,2,16,55,-6,32,1001,16.09,0,0 +2010,5,2,17,45,-7,31,1002,25.03,0,0 +2010,5,2,18,39,-8,30,1002,33.08,0,0 +2010,5,2,19,32,-4,25,1003,38,0,0 +2010,5,2,20,32,-9,26,1004,43.81,0,0 +2010,5,2,21,31,-8,24,1005,3.13,0,0 +2010,5,2,22,32,-9,22,1006,0.89,0,0 +2010,5,2,23,24,-9,22,1006,1.79,0,0 +2010,5,3,0,36,-2,18,1006,0.89,0,0 +2010,5,3,1,56,0,14,1007,1.78,0,0 +2010,5,3,2,43,-1,15,1007,2.23,0,0 +2010,5,3,3,69,0,14,1007,3.12,0,0 +2010,5,3,4,45,3,13,1008,1.79,0,0 +2010,5,3,5,44,-2,13,1008,3.58,0,0 +2010,5,3,6,64,-2,15,1009,0.45,0,0 +2010,5,3,7,80,5,16,1010,1.79,0,0 +2010,5,3,8,53,-2,21,1010,3.58,0,0 +2010,5,3,9,102,3,23,1010,0.89,0,0 +2010,5,3,10,133,3,22,1010,4.02,0,0 +2010,5,3,11,151,4,23,1010,7.15,0,0 +2010,5,3,12,149,6,23,1009,8.94,0,0 +2010,5,3,13,141,6,23,1008,12.07,0,0 +2010,5,3,14,146,6,24,1007,15.2,0,0 +2010,5,3,15,150,6,23,1006,18.33,0,0 +2010,5,3,16,150,7,23,1005,3.13,0,0 +2010,5,3,17,151,7,23,1005,0.89,0,0 +2010,5,3,18,142,6,22,1006,1.79,0,0 +2010,5,3,19,138,5,23,1006,4.02,0,0 +2010,5,3,20,142,6,20,1007,8.04,0,0 +2010,5,3,21,142,4,21,1007,0.89,0,0 +2010,5,3,22,150,5,19,1005,4.02,0,0 +2010,5,3,23,160,5,21,1006,1.79,0,0 +2010,5,4,0,118,4,20,1006,4.02,0,1 +2010,5,4,1,114,7,18,1005,0.89,0,0 +2010,5,4,2,111,9,16,1005,0.89,0,0 +2010,5,4,3,113,10,15,1005,4.02,0,0 +2010,5,4,4,120,10,15,1005,0.89,0,0 +2010,5,4,5,129,9,14,1005,0.89,0,0 +2010,5,4,6,133,10,13,1005,0.89,0,0 +2010,5,4,7,118,10,16,1005,1.79,0,0 +2010,5,4,8,119,8,19,1005,0.89,0,0 +2010,5,4,9,118,8,20,1005,1.79,0,0 +2010,5,4,10,132,9,21,1005,3.58,0,0 +2010,5,4,11,128,8,23,1004,1.79,0,0 +2010,5,4,12,135,8,24,1002,3.58,0,0 +2010,5,4,13,123,11,25,1002,4.02,0,0 +2010,5,4,14,118,13,25,1001,7.15,0,0 +2010,5,4,15,104,15,25,1000,11.17,0,0 +2010,5,4,16,121,15,24,999,16.98,0,0 +2010,5,4,17,136,15,23,999,21.9,0,0 +2010,5,4,18,172,16,22,999,26.82,0,1 +2010,5,4,19,208,17,19,1000,29.95,0,3 +2010,5,4,20,228,18,19,999,31.74,0,4 +2010,5,4,21,201,17,19,1000,35.76,0,0 +2010,5,4,22,142,16,19,1000,39.78,0,1 +2010,5,4,23,154,16,18,1000,44.7,0,2 +2010,5,5,0,165,16,17,999,49.62,0,3 +2010,5,5,1,182,15,17,999,53.64,0,5 +2010,5,5,2,158,15,16,997,57.66,0,6 +2010,5,5,3,181,15,16,997,59.45,0,0 +2010,5,5,4,202,15,16,996,63.47,0,0 +2010,5,5,5,202,15,16,996,65.26,0,0 +2010,5,5,6,198,14,16,997,67.05,0,0 +2010,5,5,7,215,15,16,998,68.84,0,0 +2010,5,5,8,216,15,16,997,71.97,0,0 +2010,5,5,9,261,15,17,998,73.76,0,0 +2010,5,5,10,283,16,18,997,76.89,0,0 +2010,5,5,11,282,17,20,997,78.68,0,0 +2010,5,5,12,260,16,22,997,3.13,0,0 +2010,5,5,13,70,8,24,996,12.07,0,0 +2010,5,5,14,32,7,26,996,21.9,0,0 +2010,5,5,15,13,7,26,996,33.08,0,0 +2010,5,5,16,9,6,25,996,46.94,0,0 +2010,5,5,17,7,6,24,997,59.01,0,0 +2010,5,5,18,11,3,21,999,71.08,0,0 +2010,5,5,19,14,0,18,1000,84.04,0,0 +2010,5,5,20,14,0,17,1002,97,0,0 +2010,5,5,21,14,-1,17,1003,113.09,0,0 +2010,5,5,22,12,-1,16,1004,124.27,0,0 +2010,5,5,23,10,-1,15,1004,134.1,0,0 +2010,5,6,0,16,-1,15,1005,143.04,0,0 +2010,5,6,1,9,-1,15,1006,151.98,0,0 +2010,5,6,2,10,-1,13,1007,153.77,0,0 +2010,5,6,3,10,0,12,1007,3.13,0,0 +2010,5,6,4,11,0,12,1008,4.02,0,0 +2010,5,6,5,13,0,13,1008,8.04,0,0 +2010,5,6,6,12,1,13,1009,11.17,0,0 +2010,5,6,7,17,1,15,1009,18.32,0,0 +2010,5,6,8,15,0,17,1009,25.47,0,0 +2010,5,6,9,19,2,19,1009,32.62,0,0 +2010,5,6,10,20,2,21,1008,39.77,0,0 +2010,5,6,11,16,2,23,1007,45.58,0,0 +2010,5,6,12,20,1,25,1006,52.73,0,0 +2010,5,6,13,17,1,26,1005,59.88,0,0 +2010,5,6,14,14,-1,28,1005,67.03,0,0 +2010,5,6,15,18,-1,28,1004,75.08,0,0 +2010,5,6,16,16,0,28,1004,80.89,0,0 +2010,5,6,17,16,0,27,1003,88.04,0,0 +2010,5,6,18,18,-2,27,1003,93.85,0,0 +2010,5,6,19,25,-3,26,1004,97.87,0,0 +2010,5,6,20,32,2,24,1004,4.92,0,0 +2010,5,6,21,35,2,23,1005,9.84,0,0 +2010,5,6,22,35,2,22,1005,13.86,0,0 +2010,5,6,23,41,0,23,1004,22.8,0,0 +2010,5,7,0,48,2,21,1003,26.82,0,0 +2010,5,7,1,50,2,20,1004,30.84,0,0 +2010,5,7,2,52,4,18,1003,33.97,0,0 +2010,5,7,3,57,4,19,1003,37.99,0,0 +2010,5,7,4,64,6,17,1002,41.12,0,0 +2010,5,7,5,66,6,17,1002,0.89,0,0 +2010,5,7,6,73,8,15,1002,1.79,0,0 +2010,5,7,7,72,8,17,1003,0.89,0,0 +2010,5,7,8,81,8,19,1004,1.79,0,0 +2010,5,7,9,85,8,21,1004,3.58,0,0 +2010,5,7,10,87,10,23,1003,1.79,0,0 +2010,5,7,11,165,0,25,1003,8.94,0,0 +2010,5,7,12,219,-2,26,1003,18.77,0,0 +2010,5,7,13,228,0,27,1002,26.82,0,0 +2010,5,7,14,204,-3,28,1002,38,0,0 +2010,5,7,15,173,-1,27,1002,47.83,0,0 +2010,5,7,16,126,-1,28,1002,57.66,0,0 +2010,5,7,17,130,-2,28,1002,66.6,0,0 +2010,5,7,18,115,-2,27,1003,73.75,0,0 +2010,5,7,19,58,1,23,1004,76.88,0,0 +2010,5,7,20,45,0,20,1005,78.67,0,0 +2010,5,7,21,30,2,19,1006,80.46,0,0 +2010,5,7,22,38,0,19,1006,85.38,0,0 +2010,5,7,23,48,0,16,1007,89.4,0,0 +2010,5,8,0,50,-1,18,1007,90.29,0,0 +2010,5,8,1,71,5,14,1007,1.79,0,0 +2010,5,8,2,49,6,14,1007,0.89,0,0 +2010,5,8,3,67,6,13,1007,1.79,0,0 +2010,5,8,4,59,7,15,1008,1.79,0,0 +2010,5,8,5,78,7,13,1008,0.89,0,0 +2010,5,8,6,140,8,12,1009,1.78,0,0 +2010,5,8,7,159,9,16,1009,2.67,0,0 +2010,5,8,8,191,10,18,1009,1.79,0,0 +2010,5,8,9,201,9,20,1009,1.79,0,0 +2010,5,8,10,203,9,22,1009,3.58,0,0 +2010,5,8,11,202,7,24,1009,1.79,0,0 +2010,5,8,12,163,4,24,1008,4.92,0,0 +2010,5,8,13,135,6,25,1007,9.84,0,0 +2010,5,8,14,119,7,25,1006,13.86,0,0 +2010,5,8,15,107,8,25,1005,17.88,0,0 +2010,5,8,16,110,7,24,1004,23.69,0,0 +2010,5,8,17,114,7,23,1004,28.61,0,0 +2010,5,8,18,132,7,23,1004,33.53,0,0 +2010,5,8,19,150,6,22,1004,38.45,0,0 +2010,5,8,20,154,5,21,1005,42.47,0,0 +2010,5,8,21,148,5,20,1006,45.6,0,0 +2010,5,8,22,144,6,19,1005,48.73,0,0 +2010,5,8,23,133,6,19,1007,0.89,0,0 +2010,5,9,0,139,11,15,1007,1.79,0,1 +2010,5,9,1,129,12,15,1004,1.79,0,2 +2010,5,9,2,122,12,14,1004,4.02,0,0 +2010,5,9,3,120,12,15,1005,8.94,0,0 +2010,5,9,4,129,10,16,1005,14.75,0,0 +2010,5,9,5,294,6,16,1006,19.67,0,0 +2010,5,9,6,100,3,15,1007,27.72,0,0 +2010,5,9,7,55,0,15,1008,35.77,0,0 +2010,5,9,8,32,-1,15,1008,47.84,0,0 +2010,5,9,9,19,-1,16,1008,59.02,0,0 +2010,5,9,10,18,-1,17,1008,67.96,0,0 +2010,5,9,11,14,0,19,1008,73.77,0,0 +2010,5,9,12,20,-1,20,1007,81.82,0,0 +2010,5,9,13,27,-3,21,1007,93,0,0 +2010,5,9,14,31,-2,22,1007,100.15,0,0 +2010,5,9,15,39,2,20,1007,5.81,0,0 +2010,5,9,16,28,-3,21,1007,5.81,0,0 +2010,5,9,17,56,-9,21,1007,16.99,0,0 +2010,5,9,18,53,-9,20,1007,24.14,0,0 +2010,5,9,19,59,-9,19,1008,32.19,0,0 +2010,5,9,20,82,-7,18,1009,37.11,0,0 +2010,5,9,21,108,-4,16,1009,38.9,0,0 +2010,5,9,22,117,-2,15,1009,0.89,0,0 +2010,5,9,23,126,0,14,1008,1.79,0,0 +2010,5,10,0,132,-2,14,1008,4.92,0,0 +2010,5,10,1,125,-1,13,1007,6.71,0,0 +2010,5,10,2,96,0,12,1006,0.89,0,0 +2010,5,10,3,40,3,11,1006,1.79,0,0 +2010,5,10,4,46,5,10,1005,3.58,0,0 +2010,5,10,5,50,4,8,1005,0.89,0,0 +2010,5,10,6,58,6,8,1005,1.78,0,0 +2010,5,10,7,55,6,12,1004,1.79,0,0 +2010,5,10,8,50,6,15,1004,3.58,0,0 +2010,5,10,9,55,3,19,1004,1.79,0,0 +2010,5,10,10,45,-6,22,1003,7.15,0,0 +2010,5,10,11,43,-8,23,1003,20.11,0,0 +2010,5,10,12,63,-6,23,1003,35.31,0,0 +2010,5,10,13,62,-4,23,1004,48.27,0,0 +2010,5,10,14,58,-5,22,1004,61.23,0,0 +2010,5,10,15,28,-3,23,1004,73.3,0,0 +2010,5,10,16,32,-1,21,1005,82.24,0,0 +2010,5,10,17,21,-2,21,1006,92.07,0,0 +2010,5,10,18,15,-1,20,1007,101.9,0,0 +2010,5,10,19,15,-1,18,1008,113.97,0,0 +2010,5,10,20,8,-2,17,1009,122.02,0,0 +2010,5,10,21,20,-1,17,1010,129.17,0,0 +2010,5,10,22,11,-1,16,1011,138.11,0,0 +2010,5,10,23,11,0,16,1012,146.16,0,0 +2010,5,11,0,10,-1,15,1013,154.21,0,0 +2010,5,11,1,8,0,14,1013,159.13,0,0 +2010,5,11,2,7,-1,14,1013,166.28,0,0 +2010,5,11,3,5,0,14,1014,172.09,0,0 +2010,5,11,4,5,0,13,1014,177.01,0,0 +2010,5,11,5,5,0,10,1014,180.14,0,0 +2010,5,11,6,4,3,12,1015,183.27,0,0 +2010,5,11,7,6,2,15,1015,186.4,0,0 +2010,5,11,8,10,2,16,1016,3.13,0,0 +2010,5,11,9,13,1,18,1017,3.13,0,0 +2010,5,11,10,12,3,20,1016,1.79,0,0 +2010,5,11,11,12,2,21,1016,4.92,0,0 +2010,5,11,12,16,2,22,1015,4.92,0,0 +2010,5,11,13,16,2,22,1015,9.84,0,0 +2010,5,11,14,18,2,23,1014,4.02,0,0 +2010,5,11,15,16,3,23,1014,4.02,0,0 +2010,5,11,16,11,3,23,1013,1.79,0,0 +2010,5,11,17,20,3,23,1013,3.13,0,0 +2010,5,11,18,20,3,22,1013,1.79,0,0 +2010,5,11,19,19,3,21,1014,1.79,0,0 +2010,5,11,20,24,4,19,1015,2.68,0,0 +2010,5,11,21,39,4,20,1016,0.89,0,0 +2010,5,11,22,35,4,16,1017,5.81,0,0 +2010,5,11,23,54,4,15,1018,10.73,0,0 +2010,5,12,0,49,5,14,1019,15.65,0,0 +2010,5,12,1,43,6,14,1019,17.44,0,0 +2010,5,12,2,40,6,13,1019,19.23,0,0 +2010,5,12,3,37,6,12,1019,22.36,0,0 +2010,5,12,4,38,6,11,1019,0.89,0,0 +2010,5,12,5,33,6,10,1019,0.89,0,0 +2010,5,12,6,31,7,11,1020,1.78,0,0 +2010,5,12,7,55,7,15,1020,0.45,0,0 +2010,5,12,8,51,8,16,1020,1.79,0,0 +2010,5,12,9,55,8,17,1020,0.89,0,0 +2010,5,12,10,49,7,18,1020,1.79,0,0 +2010,5,12,11,42,6,20,1019,1.79,0,0 +2010,5,12,12,44,6,21,1018,4.92,0,0 +2010,5,12,13,44,5,22,1018,1.79,0,0 +2010,5,12,14,40,5,22,1016,4.92,0,0 +2010,5,12,15,43,6,24,1016,8.05,0,0 +2010,5,12,16,45,5,24,1015,12.97,0,0 +2010,5,12,17,41,5,23,1015,17.89,0,0 +2010,5,12,18,43,5,22,1015,22.81,0,0 +2010,5,12,19,38,4,21,1015,26.83,0,0 +2010,5,12,20,44,6,19,1016,30.85,0,0 +2010,5,12,21,43,6,18,1017,34.87,0,0 +2010,5,12,22,46,6,16,1017,38,0,0 +2010,5,12,23,54,6,15,1018,41.13,0,0 +2010,5,13,0,52,6,14,1018,42.92,0,0 +2010,5,13,1,54,5,13,1018,0.89,0,0 +2010,5,13,2,60,6,12,1018,0.89,0,0 +2010,5,13,3,69,5,12,1018,0.89,0,0 +2010,5,13,4,75,5,12,1018,1.79,0,0 +2010,5,13,5,73,5,12,1019,4.92,0,0 +2010,5,13,6,72,6,12,1019,0.89,0,0 +2010,5,13,7,78,6,14,1020,1.79,0,0 +2010,5,13,8,83,8,17,1020,3.58,0,0 +2010,5,13,9,83,7,19,1020,5.37,0,0 +2010,5,13,10,82,9,21,1019,0.89,0,0 +2010,5,13,11,74,6,22,1019,2.68,0,0 +2010,5,13,12,60,7,24,1018,4.47,0,0 +2010,5,13,13,43,5,25,1017,4.92,0,0 +2010,5,13,14,41,5,25,1016,8.94,0,0 +2010,5,13,15,57,6,25,1016,12.96,0,0 +2010,5,13,16,66,6,25,1015,16.09,0,0 +2010,5,13,17,68,7,24,1015,19.22,0,0 +2010,5,13,18,78,7,23,1015,22.35,0,0 +2010,5,13,19,83,7,22,1015,25.48,0,0 +2010,5,13,20,68,5,21,1016,29.5,0,0 +2010,5,13,21,64,5,20,1017,33.52,0,0 +2010,5,13,22,69,6,18,1017,36.65,0,0 +2010,5,13,23,64,7,17,1017,39.78,0,0 +2010,5,14,0,60,7,16,1017,41.57,0,0 +2010,5,14,1,65,7,15,1017,1.79,0,0 +2010,5,14,2,61,7,14,1017,0.89,0,0 +2010,5,14,3,83,6,15,1016,1.79,0,0 +2010,5,14,4,112,6,14,1016,2.68,0,0 +2010,5,14,5,99,6,14,1016,5.81,0,0 +2010,5,14,6,108,7,14,1016,7.6,0,0 +2010,5,14,7,123,8,16,1017,9.39,0,0 +2010,5,14,8,117,8,17,1017,12.52,0,0 +2010,5,14,9,130,9,19,1016,14.31,0,0 +2010,5,14,10,130,8,20,1016,16.1,0,0 +2010,5,14,11,138,9,20,1016,19.23,0,0 +2010,5,14,12,147,9,21,1015,22.36,0,0 +2010,5,14,13,157,10,22,1015,24.15,0,0 +2010,5,14,14,140,11,22,1014,25.94,0,0 +2010,5,14,15,140,10,22,1014,1.79,0,0 +2010,5,14,16,159,10,22,1013,3.13,0,1 +2010,5,14,17,158,10,21,1013,3.13,0,2 +2010,5,14,18,168,11,20,1013,6.26,0,3 +2010,5,14,19,173,11,19,1012,1.79,0,0 +2010,5,14,20,181,13,18,1013,2.68,0,1 +2010,5,14,21,193,13,17,1013,0.89,0,2 +2010,5,14,22,209,13,17,1014,0.89,0,3 +2010,5,14,23,216,14,17,1014,0.89,0,4 +2010,5,15,0,219,14,17,1014,0.89,0,0 +2010,5,15,1,224,14,17,1013,0.89,0,0 +2010,5,15,2,231,14,17,1013,0.89,0,0 +2010,5,15,3,237,13,15,1013,2.68,0,0 +2010,5,15,4,238,12,14,1013,4.47,0,0 +2010,5,15,5,235,12,14,1014,6.26,0,0 +2010,5,15,6,222,14,15,1014,8.05,0,0 +2010,5,15,7,221,14,16,1014,9.84,0,0 +2010,5,15,8,223,14,18,1014,11.63,0,0 +2010,5,15,9,203,13,20,1014,13.42,0,0 +2010,5,15,10,191,13,22,1014,15.21,0,0 +2010,5,15,11,185,13,24,1014,0.89,0,0 +2010,5,15,12,181,13,26,1013,4.02,0,0 +2010,5,15,13,162,13,27,1012,4.91,0,0 +2010,5,15,14,185,11,28,1011,1.79,0,0 +2010,5,15,15,188,11,28,1011,0.89,0,0 +2010,5,15,16,170,12,28,1010,3.13,0,0 +2010,5,15,17,142,11,27,1010,7.15,0,0 +2010,5,15,18,126,10,27,1010,11.17,0,0 +2010,5,15,19,117,10,24,1011,14.3,0,0 +2010,5,15,20,113,10,24,1012,16.09,0,0 +2010,5,15,21,122,11,23,1012,17.88,0,0 +2010,5,15,22,114,11,23,1013,21.9,0,0 +2010,5,15,23,113,11,21,1013,25.92,0,0 +2010,5,16,0,107,11,21,1014,27.71,0,0 +2010,5,16,1,105,11,20,1014,30.84,0,0 +2010,5,16,2,98,10,18,1014,33.97,0,0 +2010,5,16,3,115,11,18,1014,35.76,0,0 +2010,5,16,4,121,11,18,1014,38.89,0,0 +2010,5,16,5,144,11,17,1014,42.02,0,0 +2010,5,16,6,185,12,17,1015,43.81,0,0 +2010,5,16,7,182,12,18,1015,47.83,0,0 +2010,5,16,8,178,13,19,1015,51.85,0,0 +2010,5,16,9,185,13,20,1015,54.98,0,0 +2010,5,16,10,163,13,22,1015,56.77,0,0 +2010,5,16,11,145,13,22,1015,58.56,0,0 +2010,5,16,12,137,13,22,1014,61.69,0,0 +2010,5,16,13,136,12,22,1014,65.71,0,0 +2010,5,16,14,100,12,22,1013,70.63,0,1 +2010,5,16,15,81,13,21,1013,73.76,0,0 +2010,5,16,16,56,12,20,1013,78.68,0,1 +2010,5,16,17,33,12,19,1013,82.7,0,2 +2010,5,16,18,70,13,17,1013,88.51,0,3 +2010,5,16,19,87,14,16,1014,90.3,0,4 +2010,5,16,20,69,14,15,1014,93.43,0,5 +2010,5,16,21,47,14,15,1014,95.22,0,6 +2010,5,16,22,48,13,15,1014,97.01,0,7 +2010,5,16,23,47,14,15,1013,98.8,0,8 +2010,5,17,0,51,13,15,1013,101.93,0,0 +2010,5,17,1,49,13,14,1012,103.72,0,1 +2010,5,17,2,36,13,14,1012,0.89,0,2 +2010,5,17,3,44,13,14,1011,1.79,0,3 +2010,5,17,4,41,13,14,1011,3.58,0,4 +2010,5,17,5,50,13,14,1011,0.89,0,5 +2010,5,17,6,51,13,14,1011,1.78,0,6 +2010,5,17,7,48,13,14,1011,2.67,0,7 +2010,5,17,8,53,13,14,1011,1.79,0,0 +2010,5,17,9,77,13,15,1011,3.58,0,0 +2010,5,17,10,74,14,16,1011,5.37,0,0 +2010,5,17,11,82,14,17,1011,7.16,0,0 +2010,5,17,12,75,14,18,1010,10.29,0,0 +2010,5,17,13,76,14,19,1010,13.42,0,0 +2010,5,17,14,79,15,19,1009,16.55,0,0 +2010,5,17,15,84,14,20,1008,1.79,0,0 +2010,5,17,16,89,13,20,1007,3.13,0,0 +2010,5,17,17,NA,13,19,1007,4.92,0,0 +2010,5,17,18,NA,13,19,1008,0.89,0,0 +2010,5,17,19,90,13,18,1007,3.13,0,0 +2010,5,17,20,83,13,18,1008,7.15,0,0 +2010,5,17,21,96,13,17,1008,10.28,0,0 +2010,5,17,22,74,13,17,1008,13.41,0,0 +2010,5,17,23,84,13,16,1007,18.33,0,0 +2010,5,18,0,86,13,16,1006,22.35,0,0 +2010,5,18,1,86,13,16,1005,25.48,0,0 +2010,5,18,2,94,13,16,1005,27.27,0,0 +2010,5,18,3,105,13,16,1004,29.06,0,0 +2010,5,18,4,89,14,15,1004,3.13,0,1 +2010,5,18,5,79,12,15,1004,10.28,0,2 +2010,5,18,6,13,12,14,1005,16.09,0,3 +2010,5,18,7,12,11,14,1004,21.9,0,4 +2010,5,18,8,17,11,13,1004,29.95,0,5 +2010,5,18,9,13,12,16,1003,33.97,0,6 +2010,5,18,10,16,9,18,1003,39.78,0,0 +2010,5,18,11,37,9,19,1002,45.59,0,0 +2010,5,18,12,53,8,21,1001,48.72,0,0 +2010,5,18,13,72,6,23,1000,59.9,0,0 +2010,5,18,14,64,7,24,999,7.15,0,0 +2010,5,18,15,60,8,25,998,12.07,0,0 +2010,5,18,16,54,13,14,999,5.81,0,1 +2010,5,18,17,29,12,18,1000,3.13,0,0 +2010,5,18,18,41,13,19,1000,0.89,0,0 +2010,5,18,19,56,10,19,1000,3.13,0,0 +2010,5,18,20,33,6,20,1000,7.15,0,1 +2010,5,18,21,31,7,18,1001,11.17,0,0 +2010,5,18,22,41,7,15,1001,1.79,0,0 +2010,5,18,23,58,7,17,1001,1.79,0,0 +2010,5,19,0,49,9,13,1001,0.89,0,0 +2010,5,19,1,38,9,13,1001,1.78,0,0 +2010,5,19,2,41,9,14,1001,3.13,0,0 +2010,5,19,3,34,8,14,1001,6.26,0,0 +2010,5,19,4,27,9,11,1001,1.79,0,0 +2010,5,19,5,23,9,11,1001,0.89,0,0 +2010,5,19,6,34,10,13,1001,0.89,0,0 +2010,5,19,7,34,10,19,1001,0.89,0,0 +2010,5,19,8,43,10,21,1001,1.79,0,0 +2010,5,19,9,50,5,25,1001,4.02,0,0 +2010,5,19,10,47,6,27,1000,8.94,0,0 +2010,5,19,11,44,4,29,1000,5.81,0,0 +2010,5,19,12,36,3,30,999,4.92,0,0 +2010,5,19,13,29,0,32,998,9.83,0,0 +2010,5,19,14,27,1,33,998,21.01,0,0 +2010,5,19,15,30,0,33,998,30.84,0,0 +2010,5,19,16,30,-2,34,997,39.78,0,0 +2010,5,19,17,21,-2,33,997,47.83,0,0 +2010,5,19,18,19,-3,32,998,54.98,0,0 +2010,5,19,19,21,-2,31,999,62.13,0,0 +2010,5,19,20,28,-1,29,999,4.02,0,0 +2010,5,19,21,32,-2,28,1000,8.04,0,0 +2010,5,19,22,33,0,27,1001,3.13,0,0 +2010,5,19,23,44,0,26,1001,6.26,0,0 +2010,5,20,0,61,4,20,1002,3.13,0,0 +2010,5,20,1,65,7,19,1002,4.92,0,0 +2010,5,20,2,66,9,16,1003,0.89,0,0 +2010,5,20,3,89,8,13,1003,1.79,0,0 +2010,5,20,4,103,9,13,1004,0.89,0,0 +2010,5,20,5,113,9,13,1005,1.78,0,0 +2010,5,20,6,104,10,16,1005,2.67,0,0 +2010,5,20,7,103,9,21,1006,3.56,0,0 +2010,5,20,8,75,8,22,1006,1.79,0,0 +2010,5,20,9,65,7,25,1006,1.79,0,0 +2010,5,20,10,67,9,28,1006,0.89,0,0 +2010,5,20,11,53,9,30,1006,2.68,0,0 +2010,5,20,12,51,4,32,1005,1.79,0,0 +2010,5,20,13,58,5,33,1005,5.81,0,0 +2010,5,20,14,39,2,34,1005,8.94,0,0 +2010,5,20,15,36,0,35,1004,13.86,0,0 +2010,5,20,16,36,-2,35,1004,19.67,0,0 +2010,5,20,17,34,-1,34,1004,25.48,0,0 +2010,5,20,18,37,-1,32,1004,29.5,0,0 +2010,5,20,19,40,-1,30,1005,33.52,0,0 +2010,5,20,20,43,-1,30,1005,36.65,0,0 +2010,5,20,21,54,2,29,1006,40.67,0,0 +2010,5,20,22,68,5,25,1006,43.8,0,0 +2010,5,20,23,78,7,25,1006,46.93,0,0 +2010,5,21,0,93,9,24,1006,50.06,0,0 +2010,5,21,1,91,10,22,1007,51.85,0,0 +2010,5,21,2,96,10,22,1007,53.64,0,0 +2010,5,21,3,106,10,20,1007,55.43,0,0 +2010,5,21,4,101,12,19,1007,57.22,0,0 +2010,5,21,5,90,12,18,1007,0.45,0,0 +2010,5,21,6,95,11,19,1008,1.34,0,0 +2010,5,21,7,105,12,20,1008,2.23,0,0 +2010,5,21,8,110,13,22,1008,0.89,0,0 +2010,5,21,9,116,13,24,1009,2.68,0,0 +2010,5,21,10,129,12,25,1008,4.47,0,0 +2010,5,21,11,134,13,27,1008,6.26,0,0 +2010,5,21,12,120,13,27,1008,8.05,0,0 +2010,5,21,13,78,11,27,1008,9.84,0,0 +2010,5,21,14,72,7,27,1007,13.86,0,0 +2010,5,21,15,79,8,28,1007,17.88,0,0 +2010,5,21,16,86,9,28,1006,21.01,0,0 +2010,5,21,17,77,8,28,1006,22.8,0,0 +2010,5,21,18,82,10,27,1006,25.93,0,0 +2010,5,21,19,65,10,25,1006,27.72,0,0 +2010,5,21,20,93,12,23,1006,29.51,0,0 +2010,5,21,21,148,12,21,1006,0.89,0,0 +2010,5,21,22,197,12,20,1007,1.78,0,0 +2010,5,21,23,234,12,18,1007,0.89,0,0 +2010,5,22,0,229,12,17,1007,1.79,0,0 +2010,5,22,1,214,12,16,1006,0.89,0,0 +2010,5,22,2,229,12,16,1006,1.78,0,0 +2010,5,22,3,198,11,16,1006,1.79,0,0 +2010,5,22,4,163,11,16,1006,2.68,0,0 +2010,5,22,5,119,11,15,1006,3.57,0,0 +2010,5,22,6,124,11,16,1007,5.36,0,0 +2010,5,22,7,131,12,19,1007,8.49,0,0 +2010,5,22,8,157,12,21,1007,0.89,0,0 +2010,5,22,9,178,13,24,1007,1.78,0,0 +2010,5,22,10,187,14,25,1007,2.67,0,0 +2010,5,22,11,182,14,26,1007,4.46,0,0 +2010,5,22,12,174,14,28,1006,1.79,0,0 +2010,5,22,13,167,10,29,1005,3.13,0,0 +2010,5,22,14,128,8,30,1005,3.13,0,0 +2010,5,22,15,108,10,30,1004,6.26,0,0 +2010,5,22,16,121,12,29,1004,12.07,0,0 +2010,5,22,17,127,13,28,1004,16.09,0,0 +2010,5,22,18,118,11,27,1004,20.11,0,0 +2010,5,22,19,116,9,26,1004,24.13,0,0 +2010,5,22,20,115,9,24,1005,27.26,0,0 +2010,5,22,21,123,11,22,1006,29.05,0,0 +2010,5,22,22,106,12,19,1006,0.89,0,0 +2010,5,22,23,118,12,19,1005,1.78,0,0 +2010,5,23,0,149,12,18,1005,2.67,0,0 +2010,5,23,1,163,12,17,1004,3.56,0,0 +2010,5,23,2,155,13,16,1004,0.89,0,0 +2010,5,23,3,150,13,18,1003,2.68,0,0 +2010,5,23,4,155,12,15,1003,1.79,0,0 +2010,5,23,5,176,11,15,1003,3.58,0,0 +2010,5,23,6,165,12,16,1003,0.89,0,0 +2010,5,23,7,138,13,19,1003,3.13,0,0 +2010,5,23,8,139,12,21,1003,6.26,0,0 +2010,5,23,9,140,11,24,1003,8.05,0,0 +2010,5,23,10,139,12,26,1002,11.18,0,0 +2010,5,23,11,135,11,28,1001,12.97,0,0 +2010,5,23,12,115,10,31,1000,0.89,0,0 +2010,5,23,13,100,8,32,999,4.02,0,0 +2010,5,23,14,104,4,34,998,4.02,0,0 +2010,5,23,15,70,4,35,997,1.79,0,0 +2010,5,23,16,63,2,34,996,1.79,0,0 +2010,5,23,17,53,2,35,995,3.13,0,0 +2010,5,23,18,79,5,32,996,6.26,0,0 +2010,5,23,19,72,13,27,997,13.41,0,0 +2010,5,23,20,165,13,24,998,16.54,0,1 +2010,5,23,21,149,12,22,1000,20.56,0,0 +2010,5,23,22,139,11,21,1000,24.58,0,0 +2010,5,23,23,93,11,20,1000,27.71,0,0 +2010,5,24,0,100,11,19,1000,0.89,0,0 +2010,5,24,1,78,11,17,1000,1.78,0,0 +2010,5,24,2,78,12,17,999,0.89,0,0 +2010,5,24,3,73,11,16,1000,1.79,0,0 +2010,5,24,4,85,11,16,1000,0.89,0,0 +2010,5,24,5,98,10,16,1000,1.79,0,0 +2010,5,24,6,47,8,20,1001,4.02,0,0 +2010,5,24,7,45,8,23,1003,1.79,0,0 +2010,5,24,8,40,6,24,1004,9.84,0,0 +2010,5,24,9,35,7,26,1004,17.89,0,0 +2010,5,24,10,30,7,28,1004,25.04,0,0 +2010,5,24,11,31,7,26,1005,30.85,0,0 +2010,5,24,12,24,7,26,1005,38.9,0,0 +2010,5,24,13,42,7,29,1005,46.05,0,0 +2010,5,24,14,28,7,29,1004,51.86,0,0 +2010,5,24,15,23,6,29,1004,56.78,0,0 +2010,5,24,16,28,7,30,1003,60.8,0,0 +2010,5,24,17,26,7,29,1003,64.82,0,0 +2010,5,24,18,23,6,29,1004,69.74,0,0 +2010,5,24,19,30,7,26,1004,72.87,0,0 +2010,5,24,20,44,6,24,1005,1.79,0,0 +2010,5,24,21,45,3,23,1006,4.02,0,0 +2010,5,24,22,28,3,20,1007,7.15,0,0 +2010,5,24,23,18,1,20,1007,10.28,0,0 +2010,5,25,0,20,0,20,1008,0.89,0,0 +2010,5,25,1,24,-2,22,1009,4.92,0,0 +2010,5,25,2,18,0,17,1009,8.05,0,0 +2010,5,25,3,28,1,17,1010,9.84,0,0 +2010,5,25,4,16,1,17,1010,13.86,0,0 +2010,5,25,5,14,2,18,1011,15.65,0,0 +2010,5,25,6,14,2,19,1012,19.67,0,0 +2010,5,25,7,24,4,21,1013,3.13,0,0 +2010,5,25,8,29,4,23,1013,4.02,0,0 +2010,5,25,9,45,4,24,1013,8.94,0,0 +2010,5,25,10,33,6,26,1013,12.07,0,0 +2010,5,25,11,46,5,27,1012,16.99,0,0 +2010,5,25,12,39,6,29,1012,3.13,0,0 +2010,5,25,13,28,6,30,1011,3.13,0,0 +2010,5,25,14,55,5,30,1010,7.15,0,0 +2010,5,25,15,45,4,31,1009,1.79,0,0 +2010,5,25,16,27,4,31,1009,1.79,0,0 +2010,5,25,17,48,5,31,1008,3.58,0,0 +2010,5,25,18,57,5,30,1008,5.37,0,0 +2010,5,25,19,33,5,27,1009,7.16,0,0 +2010,5,25,20,43,7,28,1009,11.18,0,0 +2010,5,25,21,35,7,27,1010,15.2,0,0 +2010,5,25,22,37,7,26,1010,18.33,0,0 +2010,5,25,23,39,7,26,1011,1.79,0,0 +2010,5,26,0,47,10,20,1011,1.79,0,0 +2010,5,26,1,105,9,21,1011,0.89,0,0 +2010,5,26,2,104,11,20,1011,1.78,0,0 +2010,5,26,3,139,10,21,1011,0.89,0,0 +2010,5,26,4,92,10,20,1012,2.68,0,0 +2010,5,26,5,86,13,19,1012,1.79,0,0 +2010,5,26,6,81,13,20,1012,1.79,0,0 +2010,5,26,7,65,12,23,1013,0.89,0,0 +2010,5,26,8,131,12,24,1014,1.78,0,0 +2010,5,26,9,137,12,27,1014,4.92,0,0 +2010,5,26,10,118,11,27,1014,10.73,0,0 +2010,5,26,11,115,11,29,1014,16.54,0,0 +2010,5,26,12,80,11,30,1014,21.46,0,0 +2010,5,26,13,79,11,30,1013,25.48,0,0 +2010,5,26,14,62,11,30,1013,30.4,0,0 +2010,5,26,15,58,10,30,1012,35.32,0,0 +2010,5,26,16,63,10,29,1012,40.24,0,0 +2010,5,26,17,72,9,29,1012,45.16,0,0 +2010,5,26,18,59,10,28,1012,49.18,0,0 +2010,5,26,19,48,11,27,1013,52.31,0,0 +2010,5,26,20,40,10,26,1013,57.23,0,0 +2010,5,26,21,48,12,26,1015,61.25,0,0 +2010,5,26,22,59,12,24,1015,64.38,0,0 +2010,5,26,23,78,13,24,1015,1.79,0,0 +2010,5,27,0,90,14,22,1015,0.89,0,0 +2010,5,27,1,91,11,23,1015,3.13,0,0 +2010,5,27,2,98,11,22,1015,4.92,0,1 +2010,5,27,3,92,13,21,1015,0.89,0,0 +2010,5,27,4,81,13,21,1015,0.89,0,0 +2010,5,27,5,74,12,21,1015,1.79,0,0 +2010,5,27,6,80,13,21,1015,0.89,0,0 +2010,5,27,7,67,14,22,1015,1.79,0,0 +2010,5,27,8,108,15,21,1015,3.58,0,0 +2010,5,27,9,92,15,22,1015,6.71,0,1 +2010,5,27,10,71,15,23,1015,9.84,0,2 +2010,5,27,11,92,14,23,1015,0.89,0,0 +2010,5,27,12,84,15,23,1015,1.78,0,1 +2010,5,27,13,85,15,23,1015,2.67,0,2 +2010,5,27,14,83,16,22,1015,1.79,0,3 +2010,5,27,15,96,15,21,1015,4.92,0,4 +2010,5,27,16,70,16,20,1015,8.05,0,5 +2010,5,27,17,72,16,20,1015,11.18,0,6 +2010,5,27,18,85,16,19,1015,14.31,0,7 +2010,5,27,19,NA,16,18,1015,1.79,0,8 +2010,5,27,20,NA,16,18,1015,2.68,0,9 +2010,5,27,21,NA,16,18,1015,1.79,0,10 +2010,5,27,22,NA,16,18,1015,0.89,0,11 +2010,5,27,23,NA,16,18,1014,1.78,0,12 +2010,5,28,0,90,16,17,1014,0.89,0,13 +2010,5,28,1,89,15,17,1013,0.89,0,14 +2010,5,28,2,100,16,17,1013,0.89,0,15 +2010,5,28,3,86,16,17,1013,3.13,0,16 +2010,5,28,4,83,15,17,1012,1.79,0,17 +2010,5,28,5,94,16,17,1011,1.79,0,18 +2010,5,28,6,111,16,17,1011,0.89,0,19 +2010,5,28,7,133,16,17,1011,3.13,0,20 +2010,5,28,8,127,16,17,1011,6.26,0,21 +2010,5,28,9,152,16,18,1010,8.05,0,22 +2010,5,28,10,157,17,19,1010,11.18,0,0 +2010,5,28,11,142,17,20,1009,15.2,0,0 +2010,5,28,12,127,16,21,1009,1.79,0,1 +2010,5,28,13,132,16,24,1008,3.13,0,0 +2010,5,28,14,117,15,24,1008,1.79,0,0 +2010,5,28,15,111,15,23,1007,4.02,0,1 +2010,5,28,16,108,17,23,1007,1.79,0,0 +2010,5,28,17,107,18,21,1007,4.02,0,0 +2010,5,28,18,142,18,20,1008,7.15,0,1 +2010,5,28,19,180,17,20,1007,3.13,0,0 +2010,5,28,20,206,16,19,1008,0.89,0,0 +2010,5,28,21,193,16,18,1008,1.79,0,0 +2010,5,28,22,186,16,18,1009,4.92,0,0 +2010,5,28,23,168,16,18,1009,8.05,0,1 +2010,5,29,0,175,16,17,1008,1.79,0,0 +2010,5,29,1,181,16,17,1008,0.89,0,0 +2010,5,29,2,174,16,17,1008,1.78,0,0 +2010,5,29,3,205,16,17,1007,0.89,0,0 +2010,5,29,4,219,15,16,1007,4.02,0,0 +2010,5,29,5,214,15,16,1007,0.89,0,0 +2010,5,29,6,230,15,17,1007,1.78,0,0 +2010,5,29,7,242,16,17,1007,2.67,0,0 +2010,5,29,8,251,17,19,1007,3.56,0,0 +2010,5,29,9,234,17,21,1007,1.79,0,0 +2010,5,29,10,187,17,23,1006,0.89,0,0 +2010,5,29,11,220,17,25,1006,1.78,0,0 +2010,5,29,12,92,16,28,1005,2.67,0,0 +2010,5,29,13,28,8,31,1003,4.92,0,0 +2010,5,29,14,65,5,31,1003,12.07,0,0 +2010,5,29,15,16,7,30,1002,17.88,0,0 +2010,5,29,16,19,8,31,1002,22.8,0,0 +2010,5,29,17,20,6,31,1002,26.82,0,0 +2010,5,29,18,17,7,30,1001,29.95,0,0 +2010,5,29,19,22,11,26,1002,0.89,0,0 +2010,5,29,20,34,13,23,1002,0.89,0,0 +2010,5,29,21,214,15,21,1003,1.78,0,0 +2010,5,29,22,314,15,20,1003,1.79,0,0 +2010,5,29,23,71,9,23,1004,6.71,0,0 +2010,5,30,0,33,11,20,1004,4.02,0,0 +2010,5,30,1,26,11,19,1004,8.04,0,0 +2010,5,30,2,30,10,20,1004,11.17,0,0 +2010,5,30,3,33,8,23,1004,16.98,0,0 +2010,5,30,4,22,8,23,1004,24.13,0,0 +2010,5,30,5,17,8,21,1005,29.05,0,0 +2010,5,30,6,25,11,21,1005,3.13,0,0 +2010,5,30,7,29,11,23,1006,3.13,0,0 +2010,5,30,8,18,11,24,1007,8.05,0,0 +2010,5,30,9,19,10,25,1007,12.07,0,0 +2010,5,30,10,23,10,27,1007,16.99,0,0 +2010,5,30,11,54,10,26,1007,21.01,0,0 +2010,5,30,12,41,10,28,1007,4.02,0,0 +2010,5,30,13,26,9,28,1007,3.13,0,0 +2010,5,30,14,18,9,28,1007,6.26,0,0 +2010,5,30,15,37,12,22,1007,5.81,0,0 +2010,5,30,16,39,13,23,1008,9.83,0,0 +2010,5,30,17,29,11,24,1008,12.96,0,0 +2010,5,30,18,59,11,25,1009,4.02,0,0 +2010,5,30,19,97,13,23,1009,5.81,0,0 +2010,5,30,20,30,15,21,1010,7.6,0,0 +2010,5,30,21,65,15,22,1011,0.89,0,0 +2010,5,30,22,83,15,21,1012,3.13,0,1 +2010,5,30,23,94,15,20,1013,7.15,0,0 +2010,5,31,0,77,15,18,1013,8.94,0,1 +2010,5,31,1,69,15,18,1014,10.73,0,0 +2010,5,31,2,59,14,18,1014,12.52,0,0 +2010,5,31,3,75,14,18,1014,14.31,0,0 +2010,5,31,4,88,14,18,1014,16.1,0,0 +2010,5,31,5,91,14,17,1015,17.89,0,0 +2010,5,31,6,98,14,18,1015,0.89,0,0 +2010,5,31,7,118,15,20,1016,1.79,0,0 +2010,5,31,8,105,15,20,1016,2.68,0,0 +2010,5,31,9,103,15,22,1016,1.79,0,0 +2010,5,31,10,77,15,23,1016,1.79,0,0 +2010,5,31,11,76,13,24,1016,1.79,0,0 +2010,5,31,12,85,11,26,1015,3.13,0,0 +2010,5,31,13,69,9,27,1014,4.92,0,0 +2010,5,31,14,50,10,28,1014,8.05,0,0 +2010,5,31,15,39,10,27,1013,8.94,0,0 +2010,5,31,16,45,11,26,1013,3.13,0,0 +2010,5,31,17,99,12,25,1013,7.15,0,0 +2010,5,31,18,92,12,25,1013,11.17,0,0 +2010,5,31,19,79,12,24,1014,15.19,0,0 +2010,5,31,20,61,14,22,1015,0.89,0,1 +2010,5,31,21,60,16,21,1016,4.92,0,0 +2010,5,31,22,132,15,20,1016,9.84,0,0 +2010,5,31,23,179,14,19,1017,12.97,0,0 +2010,6,1,0,189,14,18,1017,16.1,0,0 +2010,6,1,1,177,13,17,1017,0.89,0,0 +2010,6,1,2,112,13,17,1017,1.79,0,0 +2010,6,1,3,94,14,17,1017,3.58,0,0 +2010,6,1,4,87,14,17,1017,4.47,0,0 +2010,6,1,5,92,14,17,1017,6.26,0,0 +2010,6,1,6,86,14,17,1017,0.89,0,0 +2010,6,1,7,87,13,18,1017,1.78,0,0 +2010,6,1,8,98,14,19,1017,2.67,0,0 +2010,6,1,9,87,14,21,1017,3.56,0,0 +2010,6,1,10,106,14,21,1017,4.45,0,0 +2010,6,1,11,107,14,23,1016,1.79,0,0 +2010,6,1,12,110,14,23,1016,4.92,0,0 +2010,6,1,13,120,14,25,1015,8.05,0,0 +2010,6,1,14,103,14,25,1014,9.84,0,0 +2010,6,1,15,98,14,25,1013,3.13,0,0 +2010,6,1,16,NA,15,25,1013,3.13,0,0 +2010,6,1,17,178,15,25,1012,7.15,0,0 +2010,6,1,18,80,14,24,1012,10.28,0,0 +2010,6,1,19,100,15,24,1014,4.02,0,1 +2010,6,1,20,115,16,22,1012,7.15,0,2 +2010,6,1,21,92,17,20,1013,8.94,0,0 +2010,6,1,22,NA,16,21,1014,0.89,0,0 +2010,6,1,23,NA,17,20,1014,1.78,0,0 +2010,6,2,0,NA,16,20,1014,1.79,0,0 +2010,6,2,1,NA,14,18,1014,4.92,0,0 +2010,6,2,2,NA,15,17,1014,1.79,0,1 +2010,6,2,3,NA,15,17,1014,0.89,0,2 +2010,6,2,4,NA,15,17,1015,1.78,0,3 +2010,6,2,5,NA,15,17,1015,0.89,0,4 +2010,6,2,6,NA,16,17,1016,0.89,0,0 +2010,6,2,7,NA,15,18,1016,0.89,0,0 +2010,6,2,8,NA,15,18,1017,3.13,0,0 +2010,6,2,9,NA,15,18,1017,4.92,0,0 +2010,6,2,10,NA,15,20,1017,6.71,0,0 +2010,6,2,11,NA,15,20,1017,8.5,0,0 +2010,6,2,12,NA,15,22,1017,0.89,0,0 +2010,6,2,13,NA,15,24,1017,1.79,0,0 +2010,6,2,14,NA,15,23,1016,3.13,0,0 +2010,6,2,15,NA,14,24,1016,1.79,0,0 +2010,6,2,16,NA,14,24,1016,3.13,0,0 +2010,6,2,17,74,14,24,1016,6.26,0,0 +2010,6,2,18,97,13,24,1016,8.05,0,0 +2010,6,2,19,119,13,23,1016,9.84,0,0 +2010,6,2,20,128,15,21,1016,0.89,0,0 +2010,6,2,21,129,15,19,1017,1.78,0,0 +2010,6,2,22,141,15,19,1017,2.67,0,0 +2010,6,2,23,159,15,20,1018,1.79,0,0 +2010,6,3,0,190,15,17,1018,3.58,0,0 +2010,6,3,1,154,15,17,1018,4.47,0,0 +2010,6,3,2,156,15,17,1018,5.36,0,0 +2010,6,3,3,134,14,17,1018,6.25,0,0 +2010,6,3,4,133,13,16,1018,8.04,0,0 +2010,6,3,5,94,13,16,1018,8.93,0,0 +2010,6,3,6,109,14,15,1019,0.89,0,0 +2010,6,3,7,140,14,19,1019,0.89,0,0 +2010,6,3,8,152,15,20,1019,1.78,0,0 +2010,6,3,9,127,13,22,1019,2.67,0,0 +2010,6,3,10,114,12,23,1019,3.56,0,0 +2010,6,3,11,121,13,24,1018,4.45,0,0 +2010,6,3,12,122,14,26,1018,5.34,0,0 +2010,6,3,13,113,12,27,1017,1.79,0,0 +2010,6,3,14,104,13,27,1017,1.79,0,0 +2010,6,3,15,108,13,28,1016,3.13,0,0 +2010,6,3,16,101,12,27,1016,6.26,0,0 +2010,6,3,17,92,13,27,1015,9.39,0,0 +2010,6,3,18,82,11,26,1015,13.41,0,0 +2010,6,3,19,83,10,25,1015,16.54,0,0 +2010,6,3,20,85,12,23,1016,19.67,0,0 +2010,6,3,21,90,12,22,1016,21.46,0,0 +2010,6,3,22,81,12,21,1017,23.25,0,0 +2010,6,3,23,86,13,20,1017,24.14,0,0 +2010,6,4,0,79,14,19,1017,25.93,0,0 +2010,6,4,1,71,14,18,1017,27.72,0,0 +2010,6,4,2,64,14,17,1016,28.61,0,0 +2010,6,4,3,63,13,17,1017,30.4,0,0 +2010,6,4,4,76,13,16,1017,0.89,0,0 +2010,6,4,5,89,12,15,1017,0.89,0,0 +2010,6,4,6,101,13,16,1017,0.89,0,0 +2010,6,4,7,95,14,18,1017,1.78,0,0 +2010,6,4,8,82,13,21,1018,2.67,0,0 +2010,6,4,9,85,13,23,1017,3.56,0,0 +2010,6,4,10,79,13,24,1017,4.45,0,0 +2010,6,4,11,76,13,26,1017,5.34,0,0 +2010,6,4,12,NA,11,26,1016,7.13,0,0 +2010,6,4,13,NA,11,29,1014,3.13,0,0 +2010,6,4,14,NA,10,29,1014,1.79,0,0 +2010,6,4,15,NA,10,29,1013,3.13,0,0 +2010,6,4,16,NA,9,29,1013,8.05,0,0 +2010,6,4,17,NA,8,28,1013,12.07,0,0 +2010,6,4,18,NA,8,27,1013,16.09,0,0 +2010,6,4,19,NA,11,26,1013,20.11,0,0 +2010,6,4,20,NA,11,25,1013,24.13,0,0 +2010,6,4,21,NA,11,24,1013,28.15,0,0 +2010,6,4,22,NA,13,21,1014,31.28,0,0 +2010,6,4,23,NA,12,21,1014,34.41,0,0 +2010,6,5,0,NA,12,20,1014,36.2,0,0 +2010,6,5,1,NA,12,19,1014,37.09,0,0 +2010,6,5,2,NA,11,18,1014,0.89,0,0 +2010,6,5,3,NA,11,17,1013,1.78,0,0 +2010,6,5,4,NA,12,17,1014,1.79,0,0 +2010,6,5,5,NA,12,16,1014,4.92,0,0 +2010,6,5,6,NA,14,18,1014,8.05,0,0 +2010,6,5,7,NA,15,19,1014,11.18,0,0 +2010,6,5,8,NA,15,21,1014,0.89,0,0 +2010,6,5,9,NA,15,23,1014,1.78,0,0 +2010,6,5,10,NA,14,24,1014,2.67,0,0 +2010,6,5,11,NA,14,27,1013,1.79,0,0 +2010,6,5,12,NA,13,28,1013,1.79,0,0 +2010,6,5,13,NA,11,30,1011,4.02,0,0 +2010,6,5,14,NA,11,30,1011,4.02,0,0 +2010,6,5,15,NA,12,31,1010,4.02,0,0 +2010,6,5,16,NA,12,30,1009,8.04,0,0 +2010,6,5,17,NA,12,29,1009,12.06,0,0 +2010,6,5,18,NA,11,29,1009,16.98,0,0 +2010,6,5,19,NA,9,28,1009,21,0,0 +2010,6,5,20,NA,11,26,1010,22.79,0,0 +2010,6,5,21,NA,13,24,1010,25.92,0,0 +2010,6,5,22,NA,12,23,1011,29.05,0,0 +2010,6,5,23,NA,12,22,1011,32.18,0,0 +2010,6,6,0,NA,12,21,1011,33.97,0,0 +2010,6,6,1,NA,12,21,1011,34.86,0,0 +2010,6,6,2,NA,11,20,1011,0.89,0,0 +2010,6,6,3,NA,11,19,1011,1.78,0,0 +2010,6,6,4,NA,12,17,1012,0.89,0,0 +2010,6,6,5,NA,12,17,1012,0.89,0,0 +2010,6,6,6,NA,13,18,1012,0.89,0,0 +2010,6,6,7,NA,14,22,1013,1.79,0,0 +2010,6,6,8,NA,14,22,1013,3.58,0,0 +2010,6,6,9,NA,14,24,1013,5.37,0,0 +2010,6,6,10,NA,15,26,1013,7.16,0,0 +2010,6,6,11,NA,15,28,1013,1.79,0,0 +2010,6,6,12,NA,15,29,1012,3.13,0,0 +2010,6,6,13,NA,10,31,1011,8.05,0,0 +2010,6,6,14,NA,10,32,1010,12.07,0,0 +2010,6,6,15,NA,11,31,1010,17.88,0,0 +2010,6,6,16,NA,12,31,1009,21.9,0,0 +2010,6,6,17,NA,12,30,1009,25.92,0,0 +2010,6,6,18,NA,11,30,1009,29.94,0,0 +2010,6,6,19,NA,11,28,1009,33.96,0,0 +2010,6,6,20,NA,13,26,1010,35.75,0,0 +2010,6,6,21,NA,14,25,1011,36.64,0,0 +2010,6,6,22,NA,14,24,1011,38.43,0,0 +2010,6,6,23,NA,15,24,1011,41.56,0,0 +2010,6,7,0,NA,16,23,1012,43.35,0,0 +2010,6,7,1,NA,17,22,1012,46.48,0,0 +2010,6,7,2,NA,17,21,1012,48.27,0,0 +2010,6,7,3,NA,16,20,1012,0.89,0,0 +2010,6,7,4,NA,16,20,1012,1.78,0,0 +2010,6,7,5,NA,16,20,1013,1.79,0,0 +2010,6,7,6,NA,17,21,1013,4.92,0,0 +2010,6,7,7,NA,17,22,1014,6.71,0,0 +2010,6,7,8,NA,18,23,1014,8.5,0,0 +2010,6,7,9,NA,18,25,1014,10.29,0,0 +2010,6,7,10,NA,17,26,1014,1.79,0,0 +2010,6,7,11,NA,17,28,1014,4.92,0,0 +2010,6,7,12,151,17,29,1013,3.13,0,0 +2010,6,7,13,129,13,31,1012,8.05,0,0 +2010,6,7,14,107,12,30,1012,13.86,0,0 +2010,6,7,15,112,11,31,1012,19.67,0,0 +2010,6,7,16,109,11,31,1011,24.59,0,0 +2010,6,7,17,113,13,31,1011,29.51,0,0 +2010,6,7,18,108,16,28,1011,33.53,0,0 +2010,6,7,19,172,15,27,1011,37.55,0,0 +2010,6,7,20,157,14,26,1012,41.57,0,0 +2010,6,7,21,142,14,25,1013,44.7,0,0 +2010,6,7,22,148,14,24,1013,47.83,0,0 +2010,6,7,23,131,13,24,1014,50.96,0,0 +2010,6,8,0,128,14,23,1014,54.09,0,0 +2010,6,8,1,148,14,22,1014,57.22,0,0 +2010,6,8,2,176,14,22,1014,0.89,0,0 +2010,6,8,3,191,14,20,1014,0.89,0,0 +2010,6,8,4,NA,14,20,1014,0.89,0,0 +2010,6,8,5,NA,14,20,1014,1.78,0,0 +2010,6,8,6,NA,15,19,1015,2.23,0,0 +2010,6,8,7,NA,13,22,1015,1.79,0,0 +2010,6,8,8,NA,14,24,1015,3.58,0,0 +2010,6,8,9,NA,14,25,1015,5.37,0,0 +2010,6,8,10,NA,14,27,1015,7.16,0,0 +2010,6,8,11,NA,15,28,1015,0.89,0,0 +2010,6,8,12,NA,14,30,1014,2.68,0,0 +2010,6,8,13,NA,11,31,1013,1.79,0,0 +2010,6,8,14,NA,11,32,1013,4.92,0,0 +2010,6,8,15,NA,11,33,1013,8.05,0,0 +2010,6,8,16,NA,11,30,1012,11.18,0,0 +2010,6,8,17,NA,10,30,1012,14.31,0,0 +2010,6,8,18,NA,11,29,1012,17.44,0,0 +2010,6,8,19,NA,12,29,1012,19.23,0,0 +2010,6,8,20,NA,10,27,1013,22.36,0,0 +2010,6,8,21,NA,11,26,1014,25.49,0,0 +2010,6,8,22,NA,12,25,1014,27.28,0,0 +2010,6,8,23,NA,12,24,1014,30.41,0,0 +2010,6,9,0,NA,12,23,1014,32.2,0,0 +2010,6,9,1,NA,12,22,1014,0.89,0,0 +2010,6,9,2,NA,13,21,1014,0.89,0,0 +2010,6,9,3,NA,13,20,1014,1.78,0,0 +2010,6,9,4,NA,14,21,1014,2.67,0,0 +2010,6,9,5,NA,13,20,1014,0.89,0,0 +2010,6,9,6,NA,14,20,1015,0.89,0,0 +2010,6,9,7,NA,14,23,1015,1.79,0,0 +2010,6,9,8,NA,13,24,1015,3.58,0,0 +2010,6,9,9,NA,12,26,1015,1.79,0,0 +2010,6,9,10,NA,11,28,1015,1.79,0,0 +2010,6,9,11,NA,11,28,1015,3.58,0,0 +2010,6,9,12,NA,11,29,1015,5.37,0,0 +2010,6,9,13,NA,11,30,1013,1.79,0,0 +2010,6,9,14,NA,10,30,1013,3.13,0,0 +2010,6,9,15,NA,11,31,1013,4.02,0,0 +2010,6,9,16,NA,10,32,1012,3.13,0,0 +2010,6,9,17,70,11,30,1012,7.15,0,0 +2010,6,9,18,60,13,29,1012,11.17,0,0 +2010,6,9,19,41,13,28,1013,15.19,0,0 +2010,6,9,20,40,12,26,1013,19.21,0,0 +2010,6,9,21,32,11,25,1014,22.34,0,0 +2010,6,9,22,51,12,24,1014,24.13,0,0 +2010,6,9,23,42,13,22,1014,0.89,0,0 +2010,6,10,0,42,12,23,1014,1.79,0,0 +2010,6,10,1,38,12,22,1013,2.68,0,0 +2010,6,10,2,43,12,21,1013,4.47,0,0 +2010,6,10,3,63,13,21,1013,5.36,0,0 +2010,6,10,4,63,13,22,1013,0.89,0,1 +2010,6,10,5,62,16,21,1014,3.13,0,2 +2010,6,10,6,60,16,20,1015,4.92,0,3 +2010,6,10,7,46,16,20,1015,8.05,0,4 +2010,6,10,8,41,16,19,1015,12.07,0,5 +2010,6,10,9,42,16,20,1015,15.2,0,0 +2010,6,10,10,39,16,20,1014,20.12,0,0 +2010,6,10,11,36,15,21,1014,24.14,0,0 +2010,6,10,12,34,16,21,1014,25.93,0,0 +2010,6,10,13,47,15,22,1013,29.06,0,0 +2010,6,10,14,36,15,22,1013,3.13,0,0 +2010,6,10,15,40,14,23,1012,3.13,0,0 +2010,6,10,16,35,14,24,1012,6.26,0,0 +2010,6,10,17,38,14,24,1012,9.39,0,0 +2010,6,10,18,46,15,23,1012,14.31,0,0 +2010,6,10,19,77,15,22,1012,18.33,0,0 +2010,6,10,20,53,15,20,1013,20.12,0,0 +2010,6,10,21,58,15,19,1013,21.91,0,0 +2010,6,10,22,78,15,19,1013,23.7,0,0 +2010,6,10,23,80,15,18,1013,24.59,0,0 +2010,6,11,0,88,16,18,1013,25.48,0,0 +2010,6,11,1,97,15,18,1013,0.89,0,0 +2010,6,11,2,77,16,18,1013,0.45,0,0 +2010,6,11,3,72,16,18,1013,1.34,0,0 +2010,6,11,4,78,15,18,1013,0.89,0,0 +2010,6,11,5,81,16,18,1013,1.78,0,0 +2010,6,11,6,97,16,18,1013,0.89,0,0 +2010,6,11,7,72,16,19,1013,1.79,0,0 +2010,6,11,8,78,15,20,1013,3.58,0,0 +2010,6,11,9,71,15,21,1013,0.89,0,0 +2010,6,11,10,74,15,23,1012,1.79,0,0 +2010,6,11,11,73,15,23,1012,3.58,0,0 +2010,6,11,12,85,15,24,1012,5.37,0,0 +2010,6,11,13,92,15,24,1011,1.79,0,0 +2010,6,11,14,100,15,24,1011,1.79,0,0 +2010,6,11,15,95,15,25,1010,4.92,0,0 +2010,6,11,16,98,15,25,1010,6.71,0,0 +2010,6,11,17,89,15,25,1009,9.84,0,0 +2010,6,11,18,95,15,24,1010,11.63,0,0 +2010,6,11,19,93,16,24,1011,15.65,0,0 +2010,6,11,20,93,16,22,1009,18.78,0,0 +2010,6,11,21,93,16,22,1010,20.57,0,0 +2010,6,11,22,94,16,22,1010,21.46,0,0 +2010,6,11,23,99,16,20,1010,0.89,0,0 +2010,6,12,0,101,16,20,1010,1.79,0,0 +2010,6,12,1,144,17,21,1009,3.58,0,0 +2010,6,12,2,160,17,20,1009,4.47,0,0 +2010,6,12,3,142,17,20,1009,6.26,0,0 +2010,6,12,4,105,16,20,1009,8.05,0,0 +2010,6,12,5,97,16,19,1009,8.94,0,0 +2010,6,12,6,94,15,20,1009,12.07,0,0 +2010,6,12,7,90,16,20,1010,13.86,0,0 +2010,6,12,8,91,16,21,1010,15.65,0,0 +2010,6,12,9,98,16,22,1010,17.44,0,0 +2010,6,12,10,104,16,25,1009,0.89,0,0 +2010,6,12,11,115,16,26,1009,1.78,0,0 +2010,6,12,12,100,16,27,1008,3.57,0,0 +2010,6,12,13,115,15,29,1007,3.13,0,0 +2010,6,12,14,111,16,30,1006,1.79,0,0 +2010,6,12,15,84,15,29,1006,4.02,0,0 +2010,6,12,16,89,15,31,1005,7.15,0,0 +2010,6,12,17,104,17,29,1004,11.17,0,0 +2010,6,12,18,96,17,28,1004,15.19,0,0 +2010,6,12,19,119,17,28,1006,3.13,0,0 +2010,6,12,20,129,17,26,1006,3.13,0,0 +2010,6,12,21,125,17,27,1007,4.92,0,0 +2010,6,12,22,127,18,27,1007,4.92,0,1 +2010,6,12,23,104,17,23,1007,14.75,0,2 +2010,6,13,0,13,15,24,1005,3.13,0,0 +2010,6,13,1,19,18,23,1006,4.92,0,0 +2010,6,13,2,23,18,23,1006,8.94,0,0 +2010,6,13,3,153,17,22,1006,12.96,0,0 +2010,6,13,4,126,17,21,1006,16.09,0,0 +2010,6,13,5,140,17,21,1006,17.88,0,0 +2010,6,13,6,131,17,21,1007,19.67,0,0 +2010,6,13,7,141,18,21,1007,22.8,0,0 +2010,6,13,8,149,18,21,1007,25.93,0,0 +2010,6,13,9,137,18,22,1007,29.95,0,0 +2010,6,13,10,137,18,24,1007,33.08,0,0 +2010,6,13,11,154,18,25,1006,34.87,0,0 +2010,6,13,12,147,18,26,1006,38,0,0 +2010,6,13,13,143,19,27,1004,41.13,0,0 +2010,6,13,14,151,19,28,1004,42.92,0,0 +2010,6,13,15,165,18,29,1003,44.71,0,0 +2010,6,13,16,174,19,29,1002,47.84,0,0 +2010,6,13,17,173,19,28,1002,51.86,0,0 +2010,6,13,18,157,19,27,1002,53.65,0,0 +2010,6,13,19,198,16,22,1005,9.83,0,2 +2010,6,13,20,37,17,19,1005,4.02,0,3 +2010,6,13,21,35,17,19,1004,7.15,0,5 +2010,6,13,22,42,17,20,1006,3.13,0,6 +2010,6,13,23,38,18,20,1006,0.89,0,0 +2010,6,14,0,57,17,19,1006,1.79,0,0 +2010,6,14,1,68,17,19,1006,3.58,0,0 +2010,6,14,2,63,17,18,1005,0.89,0,0 +2010,6,14,3,80,17,18,1004,1.79,0,0 +2010,6,14,4,63,17,18,1004,4.92,0,0 +2010,6,14,5,74,16,18,1004,0.89,0,0 +2010,6,14,6,77,18,19,1005,1.78,0,0 +2010,6,14,7,67,18,20,1005,1.79,0,0 +2010,6,14,8,78,18,21,1006,0.89,0,0 +2010,6,14,9,82,18,23,1006,1.78,0,0 +2010,6,14,10,68,18,24,1006,3.57,0,0 +2010,6,14,11,56,17,25,1005,4.46,0,0 +2010,6,14,12,65,17,27,1005,1.79,0,0 +2010,6,14,13,68,17,28,1003,1.79,0,0 +2010,6,14,14,67,18,29,1003,3.13,0,0 +2010,6,14,15,77,18,30,1002,7.15,0,0 +2010,6,14,16,82,18,30,1002,11.17,0,0 +2010,6,14,17,85,19,31,1001,16.09,0,0 +2010,6,14,18,95,19,29,1001,19.22,0,0 +2010,6,14,19,100,19,28,1001,22.35,0,0 +2010,6,14,20,118,20,28,1001,26.37,0,0 +2010,6,14,21,142,20,25,1002,28.16,0,0 +2010,6,14,22,137,21,25,1003,29.95,0,0 +2010,6,14,23,139,20,24,1003,31.74,0,0 +2010,6,15,0,151,21,24,1003,34.87,0,0 +2010,6,15,1,191,20,22,1002,0.89,0,0 +2010,6,15,2,216,20,22,1002,1.79,0,0 +2010,6,15,3,229,19,21,1002,1.79,0,0 +2010,6,15,4,198,19,21,1002,1.79,0,0 +2010,6,15,5,175,19,21,1002,0.89,0,0 +2010,6,15,6,159,19,22,1002,1.78,0,0 +2010,6,15,7,196,19,23,1003,0.89,0,0 +2010,6,15,8,199,19,25,1003,0.89,0,0 +2010,6,15,9,150,19,26,1003,1.79,0,0 +2010,6,15,10,128,20,28,1002,0.89,0,0 +2010,6,15,11,139,20,29,1002,1.79,0,0 +2010,6,15,12,148,20,30,1002,1.79,0,0 +2010,6,15,13,136,19,31,1000,1.79,0,0 +2010,6,15,14,117,19,31,1000,0.89,0,0 +2010,6,15,15,108,17,31,1000,1.79,0,0 +2010,6,15,16,75,14,32,999,3.58,0,0 +2010,6,15,17,66,15,33,999,0.89,0,0 +2010,6,15,18,89,17,32,998,1.79,0,0 +2010,6,15,19,118,18,30,999,0.89,0,0 +2010,6,15,20,152,19,28,998,1.79,0,0 +2010,6,15,21,183,17,30,1002,7.15,0,1 +2010,6,15,22,99,21,24,1000,0.89,0,0 +2010,6,15,23,91,21,24,1000,4.02,0,0 +2010,6,16,0,105,20,24,1000,0.89,0,0 +2010,6,16,1,139,20,23,1000,1.79,0,0 +2010,6,16,2,129,20,23,1000,0.89,0,0 +2010,6,16,3,149,21,23,1000,0.89,0,0 +2010,6,16,4,139,20,23,1000,1.78,0,0 +2010,6,16,5,155,20,22,1001,3.57,0,0 +2010,6,16,6,86,19,23,1001,3.13,0,0 +2010,6,16,7,67,19,24,1001,0.89,0,0 +2010,6,16,8,62,19,26,1001,1.78,0,0 +2010,6,16,9,63,18,27,1001,3.13,0,0 +2010,6,16,10,81,18,27,1000,6.26,0,0 +2010,6,16,11,95,18,28,1000,10.28,0,0 +2010,6,16,12,109,18,30,1000,3.13,0,0 +2010,6,16,13,120,19,31,999,4.92,0,0 +2010,6,16,14,125,20,30,998,9.84,0,0 +2010,6,16,15,130,20,30,998,14.76,0,0 +2010,6,16,16,133,19,30,998,19.68,0,0 +2010,6,16,17,139,20,29,998,23.7,0,0 +2010,6,16,18,109,15,26,1001,9.83,0,1 +2010,6,16,19,20,17,23,1002,14.75,0,2 +2010,6,16,20,20,16,22,1000,3.13,0,0 +2010,6,16,21,22,16,23,1002,1.79,0,0 +2010,6,16,22,27,18,21,1004,0.89,0,1 +2010,6,16,23,30,19,20,1004,3.13,0,2 +2010,6,17,0,33,18,20,1001,4.02,0,3 +2010,6,17,1,37,18,20,1002,1.79,0,0 +2010,6,17,2,45,18,20,1001,1.79,0,0 +2010,6,17,3,50,18,20,1001,3.58,0,0 +2010,6,17,4,46,18,20,1001,4.02,0,0 +2010,6,17,5,42,18,20,1001,7.15,0,0 +2010,6,17,6,35,18,20,1002,11.17,0,0 +2010,6,17,7,51,18,21,1003,16.09,0,0 +2010,6,17,8,44,18,20,1003,4.02,0,1 +2010,6,17,9,43,18,21,1003,4.92,0,0 +2010,6,17,10,30,17,18,1003,1.79,0,1 +2010,6,17,11,27,17,19,1002,3.58,0,2 +2010,6,17,12,19,17,19,1003,1.79,0,3 +2010,6,17,13,35,17,19,1002,0.89,0,4 +2010,6,17,14,21,17,19,1002,1.79,0,0 +2010,6,17,15,16,17,19,1001,1.79,0,0 +2010,6,17,16,26,18,19,1002,0.89,0,0 +2010,6,17,17,36,17,20,1001,4.92,0,0 +2010,6,17,18,29,17,20,1002,3.13,0,0 +2010,6,17,19,40,17,21,1002,1.79,0,1 +2010,6,17,20,37,17,20,1002,0.89,0,0 +2010,6,17,21,39,17,20,1003,1.78,0,0 +2010,6,17,22,50,17,19,1004,2.67,0,0 +2010,6,17,23,49,17,19,1003,3.13,0,0 +2010,6,18,0,41,17,20,1004,6.26,0,0 +2010,6,18,1,34,17,19,1003,8.05,0,0 +2010,6,18,2,58,17,18,1002,0.89,0,0 +2010,6,18,3,57,16,18,1002,1.79,0,0 +2010,6,18,4,39,17,18,1003,0.89,0,0 +2010,6,18,5,40,16,18,1003,1.78,0,0 +2010,6,18,6,41,17,19,1003,1.79,0,0 +2010,6,18,7,44,17,20,1004,4.92,0,0 +2010,6,18,8,49,16,20,1005,8.05,0,0 +2010,6,18,9,61,17,22,1005,11.18,0,0 +2010,6,18,10,74,17,23,1005,1.79,0,0 +2010,6,18,11,93,17,23,1004,0.89,0,0 +2010,6,18,12,96,17,25,1004,1.78,0,0 +2010,6,18,13,81,17,27,1003,2.67,0,0 +2010,6,18,14,74,16,27,1003,4.46,0,0 +2010,6,18,15,79,18,26,1002,1.79,0,0 +2010,6,18,16,63,18,27,1003,4.92,0,0 +2010,6,18,17,69,19,25,1002,8.94,0,0 +2010,6,18,18,95,19,25,1002,12.07,0,0 +2010,6,18,19,100,17,22,1003,15.2,0,0 +2010,6,18,20,100,16,22,1003,16.99,0,0 +2010,6,18,21,88,17,20,1004,18.78,0,0 +2010,6,18,22,65,17,20,1004,0.89,0,0 +2010,6,18,23,69,17,19,1005,0.89,0,0 +2010,6,19,0,70,16,19,1005,0.89,0,0 +2010,6,19,1,89,16,18,1005,1.34,0,0 +2010,6,19,2,98,16,18,1005,0.89,0,0 +2010,6,19,3,96,17,18,1005,0.89,0,0 +2010,6,19,4,98,16,18,1005,0.89,0,0 +2010,6,19,5,96,16,18,1005,0.89,0,0 +2010,6,19,6,90,17,19,1005,1.34,0,0 +2010,6,19,7,92,19,20,1006,1.79,0,0 +2010,6,19,8,99,17,22,1006,2.68,0,0 +2010,6,19,9,106,17,24,1005,1.79,0,0 +2010,6,19,10,120,18,26,1005,0.89,0,0 +2010,6,19,11,109,18,28,1005,2.68,0,0 +2010,6,19,12,74,18,29,1004,1.79,0,0 +2010,6,19,13,87,17,31,1003,3.58,0,0 +2010,6,19,14,93,16,31,1003,5.37,0,0 +2010,6,19,15,99,19,32,1002,8.5,0,0 +2010,6,19,16,82,19,31,1002,12.52,0,0 +2010,6,19,17,80,19,31,1002,16.54,0,0 +2010,6,19,18,78,19,28,1003,19.67,0,0 +2010,6,19,19,83,20,27,1003,21.46,0,0 +2010,6,19,20,92,21,26,1004,23.25,0,0 +2010,6,19,21,104,21,24,1005,0.89,0,0 +2010,6,19,22,116,21,24,1005,1.78,0,0 +2010,6,19,23,117,21,24,1006,2.67,0,0 +2010,6,20,0,205,21,24,1006,3.56,0,0 +2010,6,20,1,202,20,22,1006,0.89,0,0 +2010,6,20,2,227,20,22,1006,1.79,0,0 +2010,6,20,3,240,20,22,1007,4.92,0,0 +2010,6,20,4,124,19,21,1007,8.05,0,0 +2010,6,20,5,77,19,21,1007,9.84,0,0 +2010,6,20,6,95,19,24,1008,12.97,0,0 +2010,6,20,7,99,19,25,1009,16.99,0,0 +2010,6,20,8,NA,18,27,1009,20.12,0,0 +2010,6,20,9,NA,17,29,1009,25.04,0,0 +2010,6,20,10,48,15,30,1010,4.92,0,0 +2010,6,20,11,21,14,32,1009,3.13,0,0 +2010,6,20,12,15,10,33,1009,4.92,0,0 +2010,6,20,13,11,11,34,1007,0.89,0,0 +2010,6,20,14,11,10,34,1007,3.13,0,0 +2010,6,20,15,10,12,34,1006,1.79,0,0 +2010,6,20,16,10,10,35,1006,3.58,0,0 +2010,6,20,17,49,16,33,1005,5.81,0,0 +2010,6,20,18,49,16,32,1005,10.73,0,0 +2010,6,20,19,41,17,30,1005,16.54,0,0 +2010,6,20,20,71,17,28,1005,21.46,0,0 +2010,6,20,21,62,15,27,1006,26.38,0,0 +2010,6,20,22,48,15,26,1006,31.3,0,0 +2010,6,20,23,70,15,25,1006,36.22,0,0 +2010,6,21,0,61,15,24,1006,40.24,0,0 +2010,6,21,1,62,15,22,1006,43.37,0,0 +2010,6,21,2,66,14,20,1006,0.89,0,0 +2010,6,21,3,63,14,20,1006,1.78,0,0 +2010,6,21,4,55,14,20,1006,2.67,0,0 +2010,6,21,5,42,14,19,1006,3.56,0,0 +2010,6,21,6,48,15,20,1006,0.89,0,0 +2010,6,21,7,52,13,23,1006,0.89,0,0 +2010,6,21,8,59,13,24,1006,1.79,0,0 +2010,6,21,9,63,14,25,1005,3.58,0,0 +2010,6,21,10,64,15,25,1005,5.37,0,0 +2010,6,21,11,86,15,28,1004,1.79,0,0 +2010,6,21,12,85,16,28,1004,0.89,0,0 +2010,6,21,13,91,17,30,1003,1.78,0,0 +2010,6,21,14,85,16,30,1003,3.13,0,0 +2010,6,21,15,89,16,30,1002,1.79,0,0 +2010,6,21,16,78,17,30,1001,4.02,0,0 +2010,6,21,17,64,14,31,1001,5.81,0,0 +2010,6,21,18,69,15,31,1001,3.13,0,0 +2010,6,21,19,92,16,29,1001,1.79,0,0 +2010,6,21,20,89,17,28,1001,1.79,0,0 +2010,6,21,21,79,18,27,1001,3.58,0,0 +2010,6,21,22,107,18,26,1001,4.92,0,0 +2010,6,21,23,132,18,26,1002,6.71,0,1 +2010,6,22,0,124,18,26,1002,1.79,0,0 +2010,6,22,1,117,19,25,1002,1.79,0,1 +2010,6,22,2,122,19,24,1001,1.79,0,0 +2010,6,22,3,121,19,25,1001,1.79,0,0 +2010,6,22,4,135,18,25,1001,4.92,0,0 +2010,6,22,5,140,19,23,1002,1.79,0,0 +2010,6,22,6,122,20,25,1003,0.45,0,0 +2010,6,22,7,131,20,27,1003,1.34,0,0 +2010,6,22,8,135,20,28,1004,2.23,0,0 +2010,6,22,9,136,20,29,1004,4.02,0,0 +2010,6,22,10,NA,19,29,1005,5.81,0,0 +2010,6,22,11,NA,19,30,1005,11.62,0,0 +2010,6,22,12,NA,17,30,1006,13.41,0,0 +2010,6,22,13,NA,16,29,1006,20.56,0,0 +2010,6,22,14,NA,15,29,1005,26.37,0,0 +2010,6,22,15,NA,16,30,1005,31.29,0,0 +2010,6,22,16,NA,15,29,1005,36.21,0,0 +2010,6,22,17,NA,15,29,1006,41.13,0,0 +2010,6,22,18,NA,16,28,1006,45.15,0,0 +2010,6,22,19,NA,15,26,1007,49.17,0,0 +2010,6,22,20,NA,14,25,1008,52.3,0,0 +2010,6,22,21,NA,13,24,1009,56.32,0,0 +2010,6,22,22,NA,13,24,1009,59.45,0,0 +2010,6,22,23,NA,14,23,1009,0.89,0,0 +2010,6,23,0,NA,13,23,1010,1.79,0,0 +2010,6,23,1,NA,10,23,1011,5.81,0,0 +2010,6,23,2,NA,12,22,1011,6.7,0,0 +2010,6,23,3,NA,12,21,1011,8.49,0,0 +2010,6,23,4,NA,13,20,1011,9.38,0,0 +2010,6,23,5,NA,13,19,1011,0.89,0,0 +2010,6,23,6,NA,14,22,1011,0.89,0,0 +2010,6,23,7,NA,10,23,1011,1.78,0,0 +2010,6,23,8,NA,8,24,1011,2.67,0,0 +2010,6,23,9,NA,8,27,1011,3.56,0,0 +2010,6,23,10,136,8,28,1011,4.45,0,0 +2010,6,23,11,48,9,29,1011,4.02,0,0 +2010,6,23,12,48,9,30,1010,7.15,0,0 +2010,6,23,13,42,10,30,1009,11.17,0,0 +2010,6,23,14,39,10,31,1008,15.19,0,0 +2010,6,23,15,35,10,31,1008,19.21,0,0 +2010,6,23,16,40,11,31,1007,23.23,0,0 +2010,6,23,17,48,11,30,1007,26.36,0,0 +2010,6,23,18,55,11,29,1007,29.49,0,0 +2010,6,23,19,62,12,27,1007,31.28,0,0 +2010,6,23,20,61,12,26,1007,0.89,0,0 +2010,6,23,21,66,13,26,1008,3.13,0,0 +2010,6,23,22,68,13,26,1009,6.26,0,0 +2010,6,23,23,69,11,25,1009,11.18,0,0 +2010,6,24,0,54,12,23,1009,14.31,0,0 +2010,6,24,1,78,13,21,1009,16.1,0,0 +2010,6,24,2,63,14,21,1009,19.23,0,0 +2010,6,24,3,69,14,20,1009,21.02,0,0 +2010,6,24,4,76,14,19,1009,22.81,0,0 +2010,6,24,5,68,14,17,1009,0.89,0,0 +2010,6,24,6,100,16,21,1010,0.45,0,0 +2010,6,24,7,109,15,23,1010,1.34,0,0 +2010,6,24,8,120,15,23,1010,2.23,0,0 +2010,6,24,9,104,13,26,1009,3.12,0,0 +2010,6,24,10,104,13,27,1009,1.79,0,0 +2010,6,24,11,106,13,28,1009,4.92,0,0 +2010,6,24,12,96,12,31,1008,8.05,0,0 +2010,6,24,13,70,12,31,1007,11.18,0,0 +2010,6,24,14,49,11,32,1007,14.31,0,0 +2010,6,24,15,49,12,32,1006,18.33,0,0 +2010,6,24,16,38,12,33,1006,22.35,0,0 +2010,6,24,17,48,11,33,1006,1.79,0,0 +2010,6,24,18,57,11,32,1006,4.02,0,0 +2010,6,24,19,60,12,29,1006,7.15,0,0 +2010,6,24,20,64,11,29,1006,10.28,0,0 +2010,6,24,21,81,14,27,1007,13.41,0,0 +2010,6,24,22,83,15,25,1008,15.2,0,0 +2010,6,24,23,98,14,23,1008,0.89,0,0 +2010,6,25,0,114,15,23,1009,1.79,0,0 +2010,6,25,1,169,15,22,1008,3.58,0,0 +2010,6,25,2,231,15,22,1008,5.37,0,0 +2010,6,25,3,214,15,21,1009,7.16,0,0 +2010,6,25,4,188,16,21,1009,0.45,0,0 +2010,6,25,5,165,16,19,1009,0.89,0,0 +2010,6,25,6,191,18,22,1010,0.45,0,0 +2010,6,25,7,149,17,23,1010,1.34,0,0 +2010,6,25,8,140,17,24,1010,2.23,0,0 +2010,6,25,9,149,16,25,1010,1.79,0,0 +2010,6,25,10,151,16,26,1009,3.58,0,0 +2010,6,25,11,159,15,28,1009,1.79,0,0 +2010,6,25,12,166,15,29,1008,3.58,0,0 +2010,6,25,13,136,14,31,1007,5.37,0,0 +2010,6,25,14,116,15,32,1007,3.13,0,0 +2010,6,25,15,118,14,31,1007,7.15,0,0 +2010,6,25,16,107,14,31,1006,11.17,0,0 +2010,6,25,17,171,15,31,1006,14.3,0,0 +2010,6,25,18,170,15,30,1006,16.09,0,0 +2010,6,25,19,150,16,28,1006,19.22,0,0 +2010,6,25,20,131,16,28,1006,22.35,0,0 +2010,6,25,21,195,18,26,1007,25.48,0,0 +2010,6,25,22,215,17,25,1008,27.27,0,0 +2010,6,25,23,227,17,24,1008,29.06,0,0 +2010,6,26,0,190,17,24,1007,0.89,0,0 +2010,6,26,1,207,17,23,1007,1.78,0,0 +2010,6,26,2,204,18,22,1008,0.89,0,0 +2010,6,26,3,251,17,22,1008,4.02,0,0 +2010,6,26,4,252,18,22,1008,5.81,0,0 +2010,6,26,5,238,18,21,1008,7.6,0,0 +2010,6,26,6,224,18,21,1008,8.49,0,0 +2010,6,26,7,211,18,23,1008,0.89,0,0 +2010,6,26,8,205,18,24,1009,1.78,0,0 +2010,6,26,9,209,17,25,1008,1.79,0,0 +2010,6,26,10,205,17,27,1008,1.79,0,0 +2010,6,26,11,184,17,28,1007,1.79,0,0 +2010,6,26,12,160,17,30,1006,4.92,0,0 +2010,6,26,13,150,17,30,1006,8.05,0,0 +2010,6,26,14,160,17,31,1005,3.13,0,0 +2010,6,26,15,152,17,32,1005,3.13,0,0 +2010,6,26,16,149,16,31,1005,6.26,0,0 +2010,6,26,17,133,16,31,1004,9.39,0,0 +2010,6,26,18,115,15,30,1004,13.41,0,0 +2010,6,26,19,127,15,29,1004,16.54,0,0 +2010,6,26,20,119,16,27,1005,19.67,0,0 +2010,6,26,21,124,14,26,1005,23.69,0,0 +2010,6,26,22,102,14,25,1006,26.82,0,0 +2010,6,26,23,91,14,24,1006,28.61,0,0 +2010,6,27,0,92,14,23,1006,30.4,0,0 +2010,6,27,1,97,13,23,1006,32.19,0,0 +2010,6,27,2,100,12,22,1006,33.98,0,0 +2010,6,27,3,97,13,22,1006,35.77,0,0 +2010,6,27,4,80,14,22,1006,37.56,0,0 +2010,6,27,5,94,15,22,1006,0.45,0,0 +2010,6,27,6,121,17,21,1006,3.13,0,1 +2010,6,27,7,153,16,22,1006,0.89,0,0 +2010,6,27,8,133,14,24,1006,1.78,0,0 +2010,6,27,9,163,16,25,1005,3.13,0,0 +2010,6,27,10,153,16,27,1005,0.89,0,0 +2010,6,27,11,139,14,27,1004,2.68,0,0 +2010,6,27,12,131,15,29,1004,1.79,0,0 +2010,6,27,13,120,15,29,1003,1.79,0,0 +2010,6,27,14,118,15,30,1002,3.13,0,0 +2010,6,27,15,117,14,32,1001,8.05,0,0 +2010,6,27,16,111,16,32,1001,12.97,0,0 +2010,6,27,17,117,16,31,1001,16.99,0,0 +2010,6,27,18,116,16,30,1004,4.92,0,0 +2010,6,27,19,131,19,26,1002,4.92,0,1 +2010,6,27,20,118,20,25,1002,8.05,0,2 +2010,6,27,21,121,18,24,1003,4.92,0,0 +2010,6,27,22,112,19,24,1002,4.92,0,0 +2010,6,27,23,97,18,26,1002,8.05,0,0 +2010,6,28,0,111,19,23,1002,0.89,0,0 +2010,6,28,1,126,19,23,1002,1.79,0,0 +2010,6,28,2,151,19,22,1002,1.79,0,0 +2010,6,28,3,140,19,22,1002,0.89,0,0 +2010,6,28,4,134,19,21,1002,1.79,0,0 +2010,6,28,5,118,19,21,1002,3.13,0,0 +2010,6,28,6,124,19,21,1002,4.92,0,0 +2010,6,28,7,112,20,23,1002,8.05,0,0 +2010,6,28,8,101,19,25,1002,9.84,0,0 +2010,6,28,9,131,19,26,1002,0.89,0,0 +2010,6,28,10,137,18,27,1002,1.78,0,0 +2010,6,28,11,166,19,29,1002,3.57,0,0 +2010,6,28,12,152,18,30,1002,3.13,0,0 +2010,6,28,13,153,18,30,1001,6.26,0,0 +2010,6,28,14,164,18,31,1001,10.28,0,0 +2010,6,28,15,174,19,32,1001,14.3,0,0 +2010,6,28,16,159,19,31,1001,18.32,0,0 +2010,6,28,17,174,18,31,1001,22.34,0,0 +2010,6,28,18,189,19,30,1001,25.47,0,0 +2010,6,28,19,198,19,29,1001,28.6,0,0 +2010,6,28,20,184,20,28,1002,31.73,0,0 +2010,6,28,21,194,20,27,1002,33.52,0,0 +2010,6,28,22,200,20,26,1003,35.31,0,0 +2010,6,28,23,190,19,26,1003,38.44,0,0 +2010,6,29,0,196,19,25,1003,40.23,0,0 +2010,6,29,1,228,20,24,1003,42.02,0,0 +2010,6,29,2,199,20,24,1003,43.81,0,0 +2010,6,29,3,201,19,23,1003,45.6,0,0 +2010,6,29,4,215,19,22,1003,0.45,0,0 +2010,6,29,5,247,19,22,1003,0.89,0,0 +2010,6,29,6,249,20,22,1004,1.78,0,0 +2010,6,29,7,198,20,23,1004,0.89,0,0 +2010,6,29,8,201,20,25,1004,1.78,0,0 +2010,6,29,9,211,20,26,1004,2.67,0,0 +2010,6,29,10,181,20,27,1004,3.56,0,0 +2010,6,29,11,184,20,30,1004,5.35,0,0 +2010,6,29,12,172,18,31,1003,3.13,0,0 +2010,6,29,13,134,18,31,1003,6.26,0,0 +2010,6,29,14,153,18,32,1002,9.39,0,0 +2010,6,29,15,170,19,32,1002,12.52,0,0 +2010,6,29,16,194,19,32,1002,16.54,0,0 +2010,6,29,17,196,19,31,1001,19.67,0,0 +2010,6,29,18,175,20,31,1002,21.46,0,0 +2010,6,29,19,166,20,30,1002,23.25,0,0 +2010,6,29,20,174,20,29,1002,26.38,0,0 +2010,6,29,21,158,21,28,1003,0.89,0,0 +2010,6,29,22,153,21,27,1003,0.89,0,0 +2010,6,29,23,170,20,27,1003,4.02,0,0 +2010,6,30,0,188,20,26,1003,7.15,0,0 +2010,6,30,1,155,21,26,1003,8.94,0,0 +2010,6,30,2,158,20,25,1004,10.73,0,0 +2010,6,30,3,154,20,25,1003,12.52,0,0 +2010,6,30,4,160,19,25,1003,0.89,0,0 +2010,6,30,5,175,20,24,1003,1.34,0,0 +2010,6,30,6,172,20,24,1003,0.89,0,0 +2010,6,30,7,197,20,24,1004,0.89,0,0 +2010,6,30,8,205,20,25,1004,1.79,0,0 +2010,6,30,9,195,20,25,1004,3.58,0,0 +2010,6,30,10,164,20,26,1004,5.37,0,0 +2010,6,30,11,159,20,26,1004,0.89,0,0 +2010,6,30,12,156,20,27,1003,1.78,0,0 +2010,6,30,13,162,19,28,1003,3.13,0,0 +2010,6,30,14,152,20,28,1002,6.26,0,0 +2010,6,30,15,150,20,29,1002,8.05,0,0 +2010,6,30,16,159,19,29,1002,11.18,0,0 +2010,6,30,17,154,20,29,1001,12.97,0,0 +2010,6,30,18,174,20,28,1001,14.76,0,0 +2010,6,30,19,184,20,28,1001,15.65,0,0 +2010,6,30,20,230,20,26,1001,17.44,0,0 +2010,6,30,21,235,21,26,1002,19.23,0,0 +2010,6,30,22,203,21,26,1002,21.02,0,0 +2010,6,30,23,219,21,25,1002,0.89,0,0 +2010,7,1,0,209,21,25,1002,1.79,0,0 +2010,7,1,1,198,21,25,1001,3.58,0,0 +2010,7,1,2,203,22,25,1001,5.37,0,0 +2010,7,1,3,241,22,25,1000,7.16,0,0 +2010,7,1,4,239,22,25,1000,8.95,0,0 +2010,7,1,5,225,22,24,1000,12.08,0,0 +2010,7,1,6,204,22,24,1000,15.21,0,0 +2010,7,1,7,163,22,24,1001,0.89,0,0 +2010,7,1,8,174,22,24,1001,1.79,0,0 +2010,7,1,9,154,21,24,1001,3.58,0,1 +2010,7,1,10,150,22,23,1001,0.89,0,2 +2010,7,1,11,126,22,23,1001,3.13,0,3 +2010,7,1,12,121,22,24,1000,0.89,0,4 +2010,7,1,13,124,22,24,1000,3.13,0,5 +2010,7,1,14,121,22,24,1000,7.15,0,6 +2010,7,1,15,92,22,25,1000,11.17,0,7 +2010,7,1,16,38,21,24,1000,3.13,0,8 +2010,7,1,17,28,19,23,1000,7.15,0,0 +2010,7,1,18,46,20,23,1000,4.02,0,0 +2010,7,1,19,32,20,24,1000,7.15,0,0 +2010,7,1,20,40,20,24,1000,11.17,0,0 +2010,7,1,21,36,20,23,1000,1.79,0,0 +2010,7,1,22,27,20,22,1000,3.13,0,0 +2010,7,1,23,37,19,22,1000,4.92,0,0 +2010,7,2,0,46,19,23,1000,8.05,0,0 +2010,7,2,1,43,20,22,1000,12.07,0,0 +2010,7,2,2,37,19,22,1000,16.09,0,0 +2010,7,2,3,40,18,21,999,19.22,0,0 +2010,7,2,4,45,19,21,1000,23.24,0,0 +2010,7,2,5,17,18,20,1000,28.16,0,0 +2010,7,2,6,11,19,21,1000,29.95,0,0 +2010,7,2,7,17,20,23,1000,31.74,0,0 +2010,7,2,8,20,19,25,1001,35.76,0,0 +2010,7,2,9,29,19,27,1001,3.13,0,0 +2010,7,2,10,34,18,30,1000,0.89,0,0 +2010,7,2,11,40,18,32,1000,2.68,0,0 +2010,7,2,12,23,16,34,1000,4.47,0,0 +2010,7,2,13,17,15,35,999,6.26,0,0 +2010,7,2,14,14,14,36,999,8.05,0,0 +2010,7,2,15,12,13,37,999,3.13,0,0 +2010,7,2,16,13,12,37,998,1.79,0,0 +2010,7,2,17,14,11,37,998,4.92,0,0 +2010,7,2,18,15,13,36,998,5.81,0,0 +2010,7,2,19,31,16,33,999,4.92,0,0 +2010,7,2,20,34,16,31,999,8.94,0,0 +2010,7,2,21,35,17,29,1000,12.07,0,0 +2010,7,2,22,33,18,28,1000,0.89,0,0 +2010,7,2,23,41,19,26,1000,1.78,0,0 +2010,7,3,0,34,19,25,1001,0.89,0,0 +2010,7,3,1,43,20,25,1000,0.89,0,0 +2010,7,3,2,54,20,24,1000,1.78,0,0 +2010,7,3,3,73,20,24,1000,0.89,0,0 +2010,7,3,4,94,21,23,1000,0.89,0,0 +2010,7,3,5,90,20,24,1001,0.89,0,0 +2010,7,3,6,55,20,25,1001,0.89,0,0 +2010,7,3,7,50,21,27,1002,1.78,0,0 +2010,7,3,8,73,22,28,1002,2.67,0,0 +2010,7,3,9,77,21,29,1001,1.79,0,0 +2010,7,3,10,89,20,31,1001,1.79,0,0 +2010,7,3,11,93,20,33,1001,1.79,0,0 +2010,7,3,12,100,20,35,1000,1.79,0,0 +2010,7,3,13,95,19,36,1000,3.13,0,0 +2010,7,3,14,88,17,37,999,6.26,0,0 +2010,7,3,15,85,16,37,998,9.39,0,0 +2010,7,3,16,68,16,38,998,13.41,0,0 +2010,7,3,17,72,17,37,998,17.43,0,0 +2010,7,3,18,57,17,37,998,20.56,0,0 +2010,7,3,19,48,17,34,998,22.35,0,0 +2010,7,3,20,37,17,33,998,23.24,0,0 +2010,7,3,21,67,18,30,999,25.03,0,0 +2010,7,3,22,54,20,30,999,26.82,0,0 +2010,7,3,23,59,21,30,1000,30.84,0,0 +2010,7,4,0,79,22,29,1000,34.86,0,0 +2010,7,4,1,94,21,28,1000,0.89,0,0 +2010,7,4,2,127,21,27,1000,1.79,0,0 +2010,7,4,3,153,21,27,1000,1.79,0,0 +2010,7,4,4,168,20,26,1000,0.89,0,0 +2010,7,4,5,161,20,26,1000,1.78,0,0 +2010,7,4,6,145,21,26,1001,0.89,0,0 +2010,7,4,7,127,21,28,1001,0.89,0,0 +2010,7,4,8,119,20,29,1001,1.79,0,0 +2010,7,4,9,103,20,30,1001,5.81,0,0 +2010,7,4,10,100,20,30,1001,7.6,0,0 +2010,7,4,11,78,22,29,1001,0.89,0,1 +2010,7,4,12,77,19,33,1000,1.78,0,0 +2010,7,4,13,58,14,37,1000,3.13,0,0 +2010,7,4,14,47,15,37,999,6.26,0,0 +2010,7,4,15,43,14,38,998,3.13,0,0 +2010,7,4,16,66,18,37,998,5.81,0,0 +2010,7,4,17,56,17,37,998,10.73,0,0 +2010,7,4,18,51,16,36,997,15.65,0,0 +2010,7,4,19,60,18,34,998,18.78,0,0 +2010,7,4,20,61,19,31,998,21.91,0,0 +2010,7,4,21,66,20,30,999,23.7,0,0 +2010,7,4,22,77,20,29,1000,25.49,0,0 +2010,7,4,23,87,20,29,1000,27.28,0,0 +2010,7,5,0,105,20,27,1000,0.89,0,0 +2010,7,5,1,117,20,26,1001,1.78,0,0 +2010,7,5,2,122,21,25,1001,0.89,0,0 +2010,7,5,3,132,21,24,1001,0.89,0,0 +2010,7,5,4,114,20,24,1001,2.68,0,0 +2010,7,5,5,104,19,23,1002,5.81,0,0 +2010,7,5,6,78,20,24,1002,9.83,0,0 +2010,7,5,7,59,15,28,1002,12.96,0,0 +2010,7,5,8,35,13,31,1003,16.98,0,0 +2010,7,5,9,31,11,33,1002,21,0,0 +2010,7,5,10,15,11,35,1001,22.79,0,0 +2010,7,5,11,15,9,38,1001,1.79,0,0 +2010,7,5,12,13,7,39,1000,3.13,0,0 +2010,7,5,13,11,7,40,999,8.05,0,0 +2010,7,5,14,8,6,40,998,12.07,0,0 +2010,7,5,15,10,5,41,998,20.12,0,0 +2010,7,5,16,13,5,41,997,28.17,0,0 +2010,7,5,17,12,5,41,997,33.09,0,0 +2010,7,5,18,9,4,40,997,34.88,0,0 +2010,7,5,19,21,9,36,997,38.01,0,0 +2010,7,5,20,20,12,33,997,39.8,0,0 +2010,7,5,21,34,8,33,998,41.59,0,0 +2010,7,5,22,32,12,29,998,0.45,0,0 +2010,7,5,23,44,15,26,998,1.34,0,0 +2010,7,6,0,62,14,27,999,3.13,0,0 +2010,7,6,1,34,10,26,999,3.13,0,0 +2010,7,6,2,24,8,28,1000,7.15,0,0 +2010,7,6,3,25,9,28,1000,11.17,0,0 +2010,7,6,4,28,9,26,1000,12.96,0,0 +2010,7,6,5,28,12,23,1001,0.89,0,0 +2010,7,6,6,24,13,26,1001,3.13,0,0 +2010,7,6,7,22,10,30,1002,7.15,0,0 +2010,7,6,8,21,10,32,1002,10.28,0,0 +2010,7,6,9,27,8,34,1002,3.13,0,0 +2010,7,6,10,19,8,36,1002,1.79,0,0 +2010,7,6,11,4,6,38,1002,1.79,0,0 +2010,7,6,12,2,5,38,1001,3.58,0,0 +2010,7,6,13,3,6,40,1000,6.71,0,0 +2010,7,6,14,9,7,40,999,3.13,0,0 +2010,7,6,15,7,6,41,999,7.15,0,0 +2010,7,6,16,21,6,40,998,12.96,0,0 +2010,7,6,17,23,7,39,998,18.77,0,0 +2010,7,6,18,17,8,37,999,23.69,0,0 +2010,7,6,19,25,10,36,999,27.71,0,0 +2010,7,6,20,36,12,35,999,33.52,0,0 +2010,7,6,21,50,12,32,1000,35.31,0,0 +2010,7,6,22,44,18,30,1001,42.46,0,0 +2010,7,6,23,59,18,27,1002,49.61,0,0 +2010,7,7,0,47,15,26,1003,55.42,0,0 +2010,7,7,1,24,14,25,1003,58.55,0,0 +2010,7,7,2,29,14,25,1003,0.89,0,0 +2010,7,7,3,35,14,25,1003,1.78,0,0 +2010,7,7,4,45,15,23,1003,0.89,0,0 +2010,7,7,5,52,15,23,1003,0.89,0,0 +2010,7,7,6,46,15,25,1004,1.34,0,0 +2010,7,7,7,46,15,27,1004,1.79,0,0 +2010,7,7,8,49,15,27,1004,4.92,0,0 +2010,7,7,9,39,15,28,1004,8.94,0,0 +2010,7,7,10,54,16,28,1004,10.73,0,0 +2010,7,7,11,57,16,29,1004,12.52,0,0 +2010,7,7,12,59,16,29,1004,15.65,0,0 +2010,7,7,13,69,16,30,1004,17.44,0,0 +2010,7,7,14,79,17,30,1004,0.89,0,0 +2010,7,7,15,79,17,30,1004,1.79,0,0 +2010,7,7,16,61,17,30,1004,1.79,0,0 +2010,7,7,17,65,18,30,1004,1.79,0,0 +2010,7,7,18,68,18,29,1005,1.79,0,0 +2010,7,7,19,76,18,29,1005,0.89,0,0 +2010,7,7,20,79,19,28,1005,1.79,0,0 +2010,7,7,21,80,18,27,1007,4.02,0,0 +2010,7,7,22,67,17,27,1007,8.94,0,0 +2010,7,7,23,61,17,26,1007,10.73,0,0 +2010,7,8,0,52,17,25,1006,12.52,0,0 +2010,7,8,1,55,17,25,1007,16.54,0,0 +2010,7,8,2,45,17,25,1007,19.67,0,0 +2010,7,8,3,50,17,25,1007,21.46,0,0 +2010,7,8,4,52,17,23,1007,23.25,0,0 +2010,7,8,5,52,17,22,1007,0.89,0,0 +2010,7,8,6,46,18,23,1007,1.34,0,0 +2010,7,8,7,63,18,26,1008,2.23,0,0 +2010,7,8,8,74,17,27,1008,1.79,0,0 +2010,7,8,9,77,17,29,1008,3.58,0,0 +2010,7,8,10,71,16,31,1007,3.13,0,0 +2010,7,8,11,49,14,33,1006,6.26,0,0 +2010,7,8,12,31,12,35,1005,1.79,0,0 +2010,7,8,13,28,11,35,1004,3.58,0,0 +2010,7,8,14,21,11,36,1004,1.79,0,0 +2010,7,8,15,21,11,36,1003,4.92,0,0 +2010,7,8,16,14,11,37,1003,3.13,0,0 +2010,7,8,17,23,10,36,1003,1.79,0,0 +2010,7,8,18,31,10,36,1003,3.13,0,0 +2010,7,8,19,32,11,34,1003,8.94,0,0 +2010,7,8,20,32,11,32,1004,12.07,0,0 +2010,7,8,21,33,16,30,1005,16.99,0,0 +2010,7,8,22,70,18,29,1006,20.12,0,0 +2010,7,8,23,73,17,28,1007,25.04,0,0 +2010,7,9,0,69,17,26,1007,29.06,0,0 +2010,7,9,1,86,16,25,1007,33.08,0,0 +2010,7,9,2,81,16,25,1007,37.1,0,0 +2010,7,9,3,67,16,25,1007,40.23,0,0 +2010,7,9,4,69,16,23,1007,1.79,0,0 +2010,7,9,5,69,17,23,1007,0.89,0,0 +2010,7,9,6,79,18,24,1007,1.78,0,0 +2010,7,9,7,84,17,26,1007,2.67,0,0 +2010,7,9,8,79,17,27,1007,1.79,0,0 +2010,7,9,9,86,17,28,1007,4.92,0,0 +2010,7,9,10,96,17,28,1007,8.05,0,0 +2010,7,9,11,90,17,29,1007,11.18,0,0 +2010,7,9,12,90,18,30,1006,15.2,0,0 +2010,7,9,13,97,19,29,1006,18.33,0,0 +2010,7,9,14,112,19,30,1005,20.12,0,0 +2010,7,9,15,119,20,27,1005,21.91,0,1 +2010,7,9,16,123,21,27,1004,23.7,0,0 +2010,7,9,17,105,21,28,1004,0.89,0,0 +2010,7,9,18,70,19,23,1005,4.02,0,1 +2010,7,9,19,83,21,23,1006,5.81,0,2 +2010,7,9,20,91,20,22,1006,8.94,0,3 +2010,7,9,21,91,20,22,1007,0.89,0,5 +2010,7,9,22,74,20,21,1006,4.02,0,6 +2010,7,9,23,58,19,21,1006,0.89,0,8 +2010,7,10,0,31,20,21,1006,1.79,0,10 +2010,7,10,1,40,19,20,1006,1.79,0,11 +2010,7,10,2,43,19,21,1006,1.79,0,12 +2010,7,10,3,68,19,21,1005,1.79,0,13 +2010,7,10,4,73,20,21,1005,3.58,0,14 +2010,7,10,5,88,20,21,1005,6.71,0,0 +2010,7,10,6,108,19,20,1006,9.84,0,0 +2010,7,10,7,106,19,20,1006,12.97,0,1 +2010,7,10,8,98,19,21,1006,16.1,0,2 +2010,7,10,9,84,19,21,1006,17.89,0,3 +2010,7,10,10,79,20,21,1006,21.02,0,4 +2010,7,10,11,83,20,22,1006,24.15,0,0 +2010,7,10,12,100,21,23,1006,25.94,0,0 +2010,7,10,13,116,20,22,1006,27.73,0,1 +2010,7,10,14,117,21,22,1006,29.52,0,2 +2010,7,10,15,92,21,22,1005,0.89,0,3 +2010,7,10,16,72,21,23,1005,1.79,0,0 +2010,7,10,17,83,21,23,1005,4.92,0,0 +2010,7,10,18,88,20,23,1005,8.05,0,0 +2010,7,10,19,77,19,23,1005,11.18,0,0 +2010,7,10,20,72,20,23,1005,14.31,0,0 +2010,7,10,21,80,19,22,1006,17.44,0,0 +2010,7,10,22,71,20,22,1005,20.57,0,0 +2010,7,10,23,72,19,22,1005,23.7,0,1 +2010,7,11,0,77,19,22,1005,27.72,0,2 +2010,7,11,1,99,19,22,1005,29.51,0,3 +2010,7,11,2,107,19,22,1004,31.3,0,4 +2010,7,11,3,107,19,21,1004,34.43,0,0 +2010,7,11,4,110,19,22,1004,37.56,0,0 +2010,7,11,5,110,20,22,1004,39.35,0,0 +2010,7,11,6,130,20,22,1005,41.14,0,0 +2010,7,11,7,165,20,22,1005,42.93,0,0 +2010,7,11,8,168,20,22,1005,44.72,0,0 +2010,7,11,9,155,20,23,1005,46.51,0,0 +2010,7,11,10,181,20,23,1005,48.3,0,0 +2010,7,11,11,164,20,23,1005,50.09,0,0 +2010,7,11,12,154,21,23,1005,51.88,0,1 +2010,7,11,13,178,21,23,1004,1.79,0,2 +2010,7,11,14,193,21,23,1004,3.58,0,3 +2010,7,11,15,186,21,23,1004,0.89,0,0 +2010,7,11,16,192,22,24,1004,1.79,0,0 +2010,7,11,17,223,22,24,1003,0.89,0,0 +2010,7,11,18,220,22,24,1004,1.79,0,0 +2010,7,11,19,193,22,24,1004,1.79,0,0 +2010,7,11,20,231,21,24,1004,3.58,0,0 +2010,7,11,21,219,21,24,1005,6.71,0,0 +2010,7,11,22,211,21,24,1006,3.13,0,0 +2010,7,11,23,161,20,23,1006,3.13,0,1 +2010,7,12,0,164,18,21,1005,4.92,0,0 +2010,7,12,1,60,19,21,1005,6.71,0,0 +2010,7,12,2,42,19,21,1006,0.89,0,0 +2010,7,12,3,45,19,21,1006,1.79,0,0 +2010,7,12,4,45,20,21,1006,0.89,0,0 +2010,7,12,5,49,20,21,1006,1.78,0,0 +2010,7,12,6,87,20,22,1006,2.67,0,0 +2010,7,12,7,92,20,22,1006,1.79,0,0 +2010,7,12,8,69,20,22,1006,3.58,0,0 +2010,7,12,9,83,20,23,1006,5.37,0,0 +2010,7,12,10,86,20,24,1006,7.16,0,0 +2010,7,12,11,111,21,24,1007,0.89,0,0 +2010,7,12,12,100,21,24,1007,1.79,0,0 +2010,7,12,13,104,21,24,1006,3.58,0,0 +2010,7,12,14,188,22,24,1006,1.79,0,0 +2010,7,12,15,122,21,24,1006,3.58,0,0 +2010,7,12,16,155,21,24,1006,1.79,0,0 +2010,7,12,17,184,21,25,1006,3.58,0,0 +2010,7,12,18,181,21,24,1006,1.79,0,0 +2010,7,12,19,211,21,24,1006,0.89,0,0 +2010,7,12,20,181,21,24,1007,0.89,0,0 +2010,7,12,21,185,21,23,1008,1.79,0,1 +2010,7,12,22,186,21,23,1008,0.89,0,0 +2010,7,12,23,173,22,23,1008,0.89,0,0 +2010,7,13,0,189,21,23,1008,0.89,0,0 +2010,7,13,1,148,21,23,1009,0.89,0,0 +2010,7,13,2,145,20,23,1008,1.79,0,0 +2010,7,13,3,206,20,22,1008,3.58,0,1 +2010,7,13,4,107,20,22,1009,5.37,0,2 +2010,7,13,5,62,21,22,1009,7.16,0,3 +2010,7,13,6,51,21,22,1009,0.89,0,4 +2010,7,13,7,58,21,23,1009,3.13,0,0 +2010,7,13,8,61,21,24,1010,6.26,0,0 +2010,7,13,9,72,21,24,1010,8.05,0,0 +2010,7,13,10,85,22,25,1010,1.79,0,0 +2010,7,13,11,105,22,26,1010,0.89,0,0 +2010,7,13,12,84,21,26,1010,1.78,0,0 +2010,7,13,13,92,21,25,1010,3.13,0,0 +2010,7,13,14,99,21,26,1010,6.26,0,0 +2010,7,13,15,96,21,26,1010,9.39,0,0 +2010,7,13,16,107,21,26,1009,12.52,0,0 +2010,7,13,17,110,21,26,1009,15.65,0,0 +2010,7,13,18,114,21,26,1009,19.67,0,0 +2010,7,13,19,121,21,26,1009,22.8,0,0 +2010,7,13,20,110,20,26,1010,25.93,0,0 +2010,7,13,21,99,20,26,1010,29.06,0,0 +2010,7,13,22,117,21,25,1011,0.89,0,0 +2010,7,13,23,112,21,24,1010,0.89,0,0 +2010,7,14,0,122,22,24,1010,0.89,0,0 +2010,7,14,1,139,21,24,1010,0.89,0,0 +2010,7,14,2,141,21,23,1010,1.78,0,0 +2010,7,14,3,111,22,23,1010,1.79,0,0 +2010,7,14,4,120,22,24,1010,2.68,0,0 +2010,7,14,5,103,22,23,1010,0.89,0,0 +2010,7,14,6,118,22,24,1011,0.89,0,0 +2010,7,14,7,118,22,24,1011,2.68,0,0 +2010,7,14,8,124,22,24,1011,0.89,0,0 +2010,7,14,9,120,22,25,1011,1.78,0,0 +2010,7,14,10,134,23,26,1010,1.79,0,0 +2010,7,14,11,143,22,27,1010,0.89,0,0 +2010,7,14,12,161,21,27,1010,1.79,0,0 +2010,7,14,13,116,22,27,1010,3.58,0,0 +2010,7,14,14,95,21,27,1010,5.37,0,0 +2010,7,14,15,131,21,27,1010,8.5,0,0 +2010,7,14,16,105,21,27,1010,9.39,0,0 +2010,7,14,17,102,21,27,1009,11.18,0,0 +2010,7,14,18,118,21,26,1009,12.97,0,0 +2010,7,14,19,130,22,26,1009,13.86,0,0 +2010,7,14,20,150,22,25,1009,0.89,0,0 +2010,7,14,21,167,22,25,1009,1.34,0,0 +2010,7,14,22,174,22,25,1009,0.89,0,0 +2010,7,14,23,161,22,25,1008,1.78,0,0 +2010,7,15,0,168,22,24,1008,2.67,0,0 +2010,7,15,1,176,22,24,1008,0.89,0,0 +2010,7,15,2,152,22,24,1008,1.78,0,0 +2010,7,15,3,126,22,24,1007,1.79,0,0 +2010,7,15,4,162,22,24,1007,3.58,0,0 +2010,7,15,5,141,21,24,1007,5.37,0,0 +2010,7,15,6,167,21,24,1007,0.89,0,0 +2010,7,15,7,163,21,24,1007,1.78,0,0 +2010,7,15,8,175,21,24,1007,2.67,0,0 +2010,7,15,9,208,22,24,1007,1.79,0,1 +2010,7,15,10,216,22,24,1007,3.58,0,0 +2010,7,15,11,193,22,25,1006,5.37,0,0 +2010,7,15,12,167,22,25,1006,7.16,0,1 +2010,7,15,13,193,22,24,1006,8.95,0,2 +2010,7,15,14,196,22,24,1006,10.74,0,3 +2010,7,15,15,172,22,24,1006,0.89,0,0 +2010,7,15,16,211,22,24,1006,2.68,0,1 +2010,7,15,17,183,22,24,1005,3.13,0,0 +2010,7,15,18,220,22,24,1005,6.26,0,0 +2010,7,15,19,262,22,24,1005,9.39,0,0 +2010,7,15,20,235,22,24,1005,11.18,0,0 +2010,7,15,21,286,22,24,1005,12.97,0,0 +2010,7,15,22,264,22,24,1006,1.79,0,0 +2010,7,15,23,266,22,24,1005,3.13,0,1 +2010,7,16,0,213,22,24,1005,4.92,0,0 +2010,7,16,1,196,22,23,1005,6.71,0,0 +2010,7,16,2,185,22,23,1005,0.89,0,0 +2010,7,16,3,199,22,23,1005,1.78,0,0 +2010,7,16,4,225,22,23,1005,2.67,0,0 +2010,7,16,5,231,22,23,1005,1.79,0,0 +2010,7,16,6,209,22,23,1005,3.58,0,0 +2010,7,16,7,231,22,24,1005,5.37,0,0 +2010,7,16,8,259,23,24,1005,7.16,0,0 +2010,7,16,9,310,23,25,1005,8.95,0,0 +2010,7,16,10,304,23,27,1005,10.74,0,0 +2010,7,16,11,290,23,28,1005,0.89,0,0 +2010,7,16,12,283,23,29,1004,1.79,0,0 +2010,7,16,13,212,23,30,1004,3.58,0,0 +2010,7,16,14,185,23,30,1003,7.6,0,0 +2010,7,16,15,195,24,31,1003,10.73,0,0 +2010,7,16,16,188,23,31,1003,14.75,0,0 +2010,7,16,17,169,23,31,1002,17.88,0,0 +2010,7,16,18,178,23,30,1003,21.01,0,0 +2010,7,16,19,161,23,29,1003,22.8,0,0 +2010,7,16,20,139,23,28,1004,23.69,0,0 +2010,7,16,21,116,23,28,1005,25.48,0,1 +2010,7,16,22,127,22,28,1005,3.13,0,2 +2010,7,16,23,61,21,27,1005,1.79,0,0 +2010,7,17,0,50,22,26,1004,3.13,0,0 +2010,7,17,1,57,21,26,1005,1.79,0,0 +2010,7,17,2,55,22,25,1005,0.89,0,0 +2010,7,17,3,47,22,24,1005,1.79,0,0 +2010,7,17,4,53,22,24,1006,3.58,0,0 +2010,7,17,5,68,22,24,1006,0.89,0,0 +2010,7,17,6,60,23,24,1006,1.79,0,0 +2010,7,17,7,73,23,25,1007,0.89,0,0 +2010,7,17,8,100,23,26,1007,1.79,0,0 +2010,7,17,9,196,23,27,1007,3.58,0,0 +2010,7,17,10,154,23,28,1007,5.37,0,0 +2010,7,17,11,172,22,30,1007,7.16,0,0 +2010,7,17,12,186,23,30,1007,8.95,0,0 +2010,7,17,13,176,23,30,1007,1.79,0,0 +2010,7,17,14,177,24,31,1006,3.13,0,0 +2010,7,17,15,120,23,32,1005,6.26,0,0 +2010,7,17,16,98,22,32,1005,10.28,0,0 +2010,7,17,17,90,21,32,1004,13.41,0,0 +2010,7,17,18,91,21,31,1005,16.54,0,0 +2010,7,17,19,130,21,29,1005,19.67,0,0 +2010,7,17,20,111,21,29,1006,21.46,0,0 +2010,7,17,21,143,21,28,1006,24.59,0,1 +2010,7,17,22,145,22,28,1007,27.72,0,0 +2010,7,17,23,176,22,28,1007,29.51,0,0 +2010,7,18,0,219,22,26,1006,31.3,0,0 +2010,7,18,1,245,23,26,1006,33.09,0,0 +2010,7,18,2,267,23,26,1006,34.88,0,0 +2010,7,18,3,203,23,25,1006,39.8,0,1 +2010,7,18,4,140,19,23,1006,41.59,0,0 +2010,7,18,5,164,20,22,1007,1.79,0,0 +2010,7,18,6,122,21,23,1007,2.68,0,0 +2010,7,18,7,98,21,24,1007,1.79,0,0 +2010,7,18,8,83,21,24,1007,0.89,0,0 +2010,7,18,9,123,22,26,1007,1.78,0,0 +2010,7,18,10,167,22,28,1007,2.67,0,0 +2010,7,18,11,153,23,30,1007,3.56,0,0 +2010,7,18,12,149,23,32,1006,1.79,0,0 +2010,7,18,13,140,24,32,1005,4.92,0,0 +2010,7,18,14,158,23,33,1005,8.05,0,0 +2010,7,18,15,122,23,34,1005,11.18,0,0 +2010,7,18,16,113,22,34,1004,15.2,0,0 +2010,7,18,17,164,23,33,1004,19.22,0,0 +2010,7,18,18,177,24,32,1004,23.24,0,0 +2010,7,18,19,165,24,31,1004,27.26,0,0 +2010,7,18,20,148,24,29,1005,29.05,0,0 +2010,7,18,21,142,24,29,1005,30.84,0,0 +2010,7,18,22,142,24,28,1005,32.63,0,0 +2010,7,18,23,141,24,28,1005,35.76,0,0 +2010,7,19,0,147,23,28,1005,38.89,0,0 +2010,7,19,1,161,23,27,1005,42.02,0,0 +2010,7,19,2,188,23,27,1005,45.15,0,0 +2010,7,19,3,227,23,27,1006,46.94,0,0 +2010,7,19,4,236,23,26,1006,48.73,0,0 +2010,7,19,5,275,23,25,1006,50.52,0,0 +2010,7,19,6,258,23,26,1007,52.31,0,0 +2010,7,19,7,250,23,26,1007,54.1,0,0 +2010,7,19,8,241,24,26,1007,57.23,0,0 +2010,7,19,9,232,24,27,1007,59.02,0,0 +2010,7,19,10,246,24,28,1007,60.81,0,0 +2010,7,19,11,263,24,27,1007,63.94,0,0 +2010,7,19,12,270,24,27,1007,65.73,0,0 +2010,7,19,13,286,24,27,1007,67.52,0,1 +2010,7,19,14,212,22,25,1007,73.33,0,0 +2010,7,19,15,75,22,25,1008,0.89,0,1 +2010,7,19,16,78,20,24,1007,4.02,0,2 +2010,7,19,17,76,21,24,1006,1.79,0,3 +2010,7,19,18,47,21,23,1007,3.58,0,4 +2010,7,19,19,56,21,23,1006,1.79,0,5 +2010,7,19,20,55,20,22,1006,4.92,0,6 +2010,7,19,21,63,19,22,1006,1.79,0,7 +2010,7,19,22,36,20,22,1005,3.58,0,0 +2010,7,19,23,50,20,22,1005,1.79,0,1 +2010,7,20,0,63,20,22,1004,0.45,0,2 +2010,7,20,1,60,19,22,1003,4.02,0,3 +2010,7,20,2,53,19,22,1003,1.79,0,4 +2010,7,20,3,54,19,22,1003,0.89,0,5 +2010,7,20,4,58,19,22,1003,4.91,0,0 +2010,7,20,5,46,19,22,1003,8.04,0,0 +2010,7,20,6,49,19,22,1003,9.83,0,0 +2010,7,20,7,35,19,23,1004,12.96,0,0 +2010,7,20,8,43,20,24,1004,16.09,0,0 +2010,7,20,9,35,19,26,1003,17.88,0,0 +2010,7,20,10,46,19,28,1004,19.67,0,0 +2010,7,20,11,34,19,29,1003,23.69,0,0 +2010,7,20,12,34,19,30,1003,26.82,0,0 +2010,7,20,13,27,20,32,1003,0.89,0,0 +2010,7,20,14,34,20,32,1002,3.13,0,0 +2010,7,20,15,47,20,32,1002,6.26,0,0 +2010,7,20,16,45,21,30,1003,12.07,0,0 +2010,7,20,17,36,21,29,1003,17.88,0,0 +2010,7,20,18,40,21,28,1003,21.9,0,0 +2010,7,20,19,31,20,27,1004,25.92,0,0 +2010,7,20,20,39,21,26,1004,29.05,0,0 +2010,7,20,21,44,21,25,1005,30.84,0,0 +2010,7,20,22,49,21,24,1005,32.63,0,0 +2010,7,20,23,34,21,24,1006,33.52,0,0 +2010,7,21,0,47,22,23,1006,34.41,0,0 +2010,7,21,1,77,21,23,1006,35.3,0,0 +2010,7,21,2,75,21,23,1006,36.19,0,0 +2010,7,21,3,109,21,23,1006,37.98,0,0 +2010,7,21,4,101,21,22,1006,0.89,0,0 +2010,7,21,5,92,20,22,1006,0.89,0,0 +2010,7,21,6,99,21,22,1006,1.79,0,0 +2010,7,21,7,88,22,24,1007,0.89,0,0 +2010,7,21,8,96,22,26,1007,0.89,0,0 +2010,7,21,9,81,22,27,1007,1.79,0,0 +2010,7,21,10,70,22,29,1007,0.89,0,0 +2010,7,21,11,59,22,30,1007,1.78,0,0 +2010,7,21,12,60,22,31,1006,1.79,0,0 +2010,7,21,13,54,21,32,1006,4.92,0,0 +2010,7,21,14,47,21,32,1006,8.05,0,0 +2010,7,21,15,54,22,32,1005,12.07,0,0 +2010,7,21,16,42,22,32,1005,16.09,0,0 +2010,7,21,17,53,22,31,1005,20.11,0,0 +2010,7,21,18,51,22,30,1005,24.13,0,0 +2010,7,21,19,62,22,29,1006,27.26,0,0 +2010,7,21,20,70,22,28,1006,30.39,0,0 +2010,7,21,21,70,22,27,1007,33.52,0,0 +2010,7,21,22,79,22,26,1007,0.89,0,0 +2010,7,21,23,64,22,25,1008,0.89,0,0 +2010,7,22,0,67,22,25,1008,2.68,0,0 +2010,7,22,1,73,22,25,1008,3.57,0,0 +2010,7,22,2,91,22,24,1008,5.36,0,0 +2010,7,22,3,121,22,24,1008,7.15,0,0 +2010,7,22,4,147,22,24,1008,8.94,0,0 +2010,7,22,5,202,22,24,1008,12.07,0,0 +2010,7,22,6,209,22,24,1009,0.89,0,0 +2010,7,22,7,227,23,24,1009,0.89,0,0 +2010,7,22,8,227,23,25,1009,0.89,0,0 +2010,7,22,9,202,23,27,1009,1.78,0,0 +2010,7,22,10,197,24,28,1009,2.67,0,0 +2010,7,22,11,202,24,30,1009,1.79,0,0 +2010,7,22,12,206,24,31,1009,1.79,0,0 +2010,7,22,13,201,25,33,1008,3.13,0,0 +2010,7,22,14,153,25,33,1007,4.92,0,0 +2010,7,22,15,130,23,33,1007,8.05,0,0 +2010,7,22,16,130,24,33,1006,11.18,0,0 +2010,7,22,17,137,23,33,1006,14.31,0,0 +2010,7,22,18,139,24,32,1006,16.1,0,0 +2010,7,22,19,153,24,31,1007,17.89,0,0 +2010,7,22,20,194,25,30,1007,21.02,0,0 +2010,7,22,21,176,25,29,1008,24.15,0,0 +2010,7,22,22,170,25,28,1008,27.28,0,0 +2010,7,22,23,169,25,28,1008,29.07,0,0 +2010,7,23,0,189,24,27,1008,30.86,0,0 +2010,7,23,1,207,24,27,1008,32.65,0,0 +2010,7,23,2,202,24,26,1008,34.44,0,0 +2010,7,23,3,172,24,25,1008,0.89,0,0 +2010,7,23,4,247,23,25,1008,1.78,0,0 +2010,7,23,5,233,24,25,1008,0.89,0,0 +2010,7,23,6,227,23,25,1009,1.78,0,0 +2010,7,23,7,255,24,26,1009,2.67,0,0 +2010,7,23,8,245,24,28,1009,3.12,0,0 +2010,7,23,9,222,24,29,1009,4.01,0,0 +2010,7,23,10,226,25,30,1009,4.9,0,0 +2010,7,23,11,239,24,32,1009,5.79,0,0 +2010,7,23,12,201,24,33,1008,1.79,0,0 +2010,7,23,13,154,25,34,1008,4.92,0,0 +2010,7,23,14,144,23,35,1007,9.84,0,0 +2010,7,23,15,122,23,35,1007,14.76,0,0 +2010,7,23,16,132,23,34,1007,20.57,0,0 +2010,7,23,17,114,22,34,1007,24.59,0,0 +2010,7,23,18,102,22,33,1007,28.61,0,0 +2010,7,23,19,110,22,32,1007,31.74,0,0 +2010,7,23,20,117,23,30,1008,34.87,0,0 +2010,7,23,21,99,23,31,1008,38,0,0 +2010,7,23,22,101,23,30,1009,41.13,0,0 +2010,7,23,23,105,23,30,1009,44.26,0,0 +2010,7,24,0,84,23,29,1009,46.05,0,0 +2010,7,24,1,84,23,28,1009,47.84,0,0 +2010,7,24,2,85,23,27,1009,49.63,0,0 +2010,7,24,3,101,23,26,1010,51.42,0,0 +2010,7,24,4,105,24,26,1010,53.21,0,0 +2010,7,24,5,114,23,25,1010,54.1,0,0 +2010,7,24,6,116,24,26,1010,0.89,0,0 +2010,7,24,7,118,24,27,1011,1.79,0,0 +2010,7,24,8,114,25,29,1011,3.58,0,0 +2010,7,24,9,125,25,30,1011,5.37,0,0 +2010,7,24,10,104,25,32,1011,7.16,0,0 +2010,7,24,11,85,24,32,1011,1.79,0,0 +2010,7,24,12,88,23,33,1011,3.13,0,0 +2010,7,24,13,82,24,34,1010,7.15,0,0 +2010,7,24,14,77,22,35,1010,10.28,0,0 +2010,7,24,15,71,22,35,1010,14.3,0,0 +2010,7,24,16,77,21,34,1009,19.22,0,0 +2010,7,24,17,60,20,34,1009,24.14,0,0 +2010,7,24,18,64,21,33,1009,28.16,0,0 +2010,7,24,19,71,22,32,1009,31.29,0,0 +2010,7,24,20,81,22,30,1010,33.08,0,0 +2010,7,24,21,96,23,30,1011,34.87,0,0 +2010,7,24,22,111,23,29,1011,36.66,0,0 +2010,7,24,23,145,24,28,1011,37.55,0,0 +2010,7,25,0,165,24,29,1010,41.57,0,0 +2010,7,25,1,196,24,27,1010,43.36,0,0 +2010,7,25,2,211,23,28,1010,46.49,0,0 +2010,7,25,3,204,23,27,1010,49.62,0,0 +2010,7,25,4,201,23,26,1010,51.41,0,0 +2010,7,25,5,201,22,25,1011,52.3,0,0 +2010,7,25,6,201,23,25,1010,0.89,0,0 +2010,7,25,7,204,24,26,1011,1.79,0,0 +2010,7,25,8,228,24,28,1011,0.89,0,0 +2010,7,25,9,231,24,29,1011,1.78,0,0 +2010,7,25,10,199,25,31,1011,1.79,0,0 +2010,7,25,11,201,25,32,1010,1.79,0,0 +2010,7,25,12,137,24,34,1010,3.58,0,0 +2010,7,25,13,113,24,34,1009,4.02,0,0 +2010,7,25,14,106,23,35,1009,8.94,0,0 +2010,7,25,15,96,24,35,1008,13.86,0,0 +2010,7,25,16,111,23,34,1008,17.88,0,0 +2010,7,25,17,118,22,33,1008,22.8,0,0 +2010,7,25,18,128,21,33,1008,27.72,0,0 +2010,7,25,19,125,23,32,1009,31.74,0,0 +2010,7,25,20,131,23,31,1009,34.87,0,0 +2010,7,25,21,138,23,30,1009,36.66,0,0 +2010,7,25,22,150,23,29,1010,38.45,0,0 +2010,7,25,23,146,23,28,1010,39.34,0,0 +2010,7,26,0,161,23,28,1009,41.13,0,0 +2010,7,26,1,159,23,28,1009,42.92,0,0 +2010,7,26,2,173,24,27,1008,44.71,0,0 +2010,7,26,3,186,23,27,1008,0.89,0,0 +2010,7,26,4,204,24,26,1008,1.78,0,0 +2010,7,26,5,211,24,26,1008,1.79,0,0 +2010,7,26,6,225,24,26,1009,0.89,0,0 +2010,7,26,7,215,24,27,1009,1.78,0,0 +2010,7,26,8,227,24,28,1009,3.13,0,0 +2010,7,26,9,244,24,29,1009,4.92,0,0 +2010,7,26,10,251,25,30,1010,1.79,0,0 +2010,7,26,11,245,25,30,1009,3.58,0,0 +2010,7,26,12,249,25,31,1009,1.79,0,0 +2010,7,26,13,234,25,31,1009,4.92,0,0 +2010,7,26,14,262,25,32,1008,6.71,0,0 +2010,7,26,15,269,24,32,1008,9.84,0,0 +2010,7,26,16,240,24,32,1008,12.97,0,0 +2010,7,26,17,194,23,33,1007,16.1,0,0 +2010,7,26,18,174,24,32,1007,19.23,0,0 +2010,7,26,19,202,24,31,1007,0.89,0,0 +2010,7,26,20,196,26,29,1007,1.78,0,0 +2010,7,26,21,171,26,29,1007,0.89,0,0 +2010,7,26,22,195,25,29,1007,1.78,0,0 +2010,7,26,23,174,26,28,1006,3.57,0,0 +2010,7,27,0,178,25,28,1007,5.36,0,0 +2010,7,27,1,193,24,28,1006,7.15,0,0 +2010,7,27,2,196,24,27,1006,8.94,0,0 +2010,7,27,3,220,24,27,1006,10.73,0,0 +2010,7,27,4,208,24,27,1006,12.52,0,0 +2010,7,27,5,225,24,26,1006,13.41,0,0 +2010,7,27,6,224,24,26,1006,15.2,0,0 +2010,7,27,7,243,25,27,1006,16.09,0,0 +2010,7,27,8,230,25,29,1006,17.88,0,0 +2010,7,27,9,210,25,31,1006,0.89,0,0 +2010,7,27,10,201,26,31,1005,3.13,0,0 +2010,7,27,11,199,25,33,1005,7.15,0,0 +2010,7,27,12,176,24,34,1005,11.17,0,0 +2010,7,27,13,192,23,34,1004,16.09,0,0 +2010,7,27,14,189,22,34,1004,21.01,0,0 +2010,7,27,15,173,22,35,1003,26.82,0,0 +2010,7,27,16,171,23,35,1003,32.63,0,0 +2010,7,27,17,161,22,34,1002,37.55,0,0 +2010,7,27,18,148,23,33,1002,42.47,0,0 +2010,7,27,19,140,23,32,1002,45.6,0,0 +2010,7,27,20,130,23,31,1002,47.39,0,0 +2010,7,27,21,126,23,30,1003,52.31,0,0 +2010,7,27,22,139,23,30,1002,55.44,0,0 +2010,7,27,23,138,24,29,1002,57.23,0,0 +2010,7,28,0,152,24,29,1002,60.36,0,0 +2010,7,28,1,150,24,28,1002,63.49,0,0 +2010,7,28,2,157,24,28,1001,65.28,0,0 +2010,7,28,3,147,24,26,1001,68.41,0,0 +2010,7,28,4,154,24,27,1001,71.54,0,0 +2010,7,28,5,185,24,26,1002,73.33,0,0 +2010,7,28,6,189,24,26,1002,75.12,0,0 +2010,7,28,7,198,24,27,1002,76.91,0,0 +2010,7,28,8,179,24,28,1002,80.04,0,0 +2010,7,28,9,180,25,29,1002,81.83,0,0 +2010,7,28,10,180,25,30,1001,83.62,0,0 +2010,7,28,11,178,25,32,1001,85.41,0,0 +2010,7,28,12,181,24,34,1000,89.43,0,0 +2010,7,28,13,177,23,34,999,3.13,0,0 +2010,7,28,14,165,23,36,998,4.02,0,0 +2010,7,28,15,198,22,36,997,8.04,0,0 +2010,7,28,16,193,23,35,997,12.06,0,0 +2010,7,28,17,183,23,35,996,16.98,0,0 +2010,7,28,18,189,24,34,996,21,0,0 +2010,7,28,19,184,24,32,997,22.79,0,0 +2010,7,28,20,194,24,32,997,25.92,0,0 +2010,7,28,21,169,24,31,997,27.71,0,0 +2010,7,28,22,172,25,30,997,29.5,0,0 +2010,7,28,23,188,25,29,997,32.63,0,0 +2010,7,29,0,159,25,29,997,35.76,0,0 +2010,7,29,1,151,25,29,996,38.89,0,0 +2010,7,29,2,149,25,29,996,40.68,0,0 +2010,7,29,3,151,25,28,996,43.81,0,0 +2010,7,29,4,131,25,28,996,45.6,0,0 +2010,7,29,5,142,25,27,996,47.39,0,0 +2010,7,29,6,149,25,27,997,49.18,0,0 +2010,7,29,7,155,25,28,997,50.97,0,0 +2010,7,29,8,156,26,29,997,52.76,0,0 +2010,7,29,9,169,25,30,997,1.79,0,0 +2010,7,29,10,171,26,31,996,1.79,0,0 +2010,7,29,11,192,26,31,997,4.92,0,0 +2010,7,29,12,215,26,32,996,8.05,0,0 +2010,7,29,13,204,27,33,996,11.18,0,0 +2010,7,29,14,178,27,33,995,14.31,0,0 +2010,7,29,15,197,27,33,995,18.33,0,0 +2010,7,29,16,210,27,33,994,21.46,0,0 +2010,7,29,17,244,28,32,995,3.13,0,0 +2010,7,29,18,278,27,32,996,7.15,0,0 +2010,7,29,19,257,27,31,997,1.79,0,1 +2010,7,29,20,277,28,30,996,3.58,0,2 +2010,7,29,21,276,28,30,997,1.79,0,3 +2010,7,29,22,246,27,30,997,0.89,0,0 +2010,7,29,23,285,27,30,997,4.02,0,0 +2010,7,30,0,246,26,29,998,8.94,0,0 +2010,7,30,1,230,25,27,998,12.96,0,0 +2010,7,30,2,224,24,27,998,16.98,0,0 +2010,7,30,3,210,24,27,998,20.11,0,0 +2010,7,30,4,216,23,26,998,23.24,0,0 +2010,7,30,5,199,23,26,999,25.03,0,0 +2010,7,30,6,179,24,26,1000,1.79,0,0 +2010,7,30,7,190,24,26,1000,4.02,0,0 +2010,7,30,8,206,25,26,1000,0.89,0,1 +2010,7,30,9,215,25,26,1000,0.89,0,2 +2010,7,30,10,204,25,27,1000,0.89,0,0 +2010,7,30,11,229,25,27,1000,0.89,0,0 +2010,7,30,12,219,26,28,1000,0.89,0,0 +2010,7,30,13,227,26,28,999,0.89,0,0 +2010,7,30,14,192,26,29,999,1.79,0,0 +2010,7,30,15,240,26,29,999,3.58,0,0 +2010,7,30,16,280,26,30,998,5.37,0,0 +2010,7,30,17,302,26,30,997,7.16,0,0 +2010,7,30,18,267,27,30,998,1.79,0,0 +2010,7,30,19,205,26,29,998,0.89,0,0 +2010,7,30,20,188,26,29,998,1.78,0,0 +2010,7,30,21,165,26,29,999,0.89,0,0 +2010,7,30,22,170,26,29,999,1.78,0,0 +2010,7,30,23,155,26,29,999,3.57,0,0 +2010,7,31,0,185,26,28,999,0.89,0,0 +2010,7,31,1,201,26,28,998,1.78,0,0 +2010,7,31,2,197,26,28,998,1.79,0,0 +2010,7,31,3,201,26,27,997,0.89,0,0 +2010,7,31,4,208,26,28,997,1.78,0,0 +2010,7,31,5,242,26,28,997,2.23,0,0 +2010,7,31,6,259,27,28,998,1.79,0,0 +2010,7,31,7,234,27,28,998,0.89,0,0 +2010,7,31,8,248,28,29,998,1.78,0,0 +2010,7,31,9,281,28,30,999,2.67,0,0 +2010,7,31,10,306,24,28,999,5.81,0,1 +2010,7,31,11,182,23,28,1000,9.83,0,2 +2010,7,31,12,46,24,30,1000,15.64,0,0 +2010,7,31,13,22,24,31,1000,22.79,0,0 +2010,7,31,14,22,21,31,1000,29.94,0,0 +2010,7,31,15,20,20,32,1000,35.75,0,0 +2010,7,31,16,21,19,31,1001,41.56,0,0 +2010,7,31,17,18,18,31,1001,47.37,0,0 +2010,7,31,18,18,16,30,1001,53.18,0,0 +2010,7,31,19,13,15,29,1003,57.2,0,0 +2010,7,31,20,13,16,28,1004,3.13,0,1 +2010,7,31,21,13,17,28,1005,0.89,0,2 +2010,7,31,22,30,19,26,1005,1.79,0,0 +2010,7,31,23,34,21,26,1005,3.58,0,0 +2010,8,1,0,44,22,25,1006,5.37,0,0 +2010,8,1,1,22,21,26,1006,7.16,0,1 +2010,8,1,2,35,22,25,1006,8.05,0,0 +2010,8,1,3,53,22,24,1006,9.84,0,1 +2010,8,1,4,46,22,24,1006,10.73,0,0 +2010,8,1,5,48,21,24,1007,12.52,0,0 +2010,8,1,6,33,22,24,1008,0.45,0,0 +2010,8,1,7,45,22,25,1009,1.34,0,0 +2010,8,1,8,53,22,25,1009,1.79,0,0 +2010,8,1,9,57,21,27,1010,1.79,0,0 +2010,8,1,10,66,20,28,1010,3.58,0,0 +2010,8,1,11,45,20,30,1010,0.89,0,0 +2010,8,1,12,47,16,31,1009,2.68,0,0 +2010,8,1,13,49,14,31,1009,4.02,0,0 +2010,8,1,14,27,13,31,1009,8.04,0,0 +2010,8,1,15,30,12,30,1009,12.06,0,0 +2010,8,1,16,26,13,31,1008,16.98,0,0 +2010,8,1,17,22,13,31,1008,20.11,0,0 +2010,8,1,18,25,14,29,1008,23.24,0,0 +2010,8,1,19,28,16,27,1008,26.37,0,0 +2010,8,1,20,23,16,26,1009,28.16,0,0 +2010,8,1,21,37,17,25,1009,29.95,0,0 +2010,8,1,22,66,18,24,1010,31.74,0,0 +2010,8,1,23,70,18,24,1010,33.53,0,0 +2010,8,2,0,85,18,23,1010,35.32,0,0 +2010,8,2,1,105,19,23,1010,37.11,0,0 +2010,8,2,2,78,18,23,1010,0.89,0,0 +2010,8,2,3,64,19,23,1010,0.89,0,0 +2010,8,2,4,38,18,22,1010,1.78,0,0 +2010,8,2,5,41,18,22,1010,2.67,0,0 +2010,8,2,6,91,18,23,1010,0.89,0,0 +2010,8,2,7,90,18,23,1010,1.78,0,0 +2010,8,2,8,100,18,24,1011,2.67,0,0 +2010,8,2,9,114,17,25,1011,3.56,0,0 +2010,8,2,10,117,17,27,1010,4.45,0,0 +2010,8,2,11,90,16,28,1010,5.34,0,0 +2010,8,2,12,100,16,29,1009,1.79,0,0 +2010,8,2,13,71,15,30,1008,1.79,0,0 +2010,8,2,14,65,14,31,1007,1.79,0,0 +2010,8,2,15,44,13,32,1006,0.89,0,0 +2010,8,2,16,36,14,31,1006,3.13,0,0 +2010,8,2,17,29,14,31,1005,6.26,0,0 +2010,8,2,18,38,13,30,1005,10.28,0,0 +2010,8,2,19,41,14,28,1005,13.41,0,0 +2010,8,2,20,48,15,27,1005,15.2,0,0 +2010,8,2,21,50,16,25,1006,16.99,0,0 +2010,8,2,22,52,16,25,1005,18.78,0,0 +2010,8,2,23,64,16,24,1005,20.57,0,0 +2010,8,3,0,74,16,25,1005,22.36,0,0 +2010,8,3,1,71,17,23,1004,24.15,0,0 +2010,8,3,2,83,18,23,1004,27.28,0,0 +2010,8,3,3,94,18,23,1003,29.07,0,0 +2010,8,3,4,85,18,23,1003,30.86,0,0 +2010,8,3,5,96,17,23,1003,32.65,0,0 +2010,8,3,6,154,18,22,1003,1.79,0,0 +2010,8,3,7,121,18,23,1004,3.58,0,0 +2010,8,3,8,142,18,23,1004,1.79,0,0 +2010,8,3,9,179,18,24,1004,0.89,0,0 +2010,8,3,10,184,19,25,1004,3.13,0,0 +2010,8,3,11,166,19,26,1003,4.92,0,0 +2010,8,3,12,163,18,27,1002,1.79,0,0 +2010,8,3,13,144,19,28,1002,1.79,0,0 +2010,8,3,14,139,19,30,1001,0.89,0,0 +2010,8,3,15,161,19,29,1001,1.79,0,0 +2010,8,3,16,165,20,29,1001,3.58,0,0 +2010,8,3,17,174,20,29,1000,5.37,0,0 +2010,8,3,18,195,21,28,1001,7.16,0,0 +2010,8,3,19,176,21,28,1001,8.95,0,0 +2010,8,3,20,171,21,27,1002,0.89,0,0 +2010,8,3,21,174,21,27,1002,0.89,0,0 +2010,8,3,22,199,21,26,1002,0.89,0,0 +2010,8,3,23,198,21,26,1002,1.78,0,0 +2010,8,4,0,198,21,26,1002,0.89,0,0 +2010,8,4,1,202,21,25,1002,1.78,0,0 +2010,8,4,2,199,21,26,1001,3.57,0,0 +2010,8,4,3,202,21,25,1001,4.46,0,0 +2010,8,4,4,178,21,25,1002,0.45,0,0 +2010,8,4,5,203,22,25,1002,0.89,0,0 +2010,8,4,6,178,22,24,1002,0.89,0,1 +2010,8,4,7,155,22,24,1002,1.78,0,0 +2010,8,4,8,159,22,25,1003,2.67,0,0 +2010,8,4,9,170,22,25,1003,0.89,0,0 +2010,8,4,10,205,22,25,1004,0.45,0,0 +2010,8,4,11,202,22,26,1004,4.02,0,1 +2010,8,4,12,137,22,25,1004,9.83,0,2 +2010,8,4,13,86,22,23,1004,13.85,0,0 +2010,8,4,14,69,22,24,1004,17.87,0,1 +2010,8,4,15,35,22,23,1004,22.79,0,2 +2010,8,4,16,35,22,24,1003,27.71,0,3 +2010,8,4,17,41,21,24,1003,32.63,0,0 +2010,8,4,18,33,21,25,1003,35.76,0,0 +2010,8,4,19,36,21,24,1003,38.89,0,0 +2010,8,4,20,38,21,24,1004,42.91,0,0 +2010,8,4,21,41,20,24,1004,46.04,0,0 +2010,8,4,22,37,20,24,1004,47.83,0,0 +2010,8,4,23,43,20,24,1004,3.13,0,0 +2010,8,5,0,45,20,24,1004,4.92,0,0 +2010,8,5,1,36,20,23,1003,6.71,0,0 +2010,8,5,2,28,20,23,1003,3.13,0,0 +2010,8,5,3,19,20,23,1003,6.26,0,0 +2010,8,5,4,31,20,23,1004,9.39,0,0 +2010,8,5,5,35,20,23,1004,13.41,0,0 +2010,8,5,6,32,20,23,1005,16.54,0,0 +2010,8,5,7,28,20,24,1006,19.67,0,0 +2010,8,5,8,33,20,25,1006,22.8,0,0 +2010,8,5,9,24,11,30,1007,30.85,0,0 +2010,8,5,10,23,10,30,1008,40.68,0,0 +2010,8,5,11,15,9,28,1009,51.86,0,0 +2010,8,5,12,13,10,30,1009,59.91,0,0 +2010,8,5,13,30,10,30,1009,67.96,0,0 +2010,8,5,14,11,10,31,1009,73.77,0,0 +2010,8,5,15,13,10,32,1009,79.58,0,0 +2010,8,5,16,16,9,31,1010,85.39,0,0 +2010,8,5,17,24,8,31,1010,94.33,0,0 +2010,8,5,18,20,8,30,1010,101.48,0,0 +2010,8,5,19,9,8,29,1011,108.63,0,0 +2010,8,5,20,18,8,27,1012,114.44,0,0 +2010,8,5,21,12,10,25,1013,118.46,0,0 +2010,8,5,22,12,8,25,1014,123.38,0,0 +2010,8,5,23,11,8,25,1014,128.3,0,0 +2010,8,6,0,11,9,24,1014,132.32,0,0 +2010,8,6,1,12,9,23,1014,135.45,0,0 +2010,8,6,2,10,12,18,1014,138.58,0,0 +2010,8,6,3,15,12,18,1015,140.37,0,0 +2010,8,6,4,16,13,18,1015,142.16,0,0 +2010,8,6,5,14,13,16,1016,145.29,0,0 +2010,8,6,6,13,14,18,1016,148.42,0,0 +2010,8,6,7,25,13,21,1017,152.44,0,0 +2010,8,6,8,21,13,23,1017,155.57,0,0 +2010,8,6,9,23,13,25,1017,159.59,0,0 +2010,8,6,10,16,12,27,1017,4.02,0,0 +2010,8,6,11,12,11,28,1017,7.15,0,0 +2010,8,6,12,10,11,29,1016,3.13,0,0 +2010,8,6,13,11,12,29,1016,6.26,0,0 +2010,8,6,14,18,12,30,1015,9.39,0,0 +2010,8,6,15,18,12,30,1015,12.52,0,0 +2010,8,6,16,16,11,31,1014,14.31,0,0 +2010,8,6,17,20,14,29,1014,16.1,0,0 +2010,8,6,18,19,15,28,1013,19.23,0,0 +2010,8,6,19,18,15,26,1014,21.02,0,0 +2010,8,6,20,27,17,25,1014,24.15,0,0 +2010,8,6,21,46,16,24,1015,27.28,0,0 +2010,8,6,22,38,17,23,1015,29.07,0,0 +2010,8,6,23,48,17,23,1015,30.86,0,0 +2010,8,7,0,62,17,22,1015,32.65,0,0 +2010,8,7,1,65,17,22,1015,0.89,0,0 +2010,8,7,2,78,17,22,1014,1.78,0,0 +2010,8,7,3,85,17,21,1013,2.67,0,0 +2010,8,7,4,76,17,22,1013,3.56,0,0 +2010,8,7,5,79,17,21,1013,4.45,0,0 +2010,8,7,6,92,18,21,1013,0.89,0,0 +2010,8,7,7,105,18,22,1013,0.89,0,0 +2010,8,7,8,90,18,23,1013,1.79,0,0 +2010,8,7,9,108,18,24,1013,3.58,0,0 +2010,8,7,10,105,20,26,1012,5.37,0,0 +2010,8,7,11,106,20,27,1012,8.5,0,0 +2010,8,7,12,106,20,27,1011,11.63,0,0 +2010,8,7,13,107,20,28,1010,15.65,0,0 +2010,8,7,14,119,20,29,1009,19.67,0,0 +2010,8,7,15,108,21,28,1008,23.69,0,0 +2010,8,7,16,101,20,28,1008,28.61,0,0 +2010,8,7,17,91,19,27,1007,33.53,0,0 +2010,8,7,18,38,17,27,1007,37.55,0,0 +2010,8,7,19,39,15,26,1008,41.57,0,0 +2010,8,7,20,42,15,25,1008,44.7,0,0 +2010,8,7,21,39,16,25,1009,47.83,0,1 +2010,8,7,22,38,18,23,1009,0.89,0,2 +2010,8,7,23,38,18,22,1008,0.89,0,3 +2010,8,8,0,45,19,22,1007,3.13,0,4 +2010,8,8,1,40,19,20,1006,0.89,0,5 +2010,8,8,2,44,19,21,1006,3.13,0,6 +2010,8,8,3,44,19,21,1005,0.45,0,7 +2010,8,8,4,55,19,21,1005,1.34,0,0 +2010,8,8,5,62,19,21,1005,2.23,0,0 +2010,8,8,6,63,19,21,1005,2.68,0,0 +2010,8,8,7,56,19,21,1005,3.13,0,0 +2010,8,8,8,68,19,23,1005,7.15,0,0 +2010,8,8,9,57,19,23,1005,10.28,0,0 +2010,8,8,10,71,19,26,1004,12.07,0,0 +2010,8,8,11,66,19,27,1004,0.89,0,0 +2010,8,8,12,75,19,28,1004,3.13,0,0 +2010,8,8,13,81,19,30,1003,4.92,0,0 +2010,8,8,14,98,20,31,1003,6.71,0,0 +2010,8,8,15,114,20,32,1003,8.5,0,0 +2010,8,8,16,119,21,32,1002,10.29,0,0 +2010,8,8,17,125,21,30,1002,14.31,0,0 +2010,8,8,18,115,22,29,1002,17.44,0,0 +2010,8,8,19,124,22,27,1002,20.57,0,0 +2010,8,8,20,141,22,26,1003,23.7,0,0 +2010,8,8,21,157,22,26,1004,25.49,0,0 +2010,8,8,22,187,23,26,1004,28.62,0,0 +2010,8,8,23,221,22,26,1005,0.89,0,0 +2010,8,9,0,221,23,25,1005,1.79,0,0 +2010,8,9,1,221,22,25,1005,0.89,0,0 +2010,8,9,2,218,23,25,1003,3.13,0,0 +2010,8,9,3,231,22,24,1003,0.89,0,0 +2010,8,9,4,231,22,25,1003,3.13,0,0 +2010,8,9,5,231,22,24,1002,1.79,0,0 +2010,8,9,6,208,22,24,1003,0.89,0,0 +2010,8,9,7,211,23,25,1004,1.78,0,0 +2010,8,9,8,255,23,25,1004,1.79,0,0 +2010,8,9,9,258,22,26,1004,3.58,0,0 +2010,8,9,10,246,23,27,1004,5.37,0,0 +2010,8,9,11,239,23,29,1005,1.79,0,0 +2010,8,9,12,207,22,29,1005,1.79,0,0 +2010,8,9,13,224,22,31,1004,3.58,0,0 +2010,8,9,14,245,22,31,1003,6.71,0,0 +2010,8,9,15,221,22,31,1003,8.5,0,0 +2010,8,9,16,208,22,31,1003,11.63,0,0 +2010,8,9,17,209,22,31,1003,14.76,0,0 +2010,8,9,18,224,22,30,1003,17.89,0,0 +2010,8,9,19,270,22,29,1003,21.02,0,0 +2010,8,9,20,271,23,28,1004,0.89,0,0 +2010,8,9,21,297,23,27,1005,1.79,0,0 +2010,8,9,22,275,23,26,1006,0.89,0,0 +2010,8,9,23,295,23,27,1006,1.79,0,0 +2010,8,10,0,336,24,26,1006,2.68,0,0 +2010,8,10,1,360,23,26,1006,1.79,0,0 +2010,8,10,2,263,24,26,1006,0.89,0,0 +2010,8,10,3,271,23,25,1006,1.79,0,0 +2010,8,10,4,284,20,24,1006,3.13,0,0 +2010,8,10,5,145,17,24,1006,4.92,0,0 +2010,8,10,6,123,15,23,1007,8.05,0,0 +2010,8,10,7,65,14,24,1007,12.07,0,0 +2010,8,10,8,32,13,25,1008,16.99,0,0 +2010,8,10,9,34,12,26,1008,21.01,0,0 +2010,8,10,10,45,11,28,1007,4.02,0,0 +2010,8,10,11,17,12,30,1007,1.79,0,0 +2010,8,10,12,40,14,31,1007,0.89,0,0 +2010,8,10,13,60,18,32,1007,3.13,0,0 +2010,8,10,14,83,20,33,1006,6.26,0,0 +2010,8,10,15,110,20,33,1006,8.05,0,0 +2010,8,10,16,121,20,32,1005,11.18,0,0 +2010,8,10,17,155,21,30,1005,16.1,0,0 +2010,8,10,18,168,21,29,1005,19.23,0,0 +2010,8,10,19,154,21,27,1005,23.25,0,0 +2010,8,10,20,160,21,27,1006,26.38,0,0 +2010,8,10,21,175,22,26,1006,28.17,0,0 +2010,8,10,22,172,23,26,1006,29.96,0,0 +2010,8,10,23,183,23,26,1006,31.75,0,0 +2010,8,11,0,182,23,25,1007,0.89,0,0 +2010,8,11,1,194,23,25,1006,1.79,0,0 +2010,8,11,2,132,23,25,1006,3.58,0,0 +2010,8,11,3,141,23,24,1006,5.37,0,0 +2010,8,11,4,122,23,24,1005,7.16,0,0 +2010,8,11,5,101,22,24,1005,8.95,0,0 +2010,8,11,6,100,22,24,1005,10.74,0,0 +2010,8,11,7,92,22,25,1006,12.53,0,0 +2010,8,11,8,94,23,25,1006,14.32,0,0 +2010,8,11,9,120,23,26,1005,17.45,0,0 +2010,8,11,10,166,23,26,1006,20.58,0,1 +2010,8,11,11,223,23,26,1005,23.71,0,0 +2010,8,11,12,181,23,26,1005,27.73,0,1 +2010,8,11,13,128,23,26,1005,31.75,0,2 +2010,8,11,14,117,22,26,1005,35.77,0,0 +2010,8,11,15,97,22,27,1004,38.9,0,1 +2010,8,11,16,96,21,27,1004,42.92,0,0 +2010,8,11,17,79,21,26,1004,46.94,0,0 +2010,8,11,18,61,21,26,1004,50.96,0,1 +2010,8,11,19,66,21,25,1004,54.09,0,0 +2010,8,11,20,69,20,25,1004,57.22,0,0 +2010,8,11,21,76,20,25,1004,1.79,0,0 +2010,8,11,22,81,20,24,1004,1.79,0,0 +2010,8,11,23,70,20,23,1004,3.58,0,0 +2010,8,12,0,83,20,22,1004,5.37,0,0 +2010,8,12,1,81,20,22,1003,0.89,0,0 +2010,8,12,2,74,20,21,1003,1.34,0,0 +2010,8,12,3,70,19,21,1003,0.89,0,0 +2010,8,12,4,74,20,21,1003,0.45,0,0 +2010,8,12,5,94,20,21,1003,0.89,0,0 +2010,8,12,6,111,20,21,1002,1.79,0,0 +2010,8,12,7,115,21,22,1002,0.45,0,0 +2010,8,12,8,117,22,24,1002,1.79,0,0 +2010,8,12,9,110,21,27,1002,0.89,0,0 +2010,8,12,10,96,20,28,1002,1.79,0,0 +2010,8,12,11,93,21,29,1001,0.89,0,0 +2010,8,12,12,113,22,31,1001,1.78,0,0 +2010,8,12,13,111,21,33,1001,1.79,0,0 +2010,8,12,14,115,21,33,1000,1.79,0,0 +2010,8,12,15,105,21,33,999,1.79,0,0 +2010,8,12,16,94,22,33,998,5.81,0,0 +2010,8,12,17,99,21,32,998,9.83,0,0 +2010,8,12,18,71,22,31,998,13.85,0,0 +2010,8,12,19,53,23,29,999,16.98,0,0 +2010,8,12,20,47,23,27,1000,18.77,0,0 +2010,8,12,21,83,23,27,1001,20.56,0,0 +2010,8,12,22,96,24,27,1002,0.89,0,0 +2010,8,12,23,111,24,27,1001,3.13,0,0 +2010,8,13,0,100,24,27,1001,6.26,0,0 +2010,8,13,1,84,24,27,1001,8.05,0,0 +2010,8,13,2,88,24,26,1000,9.84,0,0 +2010,8,13,3,79,24,26,1000,11.63,0,0 +2010,8,13,4,75,24,26,1000,13.42,0,0 +2010,8,13,5,88,24,26,1000,15.21,0,0 +2010,8,13,6,87,24,26,1001,16.1,0,0 +2010,8,13,7,96,24,26,1001,0.89,0,0 +2010,8,13,8,96,24,26,1002,0.89,0,0 +2010,8,13,9,132,24,27,1002,0.89,0,1 +2010,8,13,10,141,25,28,1002,1.78,0,0 +2010,8,13,11,174,25,28,1002,1.79,0,0 +2010,8,13,12,167,25,28,1002,3.58,0,0 +2010,8,13,13,184,25,29,1002,0.89,0,0 +2010,8,13,14,194,25,29,1001,3.13,0,0 +2010,8,13,15,190,25,30,1001,7.15,0,0 +2010,8,13,16,146,25,30,1001,11.17,0,0 +2010,8,13,17,183,25,29,1000,12.96,0,0 +2010,8,13,18,157,25,29,1000,16.09,0,0 +2010,8,13,19,144,25,28,1000,19.22,0,1 +2010,8,13,20,NA,25,27,1001,3.13,0,0 +2010,8,13,21,NA,25,27,1001,1.79,0,0 +2010,8,13,22,NA,25,27,1001,1.79,0,0 +2010,8,13,23,NA,25,26,1000,3.13,0,0 +2010,8,14,0,NA,24,26,999,6.26,0,0 +2010,8,14,1,NA,24,26,999,8.05,0,0 +2010,8,14,2,NA,24,25,999,1.79,0,0 +2010,8,14,3,NA,24,25,999,1.79,0,0 +2010,8,14,4,NA,23,25,999,0.45,0,0 +2010,8,14,5,NA,23,24,998,0.89,0,0 +2010,8,14,6,NA,22,23,999,1.79,0,0 +2010,8,14,7,NA,23,25,999,0.89,0,0 +2010,8,14,8,NA,24,26,999,1.78,0,0 +2010,8,14,9,NA,23,29,999,2.67,0,0 +2010,8,14,10,NA,18,31,999,1.79,0,0 +2010,8,14,11,NA,13,33,999,5.81,0,0 +2010,8,14,12,NA,-1,35,999,15.64,0,0 +2010,8,14,13,NA,0,35,998,26.82,0,0 +2010,8,14,14,NA,1,36,998,38,0,0 +2010,8,14,15,NA,3,36,998,46.94,0,0 +2010,8,14,16,NA,2,35,998,58.12,0,0 +2010,8,14,17,NA,5,35,999,67.95,0,0 +2010,8,14,18,NA,7,32,1000,77.78,0,0 +2010,8,14,19,NA,6,31,1001,85.83,0,0 +2010,8,14,20,NA,7,30,1001,90.75,0,0 +2010,8,14,21,NA,9,26,1002,0.89,0,0 +2010,8,14,22,NA,9,28,1002,1.79,0,0 +2010,8,14,23,NA,9,28,1003,5.81,0,0 +2010,8,15,0,NA,9,27,1003,8.94,0,0 +2010,8,15,1,NA,9,27,1003,12.96,0,0 +2010,8,15,2,NA,9,26,1003,16.98,0,0 +2010,8,15,3,NA,9,26,1003,20.11,0,0 +2010,8,15,4,NA,10,25,1003,23.24,0,0 +2010,8,15,5,NA,10,24,1004,27.26,0,0 +2010,8,15,6,NA,12,21,1004,29.05,0,0 +2010,8,15,7,NA,11,26,1005,33.07,0,0 +2010,8,15,8,NA,12,28,1005,36.2,0,0 +2010,8,15,9,NA,10,30,1005,41.12,0,0 +2010,8,15,10,NA,9,31,1006,45.14,0,0 +2010,8,15,11,NA,8,33,1005,52.29,0,0 +2010,8,15,12,NA,9,34,1005,57.21,0,0 +2010,8,15,13,NA,9,35,1004,4.02,0,0 +2010,8,15,14,NA,10,33,1004,4.02,0,0 +2010,8,15,15,NA,11,33,1004,0.89,0,0 +2010,8,15,16,NA,12,33,1004,1.79,0,0 +2010,8,15,17,NA,12,32,1004,5.81,0,0 +2010,8,15,18,NA,13,31,1004,8.94,0,0 +2010,8,15,19,NA,13,30,1004,10.73,0,0 +2010,8,15,20,NA,13,29,1005,13.86,0,0 +2010,8,15,21,NA,16,26,1005,15.65,0,0 +2010,8,15,22,NA,15,25,1006,0.89,0,0 +2010,8,15,23,NA,17,23,1006,1.78,0,0 +2010,8,16,0,NA,17,23,1006,0.89,0,0 +2010,8,16,1,NA,17,23,1006,0.89,0,0 +2010,8,16,2,NA,17,22,1007,0.89,0,0 +2010,8,16,3,NA,17,21,1007,1.79,0,0 +2010,8,16,4,NA,17,21,1007,1.79,0,0 +2010,8,16,5,NA,17,20,1007,1.79,0,0 +2010,8,16,6,NA,17,20,1008,3.58,0,0 +2010,8,16,7,NA,17,24,1008,5.37,0,0 +2010,8,16,8,NA,15,26,1008,3.13,0,0 +2010,8,16,9,NA,16,28,1008,4.92,0,0 +2010,8,16,10,NA,15,31,1008,0.89,0,0 +2010,8,16,11,NA,16,31,1008,1.78,0,0 +2010,8,16,12,NA,16,33,1007,3.57,0,0 +2010,8,16,13,NA,16,34,1007,6.7,0,0 +2010,8,16,14,NA,14,35,1006,4.02,0,0 +2010,8,16,15,NA,14,35,1005,8.04,0,0 +2010,8,16,16,112,15,34,1005,12.96,0,0 +2010,8,16,17,68,15,33,1005,17.88,0,0 +2010,8,16,18,45,17,32,1005,21.9,0,0 +2010,8,16,19,69,15,30,1006,25.92,0,0 +2010,8,16,20,68,15,30,1007,29.94,0,0 +2010,8,16,21,92,16,29,1007,33.96,0,0 +2010,8,16,22,79,17,28,1007,37.09,0,0 +2010,8,16,23,68,18,26,1007,38.88,0,0 +2010,8,17,0,76,18,26,1008,40.67,0,0 +2010,8,17,1,81,19,24,1008,42.46,0,0 +2010,8,17,2,97,19,23,1007,44.25,0,0 +2010,8,17,3,114,19,23,1007,46.04,0,0 +2010,8,17,4,140,19,22,1008,47.83,0,0 +2010,8,17,5,158,18,22,1008,0.89,0,0 +2010,8,17,6,158,19,22,1009,1.78,0,0 +2010,8,17,7,182,20,24,1009,2.67,0,0 +2010,8,17,8,166,21,26,1009,3.13,0,0 +2010,8,17,9,188,20,28,1009,6.26,0,0 +2010,8,17,10,194,21,29,1009,9.39,0,0 +2010,8,17,11,174,19,31,1008,14.31,0,0 +2010,8,17,12,173,17,33,1008,20.12,0,0 +2010,8,17,13,121,17,33,1008,25.04,0,0 +2010,8,17,14,125,16,33,1007,30.85,0,0 +2010,8,17,15,122,16,33,1007,36.66,0,0 +2010,8,17,16,118,17,32,1006,41.58,0,0 +2010,8,17,17,123,16,32,1006,47.39,0,0 +2010,8,17,18,127,17,31,1006,52.31,0,0 +2010,8,17,19,152,18,30,1007,56.33,0,0 +2010,8,17,20,144,18,28,1008,59.46,0,0 +2010,8,17,21,145,19,27,1008,62.59,0,0 +2010,8,17,22,156,19,26,1009,65.72,0,0 +2010,8,17,23,171,21,25,1009,67.51,0,0 +2010,8,18,0,199,22,25,1009,69.3,0,0 +2010,8,18,1,249,22,24,1009,71.09,0,0 +2010,8,18,2,300,22,25,1009,72.88,0,0 +2010,8,18,3,285,22,24,1009,76.01,0,0 +2010,8,18,4,281,22,24,1009,79.14,0,0 +2010,8,18,5,318,22,24,1009,80.93,0,0 +2010,8,18,6,326,22,24,1010,84.06,0,0 +2010,8,18,7,337,23,25,1010,87.19,0,0 +2010,8,18,8,312,23,26,1010,90.32,0,0 +2010,8,18,9,311,23,27,1011,93.45,0,0 +2010,8,18,10,291,23,27,1011,95.24,0,0 +2010,8,18,11,230,23,27,1011,0.89,0,0 +2010,8,18,12,246,23,28,1011,1.79,0,1 +2010,8,18,13,309,23,28,1011,3.58,0,2 +2010,8,18,14,283,23,28,1010,6.71,0,3 +2010,8,18,15,237,23,28,1010,8.5,0,0 +2010,8,18,16,164,24,27,1010,10.29,0,1 +2010,8,18,17,140,24,27,1010,12.08,0,2 +2010,8,18,18,140,24,27,1010,12.97,0,3 +2010,8,18,19,152,23,25,1010,14.76,0,4 +2010,8,18,20,173,23,24,1011,1.79,0,6 +2010,8,18,21,136,22,24,1011,3.13,0,8 +2010,8,18,22,90,19,22,1012,6.26,0,9 +2010,8,18,23,103,20,22,1011,10.28,0,10 +2010,8,19,0,104,20,22,1011,14.3,0,11 +2010,8,19,1,106,20,22,1011,0.89,0,12 +2010,8,19,2,89,21,22,1010,1.79,0,13 +2010,8,19,3,85,21,22,1011,3.58,0,14 +2010,8,19,4,84,21,22,1010,6.71,0,0 +2010,8,19,5,88,21,22,1011,9.84,0,0 +2010,8,19,6,97,21,22,1011,12.97,0,0 +2010,8,19,7,102,21,23,1011,16.1,0,1 +2010,8,19,8,93,22,23,1012,17.89,0,0 +2010,8,19,9,102,21,24,1013,21.02,0,0 +2010,8,19,10,100,21,24,1013,22.81,0,0 +2010,8,19,11,100,21,26,1012,25.94,0,0 +2010,8,19,12,78,21,28,1012,27.73,0,0 +2010,8,19,13,75,21,28,1012,0.89,0,0 +2010,8,19,14,93,21,27,1012,1.79,0,0 +2010,8,19,15,123,22,27,1012,3.58,0,0 +2010,8,19,16,124,21,28,1012,6.71,0,0 +2010,8,19,17,116,22,28,1012,8.5,0,0 +2010,8,19,18,129,22,27,1012,10.29,0,0 +2010,8,19,19,121,23,26,1012,0.89,0,0 +2010,8,19,20,134,22,25,1012,0.89,0,0 +2010,8,19,21,137,22,25,1013,0.89,0,0 +2010,8,19,22,154,22,24,1013,1.79,0,0 +2010,8,19,23,210,22,24,1014,0.89,0,0 +2010,8,20,0,195,22,24,1014,1.79,0,0 +2010,8,20,1,154,22,24,1014,0.89,0,0 +2010,8,20,2,152,22,24,1014,0.89,0,0 +2010,8,20,3,164,22,24,1014,0.89,0,0 +2010,8,20,4,165,22,23,1014,0.89,0,0 +2010,8,20,5,126,22,23,1015,1.79,0,0 +2010,8,20,6,121,21,23,1015,4.92,0,0 +2010,8,20,7,132,22,23,1015,8.05,0,0 +2010,8,20,8,119,22,25,1015,9.84,0,0 +2010,8,20,9,88,23,27,1015,12.97,0,0 +2010,8,20,10,79,22,28,1015,16.99,0,0 +2010,8,20,11,65,22,30,1015,1.79,0,0 +2010,8,20,12,63,21,30,1014,2.68,0,0 +2010,8,20,13,79,23,31,1014,1.79,0,0 +2010,8,20,14,93,21,32,1013,3.58,0,0 +2010,8,20,15,116,22,31,1013,5.37,0,0 +2010,8,20,16,146,23,30,1012,9.39,0,0 +2010,8,20,17,138,22,29,1012,13.41,0,0 +2010,8,20,18,144,23,28,1012,17.43,0,0 +2010,8,20,19,151,23,27,1013,20.56,0,0 +2010,8,20,20,132,23,26,1013,23.69,0,0 +2010,8,20,21,113,22,25,1014,26.82,0,0 +2010,8,20,22,63,22,24,1014,28.61,0,0 +2010,8,20,23,64,23,24,1014,30.4,0,0 +2010,8,21,0,45,21,24,1014,32.19,0,0 +2010,8,21,1,62,21,24,1013,33.98,0,0 +2010,8,21,2,60,21,23,1013,35.77,0,0 +2010,8,21,3,39,21,23,1013,37.56,0,0 +2010,8,21,4,47,21,23,1012,0.89,0,1 +2010,8,21,5,45,21,22,1012,1.78,0,0 +2010,8,21,6,52,21,22,1012,2.67,0,1 +2010,8,21,7,52,21,22,1012,3.56,0,2 +2010,8,21,8,53,21,22,1012,1.79,0,3 +2010,8,21,9,50,20,23,1012,4.92,0,4 +2010,8,21,10,35,19,22,1013,12.07,0,5 +2010,8,21,11,46,20,22,1012,16.99,0,6 +2010,8,21,12,51,19,21,1012,21.91,0,7 +2010,8,21,13,38,19,21,1011,27.72,0,8 +2010,8,21,14,34,18,20,1011,32.64,0,10 +2010,8,21,15,44,19,20,1011,37.56,0,11 +2010,8,21,16,19,18,20,1011,4.02,0,12 +2010,8,21,17,21,19,20,1011,8.04,0,13 +2010,8,21,18,12,19,21,1011,4.92,0,14 +2010,8,21,19,26,18,20,1011,8.05,0,0 +2010,8,21,20,46,18,20,1012,9.84,0,1 +2010,8,21,21,29,17,20,1013,13.86,0,2 +2010,8,21,22,13,17,20,1012,4.02,0,3 +2010,8,21,23,9,16,19,1012,7.15,0,4 +2010,8,22,0,9,16,19,1013,10.28,0,5 +2010,8,22,1,9,16,19,1013,4.02,0,0 +2010,8,22,2,17,16,20,1013,4.02,0,0 +2010,8,22,3,4,16,19,1013,8.04,0,0 +2010,8,22,4,8,15,19,1012,12.96,0,0 +2010,8,22,5,15,16,18,1012,1.79,0,0 +2010,8,22,6,23,16,18,1013,4.92,0,0 +2010,8,22,7,11,16,19,1013,3.13,0,0 +2010,8,22,8,23,16,21,1014,4.92,0,0 +2010,8,22,9,32,17,23,1014,6.71,0,0 +2010,8,22,10,30,16,24,1014,3.13,0,0 +2010,8,22,11,12,16,26,1014,7.15,0,0 +2010,8,22,12,11,15,27,1014,4.02,0,0 +2010,8,22,13,17,16,28,1013,7.15,0,0 +2010,8,22,14,18,15,28,1013,3.13,0,0 +2010,8,22,15,21,15,28,1012,7.15,0,0 +2010,8,22,16,15,14,29,1012,10.28,0,0 +2010,8,22,17,11,17,28,1012,13.41,0,0 +2010,8,22,18,24,17,26,1012,17.43,0,0 +2010,8,22,19,38,18,25,1013,20.56,0,0 +2010,8,22,20,45,18,23,1014,22.35,0,0 +2010,8,22,21,44,18,22,1014,24.14,0,0 +2010,8,22,22,48,18,22,1014,27.27,0,0 +2010,8,22,23,42,18,21,1015,28.16,0,0 +2010,8,23,0,49,18,20,1015,0.89,0,0 +2010,8,23,1,61,18,20,1014,1.78,0,0 +2010,8,23,2,57,18,19,1014,2.67,0,0 +2010,8,23,3,56,17,19,1014,0.89,0,0 +2010,8,23,4,61,17,19,1014,1.78,0,0 +2010,8,23,5,64,17,19,1014,0.45,0,0 +2010,8,23,6,83,17,19,1014,1.79,0,0 +2010,8,23,7,82,18,20,1015,2.68,0,0 +2010,8,23,8,76,18,21,1015,4.47,0,0 +2010,8,23,9,61,18,22,1015,7.6,0,0 +2010,8,23,10,66,19,24,1015,9.39,0,0 +2010,8,23,11,62,18,26,1015,11.18,0,0 +2010,8,23,12,70,18,27,1014,0.89,0,0 +2010,8,23,13,63,19,29,1013,1.78,0,0 +2010,8,23,14,57,18,29,1013,2.67,0,0 +2010,8,23,15,65,18,29,1012,3.13,0,0 +2010,8,23,16,84,19,29,1012,4.92,0,0 +2010,8,23,17,76,20,28,1012,6.71,0,0 +2010,8,23,18,59,20,28,1012,10.73,0,0 +2010,8,23,19,70,20,27,1012,12.52,0,0 +2010,8,23,20,108,20,25,1013,14.31,0,0 +2010,8,23,21,149,21,24,1014,16.1,0,0 +2010,8,23,22,169,21,23,1014,17.89,0,0 +2010,8,23,23,157,21,23,1014,19.68,0,0 +2010,8,24,0,186,21,23,1014,22.81,0,0 +2010,8,24,1,166,20,22,1014,24.6,0,0 +2010,8,24,2,173,20,21,1014,26.39,0,0 +2010,8,24,3,178,20,22,1013,0.89,0,0 +2010,8,24,4,190,20,22,1013,1.78,0,0 +2010,8,24,5,211,20,22,1013,0.89,0,0 +2010,8,24,6,177,20,21,1014,0.89,0,0 +2010,8,24,7,182,21,22,1014,1.78,0,0 +2010,8,24,8,159,21,23,1014,1.79,0,0 +2010,8,24,9,181,21,24,1014,0.89,0,0 +2010,8,24,10,169,21,26,1014,1.79,0,0 +2010,8,24,11,157,20,27,1014,3.58,0,0 +2010,8,24,12,202,22,28,1013,6.71,0,0 +2010,8,24,13,186,21,28,1013,9.84,0,0 +2010,8,24,14,127,21,29,1013,13.86,0,0 +2010,8,24,15,117,19,29,1013,18.78,0,0 +2010,8,24,16,87,18,29,1012,24.59,0,0 +2010,8,24,17,70,18,28,1013,31.74,0,0 +2010,8,24,18,60,18,26,1013,37.55,0,0 +2010,8,24,19,34,17,24,1013,42.47,0,0 +2010,8,24,20,34,16,24,1014,47.39,0,0 +2010,8,24,21,47,17,23,1015,51.41,0,0 +2010,8,24,22,45,17,21,1015,0.89,0,0 +2010,8,24,23,39,17,20,1015,0.89,0,0 +2010,8,25,0,61,17,19,1015,2.68,0,0 +2010,8,25,1,72,17,19,1015,4.47,0,0 +2010,8,25,2,73,17,19,1015,6.26,0,0 +2010,8,25,3,77,17,18,1015,7.15,0,0 +2010,8,25,4,69,16,18,1015,0.89,0,0 +2010,8,25,5,83,16,17,1015,0.89,0,0 +2010,8,25,6,76,16,17,1015,1.78,0,0 +2010,8,25,7,93,18,19,1015,0.89,0,0 +2010,8,25,8,97,18,22,1015,3.13,0,0 +2010,8,25,9,100,18,23,1015,0.89,0,0 +2010,8,25,10,74,18,25,1015,3.13,0,0 +2010,8,25,11,81,19,26,1015,0.89,0,0 +2010,8,25,12,77,18,27,1015,2.68,0,0 +2010,8,25,13,82,17,28,1014,3.57,0,0 +2010,8,25,14,75,19,29,1014,1.79,0,0 +2010,8,25,15,97,18,30,1014,3.58,0,0 +2010,8,25,16,73,17,29,1013,5.37,0,0 +2010,8,25,17,71,16,29,1013,8.5,0,0 +2010,8,25,18,70,18,27,1013,10.29,0,0 +2010,8,25,19,81,18,25,1014,11.18,0,0 +2010,8,25,20,90,19,23,1014,0.89,0,0 +2010,8,25,21,132,19,22,1015,1.78,0,0 +2010,8,25,22,184,19,21,1015,2.67,0,0 +2010,8,25,23,115,18,21,1015,0.89,0,0 +2010,8,26,0,122,19,21,1015,1.79,0,0 +2010,8,26,1,124,18,20,1015,0.89,0,0 +2010,8,26,2,133,18,20,1015,0.89,0,0 +2010,8,26,3,98,17,19,1014,2.68,0,0 +2010,8,26,4,70,17,19,1014,4.47,0,0 +2010,8,26,5,62,16,19,1015,7.6,0,0 +2010,8,26,6,81,17,19,1015,12.52,0,0 +2010,8,26,7,81,17,21,1015,15.65,0,0 +2010,8,26,8,71,17,23,1016,19.67,0,0 +2010,8,26,9,57,18,26,1016,24.59,0,0 +2010,8,26,10,60,17,27,1016,29.51,0,0 +2010,8,26,11,44,16,29,1016,4.02,0,0 +2010,8,26,12,42,16,29,1015,4.02,0,0 +2010,8,26,13,31,18,31,1014,0.89,0,0 +2010,8,26,14,41,16,31,1014,3.13,0,0 +2010,8,26,15,27,16,31,1013,4.92,0,0 +2010,8,26,16,33,18,32,1013,0.89,0,0 +2010,8,26,17,35,19,30,1012,1.79,0,0 +2010,8,26,18,45,19,29,1012,2.68,0,0 +2010,8,26,19,102,20,26,1012,4.47,0,0 +2010,8,26,20,101,16,25,1013,3.13,0,0 +2010,8,26,21,26,14,26,1014,6.26,0,0 +2010,8,26,22,22,14,24,1014,9.39,0,0 +2010,8,26,23,25,15,24,1014,13.41,0,0 +2010,8,27,0,33,15,23,1014,16.54,0,0 +2010,8,27,1,33,15,22,1014,19.67,0,0 +2010,8,27,2,36,15,22,1013,21.46,0,0 +2010,8,27,3,33,15,22,1013,24.59,0,0 +2010,8,27,4,43,16,19,1013,27.72,0,0 +2010,8,27,5,46,16,21,1013,31.74,0,0 +2010,8,27,6,35,16,19,1013,34.87,0,0 +2010,8,27,7,42,16,21,1013,38,0,0 +2010,8,27,8,64,17,22,1014,41.13,0,0 +2010,8,27,9,41,16,23,1014,42.92,0,0 +2010,8,27,10,40,18,25,1014,46.05,0,0 +2010,8,27,11,46,15,28,1014,4.92,0,0 +2010,8,27,12,35,14,29,1013,9.84,0,0 +2010,8,27,13,26,15,29,1013,12.97,0,0 +2010,8,27,14,31,14,29,1012,0.89,0,0 +2010,8,27,15,24,13,30,1011,1.78,0,0 +2010,8,27,16,17,13,29,1011,2.67,0,0 +2010,8,27,17,26,15,29,1011,1.79,0,0 +2010,8,27,18,30,14,27,1011,3.58,0,0 +2010,8,27,19,42,15,26,1011,5.37,0,0 +2010,8,27,20,59,14,26,1011,8.5,0,0 +2010,8,27,21,66,15,26,1012,11.63,0,0 +2010,8,27,22,54,15,26,1012,1.79,0,0 +2010,8,27,23,44,17,23,1012,0.89,0,0 +2010,8,28,0,44,17,20,1012,1.78,0,0 +2010,8,28,1,94,17,21,1012,0.89,0,0 +2010,8,28,2,127,17,19,1011,0.89,0,0 +2010,8,28,3,48,17,19,1011,1.79,0,0 +2010,8,28,4,69,17,19,1011,3.58,0,0 +2010,8,28,5,73,17,19,1011,6.71,0,0 +2010,8,28,6,51,16,18,1011,9.84,0,0 +2010,8,28,7,33,17,21,1012,13.86,0,0 +2010,8,28,8,47,17,23,1012,17.88,0,0 +2010,8,28,9,24,16,25,1012,21.9,0,0 +2010,8,28,10,37,17,27,1012,25.92,0,0 +2010,8,28,11,20,17,28,1011,29.05,0,0 +2010,8,28,12,24,16,30,1010,30.84,0,0 +2010,8,28,13,17,14,31,1009,1.79,0,0 +2010,8,28,14,22,14,32,1009,0.89,0,0 +2010,8,28,15,16,14,31,1008,1.79,0,0 +2010,8,28,16,11,13,31,1008,4.92,0,0 +2010,8,28,17,35,16,30,1008,8.05,0,0 +2010,8,28,18,42,16,29,1008,11.18,0,0 +2010,8,28,19,44,17,27,1008,12.97,0,0 +2010,8,28,20,55,18,24,1009,14.76,0,0 +2010,8,28,21,49,18,23,1010,15.65,0,0 +2010,8,28,22,65,19,24,1010,19.67,0,0 +2010,8,28,23,97,20,23,1010,21.46,0,0 +2010,8,29,0,132,20,23,1010,23.25,0,0 +2010,8,29,1,110,19,21,1009,0.89,0,0 +2010,8,29,2,136,19,21,1009,0.89,0,0 +2010,8,29,3,168,20,21,1009,1.79,0,0 +2010,8,29,4,157,20,22,1009,3.58,0,0 +2010,8,29,5,191,20,21,1008,0.89,0,0 +2010,8,29,6,160,20,21,1009,0.89,0,0 +2010,8,29,7,195,19,21,1009,1.79,0,0 +2010,8,29,8,135,20,22,1009,4.92,0,0 +2010,8,29,9,121,19,25,1009,8.05,0,0 +2010,8,29,10,102,19,27,1008,1.79,0,0 +2010,8,29,11,82,20,29,1008,1.79,0,0 +2010,8,29,12,98,20,29,1007,1.79,0,0 +2010,8,29,13,110,19,31,1007,3.58,0,0 +2010,8,29,14,138,19,32,1006,5.37,0,0 +2010,8,29,15,79,18,32,1005,9.39,0,0 +2010,8,29,16,60,17,32,1005,14.31,0,0 +2010,8,29,17,54,17,31,1005,18.33,0,0 +2010,8,29,18,62,17,30,1005,22.35,0,0 +2010,8,29,19,91,18,28,1006,25.48,0,0 +2010,8,29,20,99,18,27,1007,28.61,0,0 +2010,8,29,21,93,19,25,1007,30.4,0,0 +2010,8,29,22,88,19,24,1007,32.19,0,0 +2010,8,29,23,95,19,22,1007,0.89,0,0 +2010,8,30,0,102,19,22,1007,2.68,0,0 +2010,8,30,1,145,19,22,1007,0.89,0,0 +2010,8,30,2,107,19,22,1007,0.89,0,0 +2010,8,30,3,100,19,21,1008,1.79,0,0 +2010,8,30,4,103,18,20,1008,1.79,0,0 +2010,8,30,5,114,18,20,1008,1.79,0,0 +2010,8,30,6,130,18,20,1008,3.58,0,0 +2010,8,30,7,111,19,21,1009,0.89,0,0 +2010,8,30,8,77,19,23,1009,1.79,0,0 +2010,8,30,9,67,19,26,1009,3.58,0,0 +2010,8,30,10,95,19,27,1009,6.71,0,0 +2010,8,30,11,83,18,29,1009,9.84,0,0 +2010,8,30,12,54,18,30,1008,0.45,0,0 +2010,8,30,13,61,20,30,1008,1.79,0,0 +2010,8,30,14,77,20,30,1008,0.89,0,0 +2010,8,30,15,58,20,30,1007,3.13,0,0 +2010,8,30,16,88,21,30,1007,6.26,0,0 +2010,8,30,17,74,22,29,1007,8.05,0,0 +2010,8,30,18,70,21,28,1006,9.84,0,0 +2010,8,30,19,128,22,27,1007,11.63,0,1 +2010,8,30,20,217,22,26,1007,13.42,0,0 +2010,8,30,21,224,22,26,1008,16.55,0,0 +2010,8,30,22,176,21,26,1008,18.34,0,0 +2010,8,30,23,165,21,26,1008,22.36,0,0 +2010,8,31,0,116,19,25,1009,4.02,0,1 +2010,8,31,1,73,21,23,1009,0.89,0,2 +2010,8,31,2,60,20,22,1009,1.78,0,0 +2010,8,31,3,59,19,21,1009,1.79,0,0 +2010,8,31,4,45,19,21,1010,0.89,0,0 +2010,8,31,5,65,18,20,1010,0.89,0,0 +2010,8,31,6,72,18,20,1010,2.68,0,0 +2010,8,31,7,112,19,21,1010,0.89,0,0 +2010,8,31,8,105,20,23,1010,2.68,0,0 +2010,8,31,9,93,19,25,1010,1.79,0,0 +2010,8,31,10,81,19,25,1011,4.92,0,0 +2010,8,31,11,92,19,25,1010,6.71,0,0 +2010,8,31,12,102,20,24,1010,9.84,0,0 +2010,8,31,13,92,20,26,1010,11.63,0,0 +2010,8,31,14,89,20,27,1010,13.42,0,0 +2010,8,31,15,103,20,27,1010,15.21,0,0 +2010,8,31,16,80,20,27,1010,17,0,0 +2010,8,31,17,101,19,27,1010,18.79,0,0 +2010,8,31,18,95,20,26,1010,1.79,0,0 +2010,8,31,19,85,20,24,1011,2.68,0,0 +2010,8,31,20,89,20,23,1011,0.89,0,0 +2010,8,31,21,107,20,23,1011,0.89,0,0 +2010,8,31,22,115,20,23,1011,1.79,0,0 +2010,8,31,23,100,20,23,1012,0.89,0,0 +2010,9,1,0,99,19,22,1012,0.89,0,0 +2010,9,1,1,105,20,22,1012,0.89,0,0 +2010,9,1,2,108,20,22,1012,0.89,0,0 +2010,9,1,3,116,21,22,1013,1.79,0,1 +2010,9,1,4,95,18,20,1012,4.02,0,2 +2010,9,1,5,79,18,20,1012,1.79,0,0 +2010,9,1,6,82,19,21,1013,0.89,0,1 +2010,9,1,7,90,20,21,1014,1.78,0,2 +2010,9,1,8,78,20,22,1014,3.13,0,0 +2010,9,1,9,76,19,22,1014,6.26,0,0 +2010,9,1,10,44,19,23,1015,11.18,0,0 +2010,9,1,11,61,19,24,1015,14.31,0,0 +2010,9,1,12,52,19,25,1014,17.44,0,0 +2010,9,1,13,45,19,25,1014,20.57,0,0 +2010,9,1,14,55,19,27,1014,23.7,0,0 +2010,9,1,15,44,19,27,1013,26.83,0,0 +2010,9,1,16,55,18,27,1013,29.96,0,0 +2010,9,1,17,43,19,26,1013,33.09,0,0 +2010,9,1,18,37,18,26,1013,37.11,0,0 +2010,9,1,19,59,19,24,1013,38.9,0,0 +2010,9,1,20,59,19,23,1014,0.89,0,0 +2010,9,1,21,53,18,24,1014,3.13,0,0 +2010,9,1,22,73,18,22,1014,0.89,0,0 +2010,9,1,23,81,18,21,1015,1.78,0,0 +2010,9,2,0,82,18,22,1015,1.79,0,0 +2010,9,2,1,81,18,21,1014,0.89,0,0 +2010,9,2,2,105,19,21,1014,1.79,0,0 +2010,9,2,3,105,18,21,1014,2.68,0,0 +2010,9,2,4,85,18,22,1014,5.81,0,0 +2010,9,2,5,49,18,21,1015,7.6,0,0 +2010,9,2,6,45,18,21,1015,10.73,0,0 +2010,9,2,7,36,18,22,1015,1.79,0,0 +2010,9,2,8,46,18,23,1016,3.13,0,0 +2010,9,2,9,59,18,24,1016,6.26,0,0 +2010,9,2,10,51,18,26,1016,9.39,0,0 +2010,9,2,11,50,18,26,1016,12.52,0,0 +2010,9,2,12,39,18,27,1016,14.31,0,0 +2010,9,2,13,39,18,28,1015,0.89,0,0 +2010,9,2,14,46,19,29,1014,2.68,0,0 +2010,9,2,15,77,18,29,1014,4.47,0,0 +2010,9,2,16,104,19,29,1014,1.79,0,0 +2010,9,2,17,80,20,28,1014,3.58,0,0 +2010,9,2,18,112,20,27,1014,4.47,0,0 +2010,9,2,19,143,21,25,1014,5.36,0,0 +2010,9,2,20,191,21,24,1015,6.25,0,0 +2010,9,2,21,211,21,23,1015,7.14,0,0 +2010,9,2,22,224,20,22,1015,8.03,0,0 +2010,9,2,23,249,20,22,1015,0.89,0,0 +2010,9,3,0,254,20,22,1015,0.89,0,0 +2010,9,3,1,284,20,22,1015,0.89,0,0 +2010,9,3,2,228,20,22,1015,1.78,0,0 +2010,9,3,3,135,20,22,1014,3.57,0,0 +2010,9,3,4,94,20,22,1014,4.46,0,0 +2010,9,3,5,101,20,22,1014,0.45,0,0 +2010,9,3,6,93,20,22,1014,1.34,0,1 +2010,9,3,7,97,20,22,1015,1.79,0,2 +2010,9,3,8,100,19,22,1015,6.71,0,3 +2010,9,3,9,95,18,22,1015,12.52,0,4 +2010,9,3,10,56,18,22,1015,16.54,0,5 +2010,9,3,11,52,18,23,1014,19.67,0,0 +2010,9,3,12,52,17,23,1014,23.69,0,1 +2010,9,3,13,51,18,25,1013,26.82,0,0 +2010,9,3,14,46,19,25,1013,1.79,0,0 +2010,9,3,15,62,19,26,1012,0.89,0,0 +2010,9,3,16,70,19,26,1012,1.78,0,0 +2010,9,3,17,79,18,26,1012,2.67,0,0 +2010,9,3,18,90,19,25,1012,1.79,0,0 +2010,9,3,19,103,20,23,1012,0.89,0,0 +2010,9,3,20,103,20,23,1013,1.34,0,0 +2010,9,3,21,118,19,22,1013,2.23,0,0 +2010,9,3,22,142,19,22,1012,3.12,0,0 +2010,9,3,23,157,18,21,1013,0.89,0,0 +2010,9,4,0,155,18,21,1013,1.78,0,0 +2010,9,4,1,159,18,20,1013,2.67,0,0 +2010,9,4,2,82,18,19,1012,1.79,0,0 +2010,9,4,3,81,17,19,1012,0.45,0,0 +2010,9,4,4,105,17,19,1013,0.89,0,0 +2010,9,4,5,101,17,19,1013,1.79,0,0 +2010,9,4,6,93,17,19,1013,0.89,0,0 +2010,9,4,7,92,18,20,1013,0.45,0,0 +2010,9,4,8,81,17,22,1013,1.79,0,0 +2010,9,4,9,58,17,24,1014,3.58,0,0 +2010,9,4,10,72,17,25,1014,5.37,0,0 +2010,9,4,11,74,16,26,1013,1.79,0,0 +2010,9,4,12,81,17,27,1013,2.68,0,0 +2010,9,4,13,86,15,28,1012,1.79,0,0 +2010,9,4,14,77,16,28,1012,1.79,0,0 +2010,9,4,15,71,16,28,1011,3.13,0,0 +2010,9,4,16,78,16,28,1011,6.26,0,0 +2010,9,4,17,80,17,27,1011,9.39,0,0 +2010,9,4,18,88,16,26,1011,11.18,0,0 +2010,9,4,19,89,16,24,1012,12.97,0,0 +2010,9,4,20,93,17,23,1012,14.76,0,0 +2010,9,4,21,90,16,23,1012,16.55,0,0 +2010,9,4,22,135,18,22,1012,0.89,0,0 +2010,9,4,23,125,18,21,1012,1.78,0,0 +2010,9,5,0,112,19,22,1012,1.79,0,0 +2010,9,5,1,88,19,21,1012,2.68,0,0 +2010,9,5,2,92,19,21,1012,0.89,0,0 +2010,9,5,3,91,19,20,1012,0.89,0,0 +2010,9,5,4,103,18,20,1012,0.89,0,0 +2010,9,5,5,100,18,20,1012,1.78,0,0 +2010,9,5,6,91,18,19,1012,2.67,0,0 +2010,9,5,7,161,19,20,1013,3.56,0,0 +2010,9,5,8,185,19,22,1013,4.45,0,0 +2010,9,5,9,170,19,23,1013,5.34,0,0 +2010,9,5,10,146,19,25,1013,6.23,0,0 +2010,9,5,11,133,19,26,1012,7.12,0,0 +2010,9,5,12,125,20,27,1011,8.91,0,0 +2010,9,5,13,131,19,28,1011,1.79,0,0 +2010,9,5,14,123,20,29,1010,1.79,0,0 +2010,9,5,15,89,20,29,1009,3.13,0,0 +2010,9,5,16,96,20,29,1008,6.26,0,0 +2010,9,5,17,87,19,29,1008,9.39,0,0 +2010,9,5,18,95,19,27,1008,11.18,0,0 +2010,9,5,19,97,20,26,1009,15.2,0,0 +2010,9,5,20,117,20,26,1010,19.22,0,0 +2010,9,5,21,128,19,25,1010,22.35,0,0 +2010,9,5,22,144,19,22,1010,24.14,0,0 +2010,9,5,23,147,20,22,1010,0.89,0,0 +2010,9,6,0,129,19,21,1010,1.78,0,0 +2010,9,6,1,148,19,21,1010,1.79,0,0 +2010,9,6,2,163,19,21,1010,0.89,0,0 +2010,9,6,3,168,18,20,1010,1.78,0,0 +2010,9,6,4,152,18,19,1010,0.89,0,0 +2010,9,6,5,177,19,20,1010,1.78,0,0 +2010,9,6,6,182,19,20,1011,0.45,0,0 +2010,9,6,7,177,20,21,1011,1.79,0,0 +2010,9,6,8,151,21,23,1011,0.89,0,0 +2010,9,6,9,150,20,25,1011,1.79,0,0 +2010,9,6,10,131,20,27,1011,3.13,0,0 +2010,9,6,11,135,19,28,1011,7.15,0,0 +2010,9,6,12,117,19,29,1010,1.79,0,0 +2010,9,6,13,103,18,30,1010,0.89,0,0 +2010,9,6,14,103,19,30,1009,1.79,0,0 +2010,9,6,15,95,20,30,1009,3.58,0,0 +2010,9,6,16,99,20,30,1009,5.37,0,0 +2010,9,6,17,104,21,30,1009,6.26,0,0 +2010,9,6,18,128,22,28,1009,7.15,0,0 +2010,9,6,19,182,21,26,1009,8.04,0,0 +2010,9,6,20,224,22,25,1010,8.93,0,0 +2010,9,6,21,232,22,25,1011,0.89,0,0 +2010,9,6,22,216,22,25,1011,0.89,0,0 +2010,9,6,23,229,22,24,1011,0.89,0,0 +2010,9,7,0,226,22,24,1011,1.79,0,0 +2010,9,7,1,250,22,24,1011,3.58,0,0 +2010,9,7,2,278,22,24,1011,0.89,0,0 +2010,9,7,3,249,21,24,1012,1.78,0,0 +2010,9,7,4,201,22,23,1012,0.89,0,0 +2010,9,7,5,161,21,24,1012,1.34,0,0 +2010,9,7,6,203,21,23,1012,0.89,0,0 +2010,9,7,7,222,21,24,1013,0.89,0,0 +2010,9,7,8,226,22,24,1013,1.78,0,0 +2010,9,7,9,197,21,25,1014,2.67,0,0 +2010,9,7,10,210,20,26,1014,1.79,0,0 +2010,9,7,11,148,17,27,1015,4.92,0,0 +2010,9,7,12,56,16,27,1015,8.94,0,0 +2010,9,7,13,52,14,27,1015,12.96,0,0 +2010,9,7,14,38,13,26,1014,16.98,0,0 +2010,9,7,15,41,14,26,1014,18.77,0,0 +2010,9,7,16,48,15,26,1014,1.79,0,0 +2010,9,7,17,43,17,26,1014,2.68,0,0 +2010,9,7,18,54,17,24,1014,4.47,0,0 +2010,9,7,19,70,17,22,1014,6.26,0,0 +2010,9,7,20,58,14,23,1015,8.05,0,0 +2010,9,7,21,61,17,21,1015,0.89,0,0 +2010,9,7,22,64,17,21,1015,1.78,0,0 +2010,9,7,23,74,15,20,1015,2.67,0,0 +2010,9,8,0,103,16,19,1016,3.12,0,0 +2010,9,8,1,102,16,18,1015,4.01,0,0 +2010,9,8,2,61,16,18,1015,4.46,0,0 +2010,9,8,3,69,16,17,1015,5.35,0,0 +2010,9,8,4,81,15,17,1015,0.89,0,0 +2010,9,8,5,79,15,17,1015,1.78,0,0 +2010,9,8,6,129,15,17,1016,0.89,0,0 +2010,9,8,7,133,17,18,1016,0.89,0,0 +2010,9,8,8,96,17,20,1017,1.78,0,0 +2010,9,8,9,81,17,21,1017,0.89,0,0 +2010,9,8,10,58,13,24,1017,4.02,0,0 +2010,9,8,11,63,10,25,1016,5.81,0,0 +2010,9,8,12,67,10,27,1015,3.13,0,0 +2010,9,8,13,81,10,27,1015,3.13,0,0 +2010,9,8,14,113,12,28,1014,6.26,0,0 +2010,9,8,15,97,12,28,1013,9.39,0,0 +2010,9,8,16,97,11,26,1013,12.52,0,0 +2010,9,8,17,111,12,26,1013,15.65,0,0 +2010,9,8,18,102,13,25,1013,17.44,0,0 +2010,9,8,19,100,15,22,1013,19.23,0,0 +2010,9,8,20,123,15,22,1014,0.89,0,0 +2010,9,8,21,125,15,22,1014,1.79,0,0 +2010,9,8,22,128,14,23,1014,5.81,0,0 +2010,9,8,23,118,16,20,1014,6.7,0,0 +2010,9,9,0,115,16,19,1014,8.49,0,0 +2010,9,9,1,140,16,19,1014,10.28,0,0 +2010,9,9,2,142,15,18,1013,12.07,0,0 +2010,9,9,3,122,14,18,1013,13.86,0,0 +2010,9,9,4,109,14,18,1013,0.89,0,0 +2010,9,9,5,159,14,17,1013,1.78,0,0 +2010,9,9,6,154,15,17,1013,0.89,0,0 +2010,9,9,7,213,15,18,1014,0.89,0,0 +2010,9,9,8,216,15,19,1013,0.89,0,0 +2010,9,9,9,227,15,21,1013,1.78,0,0 +2010,9,9,10,210,15,23,1013,1.79,0,0 +2010,9,9,11,220,15,24,1012,0.89,0,0 +2010,9,9,12,216,16,26,1012,2.68,0,0 +2010,9,9,13,211,16,26,1011,3.13,0,0 +2010,9,9,14,204,17,26,1010,7.15,0,0 +2010,9,9,15,122,16,27,1009,12.07,0,0 +2010,9,9,16,121,16,27,1009,16.99,0,0 +2010,9,9,17,85,16,26,1009,21.01,0,0 +2010,9,9,18,114,17,25,1009,25.03,0,0 +2010,9,9,19,152,17,23,1009,28.16,0,0 +2010,9,9,20,131,16,22,1010,29.95,0,0 +2010,9,9,21,144,17,21,1010,31.74,0,0 +2010,9,9,22,147,17,21,1010,34.87,0,0 +2010,9,9,23,160,16,21,1009,36.66,0,0 +2010,9,10,0,175,17,20,1009,38.45,0,0 +2010,9,10,1,176,16,19,1009,1.79,0,0 +2010,9,10,2,193,16,19,1009,2.68,0,0 +2010,9,10,3,200,16,18,1009,0.89,0,0 +2010,9,10,4,189,16,18,1009,0.89,0,0 +2010,9,10,5,108,15,16,1009,0.89,0,0 +2010,9,10,6,50,15,16,1009,0.89,0,0 +2010,9,10,7,73,16,18,1009,0.89,0,0 +2010,9,10,8,57,12,23,1009,4.02,0,0 +2010,9,10,9,57,10,25,1010,9.83,0,0 +2010,9,10,10,45,9,27,1009,14.75,0,0 +2010,9,10,11,62,9,27,1009,18.77,0,0 +2010,9,10,12,62,8,29,1008,23.69,0,0 +2010,9,10,13,24,8,29,1007,28.61,0,0 +2010,9,10,14,24,8,30,1007,33.53,0,0 +2010,9,10,15,27,8,30,1006,38.45,0,0 +2010,9,10,16,38,9,30,1006,43.37,0,0 +2010,9,10,17,39,8,30,1006,48.29,0,0 +2010,9,10,18,27,9,29,1006,53.21,0,0 +2010,9,10,19,66,10,25,1006,55,0,0 +2010,9,10,20,111,13,22,1007,0.89,0,0 +2010,9,10,21,198,16,20,1008,0.45,0,0 +2010,9,10,22,181,13,21,1008,1.79,0,0 +2010,9,10,23,212,12,20,1009,4.92,0,0 +2010,9,11,0,174,12,20,1009,6.71,0,0 +2010,9,11,1,170,11,22,1009,9.84,0,0 +2010,9,11,2,133,12,18,1009,12.97,0,0 +2010,9,11,3,50,12,17,1009,16.1,0,0 +2010,9,11,4,28,12,17,1009,17.89,0,0 +2010,9,11,5,18,12,16,1009,0.89,0,0 +2010,9,11,6,NA,12,16,1010,1.79,0,0 +2010,9,11,7,NA,13,19,1010,4.92,0,0 +2010,9,11,8,NA,13,23,1010,6.71,0,0 +2010,9,11,9,NA,12,25,1011,10.73,0,0 +2010,9,11,10,NA,11,27,1011,14.75,0,0 +2010,9,11,11,NA,11,29,1010,18.77,0,0 +2010,9,11,12,NA,11,31,1010,22.79,0,0 +2010,9,11,13,NA,11,31,1009,25.92,0,0 +2010,9,11,14,NA,12,31,1008,1.79,0,0 +2010,9,11,15,NA,12,32,1008,0.89,0,0 +2010,9,11,16,NA,11,32,1008,3.13,0,0 +2010,9,11,17,NA,12,31,1008,6.26,0,0 +2010,9,11,18,NA,14,28,1008,8.05,0,0 +2010,9,11,19,NA,13,26,1008,11.18,0,0 +2010,9,11,20,NA,14,23,1009,12.97,0,0 +2010,9,11,21,NA,14,23,1010,14.76,0,0 +2010,9,11,22,76,16,21,1010,15.65,0,0 +2010,9,11,23,72,16,21,1010,0.89,0,0 +2010,9,12,0,72,15,19,1010,0.89,0,0 +2010,9,12,1,207,16,20,1010,0.89,0,0 +2010,9,12,2,240,15,19,1010,0.89,0,0 +2010,9,12,3,221,16,18,1010,1.78,0,0 +2010,9,12,4,164,16,18,1010,2.67,0,0 +2010,9,12,5,98,15,17,1011,3.56,0,0 +2010,9,12,6,69,15,17,1011,0.89,0,0 +2010,9,12,7,96,17,19,1012,1.79,0,0 +2010,9,12,8,119,16,22,1012,3.58,0,0 +2010,9,12,9,110,14,25,1012,3.13,0,0 +2010,9,12,10,107,14,27,1012,4.92,0,0 +2010,9,12,11,91,13,29,1012,6.71,0,0 +2010,9,12,12,71,13,31,1011,0.89,0,0 +2010,9,12,13,69,12,32,1011,1.78,0,0 +2010,9,12,14,85,12,33,1010,1.79,0,0 +2010,9,12,15,87,13,33,1009,4.92,0,0 +2010,9,12,16,103,13,33,1009,6.71,0,0 +2010,9,12,17,115,15,31,1009,9.84,0,0 +2010,9,12,18,123,16,28,1009,11.63,0,0 +2010,9,12,19,123,14,28,1009,15.65,0,0 +2010,9,12,20,132,15,25,1010,17.44,0,0 +2010,9,12,21,120,15,24,1010,19.23,0,0 +2010,9,12,22,132,16,23,1011,21.02,0,0 +2010,9,12,23,155,16,21,1011,1.79,0,0 +2010,9,13,0,142,17,20,1011,0.89,0,0 +2010,9,13,1,155,17,21,1011,1.78,0,0 +2010,9,13,2,218,17,20,1012,2.67,0,0 +2010,9,13,3,261,17,19,1012,0.89,0,0 +2010,9,13,4,269,17,19,1012,1.78,0,0 +2010,9,13,5,206,16,18,1012,0.89,0,0 +2010,9,13,6,117,16,18,1012,1.79,0,0 +2010,9,13,7,111,17,19,1013,4.92,0,0 +2010,9,13,8,114,17,23,1013,6.71,0,0 +2010,9,13,9,120,17,26,1013,0.89,0,0 +2010,9,13,10,134,17,27,1013,1.78,0,0 +2010,9,13,11,131,16,30,1013,2.67,0,0 +2010,9,13,12,141,16,30,1012,3.56,0,0 +2010,9,13,13,141,17,32,1011,5.35,0,0 +2010,9,13,14,159,16,33,1011,7.14,0,0 +2010,9,13,15,129,15,34,1010,3.13,0,0 +2010,9,13,16,150,16,33,1010,4.92,0,0 +2010,9,13,17,124,15,32,1010,8.94,0,0 +2010,9,13,18,121,15,30,1010,12.96,0,0 +2010,9,13,19,138,15,29,1010,16.98,0,0 +2010,9,13,20,139,16,27,1011,21,0,0 +2010,9,13,21,180,17,24,1012,21.89,0,0 +2010,9,13,22,167,18,23,1012,23.68,0,0 +2010,9,13,23,191,18,20,1012,1.79,0,0 +2010,9,14,0,209,18,21,1012,0.89,0,0 +2010,9,14,1,275,18,20,1013,1.78,0,0 +2010,9,14,2,277,17,19,1013,2.67,0,0 +2010,9,14,3,310,17,19,1013,0.89,0,0 +2010,9,14,4,242,17,19,1013,2.68,0,0 +2010,9,14,5,206,16,18,1013,4.47,0,0 +2010,9,14,6,136,17,18,1014,6.26,0,0 +2010,9,14,7,166,17,20,1014,8.05,0,0 +2010,9,14,8,175,18,23,1014,11.18,0,0 +2010,9,14,9,160,17,26,1014,14.31,0,0 +2010,9,14,10,179,17,28,1015,16.1,0,0 +2010,9,14,11,182,17,30,1014,1.79,0,0 +2010,9,14,12,177,17,31,1014,3.58,0,0 +2010,9,14,13,183,15,32,1013,1.79,0,0 +2010,9,14,14,153,15,33,1012,3.58,0,0 +2010,9,14,15,128,14,32,1012,5.37,0,0 +2010,9,14,16,165,13,32,1012,9.39,0,0 +2010,9,14,17,166,15,31,1012,12.52,0,0 +2010,9,14,18,152,16,29,1012,14.31,0,0 +2010,9,14,19,169,17,26,1012,15.2,0,0 +2010,9,14,20,212,18,24,1012,16.99,0,0 +2010,9,14,21,225,18,23,1013,18.78,0,0 +2010,9,14,22,227,18,22,1013,19.67,0,0 +2010,9,14,23,225,19,22,1013,0.89,0,0 +2010,9,15,0,321,19,22,1013,0.89,0,0 +2010,9,15,1,358,19,21,1013,0.89,0,0 +2010,9,15,2,360,19,21,1012,0.89,0,0 +2010,9,15,3,411,18,21,1012,0.89,0,0 +2010,9,15,4,374,18,21,1012,1.78,0,0 +2010,9,15,5,414,18,20,1012,0.89,0,0 +2010,9,15,6,349,18,20,1013,1.34,0,0 +2010,9,15,7,298,18,20,1013,2.23,0,0 +2010,9,15,8,334,19,22,1013,3.12,0,0 +2010,9,15,9,333,20,24,1013,4.01,0,0 +2010,9,15,10,455,20,26,1013,1.79,0,0 +2010,9,15,11,440,20,27,1012,3.58,0,0 +2010,9,15,12,313,20,29,1011,7.6,0,0 +2010,9,15,13,215,19,30,1010,13.41,0,0 +2010,9,15,14,190,19,30,1010,18.33,0,0 +2010,9,15,15,236,19,30,1009,23.25,0,0 +2010,9,15,16,187,19,29,1009,29.06,0,0 +2010,9,15,17,211,19,29,1009,33.98,0,0 +2010,9,15,18,196,19,28,1009,38,0,0 +2010,9,15,19,240,20,27,1010,41.13,0,0 +2010,9,15,20,281,21,26,1010,42.92,0,0 +2010,9,15,21,297,21,25,1010,46.05,0,0 +2010,9,15,22,293,21,26,1010,50.07,0,0 +2010,9,15,23,254,21,27,1010,54.09,0,0 +2010,9,16,0,203,21,26,1010,55.88,0,0 +2010,9,16,1,186,21,25,1010,59.01,0,1 +2010,9,16,2,205,21,24,1010,60.8,0,2 +2010,9,16,3,200,21,24,1010,62.59,0,0 +2010,9,16,4,181,21,24,1010,64.38,0,1 +2010,9,16,5,176,21,24,1010,66.17,0,2 +2010,9,16,6,188,21,23,1010,67.96,0,0 +2010,9,16,7,181,21,23,1011,68.85,0,1 +2010,9,16,8,219,21,24,1011,70.64,0,0 +2010,9,16,9,226,21,24,1011,72.43,0,0 +2010,9,16,10,238,20,24,1012,74.22,0,0 +2010,9,16,11,247,20,25,1011,77.35,0,0 +2010,9,16,12,225,20,25,1011,79.14,0,0 +2010,9,16,13,244,20,25,1011,82.27,0,0 +2010,9,16,14,199,20,25,1010,84.06,0,0 +2010,9,16,15,157,20,25,1010,85.85,0,0 +2010,9,16,16,194,20,25,1009,88.98,0,1 +2010,9,16,17,262,19,24,1009,93,0,2 +2010,9,16,18,224,19,21,1010,97.02,0,3 +2010,9,16,19,174,18,21,1010,102.83,0,0 +2010,9,16,20,170,18,21,1011,108.64,0,0 +2010,9,16,21,153,18,20,1011,115.79,0,1 +2010,9,16,22,104,18,20,1011,119.81,0,2 +2010,9,16,23,136,17,20,1010,124.73,0,3 +2010,9,17,0,117,17,19,1010,129.65,0,4 +2010,9,17,1,94,17,18,1010,132.78,0,5 +2010,9,17,2,112,16,18,1010,137.7,0,6 +2010,9,17,3,71,16,18,1010,139.49,0,7 +2010,9,17,4,89,16,17,1010,142.62,0,8 +2010,9,17,5,84,15,17,1010,144.41,0,9 +2010,9,17,6,70,16,17,1011,1.79,0,10 +2010,9,17,7,61,16,17,1011,0.89,0,11 +2010,9,17,8,53,16,18,1012,1.79,0,12 +2010,9,17,9,34,17,18,1012,0.89,0,13 +2010,9,17,10,26,16,17,1013,3.13,0,14 +2010,9,17,11,43,15,16,1013,6.26,0,15 +2010,9,17,12,21,15,16,1013,9.39,0,16 +2010,9,17,13,13,14,16,1013,12.52,0,17 +2010,9,17,14,24,14,15,1013,16.54,0,18 +2010,9,17,15,33,14,15,1013,19.67,0,19 +2010,9,17,16,9,14,15,1014,21.46,0,20 +2010,9,17,17,26,13,15,1014,1.79,0,21 +2010,9,17,18,30,13,14,1014,1.79,0,22 +2010,9,17,19,14,13,14,1014,0.89,0,23 +2010,9,17,20,12,13,14,1015,1.78,0,24 +2010,9,17,21,8,13,14,1015,0.89,0,25 +2010,9,17,22,6,13,14,1015,2.68,0,26 +2010,9,17,23,31,12,14,1015,4.47,0,27 +2010,9,18,0,26,12,14,1015,7.6,0,28 +2010,9,18,1,20,12,14,1015,9.39,0,29 +2010,9,18,2,19,12,14,1014,0.89,0,30 +2010,9,18,3,16,12,14,1014,1.34,0,31 +2010,9,18,4,17,12,13,1014,1.79,0,32 +2010,9,18,5,14,12,13,1014,1.79,0,33 +2010,9,18,6,12,12,13,1014,3.58,0,34 +2010,9,18,7,5,12,13,1015,5.37,0,35 +2010,9,18,8,20,12,13,1015,8.5,0,36 +2010,9,18,9,5,12,14,1015,11.63,0,0 +2010,9,18,10,20,12,14,1016,14.76,0,1 +2010,9,18,11,23,12,14,1015,3.13,0,2 +2010,9,18,12,33,13,15,1015,4.02,0,3 +2010,9,18,13,21,13,16,1015,1.79,0,0 +2010,9,18,14,3,13,17,1014,1.79,0,0 +2010,9,18,15,6,13,18,1014,1.79,0,0 +2010,9,18,16,27,14,19,1014,4.92,0,0 +2010,9,18,17,23,14,18,1014,8.05,0,0 +2010,9,18,18,35,13,17,1014,11.18,0,0 +2010,9,18,19,48,13,16,1015,0.89,0,0 +2010,9,18,20,54,13,16,1015,1.78,0,0 +2010,9,18,21,44,14,15,1016,1.79,0,0 +2010,9,18,22,44,14,16,1015,4.92,0,0 +2010,9,18,23,24,14,16,1016,6.71,0,0 +2010,9,19,0,34,14,16,1015,8.5,0,1 +2010,9,19,1,45,14,15,1015,1.79,0,2 +2010,9,19,2,54,14,15,1015,2.68,0,3 +2010,9,19,3,55,14,15,1014,0.89,0,0 +2010,9,19,4,55,13,15,1014,1.79,0,0 +2010,9,19,5,55,14,15,1014,1.79,0,0 +2010,9,19,6,55,14,15,1014,3.58,0,0 +2010,9,19,7,84,15,16,1014,5.37,0,0 +2010,9,19,8,106,15,17,1014,8.5,0,0 +2010,9,19,9,73,15,19,1015,10.29,0,0 +2010,9,19,10,87,15,20,1015,12.08,0,0 +2010,9,19,11,118,15,20,1014,0.89,0,0 +2010,9,19,12,138,15,21,1014,1.79,0,0 +2010,9,19,13,118,14,24,1012,1.79,0,0 +2010,9,19,14,79,13,25,1012,3.13,0,0 +2010,9,19,15,70,13,24,1011,4.92,0,0 +2010,9,19,16,49,13,24,1011,8.05,0,0 +2010,9,19,17,37,13,23,1011,12.07,0,0 +2010,9,19,18,47,12,22,1010,15.2,0,0 +2010,9,19,19,NA,12,22,1011,16.99,0,0 +2010,9,19,20,NA,14,20,1011,0.89,0,0 +2010,9,19,21,NA,14,20,1011,1.78,0,0 +2010,9,19,22,NA,15,18,1011,2.67,0,0 +2010,9,19,23,NA,15,20,1011,1.79,0,0 +2010,9,20,0,84,15,18,1011,3.58,0,0 +2010,9,20,1,91,15,17,1011,4.47,0,0 +2010,9,20,2,82,16,17,1011,5.36,0,0 +2010,9,20,3,76,15,17,1011,1.79,0,0 +2010,9,20,4,129,15,17,1011,0.89,0,0 +2010,9,20,5,147,15,17,1011,1.78,0,0 +2010,9,20,6,115,15,16,1011,0.89,0,0 +2010,9,20,7,121,15,17,1012,1.79,0,0 +2010,9,20,8,109,15,18,1012,3.58,0,0 +2010,9,20,9,99,15,19,1013,5.37,0,0 +2010,9,20,10,121,15,19,1013,7.16,0,0 +2010,9,20,11,119,15,19,1013,8.95,0,0 +2010,9,20,12,124,15,19,1013,10.74,0,1 +2010,9,20,13,143,15,19,1012,13.87,0,0 +2010,9,20,14,128,15,19,1012,1.79,0,1 +2010,9,20,15,106,15,19,1012,3.13,0,0 +2010,9,20,16,101,16,19,1012,4.92,0,0 +2010,9,20,17,57,15,18,1012,8.05,0,0 +2010,9,20,18,48,15,18,1013,12.07,0,0 +2010,9,20,19,34,15,17,1013,13.86,0,1 +2010,9,20,20,35,15,17,1014,15.65,0,2 +2010,9,20,21,52,15,16,1015,18.78,0,3 +2010,9,20,22,56,15,16,1014,22.8,0,4 +2010,9,20,23,49,15,16,1014,25.93,0,5 +2010,9,21,0,28,15,16,1015,29.06,0,6 +2010,9,21,1,20,14,15,1014,34.87,0,7 +2010,9,21,2,11,13,15,1015,42.92,0,8 +2010,9,21,3,1,13,14,1015,48.73,0,9 +2010,9,21,4,4,12,13,1016,55.88,0,10 +2010,9,21,5,NA,11,13,1018,61.69,0,11 +2010,9,21,6,NA,11,13,1018,67.5,0,0 +2010,9,21,7,NA,11,13,1020,4.02,0,1 +2010,9,21,8,NA,11,13,1020,3.13,0,2 +2010,9,21,9,NA,11,14,1021,1.79,0,3 +2010,9,21,10,NA,10,14,1022,3.13,0,4 +2010,9,21,11,NA,11,14,1023,4.02,0,5 +2010,9,21,12,NA,11,13,1023,8.04,0,6 +2010,9,21,13,NA,11,15,1023,3.13,0,0 +2010,9,21,14,NA,10,17,1022,6.26,0,0 +2010,9,21,15,NA,10,18,1022,9.39,0,0 +2010,9,21,16,NA,10,17,1022,3.13,0,0 +2010,9,21,17,NA,10,17,1023,0.45,0,0 +2010,9,21,18,NA,10,17,1023,3.13,0,0 +2010,9,21,19,NA,10,16,1023,4.92,0,0 +2010,9,21,20,NA,10,16,1024,8.05,0,0 +2010,9,21,21,NA,11,15,1024,1.79,0,0 +2010,9,21,22,NA,11,13,1025,0.89,0,0 +2010,9,21,23,NA,11,14,1025,3.13,0,0 +2010,9,22,0,NA,11,13,1025,6.26,0,0 +2010,9,22,1,NA,9,10,1025,8.05,0,0 +2010,9,22,2,NA,10,11,1025,9.84,0,0 +2010,9,22,3,NA,8,9,1025,12.97,0,0 +2010,9,22,4,NA,6,8,1025,16.1,0,0 +2010,9,22,5,NA,7,8,1025,17.89,0,0 +2010,9,22,6,NA,6,7,1026,18.78,0,0 +2010,9,22,7,NA,8,10,1026,0.89,0,0 +2010,9,22,8,NA,5,14,1026,2.68,0,0 +2010,9,22,9,NA,2,16,1026,1.79,0,0 +2010,9,22,10,NA,3,18,1026,1.79,0,0 +2010,9,22,11,NA,-2,19,1025,5.81,0,0 +2010,9,22,12,NA,-2,20,1024,9.83,0,0 +2010,9,22,13,NA,-3,21,1023,13.85,0,0 +2010,9,22,14,NA,-3,22,1021,18.77,0,0 +2010,9,22,15,NA,-3,22,1020,4.92,0,0 +2010,9,22,16,NA,-3,22,1020,4.02,0,0 +2010,9,22,17,NA,0,22,1019,4.92,0,0 +2010,9,22,18,NA,1,19,1019,8.94,0,0 +2010,9,22,19,NA,3,17,1020,12.96,0,0 +2010,9,22,20,NA,5,16,1020,16.09,0,0 +2010,9,22,21,NA,4,17,1020,20.11,0,0 +2010,9,22,22,NA,7,13,1020,0.89,0,0 +2010,9,22,23,NA,8,11,1020,0.89,0,0 +2010,9,23,0,NA,8,10,1020,0.89,0,0 +2010,9,23,1,NA,7,9,1020,1.79,0,0 +2010,9,23,2,NA,8,10,1020,2.68,0,0 +2010,9,23,3,NA,7,9,1020,4.47,0,0 +2010,9,23,4,NA,7,10,1020,8.49,0,0 +2010,9,23,5,NA,7,9,1020,11.62,0,0 +2010,9,23,6,NA,7,9,1020,13.41,0,0 +2010,9,23,7,NA,7,11,1020,16.54,0,0 +2010,9,23,8,NA,8,15,1020,19.67,0,0 +2010,9,23,9,NA,7,18,1020,23.69,0,0 +2010,9,23,10,NA,8,20,1020,1.79,0,0 +2010,9,23,11,NA,8,22,1020,1.79,0,0 +2010,9,23,12,NA,6,24,1019,0.89,0,0 +2010,9,23,13,NA,4,24,1018,1.78,0,0 +2010,9,23,14,NA,4,25,1017,3.13,0,0 +2010,9,23,15,NA,8,25,1017,7.15,0,0 +2010,9,23,16,NA,7,25,1016,11.17,0,0 +2010,9,23,17,NA,7,23,1016,14.3,0,0 +2010,9,23,18,NA,9,21,1016,17.43,0,0 +2010,9,23,19,NA,10,18,1017,20.56,0,0 +2010,9,23,20,NA,10,17,1017,24.58,0,0 +2010,9,23,21,NA,10,16,1018,26.37,0,0 +2010,9,23,22,NA,11,15,1018,29.5,0,0 +2010,9,23,23,NA,11,14,1018,31.29,0,0 +2010,9,24,0,NA,10,13,1018,0.89,0,0 +2010,9,24,1,NA,10,12,1019,1.79,0,0 +2010,9,24,2,NA,11,13,1019,3.58,0,0 +2010,9,24,3,NA,10,12,1019,0.89,0,0 +2010,9,24,4,NA,10,12,1018,0.89,0,0 +2010,9,24,5,NA,10,11,1018,1.78,0,0 +2010,9,24,6,NA,10,12,1018,2.67,0,0 +2010,9,24,7,NA,11,12,1019,0.89,0,0 +2010,9,24,8,NA,13,15,1019,1.79,0,0 +2010,9,24,9,NA,12,17,1019,3.58,0,0 +2010,9,24,10,NA,12,18,1019,1.79,0,0 +2010,9,24,11,NA,12,20,1018,3.58,0,0 +2010,9,24,12,NA,13,21,1017,1.79,0,0 +2010,9,24,13,NA,12,22,1016,3.58,0,0 +2010,9,24,14,NA,8,24,1015,4.92,0,0 +2010,9,24,15,NA,8,25,1015,4.92,0,0 +2010,9,24,16,NA,10,24,1015,8.94,0,0 +2010,9,24,17,NA,9,23,1015,12.96,0,0 +2010,9,24,18,NA,9,21,1015,16.98,0,0 +2010,9,24,19,NA,9,19,1015,18.77,0,0 +2010,9,24,20,NA,9,18,1016,21.9,0,0 +2010,9,24,21,NA,7,18,1016,25.92,0,0 +2010,9,24,22,NA,7,17,1017,29.94,0,0 +2010,9,24,23,NA,8,16,1017,33.07,0,0 +2010,9,25,0,NA,8,16,1017,36.2,0,0 +2010,9,25,1,NA,9,14,1017,37.99,0,0 +2010,9,25,2,NA,9,13,1017,39.78,0,0 +2010,9,25,3,NA,9,12,1017,40.67,0,0 +2010,9,25,4,NA,9,11,1016,0.89,0,0 +2010,9,25,5,NA,9,11,1017,1.78,0,0 +2010,9,25,6,NA,9,11,1016,2.67,0,0 +2010,9,25,7,NA,11,12,1017,3.56,0,0 +2010,9,25,8,NA,11,13,1017,0.89,0,0 +2010,9,25,9,NA,12,15,1017,0.89,0,0 +2010,9,25,10,NA,12,17,1017,1.79,0,0 +2010,9,25,11,NA,12,19,1017,3.58,0,0 +2010,9,25,12,NA,12,19,1016,6.71,0,0 +2010,9,25,13,NA,12,20,1015,10.73,0,0 +2010,9,25,14,NA,13,21,1014,12.52,0,0 +2010,9,25,15,NA,12,22,1014,17.44,0,0 +2010,9,25,16,NA,12,21,1013,21.46,0,0 +2010,9,25,17,NA,12,21,1013,25.48,0,0 +2010,9,25,18,NA,12,19,1013,29.5,0,0 +2010,9,25,19,NA,13,17,1015,0.89,0,0 +2010,9,25,20,NA,13,19,1016,3.13,0,0 +2010,9,25,21,NA,14,17,1016,4.02,0,1 +2010,9,25,22,NA,14,16,1016,0.89,0,0 +2010,9,25,23,NA,14,16,1016,1.78,0,0 +2010,9,26,0,NA,13,14,1017,0.89,0,0 +2010,9,26,1,NA,14,15,1017,1.34,0,0 +2010,9,26,2,NA,13,14,1017,1.79,0,0 +2010,9,26,3,NA,14,15,1017,3.58,0,0 +2010,9,26,4,NA,14,15,1018,4.47,0,0 +2010,9,26,5,NA,13,14,1018,7.6,0,0 +2010,9,26,6,NA,12,13,1019,9.39,0,0 +2010,9,26,7,NA,11,12,1019,1.79,0,0 +2010,9,26,8,NA,13,14,1020,0.89,0,0 +2010,9,26,9,NA,9,18,1020,1.78,0,0 +2010,9,26,10,NA,3,20,1020,3.13,0,0 +2010,9,26,11,NA,0,22,1019,6.26,0,0 +2010,9,26,12,NA,2,23,1019,8.05,0,0 +2010,9,26,13,NA,0,24,1018,12.07,0,0 +2010,9,26,14,NA,-1,24,1017,16.09,0,0 +2010,9,26,15,NA,-1,24,1016,1.79,0,0 +2010,9,26,16,NA,-2,24,1015,3.13,0,0 +2010,9,26,17,NA,-2,24,1015,1.79,0,0 +2010,9,26,18,NA,2,21,1015,6.71,0,0 +2010,9,26,19,NA,4,17,1015,9.84,0,0 +2010,9,26,20,NA,6,16,1015,12.97,0,0 +2010,9,26,21,NA,6,16,1015,16.1,0,0 +2010,9,26,22,NA,7,15,1015,17.89,0,0 +2010,9,26,23,NA,8,18,1015,21.02,0,0 +2010,9,27,0,NA,8,15,1014,24.15,0,0 +2010,9,27,1,NA,9,14,1014,27.28,0,0 +2010,9,27,2,NA,9,11,1014,3.13,0,0 +2010,9,27,3,NA,9,10,1014,4.92,0,0 +2010,9,27,4,NA,8,10,1014,8.05,0,0 +2010,9,27,5,NA,8,11,1015,0.89,0,0 +2010,9,27,6,NA,8,11,1015,1.78,0,0 +2010,9,27,7,NA,8,12,1016,1.79,0,0 +2010,9,27,8,NA,1,18,1016,8.05,0,0 +2010,9,27,9,NA,1,19,1017,16.1,0,0 +2010,9,27,10,NA,-2,20,1017,24.15,0,0 +2010,9,27,11,NA,-1,21,1017,29.07,0,0 +2010,9,27,12,NA,-3,21,1017,37.12,0,0 +2010,9,27,13,NA,-3,21,1016,45.17,0,0 +2010,9,27,14,NA,-3,22,1016,53.22,0,0 +2010,9,27,15,NA,-4,22,1016,61.27,0,0 +2010,9,27,16,45,-4,22,1016,70.21,0,0 +2010,9,27,17,NA,-4,21,1016,79.15,0,0 +2010,9,27,18,NA,-4,20,1017,86.3,0,0 +2010,9,27,19,NA,-3,19,1018,92.11,0,0 +2010,9,27,20,NA,-2,18,1019,97.03,0,0 +2010,9,27,21,NA,-2,16,1020,101.95,0,0 +2010,9,27,22,NA,-4,17,1021,110.89,0,0 +2010,9,27,23,NA,-4,16,1022,118.94,0,0 +2010,9,28,0,NA,-4,14,1023,123.86,0,0 +2010,9,28,1,NA,-2,12,1023,125.65,0,0 +2010,9,28,2,NA,-3,13,1024,129.67,0,0 +2010,9,28,3,NA,-1,9,1024,132.8,0,0 +2010,9,28,4,NA,0,10,1025,1.79,0,0 +2010,9,28,5,NA,0,10,1026,4.92,0,0 +2010,9,28,6,NA,-1,10,1027,8.94,0,0 +2010,9,28,7,NA,0,11,1028,13.86,0,0 +2010,9,28,8,NA,-1,12,1028,19.67,0,0 +2010,9,28,9,NA,-1,14,1028,24.59,0,0 +2010,9,28,10,NA,-1,15,1028,28.61,0,0 +2010,9,28,11,NA,-1,16,1027,4.02,0,0 +2010,9,28,12,NA,0,17,1026,3.13,0,0 +2010,9,28,13,NA,-2,18,1025,1.79,0,0 +2010,9,28,14,NA,-2,19,1024,1.79,0,0 +2010,9,28,15,NA,-1,19,1023,0.89,0,0 +2010,9,28,16,NA,-2,19,1023,3.13,0,0 +2010,9,28,17,NA,-3,18,1023,6.26,0,0 +2010,9,28,18,NA,0,15,1023,9.39,0,0 +2010,9,28,19,NA,2,12,1024,11.18,0,0 +2010,9,28,20,NA,3,12,1024,12.07,0,0 +2010,9,28,21,NA,5,10,1024,0.89,0,0 +2010,9,28,22,NA,6,9,1024,1.78,0,0 +2010,9,28,23,NA,5,8,1024,2.67,0,0 +2010,9,29,0,NA,5,8,1024,3.56,0,0 +2010,9,29,1,NA,5,8,1024,4.45,0,0 +2010,9,29,2,NA,5,7,1024,0.89,0,0 +2010,9,29,3,NA,5,7,1024,1.78,0,0 +2010,9,29,4,NA,5,7,1023,0.45,0,0 +2010,9,29,5,NA,5,6,1023,0.9,0,0 +2010,9,29,6,NA,5,6,1023,0.89,0,0 +2010,9,29,7,NA,5,7,1024,1.78,0,0 +2010,9,29,8,NA,6,10,1024,3.57,0,0 +2010,9,29,9,NA,7,12,1023,0.45,0,0 +2010,9,29,10,NA,7,14,1023,1.34,0,0 +2010,9,29,11,NA,6,16,1022,2.23,0,0 +2010,9,29,12,NA,6,18,1021,1.79,0,0 +2010,9,29,13,NA,5,19,1020,1.79,0,0 +2010,9,29,14,NA,5,20,1019,3.13,0,0 +2010,9,29,15,NA,5,21,1018,6.26,0,0 +2010,9,29,16,NA,4,21,1018,10.28,0,0 +2010,9,29,17,NA,5,20,1018,13.41,0,0 +2010,9,29,18,NA,7,17,1018,15.2,0,0 +2010,9,29,19,NA,8,14,1018,16.99,0,0 +2010,9,29,20,NA,8,14,1019,0.89,0,0 +2010,9,29,21,NA,9,12,1019,0.89,0,0 +2010,9,29,22,NA,9,12,1019,0.89,0,0 +2010,9,29,23,NA,9,10,1019,1.79,0,0 +2010,9,30,0,NA,9,10,1019,0.89,0,0 +2010,9,30,1,NA,9,10,1019,0.89,0,0 +2010,9,30,2,NA,8,9,1019,1.34,0,0 +2010,9,30,3,NA,8,9,1019,0.89,0,0 +2010,9,30,4,NA,8,9,1019,2.68,0,0 +2010,9,30,5,NA,8,8,1019,4.47,0,0 +2010,9,30,6,NA,8,8,1019,0.45,0,0 +2010,9,30,7,NA,8,9,1019,0.89,0,0 +2010,9,30,8,NA,10,12,1020,4.02,0,0 +2010,9,30,9,NA,10,15,1020,5.81,0,0 +2010,9,30,10,NA,9,17,1019,0.89,0,0 +2010,9,30,11,NA,9,19,1020,1.79,0,0 +2010,9,30,12,NA,9,21,1018,0.89,0,0 +2010,9,30,13,NA,10,23,1018,1.78,0,0 +2010,9,30,14,NA,10,24,1017,1.79,0,0 +2010,9,30,15,NA,10,25,1016,4.92,0,0 +2010,9,30,16,NA,11,24,1016,8.05,0,0 +2010,9,30,17,NA,12,23,1016,9.84,0,0 +2010,9,30,18,NA,13,20,1017,11.63,0,0 +2010,9,30,19,NA,13,17,1017,0.89,0,0 +2010,9,30,20,NA,13,16,1018,0.89,0,0 +2010,9,30,21,209,13,15,1018,0.89,0,0 +2010,9,30,22,204,13,14,1019,1.78,0,0 +2010,9,30,23,222,13,14,1018,0.89,0,0 +2010,10,1,0,231,12,12,1018,1.78,0,0 +2010,10,1,1,268,12,13,1018,0.89,0,0 +2010,10,1,2,258,12,13,1018,1.78,0,0 +2010,10,1,3,196,11,12,1019,3.13,0,0 +2010,10,1,4,192,11,13,1019,4.02,0,0 +2010,10,1,5,165,11,12,1019,0.89,0,0 +2010,10,1,6,156,10,11,1019,1.79,0,0 +2010,10,1,7,155,11,12,1020,2.68,0,0 +2010,10,1,8,162,13,14,1020,1.79,0,0 +2010,10,1,9,211,13,17,1020,0.89,0,0 +2010,10,1,10,274,13,19,1020,1.79,0,0 +2010,10,1,11,266,13,20,1019,0.89,0,0 +2010,10,1,12,225,14,21,1018,1.79,0,0 +2010,10,1,13,194,14,24,1018,0.89,0,0 +2010,10,1,14,203,15,23,1017,1.79,0,0 +2010,10,1,15,224,15,23,1016,0.89,0,0 +2010,10,1,16,234,16,23,1016,1.78,0,0 +2010,10,1,17,259,16,22,1015,0.89,0,0 +2010,10,1,18,247,17,20,1016,0.89,0,0 +2010,10,1,19,266,17,20,1016,1.34,0,0 +2010,10,1,20,237,17,21,1016,1.79,0,0 +2010,10,1,21,243,16,21,1015,3.58,0,0 +2010,10,1,22,242,15,19,1016,3.13,0,0 +2010,10,1,23,256,16,19,1015,6.26,0,1 +2010,10,2,0,168,15,18,1015,10.28,0,2 +2010,10,2,1,74,15,16,1015,3.13,0,3 +2010,10,2,2,67,15,16,1015,3.13,0,4 +2010,10,2,3,80,15,16,1014,0.89,0,5 +2010,10,2,4,75,16,16,1014,1.79,0,6 +2010,10,2,5,54,16,16,1014,4.92,0,7 +2010,10,2,6,47,15,16,1014,6.71,0,8 +2010,10,2,7,51,15,16,1013,9.84,0,0 +2010,10,2,8,47,15,16,1013,12.97,0,0 +2010,10,2,9,34,15,18,1013,14.76,0,0 +2010,10,2,10,22,12,20,1013,22.81,0,0 +2010,10,2,11,9,10,21,1013,31.75,0,0 +2010,10,2,12,11,10,21,1012,41.58,0,0 +2010,10,2,13,12,9,21,1012,51.41,0,0 +2010,10,2,14,11,9,21,1011,59.46,0,0 +2010,10,2,15,7,7,20,1012,68.4,0,0 +2010,10,2,16,3,7,19,1012,78.23,0,0 +2010,10,2,17,5,8,18,1012,87.17,0,0 +2010,10,2,18,7,8,17,1013,96.11,0,0 +2010,10,2,19,6,8,17,1014,104.16,0,0 +2010,10,2,20,10,8,16,1015,108.18,0,0 +2010,10,2,21,13,8,16,1015,113.1,0,0 +2010,10,2,22,16,9,14,1015,114.89,0,0 +2010,10,2,23,12,8,13,1015,118.02,0,0 +2010,10,3,0,12,7,13,1015,122.04,0,0 +2010,10,3,1,13,7,13,1015,125.17,0,0 +2010,10,3,2,12,7,14,1015,128.3,0,0 +2010,10,3,3,12,5,14,1015,131.43,0,0 +2010,10,3,4,13,3,15,1015,138.58,0,0 +2010,10,3,5,12,3,16,1015,144.39,0,0 +2010,10,3,6,12,3,16,1015,149.31,0,0 +2010,10,3,7,13,3,16,1016,156.46,0,0 +2010,10,3,8,10,2,17,1016,165.4,0,0 +2010,10,3,9,12,0,18,1017,174.34,0,0 +2010,10,3,10,13,0,18,1017,184.17,0,0 +2010,10,3,11,12,0,19,1017,193.11,0,0 +2010,10,3,12,11,1,21,1016,202.94,0,0 +2010,10,3,13,11,-1,22,1015,211.88,0,0 +2010,10,3,14,10,-2,23,1015,219.93,0,0 +2010,10,3,15,8,0,23,1015,228.87,0,0 +2010,10,3,16,6,0,23,1015,237.81,0,0 +2010,10,3,17,5,2,22,1016,245.86,0,0 +2010,10,3,18,6,2,21,1016,253.91,0,0 +2010,10,3,19,8,2,20,1017,261.96,0,0 +2010,10,3,20,7,2,19,1018,269.11,0,0 +2010,10,3,21,7,4,13,1018,272.24,0,0 +2010,10,3,22,8,3,16,1018,275.37,0,0 +2010,10,3,23,7,4,14,1018,278.5,0,0 +2010,10,4,0,9,6,11,1018,3.13,0,0 +2010,10,4,1,23,6,11,1018,0.89,0,0 +2010,10,4,2,21,6,10,1018,2.68,0,0 +2010,10,4,3,25,6,9,1018,6.7,0,0 +2010,10,4,4,22,6,9,1018,9.83,0,0 +2010,10,4,5,14,5,9,1019,11.62,0,0 +2010,10,4,6,22,6,9,1019,13.41,0,0 +2010,10,4,7,27,7,9,1020,15.2,0,0 +2010,10,4,8,22,6,14,1020,1.79,0,0 +2010,10,4,9,14,5,17,1020,4.92,0,0 +2010,10,4,10,19,4,19,1020,0.89,0,0 +2010,10,4,11,15,5,20,1020,1.78,0,0 +2010,10,4,12,17,5,22,1019,3.57,0,0 +2010,10,4,13,21,6,23,1017,4.46,0,0 +2010,10,4,14,24,6,23,1017,1.79,0,0 +2010,10,4,15,24,6,23,1016,4.92,0,0 +2010,10,4,16,26,7,23,1016,8.05,0,0 +2010,10,4,17,24,8,22,1015,11.18,0,0 +2010,10,4,18,25,8,20,1015,14.31,0,0 +2010,10,4,19,27,10,17,1016,0.89,0,0 +2010,10,4,20,38,11,14,1016,0.89,0,0 +2010,10,4,21,47,10,13,1017,0.89,0,0 +2010,10,4,22,41,11,13,1017,0.89,0,0 +2010,10,4,23,42,10,13,1016,1.78,0,0 +2010,10,5,0,85,9,12,1016,0.89,0,0 +2010,10,5,1,88,10,11,1016,0.89,0,0 +2010,10,5,2,90,8,9,1016,1.79,0,0 +2010,10,5,3,73,8,9,1016,1.79,0,0 +2010,10,5,4,57,9,10,1016,1.79,0,0 +2010,10,5,5,50,8,9,1016,4.92,0,0 +2010,10,5,6,43,8,10,1016,8.05,0,0 +2010,10,5,7,52,9,10,1016,9.84,0,0 +2010,10,5,8,52,11,14,1016,12.97,0,0 +2010,10,5,9,31,10,17,1016,1.79,0,0 +2010,10,5,10,38,10,19,1016,1.79,0,0 +2010,10,5,11,44,10,21,1015,3.58,0,0 +2010,10,5,12,39,9,23,1014,0.89,0,0 +2010,10,5,13,43,9,25,1013,1.78,0,0 +2010,10,5,14,55,10,26,1013,3.13,0,0 +2010,10,5,15,56,10,26,1012,7.15,0,0 +2010,10,5,16,60,11,25,1012,10.28,0,0 +2010,10,5,17,72,12,24,1011,12.07,0,0 +2010,10,5,18,80,13,20,1012,13.86,0,0 +2010,10,5,19,99,13,18,1012,0.89,0,0 +2010,10,5,20,133,14,16,1013,1.78,0,0 +2010,10,5,21,166,13,15,1013,2.67,0,0 +2010,10,5,22,152,13,16,1013,1.79,0,0 +2010,10,5,23,192,13,14,1014,0.89,0,0 +2010,10,6,0,206,11,13,1014,0.89,0,0 +2010,10,6,1,260,12,13,1014,0.89,0,0 +2010,10,6,2,217,12,12,1014,0.89,0,0 +2010,10,6,3,170,12,12,1014,0.89,0,0 +2010,10,6,4,151,11,12,1013,1.79,0,0 +2010,10,6,5,136,11,12,1014,0.89,0,0 +2010,10,6,6,115,10,11,1014,0.89,0,0 +2010,10,6,7,144,11,11,1014,2.68,0,0 +2010,10,6,8,154,13,14,1015,4.47,0,0 +2010,10,6,9,198,14,17,1014,0.89,0,0 +2010,10,6,10,252,13,19,1014,1.78,0,0 +2010,10,6,11,263,13,22,1014,2.67,0,0 +2010,10,6,12,243,13,23,1013,3.56,0,0 +2010,10,6,13,217,14,24,1013,1.79,0,0 +2010,10,6,14,256,14,25,1012,4.92,0,0 +2010,10,6,15,284,14,25,1012,8.05,0,0 +2010,10,6,16,252,15,24,1012,11.18,0,0 +2010,10,6,17,264,15,22,1012,12.97,0,0 +2010,10,6,18,320,16,19,1012,14.76,0,0 +2010,10,6,19,372,15,17,1013,0.89,0,0 +2010,10,6,20,435,16,16,1014,0.89,0,0 +2010,10,6,21,503,14,15,1014,0.89,0,0 +2010,10,6,22,534,15,15,1014,0.89,0,0 +2010,10,6,23,506,15,15,1014,1.78,0,0 +2010,10,7,0,505,14,15,1015,2.67,0,0 +2010,10,7,1,465,13,14,1015,0.89,0,0 +2010,10,7,2,468,13,13,1015,0.89,0,0 +2010,10,7,3,449,12,13,1014,1.78,0,0 +2010,10,7,4,418,12,12,1014,0.89,0,0 +2010,10,7,5,405,12,13,1015,1.78,0,0 +2010,10,7,6,374,12,12,1015,0.89,0,0 +2010,10,7,7,388,12,13,1016,1.78,0,0 +2010,10,7,8,384,14,15,1016,0.89,0,0 +2010,10,7,9,384,14,16,1017,1.79,0,0 +2010,10,7,10,381,15,18,1017,0.89,0,0 +2010,10,7,11,359,13,20,1017,1.79,0,0 +2010,10,7,12,369,13,21,1016,0.89,0,0 +2010,10,7,13,355,13,22,1015,1.79,0,0 +2010,10,7,14,348,13,22,1015,4.92,0,0 +2010,10,7,15,344,13,23,1015,8.05,0,0 +2010,10,7,16,330,13,23,1015,11.18,0,0 +2010,10,7,17,279,14,22,1015,12.07,0,0 +2010,10,7,18,252,15,18,1015,0.89,0,0 +2010,10,7,19,287,15,17,1016,1.34,0,0 +2010,10,7,20,327,14,16,1016,0.89,0,0 +2010,10,7,21,365,14,15,1016,1.78,0,0 +2010,10,7,22,391,14,14,1017,0.89,0,0 +2010,10,7,23,423,14,14,1017,0.89,0,0 +2010,10,8,0,429,14,14,1017,0.89,0,0 +2010,10,8,1,439,13,14,1016,0.89,0,0 +2010,10,8,2,458,11,12,1016,0.89,0,0 +2010,10,8,3,436,12,12,1016,0.45,0,0 +2010,10,8,4,431,12,12,1016,0.89,0,0 +2010,10,8,5,408,11,11,1016,0.89,0,0 +2010,10,8,6,387,11,11,1016,1.34,0,0 +2010,10,8,7,349,13,12,1016,0.89,0,0 +2010,10,8,8,332,13,13,1017,0.89,0,0 +2010,10,8,9,348,14,15,1017,2.68,0,0 +2010,10,8,10,350,15,17,1017,0.89,0,0 +2010,10,8,11,344,15,19,1016,1.78,0,0 +2010,10,8,12,369,15,20,1016,1.79,0,0 +2010,10,8,13,406,14,22,1015,3.58,0,0 +2010,10,8,14,422,14,23,1014,5.37,0,0 +2010,10,8,15,310,12,24,1013,7.16,0,0 +2010,10,8,16,175,10,24,1013,11.18,0,0 +2010,10,8,17,253,12,21,1013,12.97,0,0 +2010,10,8,18,263,13,18,1013,0.89,0,0 +2010,10,8,19,223,13,16,1013,1.78,0,0 +2010,10,8,20,400,13,14,1013,0.89,0,0 +2010,10,8,21,470,13,14,1013,0.89,0,0 +2010,10,8,22,459,13,14,1013,1.78,0,0 +2010,10,8,23,378,13,14,1013,1.79,0,0 +2010,10,9,0,290,13,14,1013,2.68,0,0 +2010,10,9,1,290,12,13,1013,3.57,0,0 +2010,10,9,2,287,11,12,1013,4.46,0,0 +2010,10,9,3,283,12,12,1012,0.89,0,0 +2010,10,9,4,283,11,12,1012,1.78,0,0 +2010,10,9,5,337,11,12,1012,2.67,0,0 +2010,10,9,6,329,11,11,1012,3.12,0,0 +2010,10,9,7,311,10,11,1012,0.89,0,0 +2010,10,9,8,318,12,13,1012,0.89,0,0 +2010,10,9,9,335,12,14,1012,0.89,0,0 +2010,10,9,10,324,12,16,1012,1.78,0,0 +2010,10,9,11,307,13,17,1011,2.67,0,0 +2010,10,9,12,323,13,19,1011,3.56,0,0 +2010,10,9,13,350,13,20,1010,1.79,0,0 +2010,10,9,14,374,14,20,1009,3.58,0,0 +2010,10,9,15,396,14,21,1009,5.37,0,0 +2010,10,9,16,435,14,21,1008,0.89,0,0 +2010,10,9,17,412,15,20,1008,0.89,0,0 +2010,10,9,18,449,14,17,1009,0.89,0,0 +2010,10,9,19,429,14,16,1009,1.79,0,0 +2010,10,9,20,411,14,15,1010,2.68,0,0 +2010,10,9,21,430,13,14,1010,0.89,0,0 +2010,10,9,22,431,13,14,1010,1.78,0,0 +2010,10,9,23,440,13,14,1010,2.67,0,0 +2010,10,10,0,434,13,13,1010,3.56,0,0 +2010,10,10,1,438,12,12,1010,4.45,0,0 +2010,10,10,2,416,12,13,1011,0.89,0,0 +2010,10,10,3,417,12,13,1010,0.89,0,0 +2010,10,10,4,394,12,12,1010,0.89,0,0 +2010,10,10,5,380,12,12,1011,0.89,0,0 +2010,10,10,6,376,12,12,1011,0.89,0,0 +2010,10,10,7,385,13,13,1011,0.89,0,0 +2010,10,10,8,383,13,14,1012,0.89,0,0 +2010,10,10,9,397,14,15,1012,0.89,0,0 +2010,10,10,10,392,14,16,1013,0.89,0,0 +2010,10,10,11,408,15,17,1012,1.79,0,0 +2010,10,10,12,428,15,18,1011,3.58,0,0 +2010,10,10,13,420,14,18,1011,5.37,0,0 +2010,10,10,14,399,14,19,1010,0.89,0,0 +2010,10,10,15,391,15,18,1010,1.34,0,1 +2010,10,10,16,393,16,17,1010,1.79,0,2 +2010,10,10,17,415,16,17,1011,3.13,0,3 +2010,10,10,18,395,16,16,1011,0.89,0,4 +2010,10,10,19,420,16,16,1011,1.79,0,5 +2010,10,10,20,414,16,17,1011,0.89,0,0 +2010,10,10,21,394,16,17,1012,3.13,0,1 +2010,10,10,22,348,15,16,1012,7.15,0,2 +2010,10,10,23,74,13,15,1013,12.07,0,4 +2010,10,11,0,32,12,14,1013,17.88,0,5 +2010,10,11,1,17,12,13,1014,23.69,0,6 +2010,10,11,2,6,11,13,1013,27.71,0,7 +2010,10,11,3,5,11,13,1013,32.63,0,0 +2010,10,11,4,4,9,14,1013,38.44,0,0 +2010,10,11,5,6,7,14,1014,45.59,0,0 +2010,10,11,6,8,7,11,1015,48.72,0,0 +2010,10,11,7,16,8,12,1016,50.51,0,0 +2010,10,11,8,2,8,14,1017,3.13,0,0 +2010,10,11,9,4,8,15,1018,8.05,0,0 +2010,10,11,10,7,7,16,1018,12.07,0,0 +2010,10,11,11,5,6,17,1018,16.99,0,0 +2010,10,11,12,4,5,18,1018,4.92,0,0 +2010,10,11,13,7,6,18,1018,4.02,0,0 +2010,10,11,14,7,5,19,1018,8.04,0,0 +2010,10,11,15,7,5,19,1018,11.17,0,0 +2010,10,11,16,10,6,19,1019,15.19,0,0 +2010,10,11,17,10,5,17,1019,16.98,0,0 +2010,10,11,18,13,4,17,1020,1.79,0,0 +2010,10,11,19,17,4,15,1021,5.81,0,0 +2010,10,11,20,9,4,13,1022,1.79,0,0 +2010,10,11,21,11,4,12,1023,3.58,0,0 +2010,10,11,22,21,6,10,1023,0.89,0,0 +2010,10,11,23,21,6,10,1023,0.89,0,0 +2010,10,12,0,37,6,8,1024,2.68,0,0 +2010,10,12,1,30,5,8,1024,0.89,0,0 +2010,10,12,2,45,6,7,1024,1.79,0,0 +2010,10,12,3,56,6,7,1024,0.89,0,0 +2010,10,12,4,42,6,7,1024,1.34,0,0 +2010,10,12,5,51,6,6,1024,2.23,0,0 +2010,10,12,6,33,5,6,1024,3.13,0,0 +2010,10,12,7,48,6,7,1024,0.89,0,0 +2010,10,12,8,42,7,10,1024,1.79,0,0 +2010,10,12,9,32,6,12,1025,3.58,0,0 +2010,10,12,10,29,4,14,1024,5.37,0,0 +2010,10,12,11,31,5,16,1023,7.16,0,0 +2010,10,12,12,22,5,18,1023,0.89,0,0 +2010,10,12,13,24,4,18,1021,2.68,0,0 +2010,10,12,14,NA,4,19,1020,3.13,0,0 +2010,10,12,15,NA,4,20,1020,3.13,0,0 +2010,10,12,16,40,5,19,1020,6.26,0,0 +2010,10,12,17,32,6,17,1019,9.39,0,0 +2010,10,12,18,45,6,15,1019,12.52,0,0 +2010,10,12,19,55,7,14,1020,14.31,0,0 +2010,10,12,20,75,8,13,1020,15.2,0,0 +2010,10,12,21,107,9,12,1020,16.09,0,0 +2010,10,12,22,125,8,11,1020,0.89,0,0 +2010,10,12,23,154,8,11,1020,1.78,0,0 +2010,10,13,0,168,8,10,1019,2.67,0,0 +2010,10,13,1,187,8,9,1019,3.56,0,0 +2010,10,13,2,163,8,9,1019,4.01,0,0 +2010,10,13,3,143,8,8,1019,4.46,0,0 +2010,10,13,4,118,8,8,1018,4.91,0,0 +2010,10,13,5,111,7,8,1018,0.89,0,0 +2010,10,13,6,99,7,7,1018,0.89,0,0 +2010,10,13,7,86,7,8,1018,1.79,0,0 +2010,10,13,8,66,8,10,1019,2.68,0,0 +2010,10,13,9,56,9,13,1019,0.89,0,0 +2010,10,13,10,55,9,16,1019,1.79,0,0 +2010,10,13,11,63,8,17,1018,0.89,0,0 +2010,10,13,12,67,7,19,1017,1.79,0,0 +2010,10,13,13,70,8,21,1016,1.79,0,0 +2010,10,13,14,91,8,22,1015,4.92,0,0 +2010,10,13,15,107,7,22,1014,8.94,0,0 +2010,10,13,16,137,8,22,1013,12.96,0,0 +2010,10,13,17,102,9,20,1013,16.09,0,0 +2010,10,13,18,114,9,18,1014,19.22,0,0 +2010,10,13,19,148,11,15,1014,0.89,0,0 +2010,10,13,20,164,12,14,1015,0.89,0,0 +2010,10,13,21,193,11,15,1015,0.89,0,0 +2010,10,13,22,164,10,12,1016,3.13,0,0 +2010,10,13,23,118,11,14,1017,7.15,0,0 +2010,10,14,0,41,9,15,1017,8.94,0,0 +2010,10,14,1,17,8,13,1018,12.07,0,0 +2010,10,14,2,13,0,15,1018,16.99,0,0 +2010,10,14,3,13,3,11,1019,1.79,0,0 +2010,10,14,4,10,1,12,1019,4.92,0,0 +2010,10,14,5,5,0,11,1020,8.05,0,0 +2010,10,14,6,2,1,9,1020,9.84,0,0 +2010,10,14,7,7,1,8,1021,12.97,0,0 +2010,10,14,8,22,-2,14,1022,16.99,0,0 +2010,10,14,9,12,-2,16,1022,21.01,0,0 +2010,10,14,10,22,-3,18,1021,26.82,0,0 +2010,10,14,11,11,-3,19,1021,36.65,0,0 +2010,10,14,12,16,-2,19,1020,42.46,0,0 +2010,10,14,13,18,-1,20,1019,47.38,0,0 +2010,10,14,14,7,-1,20,1018,54.53,0,0 +2010,10,14,15,17,-1,20,1017,61.68,0,0 +2010,10,14,16,11,-1,20,1018,69.73,0,0 +2010,10,14,17,18,-2,19,1018,77.78,0,0 +2010,10,14,18,21,-2,18,1018,84.93,0,0 +2010,10,14,19,16,-2,17,1019,90.74,0,0 +2010,10,14,20,22,1,12,1020,93.87,0,0 +2010,10,14,21,13,0,12,1020,97,0,0 +2010,10,14,22,21,0,11,1021,100.13,0,0 +2010,10,14,23,30,-2,15,1021,104.15,0,0 +2010,10,15,0,19,-2,14,1021,109.07,0,0 +2010,10,15,1,19,-3,13,1020,114.88,0,0 +2010,10,15,2,15,-3,13,1020,119.8,0,0 +2010,10,15,3,18,-1,12,1020,121.59,0,0 +2010,10,15,4,56,0,11,1019,123.38,0,0 +2010,10,15,5,38,0,10,1019,125.17,0,0 +2010,10,15,6,18,-1,11,1019,129.19,0,0 +2010,10,15,7,48,-2,11,1019,133.21,0,0 +2010,10,15,8,37,-2,13,1019,136.34,0,0 +2010,10,15,9,19,-1,14,1019,140.36,0,0 +2010,10,15,10,9,-2,17,1018,144.38,0,0 +2010,10,15,11,8,-2,18,1017,147.51,0,0 +2010,10,15,12,23,-2,19,1016,4.92,0,0 +2010,10,15,13,24,-1,20,1014,10.73,0,0 +2010,10,15,14,26,1,20,1013,17.88,0,0 +2010,10,15,15,31,0,20,1012,25.03,0,0 +2010,10,15,16,46,0,20,1011,33.08,0,0 +2010,10,15,17,67,1,19,1011,38.89,0,0 +2010,10,15,18,73,1,18,1011,44.7,0,0 +2010,10,15,19,67,1,19,1011,53.64,0,0 +2010,10,15,20,71,2,18,1012,3.13,0,0 +2010,10,15,21,64,5,12,1013,4.02,0,0 +2010,10,15,22,77,5,9,1013,1.79,0,0 +2010,10,15,23,71,5,8,1013,2.68,0,0 +2010,10,16,0,111,6,9,1014,0.89,0,0 +2010,10,16,1,125,5,7,1014,1.78,0,0 +2010,10,16,2,133,5,6,1015,1.79,0,0 +2010,10,16,3,134,4,6,1015,4.92,0,0 +2010,10,16,4,143,4,5,1015,8.94,0,0 +2010,10,16,5,123,4,6,1015,12.07,0,0 +2010,10,16,6,97,4,5,1016,0.45,0,0 +2010,10,16,7,73,4,6,1017,1.79,0,0 +2010,10,16,8,37,5,11,1018,5.81,0,0 +2010,10,16,9,34,2,15,1018,4.02,0,0 +2010,10,16,10,63,5,16,1019,4.02,0,0 +2010,10,16,11,106,5,16,1019,9.83,0,0 +2010,10,16,12,123,6,17,1019,13.85,0,0 +2010,10,16,13,79,7,18,1018,17.87,0,0 +2010,10,16,14,50,8,18,1018,21.89,0,0 +2010,10,16,15,46,7,18,1018,25.91,0,0 +2010,10,16,16,45,7,18,1018,29.04,0,0 +2010,10,16,17,47,7,16,1018,33.06,0,0 +2010,10,16,18,49,7,15,1019,36.19,0,0 +2010,10,16,19,53,6,13,1020,39.32,0,0 +2010,10,16,20,49,6,13,1022,44.24,0,0 +2010,10,16,21,46,5,13,1023,49.16,0,0 +2010,10,16,22,32,3,11,1025,54.97,0,0 +2010,10,16,23,32,2,11,1025,58.99,0,0 +2010,10,17,0,23,1,11,1026,63.01,0,0 +2010,10,17,1,31,0,10,1027,67.03,0,0 +2010,10,17,2,27,-2,10,1027,70.16,0,0 +2010,10,17,3,24,-4,9,1027,73.29,0,0 +2010,10,17,4,20,-5,8,1027,76.42,0,0 +2010,10,17,5,23,-6,8,1028,79.55,0,0 +2010,10,17,6,34,-6,8,1028,82.68,0,0 +2010,10,17,7,34,-6,8,1029,85.81,0,0 +2010,10,17,8,44,-5,8,1029,88.94,0,0 +2010,10,17,9,41,-4,8,1030,90.73,0,0 +2010,10,17,10,39,-4,8,1030,92.52,0,0 +2010,10,17,11,38,-5,9,1030,95.65,0,0 +2010,10,17,12,40,-5,9,1030,97.44,0,0 +2010,10,17,13,46,-4,9,1029,99.23,0,0 +2010,10,17,14,47,-4,9,1028,101.02,0,0 +2010,10,17,15,44,-4,9,1028,102.81,0,0 +2010,10,17,16,51,-4,9,1028,104.6,0,0 +2010,10,17,17,64,-3,9,1027,106.39,0,0 +2010,10,17,18,59,-3,8,1028,0.89,0,0 +2010,10,17,19,64,-1,8,1028,1.78,0,0 +2010,10,17,20,62,0,8,1029,2.67,0,0 +2010,10,17,21,73,0,8,1029,3.56,0,0 +2010,10,17,22,80,2,8,1029,0.89,0,0 +2010,10,17,23,89,3,8,1029,0.45,0,0 +2010,10,18,0,81,1,8,1029,0.89,0,0 +2010,10,18,1,73,1,8,1029,1.78,0,0 +2010,10,18,2,92,1,8,1028,0.89,0,0 +2010,10,18,3,86,1,8,1028,0.89,0,0 +2010,10,18,4,90,2,8,1028,0.89,0,0 +2010,10,18,5,80,2,8,1028,1.78,0,0 +2010,10,18,6,71,2,8,1028,2.67,0,0 +2010,10,18,7,83,1,8,1028,3.56,0,0 +2010,10,18,8,77,0,8,1029,4.45,0,0 +2010,10,18,9,69,0,8,1029,3.13,0,0 +2010,10,18,10,64,2,8,1028,0.45,0,0 +2010,10,18,11,66,3,8,1027,3.13,0,0 +2010,10,18,12,71,2,8,1027,4.92,0,0 +2010,10,18,13,75,5,7,1026,9.84,0,1 +2010,10,18,14,79,5,6,1025,12.97,0,2 +2010,10,18,15,75,5,6,1025,16.99,0,3 +2010,10,18,16,68,6,6,1025,20.12,0,4 +2010,10,18,17,64,6,6,1025,23.25,0,5 +2010,10,18,18,50,6,6,1025,26.38,0,6 +2010,10,18,19,47,6,6,1026,0.89,0,7 +2010,10,18,20,47,6,6,1026,1.79,0,8 +2010,10,18,21,41,6,7,1026,0.89,0,9 +2010,10,18,22,38,6,6,1026,1.78,0,10 +2010,10,18,23,39,6,7,1026,1.79,0,0 +2010,10,19,0,42,5,7,1026,1.79,0,0 +2010,10,19,1,43,6,7,1025,3.13,0,0 +2010,10,19,2,40,6,7,1025,0.89,0,0 +2010,10,19,3,45,6,7,1025,1.78,0,0 +2010,10,19,4,52,6,7,1025,0.89,0,0 +2010,10,19,5,57,6,7,1025,1.78,0,0 +2010,10,19,6,61,6,7,1026,0.89,0,0 +2010,10,19,7,57,7,7,1026,1.34,0,0 +2010,10,19,8,63,7,8,1027,0.89,0,0 +2010,10,19,9,73,7,8,1028,0.89,0,0 +2010,10,19,10,60,7,8,1028,1.78,0,0 +2010,10,19,11,81,7,9,1028,2.67,0,0 +2010,10,19,12,79,7,10,1027,1.79,0,0 +2010,10,19,13,89,7,10,1026,3.58,0,0 +2010,10,19,14,93,7,11,1026,1.79,0,0 +2010,10,19,15,87,7,10,1026,1.79,0,0 +2010,10,19,16,93,7,11,1027,0.89,0,0 +2010,10,19,17,84,7,11,1027,1.34,0,0 +2010,10,19,18,79,8,10,1027,0.89,0,0 +2010,10,19,19,89,8,10,1028,0.89,0,0 +2010,10,19,20,88,8,10,1028,0.89,0,0 +2010,10,19,21,82,7,9,1029,2.68,0,0 +2010,10,19,22,92,7,9,1029,4.47,0,0 +2010,10,19,23,91,6,9,1029,6.26,0,0 +2010,10,20,0,83,6,9,1028,8.05,0,0 +2010,10,20,1,80,7,9,1028,0.89,0,0 +2010,10,20,2,82,7,8,1028,1.78,0,0 +2010,10,20,3,88,7,8,1028,1.79,0,0 +2010,10,20,4,92,6,8,1028,1.79,0,0 +2010,10,20,5,81,7,8,1028,2.68,0,0 +2010,10,20,6,79,7,8,1028,0.89,0,0 +2010,10,20,7,86,6,8,1028,0.89,0,0 +2010,10,20,8,84,6,8,1029,0.89,0,0 +2010,10,20,9,75,7,9,1029,0.89,0,0 +2010,10,20,10,82,6,9,1029,1.79,0,0 +2010,10,20,11,89,7,10,1028,1.79,0,0 +2010,10,20,12,74,6,10,1028,0.89,0,0 +2010,10,20,13,87,6,10,1027,1.78,0,0 +2010,10,20,14,79,6,10,1026,2.67,0,0 +2010,10,20,15,81,7,10,1026,3.56,0,0 +2010,10,20,16,78,7,10,1026,1.79,0,0 +2010,10,20,17,73,7,10,1025,2.68,0,0 +2010,10,20,18,80,7,10,1026,3.57,0,0 +2010,10,20,19,74,7,10,1026,0.89,0,0 +2010,10,20,20,85,7,9,1026,0.89,0,0 +2010,10,20,21,91,7,9,1026,1.78,0,0 +2010,10,20,22,86,7,9,1026,0.45,0,0 +2010,10,20,23,84,7,9,1026,0.9,0,0 +2010,10,21,0,88,8,9,1025,1.35,0,0 +2010,10,21,1,92,8,9,1025,0.89,0,0 +2010,10,21,2,93,8,9,1025,0.45,0,0 +2010,10,21,3,98,8,9,1024,0.89,0,0 +2010,10,21,4,101,8,9,1024,1.78,0,0 +2010,10,21,5,102,8,9,1023,0.89,0,0 +2010,10,21,6,95,8,9,1023,0.45,0,0 +2010,10,21,7,100,8,9,1023,0.89,0,0 +2010,10,21,8,103,8,10,1024,0.89,0,0 +2010,10,21,9,115,8,10,1024,1.78,0,0 +2010,10,21,10,130,8,10,1024,0.89,0,0 +2010,10,21,11,140,8,11,1023,1.79,0,0 +2010,10,21,12,154,8,11,1023,0.89,0,0 +2010,10,21,13,172,8,12,1022,1.79,0,0 +2010,10,21,14,151,9,12,1021,0.89,0,0 +2010,10,21,15,127,9,12,1021,1.79,0,0 +2010,10,21,16,109,8,12,1021,3.58,0,0 +2010,10,21,17,105,8,12,1021,5.37,0,0 +2010,10,21,18,96,8,12,1020,6.26,0,0 +2010,10,21,19,115,9,11,1021,7.15,0,0 +2010,10,21,20,161,10,11,1021,0.89,0,0 +2010,10,21,21,180,9,11,1021,0.89,0,0 +2010,10,21,22,179,9,11,1021,0.89,0,0 +2010,10,21,23,179,9,11,1021,1.78,0,0 +2010,10,22,0,184,9,11,1021,2.67,0,0 +2010,10,22,1,176,9,11,1021,3.56,0,0 +2010,10,22,2,164,9,11,1021,0.45,0,0 +2010,10,22,3,152,9,11,1020,1.34,0,0 +2010,10,22,4,143,9,11,1020,0.89,0,0 +2010,10,22,5,142,9,10,1021,1.78,0,0 +2010,10,22,6,137,9,10,1021,3.57,0,0 +2010,10,22,7,145,9,10,1021,4.46,0,0 +2010,10,22,8,146,9,11,1021,0.89,0,0 +2010,10,22,9,147,9,11,1022,0.89,0,0 +2010,10,22,10,154,9,12,1022,1.78,0,0 +2010,10,22,11,161,9,12,1021,1.79,0,0 +2010,10,22,12,165,9,12,1021,1.79,0,0 +2010,10,22,13,161,10,13,1020,0.89,0,0 +2010,10,22,14,162,10,13,1019,1.79,0,0 +2010,10,22,15,182,10,13,1019,0.89,0,0 +2010,10,22,16,201,10,13,1019,1.78,0,0 +2010,10,22,17,197,11,13,1019,0.89,0,0 +2010,10,22,18,201,11,12,1019,1.78,0,0 +2010,10,22,19,195,10,12,1020,1.79,0,0 +2010,10,22,20,204,10,12,1020,0.89,0,0 +2010,10,22,21,187,10,12,1020,0.89,0,0 +2010,10,22,22,174,10,12,1021,1.79,0,0 +2010,10,22,23,174,10,12,1020,4.92,0,0 +2010,10,23,0,178,10,12,1020,6.71,0,0 +2010,10,23,1,164,10,12,1020,8.5,0,0 +2010,10,23,2,158,10,12,1020,0.89,0,0 +2010,10,23,3,158,10,11,1019,1.78,0,0 +2010,10,23,4,158,10,12,1019,2.67,0,0 +2010,10,23,5,160,10,12,1019,1.79,0,1 +2010,10,23,6,164,10,11,1019,3.58,0,2 +2010,10,23,7,167,11,11,1019,4.47,0,3 +2010,10,23,8,173,11,12,1020,0.89,0,4 +2010,10,23,9,179,11,12,1020,0.89,0,0 +2010,10,23,10,188,11,12,1020,1.79,0,0 +2010,10,23,11,194,11,13,1019,3.58,0,0 +2010,10,23,12,225,12,15,1018,1.79,0,0 +2010,10,23,13,252,12,16,1018,0.89,0,0 +2010,10,23,14,257,12,16,1017,1.78,0,0 +2010,10,23,15,260,12,16,1017,2.67,0,0 +2010,10,23,16,271,13,15,1017,3.56,0,0 +2010,10,23,17,265,13,14,1017,0.89,0,0 +2010,10,23,18,271,13,14,1017,1.78,0,0 +2010,10,23,19,273,13,14,1018,0.89,0,0 +2010,10,23,20,261,12,13,1018,1.78,0,0 +2010,10,23,21,275,12,13,1018,2.67,0,0 +2010,10,23,22,278,12,13,1019,0.89,0,0 +2010,10,23,23,270,12,13,1019,2.68,0,0 +2010,10,24,0,262,12,13,1019,3.57,0,0 +2010,10,24,1,256,12,13,1019,4.46,0,1 +2010,10,24,2,251,13,13,1018,6.25,0,2 +2010,10,24,3,259,13,13,1018,8.04,0,3 +2010,10,24,4,260,13,13,1018,9.83,0,4 +2010,10,24,5,252,13,13,1018,11.62,0,5 +2010,10,24,6,234,13,13,1018,13.41,0,6 +2010,10,24,7,212,13,13,1018,1.79,0,7 +2010,10,24,8,218,12,13,1020,7.6,0,8 +2010,10,24,9,63,9,11,1022,13.41,0,9 +2010,10,24,10,13,7,9,1024,19.22,0,10 +2010,10,24,11,11,6,8,1025,27.27,0,11 +2010,10,24,12,12,5,7,1026,35.32,0,12 +2010,10,24,13,12,4,7,1025,43.37,0,13 +2010,10,24,14,8,3,7,1025,50.52,0,0 +2010,10,24,15,9,3,7,1025,56.33,0,0 +2010,10,24,16,10,2,8,1025,61.25,0,0 +2010,10,24,17,11,2,8,1026,65.27,0,0 +2010,10,24,18,13,1,7,1026,70.19,0,0 +2010,10,24,19,17,1,7,1028,4.02,0,0 +2010,10,24,20,11,1,6,1029,8.94,0,0 +2010,10,24,21,12,0,5,1029,12.07,0,0 +2010,10,24,22,14,-1,6,1029,15.2,0,0 +2010,10,24,23,15,-1,6,1030,18.33,0,0 +2010,10,25,0,12,-3,6,1031,22.35,0,0 +2010,10,25,1,10,-2,3,1031,25.48,0,0 +2010,10,25,2,10,-6,6,1031,30.4,0,0 +2010,10,25,3,8,-7,6,1031,35.32,0,0 +2010,10,25,4,6,-9,6,1031,41.13,0,0 +2010,10,25,5,7,-10,6,1031,49.18,0,0 +2010,10,25,6,16,-10,5,1031,53.2,0,0 +2010,10,25,7,10,-10,6,1032,59.01,0,0 +2010,10,25,8,17,-7,7,1032,64.82,0,0 +2010,10,25,9,15,-7,8,1032,70.63,0,0 +2010,10,25,10,15,-6,9,1032,75.55,0,0 +2010,10,25,11,14,-7,11,1031,82.7,0,0 +2010,10,25,12,11,-7,12,1030,90.75,0,0 +2010,10,25,13,12,-7,13,1029,99.69,0,0 +2010,10,25,14,12,-9,14,1028,111.76,0,0 +2010,10,25,15,7,-9,13,1028,122.94,0,0 +2010,10,25,16,6,-10,12,1028,135.9,0,0 +2010,10,25,17,8,-11,11,1029,147.97,0,0 +2010,10,25,18,12,-11,10,1030,159.15,0,0 +2010,10,25,19,14,-11,9,1032,168.09,0,0 +2010,10,25,20,14,-11,8,1033,173.9,0,0 +2010,10,25,21,11,-12,7,1035,181.95,0,0 +2010,10,25,22,9,-10,5,1036,4.02,0,0 +2010,10,25,23,11,-9,5,1037,8.94,0,0 +2010,10,26,0,8,-9,4,1038,13.86,0,0 +2010,10,26,1,9,-14,4,1039,19.67,0,0 +2010,10,26,2,7,-13,3,1040,4.92,0,0 +2010,10,26,3,4,-13,2,1040,9.84,0,0 +2010,10,26,4,6,-12,1,1041,14.76,0,0 +2010,10,26,5,4,-10,0,1042,16.55,0,0 +2010,10,26,6,4,-10,0,1042,18.34,0,0 +2010,10,26,7,5,-7,-1,1042,21.47,0,0 +2010,10,26,8,6,-9,1,1042,1.79,0,0 +2010,10,26,9,7,-12,3,1043,4.02,0,0 +2010,10,26,10,8,-12,4,1042,4.92,0,0 +2010,10,26,11,9,-12,6,1042,4.02,0,0 +2010,10,26,12,10,-14,7,1041,8.04,0,0 +2010,10,26,13,8,-14,7,1040,12.06,0,0 +2010,10,26,14,9,-14,8,1038,0.89,0,0 +2010,10,26,15,12,-15,8,1037,2.68,0,0 +2010,10,26,16,11,-15,7,1037,3.57,0,0 +2010,10,26,17,8,-14,6,1036,3.13,0,0 +2010,10,26,18,19,-10,4,1036,4.02,0,0 +2010,10,26,19,28,-8,3,1036,0.89,0,0 +2010,10,26,20,35,-7,3,1036,1.78,0,0 +2010,10,26,21,32,-10,4,1036,3.13,0,0 +2010,10,26,22,29,-6,1,1036,4.02,0,0 +2010,10,26,23,35,-5,0,1036,4.91,0,0 +2010,10,27,0,41,-3,0,1035,5.8,0,0 +2010,10,27,1,42,-4,-2,1034,0.89,0,0 +2010,10,27,2,46,-4,-2,1034,1.78,0,0 +2010,10,27,3,68,-4,-2,1034,2.23,0,0 +2010,10,27,4,80,-4,-2,1033,0.89,0,0 +2010,10,27,5,76,-3,-1,1033,1.78,0,0 +2010,10,27,6,74,-3,-1,1033,0.45,0,0 +2010,10,27,7,88,-3,-1,1032,0.9,0,0 +2010,10,27,8,92,-2,1,1033,0.89,0,0 +2010,10,27,9,82,-2,3,1033,0.89,0,0 +2010,10,27,10,69,-4,5,1032,1.78,0,0 +2010,10,27,11,73,-6,8,1031,2.67,0,0 +2010,10,27,12,71,-5,9,1030,4.46,0,0 +2010,10,27,13,89,-6,10,1029,6.25,0,0 +2010,10,27,14,83,-5,11,1028,3.13,0,0 +2010,10,27,15,77,-4,11,1027,7.15,0,0 +2010,10,27,16,97,-4,11,1027,11.17,0,0 +2010,10,27,17,90,-3,9,1027,12.96,0,0 +2010,10,27,18,88,-1,6,1028,0.89,0,0 +2010,10,27,19,101,0,4,1028,1.78,0,0 +2010,10,27,20,112,0,4,1028,2.67,0,0 +2010,10,27,21,124,-1,2,1028,1.79,0,0 +2010,10,27,22,158,0,1,1029,0.89,0,0 +2010,10,27,23,164,-1,1,1029,0.89,0,0 +2010,10,28,0,176,-1,1,1029,3.13,0,0 +2010,10,28,1,189,-1,1,1029,4.92,0,0 +2010,10,28,2,174,-1,0,1029,0.89,0,0 +2010,10,28,3,107,-1,1,1028,3.13,0,0 +2010,10,28,4,111,-1,1,1028,0.89,0,0 +2010,10,28,5,91,-2,1,1029,0.89,0,0 +2010,10,28,6,89,-3,-1,1029,4.02,0,0 +2010,10,28,7,82,-2,0,1029,7.15,0,0 +2010,10,28,8,76,-1,3,1030,10.28,0,0 +2010,10,28,9,83,-1,7,1030,13.41,0,0 +2010,10,28,10,83,-1,10,1030,16.54,0,0 +2010,10,28,11,67,-1,12,1029,18.33,0,0 +2010,10,28,12,45,-1,14,1028,1.79,0,0 +2010,10,28,13,42,0,17,1027,2.68,0,0 +2010,10,28,14,37,0,17,1026,3.13,0,0 +2010,10,28,15,44,-1,17,1026,6.26,0,0 +2010,10,28,16,38,-1,17,1026,4.02,0,0 +2010,10,28,17,59,0,16,1026,7.15,0,0 +2010,10,28,18,34,0,14,1026,10.28,0,0 +2010,10,28,19,48,0,13,1027,14.3,0,0 +2010,10,28,20,48,0,13,1027,17.43,0,0 +2010,10,28,21,41,-1,13,1027,21.45,0,0 +2010,10,28,22,41,1,8,1028,0.89,0,0 +2010,10,28,23,50,2,6,1028,1.78,0,0 +2010,10,29,0,47,2,4,1028,1.79,0,0 +2010,10,29,1,67,2,4,1028,3.58,0,0 +2010,10,29,2,76,1,3,1028,5.37,0,0 +2010,10,29,3,79,2,3,1028,0.45,0,0 +2010,10,29,4,72,0,3,1028,1.79,0,0 +2010,10,29,5,88,1,3,1028,3.58,0,0 +2010,10,29,6,72,0,2,1028,0.89,0,0 +2010,10,29,7,76,0,3,1028,1.79,0,0 +2010,10,29,8,93,2,6,1029,2.68,0,0 +2010,10,29,9,66,2,9,1029,4.47,0,0 +2010,10,29,10,49,0,13,1029,7.6,0,0 +2010,10,29,11,60,0,16,1029,11.62,0,0 +2010,10,29,12,40,-2,18,1028,16.54,0,0 +2010,10,29,13,34,-3,18,1026,21.46,0,0 +2010,10,29,14,21,-3,19,1026,24.59,0,0 +2010,10,29,15,19,-4,19,1026,29.51,0,0 +2010,10,29,16,20,-3,19,1025,32.64,0,0 +2010,10,29,17,24,-2,17,1025,3.13,0,0 +2010,10,29,18,31,-3,16,1026,7.15,0,0 +2010,10,29,19,31,-2,15,1026,11.17,0,0 +2010,10,29,20,42,-2,15,1026,15.19,0,0 +2010,10,29,21,49,-2,14,1027,19.21,0,0 +2010,10,29,22,44,1,8,1027,0.89,0,0 +2010,10,29,23,51,1,6,1027,2.68,0,0 +2010,10,30,0,52,2,6,1027,4.47,0,0 +2010,10,30,1,69,2,4,1028,7.6,0,0 +2010,10,30,2,75,1,4,1027,10.73,0,0 +2010,10,30,3,69,1,5,1027,12.52,0,0 +2010,10,30,4,57,0,4,1027,14.31,0,0 +2010,10,30,5,47,0,3,1027,17.44,0,0 +2010,10,30,6,51,0,3,1028,20.57,0,0 +2010,10,30,7,48,0,2,1028,24.59,0,0 +2010,10,30,8,45,2,7,1029,27.72,0,0 +2010,10,30,9,38,1,11,1029,3.13,0,0 +2010,10,30,10,34,0,14,1029,4.02,0,0 +2010,10,30,11,39,-2,16,1028,7.15,0,0 +2010,10,30,12,38,-2,18,1027,8.94,0,0 +2010,10,30,13,23,-3,18,1026,10.73,0,0 +2010,10,30,14,12,-6,20,1026,1.79,0,0 +2010,10,30,15,9,-4,20,1025,2.68,0,0 +2010,10,30,16,15,-2,19,1025,3.57,0,0 +2010,10,30,17,24,-2,16,1025,1.79,0,0 +2010,10,30,18,45,3,10,1025,0.89,0,0 +2010,10,30,19,102,-3,16,1026,4.02,0,0 +2010,10,30,20,101,1,11,1026,5.81,0,0 +2010,10,30,21,92,2,7,1026,0.89,0,0 +2010,10,30,22,68,3,7,1026,1.78,0,0 +2010,10,30,23,64,2,6,1026,1.79,0,0 +2010,10,31,0,101,2,6,1026,3.58,0,0 +2010,10,31,1,102,2,5,1026,5.37,0,0 +2010,10,31,2,129,1,5,1026,7.16,0,0 +2010,10,31,3,123,0,3,1025,8.95,0,0 +2010,10,31,4,117,0,4,1025,10.74,0,0 +2010,10,31,5,76,0,4,1025,13.87,0,0 +2010,10,31,6,51,-1,3,1025,15.66,0,0 +2010,10,31,7,57,-1,3,1025,18.79,0,0 +2010,10,31,8,47,2,7,1026,21.92,0,0 +2010,10,31,9,47,1,11,1026,25.05,0,0 +2010,10,31,10,53,0,14,1026,28.18,0,0 +2010,10,31,11,64,0,15,1025,29.97,0,0 +2010,10,31,12,57,0,16,1024,33.1,0,0 +2010,10,31,13,60,-1,18,1024,36.23,0,0 +2010,10,31,14,42,-1,19,1023,39.36,0,0 +2010,10,31,15,30,-1,19,1022,0.89,0,0 +2010,10,31,16,22,-4,19,1022,4.02,0,0 +2010,10,31,17,24,-3,18,1022,8.04,0,0 +2010,10,31,18,76,0,11,1023,0.89,0,0 +2010,10,31,19,111,2,10,1023,1.78,0,0 +2010,10,31,20,98,2,8,1023,2.67,0,0 +2010,10,31,21,89,2,8,1024,1.79,0,0 +2010,10,31,22,77,2,6,1024,4.92,0,0 +2010,10,31,23,92,2,6,1024,6.71,0,0 +2010,11,1,0,84,1,5,1024,9.84,0,0 +2010,11,1,1,83,1,4,1025,12.97,0,0 +2010,11,1,2,71,1,7,1025,16.1,0,0 +2010,11,1,3,64,0,4,1025,19.23,0,0 +2010,11,1,4,86,0,4,1025,22.36,0,0 +2010,11,1,5,61,0,6,1026,24.15,0,0 +2010,11,1,6,58,-1,2,1027,28.17,0,0 +2010,11,1,7,60,-1,5,1028,32.19,0,0 +2010,11,1,8,41,-2,10,1029,4.92,0,0 +2010,11,1,9,NA,-3,12,1030,7.15,0,0 +2010,11,1,10,NA,-4,12,1030,5.81,0,0 +2010,11,1,11,NA,-5,14,1030,11.62,0,0 +2010,11,1,12,NA,-7,14,1029,7.15,0,0 +2010,11,1,13,NA,-6,15,1029,4.92,0,0 +2010,11,1,14,NA,-7,15,1028,4.02,0,0 +2010,11,1,15,NA,-9,14,1028,5.81,0,0 +2010,11,1,16,NA,-10,13,1028,11.62,0,0 +2010,11,1,17,NA,-10,11,1028,15.64,0,0 +2010,11,1,18,NA,-9,9,1029,17.43,0,0 +2010,11,1,19,NA,-6,6,1030,18.32,0,0 +2010,11,1,20,NA,-6,4,1030,4.02,0,0 +2010,11,1,21,NA,-9,7,1031,4.02,0,0 +2010,11,1,22,NA,-9,7,1032,8.04,0,0 +2010,11,1,23,NA,-8,6,1032,12.06,0,0 +2010,11,2,0,NA,-9,5,1032,1.79,0,0 +2010,11,2,1,NA,-8,4,1031,1.79,0,0 +2010,11,2,2,NA,-8,3,1032,1.79,0,0 +2010,11,2,3,NA,-6,1,1031,4.92,0,0 +2010,11,2,4,NA,-6,-2,1031,8.94,0,0 +2010,11,2,5,NA,-6,-1,1031,10.73,0,0 +2010,11,2,6,NA,-6,0,1031,3.13,0,0 +2010,11,2,7,NA,-6,-1,1031,1.79,0,0 +2010,11,2,8,NA,-5,3,1032,3.58,0,0 +2010,11,2,9,NA,-7,7,1032,6.71,0,0 +2010,11,2,10,NA,-10,9,1031,9.84,0,0 +2010,11,2,11,NA,-9,10,1031,11.63,0,0 +2010,11,2,12,NA,-9,11,1029,1.79,0,0 +2010,11,2,13,NA,-9,12,1028,3.58,0,0 +2010,11,2,14,NA,-8,13,1026,3.13,0,0 +2010,11,2,15,NA,-7,13,1025,7.15,0,0 +2010,11,2,16,NA,-7,13,1025,11.17,0,0 +2010,11,2,17,NA,-7,11,1024,14.3,0,0 +2010,11,2,18,NA,-7,10,1024,17.43,0,0 +2010,11,2,19,NA,-6,10,1025,20.56,0,0 +2010,11,2,20,NA,-6,10,1024,24.58,0,0 +2010,11,2,21,NA,-6,10,1025,26.37,0,0 +2010,11,2,22,NA,-2,4,1025,0.89,0,0 +2010,11,2,23,NA,-2,3,1024,1.78,0,0 +2010,11,3,0,NA,-1,1,1024,2.67,0,0 +2010,11,3,1,NA,-2,0,1023,3.56,0,0 +2010,11,3,2,NA,-1,0,1024,1.79,0,0 +2010,11,3,3,NA,-1,0,1023,0.89,0,0 +2010,11,3,4,NA,-1,0,1023,1.78,0,0 +2010,11,3,5,NA,-1,0,1023,0.89,0,0 +2010,11,3,6,NA,-3,-2,1023,2.68,0,0 +2010,11,3,7,NA,-2,-1,1023,0.89,0,0 +2010,11,3,8,NA,0,2,1024,3.13,0,0 +2010,11,3,9,NA,-1,6,1024,7.15,0,0 +2010,11,3,10,NA,0,10,1023,0.89,0,0 +2010,11,3,11,NA,-2,13,1023,1.79,0,0 +2010,11,3,12,NA,-3,16,1022,5.81,0,0 +2010,11,3,13,NA,-3,18,1021,8.94,0,0 +2010,11,3,14,NA,-4,19,1020,12.07,0,0 +2010,11,3,15,50,-3,19,1020,3.13,0,0 +2010,11,3,16,42,-3,18,1020,3.13,0,0 +2010,11,3,17,61,1,14,1021,4.92,0,0 +2010,11,3,18,75,1,11,1021,0.89,0,0 +2010,11,3,19,109,1,8,1022,1.34,0,0 +2010,11,3,20,143,2,7,1022,2.23,0,0 +2010,11,3,21,167,2,6,1022,3.12,0,0 +2010,11,3,22,183,1,4,1023,0.89,0,0 +2010,11,3,23,154,1,5,1023,2.68,0,0 +2010,11,4,0,127,0,4,1022,4.47,0,0 +2010,11,4,1,94,1,3,1022,6.26,0,0 +2010,11,4,2,86,0,3,1023,8.05,0,0 +2010,11,4,3,51,-1,3,1022,11.18,0,0 +2010,11,4,4,95,-1,3,1022,12.97,0,0 +2010,11,4,5,85,-1,2,1022,16.1,0,0 +2010,11,4,6,53,-2,3,1022,17.89,0,0 +2010,11,4,7,56,-2,5,1023,21.02,0,0 +2010,11,4,8,55,-1,7,1023,24.15,0,0 +2010,11,4,9,50,0,10,1023,27.28,0,0 +2010,11,4,10,41,-2,13,1023,30.41,0,0 +2010,11,4,11,39,-3,15,1023,1.79,0,0 +2010,11,4,12,30,-2,17,1022,1.79,0,0 +2010,11,4,13,34,-3,19,1021,3.58,0,0 +2010,11,4,14,43,-3,19,1020,3.13,0,0 +2010,11,4,15,52,-2,19,1020,7.15,0,0 +2010,11,4,16,44,-2,19,1020,11.17,0,0 +2010,11,4,17,39,-2,17,1020,14.3,0,0 +2010,11,4,18,43,-1,15,1020,17.43,0,0 +2010,11,4,19,53,0,11,1021,19.22,0,0 +2010,11,4,20,63,2,8,1021,0.89,0,0 +2010,11,4,21,91,2,7,1022,0.89,0,0 +2010,11,4,22,119,2,7,1022,0.89,0,0 +2010,11,4,23,136,1,7,1022,0.89,0,0 +2010,11,5,0,144,1,4,1022,2.68,0,0 +2010,11,5,1,148,1,4,1022,5.81,0,0 +2010,11,5,2,130,2,4,1022,0.89,0,0 +2010,11,5,3,98,1,3,1021,1.79,0,0 +2010,11,5,4,70,0,3,1021,2.68,0,0 +2010,11,5,5,95,0,3,1021,5.81,0,0 +2010,11,5,6,79,0,4,1021,7.6,0,0 +2010,11,5,7,67,0,5,1022,9.39,0,0 +2010,11,5,8,52,0,7,1022,12.52,0,0 +2010,11,5,9,54,0,9,1022,14.31,0,0 +2010,11,5,10,53,1,12,1022,16.1,0,0 +2010,11,5,11,49,-1,15,1021,17.89,0,0 +2010,11,5,12,51,1,18,1020,0.89,0,0 +2010,11,5,13,52,0,20,1019,1.79,0,0 +2010,11,5,14,58,0,21,1018,4.92,0,0 +2010,11,5,15,83,0,21,1017,8.05,0,0 +2010,11,5,16,98,0,19,1017,11.18,0,0 +2010,11,5,17,86,1,18,1017,14.31,0,0 +2010,11,5,18,95,4,12,1017,15.2,0,0 +2010,11,5,19,129,4,9,1018,16.09,0,0 +2010,11,5,20,154,4,8,1018,0.89,0,0 +2010,11,5,21,172,4,9,1018,1.78,0,0 +2010,11,5,22,187,3,7,1018,2.67,0,0 +2010,11,5,23,232,3,6,1018,3.56,0,0 +2010,11,6,0,238,3,5,1018,1.79,0,0 +2010,11,6,1,227,3,4,1018,0.89,0,0 +2010,11,6,2,221,3,4,1017,1.79,0,0 +2010,11,6,3,195,2,4,1017,3.58,0,0 +2010,11,6,4,122,2,4,1017,5.37,0,0 +2010,11,6,5,143,1,3,1017,7.16,0,0 +2010,11,6,6,147,1,2,1017,0.89,0,0 +2010,11,6,7,124,2,2,1017,1.78,0,0 +2010,11,6,8,127,3,3,1017,0.89,0,0 +2010,11,6,9,122,5,8,1018,0.89,0,0 +2010,11,6,10,164,4,11,1017,0.89,0,0 +2010,11,6,11,249,4,14,1017,1.78,0,0 +2010,11,6,12,272,4,15,1016,2.67,0,0 +2010,11,6,13,298,4,16,1015,3.56,0,0 +2010,11,6,14,314,5,17,1014,1.79,0,0 +2010,11,6,15,307,5,18,1014,3.58,0,0 +2010,11,6,16,352,6,15,1014,0.89,0,0 +2010,11,6,17,374,6,12,1014,1.79,0,0 +2010,11,6,18,366,7,10,1014,2.68,0,0 +2010,11,6,19,381,7,9,1015,0.89,0,0 +2010,11,6,20,400,6,8,1015,0.89,0,0 +2010,11,6,21,419,6,8,1015,0.89,0,0 +2010,11,6,22,463,6,7,1015,0.89,0,0 +2010,11,6,23,481,6,7,1015,2.68,0,0 +2010,11,7,0,466,5,6,1015,3.57,0,0 +2010,11,7,1,479,6,8,1015,5.36,0,0 +2010,11,7,2,343,6,8,1015,8.49,0,0 +2010,11,7,3,95,6,10,1015,11.62,0,0 +2010,11,7,4,34,6,9,1015,0.89,0,0 +2010,11,7,5,18,5,8,1015,1.78,0,0 +2010,11,7,6,20,5,8,1015,1.79,0,0 +2010,11,7,7,14,2,11,1015,5.81,0,0 +2010,11,7,8,12,1,12,1016,11.62,0,0 +2010,11,7,9,11,-3,12,1016,21.45,0,0 +2010,11,7,10,13,-4,13,1016,31.28,0,0 +2010,11,7,11,16,-4,14,1016,42.46,0,0 +2010,11,7,12,17,-5,13,1016,54.53,0,0 +2010,11,7,13,11,-4,13,1017,65.71,0,0 +2010,11,7,14,15,-5,13,1017,77.78,0,0 +2010,11,7,15,14,-5,13,1017,89.85,0,0 +2010,11,7,16,13,-6,12,1018,102.81,0,0 +2010,11,7,17,16,-7,10,1020,113.99,0,0 +2010,11,7,18,10,-8,10,1021,122.93,0,0 +2010,11,7,19,10,-8,9,1022,128.74,0,0 +2010,11,7,20,9,-8,8,1022,133.66,0,0 +2010,11,7,21,10,-8,8,1023,138.58,0,0 +2010,11,7,22,10,-7,6,1023,141.71,0,0 +2010,11,7,23,9,-8,8,1023,144.84,0,0 +2010,11,8,0,18,-8,7,1023,148.86,0,0 +2010,11,8,1,15,-7,5,1022,150.65,0,0 +2010,11,8,2,14,-7,7,1022,153.78,0,0 +2010,11,8,3,13,-7,7,1022,155.57,0,0 +2010,11,8,4,12,-7,6,1022,159.59,0,0 +2010,11,8,5,7,-5,7,1022,164.51,0,0 +2010,11,8,6,7,-4,7,1023,171.66,0,0 +2010,11,8,7,9,-7,7,1024,178.81,0,0 +2010,11,8,8,7,-3,6,1025,181.94,0,0 +2010,11,8,9,7,-4,8,1026,3.13,0,0 +2010,11,8,10,8,-8,9,1026,8.94,0,0 +2010,11,8,11,10,-11,10,1026,16.99,0,0 +2010,11,8,12,10,-10,11,1025,24.14,0,0 +2010,11,8,13,6,-11,11,1025,31.29,0,0 +2010,11,8,14,6,-13,11,1025,39.34,0,0 +2010,11,8,15,8,-12,11,1025,47.39,0,0 +2010,11,8,16,7,-14,11,1025,55.44,0,0 +2010,11,8,17,10,-13,9,1025,61.25,0,0 +2010,11,8,18,17,-13,9,1025,68.4,0,0 +2010,11,8,19,19,-11,7,1026,72.42,0,0 +2010,11,8,20,21,-11,5,1026,1.79,0,0 +2010,11,8,21,42,-11,5,1026,4.92,0,0 +2010,11,8,22,23,-11,4,1026,6.71,0,0 +2010,11,8,23,13,-10,4,1026,0.89,0,0 +2010,11,9,0,21,-6,-2,1025,1.79,0,0 +2010,11,9,1,25,-5,-1,1025,4.92,0,0 +2010,11,9,2,16,-5,-1,1025,1.79,0,0 +2010,11,9,3,15,-7,-1,1024,3.58,0,0 +2010,11,9,4,12,-8,1,1024,0.89,0,0 +2010,11,9,5,19,-6,-1,1023,1.79,0,0 +2010,11,9,6,21,-5,-3,1023,3.58,0,0 +2010,11,9,7,39,-6,-1,1023,0.45,0,0 +2010,11,9,8,45,-4,0,1023,0.89,0,0 +2010,11,9,9,25,-5,3,1023,0.89,0,0 +2010,11,9,10,31,-5,4,1022,1.79,0,0 +2010,11,9,11,47,-6,5,1021,1.79,0,0 +2010,11,9,12,75,-7,6,1020,1.79,0,0 +2010,11,9,13,75,-8,8,1019,3.58,0,0 +2010,11,9,14,71,-8,8,1018,0.89,0,0 +2010,11,9,15,62,-8,8,1017,1.78,0,0 +2010,11,9,16,57,-8,8,1017,2.67,0,0 +2010,11,9,17,66,-6,7,1018,0.89,0,0 +2010,11,9,18,78,-4,3,1018,0.89,0,0 +2010,11,9,19,99,-3,3,1018,1.78,0,0 +2010,11,9,20,111,-4,1,1019,1.79,0,0 +2010,11,9,21,128,-3,1,1019,0.45,0,0 +2010,11,9,22,146,-3,-1,1020,0.89,0,0 +2010,11,9,23,140,-4,-1,1019,0.89,0,0 +2010,11,10,0,144,-2,0,1020,1.78,0,0 +2010,11,10,1,142,-3,-1,1020,3.57,0,0 +2010,11,10,2,143,-4,-1,1020,5.36,0,0 +2010,11,10,3,129,-4,-1,1019,6.25,0,0 +2010,11,10,4,114,-4,-2,1019,8.04,0,0 +2010,11,10,5,94,-4,-1,1019,8.93,0,0 +2010,11,10,6,70,-4,-2,1019,10.72,0,0 +2010,11,10,7,99,-5,-3,1019,12.51,0,0 +2010,11,10,8,89,-3,-1,1019,14.3,0,0 +2010,11,10,9,80,-2,3,1019,0.89,0,0 +2010,11,10,10,59,-3,6,1019,1.78,0,0 +2010,11,10,11,77,-4,8,1018,2.67,0,0 +2010,11,10,12,97,-4,9,1017,3.56,0,0 +2010,11,10,13,116,-3,10,1016,4.45,0,0 +2010,11,10,14,126,-4,11,1015,3.13,0,0 +2010,11,10,15,121,-4,11,1015,4.92,0,0 +2010,11,10,16,122,-2,10,1015,5.81,0,0 +2010,11,10,17,118,-2,7,1014,6.7,0,0 +2010,11,10,18,142,-1,5,1014,7.59,0,0 +2010,11,10,19,184,0,4,1015,0.89,0,0 +2010,11,10,20,230,0,4,1014,0.89,0,0 +2010,11,10,21,207,-1,2,1014,0.89,0,0 +2010,11,10,22,201,-1,2,1013,1.79,0,0 +2010,11,10,23,227,-1,3,1013,4.92,0,0 +2010,11,11,0,213,-1,5,1012,0.89,0,0 +2010,11,11,1,193,-1,6,1011,1.79,0,0 +2010,11,11,2,155,-1,6,1010,1.79,0,0 +2010,11,11,3,154,-1,6,1010,1.79,0,0 +2010,11,11,4,149,0,5,1009,0.89,0,0 +2010,11,11,5,138,0,5,1009,1.79,0,0 +2010,11,11,6,177,0,5,1009,0.89,0,0 +2010,11,11,7,302,-3,9,1009,5.81,0,0 +2010,11,11,8,153,-2,9,1010,15.64,0,0 +2010,11,11,9,27,-2,9,1010,25.47,0,0 +2010,11,11,10,17,-2,9,1010,35.3,0,0 +2010,11,11,11,16,-2,9,1010,45.13,0,0 +2010,11,11,12,10,-2,9,1010,54.96,0,0 +2010,11,11,13,12,-2,9,1010,64.79,0,0 +2010,11,11,14,16,-2,9,1010,74.62,0,0 +2010,11,11,15,11,-2,9,1010,84.45,0,0 +2010,11,11,16,12,-2,9,1010,94.28,0,0 +2010,11,11,17,12,-2,9,1010,104.11,0,0 +2010,11,11,18,9,-2,9,1010,113.94,0,0 +2010,11,11,19,10,-3,4,1013,123.77,0,0 +2010,11,11,20,11,-6,-1,1016,125.56,0,0 +2010,11,11,21,8,-6,-1,1016,127.35,0,0 +2010,11,11,22,6,-6,-1,1016,129.14,0,0 +2010,11,11,23,7,-6,-1,1016,130.93,0,0 +2010,11,12,0,8,-6,-1,1016,132.72,0,0 +2010,11,12,1,11,-6,-1,1016,134.51,0,0 +2010,11,12,2,10,-6,-1,1016,136.3,0,0 +2010,11,12,3,9,-6,-1,1016,138.09,0,0 +2010,11,12,4,12,-6,-1,1016,139.88,0,0 +2010,11,12,5,30,-6,-1,1016,141.67,0,0 +2010,11,12,6,37,-5,-2,1016,142.56,0,0 +2010,11,12,7,45,-5,-1,1016,144.35,0,0 +2010,11,12,8,43,-5,-1,1016,146.14,0,0 +2010,11,12,9,35,-5,-1,1016,147.93,0,0 +2010,11,12,10,48,-5,-1,1016,149.72,0,0 +2010,11,12,11,81,-5,-1,1016,151.51,0,0 +2010,11,12,12,92,-5,-1,1016,153.3,0,0 +2010,11,12,13,68,-5,-1,1016,155.09,0,0 +2010,11,12,14,77,-5,-1,1016,156.88,0,0 +2010,11,12,15,88,-5,-1,1016,158.67,0,0 +2010,11,12,16,99,-5,-1,1016,160.46,0,0 +2010,11,12,17,120,-7,3,1019.5,3.13,0,0 +2010,11,12,18,114,-14,7,1023,8.94,0,0 +2010,11,12,19,115,-14,7,1023,16.99,0,0 +2010,11,12,20,125,-14,7,1023,25.93,0,0 +2010,11,12,21,121,-14,7,1023,33.98,0,0 +2010,11,12,22,112,-14,7,1023,42.92,0,0 +2010,11,12,23,85,-14,7,1023,50.97,0,0 +2010,11,13,0,42,-14,7,1023,59.91,0,0 +2010,11,13,1,16,-14,7,1023,67.96,0,0 +2010,11,13,2,12,-14,7,1023,76.9,0,0 +2010,11,13,3,10,-14,6,1023,84.95,0,0 +2010,11,13,4,10,-14,5,1023,93.89,0,0 +2010,11,13,5,7,-14,4,1024,101.94,0,0 +2010,11,13,6,3,-14,4,1025,109.09,0,0 +2010,11,13,7,5,-14,3,1026,118.03,0,0 +2010,11,13,8,10,-14,4,1027,126.08,0,0 +2010,11,13,9,12,-14,3,1027,133.23,0,0 +2010,11,13,10,15,-14,3,1027,136.36,0,0 +2010,11,13,11,8,-14,3,1027,139.49,0,0 +2010,11,13,12,11,-14,3,1027,142.62,0,0 +2010,11,13,13,15,-14,3,1027,145.75,0,0 +2010,11,13,14,17,-14,3,1027,148.88,0,0 +2010,11,13,15,14,-14,3,1027,152.01,0,0 +2010,11,13,16,11,-14,3,1027,155.14,0,0 +2010,11,13,17,10,-14,3,1027,158.27,0,0 +2010,11,13,18,10,-13,1,1029.666667,161.4,0,0 +2010,11,13,19,26,-11,0,1032.333333,164.53,0,0 +2010,11,13,20,31,-8,0,1035,166.32,0,0 +2010,11,13,21,43,-8,0,1035,168.11,0,0 +2010,11,13,22,42,-8,0,1035,169.9,0,0 +2010,11,13,23,29,-8,0,1035,171.69,0,0 +2010,11,14,0,17,-8,0,1035,173.48,0,0 +2010,11,14,1,26,-8,0,1035,175.27,0,0 +2010,11,14,2,23,-8,0,1035,177.06,0,0 +2010,11,14,3,25,-8,0,1035,178.85,0,0 +2010,11,14,4,21,-8,0,1035,180.64,0,0 +2010,11,14,5,41,-8,0,1035,182.43,0,0 +2010,11,14,6,27,-8,0,1035,184.22,0,0 +2010,11,14,7,27,-8,0,1035,186.01,0,0 +2010,11,14,8,22,-8,0,1035,187.8,0,0 +2010,11,14,9,34,-8,0,1035,189.59,0,0 +2010,11,14,10,35,-8,0,1035,191.38,0,0 +2010,11,14,11,44,-8,0,1035,193.17,0,0 +2010,11,14,12,25,-8,0,1035,194.96,0,0 +2010,11,14,13,24,-8,0,1035,196.75,0,0 +2010,11,14,14,22,-8,0,1035,198.54,0,0 +2010,11,14,15,19,-8,0,1035,200.33,0,0 +2010,11,14,16,19,-8,0,1035,202.12,0,0 +2010,11,14,17,11,-8,0,1035,203.91,0,0 +2010,11,14,18,10,-8,0,1035,205.7,0,0 +2010,11,14,19,11,-8,0,1035,207.49,0,0 +2010,11,14,20,12,-8,0,1035,209.28,0,0 +2010,11,14,21,10,-8,0,1035,211.07,0,0 +2010,11,14,22,11,-8,0,1035,212.86,0,0 +2010,11,14,23,12,-8,0,1035,214.65,0,0 +2010,11,15,0,10,-8,0,1035,216.44,0,0 +2010,11,15,1,16,-8,0,1035,218.23,0,0 +2010,11,15,2,19,-8,0,1035,220.02,0,0 +2010,11,15,3,17,-8,0,1035,221.81,0,0 +2010,11,15,4,10,-8,0,1035,223.6,0,0 +2010,11,15,5,9,-8,0,1035,225.39,0,0 +2010,11,15,6,9,-8,0,1035,227.18,0,0 +2010,11,15,7,20,-8,0,1035,228.97,0,0 +2010,11,15,8,26,-8,0,1035,230.76,0,0 +2010,11,15,9,21,-8,0,1035,232.55,0,0 +2010,11,15,10,32,-8,0,1035,234.34,0,0 +2010,11,15,11,47,-8,0,1035,236.13,0,0 +2010,11,15,12,45,-8,0,1035,237.92,0,0 +2010,11,15,13,50,-8,0,1035,239.71,0,0 +2010,11,15,14,60,-8,0,1035,241.5,0,0 +2010,11,15,15,70,-6,0,1031,243.29,0,0 +2010,11,15,16,60,-6,0,1027,245.08,0,0 +2010,11,15,17,68,-4,-1,1023,246.87,0,0 +2010,11,15,18,77,-4,-1,1023,248.66,0,0 +2010,11,15,19,82,-4,-1,1023,250.45,0,0 +2010,11,15,20,107,-4,-1,1023,252.24,0,0 +2010,11,15,21,124,-4,-1,1023,254.03,0,0 +2010,11,15,22,139,-4,-1,1023,255.82,0,0 +2010,11,15,23,152,-4,-1,1023,257.61,0,0 +2010,11,16,0,166,-4,-1,1023,259.4,0,0 +2010,11,16,1,213,-4,-1,1023,261.19,0,0 +2010,11,16,2,206,-4,-1,1023,262.98,0,0 +2010,11,16,3,208,-4,-1,1023,264.77,0,0 +2010,11,16,4,191,-4,-1,1023,266.56,0,0 +2010,11,16,5,152,-4,-1,1023,268.35,0,0 +2010,11,16,6,124,-4,-1,1023,270.14,0,0 +2010,11,16,7,108,-4,-1,1023,271.93,0,0 +2010,11,16,8,94,-4,-1,1023,273.72,0,0 +2010,11,16,9,92,-4,-1,1023,275.51,0,0 +2010,11,16,10,94,-4,-1,1023,277.3,0,0 +2010,11,16,11,105,-4,-1,1023,279.09,0,0 +2010,11,16,12,90,-4,-1,1023,280.88,0,0 +2010,11,16,13,78,-4,-1,1023,282.67,0,0 +2010,11,16,14,63,-4,-1,1023,284.46,0,0 +2010,11,16,15,66,-4,-1,1023,286.25,0,0 +2010,11,16,16,77,-4,-1,1023,288.04,0,0 +2010,11,16,17,116,-4,-1,1023,289.83,0,0 +2010,11,16,18,142,-4,-1,1023,291.62,0,0 +2010,11,16,19,135,-4,-1,1023,293.41,0,0 +2010,11,16,20,175,-4,-1,1023,295.2,0,0 +2010,11,16,21,196,-4,-1,1023,296.99,0,0 +2010,11,16,22,217,-4,-1,1023,298.78,0,0 +2010,11,16,23,227,-4,-1,1023,300.57,0,0 +2010,11,17,0,264,-4,-1,1023,302.36,0,0 +2010,11,17,1,276,-4,-1,1023,304.15,0,0 +2010,11,17,2,275,-4,-2,1023,305.94,0,0 +2010,11,17,3,255,-4,-2,1023,306.83,0,0 +2010,11,17,4,220,-4,-3,1023,0.89,0,0 +2010,11,17,5,220,-4,-3,1023,1.78,0,0 +2010,11,17,6,177,-5,-4,1023,1.79,0,0 +2010,11,17,7,138,-5,-4,1023,3.58,0,0 +2010,11,17,8,133,-4,-2,1024,4.47,0,0 +2010,11,17,9,129,-2,1,1024,1.79,0,0 +2010,11,17,10,154,-3,3,1024,0.89,0,0 +2010,11,17,11,197,-4,5,1024,1.78,0,0 +2010,11,17,12,247,-3,6,1023,1.79,0,0 +2010,11,17,13,288,-3,8,1022,1.79,0,0 +2010,11,17,14,331,-3,9,1022,3.58,0,0 +2010,11,17,15,400,-2,9,1021,5.37,0,0 +2010,11,17,16,472,-2,9,1021,0.89,0,0 +2010,11,17,17,467,-2,5,1021,0.89,0,0 +2010,11,17,18,503,-1,3,1022,0.45,0,0 +2010,11,17,19,503,-1,3,1022,0.89,0,0 +2010,11,17,20,525,-2,1,1022,1.78,0,0 +2010,11,17,21,504,-2,1,1023,3.57,0,0 +2010,11,17,22,502,-2,0,1022,0.89,0,0 +2010,11,17,23,504,-2,0,1022,0.89,0,0 +2010,11,18,0,521,-3,-1,1022,0.89,0,0 +2010,11,18,1,446,-2,0,1021,1.79,0,0 +2010,11,18,2,391,-3,-1,1021,3.58,0,0 +2010,11,18,3,354,-3,-1,1021,6.71,0,0 +2010,11,18,4,350,-3,-1,1021,8.5,0,0 +2010,11,18,5,354,-3,-2,1020,0.89,0,0 +2010,11,18,6,353,-3,-1,1020,1.79,0,0 +2010,11,18,7,362,-3,-2,1021,4.92,0,0 +2010,11,18,8,339,-1,0,1021,8.05,0,0 +2010,11,18,9,325,1,4,1021,11.18,0,0 +2010,11,18,10,336,1,5,1021,12.97,0,0 +2010,11,18,11,346,0,7,1020,14.76,0,0 +2010,11,18,12,299,0,8,1019,0.89,0,0 +2010,11,18,13,307,1,9,1018,1.79,0,0 +2010,11,18,14,304,2,9,1018,4.92,0,0 +2010,11,18,15,307,2,9,1018,6.71,0,0 +2010,11,18,16,324,2,8,1018,7.6,0,0 +2010,11,18,17,361,2,7,1018,0.89,0,0 +2010,11,18,18,438,2,6,1018,0.89,0,0 +2010,11,18,19,476,2,5,1018,0.89,0,0 +2010,11,18,20,503,2,4,1018,1.78,0,0 +2010,11,18,21,470,3,5,1019,2.67,0,0 +2010,11,18,22,480,2,4,1019,3.56,0,0 +2010,11,18,23,482,2,4,1018,0.89,0,0 +2010,11,19,0,535,2,3,1018,0.89,0,0 +2010,11,19,1,569,1,3,1018,1.78,0,0 +2010,11,19,2,562,0,1,1018,4.91,0,0 +2010,11,19,3,485,0,0,1017,8.04,0,0 +2010,11,19,4,488,-1,0,1017,9.83,0,0 +2010,11,19,5,470,-1,0,1016,11.62,0,0 +2010,11,19,6,453,-1,-1,1016,13.41,0,0 +2010,11,19,7,466,-2,-1,1016,15.2,0,0 +2010,11,19,8,466,1,2,1017,18.33,0,0 +2010,11,19,9,501,2,4,1016,21.46,0,0 +2010,11,19,10,559,3,7,1016,0.89,0,0 +2010,11,19,11,557,1,9,1015,1.78,0,0 +2010,11,19,12,540,2,11,1014,1.79,0,0 +2010,11,19,13,364,1,12,1012,5.81,0,0 +2010,11,19,14,230,-3,12,1011,9.83,0,0 +2010,11,19,15,185,-2,13,1011,13.85,0,0 +2010,11,19,16,135,0,10,1011,0.89,0,0 +2010,11,19,17,95,-1,8,1011,0.89,0,0 +2010,11,19,18,106,-1,4,1011,1.78,0,0 +2010,11,19,19,123,-1,3,1012,0.89,0,0 +2010,11,19,20,159,0,6,1012,1.79,0,0 +2010,11,19,21,192,1,5,1012,0.89,0,0 +2010,11,19,22,277,1,5,1012,1.78,0,0 +2010,11,19,23,371,0,2,1012,1.79,0,0 +2010,11,20,0,454,0,1,1012,0.89,0,0 +2010,11,20,1,422,-1,1,1012,0.89,0,0 +2010,11,20,2,389,-1,1,1012,2.68,0,0 +2010,11,20,3,328,-1,0,1011,3.57,0,0 +2010,11,20,4,296,-2,0,1011,5.36,0,0 +2010,11,20,5,282,-2,1,1011,7.15,0,0 +2010,11,20,6,286,-2,0,1012,8.94,0,0 +2010,11,20,7,271,-2,0,1012,12.07,0,0 +2010,11,20,8,262,-1,3,1013,15.2,0,0 +2010,11,20,9,224,0,4,1013,18.33,0,0 +2010,11,20,10,200,-1,8,1013,21.46,0,0 +2010,11,20,11,202,-1,10,1012,23.25,0,0 +2010,11,20,12,219,-2,11,1011,1.79,0,0 +2010,11,20,13,214,-2,12,1010,2.68,0,0 +2010,11,20,14,200,-2,13,1010,3.57,0,0 +2010,11,20,15,207,-2,13,1009,4.46,0,0 +2010,11,20,16,217,0,11,1009,0.89,0,0 +2010,11,20,17,241,0,9,1009,1.78,0,0 +2010,11,20,18,280,1,7,1010,0.45,0,0 +2010,11,20,19,306,1,5,1010,0.9,0,0 +2010,11,20,20,353,1,5,1010,1.79,0,0 +2010,11,20,21,395,1,4,1010,2.24,0,0 +2010,11,20,22,432,1,3,1010,2.69,0,0 +2010,11,20,23,438,0,3,1009,3.58,0,0 +2010,11,21,0,433,1,2,1009,0.89,0,0 +2010,11,21,1,462,1,2,1009,0.45,0,0 +2010,11,21,2,439,-1,1,1008,3.13,0,0 +2010,11,21,3,398,0,2,1008,4.92,0,0 +2010,11,21,4,355,0,2,1008,6.71,0,0 +2010,11,21,5,316,1,3,1008,9.84,0,0 +2010,11,21,6,287,1,4,1009,12.97,0,0 +2010,11,21,7,210,-2,8,1011,21.91,0,0 +2010,11,21,8,139,-7,10,1012,29.96,0,0 +2010,11,21,9,31,-14,9,1014,39.79,0,0 +2010,11,21,10,28,-12,9,1015,51.86,0,0 +2010,11,21,11,19,-11,9,1015,63.93,0,0 +2010,11,21,12,16,-12,10,1015,76.89,0,0 +2010,11,21,13,14,-12,10,1015,89.85,0,0 +2010,11,21,14,10,-12,10,1015,101.92,0,0 +2010,11,21,15,8,-13,9,1017,114.88,0,0 +2010,11,21,16,10,-13,9,1017,126.06,0,0 +2010,11,21,17,11,-12,7,1018,134.11,0,0 +2010,11,21,18,11,-13,7,1019,143.05,0,0 +2010,11,21,19,12,-13,6,1020,148.86,0,0 +2010,11,21,20,18,-13,2,1020,153.78,0,0 +2010,11,21,21,19,-13,2,1021,156.91,0,0 +2010,11,21,22,11,-11,0,1021,158.7,0,0 +2010,11,21,23,24,-12,0,1021,0.89,0,0 +2010,11,22,0,19,-11,1,1021,1.79,0,0 +2010,11,22,1,25,-8,-2,1021,0.45,0,0 +2010,11,22,2,23,-10,-1,1021,1.34,0,0 +2010,11,22,3,49,-8,-3,1021,1.79,0,0 +2010,11,22,4,55,-10,-5,1021,0.89,0,0 +2010,11,22,5,68,-9,-6,1021,1.78,0,0 +2010,11,22,6,66,-7,-4,1021,2.23,0,0 +2010,11,22,7,73,-9,-5,1021,1.79,0,0 +2010,11,22,8,69,-8,-2,1022,3.58,0,0 +2010,11,22,9,80,-11,4,1023,5.37,0,0 +2010,11,22,10,46,-11,5,1023,8.5,0,0 +2010,11,22,11,NA,-12,7,1022,11.63,0,0 +2010,11,22,12,NA,-14,9,1021,15.65,0,0 +2010,11,22,13,33,-14,9,1020,17.44,0,0 +2010,11,22,14,24,-14,10,1020,1.79,0,0 +2010,11,22,15,28,-13,10,1020,2.68,0,0 +2010,11,22,16,43,-12,8,1020,3.13,0,0 +2010,11,22,17,73,-12,5,1020,4.92,0,0 +2010,11,22,18,69,-12,4,1021,6.71,0,0 +2010,11,22,19,74,-9,2,1021,7.6,0,0 +2010,11,22,20,91,-9,1,1021,0.89,0,0 +2010,11,22,21,123,-7,0,1021,1.78,0,0 +2010,11,22,22,181,-7,-1,1022,0.89,0,0 +2010,11,22,23,223,-7,-1,1022,0.89,0,0 +2010,11,23,0,228,-7,-3,1022,0.89,0,0 +2010,11,23,1,249,-9,-2,1021,0.89,0,0 +2010,11,23,2,236,-9,-2,1021,1.78,0,0 +2010,11,23,3,218,-9,-1,1021,2.67,0,0 +2010,11,23,4,222,-8,-2,1021,3.56,0,0 +2010,11,23,5,238,-8,-3,1021,4.45,0,0 +2010,11,23,6,225,-7,-4,1021,0.89,0,0 +2010,11,23,7,244,-8,-2,1021,0.89,0,0 +2010,11,23,8,232,-5,-1,1021,0.89,0,0 +2010,11,23,9,203,-7,3,1022,0.89,0,0 +2010,11,23,10,213,-8,3,1021,1.78,0,0 +2010,11,23,11,241,-7,6,1020,1.79,0,0 +2010,11,23,12,238,-7,7,1020,0.89,0,0 +2010,11,23,13,213,-8,7,1019,1.79,0,0 +2010,11,23,14,214,-8,8,1018,3.58,0,0 +2010,11,23,15,201,-7,8,1017,7.6,0,0 +2010,11,23,16,208,-7,7,1018,0.89,0,0 +2010,11,23,17,217,-6,5,1018,1.78,0,0 +2010,11,23,18,258,-4,3,1018,0.89,0,0 +2010,11,23,19,251,-6,4,1019,1.79,0,0 +2010,11,23,20,235,-6,5,1019,3.58,0,0 +2010,11,23,21,240,-6,2,1019,5.37,0,0 +2010,11,23,22,253,-6,3,1020,7.16,0,0 +2010,11,23,23,264,-6,3,1020,8.95,0,0 +2010,11,24,0,256,-6,-1,1020,9.84,0,0 +2010,11,24,1,238,-6,-1,1019,11.63,0,0 +2010,11,24,2,223,-6,-1,1019,13.42,0,0 +2010,11,24,3,225,-6,-1,1020,15.21,0,0 +2010,11,24,4,194,-6,0,1020,18.34,0,0 +2010,11,24,5,181,-6,0,1020,0.89,0,0 +2010,11,24,6,88,-5,-1,1021,4.92,0,0 +2010,11,24,7,27,-11,5,1022,10.73,0,0 +2010,11,24,8,19,-14,5,1023,14.75,0,0 +2010,11,24,9,17,-15,6,1025,21.9,0,0 +2010,11,24,10,17,-16,5,1026,33.08,0,0 +2010,11,24,11,17,-16,6,1026,45.15,0,0 +2010,11,24,12,18,-16,6,1026,56.33,0,0 +2010,11,24,13,17,-16,7,1025,64.38,0,0 +2010,11,24,14,16,-17,6,1025,73.32,0,0 +2010,11,24,15,12,-17,6,1025,82.26,0,0 +2010,11,24,16,13,-16,6,1026,90.31,0,0 +2010,11,24,17,16,-16,5,1026,94.33,0,0 +2010,11,24,18,12,-16,4,1027,97.46,0,0 +2010,11,24,19,30,-17,3,1028,102.38,0,0 +2010,11,24,20,19,-16,2,1028,108.19,0,0 +2010,11,24,21,22,-15,0,1029,112.21,0,0 +2010,11,24,22,22,-16,0,1029,1.79,0,0 +2010,11,24,23,23,-13,-3,1029,3.13,0,0 +2010,11,25,0,16,-13,-4,1029,4.92,0,0 +2010,11,25,1,26,-13,-3,1029,5.81,0,0 +2010,11,25,2,24,-10,-4,1029,0.45,0,0 +2010,11,25,3,20,-12,-5,1029,1.79,0,0 +2010,11,25,4,28,-12,-3,1028,4.92,0,0 +2010,11,25,5,23,-12,-4,1028,6.71,0,0 +2010,11,25,6,22,-11,-6,1028,0.89,0,0 +2010,11,25,7,45,-11,-7,1028,1.79,0,0 +2010,11,25,8,62,-11,-4,1028,1.79,0,0 +2010,11,25,9,63,-10,-2,1028,3.58,0,0 +2010,11,25,10,80,-12,0,1027,5.37,0,0 +2010,11,25,11,55,-12,3,1026,0.89,0,0 +2010,11,25,12,49,-12,5,1025,2.68,0,0 +2010,11,25,13,54,-14,6,1024,3.13,0,0 +2010,11,25,14,57,-14,7,1023,7.15,0,0 +2010,11,25,15,54,-14,7,1022,12.07,0,0 +2010,11,25,16,52,-14,6,1022,16.09,0,0 +2010,11,25,17,63,-14,5,1022,1.79,0,0 +2010,11,25,18,105,-11,1,1023,1.79,0,0 +2010,11,25,19,106,-9,-3,1023,3.58,0,0 +2010,11,25,20,104,-10,-2,1024,4.47,0,0 +2010,11,25,21,146,-10,-4,1024,5.36,0,0 +2010,11,25,22,177,-10,-2,1024,6.25,0,0 +2010,11,25,23,168,-10,-3,1024,1.79,0,0 +2010,11,26,0,142,-9,-2,1024,2.68,0,0 +2010,11,26,1,120,-10,-6,1023,5.81,0,0 +2010,11,26,2,94,-10,-5,1022,7.6,0,0 +2010,11,26,3,89,-10,-4,1022,8.49,0,0 +2010,11,26,4,85,-9,-5,1022,0.89,0,0 +2010,11,26,5,62,-10,-6,1021,0.89,0,0 +2010,11,26,6,70,-10,-6,1021,2.68,0,0 +2010,11,26,7,80,-10,-6,1021,3.57,0,0 +2010,11,26,8,94,-9,-6,1020,0.89,0,0 +2010,11,26,9,93,-7,-2,1020,3.13,0,0 +2010,11,26,10,96,-8,-1,1019,6.26,0,0 +2010,11,26,11,96,-11,1,1017,8.05,0,0 +2010,11,26,12,124,-12,3,1015,0.89,0,0 +2010,11,26,13,137,-12,3,1014,1.79,0,0 +2010,11,26,14,127,-12,4,1012,0.89,0,0 +2010,11,26,15,130,-12,4,1012,1.78,0,0 +2010,11,26,16,135,-12,4,1012,1.79,0,0 +2010,11,26,17,127,-12,3,1012,1.79,0,0 +2010,11,26,18,148,-11,3,1013,3.13,0,0 +2010,11,26,19,159,-11,2,1013,6.26,0,0 +2010,11,26,20,206,-8,-2,1013,0.89,0,0 +2010,11,26,21,174,-8,-3,1014,1.78,0,0 +2010,11,26,22,156,-8,-3,1014,2.67,0,0 +2010,11,26,23,56,-19,8,1014,13.86,0,0 +2010,11,27,0,43,-21,7,1015,27.72,0,0 +2010,11,27,1,61,-23,6,1017,43.81,0,0 +2010,11,27,2,69,-23,4,1018,56.77,0,0 +2010,11,27,3,49,-22,3,1019,68.84,0,0 +2010,11,27,4,30,-20,2,1019,77.78,0,0 +2010,11,27,5,21,-20,1,1020,86.72,0,0 +2010,11,27,6,12,-20,1,1021,94.77,0,0 +2010,11,27,7,20,-18,0,1022,101.92,0,0 +2010,11,27,8,19,-21,0,1024,105.05,0,0 +2010,11,27,9,12,-22,1,1025,117.12,0,0 +2010,11,27,10,13,-22,2,1026,129.19,0,0 +2010,11,27,11,11,-22,2,1025,140.37,0,0 +2010,11,27,12,8,-22,3,1025,148.42,0,0 +2010,11,27,13,7,-22,4,1024,155.57,0,0 +2010,11,27,14,8,-22,4,1024,161.38,0,0 +2010,11,27,15,9,-22,3,1024,164.51,0,0 +2010,11,27,16,12,-20,3,1024,166.3,0,0 +2010,11,27,17,18,-19,1,1024,3.13,0,0 +2010,11,27,18,32,-17,0,1024,3.13,0,0 +2010,11,27,19,43,-17,-2,1025,6.26,0,0 +2010,11,27,20,41,-16,-2,1026,8.05,0,0 +2010,11,27,21,35,-16,-3,1026,9.84,0,0 +2010,11,27,22,45,-15,-3,1026,11.63,0,0 +2010,11,27,23,46,-13,-4,1026,0.89,0,0 +2010,11,28,0,47,-11,-5,1026,1.79,0,0 +2010,11,28,1,59,-13,-4,1025,3.58,0,0 +2010,11,28,2,83,-14,-4,1025,5.37,0,0 +2010,11,28,3,74,-13,-5,1024,0.89,0,0 +2010,11,28,4,74,-14,-5,1024,1.78,0,0 +2010,11,28,5,74,-14,-5,1023,2.67,0,0 +2010,11,28,6,64,-13,-6,1023,0.89,0,0 +2010,11,28,7,79,-12,-6,1022,2.68,0,0 +2010,11,28,8,97,-12,-6,1022,1.79,0,0 +2010,11,28,9,142,-13,-3,1023,1.79,0,0 +2010,11,28,10,150,-13,-1,1022,0.89,0,0 +2010,11,28,11,173,-14,1,1021,1.79,0,0 +2010,11,28,12,136,-14,3,1020,0.89,0,0 +2010,11,28,13,141,-14,4,1020,1.78,0,0 +2010,11,28,14,148,-15,5,1019,2.67,0,0 +2010,11,28,15,184,-12,5,1019,1.79,0,0 +2010,11,28,16,202,-12,4,1019,3.58,0,0 +2010,11,28,17,229,-12,1,1019,0.89,0,0 +2010,11,28,18,268,-12,0,1020,2.68,0,0 +2010,11,28,19,281,-10,-1,1021,0.89,0,0 +2010,11,28,20,316,-9,-3,1021,1.78,0,0 +2010,11,28,21,354,-9,-2,1021,0.89,0,0 +2010,11,28,22,360,-8,-3,1021,0.89,0,0 +2010,11,28,23,379,-10,-3,1021,1.78,0,0 +2010,11,29,0,443,-9,-5,1021,2.67,0,0 +2010,11,29,1,418,-9,-6,1022,0.89,0,0 +2010,11,29,2,376,-9,-2,1021,0.89,0,0 +2010,11,29,3,309,-9,-3,1021,1.78,0,0 +2010,11,29,4,248,-9,-4,1021,0.89,0,0 +2010,11,29,5,166,-8,-4,1021,2.68,0,0 +2010,11,29,6,154,-8,-4,1020,4.47,0,0 +2010,11,29,7,159,-7,-3,1020,7.6,0,0 +2010,11,29,8,144,-5,0,1020,9.39,0,0 +2010,11,29,9,124,-4,1,1020,0.89,0,0 +2010,11,29,10,121,-4,2,1020,1.79,0,0 +2010,11,29,11,121,-4,2,1020,3.58,0,0 +2010,11,29,12,136,-2,3,1018,6.71,0,0 +2010,11,29,13,130,-2,4,1018,9.84,0,0 +2010,11,29,14,103,-1,4,1017,11.63,0,0 +2010,11,29,15,98,-1,4,1017,0.89,0,0 +2010,11,29,16,111,-1,4,1017,1.78,0,0 +2010,11,29,17,130,-1,2,1017,2.67,0,0 +2010,11,29,18,168,-2,-1,1018,3.56,0,0 +2010,11,29,19,179,-1,0,1018,4.45,0,0 +2010,11,29,20,173,-3,-2,1019,5.34,0,0 +2010,11,29,21,174,-2,-1,1019,0.89,0,0 +2010,11,29,22,172,-3,-2,1020,2.68,0,0 +2010,11,29,23,213,-2,-1,1020,4.47,0,0 +2010,11,30,0,236,-3,-2,1020,6.26,0,0 +2010,11,30,1,267,-4,-3,1020,9.39,0,0 +2010,11,30,2,237,-4,-3,1019,11.18,0,0 +2010,11,30,3,236,-4,-3,1019,12.97,0,0 +2010,11,30,4,198,-5,-4,1019,13.86,0,0 +2010,11,30,5,176,-5,-5,1019,15.65,0,0 +2010,11,30,6,159,-5,-4,1019,0.89,0,0 +2010,11,30,7,170,-5,-5,1019,1.78,0,0 +2010,11,30,8,164,-5,-4,1020,2.67,0,0 +2010,11,30,9,170,-3,-2,1020,0.89,0,0 +2010,11,30,10,182,-1,-1,1021,0.89,0,0 +2010,11,30,11,240,-1,0,1020,1.78,0,0 +2010,11,30,12,256,0,1,1019,2.67,0,0 +2010,11,30,13,261,0,2,1017,1.79,0,0 +2010,11,30,14,247,0,3,1017,3.58,0,0 +2010,11,30,15,242,0,3,1016,0.89,0,0 +2010,11,30,16,237,0,2,1016,0.89,0,0 +2010,11,30,17,236,-1,2,1016,0.89,0,0 +2010,11,30,18,247,0,2,1017,1.79,0,0 +2010,11,30,19,234,0,2,1017,3.58,0,0 +2010,11,30,20,245,0,2,1018,0.89,0,0 +2010,11,30,21,243,0,2,1018,1.78,0,0 +2010,11,30,22,249,0,2,1017,1.79,0,0 +2010,11,30,23,249,0,2,1017,3.58,0,0 +2010,12,1,0,259,0,1,1017,0.89,0,0 +2010,12,1,1,256,0,1,1016,1.78,0,0 +2010,12,1,2,252,0,1,1016,2.67,0,0 +2010,12,1,3,248,-1,1,1016,3.56,0,0 +2010,12,1,4,239,-1,1,1015,4.01,0,0 +2010,12,1,5,238,-1,1,1015,4.9,0,0 +2010,12,1,6,239,-1,1,1015,1.79,0,0 +2010,12,1,7,245,-1,0,1015,0.89,0,0 +2010,12,1,8,235,-1,0,1015,0.89,0,0 +2010,12,1,9,233,-1,0,1015,1.78,0,0 +2010,12,1,10,248,-1,0,1015,2.67,0,0 +2010,12,1,11,258,-1,0,1014,1.79,0,0 +2010,12,1,12,264,-1,1,1012,3.58,0,0 +2010,12,1,13,261,-1,1,1012,0.89,0,0 +2010,12,1,14,276,-1,1,1011,1.78,0,0 +2010,12,1,15,268,-1,1,1010,2.67,0,0 +2010,12,1,16,259,-1,1,1010,3.56,0,0 +2010,12,1,17,257,-1,1,1010,4.45,0,0 +2010,12,1,18,266,-1,1,1011,1.79,0,0 +2010,12,1,19,271,-1,1,1011,0.89,0,0 +2010,12,1,20,264,-1,1,1012,1.79,0,0 +2010,12,1,21,288,-2,0,1012,4.92,0,0 +2010,12,1,22,284,-2,0,1012,8.94,0,0 +2010,12,1,23,275,-1,0,1012,12.07,0,0 +2010,12,2,0,270,-2,0,1012,15.2,0,0 +2010,12,2,1,259,-2,0,1012,18.33,0,0 +2010,12,2,2,240,-2,0,1012,22.35,0,0 +2010,12,2,3,218,-2,0,1013,25.48,0,0 +2010,12,2,4,235,-1,1,1012,29.5,0,0 +2010,12,2,5,142,-9,6,1013,35.31,0,0 +2010,12,2,6,128,-11,8,1013,42.46,0,0 +2010,12,2,7,85,-14,7,1015,48.27,0,0 +2010,12,2,8,71,-14,8,1016,57.21,0,0 +2010,12,2,9,61,-14,8,1016,68.39,0,0 +2010,12,2,10,39,-14,6,1017,83.59,0,0 +2010,12,2,11,33,-14,5,1019,97.45,0,0 +2010,12,2,12,26,-17,4,1020,112.65,0,0 +2010,12,2,13,22,-19,3,1021,129.64,0,0 +2010,12,2,14,14,-19,3,1021,142.6,0,0 +2010,12,2,15,19,-18,3,1022,151.54,0,0 +2010,12,2,16,12,-18,2,1023,159.59,0,0 +2010,12,2,17,14,-18,2,1024,166.74,0,0 +2010,12,2,18,13,-18,1,1025,173.89,0,0 +2010,12,2,19,17,-16,-1,1026,3.13,0,0 +2010,12,2,20,21,-15,-1,1026,4.02,0,0 +2010,12,2,21,17,-15,-2,1027,3.13,0,0 +2010,12,2,22,25,-15,-2,1027,7.15,0,0 +2010,12,2,23,23,-16,-3,1027,3.13,0,0 +2010,12,3,0,15,-15,-3,1027,4.02,0,0 +2010,12,3,1,23,-15,-3,1026,1.79,0,0 +2010,12,3,2,22,-15,-4,1026,0.89,0,0 +2010,12,3,3,16,-14,-5,1026,3.13,0,0 +2010,12,3,4,11,-15,-4,1025,4.92,0,0 +2010,12,3,5,13,-14,-5,1025,8.05,0,0 +2010,12,3,6,28,-13,-6,1025,0.89,0,0 +2010,12,3,7,36,-14,-5,1025,1.78,0,0 +2010,12,3,8,41,-12,-7,1025,2.67,0,0 +2010,12,3,9,107,-11,-3,1025,1.79,0,0 +2010,12,3,10,72,-12,-1,1025,3.58,0,0 +2010,12,3,11,72,-12,1,1025,5.37,0,0 +2010,12,3,12,72,-14,2,1023,1.79,0,0 +2010,12,3,13,73,-14,5,1022,4.02,0,0 +2010,12,3,14,65,-14,6,1020,8.04,0,0 +2010,12,3,15,58,-13,4,1019,1.79,0,0 +2010,12,3,16,71,-12,3,1019,1.79,0,0 +2010,12,3,17,69,-11,-1,1018,1.79,0,0 +2010,12,3,18,88,-11,-2,1018,1.79,0,0 +2010,12,3,19,146,-11,-3,1018,0.89,0,0 +2010,12,3,20,162,-9,-4,1018,0.89,0,0 +2010,12,3,21,174,-11,-2,1017,0.89,0,0 +2010,12,3,22,193,-11,-4,1017,1.78,0,0 +2010,12,3,23,167,-10,-5,1016,1.79,0,0 +2010,12,4,0,193,-10,-5,1015,4.92,0,0 +2010,12,4,1,167,-10,-4,1015,8.05,0,0 +2010,12,4,2,145,-10,-5,1014,9.84,0,0 +2010,12,4,3,128,-10,-3,1014,12.97,0,0 +2010,12,4,4,129,-10,-5,1013,14.76,0,0 +2010,12,4,5,122,-10,-3,1013,16.55,0,0 +2010,12,4,6,158,-10,-3,1012,19.68,0,0 +2010,12,4,7,154,-10,-5,1013,22.81,0,0 +2010,12,4,8,133,-9,-5,1013,0.89,0,0 +2010,12,4,9,100,-8,-1,1013,3.13,0,0 +2010,12,4,10,113,-9,2,1013,0.89,0,0 +2010,12,4,11,139,-10,3,1013,1.79,0,0 +2010,12,4,12,120,-11,5,1012,1.79,0,0 +2010,12,4,13,116,-11,6,1011,1.79,0,0 +2010,12,4,14,118,-11,7,1010,0.89,0,0 +2010,12,4,15,121,-10,7,1009,1.78,0,0 +2010,12,4,16,127,-10,6,1009,2.67,0,0 +2010,12,4,17,173,-9,4,1009,3.56,0,0 +2010,12,4,18,216,-9,0,1010,1.79,0,0 +2010,12,4,19,233,-8,0,1010,0.89,0,0 +2010,12,4,20,248,-7,-1,1011,1.34,0,0 +2010,12,4,21,281,-7,-2,1011,2.23,0,0 +2010,12,4,22,273,-7,-3,1012,1.79,0,0 +2010,12,4,23,309,-7,-3,1012,0.89,0,0 +2010,12,5,0,322,-7,-1,1012,1.79,0,0 +2010,12,5,1,271,-7,-1,1013,5.81,0,0 +2010,12,5,2,204,-7,-1,1013,8.94,0,0 +2010,12,5,3,169,-7,-3,1013,12.07,0,0 +2010,12,5,4,130,-8,-3,1013,15.2,0,0 +2010,12,5,5,161,-8,-2,1014,18.33,0,0 +2010,12,5,6,153,-7,-2,1014,21.46,0,0 +2010,12,5,7,125,-7,0,1015,25.48,0,0 +2010,12,5,8,113,-8,2,1016,28.61,0,0 +2010,12,5,9,35,-7,5,1018,31.74,0,0 +2010,12,5,10,22,-7,9,1019,4.02,0,0 +2010,12,5,11,16,-8,12,1019,4.02,0,0 +2010,12,5,12,18,-8,12,1020,8.04,0,0 +2010,12,5,13,27,-8,13,1020,12.06,0,0 +2010,12,5,14,16,-10,11,1021,21.89,0,0 +2010,12,5,15,22,-11,11,1022,31.72,0,0 +2010,12,5,16,11,-10,9,1023,41.55,0,0 +2010,12,5,17,9,-11,7,1025,50.49,0,0 +2010,12,5,18,10,-11,6,1027,56.3,0,0 +2010,12,5,19,10,-12,5,1028,63.45,0,0 +2010,12,5,20,11,-13,4,1030,69.26,0,0 +2010,12,5,21,11,-14,4,1030,77.31,0,0 +2010,12,5,22,8,-15,3,1031,85.36,0,0 +2010,12,5,23,9,-15,3,1032,95.19,0,0 +2010,12,6,0,11,-15,2,1032,104.13,0,0 +2010,12,6,1,9,-15,1,1032,112.18,0,0 +2010,12,6,2,9,-16,2,1032,122.01,0,0 +2010,12,6,3,10,-15,1,1033,134.97,0,0 +2010,12,6,4,10,-16,0,1033,147.93,0,0 +2010,12,6,5,9,-16,0,1033,159.11,0,0 +2010,12,6,6,9,-15,0,1032,168.05,0,0 +2010,12,6,7,17,-16,-1,1033,177.88,0,0 +2010,12,6,8,15,-16,-1,1034,186.82,0,0 +2010,12,6,9,15,-18,0,1035,193.97,0,0 +2010,12,6,10,19,-18,1,1035,203.8,0,0 +2010,12,6,11,15,-18,2,1034,212.74,0,0 +2010,12,6,12,15,-19,3,1033,222.57,0,0 +2010,12,6,13,13,-21,4,1031,231.51,0,0 +2010,12,6,14,16,-20,4,1031,240.45,0,0 +2010,12,6,15,14,-20,4,1030,247.6,0,0 +2010,12,6,16,13,-21,4,1029,255.65,0,0 +2010,12,6,17,12,-20,4,1029,260.57,0,0 +2010,12,6,18,16,-21,3,1029,264.59,0,0 +2010,12,6,19,20,-20,4,1028,4.02,0,0 +2010,12,6,20,32,-19,3,1028,8.04,0,0 +2010,12,6,21,40,-18,2,1027,9.83,0,0 +2010,12,6,22,46,-16,1,1027,12.96,0,0 +2010,12,6,23,44,-17,1,1026,14.75,0,0 +2010,12,7,0,61,-17,2,1025,17.88,0,0 +2010,12,7,1,53,-13,-2,1025,0.89,0,0 +2010,12,7,2,45,-18,1,1024,3.13,0,0 +2010,12,7,3,43,-18,1,1023,3.13,0,0 +2010,12,7,4,43,-18,1,1022,6.26,0,0 +2010,12,7,5,46,-19,2,1021,1.79,0,0 +2010,12,7,6,76,-13,-3,1021,1.79,0,0 +2010,12,7,7,94,-11,-3,1020,3.58,0,0 +2010,12,7,8,127,-11,-3,1020,4.47,0,0 +2010,12,7,9,68,-14,2,1019,7.6,0,0 +2010,12,7,10,74,-18,5,1019,4.02,0,0 +2010,12,7,11,27,-17,6,1018,1.79,0,0 +2010,12,7,12,23,-20,7,1016,4.02,0,0 +2010,12,7,13,83,-19,7,1014,4.02,0,0 +2010,12,7,14,41,-17,7,1013,8.04,0,0 +2010,12,7,15,50,-17,6,1013,12.96,0,0 +2010,12,7,16,46,-17,6,1012,17.88,0,0 +2010,12,7,17,53,-16,4,1012,21.9,0,0 +2010,12,7,18,41,-16,4,1012,26.82,0,0 +2010,12,7,19,50,-16,4,1012,30.84,0,0 +2010,12,7,20,49,-17,5,1012,35.76,0,0 +2010,12,7,21,56,-16,5,1012,40.68,0,0 +2010,12,7,22,52,-14,1,1012,0.89,0,0 +2010,12,7,23,67,-12,-3,1012,1.79,0,0 +2010,12,8,0,93,-11,-1,1012,0.89,0,0 +2010,12,8,1,100,-11,-1,1012,0.45,0,0 +2010,12,8,2,73,-10,0,1013,3.13,0,0 +2010,12,8,3,46,-11,0,1013,4.92,0,0 +2010,12,8,4,40,-8,2,1014,9.84,0,0 +2010,12,8,5,62,-10,2,1014,13.86,0,0 +2010,12,8,6,29,-12,2,1015,21.01,0,0 +2010,12,8,7,28,-10,0,1016,1.79,0,0 +2010,12,8,8,19,-9,-2,1017,3.58,0,0 +2010,12,8,9,26,-10,2,1017,1.79,0,0 +2010,12,8,10,38,-12,5,1017,3.58,0,0 +2010,12,8,11,36,-12,6,1017,6.71,0,0 +2010,12,8,12,27,-12,6,1016,11.63,0,0 +2010,12,8,13,21,-12,7,1015,1.79,0,0 +2010,12,8,14,16,-12,7,1015,3.13,0,0 +2010,12,8,15,13,-13,8,1015,6.26,0,0 +2010,12,8,16,9,-15,7,1015,11.18,0,0 +2010,12,8,17,18,-16,6,1016,16.1,0,0 +2010,12,8,18,13,-15,5,1016,20.12,0,0 +2010,12,8,19,20,-16,4,1016,21.91,0,0 +2010,12,8,20,29,-15,1,1017,23.7,0,0 +2010,12,8,21,61,-14,1,1017,0.89,0,0 +2010,12,8,22,52,-9,-2,1017,1.79,0,0 +2010,12,8,23,77,-10,-1,1016,2.68,0,0 +2010,12,9,0,116,-10,-2,1016,4.47,0,0 +2010,12,9,1,126,-10,-2,1016,0.89,0,0 +2010,12,9,2,132,-9,-2,1015,1.79,0,0 +2010,12,9,3,151,-8,-3,1015,2.68,0,0 +2010,12,9,4,158,-8,-2,1014,5.81,0,0 +2010,12,9,5,152,-8,-3,1014,6.7,0,0 +2010,12,9,6,154,-8,-3,1014,0.89,0,0 +2010,12,9,7,155,-8,-3,1014,1.79,0,0 +2010,12,9,8,185,-8,-3,1014,3.58,0,0 +2010,12,9,9,146,-7,-1,1014,0.89,0,0 +2010,12,9,10,159,-8,0,1014,1.78,0,0 +2010,12,9,11,212,-7,2,1012,1.79,0,0 +2010,12,9,12,209,-7,3,1011,3.58,0,0 +2010,12,9,13,215,-7,4,1009,5.37,0,0 +2010,12,9,14,211,-8,5,1008,0.89,0,0 +2010,12,9,15,218,-8,5,1007,3.13,0,0 +2010,12,9,16,220,-9,4,1007,4.92,0,0 +2010,12,9,17,238,-8,2,1006,0.45,0,0 +2010,12,9,18,243,-8,0,1007,3.13,0,0 +2010,12,9,19,257,-8,1,1006,0.89,0,0 +2010,12,9,20,257,-8,2,1007,3.13,0,0 +2010,12,9,21,254,-7,1,1006,4.92,0,0 +2010,12,9,22,246,-7,0,1005,6.71,0,0 +2010,12,9,23,241,-7,0,1005,8.5,0,0 +2010,12,10,0,230,-7,-2,1004,10.29,0,0 +2010,12,10,1,218,-7,-2,1003,12.08,0,0 +2010,12,10,2,215,-7,-2,1003,15.21,0,0 +2010,12,10,3,212,-6,-1,1003,18.34,0,0 +2010,12,10,4,231,-7,-3,1002,21.47,0,0 +2010,12,10,5,237,-7,-1,1002,23.26,0,0 +2010,12,10,6,230,-7,-2,1002,25.05,0,0 +2010,12,10,7,231,-7,-3,1002,0.89,0,0 +2010,12,10,8,221,-6,2,1002,5.81,0,0 +2010,12,10,9,180,-8,7,1003,12.07,0,0 +2010,12,10,10,38,-9,7,1005,23.25,0,0 +2010,12,10,11,29,-11,6,1007,38.45,0,0 +2010,12,10,12,35,-12,6,1006,53.65,0,0 +2010,12,10,13,28,-15,7,1008,66.61,0,0 +2010,12,10,14,25,-18,4,1011,86.73,0,0 +2010,12,10,15,25,-18,4,1013,103.72,0,0 +2010,12,10,16,19,-18,2,1014,119.81,0,0 +2010,12,10,17,18,-19,1,1017,131.88,0,0 +2010,12,10,18,13,-20,0,1019,143.95,0,0 +2010,12,10,19,10,-18,-1,1020,152.89,0,0 +2010,12,10,20,11,-19,-1,1021,161.83,0,0 +2010,12,10,21,14,-18,-2,1021,168.98,0,0 +2010,12,10,22,16,-18,-3,1022,173.9,0,0 +2010,12,10,23,19,-17,-3,1022,179.71,0,0 +2010,12,11,0,17,-18,-4,1023,182.84,0,0 +2010,12,11,1,36,-18,-3,1023,185.97,0,0 +2010,12,11,2,18,-18,-5,1023,0.89,0,0 +2010,12,11,3,14,-18,-5,1023,1.79,0,0 +2010,12,11,4,16,-17,-6,1023,3.58,0,0 +2010,12,11,5,22,-16,-10,1023,6.71,0,0 +2010,12,11,6,23,-16,-6,1023,10.73,0,0 +2010,12,11,7,19,-16,-6,1023,13.86,0,0 +2010,12,11,8,23,-15,-6,1024,3.13,0,0 +2010,12,11,9,35,-13,-3,1024,4.92,0,0 +2010,12,11,10,47,-15,-1,1023,8.94,0,0 +2010,12,11,11,40,-17,1,1023,16.09,0,0 +2010,12,11,12,41,-17,2,1021,21.01,0,0 +2010,12,11,13,36,-17,2,1020,25.93,0,0 +2010,12,11,14,49,-17,2,1019,31.74,0,0 +2010,12,11,15,47,-17,2,1018,37.55,0,0 +2010,12,11,16,46,-17,1,1018,42.47,0,0 +2010,12,11,17,34,-15,-1,1018,44.26,0,0 +2010,12,11,18,43,-15,-2,1018,46.05,0,0 +2010,12,11,19,53,-15,-3,1018,47.84,0,0 +2010,12,11,20,82,-14,-3,1018,49.63,0,0 +2010,12,11,21,116,-13,-3,1018,52.76,0,0 +2010,12,11,22,122,-12,-4,1018,54.55,0,0 +2010,12,11,23,119,-11,-4,1018,0.89,0,0 +2010,12,12,0,109,-11,-5,1017,0.89,0,0 +2010,12,12,1,131,-10,-5,1017,0.89,0,0 +2010,12,12,2,112,-11,-5,1016,1.78,0,0 +2010,12,12,3,115,-11,-5,1015,0.89,0,0 +2010,12,12,4,102,-11,-5,1015,0.89,0,0 +2010,12,12,5,105,-12,-7,1015,4.02,0,0 +2010,12,12,6,100,-11,-6,1015,7.15,0,0 +2010,12,12,7,101,-11,-7,1015,10.28,0,0 +2010,12,12,8,107,-10,-5,1016,13.41,0,0 +2010,12,12,9,94,-10,-3,1017,17.43,0,0 +2010,12,12,10,87,-10,-1,1017,21.45,0,0 +2010,12,12,11,100,-12,0,1017,25.47,0,0 +2010,12,12,12,95,-12,1,1016,28.6,0,0 +2010,12,12,13,101,-11,2,1016,31.73,0,0 +2010,12,12,14,104,-11,2,1016,33.52,0,0 +2010,12,12,15,118,-11,2,1016,1.79,0,0 +2010,12,12,16,117,-11,2,1018,1.79,0,0 +2010,12,12,17,113,-11,1,1019,0.89,0,0 +2010,12,12,18,109,-9,-1,1020,1.79,0,0 +2010,12,12,19,122,-8,-1,1021,0.45,0,0 +2010,12,12,20,139,-8,-2,1022,1.34,0,0 +2010,12,12,21,140,-7,0,1023,2.23,0,0 +2010,12,12,22,179,-6,-1,1024,1.79,0,0 +2010,12,12,23,171,-13,0,1024,5.81,0,0 +2010,12,13,0,140,-13,-1,1025,12.96,0,0 +2010,12,13,1,15,-13,-2,1025,21.01,0,0 +2010,12,13,2,11,-16,-3,1027,28.16,0,0 +2010,12,13,3,8,-18,-4,1028,36.21,0,0 +2010,12,13,4,6,-19,-4,1028,43.36,0,0 +2010,12,13,5,7,-18,-5,1028,50.51,0,0 +2010,12,13,6,10,-19,-5,1029,57.66,0,0 +2010,12,13,7,16,-21,-6,1030,63.47,0,0 +2010,12,13,8,13,-22,-6,1030,69.28,0,0 +2010,12,13,9,19,-23,-5,1031,75.09,0,0 +2010,12,13,10,19,-24,-5,1031,82.24,0,0 +2010,12,13,11,18,-24,-5,1031,88.05,0,0 +2010,12,13,12,17,-24,-5,1030,95.2,0,0 +2010,12,13,13,20,-24,-4,1029,101.01,0,0 +2010,12,13,14,15,-24,-3,1029,105.93,0,0 +2010,12,13,15,18,-24,-3,1029,110.85,0,0 +2010,12,13,16,18,-24,-4,1029,115.77,0,0 +2010,12,13,17,9,-24,-5,1030,118.9,0,0 +2010,12,13,18,17,-23,-7,1031,1.79,0,0 +2010,12,13,19,19,-23,-6,1032,3.13,0,0 +2010,12,13,20,22,-22,-6,1032,6.26,0,0 +2010,12,13,21,27,-22,-6,1032,10.28,0,0 +2010,12,13,22,21,-23,-6,1033,15.2,0,0 +2010,12,13,23,15,-22,-7,1033,20.12,0,0 +2010,12,14,0,16,-23,-8,1034,24.14,0,0 +2010,12,14,1,24,-23,-9,1034,25.93,0,0 +2010,12,14,2,17,-22,-10,1034,3.13,0,0 +2010,12,14,3,13,-22,-11,1034,7.15,0,0 +2010,12,14,4,20,-22,-11,1033,12.07,0,0 +2010,12,14,5,17,-23,-11,1033,13.86,0,0 +2010,12,14,6,19,-23,-11,1034,15.65,0,0 +2010,12,14,7,15,-22,-11,1034,19.67,0,0 +2010,12,14,8,23,-21,-11,1035,23.69,0,0 +2010,12,14,9,22,-22,-7,1035,27.71,0,0 +2010,12,14,10,20,-23,-6,1036,33.52,0,0 +2010,12,14,11,23,-23,-6,1035,39.33,0,0 +2010,12,14,12,22,-23,-5,1034,45.14,0,0 +2010,12,14,13,16,-23,-4,1033,52.29,0,0 +2010,12,14,14,16,-23,-4,1033,60.34,0,0 +2010,12,14,15,15,-23,-4,1032,70.17,0,0 +2010,12,14,16,16,-23,-4,1033,80,0,0 +2010,12,14,17,16,-23,-4,1033,84.92,0,0 +2010,12,14,18,17,-23,-6,1034,89.84,0,0 +2010,12,14,19,15,-24,-6,1034,97.89,0,0 +2010,12,14,20,16,-23,-7,1036,105.04,0,0 +2010,12,14,21,15,-24,-7,1036,112.19,0,0 +2010,12,14,22,8,-25,-8,1037,119.34,0,0 +2010,12,14,23,15,-24,-8,1038,124.26,0,0 +2010,12,15,0,11,-24,-9,1037,130.07,0,0 +2010,12,15,1,12,-25,-9,1037,137.22,0,0 +2010,12,15,2,14,-27,-10,1037,147.05,0,0 +2010,12,15,3,13,-28,-11,1037,155.99,0,0 +2010,12,15,4,13,-28,-12,1037,163.14,0,0 +2010,12,15,5,15,-28,-12,1037,168.06,0,0 +2010,12,15,6,13,-26,-12,1036,176.11,0,0 +2010,12,15,7,13,-26,-12,1036,184.16,0,0 +2010,12,15,8,18,-26,-12,1036,191.31,0,0 +2010,12,15,9,17,-26,-10,1036,199.36,0,0 +2010,12,15,10,23,-26,-9,1036,207.41,0,0 +2010,12,15,11,22,-26,-8,1035,219.48,0,0 +2010,12,15,12,22,-26,-8,1034,228.42,0,0 +2010,12,15,13,25,-25,-7,1032,238.25,0,0 +2010,12,15,14,17,-24,-6,1031,246.3,0,0 +2010,12,15,15,21,-24,-5,1030,254.35,0,0 +2010,12,15,16,24,-24,-5,1030,262.4,0,0 +2010,12,15,17,13,-24,-5,1029,266.42,0,0 +2010,12,15,18,15,-23,-4,1028,269.55,0,0 +2010,12,15,19,43,-23,-4,1028,4.02,0,0 +2010,12,15,20,43,-22,-6,1027,8.04,0,0 +2010,12,15,21,50,-22,-6,1027,13.85,0,0 +2010,12,15,22,54,-24,-4,1027,5.81,0,0 +2010,12,15,23,27,-24,-3,1026,13.86,0,0 +2010,12,16,0,20,-24,-4,1026,19.67,0,0 +2010,12,16,1,28,-22,-9,1025,22.8,0,0 +2010,12,16,2,30,-21,-10,1025,25.93,0,0 +2010,12,16,3,22,-23,-4,1024,33.98,0,0 +2010,12,16,4,27,-23,-4,1023,39.79,0,0 +2010,12,16,5,27,-23,-4,1022,43.81,0,0 +2010,12,16,6,40,-22,-7,1022,1.79,0,0 +2010,12,16,7,41,-21,-6,1021,3.58,0,0 +2010,12,16,8,39,-19,-7,1021,0.89,0,0 +2010,12,16,9,40,-18,-5,1020,1.78,0,0 +2010,12,16,10,54,-20,-2,1020,2.67,0,0 +2010,12,16,11,56,-23,0,1018,1.79,0,0 +2010,12,16,12,63,-24,1,1016,7.6,0,0 +2010,12,16,13,58,-24,2,1014,14.75,0,0 +2010,12,16,14,50,-24,3,1013,20.56,0,0 +2010,12,16,15,89,-24,3,1012,24.58,0,0 +2010,12,16,16,81,-21,1,1012,4.92,0,0 +2010,12,16,17,74,-21,0,1012,8.94,0,0 +2010,12,16,18,55,-18,0,1012,12.07,0,0 +2010,12,16,19,60,-18,0,1011,16.09,0,0 +2010,12,16,20,72,-20,0,1011,19.22,0,0 +2010,12,16,21,82,-17,-3,1011,0.89,0,0 +2010,12,16,22,98,-16,-5,1012,1.78,0,0 +2010,12,16,23,95,-18,0,1012,3.13,0,0 +2010,12,17,0,55,-18,-1,1012,7.15,0,0 +2010,12,17,1,41,-21,0,1012,12.07,0,0 +2010,12,17,2,39,-19,-2,1012,13.86,0,0 +2010,12,17,3,36,-19,-1,1013,16.99,0,0 +2010,12,17,4,34,-18,-5,1013,18.78,0,0 +2010,12,17,5,34,-18,-5,1013,20.57,0,0 +2010,12,17,6,42,-17,-6,1013,0.89,0,0 +2010,12,17,7,40,-17,-6,1014,0.89,0,0 +2010,12,17,8,71,-15,-8,1014,2.68,0,0 +2010,12,17,9,69,-13,-3,1014,0.89,0,0 +2010,12,17,10,64,-14,2,1015,1.78,0,0 +2010,12,17,11,65,-18,5,1014,4.02,0,0 +2010,12,17,12,71,-21,6,1013,9.83,0,0 +2010,12,17,13,73,-20,7,1012,13.85,0,0 +2010,12,17,14,79,-19,8,1012,17.87,0,0 +2010,12,17,15,93,-19,7,1012,21.89,0,0 +2010,12,17,16,87,-17,5,1012,25.02,0,0 +2010,12,17,17,136,-17,3,1012,26.81,0,0 +2010,12,17,18,135,-16,1,1013,0.89,0,0 +2010,12,17,19,145,-13,-3,1013,0.89,0,0 +2010,12,17,20,205,-13,-4,1013,0.89,0,0 +2010,12,17,21,282,-13,-4,1014,0.89,0,0 +2010,12,17,22,325,-13,-3,1014,0.89,0,0 +2010,12,17,23,379,-13,-6,1014,1.79,0,0 +2010,12,18,0,374,-13,-6,1014,3.58,0,0 +2010,12,18,1,363,-13,-6,1014,6.71,0,0 +2010,12,18,2,319,-13,-6,1013,0.89,0,0 +2010,12,18,3,265,-13,-8,1013,1.78,0,0 +2010,12,18,4,247,-14,-7,1013,0.89,0,0 +2010,12,18,5,231,-13,-9,1013,3.13,0,0 +2010,12,18,6,215,-13,-6,1013,4.92,0,0 +2010,12,18,7,210,-12,-8,1013,0.89,0,0 +2010,12,18,8,242,-13,-8,1013,0.89,0,0 +2010,12,18,9,249,-11,-5,1013,1.79,0,0 +2010,12,18,10,211,-10,-1,1013,3.58,0,0 +2010,12,18,11,201,-12,0,1013,0.89,0,0 +2010,12,18,12,206,-13,3,1012,1.79,0,0 +2010,12,18,13,224,-13,4,1011,0.89,0,0 +2010,12,18,14,230,-14,5,1010,1.79,0,0 +2010,12,18,15,246,-13,5,1010,0.89,0,0 +2010,12,18,16,255,-13,4,1010,1.78,0,0 +2010,12,18,17,269,-12,2,1011,1.79,0,0 +2010,12,18,18,302,-11,-1,1012,0.89,0,0 +2010,12,18,19,315,-11,-3,1012,1.79,0,0 +2010,12,18,20,296,-10,-4,1013,2.68,0,0 +2010,12,18,21,290,-11,-6,1013,0.89,0,0 +2010,12,18,22,308,-10,-5,1014,1.78,0,0 +2010,12,18,23,454,-11,-5,1015,2.67,0,0 +2010,12,19,0,528,-11,-5,1015,0.89,0,0 +2010,12,19,1,485,-10,-5,1015,0.89,0,0 +2010,12,19,2,489,-11,-4,1014,1.78,0,0 +2010,12,19,3,404,-10,-5,1014,4.02,0,0 +2010,12,19,4,299,-12,-3,1014,1.79,0,0 +2010,12,19,5,298,-12,-3,1014,0.89,0,0 +2010,12,19,6,133,-12,-3,1014,1.79,0,0 +2010,12,19,7,105,-17,6,1015,11.62,0,0 +2010,12,19,8,72,-17,5,1015,16.54,0,0 +2010,12,19,9,64,-17,7,1017,25.48,0,0 +2010,12,19,10,52,-15,8,1017,34.42,0,0 +2010,12,19,11,49,-13,9,1017,43.36,0,0 +2010,12,19,12,40,-12,9,1016,50.51,0,0 +2010,12,19,13,43,-11,10,1016,58.56,0,0 +2010,12,19,14,35,-12,11,1016,65.71,0,0 +2010,12,19,15,22,-12,11,1016,71.52,0,0 +2010,12,19,16,16,-12,10,1016,77.33,0,0 +2010,12,19,17,10,-14,9,1017,83.14,0,0 +2010,12,19,18,34,-15,9,1017,87.16,0,0 +2010,12,19,19,64,-13,8,1018,3.13,0,0 +2010,12,19,20,59,-15,9,1018,4.92,0,0 +2010,12,19,21,104,-14,7,1018,3.13,0,0 +2010,12,19,22,126,-14,6,1018,6.26,0,0 +2010,12,19,23,144,-14,3,1018,0.89,0,0 +2010,12,20,0,161,-12,-2,1019,1.79,0,0 +2010,12,20,1,187,-11,-2,1019,0.89,0,0 +2010,12,20,2,200,-11,-4,1019,0.89,0,0 +2010,12,20,3,206,-12,-6,1019,0.89,0,0 +2010,12,20,4,233,-11,-5,1018,1.78,0,0 +2010,12,20,5,221,-12,-6,1018,2.67,0,0 +2010,12,20,6,220,-12,-7,1018,1.79,0,0 +2010,12,20,7,230,-11,-7,1018,0.89,0,0 +2010,12,20,8,254,-11,-6,1019,1.78,0,0 +2010,12,20,9,311,-7,-1,1019,2.23,0,0 +2010,12,20,10,352,-10,3,1019,3.12,0,0 +2010,12,20,11,227,-10,6,1018,4.01,0,0 +2010,12,20,12,175,-13,8,1017,1.79,0,0 +2010,12,20,13,91,-13,10,1016,0.89,0,0 +2010,12,20,14,129,-15,12,1015,3.13,0,0 +2010,12,20,15,196,-14,12,1014,6.26,0,0 +2010,12,20,16,117,-13,9,1015,11.18,0,0 +2010,12,20,17,104,-14,8,1015,14.31,0,0 +2010,12,20,18,115,-14,8,1015,16.1,0,0 +2010,12,20,19,138,-13,3,1015,17.89,0,0 +2010,12,20,20,147,-12,0,1015,18.78,0,0 +2010,12,20,21,237,-10,-2,1015,0.89,0,0 +2010,12,20,22,245,-10,-2,1015,1.78,0,0 +2010,12,20,23,311,-10,-2,1015,2.67,0,0 +2010,12,21,0,410,-11,-3,1015,0.89,0,0 +2010,12,21,1,405,-10,-5,1015,1.78,0,0 +2010,12,21,2,391,-10,-4,1014,3.57,0,0 +2010,12,21,3,397,-10,-3,1014,0.89,0,0 +2010,12,21,4,396,-9,-5,1013,1.78,0,0 +2010,12,21,5,367,-10,-5,1013,1.79,0,0 +2010,12,21,6,346,-10,-3,1013,0.89,0,0 +2010,12,21,7,340,-10,-4,1013,1.79,0,0 +2010,12,21,8,266,-10,-5,1013,0.89,0,0 +2010,12,21,9,206,-9,-3,1013,0.89,0,0 +2010,12,21,10,224,-7,2,1013,1.78,0,0 +2010,12,21,11,430,-6,3,1013,2.67,0,0 +2010,12,21,12,441,-5,4,1012,1.79,0,0 +2010,12,21,13,425,-6,5,1011,0.89,0,0 +2010,12,21,14,439,-5,6,1011,1.78,0,0 +2010,12,21,15,427,-5,6,1011,1.79,0,0 +2010,12,21,16,474,-5,4,1011,2.68,0,0 +2010,12,21,17,500,-5,1,1011,0.89,0,0 +2010,12,21,18,548,-5,-1,1011,1.78,0,0 +2010,12,21,19,584,-5,-1,1012,2.67,0,0 +2010,12,21,20,607,-6,-2,1012,3.56,0,0 +2010,12,21,21,611,-6,-2,1013,4.45,0,0 +2010,12,21,22,615,-7,-4,1013,5.34,0,0 +2010,12,21,23,587,-8,-5,1013,6.23,0,0 +2010,12,22,0,570,-7,-5,1013,7.12,0,0 +2010,12,22,1,573,-8,-6,1013,8.01,0,0 +2010,12,22,2,513,-8,-5,1014,3.13,0,0 +2010,12,22,3,397,-8,-5,1014,7.15,0,0 +2010,12,22,4,365,-9,-4,1014,8.94,0,0 +2010,12,22,5,295,-9,-2,1014,16.99,0,0 +2010,12,22,6,263,-9,-3,1015,21.91,0,0 +2010,12,22,7,87,-9,-3,1016,27.72,0,0 +2010,12,22,8,67,-10,0,1017,32.64,0,0 +2010,12,22,9,48,-10,3,1018,36.66,0,0 +2010,12,22,10,38,-12,5,1019,40.68,0,0 +2010,12,22,11,35,-14,6,1019,48.73,0,0 +2010,12,22,12,23,-14,7,1019,53.65,0,0 +2010,12,22,13,24,-14,7,1019,58.57,0,0 +2010,12,22,14,15,-14,6,1019,61.7,0,0 +2010,12,22,15,17,-15,6,1019,65.72,0,0 +2010,12,22,16,14,-16,6,1020,70.64,0,0 +2010,12,22,17,13,-19,2,1020,73.77,0,0 +2010,12,22,18,17,-20,2,1021,4.02,0,0 +2010,12,22,19,27,-20,3,1022,8.94,0,0 +2010,12,22,20,29,-21,2,1023,13.86,0,0 +2010,12,22,21,24,-21,1,1025,18.78,0,0 +2010,12,22,22,18,-22,0,1026,24.59,0,0 +2010,12,22,23,11,-19,0,1027,30.4,0,0 +2010,12,23,0,10,-16,-1,1028,36.21,0,0 +2010,12,23,1,10,-9,-3,1029,4.92,0,0 +2010,12,23,2,10,-14,-2,1030,4.02,0,0 +2010,12,23,3,12,-14,-3,1031,7.15,0,0 +2010,12,23,4,13,-16,-3,1031,16.09,0,0 +2010,12,23,5,10,-20,-4,1032,27.27,0,0 +2010,12,23,6,11,-24,-5,1033,37.1,0,0 +2010,12,23,7,17,-27,-7,1035,45.15,0,0 +2010,12,23,8,21,-26,-7,1035,56.33,0,0 +2010,12,23,9,17,-26,-7,1036,64.38,0,0 +2010,12,23,10,18,-28,-6,1037,74.21,0,0 +2010,12,23,11,21,-28,-6,1037,85.39,0,0 +2010,12,23,12,24,-26,-6,1037,94.33,0,0 +2010,12,23,13,25,-26,-5,1036,102.38,0,0 +2010,12,23,14,24,-26,-5,1036,109.53,0,0 +2010,12,23,15,33,-26,-5,1036,114.45,0,0 +2010,12,23,16,24,-28,-6,1036,3.13,0,0 +2010,12,23,17,21,-27,-8,1036,4.92,0,0 +2010,12,23,18,21,-26,-9,1037,0.89,0,0 +2010,12,23,19,42,-24,-11,1038,0.89,0,0 +2010,12,23,20,46,-24,-10,1038,1.34,0,0 +2010,12,23,21,52,-26,-9,1039,2.23,0,0 +2010,12,23,22,32,-25,-9,1039,3.13,0,0 +2010,12,23,23,42,-25,-10,1040,6.26,0,0 +2010,12,24,0,32,-25,-10,1040,10.28,0,0 +2010,12,24,1,35,-26,-11,1040,13.41,0,0 +2010,12,24,2,26,-26,-11,1040,4.02,0,0 +2010,12,24,3,27,-26,-12,1041,7.15,0,0 +2010,12,24,4,28,-26,-12,1041,11.17,0,0 +2010,12,24,5,19,-26,-13,1040,15.19,0,0 +2010,12,24,6,24,-26,-13,1041,4.02,0,0 +2010,12,24,7,23,-26,-13,1041,4.02,0,0 +2010,12,24,8,21,-26,-13,1041,8.04,0,0 +2010,12,24,9,27,-26,-11,1041,12.06,0,0 +2010,12,24,10,34,-26,-9,1040,15.19,0,0 +2010,12,24,11,34,-26,-8,1040,19.21,0,0 +2010,12,24,12,25,-26,-6,1038,23.23,0,0 +2010,12,24,13,20,-26,-5,1036,26.36,0,0 +2010,12,24,14,20,-26,-4,1035,29.49,0,0 +2010,12,24,15,26,-25,-4,1034,1.79,0,0 +2010,12,24,16,23,-24,-5,1034,3.58,0,0 +2010,12,24,17,51,-25,-7,1034,5.37,0,0 +2010,12,24,18,71,-24,-9,1034,6.26,0,0 +2010,12,24,19,71,-22,-10,1033,7.15,0,0 +2010,12,24,20,76,-23,-9,1033,0.89,0,0 +2010,12,24,21,103,-22,-12,1033,0.45,0,0 +2010,12,24,22,116,-24,-12,1033,1.79,0,0 +2010,12,24,23,111,-22,-13,1033,4.92,0,0 +2010,12,25,0,135,-23,-10,1032,0.89,0,0 +2010,12,25,1,97,-23,-8,1032,4.02,0,0 +2010,12,25,2,35,-24,-8,1032,8.94,0,0 +2010,12,25,3,17,-24,-7,1031,14.75,0,0 +2010,12,25,4,18,-25,-8,1031,19.67,0,0 +2010,12,25,5,18,-25,-8,1031,25.48,0,0 +2010,12,25,6,17,-24,-9,1030,28.61,0,0 +2010,12,25,7,19,-23,-12,1031,32.63,0,0 +2010,12,25,8,19,-23,-11,1031,0.89,0,0 +2010,12,25,9,21,-22,-9,1030,1.79,0,0 +2010,12,25,10,25,-22,-7,1030,4.92,0,0 +2010,12,25,11,35,-23,-6,1029,4.02,0,0 +2010,12,25,12,22,-23,-4,1027,8.94,0,0 +2010,12,25,13,19,-23,-3,1025,12.96,0,0 +2010,12,25,14,26,-22,-4,1025,4.02,0,0 +2010,12,25,15,21,-22,-3,1024,7.15,0,0 +2010,12,25,16,20,-22,-4,1023,8.94,0,0 +2010,12,25,17,89,-21,-6,1024,1.79,0,0 +2010,12,25,18,93,-21,-9,1023,3.58,0,0 +2010,12,25,19,104,-21,-9,1023,0.45,0,0 +2010,12,25,20,121,-21,-11,1023,1.79,0,0 +2010,12,25,21,117,-19,-12,1022,0.89,0,0 +2010,12,25,22,111,-20,-10,1022,0.89,0,0 +2010,12,25,23,135,-20,-10,1022,1.79,0,0 +2010,12,26,0,163,-20,-11,1021,3.58,0,0 +2010,12,26,1,128,-19,-12,1020,5.37,0,0 +2010,12,26,2,41,-20,-12,1020,0.89,0,0 +2010,12,26,3,26,-18,-7,1020,4.02,0,0 +2010,12,26,4,20,-17,-6,1019,8.94,0,0 +2010,12,26,5,22,-17,-6,1018,13.86,0,0 +2010,12,26,6,27,-17,-6,1018,18.78,0,0 +2010,12,26,7,25,-17,-6,1018,23.7,0,0 +2010,12,26,8,27,-17,-5,1018,28.62,0,0 +2010,12,26,9,30,-17,-4,1018,31.75,0,0 +2010,12,26,10,32,-16,-2,1017,35.77,0,0 +2010,12,26,11,29,-16,0,1016,40.69,0,0 +2010,12,26,12,30,-19,1,1015,48.74,0,0 +2010,12,26,13,48,-19,2,1014,57.68,0,0 +2010,12,26,14,51,-19,2,1013,65.73,0,0 +2010,12,26,15,55,-19,2,1013,73.78,0,0 +2010,12,26,16,43,-19,1,1013,4.02,0,0 +2010,12,26,17,34,-19,0,1013,7.15,0,0 +2010,12,26,18,55,-17,0,1013,11.17,0,0 +2010,12,26,19,60,-18,0,1013,15.19,0,0 +2010,12,26,20,76,-16,-1,1013,16.98,0,0 +2010,12,26,21,75,-17,-6,1013,0.89,0,0 +2010,12,26,22,58,-16,-6,1013,1.78,0,0 +2010,12,26,23,60,-16,-6,1013,2.67,0,0 +2010,12,27,0,81,-17,-9,1012,3.13,0,0 +2010,12,27,1,92,-16,-9,1012,4.92,0,0 +2010,12,27,2,130,-17,-9,1012,6.71,0,0 +2010,12,27,3,161,-17,-9,1011,8.5,0,0 +2010,12,27,4,151,-17,-11,1010,0.89,0,0 +2010,12,27,5,125,-16,-9,1010,1.79,0,0 +2010,12,27,6,108,-17,-9,1010,3.58,0,0 +2010,12,27,7,121,-16,-11,1011,1.79,0,0 +2010,12,27,8,124,-16,-11,1011,1.79,0,0 +2010,12,27,9,106,-14,-7,1011,0.89,0,0 +2010,12,27,10,130,-15,-3,1011,2.68,0,0 +2010,12,27,11,137,-16,0,1010,1.79,0,0 +2010,12,27,12,119,-12,4,1009,7.6,0,0 +2010,12,27,13,62,-14,6,1009,17.43,0,0 +2010,12,27,14,47,-15,6,1008,28.61,0,0 +2010,12,27,15,38,-15,6,1008,37.55,0,0 +2010,12,27,16,31,-15,5,1009,48.73,0,0 +2010,12,27,17,28,-15,4,1009,55.88,0,0 +2010,12,27,18,21,-19,3,1010,64.82,0,0 +2010,12,27,19,16,-21,2,1012,73.76,0,0 +2010,12,27,20,20,-21,1,1013,86.72,0,0 +2010,12,27,21,29,-19,-1,1014,94.77,0,0 +2010,12,27,22,15,-19,-2,1015,101.92,0,0 +2010,12,27,23,21,-19,-3,1016,107.73,0,0 +2010,12,28,0,16,-20,-4,1016,112.65,0,0 +2010,12,28,1,17,-20,-5,1016,118.46,0,0 +2010,12,28,2,18,-21,-6,1017,123.38,0,0 +2010,12,28,3,16,-20,-5,1017,132.32,0,0 +2010,12,28,4,16,-20,-5,1017,139.47,0,0 +2010,12,28,5,15,-20,-6,1017,146.62,0,0 +2010,12,28,6,16,-21,-6,1017,152.43,0,0 +2010,12,28,7,19,-21,-6,1017,156.45,0,0 +2010,12,28,8,14,-20,-5,1018,162.26,0,0 +2010,12,28,9,15,-20,-4,1018,166.28,0,0 +2010,12,28,10,18,-19,-2,1019,173.43,0,0 +2010,12,28,11,21,-19,-1,1019,181.48,0,0 +2010,12,28,12,19,-20,0,1019,188.63,0,0 +2010,12,28,13,19,-21,1,1019,193.55,0,0 +2010,12,28,14,17,-21,1,1018,198.47,0,0 +2010,12,28,15,21,-21,1,1019,204.28,0,0 +2010,12,28,16,14,-21,1,1019,208.3,0,0 +2010,12,28,17,20,-19,-1,1020,0.89,0,0 +2010,12,28,18,34,-19,-3,1020,1.78,0,0 +2010,12,28,19,31,-18,-4,1021,1.79,0,0 +2010,12,28,20,39,-18,-6,1021,2.68,0,0 +2010,12,28,21,88,-17,-7,1021,0.89,0,0 +2010,12,28,22,106,-18,-8,1021,2.68,0,0 +2010,12,28,23,67,-18,-10,1021,4.47,0,0 +2010,12,29,0,73,-18,-6,1021,3.13,0,0 +2010,12,29,1,65,-18,-8,1020,4.92,0,0 +2010,12,29,2,35,-16,-7,1020,5.81,0,0 +2010,12,29,3,31,-17,-6,1020,0.45,0,0 +2010,12,29,4,34,-16,-7,1019,0.9,0,0 +2010,12,29,5,34,-15,-6,1019,1.79,0,0 +2010,12,29,6,40,-16,-6,1018,0.89,0,0 +2010,12,29,7,42,-16,-7,1019,1.79,0,0 +2010,12,29,8,49,-16,-5,1019,4.02,0,0 +2010,12,29,9,67,-16,-5,1019,4.91,0,0 +2010,12,29,10,59,-14,-3,1019,6.7,0,0 +2010,12,29,11,56,-14,-2,1019,9.83,0,0 +2010,12,29,12,69,-13,0,1018,19.66,0,0 +2010,12,29,13,40,-17,-1,1018,33.52,0,0 +2010,12,29,14,30,-15,-1,1019,45.59,0,0 +2010,12,29,15,21,-16,-1,1020,55.42,0,0 +2010,12,29,16,20,-18,-2,1022,64.36,0,0 +2010,12,29,17,15,-20,-3,1022,75.54,0,0 +2010,12,29,18,13,-19,-4,1024,86.72,0,0 +2010,12,29,19,18,-19,-4,1025,95.66,0,0 +2010,12,29,20,22,-20,-5,1025,102.81,0,0 +2010,12,29,21,14,-19,-5,1026,109.96,0,0 +2010,12,29,22,14,-20,-6,1027,118.01,0,0 +2010,12,29,23,18,-20,-6,1027,126.06,0,0 +2010,12,30,0,17,-21,-7,1027,133.21,0,0 +2010,12,30,1,17,-21,-8,1027,138.13,0,0 +2010,12,30,2,17,-21,-8,1026,145.28,0,0 +2010,12,30,3,14,-22,-8,1026,151.09,0,0 +2010,12,30,4,14,-22,-8,1026,158.24,0,0 +2010,12,30,5,14,-22,-8,1024,169.42,0,0 +2010,12,30,6,15,-23,-8,1025,174.34,0,0 +2010,12,30,7,12,-23,-8,1025,182.39,0,0 +2010,12,30,8,10,-24,-8,1025,190.44,0,0 +2010,12,30,9,18,-23,-8,1025,199.38,0,0 +2010,12,30,10,15,-23,-7,1025,210.56,0,0 +2010,12,30,11,18,-24,-7,1025,221.74,0,0 +2010,12,30,12,13,-23,-5,1024,231.57,0,0 +2010,12,30,13,23,-23,-5,1023,243.64,0,0 +2010,12,30,14,27,-22,-5,1023,255.71,0,0 +2010,12,30,15,28,-22,-4,1023,266.89,0,0 +2010,12,30,16,19,-22,-5,1024,276.72,0,0 +2010,12,30,17,15,-23,-6,1025,288.79,0,0 +2010,12,30,18,17,-23,-6,1026,302.65,0,0 +2010,12,30,19,19,-24,-7,1026,315.61,0,0 +2010,12,30,20,23,-24,-8,1027,326.79,0,0 +2010,12,30,21,21,-24,-8,1027,337.97,0,0 +2010,12,30,22,16,-23,-8,1028,345.12,0,0 +2010,12,30,23,20,-23,-9,1028,354.06,0,0 +2010,12,31,0,20,-24,-9,1028,359.87,0,0 +2010,12,31,1,21,-23,-9,1028,367.92,0,0 +2010,12,31,2,20,-23,-9,1029,376.86,0,0 +2010,12,31,3,16,-24,-9,1028,384.91,0,0 +2010,12,31,4,16,-24,-10,1029,392.96,0,0 +2010,12,31,5,13,-23,-10,1029,400.11,0,0 +2010,12,31,6,14,-24,-10,1029,409.05,0,0 +2010,12,31,7,16,-23,-10,1029,416.2,0,0 +2010,12,31,8,18,-23,-9,1029,424.25,0,0 +2010,12,31,9,15,-23,-8,1030,434.08,0,0 +2010,12,31,10,19,-23,-7,1030,447.04,0,0 +2010,12,31,11,14,-22,-6,1029,458.22,0,0 +2010,12,31,12,18,-22,-4,1029,468.05,0,0 +2010,12,31,13,21,-21,-4,1028,479.23,0,0 +2010,12,31,14,21,-21,-3,1028,489.06,0,0 +2010,12,31,15,19,-20,-3,1028,498.89,0,0 +2010,12,31,16,18,-20,-4,1029,508.72,0,0 +2010,12,31,17,20,-20,-4,1029,518.55,0,0 +2010,12,31,18,28,-20,-5,1030,526.6,0,0 +2010,12,31,19,27,-20,-5,1030,534.65,0,0 +2010,12,31,20,17,-20,-6,1031,543.59,0,0 +2010,12,31,21,19,-21,-6,1031,552.53,0,0 +2010,12,31,22,16,-21,-7,1032,559.68,0,0 +2010,12,31,23,22,-21,-7,1033,565.49,0,0 +2011,1,1,0,NA,-21,-9,1033,570.41,0,0 +2011,1,1,1,NA,-21,-10,1033,573.54,0,0 +2011,1,1,2,NA,-21,-11,1033,577.56,0,0 +2011,1,1,3,NA,-21,-10,1034,581.58,0,0 +2011,1,1,4,NA,-21,-12,1034,585.6,0,0 +2011,1,1,5,NA,-20,-11,1034,1.79,0,0 +2011,1,1,6,NA,-20,-12,1034,3.13,0,0 +2011,1,1,7,NA,-20,-12,1035,6.26,0,0 +2011,1,1,8,NA,-20,-8,1036,11.18,0,0 +2011,1,1,9,NA,-19,-7,1037,16.1,0,0 +2011,1,1,10,NA,-19,-6,1037,21.91,0,0 +2011,1,1,11,NA,-18,-4,1036,26.83,0,0 +2011,1,1,12,NA,-18,-3,1035,30.85,0,0 +2011,1,1,13,NA,-18,-2,1035,34.87,0,0 +2011,1,1,14,NA,-18,-1,1034,39.79,0,0 +2011,1,1,15,NA,-18,-1,1034,43.81,0,0 +2011,1,1,16,NA,-18,-1,1034,46.94,0,0 +2011,1,1,17,NA,-19,-5,1034,48.73,0,0 +2011,1,1,18,NA,-18,-3,1034,51.86,0,0 +2011,1,1,19,NA,-19,-5,1035,54.99,0,0 +2011,1,1,20,NA,-19,-7,1035,58.12,0,0 +2011,1,1,21,NA,-18,-5,1035,62.14,0,0 +2011,1,1,22,NA,-18,-9,1036,66.16,0,0 +2011,1,1,23,NA,-17,-7,1037,71.08,0,0 +2011,1,2,0,36,-17,-7,1037,75.1,0,0 +2011,1,2,1,31,-17,-7,1037,4.02,0,0 +2011,1,2,2,20,-17,-7,1037,8.94,0,0 +2011,1,2,3,19,-18,-8,1037,4.02,0,0 +2011,1,2,4,18,-18,-8,1037,4.02,0,0 +2011,1,2,5,17,-18,-7,1037,8.04,0,0 +2011,1,2,6,14,-18,-8,1036,4.92,0,0 +2011,1,2,7,14,-18,-8,1037,4.02,0,0 +2011,1,2,8,22,-18,-7,1037,8.94,0,0 +2011,1,2,9,22,-18,-6,1038,12.96,0,0 +2011,1,2,10,25,-18,-4,1038,16.98,0,0 +2011,1,2,11,24,-17,-3,1037,21,0,0 +2011,1,2,12,31,-17,-3,1036,24.13,0,0 +2011,1,2,13,37,-17,-1,1035,1.79,0,0 +2011,1,2,14,35,-17,-1,1034,2.68,0,0 +2011,1,2,15,32,-17,-1,1033,3.57,0,0 +2011,1,2,16,37,-17,-1,1033,4.46,0,0 +2011,1,2,17,40,-17,-2,1033,5.35,0,0 +2011,1,2,18,52,-17,-3,1033,1.79,0,0 +2011,1,2,19,63,-16,-5,1033,0.89,0,0 +2011,1,2,20,72,-15,-5,1033,0.89,0,0 +2011,1,2,21,76,-15,-5,1033,0.89,0,0 +2011,1,2,22,76,-15,-6,1033,1.78,0,0 +2011,1,2,23,67,-15,-6,1034,2.67,0,0 +2011,1,3,0,65,-15,-7,1033,0.89,0,0 +2011,1,3,1,78,-15,-6,1033,1.78,0,0 +2011,1,3,2,85,-15,-7,1033,2.67,0,0 +2011,1,3,3,143,-15,-8,1032,3.56,0,0 +2011,1,3,4,181,-15,-8,1032,0.89,0,0 +2011,1,3,5,188,-15,-8,1032,0.89,0,0 +2011,1,3,6,167,-15,-9,1032,0.89,0,0 +2011,1,3,7,173,-15,-9,1032,0.89,0,0 +2011,1,3,8,155,-15,-9,1032,2.68,0,0 +2011,1,3,9,124,-13,-8,1033,0.89,0,0 +2011,1,3,10,105,-14,-6,1033,0.89,0,0 +2011,1,3,11,87,-13,-4,1032,0.89,0,0 +2011,1,3,12,93,-13,-3,1031,0.89,0,0 +2011,1,3,13,134,-13,-2,1030,1.78,0,0 +2011,1,3,14,164,-15,-1,1029,2.67,0,0 +2011,1,3,15,180,-15,-1,1029,1.79,0,0 +2011,1,3,16,184,-15,-2,1029,0.89,0,0 +2011,1,3,17,175,-15,-3,1029,1.34,0,0 +2011,1,3,18,188,-14,-5,1029,0.89,0,0 +2011,1,3,19,255,-14,-5,1030,1.78,0,0 +2011,1,3,20,286,-14,-8,1029,2.67,0,0 +2011,1,3,21,225,-15,-8,1029,0.45,0,0 +2011,1,3,22,234,-15,-9,1029,1.34,0,0 +2011,1,3,23,226,-15,-9,1029,1.79,0,0 +2011,1,4,0,233,-15,-10,1029,2.68,0,0 +2011,1,4,1,275,-15,-10,1028,0.45,0,0 +2011,1,4,2,153,-16,-11,1028,3.13,0,0 +2011,1,4,3,121,-15,-9,1028,4.92,0,0 +2011,1,4,4,77,-15,-11,1028,5.81,0,0 +2011,1,4,5,64,-16,-11,1028,7.6,0,0 +2011,1,4,6,67,-15,-10,1028,9.39,0,0 +2011,1,4,7,67,-15,-11,1028,0.89,0,0 +2011,1,4,8,59,-16,-11,1028,1.79,0,0 +2011,1,4,9,74,-14,-9,1028,1.79,0,0 +2011,1,4,10,89,-13,-5,1028,0.89,0,0 +2011,1,4,11,96,-14,-4,1028,1.78,0,0 +2011,1,4,12,96,-14,-2,1027,2.67,0,0 +2011,1,4,13,85,-14,0,1026,3.56,0,0 +2011,1,4,14,84,-14,2,1025,3.13,0,0 +2011,1,4,15,90,-15,2,1024,8.94,0,0 +2011,1,4,16,61,-16,2,1024,13.86,0,0 +2011,1,4,17,65,-17,0,1025,21.01,0,0 +2011,1,4,18,73,-15,0,1026,29.06,0,0 +2011,1,4,19,68,-16,0,1027,36.21,0,0 +2011,1,4,20,58,-17,-2,1027,43.36,0,0 +2011,1,4,21,53,-17,-2,1028,51.41,0,0 +2011,1,4,22,46,-18,-3,1028,58.56,0,0 +2011,1,4,23,38,-18,-4,1029,66.61,0,0 +2011,1,5,0,28,-19,-5,1029,75.55,0,0 +2011,1,5,1,31,-19,-5,1029,85.38,0,0 +2011,1,5,2,24,-20,-6,1029,95.21,0,0 +2011,1,5,3,21,-20,-7,1030,104.15,0,0 +2011,1,5,4,27,-20,-7,1030,113.09,0,0 +2011,1,5,5,23,-21,-7,1030,122.92,0,0 +2011,1,5,6,24,-21,-8,1030,130.07,0,0 +2011,1,5,7,21,-21,-8,1031,137.22,0,0 +2011,1,5,8,19,-22,-7,1032,146.16,0,0 +2011,1,5,9,21,-21,-7,1032,155.1,0,0 +2011,1,5,10,19,-21,-6,1032,164.04,0,0 +2011,1,5,11,21,-21,-4,1032,172.98,0,0 +2011,1,5,12,18,-21,-4,1031,182.81,0,0 +2011,1,5,13,19,-21,-3,1031,191.75,0,0 +2011,1,5,14,16,-20,-2,1030,199.8,0,0 +2011,1,5,15,18,-20,-2,1031,208.74,0,0 +2011,1,5,16,18,-20,-2,1031,218.57,0,0 +2011,1,5,17,16,-19,-3,1032,227.51,0,0 +2011,1,5,18,10,-19,-4,1032,235.56,0,0 +2011,1,5,19,16,-19,-4,1033,243.61,0,0 +2011,1,5,20,17,-19,-5,1035,252.55,0,0 +2011,1,5,21,17,-19,-5,1035,259.7,0,0 +2011,1,5,22,30,-19,-5,1036,266.85,0,0 +2011,1,5,23,12,-19,-5,1037,274.9,0,0 +2011,1,6,0,13,-19,-6,1037,282.95,0,0 +2011,1,6,1,17,-20,-7,1037,288.76,0,0 +2011,1,6,2,10,-20,-7,1038,294.57,0,0 +2011,1,6,3,12,-20,-7,1038,302.62,0,0 +2011,1,6,4,13,-20,-8,1038,307.54,0,0 +2011,1,6,5,13,-20,-8,1038,312.46,0,0 +2011,1,6,6,9,-20,-8,1039,317.38,0,0 +2011,1,6,7,6,-21,-9,1039,321.4,0,0 +2011,1,6,8,9,-20,-9,1040,325.42,0,0 +2011,1,6,9,14,-20,-6,1041,332.57,0,0 +2011,1,6,10,14,-20,-5,1041,341.51,0,0 +2011,1,6,11,13,-19,-4,1040,350.45,0,0 +2011,1,6,12,18,-19,-2,1039,359.39,0,0 +2011,1,6,13,15,-19,-2,1038,368.33,0,0 +2011,1,6,14,18,-19,-1,1037,377.27,0,0 +2011,1,6,15,13,-18,-1,1037,385.32,0,0 +2011,1,6,16,13,-18,-2,1037,392.47,0,0 +2011,1,6,17,20,-18,-1,1036,399.62,0,0 +2011,1,6,18,21,-18,-2,1037,406.77,0,0 +2011,1,6,19,22,-18,-3,1037,412.58,0,0 +2011,1,6,20,27,-18,-4,1037,417.5,0,0 +2011,1,6,21,21,-19,-6,1037,422.42,0,0 +2011,1,6,22,28,-19,-6,1038,425.55,0,0 +2011,1,6,23,22,-19,-7,1038,429.57,0,0 +2011,1,7,0,18,-18,-6,1037,432.7,0,0 +2011,1,7,1,17,-18,-7,1037,437.62,0,0 +2011,1,7,2,16,-19,-7,1036,441.64,0,0 +2011,1,7,3,27,-19,-10,1036,445.66,0,0 +2011,1,7,4,17,-19,-8,1035,4.02,0,0 +2011,1,7,5,13,-19,-11,1035,4.02,0,0 +2011,1,7,6,23,-19,-11,1035,7.15,0,0 +2011,1,7,7,27,-19,-11,1035,10.28,0,0 +2011,1,7,8,44,-19,-8,1035,13.41,0,0 +2011,1,7,9,24,-18,-5,1034,17.43,0,0 +2011,1,7,10,32,-18,-4,1034,22.35,0,0 +2011,1,7,11,40,-18,-3,1033,25.48,0,0 +2011,1,7,12,43,-18,-2,1032,27.27,0,0 +2011,1,7,13,51,-17,-1,1030,0.89,0,0 +2011,1,7,14,48,-18,1,1028,2.68,0,0 +2011,1,7,15,56,-18,1,1027,1.79,0,0 +2011,1,7,16,56,-18,0,1026,4.92,0,0 +2011,1,7,17,54,-17,-1,1026,8.05,0,0 +2011,1,7,18,69,-17,-1,1026,12.07,0,0 +2011,1,7,19,79,-17,-2,1025,15.2,0,0 +2011,1,7,20,79,-16,-2,1025,18.33,0,0 +2011,1,7,21,92,-17,-4,1025,19.22,0,0 +2011,1,7,22,86,-16,-4,1025,0.89,0,0 +2011,1,7,23,109,-16,-7,1025,1.78,0,0 +2011,1,8,0,120,-14,-7,1025,0.89,0,0 +2011,1,8,1,126,-15,-6,1024,2.68,0,0 +2011,1,8,2,156,-15,-8,1024,0.89,0,0 +2011,1,8,3,156,-15,-7,1024,0.89,0,0 +2011,1,8,4,173,-15,-7,1024,2.68,0,0 +2011,1,8,5,181,-15,-6,1024,5.81,0,0 +2011,1,8,6,118,-15,-5,1025,8.94,0,0 +2011,1,8,7,79,-16,-5,1025,13.86,0,0 +2011,1,8,8,25,-14,-3,1026,18.78,0,0 +2011,1,8,9,19,-16,-3,1027,25.93,0,0 +2011,1,8,10,16,-17,-3,1028,34.87,0,0 +2011,1,8,11,21,-17,-2,1028,46.05,0,0 +2011,1,8,12,20,-17,-1,1027,54.99,0,0 +2011,1,8,13,23,-17,-1,1027,63.93,0,0 +2011,1,8,14,26,-17,-1,1028,73.76,0,0 +2011,1,8,15,17,-18,-1,1028,83.59,0,0 +2011,1,8,16,NA,-18,-1,1029,93.42,0,0 +2011,1,8,17,NA,-18,-2,1030,101.47,0,0 +2011,1,8,18,NA,-19,-2,1031,111.3,0,0 +2011,1,8,19,NA,-20,-2,1032,121.13,0,0 +2011,1,8,20,NA,-20,-3,1033,138.12,0,0 +2011,1,8,21,NA,-21,-5,1034,151.08,0,0 +2011,1,8,22,NA,-22,-6,1035,160.91,0,0 +2011,1,8,23,NA,-22,-6,1036,172.98,0,0 +2011,1,9,0,NA,-22,-6,1037,184.16,0,0 +2011,1,9,1,NA,-22,-7,1037,197.12,0,0 +2011,1,9,2,NA,-23,-7,1037,209.19,0,0 +2011,1,9,3,NA,-23,-7,1037,220.37,0,0 +2011,1,9,4,NA,-24,-8,1037,230.2,0,0 +2011,1,9,5,NA,-24,-8,1038,242.27,0,0 +2011,1,9,6,NA,-23,-9,1038,250.32,0,0 +2011,1,9,7,NA,-23,-9,1039,256.13,0,0 +2011,1,9,8,NA,-22,-8,1039,260.15,0,0 +2011,1,9,9,NA,-22,-7,1040,268.2,0,0 +2011,1,9,10,NA,-23,-6,1039,277.14,0,0 +2011,1,9,11,NA,-23,-4,1039,286.08,0,0 +2011,1,9,12,NA,-22,-4,1038,295.02,0,0 +2011,1,9,13,NA,-22,-3,1037,303.96,0,0 +2011,1,9,14,NA,-22,-3,1036,312.9,0,0 +2011,1,9,15,NA,-22,-3,1035,320.95,0,0 +2011,1,9,16,NA,-22,-3,1035,328.1,0,0 +2011,1,9,17,NA,-21,-3,1035,335.25,0,0 +2011,1,9,18,NA,-22,-4,1035,341.06,0,0 +2011,1,9,19,NA,-22,-4,1035,345.98,0,0 +2011,1,9,20,NA,-22,-5,1035,350.9,0,0 +2011,1,9,21,NA,-22,-5,1035,354.92,0,0 +2011,1,9,22,NA,-22,-6,1035,358.05,0,0 +2011,1,9,23,NA,-22,-6,1035,362.07,0,0 +2011,1,10,0,NA,-21,-5,1034,369.22,0,0 +2011,1,10,1,NA,-21,-6,1034,376.37,0,0 +2011,1,10,2,NA,-21,-6,1034,382.18,0,0 +2011,1,10,3,NA,-21,-6,1033,391.12,0,0 +2011,1,10,4,NA,-21,-6,1033,398.27,0,0 +2011,1,10,5,NA,-21,-7,1032,403.19,0,0 +2011,1,10,6,NA,-22,-10,1032,0.89,0,0 +2011,1,10,7,NA,-22,-10,1032,3.13,0,0 +2011,1,10,8,NA,-22,-10,1032,4.02,0,0 +2011,1,10,9,NA,-21,-7,1031,8.04,0,0 +2011,1,10,10,NA,-21,-5,1030,4.92,0,0 +2011,1,10,11,NA,-20,-4,1029,4.92,0,0 +2011,1,10,12,NA,-20,-2,1028,8.05,0,0 +2011,1,10,13,NA,-20,0,1026,13.86,0,0 +2011,1,10,14,NA,-23,0,1025,21.01,0,0 +2011,1,10,15,NA,-23,0,1025,28.16,0,0 +2011,1,10,16,43,-22,0,1024,33.08,0,0 +2011,1,10,17,50,-20,-1,1024,4.02,0,0 +2011,1,10,18,70,-20,-3,1024,3.13,0,0 +2011,1,10,19,69,-19,-4,1023,6.26,0,0 +2011,1,10,20,88,-19,-3,1023,11.18,0,0 +2011,1,10,21,106,-20,-3,1022,15.2,0,0 +2011,1,10,22,115,-20,-4,1022,18.33,0,0 +2011,1,10,23,100,-20,-6,1022,0.89,0,0 +2011,1,11,0,105,-19,-7,1021,1.78,0,0 +2011,1,11,1,76,-19,-2,1021,4.92,0,0 +2011,1,11,2,33,-19,-5,1021,6.71,0,0 +2011,1,11,3,29,-19,-7,1021,8.5,0,0 +2011,1,11,4,26,-19,-6,1021,1.79,0,0 +2011,1,11,5,19,-19,-4,1021,8.05,0,0 +2011,1,11,6,18,-20,-5,1022,13.86,0,0 +2011,1,11,7,13,-20,-5,1022,19.67,0,0 +2011,1,11,8,14,-20,-6,1023,25.48,0,0 +2011,1,11,9,15,-20,-4,1024,33.53,0,0 +2011,1,11,10,18,-20,-3,1025,41.58,0,0 +2011,1,11,11,21,-20,-3,1025,49.63,0,0 +2011,1,11,12,21,-20,-2,1024,56.78,0,0 +2011,1,11,13,16,-21,-1,1023,64.83,0,0 +2011,1,11,14,8,-20,-1,1023,71.98,0,0 +2011,1,11,15,18,-20,-1,1022,80.92,0,0 +2011,1,11,16,12,-20,-1,1023,88.97,0,0 +2011,1,11,17,13,-22,-2,1023,94.78,0,0 +2011,1,11,18,19,-22,-3,1024,99.7,0,0 +2011,1,11,19,21,-23,-3,1024,106.85,0,0 +2011,1,11,20,20,-23,-5,1025,0.89,0,0 +2011,1,11,21,22,-23,-6,1025,4.02,0,0 +2011,1,11,22,37,-23,-7,1025,0.89,0,0 +2011,1,11,23,54,-23,-8,1025,1.78,0,0 +2011,1,12,0,45,-22,-6,1025,1.79,0,0 +2011,1,12,1,24,-21,-8,1025,3.58,0,0 +2011,1,12,2,35,-22,-9,1025,5.37,0,0 +2011,1,12,3,30,-20,-11,1025,0.89,0,0 +2011,1,12,4,34,-21,-10,1025,3.13,0,0 +2011,1,12,5,37,-21,-9,1025,4.92,0,0 +2011,1,12,6,49,-22,-13,1025,8.05,0,0 +2011,1,12,7,62,-21,-13,1025,8.94,0,0 +2011,1,12,8,59,-20,-13,1026,0.89,0,0 +2011,1,12,9,74,-19,-9,1026,1.79,0,0 +2011,1,12,10,61,-19,-6,1026,3.58,0,0 +2011,1,12,11,86,-20,-4,1026,5.37,0,0 +2011,1,12,12,71,-20,-1,1024,0.89,0,0 +2011,1,12,13,56,-19,0,1023,1.78,0,0 +2011,1,12,14,79,-21,0,1022,4.02,0,0 +2011,1,12,15,81,-22,1,1022,7.15,0,0 +2011,1,12,16,89,-20,0,1022,10.28,0,0 +2011,1,12,17,45,-20,-3,1022,12.07,0,0 +2011,1,12,18,55,-20,-3,1022,15.2,0,0 +2011,1,12,19,49,-18,-5,1022,16.09,0,0 +2011,1,12,20,66,-20,-6,1023,0.89,0,0 +2011,1,12,21,96,-19,-8,1023,0.89,0,0 +2011,1,12,22,100,-18,-8,1023,0.89,0,0 +2011,1,12,23,124,-19,-9,1023,1.78,0,0 +2011,1,13,0,144,-19,-10,1022,0.89,0,0 +2011,1,13,1,252,-20,-8,1022,1.79,0,0 +2011,1,13,2,244,-20,-11,1022,1.79,0,0 +2011,1,13,3,204,-19,-12,1022,4.92,0,0 +2011,1,13,4,166,-18,-11,1022,6.71,0,0 +2011,1,13,5,138,-18,-11,1021,7.6,0,0 +2011,1,13,6,149,-19,-10,1022,9.39,0,0 +2011,1,13,7,139,-19,-13,1022,12.52,0,0 +2011,1,13,8,168,-18,-9,1023,0.89,0,0 +2011,1,13,9,165,-17,-7,1023,1.79,0,0 +2011,1,13,10,123,-16,-3,1023,0.89,0,0 +2011,1,13,11,125,-17,-2,1023,1.78,0,0 +2011,1,13,12,111,-19,0,1021,2.67,0,0 +2011,1,13,13,79,-21,3,1020,5.81,0,0 +2011,1,13,14,51,-21,3,1020,15.64,0,0 +2011,1,13,15,69,-21,3,1020,24.58,0,0 +2011,1,13,16,75,-21,3,1021,31.73,0,0 +2011,1,13,17,60,-19,2,1022,4.02,0,0 +2011,1,13,18,62,-19,1,1022,4.02,0,0 +2011,1,13,19,74,-18,-2,1023,5.81,0,0 +2011,1,13,20,95,-18,-1,1024,0.89,0,0 +2011,1,13,21,120,-18,-7,1025,4.02,0,0 +2011,1,13,22,125,-17,-5,1025,1.79,0,0 +2011,1,13,23,95,-18,-2,1026,3.13,0,0 +2011,1,14,0,27,-17,-2,1027,8.05,0,0 +2011,1,14,1,25,-18,-3,1028,13.86,0,0 +2011,1,14,2,25,-17,-3,1028,22.8,0,0 +2011,1,14,3,18,-18,-4,1029,29.95,0,0 +2011,1,14,4,12,-19,-5,1030,38,0,0 +2011,1,14,5,17,-19,-6,1031,45.15,0,0 +2011,1,14,6,14,-19,-6,1032,50.96,0,0 +2011,1,14,7,15,-20,-8,1033,56.77,0,0 +2011,1,14,8,16,-20,-7,1034,63.92,0,0 +2011,1,14,9,19,-20,-6,1035,68.84,0,0 +2011,1,14,10,20,-20,-4,1035,73.76,0,0 +2011,1,14,11,21,-20,-3,1035,81.81,0,0 +2011,1,14,12,20,-20,-2,1034,88.96,0,0 +2011,1,14,13,14,-20,-2,1033,94.77,0,0 +2011,1,14,14,18,-20,-2,1032,98.79,0,0 +2011,1,14,15,20,-21,-1,1032,105.94,0,0 +2011,1,14,16,13,-22,-1,1032,114.88,0,0 +2011,1,14,17,16,-24,-2,1033,122.03,0,0 +2011,1,14,18,21,-23,-4,1035,129.18,0,0 +2011,1,14,19,20,-24,-5,1037,138.12,0,0 +2011,1,14,20,13,-25,-7,1038,149.3,0,0 +2011,1,14,21,13,-25,-8,1039,158.24,0,0 +2011,1,14,22,13,-26,-9,1040,167.18,0,0 +2011,1,14,23,12,-26,-9,1040,174.33,0,0 +2011,1,15,0,11,-25,-9,1040,182.38,0,0 +2011,1,15,1,12,-26,-10,1041,190.43,0,0 +2011,1,15,2,15,-27,-10,1041,203.39,0,0 +2011,1,15,3,12,-27,-10,1041,214.57,0,0 +2011,1,15,4,12,-27,-11,1041,224.4,0,0 +2011,1,15,5,9,-27,-11,1040,233.34,0,0 +2011,1,15,6,10,-27,-11,1041,240.49,0,0 +2011,1,15,7,11,-27,-11,1041,248.54,0,0 +2011,1,15,8,11,-27,-10,1042,256.59,0,0 +2011,1,15,9,18,-26,-9,1042,263.74,0,0 +2011,1,15,10,22,-26,-8,1042,271.79,0,0 +2011,1,15,11,10,-26,-7,1041,279.84,0,0 +2011,1,15,12,12,-25,-6,1040,287.89,0,0 +2011,1,15,13,17,-24,-5,1039,296.83,0,0 +2011,1,15,14,17,-25,-4,1038,306.66,0,0 +2011,1,15,15,17,-24,-4,1038,314.71,0,0 +2011,1,15,16,14,-23,-4,1037,322.76,0,0 +2011,1,15,17,15,-23,-4,1037,329.91,0,0 +2011,1,15,18,15,-23,-5,1038,335.72,0,0 +2011,1,15,19,21,-23,-8,1038,338.85,0,0 +2011,1,15,20,27,-23,-8,1038,0.89,0,0 +2011,1,15,21,17,-25,-8,1038,1.79,0,0 +2011,1,15,22,29,-25,-10,1038,3.13,0,0 +2011,1,15,23,33,-25,-10,1038,3.13,0,0 +2011,1,16,0,29,-24,-12,1038,6.26,0,0 +2011,1,16,1,24,-24,-10,1037,9.39,0,0 +2011,1,16,2,16,-24,-11,1038,1.79,0,0 +2011,1,16,3,14,-24,-12,1038,3.13,0,0 +2011,1,16,4,13,-24,-11,1037,0.89,0,0 +2011,1,16,5,13,-24,-12,1037,1.79,0,0 +2011,1,16,6,13,-24,-11,1037,0.89,0,0 +2011,1,16,7,19,-24,-13,1037,2.68,0,0 +2011,1,16,8,11,-24,-12,1038,4.47,0,0 +2011,1,16,9,17,-23,-9,1038,7.6,0,0 +2011,1,16,10,26,-22,-7,1039,4.02,0,0 +2011,1,16,11,25,-22,-5,1038,4.02,0,0 +2011,1,16,12,31,-21,-4,1037,5.81,0,0 +2011,1,16,13,37,-21,-3,1035,0.89,0,0 +2011,1,16,14,30,-21,-3,1035,1.79,0,0 +2011,1,16,15,40,-21,-2,1034,1.79,0,0 +2011,1,16,16,39,-20,-3,1034,1.79,0,0 +2011,1,16,17,56,-20,-5,1035,1.79,0,0 +2011,1,16,18,60,-19,-5,1035,2.68,0,0 +2011,1,16,19,72,-20,-7,1036,0.89,0,0 +2011,1,16,20,61,-19,-9,1036,1.78,0,0 +2011,1,16,21,70,-18,-8,1036,2.23,0,0 +2011,1,16,22,73,-19,-9,1036,0.89,0,0 +2011,1,16,23,77,-20,-12,1036,4.02,0,0 +2011,1,17,0,107,-20,-11,1036,5.81,0,0 +2011,1,17,1,130,-21,-10,1036,8.94,0,0 +2011,1,17,2,128,-22,-10,1036,12.96,0,0 +2011,1,17,3,50,-22,-11,1036,16.98,0,0 +2011,1,17,4,41,-22,-12,1036,21,0,0 +2011,1,17,5,40,-22,-12,1036,24.13,0,0 +2011,1,17,6,34,-22,-11,1036,27.26,0,0 +2011,1,17,7,37,-22,-12,1037,30.39,0,0 +2011,1,17,8,30,-21,-11,1037,33.52,0,0 +2011,1,17,9,36,-21,-8,1037,37.54,0,0 +2011,1,17,10,27,-20,-6,1037,41.56,0,0 +2011,1,17,11,26,-20,-4,1037,48.71,0,0 +2011,1,17,12,18,-20,-3,1035,56.76,0,0 +2011,1,17,13,20,-20,-3,1034,63.91,0,0 +2011,1,17,14,23,-20,-2,1033,72.85,0,0 +2011,1,17,15,14,-20,-2,1033,80.9,0,0 +2011,1,17,16,18,-20,-3,1033,88.95,0,0 +2011,1,17,17,17,-20,-3,1033,96.1,0,0 +2011,1,17,18,20,-20,-4,1033,104.15,0,0 +2011,1,17,19,17,-20,-4,1034,111.3,0,0 +2011,1,17,20,26,-21,-5,1034,116.22,0,0 +2011,1,17,21,24,-22,-7,1034,120.24,0,0 +2011,1,17,22,16,-21,-7,1035,126.05,0,0 +2011,1,17,23,10,-21,-8,1035,130.97,0,0 +2011,1,18,0,12,-22,-10,1035,1.79,0,0 +2011,1,18,1,12,-22,-10,1035,4.02,0,0 +2011,1,18,2,12,-23,-10,1035,4.02,0,0 +2011,1,18,3,13,-23,-10,1035,4.02,0,0 +2011,1,18,4,13,-23,-12,1035,8.94,0,0 +2011,1,18,5,16,-23,-12,1035,13.86,0,0 +2011,1,18,6,18,-23,-14,1035,16.99,0,0 +2011,1,18,7,17,-23,-13,1035,21.01,0,0 +2011,1,18,8,24,-23,-13,1035,24.14,0,0 +2011,1,18,9,31,-23,-10,1035,28.16,0,0 +2011,1,18,10,31,-22,-8,1035,31.29,0,0 +2011,1,18,11,30,-22,-6,1035,34.42,0,0 +2011,1,18,12,31,-21,-4,1034,42.47,0,0 +2011,1,18,13,23,-21,-4,1033,48.28,0,0 +2011,1,18,14,18,-21,-3,1032,53.2,0,0 +2011,1,18,15,19,-22,-3,1031,60.35,0,0 +2011,1,18,16,19,-22,-3,1031,67.5,0,0 +2011,1,18,17,18,-22,-3,1031,73.31,0,0 +2011,1,18,18,17,-21,-4,1032,79.12,0,0 +2011,1,18,19,21,-21,-4,1032,84.93,0,0 +2011,1,18,20,28,-22,-5,1032,88.95,0,0 +2011,1,18,21,25,-22,-7,1033,92.97,0,0 +2011,1,18,22,21,-23,-9,1033,94.76,0,0 +2011,1,18,23,16,-23,-12,1034,98.78,0,0 +2011,1,19,0,22,-22,-9,1034,101.91,0,0 +2011,1,19,1,24,-23,-11,1034,103.7,0,0 +2011,1,19,2,20,-23,-12,1034,106.83,0,0 +2011,1,19,3,26,-22,-11,1034,111.75,0,0 +2011,1,19,4,21,-23,-13,1034,114.88,0,0 +2011,1,19,5,17,-23,-11,1033,116.67,0,0 +2011,1,19,6,23,-23,-10,1033,119.8,0,0 +2011,1,19,7,23,-22,-12,1034,1.79,0,0 +2011,1,19,8,14,-23,-11,1034,1.79,0,0 +2011,1,19,9,21,-22,-8,1034,4.92,0,0 +2011,1,19,10,26,-21,-6,1034,8.94,0,0 +2011,1,19,11,25,-21,-4,1033,12.96,0,0 +2011,1,19,12,21,-21,-2,1032,18.77,0,0 +2011,1,19,13,21,-22,-2,1031,27.71,0,0 +2011,1,19,14,24,-22,-1,1030,33.52,0,0 +2011,1,19,15,13,-22,-1,1030,40.67,0,0 +2011,1,19,16,13,-22,-1,1030,46.48,0,0 +2011,1,19,17,12,-23,-1,1030,52.29,0,0 +2011,1,19,18,16,-22,-2,1031,58.1,0,0 +2011,1,19,19,18,-23,-6,1032,62.12,0,0 +2011,1,19,20,24,-23,-9,1032,65.25,0,0 +2011,1,19,21,16,-22,-6,1032,4.02,0,0 +2011,1,19,22,34,-22,-7,1033,8.04,0,0 +2011,1,19,23,24,-21,-7,1033,12.06,0,0 +2011,1,20,0,17,-22,-8,1033,1.79,0,0 +2011,1,20,1,15,-22,-11,1034,5.81,0,0 +2011,1,20,2,20,-22,-11,1034,10.73,0,0 +2011,1,20,3,13,-22,-10,1034,14.75,0,0 +2011,1,20,4,17,-22,-10,1033,3.13,0,0 +2011,1,20,5,17,-22,-13,1033,3.13,0,0 +2011,1,20,6,20,-21,-12,1033,8.05,0,0 +2011,1,20,7,20,-21,-13,1033,12.07,0,0 +2011,1,20,8,15,-21,-11,1033,15.2,0,0 +2011,1,20,9,26,-21,-8,1033,4.92,0,0 +2011,1,20,10,25,-22,-6,1033,3.13,0,0 +2011,1,20,11,27,-22,-4,1032,4.92,0,0 +2011,1,20,12,44,-21,-3,1031,8.05,0,0 +2011,1,20,13,29,-22,-2,1030,9.84,0,0 +2011,1,20,14,19,-22,0,1028,12.97,0,0 +2011,1,20,15,25,-24,0,1028,14.76,0,0 +2011,1,20,16,47,-22,-1,1028,1.79,0,0 +2011,1,20,17,58,-22,-3,1028,3.58,0,0 +2011,1,20,18,59,-21,-4,1028,0.45,0,0 +2011,1,20,19,70,-21,-5,1028,1.34,0,0 +2011,1,20,20,74,-21,-6,1028,2.23,0,0 +2011,1,20,21,82,-20,-7,1028,2.68,0,0 +2011,1,20,22,91,-19,-9,1029,3.57,0,0 +2011,1,20,23,100,-21,-9,1029,4.02,0,0 +2011,1,21,0,124,-21,-8,1029,1.79,0,0 +2011,1,21,1,120,-21,-9,1029,3.58,0,0 +2011,1,21,2,45,-21,-8,1029,6.71,0,0 +2011,1,21,3,36,-22,-10,1029,10.73,0,0 +2011,1,21,4,34,-22,-11,1028,13.86,0,0 +2011,1,21,5,39,-21,-11,1028,16.99,0,0 +2011,1,21,6,27,-22,-10,1028,21.01,0,0 +2011,1,21,7,20,-21,-11,1029,25.03,0,0 +2011,1,21,8,31,-21,-10,1029,4.02,0,0 +2011,1,21,9,32,-21,-5,1030,4.92,0,0 +2011,1,21,10,36,-21,-4,1030,4.92,0,0 +2011,1,21,11,42,-20,-3,1029,8.94,0,0 +2011,1,21,12,29,-20,-1,1028,3.13,0,0 +2011,1,21,13,24,-20,0,1027,6.26,0,0 +2011,1,21,14,30,-21,1,1026,9.39,0,0 +2011,1,21,15,35,-21,2,1026,0.89,0,0 +2011,1,21,16,37,-20,2,1026,4.92,0,0 +2011,1,21,17,67,-20,1,1026,8.05,0,0 +2011,1,21,18,102,-19,-2,1027,1.79,0,0 +2011,1,21,19,155,-17,-1,1028,4.92,0,0 +2011,1,21,20,144,-17,-1,1028,3.13,0,0 +2011,1,21,21,154,-18,-7,1029,0.89,0,0 +2011,1,21,22,170,-17,-6,1029,1.34,0,0 +2011,1,21,23,187,-19,-8,1029,1.79,0,0 +2011,1,22,0,191,-19,-11,1029,4.92,0,0 +2011,1,22,1,130,-18,-9,1028,8.05,0,0 +2011,1,22,2,74,-18,-10,1028,8.94,0,0 +2011,1,22,3,51,-18,-10,1028,0.45,0,0 +2011,1,22,4,43,-18,-9,1028,3.13,0,0 +2011,1,22,5,44,-19,-10,1028,6.26,0,0 +2011,1,22,6,61,-19,-13,1028,10.28,0,0 +2011,1,22,7,67,-18,-11,1029,12.07,0,0 +2011,1,22,8,88,-18,-10,1029,15.2,0,0 +2011,1,22,9,91,-17,-8,1029,18.33,0,0 +2011,1,22,10,54,-17,-4,1029,4.02,0,0 +2011,1,22,11,49,-17,-3,1029,4.92,0,0 +2011,1,22,12,50,-16,-2,1028,8.05,0,0 +2011,1,22,13,48,-16,-1,1027,11.18,0,0 +2011,1,22,14,44,-16,0,1026,16.99,0,0 +2011,1,22,15,46,-16,1,1026,24.14,0,0 +2011,1,22,16,48,-18,0,1026,32.19,0,0 +2011,1,22,17,45,-17,-1,1027,40.24,0,0 +2011,1,22,18,36,-18,-2,1027,45.16,0,0 +2011,1,22,19,23,-19,-2,1029,52.31,0,0 +2011,1,22,20,29,-21,-3,1029,60.36,0,0 +2011,1,22,21,11,-22,-4,1030,68.41,0,0 +2011,1,22,22,12,-21,-6,1031,75.56,0,0 +2011,1,22,23,16,-22,-6,1032,81.37,0,0 +2011,1,23,0,11,-22,-7,1032,88.52,0,0 +2011,1,23,1,12,-23,-7,1032,96.57,0,0 +2011,1,23,2,13,-23,-8,1032,103.72,0,0 +2011,1,23,3,12,-23,-10,1033,106.85,0,0 +2011,1,23,4,12,-22,-8,1032,114,0,0 +2011,1,23,5,11,-22,-8,1032,121.15,0,0 +2011,1,23,6,10,-22,-9,1032,126.07,0,0 +2011,1,23,7,10,-22,-9,1032,131.88,0,0 +2011,1,23,8,12,-22,-8,1033,139.03,0,0 +2011,1,23,9,14,-21,-7,1034,147.08,0,0 +2011,1,23,10,15,-21,-6,1033,155.13,0,0 +2011,1,23,11,14,-21,-5,1033,163.18,0,0 +2011,1,23,12,13,-21,-4,1032,172.12,0,0 +2011,1,23,13,21,-20,-3,1031,180.17,0,0 +2011,1,23,14,19,-20,-2,1030,191.35,0,0 +2011,1,23,15,18,-20,-2,1030,199.4,0,0 +2011,1,23,16,16,-20,-2,1031,206.55,0,0 +2011,1,23,17,20,-21,-2,1031,213.7,0,0 +2011,1,23,18,16,-20,-3,1032,220.85,0,0 +2011,1,23,19,21,-20,-3,1032,228,0,0 +2011,1,23,20,16,-20,-4,1033,236.94,0,0 +2011,1,23,21,11,-20,-4,1033,244.99,0,0 +2011,1,23,22,16,-21,-6,1034,250.8,0,0 +2011,1,23,23,15,-21,-8,1034,255.72,0,0 +2011,1,24,0,9,-21,-8,1034,261.53,0,0 +2011,1,24,1,11,-22,-10,1034,265.55,0,0 +2011,1,24,2,11,-21,-10,1034,268.68,0,0 +2011,1,24,3,19,-22,-12,1034,271.81,0,0 +2011,1,24,4,21,-21,-9,1034,0.89,0,0 +2011,1,24,5,23,-22,-11,1034,1.79,0,0 +2011,1,24,6,27,-22,-13,1034,4.92,0,0 +2011,1,24,7,27,-22,-15,1034,8.05,0,0 +2011,1,24,8,43,-21,-13,1034,11.18,0,0 +2011,1,24,9,33,-20,-8,1035,0.89,0,0 +2011,1,24,10,27,-20,-6,1035,3.13,0,0 +2011,1,24,11,27,-20,-4,1035,3.13,0,0 +2011,1,24,12,36,-19,-3,1033,4.92,0,0 +2011,1,24,13,49,-19,-1,1032,0.89,0,0 +2011,1,24,14,71,-19,-1,1031,3.13,0,0 +2011,1,24,15,79,-19,0,1031,0.89,0,0 +2011,1,24,16,63,-19,0,1031,3.13,0,0 +2011,1,24,17,47,-19,-1,1030,6.26,0,0 +2011,1,24,18,52,-19,-3,1031,9.39,0,0 +2011,1,24,19,58,-19,-4,1031,0.89,0,0 +2011,1,24,20,70,-18,-7,1031,0.89,0,0 +2011,1,24,21,78,-19,-6,1031,1.78,0,0 +2011,1,24,22,87,-18,-9,1031,2.67,0,0 +2011,1,24,23,95,-18,-9,1031,3.56,0,0 +2011,1,25,0,109,-20,-10,1031,4.45,0,0 +2011,1,25,1,95,-19,-10,1031,1.79,0,0 +2011,1,25,2,90,-19,-7,1032,3.58,0,0 +2011,1,25,3,69,-19,-8,1032,6.71,0,0 +2011,1,25,4,66,-20,-10,1032,9.84,0,0 +2011,1,25,5,47,-20,-11,1032,14.76,0,0 +2011,1,25,6,40,-20,-9,1032,17.89,0,0 +2011,1,25,7,16,-20,-10,1033,22.81,0,0 +2011,1,25,8,11,-20,-6,1034,27.73,0,0 +2011,1,25,9,12,-22,-5,1035,35.78,0,0 +2011,1,25,10,13,-22,-4,1035,42.93,0,0 +2011,1,25,11,13,-22,-4,1036,50.08,0,0 +2011,1,25,12,23,-21,-3,1035,57.23,0,0 +2011,1,25,13,21,-21,-2,1035,65.28,0,0 +2011,1,25,14,17,-20,-1,1034,72.43,0,0 +2011,1,25,15,13,-21,-1,1034,80.48,0,0 +2011,1,25,16,17,-21,0,1034,89.42,0,0 +2011,1,25,17,17,-21,-2,1035,95.23,0,0 +2011,1,25,18,11,-22,-4,1036,99.25,0,0 +2011,1,25,19,12,-22,-5,1036,3.13,0,0 +2011,1,25,20,25,-22,-5,1037,4.92,0,0 +2011,1,25,21,23,-22,-7,1038,0.89,0,0 +2011,1,25,22,32,-22,-10,1038,1.79,0,0 +2011,1,25,23,31,-22,-11,1038,1.79,0,0 +2011,1,26,0,34,-21,-9,1039,3.58,0,0 +2011,1,26,1,37,-22,-10,1039,6.71,0,0 +2011,1,26,2,29,-22,-10,1039,8.5,0,0 +2011,1,26,3,35,-21,-11,1039,10.29,0,0 +2011,1,26,4,21,-21,-11,1039,12.08,0,0 +2011,1,26,5,21,-21,-11,1040,13.87,0,0 +2011,1,26,6,26,-22,-13,1040,15.66,0,0 +2011,1,26,7,41,-21,-13,1040,17.45,0,0 +2011,1,26,8,82,-19,-11,1040,20.58,0,0 +2011,1,26,9,58,-19,-10,1040,22.37,0,0 +2011,1,26,10,56,-19,-7,1040,24.16,0,0 +2011,1,26,11,61,-19,-4,1040,25.95,0,0 +2011,1,26,12,54,-19,-3,1039,0.89,0,0 +2011,1,26,13,46,-19,-2,1038,1.78,0,0 +2011,1,26,14,55,-19,0,1037,3.13,0,0 +2011,1,26,15,71,-18,0,1037,4.92,0,0 +2011,1,26,16,83,-20,0,1036,0.89,0,0 +2011,1,26,17,85,-18,-2,1037,0.89,0,0 +2011,1,26,18,93,-19,-5,1038,2.68,0,0 +2011,1,26,19,103,-19,-7,1038,3.57,0,0 +2011,1,26,20,132,-18,-7,1039,0.89,0,0 +2011,1,26,21,144,-17,-8,1039,1.34,0,0 +2011,1,26,22,137,-19,-10,1040,3.13,0,0 +2011,1,26,23,117,-19,-9,1041,6.26,0,0 +2011,1,27,0,128,-19,-9,1041,9.39,0,0 +2011,1,27,1,161,-19,-8,1041,12.52,0,0 +2011,1,27,2,89,-19,-9,1041,17.44,0,0 +2011,1,27,3,40,-19,-9,1042,22.36,0,0 +2011,1,27,4,24,-19,-10,1042,27.28,0,0 +2011,1,27,5,20,-19,-9,1042,31.3,0,0 +2011,1,27,6,16,-19,-8,1043,34.43,0,0 +2011,1,27,7,19,-19,-9,1043,38.45,0,0 +2011,1,27,8,18,-20,-7,1043,5.81,0,0 +2011,1,27,9,19,-21,-6,1044,10.73,0,0 +2011,1,27,10,23,-20,-5,1045,15.65,0,0 +2011,1,27,11,8,-20,-4,1044,20.57,0,0 +2011,1,27,12,11,-20,-3,1043,23.7,0,0 +2011,1,27,13,17,-20,-3,1042,4.02,0,0 +2011,1,27,14,13,-21,-2,1040,3.13,0,0 +2011,1,27,15,14,-21,-2,1040,6.26,0,0 +2011,1,27,16,13,-21,-3,1040,9.39,0,0 +2011,1,27,17,12,-21,-3,1040,11.18,0,0 +2011,1,27,18,12,-22,-4,1040,12.97,0,0 +2011,1,27,19,11,-22,-4,1041,13.86,0,0 +2011,1,27,20,26,-21,-4,1041,3.13,0,0 +2011,1,27,21,19,-21,-4,1041,6.26,0,0 +2011,1,27,22,20,-21,-6,1041,8.05,0,0 +2011,1,27,23,32,-21,-7,1041,9.84,0,0 +2011,1,28,0,32,-21,-7,1041,12.97,0,0 +2011,1,28,1,18,-21,-8,1041,16.1,0,0 +2011,1,28,2,13,-21,-7,1040,17.89,0,0 +2011,1,28,3,23,-21,-8,1039,21.91,0,0 +2011,1,28,4,23,-21,-9,1040,25.04,0,0 +2011,1,28,5,20,-21,-10,1040,28.17,0,0 +2011,1,28,6,23,-21,-10,1039,32.19,0,0 +2011,1,28,7,31,-21,-9,1039,35.32,0,0 +2011,1,28,8,27,-20,-10,1039,1.79,0,0 +2011,1,28,9,19,-20,-5,1039,4.92,0,0 +2011,1,28,10,21,-20,-4,1039,9.84,0,0 +2011,1,28,11,21,-20,-3,1039,16.99,0,0 +2011,1,28,12,18,-20,-2,1038,24.14,0,0 +2011,1,28,13,16,-20,-2,1037,32.19,0,0 +2011,1,28,14,14,-20,-1,1036,38,0,0 +2011,1,28,15,14,-21,-1,1034,45.15,0,0 +2011,1,28,16,12,-22,-1,1034,53.2,0,0 +2011,1,28,17,12,-22,-2,1034,61.25,0,0 +2011,1,28,18,13,-23,-5,1035,67.06,0,0 +2011,1,28,19,12,-22,-3,1036,71.08,0,0 +2011,1,28,20,16,-22,-6,1036,75.1,0,0 +2011,1,28,21,14,-21,-7,1036,76.89,0,0 +2011,1,28,22,10,-21,-5,1037,81.81,0,0 +2011,1,28,23,15,-21,-6,1037,86.73,0,0 +2011,1,29,0,3,-23,-7,1037,92.54,0,0 +2011,1,29,1,6,-24,-8,1037,97.46,0,0 +2011,1,29,2,10,-24,-8,1037,102.38,0,0 +2011,1,29,3,7,-25,-9,1037,108.19,0,0 +2011,1,29,4,4,-25,-11,1037,112.21,0,0 +2011,1,29,5,2,-25,-11,1036,117.13,0,0 +2011,1,29,6,7,-24,-11,1036,122.05,0,0 +2011,1,29,7,11,-24,-12,1037,125.18,0,0 +2011,1,29,8,8,-24,-11,1037,128.31,0,0 +2011,1,29,9,9,-24,-9,1038,3.13,0,0 +2011,1,29,10,16,-23,-7,1038,6.26,0,0 +2011,1,29,11,27,-22,-6,1038,3.13,0,0 +2011,1,29,12,19,-22,-4,1037,11.18,0,0 +2011,1,29,13,11,-22,-3,1035,19.23,0,0 +2011,1,29,14,42,-22,-2,1035,27.28,0,0 +2011,1,29,15,15,-23,-1,1034,34.43,0,0 +2011,1,29,16,13,-22,-1,1034,40.24,0,0 +2011,1,29,17,11,-23,-2,1034,47.39,0,0 +2011,1,29,18,9,-24,-6,1035,51.41,0,0 +2011,1,29,19,12,-23,-7,1035,55.43,0,0 +2011,1,29,20,16,-22,-6,1035,60.35,0,0 +2011,1,29,21,9,-22,-5,1035,66.16,0,0 +2011,1,29,22,10,-23,-5,1035,74.21,0,0 +2011,1,29,23,9,-23,-7,1036,77.34,0,0 +2011,1,30,0,21,-22,-5,1036,82.26,0,0 +2011,1,30,1,22,-22,-6,1036,88.07,0,0 +2011,1,30,2,10,-21,-6,1036,93.88,0,0 +2011,1,30,3,19,-22,-6,1036,99.69,0,0 +2011,1,30,4,7,-21,-6,1036,104.61,0,0 +2011,1,30,5,8,-22,-7,1036,108.63,0,0 +2011,1,30,6,8,-21,-7,1036,114.44,0,0 +2011,1,30,7,9,-21,-7,1036,120.25,0,0 +2011,1,30,8,17,-21,-6,1036,125.17,0,0 +2011,1,30,9,19,-21,-4,1036,130.98,0,0 +2011,1,30,10,21,-20,-3,1036,138.13,0,0 +2011,1,30,11,24,-20,-1,1036,147.07,0,0 +2011,1,30,12,16,-22,0,1035,156.01,0,0 +2011,1,30,13,14,-22,1,1033,164.95,0,0 +2011,1,30,14,16,-22,2,1032,173.89,0,0 +2011,1,30,15,16,-22,2,1031,181.94,0,0 +2011,1,30,16,11,-21,3,1030,189.09,0,0 +2011,1,30,17,13,-21,3,1030,194.01,0,0 +2011,1,30,18,16,-21,2,1031,198.93,0,0 +2011,1,30,19,37,-21,0,1031,202.06,0,0 +2011,1,30,20,46,-20,1,1031,205.19,0,0 +2011,1,30,21,45,-18,-2,1032,208.32,0,0 +2011,1,30,22,49,-19,-4,1032,0.89,0,0 +2011,1,30,23,45,-19,-4,1032,1.79,0,0 +2011,1,31,0,21,-18,-3,1032,4.92,0,0 +2011,1,31,1,15,-18,-4,1031,9.84,0,0 +2011,1,31,2,12,-19,-5,1031,12.97,0,0 +2011,1,31,3,12,-18,-5,1031,16.1,0,0 +2011,1,31,4,15,-19,-6,1030,19.23,0,0 +2011,1,31,5,13,-19,-9,1030,23.25,0,0 +2011,1,31,6,10,-19,-9,1030,0.89,0,0 +2011,1,31,7,25,-18,-9,1030,4.02,0,0 +2011,1,31,8,43,-18,-8,1031,7.15,0,0 +2011,1,31,9,23,-18,-2,1031,10.28,0,0 +2011,1,31,10,27,-19,1,1030,15.2,0,0 +2011,1,31,11,19,-20,4,1030,19.22,0,0 +2011,1,31,12,23,-21,6,1029,26.37,0,0 +2011,1,31,13,23,-20,7,1027,33.52,0,0 +2011,1,31,14,17,-19,8,1026,40.67,0,0 +2011,1,31,15,21,-19,8,1025,46.48,0,0 +2011,1,31,16,18,-19,8,1025,52.29,0,0 +2011,1,31,17,28,-20,7,1025,56.31,0,0 +2011,1,31,18,24,-20,6,1025,59.44,0,0 +2011,1,31,19,46,-20,4,1025,64.36,0,0 +2011,1,31,20,61,-21,4,1025,68.38,0,0 +2011,1,31,21,69,-20,2,1025,71.51,0,0 +2011,1,31,22,68,-19,2,1025,74.64,0,0 +2011,1,31,23,55,-18,-3,1025,77.77,0,0 +2011,2,1,0,33,-17,-4,1025,80.9,0,0 +2011,2,1,1,43,-17,-5,1024,84.92,0,0 +2011,2,1,2,28,-17,-5,1025,89.84,0,0 +2011,2,1,3,34,-18,-6,1024,94.76,0,0 +2011,2,1,4,29,-17,-6,1024,98.78,0,0 +2011,2,1,5,37,-17,-4,1024,101.91,0,0 +2011,2,1,6,34,-17,-4,1023,105.04,0,0 +2011,2,1,7,27,-17,-3,1024,4.02,0,0 +2011,2,1,8,44,-17,-3,1024,7.15,0,0 +2011,2,1,9,37,-17,-1,1024,10.28,0,0 +2011,2,1,10,46,-19,0,1024,14.3,0,0 +2011,2,1,11,43,-20,4,1024,18.32,0,0 +2011,2,1,12,29,-20,6,1023,3.13,0,0 +2011,2,1,13,20,-22,7,1022,10.28,0,0 +2011,2,1,14,27,-22,9,1021,17.43,0,0 +2011,2,1,15,23,-22,9,1020,22.35,0,0 +2011,2,1,16,28,-21,8,1020,28.16,0,0 +2011,2,1,17,22,-22,7,1020,32.18,0,0 +2011,2,1,18,29,-22,6,1021,37.1,0,0 +2011,2,1,19,30,-22,5,1021,42.02,0,0 +2011,2,1,20,42,-22,3,1021,46.04,0,0 +2011,2,1,21,49,-22,4,1021,50.06,0,0 +2011,2,1,22,49,-22,3,1021,54.08,0,0 +2011,2,1,23,59,-22,3,1021,57.21,0,0 +2011,2,2,0,84,-20,-2,1021,61.23,0,0 +2011,2,2,1,73,-19,-4,1021,0.89,0,0 +2011,2,2,2,70,-19,-6,1021,3.13,0,0 +2011,2,2,3,95,-19,-6,1020,6.26,0,0 +2011,2,2,4,71,-20,-5,1020,0.89,0,0 +2011,2,2,5,47,-19,-4,1020,4.02,0,0 +2011,2,2,6,42,-19,-7,1020,7.15,0,0 +2011,2,2,7,56,-20,-7,1020,10.28,0,0 +2011,2,2,8,48,-18,-4,1021,14.3,0,0 +2011,2,2,9,42,-18,-2,1021,18.32,0,0 +2011,2,2,10,45,-20,1,1022,21.45,0,0 +2011,2,2,11,60,-21,4,1022,24.58,0,0 +2011,2,2,12,65,-21,5,1021,28.6,0,0 +2011,2,2,13,55,-20,7,1019,31.73,0,0 +2011,2,2,14,38,-20,7,1019,39.78,0,0 +2011,2,2,15,33,-18,6,1019,47.83,0,0 +2011,2,2,16,34,-16,5,1020,57.66,0,0 +2011,2,2,17,19,-17,5,1020,65.71,0,0 +2011,2,2,18,18,-17,3,1021,73.76,0,0 +2011,2,2,19,29,-18,3,1022,81.81,0,0 +2011,2,2,20,44,-17,2,1022,87.62,0,0 +2011,2,2,21,89,-17,2,1023,94.77,0,0 +2011,2,2,22,61,-17,1,1023,100.58,0,0 +2011,2,2,23,61,-16,-3,1023,103.71,0,0 +2011,2,3,0,75,-17,-4,1024,107.73,0,0 +2011,2,3,1,348,-17,-3,1024,111.75,0,0 +2011,2,3,2,79,-17,-2,1024,116.67,0,0 +2011,2,3,3,40,-17,-5,1024,119.8,0,0 +2011,2,3,4,25,-17,-8,1024,122.93,0,0 +2011,2,3,5,29,-17,-7,1023,126.06,0,0 +2011,2,3,6,23,-17,-7,1023,129.19,0,0 +2011,2,3,7,24,-17,-9,1024,133.21,0,0 +2011,2,3,8,39,-16,-5,1024,3.13,0,0 +2011,2,3,9,46,-16,-2,1024,4.92,0,0 +2011,2,3,10,47,-18,0,1023,4.02,0,0 +2011,2,3,11,30,-18,2,1023,3.13,0,0 +2011,2,3,12,36,-18,3,1022,4.92,0,0 +2011,2,3,13,28,-19,5,1020,0.89,0,0 +2011,2,3,14,44,-19,6,1019,1.79,0,0 +2011,2,3,15,47,-19,7,1019,3.13,0,0 +2011,2,3,16,78,-19,6,1018,8.05,0,0 +2011,2,3,17,100,-20,5,1018,12.07,0,0 +2011,2,3,18,146,-19,3,1019,16.09,0,0 +2011,2,3,19,143,-18,3,1019,19.22,0,0 +2011,2,3,20,167,-18,2,1019,21.01,0,0 +2011,2,3,21,250,-16,-3,1019,0.89,0,0 +2011,2,3,22,288,-15,-3,1019,1.34,0,0 +2011,2,3,23,289,-15,-5,1019,2.23,0,0 +2011,2,4,0,299,-15,-6,1019,0.89,0,0 +2011,2,4,1,378,-16,-7,1019,0.89,0,0 +2011,2,4,2,270,-15,-6,1019,1.79,0,0 +2011,2,4,3,263,-16,-7,1019,1.79,0,0 +2011,2,4,4,234,-15,-4,1019,4.92,0,0 +2011,2,4,5,171,-16,-4,1019,6.71,0,0 +2011,2,4,6,100,-16,-3,1019,9.84,0,0 +2011,2,4,7,83,-16,-3,1020,12.97,0,0 +2011,2,4,8,77,-15,0,1021,16.99,0,0 +2011,2,4,9,86,-17,0,1022,21.91,0,0 +2011,2,4,10,102,-17,2,1022,25.93,0,0 +2011,2,4,11,77,-13,4,1023,29.95,0,0 +2011,2,4,12,40,-10,6,1023,4.02,0,0 +2011,2,4,13,37,-11,7,1022,8.04,0,0 +2011,2,4,14,33,-12,8,1021,1.79,0,0 +2011,2,4,15,32,-12,8,1021,0.89,0,0 +2011,2,4,16,32,-12,7,1021,3.13,0,0 +2011,2,4,17,21,-12,6,1021,7.15,0,0 +2011,2,4,18,196,-12,2,1023,12.96,0,0 +2011,2,4,19,161,-13,0,1023,16.09,0,0 +2011,2,4,20,143,-11,-1,1024,19.22,0,0 +2011,2,4,21,149,-11,-2,1025,22.35,0,0 +2011,2,4,22,99,-11,-4,1025,24.14,0,0 +2011,2,4,23,64,-11,-3,1025,25.03,0,0 +2011,2,5,0,43,-12,-6,1025,25.92,0,0 +2011,2,5,1,53,-11,-6,1025,0.45,0,0 +2011,2,5,2,46,-11,-6,1024,1.34,0,0 +2011,2,5,3,55,-11,-8,1023,0.89,0,0 +2011,2,5,4,63,-11,-7,1023,0.89,0,0 +2011,2,5,5,74,-10,-6,1022,1.78,0,0 +2011,2,5,6,82,-12,-8,1021,2.23,0,0 +2011,2,5,7,87,-11,-7,1021,0.89,0,0 +2011,2,5,8,90,-11,-5,1022,0.89,0,0 +2011,2,5,9,92,-10,-4,1022,0.89,0,0 +2011,2,5,10,97,-8,-2,1020,3.13,0,0 +2011,2,5,11,100,-7,-1,1020,0.89,0,0 +2011,2,5,12,105,-7,0,1018,3.13,0,0 +2011,2,5,13,109,-9,1,1016,6.26,0,0 +2011,2,5,14,130,-9,2,1015,8.05,0,0 +2011,2,5,15,145,-9,2,1014,0.89,0,0 +2011,2,5,16,147,-9,2,1014,1.78,0,0 +2011,2,5,17,165,-9,1,1014,2.67,0,0 +2011,2,5,18,178,-9,-1,1014,1.79,0,0 +2011,2,5,19,169,-10,-4,1014,4.92,0,0 +2011,2,5,20,155,-10,-3,1015,6.71,0,0 +2011,2,5,21,158,-9,-3,1015,0.89,0,0 +2011,2,5,22,174,-10,-4,1015,1.78,0,0 +2011,2,5,23,201,-9,-3,1016,4.02,0,0 +2011,2,6,0,220,-9,-2,1017,8.04,0,0 +2011,2,6,1,186,-10,-5,1018,1.79,0,0 +2011,2,6,2,144,-10,-6,1018,1.79,0,0 +2011,2,6,3,89,-9,-1,1019,4.02,0,0 +2011,2,6,4,17,-11,-4,1019,7.15,0,0 +2011,2,6,5,19,-10,-3,1020,4.02,0,0 +2011,2,6,6,22,-10,-2,1020,5.81,0,0 +2011,2,6,7,34,-12,-6,1021,3.13,0,0 +2011,2,6,8,31,-10,-1,1023,1.79,0,0 +2011,2,6,9,54,-12,2,1023,3.13,0,0 +2011,2,6,10,34,-12,5,1023,8.05,0,0 +2011,2,6,11,29,-12,6,1023,4.02,0,0 +2011,2,6,12,25,-10,8,1022,7.15,0,0 +2011,2,6,13,27,-10,9,1020,11.17,0,0 +2011,2,6,14,21,-10,10,1019,1.79,0,0 +2011,2,6,15,25,-10,11,1019,3.58,0,0 +2011,2,6,16,29,-9,10,1019,4.02,0,0 +2011,2,6,17,21,-10,10,1019,7.15,0,0 +2011,2,6,18,22,-11,6,1019,8.94,0,0 +2011,2,6,19,20,-12,5,1019,12.96,0,0 +2011,2,6,20,35,-11,2,1020,0.89,0,0 +2011,2,6,21,41,-12,3,1020,4.92,0,0 +2011,2,6,22,69,-13,2,1020,6.71,0,0 +2011,2,6,23,83,-11,1,1020,9.84,0,0 +2011,2,7,0,93,-10,0,1020,12.97,0,0 +2011,2,7,1,106,-9,-1,1019,16.1,0,0 +2011,2,7,2,110,-9,-2,1019,0.89,0,0 +2011,2,7,3,110,-9,-3,1019,1.78,0,0 +2011,2,7,4,122,-9,-2,1017,3.13,0,0 +2011,2,7,5,130,-9,-2,1017,4.92,0,0 +2011,2,7,6,126,-9,-2,1018,0.45,0,0 +2011,2,7,7,117,-9,-2,1017,1.34,0,0 +2011,2,7,8,118,-9,-2,1017,2.23,0,0 +2011,2,7,9,110,-8,0,1017,1.79,0,0 +2011,2,7,10,113,-8,0,1017,0.89,0,0 +2011,2,7,11,131,-8,1,1016,1.79,0,0 +2011,2,7,12,151,-8,3,1015,1.79,0,0 +2011,2,7,13,129,-11,4,1013,4.92,0,0 +2011,2,7,14,117,-12,4,1012,8.94,0,0 +2011,2,7,15,138,-11,4,1012,12.96,0,0 +2011,2,7,16,146,-12,3,1011,16.98,0,0 +2011,2,7,17,108,-12,3,1012,20.11,0,0 +2011,2,7,18,77,-12,1,1012,21.9,0,0 +2011,2,7,19,101,-10,0,1012,0.89,0,0 +2011,2,7,20,200,-10,-3,1013,0.89,0,0 +2011,2,7,21,288,-10,-3,1014,0.45,0,0 +2011,2,7,22,295,-10,-4,1013,1.34,0,0 +2011,2,7,23,263,-10,-4,1013,1.79,0,0 +2011,2,8,0,208,-10,-6,1012,2.68,0,0 +2011,2,8,1,176,-11,-7,1012,3.13,0,0 +2011,2,8,2,121,-10,-7,1013,0.89,0,0 +2011,2,8,3,133,-12,-7,1013,2.68,0,0 +2011,2,8,4,128,-12,-8,1013,5.81,0,0 +2011,2,8,5,162,-11,-7,1012,8.94,0,0 +2011,2,8,6,168,-11,-7,1012,12.07,0,0 +2011,2,8,7,148,-10,-5,1013,16.99,0,0 +2011,2,8,8,119,-11,-4,1014,20.12,0,0 +2011,2,8,9,118,-10,-1,1015,25.04,0,0 +2011,2,8,10,109,-12,3,1015,5.81,0,0 +2011,2,8,11,80,-12,5,1016,10.73,0,0 +2011,2,8,12,52,-11,6,1016,15.65,0,0 +2011,2,8,13,43,-11,7,1016,21.46,0,0 +2011,2,8,14,37,-12,8,1016,26.38,0,0 +2011,2,8,15,26,-12,8,1016,31.3,0,0 +2011,2,8,16,27,-14,7,1017,4.02,0,0 +2011,2,8,17,27,-15,7,1017,3.13,0,0 +2011,2,8,18,17,-15,5,1018,0.89,0,0 +2011,2,8,19,37,-15,0,1019,0.89,0,0 +2011,2,8,20,47,-13,-2,1020,1.79,0,0 +2011,2,8,21,100,-11,-3,1021,0.89,0,0 +2011,2,8,22,148,-12,-3,1022,1.78,0,0 +2011,2,8,23,132,-12,-3,1022,1.79,0,0 +2011,2,9,0,84,-13,-4,1023,2.68,0,0 +2011,2,9,1,78,-12,-5,1024,1.79,0,0 +2011,2,9,2,132,-11,-5,1025,0.89,0,0 +2011,2,9,3,163,-9,-5,1026,1.79,0,0 +2011,2,9,4,171,-9,-6,1025,0.89,0,0 +2011,2,9,5,176,-8,-6,1026,1.79,0,0 +2011,2,9,6,199,-8,-7,1026,0.89,0,0 +2011,2,9,7,209,-8,-7,1027,0.89,0,0 +2011,2,9,8,208,-8,-7,1028,0.89,0,0 +2011,2,9,9,211,-6,-4,1028,0.89,0,0 +2011,2,9,10,199,-6,-3,1029,1.78,0,0 +2011,2,9,11,212,-5,-1,1028,1.79,0,0 +2011,2,9,12,225,-5,2,1027,3.58,0,0 +2011,2,9,13,151,-6,3,1026,7.6,0,0 +2011,2,9,14,96,-8,3,1026,10.73,0,0 +2011,2,9,15,68,-9,2,1025,13.86,0,0 +2011,2,9,16,68,-8,2,1025,17.88,0,0 +2011,2,9,17,79,-7,1,1025,21.9,0,0 +2011,2,9,18,80,-7,1,1025,25.03,0,0 +2011,2,9,19,82,-7,1,1026,28.16,0,0 +2011,2,9,20,43,-7,0,1026,33.08,0,0 +2011,2,9,21,38,-7,-1,1027,37.1,0,0 +2011,2,9,22,38,-8,-2,1027,40.23,0,0 +2011,2,9,23,35,-7,-3,1028,44.25,1,0 +2011,2,10,0,31,-5,-4,1028,47.38,2,0 +2011,2,10,1,30,-5,-4,1027,50.51,3,0 +2011,2,10,2,28,-5,-5,1027,53.64,4,0 +2011,2,10,3,30,-5,-5,1027,55.43,5,0 +2011,2,10,4,44,-6,-5,1027,58.56,6,0 +2011,2,10,5,55,-6,-5,1027,61.69,7,0 +2011,2,10,6,34,-6,-5,1027,64.82,8,0 +2011,2,10,7,41,-6,-5,1027,66.61,9,0 +2011,2,10,8,52,-6,-5,1028,68.4,10,0 +2011,2,10,9,66,-6,-5,1028,70.19,11,0 +2011,2,10,10,70,-5,-4,1029,71.98,12,0 +2011,2,10,11,74,-4,-4,1029,73.77,13,0 +2011,2,10,12,81,-4,-3,1028,75.56,14,0 +2011,2,10,13,68,-3,-3,1027,78.69,15,0 +2011,2,10,14,62,-3,-2,1027,80.48,16,0 +2011,2,10,15,76,-4,-2,1026,83.61,17,0 +2011,2,10,16,78,-5,-2,1026,85.4,18,0 +2011,2,10,17,79,-4,-3,1027,87.19,19,0 +2011,2,10,18,83,-4,-3,1027,88.08,20,0 +2011,2,10,19,90,-5,-3,1028,0.89,0,0 +2011,2,10,20,95,-5,-3,1028,0.89,0,0 +2011,2,10,21,98,-4,-3,1029,1.78,0,0 +2011,2,10,22,98,-4,-3,1029,0.89,0,0 +2011,2,10,23,97,-4,-3,1028,0.89,0,0 +2011,2,11,0,107,-5,-3,1028,1.78,0,0 +2011,2,11,1,100,-5,-4,1028,0.89,0,0 +2011,2,11,2,105,-6,-5,1028,1.79,0,0 +2011,2,11,3,103,-6,-5,1028,3.58,0,0 +2011,2,11,4,96,-5,-5,1027,5.37,0,0 +2011,2,11,5,104,-6,-5,1027,6.26,0,0 +2011,2,11,6,102,-6,-6,1028,8.05,0,0 +2011,2,11,7,111,-7,-6,1028,12.07,0,0 +2011,2,11,8,82,-8,-6,1029,0.89,0,0 +2011,2,11,9,239,-13,-2,1030,11.18,0,0 +2011,2,11,10,132,-13,-2,1030,23.25,0,0 +2011,2,11,11,66,-15,-2,1031,36.21,0,0 +2011,2,11,12,41,-14,-2,1031,48.28,0,0 +2011,2,11,13,13,-15,-1,1030,58.11,0,0 +2011,2,11,14,10,-15,-1,1030,69.29,0,0 +2011,2,11,15,8,-16,-1,1030,80.47,0,0 +2011,2,11,16,12,-16,-1,1030,91.65,0,0 +2011,2,11,17,12,-17,-2,1031,101.48,0,0 +2011,2,11,18,7,-17,-3,1032,109.53,0,0 +2011,2,11,19,7,-17,-5,1033,3.13,0,0 +2011,2,11,20,10,-18,-4,1034,5.81,0,0 +2011,2,11,21,8,-18,-7,1035,9.83,0,0 +2011,2,11,22,9,-19,-6,1035,1.79,0,0 +2011,2,11,23,9,-19,-7,1036,1.79,0,0 +2011,2,12,0,7,-18,-9,1035,3.13,0,0 +2011,2,12,1,12,-19,-9,1036,4.92,0,0 +2011,2,12,2,11,-19,-10,1036,8.05,0,0 +2011,2,12,3,17,-20,-9,1035,12.07,0,0 +2011,2,12,4,12,-20,-8,1035,16.09,0,0 +2011,2,12,5,10,-20,-9,1035,20.11,0,0 +2011,2,12,6,10,-20,-12,1035,4.02,0,0 +2011,2,12,7,12,-20,-10,1035,7.15,0,0 +2011,2,12,8,16,-19,-9,1035,3.13,0,0 +2011,2,12,9,21,-19,-7,1035,8.05,0,0 +2011,2,12,10,21,-19,-6,1034,3.13,0,0 +2011,2,12,11,22,-18,-4,1034,6.26,0,0 +2011,2,12,12,23,-18,-4,1033,8.05,0,0 +2011,2,12,13,27,-18,-2,1031,0.89,0,0 +2011,2,12,14,34,-17,-2,1031,3.13,0,0 +2011,2,12,15,49,-17,-2,1030,4.92,0,0 +2011,2,12,16,41,-16,-3,1029,8.05,0,0 +2011,2,12,17,48,-16,-3,1029,11.18,0,0 +2011,2,12,18,43,-16,-4,1029,14.31,0,0 +2011,2,12,19,41,-15,-4,1029,17.44,0,0 +2011,2,12,20,39,-16,-4,1029,20.57,0,0 +2011,2,12,21,41,-13,-4,1030,24.59,0,0 +2011,2,12,22,47,-11,-5,1030,27.72,1,0 +2011,2,12,23,58,-9,-6,1030,29.51,2,0 +2011,2,13,0,50,-7,-6,1030,31.3,3,0 +2011,2,13,1,52,-7,-6,1029,33.09,4,0 +2011,2,13,2,59,-7,-7,1029,34.88,5,0 +2011,2,13,3,58,-7,-7,1029,36.67,6,0 +2011,2,13,4,65,-7,-7,1029,38.46,7,0 +2011,2,13,5,68,-8,-7,1029,1.79,8,0 +2011,2,13,6,61,-8,-7,1030,3.58,9,0 +2011,2,13,7,69,-8,-7,1030,4.47,10,0 +2011,2,13,8,72,-8,-7,1031,6.26,11,0 +2011,2,13,9,69,-7,-6,1031,7.15,12,0 +2011,2,13,10,82,-7,-6,1031,0.89,13,0 +2011,2,13,11,85,-6,-4,1031,1.78,14,0 +2011,2,13,12,87,-6,-4,1031,1.79,0,0 +2011,2,13,13,79,-6,-3,1030,0.89,0,0 +2011,2,13,14,86,-8,-3,1030,1.79,0,0 +2011,2,13,15,86,-9,-2,1029,0.89,0,0 +2011,2,13,16,82,-9,-2,1030,1.78,0,0 +2011,2,13,17,92,-8,-2,1030,3.13,0,0 +2011,2,13,18,104,-9,-3,1030,4.92,0,0 +2011,2,13,19,108,-8,-4,1031,8.05,0,0 +2011,2,13,20,113,-8,-4,1031,9.84,0,0 +2011,2,13,21,115,-8,-4,1032,3.13,0,0 +2011,2,13,22,12,-13,-5,1032,8.05,0,0 +2011,2,13,23,13,-15,-5,1033,12.07,0,0 +2011,2,14,0,11,-18,-7,1033,16.09,0,0 +2011,2,14,1,6,-18,-10,1033,19.22,0,0 +2011,2,14,2,8,-20,-8,1033,23.24,0,0 +2011,2,14,3,8,-20,-11,1033,26.37,0,0 +2011,2,14,4,7,-19,-9,1033,28.16,0,0 +2011,2,14,5,10,-20,-10,1034,1.79,0,0 +2011,2,14,6,11,-19,-11,1034,5.81,0,0 +2011,2,14,7,22,-20,-11,1034,3.13,0,0 +2011,2,14,8,33,-18,-9,1034,4.02,0,0 +2011,2,14,9,29,-19,-6,1034,8.04,0,0 +2011,2,14,10,37,-19,-6,1034,12.06,0,0 +2011,2,14,11,30,-18,-4,1034,13.85,0,0 +2011,2,14,12,26,-18,-2,1033,0.89,0,0 +2011,2,14,13,22,-17,-1,1032,1.78,0,0 +2011,2,14,14,28,-17,0,1031,3.13,0,0 +2011,2,14,15,46,-19,1,1031,1.79,0,0 +2011,2,14,16,53,-18,1,1030,3.58,0,0 +2011,2,14,17,56,-14,0,1030,6.71,0,0 +2011,2,14,18,39,-14,-2,1030,9.84,0,0 +2011,2,14,19,40,-14,-2,1030,11.63,0,0 +2011,2,14,20,46,-14,-2,1030,13.42,0,0 +2011,2,14,21,61,-13,-6,1030,0.89,0,0 +2011,2,14,22,77,-13,-7,1030,1.79,0,0 +2011,2,14,23,89,-12,-8,1030,2.68,0,0 +2011,2,15,0,102,-13,-9,1030,3.57,0,0 +2011,2,15,1,125,-13,-9,1029,0.89,0,0 +2011,2,15,2,154,-14,-10,1029,1.79,0,0 +2011,2,15,3,153,-14,-10,1029,2.68,0,0 +2011,2,15,4,122,-15,-11,1029,0.89,0,0 +2011,2,15,5,113,-14,-12,1029,0.89,0,0 +2011,2,15,6,138,-14,-11,1029,0.89,0,0 +2011,2,15,7,125,-15,-12,1029,0.89,0,0 +2011,2,15,8,117,-14,-11,1029,1.78,0,0 +2011,2,15,9,152,-11,-8,1030,0.89,0,0 +2011,2,15,10,181,-10,-6,1029,1.79,0,0 +2011,2,15,11,214,-9,-4,1029,0.89,0,0 +2011,2,15,12,233,-9,-2,1028,1.78,0,0 +2011,2,15,13,252,-9,0,1027,2.67,0,0 +2011,2,15,14,274,-10,0,1025,3.56,0,0 +2011,2,15,15,248,-9,1,1025,1.79,0,0 +2011,2,15,16,216,-8,1,1024,3.58,0,0 +2011,2,15,17,209,-8,0,1024,5.37,0,0 +2011,2,15,18,213,-8,-2,1025,1.79,0,0 +2011,2,15,19,254,-7,-2,1025,3.58,0,0 +2011,2,15,20,301,-9,-5,1025,4.47,0,0 +2011,2,15,21,298,-8,-6,1025,5.36,0,0 +2011,2,15,22,331,-8,-6,1025,6.25,0,0 +2011,2,15,23,328,-9,-6,1025,1.79,0,0 +2011,2,16,0,335,-10,-7,1025,3.58,0,0 +2011,2,16,1,312,-9,-6,1025,5.37,0,0 +2011,2,16,2,299,-10,-8,1024,6.26,0,0 +2011,2,16,3,294,-11,-9,1023,0.89,0,0 +2011,2,16,4,277,-11,-9,1023,1.79,0,0 +2011,2,16,5,281,-10,-8,1023,0.45,0,0 +2011,2,16,6,267,-12,-10,1023,0.89,0,0 +2011,2,16,7,269,-11,-9,1023,0.45,0,0 +2011,2,16,8,258,-9,-7,1023,0.89,0,0 +2011,2,16,9,263,-8,-4,1024,0.89,0,0 +2011,2,16,10,285,-8,-3,1024,1.78,0,0 +2011,2,16,11,282,-8,-1,1023,2.67,0,0 +2011,2,16,12,281,-8,0,1022,1.79,0,0 +2011,2,16,13,292,-8,1,1021,1.79,0,0 +2011,2,16,14,289,-8,2,1020,3.13,0,0 +2011,2,16,15,272,-8,3,1020,4.92,0,0 +2011,2,16,16,234,-6,2,1020,0.89,0,0 +2011,2,16,17,214,-6,1,1020,1.79,0,0 +2011,2,16,18,192,-6,0,1020,3.58,0,0 +2011,2,16,19,211,-6,-1,1021,4.47,0,0 +2011,2,16,20,240,-5,-1,1022,5.36,0,0 +2011,2,16,21,249,-5,-2,1023,6.25,0,0 +2011,2,16,22,278,-5,-2,1023,7.14,0,0 +2011,2,16,23,279,-5,-2,1023,0.45,0,0 +2011,2,17,0,294,-6,-4,1023,0.89,0,0 +2011,2,17,1,327,-6,-5,1022,1.79,0,0 +2011,2,17,2,328,-6,-5,1022,2.68,0,0 +2011,2,17,3,316,-7,-5,1022,4.47,0,0 +2011,2,17,4,315,-8,-7,1022,7.6,0,0 +2011,2,17,5,273,-7,-5,1023,11.62,0,0 +2011,2,17,6,254,-6,-4,1024,15.64,0,0 +2011,2,17,7,226,-7,-4,1025,17.43,0,0 +2011,2,17,8,237,-6,-3,1026,20.56,0,0 +2011,2,17,9,224,-5,-2,1027,22.35,0,0 +2011,2,17,10,234,-6,1,1027,24.14,0,0 +2011,2,17,11,195,-7,3,1027,0.89,0,0 +2011,2,17,12,104,-8,5,1026,3.13,0,0 +2011,2,17,13,75,-8,6,1025,4.92,0,0 +2011,2,17,14,53,-8,7,1025,1.79,0,0 +2011,2,17,15,50,-10,7,1024,1.79,0,0 +2011,2,17,16,49,-9,8,1024,0.89,0,0 +2011,2,17,17,42,-10,6,1024,3.13,0,0 +2011,2,17,18,53,-8,4,1025,4.02,0,0 +2011,2,17,19,80,-8,4,1025,8.04,0,0 +2011,2,17,20,188,-8,3,1026,11.17,0,0 +2011,2,17,21,288,-8,3,1026,15.19,0,0 +2011,2,17,22,404,-7,-1,1027,0.89,0,0 +2011,2,17,23,422,-7,-2,1027,1.79,0,0 +2011,2,18,0,411,-7,-3,1028,2.68,0,0 +2011,2,18,1,345,-7,-4,1028,0.89,0,0 +2011,2,18,2,353,-7,-5,1028,0.89,0,0 +2011,2,18,3,399,-10,-8,1027,2.68,0,0 +2011,2,18,4,384,-9,-7,1027,0.89,0,0 +2011,2,18,5,282,-9,-7,1027,2.68,0,0 +2011,2,18,6,88,-12,-7,1028,5.81,0,0 +2011,2,18,7,41,-12,-5,1028,7.6,0,0 +2011,2,18,8,51,-10,-4,1029,9.39,0,0 +2011,2,18,9,46,-12,0,1029,13.41,0,0 +2011,2,18,10,48,-14,2,1029,16.54,0,0 +2011,2,18,11,51,-14,4,1029,19.67,0,0 +2011,2,18,12,50,-15,5,1028,1.79,0,0 +2011,2,18,13,50,-14,6,1026,1.79,0,0 +2011,2,18,14,101,-13,8,1025,3.13,0,0 +2011,2,18,15,127,-10,8,1025,6.26,0,0 +2011,2,18,16,140,-10,7,1024,9.39,0,0 +2011,2,18,17,206,-8,5,1024,12.52,0,0 +2011,2,18,18,226,-7,5,1024,15.65,0,0 +2011,2,18,19,203,-7,5,1025,18.78,0,0 +2011,2,18,20,240,-7,3,1025,20.57,0,0 +2011,2,18,21,258,-7,-1,1025,22.36,0,0 +2011,2,18,22,262,-6,0,1025,25.49,0,0 +2011,2,18,23,260,-6,-2,1025,0.89,0,0 +2011,2,19,0,264,-7,-3,1025,0.89,0,0 +2011,2,19,1,274,-7,-4,1026,0.89,0,0 +2011,2,19,2,253,-9,-6,1026,1.79,0,0 +2011,2,19,3,283,-8,-5,1025,4.92,0,0 +2011,2,19,4,259,-9,-3,1025,6.71,0,0 +2011,2,19,5,140,-10,-5,1025,8.5,0,0 +2011,2,19,6,120,-9,-3,1025,12.52,0,0 +2011,2,19,7,154,-10,-5,1025,15.65,0,0 +2011,2,19,8,166,-10,-2,1026,19.67,0,0 +2011,2,19,9,144,-10,0,1026,23.69,0,0 +2011,2,19,10,138,-11,3,1025,27.71,0,0 +2011,2,19,11,131,-11,5,1025,30.84,0,0 +2011,2,19,12,113,-11,7,1024,32.63,0,0 +2011,2,19,13,92,-12,9,1023,0.89,0,0 +2011,2,19,14,127,-10,11,1023,1.79,0,0 +2011,2,19,15,127,-10,12,1022,3.58,0,0 +2011,2,19,16,131,-10,11,1022,3.13,0,0 +2011,2,19,17,153,-9,10,1022,3.13,0,0 +2011,2,19,18,174,-9,8,1022,4.92,0,0 +2011,2,19,19,221,-8,8,1023,8.05,0,0 +2011,2,19,20,264,-7,4,1023,11.18,0,0 +2011,2,19,21,273,-6,6,1024,14.31,0,0 +2011,2,19,22,273,-6,0,1024,15.2,0,0 +2011,2,19,23,268,-7,-2,1025,0.89,0,0 +2011,2,20,0,288,-6,-3,1025,0.89,0,0 +2011,2,20,1,297,-6,-3,1025,0.45,0,0 +2011,2,20,2,300,-7,-4,1026,0.89,0,0 +2011,2,20,3,355,-7,-4,1026,0.89,0,0 +2011,2,20,4,342,-7,-5,1026,2.68,0,0 +2011,2,20,5,202,-7,-4,1026,3.57,0,0 +2011,2,20,6,130,-8,-6,1026,5.36,0,0 +2011,2,20,7,108,-8,-6,1027,0.45,0,0 +2011,2,20,8,113,-8,-5,1028,0.89,0,0 +2011,2,20,9,126,-7,0,1028,0.89,0,0 +2011,2,20,10,156,-7,2,1029,1.78,0,0 +2011,2,20,11,282,-7,4,1029,2.67,0,0 +2011,2,20,12,379,-6,6,1028,1.79,0,0 +2011,2,20,13,437,-5,8,1027,1.79,0,0 +2011,2,20,14,477,-4,8,1026,1.79,0,0 +2011,2,20,15,510,-5,9,1026,3.58,0,0 +2011,2,20,16,526,-4,9,1026,5.37,0,0 +2011,2,20,17,492,-5,7,1025,6.26,0,0 +2011,2,20,18,472,-4,6,1026,8.05,0,0 +2011,2,20,19,474,-4,4,1026,0.89,0,0 +2011,2,20,20,482,-4,1,1026,1.78,0,0 +2011,2,20,21,482,-5,0,1026,2.67,0,0 +2011,2,20,22,477,-4,-1,1027,3.56,0,0 +2011,2,20,23,480,-4,-1,1026,4.01,0,0 +2011,2,21,0,527,-4,-2,1026,4.9,0,0 +2011,2,21,1,486,-4,-2,1026,0.89,0,0 +2011,2,21,2,493,-5,-3,1026,0.45,0,0 +2011,2,21,3,515,-6,-4,1026,0.89,0,0 +2011,2,21,4,595,-5,-2,1025,0.89,0,0 +2011,2,21,5,563,-5,-2,1025,0.89,0,0 +2011,2,21,6,563,-5,-3,1025,1.78,0,0 +2011,2,21,7,528,-6,-4,1026,1.79,0,0 +2011,2,21,8,515,-6,-5,1027,0.89,0,0 +2011,2,21,9,495,-2,0,1027,0.89,0,0 +2011,2,21,10,486,-2,1,1027,1.79,0,0 +2011,2,21,11,508,-2,3,1026,0.89,0,0 +2011,2,21,12,464,-2,4,1025,1.78,0,0 +2011,2,21,13,430,-2,5,1024,1.79,0,0 +2011,2,21,14,452,-3,7,1023,3.58,0,0 +2011,2,21,15,489,-3,7,1022,0.89,0,0 +2011,2,21,16,477,-4,7,1022,1.79,0,0 +2011,2,21,17,425,-5,6,1022,0.89,0,0 +2011,2,21,18,407,-6,2,1022,1.78,0,0 +2011,2,21,19,498,-4,2,1023,1.79,0,0 +2011,2,21,20,492,-4,1,1023,0.89,0,0 +2011,2,21,21,492,-4,0,1024,1.78,0,0 +2011,2,21,22,476,-4,-2,1024,2.67,0,0 +2011,2,21,23,478,-4,-2,1024,3.12,0,0 +2011,2,22,0,499,-6,-4,1024,0.89,0,0 +2011,2,22,1,489,-5,-3,1024,0.89,0,0 +2011,2,22,2,494,-6,-4,1023,0.89,0,0 +2011,2,22,3,481,-5,-3,1023,1.78,0,0 +2011,2,22,4,445,-5,-4,1022,2.67,0,0 +2011,2,22,5,431,-7,-5,1022,4.46,0,0 +2011,2,22,6,423,-6,-4,1022,6.25,0,0 +2011,2,22,7,457,-6,-4,1022,8.04,0,0 +2011,2,22,8,434,-5,-3,1023,9.83,0,0 +2011,2,22,9,391,-4,-1,1023,1.79,0,0 +2011,2,22,10,347,-4,2,1023,0.89,0,0 +2011,2,22,11,292,-6,3,1022,1.79,0,0 +2011,2,22,12,343,-6,5,1021,0.89,0,0 +2011,2,22,13,322,-5,6,1019,2.68,0,0 +2011,2,22,14,360,-5,7,1019,3.57,0,0 +2011,2,22,15,376,-5,8,1017,3.13,0,0 +2011,2,22,16,369,-5,8,1017,0.89,0,0 +2011,2,22,17,354,-5,7,1017,1.79,0,0 +2011,2,22,18,335,-5,5,1017,1.79,0,0 +2011,2,22,19,394,-5,2,1018,2.68,0,0 +2011,2,22,20,397,-5,1,1018,0.45,0,0 +2011,2,22,21,395,-5,0,1018,1.34,0,0 +2011,2,22,22,401,-6,-1,1018,2.23,0,0 +2011,2,22,23,406,-5,0,1018,3.12,0,0 +2011,2,23,0,399,-4,0,1018,4.01,0,0 +2011,2,23,1,373,-5,-3,1018,1.79,0,0 +2011,2,23,2,396,-5,-3,1017,2.68,0,0 +2011,2,23,3,461,-5,-3,1017,0.45,0,0 +2011,2,23,4,455,-5,-3,1017,0.89,0,0 +2011,2,23,5,415,-5,-2,1017,1.79,0,0 +2011,2,23,6,406,-5,-3,1017,2.68,0,0 +2011,2,23,7,421,-5,-3,1017,4.47,0,0 +2011,2,23,8,420,-4,-1,1017,7.6,0,0 +2011,2,23,9,393,-4,0,1017,9.39,0,0 +2011,2,23,10,389,-4,2,1017,0.89,0,0 +2011,2,23,11,401,-4,4,1016,1.79,0,0 +2011,2,23,12,403,-4,5,1015,3.13,0,0 +2011,2,23,13,406,-3,7,1014,6.26,0,0 +2011,2,23,14,469,-3,8,1013,9.39,0,0 +2011,2,23,15,409,-3,9,1013,11.18,0,0 +2011,2,23,16,397,-3,9,1012,0.89,0,0 +2011,2,23,17,387,-2,8,1012,1.78,0,0 +2011,2,23,18,396,-3,6,1013,1.79,0,0 +2011,2,23,19,436,-4,3,1013,4.92,0,0 +2011,2,23,20,470,-4,3,1014,8.05,0,0 +2011,2,23,21,451,-4,3,1015,11.18,0,0 +2011,2,23,22,436,-3,3,1015,14.31,0,0 +2011,2,23,23,411,-3,3,1015,18.33,0,0 +2011,2,24,0,418,-3,2,1016,22.35,0,0 +2011,2,24,1,424,-3,2,1017,23.24,0,0 +2011,2,24,2,413,-4,0,1017,26.37,0,0 +2011,2,24,3,404,-4,1,1017,28.16,0,0 +2011,2,24,4,397,-5,-3,1018,32.18,0,0 +2011,2,24,5,285,-5,-3,1018,36.2,0,0 +2011,2,24,6,215,-5,-2,1019,39.33,0,0 +2011,2,24,7,136,-7,-2,1020,43.35,0,0 +2011,2,24,8,119,-8,3,1022,48.27,0,0 +2011,2,24,9,38,-10,6,1023,53.19,0,0 +2011,2,24,10,16,-13,8,1023,4.92,0,0 +2011,2,24,11,13,-14,9,1023,4.92,0,0 +2011,2,24,12,17,-15,9,1023,4.02,0,0 +2011,2,24,13,23,-16,10,1022,8.04,0,0 +2011,2,24,14,29,-17,11,1022,4.02,0,0 +2011,2,24,15,34,-18,11,1022,8.04,0,0 +2011,2,24,16,28,-19,11,1022,12.96,0,0 +2011,2,24,17,24,-19,11,1022,17.88,0,0 +2011,2,24,18,21,-17,7,1023,0.89,0,0 +2011,2,24,19,41,-16,6,1023,3.13,0,0 +2011,2,24,20,52,-3,3,1025,8.05,0,0 +2011,2,24,21,142,-5,1,1026,12.97,0,0 +2011,2,24,22,99,-6,0,1028,16.99,0,0 +2011,2,24,23,55,-6,-1,1029,20.12,0,0 +2011,2,25,0,55,-6,-1,1030,21.91,0,0 +2011,2,25,1,53,-6,-3,1030,22.8,0,0 +2011,2,25,2,67,-6,-4,1030,0.89,0,0 +2011,2,25,3,72,-6,-4,1030,0.89,0,0 +2011,2,25,4,77,-6,-4,1030,0.45,0,0 +2011,2,25,5,75,-7,-5,1031,0.9,0,0 +2011,2,25,6,80,-5,-4,1031,1.35,0,0 +2011,2,25,7,106,-7,-5,1032,1.79,0,0 +2011,2,25,8,95,-4,-3,1033,0.89,0,0 +2011,2,25,9,100,-7,0,1034,0.89,0,0 +2011,2,25,10,98,-7,2,1034,1.79,0,0 +2011,2,25,11,79,-9,4,1034,0.89,0,0 +2011,2,25,12,74,-8,5,1033,1.79,0,0 +2011,2,25,13,71,-9,6,1032,3.58,0,0 +2011,2,25,14,75,-10,6,1031,6.71,0,0 +2011,2,25,15,73,-10,7,1031,9.84,0,0 +2011,2,25,16,53,-10,6,1030,11.63,0,0 +2011,2,25,17,47,-11,5,1030,14.76,0,0 +2011,2,25,18,44,-12,5,1030,17.89,0,0 +2011,2,25,19,61,-10,4,1030,21.91,0,0 +2011,2,25,20,78,-10,4,1030,25.93,0,0 +2011,2,25,21,55,-11,3,1031,29.95,0,0 +2011,2,25,22,62,-11,2,1030,33.08,0,0 +2011,2,25,23,52,-13,1,1031,36.21,0,0 +2011,2,26,0,42,-13,1,1031,41.13,0,0 +2011,2,26,1,42,-11,0,1031,44.26,0,0 +2011,2,26,2,39,-9,0,1030,46.05,0,0 +2011,2,26,3,35,-7,0,1029,47.84,0,0 +2011,2,26,4,36,-6,-1,1029,1.79,1,0 +2011,2,26,5,30,-3,-3,1029,0.89,2,0 +2011,2,26,6,44,-3,-3,1028,1.79,3,0 +2011,2,26,7,52,-3,-3,1028,3.58,4,0 +2011,2,26,8,58,-3,-3,1028,5.37,5,0 +2011,2,26,9,71,-3,-3,1027,7.16,6,0 +2011,2,26,10,79,-3,-2,1027,8.95,7,0 +2011,2,26,11,77,-3,-2,1026,12.08,8,0 +2011,2,26,12,71,-3,-2,1026,0.89,0,0 +2011,2,26,13,80,-3,-1,1025,1.78,0,0 +2011,2,26,14,85,-3,-1,1024,2.67,0,0 +2011,2,26,15,96,-3,0,1023,3.56,0,0 +2011,2,26,16,111,-3,0,1023,4.45,0,0 +2011,2,26,17,108,-4,-1,1023,1.79,0,0 +2011,2,26,18,122,-4,-1,1024,0.89,0,0 +2011,2,26,19,109,-4,-3,1025,1.78,0,0 +2011,2,26,20,115,-3,-2,1025,0.45,0,0 +2011,2,26,21,103,-3,-1,1026,1.79,0,0 +2011,2,26,22,109,-3,0,1026,4.92,0,0 +2011,2,26,23,116,-3,0,1026,6.71,0,0 +2011,2,27,0,125,-3,-1,1026,0.89,0,0 +2011,2,27,1,127,-3,-2,1027,0.89,0,0 +2011,2,27,2,127,-3,-1,1027,1.79,0,0 +2011,2,27,3,123,-3,-1,1026,4.92,0,0 +2011,2,27,4,115,-3,-1,1026,8.05,0,0 +2011,2,27,5,105,-3,0,1026,9.84,0,0 +2011,2,27,6,90,-3,-1,1027,1.79,1,0 +2011,2,27,7,66,-4,0,1028,3.13,2,0 +2011,2,27,8,59,-2,-1,1029,7.15,3,0 +2011,2,27,9,39,-3,-1,1029,12.07,4,0 +2011,2,27,10,35,-4,-1,1030,19.22,5,0 +2011,2,27,11,35,-3,-2,1030,25.03,6,0 +2011,2,27,12,26,-3,-1,1029,32.18,7,0 +2011,2,27,13,31,-4,-1,1028,39.33,0,0 +2011,2,27,14,31,-4,1,1027,43.35,0,0 +2011,2,27,15,34,-5,1,1027,49.16,0,0 +2011,2,27,16,35,-7,2,1026,54.97,0,0 +2011,2,27,17,26,-7,1,1026,58.99,0,0 +2011,2,27,18,28,-8,1,1026,63.01,0,0 +2011,2,27,19,29,-8,0,1026,66.14,0,0 +2011,2,27,20,29,-7,0,1027,69.27,0,0 +2011,2,27,21,29,-8,0,1027,73.29,0,0 +2011,2,27,22,32,-8,-3,1028,3.13,0,0 +2011,2,27,23,29,-8,-3,1028,1.79,0,0 +2011,2,28,0,25,-9,-2,1027,1.79,0,0 +2011,2,28,1,31,-9,-3,1028,4.92,0,0 +2011,2,28,2,29,-10,-1,1027,4.02,0,0 +2011,2,28,3,26,-9,-1,1027,3.13,0,0 +2011,2,28,4,25,-10,-2,1027,6.26,0,0 +2011,2,28,5,15,-10,-2,1026,8.05,0,0 +2011,2,28,6,20,-11,-3,1026,9.84,0,0 +2011,2,28,7,32,-10,-3,1027,12.97,0,0 +2011,2,28,8,37,-9,-2,1029,16.99,0,0 +2011,2,28,9,37,-10,0,1029,20.12,0,0 +2011,2,28,10,18,-11,2,1028,23.25,0,0 +2011,2,28,11,20,-12,3,1028,29.06,0,0 +2011,2,28,12,13,-11,4,1028,33.98,0,0 +2011,2,28,13,12,-12,5,1026,39.79,0,0 +2011,2,28,14,12,-13,6,1026,46.94,0,0 +2011,2,28,15,12,-13,6,1025,51.86,0,0 +2011,2,28,16,15,-12,6,1024,55.88,0,0 +2011,2,28,17,20,-13,5,1025,59.9,0,0 +2011,2,28,18,16,-12,5,1025,64.82,0,0 +2011,2,28,19,18,-12,4,1026,69.74,0,0 +2011,2,28,20,15,-12,3,1026,74.66,0,0 +2011,2,28,21,8,-12,2,1027,78.68,0,0 +2011,2,28,22,7,-12,2,1028,83.6,0,0 +2011,2,28,23,8,-12,1,1028,88.52,0,0 +2011,3,1,0,12,-11,-1,1028,93.44,0,0 +2011,3,1,1,9,-12,-2,1028,3.13,0,0 +2011,3,1,2,9,-12,-4,1028,3.13,0,0 +2011,3,1,3,11,-12,-4,1028,6.26,0,0 +2011,3,1,4,17,-12,-3,1028,8.05,0,0 +2011,3,1,5,9,-13,-4,1028,9.84,0,0 +2011,3,1,6,11,-15,-4,1028,14.76,0,0 +2011,3,1,7,12,-15,-4,1028,19.68,0,0 +2011,3,1,8,17,-15,-2,1029,24.6,0,0 +2011,3,1,9,20,-15,0,1028,28.62,0,0 +2011,3,1,10,18,-17,2,1028,35.77,0,0 +2011,3,1,11,28,-16,2,1027,40.69,0,0 +2011,3,1,12,16,-17,4,1026,46.5,0,0 +2011,3,1,13,16,-17,4,1025,49.63,0,0 +2011,3,1,14,11,-17,6,1023,56.78,0,0 +2011,3,1,15,19,-17,7,1022,64.83,0,0 +2011,3,1,16,17,-17,7,1022,73.77,0,0 +2011,3,1,17,15,-16,6,1021,81.82,0,0 +2011,3,1,18,14,-16,5,1021,87.63,0,0 +2011,3,1,19,28,-15,5,1022,92.55,0,0 +2011,3,1,20,80,-15,3,1023,96.57,0,0 +2011,3,1,21,87,-14,0,1024,100.59,0,0 +2011,3,1,22,51,-14,0,1025,104.61,0,0 +2011,3,1,23,37,-15,0,1026,108.63,0,0 +2011,3,2,0,9,-18,1,1026,115.78,0,0 +2011,3,2,1,11,-16,-1,1026,121.59,0,0 +2011,3,2,2,14,-16,-2,1026,124.72,0,0 +2011,3,2,3,11,-16,-4,1026,3.13,0,0 +2011,3,2,4,12,-16,-3,1026,0.89,0,0 +2011,3,2,5,15,-15,-5,1026,1.78,0,0 +2011,3,2,6,29,-15,-4,1026,2.67,0,0 +2011,3,2,7,44,-14,-7,1027,3.12,0,0 +2011,3,2,8,33,-12,-3,1028,1.79,0,0 +2011,3,2,9,22,-15,0,1028,3.13,0,0 +2011,3,2,10,27,-16,2,1028,0.89,0,0 +2011,3,2,11,26,-16,4,1027,4.02,0,0 +2011,3,2,12,18,-16,5,1026,5.81,0,0 +2011,3,2,13,27,-17,6,1024,10.73,0,0 +2011,3,2,14,10,-18,7,1023,16.54,0,0 +2011,3,2,15,16,-18,8,1023,25.48,0,0 +2011,3,2,16,32,-17,8,1022,34.42,0,0 +2011,3,2,17,49,-17,7,1022,41.57,0,0 +2011,3,2,18,40,-13,6,1023,46.49,0,0 +2011,3,2,19,69,-16,4,1024,50.51,0,0 +2011,3,2,20,42,-15,3,1025,55.43,0,0 +2011,3,2,21,20,-16,3,1026,60.35,0,0 +2011,3,2,22,20,-16,3,1026,66.16,0,0 +2011,3,2,23,16,-15,2,1027,71.97,0,0 +2011,3,3,0,9,-16,1,1028,81.8,0,0 +2011,3,3,1,8,-16,0,1028,89.85,0,0 +2011,3,3,2,9,-16,0,1028,97,0,0 +2011,3,3,3,10,-14,0,1028,102.81,0,0 +2011,3,3,4,8,-14,-1,1028,107.73,0,0 +2011,3,3,5,8,-14,-2,1028,110.86,0,0 +2011,3,3,6,9,-14,-2,1028,114.88,0,0 +2011,3,3,7,10,-14,-1,1028,119.8,0,0 +2011,3,3,8,13,-15,1,1029,124.72,0,0 +2011,3,3,9,13,-16,3,1029,130.53,0,0 +2011,3,3,10,26,-16,4,1029,137.68,0,0 +2011,3,3,11,15,-17,6,1028,144.83,0,0 +2011,3,3,12,19,-17,6,1028,151.98,0,0 +2011,3,3,13,18,-17,7,1027,159.13,0,0 +2011,3,3,14,16,-17,8,1026,166.28,0,0 +2011,3,3,15,15,-17,9,1025,172.09,0,0 +2011,3,3,16,12,-16,9,1025,179.24,0,0 +2011,3,3,17,17,-15,9,1024,185.05,0,0 +2011,3,3,18,9,-15,8,1024,189.97,0,0 +2011,3,3,19,9,-14,7,1025,195.78,0,0 +2011,3,3,20,11,-14,6,1025,200.7,0,0 +2011,3,3,21,37,-14,6,1026,205.62,0,0 +2011,3,3,22,36,-13,5,1026,3.13,0,0 +2011,3,3,23,40,-13,4,1025,3.13,0,0 +2011,3,4,0,43,-13,3,1025,6.26,0,0 +2011,3,4,1,48,-10,-2,1026,0.89,0,0 +2011,3,4,2,46,-10,-2,1026,1.34,0,0 +2011,3,4,3,45,-10,-3,1025,2.23,0,0 +2011,3,4,4,46,-10,-5,1025,1.79,0,0 +2011,3,4,5,52,-10,-3,1025,2.68,0,0 +2011,3,4,6,69,-10,-5,1025,0.89,0,0 +2011,3,4,7,105,-10,-3,1025,1.79,0,0 +2011,3,4,8,83,-9,-1,1025,0.89,0,0 +2011,3,4,9,51,-10,4,1025,0.89,0,0 +2011,3,4,10,43,-12,5,1025,1.79,0,0 +2011,3,4,11,57,-12,8,1025,0.89,0,0 +2011,3,4,12,57,-13,9,1024,1.79,0,0 +2011,3,4,13,55,-12,10,1022,0.89,0,0 +2011,3,4,14,51,-13,12,1021,2.68,0,0 +2011,3,4,15,46,-14,12,1020,3.13,0,0 +2011,3,4,16,42,-14,11,1019,8.05,0,0 +2011,3,4,17,55,-13,11,1019,12.07,0,0 +2011,3,4,18,58,-12,8,1019,16.09,0,0 +2011,3,4,19,68,-10,7,1020,17.88,0,0 +2011,3,4,20,91,-10,6,1020,19.67,0,0 +2011,3,4,21,89,-9,6,1020,21.46,0,0 +2011,3,4,22,59,-9,5,1020,25.48,0,0 +2011,3,4,23,66,-9,5,1019,29.5,0,0 +2011,3,5,0,69,-8,6,1018,34.42,0,0 +2011,3,5,1,56,-8,6,1018,39.34,0,0 +2011,3,5,2,61,-8,5,1018,43.36,0,0 +2011,3,5,3,62,-7,5,1017,47.38,0,0 +2011,3,5,4,74,-7,5,1018,52.3,0,0 +2011,3,5,5,82,1,5,1017,55.43,0,0 +2011,3,5,6,77,-5,4,1016,57.22,0,0 +2011,3,5,7,82,-5,2,1016,58.11,0,0 +2011,3,5,8,79,-4,4,1017,61.24,0,0 +2011,3,5,9,91,-5,6,1017,64.37,0,0 +2011,3,5,10,96,-5,7,1017,0.89,0,0 +2011,3,5,11,109,-5,8,1017,1.78,0,0 +2011,3,5,12,106,-5,9,1016,2.67,0,0 +2011,3,5,13,109,-4,11,1015,4.46,0,0 +2011,3,5,14,136,-4,12,1014,3.13,0,0 +2011,3,5,15,133,-4,13,1014,7.15,0,0 +2011,3,5,16,92,-5,13,1014,11.17,0,0 +2011,3,5,17,90,-5,13,1014,12.96,0,0 +2011,3,5,18,92,-5,10,1015,0.89,0,0 +2011,3,5,19,108,-4,7,1016,1.79,0,0 +2011,3,5,20,122,-5,6,1017,0.89,0,0 +2011,3,5,21,130,-4,4,1018,1.79,0,0 +2011,3,5,22,142,-4,3,1019,0.89,0,0 +2011,3,5,23,129,-9,6,1020,3.13,0,0 +2011,3,6,0,55,-11,6,1021,6.26,0,0 +2011,3,6,1,20,-12,7,1021,9.39,0,0 +2011,3,6,2,15,-12,8,1021,14.31,0,0 +2011,3,6,3,14,-12,7,1022,21.46,0,0 +2011,3,6,4,12,-11,7,1022,31.29,0,0 +2011,3,6,5,12,-10,6,1022,40.23,0,0 +2011,3,6,6,24,-10,5,1024,51.41,0,0 +2011,3,6,7,6,-10,5,1025,58.56,0,0 +2011,3,6,8,19,-10,5,1026,63.48,0,0 +2011,3,6,9,13,-10,5,1026,67.5,0,0 +2011,3,6,10,18,-11,7,1026,74.65,0,0 +2011,3,6,11,14,-13,8,1026,7.15,0,0 +2011,3,6,12,16,-16,10,1026,8.94,0,0 +2011,3,6,13,15,-16,10,1024,5.81,0,0 +2011,3,6,14,11,-16,11,1023,5.81,0,0 +2011,3,6,15,12,-16,11,1024,11.62,0,0 +2011,3,6,16,17,-17,11,1023,4.92,0,0 +2011,3,6,17,14,-17,10,1023,8.94,0,0 +2011,3,6,18,10,-17,9,1023,7.15,0,0 +2011,3,6,19,7,-16,7,1024,12.96,0,0 +2011,3,6,20,NA,-16,4,1025,17.88,0,0 +2011,3,6,21,NA,-18,5,1026,22.8,0,0 +2011,3,6,22,NA,-18,4,1026,25.93,0,0 +2011,3,6,23,NA,-16,1,1027,27.72,0,0 +2011,3,7,0,NA,-15,3,1027,30.85,0,0 +2011,3,7,1,NA,-13,2,1027,32.64,0,0 +2011,3,7,2,NA,-13,4,1026,37.56,0,0 +2011,3,7,3,9,-14,3,1026,44.71,0,0 +2011,3,7,4,11,-15,3,1026,50.52,0,0 +2011,3,7,5,20,-15,1,1026,0.89,0,0 +2011,3,7,6,14,-16,1,1026,4.02,0,0 +2011,3,7,7,16,-16,2,1026,8.94,0,0 +2011,3,7,8,21,-14,4,1027,16.09,0,0 +2011,3,7,9,17,-14,5,1027,24.14,0,0 +2011,3,7,10,15,-14,7,1026,32.19,0,0 +2011,3,7,11,15,-14,8,1025,39.34,0,0 +2011,3,7,12,19,-14,9,1024,47.39,0,0 +2011,3,7,13,15,-14,10,1022,55.44,0,0 +2011,3,7,14,12,-14,11,1021,62.59,0,0 +2011,3,7,15,22,-14,10,1020,72.42,0,0 +2011,3,7,16,16,-17,10,1020,81.36,0,0 +2011,3,7,17,22,-18,10,1020,90.3,0,0 +2011,3,7,18,9,-17,9,1020,99.24,0,0 +2011,3,7,19,16,-16,7,1020,104.16,0,0 +2011,3,7,20,23,-15,7,1020,108.18,0,0 +2011,3,7,21,46,-15,4,1020,109.97,0,0 +2011,3,7,22,35,-15,6,1021,114.89,0,0 +2011,3,7,23,33,-15,5,1021,122.04,0,0 +2011,3,8,0,NA,-16,5,1022,129.19,0,0 +2011,3,8,1,25,-17,4,1022,135,0,0 +2011,3,8,2,10,-16,2,1021,139.92,0,0 +2011,3,8,3,11,-18,4,1021,147.97,0,0 +2011,3,8,4,13,-16,3,1022,153.78,0,0 +2011,3,8,5,14,-15,2,1022,158.7,0,0 +2011,3,8,6,18,-15,2,1023,164.51,0,0 +2011,3,8,7,16,-15,2,1023,170.32,0,0 +2011,3,8,8,21,-16,4,1024,178.37,0,0 +2011,3,8,9,19,-15,5,1024,185.52,0,0 +2011,3,8,10,20,-15,5,1024,4.92,0,0 +2011,3,8,11,16,-16,6,1024,8.94,0,0 +2011,3,8,12,17,-17,8,1023,1.79,0,0 +2011,3,8,13,19,-18,8,1022,3.13,0,0 +2011,3,8,14,16,-17,9,1021,1.79,0,0 +2011,3,8,15,21,-16,8,1021,3.13,0,0 +2011,3,8,16,24,-16,9,1021,4.92,0,0 +2011,3,8,17,24,-16,9,1021,0.89,0,0 +2011,3,8,18,16,-15,7,1021,3.13,0,0 +2011,3,8,19,15,-17,6,1023,3.13,0,0 +2011,3,8,20,18,-19,7,1024,8.05,0,0 +2011,3,8,21,14,-18,5,1025,11.18,0,0 +2011,3,8,22,10,-17,6,1026,16.1,0,0 +2011,3,8,23,7,-17,5,1027,24.15,0,0 +2011,3,9,0,NA,-16,4,1028,29.07,0,0 +2011,3,9,1,6,-16,4,1029,36.22,0,0 +2011,3,9,2,7,-16,3,1030,42.03,0,0 +2011,3,9,3,7,-16,2,1029,47.84,0,0 +2011,3,9,4,9,-16,2,1030,53.65,0,0 +2011,3,9,5,10,-15,2,1030,58.57,0,0 +2011,3,9,6,19,-14,-2,1030,61.7,0,0 +2011,3,9,7,31,-12,1,1031,0.89,0,0 +2011,3,9,8,35,-14,2,1031,2.68,0,0 +2011,3,9,9,34,-15,5,1031,1.79,0,0 +2011,3,9,10,21,-16,6,1031,3.58,0,0 +2011,3,9,11,19,-16,8,1030,6.71,0,0 +2011,3,9,12,15,-17,9,1029,12.52,0,0 +2011,3,9,13,17,-17,11,1028,18.33,0,0 +2011,3,9,14,16,-17,12,1026,25.48,0,0 +2011,3,9,15,24,-17,12,1025,34.42,0,0 +2011,3,9,16,17,-19,12,1024,43.36,0,0 +2011,3,9,17,12,-18,12,1024,52.3,0,0 +2011,3,9,18,12,-17,12,1023,57.22,0,0 +2011,3,9,19,18,-17,11,1024,59.01,0,0 +2011,3,9,20,34,-15,9,1024,4.02,0,0 +2011,3,9,21,35,-15,9,1024,7.15,0,0 +2011,3,9,22,36,-15,8,1024,8.94,0,0 +2011,3,9,23,46,-14,7,1023,12.07,0,0 +2011,3,10,0,66,-13,5,1023,1.79,0,0 +2011,3,10,1,51,-11,0,1024,1.79,0,0 +2011,3,10,2,58,-13,0,1024,4.92,0,0 +2011,3,10,3,79,-11,-1,1024,0.89,0,0 +2011,3,10,4,80,-11,-1,1024,3.13,0,0 +2011,3,10,5,74,-12,-1,1024,4.92,0,0 +2011,3,10,6,72,-12,-1,1024,6.71,0,0 +2011,3,10,7,83,-11,-2,1025,0.89,0,0 +2011,3,10,8,112,-12,3,1025,1.78,0,0 +2011,3,10,9,61,-12,7,1025,1.79,0,0 +2011,3,10,10,54,-14,10,1025,3.58,0,0 +2011,3,10,11,46,-15,12,1024,0.89,0,0 +2011,3,10,12,36,-14,13,1023,4.02,0,0 +2011,3,10,13,27,-14,14,1022,3.13,0,0 +2011,3,10,14,19,-15,16,1020,11.18,0,0 +2011,3,10,15,26,-14,16,1019,18.33,0,0 +2011,3,10,16,22,-14,16,1019,24.14,0,0 +2011,3,10,17,14,-15,16,1018,29.06,0,0 +2011,3,10,18,9,-15,13,1019,32.19,0,0 +2011,3,10,19,26,-16,10,1020,37.11,0,0 +2011,3,10,20,25,-17,10,1021,0.89,0,0 +2011,3,10,21,38,-17,8,1021,2.68,0,0 +2011,3,10,22,43,-17,9,1022,1.79,0,0 +2011,3,10,23,48,-15,6,1022,1.79,0,0 +2011,3,11,0,48,-16,6,1022,1.79,0,0 +2011,3,11,1,47,-14,2,1022,0.89,0,0 +2011,3,11,2,40,-14,1,1022,1.79,0,0 +2011,3,11,3,40,-14,1,1021,4.92,0,0 +2011,3,11,4,44,-12,0,1021,0.89,0,0 +2011,3,11,5,26,-9,-1,1021,0.89,0,0 +2011,3,11,6,26,-10,-1,1021,1.79,0,0 +2011,3,11,7,30,-13,0,1021,3.58,0,0 +2011,3,11,8,85,-12,3,1021,4.47,0,0 +2011,3,11,9,92,-12,6,1021,0.89,0,0 +2011,3,11,10,99,-13,9,1020,3.13,0,0 +2011,3,11,11,109,-13,10,1019,6.26,0,0 +2011,3,11,12,92,-13,11,1018,9.39,0,0 +2011,3,11,13,80,-14,12,1016,12.52,0,0 +2011,3,11,14,85,-13,14,1015,15.65,0,0 +2011,3,11,15,95,-11,14,1013,19.67,0,0 +2011,3,11,16,116,-9,13,1013,22.8,0,0 +2011,3,11,17,110,-7,13,1012,25.93,0,0 +2011,3,11,18,108,-7,10,1012,0.89,0,0 +2011,3,11,19,132,-7,8,1012,1.78,0,0 +2011,3,11,20,156,-7,6,1012,2.67,0,0 +2011,3,11,21,166,-6,5,1012,1.79,0,0 +2011,3,11,22,196,-5,5,1012,3.58,0,0 +2011,3,11,23,209,-6,5,1012,0.89,0,0 +2011,3,12,0,263,-6,5,1012,1.78,0,0 +2011,3,12,1,248,-5,3,1012,0.89,0,0 +2011,3,12,2,253,-3,2,1011,1.78,0,0 +2011,3,12,3,258,-4,3,1010,0.45,0,0 +2011,3,12,4,263,-4,2,1009,0.9,0,0 +2011,3,12,5,266,-3,1,1009,1.35,0,0 +2011,3,12,6,274,-5,1,1009,1.79,0,0 +2011,3,12,7,272,-5,2,1010,3.58,0,0 +2011,3,12,8,271,-4,3,1010,5.37,0,0 +2011,3,12,9,250,-5,6,1010,1.79,0,0 +2011,3,12,10,254,-5,9,1010,0.89,0,0 +2011,3,12,11,243,-5,11,1009,1.78,0,0 +2011,3,12,12,241,-4,13,1008,1.79,0,0 +2011,3,12,13,241,-3,14,1008,3.58,0,0 +2011,3,12,14,236,-3,16,1007,6.71,0,0 +2011,3,12,15,225,-3,17,1007,9.84,0,0 +2011,3,12,16,225,-3,17,1007,12.97,0,0 +2011,3,12,17,228,-3,16,1007,14.76,0,0 +2011,3,12,18,272,-3,13,1007,15.65,0,0 +2011,3,12,19,272,-2,11,1008,16.54,0,0 +2011,3,12,20,367,-2,10,1008,0.89,0,0 +2011,3,12,21,416,-1,7,1009,0.89,0,0 +2011,3,12,22,415,-2,6,1009,0.89,0,0 +2011,3,12,23,360,-2,6,1009,0.89,0,0 +2011,3,13,0,379,-3,4,1009,0.89,0,0 +2011,3,13,1,377,-2,4,1009,0.89,0,0 +2011,3,13,2,400,-2,4,1009,1.79,0,0 +2011,3,13,3,368,-2,4,1008,3.58,0,0 +2011,3,13,4,351,-3,3,1008,0.89,0,0 +2011,3,13,5,339,-2,3,1008,1.78,0,0 +2011,3,13,6,320,-3,4,1009,1.79,0,0 +2011,3,13,7,339,-2,4,1010,3.58,0,0 +2011,3,13,8,332,-2,6,1010,7.6,0,0 +2011,3,13,9,318,-2,9,1011,10.73,0,0 +2011,3,13,10,267,-3,13,1012,14.75,0,0 +2011,3,13,11,164,-4,15,1012,19.67,0,0 +2011,3,13,12,42,-5,18,1012,25.48,0,0 +2011,3,13,13,27,-6,19,1012,32.63,0,0 +2011,3,13,14,31,-6,19,1011,39.78,0,0 +2011,3,13,15,25,-6,18,1012,48.72,0,0 +2011,3,13,16,24,-6,17,1013,57.66,0,0 +2011,3,13,17,20,-6,15,1015,8.05,0,0 +2011,3,13,18,16,-9,12,1018,17.88,0,0 +2011,3,13,19,16,-11,10,1020,27.71,0,0 +2011,3,13,20,17,-12,9,1023,8.94,0,0 +2011,3,13,21,13,-12,8,1024,9.83,0,0 +2011,3,13,22,11,-15,7,1025,11.18,0,0 +2011,3,13,23,9,-16,7,1026,21.01,0,0 +2011,3,14,0,8,-14,6,1027,7.15,0,0 +2011,3,14,1,6,-16,6,1028,15.2,0,0 +2011,3,14,2,7,-15,5,1028,7.15,0,0 +2011,3,14,3,9,-15,5,1029,12.96,0,0 +2011,3,14,4,8,-15,5,1029,17.88,0,0 +2011,3,14,5,8,-15,4,1030,21.9,0,0 +2011,3,14,6,8,-15,4,1030,26.82,0,0 +2011,3,14,7,8,-15,4,1031,31.74,0,0 +2011,3,14,8,12,-15,5,1031,38.89,0,0 +2011,3,14,9,21,-16,6,1031,46.04,0,0 +2011,3,14,10,18,-19,6,1031,54.09,0,0 +2011,3,14,11,13,-19,7,1030,62.14,0,0 +2011,3,14,12,25,-19,8,1030,71.97,0,0 +2011,3,14,13,22,-20,8,1029,79.12,0,0 +2011,3,14,14,21,-18,9,1028,84.93,0,0 +2011,3,14,15,23,-19,9,1027,92.08,0,0 +2011,3,14,16,25,-20,8,1027,100.13,0,0 +2011,3,14,17,17,-20,8,1027,108.18,0,0 +2011,3,14,18,12,-19,8,1027,115.33,0,0 +2011,3,14,19,16,-18,8,1027,120.25,0,0 +2011,3,14,20,18,-17,8,1028,128.3,0,0 +2011,3,14,21,17,-18,7,1029,140.37,0,0 +2011,3,14,22,17,-18,6,1030,155.57,0,0 +2011,3,14,23,14,-19,5,1031,165.4,0,0 +2011,3,15,0,16,-18,5,1031,173.45,0,0 +2011,3,15,1,17,-17,4,1030,181.5,0,0 +2011,3,15,2,9,-17,3,1030,189.55,0,0 +2011,3,15,3,9,-16,3,1029,196.7,0,0 +2011,3,15,4,9,-16,2,1029,203.85,0,0 +2011,3,15,5,11,-18,2,1029,215.03,0,0 +2011,3,15,6,14,-19,2,1030,224.86,0,0 +2011,3,15,7,13,-19,1,1030,233.8,0,0 +2011,3,15,8,12,-19,2,1031,242.74,0,0 +2011,3,15,9,16,-19,3,1031,252.57,0,0 +2011,3,15,10,11,-20,4,1030,261.51,0,0 +2011,3,15,11,18,-20,6,1028,267.32,0,0 +2011,3,15,12,21,-19,6,1027,271.34,0,0 +2011,3,15,13,22,-20,7,1026,274.47,0,0 +2011,3,15,14,23,-19,9,1024,280.28,0,0 +2011,3,15,15,16,-19,9,1023,286.09,0,0 +2011,3,15,16,21,-19,10,1022,295.92,0,0 +2011,3,15,17,19,-19,9,1022,305.75,0,0 +2011,3,15,18,19,-18,9,1022,312.9,0,0 +2011,3,15,19,18,-19,7,1023,321.84,0,0 +2011,3,15,20,16,-16,6,1025,331.67,0,0 +2011,3,15,21,14,-16,5,1026,339.72,0,0 +2011,3,15,22,23,-15,5,1026,345.53,0,0 +2011,3,15,23,16,-17,5,1026,351.34,0,0 +2011,3,16,0,18,-16,4,1026,356.26,0,0 +2011,3,16,1,17,-15,1,1026,0.89,0,0 +2011,3,16,2,7,-13,1,1026,3.13,0,0 +2011,3,16,3,7,-13,1,1025,1.79,0,0 +2011,3,16,4,19,-11,0,1025,4.02,0,0 +2011,3,16,5,15,-11,-1,1026,7.15,0,0 +2011,3,16,6,23,-13,-1,1026,0.89,0,0 +2011,3,16,7,24,-11,-2,1026,1.79,0,0 +2011,3,16,8,19,-15,2,1027,3.13,0,0 +2011,3,16,9,33,-14,3,1027,4.92,0,0 +2011,3,16,10,32,-14,6,1026,1.79,0,0 +2011,3,16,11,30,-14,6,1026,1.79,0,0 +2011,3,16,12,26,-14,7,1025,1.79,0,0 +2011,3,16,13,45,-14,9,1024,4.02,0,0 +2011,3,16,14,40,-13,10,1022,8.04,0,0 +2011,3,16,15,37,-13,11,1021,12.96,0,0 +2011,3,16,16,27,-13,11,1021,16.98,0,0 +2011,3,16,17,27,-13,10,1020,21,0,0 +2011,3,16,18,44,-13,9,1020,25.02,0,0 +2011,3,16,19,79,-12,7,1021,26.81,0,0 +2011,3,16,20,117,-11,5,1021,28.6,0,0 +2011,3,16,21,70,-12,4,1021,30.39,0,0 +2011,3,16,22,76,-11,3,1022,0.89,0,0 +2011,3,16,23,90,-13,2,1022,1.78,0,0 +2011,3,17,0,105,-12,1,1021,0.89,0,0 +2011,3,17,1,125,-9,2,1021,1.79,0,0 +2011,3,17,2,138,-7,2,1021,4.92,0,0 +2011,3,17,3,128,-8,1,1021,0.89,0,0 +2011,3,17,4,110,-9,-1,1021,2.68,0,0 +2011,3,17,5,109,-10,-2,1021,4.47,0,0 +2011,3,17,6,105,-9,-3,1021,6.26,0,0 +2011,3,17,7,104,-9,-2,1021,7.15,0,0 +2011,3,17,8,107,-9,2,1021,0.89,0,0 +2011,3,17,9,101,-6,5,1021,1.79,0,0 +2011,3,17,10,115,-6,8,1020,1.79,0,0 +2011,3,17,11,116,-6,10,1020,3.13,0,0 +2011,3,17,12,132,-5,11,1019,1.79,0,0 +2011,3,17,13,135,-5,13,1018,4.02,0,0 +2011,3,17,14,123,-6,15,1016,7.15,0,0 +2011,3,17,15,112,-8,16,1015,11.17,0,0 +2011,3,17,16,96,-10,17,1014,15.19,0,0 +2011,3,17,17,96,-12,16,1014,19.21,0,0 +2011,3,17,18,90,-10,14,1013,0.89,0,0 +2011,3,17,19,99,-9,9,1013,1.79,0,0 +2011,3,17,20,126,-9,9,1014,2.68,0,0 +2011,3,17,21,NA,-8,7,1014,0.89,0,0 +2011,3,17,22,NA,-8,8,1014,3.13,0,0 +2011,3,17,23,NA,-7,7,1013,0.89,0,0 +2011,3,18,0,NA,-6,5,1013,3.13,0,0 +2011,3,18,1,NA,-6,7,1013,6.26,0,0 +2011,3,18,2,NA,-7,7,1012,0.89,0,0 +2011,3,18,3,NA,-6,4,1012,1.79,0,0 +2011,3,18,4,NA,-5,5,1012,0.45,0,0 +2011,3,18,5,NA,-6,3,1011,1.79,0,0 +2011,3,18,6,NA,-5,3,1012,1.79,0,0 +2011,3,18,7,NA,-4,6,1011,3.58,0,0 +2011,3,18,8,NA,-4,7,1012,5.37,0,0 +2011,3,18,9,NA,-4,8,1013,0.89,0,0 +2011,3,18,10,NA,-4,9,1013,1.78,0,0 +2011,3,18,11,NA,-5,11,1013,2.67,0,0 +2011,3,18,12,NA,-10,13,1013,13.86,0,0 +2011,3,18,13,NA,-17,15,1012,25.04,0,0 +2011,3,18,14,NA,-17,15,1012,38,0,0 +2011,3,18,15,NA,-15,15,1012,50.07,0,0 +2011,3,18,16,NA,-14,14,1013,63.03,0,0 +2011,3,18,17,NA,-14,13,1013,70.18,0,0 +2011,3,18,18,NA,-14,13,1014,78.23,0,0 +2011,3,18,19,NA,-12,11,1016,85.38,0,0 +2011,3,18,20,NA,-12,10,1017,91.19,0,0 +2011,3,18,21,NA,-11,8,1019,94.32,0,0 +2011,3,18,22,NA,-11,5,1018,3.13,0,0 +2011,3,18,23,NA,-12,6,1018,1.79,0,0 +2011,3,19,0,NA,-10,3,1019,0.45,0,0 +2011,3,19,1,NA,-9,2,1018,1.34,0,0 +2011,3,19,2,NA,-11,2,1018,1.79,0,0 +2011,3,19,3,NA,-10,2,1018,0.89,0,0 +2011,3,19,4,NA,-10,2,1018,1.79,0,0 +2011,3,19,5,NA,-8,0,1019,0.89,0,0 +2011,3,19,6,NA,-10,2,1019,1.79,0,0 +2011,3,19,7,NA,-8,0,1020,0.89,0,0 +2011,3,19,8,NA,-6,3,1021,1.34,0,0 +2011,3,19,9,NA,-7,5,1021,1.79,0,0 +2011,3,19,10,NA,-8,7,1020,3.58,0,0 +2011,3,19,11,NA,-8,10,1020,5.37,0,0 +2011,3,19,12,NA,-10,13,1019,7.16,0,0 +2011,3,19,13,NA,-11,13,1018,11.18,0,0 +2011,3,19,14,NA,-12,15,1016,16.1,0,0 +2011,3,19,15,NA,-12,14,1015,21.02,0,0 +2011,3,19,16,NA,-12,14,1014,25.94,0,0 +2011,3,19,17,NA,-9,13,1014,29.07,0,0 +2011,3,19,18,NA,-8,12,1014,32.2,0,0 +2011,3,19,19,NA,-7,11,1015,36.22,0,0 +2011,3,19,20,NA,-7,10,1015,39.35,0,0 +2011,3,19,21,NA,-7,9,1015,43.37,0,0 +2011,3,19,22,NA,-6,8,1015,45.16,0,0 +2011,3,19,23,NA,-5,7,1015,46.95,0,0 +2011,3,20,0,NA,-4,7,1015,50.08,0,0 +2011,3,20,1,NA,-5,7,1014,54.1,0,0 +2011,3,20,2,NA,-5,7,1015,0.89,0,0 +2011,3,20,3,NA,-4,6,1014,1.78,0,0 +2011,3,20,4,NA,-5,5,1013,1.79,0,0 +2011,3,20,5,NA,-4,4,1013,0.89,0,0 +2011,3,20,6,NA,-5,5,1015,0.89,0,0 +2011,3,20,7,NA,-4,4,1016,1.79,0,0 +2011,3,20,8,NA,-3,6,1017,0.89,0,0 +2011,3,20,9,NA,-6,8,1016,3.13,0,0 +2011,3,20,10,NA,-4,9,1018,8.05,0,0 +2011,3,20,11,NA,-3,10,1017,12.97,0,0 +2011,3,20,12,NA,-3,10,1017,18.78,0,0 +2011,3,20,13,NA,-4,10,1016,23.7,0,0 +2011,3,20,14,NA,-4,10,1016,28.62,0,0 +2011,3,20,15,NA,-4,10,1015,34.43,0,0 +2011,3,20,16,NA,-5,9,1015,40.24,0,0 +2011,3,20,17,NA,-5,8,1015,46.05,0,0 +2011,3,20,18,NA,-5,6,1015,50.07,0,0 +2011,3,20,19,NA,-6,5,1016,53.2,0,0 +2011,3,20,20,NA,-5,4,1017,57.22,0,0 +2011,3,20,21,NA,-5,4,1018,60.35,0,0 +2011,3,20,22,NA,-6,3,1019,62.14,0,0 +2011,3,20,23,NA,-5,3,1021,0.45,0,0 +2011,3,21,0,NA,-6,0,1021,0.89,0,0 +2011,3,21,1,NA,-6,1,1022,0.89,0,0 +2011,3,21,2,NA,-4,-1,1022,0.89,0,0 +2011,3,21,3,NA,-5,-1,1023,3.13,0,0 +2011,3,21,4,NA,-5,-1,1022,6.26,0,0 +2011,3,21,5,NA,-10,4,1024,13.41,0,0 +2011,3,21,6,NA,-13,3,1025,18.33,0,0 +2011,3,21,7,NA,-16,4,1027,26.38,0,0 +2011,3,21,8,NA,-16,4,1028,33.53,0,0 +2011,3,21,9,NA,-21,3,1029,40.68,0,0 +2011,3,21,10,NA,-22,4,1029,49.62,0,0 +2011,3,21,11,NA,-23,5,1028,55.43,0,0 +2011,3,21,12,NA,-22,7,1028,59.45,0,0 +2011,3,21,13,NA,-23,8,1027,69.28,0,0 +2011,3,21,14,NA,-23,9,1026,78.22,0,0 +2011,3,21,15,NA,-22,9,1025,85.37,0,0 +2011,3,21,16,13,-24,9,1025,93.42,0,0 +2011,3,21,17,13,-23,9,1025,100.57,0,0 +2011,3,21,18,28,-22,8,1025,107.72,0,0 +2011,3,21,19,33,-21,7,1027,116.66,0,0 +2011,3,21,20,14,-21,6,1028,124.71,0,0 +2011,3,21,21,20,-23,4,1030,132.76,0,0 +2011,3,21,22,23,-24,4,1031,142.59,0,0 +2011,3,21,23,24,-24,3,1031,150.64,0,0 +2011,3,22,0,17,-24,3,1031,159.58,0,0 +2011,3,22,1,12,-24,2,1031,166.73,0,0 +2011,3,22,2,12,-24,2,1031,173.88,0,0 +2011,3,22,3,9,-24,2,1031,182.82,0,0 +2011,3,22,4,8,-23,1,1031,186.84,0,0 +2011,3,22,5,9,-22,0,1031,189.97,0,0 +2011,3,22,6,15,-19,-1,1032,3.13,0,0 +2011,3,22,7,13,-22,1,1031,3.13,0,0 +2011,3,22,8,13,-22,3,1031,7.15,0,0 +2011,3,22,9,12,-21,6,1031,11.17,0,0 +2011,3,22,10,13,-20,7,1030,16.09,0,0 +2011,3,22,11,14,-20,8,1029,21.9,0,0 +2011,3,22,12,12,-20,8,1028,29.95,0,0 +2011,3,22,13,18,-19,10,1026,35.76,0,0 +2011,3,22,14,11,-19,10,1025,43.81,0,0 +2011,3,22,15,22,-19,10,1025,51.86,0,0 +2011,3,22,16,19,-19,11,1024,59.91,0,0 +2011,3,22,17,14,-19,11,1024,68.85,0,0 +2011,3,22,18,12,-19,10,1024,76,0,0 +2011,3,22,19,17,-20,6,1024,79.13,0,0 +2011,3,22,20,24,-19,7,1025,80.92,0,0 +2011,3,22,21,41,-19,5,1026,84.05,0,0 +2011,3,22,22,60,-20,7,1026,88.97,0,0 +2011,3,22,23,21,-19,3,1026,90.76,0,0 +2011,3,23,0,30,-17,1,1027,94.78,0,0 +2011,3,23,1,23,-18,1,1027,98.8,0,0 +2011,3,23,2,20,-17,1,1027,102.82,0,0 +2011,3,23,3,24,-16,-1,1027,105.95,0,0 +2011,3,23,4,24,-19,3,1026,110.87,0,0 +2011,3,23,5,20,-18,2,1026,114.89,0,0 +2011,3,23,6,20,-18,2,1026,118.02,0,0 +2011,3,23,7,20,-17,4,1027,122.04,0,0 +2011,3,23,8,30,-18,6,1027,127.85,0,0 +2011,3,23,9,25,-18,7,1027,133.66,0,0 +2011,3,23,10,12,-18,8,1027,137.68,0,0 +2011,3,23,11,20,-18,9,1026,142.6,0,0 +2011,3,23,12,16,-18,10,1025,144.39,0,0 +2011,3,23,13,19,-17,10,1023,147.52,0,0 +2011,3,23,14,16,-16,11,1021,151.54,0,0 +2011,3,23,15,18,-17,10,1021,5.81,0,0 +2011,3,23,16,22,-17,10,1020,9.83,0,0 +2011,3,23,17,16,-17,10,1020,11.62,0,0 +2011,3,23,18,22,-14,9,1021,1.79,0,0 +2011,3,23,19,28,-13,10,1021,4.92,0,0 +2011,3,23,20,32,-12,10,1022,8.94,0,0 +2011,3,23,21,23,-12,9,1023,14.75,0,0 +2011,3,23,22,24,-12,9,1023,20.56,0,0 +2011,3,23,23,15,-18,8,1024,27.71,0,0 +2011,3,24,0,NA,-20,7,1024,35.76,0,0 +2011,3,24,1,13,-20,6,1025,42.91,0,0 +2011,3,24,2,13,-21,6,1025,52.74,0,0 +2011,3,24,3,10,-21,4,1025,60.79,0,0 +2011,3,24,4,8,-21,4,1025,68.84,0,0 +2011,3,24,5,9,-20,3,1026,78.67,0,0 +2011,3,24,6,8,-20,3,1027,86.72,0,0 +2011,3,24,7,10,-20,3,1027,95.66,0,0 +2011,3,24,8,12,-19,4,1028,104.6,0,0 +2011,3,24,9,18,-18,6,1029,113.54,0,0 +2011,3,24,10,15,-19,6,1029,119.35,0,0 +2011,3,24,11,13,-18,8,1028,126.5,0,0 +2011,3,24,12,12,-18,9,1027,134.55,0,0 +2011,3,24,13,11,-18,11,1026,142.6,0,0 +2011,3,24,14,15,-17,12,1025,150.65,0,0 +2011,3,24,15,12,-18,12,1024,161.83,0,0 +2011,3,24,16,15,-18,12,1024,169.88,0,0 +2011,3,24,17,17,-19,12,1024,177.93,0,0 +2011,3,24,18,6,-20,11,1025,186.87,0,0 +2011,3,24,19,7,-19,9,1026,194.02,0,0 +2011,3,24,20,6,-19,8,1028,199.83,0,0 +2011,3,24,21,4,-19,8,1028,207.88,0,0 +2011,3,24,22,3,-19,7,1029,213.69,0,0 +2011,3,24,23,4,-19,7,1029,218.61,0,0 +2011,3,25,0,8,-19,7,1029,222.63,0,0 +2011,3,25,1,13,-19,5,1029,226.65,0,0 +2011,3,25,2,13,-18,4,1029,229.78,0,0 +2011,3,25,3,15,-18,4,1028,231.57,0,0 +2011,3,25,4,23,-16,2,1028,0.45,0,0 +2011,3,25,5,23,-17,4,1029,4.02,0,0 +2011,3,25,6,27,-16,3,1029,8.04,0,0 +2011,3,25,7,18,-15,4,1029,11.17,0,0 +2011,3,25,8,25,-15,7,1030,16.98,0,0 +2011,3,25,9,31,-15,9,1030,24.13,0,0 +2011,3,25,10,23,-15,10,1030,29.05,0,0 +2011,3,25,11,17,-15,11,1030,33.07,0,0 +2011,3,25,12,22,-14,12,1028,0.89,0,0 +2011,3,25,13,16,-14,13,1027,4.02,0,0 +2011,3,25,14,15,-14,15,1026,9.83,0,0 +2011,3,25,15,13,-13,14,1025,4.02,0,0 +2011,3,25,16,25,-13,13,1025,8.94,0,0 +2011,3,25,17,18,-13,13,1025,3.13,0,0 +2011,3,25,18,17,-13,13,1025,6.26,0,0 +2011,3,25,19,22,-12,10,1025,10.28,0,0 +2011,3,25,20,39,-12,9,1026,13.41,0,0 +2011,3,25,21,21,-11,7,1026,15.2,0,0 +2011,3,25,22,24,-11,7,1026,16.99,0,0 +2011,3,25,23,37,-11,7,1026,18.78,0,0 +2011,3,26,0,36,-10,4,1026,0.89,0,0 +2011,3,26,1,46,-10,5,1026,1.79,0,0 +2011,3,26,2,57,-10,3,1026,0.89,0,0 +2011,3,26,3,66,-9,1,1025,1.79,0,0 +2011,3,26,4,78,-10,3,1025,1.79,0,0 +2011,3,26,5,76,-9,2,1026,0.89,0,0 +2011,3,26,6,85,-7,2,1026,1.79,0,0 +2011,3,26,7,126,-8,2,1027,0.89,0,0 +2011,3,26,8,145,-7,5,1027,1.79,0,0 +2011,3,26,9,178,-8,7,1027,3.58,0,0 +2011,3,26,10,157,-10,9,1027,1.79,0,0 +2011,3,26,11,130,-12,11,1026,3.13,0,0 +2011,3,26,12,60,-12,11,1026,4.92,0,0 +2011,3,26,13,38,-11,13,1025,3.13,0,0 +2011,3,26,14,39,-13,15,1024,3.13,0,0 +2011,3,26,15,36,-12,15,1024,1.79,0,0 +2011,3,26,16,29,-13,15,1024,5.81,0,0 +2011,3,26,17,23,-13,15,1023,4.02,0,0 +2011,3,26,18,11,-13,11,1024,5.81,0,0 +2011,3,26,19,12,-13,12,1024,8.94,0,0 +2011,3,26,20,16,-12,11,1025,12.07,0,0 +2011,3,26,21,12,-14,8,1026,16.99,0,0 +2011,3,26,22,13,-17,10,1027,21.91,0,0 +2011,3,26,23,11,-17,10,1027,27.72,0,0 +2011,3,27,0,20,-16,9,1027,32.64,0,0 +2011,3,27,1,6,-16,9,1027,38.45,0,0 +2011,3,27,2,20,-15,8,1026,44.26,0,0 +2011,3,27,3,13,-15,8,1026,49.18,0,0 +2011,3,27,4,16,-16,8,1026,54.99,0,0 +2011,3,27,5,12,-16,7,1025,60.8,0,0 +2011,3,27,6,17,-16,7,1026,67.95,0,0 +2011,3,27,7,27,-16,8,1026,72.87,0,0 +2011,3,27,8,21,-16,10,1026,80.92,0,0 +2011,3,27,9,20,-15,12,1026,88.97,0,0 +2011,3,27,10,26,-15,11,1026,97.02,0,0 +2011,3,27,11,27,-15,13,1026,104.17,0,0 +2011,3,27,12,29,-13,16,1024,111.32,0,0 +2011,3,27,13,25,-14,16,1023,117.13,0,0 +2011,3,27,14,16,-13,18,1022,3.13,0,0 +2011,3,27,15,23,-14,18,1020,6.26,0,0 +2011,3,27,16,21,-13,18,1020,3.13,0,0 +2011,3,27,17,13,-13,16,1020,4.92,0,0 +2011,3,27,18,13,-13,14,1020,5.81,0,0 +2011,3,27,19,17,-14,13,1021,7.6,0,0 +2011,3,27,20,50,-13,14,1022,10.73,0,0 +2011,3,27,21,22,-12,9,1023,3.13,0,0 +2011,3,27,22,22,-5,9,1024,4.92,0,0 +2011,3,27,23,69,-8,8,1025,9.84,0,0 +2011,3,28,0,43,-9,8,1025,14.76,0,0 +2011,3,28,1,40,-9,7,1026,18.78,0,0 +2011,3,28,2,38,-9,6,1026,21.91,0,0 +2011,3,28,3,37,-10,5,1027,1.79,0,0 +2011,3,28,4,39,-10,4,1027,2.68,0,0 +2011,3,28,5,45,-9,2,1027,1.79,0,0 +2011,3,28,6,50,-9,2,1028,4.92,0,0 +2011,3,28,7,44,-8,3,1029,6.71,0,0 +2011,3,28,8,58,-9,8,1029,8.5,0,0 +2011,3,28,9,48,-9,10,1029,10.29,0,0 +2011,3,28,10,48,-9,11,1030,12.08,0,0 +2011,3,28,11,39,-9,11,1029,13.87,0,0 +2011,3,28,12,44,-9,14,1028,3.13,0,0 +2011,3,28,13,36,-10,14,1027,1.79,0,0 +2011,3,28,14,36,-11,14,1026,3.58,0,0 +2011,3,28,15,38,-10,15,1026,5.37,0,0 +2011,3,28,16,43,-10,15,1025,7.16,0,0 +2011,3,28,17,35,-11,14,1025,1.79,0,0 +2011,3,28,18,42,-10,13,1025,4.92,0,0 +2011,3,28,19,51,-9,12,1026,8.05,0,0 +2011,3,28,20,60,-3,9,1027,12.97,0,0 +2011,3,28,21,65,-3,8,1027,16.99,0,0 +2011,3,28,22,61,-3,7,1028,21.01,0,0 +2011,3,28,23,56,-3,6,1028,24.14,0,0 +2011,3,29,0,69,-3,5,1028,25.93,0,0 +2011,3,29,1,76,-3,4,1028,27.72,0,0 +2011,3,29,2,58,-3,3,1028,0.89,0,0 +2011,3,29,3,64,-4,2,1027,0.89,0,0 +2011,3,29,4,79,-3,2,1027,0.45,0,0 +2011,3,29,5,100,-3,2,1027,1.34,0,0 +2011,3,29,6,128,-3,1,1027,0.89,0,0 +2011,3,29,7,110,-2,1,1028,0.89,0,0 +2011,3,29,8,116,-3,6,1028,3.13,0,0 +2011,3,29,9,97,-3,8,1028,7.15,0,0 +2011,3,29,10,99,-3,10,1027,8.94,0,0 +2011,3,29,11,107,-3,12,1026,1.79,0,0 +2011,3,29,12,101,-4,15,1025,1.79,0,0 +2011,3,29,13,94,-4,19,1024,1.79,0,0 +2011,3,29,14,70,-10,21,1022,3.58,0,0 +2011,3,29,15,49,-10,21,1022,4.02,0,0 +2011,3,29,16,59,-10,21,1021,7.15,0,0 +2011,3,29,17,60,-11,20,1021,11.17,0,0 +2011,3,29,18,51,-10,19,1021,14.3,0,0 +2011,3,29,19,55,-10,18,1021,17.43,0,0 +2011,3,29,20,73,-11,17,1021,21.45,0,0 +2011,3,29,21,70,-10,15,1022,25.47,0,0 +2011,3,29,22,67,-9,15,1022,29.49,0,0 +2011,3,29,23,64,-8,11,1022,0.89,0,0 +2011,3,30,0,75,-6,8,1022,0.89,0,0 +2011,3,30,1,87,-6,7,1022,1.34,0,0 +2011,3,30,2,96,-6,4,1021,1.79,0,0 +2011,3,30,3,103,-5,4,1021,0.89,0,0 +2011,3,30,4,115,-4,4,1021,0.89,0,0 +2011,3,30,5,112,-6,2,1021,2.68,0,0 +2011,3,30,6,95,-5,3,1022,3.57,0,0 +2011,3,30,7,87,-5,7,1023,0.89,0,0 +2011,3,30,8,95,-5,9,1023,1.78,0,0 +2011,3,30,9,123,-5,11,1023,1.79,0,0 +2011,3,30,10,136,-5,14,1023,0.89,0,0 +2011,3,30,11,143,-4,16,1022,2.68,0,0 +2011,3,30,12,152,-4,19,1021,4.47,0,0 +2011,3,30,13,132,-6,21,1020,4.02,0,0 +2011,3,30,14,104,-8,22,1019,9.83,0,0 +2011,3,30,15,84,-8,23,1018,14.75,0,0 +2011,3,30,16,66,-8,22,1017,20.56,0,0 +2011,3,30,17,50,-10,21,1016,27.71,0,0 +2011,3,30,18,57,-10,19,1016,30.84,0,0 +2011,3,30,19,77,-9,15,1016,33.97,0,0 +2011,3,30,20,NA,-4,15,1017,0.89,0,0 +2011,3,30,21,NA,-5,14,1017,1.79,0,0 +2011,3,30,22,NA,-3,13,1017,4.92,0,0 +2011,3,30,23,NA,-3,10,1017,6.71,0,0 +2011,3,31,0,148,-3,12,1017,8.5,0,0 +2011,3,31,1,NA,-2,10,1017,11.63,0,0 +2011,3,31,2,NA,-1,8,1016,12.52,0,0 +2011,3,31,3,NA,-2,7,1016,0.89,0,0 +2011,3,31,4,NA,-1,7,1016,0.89,0,0 +2011,3,31,5,NA,-1,6,1016,0.89,0,0 +2011,3,31,6,NA,-1,6,1016,0.89,0,0 +2011,3,31,7,NA,0,5,1017,0.89,0,0 +2011,3,31,8,NA,1,10,1017,0.89,0,0 +2011,3,31,9,204,1,12,1017,1.78,0,0 +2011,3,31,10,212,2,14,1017,1.79,0,0 +2011,3,31,11,NA,2,18,1016,1.79,0,0 +2011,3,31,12,NA,2,18,1015,1.79,0,0 +2011,3,31,13,NA,2,20,1015,4.92,0,0 +2011,3,31,14,NA,2,21,1014,8.05,0,0 +2011,3,31,15,188,3,22,1013,1.79,0,0 +2011,3,31,16,179,3,21,1012,3.13,0,0 +2011,3,31,17,166,3,20,1012,6.26,0,0 +2011,3,31,18,168,3,19,1013,0.89,0,0 +2011,3,31,19,NA,4,17,1013,1.78,0,0 +2011,3,31,20,NA,4,14,1014,0.89,0,0 +2011,3,31,21,295,4,12,1015,0.89,0,0 +2011,3,31,22,328,3,14,1016,3.13,0,0 +2011,3,31,23,287,3,14,1016,6.26,0,0 +2011,4,1,0,NA,2,15,1017,10.28,0,0 +2011,4,1,1,222,1,15,1017,15.2,0,0 +2011,4,1,2,147,-3,15,1018,21.01,0,0 +2011,4,1,3,51,-4,14,1019,26.82,0,0 +2011,4,1,4,23,-5,13,1021,36.65,0,0 +2011,4,1,5,19,-5,12,1022,44.7,0,0 +2011,4,1,6,18,-6,11,1023,52.75,0,0 +2011,4,1,7,15,-8,11,1025,60.8,0,0 +2011,4,1,8,18,-9,10,1026,69.74,0,0 +2011,4,1,9,25,-10,10,1027,77.79,0,0 +2011,4,1,10,17,-11,10,1028,85.84,0,0 +2011,4,1,11,18,-13,11,1028,91.65,0,0 +2011,4,1,12,20,-15,11,1028,97.46,0,0 +2011,4,1,13,17,-16,11,1028,102.38,0,0 +2011,4,1,14,15,-16,11,1027,107.3,0,0 +2011,4,1,15,16,-10,11,1027,7.15,0,0 +2011,4,1,16,23,-7,10,1028,12.07,0,0 +2011,4,1,17,34,-5,9,1028,17.88,0,0 +2011,4,1,18,19,-3,7,1029,3.13,0,1 +2011,4,1,19,25,1,6,1030,3.13,0,2 +2011,4,1,20,33,2,5,1030,7.15,0,3 +2011,4,1,21,36,3,5,1031,3.13,0,4 +2011,4,1,22,33,3,5,1032,4.02,0,5 +2011,4,1,23,23,2,4,1032,0.89,0,6 +2011,4,2,0,NA,1,4,1032,1.78,0,7 +2011,4,2,1,30,2,4,1032,4.02,0,0 +2011,4,2,2,48,3,4,1031,8.04,0,0 +2011,4,2,3,44,2,4,1031,11.17,0,0 +2011,4,2,4,56,2,4,1031,15.19,0,0 +2011,4,2,5,46,2,4,1031,18.32,0,0 +2011,4,2,6,32,1,3,1031,21.45,0,0 +2011,4,2,7,54,2,4,1032,24.58,0,0 +2011,4,2,8,89,3,6,1032,27.71,0,0 +2011,4,2,9,79,-1,8,1032,31.73,0,0 +2011,4,2,10,45,-6,10,1032,1.79,0,0 +2011,4,2,11,68,-6,11,1031,3.58,0,0 +2011,4,2,12,158,-8,11,1030,6.71,0,0 +2011,4,2,13,178,-12,13,1029,8.5,0,0 +2011,4,2,14,63,-13,13,1028,11.63,0,0 +2011,4,2,15,108,-12,13,1026,13.42,0,0 +2011,4,2,16,92,-12,14,1026,15.21,0,0 +2011,4,2,17,127,-10,14,1025,3.13,0,0 +2011,4,2,18,11,-10,12,1025,6.26,0,0 +2011,4,2,19,5,-7,11,1025,10.28,0,0 +2011,4,2,20,24,-2,10,1026,15.2,0,0 +2011,4,2,21,5,-2,9,1026,19.22,0,0 +2011,4,2,22,5,-2,9,1026,23.24,0,0 +2011,4,2,23,55,-1,8,1026,27.26,0,0 +2011,4,3,0,79,-1,7,1027,1.79,0,0 +2011,4,3,1,124,0,4,1027,0.89,0,0 +2011,4,3,2,143,-1,4,1027,0.89,0,0 +2011,4,3,3,186,-1,2,1027,3.13,0,0 +2011,4,3,4,167,-2,3,1027,4.92,0,0 +2011,4,3,5,184,-2,2,1027,6.71,0,0 +2011,4,3,6,172,-5,0,1027,9.84,0,0 +2011,4,3,7,149,-5,3,1028,13.86,0,0 +2011,4,3,8,156,-7,8,1028,16.99,0,0 +2011,4,3,9,144,-10,11,1028,21.01,0,0 +2011,4,3,10,135,-10,13,1028,25.03,0,0 +2011,4,3,11,94,-12,15,1027,29.05,0,0 +2011,4,3,12,105,-14,17,1026,32.18,0,0 +2011,4,3,13,50,-13,17,1024,1.79,0,0 +2011,4,3,14,167,-13,18,1024,4.02,0,0 +2011,4,3,15,138,-14,18,1022,7.15,0,0 +2011,4,3,16,170,-13,19,1022,1.79,0,0 +2011,4,3,17,89,-14,18,1021,4.02,0,0 +2011,4,3,18,108,-13,17,1021,8.04,0,0 +2011,4,3,19,NA,-12,16,1021,12.96,0,0 +2011,4,3,20,NA,-10,15,1022,17.88,0,0 +2011,4,3,21,NA,-9,14,1022,21.9,0,0 +2011,4,3,22,NA,-8,13,1023,25.92,0,0 +2011,4,3,23,86,-7,13,1023,29.05,0,0 +2011,4,4,0,65,-7,11,1023,32.18,0,0 +2011,4,4,1,88,-4,7,1023,0.89,0,0 +2011,4,4,2,80,-3,6,1023,1.34,0,0 +2011,4,4,3,125,-5,5,1023,0.89,0,0 +2011,4,4,4,123,-5,4,1023,1.78,0,0 +2011,4,4,5,151,-6,5,1023,3.57,0,0 +2011,4,4,6,174,-5,3,1024,4.46,0,0 +2011,4,4,7,241,-6,6,1025,5.35,0,0 +2011,4,4,8,132,-9,10,1025,7.14,0,0 +2011,4,4,9,159,-8,14,1025,1.79,0,0 +2011,4,4,10,138,-8,16,1026,1.79,0,0 +2011,4,4,11,177,-9,17,1025,3.58,0,0 +2011,4,4,12,149,-8,20,1024,3.13,0,0 +2011,4,4,13,192,-9,20,1023,7.15,0,0 +2011,4,4,14,171,-9,21,1022,11.17,0,0 +2011,4,4,15,211,-9,22,1021,15.19,0,0 +2011,4,4,16,124,-11,21,1021,21,0,0 +2011,4,4,17,170,-11,20,1020,26.81,0,0 +2011,4,4,18,52,-12,18,1021,31.73,0,0 +2011,4,4,19,22,-12,15,1021,35.75,0,0 +2011,4,4,20,17,-10,13,1022,39.77,0,0 +2011,4,4,21,54,-9,14,1022,43.79,0,0 +2011,4,4,22,59,-8,13,1023,47.81,0,0 +2011,4,4,23,150,-8,12,1023,50.94,0,0 +2011,4,5,0,146,-7,11,1024,54.07,0,0 +2011,4,5,1,154,-7,10,1024,57.2,0,0 +2011,4,5,2,166,-7,8,1023,58.99,0,0 +2011,4,5,3,164,-6,8,1023,60.78,0,0 +2011,4,5,4,191,-6,7,1024,62.57,0,0 +2011,4,5,5,152,-5,5,1023,64.36,0,0 +2011,4,5,6,160,-5,3,1024,0.89,0,0 +2011,4,5,7,254,-1,5,1024,0.89,0,0 +2011,4,5,8,231,-3,11,1025,2.68,0,0 +2011,4,5,9,254,-4,14,1025,4.47,0,0 +2011,4,5,10,277,-5,17,1025,10.28,0,0 +2011,4,5,11,207,-4,17,1025,18.33,0,0 +2011,4,5,12,218,-4,18,1024,25.48,0,0 +2011,4,5,13,264,-5,18,1023,30.4,0,0 +2011,4,5,14,227,-7,18,1022,38.45,0,0 +2011,4,5,15,188,-5,18,1020,45.6,0,0 +2011,4,5,16,253,-4,18,1019,52.75,0,0 +2011,4,5,17,227,-5,17,1019,57.67,0,0 +2011,4,5,18,166,-5,16,1019,62.59,0,0 +2011,4,5,19,143,-5,15,1018,65.72,0,0 +2011,4,5,20,184,-4,15,1019,67.51,0,0 +2011,4,5,21,246,-4,15,1019,71.53,0,0 +2011,4,5,22,279,-4,14,1019,75.55,0,0 +2011,4,5,23,233,-3,13,1019,77.34,0,0 +2011,4,6,0,220,-3,12,1019,80.47,0,0 +2011,4,6,1,190,0,12,1019,83.6,0,0 +2011,4,6,2,196,-3,13,1018,89.41,0,0 +2011,4,6,3,96,-3,14,1019,94.33,0,0 +2011,4,6,4,103,1,12,1018,100.14,0,1 +2011,4,6,5,94,1,11,1017,0.89,0,2 +2011,4,6,6,120,1,10,1017,1.78,0,0 +2011,4,6,7,77,2,10,1017,0.89,0,0 +2011,4,6,8,158,2,11,1017,1.78,0,0 +2011,4,6,9,169,2,13,1017,4.91,0,0 +2011,4,6,10,NA,2,14,1017,9.83,0,0 +2011,4,6,11,NA,2,14,1017,15.64,0,0 +2011,4,6,12,NA,3,15,1016,20.56,0,0 +2011,4,6,13,NA,4,16,1015,25.48,0,0 +2011,4,6,14,NA,3,17,1015,30.4,0,0 +2011,4,6,15,NA,3,18,1014,34.42,0,0 +2011,4,6,16,NA,3,17,1013,39.34,0,0 +2011,4,6,17,NA,3,17,1013,44.26,0,0 +2011,4,6,18,NA,4,15,1013,48.28,0,0 +2011,4,6,19,NA,4,13,1014,52.3,0,0 +2011,4,6,20,NA,4,14,1014,54.09,0,0 +2011,4,6,21,NA,4,12,1015,55.88,0,0 +2011,4,6,22,NA,4,11,1015,57.67,0,0 +2011,4,6,23,NA,4,11,1015,59.46,0,0 +2011,4,7,0,NA,4,11,1015,61.25,0,0 +2011,4,7,1,NA,4,10,1015,63.04,0,0 +2011,4,7,2,NA,4,9,1015,1.79,0,0 +2011,4,7,3,NA,5,9,1015,1.79,0,0 +2011,4,7,4,NA,5,9,1016,1.79,0,0 +2011,4,7,5,NA,5,8,1016,0.89,0,0 +2011,4,7,6,NA,6,9,1016,3.13,0,0 +2011,4,7,7,NA,3,12,1018,8.94,0,0 +2011,4,7,8,NA,-2,13,1019,14.75,0,0 +2011,4,7,9,NA,-9,14,1019,22.8,0,0 +2011,4,7,10,NA,-8,15,1020,29.95,0,0 +2011,4,7,11,NA,-11,17,1019,37.1,0,0 +2011,4,7,12,NA,-13,18,1018,45.15,0,0 +2011,4,7,13,NA,-13,18,1017,50.96,0,0 +2011,4,7,14,NA,-13,20,1017,60.79,0,0 +2011,4,7,15,NA,-14,20,1016,72.86,0,0 +2011,4,7,16,NA,-14,19,1016,84.04,0,0 +2011,4,7,17,NA,-14,18,1017,96.11,0,0 +2011,4,7,18,NA,-15,17,1018,105.05,0,0 +2011,4,7,19,NA,-14,16,1019,113.99,0,0 +2011,4,7,20,NA,-14,15,1021,121.14,0,0 +2011,4,7,21,NA,-14,12,1022,126.06,0,0 +2011,4,7,22,NA,-13,11,1022,129.19,0,0 +2011,4,7,23,NA,-13,10,1023,130.98,0,0 +2011,4,8,0,NA,-13,8,1023,132.77,0,0 +2011,4,8,1,NA,-10,6,1023,0.89,0,0 +2011,4,8,2,NA,-10,8,1022,0.89,0,0 +2011,4,8,3,NA,-9,5,1022,0.89,0,0 +2011,4,8,4,NA,-8,4,1022,0.89,0,0 +2011,4,8,5,NA,-5,3,1022,0.89,0,0 +2011,4,8,6,NA,-9,3,1022,0.89,0,0 +2011,4,8,7,NA,-8,7,1023,0.89,0,0 +2011,4,8,8,NA,-11,11,1023,1.79,0,0 +2011,4,8,9,NA,-10,14,1023,0.89,0,0 +2011,4,8,10,NA,-12,18,1021,7.15,0,0 +2011,4,8,11,NA,-12,18,1021,14.3,0,0 +2011,4,8,12,NA,-12,19,1019,21.45,0,0 +2011,4,8,13,NA,-12,20,1018,27.26,0,0 +2011,4,8,14,24,-11,20,1017,32.18,0,0 +2011,4,8,15,27,-11,20,1015,36.2,0,0 +2011,4,8,16,39,-10,20,1014,42.01,0,0 +2011,4,8,17,NA,-11,19,1014,49.16,0,0 +2011,4,8,18,NA,-10,18,1014,56.31,0,0 +2011,4,8,19,NA,-10,16,1014,61.23,0,0 +2011,4,8,20,NA,-9,14,1015,64.36,0,0 +2011,4,8,21,NA,-7,15,1015,67.49,0,0 +2011,4,8,22,NA,-6,16,1015,72.41,0,0 +2011,4,8,23,NA,-6,16,1015,77.33,0,0 +2011,4,9,0,69,-6,13,1014,79.12,0,0 +2011,4,9,1,NA,-4,9,1014,82.25,0,0 +2011,4,9,2,NA,-3,10,1013,0.89,0,0 +2011,4,9,3,NA,-3,9,1013,1.78,0,0 +2011,4,9,4,NA,-3,7,1013,2.67,0,0 +2011,4,9,5,NA,-4,6,1013,3.56,0,0 +2011,4,9,6,NA,-3,6,1013,4.45,0,0 +2011,4,9,7,NA,-2,7,1014,0.89,0,0 +2011,4,9,8,NA,-3,12,1014,1.79,0,0 +2011,4,9,9,NA,-3,15,1014,0.89,0,0 +2011,4,9,10,NA,-2,17,1014,1.78,0,0 +2011,4,9,11,105,-3,19,1013,1.79,0,0 +2011,4,9,12,103,-4,22,1012,4.92,0,0 +2011,4,9,13,105,-5,25,1011,8.05,0,0 +2011,4,9,14,107,-8,26,1010,12.07,0,0 +2011,4,9,15,105,-7,26,1009,15.2,0,0 +2011,4,9,16,102,-7,25,1008,18.33,0,0 +2011,4,9,17,102,-8,24,1008,22.35,0,0 +2011,4,9,18,100,-7,23,1008,26.37,0,0 +2011,4,9,19,109,-6,23,1009,1.79,0,0 +2011,4,9,20,107,-5,21,1011,4.92,0,0 +2011,4,9,21,77,-4,21,1012,3.13,0,0 +2011,4,9,22,80,-3,20,1013,8.94,0,0 +2011,4,9,23,82,-6,18,1014,16.99,0,0 +2011,4,10,0,NA,-5,15,1016,28.17,0,0 +2011,4,10,1,50,-3,13,1018,37.11,0,0 +2011,4,10,2,39,-2,12,1019,44.26,0,0 +2011,4,10,3,24,-4,11,1019,52.31,0,0 +2011,4,10,4,28,-12,11,1020,59.46,0,0 +2011,4,10,5,14,-15,11,1022,4.02,0,0 +2011,4,10,6,13,-16,11,1023,8.94,0,0 +2011,4,10,7,11,-17,11,1024,12.96,0,0 +2011,4,10,8,17,-17,12,1025,17.88,0,0 +2011,4,10,9,15,-18,13,1025,25.03,0,0 +2011,4,10,10,8,-17,14,1025,4.02,0,0 +2011,4,10,11,10,-17,15,1025,3.13,0,0 +2011,4,10,12,12,-17,16,1024,3.13,0,0 +2011,4,10,13,12,-18,17,1023,1.79,0,0 +2011,4,10,14,13,-16,19,1022,4.92,0,0 +2011,4,10,15,14,-18,19,1021,7.15,0,0 +2011,4,10,16,11,-19,19,1020,14.3,0,0 +2011,4,10,17,10,-19,19,1020,23.24,0,0 +2011,4,10,18,10,-20,18,1020,32.18,0,0 +2011,4,10,19,11,-20,16,1020,37.99,0,0 +2011,4,10,20,17,-19,13,1021,42.01,0,0 +2011,4,10,21,91,-19,11,1022,45.14,0,0 +2011,4,10,22,22,-19,11,1022,49.16,0,0 +2011,4,10,23,16,-19,10,1022,53.18,0,0 +2011,4,11,0,NA,-19,10,1023,56.31,0,0 +2011,4,11,1,12,-19,9,1023,59.44,0,0 +2011,4,11,2,8,-19,7,1023,63.46,0,0 +2011,4,11,3,9,-20,11,1023,67.48,0,0 +2011,4,11,4,14,-19,10,1023,70.61,0,0 +2011,4,11,5,16,-14,6,1023,1.79,0,0 +2011,4,11,6,14,-12,7,1024,0.45,0,0 +2011,4,11,7,29,-16,10,1024,1.34,0,0 +2011,4,11,8,24,-15,14,1024,3.13,0,0 +2011,4,11,9,19,-16,15,1024,7.15,0,0 +2011,4,11,10,16,-16,17,1023,11.17,0,0 +2011,4,11,11,17,-14,18,1023,1.79,0,0 +2011,4,11,12,13,-14,19,1021,2.68,0,0 +2011,4,11,13,12,-14,20,1020,1.79,0,0 +2011,4,11,14,10,-14,21,1019,1.79,0,0 +2011,4,11,15,11,-16,22,1018,4.92,0,0 +2011,4,11,16,12,-14,22,1017,4.92,0,0 +2011,4,11,17,13,-13,21,1016,10.73,0,0 +2011,4,11,18,18,-13,20,1016,17.88,0,0 +2011,4,11,19,26,-12,17,1017,22.8,0,0 +2011,4,11,20,27,-12,17,1017,27.72,0,0 +2011,4,11,21,45,-11,17,1017,33.53,0,0 +2011,4,11,22,49,-11,17,1017,39.34,0,0 +2011,4,11,23,50,-9,15,1017,43.36,0,0 +2011,4,12,0,51,-9,16,1017,3.13,0,0 +2011,4,12,1,64,-4,10,1016,4.02,0,0 +2011,4,12,2,67,-6,10,1016,3.13,0,0 +2011,4,12,3,77,-5,8,1016,0.45,0,0 +2011,4,12,4,85,-6,8,1016,0.89,0,0 +2011,4,12,5,91,-5,4,1016,4.02,0,0 +2011,4,12,6,88,-5,6,1016,0.89,0,0 +2011,4,12,7,91,-5,8,1016,1.79,0,0 +2011,4,12,8,103,-4,12,1016,0.89,0,0 +2011,4,12,9,101,-4,15,1016,1.78,0,0 +2011,4,12,10,88,-4,18,1016,3.57,0,0 +2011,4,12,11,91,-4,20,1015,5.36,0,0 +2011,4,12,12,99,-6,23,1014,3.13,0,0 +2011,4,12,13,94,-5,24,1012,7.15,0,0 +2011,4,12,14,101,-5,25,1011,11.17,0,0 +2011,4,12,15,91,-4,26,1010,16.09,0,0 +2011,4,12,16,73,-6,25,1009,23.24,0,0 +2011,4,12,17,94,-5,25,1009,29.05,0,0 +2011,4,12,18,88,-4,23,1009,33.97,0,0 +2011,4,12,19,104,-3,19,1010,37.1,0,0 +2011,4,12,20,145,-1,19,1010,38.89,0,0 +2011,4,12,21,171,0,17,1011,40.68,0,0 +2011,4,12,22,184,1,15,1011,42.47,0,0 +2011,4,12,23,202,1,14,1011,43.36,0,0 +2011,4,13,0,196,1,13,1011,0.89,0,0 +2011,4,13,1,186,2,13,1011,1.78,0,0 +2011,4,13,2,184,2,12,1011,2.67,0,0 +2011,4,13,3,188,3,10,1011,1.79,0,0 +2011,4,13,4,189,3,11,1011,0.89,0,0 +2011,4,13,5,201,3,10,1011,0.89,0,0 +2011,4,13,6,231,3,10,1012,0.89,0,0 +2011,4,13,7,225,4,11,1013,1.79,0,0 +2011,4,13,8,169,5,16,1013,0.89,0,0 +2011,4,13,9,163,5,17,1013,1.78,0,0 +2011,4,13,10,142,5,19,1013,1.79,0,0 +2011,4,13,11,136,5,21,1013,1.79,0,0 +2011,4,13,12,141,6,22,1012,5.81,0,0 +2011,4,13,13,125,6,23,1011,8.94,0,0 +2011,4,13,14,131,6,24,1010,12.07,0,0 +2011,4,13,15,138,6,23,1009,16.09,0,0 +2011,4,13,16,152,6,24,1008,20.11,0,0 +2011,4,13,17,145,6,23,1009,23.24,0,0 +2011,4,13,18,157,7,22,1009,25.03,0,0 +2011,4,13,19,172,7,20,1009,0.89,0,0 +2011,4,13,20,170,8,21,1009,4.02,0,0 +2011,4,13,21,172,8,20,1010,7.15,0,0 +2011,4,13,22,155,7,18,1010,0.89,0,0 +2011,4,13,23,168,7,17,1009,1.78,0,0 +2011,4,14,0,163,6,16,1009,2.67,0,0 +2011,4,14,1,144,8,13,1009,3.56,0,0 +2011,4,14,2,137,6,13,1008,3.13,0,0 +2011,4,14,3,133,6,13,1008,4.02,0,0 +2011,4,14,4,158,6,14,1007,0.89,0,0 +2011,4,14,5,162,7,14,1007,1.78,0,0 +2011,4,14,6,158,7,11,1007,1.79,0,0 +2011,4,14,7,174,8,12,1007,3.58,0,0 +2011,4,14,8,184,7,17,1007,6.71,0,0 +2011,4,14,9,191,7,19,1007,9.84,0,0 +2011,4,14,10,159,3,24,1006,13.86,0,0 +2011,4,14,11,74,-4,27,1005,19.67,0,0 +2011,4,14,12,27,-6,28,1004,27.72,0,0 +2011,4,14,13,21,-6,29,1003,36.66,0,0 +2011,4,14,14,21,-5,29,1001,39.79,0,0 +2011,4,14,15,NA,-5,31,1001,1.79,0,0 +2011,4,14,16,NA,-5,31,1000,8.05,0,0 +2011,4,14,17,NA,-6,30,1000,15.2,0,0 +2011,4,14,18,NA,-5,30,1000,19.22,0,0 +2011,4,14,19,NA,-2,26,1000,5.81,0,0 +2011,4,14,20,NA,0,25,1001,10.73,0,0 +2011,4,14,21,NA,-1,25,1001,15.65,0,0 +2011,4,14,22,NA,0,25,1001,21.46,0,0 +2011,4,14,23,NA,1,24,1001,3.13,0,0 +2011,4,15,0,53,0,19,1002,8.05,0,0 +2011,4,15,1,NA,-3,18,1002,11.18,0,0 +2011,4,15,2,NA,-3,18,1002,12.97,0,0 +2011,4,15,3,NA,-3,16,1004,4.92,0,0 +2011,4,15,4,NA,-5,19,1005,10.73,0,0 +2011,4,15,5,NA,-3,19,1007,17.88,0,0 +2011,4,15,6,NA,-3,19,1008,5.81,0,0 +2011,4,15,7,NA,-3,19,1010,9.83,0,0 +2011,4,15,8,NA,-4,20,1011,8.94,0,0 +2011,4,15,9,NA,-8,20,1012,17.88,0,0 +2011,4,15,10,NA,-7,21,1013,8.05,0,0 +2011,4,15,11,NA,-8,22,1013,7.15,0,0 +2011,4,15,12,NA,-8,23,1013,8.05,0,0 +2011,4,15,13,28,-8,23,1012,13.86,0,0 +2011,4,15,14,26,-6,23,1012,19.67,0,0 +2011,4,15,15,27,-6,24,1012,24.59,0,0 +2011,4,15,16,25,-6,23,1012,29.51,0,0 +2011,4,15,17,NA,-6,23,1012,33.53,0,0 +2011,4,15,18,NA,-7,22,1012,36.66,0,0 +2011,4,15,19,NA,-6,20,1013,1.79,0,0 +2011,4,15,20,NA,-3,19,1014,4.92,0,0 +2011,4,15,21,NA,-2,15,1016,9.84,0,0 +2011,4,15,22,NA,-1,13,1018,13.86,0,0 +2011,4,15,23,NA,-3,12,1019,17.88,0,0 +2011,4,16,0,36,-3,11,1019,19.67,0,0 +2011,4,16,1,NA,-1,10,1019,0.89,0,0 +2011,4,16,2,NA,-2,9,1019,1.78,0,0 +2011,4,16,3,NA,-2,9,1019,0.89,0,0 +2011,4,16,4,NA,-3,8,1018,0.89,0,0 +2011,4,16,5,NA,-3,7,1018,1.78,0,0 +2011,4,16,6,NA,-2,7,1019,2.23,0,0 +2011,4,16,7,NA,-1,10,1019,3.12,0,0 +2011,4,16,8,NA,-2,12,1019,3.13,0,0 +2011,4,16,9,NA,-3,14,1018,1.79,0,0 +2011,4,16,10,NA,-3,16,1018,2.68,0,0 +2011,4,16,11,NA,-5,19,1017,4.92,0,0 +2011,4,16,12,NA,-4,20,1015,9.84,0,0 +2011,4,16,13,NA,-4,22,1014,14.76,0,0 +2011,4,16,14,NA,-3,23,1012,21.91,0,0 +2011,4,16,15,76,-3,24,1010,29.06,0,0 +2011,4,16,16,87,-2,24,1009,36.21,0,0 +2011,4,16,17,146,-1,23,1008,44.26,0,0 +2011,4,16,18,149,1,21,1007,53.2,0,0 +2011,4,16,19,121,2,20,1007,57.22,0,0 +2011,4,16,20,79,0,20,1007,63.03,0,0 +2011,4,16,21,58,0,20,1007,70.18,0,0 +2011,4,16,22,53,0,19,1007,77.33,0,0 +2011,4,16,23,56,1,18,1007,82.25,0,0 +2011,4,17,0,NA,1,19,1007,86.27,0,0 +2011,4,17,1,60,2,18,1007,0.89,0,0 +2011,4,17,2,65,-1,19,1007,5.81,0,0 +2011,4,17,3,41,-1,18,1007,11.62,0,0 +2011,4,17,4,23,-4,17,1007,18.77,0,0 +2011,4,17,5,20,-4,16,1008,25.92,0,0 +2011,4,17,6,12,-3,15,1009,33.07,0,0 +2011,4,17,7,13,-3,16,1010,40.22,0,0 +2011,4,17,8,16,-4,17,1011,48.27,0,0 +2011,4,17,9,22,-4,18,1011,57.21,0,0 +2011,4,17,10,35,-4,19,1011,67.04,0,0 +2011,4,17,11,72,-5,18,1011,74.19,0,0 +2011,4,17,12,249,-3,18,1011,84.02,0,0 +2011,4,17,13,76,-6,17,1011,92.07,0,0 +2011,4,17,14,90,-4,17,1012,105.03,0,0 +2011,4,17,15,74,-8,16,1012,117.1,0,0 +2011,4,17,16,44,-8,17,1013,130.06,0,0 +2011,4,17,17,93,-11,16,1013,142.13,0,0 +2011,4,17,18,118,-11,15,1015,151.07,0,0 +2011,4,17,19,120,-11,14,1015,156.88,0,0 +2011,4,17,20,97,-13,14,1016,164.03,0,0 +2011,4,17,21,14,-13,14,1017,171.18,0,0 +2011,4,17,22,15,-14,14,1017,178.33,0,0 +2011,4,17,23,18,-15,13,1018,183.25,0,0 +2011,4,18,0,NA,-14,10,1017,187.27,0,0 +2011,4,18,1,21,-14,8,1017,191.29,0,0 +2011,4,18,2,10,-13,8,1017,195.31,0,0 +2011,4,18,3,9,-14,10,1017,200.23,0,0 +2011,4,18,4,7,-11,8,1017,0.89,0,0 +2011,4,18,5,4,-8,7,1017,1.79,0,0 +2011,4,18,6,6,-11,9,1017,3.58,0,0 +2011,4,18,7,6,-11,12,1018,4.92,0,0 +2011,4,18,8,4,-13,14,1018,12.97,0,0 +2011,4,18,9,3,-11,15,1018,22.8,0,0 +2011,4,18,10,5,-12,16,1018,9.83,0,0 +2011,4,18,11,9,-11,17,1018,8.05,0,0 +2011,4,18,12,12,-10,18,1017,15.2,0,0 +2011,4,18,13,11,-10,19,1016,5.81,0,0 +2011,4,18,14,11,-11,21,1016,5.81,0,0 +2011,4,18,15,13,-9,21,1015,3.13,0,0 +2011,4,18,16,10,-10,21,1015,7.15,0,0 +2011,4,18,17,10,-10,20,1015,10.28,0,0 +2011,4,18,18,15,-10,19,1015,12.07,0,0 +2011,4,18,19,19,-7,16,1015,0.89,0,0 +2011,4,18,20,32,-6,15,1016,2.68,0,0 +2011,4,18,21,43,-5,14,1017,4.47,0,0 +2011,4,18,22,65,-5,12,1018,0.89,0,0 +2011,4,18,23,54,-4,13,1019,3.13,0,0 +2011,4,19,0,56,0,10,1020,4.92,0,0 +2011,4,19,1,63,1,9,1020,6.71,0,0 +2011,4,19,2,52,0,8,1020,0.89,0,0 +2011,4,19,3,53,1,7,1020,1.78,0,0 +2011,4,19,4,48,1,6,1020,2.67,0,0 +2011,4,19,5,62,1,7,1020,0.89,0,0 +2011,4,19,6,59,1,6,1020,1.78,0,0 +2011,4,19,7,93,2,9,1020,1.79,0,0 +2011,4,19,8,103,1,12,1021,3.58,0,0 +2011,4,19,9,79,-1,15,1020,5.37,0,0 +2011,4,19,10,80,-3,17,1020,7.16,0,0 +2011,4,19,11,84,-3,18,1019,11.18,0,0 +2011,4,19,12,73,-3,20,1018,14.31,0,0 +2011,4,19,13,52,-6,22,1016,19.23,0,0 +2011,4,19,14,44,-6,22,1015,25.04,0,0 +2011,4,19,15,58,-5,23,1014,29.96,0,0 +2011,4,19,16,68,-5,22,1013,34.88,0,0 +2011,4,19,17,86,-3,20,1012,39.8,0,0 +2011,4,19,18,99,-3,20,1012,42.93,0,0 +2011,4,19,19,105,-2,18,1012,46.06,0,0 +2011,4,19,20,93,-1,19,1012,50.08,0,0 +2011,4,19,21,120,0,16,1012,51.87,0,0 +2011,4,19,22,115,0,15,1012,53.66,0,0 +2011,4,19,23,91,1,14,1012,55.45,0,0 +2011,4,20,0,82,2,13,1012,57.24,0,0 +2011,4,20,1,84,1,12,1012,60.37,0,0 +2011,4,20,2,114,2,12,1011,62.16,0,0 +2011,4,20,3,97,2,11,1011,0.89,0,0 +2011,4,20,4,87,2,9,1010,1.78,0,0 +2011,4,20,5,91,2,9,1010,1.79,0,0 +2011,4,20,6,97,2,9,1010,0.89,0,0 +2011,4,20,7,121,3,11,1010,0.89,0,0 +2011,4,20,8,136,4,14,1010,1.79,0,0 +2011,4,20,9,160,4,15,1010,3.58,0,0 +2011,4,20,10,175,5,17,1010,1.79,0,0 +2011,4,20,11,166,5,19,1010,1.79,0,0 +2011,4,20,12,153,5,20,1009,3.58,0,0 +2011,4,20,13,165,5,22,1008,6.71,0,0 +2011,4,20,14,135,6,22,1007,9.84,0,0 +2011,4,20,15,270,6,22,1006,12.97,0,0 +2011,4,20,16,141,6,22,1006,16.1,0,0 +2011,4,20,17,137,7,22,1005,17.89,0,0 +2011,4,20,18,143,7,21,1005,19.68,0,0 +2011,4,20,19,157,7,19,1006,21.47,0,0 +2011,4,20,20,156,8,19,1007,23.26,0,0 +2011,4,20,21,189,8,18,1008,25.05,0,0 +2011,4,20,22,165,8,17,1008,1.79,0,0 +2011,4,20,23,195,7,17,1008,3.58,0,0 +2011,4,21,0,175,7,17,1008,5.37,0,0 +2011,4,21,1,157,6,17,1008,0.89,0,0 +2011,4,21,2,161,7,16,1008,1.79,0,0 +2011,4,21,3,167,8,14,1009,3.58,0,0 +2011,4,21,4,171,7,16,1009,5.37,0,0 +2011,4,21,5,160,7,15,1009,6.26,0,0 +2011,4,21,6,140,5,17,1010,1.79,0,0 +2011,4,21,7,161,4,17,1012,3.58,0,0 +2011,4,21,8,169,3,17,1013,1.79,0,0 +2011,4,21,9,113,9,13,1015,5.81,0,1 +2011,4,21,10,69,9,12,1015,9.83,0,2 +2011,4,21,11,44,8,11,1016,15.64,0,3 +2011,4,21,12,NA,9,11,1016,20.56,0,4 +2011,4,21,13,NA,9,11,1017,24.58,0,5 +2011,4,21,14,NA,7,11,1017,29.5,0,0 +2011,4,21,15,NA,8,12,1017,34.42,0,0 +2011,4,21,16,NA,7,13,1017,39.34,0,0 +2011,4,21,17,NA,6,13,1017,43.36,0,0 +2011,4,21,18,NA,6,13,1018,47.38,0,0 +2011,4,21,19,NA,5,12,1018,49.17,0,0 +2011,4,21,20,NA,5,10,1019,50.96,0,0 +2011,4,21,21,49,5,8,1019,0.89,0,0 +2011,4,21,22,NA,6,8,1019,1.78,0,0 +2011,4,21,23,NA,6,10,1019,1.79,0,0 +2011,4,22,0,74,6,7,1019,0.89,0,0 +2011,4,22,1,NA,7,8,1019,1.78,0,0 +2011,4,22,2,NA,5,7,1019,2.23,0,0 +2011,4,22,3,NA,5,6,1018,3.12,0,0 +2011,4,22,4,NA,6,7,1018,3.57,0,0 +2011,4,22,5,NA,4,7,1018,0.89,0,0 +2011,4,22,6,NA,6,6,1018,1.79,0,0 +2011,4,22,7,NA,7,8,1018,1.79,0,0 +2011,4,22,8,NA,7,11,1018,4.92,0,0 +2011,4,22,9,NA,7,14,1018,0.89,0,0 +2011,4,22,10,NA,6,16,1017,1.78,0,0 +2011,4,22,11,39,4,17,1016,2.67,0,0 +2011,4,22,12,NA,3,19,1015,1.79,0,0 +2011,4,22,13,34,1,20,1015,3.13,0,0 +2011,4,22,14,70,1,18,1014,4.92,0,0 +2011,4,22,15,36,4,14,1014,16.1,0,0 +2011,4,22,16,28,3,15,1014,5.81,0,0 +2011,4,22,17,NA,4,16,1013,11.62,0,0 +2011,4,22,18,NA,4,16,1013,16.54,0,0 +2011,4,22,19,NA,3,16,1013,21.46,0,0 +2011,4,22,20,NA,3,15,1014,23.25,0,0 +2011,4,22,21,NA,4,12,1014,1.79,0,0 +2011,4,22,22,NA,6,11,1014,3.58,0,0 +2011,4,22,23,NA,7,11,1014,7.6,0,0 +2011,4,23,0,49,7,10,1014,10.73,0,0 +2011,4,23,1,NA,7,9,1014,12.52,0,0 +2011,4,23,2,NA,6,9,1014,14.31,0,0 +2011,4,23,3,NA,6,8,1013,15.2,0,0 +2011,4,23,4,NA,6,8,1013,0.45,0,0 +2011,4,23,5,NA,6,8,1013,0.9,0,0 +2011,4,23,6,NA,7,8,1013,0.89,0,0 +2011,4,23,7,NA,7,10,1014,2.68,0,0 +2011,4,23,8,NA,7,11,1014,1.79,0,0 +2011,4,23,9,NA,6,13,1014,4.92,0,0 +2011,4,23,10,NA,3,16,1014,8.94,0,0 +2011,4,23,11,NA,1,18,1013,14.75,0,0 +2011,4,23,12,NA,-3,19,1012,4.92,0,0 +2011,4,23,13,NA,0,17,1013,7.15,0,0 +2011,4,23,14,NA,-1,18,1012,14.3,0,0 +2011,4,23,15,16,1,17,1012,4.92,0,0 +2011,4,23,16,NA,0,18,1012,1.79,0,0 +2011,4,23,17,10,0,18,1012,8.05,0,0 +2011,4,23,18,12,0,17,1012,15.2,0,0 +2011,4,23,19,16,-2,16,1013,20.12,0,0 +2011,4,23,20,14,-3,16,1013,28.17,0,0 +2011,4,23,21,13,-3,15,1014,38,0,0 +2011,4,23,22,12,-2,14,1015,49.18,0,0 +2011,4,23,23,12,-2,13,1015,59.01,0,0 +2011,4,24,0,NA,-2,13,1015,66.16,0,0 +2011,4,24,1,9,-2,12,1014,73.31,0,0 +2011,4,24,2,8,-2,12,1014,80.46,0,0 +2011,4,24,3,11,-1,11,1014,85.38,0,0 +2011,4,24,4,15,-1,11,1013,90.3,0,0 +2011,4,24,5,16,-2,11,1013,96.11,0,0 +2011,4,24,6,17,-1,11,1013,101.92,0,0 +2011,4,24,7,23,-1,13,1014,107.73,0,0 +2011,4,24,8,17,-2,15,1014,115.78,0,0 +2011,4,24,9,19,-2,16,1013,123.83,0,0 +2011,4,24,10,15,-3,18,1012,131.88,0,0 +2011,4,24,11,22,-4,18,1011,139.93,0,0 +2011,4,24,12,13,-3,19,1010,147.08,0,0 +2011,4,24,13,14,-5,19,1009,155.13,0,0 +2011,4,24,14,15,-6,20,1008,162.28,0,0 +2011,4,24,15,10,-7,21,1007,3.13,0,0 +2011,4,24,16,23,-6,22,1006,8.94,0,0 +2011,4,24,17,14,-5,22,1006,16.09,0,0 +2011,4,24,18,12,-3,20,1006,19.22,0,0 +2011,4,24,19,13,-1,17,1006,1.79,0,0 +2011,4,24,20,24,-2,16,1006,4.92,0,0 +2011,4,24,21,32,-2,14,1007,6.71,0,0 +2011,4,24,22,37,-1,13,1007,9.84,0,0 +2011,4,24,23,41,2,12,1006,11.63,0,0 +2011,4,25,0,NA,2,12,1006,13.42,0,0 +2011,4,25,1,99,1,9,1006,0.89,0,0 +2011,4,25,2,73,2,9,1006,1.78,0,0 +2011,4,25,3,60,2,8,1005,2.67,0,0 +2011,4,25,4,78,2,9,1005,3.56,0,0 +2011,4,25,5,96,2,10,1004,3.13,0,0 +2011,4,25,6,91,2,11,1004,4.92,0,0 +2011,4,25,7,93,2,12,1004,1.79,0,0 +2011,4,25,8,122,3,13,1004,0.89,0,0 +2011,4,25,9,165,3,14,1004,3.13,0,0 +2011,4,25,10,156,3,14,1004,4.92,0,0 +2011,4,25,11,162,4,15,1003,6.71,0,0 +2011,4,25,12,167,4,15,1002,9.84,0,0 +2011,4,25,13,167,5,17,1002,12.97,0,0 +2011,4,25,14,155,5,18,1001,16.1,0,0 +2011,4,25,15,126,5,18,1001,19.23,0,0 +2011,4,25,16,117,5,18,1001,24.15,0,0 +2011,4,25,17,97,5,17,1001,28.17,0,0 +2011,4,25,18,95,7,16,1002,31.3,0,0 +2011,4,25,19,90,7,15,1002,33.09,0,0 +2011,4,25,20,93,7,14,1003,33.98,0,0 +2011,4,25,21,100,7,14,1003,35.77,0,0 +2011,4,25,22,83,8,12,1003,36.66,0,0 +2011,4,25,23,96,7,11,1004,37.55,0,0 +2011,4,26,0,103,7,11,1004,40.68,0,0 +2011,4,26,1,138,7,11,1003,43.81,0,0 +2011,4,26,2,148,6,11,1003,45.6,0,0 +2011,4,26,3,126,6,9,1003,46.49,0,0 +2011,4,26,4,101,5,9,1002,0.45,0,0 +2011,4,26,5,104,6,8,1003,1.79,0,0 +2011,4,26,6,116,6,9,1003,0.89,0,0 +2011,4,26,7,143,7,12,1003,0.89,0,0 +2011,4,26,8,151,6,15,1003,3.13,0,0 +2011,4,26,9,149,7,16,1003,4.92,0,0 +2011,4,26,10,86,4,18,1003,7.15,0,0 +2011,4,26,11,51,1,20,1002,8.94,0,0 +2011,4,26,12,41,1,21,1002,20.12,0,0 +2011,4,26,13,31,1,19,1002,32.19,0,0 +2011,4,26,14,26,6,15,1004,8.94,0,1 +2011,4,26,15,20,5,14,1006,8.05,0,0 +2011,4,26,16,15,4,14,1006,15.2,0,0 +2011,4,26,17,13,3,15,1007,21.01,0,0 +2011,4,26,18,16,3,15,1007,25.03,0,0 +2011,4,26,19,18,3,15,1007,28.16,0,0 +2011,4,26,20,22,4,11,1008,0.89,0,0 +2011,4,26,21,63,4,9,1008,0.89,0,0 +2011,4,26,22,68,4,10,1008,1.78,0,0 +2011,4,26,23,82,4,7,1008,2.23,0,0 +2011,4,27,0,79,4,7,1008,1.79,0,0 +2011,4,27,1,51,4,7,1008,1.79,0,0 +2011,4,27,2,46,3,6,1009,4.92,0,0 +2011,4,27,3,67,3,5,1009,8.05,0,0 +2011,4,27,4,54,3,5,1009,9.84,0,0 +2011,4,27,5,43,2,6,1010,12.97,0,0 +2011,4,27,6,42,3,7,1010,16.1,0,0 +2011,4,27,7,44,4,10,1012,0.89,0,0 +2011,4,27,8,29,3,14,1013,1.79,0,0 +2011,4,27,9,29,-4,16,1013,5.81,0,0 +2011,4,27,10,25,-4,18,1013,10.73,0,0 +2011,4,27,11,20,-3,18,1012,13.86,0,0 +2011,4,27,12,19,-2,20,1011,3.13,0,0 +2011,4,27,13,25,-3,20,1011,3.13,0,0 +2011,4,27,14,27,-3,21,1010,1.79,0,0 +2011,4,27,15,27,-2,22,1009,4.02,0,0 +2011,4,27,16,36,2,20,1009,11.17,0,0 +2011,4,27,17,39,2,19,1009,16.98,0,0 +2011,4,27,18,47,1,18,1009,21,0,0 +2011,4,27,19,62,2,16,1009,24.13,0,0 +2011,4,27,20,64,4,15,1010,28.15,0,0 +2011,4,27,21,73,3,15,1010,33.07,0,0 +2011,4,27,22,59,4,12,1011,34.86,0,0 +2011,4,27,23,60,4,12,1011,37.99,0,0 +2011,4,28,0,61,4,12,1011,41.12,0,0 +2011,4,28,1,61,3,12,1011,42.91,0,0 +2011,4,28,2,59,4,12,1010,44.7,0,0 +2011,4,28,3,63,5,13,1009,48.72,0,0 +2011,4,28,4,70,4,11,1009,1.79,0,0 +2011,4,28,5,64,4,10,1009,1.79,0,0 +2011,4,28,6,75,4,10,1009,1.79,0,0 +2011,4,28,7,82,5,12,1009,3.58,0,0 +2011,4,28,8,93,4,13,1009,5.37,0,0 +2011,4,28,9,90,3,15,1009,3.13,0,0 +2011,4,28,10,84,2,17,1008,4.92,0,0 +2011,4,28,11,84,3,19,1007,1.79,0,0 +2011,4,28,12,99,2,20,1006,3.58,0,0 +2011,4,28,13,105,4,22,1005,3.13,0,0 +2011,4,28,14,NA,5,21,1004,6.26,0,0 +2011,4,28,15,NA,5,22,1003,9.39,0,0 +2011,4,28,16,NA,5,22,1002,12.52,0,0 +2011,4,28,17,NA,5,21,1002,15.65,0,0 +2011,4,28,18,NA,6,20,1002,17.44,0,0 +2011,4,28,19,NA,6,19,1002,19.23,0,0 +2011,4,28,20,NA,6,17,1002,20.12,0,0 +2011,4,28,21,NA,7,18,1003,21.91,0,0 +2011,4,28,22,NA,8,18,1003,23.7,0,0 +2011,4,28,23,NA,8,16,1002,25.49,0,0 +2011,4,29,0,165,8,16,1002,0.89,0,0 +2011,4,29,1,NA,10,15,1002,4.02,0,1 +2011,4,29,2,NA,11,14,1000,7.15,0,0 +2011,4,29,3,NA,10,13,1000,8.94,0,0 +2011,4,29,4,NA,10,13,999,10.73,0,0 +2011,4,29,5,NA,9,13,999,0.89,0,0 +2011,4,29,6,NA,10,13,997,1.78,0,0 +2011,4,29,7,NA,11,13,998,1.79,0,1 +2011,4,29,8,166,10,15,1000,1.79,0,0 +2011,4,29,9,185,10,16,998,1.79,0,0 +2011,4,29,10,162,10,18,997,4.92,0,0 +2011,4,29,11,163,10,19,997,8.05,0,0 +2011,4,29,12,172,10,20,997,9.84,0,0 +2011,4,29,13,165,10,20,996,12.97,0,0 +2011,4,29,14,163,10,20,996,16.1,0,0 +2011,4,29,15,152,10,20,998,19.23,0,0 +2011,4,29,16,157,10,19,998,21.02,0,0 +2011,4,29,17,162,11,18,998,24.15,0,0 +2011,4,29,18,171,13,15,998,28.17,0,1 +2011,4,29,19,172,13,15,999,31.3,0,0 +2011,4,29,20,170,13,15,1000,34.43,0,0 +2011,4,29,21,168,12,14,1001,36.22,0,0 +2011,4,29,22,172,12,13,1000,0.89,0,0 +2011,4,29,23,168,12,13,1000,0.89,0,0 +2011,4,30,0,178,12,13,1000,1.78,0,0 +2011,4,30,1,170,11,12,1000,2.67,0,0 +2011,4,30,2,172,12,12,999,1.79,0,0 +2011,4,30,3,186,12,13,1000,3.58,0,0 +2011,4,30,4,176,12,13,999,6.71,0,0 +2011,4,30,5,183,12,13,999,9.84,0,0 +2011,4,30,6,194,12,13,999,11.63,0,0 +2011,4,30,7,195,12,13,1000,13.42,0,0 +2011,4,30,8,200,12,13,999,16.55,0,0 +2011,4,30,9,218,12,14,1000,18.34,0,0 +2011,4,30,10,232,12,16,999,21.47,0,0 +2011,4,30,11,187,0,19,1000,5.81,0,0 +2011,4,30,12,97,-3,20,1001,12.96,0,0 +2011,4,30,13,76,-2,19,1001,28.16,0,0 +2011,4,30,14,53,1,18,1001,41.12,0,0 +2011,4,30,15,36,0,19,1001,53.19,0,0 +2011,4,30,16,41,-2,19,1001,66.15,0,0 +2011,4,30,17,81,-1,19,1002,74.2,0,0 +2011,4,30,18,120,-2,18,1003,85.38,0,0 +2011,4,30,19,201,-1,17,1004,98.34,0,0 +2011,4,30,20,214,-1,17,1006,109.52,0,0 +2011,4,30,21,238,-1,17,1007,119.35,0,0 +2011,4,30,22,251,-1,17,1007,125.16,0,0 +2011,4,30,23,234,-3,17,1007,132.31,0,0 +2011,5,1,0,164,-3,17,1007,139.46,0,0 +2011,5,1,1,136,-3,17,1008,145.27,0,0 +2011,5,1,2,108,-3,16,1008,150.19,0,0 +2011,5,1,3,88,-3,17,1009,156,0,0 +2011,5,1,4,65,-3,17,1009,164.05,0,0 +2011,5,1,5,83,-2,16,1009,169.86,0,0 +2011,5,1,6,96,-2,17,1011,177.01,0,0 +2011,5,1,7,100,-1,18,1011,185.06,0,0 +2011,5,1,8,99,-1,20,1011,194,0,0 +2011,5,1,9,89,0,22,1012,199.81,0,0 +2011,5,1,10,86,-1,24,1012,205.62,0,0 +2011,5,1,11,60,-1,25,1011,209.64,0,0 +2011,5,1,12,49,-2,25,1011,216.79,0,0 +2011,5,1,13,38,-3,26,1010,225.73,0,0 +2011,5,1,14,38,-3,27,1010,234.67,0,0 +2011,5,1,15,42,-2,27,1009,242.72,0,0 +2011,5,1,16,39,-3,26,1009,247.64,0,0 +2011,5,1,17,31,-3,27,1009,252.56,0,0 +2011,5,1,18,41,-4,26,1009,255.69,0,0 +2011,5,1,19,33,-1,23,1010,3.13,0,0 +2011,5,1,20,37,-1,22,1010,7.15,0,0 +2011,5,1,21,40,0,23,1011,12.07,0,0 +2011,5,1,22,37,0,22,1011,1.79,0,0 +2011,5,1,23,33,0,20,1011,4.92,0,0 +2011,5,2,0,38,1,18,1012,8.05,0,0 +2011,5,2,1,48,-1,18,1012,11.18,0,0 +2011,5,2,2,26,-3,17,1012,15.2,0,0 +2011,5,2,3,15,-1,12,1012,16.99,0,0 +2011,5,2,4,18,-1,11,1012,0.89,0,0 +2011,5,2,5,17,2,10,1012,1.78,0,0 +2011,5,2,6,15,2,10,1012,1.79,0,0 +2011,5,2,7,24,-1,16,1012,4.92,0,0 +2011,5,2,8,23,-1,19,1012,6.71,0,0 +2011,5,2,9,20,-3,23,1012,13.86,0,0 +2011,5,2,10,27,-7,25,1012,23.69,0,0 +2011,5,2,11,14,-8,25,1012,35.76,0,0 +2011,5,2,12,12,-8,26,1011,47.83,0,0 +2011,5,2,13,16,-7,26,1010,59.01,0,0 +2011,5,2,14,27,-9,26,1010,71.08,0,0 +2011,5,2,15,24,-9,26,1010,83.15,0,0 +2011,5,2,16,41,-9,26,1009,94.33,0,0 +2011,5,2,17,31,-10,25,1009,102.38,0,0 +2011,5,2,18,23,-11,23,1010,108.19,0,0 +2011,5,2,19,19,-11,21,1010,112.21,0,0 +2011,5,2,20,34,-11,17,1011,115.34,0,0 +2011,5,2,21,34,-11,17,1013,118.47,0,0 +2011,5,2,22,21,-10,17,1013,120.26,0,0 +2011,5,2,23,14,-10,18,1014,124.28,0,0 +2011,5,3,0,13,-9,15,1015,128.3,0,0 +2011,5,3,1,16,-9,16,1015,0.89,0,0 +2011,5,3,2,12,-8,17,1015,4.02,0,0 +2011,5,3,3,8,-8,17,1015,11.17,0,0 +2011,5,3,4,7,-7,16,1015,16.09,0,0 +2011,5,3,5,7,-7,15,1015,20.11,0,0 +2011,5,3,6,8,-4,13,1016,21.9,0,0 +2011,5,3,7,11,-2,17,1017,23.69,0,0 +2011,5,3,8,13,-3,19,1017,3.13,0,0 +2011,5,3,9,14,-3,20,1017,3.13,0,0 +2011,5,3,10,14,-3,23,1017,3.13,0,0 +2011,5,3,11,14,-3,22,1016,1.79,0,0 +2011,5,3,12,13,-2,23,1016,1.79,0,0 +2011,5,3,13,15,-2,24,1015,1.79,0,0 +2011,5,3,14,16,-2,24,1014,1.79,0,0 +2011,5,3,15,13,-3,24,1013,1.79,0,0 +2011,5,3,16,36,0,24,1012,1.79,0,0 +2011,5,3,17,33,-2,24,1013,5.81,0,0 +2011,5,3,18,33,-2,23,1013,10.73,0,0 +2011,5,3,19,28,-2,21,1013,14.75,0,0 +2011,5,3,20,27,-2,19,1013,17.88,0,0 +2011,5,3,21,35,0,19,1014,21.01,0,0 +2011,5,3,22,45,1,18,1014,24.14,0,0 +2011,5,3,23,57,3,18,1014,27.27,0,0 +2011,5,4,0,68,4,17,1014,31.29,0,0 +2011,5,4,1,79,4,16,1013,33.08,0,0 +2011,5,4,2,65,4,15,1013,36.21,0,0 +2011,5,4,3,70,4,15,1012,38,0,0 +2011,5,4,4,79,5,15,1012,39.79,0,0 +2011,5,4,5,81,6,14,1012,41.58,0,0 +2011,5,4,6,93,6,15,1012,44.71,0,0 +2011,5,4,7,81,6,16,1013,48.73,0,0 +2011,5,4,8,50,8,15,1013,51.86,0,0 +2011,5,4,9,63,7,16,1013,55.88,0,0 +2011,5,4,10,44,7,15,1013,59.9,0,0 +2011,5,4,11,42,7,16,1013,63.92,0,0 +2011,5,4,12,39,6,17,1012,67.05,0,0 +2011,5,4,13,55,7,16,1011,71.97,0,0 +2011,5,4,14,48,8,16,1009,75.1,0,0 +2011,5,4,15,53,8,15,1008,78.23,0,0 +2011,5,4,16,43,9,15,1007,81.36,0,0 +2011,5,4,17,54,9,15,1007,83.15,0,0 +2011,5,4,18,58,10,14,1007,84.94,0,1 +2011,5,4,19,63,11,14,1007,0.89,0,0 +2011,5,4,20,80,11,12,1006,1.78,0,0 +2011,5,4,21,95,10,14,1007,4.02,0,0 +2011,5,4,22,93,9,13,1006,5.81,0,0 +2011,5,4,23,94,9,11,1006,6.7,0,0 +2011,5,5,0,98,9,12,1005,0.89,0,1 +2011,5,5,1,110,8,13,1004,1.78,0,0 +2011,5,5,2,113,10,12,1003,2.67,0,0 +2011,5,5,3,107,10,11,1003,3.12,0,0 +2011,5,5,4,114,10,12,1002,1.79,0,1 +2011,5,5,5,123,10,11,1001,1.79,0,0 +2011,5,5,6,130,10,12,1001,4.92,0,0 +2011,5,5,7,136,11,12,1001,1.79,0,0 +2011,5,5,8,147,12,13,1001,0.89,0,0 +2011,5,5,9,148,12,14,1001,1.78,0,0 +2011,5,5,10,168,12,15,1002,4.92,0,0 +2011,5,5,11,201,11,17,1002,1.79,0,0 +2011,5,5,12,188,11,18,1002,2.68,0,0 +2011,5,5,13,150,10,21,1001,3.13,0,0 +2011,5,5,14,118,-3,23,1001,12.96,0,0 +2011,5,5,15,84,-6,23,1001,22.79,0,0 +2011,5,5,16,59,-6,23,1001,30.84,0,0 +2011,5,5,17,44,-7,23,1001,38.89,0,0 +2011,5,5,18,39,-7,22,1001,44.7,0,0 +2011,5,5,19,35,2,18,1002,1.79,0,0 +2011,5,5,20,63,1,16,1003,3.13,0,0 +2011,5,5,21,95,1,15,1003,4.92,0,0 +2011,5,5,22,79,-1,17,1004,6.71,0,0 +2011,5,5,23,75,0,14,1004,0.89,0,0 +2011,5,6,0,97,4,12,1004,1.78,0,0 +2011,5,6,1,110,4,10,1004,2.67,0,0 +2011,5,6,2,111,3,10,1003,3.13,0,0 +2011,5,6,3,98,3,10,1003,0.89,0,0 +2011,5,6,4,97,3,8,1003,1.79,0,0 +2011,5,6,5,86,2,11,1003,0.89,0,0 +2011,5,6,6,58,4,9,1003,3.13,0,0 +2011,5,6,7,60,4,13,1003,0.89,0,0 +2011,5,6,8,55,0,20,1003,1.79,0,0 +2011,5,6,9,39,0,21,1003,1.79,0,0 +2011,5,6,10,33,-4,22,1004,8.94,0,0 +2011,5,6,11,34,-4,23,1003,18.77,0,0 +2011,5,6,12,34,-5,24,1003,29.95,0,0 +2011,5,6,13,37,-5,25,1002,39.78,0,0 +2011,5,6,14,38,-5,26,1001,50.96,0,0 +2011,5,6,15,32,-5,26,1001,63.03,0,0 +2011,5,6,16,30,-5,27,1000,72.86,0,0 +2011,5,6,17,26,-4,26,1000,81.8,0,0 +2011,5,6,18,23,-1,23,1001,86.72,0,0 +2011,5,6,19,28,0,22,1001,89.85,0,0 +2011,5,6,20,37,2,19,1002,0.89,0,0 +2011,5,6,21,43,3,18,1004,1.79,0,0 +2011,5,6,22,30,3,17,1004,0.89,0,0 +2011,5,6,23,31,4,16,1004,3.13,0,0 +2011,5,7,0,40,6,16,1005,0.89,0,0 +2011,5,7,1,46,5,13,1005,1.79,0,0 +2011,5,7,2,30,4,14,1005,3.13,0,0 +2011,5,7,3,27,5,12,1006,7.15,0,0 +2011,5,7,4,26,4,13,1006,10.28,0,0 +2011,5,7,5,17,4,13,1007,13.41,0,0 +2011,5,7,6,19,4,16,1007,18.33,0,0 +2011,5,7,7,15,5,17,1008,3.13,0,0 +2011,5,7,8,27,5,19,1009,4.02,0,0 +2011,5,7,9,23,5,21,1009,8.04,0,0 +2011,5,7,10,NA,4,22,1009,4.02,0,0 +2011,5,7,11,18,3,23,1009,4.02,0,0 +2011,5,7,12,14,3,24,1008,3.13,0,0 +2011,5,7,13,16,2,25,1007,1.79,0,0 +2011,5,7,14,20,3,26,1006,3.58,0,0 +2011,5,7,15,29,4,26,1006,4.02,0,0 +2011,5,7,16,45,3,26,1006,7.15,0,0 +2011,5,7,17,46,4,25,1006,8.94,0,0 +2011,5,7,18,48,4,24,1007,12.07,0,0 +2011,5,7,19,47,3,23,1007,16.99,0,0 +2011,5,7,20,39,4,21,1008,20.12,0,0 +2011,5,7,21,53,5,20,1009,23.25,0,0 +2011,5,7,22,55,5,18,1010,25.04,0,0 +2011,5,7,23,50,5,19,1011,29.06,0,0 +2011,5,8,0,69,7,16,1012,33.08,0,0 +2011,5,8,1,80,7,15,1012,37.1,0,0 +2011,5,8,2,88,7,15,1012,38.89,0,0 +2011,5,8,3,62,6,14,1012,40.68,0,0 +2011,5,8,4,54,6,14,1012,0.89,0,0 +2011,5,8,5,66,6,14,1013,0.89,0,0 +2011,5,8,6,64,6,14,1013,1.78,0,0 +2011,5,8,7,79,7,15,1013,2.67,0,0 +2011,5,8,8,82,7,18,1013,5.8,0,0 +2011,5,8,9,80,6,19,1013,7.59,0,0 +2011,5,8,10,63,6,21,1012,9.38,0,0 +2011,5,8,11,54,5,22,1011,13.4,0,0 +2011,5,8,12,59,7,20,1010,17.42,0,0 +2011,5,8,13,53,5,20,1010,22.34,0,0 +2011,5,8,14,39,6,20,1009,25.47,0,1 +2011,5,8,15,40,9,15,1009,29.49,0,2 +2011,5,8,16,42,11,14,1009,31.28,0,3 +2011,5,8,17,46,11,13,1008,0.89,0,4 +2011,5,8,18,44,11,13,1008,4.92,0,5 +2011,5,8,19,41,11,13,1008,8.05,0,6 +2011,5,8,20,58,12,12,1007,1.79,0,7 +2011,5,8,21,63,12,12,1007,0.89,0,8 +2011,5,8,22,65,12,12,1007,1.78,0,9 +2011,5,8,23,61,12,13,1007,1.79,0,0 +2011,5,9,0,76,12,13,1006,0.89,0,0 +2011,5,9,1,78,12,13,1005,0.89,0,0 +2011,5,9,2,74,11,12,1003,2.68,0,0 +2011,5,9,3,78,12,12,1004,4.47,0,0 +2011,5,9,4,83,11,12,1004,5.36,0,0 +2011,5,9,5,79,11,12,1005,7.15,0,0 +2011,5,9,6,88,12,13,1005,8.94,0,0 +2011,5,9,7,82,12,13,1005,10.73,0,0 +2011,5,9,8,84,13,15,1006,14.75,0,0 +2011,5,9,9,94,13,17,1006,16.54,0,0 +2011,5,9,10,75,13,21,1006,18.33,0,0 +2011,5,9,11,40,12,24,1006,21.46,0,0 +2011,5,9,12,33,9,24,1006,1.79,0,0 +2011,5,9,13,38,9,25,1005,1.79,0,0 +2011,5,9,14,22,7,25,1005,1.79,0,0 +2011,5,9,15,19,9,24,1005,0.89,0,0 +2011,5,9,16,23,4,25,1006,3.13,0,0 +2011,5,9,17,18,2,24,1006,10.28,0,0 +2011,5,9,18,20,-1,23,1007,15.2,0,1 +2011,5,9,19,20,1,23,1007,21.01,0,2 +2011,5,9,20,25,0,22,1008,25.03,0,0 +2011,5,9,21,35,1,21,1009,29.95,0,0 +2011,5,9,22,28,3,21,1009,33.97,0,0 +2011,5,9,23,28,3,20,1009,37.99,0,0 +2011,5,10,0,31,3,19,1009,42.91,0,0 +2011,5,10,1,26,3,20,1009,46.93,0,0 +2011,5,10,2,22,4,18,1009,48.72,0,0 +2011,5,10,3,16,9,15,1008,0.89,0,0 +2011,5,10,4,20,7,15,1008,1.78,0,0 +2011,5,10,5,24,5,12,1009,1.79,0,0 +2011,5,10,6,33,7,14,1009,4.92,0,0 +2011,5,10,7,32,7,16,1010,8.05,0,0 +2011,5,10,8,44,5,19,1009,12.07,0,0 +2011,5,10,9,76,4,21,1009,15.2,0,0 +2011,5,10,10,27,4,23,1009,3.13,0,0 +2011,5,10,11,24,0,25,1009,0.89,0,0 +2011,5,10,12,21,3,26,1008,1.79,0,0 +2011,5,10,13,28,4,26,1008,5.81,0,0 +2011,5,10,14,32,4,26,1007,8.94,0,0 +2011,5,10,15,70,6,26,1006,12.07,0,0 +2011,5,10,16,41,5,26,1006,15.2,0,0 +2011,5,10,17,29,6,26,1006,19.22,0,0 +2011,5,10,18,23,7,24,1006,24.14,0,0 +2011,5,10,19,24,7,22,1006,29.06,0,0 +2011,5,10,20,27,9,21,1006,33.08,0,0 +2011,5,10,21,35,11,19,1007,36.21,0,0 +2011,5,10,22,46,13,18,1007,39.34,0,0 +2011,5,10,23,56,13,17,1007,42.47,0,0 +2011,5,11,0,80,13,16,1006,45.6,0,0 +2011,5,11,1,83,13,15,1006,48.73,0,0 +2011,5,11,2,86,12,15,1006,50.52,0,0 +2011,5,11,3,92,12,13,1006,51.41,0,0 +2011,5,11,4,98,12,13,1006,52.3,0,0 +2011,5,11,5,98,11,12,1006,0.89,0,0 +2011,5,11,6,104,12,13,1007,1.79,0,0 +2011,5,11,7,106,14,15,1007,0.89,0,0 +2011,5,11,8,111,14,18,1007,1.78,0,0 +2011,5,11,9,117,14,19,1006,1.79,0,0 +2011,5,11,10,102,14,22,1006,0.89,0,0 +2011,5,11,11,99,14,25,1006,2.68,0,0 +2011,5,11,12,184,7,27,1006,3.13,0,0 +2011,5,11,13,202,-2,29,1005,12.07,0,0 +2011,5,11,14,165,-2,29,1004,21.9,0,0 +2011,5,11,15,159,-2,29,1004,30.84,0,0 +2011,5,11,16,183,-4,28,1004,39.78,0,0 +2011,5,11,17,118,-5,28,1004,49.61,0,0 +2011,5,11,18,61,-6,26,1005,58.55,0,0 +2011,5,11,19,33,-8,24,1006,66.6,0,0 +2011,5,11,20,44,-10,23,1007,77.78,0,0 +2011,5,11,21,60,-12,22,1009,86.72,0,0 +2011,5,11,22,56,-12,21,1010,95.66,0,0 +2011,5,11,23,68,-12,20,1010,105.49,0,0 +2011,5,12,0,51,-12,19,1010,115.32,0,0 +2011,5,12,1,75,-12,18,1010,120.24,0,0 +2011,5,12,2,72,-13,16,1010,127.39,0,0 +2011,5,12,3,77,-14,16,1010,133.2,0,0 +2011,5,12,4,72,-13,15,1010,138.12,0,0 +2011,5,12,5,69,-12,14,1010,141.25,0,0 +2011,5,12,6,63,-10,14,1010,145.27,0,0 +2011,5,12,7,63,-9,16,1009,150.19,0,0 +2011,5,12,8,60,-11,18,1009,160.02,0,0 +2011,5,12,9,56,-11,18,1009,172.09,0,0 +2011,5,12,10,35,-7,18,1009,181.03,0,0 +2011,5,12,11,24,-4,18,1010,190.86,0,0 +2011,5,12,12,22,-2,20,1010,199.8,0,0 +2011,5,12,13,17,-1,21,1009,207.85,0,0 +2011,5,12,14,12,0,22,1009,216.79,0,0 +2011,5,12,15,12,1,23,1009,224.84,0,0 +2011,5,12,16,11,1,24,1008,233.78,0,0 +2011,5,12,17,63,-2,24,1007,243.61,0,0 +2011,5,12,18,32,-2,23,1008,254.79,0,0 +2011,5,12,19,34,-2,22,1009,260.6,0,0 +2011,5,12,20,32,-3,22,1009,268.65,0,0 +2011,5,12,21,24,-2,21,1010,273.57,0,0 +2011,5,12,22,16,0,16,1010,276.7,0,0 +2011,5,12,23,29,-2,20,1010,281.62,0,0 +2011,5,13,0,27,-2,19,1010,286.54,0,0 +2011,5,13,1,22,-2,19,1010,291.46,0,0 +2011,5,13,2,20,-2,18,1010,296.38,0,0 +2011,5,13,3,18,-3,18,1009,300.4,0,0 +2011,5,13,4,24,-5,18,1009,304.42,0,0 +2011,5,13,5,18,-4,17,1010,3.13,0,0 +2011,5,13,6,29,2,15,1010,3.58,0,0 +2011,5,13,7,20,1,19,1010,4.47,0,0 +2011,5,13,8,28,-3,22,1010,8.05,0,0 +2011,5,13,9,28,-3,23,1010,17.88,0,0 +2011,5,13,10,27,-3,24,1010,30.84,0,0 +2011,5,13,11,27,-4,25,1010,46.04,0,0 +2011,5,13,12,27,-4,26,1009,59.9,0,0 +2011,5,13,13,31,-4,26,1009,75.1,0,0 +2011,5,13,14,28,-4,26,1008,87.17,0,0 +2011,5,13,15,25,-4,26,1008,100.13,0,0 +2011,5,13,16,24,-5,26,1008,112.2,0,0 +2011,5,13,17,32,-5,25,1009,124.27,0,0 +2011,5,13,18,31,-6,24,1009,134.1,0,0 +2011,5,13,19,35,-7,24,1009,143.93,0,0 +2011,5,13,20,70,-6,23,1011,151.98,0,0 +2011,5,13,21,40,-6,22,1012,156.9,0,0 +2011,5,13,22,72,-7,22,1012,161.82,0,0 +2011,5,13,23,59,-5,21,1012,164.95,0,0 +2011,5,14,0,44,-6,21,1012,168.97,0,0 +2011,5,14,1,27,-6,21,1012,174.78,0,0 +2011,5,14,2,31,-7,21,1011,179.7,0,0 +2011,5,14,3,29,0,17,1011,0.89,0,0 +2011,5,14,4,25,-7,21,1010,7.15,0,0 +2011,5,14,5,22,-6,19,1010,11.17,0,0 +2011,5,14,6,21,-4,20,1010,12.96,0,0 +2011,5,14,7,27,-2,22,1010,4.02,0,0 +2011,5,14,8,32,0,24,1010,7.15,0,0 +2011,5,14,9,26,-2,27,1010,12.07,0,0 +2011,5,14,10,30,-3,27,1010,5.81,0,0 +2011,5,14,11,25,-4,29,1009,13.86,0,0 +2011,5,14,12,26,-4,30,1008,22.8,0,0 +2011,5,14,13,20,-4,31,1007,30.85,0,0 +2011,5,14,14,16,-4,30,1006,40.68,0,0 +2011,5,14,15,18,-4,31,1005,4.92,0,0 +2011,5,14,16,22,-1,29,1004,12.97,0,0 +2011,5,14,17,27,-1,28,1004,21.02,0,0 +2011,5,14,18,40,0,27,1004,29.07,0,0 +2011,5,14,19,29,0,26,1004,38.01,0,0 +2011,5,14,20,58,2,25,1004,45.16,0,0 +2011,5,14,21,50,2,24,1005,52.31,0,0 +2011,5,14,22,39,2,23,1004,59.46,0,0 +2011,5,14,23,46,2,23,1004,65.27,0,0 +2011,5,15,0,53,4,19,1004,0.89,0,0 +2011,5,15,1,55,5,16,1005,1.78,0,0 +2011,5,15,2,50,4,17,1006,0.89,0,0 +2011,5,15,3,58,6,13,1006,0.89,0,0 +2011,5,15,4,63,5,13,1006,1.78,0,0 +2011,5,15,5,58,3,14,1008,3.13,0,0 +2011,5,15,6,34,-1,20,1009,4.92,0,0 +2011,5,15,7,18,-3,21,1010,10.73,0,0 +2011,5,15,8,16,-1,22,1011,16.54,0,0 +2011,5,15,9,16,-2,23,1012,22.35,0,0 +2011,5,15,10,15,-4,23,1012,28.16,0,0 +2011,5,15,11,83,-3,25,1012,32.18,0,0 +2011,5,15,12,21,-4,25,1012,36.2,0,0 +2011,5,15,13,16,-3,26,1011,3.13,0,0 +2011,5,15,14,8,-3,27,1011,4.02,0,0 +2011,5,15,15,18,-1,25,1011,1.79,0,0 +2011,5,15,16,27,-1,25,1011,3.13,0,0 +2011,5,15,17,48,0,25,1011,4.92,0,0 +2011,5,15,18,57,3,24,1011,9.84,0,0 +2011,5,15,19,50,1,22,1012,17.89,0,0 +2011,5,15,20,45,1,20,1013,21.02,0,0 +2011,5,15,21,44,2,19,1015,24.15,0,0 +2011,5,15,22,40,3,16,1015,28.17,0,0 +2011,5,15,23,53,2,15,1016,31.3,0,0 +2011,5,16,0,68,2,13,1017,33.09,0,0 +2011,5,16,1,42,2,11,1017,0.89,0,0 +2011,5,16,2,37,4,11,1017,0.89,0,0 +2011,5,16,3,37,3,10,1017,1.79,0,0 +2011,5,16,4,43,3,10,1016,0.89,0,0 +2011,5,16,5,40,4,9,1016,1.78,0,0 +2011,5,16,6,55,5,10,1016,3.13,0,0 +2011,5,16,7,65,4,15,1016,4.92,0,0 +2011,5,16,8,66,4,17,1016,6.71,0,0 +2011,5,16,9,64,3,19,1016,0.89,0,0 +2011,5,16,10,71,4,21,1015,1.79,0,0 +2011,5,16,11,69,5,24,1014,3.58,0,0 +2011,5,16,12,75,4,27,1013,3.13,0,0 +2011,5,16,13,156,1,28,1012,4.02,0,0 +2011,5,16,14,70,1,29,1010,9.83,0,0 +2011,5,16,15,24,0,30,1009,15.64,0,0 +2011,5,16,16,19,-1,29,1008,22.79,0,0 +2011,5,16,17,24,-1,28,1008,29.94,0,0 +2011,5,16,18,40,1,26,1008,34.86,0,0 +2011,5,16,19,35,-1,25,1008,40.67,0,0 +2011,5,16,20,45,1,22,1008,44.69,0,0 +2011,5,16,21,41,2,21,1008,47.82,0,0 +2011,5,16,22,40,3,22,1007,52.74,0,0 +2011,5,16,23,42,4,20,1006,56.76,0,0 +2011,5,17,0,45,4,19,1006,60.78,0,0 +2011,5,17,1,52,3,18,1005,63.91,0,0 +2011,5,17,2,57,4,17,1004,67.04,0,0 +2011,5,17,3,62,5,16,1003,0.89,0,0 +2011,5,17,4,80,7,15,1002,1.79,0,0 +2011,5,17,5,134,8,16,1002,3.58,0,0 +2011,5,17,6,107,10,16,1002,0.89,0,0 +2011,5,17,7,105,10,17,1001,1.79,0,0 +2011,5,17,8,140,11,19,1001,4.92,0,0 +2011,5,17,9,147,12,21,1000,6.71,0,0 +2011,5,17,10,153,13,22,1000,9.84,0,0 +2011,5,17,11,138,13,24,999,12.97,0,0 +2011,5,17,12,149,14,24,998,16.1,0,0 +2011,5,17,13,160,14,24,998,20.12,0,0 +2011,5,17,14,159,15,24,997,23.25,0,0 +2011,5,17,15,158,16,25,997,25.04,0,0 +2011,5,17,16,155,16,25,996,28.17,0,0 +2011,5,17,17,174,16,25,996,29.96,0,0 +2011,5,17,18,184,16,24,996,0.89,0,0 +2011,5,17,19,206,16,23,997,1.78,0,0 +2011,5,17,20,217,16,21,997,1.79,0,0 +2011,5,17,21,236,16,21,997,3.58,0,0 +2011,5,17,22,237,16,20,997,1.79,0,0 +2011,5,17,23,250,16,21,997,3.13,0,0 +2011,5,18,0,264,15,19,997,0.89,0,0 +2011,5,18,1,251,16,19,997,1.79,0,0 +2011,5,18,2,255,14,20,997,3.58,0,0 +2011,5,18,3,206,14,19,997,5.37,0,0 +2011,5,18,4,204,14,17,997,0.89,0,0 +2011,5,18,5,213,14,18,998,1.78,0,0 +2011,5,18,6,219,15,17,998,2.67,0,0 +2011,5,18,7,227,14,19,998,1.79,0,0 +2011,5,18,8,212,15,21,998,4.92,0,0 +2011,5,18,9,205,15,22,999,6.71,0,0 +2011,5,18,10,209,15,24,999,0.89,0,0 +2011,5,18,11,215,16,25,998,3.13,0,0 +2011,5,18,12,217,16,26,998,6.26,0,0 +2011,5,18,13,237,16,26,998,0.89,0,0 +2011,5,18,14,226,18,27,997,3.13,0,0 +2011,5,18,15,236,17,28,997,4.92,0,0 +2011,5,18,16,221,17,27,997,6.71,0,0 +2011,5,18,17,222,17,26,998,8.5,0,0 +2011,5,18,18,205,9,27,999,9.83,0,0 +2011,5,18,19,75,9,26,1000,16.98,0,0 +2011,5,18,20,32,8,24,1002,26.81,0,0 +2011,5,18,21,27,8,23,1004,36.64,0,0 +2011,5,18,22,23,5,22,1005,46.47,0,0 +2011,5,18,23,19,5,20,1006,56.3,0,0 +2011,5,19,0,7,5,20,1007,62.11,0,0 +2011,5,19,1,6,6,18,1008,67.03,0,0 +2011,5,19,2,7,6,18,1009,71.95,0,0 +2011,5,19,3,6,6,15,1010,76.87,0,0 +2011,5,19,4,6,4,17,1011,84.92,0,0 +2011,5,19,5,5,3,17,1011,93.86,0,0 +2011,5,19,6,6,2,17,1012,98.78,0,0 +2011,5,19,7,10,1,18,1013,104.59,0,0 +2011,5,19,8,11,1,19,1013,110.4,0,0 +2011,5,19,9,11,1,21,1013,116.21,0,0 +2011,5,19,10,8,0,21,1012,7.15,0,0 +2011,5,19,11,7,1,22,1012,11.17,0,0 +2011,5,19,12,9,1,23,1012,1.79,0,0 +2011,5,19,13,13,2,24,1010,1.79,0,0 +2011,5,19,14,14,3,26,1010,3.58,0,0 +2011,5,19,15,16,5,25,1009,4.02,0,0 +2011,5,19,16,15,5,24,1008,8.04,0,0 +2011,5,19,17,19,5,24,1008,13.85,0,0 +2011,5,19,18,11,5,23,1009,17.87,0,1 +2011,5,19,19,15,5,22,1010,25.02,0,2 +2011,5,19,20,20,7,21,1010,26.81,0,0 +2011,5,19,21,29,7,20,1010,32.62,0,0 +2011,5,19,22,27,6,20,1010,37.54,0,0 +2011,5,19,23,27,6,19,1010,41.56,0,0 +2011,5,20,0,28,6,19,1010,44.69,0,0 +2011,5,20,1,33,7,20,1011,3.13,0,0 +2011,5,20,2,35,8,20,1011,8.05,0,0 +2011,5,20,3,26,7,17,1012,11.18,0,0 +2011,5,20,4,20,-4,19,1012,19.23,0,0 +2011,5,20,5,23,-6,18,1013,27.28,0,0 +2011,5,20,6,46,-7,17,1013,36.22,0,0 +2011,5,20,7,17,-7,17,1014,46.05,0,0 +2011,5,20,8,14,-6,17,1015,59.91,0,0 +2011,5,20,9,18,-5,16,1016,67.96,0,0 +2011,5,20,10,16,-5,16,1016,75.11,0,0 +2011,5,20,11,10,-4,17,1016,82.26,0,0 +2011,5,20,12,11,-4,18,1016,88.07,0,0 +2011,5,20,13,12,-4,18,1015,92.09,0,0 +2011,5,20,14,7,-4,18,1015,3.13,0,0 +2011,5,20,15,19,-4,19,1014,4.92,0,0 +2011,5,20,16,180,-3,20,1014,0.89,0,0 +2011,5,20,17,69,-2,19,1013,3.13,0,0 +2011,5,20,18,47,-1,18,1014,4.92,0,0 +2011,5,20,19,31,-1,17,1014,6.71,0,0 +2011,5,20,20,51,0,15,1014,8.5,0,0 +2011,5,20,21,32,0,15,1015,10.29,0,0 +2011,5,20,22,22,0,13,1015,11.18,0,0 +2011,5,20,23,27,-2,15,1015,14.31,0,0 +2011,5,21,0,26,-1,14,1014,16.1,0,0 +2011,5,21,1,34,2,12,1014,17.89,0,0 +2011,5,21,2,39,3,10,1014,19.68,0,0 +2011,5,21,3,44,1,9,1014,3.13,0,0 +2011,5,21,4,56,2,9,1015,4.92,0,0 +2011,5,21,5,52,1,11,1015,8.05,0,0 +2011,5,21,6,47,0,13,1015,11.18,0,0 +2011,5,21,7,41,-2,15,1016,14.31,0,0 +2011,5,21,8,54,-3,19,1016,24.14,0,0 +2011,5,21,9,52,-3,20,1016,35.32,0,0 +2011,5,21,10,55,-4,21,1016,43.37,0,0 +2011,5,21,11,53,-4,23,1014,50.52,0,0 +2011,5,21,12,91,-4,24,1014,56.33,0,0 +2011,5,21,13,25,-7,24,1013,64.38,0,0 +2011,5,21,14,15,-6,26,1012,70.19,0,0 +2011,5,21,15,34,-5,26,1011,73.32,0,0 +2011,5,21,16,43,-5,26,1011,4.02,0,0 +2011,5,21,17,16,-5,26,1010,8.05,0,0 +2011,5,21,18,9,-7,26,1010,13.86,0,0 +2011,5,21,19,30,-1,22,1011,8.05,0,0 +2011,5,21,20,51,-1,21,1011,15.2,0,0 +2011,5,21,21,55,0,20,1012,22.35,0,0 +2011,5,21,22,29,1,19,1013,27.27,0,0 +2011,5,21,23,56,1,19,1013,31.29,0,0 +2011,5,22,0,36,2,18,1012,35.31,0,0 +2011,5,22,1,40,4,14,1013,0.45,0,0 +2011,5,22,2,55,4,13,1013,0.9,0,0 +2011,5,22,3,47,6,11,1013,1.79,0,0 +2011,5,22,4,53,4,8,1012,0.89,0,0 +2011,5,22,5,58,5,9,1013,1.78,0,0 +2011,5,22,6,74,6,11,1013,1.79,0,0 +2011,5,22,7,122,5,15,1013,3.58,0,0 +2011,5,22,8,115,6,18,1014,0.89,0,0 +2011,5,22,9,67,6,21,1013,1.78,0,0 +2011,5,22,10,114,6,24,1012,3.13,0,0 +2011,5,22,11,69,5,26,1012,3.13,0,0 +2011,5,22,12,76,1,30,1011,5.81,0,0 +2011,5,22,13,55,0,31,1010,12.96,0,0 +2011,5,22,14,25,-3,31,1009,21.01,0,0 +2011,5,22,15,24,-4,31,1008,29.95,0,0 +2011,5,22,16,21,-2,30,1008,39.78,0,0 +2011,5,22,17,24,-1,29,1007,49.61,0,0 +2011,5,22,18,31,-1,28,1007,57.66,0,0 +2011,5,22,19,116,1,26,1008,64.81,0,0 +2011,5,22,20,118,-1,25,1008,72.86,0,0 +2011,5,22,21,89,3,23,1009,77.78,0,0 +2011,5,22,22,49,3,20,1010,82.7,0,0 +2011,5,22,23,83,3,20,1010,87.62,0,0 +2011,5,23,0,73,4,19,1011,89.41,0,0 +2011,5,23,1,69,5,17,1011,91.2,0,0 +2011,5,23,2,66,5,15,1011,0.89,0,0 +2011,5,23,3,77,6,13,1012,1.79,0,0 +2011,5,23,4,78,5,14,1012,3.58,0,0 +2011,5,23,5,81,7,13,1012,5.37,0,0 +2011,5,23,6,89,8,14,1013,8.5,0,0 +2011,5,23,7,123,10,17,1013,10.29,0,0 +2011,5,23,8,149,11,19,1014,0.89,0,0 +2011,5,23,9,155,11,21,1014,1.79,0,0 +2011,5,23,10,168,12,22,1014,0.89,0,0 +2011,5,23,11,200,12,23,1014,3.13,0,0 +2011,5,23,12,187,12,24,1014,6.26,0,0 +2011,5,23,13,152,13,26,1013,9.39,0,0 +2011,5,23,14,123,11,28,1012,13.41,0,0 +2011,5,23,15,114,10,28,1011,19.22,0,0 +2011,5,23,16,120,9,29,1011,25.03,0,0 +2011,5,23,17,109,9,28,1011,32.18,0,0 +2011,5,23,18,90,8,27,1011,39.33,0,0 +2011,5,23,19,70,5,26,1011,45.14,0,0 +2011,5,23,20,61,5,24,1011,50.06,0,0 +2011,5,23,21,47,5,23,1012,54.08,0,0 +2011,5,23,22,46,6,22,1013,58.1,0,0 +2011,5,23,23,41,7,22,1013,63.02,0,0 +2011,5,24,0,53,8,20,1013,67.04,0,0 +2011,5,24,1,49,9,20,1013,71.96,0,0 +2011,5,24,2,58,10,19,1013,75.98,0,0 +2011,5,24,3,57,10,18,1013,77.77,0,0 +2011,5,24,4,59,11,16,1014,79.56,0,0 +2011,5,24,5,63,11,16,1014,81.35,0,0 +2011,5,24,6,71,12,17,1014,83.14,0,0 +2011,5,24,7,81,13,18,1014,86.27,0,0 +2011,5,24,8,86,13,20,1015,88.06,0,0 +2011,5,24,9,100,13,22,1015,89.85,0,0 +2011,5,24,10,106,13,23,1015,92.98,0,0 +2011,5,24,11,109,13,25,1015,96.11,0,0 +2011,5,24,12,102,10,27,1014,100.13,0,0 +2011,5,24,13,107,9,27,1013,105.05,0,0 +2011,5,24,14,77,8,28,1013,110.86,0,0 +2011,5,24,15,57,7,27,1013,118.01,0,0 +2011,5,24,16,63,8,28,1013,125.16,0,0 +2011,5,24,17,50,7,27,1012,130.97,0,0 +2011,5,24,18,46,7,25,1013,135.89,0,0 +2011,5,24,19,35,6,25,1013,141.7,0,0 +2011,5,24,20,67,7,23,1013,146.62,0,0 +2011,5,24,21,53,9,22,1014,149.75,0,0 +2011,5,24,22,59,10,23,1015,153.77,0,0 +2011,5,24,23,53,10,22,1015,158.69,0,0 +2011,5,25,0,78,10,21,1014,163.61,0,0 +2011,5,25,1,46,10,20,1014,166.74,0,0 +2011,5,25,2,45,11,19,1014,169.87,0,0 +2011,5,25,3,51,11,18,1014,171.66,0,0 +2011,5,25,4,58,11,18,1014,174.79,0,0 +2011,5,25,5,58,12,17,1014,177.92,0,0 +2011,5,25,6,56,12,17,1014,181.05,0,0 +2011,5,25,7,66,12,20,1014,185.07,0,0 +2011,5,25,8,72,12,22,1014,188.2,0,0 +2011,5,25,9,80,12,24,1014,193.12,0,0 +2011,5,25,10,79,12,25,1014,198.04,0,0 +2011,5,25,11,69,10,27,1014,203.85,0,0 +2011,5,25,12,73,9,28,1014,211.9,0,0 +2011,5,25,13,66,8,29,1013,219.95,0,0 +2011,5,25,14,62,8,29,1012,227.1,0,0 +2011,5,25,15,66,9,29,1012,235.15,0,0 +2011,5,25,16,61,8,28,1011,243.2,0,0 +2011,5,25,17,55,6,27,1010,251.25,0,0 +2011,5,25,18,55,7,27,1010,259.3,0,0 +2011,5,25,19,NA,7,26,1011,265.11,0,0 +2011,5,25,20,47,8,23,1012,266.9,0,0 +2011,5,25,21,48,10,22,1013,0.89,0,0 +2011,5,25,22,62,10,21,1013,1.79,0,0 +2011,5,25,23,57,12,20,1014,4.92,0,0 +2011,5,26,0,88,12,20,1014,8.05,0,0 +2011,5,26,1,96,11,19,1013,11.18,0,0 +2011,5,26,2,97,10,19,1013,12.97,0,0 +2011,5,26,3,95,10,18,1013,14.76,0,0 +2011,5,26,4,100,10,19,1012,17.89,0,0 +2011,5,26,5,100,10,18,1013,19.68,0,0 +2011,5,26,6,112,11,18,1013,22.81,0,0 +2011,5,26,7,115,11,20,1013,26.83,0,0 +2011,5,26,8,116,13,19,1013,30.85,0,1 +2011,5,26,9,116,13,24,1013,33.98,0,0 +2011,5,26,10,110,12,25,1013,38,0,0 +2011,5,26,11,NA,11,27,1012,45.15,0,0 +2011,5,26,12,NA,9,29,1011,52.3,0,0 +2011,5,26,13,NA,9,28,1010,59.45,0,0 +2011,5,26,14,NA,6,28,1009,67.5,0,0 +2011,5,26,15,NA,6,26,1010,74.65,0,1 +2011,5,26,16,NA,9,23,1011,77.78,0,2 +2011,5,26,17,NA,13,17,1013,5.81,0,3 +2011,5,26,18,NA,14,17,1012,4.02,0,0 +2011,5,26,19,NA,14,17,1012,1.79,0,0 +2011,5,26,20,NA,14,16,1013,0.45,0,0 +2011,5,26,21,NA,14,15,1013,0.89,0,0 +2011,5,26,22,NA,14,15,1013,0.89,0,0 +2011,5,26,23,NA,15,16,1013,1.79,0,0 +2011,5,27,0,NA,14,15,1014,0.45,0,0 +2011,5,27,1,NA,13,14,1013,0.89,0,0 +2011,5,27,2,NA,13,14,1013,0.45,0,0 +2011,5,27,3,NA,13,14,1013,0.89,0,0 +2011,5,27,4,NA,12,13,1013,1.78,0,0 +2011,5,27,5,NA,12,13,1013,4.91,0,0 +2011,5,27,6,NA,13,14,1013,6.7,0,0 +2011,5,27,7,NA,14,15,1013,7.59,0,0 +2011,5,27,8,NA,14,18,1013,0.89,0,0 +2011,5,27,9,NA,14,21,1013,1.78,0,0 +2011,5,27,10,NA,14,23,1013,2.67,0,0 +2011,5,27,11,NA,14,23,1012,3.56,0,0 +2011,5,27,12,NA,14,25,1011,5.35,0,0 +2011,5,27,13,NA,14,28,1010,4.02,0,0 +2011,5,27,14,NA,15,28,1010,7.15,0,0 +2011,5,27,15,NA,15,28,1009,11.17,0,0 +2011,5,27,16,97,14,28,1009,15.19,0,0 +2011,5,27,17,82,14,29,1008,19.21,0,0 +2011,5,27,18,73,13,28,1008,24.13,0,0 +2011,5,27,19,96,15,26,1009,28.15,0,0 +2011,5,27,20,107,15,25,1009,32.17,0,0 +2011,5,27,21,122,16,24,1010,36.19,0,0 +2011,5,27,22,137,16,23,1010,37.98,0,0 +2011,5,27,23,132,16,21,1010,39.77,0,0 +2011,5,28,0,153,17,20,1010,41.56,0,0 +2011,5,28,1,160,17,20,1009,44.69,0,0 +2011,5,28,2,177,17,19,1009,46.48,0,0 +2011,5,28,3,190,16,19,1009,48.27,0,0 +2011,5,28,4,186,16,18,1009,0.89,0,0 +2011,5,28,5,195,16,18,1010,0.89,0,0 +2011,5,28,6,153,17,19,1010,0.89,0,0 +2011,5,28,7,184,17,20,1010,1.79,0,0 +2011,5,28,8,171,18,22,1011,4.92,0,0 +2011,5,28,9,154,18,24,1011,6.71,0,0 +2011,5,28,10,253,17,26,1010,9.84,0,0 +2011,5,28,11,140,16,28,1010,12.97,0,0 +2011,5,28,12,113,14,30,1010,16.1,0,0 +2011,5,28,13,122,12,32,1008,21.02,0,0 +2011,5,28,14,119,12,33,1008,26.83,0,0 +2011,5,28,15,124,12,33,1007,34.88,0,0 +2011,5,28,16,105,12,32,1006,40.69,0,0 +2011,5,28,17,106,14,31,1005,46.5,0,0 +2011,5,28,18,104,16,31,1006,52.31,0,0 +2011,5,28,19,112,16,29,1006,58.12,0,0 +2011,5,28,20,109,17,27,1007,59.91,0,0 +2011,5,28,21,110,17,27,1008,63.04,0,0 +2011,5,28,22,100,17,28,1009,66.17,0,0 +2011,5,28,23,100,18,27,1008,71.09,0,0 +2011,5,29,0,99,18,26,1008,74.22,0,0 +2011,5,29,1,101,18,24,1008,76.01,0,0 +2011,5,29,2,100,18,23,1008,79.14,0,0 +2011,5,29,3,99,18,22,1008,0.89,0,0 +2011,5,29,4,97,18,21,1008,1.79,0,0 +2011,5,29,5,100,18,21,1008,3.58,0,0 +2011,5,29,6,95,18,22,1009,5.37,0,0 +2011,5,29,7,90,18,22,1009,6.26,0,0 +2011,5,29,8,99,18,23,1009,9.39,0,0 +2011,5,29,9,105,17,24,1009,11.18,0,0 +2011,5,29,10,112,17,26,1009,1.79,0,0 +2011,5,29,11,109,18,26,1008,3.58,0,0 +2011,5,29,12,116,17,27,1008,1.79,0,0 +2011,5,29,13,118,18,27,1008,4.92,0,0 +2011,5,29,14,112,18,28,1007,8.05,0,0 +2011,5,29,15,104,18,27,1006,11.18,0,0 +2011,5,29,16,112,18,27,1006,0.89,0,0 +2011,5,29,17,117,18,28,1005,1.79,0,0 +2011,5,29,18,139,18,28,1006,1.79,0,0 +2011,5,29,19,146,18,26,1006,4.02,0,0 +2011,5,29,20,150,18,25,1006,4.91,0,0 +2011,5,29,21,114,16,26,1006,3.13,0,0 +2011,5,29,22,61,14,25,1006,6.26,0,0 +2011,5,29,23,92,15,23,1005,3.13,0,0 +2011,5,30,0,96,15,23,1005,1.79,0,0 +2011,5,30,1,107,15,23,1005,3.58,0,0 +2011,5,30,2,82,15,21,1005,6.71,0,0 +2011,5,30,3,71,15,21,1005,8.5,0,0 +2011,5,30,4,58,15,19,1004,1.79,0,0 +2011,5,30,5,55,15,19,1004,0.89,0,0 +2011,5,30,6,62,16,19,1004,4.02,0,0 +2011,5,30,7,56,16,22,1004,4.91,0,0 +2011,5,30,8,52,16,23,1004,8.04,0,0 +2011,5,30,9,99,15,26,1004,9.83,0,0 +2011,5,30,10,65,15,28,1004,1.79,0,0 +2011,5,30,11,72,14,29,1003,1.79,0,0 +2011,5,30,12,85,14,30,1002,1.79,0,0 +2011,5,30,13,85,14,33,1001,4.02,0,0 +2011,5,30,14,44,10,28,1000,7.15,0,0 +2011,5,30,15,67,4,32,1000,3.13,0,0 +2011,5,30,16,26,7,31,1000,4.92,0,0 +2011,5,30,17,26,8,29,999,8.94,0,0 +2011,5,30,18,28,9,27,999,12.96,0,0 +2011,5,30,19,39,11,26,1001,16.98,0,0 +2011,5,30,20,49,10,23,1002,1.79,0,0 +2011,5,30,21,15,11,20,1002,2.68,0,0 +2011,5,30,22,12,3,25,1003,4.02,0,0 +2011,5,30,23,18,3,21,1003,8.04,0,0 +2011,5,31,0,29,5,21,1002,0.89,0,0 +2011,5,31,1,13,4,22,1002,3.13,0,0 +2011,5,31,2,14,5,22,1002,7.15,0,0 +2011,5,31,3,15,6,22,1001,11.17,0,0 +2011,5,31,4,17,6,23,1001,19.22,0,0 +2011,5,31,5,10,7,22,1001,27.27,0,0 +2011,5,31,6,10,7,23,1002,35.32,0,0 +2011,5,31,7,9,7,23,1002,43.37,0,0 +2011,5,31,8,9,8,23,1002,52.31,0,0 +2011,5,31,9,9,9,26,1002,59.46,0,0 +2011,5,31,10,8,9,27,1002,66.61,0,0 +2011,5,31,11,8,9,27,1002,73.76,0,0 +2011,5,31,12,9,9,28,1001,79.57,0,0 +2011,5,31,13,11,9,28,1001,85.38,0,0 +2011,5,31,14,13,13,25,1001,94.32,0,0 +2011,5,31,15,14,12,25,1002,99.24,0,0 +2011,5,31,16,12,11,25,1002,110.42,0,1 +2011,5,31,17,16,11,24,1003,120.25,0,0 +2011,5,31,18,10,11,24,1003,129.19,0,0 +2011,5,31,19,24,9,24,1003,137.24,0,0 +2011,5,31,20,NA,9,23,1004,142.16,0,0 +2011,5,31,21,14,9,23,1004,147.08,0,0 +2011,5,31,22,20,8,22,1005,152,0,0 +2011,5,31,23,10,9,22,1005,156.92,0,0 +2011,6,1,0,12,9,21,1005,160.94,0,0 +2011,6,1,1,17,8,22,1006,165.86,0,0 +2011,6,1,2,12,8,22,1006,170.78,0,0 +2011,6,1,3,16,8,22,1006,175.7,0,0 +2011,6,1,4,8,8,22,1006,181.51,0,0 +2011,6,1,5,11,8,22,1006,185.53,0,0 +2011,6,1,6,17,10,21,1007,188.66,0,0 +2011,6,1,7,8,9,22,1007,192.68,0,0 +2011,6,1,8,16,9,25,1008,196.7,0,0 +2011,6,1,9,15,9,25,1008,200.72,0,0 +2011,6,1,10,15,8,28,1008,203.85,0,0 +2011,6,1,11,13,8,29,1008,206.98,0,0 +2011,6,1,12,20,7,31,1007,0.89,0,0 +2011,6,1,13,9,6,32,1006,1.79,0,0 +2011,6,1,14,10,3,33,1006,1.79,0,0 +2011,6,1,15,11,6,29,1006,5.81,0,0 +2011,6,1,16,17,5,31,1006,3.13,0,0 +2011,6,1,17,18,4,31,1006,7.15,0,0 +2011,6,1,18,28,6,30,1006,10.28,0,0 +2011,6,1,19,NA,6,29,1006,14.3,0,0 +2011,6,1,20,28,7,26,1007,16.09,0,0 +2011,6,1,21,29,7,24,1007,19.22,0,0 +2011,6,1,22,33,9,22,1007,21.01,0,0 +2011,6,1,23,29,9,22,1008,22.8,0,0 +2011,6,2,0,33,9,22,1008,25.93,0,0 +2011,6,2,1,33,11,20,1007,0.89,0,0 +2011,6,2,2,31,10,19,1007,1.79,0,0 +2011,6,2,3,36,13,20,1007,5.81,0,0 +2011,6,2,4,45,12,19,1007,9.83,0,0 +2011,6,2,5,48,12,18,1007,11.62,0,0 +2011,6,2,6,57,13,20,1007,0.89,0,0 +2011,6,2,7,81,13,21,1007,1.78,0,0 +2011,6,2,8,98,13,23,1007,1.79,0,0 +2011,6,2,9,99,12,26,1006,3.58,0,0 +2011,6,2,10,143,11,26,1006,1.79,0,0 +2011,6,2,11,105,12,30,1004,3.58,0,0 +2011,6,2,12,83,11,32,1004,1.79,0,0 +2011,6,2,13,81,10,35,1002,3.13,0,0 +2011,6,2,14,128,9,35,1002,5.81,0,0 +2011,6,2,15,127,8,36,1000,11.62,0,0 +2011,6,2,16,64,7,35,999,20.56,0,0 +2011,6,2,17,60,5,33,1000,27.71,0,0 +2011,6,2,18,100,8,33,999,32.63,0,0 +2011,6,2,19,92,8,32,1000,38.44,0,0 +2011,6,2,20,116,9,31,1000,44.25,0,0 +2011,6,2,21,74,10,31,1001,47.38,0,0 +2011,6,2,22,64,10,28,1001,4.02,0,0 +2011,6,2,23,73,10,24,1001,4.02,0,0 +2011,6,3,0,69,10,23,1001,8.94,0,0 +2011,6,3,1,68,11,24,1001,13.86,0,0 +2011,6,3,2,37,10,24,1002,4.02,0,0 +2011,6,3,3,37,10,24,1002,4.92,0,0 +2011,6,3,4,32,8,23,1003,5.81,0,0 +2011,6,3,5,23,6,23,1004,12.96,0,0 +2011,6,3,6,18,3,23,1005,21.01,0,0 +2011,6,3,7,25,3,24,1006,28.16,0,0 +2011,6,3,8,34,1,25,1006,8.94,0,0 +2011,6,3,9,9,2,24,1006,16.09,0,0 +2011,6,3,10,12,-1,28,1005,21.01,0,0 +2011,6,3,11,28,-1,27,1005,25.03,0,0 +2011,6,3,12,9,0,30,1005,29.95,0,0 +2011,6,3,13,9,0,31,1004,34.87,0,0 +2011,6,3,14,11,1,31,1004,39.79,0,0 +2011,6,3,15,11,4,30,1004,4.02,0,0 +2011,6,3,16,8,5,30,1003,7.15,0,0 +2011,6,3,17,56,6,30,1003,1.79,0,0 +2011,6,3,18,15,1,31,1003,4.92,0,0 +2011,6,3,19,15,3,30,1004,9.84,0,0 +2011,6,3,20,48,5,28,1004,3.13,0,0 +2011,6,3,21,56,5,26,1005,6.26,0,0 +2011,6,3,22,45,5,26,1006,9.39,0,0 +2011,6,3,23,48,9,21,1006,11.18,0,0 +2011,6,4,0,53,8,20,1006,0.89,0,0 +2011,6,4,1,53,9,19,1006,1.79,0,0 +2011,6,4,2,70,9,18,1006,2.68,0,0 +2011,6,4,3,48,10,19,1006,0.89,0,0 +2011,6,4,4,58,11,19,1006,0.45,0,0 +2011,6,4,5,68,10,19,1007,1.34,0,0 +2011,6,4,6,57,9,21,1007,1.79,0,0 +2011,6,4,7,44,9,22,1008,0.89,0,0 +2011,6,4,8,NA,7,25,1008,1.79,0,0 +2011,6,4,9,NA,7,25,1008,1.79,0,0 +2011,6,4,10,NA,7,26,1008,0.89,0,0 +2011,6,4,11,NA,8,28,1008,1.78,0,0 +2011,6,4,12,NA,8,29,1007,3.57,0,0 +2011,6,4,13,30,6,31,1006,7.59,0,0 +2011,6,4,14,44,8,32,1006,4.02,0,0 +2011,6,4,15,37,9,32,1005,8.94,0,0 +2011,6,4,16,40,6,32,1005,14.75,0,0 +2011,6,4,17,45,9,31,1004,20.56,0,0 +2011,6,4,18,21,5,30,1005,27.71,0,0 +2011,6,4,19,18,5,29,1005,31.73,0,0 +2011,6,4,20,25,6,27,1006,35.75,0,0 +2011,6,4,21,27,7,24,1007,38.88,0,0 +2011,6,4,22,24,8,22,1007,0.89,0,0 +2011,6,4,23,22,10,20,1007,1.79,0,0 +2011,6,5,0,28,10,20,1007,3.58,0,0 +2011,6,5,1,40,12,19,1007,0.89,0,0 +2011,6,5,2,63,11,18,1008,1.78,0,0 +2011,6,5,3,78,12,18,1008,2.67,0,0 +2011,6,5,4,100,12,18,1008,3.56,0,0 +2011,6,5,5,109,12,16,1009,4.45,0,0 +2011,6,5,6,124,12,19,1009,5.34,0,0 +2011,6,5,7,125,12,21,1009,1.79,0,0 +2011,6,5,8,133,13,24,1009,0.89,0,0 +2011,6,5,9,133,13,27,1009,1.78,0,0 +2011,6,5,10,126,12,28,1009,1.79,0,0 +2011,6,5,11,142,12,30,1008,4.92,0,0 +2011,6,5,12,109,10,31,1007,8.94,0,0 +2011,6,5,13,43,10,33,1006,14.75,0,0 +2011,6,5,14,57,10,33,1005,20.56,0,0 +2011,6,5,15,90,11,34,1004,26.37,0,0 +2011,6,5,16,83,11,33,1004,31.29,0,0 +2011,6,5,17,60,10,33,1003,38.44,0,0 +2011,6,5,18,67,10,31,1003,43.36,0,0 +2011,6,5,19,67,11,30,1004,49.17,0,0 +2011,6,5,20,59,12,30,1004,53.19,0,0 +2011,6,5,21,64,12,29,1004,54.98,0,0 +2011,6,5,22,69,14,26,1005,0.89,0,0 +2011,6,5,23,67,14,27,1004,1.79,0,0 +2011,6,6,0,75,14,25,1004,3.58,0,0 +2011,6,6,1,65,15,24,1003,5.37,0,0 +2011,6,6,2,50,15,23,1002,7.16,0,0 +2011,6,6,3,41,15,22,1002,1.79,0,0 +2011,6,6,4,44,15,22,1001,3.58,0,0 +2011,6,6,5,74,15,21,1001,1.79,0,0 +2011,6,6,6,92,16,21,1002,0.89,0,0 +2011,6,6,7,103,15,24,1002,1.78,0,0 +2011,6,6,8,NA,15,26,1001,2.67,0,0 +2011,6,6,9,NA,15,28,1001,3.56,0,0 +2011,6,6,10,NA,15,28,1001,1.79,0,0 +2011,6,6,11,87,15,28,1001,0.89,0,0 +2011,6,6,12,85,15,29,1000,1.78,0,0 +2011,6,6,13,100,13,29,1000,1.79,0,1 +2011,6,6,14,100,18,26,999,1.79,0,2 +2011,6,6,15,93,16,30,998,3.13,0,0 +2011,6,6,16,86,17,29,998,6.26,0,0 +2011,6,6,17,91,16,28,999,11.18,0,0 +2011,6,6,18,99,18,28,998,3.13,0,0 +2011,6,6,19,114,18,26,998,0.89,0,0 +2011,6,6,20,123,18,24,999,1.79,0,0 +2011,6,6,21,145,18,23,999,3.58,0,0 +2011,6,6,22,163,18,22,999,1.79,0,0 +2011,6,6,23,170,18,22,999,3.58,0,0 +2011,6,7,0,163,18,21,998,1.79,0,0 +2011,6,7,1,166,17,20,998,0.89,0,0 +2011,6,7,2,167,17,19,998,1.78,0,0 +2011,6,7,3,201,17,20,998,2.67,0,0 +2011,6,7,4,201,17,19,998,0.89,0,0 +2011,6,7,5,204,17,18,998,0.89,0,0 +2011,6,7,6,217,18,19,998,0.89,0,0 +2011,6,7,7,206,17,23,998,2.68,0,0 +2011,6,7,8,241,18,24,998,0.89,0,0 +2011,6,7,9,237,18,27,998,1.79,0,0 +2011,6,7,10,231,17,30,997,3.58,0,0 +2011,6,7,11,177,16,32,997,0.89,0,0 +2011,6,7,12,160,16,34,996,3.13,0,0 +2011,6,7,13,152,16,34,994,8.05,0,0 +2011,6,7,14,102,16,35,994,13.86,0,0 +2011,6,7,15,66,14,34,993,18.78,0,0 +2011,6,7,16,69,14,36,993,22.8,0,0 +2011,6,7,17,69,14,34,993,25.93,0,0 +2011,6,7,18,82,19,26,997,17.88,0,2 +2011,6,7,19,37,19,22,995,3.13,0,0 +2011,6,7,20,47,19,22,995,4.92,0,1 +2011,6,7,21,55,20,22,996,1.79,0,0 +2011,6,7,22,58,19,22,996,3.58,0,0 +2011,6,7,23,59,19,22,997,4.47,0,0 +2011,6,8,0,66,19,23,996,4.02,0,0 +2011,6,8,1,61,17,23,1000,11.18,0,2 +2011,6,8,2,35,17,19,998,20.12,0,3 +2011,6,8,3,9,16,19,995,31.3,0,4 +2011,6,8,4,7,16,19,996,37.11,0,5 +2011,6,8,5,6,16,19,997,41.13,0,6 +2011,6,8,6,7,17,19,998,0.89,0,0 +2011,6,8,7,7,17,19,998,1.78,0,0 +2011,6,8,8,20,18,23,999,3.13,0,0 +2011,6,8,9,13,16,26,999,6.26,0,0 +2011,6,8,10,27,15,28,999,10.28,0,0 +2011,6,8,11,24,14,30,999,13.41,0,0 +2011,6,8,12,14,13,31,999,16.54,0,0 +2011,6,8,13,22,14,33,999,1.79,0,0 +2011,6,8,14,33,15,32,999,4.02,0,0 +2011,6,8,15,49,14,33,998,7.15,0,0 +2011,6,8,16,26,15,33,998,10.28,0,0 +2011,6,8,17,22,15,31,998,14.3,0,0 +2011,6,8,18,27,16,30,998,17.43,0,0 +2011,6,8,19,28,17,29,999,20.56,0,0 +2011,6,8,20,40,18,27,1000,24.58,0,0 +2011,6,8,21,29,18,25,1001,28.6,0,0 +2011,6,8,22,33,19,24,1001,31.73,0,0 +2011,6,8,23,47,19,24,1002,34.86,0,0 +2011,6,9,0,53,19,23,1002,36.65,0,0 +2011,6,9,1,86,19,21,1003,38.44,0,0 +2011,6,9,2,151,19,20,1003,0.89,0,0 +2011,6,9,3,210,18,20,1004,1.79,0,0 +2011,6,9,4,188,18,19,1004,0.89,0,0 +2011,6,9,5,176,18,19,1004,2.68,0,0 +2011,6,9,6,184,19,21,1005,0.89,0,0 +2011,6,9,7,190,19,23,1005,0.89,0,0 +2011,6,9,8,196,19,24,1005,1.78,0,0 +2011,6,9,9,189,19,25,1005,3.57,0,0 +2011,6,9,10,165,19,27,1005,4.46,0,0 +2011,6,9,11,152,18,30,1004,1.79,0,0 +2011,6,9,12,126,17,30,1003,0.89,0,0 +2011,6,9,13,111,17,32,1002,3.13,0,0 +2011,6,9,14,112,16,32,1002,6.26,0,0 +2011,6,9,15,106,13,33,1001,12.07,0,0 +2011,6,9,16,76,15,32,1001,16.09,0,0 +2011,6,9,17,67,16,32,1000,19.22,0,0 +2011,6,9,18,92,13,31,1000,24.14,0,0 +2011,6,9,19,62,15,29,1000,28.16,0,0 +2011,6,9,20,75,15,29,1000,31.29,0,0 +2011,6,9,21,96,17,27,1000,33.08,0,0 +2011,6,9,22,123,18,25,1001,34.87,0,0 +2011,6,9,23,119,18,26,1001,36.66,0,0 +2011,6,10,0,121,18,25,1000,38.45,0,0 +2011,6,10,1,121,19,24,1000,41.58,0,0 +2011,6,10,2,121,19,23,1000,45.6,0,0 +2011,6,10,3,108,18,22,1000,0.89,0,0 +2011,6,10,4,117,19,22,1000,1.79,0,0 +2011,6,10,5,176,18,22,1000,3.58,0,0 +2011,6,10,6,199,18,23,1000,7.6,0,0 +2011,6,10,7,185,17,24,1001,1.79,0,0 +2011,6,10,8,173,17,25,1001,2.68,0,0 +2011,6,10,9,172,18,26,1000,3.57,0,0 +2011,6,10,10,141,18,28,1000,1.79,0,0 +2011,6,10,11,130,18,30,1000,3.58,0,0 +2011,6,10,12,132,16,32,999,1.79,0,0 +2011,6,10,13,117,14,34,998,5.81,0,0 +2011,6,10,14,91,13,35,997,12.96,0,0 +2011,6,10,15,83,12,35,997,20.11,0,0 +2011,6,10,16,71,13,35,996,25.92,0,0 +2011,6,10,17,80,12,33,997,31.73,0,0 +2011,6,10,18,92,13,33,997,36.65,0,0 +2011,6,10,19,80,15,25,1001,7.15,0,0 +2011,6,10,20,48,16,23,1002,12.96,0,1 +2011,6,10,21,64,17,23,1003,14.75,0,2 +2011,6,10,22,74,18,22,1003,17.88,0,0 +2011,6,10,23,84,18,21,1003,4.02,0,0 +2011,6,11,0,96,18,21,1004,7.15,0,0 +2011,6,11,1,122,18,20,1004,10.28,0,0 +2011,6,11,2,53,18,20,1003,12.07,0,0 +2011,6,11,3,52,18,19,1003,13.86,0,0 +2011,6,11,4,57,17,20,1003,0.89,0,0 +2011,6,11,5,87,18,19,1003,1.78,0,0 +2011,6,11,6,95,18,20,1004,1.79,0,0 +2011,6,11,7,109,17,21,1004,0.89,0,0 +2011,6,11,8,96,18,22,1004,1.78,0,0 +2011,6,11,9,91,18,24,1005,3.57,0,0 +2011,6,11,10,82,18,26,1004,5.36,0,0 +2011,6,11,11,73,18,27,1004,6.25,0,0 +2011,6,11,12,81,18,27,1003,3.13,0,0 +2011,6,11,13,67,17,28,1002,4.92,0,0 +2011,6,11,14,74,16,29,1001,3.13,0,0 +2011,6,11,15,85,17,29,1000,5.81,0,0 +2011,6,11,16,29,15,22,1002,12.96,0,0 +2011,6,11,17,25,14,22,1002,16.09,0,0 +2011,6,11,18,39,15,22,1003,0.89,0,0 +2011,6,11,19,27,15,22,1004,1.79,0,0 +2011,6,11,20,33,16,21,1005,0.89,0,0 +2011,6,11,21,43,16,21,1005,1.78,0,0 +2011,6,11,22,55,16,21,1006,1.79,0,0 +2011,6,11,23,48,14,22,1007,4.02,0,1 +2011,6,12,0,20,13,21,1007,8.04,0,0 +2011,6,12,1,16,13,20,1007,9.83,0,0 +2011,6,12,2,18,14,18,1006,0.89,0,0 +2011,6,12,3,14,14,18,1006,1.79,0,0 +2011,6,12,4,12,13,19,1006,2.68,0,0 +2011,6,12,5,18,15,17,1007,0.89,0,0 +2011,6,12,6,10,15,19,1007,0.89,0,0 +2011,6,12,7,19,13,23,1008,1.79,0,0 +2011,6,12,8,15,13,24,1008,4.92,0,0 +2011,6,12,9,12,13,26,1008,8.05,0,0 +2011,6,12,10,11,10,29,1007,12.07,0,0 +2011,6,12,11,10,10,30,1007,15.2,0,0 +2011,6,12,12,11,9,32,1007,18.33,0,0 +2011,6,12,13,13,9,32,1006,22.35,0,0 +2011,6,12,14,22,9,33,1005,3.13,0,0 +2011,6,12,15,15,9,35,1004,4.92,0,0 +2011,6,12,16,14,8,34,1004,5.81,0,0 +2011,6,12,17,17,10,33,1003,3.13,0,0 +2011,6,12,18,46,12,31,1004,6.26,0,0 +2011,6,12,19,48,12,30,1004,11.18,0,0 +2011,6,12,20,49,12,29,1005,14.31,0,0 +2011,6,12,21,39,13,27,1006,17.44,0,0 +2011,6,12,22,34,16,25,1006,20.57,0,0 +2011,6,12,23,45,16,23,1006,3.13,0,0 +2011,6,13,0,52,16,23,1006,3.13,0,0 +2011,6,13,1,63,16,23,1006,4.92,0,0 +2011,6,13,2,79,17,21,1007,0.89,0,0 +2011,6,13,3,85,17,20,1007,1.78,0,0 +2011,6,13,4,95,17,19,1007,1.79,0,0 +2011,6,13,5,96,17,19,1007,0.89,0,0 +2011,6,13,6,98,17,20,1008,1.78,0,0 +2011,6,13,7,117,17,23,1008,1.79,0,0 +2011,6,13,8,119,17,23,1008,3.58,0,0 +2011,6,13,9,103,17,26,1008,5.37,0,0 +2011,6,13,10,98,18,27,1007,1.79,0,0 +2011,6,13,11,96,17,29,1007,3.13,0,0 +2011,6,13,12,98,17,29,1007,6.26,0,0 +2011,6,13,13,95,16,31,1006,9.39,0,0 +2011,6,13,14,88,15,32,1005,1.79,0,0 +2011,6,13,15,83,14,34,1004,3.13,0,0 +2011,6,13,16,69,14,34,1004,6.26,0,0 +2011,6,13,17,65,13,34,1003,9.39,0,0 +2011,6,13,18,63,15,32,1002,13.41,0,0 +2011,6,13,19,45,16,31,1003,16.54,0,0 +2011,6,13,20,70,17,28,1003,18.33,0,0 +2011,6,13,21,63,19,28,1004,22.35,0,0 +2011,6,13,22,116,19,26,1005,24.14,0,0 +2011,6,13,23,127,19,25,1004,27.27,0,0 +2011,6,14,0,132,18,25,1004,31.29,0,0 +2011,6,14,1,147,17,23,1004,34.42,0,0 +2011,6,14,2,156,17,23,1004,37.55,0,0 +2011,6,14,3,125,16,22,1004,0.89,0,0 +2011,6,14,4,100,16,22,1004,1.78,0,0 +2011,6,14,5,93,16,21,1004,1.79,0,0 +2011,6,14,6,94,16,22,1004,3.58,0,0 +2011,6,14,7,94,16,24,1004,5.37,0,0 +2011,6,14,8,133,16,26,1004,7.16,0,0 +2011,6,14,9,153,16,28,1004,8.95,0,0 +2011,6,14,10,187,16,30,1004,1.79,0,0 +2011,6,14,11,195,16,31,1003,3.13,0,0 +2011,6,14,12,205,17,31,1003,7.15,0,0 +2011,6,14,13,234,17,33,1002,11.17,0,0 +2011,6,14,14,236,17,34,1001,18.32,0,0 +2011,6,14,15,231,17,34,1000,24.13,0,0 +2011,6,14,16,220,19,34,1000,29.94,0,0 +2011,6,14,17,175,15,33,1000,35.75,0,0 +2011,6,14,18,146,14,32,1000,38.88,0,1 +2011,6,14,19,151,17,31,1001,0.89,0,2 +2011,6,14,20,141,16,27,1002,4.02,0,3 +2011,6,14,21,142,20,23,1003,4.92,0,5 +2011,6,14,22,123,19,23,1003,8.05,0,0 +2011,6,14,23,78,19,23,1003,12.07,0,0 +2011,6,15,0,54,20,22,1002,1.79,0,0 +2011,6,15,1,71,20,22,1002,1.79,0,0 +2011,6,15,2,106,19,21,1002,0.89,0,0 +2011,6,15,3,128,19,21,1001,1.78,0,0 +2011,6,15,4,124,19,21,1002,0.89,0,0 +2011,6,15,5,135,19,21,1002,0.89,0,0 +2011,6,15,6,167,20,21,1003,1.78,0,0 +2011,6,15,7,165,19,23,1003,2.67,0,0 +2011,6,15,8,171,19,24,1003,1.79,0,0 +2011,6,15,9,170,20,25,1003,4.92,0,0 +2011,6,15,10,156,20,27,1003,8.05,0,0 +2011,6,15,11,167,20,29,1003,11.18,0,0 +2011,6,15,12,175,21,30,1003,14.31,0,0 +2011,6,15,13,164,20,31,1002,20.12,0,0 +2011,6,15,14,153,20,30,1001,25.93,0,0 +2011,6,15,15,152,20,29,1001,31.74,0,0 +2011,6,15,16,158,20,29,1001,35.76,0,1 +2011,6,15,17,144,18,29,1001,39.78,0,0 +2011,6,15,18,142,15,28,1001,42.91,0,1 +2011,6,15,19,135,19,21,1004,5.81,0,2 +2011,6,15,20,165,18,21,1003,4.02,0,0 +2011,6,15,21,198,17,22,1004,0.89,0,0 +2011,6,15,22,195,17,22,1005,1.78,0,0 +2011,6,15,23,138,18,21,1005,2.67,0,1 +2011,6,16,0,154,18,21,1004,1.79,0,2 +2011,6,16,1,192,19,21,1005,4.92,0,3 +2011,6,16,2,177,16,21,1005,10.73,0,0 +2011,6,16,3,120,16,21,1005,14.75,0,0 +2011,6,16,4,96,17,20,1006,0.89,0,0 +2011,6,16,5,80,17,18,1006,1.78,0,0 +2011,6,16,6,91,17,18,1006,1.79,0,0 +2011,6,16,7,76,17,19,1006,0.89,0,0 +2011,6,16,8,69,17,20,1007,1.78,0,0 +2011,6,16,9,69,17,20,1007,2.67,0,0 +2011,6,16,10,80,16,21,1007,1.79,0,0 +2011,6,16,11,95,17,22,1007,3.13,0,0 +2011,6,16,12,95,17,22,1006,3.13,0,0 +2011,6,16,13,97,17,23,1006,6.26,0,0 +2011,6,16,14,124,18,23,1005,8.05,0,0 +2011,6,16,15,140,18,24,1005,11.18,0,0 +2011,6,16,16,123,18,23,1005,14.31,0,0 +2011,6,16,17,139,18,23,1005,16.1,0,0 +2011,6,16,18,144,17,23,1006,16.99,0,0 +2011,6,16,19,135,18,22,1006,0.89,0,0 +2011,6,16,20,137,18,23,1007,1.79,0,0 +2011,6,16,21,113,17,22,1007,4.92,0,0 +2011,6,16,22,117,17,22,1008,0.89,0,0 +2011,6,16,23,114,17,21,1008,3.13,0,0 +2011,6,17,0,115,17,21,1008,6.26,0,0 +2011,6,17,1,134,17,21,1008,9.39,0,0 +2011,6,17,2,134,17,21,1008,11.18,0,0 +2011,6,17,3,125,17,21,1008,0.89,0,0 +2011,6,17,4,106,17,21,1008,1.78,0,0 +2011,6,17,5,121,18,20,1009,2.23,0,0 +2011,6,17,6,110,18,21,1009,3.13,0,0 +2011,6,17,7,115,18,22,1009,4.92,0,0 +2011,6,17,8,102,17,24,1009,0.89,0,0 +2011,6,17,9,88,17,24,1009,1.78,0,0 +2011,6,17,10,87,18,25,1009,2.67,0,0 +2011,6,17,11,85,18,26,1009,4.46,0,0 +2011,6,17,12,88,18,28,1008,6.25,0,0 +2011,6,17,13,85,19,29,1008,1.79,0,0 +2011,6,17,14,80,18,29,1007,3.13,0,0 +2011,6,17,15,67,18,30,1007,3.13,0,0 +2011,6,17,16,66,16,30,1006,1.79,0,0 +2011,6,17,17,69,18,30,1006,4.02,0,0 +2011,6,17,18,79,19,29,1006,8.04,0,0 +2011,6,17,19,89,19,27,1006,11.17,0,0 +2011,6,17,20,87,19,25,1007,12.96,0,0 +2011,6,17,21,104,19,24,1007,14.75,0,0 +2011,6,17,22,135,19,24,1008,15.64,0,0 +2011,6,17,23,142,19,23,1008,17.43,0,0 +2011,6,18,0,151,19,22,1008,18.32,0,0 +2011,6,18,1,179,19,21,1008,0.89,0,0 +2011,6,18,2,190,19,21,1008,1.79,0,0 +2011,6,18,3,194,19,21,1007,0.89,0,0 +2011,6,18,4,212,19,20,1008,1.78,0,0 +2011,6,18,5,257,19,20,1008,0.89,0,0 +2011,6,18,6,244,19,20,1008,1.78,0,0 +2011,6,18,7,257,20,23,1008,0.45,0,0 +2011,6,18,8,227,21,24,1007,1.34,0,0 +2011,6,18,9,198,20,26,1007,2.23,0,0 +2011,6,18,10,180,20,28,1007,3.12,0,0 +2011,6,18,11,154,19,30,1007,4.01,0,0 +2011,6,18,12,132,20,31,1007,1.79,0,0 +2011,6,18,13,106,19,32,1006,3.13,0,0 +2011,6,18,14,95,19,33,1005,1.79,0,0 +2011,6,18,15,101,15,33,1005,4.02,0,0 +2011,6,18,16,110,15,33,1005,8.04,0,0 +2011,6,18,17,99,18,33,1004,12.06,0,0 +2011,6,18,18,113,20,31,1004,16.98,0,0 +2011,6,18,19,147,19,30,1005,20.11,0,0 +2011,6,18,20,269,20,27,1006,25.03,0,0 +2011,6,18,21,285,20,26,1006,29.95,0,0 +2011,6,18,22,245,21,25,1007,33.97,0,0 +2011,6,18,23,237,20,23,1008,35.76,0,0 +2011,6,19,0,221,20,22,1008,37.55,0,0 +2011,6,19,1,221,20,22,1008,39.34,0,0 +2011,6,19,2,201,19,21,1008,42.47,0,0 +2011,6,19,3,195,19,21,1008,44.26,0,0 +2011,6,19,4,205,19,20,1008,0.89,0,0 +2011,6,19,5,242,19,20,1008,1.78,0,0 +2011,6,19,6,235,20,21,1008,0.89,0,0 +2011,6,19,7,209,20,21,1009,0.89,0,0 +2011,6,19,8,199,20,23,1009,1.78,0,0 +2011,6,19,9,198,21,25,1008,2.67,0,0 +2011,6,19,10,205,20,26,1008,3.56,0,0 +2011,6,19,11,207,21,27,1007,1.79,0,0 +2011,6,19,12,201,20,29,1007,1.79,0,0 +2011,6,19,13,182,21,30,1006,3.58,0,0 +2011,6,19,14,167,20,31,1005,1.79,0,0 +2011,6,19,15,161,20,31,1005,4.92,0,0 +2011,6,19,16,167,20,31,1005,8.94,0,0 +2011,6,19,17,155,20,32,1004,12.07,0,0 +2011,6,19,18,149,19,31,1004,16.09,0,0 +2011,6,19,19,158,19,29,1005,20.11,0,0 +2011,6,19,20,203,19,28,1005,24.13,0,0 +2011,6,19,21,209,18,26,1006,28.15,0,0 +2011,6,19,22,206,18,25,1006,32.17,0,0 +2011,6,19,23,217,17,24,1006,33.96,0,0 +2011,6,20,0,222,18,23,1006,34.85,0,0 +2011,6,20,1,236,17,23,1006,36.64,0,0 +2011,6,20,2,207,18,23,1006,38.43,0,0 +2011,6,20,3,254,18,22,1006,0.89,0,0 +2011,6,20,4,205,18,22,1006,1.78,0,0 +2011,6,20,5,230,18,20,1006,0.89,0,0 +2011,6,20,6,214,19,21,1006,0.89,0,0 +2011,6,20,7,187,19,23,1006,1.78,0,0 +2011,6,20,8,192,20,24,1006,2.67,0,0 +2011,6,20,9,184,20,25,1006,3.56,0,0 +2011,6,20,10,174,20,26,1006,1.79,0,0 +2011,6,20,11,185,20,29,1005,0.89,0,0 +2011,6,20,12,173,19,30,1004,2.68,0,0 +2011,6,20,13,158,20,31,1003,1.79,0,0 +2011,6,20,14,165,21,32,1003,4.92,0,0 +2011,6,20,15,134,20,34,1002,8.05,0,0 +2011,6,20,16,104,15,35,1002,13.86,0,0 +2011,6,20,17,120,15,34,1001,18.78,0,0 +2011,6,20,18,111,14,33,1001,22.8,0,0 +2011,6,20,19,139,14,32,1002,25.93,0,0 +2011,6,20,20,173,16,30,1002,27.72,0,0 +2011,6,20,21,192,18,28,1003,29.51,0,0 +2011,6,20,22,181,18,27,1003,30.4,0,0 +2011,6,20,23,183,18,26,1003,32.19,0,0 +2011,6,21,0,199,17,27,1003,33.98,0,0 +2011,6,21,1,183,17,27,1003,37.11,0,0 +2011,6,21,2,158,18,26,1003,40.24,0,0 +2011,6,21,3,160,18,26,1003,43.37,0,0 +2011,6,21,4,161,18,25,1003,44.26,0,0 +2011,6,21,5,171,19,24,1004,46.05,0,0 +2011,6,21,6,176,19,24,1004,47.84,0,0 +2011,6,21,7,176,20,26,1004,49.63,0,0 +2011,6,21,8,172,20,27,1004,51.42,0,0 +2011,6,21,9,169,19,29,1004,54.55,0,0 +2011,6,21,10,178,19,32,1004,58.57,0,0 +2011,6,21,11,149,19,33,1003,4.02,0,0 +2011,6,21,12,164,15,34,1003,7.15,0,0 +2011,6,21,13,117,15,34,1002,12.96,0,0 +2011,6,21,14,120,15,35,1001,18.77,0,0 +2011,6,21,15,109,15,35,1001,23.69,0,0 +2011,6,21,16,106,16,35,1000,29.5,0,0 +2011,6,21,17,137,15,34,1000,33.52,0,0 +2011,6,21,18,91,15,33,1000,37.54,0,0 +2011,6,21,19,91,15,32,1000,41.56,0,0 +2011,6,21,20,101,15,31,1000,43.35,0,0 +2011,6,21,21,116,16,30,1000,46.48,0,0 +2011,6,21,22,145,16,29,1000,49.61,0,0 +2011,6,21,23,164,17,28,1000,52.74,0,0 +2011,6,22,0,166,18,27,1000,55.87,0,0 +2011,6,22,1,156,18,27,1000,59,0,0 +2011,6,22,2,146,19,27,1000,62.13,0,0 +2011,6,22,3,137,19,26,999,65.26,0,0 +2011,6,22,4,145,19,26,999,68.39,0,0 +2011,6,22,5,146,19,25,999,70.18,0,0 +2011,6,22,6,204,19,25,999,0.89,0,0 +2011,6,22,7,231,20,26,999,1.78,0,0 +2011,6,22,8,217,20,26,999,1.79,0,0 +2011,6,22,9,187,20,26,999,4.92,0,0 +2011,6,22,10,194,20,26,999,8.05,0,0 +2011,6,22,11,204,20,26,999,11.18,0,0 +2011,6,22,12,251,21,27,999,0.89,0,0 +2011,6,22,13,337,21,27,998,1.79,0,0 +2011,6,22,14,402,21,27,998,3.58,0,0 +2011,6,22,15,432,21,27,998,5.37,0,0 +2011,6,22,16,442,21,27,998,7.16,0,0 +2011,6,22,17,459,21,27,997,8.95,0,0 +2011,6,22,18,445,22,27,997,10.74,0,0 +2011,6,22,19,352,22,27,997,12.53,0,0 +2011,6,22,20,399,22,27,997,15.66,0,0 +2011,6,22,21,132,22,26,998,18.79,0,0 +2011,6,22,22,99,22,26,998,21.92,0,0 +2011,6,22,23,136,22,24,998,0.89,0,1 +2011,6,23,0,139,22,24,998,1.79,0,2 +2011,6,23,1,145,22,24,998,3.58,0,0 +2011,6,23,2,152,23,23,997,5.37,0,1 +2011,6,23,3,150,23,23,997,0.89,0,0 +2011,6,23,4,152,22,23,997,1.79,0,0 +2011,6,23,5,185,22,23,997,3.58,0,0 +2011,6,23,6,166,22,23,998,6.71,0,0 +2011,6,23,7,169,22,23,998,9.84,0,0 +2011,6,23,8,182,22,23,998,12.97,0,0 +2011,6,23,9,166,22,24,998,16.99,0,0 +2011,6,23,10,160,22,24,998,21.01,0,0 +2011,6,23,11,169,22,25,998,24.14,0,0 +2011,6,23,12,185,22,25,998,28.16,0,0 +2011,6,23,13,184,22,25,998,32.18,0,1 +2011,6,23,14,182,22,25,997,36.2,0,0 +2011,6,23,15,163,22,25,997,40.22,0,0 +2011,6,23,16,179,21,25,998,45.14,0,1 +2011,6,23,17,194,19,20,999,3.13,0,2 +2011,6,23,18,133,19,20,999,4.92,0,3 +2011,6,23,19,51,19,20,999,3.13,0,4 +2011,6,23,20,44,19,20,1002,3.13,0,5 +2011,6,23,21,34,19,20,1003,6.26,0,6 +2011,6,23,22,36,19,20,1004,10.28,0,7 +2011,6,23,23,29,18,19,1004,3.13,0,0 +2011,6,24,0,49,19,20,1005,6.26,0,0 +2011,6,24,1,54,17,18,1005,9.39,0,1 +2011,6,24,2,22,17,18,1005,13.41,0,2 +2011,6,24,3,25,16,18,1006,5.81,0,3 +2011,6,24,4,11,16,17,1006,10.73,0,4 +2011,6,24,5,25,16,17,1007,14.75,0,5 +2011,6,24,6,18,16,17,1007,17.88,0,6 +2011,6,24,7,9,17,18,1008,21.9,0,7 +2011,6,24,8,8,17,19,1009,26.82,0,0 +2011,6,24,9,11,18,21,1009,30.84,0,0 +2011,6,24,10,16,16,23,1009,34.86,0,0 +2011,6,24,11,22,17,25,1009,3.13,0,0 +2011,6,24,12,18,15,26,1009,1.79,0,0 +2011,6,24,13,25,16,26,1009,4.92,0,0 +2011,6,24,14,20,14,28,1009,6.71,0,0 +2011,6,24,15,23,15,27,1009,3.13,0,0 +2011,6,24,16,25,15,28,1008,4.02,0,0 +2011,6,24,17,27,14,27,1009,1.79,0,0 +2011,6,24,18,34,15,27,1009,4.92,0,0 +2011,6,24,19,36,15,26,1009,4.02,0,0 +2011,6,24,20,35,15,26,1010,8.04,0,0 +2011,6,24,21,45,15,25,1011,11.17,0,0 +2011,6,24,22,48,15,24,1011,1.79,0,0 +2011,6,24,23,41,15,25,1011,3.13,0,0 +2011,6,25,0,51,17,21,1012,4.92,0,0 +2011,6,25,1,43,17,21,1012,8.05,0,0 +2011,6,25,2,50,17,19,1012,9.84,0,0 +2011,6,25,3,31,17,19,1012,11.63,0,0 +2011,6,25,4,34,17,19,1012,0.89,0,0 +2011,6,25,5,31,16,19,1012,1.79,0,0 +2011,6,25,6,22,17,20,1012,3.58,0,0 +2011,6,25,7,20,16,23,1013,5.37,0,0 +2011,6,25,8,22,16,24,1013,3.13,0,0 +2011,6,25,9,29,16,26,1013,4.92,0,0 +2011,6,25,10,23,15,27,1013,1.79,0,0 +2011,6,25,11,29,15,29,1012,1.79,0,0 +2011,6,25,12,30,13,29,1012,3.13,0,0 +2011,6,25,13,27,12,30,1011,0.89,0,0 +2011,6,25,14,18,13,30,1011,2.68,0,0 +2011,6,25,15,19,12,30,1010,1.79,0,0 +2011,6,25,16,22,12,31,1010,3.58,0,0 +2011,6,25,17,25,12,30,1009,5.37,0,0 +2011,6,25,18,25,14,29,1009,0.89,0,0 +2011,6,25,19,26,14,28,1009,1.79,0,0 +2011,6,25,20,40,15,26,1009,0.89,0,0 +2011,6,25,21,43,13,24,1009,1.78,0,0 +2011,6,25,22,62,16,23,1010,3.13,0,0 +2011,6,25,23,77,16,23,1010,6.26,0,0 +2011,6,26,0,102,17,22,1009,8.05,0,0 +2011,6,26,1,87,17,20,1009,0.89,0,0 +2011,6,26,2,91,17,20,1009,1.79,0,0 +2011,6,26,3,109,17,19,1008,1.79,0,0 +2011,6,26,4,134,15,19,1007,5.81,0,0 +2011,6,26,5,87,15,19,1007,9.83,0,0 +2011,6,26,6,56,15,20,1007,13.85,0,0 +2011,6,26,7,52,15,22,1007,17.87,0,0 +2011,6,26,8,52,15,24,1007,21,0,0 +2011,6,26,9,41,15,27,1006,25.92,0,0 +2011,6,26,10,29,13,27,1006,31.73,0,0 +2011,6,26,11,24,13,27,1005,4.92,0,0 +2011,6,26,12,16,14,30,1004,8.05,0,0 +2011,6,26,13,19,14,30,1003,12.07,0,0 +2011,6,26,14,16,15,30,1001,13.86,0,0 +2011,6,26,15,21,13,31,1001,0.89,0,0 +2011,6,26,16,20,15,31,1000,1.79,0,0 +2011,6,26,17,21,14,31,999,4.92,0,0 +2011,6,26,18,24,14,31,999,8.05,0,0 +2011,6,26,19,23,14,30,999,11.18,0,0 +2011,6,26,20,27,14,28,999,12.07,0,0 +2011,6,26,21,75,17,26,999,0.89,0,0 +2011,6,26,22,64,15,25,999,1.79,0,0 +2011,6,26,23,62,16,24,999,4.92,0,0 +2011,6,27,0,72,16,23,999,8.05,0,0 +2011,6,27,1,74,14,25,998,12.07,0,0 +2011,6,27,2,49,15,21,998,15.2,0,0 +2011,6,27,3,47,16,21,998,1.79,0,0 +2011,6,27,4,57,18,20,998,0.89,0,0 +2011,6,27,5,49,18,20,999,0.89,0,0 +2011,6,27,6,81,19,20,999,1.78,0,0 +2011,6,27,7,125,19,22,999,0.89,0,0 +2011,6,27,8,127,19,25,999,1.78,0,0 +2011,6,27,9,79,18,27,1000,2.67,0,0 +2011,6,27,10,59,14,29,999,4.46,0,0 +2011,6,27,11,36,12,30,999,4.02,0,0 +2011,6,27,12,26,12,31,998,1.79,0,0 +2011,6,27,13,30,13,31,998,3.58,0,0 +2011,6,27,14,16,14,31,998,3.13,0,0 +2011,6,27,15,31,12,30,998,6.26,0,0 +2011,6,27,16,39,15,30,998,11.18,0,0 +2011,6,27,17,40,15,30,998,15.2,0,0 +2011,6,27,18,51,14,30,999,19.22,0,0 +2011,6,27,19,53,15,28,999,22.35,0,0 +2011,6,27,20,58,15,28,1000,24.14,0,0 +2011,6,27,21,55,16,26,1000,25.93,0,0 +2011,6,27,22,56,17,26,1001,29.06,0,0 +2011,6,27,23,93,18,24,1001,32.19,0,0 +2011,6,28,0,108,18,24,1001,33.08,0,0 +2011,6,28,1,94,18,23,1001,33.97,0,0 +2011,6,28,2,112,18,22,1002,35.76,0,0 +2011,6,28,3,118,18,22,1001,37.55,0,0 +2011,6,28,4,121,18,21,1002,1.79,0,0 +2011,6,28,5,119,18,22,1002,0.45,0,0 +2011,6,28,6,127,18,22,1002,0.89,0,0 +2011,6,28,7,123,18,23,1003,1.79,0,0 +2011,6,28,8,125,18,24,1003,0.89,0,0 +2011,6,28,9,127,19,25,1003,1.79,0,0 +2011,6,28,10,205,19,27,1003,1.79,0,0 +2011,6,28,11,149,20,28,1003,1.79,0,0 +2011,6,28,12,162,20,29,1003,1.79,0,0 +2011,6,28,13,173,20,30,1002,3.13,0,0 +2011,6,28,14,202,20,32,1001,3.13,0,0 +2011,6,28,15,185,20,32,1001,7.15,0,0 +2011,6,28,16,166,19,33,1001,11.17,0,0 +2011,6,28,17,152,18,32,1001,12.96,0,0 +2011,6,28,18,137,18,31,1001,16.09,0,0 +2011,6,28,19,156,18,30,1001,19.22,0,0 +2011,6,28,20,132,19,30,1002,22.35,0,0 +2011,6,28,21,116,19,27,1003,25.48,0,0 +2011,6,28,22,113,19,27,1004,28.61,0,0 +2011,6,28,23,141,20,26,1004,30.4,0,0 +2011,6,29,0,177,19,24,1004,0.89,0,0 +2011,6,29,1,199,19,24,1004,1.79,0,0 +2011,6,29,2,226,19,23,1004,0.89,0,0 +2011,6,29,3,229,19,23,1003,1.78,0,0 +2011,6,29,4,242,20,22,1003,2.23,0,0 +2011,6,29,5,244,20,22,1004,3.12,0,0 +2011,6,29,6,265,20,23,1004,0.89,0,0 +2011,6,29,7,266,21,24,1005,0.89,0,0 +2011,6,29,8,259,21,24,1005,1.79,0,0 +2011,6,29,9,274,22,25,1005,4.92,0,0 +2011,6,29,10,275,22,26,1005,6.71,0,0 +2011,6,29,11,242,23,27,1005,8.5,0,0 +2011,6,29,12,232,22,28,1005,10.29,0,0 +2011,6,29,13,208,22,29,1004,14.31,0,0 +2011,6,29,14,204,22,30,1003,18.33,0,0 +2011,6,29,15,190,22,30,1003,22.35,0,0 +2011,6,29,16,223,22,30,1002,27.27,0,0 +2011,6,29,17,228,23,30,1002,32.19,0,0 +2011,6,29,18,243,22,28,1002,37.11,0,0 +2011,6,29,19,229,22,27,1002,42.03,0,0 +2011,6,29,20,224,22,27,1003,46.95,0,0 +2011,6,29,21,206,22,26,1003,50.97,0,0 +2011,6,29,22,226,22,25,1004,0.89,0,0 +2011,6,29,23,256,22,25,1004,3.13,0,0 +2011,6,30,0,266,22,24,1003,6.26,0,0 +2011,6,30,1,242,21,24,1003,9.39,0,0 +2011,6,30,2,255,22,23,1003,11.18,0,1 +2011,6,30,3,238,22,23,1002,1.79,0,0 +2011,6,30,4,229,22,23,1002,0.89,0,0 +2011,6,30,5,231,22,23,1002,1.79,0,0 +2011,6,30,6,219,22,23,1003,4.92,0,0 +2011,6,30,7,145,20,21,1003,6.71,0,1 +2011,6,30,8,35,21,22,1003,0.89,0,2 +2011,6,30,9,38,21,23,1003,1.78,0,0 +2011,6,30,10,37,21,23,1003,1.79,0,0 +2011,6,30,11,33,22,24,1003,0.89,0,0 +2011,6,30,12,48,21,25,1003,1.78,0,0 +2011,6,30,13,48,22,27,1002,2.67,0,0 +2011,6,30,14,47,21,28,1002,1.79,0,0 +2011,6,30,15,52,23,29,1002,1.79,0,0 +2011,6,30,16,58,23,29,1002,1.79,0,0 +2011,6,30,17,75,23,29,1001,1.79,0,0 +2011,6,30,18,113,23,28,1002,3.13,0,0 +2011,6,30,19,150,23,28,1002,1.79,0,0 +2011,6,30,20,213,23,27,1003,3.58,0,0 +2011,6,30,21,213,23,27,1003,4.47,0,0 +2011,6,30,22,218,23,27,1004,6.26,0,0 +2011,6,30,23,233,23,26,1004,9.39,0,0 +2011,7,1,0,245,23,26,1003,11.18,0,0 +2011,7,1,1,254,23,25,1003,12.97,0,0 +2011,7,1,2,232,23,25,1003,0.89,0,0 +2011,7,1,3,220,23,24,1002,1.78,0,0 +2011,7,1,4,222,22,24,1003,1.79,0,0 +2011,7,1,5,230,22,24,1003,0.89,0,0 +2011,7,1,6,226,23,24,1003,0.89,0,0 +2011,7,1,7,221,23,25,1003,0.89,0,0 +2011,7,1,8,206,23,25,1004,4.92,0,1 +2011,7,1,9,82,23,24,1004,8.05,0,2 +2011,7,1,10,29,23,24,1003,0.89,0,3 +2011,7,1,11,39,23,24,1003,1.79,0,4 +2011,7,1,12,61,24,25,1003,4.92,0,0 +2011,7,1,13,77,24,25,1003,6.71,0,0 +2011,7,1,14,93,23,26,1002,3.13,0,1 +2011,7,1,15,109,23,26,1001,1.79,0,0 +2011,7,1,16,94,23,26,1001,0.89,0,0 +2011,7,1,17,81,23,26,1001,1.79,0,0 +2011,7,1,18,91,23,26,1001,0.89,0,0 +2011,7,1,19,117,23,26,1001,1.79,0,0 +2011,7,1,20,121,23,25,1002,3.58,0,0 +2011,7,1,21,112,23,25,1002,5.37,0,0 +2011,7,1,22,108,24,25,1002,0.89,0,0 +2011,7,1,23,118,23,25,1002,1.79,0,0 +2011,7,2,0,133,23,24,1001,0.89,0,0 +2011,7,2,1,141,23,24,1001,0.89,0,1 +2011,7,2,2,150,23,24,1001,0.89,0,0 +2011,7,2,3,170,23,24,1001,1.78,0,0 +2011,7,2,4,165,23,24,1000,1.79,0,1 +2011,7,2,5,179,23,24,1000,2.68,0,2 +2011,7,2,6,153,23,24,1000,0.89,0,3 +2011,7,2,7,166,23,24,1001,1.79,0,4 +2011,7,2,8,165,24,25,1001,0.89,0,5 +2011,7,2,9,160,24,25,1001,1.78,0,0 +2011,7,2,10,176,24,26,1001,4.02,0,0 +2011,7,2,11,162,24,26,1001,5.81,0,0 +2011,7,2,12,144,23,27,1001,0.89,0,0 +2011,7,2,13,128,24,28,1000,1.79,0,0 +2011,7,2,14,140,24,28,1000,3.58,0,0 +2011,7,2,15,133,24,28,1000,5.37,0,0 +2011,7,2,16,122,24,28,999,0.89,0,0 +2011,7,2,17,164,24,28,999,1.78,0,0 +2011,7,2,18,155,24,28,999,2.67,0,0 +2011,7,2,19,170,23,27,1000,4.46,0,0 +2011,7,2,20,164,23,26,1000,1.79,0,0 +2011,7,2,21,187,23,25,1001,0.89,0,0 +2011,7,2,22,187,23,25,1001,1.78,0,0 +2011,7,2,23,198,23,24,1001,1.79,0,0 +2011,7,3,0,202,23,24,1001,2.68,0,0 +2011,7,3,1,215,23,24,1001,4.47,0,0 +2011,7,3,2,195,23,24,1001,0.89,0,0 +2011,7,3,3,195,23,23,1001,0.89,0,0 +2011,7,3,4,245,23,23,1001,0.89,0,0 +2011,7,3,5,224,22,23,1001,1.79,0,0 +2011,7,3,6,220,23,24,1002,4.92,0,0 +2011,7,3,7,244,23,25,1002,8.94,0,0 +2011,7,3,8,238,22,26,1003,12.96,0,0 +2011,7,3,9,140,19,27,1003,16.98,0,0 +2011,7,3,10,72,19,29,1003,20.11,0,0 +2011,7,3,11,47,16,31,1002,4.02,0,0 +2011,7,3,12,30,16,32,1002,3.13,0,0 +2011,7,3,13,26,15,33,1002,4.92,0,0 +2011,7,3,14,25,15,34,1001,3.13,0,0 +2011,7,3,15,19,14,34,1001,1.79,0,0 +2011,7,3,16,26,15,34,1001,1.79,0,0 +2011,7,3,17,23,15,34,1000,2.68,0,0 +2011,7,3,18,30,15,33,1001,1.79,0,0 +2011,7,3,19,44,18,31,1001,4.92,0,0 +2011,7,3,20,46,17,31,1002,8.94,0,0 +2011,7,3,21,44,18,31,1002,13.86,0,0 +2011,7,3,22,46,18,30,1002,17.88,0,0 +2011,7,3,23,56,18,29,1003,21.9,0,0 +2011,7,4,0,72,19,26,1003,1.79,0,0 +2011,7,4,1,96,20,24,1003,0.89,0,0 +2011,7,4,2,108,20,23,1004,1.79,0,0 +2011,7,4,3,104,21,23,1003,0.89,0,0 +2011,7,4,4,74,20,23,1004,0.89,0,0 +2011,7,4,5,85,19,22,1004,4.02,0,0 +2011,7,4,6,55,19,22,1005,7.15,0,0 +2011,7,4,7,48,20,25,1005,8.94,0,0 +2011,7,4,8,46,19,27,1005,10.73,0,0 +2011,7,4,9,47,17,29,1005,12.52,0,0 +2011,7,4,10,36,16,31,1005,0.89,0,0 +2011,7,4,11,42,16,31,1004,1.79,0,0 +2011,7,4,12,36,16,33,1004,0.89,0,0 +2011,7,4,13,37,15,33,1003,1.79,0,0 +2011,7,4,14,40,13,34,1003,4.92,0,0 +2011,7,4,15,52,14,35,1002,3.13,0,0 +2011,7,4,16,71,16,34,1002,1.79,0,0 +2011,7,4,17,63,21,34,1002,5.81,0,0 +2011,7,4,18,88,21,33,1002,8.94,0,0 +2011,7,4,19,96,21,32,1002,12.07,0,0 +2011,7,4,20,73,16,32,1002,16.09,0,0 +2011,7,4,21,63,18,31,1003,20.11,0,0 +2011,7,4,22,61,18,30,1003,25.03,0,0 +2011,7,4,23,57,18,30,1002,29.05,0,0 +2011,7,5,0,65,19,29,1002,33.07,0,0 +2011,7,5,1,82,19,28,1001,36.2,0,0 +2011,7,5,2,86,19,28,1001,40.22,0,0 +2011,7,5,3,89,18,27,1001,43.35,0,0 +2011,7,5,4,67,17,27,1001,46.48,0,0 +2011,7,5,5,79,17,26,1001,49.61,0,0 +2011,7,5,6,95,18,26,1001,52.74,0,0 +2011,7,5,7,112,18,26,1000,55.87,0,0 +2011,7,5,8,105,18,27,1000,59,0,0 +2011,7,5,9,119,19,27,1000,62.13,0,0 +2011,7,5,10,134,19,27,1000,63.92,0,0 +2011,7,5,11,133,20,28,1000,65.71,0,0 +2011,7,5,12,144,19,29,1000,67.5,0,0 +2011,7,5,13,165,20,28,999,3.13,0,0 +2011,7,5,14,153,20,28,999,4.02,0,0 +2011,7,5,15,142,21,28,999,7.15,0,0 +2011,7,5,16,141,20,28,998,1.79,0,0 +2011,7,5,17,139,20,28,998,3.58,0,0 +2011,7,5,18,152,20,28,998,5.37,0,0 +2011,7,5,19,160,20,28,998,7.16,0,0 +2011,7,5,20,145,21,26,998,0.89,0,0 +2011,7,5,21,132,21,25,998,0.89,0,0 +2011,7,5,22,141,21,25,998,0.89,0,0 +2011,7,5,23,145,21,24,998,0.89,0,0 +2011,7,6,0,152,21,24,998,1.34,0,0 +2011,7,6,1,173,21,23,998,2.23,0,0 +2011,7,6,2,199,20,24,998,3.12,0,0 +2011,7,6,3,193,21,23,998,0.89,0,0 +2011,7,6,4,193,20,23,998,0.89,0,0 +2011,7,6,5,185,21,23,998,1.78,0,0 +2011,7,6,6,184,21,23,998,2.67,0,0 +2011,7,6,7,178,21,24,998,3.56,0,0 +2011,7,6,8,145,21,26,998,0.89,0,0 +2011,7,6,9,142,22,28,998,1.79,0,0 +2011,7,6,10,158,22,30,998,0.89,0,0 +2011,7,6,11,173,21,30,997,1.79,0,0 +2011,7,6,12,164,21,32,997,1.79,0,0 +2011,7,6,13,180,23,33,996,3.58,0,0 +2011,7,6,14,181,22,32,996,4.92,0,0 +2011,7,6,15,172,22,32,995,9.84,0,0 +2011,7,6,16,171,22,32,995,13.86,0,0 +2011,7,6,17,167,23,32,995,18.78,0,0 +2011,7,6,18,170,22,31,995,23.7,0,0 +2011,7,6,19,174,22,30,995,26.83,0,0 +2011,7,6,20,171,22,29,995,28.62,0,0 +2011,7,6,21,188,22,28,996,29.51,0,0 +2011,7,6,22,179,21,29,996,34.43,0,0 +2011,7,6,23,121,17,25,996,4.92,0,0 +2011,7,7,0,66,17,24,995,1.79,0,0 +2011,7,7,1,54,19,24,995,3.13,0,0 +2011,7,7,2,56,20,24,995,1.79,0,1 +2011,7,7,3,87,20,24,994,0.89,0,2 +2011,7,7,4,48,19,20,996,7.15,0,3 +2011,7,7,5,42,19,20,997,12.07,0,0 +2011,7,7,6,44,18,20,997,16.09,0,0 +2011,7,7,7,31,19,21,997,3.13,0,0 +2011,7,7,8,12,18,23,998,4.02,0,0 +2011,7,7,9,10,16,25,998,8.04,0,0 +2011,7,7,10,11,11,26,998,16.09,0,0 +2011,7,7,11,11,10,27,998,24.14,0,0 +2011,7,7,12,16,10,28,997,29.95,0,0 +2011,7,7,13,9,11,28,997,35.76,0,0 +2011,7,7,14,16,9,29,996,4.02,0,0 +2011,7,7,15,17,10,30,996,4.92,0,0 +2011,7,7,16,16,10,30,996,3.13,0,0 +2011,7,7,17,12,10,30,995,3.13,0,0 +2011,7,7,18,21,12,28,996,4.02,0,0 +2011,7,7,19,20,11,27,996,8.04,0,0 +2011,7,7,20,22,14,25,997,9.83,0,0 +2011,7,7,21,19,14,25,998,11.62,0,0 +2011,7,7,22,23,13,26,998,14.75,0,0 +2011,7,7,23,28,14,25,999,7.15,0,0 +2011,7,8,0,21,16,21,998,1.79,0,1 +2011,7,8,1,22,17,21,998,0.89,0,0 +2011,7,8,2,31,16,19,998,1.79,0,0 +2011,7,8,3,37,17,19,998,2.68,0,0 +2011,7,8,4,38,17,19,998,0.89,0,0 +2011,7,8,5,32,17,18,998,1.78,0,0 +2011,7,8,6,27,18,20,998,1.79,0,0 +2011,7,8,7,40,17,22,998,0.89,0,0 +2011,7,8,8,37,18,24,999,1.78,0,0 +2011,7,8,9,32,17,27,998,1.79,0,0 +2011,7,8,10,26,14,29,998,4.92,0,0 +2011,7,8,11,25,14,31,998,5.81,0,0 +2011,7,8,12,18,14,31,998,9.83,0,0 +2011,7,8,13,23,13,32,997,16.98,0,0 +2011,7,8,14,14,15,33,997,24.13,0,0 +2011,7,8,15,12,13,32,998,31.28,0,0 +2011,7,8,16,16,13,32,998,38.43,0,0 +2011,7,8,17,7,13,31,999,44.24,0,0 +2011,7,8,18,9,12,31,999,51.39,0,0 +2011,7,8,19,12,13,29,1000,56.31,0,0 +2011,7,8,20,16,14,26,1001,60.33,0,0 +2011,7,8,21,17,13,25,1002,62.12,0,0 +2011,7,8,22,17,13,25,1003,0.89,0,0 +2011,7,8,23,28,15,24,1003,1.79,0,0 +2011,7,9,0,18,14,22,1003,4.92,0,0 +2011,7,9,1,22,15,22,1003,1.79,0,0 +2011,7,9,2,34,15,20,1004,3.13,0,0 +2011,7,9,3,41,14,21,1004,0.89,0,0 +2011,7,9,4,21,14,21,1004,0.89,0,0 +2011,7,9,5,28,15,19,1005,4.02,0,0 +2011,7,9,6,25,16,20,1005,7.15,0,0 +2011,7,9,7,19,16,23,1006,10.28,0,0 +2011,7,9,8,14,15,25,1006,12.07,0,0 +2011,7,9,9,23,16,27,1006,1.79,0,0 +2011,7,9,10,18,15,29,1006,1.79,0,0 +2011,7,9,11,18,14,31,1006,3.13,0,0 +2011,7,9,12,12,16,33,1005,4.92,0,0 +2011,7,9,13,17,16,34,1004,3.13,0,0 +2011,7,9,14,14,14,34,1004,1.79,0,0 +2011,7,9,15,23,14,35,1003,1.79,0,0 +2011,7,9,16,18,15,35,1003,5.81,0,0 +2011,7,9,17,19,15,33,1003,10.73,0,0 +2011,7,9,18,21,14,33,1003,14.75,0,0 +2011,7,9,19,18,14,31,1004,17.88,0,0 +2011,7,9,20,23,15,29,1004,19.67,0,0 +2011,7,9,21,25,16,28,1005,21.46,0,0 +2011,7,9,22,29,16,26,1005,22.35,0,0 +2011,7,9,23,28,17,25,1006,23.24,0,0 +2011,7,10,0,42,17,25,1006,0.89,0,0 +2011,7,10,1,53,18,25,1006,1.79,0,0 +2011,7,10,2,57,18,24,1006,3.58,0,0 +2011,7,10,3,62,19,24,1006,5.37,0,0 +2011,7,10,4,80,19,24,1006,0.89,0,0 +2011,7,10,5,100,19,24,1006,3.13,0,0 +2011,7,10,6,93,18,24,1007,6.26,0,0 +2011,7,10,7,82,18,24,1007,8.05,0,0 +2011,7,10,8,79,19,26,1008,9.84,0,0 +2011,7,10,9,83,19,27,1008,11.63,0,0 +2011,7,10,10,75,20,29,1008,1.79,0,0 +2011,7,10,11,88,19,29,1008,1.79,0,0 +2011,7,10,12,90,20,30,1007,0.89,0,0 +2011,7,10,13,90,20,31,1006,2.68,0,0 +2011,7,10,14,79,19,32,1006,1.79,0,0 +2011,7,10,15,89,21,31,1005,6.71,0,0 +2011,7,10,16,87,20,30,1005,10.73,0,0 +2011,7,10,17,91,20,31,1006,13.86,0,0 +2011,7,10,18,73,19,30,1006,16.99,0,0 +2011,7,10,19,66,19,29,1005,20.12,0,0 +2011,7,10,20,72,20,27,1006,21.01,0,0 +2011,7,10,21,117,21,26,1007,0.45,0,0 +2011,7,10,22,111,21,26,1008,3.13,0,0 +2011,7,10,23,125,19,26,1008,7.15,0,0 +2011,7,11,0,146,22,25,1008,10.28,0,0 +2011,7,11,1,171,22,24,1008,12.07,0,0 +2011,7,11,2,185,22,23,1008,12.96,0,0 +2011,7,11,3,296,22,23,1008,0.89,0,0 +2011,7,11,4,275,22,23,1008,1.78,0,0 +2011,7,11,5,288,22,23,1008,0.89,0,0 +2011,7,11,6,298,23,23,1008,0.45,0,0 +2011,7,11,7,274,22,24,1008,1.34,0,0 +2011,7,11,8,281,22,24,1009,3.13,0,0 +2011,7,11,9,280,23,25,1009,6.26,0,0 +2011,7,11,10,249,23,27,1009,9.39,0,0 +2011,7,11,11,239,23,28,1008,11.18,0,0 +2011,7,11,12,218,23,30,1008,1.79,0,0 +2011,7,11,13,197,23,31,1007,4.02,0,0 +2011,7,11,14,154,22,32,1007,8.04,0,0 +2011,7,11,15,130,22,32,1006,12.96,0,0 +2011,7,11,16,122,21,32,1006,16.98,0,0 +2011,7,11,17,125,20,32,1005,21,0,0 +2011,7,11,18,118,22,31,1006,24.13,0,0 +2011,7,11,19,153,22,30,1007,27.26,0,0 +2011,7,11,20,132,21,29,1007,30.39,0,0 +2011,7,11,21,91,20,28,1008,33.52,0,0 +2011,7,11,22,102,19,27,1008,37.54,0,0 +2011,7,11,23,113,20,26,1008,40.67,0,0 +2011,7,12,0,112,20,26,1009,44.69,0,0 +2011,7,12,1,96,21,25,1009,47.82,0,0 +2011,7,12,2,107,22,24,1009,50.95,0,0 +2011,7,12,3,118,22,24,1009,0.89,0,0 +2011,7,12,4,105,22,24,1009,1.79,0,0 +2011,7,12,5,104,22,24,1009,0.89,0,0 +2011,7,12,6,107,22,23,1008,3.13,0,0 +2011,7,12,7,127,22,24,1009,4.92,0,0 +2011,7,12,8,128,22,25,1009,8.05,0,0 +2011,7,12,9,111,22,26,1009,11.18,0,0 +2011,7,12,10,126,22,26,1009,12.97,0,0 +2011,7,12,11,109,21,27,1009,16.1,0,0 +2011,7,12,12,108,21,29,1009,1.79,0,0 +2011,7,12,13,122,22,30,1008,3.13,0,0 +2011,7,12,14,112,21,30,1008,6.26,0,0 +2011,7,12,15,112,21,30,1008,8.05,0,0 +2011,7,12,16,100,21,30,1007,11.18,0,0 +2011,7,12,17,109,21,30,1006,14.31,0,0 +2011,7,12,18,99,20,30,1006,17.44,0,0 +2011,7,12,19,92,21,28,1006,20.57,0,0 +2011,7,12,20,113,21,27,1006,21.46,0,0 +2011,7,12,21,117,21,27,1006,0.89,0,0 +2011,7,12,22,117,22,27,1007,3.13,0,0 +2011,7,12,23,112,23,26,1008,4.92,0,0 +2011,7,13,0,120,23,26,1006,9.84,0,1 +2011,7,13,1,116,22,25,1006,13.86,0,2 +2011,7,13,2,87,22,24,1007,16.99,0,0 +2011,7,13,3,95,22,24,1006,20.12,0,0 +2011,7,13,4,92,22,24,1006,23.25,0,0 +2011,7,13,5,89,22,24,1006,25.04,0,0 +2011,7,13,6,102,22,23,1006,28.17,0,0 +2011,7,13,7,99,21,24,1006,31.3,0,0 +2011,7,13,8,97,22,24,1006,33.09,0,0 +2011,7,13,9,92,22,26,1006,34.88,0,0 +2011,7,13,10,91,22,27,1006,38.01,0,0 +2011,7,13,11,95,22,29,1006,1.79,0,0 +2011,7,13,12,95,22,29,1006,3.13,0,0 +2011,7,13,13,102,21,29,1005,0.89,0,0 +2011,7,13,14,103,21,31,1005,1.79,0,0 +2011,7,13,15,101,21,32,1004,4.92,0,0 +2011,7,13,16,89,22,32,1004,8.05,0,0 +2011,7,13,17,87,22,32,1004,11.18,0,0 +2011,7,13,18,84,21,31,1003,14.31,0,0 +2011,7,13,19,91,22,29,1004,17.44,0,0 +2011,7,13,20,91,22,28,1004,19.23,0,0 +2011,7,13,21,97,23,28,1005,22.36,0,0 +2011,7,13,22,93,23,27,1005,26.38,0,0 +2011,7,13,23,81,23,26,1005,29.51,0,0 +2011,7,14,0,80,22,25,1005,32.64,0,0 +2011,7,14,1,77,22,25,1005,34.43,0,0 +2011,7,14,2,80,21,24,1005,0.89,0,0 +2011,7,14,3,82,21,24,1005,1.78,0,0 +2011,7,14,4,85,21,23,1005,0.89,0,0 +2011,7,14,5,78,21,24,1006,1.79,0,0 +2011,7,14,6,90,21,24,1006,0.89,0,0 +2011,7,14,7,86,22,25,1006,1.79,0,0 +2011,7,14,8,94,22,26,1006,3.58,0,0 +2011,7,14,9,95,23,26,1006,5.37,0,0 +2011,7,14,10,125,23,27,1007,7.16,0,0 +2011,7,14,11,131,23,28,1007,0.89,0,0 +2011,7,14,12,135,23,30,1006,1.79,0,0 +2011,7,14,13,144,24,30,1005,0.89,0,0 +2011,7,14,14,144,23,30,1005,1.79,0,0 +2011,7,14,15,133,23,31,1004,1.79,0,0 +2011,7,14,16,132,22,32,1003,3.13,0,0 +2011,7,14,17,137,22,32,1003,1.79,0,0 +2011,7,14,18,141,23,29,1004,3.13,0,0 +2011,7,14,19,133,23,28,1004,1.79,0,1 +2011,7,14,20,94,22,27,1005,3.58,0,0 +2011,7,14,21,83,19,27,1006,6.71,0,0 +2011,7,14,22,71,20,23,1007,13.86,0,0 +2011,7,14,23,62,20,22,1008,16.99,0,1 +2011,7,15,0,44,20,21,1007,18.78,0,2 +2011,7,15,1,39,20,22,1008,20.57,0,3 +2011,7,15,2,42,19,21,1006,1.79,0,4 +2011,7,15,3,58,19,21,1007,4.02,0,5 +2011,7,15,4,59,21,22,1007,8.04,0,0 +2011,7,15,5,57,20,22,1007,11.17,0,0 +2011,7,15,6,59,20,22,1007,1.79,0,0 +2011,7,15,7,52,21,22,1007,3.58,0,0 +2011,7,15,8,64,21,23,1007,0.89,0,0 +2011,7,15,9,70,22,24,1007,1.79,0,0 +2011,7,15,10,66,21,26,1007,0.89,0,0 +2011,7,15,11,66,21,27,1007,1.78,0,0 +2011,7,15,12,70,21,28,1006,1.79,0,0 +2011,7,15,13,83,22,29,1006,3.58,0,0 +2011,7,15,14,103,22,29,1005,8.5,0,0 +2011,7,15,15,109,22,29,1005,11.63,0,0 +2011,7,15,16,91,22,29,1005,15.65,0,0 +2011,7,15,17,90,22,28,1005,18.78,0,0 +2011,7,15,18,88,21,28,1004,21.91,0,0 +2011,7,15,19,97,21,27,1004,25.93,0,1 +2011,7,15,20,107,21,26,1004,27.72,0,0 +2011,7,15,21,94,21,26,1004,30.85,0,0 +2011,7,15,22,74,21,26,1006,32.64,0,0 +2011,7,15,23,100,20,25,1006,34.43,0,0 +2011,7,16,0,72,19,26,1005,37.56,0,0 +2011,7,16,1,78,19,25,1006,39.35,0,0 +2011,7,16,2,80,20,23,1005,41.14,0,0 +2011,7,16,3,77,20,23,1005,44.27,0,0 +2011,7,16,4,86,20,23,1005,47.4,0,0 +2011,7,16,5,108,20,22,1005,49.19,0,0 +2011,7,16,6,90,20,22,1005,0.89,0,0 +2011,7,16,7,73,21,24,1006,1.78,0,0 +2011,7,16,8,71,20,25,1006,2.67,0,0 +2011,7,16,9,54,20,27,1006,4.46,0,0 +2011,7,16,10,52,18,28,1006,5.35,0,0 +2011,7,16,11,55,19,29,1006,6.24,0,0 +2011,7,16,12,69,19,29,1005,9.37,0,0 +2011,7,16,13,70,19,30,1004,3.13,0,0 +2011,7,16,14,87,21,30,1003,6.26,0,0 +2011,7,16,15,75,19,32,1003,9.39,0,0 +2011,7,16,16,84,21,31,1003,12.52,0,0 +2011,7,16,17,91,21,30,1003,16.54,0,0 +2011,7,16,18,90,21,30,1002,19.67,0,0 +2011,7,16,19,92,21,29,1002,22.8,0,0 +2011,7,16,20,96,21,28,1003,24.59,0,0 +2011,7,16,21,84,19,25,1004,8.94,0,0 +2011,7,16,22,37,20,24,1005,7.15,0,1 +2011,7,16,23,47,20,22,1004,8.04,0,2 +2011,7,17,0,48,20,21,1004,3.13,0,3 +2011,7,17,1,63,21,22,1004,1.79,0,0 +2011,7,17,2,67,21,22,1004,0.89,0,0 +2011,7,17,3,69,21,22,1004,1.79,0,0 +2011,7,17,4,71,21,22,1004,0.89,0,0 +2011,7,17,5,80,21,22,1004,0.45,0,0 +2011,7,17,6,82,21,22,1005,1.79,0,0 +2011,7,17,7,93,21,23,1005,3.58,0,0 +2011,7,17,8,97,21,24,1005,5.37,0,0 +2011,7,17,9,121,21,24,1006,0.89,0,0 +2011,7,17,10,137,22,26,1006,1.79,0,0 +2011,7,17,11,147,22,27,1005,0.89,0,0 +2011,7,17,12,166,22,27,1005,2.68,0,0 +2011,7,17,13,181,22,28,1004,3.57,0,0 +2011,7,17,14,186,23,27,1004,1.79,0,0 +2011,7,17,15,200,23,27,1003,3.58,0,0 +2011,7,17,16,75,20,23,1003,9.39,0,1 +2011,7,17,17,77,21,24,1003,12.52,0,2 +2011,7,17,18,87,20,24,1004,14.31,0,0 +2011,7,17,19,61,20,24,1004,16.1,0,0 +2011,7,17,20,75,20,24,1005,19.23,0,0 +2011,7,17,21,87,21,24,1005,0.89,0,0 +2011,7,17,22,93,21,23,1006,1.79,0,0 +2011,7,17,23,88,20,23,1006,1.79,0,1 +2011,7,18,0,117,19,22,1005,1.79,0,0 +2011,7,18,1,77,20,22,1005,3.58,0,0 +2011,7,18,2,61,20,22,1005,4.47,0,0 +2011,7,18,3,58,20,22,1005,6.26,0,0 +2011,7,18,4,54,20,22,1005,8.05,0,0 +2011,7,18,5,69,20,21,1006,9.84,0,0 +2011,7,18,6,62,20,22,1006,11.63,0,0 +2011,7,18,7,64,20,22,1006,14.76,0,0 +2011,7,18,8,74,20,23,1006,16.55,0,0 +2011,7,18,9,74,20,23,1006,0.89,0,0 +2011,7,18,10,69,21,25,1006,1.79,0,0 +2011,7,18,11,76,21,26,1006,0.89,0,0 +2011,7,18,12,101,22,27,1005,2.68,0,0 +2011,7,18,13,119,22,28,1005,1.79,0,0 +2011,7,18,14,127,21,29,1004,0.89,0,0 +2011,7,18,15,128,21,29,1004,1.78,0,0 +2011,7,18,16,128,20,29,1003,2.67,0,0 +2011,7,18,17,144,21,28,1003,1.79,0,0 +2011,7,18,18,142,21,28,1003,5.81,0,0 +2011,7,18,19,134,21,27,1003,8.94,0,0 +2011,7,18,20,149,20,27,1003,12.96,0,0 +2011,7,18,21,151,21,25,1003,0.89,0,0 +2011,7,18,22,160,22,25,1003,1.34,0,0 +2011,7,18,23,157,22,24,1004,1.79,0,0 +2011,7,19,0,165,22,26,1004,3.13,0,0 +2011,7,19,1,181,21,25,1003,7.15,0,0 +2011,7,19,2,174,21,24,1003,8.04,0,0 +2011,7,19,3,140,21,24,1002,1.79,0,0 +2011,7,19,4,126,21,24,1002,0.89,0,0 +2011,7,19,5,121,21,23,1003,2.68,0,0 +2011,7,19,6,130,22,23,1003,4.47,0,0 +2011,7,19,7,131,22,25,1003,0.89,0,0 +2011,7,19,8,140,23,26,1003,1.79,0,0 +2011,7,19,9,150,22,26,1003,4.92,0,0 +2011,7,19,10,117,22,27,1003,4.02,0,0 +2011,7,19,11,105,21,28,1003,3.13,0,0 +2011,7,19,12,66,21,28,1003,4.92,0,0 +2011,7,19,13,62,22,29,1002,8.05,0,0 +2011,7,19,14,68,22,29,1002,0.89,0,0 +2011,7,19,15,75,22,28,1001,3.13,0,0 +2011,7,19,16,73,22,28,1001,4.92,0,0 +2011,7,19,17,72,21,29,1001,6.71,0,0 +2011,7,19,18,65,22,27,1001,8.5,0,0 +2011,7,19,19,79,22,27,1001,11.63,0,0 +2011,7,19,20,83,22,25,1001,15.65,0,1 +2011,7,19,21,50,21,24,1002,17.44,0,0 +2011,7,19,22,44,22,24,1002,0.89,0,0 +2011,7,19,23,45,22,24,1002,1.78,0,0 +2011,7,20,0,51,21,23,1002,2.67,0,0 +2011,7,20,1,57,22,23,1002,3.13,0,1 +2011,7,20,2,53,22,23,1002,4.02,0,2 +2011,7,20,3,27,22,22,1002,7.15,0,3 +2011,7,20,4,21,21,21,1002,10.28,0,5 +2011,7,20,5,28,21,21,1002,0.89,0,6 +2011,7,20,6,16,21,21,1003,1.78,0,7 +2011,7,20,7,10,21,22,1003,1.79,0,8 +2011,7,20,8,13,22,22,1003,4.92,0,9 +2011,7,20,9,14,22,23,1003,8.05,0,0 +2011,7,20,10,11,22,23,1003,11.18,0,0 +2011,7,20,11,26,22,24,1003,15.2,0,0 +2011,7,20,12,24,22,25,1003,0.89,0,0 +2011,7,20,13,21,22,25,1003,4.02,0,0 +2011,7,20,14,14,23,24,1002,7.15,0,0 +2011,7,20,15,47,23,25,1002,10.28,0,0 +2011,7,20,16,58,23,26,1002,13.41,0,0 +2011,7,20,17,61,22,26,1002,16.54,0,0 +2011,7,20,18,65,22,26,1002,19.67,0,0 +2011,7,20,19,56,22,25,1003,21.46,0,0 +2011,7,20,20,63,22,23,1003,0.89,0,0 +2011,7,20,21,80,22,23,1003,0.89,0,0 +2011,7,20,22,84,22,23,1003,1.79,0,0 +2011,7,20,23,116,22,23,1003,0.89,0,1 +2011,7,21,0,100,22,23,1003,0.89,0,0 +2011,7,21,1,107,22,23,1003,0.89,0,0 +2011,7,21,2,86,22,23,1003,1.79,0,0 +2011,7,21,3,87,22,23,1003,0.89,0,0 +2011,7,21,4,89,22,23,1003,0.89,0,0 +2011,7,21,5,83,22,23,1003,0.89,0,0 +2011,7,21,6,77,23,23,1003,1.78,0,0 +2011,7,21,7,65,23,24,1003,1.79,0,0 +2011,7,21,8,83,23,24,1003,1.79,0,0 +2011,7,21,9,103,23,24,1003,3.58,0,1 +2011,7,21,10,90,22,25,1004,5.37,0,2 +2011,7,21,11,92,23,25,1004,0.89,0,3 +2011,7,21,12,95,23,27,1003,1.78,0,0 +2011,7,21,13,111,24,29,1003,3.57,0,0 +2011,7,21,14,112,23,28,1002,4.02,0,0 +2011,7,21,15,87,23,28,1002,8.04,0,0 +2011,7,21,16,88,23,28,1002,13.85,0,0 +2011,7,21,17,88,24,28,1001,17.87,0,0 +2011,7,21,18,86,23,27,1001,21.89,0,0 +2011,7,21,19,78,24,26,1002,25.02,0,0 +2011,7,21,20,75,23,25,1002,28.15,0,0 +2011,7,21,21,91,23,25,1003,31.28,0,0 +2011,7,21,22,91,23,25,1003,33.07,0,0 +2011,7,21,23,106,23,25,1003,34.86,0,0 +2011,7,22,0,94,23,25,1004,36.65,0,1 +2011,7,22,1,118,23,24,1003,39.78,0,0 +2011,7,22,2,129,23,24,1003,42.91,0,1 +2011,7,22,3,116,23,24,1003,46.04,0,0 +2011,7,22,4,119,22,23,1003,49.17,0,0 +2011,7,22,5,118,22,23,1003,52.3,0,0 +2011,7,22,6,65,22,23,1004,54.09,0,0 +2011,7,22,7,59,23,23,1004,55.88,0,0 +2011,7,22,8,74,23,24,1004,57.67,0,0 +2011,7,22,9,111,23,24,1004,59.46,0,0 +2011,7,22,10,146,23,25,1004,61.25,0,0 +2011,7,22,11,155,24,26,1004,63.04,0,0 +2011,7,22,12,161,24,27,1003,64.83,0,0 +2011,7,22,13,170,24,28,1003,0.89,0,0 +2011,7,22,14,189,25,28,1002,1.79,0,0 +2011,7,22,15,196,25,29,1002,3.58,0,0 +2011,7,22,16,203,25,28,1001,6.71,0,0 +2011,7,22,17,211,25,28,1001,9.84,0,0 +2011,7,22,18,230,25,28,1001,11.63,0,0 +2011,7,22,19,256,25,27,1002,13.42,0,0 +2011,7,22,20,271,25,27,1002,0.89,0,0 +2011,7,22,21,291,25,27,1003,1.79,0,0 +2011,7,22,22,271,25,27,1003,3.58,0,0 +2011,7,22,23,273,25,26,1003,5.37,0,0 +2011,7,23,0,261,25,26,1003,0.89,0,0 +2011,7,23,1,296,25,26,1002,0.89,0,0 +2011,7,23,2,321,25,26,1002,1.78,0,0 +2011,7,23,3,290,25,26,1002,2.67,0,0 +2011,7,23,4,288,25,26,1002,0.89,0,0 +2011,7,23,5,293,25,26,1003,0.89,0,0 +2011,7,23,6,307,25,26,1003,1.78,0,0 +2011,7,23,7,335,25,26,1003,2.67,0,0 +2011,7,23,8,330,25,26,1004,1.79,0,0 +2011,7,23,9,334,26,27,1003,0.89,0,0 +2011,7,23,10,334,26,28,1003,1.79,0,0 +2011,7,23,11,336,26,29,1003,0.89,0,0 +2011,7,23,12,302,26,30,1003,1.79,0,0 +2011,7,23,13,308,26,30,1003,0.89,0,0 +2011,7,23,14,299,27,31,1002,1.79,0,0 +2011,7,23,15,293,27,32,1002,3.58,0,0 +2011,7,23,16,269,27,33,1001,5.37,0,0 +2011,7,23,17,206,28,32,1001,0.89,0,0 +2011,7,23,18,223,28,31,1001,1.79,0,0 +2011,7,23,19,235,28,31,1001,3.58,0,0 +2011,7,23,20,244,28,30,1002,5.37,0,0 +2011,7,23,21,237,27,29,1002,7.16,0,0 +2011,7,23,22,277,27,29,1003,8.05,0,0 +2011,7,23,23,300,27,29,1003,9.84,0,0 +2011,7,24,0,361,27,28,1002,12.97,0,0 +2011,7,24,1,325,27,28,1002,16.1,0,0 +2011,7,24,2,330,26,27,1002,19.23,0,0 +2011,7,24,3,354,26,27,1002,21.02,0,0 +2011,7,24,4,321,26,27,1002,22.81,0,0 +2011,7,24,5,383,26,27,1003,0.89,0,0 +2011,7,24,6,355,26,27,1003,3.13,0,0 +2011,7,24,7,354,26,27,1003,0.89,0,0 +2011,7,24,8,375,26,28,1003,3.13,0,0 +2011,7,24,9,390,26,28,1003,6.26,0,0 +2011,7,24,10,376,27,30,1003,9.39,0,0 +2011,7,24,11,356,27,31,1003,0.89,0,0 +2011,7,24,12,375,27,31,1002,1.79,0,0 +2011,7,24,13,337,28,32,1002,4.92,0,0 +2011,7,24,14,310,28,32,1000,8.05,0,0 +2011,7,24,15,127,24,30,1001,8.05,0,1 +2011,7,24,16,38,24,27,1000,11.18,0,0 +2011,7,24,17,62,25,28,1000,15.2,0,0 +2011,7,24,18,49,24,25,1000,21.01,0,3 +2011,7,24,19,59,24,25,1000,24.14,0,5 +2011,7,24,20,53,24,24,1000,3.13,0,7 +2011,7,24,21,51,23,24,1001,4.92,0,9 +2011,7,24,22,23,24,24,1001,3.13,0,10 +2011,7,24,23,19,24,24,1000,4.92,0,11 +2011,7,25,0,20,23,24,1000,0.89,0,12 +2011,7,25,1,32,24,24,1000,1.78,0,0 +2011,7,25,2,31,24,24,999,2.67,0,1 +2011,7,25,3,40,23,24,999,3.56,0,2 +2011,7,25,4,41,23,24,999,1.79,0,3 +2011,7,25,5,38,23,24,999,0.89,0,0 +2011,7,25,6,31,23,24,999,0.89,0,0 +2011,7,25,7,38,23,24,1000,0.89,0,0 +2011,7,25,8,57,24,24,1000,1.78,0,0 +2011,7,25,9,58,24,25,999,2.67,0,0 +2011,7,25,10,64,24,25,999,3.56,0,1 +2011,7,25,11,56,24,25,999,1.79,0,0 +2011,7,25,12,48,24,25,999,1.79,0,1 +2011,7,25,13,41,24,25,999,3.13,0,2 +2011,7,25,14,27,24,27,999,6.26,0,0 +2011,7,25,15,34,24,28,998,1.79,0,0 +2011,7,25,16,29,25,29,998,3.58,0,0 +2011,7,25,17,34,25,30,998,5.37,0,0 +2011,7,25,18,40,25,29,998,1.79,0,0 +2011,7,25,19,40,25,28,998,3.58,0,0 +2011,7,25,20,63,25,26,999,4.47,0,0 +2011,7,25,21,111,24,25,999,0.89,0,0 +2011,7,25,22,118,24,25,999,0.89,0,0 +2011,7,25,23,84,24,25,999,1.78,0,0 +2011,7,26,0,85,24,24,1000,0.89,0,0 +2011,7,26,1,100,23,23,1000,1.79,0,0 +2011,7,26,2,102,24,24,1000,0.89,0,0 +2011,7,26,3,80,24,24,999,0.89,0,0 +2011,7,26,4,73,22,23,1000,0.89,0,0 +2011,7,26,5,47,22,22,1000,2.68,0,0 +2011,7,26,6,38,23,23,1000,4.47,0,0 +2011,7,26,7,39,24,25,1001,0.45,0,0 +2011,7,26,8,25,23,26,1001,3.13,0,0 +2011,7,26,9,24,23,28,1001,1.79,0,0 +2011,7,26,10,22,22,29,1001,3.13,0,0 +2011,7,26,11,24,20,30,1001,1.79,0,0 +2011,7,26,12,23,20,32,1000,0.89,0,0 +2011,7,26,13,30,21,32,1000,1.79,0,0 +2011,7,26,14,36,22,33,999,1.79,0,0 +2011,7,26,15,45,22,33,999,4.92,0,0 +2011,7,26,16,42,22,33,998,8.94,0,0 +2011,7,26,17,33,23,33,998,12.07,0,0 +2011,7,26,18,35,23,32,998,16.09,0,0 +2011,7,26,19,55,24,30,998,19.22,0,0 +2011,7,26,20,60,24,29,999,21.01,0,0 +2011,7,26,21,60,25,28,1000,22.8,0,0 +2011,7,26,22,56,24,28,999,25.93,0,0 +2011,7,26,23,70,23,27,1001,33.98,0,1 +2011,7,27,0,32,23,24,1002,3.13,0,2 +2011,7,27,1,37,23,23,1002,3.13,0,3 +2011,7,27,2,39,23,23,1001,1.79,0,0 +2011,7,27,3,61,22,23,1001,3.58,0,1 +2011,7,27,4,53,22,23,1001,1.79,0,0 +2011,7,27,5,48,22,23,1001,5.81,0,0 +2011,7,27,6,40,22,23,1001,7.6,0,0 +2011,7,27,7,37,22,23,1002,0.89,0,0 +2011,7,27,8,42,23,25,1003,1.78,0,0 +2011,7,27,9,51,23,27,1003,2.67,0,0 +2011,7,27,10,68,24,27,1003,1.79,0,0 +2011,7,27,11,77,24,28,1002,3.58,0,0 +2011,7,27,12,88,24,28,1002,6.71,0,0 +2011,7,27,13,72,24,29,1002,9.84,0,0 +2011,7,27,14,65,25,29,1002,13.86,0,0 +2011,7,27,15,67,24,29,1002,17.88,0,0 +2011,7,27,16,55,24,29,1002,21.01,0,0 +2011,7,27,17,62,24,29,1002,24.14,0,0 +2011,7,27,18,73,23,29,1002,28.16,0,0 +2011,7,27,19,84,23,28,1002,31.29,0,0 +2011,7,27,20,90,24,27,1002,32.18,0,0 +2011,7,27,21,102,24,27,1003,33.07,0,0 +2011,7,27,22,93,24,27,1004,33.96,0,0 +2011,7,27,23,99,25,27,1004,34.85,0,0 +2011,7,28,0,116,24,27,1004,37.98,0,0 +2011,7,28,1,151,23,26,1004,39.77,0,0 +2011,7,28,2,171,24,26,1004,42.9,0,0 +2011,7,28,3,179,24,25,1004,44.69,0,0 +2011,7,28,4,183,24,25,1004,46.48,0,0 +2011,7,28,5,190,24,25,1004,48.27,0,0 +2011,7,28,6,202,24,25,1004,50.06,0,0 +2011,7,28,7,209,24,25,1005,53.19,0,0 +2011,7,28,8,226,24,26,1005,54.98,0,0 +2011,7,28,9,220,25,27,1005,0.89,0,0 +2011,7,28,10,218,25,28,1006,3.13,0,0 +2011,7,28,11,201,25,28,1006,4.92,0,0 +2011,7,28,12,201,26,30,1006,1.79,0,0 +2011,7,28,13,213,25,31,1005,3.58,0,0 +2011,7,28,14,210,26,31,1005,1.79,0,0 +2011,7,28,15,233,26,31,1004,5.81,0,0 +2011,7,28,16,215,26,31,1005,8.94,0,0 +2011,7,28,17,184,27,30,1004,12.07,0,0 +2011,7,28,18,186,27,30,1004,13.86,0,0 +2011,7,28,19,187,27,30,1005,15.65,0,0 +2011,7,28,20,209,27,29,1005,17.44,0,0 +2011,7,28,21,238,27,28,1006,19.23,0,0 +2011,7,28,22,259,27,28,1006,21.02,0,0 +2011,7,28,23,269,27,27,1007,0.89,0,0 +2011,7,29,0,286,26,27,1006,1.79,0,0 +2011,7,29,1,311,26,27,1006,4.92,0,0 +2011,7,29,2,306,26,27,1006,0.89,0,0 +2011,7,29,3,307,25,26,1006,1.79,0,0 +2011,7,29,4,313,25,26,1006,0.89,0,0 +2011,7,29,5,300,25,26,1006,1.79,0,0 +2011,7,29,6,284,25,26,1006,0.89,0,0 +2011,7,29,7,273,25,26,1005,1.79,0,0 +2011,7,29,8,264,26,26,1006,3.58,0,0 +2011,7,29,9,88,22,23,1007,7.15,0,1 +2011,7,29,10,50,22,23,1008,11.17,0,2 +2011,7,29,11,49,22,24,1007,4.02,0,3 +2011,7,29,12,42,23,24,1007,3.13,0,4 +2011,7,29,13,43,23,25,1007,4.92,0,5 +2011,7,29,14,32,22,25,1007,12.07,0,6 +2011,7,29,15,54,23,24,1006,4.02,0,7 +2011,7,29,16,66,23,25,1005,3.13,0,8 +2011,7,29,17,50,23,25,1004,6.26,0,9 +2011,7,29,18,38,23,25,1005,9.39,0,10 +2011,7,29,19,43,23,25,1005,13.41,0,11 +2011,7,29,20,56,23,24,1006,18.33,0,12 +2011,7,29,21,49,22,23,1006,24.14,0,13 +2011,7,29,22,37,22,23,1005,27.27,0,14 +2011,7,29,23,41,22,23,1005,30.4,0,15 +2011,7,30,0,39,22,22,1006,1.79,0,16 +2011,7,30,1,36,21,21,1004,1.79,0,17 +2011,7,30,2,41,21,21,1004,4.92,0,18 +2011,7,30,3,35,21,21,1003,6.71,0,19 +2011,7,30,4,40,21,21,1003,4.02,0,20 +2011,7,30,5,36,21,21,1003,7.15,0,21 +2011,7,30,6,39,20,21,1002,4.02,0,22 +2011,7,30,7,32,21,22,1003,4.02,0,23 +2011,7,30,8,27,21,23,1002,8.04,0,0 +2011,7,30,9,35,22,26,1002,9.83,0,0 +2011,7,30,10,38,22,27,1002,12.96,0,0 +2011,7,30,11,35,22,28,1002,16.09,0,0 +2011,7,30,12,18,22,29,1001,19.22,0,0 +2011,7,30,13,33,23,30,1001,1.79,0,0 +2011,7,30,14,30,21,31,1000,2.68,0,0 +2011,7,30,15,37,22,31,1000,3.57,0,0 +2011,7,30,16,51,22,31,1000,1.79,0,0 +2011,7,30,17,54,23,31,999,3.58,0,0 +2011,7,30,18,48,23,30,999,6.71,0,0 +2011,7,30,19,44,23,28,1000,10.73,0,0 +2011,7,30,20,56,23,27,1000,13.86,0,0 +2011,7,30,21,57,23,27,1001,17.88,0,0 +2011,7,30,22,62,23,26,1002,21.01,0,0 +2011,7,30,23,58,23,26,1002,25.03,0,0 +2011,7,31,0,61,23,25,1002,0.89,0,0 +2011,7,31,1,77,23,24,1003,0.89,0,0 +2011,7,31,2,78,24,24,1003,0.89,0,0 +2011,7,31,3,86,23,24,1003,1.78,0,0 +2011,7,31,4,102,22,23,1003,2.67,0,0 +2011,7,31,5,87,23,23,1003,3.56,0,0 +2011,7,31,6,72,23,23,1003,3.13,0,0 +2011,7,31,7,73,23,25,1003,6.26,0,0 +2011,7,31,8,59,23,26,1004,10.28,0,0 +2011,7,31,9,64,23,27,1004,14.3,0,0 +2011,7,31,10,51,23,29,1004,17.43,0,0 +2011,7,31,11,29,22,30,1004,19.22,0,0 +2011,7,31,12,38,20,32,1003,1.79,0,0 +2011,7,31,13,46,22,32,1003,3.58,0,0 +2011,7,31,14,37,21,33,1003,5.37,0,0 +2011,7,31,15,20,20,34,1002,6.26,0,0 +2011,7,31,16,41,19,35,1002,1.79,0,0 +2011,7,31,17,47,23,33,1002,3.13,0,0 +2011,7,31,18,82,23,31,1002,4.92,0,0 +2011,7,31,19,88,23,31,1002,8.05,0,0 +2011,7,31,20,104,24,29,1003,12.07,0,0 +2011,7,31,21,NA,24,29,1003,15.2,0,0 +2011,7,31,22,NA,25,28,1003,18.33,0,0 +2011,7,31,23,NA,24,26,1003,0.89,0,0 +2011,8,1,0,NA,25,25,1004,1.78,0,0 +2011,8,1,1,NA,24,25,1004,1.79,0,0 +2011,8,1,2,NA,24,25,1004,0.89,0,0 +2011,8,1,3,NA,23,24,1004,0.89,0,0 +2011,8,1,4,NA,23,24,1004,1.78,0,0 +2011,8,1,5,NA,23,24,1004,0.89,0,0 +2011,8,1,6,NA,24,24,1004,1.79,0,0 +2011,8,1,7,NA,24,26,1004,0.89,0,0 +2011,8,1,8,NA,25,26,1005,1.78,0,0 +2011,8,1,9,NA,25,27,1005,2.67,0,0 +2011,8,1,10,NA,26,28,1005,1.79,0,0 +2011,8,1,11,NA,26,29,1005,3.58,0,0 +2011,8,1,12,NA,27,30,1005,5.37,0,0 +2011,8,1,13,NA,26,31,1004,7.16,0,0 +2011,8,1,14,NA,27,33,1004,10.29,0,0 +2011,8,1,15,NA,26,33,1003,12.08,0,0 +2011,8,1,16,NA,27,33,1003,15.21,0,0 +2011,8,1,17,NA,25,32,1003,18.34,0,0 +2011,8,1,18,NA,26,31,1004,21.47,0,0 +2011,8,1,19,NA,24,30,1004,24.6,0,0 +2011,8,1,20,NA,26,29,1004,26.39,0,0 +2011,8,1,21,NA,26,28,1005,28.18,0,0 +2011,8,1,22,NA,26,28,1005,0.89,0,0 +2011,8,1,23,NA,26,27,1006,0.89,0,0 +2011,8,2,0,NA,26,27,1006,2.68,0,0 +2011,8,2,1,NA,25,26,1006,4.47,0,0 +2011,8,2,2,NA,25,26,1006,6.26,0,0 +2011,8,2,3,NA,25,26,1006,7.15,0,0 +2011,8,2,4,NA,24,25,1006,8.94,0,0 +2011,8,2,5,NA,23,25,1007,0.89,0,0 +2011,8,2,6,NA,22,24,1007,1.79,0,0 +2011,8,2,7,NA,22,24,1008,3.58,0,0 +2011,8,2,8,NA,22,25,1008,0.89,0,0 +2011,8,2,9,NA,20,28,1008,1.79,0,0 +2011,8,2,10,NA,19,29,1008,3.58,0,0 +2011,8,2,11,NA,20,31,1008,3.13,0,0 +2011,8,2,12,NA,15,32,1008,8.94,0,0 +2011,8,2,13,NA,16,33,1007,16.09,0,0 +2011,8,2,14,NA,15,33,1007,24.14,0,0 +2011,8,2,15,NA,15,33,1007,31.29,0,0 +2011,8,2,16,NA,15,31,1007,37.1,0,0 +2011,8,2,17,NA,16,30,1007,42.91,0,0 +2011,8,2,18,NA,17,28,1007,47.83,0,0 +2011,8,2,19,NA,19,27,1007,51.85,0,0 +2011,8,2,20,NA,19,26,1007,54.98,0,0 +2011,8,2,21,NA,20,25,1007,58.11,0,0 +2011,8,2,22,NA,20,24,1007,62.13,0,0 +2011,8,2,23,NA,20,24,1008,65.26,0,0 +2011,8,3,0,NA,20,23,1008,68.39,0,0 +2011,8,3,1,NA,20,23,1008,70.18,0,0 +2011,8,3,2,NA,20,22,1008,71.07,0,0 +2011,8,3,3,NA,20,21,1008,72.86,0,0 +2011,8,3,4,NA,20,21,1007,0.89,0,0 +2011,8,3,5,NA,20,21,1007,1.78,0,0 +2011,8,3,6,NA,20,21,1007,0.89,0,0 +2011,8,3,7,NA,21,22,1007,1.78,0,0 +2011,8,3,8,NA,21,23,1007,0.89,0,0 +2011,8,3,9,NA,22,25,1006,3.13,0,0 +2011,8,3,10,NA,22,26,1006,4.92,0,0 +2011,8,3,11,NA,22,28,1005,0.89,0,0 +2011,8,3,12,59,21,29,1004,3.13,0,0 +2011,8,3,13,46,20,31,1003,7.15,0,0 +2011,8,3,14,49,20,31,1003,12.07,0,0 +2011,8,3,15,46,21,31,1003,16.99,0,0 +2011,8,3,16,49,20,30,1003,21.91,0,0 +2011,8,3,17,50,21,29,1003,26.83,0,0 +2011,8,3,18,66,19,28,1003,30.85,0,0 +2011,8,3,19,68,20,26,1003,34.87,0,0 +2011,8,3,20,74,20,26,1003,36.66,0,0 +2011,8,3,21,75,20,26,1004,39.79,0,0 +2011,8,3,22,81,20,25,1004,43.81,0,0 +2011,8,3,23,70,20,25,1004,46.94,0,0 +2011,8,4,0,85,21,25,1004,50.07,0,0 +2011,8,4,1,77,21,23,1004,51.86,0,0 +2011,8,4,2,86,21,23,1004,53.65,0,0 +2011,8,4,3,101,21,23,1004,55.44,0,0 +2011,8,4,4,98,21,22,1004,57.23,0,0 +2011,8,4,5,91,21,22,1004,0.89,0,0 +2011,8,4,6,108,21,22,1004,0.89,0,0 +2011,8,4,7,110,22,24,1005,1.78,0,0 +2011,8,4,8,109,22,25,1006,3.57,0,0 +2011,8,4,9,119,22,27,1005,1.79,0,0 +2011,8,4,10,113,22,28,1005,2.68,0,0 +2011,8,4,11,114,23,29,1005,1.79,0,0 +2011,8,4,12,106,23,30,1004,0.89,0,0 +2011,8,4,13,103,24,31,1004,1.79,0,0 +2011,8,4,14,109,23,32,1004,3.58,0,0 +2011,8,4,15,114,22,31,1004,7.6,0,0 +2011,8,4,16,101,22,31,1004,11.62,0,0 +2011,8,4,17,100,22,30,1004,15.64,0,0 +2011,8,4,18,117,21,29,1004,20.56,0,0 +2011,8,4,19,115,21,28,1004,23.69,0,0 +2011,8,4,20,117,21,27,1005,25.48,0,0 +2011,8,4,21,NA,21,25,1006,27.27,0,0 +2011,8,4,22,101,22,25,1006,0.89,0,0 +2011,8,4,23,107,23,24,1006,1.34,0,0 +2011,8,5,0,118,23,24,1006,1.79,0,0 +2011,8,5,1,138,23,24,1006,3.58,0,0 +2011,8,5,2,166,22,24,1006,5.37,0,0 +2011,8,5,3,144,23,24,1006,7.16,0,0 +2011,8,5,4,146,23,24,1006,8.05,0,0 +2011,8,5,5,154,23,23,1006,8.94,0,0 +2011,8,5,6,166,23,24,1007,10.73,0,0 +2011,8,5,7,190,23,25,1007,12.52,0,0 +2011,8,5,8,205,24,26,1007,0.89,0,0 +2011,8,5,9,200,24,26,1007,1.79,0,0 +2011,8,5,10,178,24,27,1007,3.58,0,0 +2011,8,5,11,134,24,29,1007,5.37,0,0 +2011,8,5,12,134,24,29,1007,7.16,0,0 +2011,8,5,13,144,23,30,1006,10.29,0,0 +2011,8,5,14,142,23,30,1006,13.42,0,0 +2011,8,5,15,140,23,30,1005,17.44,0,0 +2011,8,5,16,132,23,30,1005,21.46,0,0 +2011,8,5,17,143,23,30,1005,24.59,0,0 +2011,8,5,18,144,22,29,1005,27.72,0,0 +2011,8,5,19,175,23,28,1005,30.85,0,0 +2011,8,5,20,154,23,27,1006,33.98,0,0 +2011,8,5,21,142,22,26,1006,37.11,0,0 +2011,8,5,22,119,22,26,1007,40.24,0,0 +2011,8,5,23,104,22,25,1007,42.03,0,0 +2011,8,6,0,114,22,24,1007,43.82,0,0 +2011,8,6,1,112,22,24,1006,46.95,0,0 +2011,8,6,2,NA,23,24,1006,50.08,0,0 +2011,8,6,3,NA,22,23,1006,0.89,0,0 +2011,8,6,4,NA,22,23,1006,0.89,0,0 +2011,8,6,5,NA,22,23,1006,1.78,0,0 +2011,8,6,6,NA,23,23,1006,0.89,0,0 +2011,8,6,7,NA,23,24,1007,1.79,0,0 +2011,8,6,8,NA,23,25,1007,4.92,0,0 +2011,8,6,9,NA,23,26,1007,8.94,0,0 +2011,8,6,10,NA,23,26,1007,12.96,0,0 +2011,8,6,11,NA,23,28,1007,16.09,0,0 +2011,8,6,12,NA,22,29,1006,20.11,0,0 +2011,8,6,13,NA,22,28,1006,23.24,0,0 +2011,8,6,14,NA,21,29,1005,27.26,0,0 +2011,8,6,15,NA,21,29,1005,31.28,0,0 +2011,8,6,16,NA,21,28,1005,36.2,0,0 +2011,8,6,17,NA,21,27,1006,41.12,0,0 +2011,8,6,18,NA,21,26,1006,45.14,0,0 +2011,8,6,19,NA,21,25,1006,49.16,0,0 +2011,8,6,20,NA,22,24,1007,52.29,0,0 +2011,8,6,21,NA,22,24,1007,55.42,0,0 +2011,8,6,22,NA,22,24,1008,58.55,0,0 +2011,8,6,23,NA,22,24,1008,60.34,0,0 +2011,8,7,0,105,22,24,1008,61.23,0,0 +2011,8,7,1,NA,23,24,1007,62.12,0,0 +2011,8,7,2,NA,22,23,1007,0.89,0,0 +2011,8,7,3,NA,22,23,1007,1.78,0,0 +2011,8,7,4,NA,22,23,1007,2.67,0,0 +2011,8,7,5,NA,23,23,1007,3.56,0,0 +2011,8,7,6,NA,23,24,1008,0.89,0,0 +2011,8,7,7,NA,23,24,1008,0.89,0,0 +2011,8,7,8,NA,24,25,1008,1.79,0,0 +2011,8,7,9,NA,23,26,1008,3.58,0,0 +2011,8,7,10,NA,23,28,1008,0.89,0,0 +2011,8,7,11,NA,24,28,1007,1.78,0,0 +2011,8,7,12,NA,24,29,1007,3.13,0,0 +2011,8,7,13,NA,24,29,1006,6.26,0,0 +2011,8,7,14,NA,24,31,1006,9.39,0,0 +2011,8,7,15,NA,24,30,1005,11.18,0,0 +2011,8,7,16,NA,23,30,1005,12.97,0,0 +2011,8,7,17,NA,23,30,1004,14.76,0,0 +2011,8,7,18,NA,23,30,1004,16.55,0,0 +2011,8,7,19,NA,24,28,1004,19.68,0,0 +2011,8,7,20,NA,23,26,1004,22.81,0,0 +2011,8,7,21,NA,23,26,1005,24.6,0,0 +2011,8,7,22,NA,24,25,1004,25.49,0,0 +2011,8,7,23,NA,24,25,1004,27.28,0,0 +2011,8,8,0,NA,24,25,1004,29.07,0,0 +2011,8,8,1,NA,24,25,1004,30.86,0,0 +2011,8,8,2,NA,23,24,1003,31.75,0,0 +2011,8,8,3,NA,23,24,1003,1.79,0,0 +2011,8,8,4,NA,23,24,1003,0.89,0,0 +2011,8,8,5,NA,23,23,1002,1.79,0,0 +2011,8,8,6,115,23,24,1002,4.92,0,0 +2011,8,8,7,107,23,25,1002,8.05,0,0 +2011,8,8,8,135,23,27,1002,12.07,0,0 +2011,8,8,9,109,24,28,1002,15.2,0,0 +2011,8,8,10,98,23,29,1002,1.79,0,0 +2011,8,8,11,90,23,31,1001,1.79,0,0 +2011,8,8,12,91,22,32,1001,3.58,0,0 +2011,8,8,13,84,21,33,1000,1.79,0,0 +2011,8,8,14,87,22,34,999,3.58,0,0 +2011,8,8,15,75,22,34,999,0.89,0,0 +2011,8,8,16,102,21,34,998,1.79,0,0 +2011,8,8,17,104,22,33,998,4.92,0,0 +2011,8,8,18,92,24,32,998,8.05,0,0 +2011,8,8,19,97,24,30,999,11.18,0,0 +2011,8,8,20,94,24,29,999,12.97,0,0 +2011,8,8,21,92,25,29,1000,14.76,0,0 +2011,8,8,22,98,24,28,1001,16.55,0,0 +2011,8,8,23,139,24,27,1001,18.34,0,0 +2011,8,9,0,NA,25,27,1001,20.13,0,0 +2011,8,9,1,194,25,27,1001,21.92,0,0 +2011,8,9,2,209,25,26,1001,23.71,0,0 +2011,8,9,3,208,24,26,1001,25.5,0,0 +2011,8,9,4,211,24,25,1001,27.29,0,0 +2011,8,9,5,201,24,25,1002,0.89,0,0 +2011,8,9,6,226,25,26,1002,1.78,0,0 +2011,8,9,7,214,25,26,1002,1.79,0,0 +2011,8,9,8,212,25,27,1003,3.58,0,0 +2011,8,9,9,227,25,29,1003,0.89,0,0 +2011,8,9,10,217,25,30,1003,1.78,0,0 +2011,8,9,11,212,26,31,1003,2.67,0,0 +2011,8,9,12,230,26,31,1003,1.79,0,0 +2011,8,9,13,202,26,33,1003,0.89,0,0 +2011,8,9,14,195,26,33,1002,1.79,0,0 +2011,8,9,15,221,26,34,1002,4.92,0,0 +2011,8,9,16,201,25,34,1003,8.05,0,0 +2011,8,9,17,202,25,31,1003,9.84,0,1 +2011,8,9,18,102,21,27,1004,9.83,0,2 +2011,8,9,19,34,21,23,1008,17.88,0,4 +2011,8,9,20,30,21,21,1008,0.89,0,5 +2011,8,9,21,36,21,22,1006,4.92,0,6 +2011,8,9,22,42,22,23,1009,3.13,0,7 +2011,8,9,23,NA,21,22,1009,0.89,0,8 +2011,8,10,0,NA,21,22,1008,3.13,0,0 +2011,8,10,1,NA,21,22,1008,1.79,0,0 +2011,8,10,2,NA,21,22,1007,5.81,0,0 +2011,8,10,3,NA,21,22,1007,7.6,0,0 +2011,8,10,4,NA,20,22,1007,9.39,0,0 +2011,8,10,5,NA,20,23,1007,12.52,0,0 +2011,8,10,6,NA,20,23,1008,15.65,0,0 +2011,8,10,7,NA,21,24,1008,18.78,0,0 +2011,8,10,8,NA,22,26,1009,1.79,0,0 +2011,8,10,9,NA,21,27,1008,4.02,0,0 +2011,8,10,10,NA,22,29,1009,1.79,0,0 +2011,8,10,11,NA,20,31,1008,2.68,0,0 +2011,8,10,12,NA,20,32,1008,1.79,0,0 +2011,8,10,13,NA,20,34,1007,0.89,0,0 +2011,8,10,14,NA,19,34,1006,3.13,0,0 +2011,8,10,15,NA,18,34,1006,4.92,0,0 +2011,8,10,16,NA,20,34,1005,6.71,0,0 +2011,8,10,17,NA,21,33,1005,8.5,0,0 +2011,8,10,18,NA,23,32,1005,11.63,0,0 +2011,8,10,19,NA,23,30,1005,14.76,0,0 +2011,8,10,20,NA,24,28,1006,16.55,0,0 +2011,8,10,21,58,24,28,1007,19.68,0,0 +2011,8,10,22,54,24,27,1007,21.47,0,0 +2011,8,10,23,70,24,26,1007,0.89,0,0 +2011,8,11,0,74,24,25,1007,1.79,0,0 +2011,8,11,1,64,24,25,1007,0.89,0,0 +2011,8,11,2,82,24,25,1007,1.79,0,0 +2011,8,11,3,119,23,24,1007,0.89,0,0 +2011,8,11,4,146,22,23,1006,0.89,0,0 +2011,8,11,5,121,23,23,1007,1.78,0,0 +2011,8,11,6,64,23,23,1007,1.79,0,0 +2011,8,11,7,51,24,25,1007,3.58,0,0 +2011,8,11,8,61,24,27,1007,0.89,0,0 +2011,8,11,9,83,24,29,1007,1.79,0,0 +2011,8,11,10,96,25,31,1006,0.89,0,0 +2011,8,11,11,80,24,32,1006,1.78,0,0 +2011,8,11,12,87,23,34,1006,1.79,0,0 +2011,8,11,13,91,24,32,1005,3.58,0,0 +2011,8,11,14,NA,23,34,1004,6.71,0,0 +2011,8,11,15,NA,23,33,1004,9.84,0,0 +2011,8,11,16,103,22,33,1004,12.97,0,0 +2011,8,11,17,101,23,33,1003,16.1,0,0 +2011,8,11,18,111,24,31,1003,17.89,0,0 +2011,8,11,19,108,24,30,1004,19.68,0,0 +2011,8,11,20,113,24,29,1004,21.47,0,0 +2011,8,11,21,130,25,28,1004,24.6,0,0 +2011,8,11,22,133,25,27,1004,26.39,0,0 +2011,8,11,23,161,24,27,1004,28.18,0,0 +2011,8,12,0,123,22,27,1004,29.97,0,0 +2011,8,12,1,115,24,26,1004,31.76,0,0 +2011,8,12,2,111,24,26,1004,0.89,0,0 +2011,8,12,3,105,24,25,1004,0.89,0,0 +2011,8,12,4,111,24,25,1004,0.89,0,0 +2011,8,12,5,103,24,25,1004,1.79,0,0 +2011,8,12,6,107,24,25,1004,0.89,0,0 +2011,8,12,7,125,24,26,1004,0.89,0,0 +2011,8,12,8,128,24,27,1004,0.89,0,0 +2011,8,12,9,141,25,29,1004,1.78,0,0 +2011,8,12,10,112,23,30,1004,1.79,0,0 +2011,8,12,11,118,25,31,1003,0.89,0,0 +2011,8,12,12,132,24,32,1003,3.13,0,0 +2011,8,12,13,153,24,32,1002,6.26,0,0 +2011,8,12,14,159,24,32,1002,8.05,0,0 +2011,8,12,15,151,24,33,1001,11.18,0,0 +2011,8,12,16,161,24,32,1001,14.31,0,0 +2011,8,12,17,148,23,31,1001,17.44,0,0 +2011,8,12,18,138,24,30,1001,20.57,0,0 +2011,8,12,19,171,25,29,1001,23.7,0,0 +2011,8,12,20,174,25,28,1002,27.72,0,0 +2011,8,12,21,154,25,27,1003,29.51,0,0 +2011,8,12,22,140,25,27,1003,32.64,0,0 +2011,8,12,23,130,24,26,1002,0.89,0,0 +2011,8,13,0,113,24,26,1002,1.79,0,0 +2011,8,13,1,100,24,26,1002,3.58,0,0 +2011,8,13,2,98,24,26,1001,6.71,0,0 +2011,8,13,3,91,24,25,1001,9.84,0,0 +2011,8,13,4,86,24,25,1001,11.63,0,0 +2011,8,13,5,96,24,25,1001,13.42,0,0 +2011,8,13,6,92,24,25,1001,15.21,0,0 +2011,8,13,7,99,23,25,1001,18.34,0,0 +2011,8,13,8,116,23,26,1001,21.47,0,0 +2011,8,13,9,110,24,27,1001,23.26,0,0 +2011,8,13,10,132,24,27,1001,25.05,0,0 +2011,8,13,11,137,25,29,1000,26.84,0,0 +2011,8,13,12,135,25,29,1000,29.97,0,0 +2011,8,13,13,125,24,30,1000,33.1,0,0 +2011,8,13,14,148,24,31,999,36.23,0,0 +2011,8,13,15,134,25,31,999,39.36,0,0 +2011,8,13,16,134,24,30,999,42.49,0,0 +2011,8,13,17,147,25,30,998,45.62,0,0 +2011,8,13,18,141,25,29,999,48.75,0,0 +2011,8,13,19,142,25,29,1000,50.54,0,0 +2011,8,13,20,130,25,28,1000,52.33,0,0 +2011,8,13,21,123,25,28,1001,54.12,0,0 +2011,8,13,22,131,26,28,1001,55.01,0,0 +2011,8,13,23,135,25,28,1001,56.8,0,0 +2011,8,14,0,125,25,28,1001,58.59,0,0 +2011,8,14,1,138,25,28,1001,60.38,0,0 +2011,8,14,2,153,22,23,1001,4.02,0,2 +2011,8,14,3,31,24,24,1001,3.13,0,3 +2011,8,14,4,47,23,24,1001,4.02,0,0 +2011,8,14,5,56,23,24,1001,5.81,0,0 +2011,8,14,6,46,23,24,1002,1.79,0,0 +2011,8,14,7,33,24,24,1002,0.89,0,0 +2011,8,14,8,40,24,25,1002,1.79,0,0 +2011,8,14,9,41,25,27,1003,0.89,0,0 +2011,8,14,10,65,26,28,1003,1.79,0,0 +2011,8,14,11,110,26,28,1002,3.58,0,0 +2011,8,14,12,108,26,29,1002,5.37,0,0 +2011,8,14,13,93,25,28,1002,7.16,0,0 +2011,8,14,14,107,26,28,1002,8.95,0,1 +2011,8,14,15,100,25,28,1002,10.74,0,0 +2011,8,14,16,97,26,27,1002,0.89,0,0 +2011,8,14,17,120,26,27,1001,1.78,0,1 +2011,8,14,18,122,26,26,1002,3.13,0,0 +2011,8,14,19,128,25,25,1002,3.13,0,1 +2011,8,14,20,158,25,25,1002,6.26,0,2 +2011,8,14,21,206,25,25,1002,0.89,0,0 +2011,8,14,22,221,25,25,1003,3.13,0,1 +2011,8,14,23,156,24,24,1003,3.13,0,2 +2011,8,15,0,165,24,25,1003,1.79,0,3 +2011,8,15,1,160,24,24,1002,1.79,0,0 +2011,8,15,2,175,24,24,1002,4.92,0,0 +2011,8,15,3,158,24,24,1001,8.05,0,1 +2011,8,15,4,137,24,25,1001,9.84,0,2 +2011,8,15,5,128,24,25,1001,11.63,0,3 +2011,8,15,6,126,24,25,1001,14.76,0,4 +2011,8,15,7,124,24,25,1001,16.55,0,5 +2011,8,15,8,109,24,25,1002,18.34,0,6 +2011,8,15,9,107,24,24,1002,20.13,0,0 +2011,8,15,10,99,24,25,1002,21.92,0,1 +2011,8,15,11,143,24,25,1002,1.79,0,2 +2011,8,15,12,118,24,24,1002,4.92,0,3 +2011,8,15,13,116,24,24,1002,8.05,0,4 +2011,8,15,14,109,23,24,1002,1.79,0,5 +2011,8,15,15,109,24,25,1002,0.89,0,0 +2011,8,15,16,109,24,26,1002,1.79,0,0 +2011,8,15,17,87,24,26,1002,0.89,0,0 +2011,8,15,18,100,24,25,1002,1.78,0,0 +2011,8,15,19,117,24,25,1002,0.89,0,0 +2011,8,15,20,107,24,25,1003,1.79,0,0 +2011,8,15,21,110,24,25,1003,1.79,0,0 +2011,8,15,22,107,22,25,1003,4.02,0,0 +2011,8,15,23,38,21,25,1003,3.13,0,0 +2011,8,16,0,10,21,24,1003,4.92,0,1 +2011,8,16,1,7,22,23,1003,0.89,0,2 +2011,8,16,2,6,22,23,1003,0.89,0,3 +2011,8,16,3,9,22,23,1003,0.89,0,0 +2011,8,16,4,8,21,23,1003,4.02,0,0 +2011,8,16,5,8,21,23,1003,7.15,0,0 +2011,8,16,6,17,21,23,1003,11.17,0,0 +2011,8,16,7,21,21,24,1003,14.3,0,0 +2011,8,16,8,16,21,25,1004,18.32,0,0 +2011,8,16,9,17,22,27,1004,21.45,0,0 +2011,8,16,10,13,21,28,1004,25.47,0,0 +2011,8,16,11,7,19,30,1004,30.39,0,0 +2011,8,16,12,7,19,31,1004,4.02,0,0 +2011,8,16,13,11,18,31,1004,8.04,0,0 +2011,8,16,14,7,18,32,1003,12.06,0,0 +2011,8,16,15,4,17,32,1003,3.13,0,0 +2011,8,16,16,4,17,32,1003,4.92,0,0 +2011,8,16,17,8,18,32,1003,8.05,0,0 +2011,8,16,18,17,19,30,1003,9.84,0,0 +2011,8,16,19,24,19,29,1003,13.86,0,0 +2011,8,16,20,35,21,27,1004,16.99,0,0 +2011,8,16,21,32,21,27,1005,20.12,0,0 +2011,8,16,22,37,21,27,1006,21.91,0,0 +2011,8,16,23,45,22,26,1006,23.7,0,0 +2011,8,17,0,45,22,24,1006,0.89,0,0 +2011,8,17,1,51,22,23,1006,1.79,0,0 +2011,8,17,2,67,22,23,1006,0.89,0,0 +2011,8,17,3,68,22,22,1006,1.78,0,0 +2011,8,17,4,66,22,22,1006,2.67,0,0 +2011,8,17,5,63,22,22,1006,0.89,0,0 +2011,8,17,6,74,22,22,1006,0.89,0,0 +2011,8,17,7,74,23,23,1007,0.89,0,0 +2011,8,17,8,69,23,25,1007,1.78,0,0 +2011,8,17,9,59,23,26,1007,2.67,0,0 +2011,8,17,10,69,23,28,1007,1.79,0,0 +2011,8,17,11,88,23,27,1007,4.92,0,0 +2011,8,17,12,88,23,26,1008,9.84,0,0 +2011,8,17,13,67,23,26,1007,1.79,0,1 +2011,8,17,14,48,22,27,1007,3.13,0,0 +2011,8,17,15,37,22,27,1007,8.05,0,0 +2011,8,17,16,37,19,26,1007,12.97,0,1 +2011,8,17,17,22,20,25,1008,17.89,0,2 +2011,8,17,18,16,18,25,1009,23.7,0,3 +2011,8,17,19,29,18,24,1008,26.83,0,0 +2011,8,17,20,31,19,23,1009,30.85,0,1 +2011,8,17,21,25,19,23,1010,34.87,0,2 +2011,8,17,22,19,18,23,1010,38.89,0,0 +2011,8,17,23,17,18,23,1009,42.91,0,0 +2011,8,18,0,16,18,23,1009,3.13,0,0 +2011,8,18,1,16,18,23,1010,4.92,0,0 +2011,8,18,2,18,19,23,1009,6.71,0,0 +2011,8,18,3,7,19,23,1009,0.89,0,0 +2011,8,18,4,3,19,21,1009,0.89,0,0 +2011,8,18,5,2,19,20,1010,1.78,0,0 +2011,8,18,6,5,19,20,1010,0.89,0,0 +2011,8,18,7,10,20,22,1011,0.89,0,0 +2011,8,18,8,12,18,24,1011,3.13,0,0 +2011,8,18,9,11,17,26,1011,6.26,0,0 +2011,8,18,10,10,17,27,1011,9.39,0,0 +2011,8,18,11,8,16,28,1011,1.79,0,0 +2011,8,18,12,9,16,29,1010,3.58,0,0 +2011,8,18,13,18,15,31,1010,1.79,0,0 +2011,8,18,14,22,16,31,1010,1.79,0,0 +2011,8,18,15,29,16,32,1009,4.92,0,0 +2011,8,18,16,23,19,31,1009,9.84,0,0 +2011,8,18,17,21,19,29,1010,16.99,0,0 +2011,8,18,18,29,19,27,1010,21.91,0,0 +2011,8,18,19,37,20,26,1010,26.83,0,0 +2011,8,18,20,37,20,24,1011,29.96,0,0 +2011,8,18,21,49,20,24,1012,33.98,0,0 +2011,8,18,22,44,21,23,1012,37.11,0,0 +2011,8,18,23,47,21,23,1012,40.24,0,0 +2011,8,19,0,48,21,22,1012,43.37,0,0 +2011,8,19,1,55,21,22,1012,44.26,0,0 +2011,8,19,2,61,21,21,1012,46.05,0,0 +2011,8,19,3,56,20,21,1012,0.89,0,0 +2011,8,19,4,51,20,21,1012,0.89,0,0 +2011,8,19,5,62,20,21,1012,0.89,0,0 +2011,8,19,6,63,20,20,1013,0.89,0,0 +2011,8,19,7,60,21,22,1012,0.89,0,0 +2011,8,19,8,75,22,24,1013,1.78,0,0 +2011,8,19,9,77,22,25,1013,2.67,0,0 +2011,8,19,10,67,22,27,1013,1.79,0,0 +2011,8,19,11,87,21,29,1013,3.13,0,0 +2011,8,19,12,81,22,29,1013,4.02,0,0 +2011,8,19,13,59,19,29,1012,8.94,0,0 +2011,8,19,14,48,20,30,1012,12.96,0,0 +2011,8,19,15,54,18,30,1011,16.98,0,0 +2011,8,19,16,48,17,29,1011,21,0,0 +2011,8,19,17,42,18,30,1011,24.13,0,0 +2011,8,19,18,34,19,28,1011,27.26,0,0 +2011,8,19,19,48,19,26,1011,30.39,0,0 +2011,8,19,20,54,19,25,1012,34.41,0,0 +2011,8,19,21,50,19,23,1012,36.2,0,0 +2011,8,19,22,51,19,22,1012,37.99,0,0 +2011,8,19,23,69,19,21,1012,39.78,0,0 +2011,8,20,0,67,20,22,1012,41.57,0,0 +2011,8,20,1,68,19,21,1012,43.36,0,0 +2011,8,20,2,85,19,20,1013,45.15,0,0 +2011,8,20,3,76,20,20,1013,46.94,0,0 +2011,8,20,4,81,19,19,1013,1.79,0,0 +2011,8,20,5,82,19,19,1013,3.58,0,0 +2011,8,20,6,96,19,20,1013,5.37,0,0 +2011,8,20,7,89,21,21,1013,0.89,0,0 +2011,8,20,8,120,21,22,1013,1.78,0,0 +2011,8,20,9,124,21,24,1013,1.79,0,0 +2011,8,20,10,106,22,27,1013,0.89,0,0 +2011,8,20,11,79,21,28,1013,1.78,0,0 +2011,8,20,12,85,21,30,1012,1.79,0,0 +2011,8,20,13,92,20,31,1012,3.58,0,0 +2011,8,20,14,99,20,32,1011,6.71,0,0 +2011,8,20,15,97,21,31,1010,9.84,0,0 +2011,8,20,16,74,20,31,1010,13.86,0,0 +2011,8,20,17,54,20,31,1010,16.99,0,0 +2011,8,20,18,47,20,29,1010,21.01,0,0 +2011,8,20,19,45,20,27,1011,24.14,0,0 +2011,8,20,20,44,20,25,1011,27.27,0,0 +2011,8,20,21,54,20,25,1011,31.29,0,0 +2011,8,20,22,45,21,23,1012,0.89,0,0 +2011,8,20,23,57,20,22,1012,1.79,0,0 +2011,8,21,0,60,21,22,1012,2.68,0,0 +2011,8,21,1,61,20,21,1011,4.47,0,0 +2011,8,21,2,73,20,21,1012,1.79,0,0 +2011,8,21,3,90,20,21,1012,2.68,0,0 +2011,8,21,4,92,19,20,1012,0.89,0,0 +2011,8,21,5,105,19,19,1012,0.89,0,0 +2011,8,21,6,102,20,20,1012,1.78,0,0 +2011,8,21,7,110,21,22,1012,0.89,0,0 +2011,8,21,8,118,22,24,1012,1.78,0,0 +2011,8,21,9,107,22,25,1012,1.79,0,0 +2011,8,21,10,92,22,28,1011,0.89,0,0 +2011,8,21,11,85,22,28,1011,1.79,0,0 +2011,8,21,12,91,22,30,1010,1.79,0,0 +2011,8,21,13,100,22,31,1010,1.79,0,0 +2011,8,21,14,79,21,31,1009,5.81,0,0 +2011,8,21,15,48,19,32,1008,11.62,0,0 +2011,8,21,16,52,19,31,1008,16.54,0,0 +2011,8,21,17,69,21,30,1008,20.56,0,0 +2011,8,21,18,71,18,29,1008,25.48,0,0 +2011,8,21,19,72,19,27,1009,28.61,0,0 +2011,8,21,20,77,21,24,1011,1.79,0,0 +2011,8,21,21,92,21,24,1011,0.89,0,0 +2011,8,21,22,NA,21,25,1009,5.81,0,0 +2011,8,21,23,NA,20,23,1010,0.89,0,0 +2011,8,22,0,104,21,22,1011,1.78,0,0 +2011,8,22,1,106,20,21,1010,1.79,0,0 +2011,8,22,2,120,21,22,1010,4.92,0,0 +2011,8,22,3,127,20,21,1010,1.79,0,0 +2011,8,22,4,145,20,20,1010,0.89,0,0 +2011,8,22,5,134,20,20,1010,1.78,0,0 +2011,8,22,6,139,19,20,1011,2.67,0,0 +2011,8,22,7,81,20,21,1011,1.79,0,0 +2011,8,22,8,53,20,24,1012,0.89,0,0 +2011,8,22,9,97,20,25,1012,1.79,0,0 +2011,8,22,10,94,20,27,1012,0.89,0,0 +2011,8,22,11,99,19,29,1012,1.78,0,0 +2011,8,22,12,110,20,30,1011,3.57,0,0 +2011,8,22,13,117,19,30,1011,5.36,0,0 +2011,8,22,14,114,19,31,1010,3.13,0,0 +2011,8,22,15,92,17,30,1010,7.15,0,0 +2011,8,22,16,74,17,30,1010,11.17,0,0 +2011,8,22,17,81,17,29,1010,15.19,0,0 +2011,8,22,18,98,18,28,1011,18.32,0,0 +2011,8,22,19,120,18,26,1012,20.11,0,0 +2011,8,22,20,113,20,23,1011,0.89,0,0 +2011,8,22,21,104,19,27,1011,4.02,0,0 +2011,8,22,22,NA,20,24,1012,4.91,0,0 +2011,8,22,23,NA,20,23,1012,5.8,0,0 +2011,8,23,0,130,21,22,1012,1.79,0,0 +2011,8,23,1,NA,21,23,1012,0.89,0,0 +2011,8,23,2,NA,21,22,1012,1.78,0,0 +2011,8,23,3,NA,21,21,1012,2.67,0,0 +2011,8,23,4,NA,19,20,1012,0.89,0,0 +2011,8,23,5,NA,18,19,1013,4.02,0,0 +2011,8,23,6,NA,19,20,1013,5.81,0,0 +2011,8,23,7,NA,20,21,1013,7.6,0,0 +2011,8,23,8,NA,20,23,1013,1.79,0,0 +2011,8,23,9,NA,20,25,1014,1.79,0,0 +2011,8,23,10,NA,21,26,1014,1.79,0,0 +2011,8,23,11,NA,21,27,1013,1.79,0,0 +2011,8,23,12,NA,21,29,1013,0.89,0,0 +2011,8,23,13,NA,19,29,1012,1.79,0,0 +2011,8,23,14,NA,19,29,1011,1.79,0,0 +2011,8,23,15,NA,20,29,1011,1.79,0,0 +2011,8,23,16,NA,19,30,1010,3.58,0,0 +2011,8,23,17,NA,18,29,1010,6.71,0,0 +2011,8,23,18,NA,17,28,1010,9.84,0,0 +2011,8,23,19,NA,17,26,1010,13.86,0,0 +2011,8,23,20,NA,18,24,1011,0.89,0,0 +2011,8,23,21,NA,20,22,1012,1.79,0,0 +2011,8,23,22,NA,20,24,1012,4.92,0,0 +2011,8,23,23,NA,20,23,1012,6.71,0,0 +2011,8,24,0,92,20,22,1012,8.5,0,0 +2011,8,24,1,NA,20,23,1012,10.29,0,0 +2011,8,24,2,NA,19,22,1012,12.08,0,0 +2011,8,24,3,NA,19,20,1012,1.79,0,0 +2011,8,24,4,NA,19,21,1012,1.79,0,0 +2011,8,24,5,NA,19,20,1012,0.45,0,0 +2011,8,24,6,NA,19,20,1012,1.34,0,0 +2011,8,24,7,NA,20,21,1012,0.89,0,0 +2011,8,24,8,NA,20,23,1013,2.68,0,0 +2011,8,24,9,NA,20,24,1013,0.89,0,0 +2011,8,24,10,137,21,26,1013,1.78,0,0 +2011,8,24,11,132,22,27,1012,1.79,0,0 +2011,8,24,12,134,21,28,1012,4.92,0,0 +2011,8,24,13,129,21,25,1012,4.92,0,1 +2011,8,24,14,109,21,28,1011,3.13,0,0 +2011,8,24,15,93,19,29,1010,7.15,0,0 +2011,8,24,16,87,18,28,1010,12.07,0,0 +2011,8,24,17,88,19,28,1010,13.86,0,0 +2011,8,24,18,94,20,27,1010,16.99,0,0 +2011,8,24,19,70,21,24,1011,21.91,0,1 +2011,8,24,20,88,21,22,1012,1.79,0,2 +2011,8,24,21,113,21,22,1013,1.79,0,0 +2011,8,24,22,115,20,21,1013,1.79,0,0 +2011,8,24,23,96,20,21,1013,0.89,0,0 +2011,8,25,0,87,19,20,1013,1.79,0,0 +2011,8,25,1,93,19,20,1013,3.58,0,0 +2011,8,25,2,98,19,20,1013,0.89,0,0 +2011,8,25,3,97,20,20,1013,1.79,0,0 +2011,8,25,4,97,20,21,1013,3.58,0,0 +2011,8,25,5,103,20,21,1013,5.37,0,0 +2011,8,25,6,111,20,21,1014,7.16,0,0 +2011,8,25,7,110,21,22,1014,0.89,0,0 +2011,8,25,8,117,21,23,1014,1.79,0,0 +2011,8,25,9,125,21,25,1015,4.92,0,0 +2011,8,25,10,106,21,25,1015,8.05,0,0 +2011,8,25,11,91,22,26,1015,11.18,0,0 +2011,8,25,12,80,21,27,1015,14.31,0,0 +2011,8,25,13,97,20,24,1014,3.13,0,1 +2011,8,25,14,98,20,22,1014,4.92,0,2 +2011,8,25,15,92,21,22,1014,6.71,0,0 +2011,8,25,16,93,21,23,1014,10.73,0,0 +2011,8,25,17,85,20,23,1014,12.52,0,0 +2011,8,25,18,95,20,23,1014,1.79,0,0 +2011,8,25,19,93,20,22,1014,0.45,0,0 +2011,8,25,20,96,20,21,1015,1.79,0,0 +2011,8,25,21,92,20,21,1015,0.89,0,0 +2011,8,25,22,109,20,21,1015,1.78,0,0 +2011,8,25,23,118,20,21,1015,2.67,0,0 +2011,8,26,0,121,20,21,1016,1.79,0,0 +2011,8,26,1,108,21,21,1015,2.68,0,0 +2011,8,26,2,121,21,21,1015,0.89,0,0 +2011,8,26,3,126,21,22,1015,1.34,0,0 +2011,8,26,4,114,21,22,1015,2.23,0,1 +2011,8,26,5,116,21,21,1015,3.12,0,2 +2011,8,26,6,120,20,21,1016,1.79,0,3 +2011,8,26,7,126,21,22,1016,4.92,0,0 +2011,8,26,8,35,19,20,1017,5.81,0,2 +2011,8,26,9,32,19,20,1017,8.94,0,3 +2011,8,26,10,31,19,20,1017,9.83,0,4 +2011,8,26,11,57,20,21,1017,13.85,0,5 +2011,8,26,12,69,22,24,1016,16.98,0,0 +2011,8,26,13,70,22,26,1016,20.11,0,0 +2011,8,26,14,82,22,26,1015,23.24,0,0 +2011,8,26,15,75,22,27,1015,26.37,0,0 +2011,8,26,16,80,21,27,1014,29.5,0,0 +2011,8,26,17,71,21,26,1014,32.63,0,0 +2011,8,26,18,80,21,26,1014,35.76,0,0 +2011,8,26,19,95,21,26,1014,37.55,0,1 +2011,8,26,20,108,21,24,1015,40.68,0,2 +2011,8,26,21,118,21,23,1015,0.89,0,0 +2011,8,26,22,118,21,22,1015,1.78,0,0 +2011,8,26,23,111,20,21,1015,2.67,0,0 +2011,8,27,0,118,21,21,1015,0.89,0,0 +2011,8,27,1,111,20,21,1014,0.89,0,0 +2011,8,27,2,111,21,21,1014,2.68,0,0 +2011,8,27,3,103,20,21,1014,4.47,0,0 +2011,8,27,4,92,20,20,1014,0.89,0,0 +2011,8,27,5,111,19,20,1014,1.78,0,0 +2011,8,27,6,124,20,20,1014,2.67,0,0 +2011,8,27,7,112,21,21,1014,1.79,0,0 +2011,8,27,8,120,21,22,1014,3.58,0,0 +2011,8,27,9,115,21,23,1014,5.37,0,0 +2011,8,27,10,95,21,25,1015,1.79,0,0 +2011,8,27,11,110,21,26,1014,3.58,0,0 +2011,8,27,12,109,20,26,1014,6.71,0,0 +2011,8,27,13,71,18,27,1013,9.84,0,0 +2011,8,27,14,80,18,27,1013,12.97,0,0 +2011,8,27,15,64,20,26,1012,17.89,0,0 +2011,8,27,16,87,18,26,1012,21.91,0,0 +2011,8,27,17,58,18,25,1012,25.93,0,0 +2011,8,27,18,69,17,24,1011,29.06,0,0 +2011,8,27,19,56,18,22,1012,30.85,0,0 +2011,8,27,20,55,18,22,1012,0.89,0,0 +2011,8,27,21,52,18,21,1012,0.89,0,0 +2011,8,27,22,62,18,20,1012,0.45,0,0 +2011,8,27,23,78,18,19,1012,1.34,0,0 +2011,8,28,0,78,18,19,1012,2.23,0,0 +2011,8,28,1,86,18,18,1012,1.79,0,0 +2011,8,28,2,102,18,18,1011,0.89,0,0 +2011,8,28,3,98,18,18,1011,1.34,0,0 +2011,8,28,4,122,17,17,1011,1.79,0,0 +2011,8,28,5,100,18,18,1011,2.68,0,0 +2011,8,28,6,103,17,18,1011,4.47,0,0 +2011,8,28,7,97,19,20,1011,6.26,0,0 +2011,8,28,8,101,20,21,1011,8.05,0,0 +2011,8,28,9,98,20,24,1011,11.18,0,0 +2011,8,28,10,84,20,25,1011,1.79,0,0 +2011,8,28,11,84,19,27,1011,0.89,0,0 +2011,8,28,12,77,19,27,1010,1.79,0,0 +2011,8,28,13,83,18,28,1009,3.13,0,0 +2011,8,28,14,89,19,29,1009,4.92,0,0 +2011,8,28,15,86,19,29,1008,1.79,0,0 +2011,8,28,16,84,20,29,1007,3.58,0,0 +2011,8,28,17,63,20,28,1007,5.37,0,0 +2011,8,28,18,75,19,27,1007,9.39,0,0 +2011,8,28,19,72,19,25,1008,13.41,0,0 +2011,8,28,20,75,19,26,1009,17.43,0,0 +2011,8,28,21,69,20,24,1009,0.89,0,0 +2011,8,28,22,75,20,22,1009,1.78,0,0 +2011,8,28,23,69,20,21,1010,2.67,0,0 +2011,8,29,0,65,20,21,1010,0.89,0,0 +2011,8,29,1,76,20,21,1009,0.89,0,0 +2011,8,29,2,98,20,20,1009,1.34,0,0 +2011,8,29,3,114,19,20,1009,2.23,0,0 +2011,8,29,4,124,19,20,1009,3.12,0,0 +2011,8,29,5,143,20,20,1010,4.01,0,0 +2011,8,29,6,129,19,20,1010,0.89,0,0 +2011,8,29,7,150,20,21,1010,0.89,0,0 +2011,8,29,8,151,20,23,1010,1.78,0,0 +2011,8,29,9,146,21,24,1010,1.79,0,0 +2011,8,29,10,160,21,26,1010,0.89,0,0 +2011,8,29,11,129,21,27,1010,2.68,0,0 +2011,8,29,12,104,21,28,1009,3.13,0,0 +2011,8,29,13,102,21,28,1009,4.92,0,0 +2011,8,29,14,90,21,29,1008,8.05,0,0 +2011,8,29,15,NA,20,29,1008,11.18,0,0 +2011,8,29,16,93,19,29,1007,12.97,0,0 +2011,8,29,17,93,19,29,1007,16.1,0,0 +2011,8,29,18,100,21,27,1007,17.89,0,0 +2011,8,29,19,104,19,26,1008,19.68,0,0 +2011,8,29,20,117,21,24,1009,21.47,0,0 +2011,8,29,21,135,21,24,1009,0.89,0,0 +2011,8,29,22,179,21,23,1010,0.89,0,0 +2011,8,29,23,188,22,23,1010,0.89,0,0 +2011,8,30,0,202,21,22,1010,1.79,0,0 +2011,8,30,1,184,21,22,1009,2.68,0,0 +2011,8,30,2,195,22,23,1009,3.57,0,0 +2011,8,30,3,203,22,23,1009,5.36,0,0 +2011,8,30,4,191,22,23,1009,7.15,0,0 +2011,8,30,5,178,22,23,1009,0.89,0,0 +2011,8,30,6,171,22,23,1009,0.89,0,0 +2011,8,30,7,171,22,23,1009,0.89,0,0 +2011,8,30,8,166,22,24,1010,0.89,0,0 +2011,8,30,9,172,23,25,1010,1.78,0,0 +2011,8,30,10,169,23,26,1010,2.67,0,0 +2011,8,30,11,174,23,28,1009,3.56,0,0 +2011,8,30,12,152,23,28,1009,1.79,0,0 +2011,8,30,13,143,23,31,1008,3.13,0,0 +2011,8,30,14,141,23,31,1007,1.79,0,0 +2011,8,30,15,134,23,30,1007,1.79,0,0 +2011,8,30,16,136,23,30,1006,4.92,0,0 +2011,8,30,17,141,23,30,1006,8.05,0,0 +2011,8,30,18,147,22,29,1007,12.07,0,0 +2011,8,30,19,161,22,28,1007,15.2,0,0 +2011,8,30,20,152,23,26,1008,18.33,0,0 +2011,8,30,21,148,22,26,1008,20.12,0,0 +2011,8,30,22,155,22,25,1008,0.89,0,0 +2011,8,30,23,172,23,24,1007,0.89,0,0 +2011,8,31,0,193,22,23,1007,1.78,0,0 +2011,8,31,1,205,22,23,1007,2.67,0,0 +2011,8,31,2,203,22,22,1007,0.89,0,0 +2011,8,31,3,211,22,22,1007,0.89,0,0 +2011,8,31,4,204,22,23,1006,0.89,0,0 +2011,8,31,5,201,22,22,1006,1.78,0,0 +2011,8,31,6,206,22,23,1006,0.89,0,0 +2011,8,31,7,205,23,23,1006,1.78,0,0 +2011,8,31,8,204,24,24,1006,2.67,0,0 +2011,8,31,9,205,24,26,1006,3.56,0,0 +2011,8,31,10,210,24,27,1006,1.79,0,0 +2011,8,31,11,201,24,29,1005,1.79,0,0 +2011,8,31,12,213,24,29,1005,2.68,0,0 +2011,8,31,13,204,24,30,1004,1.79,0,0 +2011,8,31,14,233,24,32,1004,1.79,0,0 +2011,8,31,15,233,24,31,1003,1.79,0,0 +2011,8,31,16,266,23,31,1003,0.89,0,0 +2011,8,31,17,240,25,30,1003,1.79,0,0 +2011,8,31,18,269,25,29,1003,3.58,0,0 +2011,8,31,19,272,25,27,1003,0.89,0,0 +2011,8,31,20,316,25,27,1004,1.78,0,0 +2011,8,31,21,309,25,26,1005,2.67,0,0 +2011,8,31,22,300,25,26,1005,3.13,0,0 +2011,8,31,23,341,25,27,1006,6.26,0,0 +2011,9,1,0,280,23,26,1005,5.81,0,1 +2011,9,1,1,96,21,23,1007,3.13,0,2 +2011,9,1,2,36,20,24,1008,4.92,0,3 +2011,9,1,3,41,21,23,1008,4.02,0,0 +2011,9,1,4,52,19,21,1010,8.94,0,1 +2011,9,1,5,25,19,21,1011,12.96,0,0 +2011,9,1,6,16,18,20,1012,16.09,0,0 +2011,9,1,7,13,18,20,1013,17.88,0,0 +2011,9,1,8,14,18,21,1013,21.01,0,0 +2011,9,1,9,16,18,22,1014,1.79,0,0 +2011,9,1,10,27,17,24,1014,1.79,0,0 +2011,9,1,11,22,18,24,1013,3.13,0,0 +2011,9,1,12,27,17,26,1013,1.79,0,0 +2011,9,1,13,23,16,28,1012,3.13,0,0 +2011,9,1,14,19,14,28,1011,7.15,0,0 +2011,9,1,15,22,13,28,1010,11.17,0,0 +2011,9,1,16,12,12,28,1010,15.19,0,0 +2011,9,1,17,23,12,28,1009,19.21,0,0 +2011,9,1,18,20,12,25,1009,23.23,0,0 +2011,9,1,19,24,13,24,1010,28.15,0,0 +2011,9,1,20,31,14,22,1010,32.17,0,0 +2011,9,1,21,32,15,21,1010,35.3,0,0 +2011,9,1,22,32,15,22,1011,37.09,0,0 +2011,9,1,23,31,17,19,1011,37.98,0,0 +2011,9,2,0,39,17,18,1010,38.87,0,0 +2011,9,2,1,32,17,18,1010,39.76,0,0 +2011,9,2,2,40,17,18,1010,40.65,0,0 +2011,9,2,3,36,17,18,1010,0.89,0,0 +2011,9,2,4,43,16,17,1010,1.78,0,0 +2011,9,2,5,58,16,17,1010,2.23,0,0 +2011,9,2,6,82,17,18,1011,2.68,0,0 +2011,9,2,7,83,18,21,1011,3.13,0,0 +2011,9,2,8,81,16,21,1011,1.79,0,0 +2011,9,2,9,88,17,23,1011,1.79,0,0 +2011,9,2,10,78,17,25,1011,1.79,0,0 +2011,9,2,11,52,17,27,1011,1.79,0,0 +2011,9,2,12,40,17,28,1010,1.79,0,0 +2011,9,2,13,39,16,29,1009,3.58,0,0 +2011,9,2,14,37,16,30,1008,6.71,0,0 +2011,9,2,15,31,17,30,1008,9.84,0,0 +2011,9,2,16,57,18,30,1008,4.02,0,0 +2011,9,2,17,59,17,28,1008,8.04,0,0 +2011,9,2,18,49,17,25,1008,12.06,0,0 +2011,9,2,19,57,18,24,1008,13.85,0,0 +2011,9,2,20,51,18,23,1009,15.64,0,0 +2011,9,2,21,71,18,23,1009,17.43,0,0 +2011,9,2,22,72,19,23,1009,20.56,0,0 +2011,9,2,23,50,19,22,1010,22.35,0,0 +2011,9,3,0,47,20,21,1010,0.89,0,0 +2011,9,3,1,39,19,21,1010,1.79,0,0 +2011,9,3,2,49,19,21,1011,1.79,0,0 +2011,9,3,3,60,19,21,1011,0.89,0,0 +2011,9,3,4,82,19,20,1011,1.78,0,0 +2011,9,3,5,78,19,20,1012,0.89,0,0 +2011,9,3,6,79,19,20,1012,0.89,0,0 +2011,9,3,7,102,20,21,1013,0.89,0,0 +2011,9,3,8,96,19,22,1013,1.78,0,0 +2011,9,3,9,103,20,24,1014,1.79,0,0 +2011,9,3,10,99,19,25,1014,4.92,0,0 +2011,9,3,11,107,19,26,1014,8.05,0,0 +2011,9,3,12,89,20,27,1014,12.07,0,0 +2011,9,3,13,69,20,27,1013,16.09,0,0 +2011,9,3,14,78,20,27,1013,20.11,0,0 +2011,9,3,15,61,17,27,1013,24.13,0,0 +2011,9,3,16,69,17,27,1012,29.05,0,0 +2011,9,3,17,69,18,26,1013,33.97,0,0 +2011,9,3,18,81,19,25,1013,37.99,0,0 +2011,9,3,19,105,19,23,1013,39.78,0,0 +2011,9,3,20,107,20,23,1014,42.91,0,0 +2011,9,3,21,102,20,22,1014,0.89,0,0 +2011,9,3,22,121,19,21,1014,3.13,0,0 +2011,9,3,23,137,19,21,1015,4.92,0,0 +2011,9,4,0,169,19,20,1014,5.81,0,0 +2011,9,4,1,191,19,19,1014,0.89,0,0 +2011,9,4,2,191,19,20,1014,2.68,0,0 +2011,9,4,3,189,18,18,1014,0.89,0,0 +2011,9,4,4,160,18,19,1014,0.89,0,0 +2011,9,4,5,159,18,18,1014,1.34,0,0 +2011,9,4,6,112,18,18,1014,2.23,0,0 +2011,9,4,7,111,19,19,1015,3.12,0,0 +2011,9,4,8,83,19,21,1015,4.02,0,0 +2011,9,4,9,65,19,24,1015,5.81,0,0 +2011,9,4,10,66,18,25,1015,1.79,0,0 +2011,9,4,11,76,18,26,1015,1.79,0,0 +2011,9,4,12,79,17,28,1014,3.13,0,0 +2011,9,4,13,63,16,28,1013,4.92,0,0 +2011,9,4,14,58,17,30,1012,1.79,0,0 +2011,9,4,15,56,17,30,1012,1.79,0,0 +2011,9,4,16,62,15,31,1011,3.58,0,0 +2011,9,4,17,58,15,30,1011,3.13,0,0 +2011,9,4,18,52,16,28,1011,7.15,0,0 +2011,9,4,19,35,16,26,1012,10.28,0,0 +2011,9,4,20,36,16,25,1012,13.41,0,0 +2011,9,4,21,39,16,25,1012,16.54,0,0 +2011,9,4,22,37,16,22,1012,18.33,0,0 +2011,9,4,23,48,17,20,1012,0.89,0,0 +2011,9,5,0,73,17,19,1012,1.79,0,0 +2011,9,5,1,78,17,18,1012,1.79,0,0 +2011,9,5,2,90,17,18,1012,0.89,0,0 +2011,9,5,3,81,17,18,1013,1.78,0,0 +2011,9,5,4,93,17,17,1013,2.67,0,0 +2011,9,5,5,83,18,18,1013,3.12,0,0 +2011,9,5,6,78,17,17,1013,0.89,0,0 +2011,9,5,7,85,19,19,1014,0.89,0,0 +2011,9,5,8,96,19,22,1015,1.78,0,0 +2011,9,5,9,146,19,24,1015,1.79,0,0 +2011,9,5,10,163,19,25,1015,3.58,0,0 +2011,9,5,11,161,18,27,1015,5.37,0,0 +2011,9,5,12,151,16,29,1014,10.29,0,0 +2011,9,5,13,105,16,28,1014,14.31,0,0 +2011,9,5,14,101,16,29,1013,18.33,0,0 +2011,9,5,15,92,17,29,1012,22.35,0,0 +2011,9,5,16,118,17,28,1012,25.48,0,0 +2011,9,5,17,96,16,27,1012,29.5,0,0 +2011,9,5,18,51,15,25,1012,32.63,0,0 +2011,9,5,19,38,15,24,1012,35.76,0,0 +2011,9,5,20,58,15,22,1013,37.55,0,0 +2011,9,5,21,55,15,22,1013,39.34,0,0 +2011,9,5,22,41,16,19,1013,0.45,0,0 +2011,9,5,23,36,17,19,1013,1.34,0,0 +2011,9,6,0,49,17,20,1013,2.23,0,0 +2011,9,6,1,50,17,20,1012,0.89,0,0 +2011,9,6,2,51,16,19,1012,0.89,0,0 +2011,9,6,3,60,16,20,1012,1.78,0,0 +2011,9,6,4,73,16,19,1012,2.67,0,0 +2011,9,6,5,86,16,18,1012,3.56,0,0 +2011,9,6,6,157,16,18,1012,4.45,0,0 +2011,9,6,7,100,17,19,1012,0.89,0,0 +2011,9,6,8,109,16,21,1012,1.79,0,0 +2011,9,6,9,117,16,23,1012,3.58,0,0 +2011,9,6,10,95,15,24,1012,0.89,0,0 +2011,9,6,11,85,15,26,1011,1.79,0,0 +2011,9,6,12,103,16,27,1010,4.92,0,0 +2011,9,6,13,127,18,28,1009,8.05,0,0 +2011,9,6,14,108,18,28,1008,12.07,0,0 +2011,9,6,15,85,18,30,1007,16.09,0,0 +2011,9,6,16,98,17,29,1007,21.01,0,0 +2011,9,6,17,125,17,28,1007,25.93,0,0 +2011,9,6,18,155,18,26,1007,29.06,0,0 +2011,9,6,19,143,19,25,1007,32.19,0,0 +2011,9,6,20,160,19,23,1008,33.98,0,0 +2011,9,6,21,164,19,23,1008,37.11,0,0 +2011,9,6,22,200,19,23,1007,38.9,0,0 +2011,9,6,23,194,19,23,1007,40.69,0,0 +2011,9,7,0,195,19,22,1007,42.48,0,0 +2011,9,7,1,206,20,21,1006,44.27,0,0 +2011,9,7,2,221,20,21,1006,46.06,0,0 +2011,9,7,3,217,20,21,1006,47.85,0,0 +2011,9,7,4,218,20,21,1006,49.64,0,0 +2011,9,7,5,230,19,20,1005,51.43,0,0 +2011,9,7,6,228,19,20,1006,53.22,0,0 +2011,9,7,7,249,20,21,1006,55.01,0,0 +2011,9,7,8,256,21,22,1006,56.8,0,0 +2011,9,7,9,264,20,23,1006,0.89,0,0 +2011,9,7,10,256,21,24,1006,1.79,0,0 +2011,9,7,11,277,21,25,1006,1.79,0,0 +2011,9,7,12,287,21,26,1005,4.92,0,0 +2011,9,7,13,202,20,27,1005,8.05,0,0 +2011,9,7,14,116,13,29,1004,15.2,0,0 +2011,9,7,15,50,14,29,1004,20.12,0,0 +2011,9,7,16,35,14,29,1004,25.04,0,0 +2011,9,7,17,30,15,25,1005,3.13,0,0 +2011,9,7,18,43,17,21,1005,4.02,0,0 +2011,9,7,19,46,16,21,1006,1.79,0,0 +2011,9,7,20,70,16,21,1007,4.02,0,0 +2011,9,7,21,66,18,20,1008,4.02,0,1 +2011,9,7,22,79,18,20,1009,0.89,0,2 +2011,9,7,23,76,18,20,1009,1.79,0,3 +2011,9,8,0,80,18,20,1009,2.68,0,4 +2011,9,8,1,69,18,20,1009,5.81,0,0 +2011,9,8,2,64,17,21,1009,9.83,0,0 +2011,9,8,3,17,16,20,1009,12.96,0,0 +2011,9,8,4,14,18,20,1010,16.09,0,0 +2011,9,8,5,13,14,20,1011,20.11,0,0 +2011,9,8,6,11,15,17,1011,0.89,0,0 +2011,9,8,7,10,15,17,1012,3.13,0,0 +2011,9,8,8,21,15,18,1012,6.26,0,1 +2011,9,8,9,17,15,18,1013,3.13,0,0 +2011,9,8,10,27,14,20,1012,4.92,0,0 +2011,9,8,11,26,14,20,1013,0.89,0,0 +2011,9,8,12,23,14,19,1013,1.78,0,0 +2011,9,8,13,24,14,20,1012,4.02,0,0 +2011,9,8,14,51,14,19,1012,7.15,0,0 +2011,9,8,15,40,15,19,1012,8.94,0,0 +2011,9,8,16,45,15,19,1013,10.73,0,0 +2011,9,8,17,57,16,19,1013,12.52,0,0 +2011,9,8,18,57,16,19,1013,14.31,0,0 +2011,9,8,19,58,16,18,1014,0.89,0,0 +2011,9,8,20,58,16,18,1016,0.89,0,1 +2011,9,8,21,44,16,17,1016,1.79,0,0 +2011,9,8,22,43,16,17,1016,0.89,0,0 +2011,9,8,23,48,17,17,1016,1.78,0,0 +2011,9,9,0,43,17,17,1017,1.79,0,0 +2011,9,9,1,58,17,17,1017,3.58,0,0 +2011,9,9,2,54,17,17,1017,0.89,0,0 +2011,9,9,3,48,15,16,1017,1.79,0,0 +2011,9,9,4,34,14,16,1018,4.92,0,0 +2011,9,9,5,26,15,16,1019,8.94,0,0 +2011,9,9,6,5,13,14,1019,12.07,0,0 +2011,9,9,7,6,14,17,1020,16.09,0,0 +2011,9,9,8,11,11,18,1021,21.01,0,0 +2011,9,9,9,13,6,20,1021,25.03,0,0 +2011,9,9,10,7,6,22,1022,29.95,0,0 +2011,9,9,11,3,8,22,1021,4.92,0,0 +2011,9,9,12,16,3,24,1021,7.15,0,0 +2011,9,9,13,9,3,24,1020,11.17,0,0 +2011,9,9,14,8,4,24,1020,4.92,0,0 +2011,9,9,15,7,3,25,1020,4.92,0,0 +2011,9,9,16,10,5,23,1020,1.79,0,0 +2011,9,9,17,12,5,23,1020,4.92,0,0 +2011,9,9,18,14,9,20,1021,0.89,0,0 +2011,9,9,19,23,12,18,1021,0.89,0,0 +2011,9,9,20,32,12,17,1022,0.89,0,0 +2011,9,9,21,32,10,19,1022,1.79,0,0 +2011,9,9,22,19,10,20,1023,3.13,0,0 +2011,9,9,23,21,10,18,1023,0.89,0,0 +2011,9,10,0,48,11,17,1023,1.79,0,0 +2011,9,10,1,31,11,17,1023,3.58,0,0 +2011,9,10,2,31,11,17,1023,5.37,0,0 +2011,9,10,3,25,12,17,1023,0.89,0,1 +2011,9,10,4,23,13,16,1024,4.92,0,2 +2011,9,10,5,22,13,15,1024,9.84,0,3 +2011,9,10,6,11,13,14,1025,13.86,0,4 +2011,9,10,7,10,13,14,1025,16.99,0,5 +2011,9,10,8,11,13,14,1025,21.01,0,6 +2011,9,10,9,12,12,14,1026,25.93,0,7 +2011,9,10,10,10,12,14,1026,29.95,0,8 +2011,9,10,11,22,12,14,1026,31.74,0,9 +2011,9,10,12,8,13,14,1025,34.87,0,10 +2011,9,10,13,19,13,14,1024,38,0,11 +2011,9,10,14,20,14,15,1024,1.79,0,12 +2011,9,10,15,14,13,14,1023,0.89,0,13 +2011,9,10,16,24,14,15,1023,1.79,0,14 +2011,9,10,17,17,13,14,1022,4.92,0,15 +2011,9,10,18,15,13,14,1022,0.89,0,16 +2011,9,10,19,29,14,14,1022,1.79,0,17 +2011,9,10,20,27,14,14,1022,3.58,0,18 +2011,9,10,21,31,14,14,1022,1.79,0,0 +2011,9,10,22,31,14,14,1022,1.79,0,0 +2011,9,10,23,28,14,14,1022,4.92,0,0 +2011,9,11,0,35,13,13,1022,6.71,0,0 +2011,9,11,1,27,14,14,1021,9.84,0,0 +2011,9,11,2,28,14,15,1021,12.97,0,0 +2011,9,11,3,28,13,14,1020,13.86,0,0 +2011,9,11,4,30,14,14,1020,15.65,0,0 +2011,9,11,5,28,13,14,1020,17.44,0,0 +2011,9,11,6,28,13,13,1020,0.89,0,0 +2011,9,11,7,27,13,15,1021,1.79,0,0 +2011,9,11,8,39,14,16,1020,3.58,0,0 +2011,9,11,9,38,14,18,1021,0.89,0,0 +2011,9,11,10,36,14,19,1020,1.79,0,0 +2011,9,11,11,37,14,20,1020,1.79,0,0 +2011,9,11,12,33,15,21,1020,2.68,0,0 +2011,9,11,13,56,14,21,1018,4.47,0,0 +2011,9,11,14,58,14,22,1018,6.26,0,0 +2011,9,11,15,67,14,22,1017,1.79,0,0 +2011,9,11,16,71,14,22,1017,3.58,0,0 +2011,9,11,17,61,15,21,1017,5.37,0,0 +2011,9,11,18,47,15,20,1017,6.26,0,0 +2011,9,11,19,56,15,20,1017,9.39,0,0 +2011,9,11,20,52,15,19,1018,11.18,0,0 +2011,9,11,21,51,15,18,1018,12.97,0,0 +2011,9,11,22,56,15,17,1018,0.89,0,0 +2011,9,11,23,54,14,15,1018,1.78,0,0 +2011,9,12,0,68,14,14,1018,1.79,0,0 +2011,9,12,1,71,14,14,1018,2.68,0,0 +2011,9,12,2,75,13,13,1018,3.57,0,0 +2011,9,12,3,83,12,13,1018,0.89,0,0 +2011,9,12,4,78,12,13,1017,1.78,0,0 +2011,9,12,5,77,12,12,1017,2.67,0,0 +2011,9,12,6,100,12,12,1017,0.89,0,0 +2011,9,12,7,94,13,14,1017,1.78,0,0 +2011,9,12,8,118,14,15,1017,2.67,0,0 +2011,9,12,9,124,15,16,1018,3.56,0,0 +2011,9,12,10,128,17,17,1017,4.45,0,0 +2011,9,12,11,131,16,19,1017,1.79,0,0 +2011,9,12,12,149,16,20,1016,4.92,0,0 +2011,9,12,13,114,16,20,1016,8.05,0,0 +2011,9,12,14,142,16,20,1015,11.18,0,0 +2011,9,12,15,168,16,20,1014,15.2,0,0 +2011,9,12,16,163,16,20,1014,18.33,0,0 +2011,9,12,17,154,16,19,1014,20.12,0,0 +2011,9,12,18,176,16,19,1013,23.25,0,0 +2011,9,12,19,180,16,18,1014,25.04,0,0 +2011,9,12,20,179,16,18,1014,26.83,0,0 +2011,9,12,21,135,16,18,1014,28.62,0,0 +2011,9,12,22,85,17,18,1014,30.41,0,0 +2011,9,12,23,102,17,19,1014,32.2,0,0 +2011,9,13,0,108,17,19,1014,33.99,0,0 +2011,9,13,1,113,18,19,1014,37.12,0,0 +2011,9,13,2,116,17,19,1013,38.91,0,0 +2011,9,13,3,113,18,19,1013,42.04,0,0 +2011,9,13,4,117,18,19,1013,43.83,0,0 +2011,9,13,5,117,18,18,1012,45.62,0,0 +2011,9,13,6,117,18,18,1013,0.89,0,0 +2011,9,13,7,113,18,19,1013,1.79,0,0 +2011,9,13,8,117,18,19,1013,3.58,0,0 +2011,9,13,9,124,18,20,1013,6.71,0,0 +2011,9,13,10,134,18,20,1013,9.84,0,0 +2011,9,13,11,120,18,21,1013,11.63,0,0 +2011,9,13,12,126,18,21,1012,13.42,0,0 +2011,9,13,13,117,18,21,1012,16.55,0,0 +2011,9,13,14,117,18,22,1012,19.68,0,0 +2011,9,13,15,132,18,22,1011,22.81,0,0 +2011,9,13,16,118,17,22,1011,25.94,0,0 +2011,9,13,17,102,17,21,1011,27.73,0,0 +2011,9,13,18,102,18,21,1011,29.52,0,0 +2011,9,13,19,128,18,20,1011,31.31,0,0 +2011,9,13,20,140,18,20,1011,33.1,0,0 +2011,9,13,21,134,18,20,1012,0.89,0,0 +2011,9,13,22,136,18,20,1012,1.78,0,0 +2011,9,13,23,120,18,20,1012,2.67,0,0 +2011,9,14,0,142,18,19,1011,0.89,0,0 +2011,9,14,1,146,18,19,1011,0.89,0,0 +2011,9,14,2,164,18,19,1011,1.78,0,0 +2011,9,14,3,163,18,18,1011,2.23,0,0 +2011,9,14,4,180,18,19,1010,0.89,0,0 +2011,9,14,5,172,18,18,1010,0.89,0,0 +2011,9,14,6,170,18,19,1010,0.89,0,0 +2011,9,14,7,178,18,19,1010,0.89,0,0 +2011,9,14,8,188,19,20,1011,1.78,0,0 +2011,9,14,9,177,19,21,1011,1.79,0,0 +2011,9,14,10,188,18,22,1011,0.89,0,0 +2011,9,14,11,186,18,22,1011,3.13,0,0 +2011,9,14,12,196,19,23,1011,7.15,0,0 +2011,9,14,13,155,18,24,1010,10.28,0,0 +2011,9,14,14,133,19,26,1009,13.41,0,0 +2011,9,14,15,145,18,25,1009,15.2,0,0 +2011,9,14,16,147,17,25,1009,1.79,0,0 +2011,9,14,17,113,18,24,1009,3.13,0,0 +2011,9,14,18,99,18,22,1009,4.92,0,0 +2011,9,14,19,96,19,21,1009,6.71,0,0 +2011,9,14,20,131,18,20,1009,7.6,0,0 +2011,9,14,21,146,17,18,1010,0.89,0,0 +2011,9,14,22,155,18,18,1010,0.89,0,0 +2011,9,14,23,172,17,18,1010,1.78,0,0 +2011,9,15,0,204,17,18,1011,0.89,0,0 +2011,9,15,1,188,17,18,1010,0.45,0,0 +2011,9,15,2,180,18,18,1010,0.89,0,0 +2011,9,15,3,186,17,18,1010,1.79,0,0 +2011,9,15,4,182,17,17,1009,0.89,0,0 +2011,9,15,5,187,16,16,1010,0.89,0,0 +2011,9,15,6,203,16,16,1010,0.89,0,0 +2011,9,15,7,173,17,17,1010,0.89,0,0 +2011,9,15,8,183,18,19,1010,2.68,0,0 +2011,9,15,9,195,19,21,1011,0.89,0,0 +2011,9,15,10,174,19,23,1011,1.79,0,0 +2011,9,15,11,168,19,24,1010,1.79,0,0 +2011,9,15,12,160,19,24,1010,1.79,0,0 +2011,9,15,13,168,18,26,1009,0.89,0,0 +2011,9,15,14,164,19,26,1009,2.68,0,0 +2011,9,15,15,164,18,26,1008,1.79,0,0 +2011,9,15,16,145,19,26,1008,4.92,0,0 +2011,9,15,17,123,19,25,1008,6.71,0,0 +2011,9,15,18,102,19,24,1009,8.5,0,0 +2011,9,15,19,105,20,23,1010,9.39,0,0 +2011,9,15,20,124,20,22,1010,10.28,0,0 +2011,9,15,21,108,17,21,1011,5.81,0,1 +2011,9,15,22,70,17,20,1011,10.73,0,2 +2011,9,15,23,92,18,20,1011,12.52,0,0 +2011,9,16,0,36,17,19,1012,1.79,0,0 +2011,9,16,1,31,17,19,1012,0.89,0,1 +2011,9,16,2,24,17,18,1012,1.79,0,0 +2011,9,16,3,17,17,18,1012,1.79,0,0 +2011,9,16,4,25,16,17,1012,3.58,0,0 +2011,9,16,5,22,17,17,1012,0.89,0,0 +2011,9,16,6,26,17,18,1013,1.78,0,0 +2011,9,16,7,28,18,19,1014,2.67,0,0 +2011,9,16,8,33,18,20,1014,4.02,0,0 +2011,9,16,9,26,16,22,1015,8.94,0,0 +2011,9,16,10,22,13,22,1016,4.92,0,0 +2011,9,16,11,21,13,23,1015,9.84,0,0 +2011,9,16,12,11,12,23,1015,14.76,0,0 +2011,9,16,13,12,10,22,1015,20.57,0,0 +2011,9,16,14,19,9,21,1014,26.38,0,0 +2011,9,16,15,23,8,21,1014,31.3,0,0 +2011,9,16,16,12,8,20,1015,34.43,0,0 +2011,9,16,17,16,9,19,1015,1.79,0,0 +2011,9,16,18,16,9,18,1015,4.92,0,0 +2011,9,16,19,25,9,18,1016,8.94,0,0 +2011,9,16,20,26,10,17,1016,12.07,0,0 +2011,9,16,21,28,10,17,1017,15.2,0,1 +2011,9,16,22,26,11,17,1017,16.99,0,2 +2011,9,16,23,31,12,18,1017,20.12,0,3 +2011,9,17,0,25,13,15,1018,21.91,0,4 +2011,9,17,1,26,13,15,1018,0.89,0,5 +2011,9,17,2,24,12,16,1018,1.78,0,6 +2011,9,17,3,10,11,15,1018,3.13,0,0 +2011,9,17,4,21,6,15,1019,3.13,0,0 +2011,9,17,5,5,2,16,1019,4.02,0,0 +2011,9,17,6,8,3,15,1019,7.15,0,0 +2011,9,17,7,9,3,15,1020,3.13,0,0 +2011,9,17,8,10,2,15,1020,6.26,0,0 +2011,9,17,9,18,4,17,1021,10.28,0,0 +2011,9,17,10,12,-1,18,1021,15.2,0,0 +2011,9,17,11,12,-1,20,1020,4.02,0,0 +2011,9,17,12,11,-1,20,1020,8.94,0,0 +2011,9,17,13,6,-1,21,1019,12.96,0,0 +2011,9,17,14,4,-2,21,1019,16.09,0,0 +2011,9,17,15,7,0,20,1019,1.79,0,0 +2011,9,17,16,7,2,20,1019,0.89,0,0 +2011,9,17,17,7,4,19,1018,1.79,0,0 +2011,9,17,18,21,2,19,1019,0.89,0,0 +2011,9,17,19,20,2,18,1020,4.02,0,0 +2011,9,17,20,26,1,18,1021,7.15,0,0 +2011,9,17,21,35,5,16,1022,3.13,0,0 +2011,9,17,22,47,6,14,1023,1.79,0,0 +2011,9,17,23,59,7,11,1023,3.13,0,0 +2011,9,18,0,49,6,11,1024,4.92,0,0 +2011,9,18,1,35,6,10,1024,8.05,0,0 +2011,9,18,2,28,6,10,1025,9.84,0,0 +2011,9,18,3,28,5,9,1025,12.97,0,0 +2011,9,18,4,29,5,10,1025,1.79,0,0 +2011,9,18,5,32,4,10,1026,0.89,0,0 +2011,9,18,6,31,4,9,1027,3.13,0,0 +2011,9,18,7,44,4,12,1027,7.15,0,0 +2011,9,18,8,17,4,15,1028,4.02,0,0 +2011,9,18,9,14,2,16,1028,4.02,0,0 +2011,9,18,10,9,0,18,1028,8.04,0,0 +2011,9,18,11,17,0,18,1027,12.06,0,0 +2011,9,18,12,5,0,19,1027,16.08,0,0 +2011,9,18,13,8,0,19,1026,19.21,0,0 +2011,9,18,14,11,1,19,1025,21,0,0 +2011,9,18,15,8,1,21,1025,0.89,0,0 +2011,9,18,16,8,-1,20,1024,1.79,0,0 +2011,9,18,17,9,0,19,1024,5.81,0,0 +2011,9,18,18,8,0,18,1024,8.94,0,0 +2011,9,18,19,21,1,18,1024,12.07,0,0 +2011,9,18,20,26,1,18,1024,16.09,0,0 +2011,9,18,21,28,0,17,1025,19.22,0,0 +2011,9,18,22,24,5,12,1025,0.89,0,0 +2011,9,18,23,27,7,10,1025,1.78,0,0 +2011,9,19,0,48,8,10,1025,1.79,0,0 +2011,9,19,1,48,7,10,1025,3.13,0,0 +2011,9,19,2,64,7,9,1025,4.92,0,0 +2011,9,19,3,41,6,8,1025,8.05,0,0 +2011,9,19,4,21,6,8,1025,11.18,0,0 +2011,9,19,5,25,6,8,1026,15.2,0,0 +2011,9,19,6,28,5,7,1026,19.22,0,0 +2011,9,19,7,41,8,10,1026,22.35,0,0 +2011,9,19,8,29,6,14,1026,27.27,0,0 +2011,9,19,9,22,6,16,1026,31.29,0,0 +2011,9,19,10,23,6,18,1025,34.42,0,0 +2011,9,19,11,15,6,20,1025,36.21,0,0 +2011,9,19,12,12,4,22,1024,38,0,0 +2011,9,19,13,10,0,22,1022,41.13,0,0 +2011,9,19,14,11,1,23,1022,44.26,0,0 +2011,9,19,15,13,1,23,1021,1.79,0,0 +2011,9,19,16,22,2,22,1020,3.13,0,0 +2011,9,19,17,12,1,22,1020,0.89,0,0 +2011,9,19,18,18,2,20,1020,1.79,0,0 +2011,9,19,19,55,8,17,1021,3.58,0,0 +2011,9,19,20,22,2,18,1021,6.71,0,0 +2011,9,19,21,29,3,20,1022,8.5,0,0 +2011,9,19,22,37,7,13,1021,10.29,0,0 +2011,9,19,23,43,8,11,1021,0.45,0,0 +2011,9,20,0,64,9,11,1021,0.89,0,0 +2011,9,20,1,78,8,10,1021,0.45,0,0 +2011,9,20,2,78,8,10,1021,1.34,0,0 +2011,9,20,3,76,8,9,1021,1.79,0,0 +2011,9,20,4,59,7,9,1021,4.92,0,0 +2011,9,20,5,23,6,8,1022,8.05,0,0 +2011,9,20,6,25,6,8,1022,11.18,0,0 +2011,9,20,7,27,8,11,1022,14.31,0,0 +2011,9,20,8,49,6,16,1022,18.33,0,0 +2011,9,20,9,38,6,17,1022,22.35,0,0 +2011,9,20,10,39,6,19,1022,25.48,0,0 +2011,9,20,11,42,4,22,1021,28.61,0,0 +2011,9,20,12,24,3,23,1020,30.4,0,0 +2011,9,20,13,26,2,25,1019,33.53,0,0 +2011,9,20,14,16,1,25,1018,35.32,0,0 +2011,9,20,15,12,2,25,1017,38.45,0,0 +2011,9,20,16,7,2,26,1016,1.79,0,0 +2011,9,20,17,7,1,25,1016,3.13,0,0 +2011,9,20,18,32,3,21,1016,4.92,0,0 +2011,9,20,19,26,4,19,1016,6.71,0,0 +2011,9,20,20,28,2,21,1017,9.84,0,0 +2011,9,20,21,49,7,16,1017,11.63,0,0 +2011,9,20,22,39,8,13,1017,0.45,0,0 +2011,9,20,23,24,8,11,1016,1.34,0,0 +2011,9,21,0,57,9,12,1017,2.23,0,0 +2011,9,21,1,47,8,10,1016,3.12,0,0 +2011,9,21,2,60,9,10,1016,1.79,0,0 +2011,9,21,3,66,8,11,1016,3.58,0,0 +2011,9,21,4,37,7,10,1016,6.71,0,0 +2011,9,21,5,45,6,9,1016,9.84,0,0 +2011,9,21,6,57,6,10,1016,11.63,0,0 +2011,9,21,7,58,8,12,1017,14.76,0,0 +2011,9,21,8,45,8,15,1017,18.78,0,0 +2011,9,21,9,30,5,19,1017,4.02,0,0 +2011,9,21,10,38,4,21,1017,3.13,0,0 +2011,9,21,11,31,4,23,1016,6.26,0,0 +2011,9,21,12,18,4,25,1015,10.28,0,0 +2011,9,21,13,14,3,26,1015,3.13,0,0 +2011,9,21,14,18,3,26,1014,6.26,0,0 +2011,9,21,15,21,1,27,1013,8.05,0,0 +2011,9,21,16,18,4,26,1012,1.79,0,0 +2011,9,21,17,19,4,25,1012,3.58,0,0 +2011,9,21,18,28,4,22,1013,6.71,0,0 +2011,9,21,19,37,4,22,1013,9.84,0,0 +2011,9,21,20,36,4,22,1014,13.86,0,0 +2011,9,21,21,50,9,15,1014,0.89,0,0 +2011,9,21,22,50,10,13,1014,1.78,0,0 +2011,9,21,23,68,10,13,1014,2.67,0,0 +2011,9,22,0,92,10,12,1014,0.89,0,0 +2011,9,22,1,87,10,11,1014,3.13,0,0 +2011,9,22,2,66,9,12,1014,6.26,0,0 +2011,9,22,3,59,8,11,1014,8.05,0,0 +2011,9,22,4,40,7,10,1014,12.07,0,0 +2011,9,22,5,42,6,10,1015,15.2,0,0 +2011,9,22,6,36,6,10,1015,19.22,0,0 +2011,9,22,7,32,8,12,1016,24.14,0,0 +2011,9,22,8,32,7,17,1016,28.16,0,0 +2011,9,22,9,46,5,21,1016,33.08,0,0 +2011,9,22,10,28,5,23,1017,38,0,0 +2011,9,22,11,40,5,26,1016,42.02,0,0 +2011,9,22,12,33,5,27,1016,46.04,0,0 +2011,9,22,13,16,1,28,1015,4.02,0,0 +2011,9,22,14,15,0,28,1014,7.15,0,0 +2011,9,22,15,14,1,28,1013,8.94,0,0 +2011,9,22,16,21,1,28,1013,1.79,0,0 +2011,9,22,17,26,6,27,1013,3.58,0,0 +2011,9,22,18,31,10,22,1013,0.45,0,0 +2011,9,22,19,56,4,21,1014,3.13,0,0 +2011,9,22,20,74,10,16,1015,0.89,0,0 +2011,9,22,21,60,10,15,1015,1.78,0,0 +2011,9,22,22,133,11,14,1016,2.23,0,0 +2011,9,22,23,125,9,12,1016,0.89,0,0 +2011,9,23,0,164,11,15,1016,1.79,0,0 +2011,9,23,1,140,11,14,1016,3.58,0,0 +2011,9,23,2,138,10,12,1016,0.89,0,0 +2011,9,23,3,137,9,12,1017,2.68,0,0 +2011,9,23,4,114,8,11,1017,5.81,0,0 +2011,9,23,5,58,9,12,1017,7.6,0,0 +2011,9,23,6,57,9,12,1018,9.39,0,0 +2011,9,23,7,84,10,14,1018,12.52,0,0 +2011,9,23,8,94,11,15,1018,14.31,0,0 +2011,9,23,9,74,10,19,1018,0.89,0,0 +2011,9,23,10,76,11,22,1018,1.79,0,0 +2011,9,23,11,94,11,24,1017,3.13,0,0 +2011,9,23,12,106,10,24,1017,3.13,0,0 +2011,9,23,13,104,9,26,1015,7.15,0,0 +2011,9,23,14,100,9,26,1014,11.17,0,0 +2011,9,23,15,100,8,26,1014,15.19,0,0 +2011,9,23,16,104,9,26,1013,19.21,0,0 +2011,9,23,17,113,10,25,1013,23.23,0,0 +2011,9,23,18,90,10,22,1013,25.02,0,0 +2011,9,23,19,115,12,17,1014,0.89,0,0 +2011,9,23,20,120,12,19,1014,3.13,0,0 +2011,9,23,21,179,13,18,1014,4.92,0,0 +2011,9,23,22,208,14,17,1014,6.71,0,0 +2011,9,23,23,225,14,16,1014,8.5,0,0 +2011,9,24,0,227,14,16,1014,10.29,0,0 +2011,9,24,1,197,12,13,1014,1.79,0,0 +2011,9,24,2,162,13,14,1014,0.89,0,0 +2011,9,24,3,152,13,13,1014,0.89,0,0 +2011,9,24,4,130,12,13,1014,2.68,0,0 +2011,9,24,5,125,12,13,1014,4.47,0,0 +2011,9,24,6,119,12,12,1014,5.36,0,0 +2011,9,24,7,139,12,12,1015,7.15,0,0 +2011,9,24,8,122,14,18,1015,1.79,0,0 +2011,9,24,9,132,14,19,1015,0.89,0,0 +2011,9,24,10,137,14,21,1015,1.78,0,0 +2011,9,24,11,150,14,23,1015,1.79,0,0 +2011,9,24,12,134,13,26,1014,0.89,0,0 +2011,9,24,13,125,10,27,1013,3.13,0,0 +2011,9,24,14,127,10,28,1013,4.92,0,0 +2011,9,24,15,135,10,28,1013,8.05,0,0 +2011,9,24,16,153,11,28,1012,12.07,0,0 +2011,9,24,17,141,12,26,1012,16.09,0,0 +2011,9,24,18,139,11,24,1013,20.11,0,0 +2011,9,24,19,140,11,23,1013,23.24,0,0 +2011,9,24,20,158,13,17,1014,0.89,0,0 +2011,9,24,21,163,13,17,1015,1.79,0,0 +2011,9,24,22,176,13,17,1014,3.58,0,0 +2011,9,24,23,177,14,16,1015,0.89,0,0 +2011,9,25,0,181,14,16,1015,1.78,0,0 +2011,9,25,1,209,13,15,1015,2.67,0,0 +2011,9,25,2,252,13,15,1015,3.56,0,0 +2011,9,25,3,247,12,13,1015,1.79,0,0 +2011,9,25,4,253,13,13,1015,0.89,0,0 +2011,9,25,5,240,12,13,1016,1.78,0,0 +2011,9,25,6,231,11,12,1016,0.89,0,0 +2011,9,25,7,240,13,13,1017,0.45,0,0 +2011,9,25,8,273,15,16,1017,0.89,0,0 +2011,9,25,9,275,15,18,1017,0.89,0,0 +2011,9,25,10,274,15,21,1018,1.78,0,0 +2011,9,25,11,271,15,22,1017,2.67,0,0 +2011,9,25,12,284,16,23,1017,1.79,0,0 +2011,9,25,13,280,15,25,1016,4.92,0,0 +2011,9,25,14,277,16,25,1016,8.05,0,0 +2011,9,25,15,260,15,25,1015,11.18,0,0 +2011,9,25,16,256,15,26,1015,14.31,0,0 +2011,9,25,17,256,16,24,1015,16.1,0,0 +2011,9,25,18,269,16,22,1016,0.89,0,0 +2011,9,25,19,259,16,19,1017,0.89,0,0 +2011,9,25,20,255,16,19,1017,1.78,0,0 +2011,9,25,21,289,16,17,1017,2.67,0,0 +2011,9,25,22,316,17,18,1018,3.56,0,0 +2011,9,25,23,301,16,17,1018,4.45,0,0 +2011,9,26,0,299,17,18,1018,0.89,0,0 +2011,9,26,1,307,16,17,1018,0.89,0,0 +2011,9,26,2,299,15,15,1018,0.89,0,0 +2011,9,26,3,295,15,15,1018,1.78,0,0 +2011,9,26,4,298,14,15,1018,2.67,0,0 +2011,9,26,5,316,13,14,1018,0.89,0,0 +2011,9,26,6,279,14,14,1019,1.78,0,0 +2011,9,26,7,247,13,14,1019,2.67,0,0 +2011,9,26,8,256,15,16,1020,0.89,0,0 +2011,9,26,9,290,15,19,1020,1.78,0,0 +2011,9,26,10,282,15,22,1020,2.67,0,0 +2011,9,26,11,309,15,23,1019,4.46,0,0 +2011,9,26,12,NA,15,25,1019,1.79,0,0 +2011,9,26,13,263,14,26,1018,4.92,0,0 +2011,9,26,14,156,10,27,1017,10.73,0,0 +2011,9,26,15,108,9,27,1016,16.54,0,0 +2011,9,26,16,107,10,27,1016,21.46,0,0 +2011,9,26,17,118,10,25,1016,25.48,0,0 +2011,9,26,18,130,11,23,1016,28.61,0,0 +2011,9,26,19,112,13,19,1016,30.4,0,0 +2011,9,26,20,112,14,17,1017,31.29,0,0 +2011,9,26,21,129,14,16,1017,33.08,0,0 +2011,9,26,22,139,14,17,1017,34.87,0,0 +2011,9,26,23,153,13,16,1017,36.66,0,0 +2011,9,27,0,159,13,16,1017,38.45,0,0 +2011,9,27,1,200,14,16,1017,40.24,0,0 +2011,9,27,2,219,13,15,1017,41.13,0,0 +2011,9,27,3,226,14,14,1017,0.89,0,0 +2011,9,27,4,218,14,15,1017,0.89,0,0 +2011,9,27,5,251,13,14,1018,1.78,0,0 +2011,9,27,6,250,13,14,1018,0.89,0,0 +2011,9,27,7,253,13,13,1018,0.89,0,0 +2011,9,27,8,282,15,16,1018,0.89,0,0 +2011,9,27,9,290,15,18,1018,1.79,0,0 +2011,9,27,10,294,16,20,1018,1.79,0,0 +2011,9,27,11,288,16,22,1018,1.79,0,0 +2011,9,27,12,281,14,24,1017,3.58,0,0 +2011,9,27,13,247,14,24,1016,1.79,0,0 +2011,9,27,14,199,13,25,1015,1.79,0,0 +2011,9,27,15,132,12,25,1015,5.81,0,0 +2011,9,27,16,130,12,25,1014,9.83,0,0 +2011,9,27,17,140,12,24,1015,12.96,0,0 +2011,9,27,18,155,13,21,1015,14.75,0,0 +2011,9,27,19,161,14,20,1015,16.54,0,0 +2011,9,27,20,167,14,20,1016,18.33,0,0 +2011,9,27,21,242,14,20,1016,20.12,0,0 +2011,9,27,22,235,14,19,1016,21.91,0,0 +2011,9,27,23,178,14,17,1015,0.89,0,0 +2011,9,28,0,177,14,16,1015,1.79,0,0 +2011,9,28,1,204,14,16,1015,0.89,0,0 +2011,9,28,2,221,15,16,1015,1.79,0,0 +2011,9,28,3,239,15,16,1015,0.89,0,0 +2011,9,28,4,246,15,16,1014,1.79,0,0 +2011,9,28,5,244,15,16,1014,0.89,0,0 +2011,9,28,6,280,15,16,1015,0.89,0,0 +2011,9,28,7,277,16,16,1015,1.79,0,0 +2011,9,28,8,278,16,17,1015,0.89,0,0 +2011,9,28,9,273,16,19,1015,1.78,0,0 +2011,9,28,10,286,16,20,1015,2.67,0,0 +2011,9,28,11,282,17,21,1015,3.56,0,0 +2011,9,28,12,291,17,20,1014,4.45,0,0 +2011,9,28,13,302,17,21,1014,5.34,0,0 +2011,9,28,14,283,17,21,1013,6.23,0,0 +2011,9,28,15,288,17,21,1013,1.79,0,0 +2011,9,28,16,300,17,21,1013,2.68,0,0 +2011,9,28,17,326,17,21,1013,3.57,0,0 +2011,9,28,18,338,17,20,1013,0.89,0,0 +2011,9,28,19,349,17,18,1013,0.89,0,0 +2011,9,28,20,346,17,18,1014,0.89,0,0 +2011,9,28,21,373,17,18,1014,0.89,0,0 +2011,9,28,22,386,17,18,1014,1.78,0,0 +2011,9,28,23,350,16,17,1015,1.79,0,0 +2011,9,29,0,339,15,16,1015,3.58,0,0 +2011,9,29,1,328,14,15,1015,6.71,0,0 +2011,9,29,2,241,13,14,1016,9.84,0,0 +2011,9,29,3,80,14,15,1015,13.86,0,0 +2011,9,29,4,28,5,17,1016,19.67,0,0 +2011,9,29,5,18,0,17,1017,25.48,0,0 +2011,9,29,6,15,-1,16,1017,29.5,0,0 +2011,9,29,7,25,-2,17,1018,37.55,0,0 +2011,9,29,8,15,-2,18,1019,46.49,0,0 +2011,9,29,9,15,-1,19,1019,55.43,0,0 +2011,9,29,10,14,-1,19,1019,63.48,0,0 +2011,9,29,11,26,0,20,1019,68.4,0,0 +2011,9,29,12,26,0,21,1019,74.21,0,0 +2011,9,29,13,19,1,22,1018,80.02,0,0 +2011,9,29,14,25,0,24,1017,85.83,0,0 +2011,9,29,15,19,0,24,1017,90.75,0,0 +2011,9,29,16,18,-1,24,1017,95.67,0,0 +2011,9,29,17,14,-1,23,1017,101.48,0,0 +2011,9,29,18,13,-1,22,1018,106.4,0,0 +2011,9,29,19,25,0,19,1020,111.32,0,0 +2011,9,29,20,24,1,16,1020,114.45,0,0 +2011,9,29,21,33,2,14,1021,119.37,0,0 +2011,9,29,22,23,1,14,1022,0.89,0,0 +2011,9,29,23,27,1,13,1023,1.79,0,0 +2011,9,30,0,19,1,12,1023,3.13,0,0 +2011,9,30,1,20,0,14,1024,7.15,0,0 +2011,9,30,2,19,-2,13,1024,11.17,0,0 +2011,9,30,3,11,-2,12,1025,15.19,0,0 +2011,9,30,4,26,-3,11,1025,20.11,0,0 +2011,9,30,5,20,-4,11,1026,25.03,0,0 +2011,9,30,6,16,-4,10,1026,29.95,0,0 +2011,9,30,7,20,-4,10,1027,34.87,0,0 +2011,9,30,8,19,-4,12,1027,38.89,0,0 +2011,9,30,9,18,-4,14,1027,42.91,0,0 +2011,9,30,10,20,-3,16,1027,46.04,0,0 +2011,9,30,11,21,-3,16,1026,3.13,0,0 +2011,9,30,12,18,-2,18,1025,3.13,0,0 +2011,9,30,13,13,-3,19,1024,0.89,0,0 +2011,9,30,14,18,-3,19,1023,1.79,0,0 +2011,9,30,15,21,-2,19,1023,3.13,0,0 +2011,9,30,16,28,-2,19,1023,4.92,0,0 +2011,9,30,17,35,-1,18,1023,9.84,0,0 +2011,9,30,18,37,0,17,1023,12.97,0,0 +2011,9,30,19,26,1,16,1023,16.99,0,0 +2011,9,30,20,41,2,13,1023,0.89,0,0 +2011,9,30,21,35,5,10,1024,1.78,0,0 +2011,9,30,22,50,6,9,1024,2.67,0,0 +2011,9,30,23,49,6,9,1024,3.56,0,0 +2011,10,1,0,62,6,8,1025,0.89,0,0 +2011,10,1,1,72,6,8,1025,0.89,0,0 +2011,10,1,2,85,5,10,1025,1.34,0,0 +2011,10,1,3,72,6,9,1025,1.79,0,0 +2011,10,1,4,59,6,9,1024,3.58,0,0 +2011,10,1,5,50,5,9,1025,5.37,0,0 +2011,10,1,6,31,4,7,1025,7.16,0,0 +2011,10,1,7,59,5,8,1026,8.95,0,0 +2011,10,1,8,60,4,13,1026,10.74,0,0 +2011,10,1,9,39,5,16,1026,12.53,0,0 +2011,10,1,10,10,-1,19,1026,15.66,0,0 +2011,10,1,11,15,-3,21,1026,22.81,0,0 +2011,10,1,12,4,-4,22,1025,29.96,0,0 +2011,10,1,13,23,-3,22,1024,35.77,0,0 +2011,10,1,14,11,-4,22,1023,4.92,0,0 +2011,10,1,15,18,-2,21,1023,1.79,0,0 +2011,10,1,16,6,-2,21,1022,1.79,0,0 +2011,10,1,17,27,-1,20,1023,3.13,0,0 +2011,10,1,18,26,0,16,1024,4.92,0,0 +2011,10,1,19,60,1,14,1025,6.71,0,0 +2011,10,1,20,53,2,15,1026,3.13,0,0 +2011,10,1,21,79,-3,16,1027,7.15,0,0 +2011,10,1,22,51,-3,14,1028,8.94,0,0 +2011,10,1,23,24,-3,13,1029,10.73,0,0 +2011,10,2,0,19,-1,9,1030,3.13,0,0 +2011,10,2,1,24,-1,8,1030,4.92,0,0 +2011,10,2,2,20,2,7,1030,1.79,0,0 +2011,10,2,3,22,1,5,1031,3.13,0,0 +2011,10,2,4,47,1,6,1031,1.79,0,0 +2011,10,2,5,36,1,6,1032,1.79,0,0 +2011,10,2,6,22,0,5,1032,3.58,0,0 +2011,10,2,7,19,3,10,1033,6.71,0,0 +2011,10,2,8,8,2,13,1033,10.73,0,0 +2011,10,2,9,23,0,15,1033,14.75,0,0 +2011,10,2,10,18,-2,18,1033,3.13,0,0 +2011,10,2,11,19,-5,18,1033,7.15,0,0 +2011,10,2,12,22,-5,19,1031,1.79,0,0 +2011,10,2,13,13,-2,20,1030,2.68,0,0 +2011,10,2,14,11,-3,20,1029,3.13,0,0 +2011,10,2,15,11,-3,19,1029,1.79,0,0 +2011,10,2,16,8,-3,19,1028,1.79,0,0 +2011,10,2,17,9,-3,18,1028,1.79,0,0 +2011,10,2,18,22,0,14,1029,3.58,0,0 +2011,10,2,19,23,0,14,1029,5.37,0,0 +2011,10,2,20,29,2,12,1029,7.16,0,0 +2011,10,2,21,35,1,12,1030,8.95,0,0 +2011,10,2,22,53,-1,15,1030,12.97,0,0 +2011,10,2,23,55,2,8,1030,1.79,0,0 +2011,10,3,0,63,3,8,1030,0.89,0,0 +2011,10,3,1,67,3,7,1030,1.79,0,0 +2011,10,3,2,61,3,8,1030,3.58,0,0 +2011,10,3,3,32,2,4,1030,7.6,0,0 +2011,10,3,4,37,2,5,1030,9.39,0,0 +2011,10,3,5,36,1,5,1030,12.52,0,0 +2011,10,3,6,28,1,3,1030,15.65,0,0 +2011,10,3,7,50,3,6,1030,19.67,0,0 +2011,10,3,8,40,2,11,1030,23.69,0,0 +2011,10,3,9,36,0,16,1030,26.82,0,0 +2011,10,3,10,24,0,18,1030,28.61,0,0 +2011,10,3,11,26,-2,19,1029,1.79,0,0 +2011,10,3,12,14,-4,21,1027,2.68,0,0 +2011,10,3,13,NA,-3,21,1026,3.13,0,0 +2011,10,3,14,NA,-3,21,1024,7.15,0,0 +2011,10,3,15,NA,-2,22,1024,11.17,0,0 +2011,10,3,16,NA,-1,21,1023,15.19,0,0 +2011,10,3,17,NA,-1,20,1023,18.32,0,0 +2011,10,3,18,NA,1,17,1023,20.11,0,0 +2011,10,3,19,NA,3,16,1023,21.9,0,0 +2011,10,3,20,NA,7,12,1023,23.69,0,0 +2011,10,3,21,NA,5,11,1023,25.48,0,0 +2011,10,3,22,NA,7,10,1023,26.37,0,0 +2011,10,3,23,NA,6,10,1023,27.26,0,0 +2011,10,4,0,NA,6,10,1023,0.89,0,0 +2011,10,4,1,NA,4,7,1023,0.89,0,0 +2011,10,4,2,NA,5,7,1022,0.89,0,0 +2011,10,4,3,NA,5,7,1022,2.68,0,0 +2011,10,4,4,NA,6,7,1022,0.89,0,0 +2011,10,4,5,NA,5,6,1022,0.45,0,0 +2011,10,4,6,NA,5,7,1022,1.34,0,0 +2011,10,4,7,NA,6,7,1022,1.79,0,0 +2011,10,4,8,NA,7,10,1022,3.58,0,0 +2011,10,4,9,NA,7,13,1022,1.79,0,0 +2011,10,4,10,NA,7,15,1022,3.58,0,0 +2011,10,4,11,NA,7,18,1021,1.79,0,0 +2011,10,4,12,NA,8,19,1020,1.79,0,0 +2011,10,4,13,NA,7,20,1019,1.79,0,0 +2011,10,4,14,NA,8,20,1018,3.13,0,0 +2011,10,4,15,NA,8,19,1017,6.26,0,0 +2011,10,4,16,NA,9,19,1017,10.28,0,0 +2011,10,4,17,NA,9,18,1016,12.07,0,0 +2011,10,4,18,NA,9,16,1016,0.89,0,0 +2011,10,4,19,NA,10,15,1017,0.89,0,0 +2011,10,4,20,NA,11,14,1017,1.78,0,0 +2011,10,4,21,NA,10,13,1017,0.89,0,0 +2011,10,4,22,NA,11,13,1017,1.78,0,0 +2011,10,4,23,NA,11,14,1017,1.79,0,0 +2011,10,5,0,NA,11,15,1016,3.58,0,0 +2011,10,5,1,NA,11,14,1016,0.89,0,0 +2011,10,5,2,NA,11,13,1016,1.79,0,0 +2011,10,5,3,NA,10,12,1015,0.89,0,0 +2011,10,5,4,NA,10,11,1015,0.89,0,0 +2011,10,5,5,NA,10,11,1015,1.78,0,0 +2011,10,5,6,NA,9,10,1015,0.89,0,0 +2011,10,5,7,NA,9,9,1015,0.89,0,0 +2011,10,5,8,NA,12,12,1015,1.78,0,0 +2011,10,5,9,NA,12,15,1015,2.67,0,0 +2011,10,5,10,NA,12,17,1015,3.56,0,0 +2011,10,5,11,NA,12,19,1014,1.79,0,0 +2011,10,5,12,NA,12,22,1012,4.92,0,0 +2011,10,5,13,NA,11,23,1011,9.84,0,0 +2011,10,5,14,NA,11,24,1010,13.86,0,0 +2011,10,5,15,NA,9,25,1009,19.67,0,0 +2011,10,5,16,NA,10,24,1009,25.48,0,0 +2011,10,5,17,NA,10,23,1010,30.4,0,0 +2011,10,5,18,NA,11,20,1011,1.79,0,0 +2011,10,5,19,NA,11,14,1012,3.13,0,0 +2011,10,5,20,NA,12,17,1013,1.79,0,0 +2011,10,5,21,NA,10,15,1015,3.13,0,0 +2011,10,5,22,NA,4,17,1015,4.02,0,0 +2011,10,5,23,NA,-1,17,1016,3.13,0,0 +2011,10,6,0,NA,-1,16,1016,0.89,0,0 +2011,10,6,1,NA,0,13,1017,1.78,0,0 +2011,10,6,2,NA,2,10,1017,1.79,0,0 +2011,10,6,3,NA,2,10,1017,3.58,0,0 +2011,10,6,4,NA,2,8,1018,5.37,0,0 +2011,10,6,5,NA,3,8,1018,8.5,0,0 +2011,10,6,6,NA,0,10,1019,0.89,0,0 +2011,10,6,7,NA,2,10,1019,1.78,0,0 +2011,10,6,8,NA,-1,16,1020,4.02,0,0 +2011,10,6,9,NA,-1,18,1021,8.94,0,0 +2011,10,6,10,NA,-2,20,1021,12.07,0,0 +2011,10,6,11,NA,-2,22,1020,13.86,0,0 +2011,10,6,12,NA,-2,22,1019,15.65,0,0 +2011,10,6,13,NA,-2,23,1019,17.44,0,0 +2011,10,6,14,NA,-2,23,1017,1.79,0,0 +2011,10,6,15,NA,-2,23,1017,3.13,0,0 +2011,10,6,16,NA,-1,22,1017,6.26,0,0 +2011,10,6,17,NA,-1,21,1017,9.39,0,0 +2011,10,6,18,NA,5,15,1018,11.18,0,0 +2011,10,6,19,NA,7,14,1018,12.07,0,0 +2011,10,6,20,NA,8,12,1019,0.89,0,0 +2011,10,6,21,NA,7,12,1019,1.34,0,0 +2011,10,6,22,NA,6,12,1020,1.79,0,0 +2011,10,6,23,NA,6,9,1020,0.89,0,0 +2011,10,7,0,NA,6,9,1020,2.68,0,0 +2011,10,7,1,NA,7,9,1020,0.89,0,0 +2011,10,7,2,NA,6,10,1020,0.89,0,0 +2011,10,7,3,NA,6,8,1020,1.78,0,0 +2011,10,7,4,NA,5,8,1020,0.89,0,0 +2011,10,7,5,NA,5,7,1020,0.89,0,0 +2011,10,7,6,NA,5,8,1020,1.78,0,0 +2011,10,7,7,NA,5,7,1020,1.79,0,0 +2011,10,7,8,NA,8,10,1021,4.92,0,0 +2011,10,7,9,NA,9,14,1021,0.89,0,0 +2011,10,7,10,NA,9,16,1021,1.78,0,0 +2011,10,7,11,NA,10,18,1021,3.57,0,0 +2011,10,7,12,NA,10,20,1019,1.79,0,0 +2011,10,7,13,NA,9,20,1019,4.92,0,0 +2011,10,7,14,NA,9,21,1018,8.94,0,0 +2011,10,7,15,NA,9,21,1017,12.96,0,0 +2011,10,7,16,162,9,21,1017,16.98,0,0 +2011,10,7,17,139,10,20,1017,20.11,0,0 +2011,10,7,18,104,11,16,1017,21,0,0 +2011,10,7,19,144,11,14,1018,21.89,0,0 +2011,10,7,20,146,11,14,1018,23.68,0,0 +2011,10,7,21,162,11,15,1018,25.47,0,0 +2011,10,7,22,168,10,13,1019,0.89,0,0 +2011,10,7,23,145,10,12,1019,1.78,0,0 +2011,10,8,0,164,10,12,1019,2.67,0,0 +2011,10,8,1,146,9,11,1019,0.89,0,0 +2011,10,8,2,122,9,10,1019,2.68,0,0 +2011,10,8,3,103,9,10,1019,4.47,0,0 +2011,10,8,4,125,10,11,1018,7.6,0,0 +2011,10,8,5,138,10,11,1019,9.39,0,0 +2011,10,8,6,144,10,12,1019,11.18,0,0 +2011,10,8,7,150,9,11,1019,12.97,0,0 +2011,10,8,8,164,10,13,1020,16.1,0,0 +2011,10,8,9,113,10,15,1020,17.89,0,0 +2011,10,8,10,110,10,17,1020,19.68,0,0 +2011,10,8,11,111,10,18,1019,0.89,0,0 +2011,10,8,12,111,13,20,1018,1.79,0,0 +2011,10,8,13,150,13,21,1017,0.89,0,0 +2011,10,8,14,160,13,21,1016,1.78,0,0 +2011,10,8,15,181,14,21,1016,2.67,0,0 +2011,10,8,16,230,14,20,1016,1.79,0,0 +2011,10,8,17,250,14,20,1016,1.79,0,0 +2011,10,8,18,258,14,19,1016,2.68,0,0 +2011,10,8,19,293,15,17,1016,0.89,0,0 +2011,10,8,20,325,15,17,1017,1.78,0,0 +2011,10,8,21,324,15,16,1017,2.67,0,0 +2011,10,8,22,329,15,16,1017,0.89,0,0 +2011,10,8,23,347,15,16,1017,0.89,0,0 +2011,10,9,0,351,14,15,1017,1.78,0,0 +2011,10,9,1,354,15,15,1017,0.89,0,0 +2011,10,9,2,314,15,17,1017,1.78,0,0 +2011,10,9,3,349,14,16,1017,2.67,0,0 +2011,10,9,4,341,14,16,1016,1.79,0,0 +2011,10,9,5,324,14,15,1017,3.58,0,0 +2011,10,9,6,274,14,15,1017,0.89,0,0 +2011,10,9,7,269,14,14,1018,3.13,0,0 +2011,10,9,8,284,15,17,1019,0.89,0,0 +2011,10,9,9,312,15,19,1020,1.79,0,0 +2011,10,9,10,360,16,20,1020,4.92,0,0 +2011,10,9,11,404,15,20,1019,6.71,0,0 +2011,10,9,12,378,13,24,1018,3.13,0,0 +2011,10,9,13,318,11,25,1017,4.92,0,0 +2011,10,9,14,297,13,25,1017,1.79,0,0 +2011,10,9,15,271,14,24,1017,5.81,0,0 +2011,10,9,16,303,15,23,1018,8.94,0,0 +2011,10,9,17,335,15,21,1018,10.73,0,0 +2011,10,9,18,340,16,20,1018,12.52,0,0 +2011,10,9,19,435,16,19,1019,14.31,0,0 +2011,10,9,20,562,15,18,1020,16.1,0,0 +2011,10,9,21,514,15,18,1021,19.23,0,0 +2011,10,9,22,485,15,18,1022,23.25,0,0 +2011,10,9,23,453,12,17,1023,26.38,0,0 +2011,10,10,0,283,11,16,1023,0.89,0,0 +2011,10,10,1,153,11,16,1024,1.78,0,0 +2011,10,10,2,105,11,15,1024,0.89,0,0 +2011,10,10,3,66,11,15,1023,0.89,0,0 +2011,10,10,4,71,12,15,1024,0.89,0,0 +2011,10,10,5,63,12,15,1024,1.78,0,0 +2011,10,10,6,54,12,16,1025,0.89,0,0 +2011,10,10,7,69,11,16,1025,1.79,0,0 +2011,10,10,8,69,10,17,1026,1.79,0,0 +2011,10,10,9,75,4,18,1027,3.13,0,0 +2011,10,10,10,75,3,20,1027,6.26,0,0 +2011,10,10,11,64,2,19,1026,1.79,0,0 +2011,10,10,12,49,3,20,1026,3.58,0,0 +2011,10,10,13,69,5,21,1025,1.79,0,0 +2011,10,10,14,89,4,21,1025,3.58,0,0 +2011,10,10,15,56,1,20,1024,7.6,0,0 +2011,10,10,16,67,4,20,1024,10.73,0,0 +2011,10,10,17,63,5,19,1024,12.52,0,0 +2011,10,10,18,62,6,17,1025,14.31,0,0 +2011,10,10,19,69,7,17,1025,16.1,0,0 +2011,10,10,20,64,9,16,1026,16.99,0,0 +2011,10,10,21,65,8,16,1026,0.89,0,0 +2011,10,10,22,65,9,16,1026,1.78,0,0 +2011,10,10,23,59,9,16,1026,0.89,0,0 +2011,10,11,0,42,10,16,1025,0.89,0,0 +2011,10,11,1,38,10,16,1025,1.79,0,0 +2011,10,11,2,35,11,15,1025,2.68,0,0 +2011,10,11,3,39,11,15,1025,0.45,0,0 +2011,10,11,4,41,12,15,1025,0.89,0,0 +2011,10,11,5,64,12,15,1025,1.78,0,0 +2011,10,11,6,90,12,15,1025,2.67,0,0 +2011,10,11,7,102,12,15,1026,0.89,0,0 +2011,10,11,8,86,11,16,1026,2.68,0,0 +2011,10,11,9,89,9,17,1027,1.79,0,0 +2011,10,11,10,81,9,19,1026,2.68,0,0 +2011,10,11,11,87,10,20,1025,3.57,0,0 +2011,10,11,12,93,10,21,1023,5.36,0,0 +2011,10,11,13,109,11,21,1023,3.13,0,0 +2011,10,11,14,139,11,22,1022,6.26,0,0 +2011,10,11,15,139,11,22,1021,8.05,0,0 +2011,10,11,16,132,11,21,1021,9.84,0,0 +2011,10,11,17,151,12,20,1021,12.97,0,0 +2011,10,11,18,155,12,19,1021,16.99,0,0 +2011,10,11,19,179,12,18,1021,18.78,0,0 +2011,10,11,20,188,14,16,1021,19.67,0,0 +2011,10,11,21,198,14,16,1022,20.56,0,0 +2011,10,11,22,197,14,16,1021,0.89,0,0 +2011,10,11,23,199,14,15,1021,0.89,0,0 +2011,10,12,0,212,14,15,1021,1.78,0,0 +2011,10,12,1,212,12,14,1021,0.89,0,0 +2011,10,12,2,221,13,14,1020,1.78,0,0 +2011,10,12,3,234,13,15,1020,3.57,0,0 +2011,10,12,4,235,13,15,1019,4.46,0,0 +2011,10,12,5,221,13,14,1019,0.89,0,0 +2011,10,12,6,205,12,14,1019,0.89,0,0 +2011,10,12,7,211,13,14,1019,0.89,0,0 +2011,10,12,8,216,13,15,1019,1.79,0,0 +2011,10,12,9,216,13,16,1020,0.89,0,0 +2011,10,12,10,220,14,18,1019,1.78,0,0 +2011,10,12,11,238,14,19,1018,2.67,0,0 +2011,10,12,12,266,14,19,1017,3.56,0,0 +2011,10,12,13,307,14,21,1016,4.45,0,0 +2011,10,12,14,313,14,21,1015,1.79,0,0 +2011,10,12,15,296,15,21,1014,1.79,0,0 +2011,10,12,16,NA,15,21,1015,0.89,0,0 +2011,10,12,17,186,12,22,1014,3.13,0,0 +2011,10,12,18,149,12,20,1014,4.92,0,0 +2011,10,12,19,205,12,19,1014,8.05,0,0 +2011,10,12,20,246,13,17,1015,0.89,0,0 +2011,10,12,21,239,13,16,1015,1.79,0,0 +2011,10,12,22,196,13,16,1015,3.58,0,0 +2011,10,12,23,225,13,16,1015,5.37,0,0 +2011,10,13,0,225,13,15,1014,7.16,0,0 +2011,10,13,1,236,14,15,1014,8.95,0,0 +2011,10,13,2,248,14,15,1014,10.74,0,0 +2011,10,13,3,259,14,15,1013,12.53,0,0 +2011,10,13,4,279,14,15,1013,14.32,0,0 +2011,10,13,5,280,15,15,1012,0.89,0,1 +2011,10,13,6,258,15,16,1012,3.13,0,2 +2011,10,13,7,108,13,14,1012,7.15,0,3 +2011,10,13,8,36,12,14,1012,12.07,0,4 +2011,10,13,9,8,12,14,1012,15.2,0,5 +2011,10,13,10,11,13,17,1012,20.12,0,0 +2011,10,13,11,13,10,19,1011,1.79,0,0 +2011,10,13,12,17,10,20,1011,1.79,0,0 +2011,10,13,13,21,9,21,1010,0.89,0,0 +2011,10,13,14,26,10,20,1010,1.79,0,0 +2011,10,13,15,36,11,21,1009,4.92,0,0 +2011,10,13,16,43,10,21,1009,8.05,0,0 +2011,10,13,17,33,11,20,1009,12.97,0,0 +2011,10,13,18,40,11,19,1010,16.1,0,0 +2011,10,13,19,52,11,19,1011,3.13,0,0 +2011,10,13,20,46,12,19,1011,3.13,0,0 +2011,10,13,21,57,7,18,1011,7.15,0,0 +2011,10,13,22,30,7,17,1012,10.28,0,0 +2011,10,13,23,39,8,14,1011,0.89,0,0 +2011,10,14,0,23,5,16,1011,4.02,0,0 +2011,10,14,1,23,5,16,1012,8.04,0,0 +2011,10,14,2,32,4,16,1011,11.17,0,0 +2011,10,14,3,21,4,14,1011,15.19,0,0 +2011,10,14,4,24,6,11,1011,18.32,0,0 +2011,10,14,5,15,7,11,1011,0.89,0,0 +2011,10,14,6,9,6,12,1011,1.79,0,0 +2011,10,14,7,6,3,14,1012,6.71,0,0 +2011,10,14,8,4,2,15,1012,14.76,0,0 +2011,10,14,9,3,1,16,1012,20.57,0,0 +2011,10,14,10,6,0,19,1012,28.62,0,0 +2011,10,14,11,9,-2,18,1012,35.77,0,0 +2011,10,14,12,5,-3,19,1011,44.71,0,0 +2011,10,14,13,7,-4,20,1011,53.65,0,0 +2011,10,14,14,13,-3,19,1010,62.59,0,0 +2011,10,14,15,11,-3,18,1010,69.74,0,0 +2011,10,14,16,10,-3,19,1010,73.76,0,0 +2011,10,14,17,11,0,16,1010,0.89,0,0 +2011,10,14,18,16,0,15,1010,1.79,0,0 +2011,10,14,19,17,2,16,1011,4.02,0,0 +2011,10,14,20,16,3,14,1011,4.02,0,0 +2011,10,14,21,17,2,15,1011,4.02,0,0 +2011,10,14,22,10,3,14,1011,11.17,0,0 +2011,10,14,23,9,3,14,1011,18.32,0,0 +2011,10,15,0,9,3,13,1011,23.24,0,0 +2011,10,15,1,11,3,13,1010,27.26,0,0 +2011,10,15,2,14,2,13,1011,32.18,0,0 +2011,10,15,3,16,3,8,1010,35.31,0,0 +2011,10,15,4,15,2,9,1010,38.44,0,0 +2011,10,15,5,21,2,12,1010,43.36,0,0 +2011,10,15,6,23,2,11,1010,48.28,0,0 +2011,10,15,7,16,4,10,1010,50.07,0,0 +2011,10,15,8,32,3,12,1010,53.2,0,0 +2011,10,15,9,23,4,17,1009,57.22,0,0 +2011,10,15,10,15,3,18,1009,64.37,0,0 +2011,10,15,11,11,2,18,1009,71.52,0,0 +2011,10,15,12,8,3,20,1008,80.46,0,0 +2011,10,15,13,10,3,20,1007,89.4,0,0 +2011,10,15,14,14,2,20,1007,100.58,0,0 +2011,10,15,15,9,3,19,1008,112.65,0,0 +2011,10,15,16,9,3,18,1008,124.72,0,0 +2011,10,15,17,9,3,17,1009,130.53,0,0 +2011,10,15,18,7,3,17,1009,139.47,0,0 +2011,10,15,19,8,3,17,1009,144.39,0,0 +2011,10,15,20,20,3,17,1009,147.52,0,0 +2011,10,15,21,13,4,16,1010,150.65,0,0 +2011,10,15,22,11,4,17,1009,154.67,0,0 +2011,10,15,23,20,3,18,1009,159.59,0,0 +2011,10,16,0,16,3,18,1010,4.02,0,0 +2011,10,16,1,16,4,17,1010,3.13,0,0 +2011,10,16,2,9,4,16,1010,7.15,0,0 +2011,10,16,3,9,4,10,1010,1.79,0,0 +2011,10,16,4,35,5,13,1010,5.81,0,0 +2011,10,16,5,29,3,13,1011,3.13,0,0 +2011,10,16,6,22,4,9,1012,6.26,0,0 +2011,10,16,7,16,4,14,1013,0.89,0,0 +2011,10,16,8,20,2,17,1014,3.13,0,0 +2011,10,16,9,12,2,18,1014,4.02,0,0 +2011,10,16,10,10,1,19,1014,4.02,0,0 +2011,10,16,11,8,1,21,1014,8.04,0,0 +2011,10,16,12,8,0,22,1013,17.87,0,0 +2011,10,16,13,8,0,22,1012,27.7,0,0 +2011,10,16,14,7,-1,23,1012,36.64,0,0 +2011,10,16,15,7,-3,22,1013,46.47,0,0 +2011,10,16,16,4,-3,21,1013,55.41,0,0 +2011,10,16,17,3,-2,19,1014,60.33,0,0 +2011,10,16,18,6,-1,18,1014,66.14,0,0 +2011,10,16,19,10,-1,15,1015,71.06,0,0 +2011,10,16,20,33,0,16,1015,76.87,0,0 +2011,10,16,21,54,0,16,1016,4.02,0,0 +2011,10,16,22,26,0,15,1017,7.15,0,0 +2011,10,16,23,14,1,12,1018,1.79,0,0 +2011,10,17,0,23,2,10,1018,0.45,0,0 +2011,10,17,1,20,3,8,1019,1.34,0,0 +2011,10,17,2,28,3,7,1020,2.23,0,0 +2011,10,17,3,31,4,8,1020,0.89,0,0 +2011,10,17,4,28,2,4,1021,3.13,0,0 +2011,10,17,5,22,1,6,1022,4.02,0,0 +2011,10,17,6,17,1,7,1022,5.81,0,0 +2011,10,17,7,23,2,7,1023,0.45,0,0 +2011,10,17,8,22,1,14,1025,1.79,0,0 +2011,10,17,9,12,-1,16,1025,0.89,0,0 +2011,10,17,10,11,-2,16,1025,1.78,0,0 +2011,10,17,11,18,-3,18,1025,3.13,0,0 +2011,10,17,12,9,-2,19,1024,6.26,0,0 +2011,10,17,13,22,-1,19,1023,1.79,0,0 +2011,10,17,14,16,-1,19,1023,3.13,0,0 +2011,10,17,15,19,-1,19,1022,7.15,0,0 +2011,10,17,16,21,-1,18,1022,10.28,0,0 +2011,10,17,17,22,0,16,1022,13.41,0,0 +2011,10,17,18,30,2,15,1022,15.2,0,0 +2011,10,17,19,43,4,11,1023,0.89,0,0 +2011,10,17,20,65,5,9,1023,1.34,0,0 +2011,10,17,21,76,5,9,1024,0.89,0,0 +2011,10,17,22,73,5,8,1024,0.89,0,0 +2011,10,17,23,78,4,7,1024,0.89,0,0 +2011,10,18,0,83,5,8,1024,2.68,0,0 +2011,10,18,1,78,7,9,1025,0.89,0,0 +2011,10,18,2,96,7,8,1025,1.78,0,0 +2011,10,18,3,96,6,8,1025,2.67,0,0 +2011,10,18,4,102,6,7,1025,3.56,0,0 +2011,10,18,5,102,5,5,1025,0.89,0,0 +2011,10,18,6,93,5,6,1026,4.02,0,0 +2011,10,18,7,78,4,5,1026,5.81,0,0 +2011,10,18,8,72,8,11,1027,1.79,0,0 +2011,10,18,9,60,9,12,1027,3.58,0,0 +2011,10,18,10,69,9,15,1026,5.37,0,0 +2011,10,18,11,75,8,16,1026,0.89,0,0 +2011,10,18,12,83,8,17,1025,1.78,0,0 +2011,10,18,13,86,8,19,1024,2.67,0,0 +2011,10,18,14,91,7,20,1023,4.46,0,0 +2011,10,18,15,86,5,20,1022,3.13,0,0 +2011,10,18,16,82,6,19,1022,4.92,0,0 +2011,10,18,17,72,7,18,1022,6.71,0,0 +2011,10,18,18,87,8,14,1022,7.6,0,0 +2011,10,18,19,98,9,13,1022,0.89,0,0 +2011,10,18,20,123,10,12,1022,1.34,0,0 +2011,10,18,21,122,8,10,1023,0.89,0,0 +2011,10,18,22,123,8,9,1023,1.78,0,0 +2011,10,18,23,129,9,10,1023,0.89,0,0 +2011,10,19,0,141,7,9,1023,0.89,0,0 +2011,10,19,1,163,8,9,1023,0.89,0,0 +2011,10,19,2,154,8,9,1023,1.34,0,0 +2011,10,19,3,150,7,7,1023,0.89,0,0 +2011,10,19,4,100,7,8,1022,1.78,0,0 +2011,10,19,5,103,7,8,1023,3.57,0,0 +2011,10,19,6,100,7,8,1023,5.36,0,0 +2011,10,19,7,96,7,8,1023,7.15,0,0 +2011,10,19,8,96,8,10,1023,10.28,0,0 +2011,10,19,9,99,9,12,1023,12.07,0,0 +2011,10,19,10,88,9,15,1023,13.86,0,0 +2011,10,19,11,103,8,16,1023,15.65,0,0 +2011,10,19,12,133,11,17,1022,3.13,0,0 +2011,10,19,13,157,12,18,1021,6.26,0,0 +2011,10,19,14,170,12,18,1020,9.39,0,0 +2011,10,19,15,187,13,18,1020,12.52,0,0 +2011,10,19,16,190,13,18,1019,15.65,0,0 +2011,10,19,17,209,13,16,1019,17.44,0,0 +2011,10,19,18,253,12,14,1020,19.23,0,0 +2011,10,19,19,254,12,13,1021,0.89,0,0 +2011,10,19,20,275,12,13,1021,1.34,0,0 +2011,10,19,21,292,11,12,1021,2.23,0,0 +2011,10,19,22,315,11,11,1022,3.12,0,0 +2011,10,19,23,294,11,12,1022,1.79,0,0 +2011,10,20,0,280,11,11,1021,0.89,0,0 +2011,10,20,1,270,11,11,1021,1.34,0,0 +2011,10,20,2,255,11,11,1021,2.23,0,0 +2011,10,20,3,243,11,12,1020,0.89,0,0 +2011,10,20,4,258,11,12,1020,0.89,0,0 +2011,10,20,5,269,12,13,1020,0.89,0,0 +2011,10,20,6,263,12,12,1021,1.78,0,0 +2011,10,20,7,258,12,13,1020,1.79,0,0 +2011,10,20,8,269,12,12,1021,2.68,0,1 +2011,10,20,9,278,12,13,1022,0.89,0,0 +2011,10,20,10,296,12,14,1022,1.79,0,0 +2011,10,20,11,339,12,14,1022,3.58,0,0 +2011,10,20,12,332,12,15,1021,5.37,0,0 +2011,10,20,13,304,12,16,1020,7.16,0,0 +2011,10,20,14,293,12,17,1019,10.29,0,0 +2011,10,20,15,284,12,17,1019,12.08,0,0 +2011,10,20,16,293,12,16,1019,0.89,0,0 +2011,10,20,17,302,12,15,1019,1.78,0,0 +2011,10,20,18,300,12,15,1020,1.79,0,0 +2011,10,20,19,298,12,13,1020,0.89,0,0 +2011,10,20,20,299,12,12,1021,0.89,0,0 +2011,10,20,21,300,11,11,1021,1.78,0,0 +2011,10,20,22,308,11,12,1021,2.67,0,0 +2011,10,20,23,302,11,11,1021,3.56,0,0 +2011,10,21,0,298,11,11,1021,4.45,0,0 +2011,10,21,1,266,9,10,1021,5.34,0,0 +2011,10,21,2,285,10,10,1021,0.89,0,0 +2011,10,21,3,282,9,9,1020,0.89,0,0 +2011,10,21,4,273,8,9,1020,0.89,0,0 +2011,10,21,5,275,9,9,1021,1.78,0,0 +2011,10,21,6,248,8,9,1021,2.67,0,0 +2011,10,21,7,227,8,8,1021,1.79,0,0 +2011,10,21,8,251,10,11,1021,3.58,0,0 +2011,10,21,9,249,13,14,1021,0.89,0,0 +2011,10,21,10,202,13,15,1021,1.78,0,0 +2011,10,21,11,190,12,17,1020,2.67,0,0 +2011,10,21,12,175,12,19,1020,3.56,0,0 +2011,10,21,13,204,12,19,1019,4.45,0,0 +2011,10,21,14,224,12,19,1018,1.79,0,0 +2011,10,21,15,231,11,20,1018,1.79,0,0 +2011,10,21,16,233,11,19,1018,1.79,0,0 +2011,10,21,17,222,13,17,1018,2.68,0,0 +2011,10,21,18,254,12,14,1018,3.57,0,0 +2011,10,21,19,269,12,13,1019,0.89,0,0 +2011,10,21,20,285,12,13,1019,0.89,0,0 +2011,10,21,21,298,11,11,1019,0.89,0,0 +2011,10,21,22,306,10,11,1019,1.78,0,0 +2011,10,21,23,313,10,11,1019,2.67,0,0 +2011,10,22,0,318,10,11,1019,0.45,0,0 +2011,10,22,1,311,10,11,1019,0.9,0,0 +2011,10,22,2,267,9,10,1019,1.35,0,0 +2011,10,22,3,261,9,10,1018,0.89,0,0 +2011,10,22,4,248,10,10,1018,2.68,0,0 +2011,10,22,5,227,9,9,1018,4.47,0,0 +2011,10,22,6,212,9,9,1018,6.26,0,0 +2011,10,22,7,215,9,10,1018,8.05,0,0 +2011,10,22,8,239,11,11,1019,9.84,0,0 +2011,10,22,9,259,13,13,1019,1.79,0,0 +2011,10,22,10,270,12,16,1018,3.13,0,0 +2011,10,22,11,285,12,18,1018,4.92,0,0 +2011,10,22,12,287,11,18,1018,6.71,0,0 +2011,10,22,13,297,11,18,1017,8.5,0,0 +2011,10,22,14,318,12,18,1016,0.89,0,0 +2011,10,22,15,347,12,18,1016,1.79,0,0 +2011,10,22,16,370,13,17,1016,3.58,0,0 +2011,10,22,17,401,13,15,1015,4.47,0,0 +2011,10,22,18,416,13,15,1016,0.89,0,0 +2011,10,22,19,399,13,14,1016,1.78,0,0 +2011,10,22,20,389,13,14,1016,1.79,0,0 +2011,10,22,21,408,13,14,1016,0.89,0,0 +2011,10,22,22,395,13,13,1017,1.78,0,0 +2011,10,22,23,402,13,13,1016,0.89,0,0 +2011,10,23,0,430,12,13,1016,0.89,0,0 +2011,10,23,1,414,12,13,1016,1.79,0,0 +2011,10,23,2,418,12,13,1017,0.89,0,0 +2011,10,23,3,408,12,13,1016,1.79,0,0 +2011,10,23,4,414,12,12,1016,0.89,0,0 +2011,10,23,5,418,12,12,1016,1.78,0,0 +2011,10,23,6,409,12,13,1017,3.13,0,1 +2011,10,23,7,420,12,13,1017,4.92,0,2 +2011,10,23,8,407,13,13,1018,8.05,0,3 +2011,10,23,9,390,13,14,1019,11.18,0,4 +2011,10,23,10,291,7,12,1020,20.12,0,5 +2011,10,23,11,26,7,11,1021,8.94,0,6 +2011,10,23,12,19,7,10,1022,7.15,0,7 +2011,10,23,13,12,6,9,1022,14.3,0,8 +2011,10,23,14,11,5,9,1022,22.35,0,9 +2011,10,23,15,8,4,10,1023,5.81,0,0 +2011,10,23,16,16,3,9,1024,12.96,0,0 +2011,10,23,17,12,2,9,1025,17.88,0,0 +2011,10,23,18,13,1,9,1026,22.8,0,0 +2011,10,23,19,13,0,9,1026,27.72,0,0 +2011,10,23,20,18,-1,9,1027,31.74,0,0 +2011,10,23,21,17,0,7,1028,33.53,0,0 +2011,10,23,22,16,-1,5,1028,1.79,0,0 +2011,10,23,23,9,1,4,1028,0.89,0,0 +2011,10,24,0,7,1,3,1028,0.89,0,0 +2011,10,24,1,6,1,2,1028,0.89,0,0 +2011,10,24,2,6,1,2,1028,0.89,0,0 +2011,10,24,3,8,0,1,1028,1.79,0,0 +2011,10,24,4,9,0,1,1028,3.58,0,0 +2011,10,24,5,15,-2,-1,1028,6.71,0,0 +2011,10,24,6,15,-1,1,1029,8.5,0,0 +2011,10,24,7,12,-1,3,1029,11.63,0,0 +2011,10,24,8,11,1,6,1030,13.42,0,0 +2011,10,24,9,18,-3,12,1030,16.55,0,0 +2011,10,24,10,16,-6,13,1029,20.57,0,0 +2011,10,24,11,8,-7,13,1029,22.36,0,0 +2011,10,24,12,6,-7,14,1028,3.13,0,0 +2011,10,24,13,7,-7,14,1027,1.79,0,0 +2011,10,24,14,16,-5,16,1026,2.68,0,0 +2011,10,24,15,9,-6,15,1026,1.79,0,0 +2011,10,24,16,9,-5,14,1026,4.92,0,0 +2011,10,24,17,24,-4,13,1026,0.89,0,0 +2011,10,24,18,35,1,7,1028,1.78,0,0 +2011,10,24,19,NA,0,4,1029,1.79,0,0 +2011,10,24,20,60,0,5,1029,3.58,0,0 +2011,10,24,21,38,-6,9,1030,5.37,0,0 +2011,10,24,22,45,-6,8,1031,0.89,0,0 +2011,10,24,23,9,-6,5,1031,1.79,0,0 +2011,10,25,0,8,-6,4,1031,4.92,0,0 +2011,10,25,1,8,-4,3,1032,6.71,0,0 +2011,10,25,2,8,-3,2,1032,0.89,0,0 +2011,10,25,3,19,-3,2,1032,1.78,0,0 +2011,10,25,4,19,-2,1,1031,2.23,0,0 +2011,10,25,5,18,-2,0,1032,3.12,0,0 +2011,10,25,6,38,-3,0,1032,3.13,0,0 +2011,10,25,7,35,-4,2,1032,4.92,0,0 +2011,10,25,8,27,-2,6,1032,8.05,0,0 +2011,10,25,9,15,-3,8,1033,11.18,0,0 +2011,10,25,10,16,-5,11,1032,0.89,0,0 +2011,10,25,11,21,-6,13,1031,2.68,0,0 +2011,10,25,12,27,-5,14,1030,1.79,0,0 +2011,10,25,13,26,-6,15,1028,4.92,0,0 +2011,10,25,14,27,-6,15,1028,8.05,0,0 +2011,10,25,15,25,-6,15,1027,12.07,0,0 +2011,10,25,16,NA,-6,15,1027,16.09,0,0 +2011,10,25,17,55,-5,13,1027,19.22,0,0 +2011,10,25,18,NA,-2,10,1027,21.01,0,0 +2011,10,25,19,NA,1,7,1027,21.9,0,0 +2011,10,25,20,NA,2,6,1027,22.79,0,0 +2011,10,25,21,NA,2,6,1027,0.89,0,0 +2011,10,25,22,NA,2,6,1027,1.78,0,0 +2011,10,25,23,NA,1,5,1027,0.89,0,0 +2011,10,26,0,127,1,5,1027,1.78,0,0 +2011,10,26,1,131,1,4,1026,0.89,0,0 +2011,10,26,2,158,2,5,1026,0.89,0,0 +2011,10,26,3,163,2,5,1025,1.34,0,0 +2011,10,26,4,138,2,5,1025,1.79,0,0 +2011,10,26,5,114,2,4,1024,2.24,0,0 +2011,10,26,6,102,2,3,1025,2.69,0,0 +2011,10,26,7,98,2,4,1025,0.89,0,0 +2011,10,26,8,80,3,6,1025,0.89,0,0 +2011,10,26,9,80,3,7,1026,0.89,0,0 +2011,10,26,10,80,4,9,1025,1.79,0,0 +2011,10,26,11,101,4,10,1026,0.89,0,0 +2011,10,26,12,110,2,10,1025,2.68,0,1 +2011,10,26,13,110,4,10,1024,1.79,0,0 +2011,10,26,14,110,4,11,1023,1.79,0,0 +2011,10,26,15,110,3,11,1023,1.79,0,0 +2011,10,26,16,280,4,11,1023,0.89,0,0 +2011,10,26,17,NA,5,10,1023,0.89,0,0 +2011,10,26,18,NA,5,9,1023,1.78,0,0 +2011,10,26,19,NA,6,8,1024,2.67,0,0 +2011,10,26,20,NA,6,8,1024,0.45,0,0 +2011,10,26,21,NA,6,8,1024,0.9,0,0 +2011,10,26,22,NA,6,8,1024,1.79,0,0 +2011,10,26,23,NA,6,8,1024,2.68,0,0 +2011,10,27,0,NA,6,8,1024,0.89,0,0 +2011,10,27,1,NA,6,8,1024,0.89,0,0 +2011,10,27,2,NA,5,6,1024,1.78,0,0 +2011,10,27,3,NA,4,4,1023,2.23,0,0 +2011,10,27,4,NA,4,4,1023,0.89,0,0 +2011,10,27,5,NA,2,2,1023,0.89,0,0 +2011,10,27,6,NA,3,3,1023,1.78,0,0 +2011,10,27,7,NA,3,3,1024,0.89,0,0 +2011,10,27,8,NA,4,4,1024,0.89,0,0 +2011,10,27,9,NA,7,8,1024,1.78,0,0 +2011,10,27,10,NA,5,12,1024,2.67,0,0 +2011,10,27,11,NA,5,13,1023,1.79,0,0 +2011,10,27,12,199,6,14,1023,3.58,0,0 +2011,10,27,13,242,5,15,1022,6.71,0,0 +2011,10,27,14,237,5,17,1021,9.84,0,0 +2011,10,27,15,207,5,17,1021,13.86,0,0 +2011,10,27,16,176,5,16,1021,16.99,0,0 +2011,10,27,17,145,6,13,1022,17.88,0,0 +2011,10,27,18,137,6,11,1022,1.79,0,0 +2011,10,27,19,184,6,8,1023,1.79,0,0 +2011,10,27,20,208,6,9,1023,2.68,0,0 +2011,10,27,21,224,6,8,1024,0.89,0,0 +2011,10,27,22,235,6,7,1024,1.78,0,0 +2011,10,27,23,273,4,5,1024,2.67,0,0 +2011,10,28,0,NA,5,5,1024,3.12,0,0 +2011,10,28,1,261,5,5,1025,3.13,0,0 +2011,10,28,2,242,3,4,1025,6.26,0,0 +2011,10,28,3,186,2,3,1025,10.28,0,0 +2011,10,28,4,187,3,4,1025,0.89,0,0 +2011,10,28,5,180,3,5,1025,3.13,0,0 +2011,10,28,6,166,3,4,1025,0.89,0,0 +2011,10,28,7,141,2,3,1025,1.79,0,0 +2011,10,28,8,125,4,11,1026,5.81,0,0 +2011,10,28,9,109,2,12,1026,4.92,0,0 +2011,10,28,10,92,2,15,1026,0.89,0,0 +2011,10,28,11,82,4,15,1026,1.78,0,0 +2011,10,28,12,92,3,16,1025,2.67,0,0 +2011,10,28,13,118,4,17,1024,3.56,0,0 +2011,10,28,14,118,4,17,1023,4.45,0,0 +2011,10,28,15,106,3,17,1023,1.79,0,0 +2011,10,28,16,129,4,17,1022,3.58,0,0 +2011,10,28,17,146,5,14,1022,4.47,0,0 +2011,10,28,18,162,6,10,1023,0.45,0,0 +2011,10,28,19,214,6,8,1023,0.89,0,0 +2011,10,28,20,227,7,8,1023,1.78,0,0 +2011,10,28,21,259,6,7,1024,0.89,0,0 +2011,10,28,22,280,5,6,1024,0.89,0,0 +2011,10,28,23,301,4,5,1023,0.89,0,0 +2011,10,29,0,336,4,5,1023,0.89,0,0 +2011,10,29,1,342,5,5,1023,0.89,0,0 +2011,10,29,2,316,4,4,1023,1.78,0,0 +2011,10,29,3,285,4,4,1022,2.67,0,0 +2011,10,29,4,218,4,4,1022,4.46,0,0 +2011,10,29,5,148,4,4,1022,0.45,0,0 +2011,10,29,6,139,3,4,1023,0.89,0,0 +2011,10,29,7,135,3,4,1023,0.89,0,0 +2011,10,29,8,146,6,6,1024,1.78,0,0 +2011,10,29,9,161,6,7,1024,2.23,0,0 +2011,10,29,10,215,8,10,1023,3.12,0,0 +2011,10,29,11,248,7,12,1023,1.79,0,0 +2011,10,29,12,283,8,14,1022,3.58,0,0 +2011,10,29,13,302,8,15,1020,5.37,0,0 +2011,10,29,14,282,8,15,1020,8.5,0,0 +2011,10,29,15,226,9,16,1020,11.63,0,0 +2011,10,29,16,207,8,16,1019,16.55,0,0 +2011,10,29,17,216,8,13,1019,18.34,0,0 +2011,10,29,18,170,9,11,1020,19.23,0,0 +2011,10,29,19,167,8,9,1020,21.02,0,0 +2011,10,29,20,193,6,8,1020,21.91,0,0 +2011,10,29,21,220,7,8,1021,22.8,0,0 +2011,10,29,22,267,8,8,1021,23.69,0,0 +2011,10,29,23,332,6,7,1021,0.89,0,0 +2011,10,30,0,350,7,7,1021,0.89,0,0 +2011,10,30,1,329,7,7,1020,0.89,0,0 +2011,10,30,2,306,5,5,1020,0.89,0,0 +2011,10,30,3,302,4,5,1020,0.89,0,0 +2011,10,30,4,297,4,4,1020,0.45,0,0 +2011,10,30,5,288,3,4,1020,0.89,0,0 +2011,10,30,6,293,4,4,1021,0.45,0,0 +2011,10,30,7,275,6,6,1021,1.34,0,0 +2011,10,30,8,257,5,6,1022,1.79,0,0 +2011,10,30,9,248,6,6,1022,0.89,0,0 +2011,10,30,10,291,6,6,1022,1.78,0,0 +2011,10,30,11,354,9,9,1022,2.67,0,0 +2011,10,30,12,387,9,10,1021,1.79,0,0 +2011,10,30,13,420,9,13,1020,3.58,0,0 +2011,10,30,14,460,10,13,1021,5.37,0,0 +2011,10,30,15,270,10,14,1020,7.16,0,0 +2011,10,30,16,181,9,13,1021,12.08,0,0 +2011,10,30,17,211,9,12,1021,15.21,0,0 +2011,10,30,18,206,9,12,1022,17,0,0 +2011,10,30,19,224,9,12,1022,18.79,0,0 +2011,10,30,20,217,9,11,1023,19.68,0,0 +2011,10,30,21,207,9,11,1023,20.57,0,0 +2011,10,30,22,233,10,11,1023,22.36,0,0 +2011,10,30,23,253,9,11,1023,24.15,0,0 +2011,10,31,0,257,9,11,1023,25.04,0,0 +2011,10,31,1,258,10,11,1023,0.89,0,0 +2011,10,31,2,255,9,11,1023,1.34,0,0 +2011,10,31,3,258,10,11,1023,0.89,0,0 +2011,10,31,4,257,9,11,1022,0.89,0,0 +2011,10,31,5,257,10,11,1023,1.78,0,0 +2011,10,31,6,257,10,11,1023,0.89,0,0 +2011,10,31,7,256,10,11,1023,0.89,0,0 +2011,10,31,8,264,10,11,1023,1.78,0,0 +2011,10,31,9,264,11,12,1024,2.67,0,0 +2011,10,31,10,268,11,13,1023,1.79,0,0 +2011,10,31,11,269,11,13,1023,4.92,0,0 +2011,10,31,12,263,11,14,1023,8.05,0,0 +2011,10,31,13,214,11,14,1022,9.84,0,0 +2011,10,31,14,205,12,15,1022,0.89,0,0 +2011,10,31,15,202,12,15,1022,1.78,0,0 +2011,10,31,16,208,12,15,1022,2.67,0,0 +2011,10,31,17,214,12,14,1022,0.89,0,0 +2011,10,31,18,227,12,14,1022,0.89,0,0 +2011,10,31,19,245,12,14,1023,1.78,0,0 +2011,10,31,20,252,12,13,1023,2.67,0,0 +2011,10,31,21,248,12,13,1023,3.12,0,0 +2011,10,31,22,248,12,13,1023,3.57,0,0 +2011,10,31,23,264,12,13,1023,4.02,0,0 +2011,11,1,0,266,12,13,1023,4.91,0,0 +2011,11,1,1,253,12,13,1023,1.79,0,0 +2011,11,1,2,245,13,14,1022,4.92,0,0 +2011,11,1,3,246,12,13,1022,0.89,0,0 +2011,11,1,4,254,12,13,1022,0.89,0,0 +2011,11,1,5,259,12,12,1022,0.89,0,0 +2011,11,1,6,252,11,12,1022,0.89,0,0 +2011,11,1,7,256,11,12,1022,1.78,0,0 +2011,11,1,8,283,12,13,1023,2.67,0,0 +2011,11,1,9,284,12,13,1023,1.79,0,0 +2011,11,1,10,297,12,14,1023,3.58,0,0 +2011,11,1,11,NA,12,15,1023,5.37,0,0 +2011,11,1,12,NA,12,15,1023,0.89,0,0 +2011,11,1,13,312,12,15,1023,1.78,0,1 +2011,11,1,14,343,13,15,1022,2.67,0,0 +2011,11,1,15,339,13,14,1022,1.79,0,0 +2011,11,1,16,288,12,15,1023,3.13,0,0 +2011,11,1,17,102,9,14,1023,8.05,0,0 +2011,11,1,18,48,10,13,1024,12.07,0,0 +2011,11,1,19,43,11,13,1025,16.09,0,0 +2011,11,1,20,44,9,12,1025,20.11,0,0 +2011,11,1,21,57,7,10,1026,24.13,0,0 +2011,11,1,22,32,7,9,1026,28.15,0,0 +2011,11,1,23,38,6,8,1026,31.28,0,0 +2011,11,2,0,38,6,7,1026,34.41,0,0 +2011,11,2,1,44,5,7,1027,37.54,0,0 +2011,11,2,2,45,6,7,1027,40.67,0,0 +2011,11,2,3,34,5,6,1027,43.8,0,0 +2011,11,2,4,30,3,4,1028,47.82,0,0 +2011,11,2,5,33,5,5,1028,51.84,0,0 +2011,11,2,6,29,3,5,1028,55.86,0,0 +2011,11,2,7,44,3,6,1028,59.88,0,0 +2011,11,2,8,32,2,10,1029,63.9,0,0 +2011,11,2,9,17,0,11,1029,67.92,0,0 +2011,11,2,10,25,-3,13,1029,4.92,0,0 +2011,11,2,11,26,-3,15,1029,4.02,0,0 +2011,11,2,12,20,-2,15,1028,3.13,0,0 +2011,11,2,13,27,-1,16,1028,3.13,0,0 +2011,11,2,14,32,0,15,1028,0.89,0,0 +2011,11,2,15,35,2,14,1027,1.78,0,0 +2011,11,2,16,33,2,14,1027,1.79,0,0 +2011,11,2,17,55,3,13,1027,3.58,0,0 +2011,11,2,18,66,5,12,1028,5.37,0,0 +2011,11,2,19,63,5,12,1028,0.89,0,0 +2011,11,2,20,70,6,12,1029,1.34,0,0 +2011,11,2,21,86,7,11,1028,0.89,0,0 +2011,11,2,22,67,6,12,1029,2.68,0,0 +2011,11,2,23,42,6,12,1028,4.47,0,0 +2011,11,3,0,27,7,11,1028,5.36,0,0 +2011,11,3,1,24,7,11,1028,0.89,0,0 +2011,11,3,2,27,7,11,1028,1.78,0,0 +2011,11,3,3,46,7,11,1028,0.89,0,0 +2011,11,3,4,34,6,10,1028,0.89,0,0 +2011,11,3,5,59,7,9,1027,0.89,0,0 +2011,11,3,6,61,7,10,1027,0.45,0,0 +2011,11,3,7,60,7,10,1027,1.34,0,0 +2011,11,3,8,72,6,11,1028,2.23,0,0 +2011,11,3,9,79,5,13,1028,3.12,0,0 +2011,11,3,10,64,2,13,1028,4.01,0,0 +2011,11,3,11,68,5,15,1027,1.79,0,0 +2011,11,3,12,71,5,15,1027,0.89,0,0 +2011,11,3,13,62,4,16,1026,1.78,0,0 +2011,11,3,14,54,5,15,1025,2.67,0,0 +2011,11,3,15,54,5,15,1025,1.79,0,0 +2011,11,3,16,79,5,15,1025,0.89,0,0 +2011,11,3,17,85,7,13,1025,1.78,0,0 +2011,11,3,18,80,7,12,1025,0.89,0,0 +2011,11,3,19,81,8,12,1025,0.89,0,0 +2011,11,3,20,100,8,12,1025,0.89,0,1 +2011,11,3,21,94,8,11,1025,0.89,0,2 +2011,11,3,22,108,9,12,1025,2.68,0,0 +2011,11,3,23,123,8,11,1024,0.45,0,0 +2011,11,4,0,102,8,11,1024,1.79,0,0 +2011,11,4,1,89,8,11,1024,2.68,0,0 +2011,11,4,2,78,8,11,1023,3.57,0,0 +2011,11,4,3,76,9,11,1023,4.46,0,0 +2011,11,4,4,108,9,11,1023,5.35,0,0 +2011,11,4,5,107,9,11,1022,0.45,0,0 +2011,11,4,6,91,9,11,1023,1.79,0,0 +2011,11,4,7,90,9,11,1023,0.89,0,0 +2011,11,4,8,87,10,12,1024,1.78,0,0 +2011,11,4,9,90,9,13,1024,3.57,0,0 +2011,11,4,10,85,9,14,1024,1.79,0,0 +2011,11,4,11,87,7,15,1024,0.89,0,0 +2011,11,4,12,96,6,16,1023,1.79,0,0 +2011,11,4,13,102,6,16,1022,1.79,0,0 +2011,11,4,14,103,6,16,1021,3.58,0,0 +2011,11,4,15,113,6,16,1021,5.37,0,0 +2011,11,4,16,106,6,16,1021,7.16,0,0 +2011,11,4,17,107,7,14,1022,1.79,0,0 +2011,11,4,18,121,7,14,1022,0.89,0,0 +2011,11,4,19,121,8,14,1023,1.78,0,0 +2011,11,4,20,143,8,14,1023,1.79,0,1 +2011,11,4,21,123,8,13,1024,4.92,0,0 +2011,11,4,22,102,6,14,1024,10.73,0,1 +2011,11,4,23,55,6,13,1024,15.65,0,2 +2011,11,5,0,38,6,13,1024,19.67,0,3 +2011,11,5,1,14,4,13,1025,23.69,0,4 +2011,11,5,2,27,2,13,1025,29.5,0,5 +2011,11,5,3,24,2,13,1025,35.31,0,0 +2011,11,5,4,18,2,13,1025,40.23,0,0 +2011,11,5,5,23,3,12,1026,45.15,0,1 +2011,11,5,6,11,3,12,1026,50.07,0,2 +2011,11,5,7,15,5,11,1027,54.09,0,3 +2011,11,5,8,27,5,11,1028,55.88,0,4 +2011,11,5,9,31,5,11,1029,4.02,0,5 +2011,11,5,10,37,5,10,1029,7.15,0,6 +2011,11,5,11,37,6,11,1029,8.94,0,0 +2011,11,5,12,33,5,11,1028,0.89,0,0 +2011,11,5,13,32,5,11,1028,3.13,0,0 +2011,11,5,14,33,6,12,1027,6.26,0,0 +2011,11,5,15,28,3,12,1026,3.13,0,0 +2011,11,5,16,35,3,12,1026,7.15,0,0 +2011,11,5,17,26,4,11,1027,0.89,0,0 +2011,11,5,18,23,5,11,1027,2.68,0,0 +2011,11,5,19,28,4,11,1027,4.47,0,0 +2011,11,5,20,38,4,11,1027,1.79,0,0 +2011,11,5,21,37,6,10,1028,0.89,0,0 +2011,11,5,22,41,7,10,1028,1.34,0,0 +2011,11,5,23,61,8,9,1028,2.23,0,0 +2011,11,6,0,85,8,10,1028,1.79,0,0 +2011,11,6,1,77,8,10,1027,0.45,0,0 +2011,11,6,2,66,7,10,1027,1.34,0,0 +2011,11,6,3,69,7,8,1027,0.89,0,0 +2011,11,6,4,76,6,6,1028,1.79,0,0 +2011,11,6,5,78,7,8,1028,2.68,0,0 +2011,11,6,6,49,3,7,1028,0.89,0,0 +2011,11,6,7,59,3,7,1028,0.89,0,0 +2011,11,6,8,48,2,8,1029,1.79,0,0 +2011,11,6,9,35,1,10,1029,1.79,0,0 +2011,11,6,10,39,1,10,1029,3.58,0,0 +2011,11,6,11,36,0,11,1029,6.71,0,0 +2011,11,6,12,45,1,14,1028,3.13,0,0 +2011,11,6,13,45,1,15,1027,6.26,0,0 +2011,11,6,14,47,0,14,1026,9.39,0,0 +2011,11,6,15,45,1,14,1026,0.89,0,0 +2011,11,6,16,46,1,12,1026,1.78,0,0 +2011,11,6,17,46,1,12,1026,2.67,0,0 +2011,11,6,18,60,3,8,1027,3.56,0,0 +2011,11,6,19,79,3,10,1027,1.79,0,0 +2011,11,6,20,97,3,8,1028,0.89,0,0 +2011,11,6,21,114,3,7,1028,1.34,0,0 +2011,11,6,22,102,3,5,1029,3.13,0,0 +2011,11,6,23,121,2,6,1029,4.02,0,0 +2011,11,7,0,111,3,5,1028,0.89,0,0 +2011,11,7,1,129,2,4,1028,1.79,0,0 +2011,11,7,2,87,1,4,1028,4.92,0,0 +2011,11,7,3,63,1,4,1028,6.71,0,0 +2011,11,7,4,43,0,3,1028,9.84,0,0 +2011,11,7,5,44,0,2,1028,12.97,0,0 +2011,11,7,6,32,0,3,1028,14.76,0,0 +2011,11,7,7,39,1,4,1029,17.89,0,0 +2011,11,7,8,34,2,7,1029,21.91,0,0 +2011,11,7,9,27,1,10,1030,25.04,0,0 +2011,11,7,10,31,1,12,1029,28.17,0,0 +2011,11,7,11,36,1,14,1028,3.13,0,0 +2011,11,7,12,40,1,15,1027,6.26,0,0 +2011,11,7,13,36,0,15,1027,0.89,0,0 +2011,11,7,14,37,-1,15,1026,1.78,0,0 +2011,11,7,15,37,0,15,1026,3.57,0,0 +2011,11,7,16,46,1,14,1026,1.79,0,0 +2011,11,7,17,49,1,13,1026,4.92,0,0 +2011,11,7,18,56,2,12,1027,8.05,0,0 +2011,11,7,19,68,2,12,1027,9.84,0,0 +2011,11,7,20,71,3,9,1027,0.89,0,0 +2011,11,7,21,79,4,8,1028,0.89,0,0 +2011,11,7,22,90,4,8,1028,0.89,0,0 +2011,11,7,23,94,3,8,1028,0.45,0,0 +2011,11,8,0,127,4,8,1027,0.89,0,0 +2011,11,8,1,122,5,8,1027,0.45,0,0 +2011,11,8,2,157,4,8,1027,1.79,0,0 +2011,11,8,3,144,4,9,1027,4.92,0,0 +2011,11,8,4,91,4,8,1027,6.71,0,0 +2011,11,8,5,68,2,10,1027,8.5,0,0 +2011,11,8,6,56,2,9,1027,11.63,0,0 +2011,11,8,7,42,0,11,1028,15.65,0,0 +2011,11,8,8,47,3,12,1028,19.67,0,0 +2011,11,8,9,33,3,13,1028,23.69,0,0 +2011,11,8,10,27,4,13,1028,27.71,0,0 +2011,11,8,11,27,4,15,1028,30.84,0,0 +2011,11,8,12,25,3,15,1027,33.97,0,0 +2011,11,8,13,8,-1,16,1026,0.89,0,0 +2011,11,8,14,16,-1,16,1026,2.68,0,0 +2011,11,8,15,11,0,16,1026,3.13,0,0 +2011,11,8,16,17,1,15,1026,1.79,0,0 +2011,11,8,17,19,3,12,1026,1.79,0,0 +2011,11,8,18,21,3,10,1026,3.58,0,0 +2011,11,8,19,31,3,9,1026,0.89,0,0 +2011,11,8,20,45,3,13,1026,3.13,0,0 +2011,11,8,21,56,3,11,1026,6.26,0,0 +2011,11,8,22,62,3,8,1026,1.79,0,0 +2011,11,8,23,77,1,3,1027,1.79,0,0 +2011,11,9,0,65,3,5,1027,2.68,0,0 +2011,11,9,1,61,2,4,1027,4.47,0,0 +2011,11,9,2,54,0,2,1027,8.49,0,0 +2011,11,9,3,33,-1,2,1027,11.62,0,0 +2011,11,9,4,33,-1,2,1027,14.75,0,0 +2011,11,9,5,27,-2,2,1027,18.77,0,0 +2011,11,9,6,22,-1,3,1027,21.9,0,0 +2011,11,9,7,24,-1,2,1028,25.92,0,0 +2011,11,9,8,32,0,9,1028,29.94,0,0 +2011,11,9,9,23,-3,12,1028,3.13,0,0 +2011,11,9,10,21,-4,14,1028,4.02,0,0 +2011,11,9,11,28,-4,15,1027,5.81,0,0 +2011,11,9,12,23,-4,15,1026,8.94,0,0 +2011,11,9,13,24,-5,15,1025,10.73,0,0 +2011,11,9,14,23,-5,16,1023,0.89,0,0 +2011,11,9,15,23,-5,16,1023,1.79,0,0 +2011,11,9,16,21,-4,14,1023,0.89,0,0 +2011,11,9,17,25,-3,10,1023,0.89,0,0 +2011,11,9,18,30,0,8,1023,0.89,0,0 +2011,11,9,19,44,1,6,1023,1.79,0,0 +2011,11,9,20,43,-2,6,1023,3.58,0,0 +2011,11,9,21,66,-1,6,1023,0.89,0,0 +2011,11,9,22,69,0,5,1023,1.78,0,0 +2011,11,9,23,79,0,4,1023,0.89,0,0 +2011,11,10,0,84,-2,3,1022,1.79,0,0 +2011,11,10,1,115,0,2,1022,2.68,0,0 +2011,11,10,2,113,-1,1,1022,4.47,0,0 +2011,11,10,3,91,-2,0,1021,6.26,0,0 +2011,11,10,4,56,-3,0,1021,8.05,0,0 +2011,11,10,5,42,-4,0,1021,9.84,0,0 +2011,11,10,6,29,-4,-1,1021,12.97,0,0 +2011,11,10,7,34,-3,2,1022,16.1,0,0 +2011,11,10,8,40,-2,4,1022,20.12,0,0 +2011,11,10,9,44,-4,10,1021,23.25,0,0 +2011,11,10,10,43,-5,12,1021,26.38,0,0 +2011,11,10,11,50,-5,13,1020,28.17,0,0 +2011,11,10,12,47,-5,14,1019,0.89,0,0 +2011,11,10,13,42,-6,15,1018,1.78,0,0 +2011,11,10,14,48,-6,15,1017,3.57,0,0 +2011,11,10,15,45,-5,16,1017,1.79,0,0 +2011,11,10,16,59,-5,14,1017,4.92,0,0 +2011,11,10,17,54,-4,13,1017,8.94,0,0 +2011,11,10,18,55,-2,12,1017,12.07,0,0 +2011,11,10,19,63,-1,10,1017,15.2,0,0 +2011,11,10,20,84,0,7,1017,0.89,0,0 +2011,11,10,21,92,0,4,1017,0.89,0,0 +2011,11,10,22,113,1,3,1017,0.89,0,0 +2011,11,10,23,139,1,4,1017,1.78,0,0 +2011,11,11,0,135,-1,2,1017,1.79,0,0 +2011,11,11,1,161,0,2,1017,0.45,0,0 +2011,11,11,2,145,-1,1,1016,0.89,0,0 +2011,11,11,3,154,-1,0,1016,2.68,0,0 +2011,11,11,4,139,-1,1,1016,0.89,0,0 +2011,11,11,5,153,-2,-1,1016,0.89,0,0 +2011,11,11,6,105,-1,0,1016,1.78,0,0 +2011,11,11,7,115,-1,1,1016,0.89,0,0 +2011,11,11,8,102,-1,3,1016,3.13,0,0 +2011,11,11,9,78,0,5,1016,4.92,0,0 +2011,11,11,10,83,0,9,1015,0.89,0,0 +2011,11,11,11,90,-1,10,1016,1.78,0,0 +2011,11,11,12,96,0,12,1014,1.79,0,0 +2011,11,11,13,96,0,13,1013,3.58,0,0 +2011,11,11,14,114,0,13,1013,5.37,0,0 +2011,11,11,15,153,1,12,1013,0.89,0,0 +2011,11,11,16,152,2,11,1013,0.89,0,0 +2011,11,11,17,179,2,10,1014,1.78,0,0 +2011,11,11,18,208,3,9,1014,0.89,0,0 +2011,11,11,19,255,4,8,1014,1.78,0,0 +2011,11,11,20,197,3,8,1014,2.67,0,0 +2011,11,11,21,211,4,8,1014,3.56,0,0 +2011,11,11,22,228,4,8,1014,4.45,0,0 +2011,11,11,23,252,4,6,1014,5.34,0,0 +2011,11,12,0,277,4,6,1014,1.79,0,0 +2011,11,12,1,208,4,5,1015,3.58,0,0 +2011,11,12,2,73,3,7,1016,5.37,0,0 +2011,11,12,3,43,0,11,1016,11.18,0,0 +2011,11,12,4,19,0,10,1016,16.99,0,0 +2011,11,12,5,16,-1,7,1017,20.12,0,0 +2011,11,12,6,21,-3,9,1018,24.14,0,0 +2011,11,12,7,30,-2,5,1018,0.89,0,0 +2011,11,12,8,38,-4,12,1020,4.02,0,0 +2011,11,12,9,43,-5,12,1020,7.15,0,0 +2011,11,12,10,39,-6,14,1020,15.2,0,0 +2011,11,12,11,43,-9,15,1019,24.14,0,0 +2011,11,12,12,34,-8,16,1018,33.97,0,0 +2011,11,12,13,42,-10,16,1018,43.8,0,0 +2011,11,12,14,41,-11,16,1017,53.63,0,0 +2011,11,12,15,46,-11,15,1017,60.78,0,0 +2011,11,12,16,41,-11,15,1017,69.72,0,0 +2011,11,12,17,45,-11,14,1017,73.74,0,0 +2011,11,12,18,50,-11,14,1018,78.66,0,0 +2011,11,12,19,47,-11,14,1019,3.13,0,0 +2011,11,12,20,65,-10,11,1019,4.02,0,0 +2011,11,12,21,75,-9,6,1020,7.15,0,0 +2011,11,12,22,23,-8,7,1021,0.89,0,0 +2011,11,12,23,17,-13,9,1021,5.81,0,0 +2011,11,13,0,21,-13,6,1022,10.73,0,0 +2011,11,13,1,15,-13,7,1023,17.88,0,0 +2011,11,13,2,7,-13,6,1023,22.8,0,0 +2011,11,13,3,6,-13,3,1024,26.82,0,0 +2011,11,13,4,6,-13,2,1024,30.84,0,0 +2011,11,13,5,7,-13,5,1025,35.76,0,0 +2011,11,13,6,8,-13,1,1025,38.89,0,0 +2011,11,13,7,9,-13,4,1026,44.7,0,0 +2011,11,13,8,10,-13,6,1027,51.85,0,0 +2011,11,13,9,9,-15,8,1027,59,0,0 +2011,11,13,10,12,-15,9,1027,63.92,0,0 +2011,11,13,11,12,-14,9,1027,67.05,0,0 +2011,11,13,12,18,-14,9,1026,3.13,0,0 +2011,11,13,13,9,-14,10,1025,4.02,0,0 +2011,11,13,14,18,-15,10,1024,3.13,0,0 +2011,11,13,15,19,-15,10,1024,1.79,0,0 +2011,11,13,16,17,-14,8,1025,3.58,0,0 +2011,11,13,17,19,-11,7,1025,4.47,0,0 +2011,11,13,18,34,-9,4,1025,5.36,0,0 +2011,11,13,19,58,-4,2,1026,0.89,0,0 +2011,11,13,20,74,-6,2,1026,1.34,0,0 +2011,11,13,21,89,-6,0,1026,0.89,0,0 +2011,11,13,22,88,-6,0,1026,1.78,0,0 +2011,11,13,23,78,-6,1,1026,3.57,0,0 +2011,11,14,0,93,-8,1,1026,5.36,0,0 +2011,11,14,1,126,-9,-1,1026,0.89,0,0 +2011,11,14,2,145,-9,0,1025,0.89,0,0 +2011,11,14,3,141,-9,0,1025,0.89,0,0 +2011,11,14,4,144,-8,1,1025,1.79,0,0 +2011,11,14,5,127,-9,0,1025,0.89,0,0 +2011,11,14,6,124,-8,-1,1025,0.89,0,0 +2011,11,14,7,134,-6,-3,1026,0.89,0,0 +2011,11,14,8,114,-6,2,1027,1.79,0,0 +2011,11,14,9,84,-6,4,1027,3.58,0,0 +2011,11,14,10,88,-8,7,1027,0.89,0,0 +2011,11,14,11,118,-9,8,1026,1.79,0,0 +2011,11,14,12,113,-8,9,1025,3.58,0,0 +2011,11,14,13,110,-8,11,1024,1.79,0,0 +2011,11,14,14,129,-8,11,1024,1.79,0,0 +2011,11,14,15,124,-8,11,1024,0.89,0,0 +2011,11,14,16,128,-5,8,1024,1.78,0,0 +2011,11,14,17,150,-6,8,1024,2.67,0,0 +2011,11,14,18,178,-4,5,1025,0.89,0,0 +2011,11,14,19,178,-3,3,1025,0.89,0,0 +2011,11,14,20,204,-3,3,1026,1.78,0,0 +2011,11,14,21,242,-3,2,1026,0.89,0,0 +2011,11,14,22,235,-4,1,1027,2.68,0,0 +2011,11,14,23,204,-4,0,1027,4.47,0,0 +2011,11,15,0,314,-4,2,1027,0.45,0,0 +2011,11,15,1,333,-4,-1,1028,0.89,0,0 +2011,11,15,2,285,-3,-1,1028,0.89,0,0 +2011,11,15,3,289,-3,-1,1028,0.89,0,0 +2011,11,15,4,225,-3,-1,1027,0.89,0,0 +2011,11,15,5,183,-3,-1,1027,1.34,0,0 +2011,11,15,6,190,-3,-2,1028,2.23,0,0 +2011,11,15,7,185,-5,-3,1028,1.79,0,0 +2011,11,15,8,188,-3,0,1029,3.58,0,0 +2011,11,15,9,172,-2,4,1029,6.71,0,0 +2011,11,15,10,185,-2,6,1029,8.5,0,0 +2011,11,15,11,185,1,9,1028,10.29,0,0 +2011,11,15,12,194,1,10,1027,0.89,0,0 +2011,11,15,13,195,2,10,1027,1.78,0,0 +2011,11,15,14,173,3,10,1026,1.79,0,0 +2011,11,15,15,158,3,10,1025,0.89,0,0 +2011,11,15,16,165,3,8,1025,1.78,0,0 +2011,11,15,17,168,2,7,1025,2.23,0,0 +2011,11,15,18,184,3,7,1025,3.12,0,0 +2011,11,15,19,206,2,5,1025,0.89,0,0 +2011,11,15,20,205,3,5,1025,0.89,0,0 +2011,11,15,21,185,3,5,1026,1.78,0,0 +2011,11,15,22,225,3,4,1026,2.67,0,0 +2011,11,15,23,206,4,5,1026,3.56,0,0 +2011,11,16,0,206,4,5,1026,1.79,0,0 +2011,11,16,1,187,3,5,1026,2.68,0,0 +2011,11,16,2,195,2,5,1026,4.47,0,0 +2011,11,16,3,202,2,5,1026,6.26,0,0 +2011,11,16,4,184,1,2,1026,8.05,0,0 +2011,11,16,5,186,2,3,1026,0.89,0,0 +2011,11,16,6,177,1,2,1026,0.89,0,0 +2011,11,16,7,193,0,0,1027,4.02,0,0 +2011,11,16,8,193,2,2,1027,5.81,0,0 +2011,11,16,9,149,4,7,1028,0.89,0,0 +2011,11,16,10,154,4,9,1028,1.79,0,0 +2011,11,16,11,NA,5,10,1027,0.89,0,0 +2011,11,16,12,NA,5,10,1027,1.78,0,0 +2011,11,16,13,210,6,11,1026,1.79,0,0 +2011,11,16,14,224,6,11,1026,3.58,0,0 +2011,11,16,15,271,6,11,1026,0.89,0,0 +2011,11,16,16,306,7,10,1026,0.89,0,0 +2011,11,16,17,342,6,9,1027,0.89,0,0 +2011,11,16,18,358,7,10,1027,1.78,0,0 +2011,11,16,19,380,7,10,1027,2.67,0,0 +2011,11,16,20,336,6,9,1028,3.13,0,0 +2011,11,16,21,166,5,9,1028,4.92,0,0 +2011,11,16,22,132,3,9,1028,8.05,0,0 +2011,11,16,23,128,2,8,1027,9.84,0,0 +2011,11,17,0,127,2,8,1027,11.63,0,0 +2011,11,17,1,100,3,7,1026,0.89,0,1 +2011,11,17,2,93,3,7,1026,1.78,0,2 +2011,11,17,3,103,4,5,1026,2.67,0,3 +2011,11,17,4,113,5,5,1025,0.89,0,4 +2011,11,17,5,111,5,6,1024,4.02,0,5 +2011,11,17,6,111,5,6,1023,5.81,0,6 +2011,11,17,7,113,6,6,1023,8.94,0,7 +2011,11,17,8,126,6,6,1023,12.07,0,8 +2011,11,17,9,121,6,6,1022,1.79,0,9 +2011,11,17,10,127,6,7,1021,3.13,0,10 +2011,11,17,11,119,7,7,1020,4.92,0,11 +2011,11,17,12,99,7,7,1020,6.71,0,12 +2011,11,17,13,107,7,8,1019,1.79,0,0 +2011,11,17,14,114,8,8,1018,1.79,0,0 +2011,11,17,15,119,8,9,1018,3.58,0,0 +2011,11,17,16,153,8,9,1017,6.71,0,0 +2011,11,17,17,160,8,9,1017,9.84,0,0 +2011,11,17,18,109,6,7,1017,11.63,0,0 +2011,11,17,19,112,6,6,1017,13.42,0,0 +2011,11,17,20,166,6,6,1017,17.44,0,0 +2011,11,17,21,144,7,8,1017,21.46,0,0 +2011,11,17,22,136,7,7,1018,24.59,0,0 +2011,11,17,23,128,6,6,1017,0.89,0,0 +2011,11,18,0,139,6,6,1016,1.79,0,0 +2011,11,18,1,141,5,5,1016,3.58,0,0 +2011,11,18,2,157,5,5,1016,6.71,0,0 +2011,11,18,3,142,6,6,1015,10.73,0,0 +2011,11,18,4,127,6,7,1015,13.86,0,0 +2011,11,18,5,95,7,7,1015,17.88,0,0 +2011,11,18,6,85,7,8,1015,19.67,0,0 +2011,11,18,7,97,7,8,1016,22.8,0,0 +2011,11,18,8,89,6,9,1016,25.93,0,0 +2011,11,18,9,78,4,11,1015,4.02,0,0 +2011,11,18,10,54,4,13,1015,4.92,0,0 +2011,11,18,11,56,3,14,1015,9.84,0,0 +2011,11,18,12,60,1,16,1014,3.13,0,0 +2011,11,18,13,59,1,15,1014,6.26,0,0 +2011,11,18,14,53,1,16,1013,0.89,0,0 +2011,11,18,15,40,0,15,1013,1.79,0,0 +2011,11,18,16,43,6,13,1013,6.71,0,0 +2011,11,18,17,70,6,11,1014,11.63,0,0 +2011,11,18,18,55,6,9,1015,16.55,0,0 +2011,11,18,19,70,6,8,1017,21.47,0,0 +2011,11,18,20,77,5,8,1017,25.49,0,0 +2011,11,18,21,91,5,8,1018,27.28,0,1 +2011,11,18,22,91,4,6,1018,29.07,0,2 +2011,11,18,23,99,5,6,1019,32.2,0,3 +2011,11,19,0,90,3,5,1019,4.02,0,4 +2011,11,19,1,82,2,4,1019,4.02,0,0 +2011,11,19,2,72,2,4,1020,5.81,0,0 +2011,11,19,3,71,1,2,1020,6.7,0,0 +2011,11,19,4,34,-1,5,1021,7.15,0,0 +2011,11,19,5,13,-6,4,1021,15.2,0,0 +2011,11,19,6,7,-7,3,1021,23.25,0,0 +2011,11,19,7,3,-9,3,1022,34.43,0,0 +2011,11,19,8,9,-10,4,1024,46.5,0,0 +2011,11,19,9,13,-11,5,1024,55.44,0,0 +2011,11,19,10,15,-11,6,1024,66.62,0,0 +2011,11,19,11,9,-11,7,1024,77.8,0,0 +2011,11,19,12,16,-12,7,1024,88.98,0,0 +2011,11,19,13,13,-12,8,1023,97.03,0,0 +2011,11,19,14,15,-12,7,1023,105.08,0,0 +2011,11,19,15,10,-12,7,1023,112.23,0,0 +2011,11,19,16,6,-11,6,1024,119.38,0,0 +2011,11,19,17,7,-11,6,1025,125.19,0,0 +2011,11,19,18,8,-12,4,1026,129.21,0,0 +2011,11,19,19,10,-11,4,1026,132.34,0,0 +2011,11,19,20,18,-12,3,1027,136.36,0,0 +2011,11,19,21,12,-11,0,1027,139.49,0,0 +2011,11,19,22,22,-11,0,1028,144.41,0,0 +2011,11,19,23,9,-11,0,1029,150.22,0,0 +2011,11,20,0,9,-14,0,1029,155.14,0,0 +2011,11,20,1,10,-11,-2,1029,160.06,0,0 +2011,11,20,2,12,-11,-3,1029,163.19,0,0 +2011,11,20,3,16,-11,-2,1029,3.13,0,0 +2011,11,20,4,17,-11,-2,1029,7.15,0,0 +2011,11,20,5,25,-12,-3,1029,1.79,0,0 +2011,11,20,6,20,-10,-3,1030,3.58,0,0 +2011,11,20,7,29,-10,-5,1030,5.37,0,0 +2011,11,20,8,19,-10,-2,1031,8.5,0,0 +2011,11,20,9,21,-11,1,1032,10.29,0,0 +2011,11,20,10,30,-11,3,1032,12.08,0,0 +2011,11,20,11,42,-12,4,1032,13.87,0,0 +2011,11,20,12,58,-11,5,1030,0.89,0,0 +2011,11,20,13,53,-10,7,1029,1.79,0,0 +2011,11,20,14,55,-10,6,1029,4.92,0,0 +2011,11,20,15,55,-9,6,1029,8.05,0,0 +2011,11,20,16,47,-9,5,1029,11.18,0,0 +2011,11,20,17,48,-9,4,1029,12.97,0,0 +2011,11,20,18,45,-5,2,1030,0.89,0,0 +2011,11,20,19,52,-4,0,1030,0.89,0,0 +2011,11,20,20,77,-7,0,1030,0.89,0,0 +2011,11,20,21,113,-6,-2,1030,1.78,0,0 +2011,11,20,22,172,-6,-2,1031,2.67,0,0 +2011,11,20,23,249,-4,-2,1031,0.89,0,0 +2011,11,21,0,226,-5,0,1031,1.34,0,0 +2011,11,21,1,191,-5,-3,1031,1.79,0,0 +2011,11,21,2,154,-5,-3,1031,1.79,0,0 +2011,11,21,3,123,-5,-3,1031,3.58,0,0 +2011,11,21,4,106,-6,-4,1031,6.71,0,0 +2011,11,21,5,102,-6,-4,1031,8.5,0,0 +2011,11,21,6,80,-5,-4,1031,10.29,0,0 +2011,11,21,7,71,-7,-5,1031,13.42,0,0 +2011,11,21,8,91,-4,-1,1031,0.89,0,0 +2011,11,21,9,81,-3,1,1032,3.13,0,0 +2011,11,21,10,73,-4,3,1032,6.26,0,0 +2011,11,21,11,89,-6,5,1030,0.89,0,0 +2011,11,21,12,99,-6,8,1029,1.78,0,0 +2011,11,21,13,99,-6,7,1028,2.67,0,0 +2011,11,21,14,115,-4,7,1027,1.79,0,0 +2011,11,21,15,132,-4,8,1026,4.92,0,0 +2011,11,21,16,135,-4,7,1026,6.71,0,0 +2011,11,21,17,165,-2,5,1026,8.5,0,0 +2011,11,21,18,163,-2,3,1027,0.89,0,0 +2011,11,21,19,172,-2,1,1027,1.78,0,0 +2011,11,21,20,190,-1,2,1027,1.79,0,0 +2011,11,21,21,196,-1,3,1027,0.89,0,0 +2011,11,21,22,210,-1,2,1026,1.78,0,0 +2011,11,21,23,239,-2,3,1026,1.79,0,0 +2011,11,22,0,380,-2,3,1025,0.89,0,0 +2011,11,22,1,358,-2,2,1024,1.78,0,0 +2011,11,22,2,364,-2,2,1024,2.23,0,0 +2011,11,22,3,363,-1,2,1023,1.79,0,0 +2011,11,22,4,322,-2,1,1022,0.89,0,0 +2011,11,22,5,315,-2,0,1022,0.89,0,0 +2011,11,22,6,271,-1,0,1021,2.68,0,0 +2011,11,22,7,227,-1,2,1021,0.89,0,0 +2011,11,22,8,197,0,2,1021,1.79,0,0 +2011,11,22,9,194,0,4,1021,0.89,0,0 +2011,11,22,10,198,0,5,1021,3.13,0,0 +2011,11,22,11,223,1,7,1020,4.92,0,0 +2011,11,22,12,241,1,9,1019,0.89,0,0 +2011,11,22,13,208,-7,12,1018,7.15,0,0 +2011,11,22,14,55,-8,11,1018,19.22,0,0 +2011,11,22,15,12,-10,10,1019,28.16,0,0 +2011,11,22,16,17,-10,8,1021,40.23,0,0 +2011,11,22,17,7,-11,6,1022,52.3,0,0 +2011,11,22,18,7,-12,4,1025,64.37,0,0 +2011,11,22,19,9,-14,3,1026,80.46,0,0 +2011,11,22,20,10,-14,2,1026,98.34,0,0 +2011,11,22,21,7,-15,2,1028,113.54,0,0 +2011,11,22,22,9,-15,1,1028,123.37,0,0 +2011,11,22,23,19,-15,1,1029,135.44,0,0 +2011,11,23,0,6,-15,0,1029,146.62,0,0 +2011,11,23,1,7,-14,0,1030,157.8,0,0 +2011,11,23,2,9,-16,0,1030,169.87,0,0 +2011,11,23,3,10,-15,-1,1031,179.7,0,0 +2011,11,23,4,10,-15,-1,1031,186.85,0,0 +2011,11,23,5,10,-15,-1,1032,195.79,0,0 +2011,11,23,6,8,-14,-2,1033,203.84,0,0 +2011,11,23,7,9,-15,-2,1034,210.99,0,0 +2011,11,23,8,12,-14,0,1035,215.01,0,0 +2011,11,23,9,13,-16,1,1036,222.16,0,0 +2011,11,23,10,14,-17,1,1036,229.31,0,0 +2011,11,23,11,14,-16,3,1035,235.12,0,0 +2011,11,23,12,14,-17,4,1033,240.93,0,0 +2011,11,23,13,11,-18,4,1032,248.08,0,0 +2011,11,23,14,10,-17,4,1032,255.23,0,0 +2011,11,23,15,15,-18,5,1032,261.04,0,0 +2011,11,23,16,13,-17,4,1031,266.85,0,0 +2011,11,23,17,13,-16,2,1031,268.64,0,0 +2011,11,23,18,18,-11,0,1031,0.89,0,0 +2011,11,23,19,28,-11,-1,1031,0.89,0,0 +2011,11,23,20,40,-11,-1,1032,1.79,0,0 +2011,11,23,21,48,-13,1,1031,4.92,0,0 +2011,11,23,22,63,-13,1,1031,8.05,0,0 +2011,11,23,23,72,-10,-1,1030,0.89,0,0 +2011,11,24,0,91,-10,-1,1029,1.79,0,0 +2011,11,24,1,98,-9,-5,1030,0.89,0,0 +2011,11,24,2,99,-10,-6,1030,1.79,0,0 +2011,11,24,3,100,-10,-6,1029,2.68,0,0 +2011,11,24,4,118,-10,-6,1029,0.89,0,0 +2011,11,24,5,135,-9,-6,1028,0.89,0,0 +2011,11,24,6,134,-10,-7,1028,0.89,0,0 +2011,11,24,7,121,-9,-6,1028,0.89,0,0 +2011,11,24,8,96,-8,-5,1028,1.78,0,0 +2011,11,24,9,90,-7,-2,1029,0.89,0,0 +2011,11,24,10,96,-8,-1,1028,1.78,0,0 +2011,11,24,11,114,-8,0,1028,0.89,0,0 +2011,11,24,12,135,-8,1,1026,2.68,0,0 +2011,11,24,13,151,-8,1,1026,0.89,0,0 +2011,11,24,14,180,-8,1,1026,1.79,0,0 +2011,11,24,15,211,-7,2,1025,3.58,0,0 +2011,11,24,16,251,-7,1,1025,0.89,0,0 +2011,11,24,17,278,-6,-1,1026,0.89,0,0 +2011,11,24,18,266,-5,-1,1027,1.78,0,0 +2011,11,24,19,273,-5,-1,1027,0.45,0,0 +2011,11,24,20,282,-6,-1,1028,1.34,0,0 +2011,11,24,21,290,-5,-3,1029,0.89,0,0 +2011,11,24,22,323,-6,-3,1029,2.68,0,0 +2011,11,24,23,333,-7,-4,1029,4.47,0,0 +2011,11,25,0,305,-8,-5,1030,7.6,0,0 +2011,11,25,1,280,-7,-4,1030,8.49,0,0 +2011,11,25,2,260,-8,-4,1030,11.62,0,0 +2011,11,25,3,207,-9,-5,1030,13.41,0,0 +2011,11,25,4,232,-9,-5,1030,16.54,0,0 +2011,11,25,5,155,-8,-6,1030,18.33,0,0 +2011,11,25,6,128,-8,-5,1030,19.22,0,0 +2011,11,25,7,105,-7,-5,1030,20.11,0,0 +2011,11,25,8,120,-6,-3,1031,21.9,0,0 +2011,11,25,9,121,-5,-1,1031,0.89,0,0 +2011,11,25,10,126,-5,1,1031,1.78,0,0 +2011,11,25,11,145,-6,2,1030,2.67,0,0 +2011,11,25,12,163,-6,3,1029,3.56,0,0 +2011,11,25,13,206,-5,4,1028,1.79,0,0 +2011,11,25,14,227,-5,4,1027,3.58,0,0 +2011,11,25,15,196,-4,4,1026,0.89,0,0 +2011,11,25,16,193,-5,3,1026,1.78,0,0 +2011,11,25,17,229,-4,2,1025,0.89,0,0 +2011,11,25,18,259,-5,1,1026,0.89,0,0 +2011,11,25,19,256,-4,1,1025,1.78,0,0 +2011,11,25,20,258,-4,-1,1026,2.67,0,0 +2011,11,25,21,274,-3,-1,1026,0.89,0,0 +2011,11,25,22,332,-3,-1,1025,0.89,0,0 +2011,11,25,23,343,-4,-2,1025,0.89,0,0 +2011,11,26,0,337,-4,-3,1024,1.78,0,0 +2011,11,26,1,311,-4,-3,1024,1.79,0,0 +2011,11,26,2,292,-4,-3,1023,2.68,0,0 +2011,11,26,3,227,-4,-1,1023,5.81,0,0 +2011,11,26,4,222,-5,-3,1023,7.6,0,0 +2011,11,26,5,229,-4,-1,1022,10.73,0,0 +2011,11,26,6,222,-4,-2,1022,12.52,0,0 +2011,11,26,7,233,-5,-2,1022,15.65,0,0 +2011,11,26,8,234,-3,-1,1022,17.44,0,0 +2011,11,26,9,223,-2,2,1022,20.57,0,0 +2011,11,26,10,203,-3,3,1023,24.59,0,0 +2011,11,26,11,223,-4,5,1022,27.72,0,0 +2011,11,26,12,209,-4,6,1021,29.51,0,0 +2011,11,26,13,193,-4,6,1020,31.3,0,0 +2011,11,26,14,200,-5,7,1019,33.09,0,0 +2011,11,26,15,200,-5,7,1019,34.88,0,0 +2011,11,26,16,210,-5,6,1019,0.89,0,0 +2011,11,26,17,222,-4,5,1020,1.79,0,0 +2011,11,26,18,250,-4,3,1020,0.89,0,0 +2011,11,26,19,271,-5,0,1020,1.78,0,0 +2011,11,26,20,300,-4,-1,1020,1.79,0,0 +2011,11,26,21,292,-4,4,1020,4.92,0,0 +2011,11,26,22,305,-3,2,1021,0.89,0,0 +2011,11,26,23,322,-4,0,1021,1.79,0,0 +2011,11,27,0,330,-4,0,1021,3.58,0,0 +2011,11,27,1,326,-4,-1,1021,5.37,0,0 +2011,11,27,2,306,-4,-2,1021,0.89,0,0 +2011,11,27,3,295,-3,-1,1021,1.79,0,0 +2011,11,27,4,302,-4,-2,1021,3.58,0,0 +2011,11,27,5,237,-4,-1,1021,6.71,0,0 +2011,11,27,6,176,-5,-2,1021,9.84,0,0 +2011,11,27,7,143,-5,-3,1021,12.97,0,0 +2011,11,27,8,135,-3,0,1022,16.99,0,0 +2011,11,27,9,133,-3,5,1022,21.01,0,0 +2011,11,27,10,115,-3,6,1022,25.03,0,0 +2011,11,27,11,115,-4,8,1022,0.89,0,0 +2011,11,27,12,106,-4,10,1021,1.78,0,0 +2011,11,27,13,86,-5,10,1020,4.92,0,0 +2011,11,27,14,102,-4,10,1019,8.05,0,0 +2011,11,27,15,140,-2,9,1019,9.84,0,0 +2011,11,27,16,178,-2,7,1019,11.63,0,0 +2011,11,27,17,216,-1,5,1019,0.45,0,0 +2011,11,27,18,243,-2,3,1020,0.89,0,0 +2011,11,27,19,277,-1,2,1020,0.89,0,0 +2011,11,27,20,313,-3,1,1021,0.89,0,0 +2011,11,27,21,350,-2,0,1021,0.45,0,0 +2011,11,27,22,404,-2,0,1022,1.34,0,0 +2011,11,27,23,391,-2,1,1022,1.79,0,0 +2011,11,28,0,362,-2,1,1022,2.68,0,0 +2011,11,28,1,289,-2,1,1022,4.47,0,0 +2011,11,28,2,257,-2,0,1022,6.26,0,0 +2011,11,28,3,222,-2,1,1022,8.05,0,0 +2011,11,28,4,177,-2,1,1022,9.84,0,0 +2011,11,28,5,174,-2,2,1022,11.63,0,0 +2011,11,28,6,148,-2,1,1023,15.65,0,0 +2011,11,28,7,140,-3,0,1024,18.78,0,0 +2011,11,28,8,124,-2,5,1026,23.7,0,0 +2011,11,28,9,127,-2,8,1026,27.72,0,0 +2011,11,28,10,143,-4,10,1026,4.02,0,0 +2011,11,28,11,138,-5,10,1026,9.83,0,0 +2011,11,28,12,96,-7,12,1026,13.85,0,0 +2011,11,28,13,34,-4,11,1025,3.13,0,0 +2011,11,28,14,131,1,9,1025,8.94,0,0 +2011,11,28,15,180,1,8,1026,14.75,0,0 +2011,11,28,16,143,0,7,1027,19.67,0,0 +2011,11,28,17,100,-1,5,1027,23.69,0,0 +2011,11,28,18,93,0,5,1028,25.48,0,0 +2011,11,28,19,64,-2,4,1029,27.27,0,0 +2011,11,28,20,48,-3,4,1030,30.4,0,0 +2011,11,28,21,50,-3,4,1031,0.89,0,0 +2011,11,28,22,48,-3,4,1031,1.78,0,0 +2011,11,28,23,39,-5,4,1032,1.79,0,0 +2011,11,29,0,40,-5,3,1032,4.92,0,0 +2011,11,29,1,48,-6,2,1032,6.71,0,0 +2011,11,29,2,47,-7,1,1032,0.89,0,0 +2011,11,29,3,50,-6,1,1033,1.78,0,0 +2011,11,29,4,49,-6,1,1032,0.89,0,0 +2011,11,29,5,50,-6,1,1032,2.68,0,0 +2011,11,29,6,52,-6,1,1032,3.57,0,0 +2011,11,29,7,45,-5,1,1033,5.36,0,0 +2011,11,29,8,40,-10,3,1034,0.89,0,0 +2011,11,29,9,31,-8,3,1034,1.79,0,0 +2011,11,29,10,34,-9,3,1034,3.58,0,0 +2011,11,29,11,42,-12,3,1034,6.71,0,0 +2011,11,29,12,45,-12,3,1033,8.5,0,0 +2011,11,29,13,39,-13,3,1032,10.29,0,0 +2011,11,29,14,51,-12,3,1032,12.08,0,0 +2011,11,29,15,48,-12,3,1032,13.87,0,0 +2011,11,29,16,63,-12,3,1033,17,0,0 +2011,11,29,17,73,-10,2,1033,0.45,0,0 +2011,11,29,18,78,-8,2,1034,1.34,0,0 +2011,11,29,19,97,-8,2,1034,2.23,0,0 +2011,11,29,20,90,-6,1,1035,3.12,0,0 +2011,11,29,21,115,-7,1,1035,0.89,0,0 +2011,11,29,22,145,-7,0,1036,1.78,0,0 +2011,11,29,23,155,-7,0,1036,0.45,0,0 +2011,11,30,0,127,-6,-2,1036,1.34,0,0 +2011,11,30,1,112,-5,-2,1037,2.23,0,0 +2011,11,30,2,119,-6,-3,1037,1.79,0,0 +2011,11,30,3,174,-5,-3,1037,0.89,0,0 +2011,11,30,4,238,-7,-3,1037,0.89,0,0 +2011,11,30,5,229,-7,-5,1037,2.68,0,0 +2011,11,30,6,208,-9,-6,1038,5.81,0,0 +2011,11,30,7,163,-9,-5,1038,7.6,0,0 +2011,11,30,8,146,-11,0,1039,4.02,0,0 +2011,11,30,9,43,-14,2,1040,9.83,0,0 +2011,11,30,10,21,-14,4,1040,13.85,0,0 +2011,11,30,11,NA,-14,4,1039,3.13,0,0 +2011,11,30,12,20,-15,6,1038,7.15,0,0 +2011,11,30,13,32,-15,6,1037,10.28,0,0 +2011,11,30,14,33,-16,6,1036,12.07,0,0 +2011,11,30,15,25,-15,6,1036,13.86,0,0 +2011,11,30,16,31,-15,5,1036,15.65,0,0 +2011,11,30,17,40,-14,2,1037,0.89,0,0 +2011,11,30,18,40,-12,1,1037,1.78,0,0 +2011,11,30,19,70,-15,3,1037,4.91,0,0 +2011,11,30,20,97,-13,3,1037,8.04,0,0 +2011,11,30,21,100,-9,3,1037,1.79,0,0 +2011,11,30,22,111,-10,-2,1037,1.79,0,0 +2011,11,30,23,99,-9,-3,1037,0.89,0,0 +2011,12,1,0,125,-9,-2,1037,1.34,0,0 +2011,12,1,1,149,-10,-5,1037,1.79,0,0 +2011,12,1,2,174,-8,-6,1036,0.89,0,0 +2011,12,1,3,224,-9,-5,1036,0.45,0,0 +2011,12,1,4,215,-9,-7,1036,1.79,0,0 +2011,12,1,5,186,-9,-6,1035,3.58,0,0 +2011,12,1,6,107,-9,-7,1035,4.47,0,0 +2011,12,1,7,73,-8,-6,1035,5.36,0,0 +2011,12,1,8,87,-7,-4,1034,8.49,0,0 +2011,12,1,9,82,-6,-2,1034,0.89,0,0 +2011,12,1,10,53,-7,0,1033,1.78,0,0 +2011,12,1,11,69,-8,2,1032,2.67,0,0 +2011,12,1,12,75,-9,2,1031,3.56,0,0 +2011,12,1,13,92,-10,4,1029,4.45,0,0 +2011,12,1,14,116,-11,4,1029,5.34,0,0 +2011,12,1,15,160,-8,3,1028,1.79,0,0 +2011,12,1,16,271,-8,1,1028,3.58,0,0 +2011,12,1,17,301,-7,0,1028,0.89,0,0 +2011,12,1,18,238,-5,-1,1027,1.78,0,0 +2011,12,1,19,183,-6,-1,1027,2.67,0,0 +2011,12,1,20,209,-4,-1,1027,1.79,0,0 +2011,12,1,21,187,-4,-2,1027,0.89,0,0 +2011,12,1,22,198,-3,-2,1027,1.79,0,0 +2011,12,1,23,219,-4,-3,1026,2.68,0,0 +2011,12,2,0,243,-4,-2,1026,4.47,0,0 +2011,12,2,1,287,-4,-2,1025,0.89,0,0 +2011,12,2,2,303,-4,-2,1025,1.78,0,0 +2011,12,2,3,282,-3,-2,1024,0.89,0,0 +2011,12,2,4,284,-3,-2,1024,1.78,0,0 +2011,12,2,5,290,-3,-2,1023,3.57,0,0 +2011,12,2,6,296,-3,-2,1023,0.89,0,0 +2011,12,2,7,306,-3,-2,1024,1.78,1,0 +2011,12,2,8,309,-2,-2,1024,2.67,2,0 +2011,12,2,9,301,-2,-1,1024,0.89,3,0 +2011,12,2,10,265,-2,-1,1024,1.34,4,0 +2011,12,2,11,293,-2,-1,1023,0.89,5,0 +2011,12,2,12,289,-1,-1,1023,0.89,6,0 +2011,12,2,13,254,-1,0,1022,1.79,7,0 +2011,12,2,14,279,-1,0,1022,3.58,0,0 +2011,12,2,15,278,-1,0,1022,5.37,0,0 +2011,12,2,16,281,-1,0,1022,6.26,0,0 +2011,12,2,17,295,-2,-1,1022,7.15,0,0 +2011,12,2,18,307,-2,-1,1023,8.04,0,0 +2011,12,2,19,365,-1,-1,1023,0.89,0,0 +2011,12,2,20,431,-1,0,1023,0.89,0,0 +2011,12,2,21,494,-1,0,1023,0.89,0,0 +2011,12,2,22,490,-1,0,1024,0.89,0,0 +2011,12,2,23,473,-1,0,1024,1.78,0,0 +2011,12,3,0,473,-1,-1,1024,0.89,0,0 +2011,12,3,1,477,-3,-3,1024,1.79,0,0 +2011,12,3,2,301,-4,-4,1024,4.92,0,0 +2011,12,3,3,288,-6,-6,1024,8.05,0,0 +2011,12,3,4,96,-5,-5,1024,12.07,0,0 +2011,12,3,5,17,-6,-4,1024,13.86,0,0 +2011,12,3,6,26,-8,-6,1025,3.13,0,0 +2011,12,3,7,37,-8,-4,1026,8.05,0,0 +2011,12,3,8,36,-7,-2,1027,11.18,0,0 +2011,12,3,9,34,-8,-1,1027,15.2,0,0 +2011,12,3,10,34,-7,0,1027,4.92,0,0 +2011,12,3,11,27,-7,2,1027,8.94,0,0 +2011,12,3,12,29,-9,2,1027,4.02,0,0 +2011,12,3,13,29,-10,5,1025,1.79,0,0 +2011,12,3,14,32,-11,6,1025,1.79,0,0 +2011,12,3,15,35,-11,5,1025,3.58,0,0 +2011,12,3,16,39,-12,5,1026,0.89,0,0 +2011,12,3,17,42,-11,2,1026,1.79,0,0 +2011,12,3,18,99,-10,1,1026,3.58,0,0 +2011,12,3,19,137,-6,1,1027,5.37,0,0 +2011,12,3,20,168,-4,1,1027,7.16,0,0 +2011,12,3,21,177,-5,-2,1027,0.89,0,0 +2011,12,3,22,184,-5,-3,1027,1.78,0,0 +2011,12,3,23,195,-5,-3,1027,0.89,0,0 +2011,12,4,0,193,-6,-3,1027,1.78,0,0 +2011,12,4,1,187,-5,-3,1028,0.89,0,0 +2011,12,4,2,178,-4,-3,1028,1.78,0,0 +2011,12,4,3,206,-5,-3,1028,2.67,0,0 +2011,12,4,4,260,-6,-3,1027,0.89,0,0 +2011,12,4,5,276,-6,-4,1028,0.89,0,0 +2011,12,4,6,250,-7,-5,1028,0.89,0,0 +2011,12,4,7,203,-7,-6,1028,1.34,0,0 +2011,12,4,8,222,-6,-5,1029,1.79,0,0 +2011,12,4,9,113,-6,-2,1029,0.89,0,0 +2011,12,4,10,54,-5,-1,1030,1.78,0,0 +2011,12,4,11,63,-5,1,1029,2.67,0,0 +2011,12,4,12,65,-5,1,1028,3.56,0,0 +2011,12,4,13,100,-4,2,1027,4.45,0,0 +2011,12,4,14,126,-3,2,1027,1.79,0,0 +2011,12,4,15,221,-3,1,1028,3.58,0,0 +2011,12,4,16,406,-3,-1,1028,5.37,0,0 +2011,12,4,17,472,-3,-2,1029,7.16,0,0 +2011,12,4,18,498,-3,-3,1029,8.95,0,0 +2011,12,4,19,522,-3,-3,1030,10.74,0,0 +2011,12,4,20,469,-3,-3,1031,13.87,0,0 +2011,12,4,21,412,-3,-3,1031,15.66,0,0 +2011,12,4,22,372,-4,-3,1032,1.79,0,0 +2011,12,4,23,355,-4,-4,1032,1.79,0,0 +2011,12,5,0,365,-4,-4,1032,0.89,0,0 +2011,12,5,1,392,-4,-4,1031,0.89,0,0 +2011,12,5,2,324,-3,-3,1032,0.89,0,0 +2011,12,5,3,386,-3,-3,1032,4.02,0,0 +2011,12,5,4,397,-3,-3,1032,5.81,0,0 +2011,12,5,5,416,-3,-3,1032,6.7,0,0 +2011,12,5,6,395,-3,-3,1032,7.59,0,0 +2011,12,5,7,370,-3,-2,1032,1.79,0,0 +2011,12,5,8,296,-2,-2,1032,0.45,0,0 +2011,12,5,9,275,-2,-1,1033,1.34,0,0 +2011,12,5,10,282,-2,-1,1033,1.79,1,0 +2011,12,5,11,270,-2,-1,1032,3.58,2,0 +2011,12,5,12,283,-1,-1,1031,0.89,3,0 +2011,12,5,13,269,-1,-1,1031,1.78,0,0 +2011,12,5,14,248,-1,0,1031,2.67,0,0 +2011,12,5,15,268,-1,0,1031,3.56,0,0 +2011,12,5,16,306,-1,0,1031,4.45,0,0 +2011,12,5,17,270,-1,0,1031,0.89,0,0 +2011,12,5,18,332,-1,0,1032,0.89,0,0 +2011,12,5,19,335,-1,0,1032,0.89,0,0 +2011,12,5,20,324,-1,0,1032,1.78,0,0 +2011,12,5,21,318,-1,0,1032,0.89,0,0 +2011,12,5,22,304,-1,0,1032,0.89,1,0 +2011,12,5,23,266,0,0,1032,1.78,2,0 +2011,12,6,0,226,-1,0,1032,1.79,3,0 +2011,12,6,1,252,0,0,1031,3.58,4,0 +2011,12,6,2,247,0,0,1031,5.37,5,0 +2011,12,6,3,247,-1,0,1031,6.26,6,0 +2011,12,6,4,237,-1,0,1031,8.05,0,0 +2011,12,6,5,229,-1,0,1030,9.84,0,0 +2011,12,6,6,225,-1,0,1031,11.63,0,0 +2011,12,6,7,231,0,0,1032,12.52,1,0 +2011,12,6,8,211,0,0,1032,14.31,2,0 +2011,12,6,9,181,0,0,1032,16.1,0,0 +2011,12,6,10,210,0,1,1032,0.89,0,0 +2011,12,6,11,211,1,2,1032,1.78,0,0 +2011,12,6,12,209,1,2,1031,3.13,0,0 +2011,12,6,13,215,1,3,1030,0.89,0,0 +2011,12,6,14,215,1,3,1029,1.78,0,0 +2011,12,6,15,201,0,3,1030,2.67,0,0 +2011,12,6,16,193,0,2,1030,0.89,0,0 +2011,12,6,17,189,0,1,1030,2.68,0,0 +2011,12,6,18,187,-1,0,1031,4.47,0,0 +2011,12,6,19,240,-2,-2,1032,6.26,0,0 +2011,12,6,20,232,-2,-2,1032,8.05,0,0 +2011,12,6,21,279,-2,-2,1032,0.89,0,0 +2011,12,6,22,456,-2,-2,1032,1.79,0,0 +2011,12,6,23,420,-2,-2,1032,0.89,0,0 +2011,12,7,0,340,-1,-1,1032,1.78,0,0 +2011,12,7,1,306,-1,-1,1032,2.67,0,0 +2011,12,7,2,286,-1,-1,1032,1.79,0,0 +2011,12,7,3,284,-1,-1,1032,0.89,1,0 +2011,12,7,4,287,-1,0,1031,0.45,2,0 +2011,12,7,5,281,0,0,1031,0.89,3,0 +2011,12,7,6,265,0,0,1032,0.45,4,0 +2011,12,7,7,232,0,0,1032,0.89,0,0 +2011,12,7,8,218,0,0,1033,0.89,0,0 +2011,12,7,9,202,1,1,1034,1.79,0,0 +2011,12,7,10,146,0,3,1034,4.92,0,0 +2011,12,7,11,27,-8,4,1034,12.97,0,0 +2011,12,7,12,28,-8,5,1033,21.02,0,0 +2011,12,7,13,17,-10,5,1033,30.85,0,0 +2011,12,7,14,18,-11,4,1032,40.68,0,0 +2011,12,7,15,17,-11,3,1033,51.86,0,0 +2011,12,7,16,18,-12,3,1033,59.91,0,0 +2011,12,7,17,15,-13,2,1033,67.96,0,0 +2011,12,7,18,16,-13,1,1034,75.11,0,0 +2011,12,7,19,13,-15,1,1035,84.94,0,0 +2011,12,7,20,12,-14,0,1036,93.88,0,0 +2011,12,7,21,11,-15,-1,1036,105.95,0,0 +2011,12,7,22,12,-16,-2,1037,115.78,0,0 +2011,12,7,23,11,-16,-3,1038,123.83,0,0 +2011,12,8,0,12,-17,-4,1038,133.66,0,0 +2011,12,8,1,11,-17,-4,1038,143.49,0,0 +2011,12,8,2,8,-18,-4,1039,152.43,0,0 +2011,12,8,3,9,-18,-6,1039,160.48,0,0 +2011,12,8,4,11,-19,-6,1039,168.53,0,0 +2011,12,8,5,8,-19,-6,1039,176.58,0,0 +2011,12,8,6,8,-18,-6,1039,183.73,0,0 +2011,12,8,7,16,-17,-7,1040,3.13,0,0 +2011,12,8,8,10,-17,-6,1041,7.15,0,0 +2011,12,8,9,16,-16,-4,1041,4.02,0,0 +2011,12,8,10,12,-16,-3,1041,9.83,0,0 +2011,12,8,11,11,-15,-2,1040,13.85,0,0 +2011,12,8,12,11,-16,-1,1039,23.68,0,0 +2011,12,8,13,17,-16,-1,1039,30.83,0,0 +2011,12,8,14,9,-18,0,1038,39.77,0,0 +2011,12,8,15,10,-18,-1,1039,50.95,0,0 +2011,12,8,16,12,-18,-2,1039,60.78,0,0 +2011,12,8,17,14,-18,-3,1039,69.72,0,0 +2011,12,8,18,15,-18,-3,1040,78.66,0,0 +2011,12,8,19,15,-18,-3,1040,87.6,0,0 +2011,12,8,20,9,-18,-4,1041,96.54,0,0 +2011,12,8,21,7,-18,-4,1041,104.59,0,0 +2011,12,8,22,7,-18,-5,1042,111.74,0,0 +2011,12,8,23,7,-18,-5,1042,119.79,0,0 +2011,12,9,0,7,-18,-7,1042,123.81,0,0 +2011,12,9,1,17,-17,-8,1041,127.83,0,0 +2011,12,9,2,17,-17,-7,1041,131.85,0,0 +2011,12,9,3,18,-17,-8,1041,134.98,0,0 +2011,12,9,4,13,-17,-6,1040,142.13,0,0 +2011,12,9,5,13,-17,-7,1040,147.05,0,0 +2011,12,9,6,14,-17,-8,1040,1.79,0,0 +2011,12,9,7,12,-16,-8,1040,5.81,0,0 +2011,12,9,8,22,-15,-7,1040,8.94,0,0 +2011,12,9,9,32,-16,-5,1040,4.92,0,0 +2011,12,9,10,26,-16,-3,1039,3.13,0,0 +2011,12,9,11,25,-16,-1,1037,8.94,0,0 +2011,12,9,12,19,-18,0,1036,17.88,0,0 +2011,12,9,13,19,-18,0,1035,25.03,0,0 +2011,12,9,14,18,-18,0,1034,33.08,0,0 +2011,12,9,15,16,-18,0,1033,41.13,0,0 +2011,12,9,16,17,-18,0,1033,48.28,0,0 +2011,12,9,17,18,-15,0,1033,55.43,0,0 +2011,12,9,18,18,-16,-1,1034,60.35,0,0 +2011,12,9,19,20,-16,-1,1033,67.5,0,0 +2011,12,9,20,29,-16,-2,1034,75.55,0,0 +2011,12,9,21,17,-16,-2,1034,82.7,0,0 +2011,12,9,22,19,-16,-3,1033,87.62,0,0 +2011,12,9,23,11,-16,-3,1033,93.43,0,0 +2011,12,10,0,18,-16,-4,1033,99.24,0,0 +2011,12,10,1,11,-16,-4,1032,106.39,0,0 +2011,12,10,2,16,-16,-4,1032,114.44,0,0 +2011,12,10,3,17,-16,-4,1032,121.59,0,0 +2011,12,10,4,14,-16,-6,1031,125.61,0,0 +2011,12,10,5,15,-16,-4,1030,129.63,0,0 +2011,12,10,6,13,-17,-4,1030,134.55,0,0 +2011,12,10,7,15,-17,-4,1030,136.34,0,0 +2011,12,10,8,15,-15,-2,1031,140.36,0,0 +2011,12,10,9,21,-15,0,1031,144.38,0,0 +2011,12,10,10,22,-15,2,1031,147.51,0,0 +2011,12,10,11,27,-15,3,1030,155.56,0,0 +2011,12,10,12,23,-14,4,1029,161.37,0,0 +2011,12,10,13,21,-13,5,1028,167.18,0,0 +2011,12,10,14,16,-13,5,1027,175.23,0,0 +2011,12,10,15,15,-13,5,1027,183.28,0,0 +2011,12,10,16,12,-12,5,1027,188.2,0,0 +2011,12,10,17,63,-12,4,1027,193.12,0,0 +2011,12,10,18,74,-13,3,1027,4.92,0,0 +2011,12,10,19,77,-13,2,1027,9.84,0,0 +2011,12,10,20,80,-12,2,1026,12.97,0,0 +2011,12,10,21,85,-10,0,1026,14.76,0,0 +2011,12,10,22,77,-10,-1,1026,16.55,0,0 +2011,12,10,23,76,-11,0,1026,0.89,0,0 +2011,12,11,0,73,-10,-3,1026,1.79,0,0 +2011,12,11,1,82,-9,-3,1026,0.89,0,0 +2011,12,11,2,87,-11,-5,1026,1.78,0,0 +2011,12,11,3,100,-10,-6,1026,2.67,0,0 +2011,12,11,4,113,-9,-5,1026,3.56,0,0 +2011,12,11,5,135,-10,-7,1026,4.45,0,0 +2011,12,11,6,148,-10,-7,1026,1.79,0,0 +2011,12,11,7,153,-10,-8,1026,3.58,0,0 +2011,12,11,8,187,-7,-4,1027,1.79,0,0 +2011,12,11,9,180,-6,1,1027,3.58,0,0 +2011,12,11,10,176,-9,6,1027,0.89,0,0 +2011,12,11,11,70,-10,7,1027,1.78,0,0 +2011,12,11,12,46,-10,10,1026,7.15,0,0 +2011,12,11,13,30,-10,10,1025,15.2,0,0 +2011,12,11,14,24,-10,10,1024,23.25,0,0 +2011,12,11,15,28,-11,9,1025,30.4,0,0 +2011,12,11,16,30,-10,9,1025,38.45,0,0 +2011,12,11,17,33,-10,8,1025,45.6,0,0 +2011,12,11,18,49,-10,7,1026,51.41,0,0 +2011,12,11,19,47,-10,7,1026,57.22,0,0 +2011,12,11,20,55,-10,6,1027,60.35,0,0 +2011,12,11,21,59,-10,6,1027,65.27,0,0 +2011,12,11,22,79,-10,3,1027,1.79,0,0 +2011,12,11,23,48,-8,0,1027,1.79,0,0 +2011,12,12,0,50,-8,0,1028,2.68,0,0 +2011,12,12,1,62,-7,0,1028,4.47,0,0 +2011,12,12,2,61,-8,-2,1029,7.6,0,0 +2011,12,12,3,46,-8,-4,1029,11.62,0,0 +2011,12,12,4,35,-8,-3,1029,15.64,0,0 +2011,12,12,5,25,-8,-1,1029,19.66,0,0 +2011,12,12,6,17,-8,-1,1029,23.68,0,0 +2011,12,12,7,16,-9,1,1030,4.02,0,0 +2011,12,12,8,20,-10,1,1031,8.94,0,0 +2011,12,12,9,25,-9,3,1031,12.07,0,0 +2011,12,12,10,29,-10,4,1032,15.2,0,0 +2011,12,12,11,26,-10,6,1031,1.79,0,0 +2011,12,12,12,43,-9,6,1030,1.79,0,0 +2011,12,12,13,31,-9,7,1030,1.79,0,0 +2011,12,12,14,38,-10,7,1029,3.58,0,0 +2011,12,12,15,61,-10,6,1029,5.37,0,0 +2011,12,12,16,65,-10,5,1029,8.5,0,0 +2011,12,12,17,57,-9,3,1029,9.39,0,0 +2011,12,12,18,82,-9,1,1029,11.18,0,0 +2011,12,12,19,91,-4,1,1030,12.97,0,0 +2011,12,12,20,79,-4,0,1030,16.1,0,0 +2011,12,12,21,62,-4,0,1030,17.89,0,0 +2011,12,12,22,62,-3,0,1030,21.02,0,0 +2011,12,12,23,71,-4,0,1030,24.15,0,0 +2011,12,13,0,82,-4,-1,1029,25.04,0,0 +2011,12,13,1,81,-4,-1,1029,0.89,0,0 +2011,12,13,2,90,-4,-1,1028,0.89,0,0 +2011,12,13,3,100,-4,-1,1028,0.89,0,0 +2011,12,13,4,88,-3,-1,1027,0.89,0,0 +2011,12,13,5,79,-3,0,1027,0.89,0,0 +2011,12,13,6,87,-2,1,1026,1.79,0,0 +2011,12,13,7,90,-2,1,1026,3.58,0,0 +2011,12,13,8,83,-2,1,1025,5.37,0,0 +2011,12,13,9,93,-1,1,1025,7.16,0,0 +2011,12,13,10,121,-1,2,1025,0.89,0,0 +2011,12,13,11,147,-1,2,1024,0.89,0,0 +2011,12,13,12,175,-1,2,1023,0.89,0,0 +2011,12,13,13,181,-1,3,1022,1.78,0,0 +2011,12,13,14,186,-1,3,1022,2.67,0,0 +2011,12,13,15,200,-1,3,1021,3.56,0,0 +2011,12,13,16,223,-1,2,1022,0.89,0,0 +2011,12,13,17,238,-2,0,1022,0.89,0,0 +2011,12,13,18,247,-2,-1,1022,1.78,0,0 +2011,12,13,19,279,0,2,1022,2.67,0,0 +2011,12,13,20,304,-4,-3,1023,3.56,0,0 +2011,12,13,21,320,-5,-4,1023,4.45,0,0 +2011,12,13,22,282,-1,0,1024,3.13,0,0 +2011,12,13,23,56,-3,-1,1025,8.05,0,0 +2011,12,14,0,16,-14,1,1025,20.12,0,0 +2011,12,14,1,15,-13,0,1027,31.3,0,0 +2011,12,14,2,14,-15,-1,1029,41.13,0,0 +2011,12,14,3,14,-16,-3,1031,49.18,0,0 +2011,12,14,4,13,-16,-3,1031,57.23,0,0 +2011,12,14,5,10,-16,-4,1032,62.15,0,0 +2011,12,14,6,10,-17,-5,1032,70.2,0,0 +2011,12,14,7,11,-17,-4,1034,78.25,0,0 +2011,12,14,8,11,-16,-4,1034,85.4,0,0 +2011,12,14,9,13,-16,-2,1035,92.55,0,0 +2011,12,14,10,16,-17,-1,1035,100.6,0,0 +2011,12,14,11,18,-19,0,1034,107.75,0,0 +2011,12,14,12,19,-18,0,1033,114.9,0,0 +2011,12,14,13,13,-19,1,1033,123.84,0,0 +2011,12,14,14,17,-19,1,1033,132.78,0,0 +2011,12,14,15,14,-20,1,1033,140.83,0,0 +2011,12,14,16,11,-20,0,1034,148.88,0,0 +2011,12,14,17,10,-18,-1,1034,156.03,0,0 +2011,12,14,18,16,-18,-2,1035,163.18,0,0 +2011,12,14,19,17,-18,-3,1036,172.12,0,0 +2011,12,14,20,20,-18,-3,1036,180.17,0,0 +2011,12,14,21,14,-18,-3,1037,188.22,0,0 +2011,12,14,22,12,-18,-4,1037,195.37,0,0 +2011,12,14,23,23,-18,-4,1038,202.52,0,0 +2011,12,15,0,16,-18,-5,1038,207.44,0,0 +2011,12,15,1,16,-18,-6,1037,212.36,0,0 +2011,12,15,2,15,-18,-6,1037,217.28,0,0 +2011,12,15,3,16,-18,-8,1037,219.07,0,0 +2011,12,15,4,22,-18,-7,1036,0.89,0,0 +2011,12,15,5,21,-18,-7,1036,1.79,0,0 +2011,12,15,6,19,-18,-7,1036,4.92,0,0 +2011,12,15,7,28,-18,-7,1036,9.84,0,0 +2011,12,15,8,11,-17,-5,1037,13.86,0,0 +2011,12,15,9,13,-18,-4,1037,18.78,0,0 +2011,12,15,10,19,-18,-2,1037,26.83,0,0 +2011,12,15,11,15,-18,-1,1036,34.88,0,0 +2011,12,15,12,19,-18,-1,1035,40.69,0,0 +2011,12,15,13,12,-19,0,1034,46.5,0,0 +2011,12,15,14,16,-17,-1,1034,52.31,0,0 +2011,12,15,15,15,-16,-1,1034,61.25,0,0 +2011,12,15,16,7,-19,-2,1035,70.19,0,0 +2011,12,15,17,11,-21,-2,1036,80.02,0,0 +2011,12,15,18,11,-21,-4,1037,92.09,0,0 +2011,12,15,19,7,-21,-4,1038,103.27,0,0 +2011,12,15,20,9,-21,-5,1039,114.45,0,0 +2011,12,15,21,10,-21,-6,1039,124.28,0,0 +2011,12,15,22,11,-22,-7,1039,134.11,0,0 +2011,12,15,23,10,-23,-7,1039,145.29,0,0 +2011,12,16,0,15,-22,-7,1040,152.44,0,0 +2011,12,16,1,11,-22,-8,1040,159.59,0,0 +2011,12,16,2,10,-22,-8,1040,168.53,0,0 +2011,12,16,3,9,-22,-8,1040,177.47,0,0 +2011,12,16,4,8,-23,-8,1040,184.62,0,0 +2011,12,16,5,8,-23,-8,1040,192.67,0,0 +2011,12,16,6,7,-22,-8,1040,200.72,0,0 +2011,12,16,7,10,-22,-8,1041,207.87,0,0 +2011,12,16,8,14,-23,-7,1041,215.92,0,0 +2011,12,16,9,17,-23,-6,1041,224.86,0,0 +2011,12,16,10,13,-23,-5,1041,233.8,0,0 +2011,12,16,11,12,-23,-3,1040,242.74,0,0 +2011,12,16,12,15,-23,-3,1039,250.79,0,0 +2011,12,16,13,16,-23,-2,1038,258.84,0,0 +2011,12,16,14,20,-23,-1,1037,266.89,0,0 +2011,12,16,15,17,-23,-1,1037,274.04,0,0 +2011,12,16,16,17,-23,-2,1036,278.96,0,0 +2011,12,16,17,18,-21,-5,1036,0.89,0,0 +2011,12,16,18,19,-21,-5,1036,1.34,0,0 +2011,12,16,19,36,-19,-7,1036,2.23,0,0 +2011,12,16,20,53,-19,-8,1036,2.68,0,0 +2011,12,16,21,49,-17,-9,1036,3.57,0,0 +2011,12,16,22,74,-19,-11,1036,0.89,0,0 +2011,12,16,23,85,-17,-11,1036,0.89,0,0 +2011,12,17,0,96,-17,-11,1035,1.78,0,0 +2011,12,17,1,123,-19,-13,1035,1.79,0,0 +2011,12,17,2,122,-19,-12,1035,3.58,0,0 +2011,12,17,3,140,-18,-11,1035,5.37,0,0 +2011,12,17,4,114,-19,-13,1035,7.16,0,0 +2011,12,17,5,109,-18,-12,1034,10.29,0,0 +2011,12,17,6,92,-18,-10,1034,11.18,0,0 +2011,12,17,7,68,-18,-11,1035,12.97,0,0 +2011,12,17,8,55,-17,-9,1035,16.1,0,0 +2011,12,17,9,58,-17,-7,1035,17.89,0,0 +2011,12,17,10,57,-16,-3,1035,19.68,0,0 +2011,12,17,11,69,-16,-2,1034,0.89,0,0 +2011,12,17,12,75,-18,1,1033,1.79,0,0 +2011,12,17,13,77,-20,1,1032,3.58,0,0 +2011,12,17,14,77,-19,2,1031,5.37,0,0 +2011,12,17,15,129,-19,3,1031,1.79,0,0 +2011,12,17,16,107,-18,2,1031,2.68,0,0 +2011,12,17,17,156,-16,-1,1031,1.79,0,0 +2011,12,17,18,108,-14,-4,1032,0.89,0,0 +2011,12,17,19,109,-15,-6,1033,0.45,0,0 +2011,12,17,20,128,-15,-6,1033,1.34,0,0 +2011,12,17,21,170,-14,-7,1033,1.79,0,0 +2011,12,17,22,151,-16,-3,1034,1.79,0,0 +2011,12,17,23,177,-16,-7,1034,4.92,0,0 +2011,12,18,0,111,-16,-5,1035,8.94,0,0 +2011,12,18,1,50,-16,-7,1035,12.07,0,0 +2011,12,18,2,39,-16,-8,1035,16.09,0,0 +2011,12,18,3,28,-16,-6,1035,20.11,0,0 +2011,12,18,4,14,-17,-4,1034,4.92,0,0 +2011,12,18,5,13,-18,-5,1034,8.94,0,0 +2011,12,18,6,19,-18,-5,1035,13.86,0,0 +2011,12,18,7,19,-18,-6,1035,17.88,0,0 +2011,12,18,8,15,-18,-5,1035,21.9,0,0 +2011,12,18,9,22,-18,-4,1035,25.92,0,0 +2011,12,18,10,33,-18,-2,1035,30.84,0,0 +2011,12,18,11,40,-20,0,1034,33.97,0,0 +2011,12,18,12,35,-20,1,1033,1.79,0,0 +2011,12,18,13,38,-20,2,1032,0.89,0,0 +2011,12,18,14,40,-20,3,1031,1.78,0,0 +2011,12,18,15,44,-20,3,1031,1.79,0,0 +2011,12,18,16,45,-18,0,1031,1.79,0,0 +2011,12,18,17,67,-18,-2,1031,0.89,0,0 +2011,12,18,18,88,-17,-6,1031,1.79,0,0 +2011,12,18,19,111,-16,-5,1032,0.89,0,0 +2011,12,18,20,107,-16,-3,1032,2.68,0,0 +2011,12,18,21,99,-17,-2,1032,5.81,0,0 +2011,12,18,22,116,-15,-4,1032,9.83,0,0 +2011,12,18,23,105,-16,-3,1032,12.96,0,0 +2011,12,19,0,64,-17,-2,1032,16.09,0,0 +2011,12,19,1,31,-17,-3,1032,20.11,0,0 +2011,12,19,2,27,-18,-2,1032,4.02,0,0 +2011,12,19,3,26,-18,-4,1031,4.02,0,0 +2011,12,19,4,23,-18,-3,1031,4.02,0,0 +2011,12,19,5,18,-19,-6,1031,3.13,0,0 +2011,12,19,6,13,-19,-5,1031,3.13,0,0 +2011,12,19,7,21,-18,-7,1032,3.13,0,0 +2011,12,19,8,18,-17,-3,1032,4.92,0,0 +2011,12,19,9,30,-18,-2,1033,4.02,0,0 +2011,12,19,10,30,-19,-1,1033,8.94,0,0 +2011,12,19,11,36,-21,1,1032,12.07,0,0 +2011,12,19,12,38,-21,3,1031,1.79,0,0 +2011,12,19,13,39,-23,3,1030,1.79,0,0 +2011,12,19,14,29,-22,3,1029,0.89,0,0 +2011,12,19,15,34,-23,3,1029,1.78,0,0 +2011,12,19,16,42,-22,1,1029,2.67,0,0 +2011,12,19,17,51,-19,-1,1029,3.56,0,0 +2011,12,19,18,58,-17,-3,1030,4.45,0,0 +2011,12,19,19,150,-16,-4,1031,0.89,0,0 +2011,12,19,20,152,-15,-6,1031,0.89,0,0 +2011,12,19,21,161,-15,-6,1031,1.78,0,0 +2011,12,19,22,153,-15,-6,1031,1.79,0,0 +2011,12,19,23,142,-15,-6,1032,2.68,0,0 +2011,12,20,0,185,-14,-7,1032,0.89,0,0 +2011,12,20,1,191,-15,-7,1032,1.79,0,0 +2011,12,20,2,188,-15,-8,1031,3.58,0,0 +2011,12,20,3,123,-15,-8,1031,5.37,0,0 +2011,12,20,4,72,-15,-8,1031,7.16,0,0 +2011,12,20,5,69,-15,-9,1030,11.18,0,0 +2011,12,20,6,35,-16,-10,1030,14.31,0,0 +2011,12,20,7,36,-15,-8,1030,19.23,0,0 +2011,12,20,8,31,-15,-6,1031,23.25,0,0 +2011,12,20,9,42,-16,-3,1031,26.38,0,0 +2011,12,20,10,48,-17,0,1030,30.4,0,0 +2011,12,20,11,66,-17,1,1029,33.53,0,0 +2011,12,20,12,66,-17,3,1028,35.32,0,0 +2011,12,20,13,62,-16,3,1026,1.79,0,0 +2011,12,20,14,65,-16,3,1026,1.79,0,0 +2011,12,20,15,85,-15,4,1026,3.58,0,0 +2011,12,20,16,112,-15,3,1026,1.79,0,0 +2011,12,20,17,94,-15,2,1026,4.92,0,0 +2011,12,20,18,105,-15,2,1026,8.05,0,0 +2011,12,20,19,62,-13,-1,1027,4.02,0,0 +2011,12,20,20,47,-14,-1,1027,0.45,0,0 +2011,12,20,21,103,-14,-5,1028,0.89,0,0 +2011,12,20,22,100,-13,-5,1028,0.89,0,0 +2011,12,20,23,123,-14,-1,1028,1.78,0,0 +2011,12,21,0,100,-15,-3,1028,1.79,0,0 +2011,12,21,1,74,-13,-8,1028,0.89,0,0 +2011,12,21,2,81,-13,-8,1028,1.78,0,0 +2011,12,21,3,30,-14,-7,1028,0.89,0,0 +2011,12,21,4,21,-15,-5,1028,4.02,0,0 +2011,12,21,5,11,-15,-6,1027,0.89,0,0 +2011,12,21,6,13,-15,-8,1027,1.78,0,0 +2011,12,21,7,13,-16,-4,1028,3.13,0,0 +2011,12,21,8,17,-16,-1,1029,7.15,0,0 +2011,12,21,9,12,-17,0,1030,12.96,0,0 +2011,12,21,10,18,-17,1,1030,18.77,0,0 +2011,12,21,11,17,-18,1,1030,27.71,0,0 +2011,12,21,12,NA,-18,2,1030,35.76,0,0 +2011,12,21,13,18,-18,3,1029,43.81,0,0 +2011,12,21,14,19,-21,3,1029,53.64,0,0 +2011,12,21,15,14,-23,3,1029,65.71,0,0 +2011,12,21,16,14,-22,2,1030,78.67,0,0 +2011,12,21,17,13,-22,1,1031,91.63,0,0 +2011,12,21,18,10,-23,1,1031,101.46,0,0 +2011,12,21,19,9,-20,0,1032,110.4,0,0 +2011,12,21,20,12,-20,-1,1033,118.45,0,0 +2011,12,21,21,18,-21,-5,1033,121.58,0,0 +2011,12,21,22,14,-20,-5,1033,0.89,0,0 +2011,12,21,23,16,-19,-3,1034,3.13,0,0 +2011,12,22,0,19,-18,-3,1034,3.13,0,0 +2011,12,22,1,13,-17,-3,1034,6.26,0,0 +2011,12,22,2,14,-17,-4,1034,9.39,0,0 +2011,12,22,3,14,-17,-3,1034,15.2,0,0 +2011,12,22,4,13,-17,-3,1034,19.22,0,0 +2011,12,22,5,12,-17,-5,1034,23.24,0,0 +2011,12,22,6,12,-17,-5,1034,26.37,0,0 +2011,12,22,7,13,-17,-4,1034,31.29,0,0 +2011,12,22,8,18,-17,-4,1035,36.21,0,0 +2011,12,22,9,24,-17,-1,1035,40.23,0,0 +2011,12,22,10,31,-18,0,1035,47.38,0,0 +2011,12,22,11,27,-19,1,1034,54.53,0,0 +2011,12,22,12,19,-19,3,1032,60.34,0,0 +2011,12,22,13,14,-20,3,1031,65.26,0,0 +2011,12,22,14,16,-20,2,1031,71.07,0,0 +2011,12,22,15,15,-20,2,1030,75.99,0,0 +2011,12,22,16,22,-19,2,1029,80.01,0,0 +2011,12,22,17,27,-18,2,1029,84.03,0,0 +2011,12,22,18,38,-18,1,1029,3.13,0,0 +2011,12,22,19,40,-17,-5,1029,1.79,0,0 +2011,12,22,20,51,-17,-5,1029,3.58,0,0 +2011,12,22,21,64,-16,-7,1029,0.89,0,0 +2011,12,22,22,68,-16,-7,1029,0.89,0,0 +2011,12,22,23,70,-16,-8,1029,0.89,0,0 +2011,12,23,0,95,-15,-8,1029,1.78,0,0 +2011,12,23,1,118,-15,-8,1028,0.89,0,0 +2011,12,23,2,117,-14,-9,1028,0.89,0,0 +2011,12,23,3,119,-15,-10,1027,1.79,0,0 +2011,12,23,4,106,-14,-9,1026,2.68,0,0 +2011,12,23,5,98,-15,-9,1026,4.47,0,0 +2011,12,23,6,73,-16,-11,1026,7.6,0,0 +2011,12,23,7,76,-15,-10,1027,1.79,0,0 +2011,12,23,8,87,-14,-8,1027,0.89,0,0 +2011,12,23,9,98,-12,-4,1027,1.79,0,0 +2011,12,23,10,90,-12,-1,1027,4.92,0,0 +2011,12,23,11,70,-14,3,1026,8.05,0,0 +2011,12,23,12,63,-15,3,1025,12.07,0,0 +2011,12,23,13,33,-15,4,1024,16.99,0,0 +2011,12,23,14,15,-17,5,1024,24.14,0,0 +2011,12,23,15,18,-16,4,1024,32.19,0,0 +2011,12,23,16,16,-16,3,1025,38,0,0 +2011,12,23,17,18,-17,3,1025,42.02,0,0 +2011,12,23,18,8,-18,2,1026,46.94,0,0 +2011,12,23,19,16,-17,-1,1027,0.89,0,0 +2011,12,23,20,17,-17,1,1027,3.13,0,0 +2011,12,23,21,15,-16,1,1027,7.15,0,0 +2011,12,23,22,16,-15,0,1027,11.17,0,0 +2011,12,23,23,13,-15,0,1027,16.09,0,0 +2011,12,24,0,11,-15,-1,1027,21.9,0,0 +2011,12,24,1,10,-16,-1,1028,25.92,0,0 +2011,12,24,2,11,-16,-1,1028,30.84,0,0 +2011,12,24,3,11,-16,-5,1027,0.89,0,0 +2011,12,24,4,16,-16,-8,1027,1.79,0,0 +2011,12,24,5,14,-16,-4,1027,4.92,0,0 +2011,12,24,6,13,-16,-10,1028,8.94,0,0 +2011,12,24,7,21,-16,-9,1028,12.96,0,0 +2011,12,24,8,20,-15,-8,1028,14.75,0,0 +2011,12,24,9,19,-15,-1,1029,0.89,0,0 +2011,12,24,10,21,-17,1,1029,1.79,0,0 +2011,12,24,11,27,-17,3,1028,4.92,0,0 +2011,12,24,12,22,-17,3,1027,9.84,0,0 +2011,12,24,13,27,-17,4,1026,16.99,0,0 +2011,12,24,14,38,-16,4,1026,22.8,0,0 +2011,12,24,15,30,-17,4,1026,29.95,0,0 +2011,12,24,16,22,-17,3,1026,35.76,0,0 +2011,12,24,17,21,-17,3,1026,38.89,0,0 +2011,12,24,18,53,-16,2,1027,40.68,0,0 +2011,12,24,19,90,-15,-3,1028,1.79,0,0 +2011,12,24,20,102,-15,-5,1028,1.79,0,0 +2011,12,24,21,90,-15,-7,1028,4.92,0,0 +2011,12,24,22,126,-14,-7,1028,1.79,0,0 +2011,12,24,23,131,-14,-3,1029,0.45,0,0 +2011,12,25,0,105,-13,-7,1029,0.9,0,0 +2011,12,25,1,103,-14,-8,1029,0.89,0,0 +2011,12,25,2,99,-14,-8,1030,0.45,0,0 +2011,12,25,3,79,-14,-8,1030,1.79,0,0 +2011,12,25,4,64,-14,-9,1030,3.58,0,0 +2011,12,25,5,37,-15,-8,1030,5.37,0,0 +2011,12,25,6,54,-15,-9,1030,8.5,0,0 +2011,12,25,7,45,-15,-10,1031,12.52,0,0 +2011,12,25,8,38,-12,-7,1032,0.89,0,0 +2011,12,25,9,37,-12,-2,1033,1.79,0,0 +2011,12,25,10,48,-15,0,1033,4.92,0,0 +2011,12,25,11,48,-15,1,1033,1.79,0,0 +2011,12,25,12,66,-15,3,1032,1.79,0,0 +2011,12,25,13,65,-15,4,1031,0.89,0,0 +2011,12,25,14,63,-15,4,1031,1.78,0,0 +2011,12,25,15,56,-16,4,1031,1.79,0,0 +2011,12,25,16,61,-15,2,1032,1.79,0,0 +2011,12,25,17,70,-15,0,1032,1.79,0,0 +2011,12,25,18,90,-13,-3,1034,2.68,0,0 +2011,12,25,19,136,-13,-3,1034,0.89,0,0 +2011,12,25,20,165,-13,-4,1035,1.34,0,0 +2011,12,25,21,193,-13,-5,1035,2.23,0,0 +2011,12,25,22,214,-14,-6,1036,1.79,0,0 +2011,12,25,23,147,-14,-7,1036,3.58,0,0 +2011,12,26,0,77,-13,-8,1037,1.79,0,0 +2011,12,26,1,69,-13,-8,1037,0.89,0,0 +2011,12,26,2,79,-11,-7,1037,0.89,0,0 +2011,12,26,3,146,-12,-7,1037,0.45,0,0 +2011,12,26,4,139,-13,-11,1037,1.79,0,0 +2011,12,26,5,124,-13,-10,1037,2.68,0,0 +2011,12,26,6,134,-13,-10,1037,4.47,0,0 +2011,12,26,7,95,-14,-9,1038,0.89,0,0 +2011,12,26,8,79,-13,-9,1039,3.13,0,0 +2011,12,26,9,58,-12,-5,1039,4.92,0,0 +2011,12,26,10,49,-13,-3,1038,1.79,0,0 +2011,12,26,11,60,-13,-2,1038,3.58,0,0 +2011,12,26,12,56,-14,1,1036,0.89,0,0 +2011,12,26,13,57,-14,1,1035,1.78,0,0 +2011,12,26,14,67,-14,1,1035,1.79,0,0 +2011,12,26,15,92,-14,0,1034,3.58,0,0 +2011,12,26,16,121,-12,-1,1034,4.47,0,0 +2011,12,26,17,151,-12,-3,1034,5.36,0,0 +2011,12,26,18,183,-13,-5,1034,0.89,0,0 +2011,12,26,19,221,-11,-5,1034,2.68,0,0 +2011,12,26,20,268,-9,-3,1034,3.57,0,0 +2011,12,26,21,307,-9,-4,1035,4.46,0,0 +2011,12,26,22,302,-10,-6,1035,5.35,0,0 +2011,12,26,23,316,-8,-5,1035,6.24,0,0 +2011,12,27,0,327,-11,-6,1034,0.89,0,0 +2011,12,27,1,286,-11,-7,1034,1.79,0,0 +2011,12,27,2,249,-12,-7,1034,3.58,0,0 +2011,12,27,3,187,-12,-8,1033,5.37,0,0 +2011,12,27,4,146,-12,-8,1033,7.16,0,0 +2011,12,27,5,150,-12,-8,1032,8.95,0,0 +2011,12,27,6,121,-12,-8,1032,10.74,0,0 +2011,12,27,7,116,-13,-9,1032,12.53,0,0 +2011,12,27,8,122,-11,-7,1032,14.32,0,0 +2011,12,27,9,122,-10,-4,1033,17.45,0,0 +2011,12,27,10,132,-10,-2,1032,20.58,0,0 +2011,12,27,11,142,-10,0,1031,22.37,0,0 +2011,12,27,12,146,-11,1,1030,0.89,0,0 +2011,12,27,13,161,-10,1,1029,1.78,0,0 +2011,12,27,14,157,-10,2,1029,3.57,0,0 +2011,12,27,15,162,-10,2,1028,4.46,0,0 +2011,12,27,16,161,-9,-1,1028,0.89,0,0 +2011,12,27,17,183,-9,-2,1028,0.89,0,0 +2011,12,27,18,204,-8,-3,1029,0.89,0,0 +2011,12,27,19,250,-8,-4,1029,0.89,0,0 +2011,12,27,20,271,-9,-5,1030,0.89,0,0 +2011,12,27,21,292,-9,-5,1030,0.89,0,0 +2011,12,27,22,324,-9,-6,1030,1.79,0,0 +2011,12,27,23,356,-9,-6,1030,0.89,0,0 +2011,12,28,0,333,-10,-7,1030,3.13,0,0 +2011,12,28,1,262,-10,-7,1030,6.26,0,0 +2011,12,28,2,199,-10,-7,1030,9.39,0,0 +2011,12,28,3,169,-11,-8,1030,12.52,0,0 +2011,12,28,4,154,-11,-8,1030,16.54,0,0 +2011,12,28,5,145,-11,-8,1030,20.56,0,0 +2011,12,28,6,163,-12,-8,1030,24.58,0,0 +2011,12,28,7,184,-12,-7,1030,28.6,0,0 +2011,12,28,8,187,-11,-7,1031,31.73,0,0 +2011,12,28,9,132,-10,-3,1032,0.45,0,0 +2011,12,28,10,134,-11,1,1032,3.13,0,0 +2011,12,28,11,108,-12,3,1031,6.26,0,0 +2011,12,28,12,115,-12,5,1030,1.79,0,0 +2011,12,28,13,101,-13,5,1030,1.79,0,0 +2011,12,28,14,63,-11,5,1030,5.81,0,0 +2011,12,28,15,99,-9,4,1030,9.83,0,0 +2011,12,28,16,137,-9,1,1031,11.62,0,0 +2011,12,28,17,200,-8,1,1031,15.64,0,0 +2011,12,28,18,316,-6,-1,1033,17.43,0,0 +2011,12,28,19,437,-6,-2,1033,20.56,0,0 +2011,12,28,20,228,-6,-3,1034,22.35,0,0 +2011,12,28,21,104,-6,-4,1035,23.24,0,0 +2011,12,28,22,87,-7,-4,1036,25.03,0,0 +2011,12,28,23,66,-8,-4,1037,26.82,0,0 +2011,12,29,0,62,-8,-6,1037,0.89,0,0 +2011,12,29,1,61,-9,-7,1037,0.89,0,0 +2011,12,29,2,47,-9,-7,1038,1.78,0,0 +2011,12,29,3,52,-8,-5,1038,2.67,0,0 +2011,12,29,4,50,-10,-4,1038,1.79,0,0 +2011,12,29,5,42,-11,-5,1038,4.92,0,0 +2011,12,29,6,33,-11,-5,1038,8.05,0,0 +2011,12,29,7,41,-12,-6,1039,9.84,0,0 +2011,12,29,8,36,-12,-6,1039,11.63,1,0 +2011,12,29,9,40,-11,-6,1040,14.76,2,0 +2011,12,29,10,46,-12,-5,1040,18.78,3,0 +2011,12,29,11,57,-11,-4,1039,21.91,0,0 +2011,12,29,12,64,-12,-4,1038,25.93,0,0 +2011,12,29,13,68,-12,-3,1037,29.06,0,0 +2011,12,29,14,72,-11,-4,1037,30.85,0,0 +2011,12,29,15,73,-11,-4,1037,32.64,0,0 +2011,12,29,16,78,-11,-4,1037,34.43,0,0 +2011,12,29,17,79,-11,-4,1037,36.22,0,0 +2011,12,29,18,82,-11,-4,1037,38.01,0,0 +2011,12,29,19,87,-11,-4,1038,39.8,0,0 +2011,12,29,20,91,-10,-4,1038,41.59,0,0 +2011,12,29,21,79,-10,-4,1038,43.38,0,0 +2011,12,29,22,64,-10,-4,1038,45.17,0,0 +2011,12,29,23,69,-10,-4,1038,46.96,0,0 +2011,12,30,0,71,-10,-4,1038,48.75,0,0 +2011,12,30,1,67,-9,-5,1037,49.64,0,0 +2011,12,30,2,64,-9,-4,1037,0.89,0,0 +2011,12,30,3,64,-8,-5,1037,0.89,0,0 +2011,12,30,4,70,-8,-5,1036,1.78,0,0 +2011,12,30,5,71,-9,-5,1035,0.89,0,0 +2011,12,30,6,79,-10,-5,1035,1.78,0,0 +2011,12,30,7,80,-9,-5,1035,2.23,0,0 +2011,12,30,8,81,-9,-4,1035,0.89,0,0 +2011,12,30,9,91,-9,-4,1036,0.89,0,0 +2011,12,30,10,101,-9,-3,1035,1.79,0,0 +2011,12,30,11,112,-9,-3,1035,0.89,0,0 +2011,12,30,12,123,-9,-3,1033,1.78,0,0 +2011,12,30,13,141,-8,-3,1032,2.67,0,0 +2011,12,30,14,150,-8,-2,1031,1.79,0,0 +2011,12,30,15,170,-8,-2,1031,0.89,0,0 +2011,12,30,16,190,-8,-2,1031,1.78,0,0 +2011,12,30,17,201,-9,-4,1031,0.89,0,0 +2011,12,30,18,211,-8,-4,1031,0.89,0,0 +2011,12,30,19,218,-9,-4,1031,0.89,0,0 +2011,12,30,20,222,-8,-4,1031,0.89,0,0 +2011,12,30,21,207,-8,-4,1031,0.89,0,0 +2011,12,30,22,208,-9,-6,1031,3.13,0,0 +2011,12,30,23,195,-10,-8,1030,6.26,0,0 +2011,12,31,0,163,-10,-7,1030,7.15,0,0 +2011,12,31,1,196,-9,-7,1030,10.28,0,0 +2011,12,31,2,175,-10,-7,1030,13.41,0,0 +2011,12,31,3,144,-11,-8,1030,15.2,0,0 +2011,12,31,4,137,-10,-9,1029,18.33,0,0 +2011,12,31,5,128,-11,-9,1029,21.46,0,0 +2011,12,31,6,135,-11,-9,1029,23.25,0,0 +2011,12,31,7,157,-10,-8,1029,26.38,0,0 +2011,12,31,8,164,-11,-9,1030,29.51,0,0 +2011,12,31,9,155,-9,-6,1030,32.64,0,0 +2011,12,31,10,159,-11,1,1030,35.77,0,0 +2011,12,31,11,130,-13,2,1029,4.02,0,0 +2011,12,31,12,98,-13,3,1028,8.94,0,0 +2011,12,31,13,82,-15,4,1027,3.13,0,0 +2011,12,31,14,71,-14,5,1027,1.79,0,0 +2011,12,31,15,66,-14,4,1027,3.58,0,0 +2011,12,31,16,72,-15,2,1027,0.89,0,0 +2011,12,31,17,192,-14,-1,1028,1.34,0,0 +2011,12,31,18,209,-12,-2,1029,1.79,0,0 +2011,12,31,19,209,-12,-3,1029,2.68,0,0 +2011,12,31,20,217,-12,-6,1029,0.89,0,0 +2011,12,31,21,223,-12,-6,1030,0.89,0,0 +2011,12,31,22,251,-12,-6,1030,1.79,0,0 +2011,12,31,23,283,-12,-6,1030,0.89,0,0 +2012,1,1,0,275,-12,-8,1030,0.89,0,0 +2012,1,1,1,303,-13,-10,1031,1.79,0,0 +2012,1,1,2,215,-13,-9,1032,3.58,0,0 +2012,1,1,3,222,-13,-9,1033,6.71,0,0 +2012,1,1,4,85,-13,-4,1033,4.92,0,0 +2012,1,1,5,38,-14,-4,1034,10.73,0,0 +2012,1,1,6,23,-14,-4,1034,14.75,0,0 +2012,1,1,7,19,-15,-5,1035,17.88,0,0 +2012,1,1,8,14,-15,-4,1035,21.9,0,0 +2012,1,1,9,16,-16,-3,1036,27.71,0,0 +2012,1,1,10,21,-16,-2,1036,33.52,0,0 +2012,1,1,11,22,-16,0,1035,38.44,0,0 +2012,1,1,12,22,-18,0,1034,42.46,0,0 +2012,1,1,13,17,-18,1,1033,45.59,0,0 +2012,1,1,14,22,-18,2,1033,47.38,0,0 +2012,1,1,15,29,-18,2,1033,1.79,0,0 +2012,1,1,16,39,-16,0,1032,0.89,0,0 +2012,1,1,17,42,-18,0,1032,1.78,0,0 +2012,1,1,18,55,-15,-2,1032,2.23,0,0 +2012,1,1,19,55,-14,-4,1032,2.68,0,0 +2012,1,1,20,72,-15,-5,1032,0.89,0,0 +2012,1,1,21,81,-15,-6,1032,0.89,0,0 +2012,1,1,22,80,-13,-4,1032,3.13,0,0 +2012,1,1,23,128,-11,-3,1031,7.15,0,0 +2012,1,2,0,114,-10,-3,1030,10.28,0,0 +2012,1,2,1,97,-10,-4,1030,12.07,0,0 +2012,1,2,2,79,-9,-4,1030,13.86,0,0 +2012,1,2,3,88,-10,-4,1029,15.65,0,0 +2012,1,2,4,87,-9,-4,1029,17.44,0,0 +2012,1,2,5,98,-9,-4,1028,20.57,0,0 +2012,1,2,6,99,-9,-5,1028,22.36,0,0 +2012,1,2,7,100,-10,-8,1029,0.45,0,0 +2012,1,2,8,104,-11,-8,1029,1.34,0,0 +2012,1,2,9,131,-9,-7,1029,0.89,0,0 +2012,1,2,10,134,-8,-3,1029,0.89,0,0 +2012,1,2,11,169,-8,-1,1028,1.78,0,0 +2012,1,2,12,133,-8,0,1027,1.79,0,0 +2012,1,2,13,39,-17,1,1026,8.94,0,0 +2012,1,2,14,12,-17,2,1026,17.88,0,0 +2012,1,2,15,19,-19,2,1026,29.06,0,0 +2012,1,2,16,14,-19,1,1027,40.24,0,0 +2012,1,2,17,13,-17,0,1028,52.31,0,0 +2012,1,2,18,13,-17,-1,1029,62.14,0,0 +2012,1,2,19,16,-18,-2,1030,74.21,0,0 +2012,1,2,20,8,-18,-3,1031,85.39,0,0 +2012,1,2,21,10,-18,-3,1031,95.22,0,0 +2012,1,2,22,27,-19,-3,1032,108.18,0,0 +2012,1,2,23,18,-19,-4,1032,119.36,0,0 +2012,1,3,0,14,-19,-4,1032,127.41,0,0 +2012,1,3,1,16,-19,-5,1032,134.56,0,0 +2012,1,3,2,17,-19,-5,1032,138.58,0,0 +2012,1,3,3,11,-19,-6,1032,144.39,0,0 +2012,1,3,4,11,-19,-5,1032,151.54,0,0 +2012,1,3,5,12,-19,-6,1032,157.35,0,0 +2012,1,3,6,15,-19,-6,1032,165.4,0,0 +2012,1,3,7,17,-19,-6,1032,172.55,0,0 +2012,1,3,8,14,-19,-5,1033,177.47,0,0 +2012,1,3,9,16,-19,-4,1033,183.28,0,0 +2012,1,3,10,18,-19,-4,1033,191.33,0,0 +2012,1,3,11,24,-19,-1,1031,202.51,0,0 +2012,1,3,12,16,-19,-1,1031,211.45,0,0 +2012,1,3,13,18,-19,-1,1030,223.52,0,0 +2012,1,3,14,13,-21,-1,1030,236.48,0,0 +2012,1,3,15,23,-20,-1,1030,250.34,0,0 +2012,1,3,16,13,-20,-2,1032,262.41,0,0 +2012,1,3,17,11,-20,-3,1032,273.59,0,0 +2012,1,3,18,8,-20,-3,1033,285.66,0,0 +2012,1,3,19,19,-20,-4,1034,297.73,0,0 +2012,1,3,20,11,-20,-4,1034,307.56,0,0 +2012,1,3,21,12,-20,-5,1035,318.74,0,0 +2012,1,3,22,10,-20,-6,1035,329.92,0,0 +2012,1,3,23,16,-20,-6,1035,339.75,0,0 +2012,1,4,0,14,-20,-6,1035,348.69,0,0 +2012,1,4,1,13,-20,-6,1036,355.84,0,0 +2012,1,4,2,10,-21,-10,1036,358.97,0,0 +2012,1,4,3,11,-20,-9,1036,360.76,0,0 +2012,1,4,4,18,-20,-10,1036,363.89,0,0 +2012,1,4,5,13,-20,-9,1036,0.89,0,0 +2012,1,4,6,13,-19,-10,1036,3.13,0,0 +2012,1,4,7,15,-19,-9,1036,1.79,0,0 +2012,1,4,8,20,-18,-6,1037,4.92,0,0 +2012,1,4,9,19,-19,-4,1037,9.84,0,0 +2012,1,4,10,16,-19,-2,1037,16.99,0,0 +2012,1,4,11,17,-19,-2,1036,22.8,0,0 +2012,1,4,12,12,-21,0,1035,28.61,0,0 +2012,1,4,13,25,-22,1,1033,34.42,0,0 +2012,1,4,14,16,-22,1,1032,40.23,0,0 +2012,1,4,15,15,-22,1,1032,46.04,0,0 +2012,1,4,16,21,-22,1,1032,50.96,0,0 +2012,1,4,17,37,-20,0,1032,55.88,0,0 +2012,1,4,18,42,-18,-1,1032,4.02,0,0 +2012,1,4,19,55,-18,-1,1032,4.02,0,0 +2012,1,4,20,74,-19,-6,1032,0.89,0,0 +2012,1,4,21,87,-17,-3,1032,1.79,0,0 +2012,1,4,22,86,-17,-8,1032,0.89,0,0 +2012,1,4,23,87,-17,-9,1031,1.78,0,0 +2012,1,5,0,90,-17,-10,1031,0.89,0,0 +2012,1,5,1,89,-17,-11,1031,1.78,0,0 +2012,1,5,2,91,-17,-11,1031,2.67,0,0 +2012,1,5,3,99,-18,-12,1031,3.12,0,0 +2012,1,5,4,150,-19,-14,1030,3.13,0,0 +2012,1,5,5,143,-18,-14,1030,4.92,0,0 +2012,1,5,6,130,-17,-12,1030,6.71,0,0 +2012,1,5,7,102,-17,-13,1030,8.5,0,0 +2012,1,5,8,96,-17,-12,1030,10.29,0,0 +2012,1,5,9,72,-17,-6,1031,13.42,0,0 +2012,1,5,10,60,-16,-4,1030,16.55,0,0 +2012,1,5,11,70,-16,-1,1029,18.34,0,0 +2012,1,5,12,76,-19,0,1028,20.13,0,0 +2012,1,5,13,69,-19,2,1027,0.89,0,0 +2012,1,5,14,53,-19,3,1026,1.78,0,0 +2012,1,5,15,53,-20,3,1026,2.67,0,0 +2012,1,5,16,65,-19,1,1027,0.89,0,0 +2012,1,5,17,86,-19,0,1027,1.78,0,0 +2012,1,5,18,115,-17,-3,1028,0.45,0,0 +2012,1,5,19,179,-16,-4,1028,0.9,0,0 +2012,1,5,20,235,-16,-5,1029,1.79,0,0 +2012,1,5,21,241,-16,-7,1030,0.89,0,0 +2012,1,5,22,189,-16,-7,1030,0.89,0,0 +2012,1,5,23,161,-17,-10,1031,1.79,0,0 +2012,1,6,0,189,-17,-9,1031,4.92,0,0 +2012,1,6,1,161,-16,-9,1032,0.89,0,0 +2012,1,6,2,161,-16,-8,1033,1.78,0,0 +2012,1,6,3,159,-15,-7,1034,0.89,0,0 +2012,1,6,4,197,-16,-9,1035,0.89,0,0 +2012,1,6,5,183,-16,-10,1035,1.79,0,0 +2012,1,6,6,172,-15,-9,1036,3.58,0,0 +2012,1,6,7,170,-15,-9,1036,5.37,0,0 +2012,1,6,8,179,-14,-10,1037,0.89,0,0 +2012,1,6,9,194,-11,-7,1039,1.79,0,0 +2012,1,6,10,183,-13,-3,1039,4.92,0,0 +2012,1,6,11,150,-14,-3,1039,8.05,0,0 +2012,1,6,12,87,-14,-3,1038,11.18,0,0 +2012,1,6,13,93,-14,-2,1038,12.97,0,0 +2012,1,6,14,91,-14,-2,1037,14.76,0,0 +2012,1,6,15,67,-13,-2,1038,16.55,0,0 +2012,1,6,16,54,-11,-3,1038,19.68,0,0 +2012,1,6,17,62,-12,-3,1038,21.47,0,0 +2012,1,6,18,56,-11,-3,1039,25.49,0,0 +2012,1,6,19,58,-11,-3,1039,27.28,0,0 +2012,1,6,20,54,-12,-3,1039,29.07,0,0 +2012,1,6,21,51,-12,-4,1040,30.86,0,0 +2012,1,6,22,52,-12,-4,1040,32.65,0,0 +2012,1,6,23,45,-11,-4,1040,34.44,0,0 +2012,1,7,0,45,-11,-4,1040,36.23,1,0 +2012,1,7,1,49,-11,-4,1040,38.02,2,0 +2012,1,7,2,47,-10,-4,1040,39.81,3,0 +2012,1,7,3,40,-10,-4,1040,42.94,4,0 +2012,1,7,4,46,-10,-4,1039,44.73,5,0 +2012,1,7,5,51,-10,-5,1039,46.52,6,0 +2012,1,7,6,49,-9,-5,1039,48.31,7,0 +2012,1,7,7,60,-9,-5,1039,51.44,8,0 +2012,1,7,8,69,-8,-5,1039,53.23,9,0 +2012,1,7,9,66,-7,-6,1040,55.02,10,0 +2012,1,7,10,68,-6,-5,1040,56.81,11,0 +2012,1,7,11,83,-6,-5,1039,1.79,12,0 +2012,1,7,12,132,-6,-5,1038,3.58,13,0 +2012,1,7,13,175,-5,-5,1037,0.89,14,0 +2012,1,7,14,170,-5,-4,1036,1.79,15,0 +2012,1,7,15,172,-5,-4,1036,3.58,16,0 +2012,1,7,16,169,-5,-4,1036,5.37,17,0 +2012,1,7,17,176,-5,-4,1036,6.26,18,0 +2012,1,7,18,175,-5,-4,1036,7.15,0,0 +2012,1,7,19,172,-5,-4,1036,1.79,0,0 +2012,1,7,20,166,-6,-4,1036,0.89,0,0 +2012,1,7,21,171,-6,-5,1036,0.89,0,0 +2012,1,7,22,164,-6,-4,1036,0.89,0,0 +2012,1,7,23,191,-6,-5,1036,1.78,0,0 +2012,1,8,0,171,-6,-4,1035,2.23,0,0 +2012,1,8,1,184,-6,-5,1035,0.89,0,0 +2012,1,8,2,191,-6,-5,1035,0.89,0,0 +2012,1,8,3,169,-6,-5,1034,3.13,0,0 +2012,1,8,4,156,-6,-5,1033,4.92,0,0 +2012,1,8,5,151,-6,-5,1033,6.71,0,0 +2012,1,8,6,120,-7,-5,1033,9.84,0,0 +2012,1,8,7,116,-10,-8,1033,11.63,0,0 +2012,1,8,8,109,-9,-7,1034,15.65,0,0 +2012,1,8,9,111,-9,-7,1034,1.79,0,0 +2012,1,8,10,124,-7,-3,1035,1.79,0,0 +2012,1,8,11,125,-7,-1,1034,3.58,0,0 +2012,1,8,12,151,-6,-1,1032,0.89,0,0 +2012,1,8,13,149,-8,0,1031,1.78,0,0 +2012,1,8,14,153,-8,1,1031,2.67,0,0 +2012,1,8,15,152,-9,1,1031,4.46,0,0 +2012,1,8,16,144,-9,0,1031,5.35,0,0 +2012,1,8,17,197,-8,-2,1031,6.24,0,0 +2012,1,8,18,181,-9,-4,1031,0.89,0,0 +2012,1,8,19,197,-8,-5,1031,0.45,0,0 +2012,1,8,20,201,-10,-7,1032,0.9,0,0 +2012,1,8,21,211,-8,-6,1032,1.35,0,0 +2012,1,8,22,235,-10,-8,1032,2.24,0,0 +2012,1,8,23,275,-10,-8,1032,0.89,0,0 +2012,1,9,0,289,-10,-8,1033,0.45,0,0 +2012,1,9,1,284,-10,-9,1033,0.89,0,0 +2012,1,9,2,205,-12,-11,1033,1.78,0,0 +2012,1,9,3,146,-11,-10,1033,2.67,0,0 +2012,1,9,4,127,-12,-10,1033,4.46,0,0 +2012,1,9,5,79,-12,-9,1032,6.25,0,0 +2012,1,9,6,63,-14,-12,1032,8.04,0,0 +2012,1,9,7,79,-13,-11,1032,11.17,0,0 +2012,1,9,8,126,-14,-10,1032,12.96,0,0 +2012,1,9,9,100,-10,-6,1033,14.75,0,0 +2012,1,9,10,96,-11,-2,1033,1.79,0,0 +2012,1,9,11,74,-11,-1,1032,0.89,0,0 +2012,1,9,12,77,-13,1,1031,1.78,0,0 +2012,1,9,13,85,-14,2,1030,2.67,0,0 +2012,1,9,14,204,-13,2,1030,1.79,0,0 +2012,1,9,15,184,-11,2,1030,4.92,0,0 +2012,1,9,16,177,-11,1,1030,8.94,0,0 +2012,1,9,17,175,-11,0,1030,12.96,0,0 +2012,1,9,18,149,-10,-3,1030,0.89,0,0 +2012,1,9,19,180,-11,-6,1030,1.79,0,0 +2012,1,9,20,267,-11,-6,1031,3.58,0,0 +2012,1,9,21,314,-10,-5,1031,4.47,0,0 +2012,1,9,22,350,-13,-9,1031,1.79,0,0 +2012,1,9,23,335,-11,-7,1032,2.68,0,0 +2012,1,10,0,366,-11,-7,1032,3.57,0,0 +2012,1,10,1,358,-11,-8,1032,1.79,0,0 +2012,1,10,2,388,-8,-6,1032,3.58,0,0 +2012,1,10,3,433,-9,-8,1032,1.79,0,0 +2012,1,10,4,524,-10,-9,1031,3.58,0,0 +2012,1,10,5,534,-8,-8,1031,4.47,0,0 +2012,1,10,6,501,-8,-8,1031,0.89,0,0 +2012,1,10,7,513,-9,-9,1031,1.78,0,0 +2012,1,10,8,491,-10,-9,1032,2.67,0,0 +2012,1,10,9,502,-9,-8,1033,3.56,0,0 +2012,1,10,10,484,-5,-5,1033,1.79,0,0 +2012,1,10,11,494,-12,1,1032,6.71,0,0 +2012,1,10,12,155,-15,3,1031,12.52,0,0 +2012,1,10,13,21,-16,3,1031,20.57,0,0 +2012,1,10,14,21,-16,3,1031,29.51,0,0 +2012,1,10,15,19,-16,3,1031,37.56,0,0 +2012,1,10,16,17,-17,3,1032,46.5,0,0 +2012,1,10,17,13,-20,0,1034,54.55,0,0 +2012,1,10,18,15,-17,0,1035,61.7,0,0 +2012,1,10,19,11,-18,-1,1036,70.64,0,0 +2012,1,10,20,12,-18,-2,1038,79.58,0,0 +2012,1,10,21,12,-18,-4,1039,85.39,0,0 +2012,1,10,22,10,-18,-5,1039,89.41,0,0 +2012,1,10,23,18,-19,-6,1040,3.13,0,0 +2012,1,11,0,17,-18,-5,1040,7.15,0,0 +2012,1,11,1,14,-18,-7,1041,10.28,0,0 +2012,1,11,2,17,-18,-8,1041,0.89,0,0 +2012,1,11,3,13,-19,-10,1041,3.13,0,0 +2012,1,11,4,14,-19,-11,1041,3.13,0,0 +2012,1,11,5,24,-18,-10,1040,0.89,0,0 +2012,1,11,6,25,-19,-10,1040,1.34,0,0 +2012,1,11,7,32,-18,-12,1040,1.79,0,0 +2012,1,11,8,29,-17,-7,1041,3.58,0,0 +2012,1,11,9,53,-17,-6,1041,6.71,0,0 +2012,1,11,10,31,-17,-4,1041,8.5,0,0 +2012,1,11,11,33,-17,-3,1039,1.79,0,0 +2012,1,11,12,47,-16,-2,1038,2.68,0,0 +2012,1,11,13,57,-16,-1,1035,4.02,0,0 +2012,1,11,14,47,-16,-1,1035,8.04,0,0 +2012,1,11,15,46,-16,-1,1033,12.96,0,0 +2012,1,11,16,65,-16,-2,1033,17.88,0,0 +2012,1,11,17,71,-16,-2,1033,21.9,0,0 +2012,1,11,18,68,-16,-3,1033,25.03,0,0 +2012,1,11,19,71,-16,-4,1032,28.16,0,0 +2012,1,11,20,93,-15,-4,1032,31.29,0,0 +2012,1,11,21,120,-15,-4,1032,34.42,0,0 +2012,1,11,22,112,-15,-6,1032,36.21,0,0 +2012,1,11,23,123,-15,-8,1031,0.89,0,0 +2012,1,12,0,138,-15,-9,1030,1.79,0,0 +2012,1,12,1,147,-17,-12,1030,1.79,0,0 +2012,1,12,2,156,-16,-12,1030,2.68,0,0 +2012,1,12,3,169,-16,-12,1029,3.57,0,0 +2012,1,12,4,156,-17,-13,1029,0.89,0,0 +2012,1,12,5,148,-16,-12,1028,1.78,0,0 +2012,1,12,6,137,-17,-13,1027,3.57,0,0 +2012,1,12,7,146,-16,-11,1027,4.46,0,0 +2012,1,12,8,130,-14,-9,1027,6.25,0,0 +2012,1,12,9,123,-14,-7,1027,9.38,0,0 +2012,1,12,10,126,-14,-4,1027,13.4,0,0 +2012,1,12,11,130,-14,-2,1026,15.19,0,0 +2012,1,12,12,136,-14,0,1025,16.98,0,0 +2012,1,12,13,149,-15,3,1023,1.79,0,0 +2012,1,12,14,196,-15,4,1023,3.13,0,0 +2012,1,12,15,162,-15,5,1022,6.26,0,0 +2012,1,12,16,160,-15,4,1022,8.05,0,0 +2012,1,12,17,160,-14,0,1023,0.45,0,0 +2012,1,12,18,174,-13,-3,1024,0.9,0,0 +2012,1,12,19,144,-13,-3,1025,1.35,0,0 +2012,1,12,20,171,-13,-4,1025,2.24,0,0 +2012,1,12,21,156,-14,-5,1026,1.79,0,0 +2012,1,12,22,230,-14,-5,1027,3.58,0,0 +2012,1,12,23,182,-14,-4,1028,8.5,0,0 +2012,1,13,0,100,-14,-7,1028,11.63,0,0 +2012,1,13,1,60,-14,-7,1028,15.65,0,0 +2012,1,13,2,50,-14,-7,1028,18.78,0,0 +2012,1,13,3,52,-15,-9,1028,22.8,0,0 +2012,1,13,4,57,-14,-8,1028,25.93,0,0 +2012,1,13,5,41,-14,-7,1028,29.95,0,0 +2012,1,13,6,47,-15,-9,1028,31.74,0,0 +2012,1,13,7,44,-15,-7,1028,33.53,0,0 +2012,1,13,8,37,-13,-6,1029,36.66,0,0 +2012,1,13,9,52,-13,-2,1030,39.79,0,0 +2012,1,13,10,46,-14,0,1030,43.81,0,0 +2012,1,13,11,43,-15,3,1029,45.6,0,0 +2012,1,13,12,45,-14,4,1028,47.39,0,0 +2012,1,13,13,45,-15,4,1027,1.79,0,0 +2012,1,13,14,61,-14,5,1026,2.68,0,0 +2012,1,13,15,69,-14,4,1026,4.02,0,0 +2012,1,13,16,66,-13,4,1026,7.15,0,0 +2012,1,13,17,39,-14,3,1026,10.28,0,0 +2012,1,13,18,57,-14,3,1026,13.41,0,0 +2012,1,13,19,72,-14,0,1026,15.2,0,0 +2012,1,13,20,73,-13,-1,1026,16.99,0,0 +2012,1,13,21,73,-13,-5,1026,0.89,0,0 +2012,1,13,22,91,-12,-5,1026,1.78,0,0 +2012,1,13,23,101,-13,-7,1026,1.79,0,0 +2012,1,14,0,137,-14,-8,1026,3.58,0,0 +2012,1,14,1,133,-14,-8,1026,6.71,0,0 +2012,1,14,2,130,-14,-9,1026,8.5,0,0 +2012,1,14,3,125,-13,-6,1026,0.89,0,0 +2012,1,14,4,125,-13,-6,1026,4.92,0,0 +2012,1,14,5,98,-14,-7,1026,0.45,0,0 +2012,1,14,6,58,-15,-9,1026,1.79,0,0 +2012,1,14,7,42,-15,-9,1027,3.58,0,0 +2012,1,14,8,39,-15,-8,1027,6.71,0,0 +2012,1,14,9,33,-15,0,1028,4.02,0,0 +2012,1,14,10,NA,-16,1,1028,9.83,0,0 +2012,1,14,11,NA,-16,2,1028,14.75,0,0 +2012,1,14,12,NA,-16,4,1026,17.88,0,0 +2012,1,14,13,NA,-16,5,1025,3.13,0,0 +2012,1,14,14,NA,-16,5,1024,3.13,0,0 +2012,1,14,15,NA,-17,5,1024,4.92,0,0 +2012,1,14,16,NA,-17,4,1024,1.79,0,0 +2012,1,14,17,NA,-17,2,1024,2.68,0,0 +2012,1,14,18,NA,-15,-2,1025,3.57,0,0 +2012,1,14,19,NA,-15,-2,1025,0.89,0,0 +2012,1,14,20,NA,-15,-4,1025,1.78,0,0 +2012,1,14,21,NA,-14,-4,1026,2.67,0,0 +2012,1,14,22,NA,-13,-5,1026,3.12,0,0 +2012,1,14,23,NA,-14,-7,1026,0.89,0,0 +2012,1,15,0,NA,-15,-6,1026,0.89,0,0 +2012,1,15,1,NA,-14,-7,1025,1.79,0,0 +2012,1,15,2,NA,-14,-7,1025,4.92,0,0 +2012,1,15,3,NA,-14,-7,1025,5.81,0,0 +2012,1,15,4,NA,-14,-8,1025,7.6,0,0 +2012,1,15,5,NA,-15,-7,1024,0.45,0,0 +2012,1,15,6,NA,-15,-9,1025,1.34,0,0 +2012,1,15,7,NA,-15,-9,1025,1.79,0,0 +2012,1,15,8,NA,-14,-7,1025,0.89,0,0 +2012,1,15,9,NA,-10,-5,1025,0.89,0,0 +2012,1,15,10,NA,-10,-2,1025,2.68,0,0 +2012,1,15,11,NA,-9,0,1024,0.89,0,0 +2012,1,15,12,NA,-10,1,1023,1.79,0,0 +2012,1,15,13,NA,-11,1,1022,0.89,0,0 +2012,1,15,14,NA,-11,2,1022,1.78,0,0 +2012,1,15,15,NA,-11,2,1021,2.67,0,0 +2012,1,15,16,NA,-11,2,1021,3.56,0,0 +2012,1,15,17,NA,-11,1,1021,0.89,0,0 +2012,1,15,18,NA,-12,-3,1021,3.13,0,0 +2012,1,15,19,NA,-13,-4,1022,4.92,0,0 +2012,1,15,20,NA,-12,-4,1022,0.89,0,0 +2012,1,15,21,NA,-11,-5,1022,0.45,0,0 +2012,1,15,22,NA,-12,-6,1022,0.89,0,0 +2012,1,15,23,NA,-12,-6,1022,0.89,0,0 +2012,1,16,0,NA,-12,-7,1022,0.89,0,0 +2012,1,16,1,NA,-12,-8,1021,0.89,0,0 +2012,1,16,2,NA,-13,-9,1021,0.45,0,0 +2012,1,16,3,NA,-12,-9,1021,0.89,0,0 +2012,1,16,4,NA,-12,-9,1020,1.79,0,0 +2012,1,16,5,NA,-12,-8,1020,3.58,0,0 +2012,1,16,6,NA,-12,-7,1020,5.37,0,0 +2012,1,16,7,NA,-12,-8,1021,8.5,0,0 +2012,1,16,8,NA,-10,-6,1021,11.63,0,0 +2012,1,16,9,NA,-11,-5,1022,13.42,0,0 +2012,1,16,10,NA,-10,-4,1021,15.21,0,0 +2012,1,16,11,NA,-10,-3,1021,0.89,0,0 +2012,1,16,12,NA,-10,-2,1020,1.79,0,0 +2012,1,16,13,NA,-10,-2,1020,0.89,0,0 +2012,1,16,14,NA,-9,-1,1019,1.78,0,0 +2012,1,16,15,NA,-8,-1,1020,2.23,0,0 +2012,1,16,16,NA,-7,-2,1020,3.12,0,0 +2012,1,16,17,NA,-7,-2,1020,0.89,0,0 +2012,1,16,18,NA,-8,-4,1020,0.89,0,0 +2012,1,16,19,NA,-8,-4,1021,2.68,0,0 +2012,1,16,20,NA,-7,-3,1021,0.89,0,0 +2012,1,16,21,NA,-7,-2,1022,1.34,0,0 +2012,1,16,22,NA,-7,-3,1022,1.79,0,0 +2012,1,16,23,NA,-8,-3,1022,1.79,0,0 +2012,1,17,0,NA,-8,-3,1022,3.58,0,0 +2012,1,17,1,NA,-8,-3,1022,5.37,0,0 +2012,1,17,2,NA,-8,-3,1022,7.16,0,0 +2012,1,17,3,NA,-8,-3,1022,0.89,0,0 +2012,1,17,4,NA,-8,-3,1021,1.78,0,0 +2012,1,17,5,NA,-8,-3,1022,0.89,0,0 +2012,1,17,6,NA,-7,-3,1021,2.68,0,0 +2012,1,17,7,NA,-7,-2,1022,0.89,0,0 +2012,1,17,8,NA,-7,-2,1023,4.02,0,0 +2012,1,17,9,NA,-7,-1,1024,1.79,0,0 +2012,1,17,10,NA,-7,-1,1024,1.79,0,0 +2012,1,17,11,NA,-8,0,1023,3.58,0,0 +2012,1,17,12,254,-8,0,1022,5.37,0,0 +2012,1,17,13,281,-7,1,1022,0.89,0,0 +2012,1,17,14,301,-7,1,1021,1.78,0,0 +2012,1,17,15,285,-7,1,1021,2.67,0,0 +2012,1,17,16,304,-7,0,1021,0.89,0,0 +2012,1,17,17,309,-8,-4,1022,1.78,0,0 +2012,1,17,18,308,-6,-3,1023,0.89,0,0 +2012,1,17,19,338,-7,-4,1023,0.89,0,0 +2012,1,17,20,416,-7,-5,1024,0.89,0,0 +2012,1,17,21,398,-5,-4,1024,0.89,0,0 +2012,1,17,22,385,-4,-3,1025,0.89,0,0 +2012,1,17,23,348,-4,-3,1025,1.78,0,0 +2012,1,18,0,332,-3,-2,1025,1.79,0,0 +2012,1,18,1,339,-3,-2,1025,0.89,0,0 +2012,1,18,2,344,-3,-2,1025,1.78,0,0 +2012,1,18,3,353,-4,-2,1026,1.79,0,0 +2012,1,18,4,385,-4,-2,1025,3.58,0,0 +2012,1,18,5,394,-4,-3,1025,5.37,0,0 +2012,1,18,6,401,-7,-5,1025,8.5,0,0 +2012,1,18,7,393,-7,-6,1026,12.52,0,0 +2012,1,18,8,381,-7,-6,1026,15.65,0,0 +2012,1,18,9,384,-7,-5,1027,19.67,0,0 +2012,1,18,10,372,-8,-4,1027,22.8,0,0 +2012,1,18,11,370,-8,-2,1026,24.59,0,0 +2012,1,18,12,345,-8,-2,1025,0.89,0,0 +2012,1,18,13,359,-8,-1,1024,1.78,0,0 +2012,1,18,14,382,-9,0,1024,2.67,0,0 +2012,1,18,15,412,-7,0,1024,3.56,0,0 +2012,1,18,16,440,-7,-1,1025,4.45,0,0 +2012,1,18,17,436,-7,-2,1025,0.89,0,0 +2012,1,18,18,450,-7,-3,1026,1.78,0,0 +2012,1,18,19,456,-8,-4,1026,2.67,0,0 +2012,1,18,20,469,-7,-4,1026,3.56,0,0 +2012,1,18,21,450,-8,-4,1027,4.45,0,0 +2012,1,18,22,458,-7,-3,1027,0.89,0,0 +2012,1,18,23,446,-6,-3,1027,1.78,0,0 +2012,1,19,0,437,-6,-3,1028,2.67,0,0 +2012,1,19,1,460,-6,-3,1028,3.56,0,0 +2012,1,19,2,503,-6,-3,1028,4.45,0,0 +2012,1,19,3,558,-6,-3,1028,0.89,0,0 +2012,1,19,4,486,-7,-3,1028,1.78,0,0 +2012,1,19,5,441,-7,-3,1028,3.57,0,0 +2012,1,19,6,388,-7,-3,1028,5.36,0,0 +2012,1,19,7,351,-7,-3,1028,8.49,0,0 +2012,1,19,8,348,-7,-3,1029,11.62,0,0 +2012,1,19,9,390,-7,-2,1030,0.89,0,0 +2012,1,19,10,405,-6,-1,1030,1.79,0,0 +2012,1,19,11,380,-7,-1,1029,4.92,0,0 +2012,1,19,12,373,-7,0,1028,0.89,0,0 +2012,1,19,13,379,-7,1,1028,1.78,0,0 +2012,1,19,14,387,-6,1,1027,2.67,0,0 +2012,1,19,15,378,-6,1,1027,0.89,0,0 +2012,1,19,16,399,-6,0,1028,1.78,0,0 +2012,1,19,17,414,-6,-1,1029,2.67,0,0 +2012,1,19,18,419,-7,-4,1029,3.56,0,0 +2012,1,19,19,436,-7,-4,1029,4.45,0,0 +2012,1,19,20,436,-6,-4,1030,5.34,0,0 +2012,1,19,21,459,-8,-6,1030,6.23,0,0 +2012,1,19,22,494,-8,-6,1031,1.79,0,0 +2012,1,19,23,522,-7,-5,1032,4.92,0,0 +2012,1,20,0,479,-7,-6,1032,6.71,0,0 +2012,1,20,1,315,-7,-4,1032,9.84,0,0 +2012,1,20,2,121,-14,-1,1032,7.15,0,0 +2012,1,20,3,49,-15,-1,1032,12.96,0,0 +2012,1,20,4,52,-16,-2,1033,17.88,0,0 +2012,1,20,5,47,-16,-2,1033,22.8,0,0 +2012,1,20,6,29,-17,-3,1034,27.72,0,0 +2012,1,20,7,17,-18,-3,1035,32.64,0,0 +2012,1,20,8,15,-19,-3,1036,38.45,0,0 +2012,1,20,9,24,-18,-4,1037,42.47,0,0 +2012,1,20,10,22,-18,-3,1037,47.39,0,0 +2012,1,20,11,21,-19,-2,1037,53.2,0,0 +2012,1,20,12,20,-20,-2,1036,60.35,0,0 +2012,1,20,13,22,-21,-2,1035,66.16,0,0 +2012,1,20,14,21,-21,-3,1034,71.08,0,0 +2012,1,20,15,12,-23,-3,1035,76.89,0,0 +2012,1,20,16,13,-23,-3,1035,84.04,0,0 +2012,1,20,17,13,-23,-4,1035,89.85,0,0 +2012,1,20,18,22,-23,-4,1036,94.77,0,0 +2012,1,20,19,16,-23,-5,1036,99.69,0,0 +2012,1,20,20,22,-22,-5,1037,105.5,0,0 +2012,1,20,21,17,-23,-5,1037,111.31,0,0 +2012,1,20,22,21,-23,-6,1037,118.46,0,0 +2012,1,20,23,18,-24,-7,1038,125.61,0,0 +2012,1,21,0,17,-25,-8,1038,131.42,0,0 +2012,1,21,1,10,-25,-9,1038,138.57,0,0 +2012,1,21,2,10,-25,-10,1039,145.72,0,0 +2012,1,21,3,11,-25,-10,1039,152.87,0,0 +2012,1,21,4,11,-26,-11,1038,158.68,0,0 +2012,1,21,5,9,-26,-11,1038,165.83,0,0 +2012,1,21,6,7,-26,-11,1038,171.64,0,0 +2012,1,21,7,10,-27,-11,1038,175.66,0,0 +2012,1,21,8,12,-27,-11,1039,180.58,0,0 +2012,1,21,9,16,-27,-9,1040,185.5,0,0 +2012,1,21,10,21,-26,-8,1039,4.92,0,0 +2012,1,21,11,17,-26,-7,1039,8.94,0,0 +2012,1,21,12,18,-26,-6,1038,12.96,0,0 +2012,1,21,13,20,-26,-4,1036,16.98,0,0 +2012,1,21,14,20,-26,-5,1035,3.13,0,0 +2012,1,21,15,17,-25,-5,1035,4.92,0,0 +2012,1,21,16,13,-26,-6,1035,8.05,0,0 +2012,1,21,17,20,-25,-6,1035,8.94,0,0 +2012,1,21,18,15,-26,-8,1035,10.73,0,0 +2012,1,21,19,14,-26,-10,1036,1.79,0,0 +2012,1,21,20,22,-25,-7,1036,5.81,0,0 +2012,1,21,21,28,-25,-8,1036,9.83,0,0 +2012,1,21,22,20,-24,-8,1037,4.02,0,0 +2012,1,21,23,13,-26,-10,1037,5.81,0,0 +2012,1,22,0,21,-25,-11,1037,8.94,0,0 +2012,1,22,1,29,-25,-12,1037,4.02,0,0 +2012,1,22,2,19,-25,-13,1038,8.94,0,0 +2012,1,22,3,14,-25,-12,1038,13.86,0,0 +2012,1,22,4,11,-27,-13,1037,3.13,0,0 +2012,1,22,5,15,-26,-12,1037,8.05,0,0 +2012,1,22,6,16,-26,-12,1037,12.07,0,0 +2012,1,22,7,16,-26,-15,1037,3.13,0,0 +2012,1,22,8,19,-26,-12,1038,4.92,0,0 +2012,1,22,9,16,-26,-11,1038,9.84,0,0 +2012,1,22,10,21,-25,-8,1037,13.86,0,0 +2012,1,22,11,21,-25,-7,1036,17.88,0,0 +2012,1,22,12,22,-24,-5,1035,4.02,0,0 +2012,1,22,13,20,-24,-4,1034,8.94,0,0 +2012,1,22,14,18,-25,-3,1032,14.75,0,0 +2012,1,22,15,23,-26,-3,1031,22.8,0,0 +2012,1,22,16,35,-24,-4,1031,4.92,0,0 +2012,1,22,17,51,-23,-5,1031,8.94,0,0 +2012,1,22,18,54,-23,-5,1032,10.73,0,0 +2012,1,22,19,68,-24,-10,1032,1.79,0,0 +2012,1,22,20,108,-24,-11,1032,4.92,0,0 +2012,1,22,21,193,-24,-10,1032,0.89,0,0 +2012,1,22,22,182,-25,-14,1033,1.79,0,0 +2012,1,22,23,161,-23,-11,1033,0.89,0,0 +2012,1,23,0,225,-24,-11,1032,3.13,0,0 +2012,1,23,1,994,-24,-12,1032,4.92,0,0 +2012,1,23,2,972,-24,-12,1032,8.05,0,0 +2012,1,23,3,430,-24,-14,1031,11.18,0,0 +2012,1,23,4,296,-25,-15,1031,0.89,0,0 +2012,1,23,5,284,-23,-15,1031,1.79,0,0 +2012,1,23,6,285,-24,-16,1030,3.58,0,0 +2012,1,23,7,223,-22,-15,1030,0.45,0,0 +2012,1,23,8,141,-23,-17,1030,1.79,0,0 +2012,1,23,9,66,-20,-8,1030,0.89,0,0 +2012,1,23,10,33,-22,-6,1030,1.78,0,0 +2012,1,23,11,36,-24,-3,1029,8.05,0,0 +2012,1,23,12,27,-23,-2,1028,16.1,0,0 +2012,1,23,13,27,-24,-2,1027,23.25,0,0 +2012,1,23,14,20,-24,-2,1026,30.4,0,0 +2012,1,23,15,32,-23,-1,1026,37.55,0,0 +2012,1,23,16,24,-23,-1,1025,44.7,0,0 +2012,1,23,17,13,-23,-2,1026,49.62,0,0 +2012,1,23,18,24,-22,-3,1026,52.75,0,0 +2012,1,23,19,88,-23,-6,1027,55.88,0,0 +2012,1,23,20,103,-23,-8,1027,57.67,0,0 +2012,1,23,21,200,-23,-10,1027,60.8,0,0 +2012,1,23,22,157,-23,-10,1028,63.93,0,0 +2012,1,23,23,97,-21,-8,1028,0.89,0,0 +2012,1,24,0,79,-22,-7,1028,1.79,0,0 +2012,1,24,1,14,-22,-9,1028,3.58,0,0 +2012,1,24,2,12,-21,-7,1028,7.6,0,0 +2012,1,24,3,10,-21,-7,1028,12.52,0,0 +2012,1,24,4,8,-22,-7,1028,17.44,0,0 +2012,1,24,5,6,-22,-7,1028,23.25,0,0 +2012,1,24,6,8,-22,-7,1029,29.06,0,0 +2012,1,24,7,11,-22,-8,1029,33.08,0,0 +2012,1,24,8,9,-21,-7,1029,37.1,0,0 +2012,1,24,9,7,-22,-5,1030,41.12,0,0 +2012,1,24,10,10,-22,-4,1030,44.25,0,0 +2012,1,24,11,12,-22,-4,1029,48.27,0,0 +2012,1,24,12,13,-22,-2,1028,52.29,0,0 +2012,1,24,13,13,-23,-2,1027,59.44,0,0 +2012,1,24,14,11,-23,-1,1026,66.59,0,0 +2012,1,24,15,16,-23,-1,1026,73.74,0,0 +2012,1,24,16,15,-23,0,1025,78.66,0,0 +2012,1,24,17,12,-25,-6,1026,1.79,0,0 +2012,1,24,18,9,-25,-6,1026,1.79,0,0 +2012,1,24,19,18,-25,-8,1027,4.92,0,0 +2012,1,24,20,25,-24,-6,1027,0.89,0,0 +2012,1,24,21,37,-22,-9,1027,1.78,0,0 +2012,1,24,22,85,-22,-9,1027,2.67,0,0 +2012,1,24,23,73,-21,-11,1027,3.12,0,0 +2012,1,25,0,33,-23,-13,1027,1.79,0,0 +2012,1,25,1,45,-22,-11,1026,2.68,0,0 +2012,1,25,2,61,-23,-12,1026,3.57,0,0 +2012,1,25,3,66,-22,-13,1026,0.45,0,0 +2012,1,25,4,50,-22,-13,1025,1.79,0,0 +2012,1,25,5,37,-22,-14,1024,3.58,0,0 +2012,1,25,6,42,-23,-16,1024,6.71,0,0 +2012,1,25,7,43,-21,-15,1024,0.89,0,0 +2012,1,25,8,48,-21,-15,1025,1.78,0,0 +2012,1,25,9,44,-20,-8,1024,2.67,0,0 +2012,1,25,10,50,-21,-6,1024,3.56,0,0 +2012,1,25,11,52,-20,-4,1023,4.45,0,0 +2012,1,25,12,51,-19,-1,1022,5.34,0,0 +2012,1,25,13,47,-21,0,1021,3.13,0,0 +2012,1,25,14,51,-22,2,1020,4.92,0,0 +2012,1,25,15,47,-22,2,1020,12.97,0,0 +2012,1,25,16,43,-21,2,1020,22.8,0,0 +2012,1,25,17,39,-21,1,1020,26.82,0,0 +2012,1,25,18,60,-18,0,1021,4.02,0,0 +2012,1,25,19,69,-18,0,1021,8.04,0,0 +2012,1,25,20,93,-17,0,1021,12.06,0,0 +2012,1,25,21,127,-17,-1,1022,15.19,0,0 +2012,1,25,22,122,-19,-8,1022,3.13,0,0 +2012,1,25,23,156,-18,-7,1022,0.89,0,0 +2012,1,26,0,176,-19,-10,1022,1.79,0,0 +2012,1,26,1,169,-19,-11,1022,3.58,0,0 +2012,1,26,2,166,-18,-10,1022,0.89,0,0 +2012,1,26,3,151,-19,-11,1022,1.79,0,0 +2012,1,26,4,113,-19,-12,1021,3.58,0,0 +2012,1,26,5,77,-20,-13,1022,5.37,0,0 +2012,1,26,6,80,-19,-14,1022,7.16,0,0 +2012,1,26,7,80,-19,-13,1022,0.89,0,0 +2012,1,26,8,62,-17,-10,1023,3.13,0,0 +2012,1,26,9,47,-17,-8,1023,0.89,0,0 +2012,1,26,10,55,-17,-3,1023,1.78,0,0 +2012,1,26,11,70,-19,1,1021,2.67,0,0 +2012,1,26,12,79,-19,2,1020,3.56,0,0 +2012,1,26,13,66,-19,3,1019,1.79,0,0 +2012,1,26,14,67,-18,3,1018,3.58,0,0 +2012,1,26,15,77,-19,3,1018,5.37,0,0 +2012,1,26,16,84,-19,3,1018,9.39,0,0 +2012,1,26,17,87,-16,0,1018,12.52,0,0 +2012,1,26,18,115,-17,-2,1019,0.45,0,0 +2012,1,26,19,111,-16,-5,1020,1.34,0,0 +2012,1,26,20,125,-16,-5,1020,2.23,0,0 +2012,1,26,21,158,-17,-8,1021,3.12,0,0 +2012,1,26,22,227,-16,-7,1021,3.13,0,0 +2012,1,26,23,192,-16,-6,1022,0.89,0,0 +2012,1,27,0,113,-17,-3,1023,4.92,0,0 +2012,1,27,1,51,-18,-2,1023,10.73,0,0 +2012,1,27,2,25,-19,-4,1024,12.52,0,0 +2012,1,27,3,18,-19,-5,1024,0.89,0,0 +2012,1,27,4,40,-20,-9,1024,0.89,0,0 +2012,1,27,5,139,-17,-10,1025,0.89,0,0 +2012,1,27,6,162,-16,-10,1026,1.78,0,0 +2012,1,27,7,95,-21,-9,1027,1.79,0,0 +2012,1,27,8,8,-21,-5,1028,4.02,0,0 +2012,1,27,9,21,-21,-4,1029,8.94,0,0 +2012,1,27,10,20,-20,-2,1029,1.79,0,0 +2012,1,27,11,29,-19,0,1029,2.68,0,0 +2012,1,27,12,24,-20,0,1029,3.57,0,0 +2012,1,27,13,23,-22,1,1027,5.36,0,0 +2012,1,27,14,36,-19,1,1026,3.13,0,0 +2012,1,27,15,76,-18,0,1026,6.26,0,0 +2012,1,27,16,84,-18,0,1026,9.39,0,0 +2012,1,27,17,75,-17,-2,1026,11.18,0,0 +2012,1,27,18,93,-16,-2,1027,12.07,0,0 +2012,1,27,19,103,-16,-4,1027,12.96,0,0 +2012,1,27,20,128,-15,-6,1028,16.09,0,0 +2012,1,27,21,158,-13,-6,1028,19.22,0,0 +2012,1,27,22,124,-11,-6,1029,22.35,0,0 +2012,1,27,23,122,-10,-6,1029,26.37,0,0 +2012,1,28,0,74,-11,-8,1030,0.89,0,0 +2012,1,28,1,51,-11,-7,1030,1.78,0,0 +2012,1,28,2,58,-12,-7,1030,1.79,0,0 +2012,1,28,3,59,-12,-6,1030,3.58,0,0 +2012,1,28,4,69,-12,-6,1030,5.37,0,0 +2012,1,28,5,67,-11,-6,1030,0.45,1,0 +2012,1,28,6,69,-11,-6,1030,0.89,2,0 +2012,1,28,7,63,-11,-6,1030,1.78,0,0 +2012,1,28,8,60,-11,-6,1031,0.89,0,0 +2012,1,28,9,72,-10,-5,1031,1.79,0,0 +2012,1,28,10,75,-10,-4,1031,0.89,0,0 +2012,1,28,11,79,-10,-3,1030,1.78,0,0 +2012,1,28,12,82,-9,-3,1030,1.79,0,0 +2012,1,28,13,79,-9,-2,1029,0.89,0,0 +2012,1,28,14,76,-9,-2,1029,1.78,0,0 +2012,1,28,15,87,-9,-2,1029,2.67,0,0 +2012,1,28,16,91,-9,-2,1029,3.12,0,0 +2012,1,28,17,88,-9,-3,1029,4.01,0,0 +2012,1,28,18,96,-9,-4,1030,4.9,0,0 +2012,1,28,19,103,-10,-6,1031,5.79,0,0 +2012,1,28,20,129,-10,-7,1031,6.24,0,0 +2012,1,28,21,132,-11,-7,1032,7.13,0,0 +2012,1,28,22,159,-12,-10,1033,0.89,0,0 +2012,1,28,23,164,-13,-11,1033,2.68,0,0 +2012,1,29,0,170,-12,-9,1033,4.47,0,0 +2012,1,29,1,165,-16,-7,1033,8.49,0,0 +2012,1,29,2,68,-17,-8,1033,12.51,0,0 +2012,1,29,3,44,-18,-9,1034,4.02,0,0 +2012,1,29,4,25,-20,-7,1034,3.13,0,0 +2012,1,29,5,15,-20,-8,1034,7.15,0,0 +2012,1,29,6,14,-21,-8,1034,11.17,0,0 +2012,1,29,7,14,-21,-9,1035,14.3,0,0 +2012,1,29,8,13,-20,-8,1036,3.13,0,0 +2012,1,29,9,12,-20,-6,1036,3.13,0,0 +2012,1,29,10,23,-20,-4,1036,6.26,0,0 +2012,1,29,11,19,-21,-2,1036,8.05,0,0 +2012,1,29,12,18,-21,-1,1034,0.89,0,0 +2012,1,29,13,20,-21,-1,1033,1.79,0,0 +2012,1,29,14,20,-21,0,1032,3.58,0,0 +2012,1,29,15,31,-23,0,1032,1.79,0,0 +2012,1,29,16,35,-19,-1,1032,2.68,0,0 +2012,1,29,17,46,-19,-2,1032,1.79,0,0 +2012,1,29,18,38,-19,-4,1033,2.68,0,0 +2012,1,29,19,49,-17,-6,1033,3.57,0,0 +2012,1,29,20,47,-18,-5,1034,5.36,0,0 +2012,1,29,21,69,-17,-4,1034,8.49,0,0 +2012,1,29,22,76,-16,-4,1034,11.62,0,0 +2012,1,29,23,87,-15,-5,1035,15.64,0,0 +2012,1,30,0,91,-15,-8,1035,17.43,0,0 +2012,1,30,1,73,-13,-8,1035,19.22,0,0 +2012,1,30,2,77,-13,-8,1035,22.35,0,0 +2012,1,30,3,109,-13,-8,1034,24.14,0,0 +2012,1,30,4,81,-13,-8,1034,25.93,0,0 +2012,1,30,5,89,-13,-7,1034,27.72,1,0 +2012,1,30,6,91,-12,-7,1034,0.45,2,0 +2012,1,30,7,91,-12,-8,1034,0.9,3,0 +2012,1,30,8,84,-12,-7,1034,1.35,4,0 +2012,1,30,9,99,-12,-7,1034,2.24,5,0 +2012,1,30,10,112,-11,-7,1033,1.79,6,0 +2012,1,30,11,127,-11,-6,1032,3.58,7,0 +2012,1,30,12,133,-9,-6,1031,5.37,8,0 +2012,1,30,13,133,-8,-5,1029,7.16,9,0 +2012,1,30,14,129,-7,-5,1028,0.89,10,0 +2012,1,30,15,134,-8,-4,1028,1.78,0,0 +2012,1,30,16,135,-8,-4,1027,2.67,0,0 +2012,1,30,17,138,-9,-5,1027,3.56,0,0 +2012,1,30,18,155,-10,-7,1027,0.89,0,0 +2012,1,30,19,157,-9,-7,1027,1.78,0,0 +2012,1,30,20,147,-9,-7,1027,2.67,0,0 +2012,1,30,21,151,-8,-5,1026,3.56,0,0 +2012,1,30,22,152,-8,-5,1026,5.35,0,0 +2012,1,30,23,142,-8,-5,1025,7.14,0,0 +2012,1,31,0,132,-8,-5,1024,0.45,0,0 +2012,1,31,1,139,-8,-5,1024,1.79,0,0 +2012,1,31,2,150,-8,-5,1024,4.92,0,0 +2012,1,31,3,150,-8,-5,1023,8.05,0,0 +2012,1,31,4,143,-9,-7,1022,9.84,0,0 +2012,1,31,5,144,-10,-8,1021,11.63,0,0 +2012,1,31,6,154,-10,-8,1021,14.76,0,0 +2012,1,31,7,152,-10,-7,1022,18.78,0,0 +2012,1,31,8,147,-10,-5,1023,4.02,0,0 +2012,1,31,9,141,-12,-4,1024,8.94,0,0 +2012,1,31,10,135,-17,-3,1024,14.75,0,0 +2012,1,31,11,58,-19,-3,1024,21.9,0,0 +2012,1,31,12,14,-21,-2,1024,27.71,0,0 +2012,1,31,13,19,-23,-1,1023,32.63,0,0 +2012,1,31,14,22,-23,-1,1023,37.55,0,0 +2012,1,31,15,23,-25,-2,1024,43.36,0,0 +2012,1,31,16,18,-27,-2,1025,47.38,0,0 +2012,1,31,17,17,-26,-3,1026,52.3,0,0 +2012,1,31,18,14,-27,-5,1027,56.32,0,0 +2012,1,31,19,13,-27,-5,1028,61.24,0,0 +2012,1,31,20,13,-28,-7,1029,65.26,0,0 +2012,1,31,21,18,-28,-7,1030,70.18,0,0 +2012,1,31,22,8,-28,-8,1031,74.2,0,0 +2012,1,31,23,9,-28,-8,1032,80.01,0,0 +2012,2,1,0,15,-27,-10,1033,85.82,0,0 +2012,2,1,1,14,-28,-10,1034,5.81,0,0 +2012,2,1,2,13,-27,-11,1034,4.92,0,0 +2012,2,1,3,9,-27,-11,1034,9.84,0,0 +2012,2,1,4,11,-27,-11,1034,14.76,0,0 +2012,2,1,5,12,-27,-13,1034,18.78,0,0 +2012,2,1,6,11,-27,-13,1034,20.57,0,0 +2012,2,1,7,13,-28,-12,1036,23.7,0,0 +2012,2,1,8,13,-27,-11,1036,4.02,0,0 +2012,2,1,9,13,-27,-8,1037,12.07,0,0 +2012,2,1,10,14,-27,-7,1037,20.12,0,0 +2012,2,1,11,14,-26,-6,1037,23.25,0,0 +2012,2,1,12,14,-26,-6,1036,4.02,0,0 +2012,2,1,13,12,-25,-6,1035,8.04,0,0 +2012,2,1,14,13,-24,-5,1035,11.17,0,0 +2012,2,1,15,16,-25,-5,1035,14.3,0,0 +2012,2,1,16,16,-24,-5,1035,16.09,0,0 +2012,2,1,17,17,-24,-6,1035,17.88,0,0 +2012,2,1,18,23,-24,-8,1036,0.89,0,0 +2012,2,1,19,25,-23,-9,1037,0.89,0,0 +2012,2,1,20,39,-24,-8,1038,2.68,0,0 +2012,2,1,21,52,-23,-8,1038,6.7,0,0 +2012,2,1,22,42,-24,-9,1039,10.72,0,0 +2012,2,1,23,24,-24,-9,1040,15.64,0,0 +2012,2,2,0,24,-25,-11,1041,4.02,0,0 +2012,2,2,1,30,-24,-9,1041,11.17,0,0 +2012,2,2,2,13,-24,-9,1041,16.09,0,0 +2012,2,2,3,10,-24,-10,1041,21.01,0,0 +2012,2,2,4,8,-24,-10,1041,26.82,0,0 +2012,2,2,5,7,-25,-10,1041,30.84,0,0 +2012,2,2,6,6,-25,-10,1041,37.99,0,0 +2012,2,2,7,9,-25,-11,1041,43.8,0,0 +2012,2,2,8,11,-25,-10,1041,47.82,0,0 +2012,2,2,9,16,-25,-8,1041,52.74,0,0 +2012,2,2,10,13,-25,-7,1040,57.66,0,0 +2012,2,2,11,16,-26,-4,1039,62.58,0,0 +2012,2,2,12,14,-26,-4,1037,69.73,0,0 +2012,2,2,13,17,-25,-4,1036,75.54,0,0 +2012,2,2,14,14,-24,-2,1034,3.13,0,0 +2012,2,2,15,13,-24,-2,1033,3.13,0,0 +2012,2,2,16,18,-25,-3,1033,1.79,0,0 +2012,2,2,17,24,-24,-5,1032,0.89,0,0 +2012,2,2,18,39,-24,-5,1032,1.78,0,0 +2012,2,2,19,57,-24,-5,1032,2.67,0,0 +2012,2,2,20,69,-23,-5,1031,3.56,0,0 +2012,2,2,21,75,-23,-10,1031,4.45,0,0 +2012,2,2,22,82,-23,-11,1031,5.34,0,0 +2012,2,2,23,92,-23,-12,1031,6.23,0,0 +2012,2,3,0,64,-23,-12,1030,0.89,0,0 +2012,2,3,1,61,-24,-14,1030,4.02,0,0 +2012,2,3,2,40,-24,-15,1029,7.15,0,0 +2012,2,3,3,60,-23,-14,1029,8.94,0,0 +2012,2,3,4,68,-23,-14,1028,12.07,0,0 +2012,2,3,5,68,-24,-13,1027,13.86,0,0 +2012,2,3,6,63,-24,-14,1026,16.99,0,0 +2012,2,3,7,63,-24,-16,1026,20.12,0,0 +2012,2,3,8,60,-23,-12,1026,24.14,0,0 +2012,2,3,9,52,-23,-8,1026,27.27,0,0 +2012,2,3,10,49,-22,-5,1025,30.4,0,0 +2012,2,3,11,59,-22,-3,1024,32.19,0,0 +2012,2,3,12,60,-24,1,1023,1.79,0,0 +2012,2,3,13,52,-23,3,1022,4.02,0,0 +2012,2,3,14,50,-23,4,1021,13.85,0,0 +2012,2,3,15,76,-25,4,1021,25.03,0,0 +2012,2,3,16,66,-26,4,1021,33.97,0,0 +2012,2,3,17,72,-25,3,1021,39.78,0,0 +2012,2,3,18,82,-22,-1,1022,1.79,0,0 +2012,2,3,19,100,-21,-3,1023,0.89,0,0 +2012,2,3,20,129,-21,-6,1024,0.89,0,0 +2012,2,3,21,147,-21,-6,1025,1.78,0,0 +2012,2,3,22,114,-22,-4,1026,1.79,0,0 +2012,2,3,23,50,-24,-4,1026,4.92,0,0 +2012,2,4,0,14,-25,-4,1027,10.73,0,0 +2012,2,4,1,16,-24,-5,1027,15.65,0,0 +2012,2,4,2,9,-25,-6,1027,21.46,0,0 +2012,2,4,3,9,-25,-7,1028,3.13,0,0 +2012,2,4,4,8,-23,-8,1028,6.26,0,0 +2012,2,4,5,9,-23,-10,1028,3.13,0,0 +2012,2,4,6,9,-23,-8,1028,5.81,0,0 +2012,2,4,7,9,-23,-7,1028,11.62,0,0 +2012,2,4,8,17,-23,-7,1029,16.54,0,0 +2012,2,4,9,21,-22,-5,1029,20.56,0,0 +2012,2,4,10,19,-22,-4,1029,24.58,0,0 +2012,2,4,11,19,-22,0,1028,26.37,0,0 +2012,2,4,12,20,-24,1,1027,1.79,0,0 +2012,2,4,13,22,-25,1,1026,2.68,0,0 +2012,2,4,14,29,-24,1,1025,4.47,0,0 +2012,2,4,15,40,-24,2,1024,1.79,0,0 +2012,2,4,16,69,-24,1,1024,6.71,0,0 +2012,2,4,17,70,-21,0,1023,10.73,0,0 +2012,2,4,18,62,-21,-1,1023,13.86,0,0 +2012,2,4,19,60,-21,-2,1024,15.65,0,0 +2012,2,4,20,61,-20,-3,1023,0.89,0,0 +2012,2,4,21,72,-19,-5,1023,1.78,0,0 +2012,2,4,22,82,-19,-5,1023,0.89,0,0 +2012,2,4,23,100,-19,-5,1023,2.68,0,0 +2012,2,5,0,118,-19,-5,1023,4.47,0,0 +2012,2,5,1,126,-14,-5,1022,0.89,0,0 +2012,2,5,2,140,-15,-5,1022,1.79,0,0 +2012,2,5,3,132,-14,-5,1021,2.68,0,0 +2012,2,5,4,119,-13,-4,1020,0.45,0,0 +2012,2,5,5,100,-14,-4,1019,0.89,0,0 +2012,2,5,6,92,-14,-5,1019,0.89,0,0 +2012,2,5,7,90,-14,-5,1019,1.79,0,0 +2012,2,5,8,109,-15,-4,1020,1.79,0,0 +2012,2,5,9,103,-15,-4,1020,1.79,0,0 +2012,2,5,10,81,-13,0,1020,3.58,0,0 +2012,2,5,11,82,-12,1,1020,0.89,0,0 +2012,2,5,12,100,-12,1,1019,1.78,0,0 +2012,2,5,13,103,-14,2,1018,1.79,0,0 +2012,2,5,14,116,-14,3,1017,1.79,0,0 +2012,2,5,15,108,-14,4,1016,1.79,0,0 +2012,2,5,16,113,-14,4,1016,4.92,0,0 +2012,2,5,17,127,-14,3,1017,8.94,0,0 +2012,2,5,18,132,-14,1,1017,12.96,0,0 +2012,2,5,19,128,-12,-2,1018,13.85,0,0 +2012,2,5,20,132,-12,-4,1018,14.74,0,0 +2012,2,5,21,201,-11,-3,1018,0.45,0,0 +2012,2,5,22,265,-12,-5,1018,0.89,0,0 +2012,2,5,23,252,-11,-4,1019,0.89,0,0 +2012,2,6,0,254,-11,-4,1019,0.89,0,0 +2012,2,6,1,262,-10,-4,1019,2.68,0,0 +2012,2,6,2,251,-12,-6,1020,0.89,0,0 +2012,2,6,3,278,-12,-6,1020,2.68,0,0 +2012,2,6,4,263,-13,-6,1020,5.81,0,0 +2012,2,6,5,183,-17,-2,1021,13.86,0,0 +2012,2,6,6,18,-20,-3,1023,23.69,0,0 +2012,2,6,7,20,-20,-3,1024,32.63,0,0 +2012,2,6,8,19,-20,-4,1026,43.81,0,0 +2012,2,6,9,12,-21,-3,1027,59.01,0,0 +2012,2,6,10,11,-21,-2,1027,71.97,0,0 +2012,2,6,11,10,-22,-2,1027,87.17,0,0 +2012,2,6,12,12,-23,-1,1027,99.24,0,0 +2012,2,6,13,19,-25,-1,1027,111.31,0,0 +2012,2,6,14,22,-25,-2,1027,124.27,0,0 +2012,2,6,15,23,-26,-2,1028,137.23,0,0 +2012,2,6,16,13,-28,-4,1029,153.32,0,0 +2012,2,6,17,12,-28,-6,1030,165.39,0,0 +2012,2,6,18,10,-28,-7,1031,174.33,0,0 +2012,2,6,19,27,-27,-7,1032,188.19,0,0 +2012,2,6,20,32,-27,-8,1033,200.26,0,0 +2012,2,6,21,42,-26,-8,1033,213.22,0,0 +2012,2,6,22,66,-26,-9,1033,224.4,0,0 +2012,2,6,23,56,-25,-9,1033,233.34,0,0 +2012,2,7,0,163,-26,-10,1033,239.15,0,0 +2012,2,7,1,17,-27,-9,1034,252.11,0,0 +2012,2,7,2,7,-28,-10,1034,265.97,0,0 +2012,2,7,3,7,-28,-10,1034,278.04,0,0 +2012,2,7,4,7,-28,-12,1034,283.85,0,0 +2012,2,7,5,9,-27,-10,1033,293.68,0,0 +2012,2,7,6,9,-27,-11,1034,300.83,0,0 +2012,2,7,7,7,-27,-13,1034,304.85,0,0 +2012,2,7,8,9,-27,-10,1034,0.89,0,0 +2012,2,7,9,13,-25,-8,1034,7.15,0,0 +2012,2,7,10,22,-25,-7,1033,14.3,0,0 +2012,2,7,11,15,-24,-6,1032,21.45,0,0 +2012,2,7,12,13,-25,-5,1031,30.39,0,0 +2012,2,7,13,18,-24,-3,1030,39.33,0,0 +2012,2,7,14,14,-24,-3,1029,46.48,0,0 +2012,2,7,15,12,-24,-2,1028,54.53,0,0 +2012,2,7,16,12,-24,-1,1028,60.34,0,0 +2012,2,7,17,13,-24,-3,1028,66.15,0,0 +2012,2,7,18,12,-25,-4,1028,71.07,0,0 +2012,2,7,19,12,-25,-6,1029,75.99,0,0 +2012,2,7,20,12,-23,-6,1029,83.14,0,0 +2012,2,7,21,12,-24,-8,1030,88.06,0,0 +2012,2,7,22,9,-24,-8,1030,92.98,0,0 +2012,2,7,23,9,-24,-8,1030,98.79,0,0 +2012,2,8,0,11,-24,-7,1030,105.94,0,0 +2012,2,8,1,9,-24,-7,1030,113.09,0,0 +2012,2,8,2,10,-25,-8,1030,118.9,0,0 +2012,2,8,3,10,-24,-9,1030,124.71,0,0 +2012,2,8,4,11,-25,-9,1029,129.63,0,0 +2012,2,8,5,10,-25,-9,1029,131.42,0,0 +2012,2,8,6,8,-25,-11,1028,134.55,0,0 +2012,2,8,7,9,-25,-11,1028,136.34,0,0 +2012,2,8,8,12,-23,-7,1028,1.79,0,0 +2012,2,8,9,16,-22,-5,1028,3.13,0,0 +2012,2,8,10,11,-22,-3,1027,7.15,0,0 +2012,2,8,11,27,-22,-2,1026,16.09,0,0 +2012,2,8,12,21,-22,0,1025,25.92,0,0 +2012,2,8,13,16,-24,0,1024,35.75,0,0 +2012,2,8,14,11,-23,2,1023,43.8,0,0 +2012,2,8,15,11,-23,2,1023,49.61,0,0 +2012,2,8,16,17,-24,2,1023,56.76,0,0 +2012,2,8,17,17,-23,2,1023,63.91,0,0 +2012,2,8,18,12,-21,-3,1024,1.79,0,0 +2012,2,8,19,20,-21,-3,1025,3.13,0,0 +2012,2,8,20,21,-21,-5,1026,1.79,0,0 +2012,2,8,21,26,-21,-5,1026,3.13,0,0 +2012,2,8,22,18,-20,-6,1026,0.89,0,0 +2012,2,8,23,22,-21,-7,1027,3.13,0,0 +2012,2,9,0,18,-20,-7,1027,4.92,0,0 +2012,2,9,1,39,-19,-8,1027,0.89,0,0 +2012,2,9,2,36,-20,-9,1026,0.45,0,0 +2012,2,9,3,28,-19,-11,1026,0.9,0,0 +2012,2,9,4,28,-21,-13,1026,0.89,0,0 +2012,2,9,5,33,-20,-13,1027,4.02,0,0 +2012,2,9,6,33,-20,-11,1026,5.81,0,0 +2012,2,9,7,38,-20,-11,1027,0.89,0,0 +2012,2,9,8,77,-17,-8,1027,3.13,0,0 +2012,2,9,9,84,-18,-5,1027,6.26,0,0 +2012,2,9,10,84,-17,-3,1028,1.79,0,0 +2012,2,9,11,63,-18,-1,1027,1.79,0,0 +2012,2,9,12,52,-20,1,1026,3.58,0,0 +2012,2,9,13,40,-21,1,1025,5.37,0,0 +2012,2,9,14,36,-21,3,1024,4.02,0,0 +2012,2,9,15,37,-21,2,1024,8.94,0,0 +2012,2,9,16,45,-21,2,1023,14.75,0,0 +2012,2,9,17,51,-21,2,1023,19.67,0,0 +2012,2,9,18,55,-21,1,1024,23.69,0,0 +2012,2,9,19,66,-20,0,1024,28.61,0,0 +2012,2,9,20,64,-18,0,1025,3.13,0,0 +2012,2,9,21,56,-18,-2,1027,6.26,0,0 +2012,2,9,22,57,-19,-5,1027,9.39,0,0 +2012,2,9,23,21,-22,-1,1028,17.44,0,0 +2012,2,10,0,6,-22,-2,1028,28.62,0,0 +2012,2,10,1,4,-22,-3,1029,36.67,0,0 +2012,2,10,2,6,-21,-2,1029,45.61,0,0 +2012,2,10,3,8,-20,-3,1030,52.76,0,0 +2012,2,10,4,7,-21,-4,1030,55.89,0,0 +2012,2,10,5,9,-21,-6,1030,57.68,0,0 +2012,2,10,6,11,-22,-6,1031,59.47,0,0 +2012,2,10,7,13,-20,-9,1031,0.89,0,0 +2012,2,10,8,21,-19,-3,1032,1.78,0,0 +2012,2,10,9,29,-20,-2,1033,1.79,0,0 +2012,2,10,10,19,-21,0,1032,3.13,0,0 +2012,2,10,11,36,-22,2,1032,0.89,0,0 +2012,2,10,12,23,-21,2,1030,2.68,0,0 +2012,2,10,13,23,-22,3,1029,3.57,0,0 +2012,2,10,14,18,-23,3,1028,3.13,0,0 +2012,2,10,15,19,-22,3,1027,8.05,0,0 +2012,2,10,16,27,-22,3,1027,13.86,0,0 +2012,2,10,17,36,-22,2,1027,18.78,0,0 +2012,2,10,18,23,-22,1,1026,23.7,0,0 +2012,2,10,19,34,-21,0,1027,27.72,0,0 +2012,2,10,20,46,-19,0,1027,32.64,0,0 +2012,2,10,21,54,-19,0,1027,37.56,0,0 +2012,2,10,22,63,-19,-1,1027,41.58,0,0 +2012,2,10,23,82,-19,-2,1026,43.37,0,0 +2012,2,11,0,74,-19,-3,1026,45.16,0,0 +2012,2,11,1,95,-19,-4,1026,46.95,0,0 +2012,2,11,2,102,-20,-7,1026,48.74,0,0 +2012,2,11,3,114,-19,-7,1026,50.53,0,0 +2012,2,11,4,121,-19,-10,1026,1.79,0,0 +2012,2,11,5,117,-17,-9,1026,2.68,0,0 +2012,2,11,6,112,-17,-10,1027,0.89,0,0 +2012,2,11,7,123,-17,-11,1027,1.79,0,0 +2012,2,11,8,126,-16,-10,1028,3.58,0,0 +2012,2,11,9,129,-16,-3,1029,6.71,0,0 +2012,2,11,10,121,-17,-1,1029,9.84,0,0 +2012,2,11,11,111,-21,2,1029,4.02,0,0 +2012,2,11,12,73,-22,3,1028,7.15,0,0 +2012,2,11,13,49,-22,6,1027,3.13,0,0 +2012,2,11,14,40,-22,5,1026,4.02,0,0 +2012,2,11,15,39,-23,5,1026,7.15,0,0 +2012,2,11,16,40,-23,5,1026,8.94,0,0 +2012,2,11,17,34,-23,4,1026,10.73,0,0 +2012,2,11,18,29,-23,2,1027,0.89,0,0 +2012,2,11,19,44,-20,-1,1027,2.68,0,0 +2012,2,11,20,74,-18,-3,1028,3.57,0,0 +2012,2,11,21,87,-18,-3,1029,4.46,0,0 +2012,2,11,22,106,-16,-2,1030,8.48,0,0 +2012,2,11,23,147,-9,-4,1030,12.5,0,0 +2012,2,12,0,175,-9,-5,1030,16.52,0,0 +2012,2,12,1,107,-9,-5,1030,19.65,0,0 +2012,2,12,2,99,-9,-4,1031,21.44,0,0 +2012,2,12,3,86,-10,-6,1030,0.89,0,0 +2012,2,12,4,74,-12,-7,1030,0.89,0,0 +2012,2,12,5,78,-12,-8,1029,0.89,0,0 +2012,2,12,6,75,-12,-10,1029,1.79,0,0 +2012,2,12,7,70,-12,-10,1030,1.79,0,0 +2012,2,12,8,70,-7,-4,1029,4.92,0,0 +2012,2,12,9,77,-8,-2,1030,8.05,0,0 +2012,2,12,10,90,-9,-1,1029,9.84,0,0 +2012,2,12,11,113,-9,-1,1029,1.79,0,0 +2012,2,12,12,97,-9,1,1027,1.79,0,0 +2012,2,12,13,111,-8,0,1025,1.79,0,0 +2012,2,12,14,104,-8,1,1024,1.79,0,0 +2012,2,12,15,106,-9,0,1023,0.89,0,0 +2012,2,12,16,105,-9,0,1024,1.78,0,0 +2012,2,12,17,105,-9,0,1024,2.67,0,0 +2012,2,12,18,112,-9,0,1024,1.79,0,0 +2012,2,12,19,120,-8,-1,1024,0.89,0,0 +2012,2,12,20,134,-9,-4,1024,0.89,0,0 +2012,2,12,21,143,-8,-3,1024,0.89,0,0 +2012,2,12,22,157,-10,-5,1025,0.89,0,0 +2012,2,12,23,149,-8,-4,1025,1.78,0,0 +2012,2,13,0,170,-11,-7,1024,3.13,0,0 +2012,2,13,1,183,-10,-6,1024,0.89,0,0 +2012,2,13,2,175,-12,-8,1024,1.78,0,0 +2012,2,13,3,154,-11,-6,1024,1.79,0,0 +2012,2,13,4,145,-11,-7,1023,3.58,0,0 +2012,2,13,5,144,-10,-6,1023,5.37,0,0 +2012,2,13,6,163,-10,-6,1024,6.26,0,0 +2012,2,13,7,172,-9,-5,1024,9.39,0,0 +2012,2,13,8,170,-9,-4,1025,11.18,0,0 +2012,2,13,9,182,-9,-2,1025,12.97,0,0 +2012,2,13,10,138,-9,-1,1025,1.79,0,0 +2012,2,13,11,149,-10,1,1024,1.79,0,0 +2012,2,13,12,163,-10,2,1023,0.89,0,0 +2012,2,13,13,165,-9,4,1022,1.78,0,0 +2012,2,13,14,192,-10,4,1022,3.13,0,0 +2012,2,13,15,226,-9,4,1021,6.26,0,0 +2012,2,13,16,221,-9,3,1021,9.39,0,0 +2012,2,13,17,224,-9,2,1021,0.89,0,0 +2012,2,13,18,228,-9,1,1021,0.89,0,0 +2012,2,13,19,254,-8,-2,1021,1.78,0,0 +2012,2,13,20,288,-8,-2,1021,2.67,0,0 +2012,2,13,21,295,-8,-1,1022,0.89,0,0 +2012,2,13,22,332,-9,-4,1023,1.78,0,0 +2012,2,13,23,352,-9,-3,1023,2.67,0,0 +2012,2,14,0,376,-10,-5,1023,0.89,0,0 +2012,2,14,1,380,-10,-6,1023,0.89,0,0 +2012,2,14,2,327,-9,-6,1023,3.13,0,0 +2012,2,14,3,306,-10,-6,1023,7.15,0,0 +2012,2,14,4,248,-9,-5,1024,11.17,0,0 +2012,2,14,5,235,-8,-3,1024,16.98,0,0 +2012,2,14,6,236,-11,-2,1025,21,0,0 +2012,2,14,7,151,-13,-3,1026,25.02,0,0 +2012,2,14,8,14,-15,-1,1027,4.02,0,0 +2012,2,14,9,13,-17,-1,1028,7.15,0,0 +2012,2,14,10,16,-19,1,1028,4.92,0,0 +2012,2,14,11,16,-20,2,1028,8.94,0,0 +2012,2,14,12,15,-22,5,1028,5.81,0,0 +2012,2,14,13,22,-26,4,1027,12.96,0,0 +2012,2,14,14,25,-24,3,1027,4.02,0,0 +2012,2,14,15,26,-23,4,1027,5.81,0,0 +2012,2,14,16,23,-24,3,1027,1.79,0,0 +2012,2,14,17,24,-24,3,1028,3.58,0,0 +2012,2,14,18,25,-21,-2,1028,4.47,0,0 +2012,2,14,19,22,-20,-2,1029,0.89,0,0 +2012,2,14,20,38,-22,-6,1029,2.68,0,0 +2012,2,14,21,57,-22,-1,1030,3.13,0,0 +2012,2,14,22,41,-21,-4,1030,1.79,0,0 +2012,2,14,23,71,-16,-5,1030,3.13,0,0 +2012,2,15,0,80,-10,-6,1031,4.92,0,0 +2012,2,15,1,129,-9,-6,1031,6.71,0,0 +2012,2,15,2,149,-9,-6,1031,9.84,0,0 +2012,2,15,3,132,-9,-6,1031,12.97,0,0 +2012,2,15,4,97,-10,-6,1031,14.76,0,0 +2012,2,15,5,83,-10,-7,1031,15.65,0,0 +2012,2,15,6,78,-13,-10,1031,0.89,0,0 +2012,2,15,7,92,-11,-9,1031,1.78,0,0 +2012,2,15,8,95,-11,-9,1031,0.89,0,0 +2012,2,15,9,92,-10,-5,1031,0.89,0,0 +2012,2,15,10,87,-9,-3,1031,0.89,0,0 +2012,2,15,11,96,-9,-2,1030,1.78,0,0 +2012,2,15,12,113,-10,0,1028,3.57,0,0 +2012,2,15,13,138,-10,1,1026,1.79,0,0 +2012,2,15,14,167,-10,2,1025,1.79,0,0 +2012,2,15,15,134,-10,2,1025,4.02,0,0 +2012,2,15,16,112,-11,2,1024,8.04,0,0 +2012,2,15,17,118,-10,2,1025,1.79,0,0 +2012,2,15,18,114,-11,0,1025,2.68,0,0 +2012,2,15,19,126,-9,-2,1026,1.79,0,0 +2012,2,15,20,133,-11,-4,1027,1.79,0,0 +2012,2,15,21,145,-9,-2,1028,3.13,0,0 +2012,2,15,22,163,-19,-2,1030,8.94,0,0 +2012,2,15,23,16,-20,-2,1030,16.09,0,0 +2012,2,16,0,4,-20,-3,1031,20.11,0,0 +2012,2,16,1,5,-20,-3,1030,25.03,0,0 +2012,2,16,2,8,-20,-4,1030,28.16,0,0 +2012,2,16,3,12,-20,-4,1030,33.08,0,0 +2012,2,16,4,11,-20,-4,1030,36.21,0,0 +2012,2,16,5,10,-21,-7,1030,38,0,0 +2012,2,16,6,11,-20,-6,1030,41.13,0,0 +2012,2,16,7,9,-20,-5,1031,44.26,0,0 +2012,2,16,8,12,-19,-2,1031,49.18,0,0 +2012,2,16,9,17,-19,-1,1032,54.1,0,0 +2012,2,16,10,15,-21,0,1031,61.25,0,0 +2012,2,16,11,24,-21,1,1031,66.17,0,0 +2012,2,16,12,17,-22,2,1030,71.98,0,0 +2012,2,16,13,11,-23,2,1029,76.9,0,0 +2012,2,16,14,15,-22,3,1028,80.92,0,0 +2012,2,16,15,18,-22,2,1028,88.07,0,0 +2012,2,16,16,18,-22,1,1028,95.22,0,0 +2012,2,16,17,16,-20,0,1028,102.37,0,0 +2012,2,16,18,14,-19,-2,1029,109.52,0,0 +2012,2,16,19,16,-20,-3,1029,115.33,0,0 +2012,2,16,20,13,-20,-3,1030,120.25,0,0 +2012,2,16,21,15,-20,-5,1030,125.17,0,0 +2012,2,16,22,15,-20,-5,1030,130.09,0,0 +2012,2,16,23,20,-19,-4,1030,133.22,0,0 +2012,2,17,0,7,-19,-4,1030,136.35,0,0 +2012,2,17,1,8,-21,-8,1030,140.37,0,0 +2012,2,17,2,9,-21,-8,1031,145.29,0,0 +2012,2,17,3,11,-20,-7,1031,150.21,0,0 +2012,2,17,4,12,-21,-7,1031,156.02,0,0 +2012,2,17,5,11,-22,-7,1032,161.83,0,0 +2012,2,17,6,9,-23,-7,1033,170.77,0,0 +2012,2,17,7,10,-23,-8,1033,178.82,0,0 +2012,2,17,8,16,-23,-7,1034,188.65,0,0 +2012,2,17,9,17,-23,-5,1035,201.61,0,0 +2012,2,17,10,15,-22,-3,1035,212.79,0,0 +2012,2,17,11,18,-23,-2,1034,223.97,0,0 +2012,2,17,12,19,-23,-1,1033,235.15,0,0 +2012,2,17,13,26,-22,0,1032,246.33,0,0 +2012,2,17,14,16,-24,1,1032,257.51,0,0 +2012,2,17,15,24,-24,0,1032,268.69,0,0 +2012,2,17,16,15,-25,0,1032,277.63,0,0 +2012,2,17,17,13,-22,-1,1031,286.57,0,0 +2012,2,17,18,9,-21,-2,1032,294.62,0,0 +2012,2,17,19,8,-21,-3,1033,302.67,0,0 +2012,2,17,20,11,-21,-4,1033,307.59,0,0 +2012,2,17,21,10,-20,-5,1034,313.4,0,0 +2012,2,17,22,18,-20,-4,1034,322.34,0,0 +2012,2,17,23,18,-20,-4,1034,331.28,0,0 +2012,2,18,0,14,-21,-5,1034,338.43,0,0 +2012,2,18,1,16,-21,-5,1034,344.24,0,0 +2012,2,18,2,12,-22,-7,1035,0.89,0,0 +2012,2,18,3,9,-23,-8,1034,3.13,0,0 +2012,2,18,4,8,-22,-9,1034,6.26,0,0 +2012,2,18,5,10,-23,-9,1035,8.05,0,0 +2012,2,18,6,8,-22,-10,1035,12.07,0,0 +2012,2,18,7,17,-22,-10,1035,16.09,0,0 +2012,2,18,8,17,-20,-7,1035,21.01,0,0 +2012,2,18,9,30,-21,-5,1035,25.93,0,0 +2012,2,18,10,27,-21,-3,1034,4.02,0,0 +2012,2,18,11,23,-20,0,1032,5.81,0,0 +2012,2,18,12,27,-23,1,1031,11.62,0,0 +2012,2,18,13,19,-23,2,1030,18.77,0,0 +2012,2,18,14,26,-22,3,1028,24.58,0,0 +2012,2,18,15,45,-22,4,1028,30.39,0,0 +2012,2,18,16,40,-22,4,1027,36.2,0,0 +2012,2,18,17,52,-22,4,1026,41.12,0,0 +2012,2,18,18,50,-22,2,1026,4.02,0,0 +2012,2,18,19,53,-22,1,1027,7.15,0,0 +2012,2,18,20,52,-20,-1,1027,3.13,0,0 +2012,2,18,21,59,-19,-3,1028,0.89,0,0 +2012,2,18,22,74,-19,-5,1028,1.78,0,0 +2012,2,18,23,90,-18,-3,1028,2.67,0,0 +2012,2,19,0,109,-18,-3,1028,1.79,0,0 +2012,2,19,1,105,-19,-7,1028,6.71,0,0 +2012,2,19,2,98,-19,-6,1027,0.89,0,0 +2012,2,19,3,91,-20,-8,1027,1.79,0,0 +2012,2,19,4,99,-19,-10,1027,0.89,0,0 +2012,2,19,5,98,-21,-11,1027,1.79,0,0 +2012,2,19,6,91,-20,-10,1026,3.58,0,0 +2012,2,19,7,87,-20,-11,1026,6.71,0,0 +2012,2,19,8,84,-17,-7,1027,8.5,0,0 +2012,2,19,9,89,-18,-4,1026,1.79,0,0 +2012,2,19,10,87,-18,-1,1026,0.89,0,0 +2012,2,19,11,80,-20,2,1026,1.79,0,0 +2012,2,19,12,73,-21,5,1025,3.58,0,0 +2012,2,19,13,52,-20,7,1022,1.79,0,0 +2012,2,19,14,38,-21,6,1022,3.58,0,0 +2012,2,19,15,33,-21,7,1021,5.37,0,0 +2012,2,19,16,33,-22,7,1020,4.02,0,0 +2012,2,19,17,25,-22,6,1020,8.04,0,0 +2012,2,19,18,32,-23,4,1021,12.06,0,0 +2012,2,19,19,41,-23,1,1021,13.85,0,0 +2012,2,19,20,52,-22,2,1021,16.98,0,0 +2012,2,19,21,64,-19,-3,1021,0.89,0,0 +2012,2,19,22,64,-18,-3,1021,1.78,0,0 +2012,2,19,23,56,-18,-5,1022,2.67,0,0 +2012,2,20,0,65,-18,-4,1021,3.56,0,0 +2012,2,20,1,81,-19,-7,1021,1.79,0,0 +2012,2,20,2,113,-19,-9,1021,2.68,0,0 +2012,2,20,3,163,-18,-9,1021,1.79,0,0 +2012,2,20,4,162,-17,-9,1021,2.68,0,0 +2012,2,20,5,152,-18,-9,1021,5.81,0,0 +2012,2,20,6,129,-18,-9,1021,8.94,0,0 +2012,2,20,7,105,-18,-9,1021,10.73,0,0 +2012,2,20,8,99,-16,-8,1021,12.52,0,0 +2012,2,20,9,94,-16,-1,1022,0.89,0,0 +2012,2,20,10,85,-19,1,1022,2.68,0,0 +2012,2,20,11,110,-19,3,1022,4.47,0,0 +2012,2,20,12,139,-21,6,1020,1.79,0,0 +2012,2,20,13,139,-20,7,1020,0.89,0,0 +2012,2,20,14,127,-20,8,1019,1.79,0,0 +2012,2,20,15,129,-18,8,1019,3.58,0,0 +2012,2,20,16,135,-17,8,1019,5.37,0,0 +2012,2,20,17,145,-15,7,1018,7.16,0,0 +2012,2,20,18,184,-15,3,1019,8.05,0,0 +2012,2,20,19,236,-15,1,1020,8.94,0,0 +2012,2,20,20,254,-12,-2,1020,10.73,0,0 +2012,2,20,21,260,-9,-1,1021,12.52,0,0 +2012,2,20,22,216,-8,0,1022,18.33,0,0 +2012,2,20,23,160,-11,-1,1022,23.25,0,0 +2012,2,21,0,93,-12,-2,1022,0.89,0,0 +2012,2,21,1,75,-12,-2,1023,1.34,0,0 +2012,2,21,2,73,-12,-4,1023,2.23,0,0 +2012,2,21,3,68,-13,-6,1023,3.12,0,0 +2012,2,21,4,69,-12,-6,1022,0.89,0,0 +2012,2,21,5,82,-12,-6,1022,0.89,0,0 +2012,2,21,6,83,-13,-6,1021,1.78,0,0 +2012,2,21,7,99,-11,-3,1021,1.79,0,0 +2012,2,21,8,117,-11,-3,1022,3.58,0,0 +2012,2,21,9,125,-11,-1,1022,5.37,0,0 +2012,2,21,10,95,-12,1,1022,7.16,0,0 +2012,2,21,11,103,-13,2,1021,0.89,0,0 +2012,2,21,12,123,-12,3,1020,2.68,0,0 +2012,2,21,13,117,-11,4,1020,1.79,0,0 +2012,2,21,14,140,-11,4,1018,3.58,0,0 +2012,2,21,15,151,-11,4,1017,6.71,0,0 +2012,2,21,16,143,-9,4,1017,9.84,0,0 +2012,2,21,17,148,-11,3,1018,11.63,0,0 +2012,2,21,18,162,-11,1,1018,13.42,0,0 +2012,2,21,19,134,-10,0,1018,0.89,0,0 +2012,2,21,20,138,-10,-2,1018,1.34,0,0 +2012,2,21,21,155,-9,-3,1018,1.79,0,0 +2012,2,21,22,172,-6,-2,1018,4.92,0,0 +2012,2,21,23,151,-5,-3,1018,5.81,0,0 +2012,2,22,0,141,-6,-4,1018,0.89,0,0 +2012,2,22,1,153,-5,-3,1018,2.68,0,0 +2012,2,22,2,141,-5,-2,1017,3.57,0,0 +2012,2,22,3,158,-5,-2,1016,0.89,0,0 +2012,2,22,4,153,-3,-1,1015,1.78,0,0 +2012,2,22,5,164,-3,-1,1016,1.79,0,0 +2012,2,22,6,173,-3,-1,1015,4.92,0,0 +2012,2,22,7,174,-3,-1,1014,0.89,0,0 +2012,2,22,8,183,-2,0,1014,0.89,0,0 +2012,2,22,9,178,-2,0,1015,0.45,0,0 +2012,2,22,10,193,-4,0,1014,1.79,0,0 +2012,2,22,11,203,-3,1,1014,3.13,0,0 +2012,2,22,12,206,-3,1,1013,1.79,0,0 +2012,2,22,13,203,-3,2,1012,1.79,0,0 +2012,2,22,14,208,-3,3,1012,2.68,0,0 +2012,2,22,15,219,-4,4,1012,3.57,0,0 +2012,2,22,16,235,-4,5,1012,1.79,0,0 +2012,2,22,17,240,-5,2,1013,3.58,0,0 +2012,2,22,18,241,-4,2,1014,6.71,0,0 +2012,2,22,19,248,-8,4,1017,4.02,0,0 +2012,2,22,20,154,-13,4,1018,7.15,0,0 +2012,2,22,21,25,-15,3,1020,12.96,0,0 +2012,2,22,22,16,-14,3,1022,16.98,0,0 +2012,2,22,23,18,-14,-2,1023,21,0,0 +2012,2,23,0,16,-14,-2,1025,24.13,0,0 +2012,2,23,1,11,-14,-1,1025,29.05,0,0 +2012,2,23,2,11,-14,-2,1026,1.79,0,0 +2012,2,23,3,16,-15,-7,1027,4.92,0,0 +2012,2,23,4,8,-14,-4,1026,9.84,0,0 +2012,2,23,5,9,-15,-4,1027,4.92,0,0 +2012,2,23,6,11,-15,-3,1027,9.84,0,0 +2012,2,23,7,14,-15,-3,1028,14.76,0,0 +2012,2,23,8,16,-15,-2,1029,19.68,0,0 +2012,2,23,9,15,-15,-1,1029,25.49,0,0 +2012,2,23,10,20,-17,1,1028,30.41,0,0 +2012,2,23,11,20,-17,2,1028,33.54,0,0 +2012,2,23,12,18,-18,3,1027,36.67,0,0 +2012,2,23,13,15,-19,5,1026,1.79,0,0 +2012,2,23,14,19,-19,5,1025,1.79,0,0 +2012,2,23,15,20,-20,6,1024,3.58,0,0 +2012,2,23,16,20,-20,6,1024,4.02,0,0 +2012,2,23,17,18,-20,5,1024,8.04,0,0 +2012,2,23,18,18,-19,2,1024,11.17,0,0 +2012,2,23,19,27,-18,1,1025,12.96,0,0 +2012,2,23,20,33,-16,1,1025,16.98,0,0 +2012,2,23,21,57,-14,-2,1025,18.77,0,0 +2012,2,23,22,70,-14,-3,1025,21.9,0,0 +2012,2,23,23,81,-13,-2,1025,23.69,0,0 +2012,2,24,0,84,-12,-4,1025,25.48,0,0 +2012,2,24,1,78,-11,-3,1025,28.61,0,0 +2012,2,24,2,74,-11,-4,1024,30.4,0,0 +2012,2,24,3,79,-11,-4,1024,32.19,0,0 +2012,2,24,4,99,-9,-3,1023,33.98,0,0 +2012,2,24,5,99,-9,-3,1023,37.11,0,0 +2012,2,24,6,108,-9,-3,1023,38.9,0,0 +2012,2,24,7,103,-9,-4,1024,0.89,0,0 +2012,2,24,8,98,-9,-3,1024,2.68,0,0 +2012,2,24,9,98,-8,-3,1024,0.89,0,0 +2012,2,24,10,102,-9,-2,1025,1.78,0,0 +2012,2,24,11,125,-9,-1,1024,2.67,0,0 +2012,2,24,12,126,-10,1,1024,1.79,0,0 +2012,2,24,13,122,-11,3,1023,1.79,0,0 +2012,2,24,14,129,-10,4,1021,0.89,0,0 +2012,2,24,15,119,-11,5,1021,1.78,0,0 +2012,2,24,16,134,-11,5,1021,3.13,0,0 +2012,2,24,17,142,-12,5,1022,1.79,0,0 +2012,2,24,18,153,-12,4,1023,5.81,0,0 +2012,2,24,19,149,-12,1,1024,7.6,0,0 +2012,2,24,20,48,-14,2,1026,4.92,0,0 +2012,2,24,21,11,-15,0,1027,4.92,0,0 +2012,2,24,22,15,-15,-1,1029,9.84,0,0 +2012,2,24,23,10,-16,-2,1029,12.97,0,0 +2012,2,25,0,9,-15,-2,1029,17.89,0,0 +2012,2,25,1,10,-15,-1,1029,23.7,0,0 +2012,2,25,2,10,-15,-2,1030,29.51,0,0 +2012,2,25,3,6,-16,-3,1031,33.53,0,0 +2012,2,25,4,17,-18,-3,1032,39.34,0,0 +2012,2,25,5,19,-19,-4,1032,44.26,0,0 +2012,2,25,6,13,-19,-6,1033,48.28,0,0 +2012,2,25,7,16,-18,-7,1033,52.3,0,0 +2012,2,25,8,14,-18,-4,1034,56.32,0,0 +2012,2,25,9,21,-18,-2,1034,60.34,0,0 +2012,2,25,10,21,-18,-1,1033,64.36,0,0 +2012,2,25,11,21,-21,0,1033,68.38,0,0 +2012,2,25,12,14,-21,1,1032,72.4,0,0 +2012,2,25,13,21,-21,3,1031,76.42,0,0 +2012,2,25,14,25,-21,3,1030,79.55,0,0 +2012,2,25,15,18,-21,2,1029,81.34,0,0 +2012,2,25,16,20,-21,3,1029,1.79,0,0 +2012,2,25,17,25,-21,2,1028,3.58,0,0 +2012,2,25,18,30,-21,0,1029,0.45,0,0 +2012,2,25,19,42,-19,2,1029,3.13,0,0 +2012,2,25,20,41,-19,1,1030,6.26,0,0 +2012,2,25,21,36,-17,0,1029,8.05,0,0 +2012,2,25,22,44,-19,1,1030,11.18,0,0 +2012,2,25,23,49,-20,0,1030,14.31,0,0 +2012,2,26,0,62,-17,-1,1030,15.2,0,0 +2012,2,26,1,70,-18,-5,1030,0.89,0,0 +2012,2,26,2,78,-16,-5,1030,4.02,0,0 +2012,2,26,3,108,-17,-6,1030,7.15,0,0 +2012,2,26,4,139,-16,-7,1030,8.94,0,0 +2012,2,26,5,73,-16,-7,1030,12.07,0,0 +2012,2,26,6,34,-16,-5,1030,1.79,0,0 +2012,2,26,7,29,-17,-4,1030,4.92,0,0 +2012,2,26,8,35,-15,-2,1031,3.13,0,0 +2012,2,26,9,37,-17,0,1031,1.79,0,0 +2012,2,26,10,46,-17,2,1031,4.92,0,0 +2012,2,26,11,30,-18,3,1030,1.79,0,0 +2012,2,26,12,24,-18,5,1030,3.58,0,0 +2012,2,26,13,25,-19,5,1028,1.79,0,0 +2012,2,26,14,22,-19,6,1027,3.58,0,0 +2012,2,26,15,33,-19,6,1026,5.37,0,0 +2012,2,26,16,41,-19,6,1026,3.13,0,0 +2012,2,26,17,47,-18,5,1026,3.13,0,0 +2012,2,26,18,46,-17,2,1026,4.92,0,0 +2012,2,26,19,42,-17,1,1027,6.71,0,0 +2012,2,26,20,59,-16,1,1028,8.5,0,0 +2012,2,26,21,83,-15,-1,1028,10.29,0,0 +2012,2,26,22,111,-17,0,1028,13.42,0,0 +2012,2,26,23,140,-14,-3,1028,15.21,0,0 +2012,2,27,0,139,-12,-2,1028,19.23,0,0 +2012,2,27,1,161,-12,-3,1028,21.02,0,0 +2012,2,27,2,153,-11,-3,1028,22.81,0,0 +2012,2,27,3,133,-9,-3,1027,24.6,0,0 +2012,2,27,4,127,-10,-5,1027,25.49,0,0 +2012,2,27,5,134,-9,-5,1027,0.89,0,0 +2012,2,27,6,155,-9,-6,1027,0.89,0,0 +2012,2,27,7,172,-10,-8,1027,1.78,0,0 +2012,2,27,8,191,-7,-5,1028,1.79,0,0 +2012,2,27,9,201,-6,-3,1028,5.81,0,0 +2012,2,27,10,216,-6,-1,1027,9.83,0,0 +2012,2,27,11,233,-6,0,1026,11.62,0,0 +2012,2,27,12,228,-7,2,1025,13.41,0,0 +2012,2,27,13,230,-8,4,1024,1.79,0,0 +2012,2,27,14,212,-10,6,1022,3.13,0,0 +2012,2,27,15,188,-10,6,1021,7.15,0,0 +2012,2,27,16,193,-11,6,1021,11.17,0,0 +2012,2,27,17,173,-11,5,1021,12.06,0,0 +2012,2,27,18,176,-11,3,1021,12.95,0,0 +2012,2,27,19,179,-10,1,1021,13.84,0,0 +2012,2,27,20,190,-10,1,1021,15.63,0,0 +2012,2,27,21,211,-10,1,1021,0.89,0,0 +2012,2,27,22,278,-10,1,1022,1.78,0,0 +2012,2,27,23,286,-9,0,1021,2.67,0,0 +2012,2,28,0,267,-8,-1,1021,3.56,0,0 +2012,2,28,1,278,-8,-3,1021,0.89,0,0 +2012,2,28,2,290,-8,-3,1021,0.45,0,0 +2012,2,28,3,287,-9,-3,1021,0.9,0,0 +2012,2,28,4,276,-10,-6,1020,0.89,0,0 +2012,2,28,5,274,-9,-6,1020,1.78,0,0 +2012,2,28,6,264,-8,-5,1021,0.89,0,0 +2012,2,28,7,245,-9,-5,1021,1.34,0,0 +2012,2,28,8,259,-8,-4,1022,0.89,0,0 +2012,2,28,9,264,-7,0,1022,0.89,0,0 +2012,2,28,10,299,-7,2,1022,1.79,0,0 +2012,2,28,11,308,-8,3,1022,3.58,0,0 +2012,2,28,12,281,-8,5,1021,5.37,0,0 +2012,2,28,13,192,-8,6,1020,9.39,0,0 +2012,2,28,14,154,-7,7,1019,13.41,0,0 +2012,2,28,15,140,-8,8,1019,19.22,0,0 +2012,2,28,16,112,-9,8,1019,24.14,0,0 +2012,2,28,17,101,-9,7,1019,28.16,0,0 +2012,2,28,18,99,-9,5,1019,29.95,0,0 +2012,2,28,19,106,-9,2,1020,30.84,0,0 +2012,2,28,20,109,-8,0,1020,32.63,0,0 +2012,2,28,21,111,-9,1,1020,35.76,0,0 +2012,2,28,22,129,-9,1,1021,38.89,0,0 +2012,2,28,23,161,-9,1,1020,42.91,0,0 +2012,2,29,0,175,-8,0,1021,46.04,0,0 +2012,2,29,1,173,-7,-2,1021,49.17,0,0 +2012,2,29,2,185,-8,-2,1021,0.89,0,0 +2012,2,29,3,190,-8,-4,1021,0.89,0,0 +2012,2,29,4,202,-8,-5,1020,0.89,0,0 +2012,2,29,5,200,-8,-5,1021,0.89,0,0 +2012,2,29,6,190,-8,-6,1021,0.89,0,0 +2012,2,29,7,184,-8,-6,1021,1.78,0,0 +2012,2,29,8,193,-6,-4,1021,3.57,0,0 +2012,2,29,9,176,-7,1,1022,0.89,0,0 +2012,2,29,10,NA,-6,3,1022,1.79,0,0 +2012,2,29,11,NA,-6,4,1022,1.79,0,0 +2012,2,29,12,NA,-6,5,1021,1.79,0,0 +2012,2,29,13,NA,-5,7,1020,3.58,0,0 +2012,2,29,14,NA,-5,8,1019,5.37,0,0 +2012,2,29,15,NA,-5,8,1019,7.16,0,0 +2012,2,29,16,229,-5,9,1019,8.95,0,0 +2012,2,29,17,203,-5,8,1019,9.84,0,0 +2012,2,29,18,214,-5,6,1019,0.89,0,0 +2012,2,29,19,253,-6,2,1019,1.78,0,0 +2012,2,29,20,285,-6,0,1020,0.45,0,0 +2012,2,29,21,286,-6,-1,1020,1.79,0,0 +2012,2,29,22,318,-5,0,1021,2.68,0,0 +2012,2,29,23,324,-6,-2,1021,0.89,0,0 +2012,3,1,0,322,-5,-1,1021,1.79,0,0 +2012,3,1,1,293,-5,-2,1022,0.89,0,0 +2012,3,1,2,290,-6,-3,1022,3.13,0,0 +2012,3,1,3,270,-6,-3,1022,6.26,0,0 +2012,3,1,4,249,-6,-3,1022,8.05,0,0 +2012,3,1,5,230,-6,-3,1023,9.84,0,0 +2012,3,1,6,227,-6,-2,1022,10.73,0,0 +2012,3,1,7,220,-7,-4,1023,12.52,0,0 +2012,3,1,8,222,-5,-1,1024,0.89,0,0 +2012,3,1,9,209,-5,1,1024,0.89,0,0 +2012,3,1,10,231,-4,3,1025,0.89,0,0 +2012,3,1,11,262,-4,4,1025,3.13,0,0 +2012,3,1,12,326,-4,4,1025,4.92,0,0 +2012,3,1,13,347,-4,5,1024,0.89,0,0 +2012,3,1,14,314,-4,5,1023,0.89,0,0 +2012,3,1,15,268,-2,4,1023,0.89,0,1 +2012,3,1,16,203,0,3,1023,1.78,0,2 +2012,3,1,17,192,1,2,1023,2.23,0,3 +2012,3,1,18,200,0,2,1024,0.89,0,4 +2012,3,1,19,208,1,1,1024,1.78,0,5 +2012,3,1,20,213,1,2,1024,0.89,0,0 +2012,3,1,21,235,0,2,1025,1.78,0,0 +2012,3,1,22,267,1,2,1025,1.79,0,1 +2012,3,1,23,278,1,2,1025,5.81,0,2 +2012,3,2,0,269,1,2,1025,8.94,0,3 +2012,3,2,1,165,1,1,1026,12.07,0,4 +2012,3,2,2,154,0,1,1026,15.2,0,5 +2012,3,2,3,112,0,1,1026,16.99,0,6 +2012,3,2,4,126,-1,0,1026,20.12,1,0 +2012,3,2,5,113,-1,0,1026,23.25,2,0 +2012,3,2,6,119,-1,0,1026,27.27,3,0 +2012,3,2,7,132,-1,0,1027,29.06,4,0 +2012,3,2,8,136,-1,0,1028,30.85,5,0 +2012,3,2,9,121,0,0,1028,33.98,6,0 +2012,3,2,10,111,0,1,1028,37.11,7,0 +2012,3,2,11,120,0,1,1028,40.24,8,0 +2012,3,2,12,121,0,1,1028,44.26,9,0 +2012,3,2,13,110,-1,0,1028,0.89,10,0 +2012,3,2,14,110,0,1,1027,1.79,11,0 +2012,3,2,15,113,1,1,1027,3.58,12,0 +2012,3,2,16,110,0,2,1026,0.89,0,0 +2012,3,2,17,110,-2,2,1027,3.13,0,0 +2012,3,2,18,100,-1,1,1028,4.92,0,0 +2012,3,2,19,83,-2,1,1028,6.71,0,0 +2012,3,2,20,75,-2,1,1028,1.79,0,0 +2012,3,2,21,132,-2,0,1028,0.89,0,0 +2012,3,2,22,159,-2,0,1029,0.89,0,0 +2012,3,2,23,151,-2,-1,1029,0.45,0,0 +2012,3,3,0,130,-1,-1,1029,1.34,0,0 +2012,3,3,1,93,-1,-1,1028,0.89,0,0 +2012,3,3,2,79,-4,-1,1029,2.68,0,0 +2012,3,3,3,79,-5,0,1029,4.47,0,0 +2012,3,3,4,66,-6,0,1029,7.6,0,0 +2012,3,3,5,77,-6,0,1029,11.62,0,0 +2012,3,3,6,73,-5,-1,1029,14.75,0,0 +2012,3,3,7,72,-5,-1,1030,16.54,0,0 +2012,3,3,8,76,-5,-1,1030,18.33,0,0 +2012,3,3,9,101,-4,-1,1030,21.46,0,0 +2012,3,3,10,NA,-5,0,1031,25.48,0,0 +2012,3,3,11,81,-5,1,1030,29.5,0,0 +2012,3,3,12,91,-7,2,1030,32.63,0,0 +2012,3,3,13,98,-7,3,1029,35.76,0,0 +2012,3,3,14,NA,-7,3,1028,37.55,0,0 +2012,3,3,15,94,-7,3,1028,40.68,0,0 +2012,3,3,16,108,-7,2,1027,43.81,0,0 +2012,3,3,17,107,-6,2,1027,46.94,0,0 +2012,3,3,18,108,-8,1,1027,48.73,0,0 +2012,3,3,19,117,-8,0,1027,51.86,0,0 +2012,3,3,20,98,-8,0,1027,52.75,0,0 +2012,3,3,21,112,-8,-1,1027,54.54,0,0 +2012,3,3,22,104,-8,-1,1027,56.33,0,0 +2012,3,3,23,106,-8,0,1028,57.22,0,0 +2012,3,4,0,108,-6,-1,1027,58.11,0,0 +2012,3,4,1,107,-6,-1,1026,59,0,0 +2012,3,4,2,106,-7,-1,1026,0.89,0,0 +2012,3,4,3,113,-8,-2,1025,0.45,0,0 +2012,3,4,4,131,-7,-2,1024,0.9,0,0 +2012,3,4,5,170,-7,-3,1024,0.89,0,0 +2012,3,4,6,132,-7,-4,1024,0.89,0,0 +2012,3,4,7,122,-7,-3,1024,0.89,0,0 +2012,3,4,8,127,-7,-2,1025,0.89,0,0 +2012,3,4,9,114,-6,0,1025,1.78,0,0 +2012,3,4,10,121,-8,2,1024,3.57,0,0 +2012,3,4,11,149,-9,3,1023,6.7,0,0 +2012,3,4,12,149,-9,4,1023,1.79,0,0 +2012,3,4,13,143,-9,5,1021,0.89,0,0 +2012,3,4,14,142,-10,5,1020,1.78,0,0 +2012,3,4,15,124,-10,5,1020,2.67,0,0 +2012,3,4,16,93,-10,5,1019,4.46,0,0 +2012,3,4,17,104,-10,5,1018,5.35,0,0 +2012,3,4,18,104,-9,3,1018,0.89,0,0 +2012,3,4,19,97,-9,0,1018,1.78,0,0 +2012,3,4,20,111,-8,0,1019,0.89,0,0 +2012,3,4,21,143,-7,-1,1019,0.89,0,0 +2012,3,4,22,170,-7,-2,1019,0.89,0,0 +2012,3,4,23,189,-7,-3,1018,2.68,0,0 +2012,3,5,0,201,-7,-3,1018,1.79,0,0 +2012,3,5,1,200,-7,-4,1017,1.79,0,0 +2012,3,5,2,220,-6,-4,1017,0.89,0,0 +2012,3,5,3,229,-6,-3,1016,0.45,0,0 +2012,3,5,4,213,-6,-3,1016,1.79,0,0 +2012,3,5,5,172,-6,-4,1015,0.89,0,0 +2012,3,5,6,183,-7,-5,1015,1.79,0,0 +2012,3,5,7,179,-7,-5,1015,3.58,0,0 +2012,3,5,8,181,-4,-3,1015,0.89,0,0 +2012,3,5,9,182,-5,0,1014,1.79,0,0 +2012,3,5,10,185,-6,1,1014,1.79,0,0 +2012,3,5,11,176,-7,2,1014,1.79,0,0 +2012,3,5,12,176,-7,3,1014,3.58,0,0 +2012,3,5,13,166,-7,3,1013,6.71,0,0 +2012,3,5,14,145,-7,3,1012,9.84,0,0 +2012,3,5,15,130,-6,3,1011,11.63,0,0 +2012,3,5,16,137,-7,4,1011,13.42,0,0 +2012,3,5,17,148,-7,3,1011,15.21,0,0 +2012,3,5,18,154,-7,2,1011,0.89,0,0 +2012,3,5,19,150,-7,1,1011,0.89,0,0 +2012,3,5,20,162,-6,1,1011,0.89,0,0 +2012,3,5,21,177,-6,-3,1012,1.78,0,0 +2012,3,5,22,180,-4,-1,1012,3.13,0,0 +2012,3,5,23,97,-7,1,1012,8.94,0,0 +2012,3,6,0,28,-9,2,1012,14.75,0,0 +2012,3,6,1,24,-13,2,1013,23.69,0,0 +2012,3,6,2,19,-12,1,1013,28.61,0,0 +2012,3,6,3,20,-13,1,1013,34.42,0,0 +2012,3,6,4,13,-15,0,1013,40.23,0,0 +2012,3,6,5,12,-17,1,1014,50.06,0,0 +2012,3,6,6,12,-16,1,1014,61.24,0,0 +2012,3,6,7,16,-16,1,1015,72.42,0,0 +2012,3,6,8,15,-15,2,1016,83.6,0,0 +2012,3,6,9,20,-15,4,1017,94.78,0,0 +2012,3,6,10,27,-14,5,1018,105.96,0,0 +2012,3,6,11,17,-14,6,1018,114.9,0,0 +2012,3,6,12,13,-15,7,1018,123.84,0,0 +2012,3,6,13,17,-15,8,1017,132.78,0,0 +2012,3,6,14,13,-16,9,1017,140.83,0,0 +2012,3,6,15,14,-16,9,1017,148.88,0,0 +2012,3,6,16,18,-16,10,1016,154.69,0,0 +2012,3,6,17,14,-17,9,1016,161.84,0,0 +2012,3,6,18,13,-17,8,1016,167.65,0,0 +2012,3,6,19,18,-16,7,1016,170.78,0,0 +2012,3,6,20,40,-16,7,1017,174.8,0,0 +2012,3,6,21,42,-15,7,1018,177.93,0,0 +2012,3,6,22,35,-14,5,1019,1.79,0,0 +2012,3,6,23,50,-14,2,1018,0.45,0,0 +2012,3,7,0,47,-15,4,1018,4.02,0,0 +2012,3,7,1,33,-14,1,1019,0.89,0,0 +2012,3,7,2,34,-13,2,1019,3.13,0,0 +2012,3,7,3,38,-13,1,1019,0.89,0,0 +2012,3,7,4,26,-12,0,1020,1.79,0,0 +2012,3,7,5,14,-10,0,1021,0.89,0,0 +2012,3,7,6,14,-13,1,1021,4.92,0,0 +2012,3,7,7,11,-13,2,1022,9.84,0,0 +2012,3,7,8,10,-12,5,1023,14.76,0,0 +2012,3,7,9,21,-12,5,1023,19.68,0,0 +2012,3,7,10,18,-12,7,1023,25.49,0,0 +2012,3,7,11,17,-12,8,1022,31.3,0,0 +2012,3,7,12,19,-13,8,1022,37.11,0,0 +2012,3,7,13,20,-14,9,1021,44.26,0,0 +2012,3,7,14,NA,-17,10,1020,51.41,0,0 +2012,3,7,15,23,-17,10,1020,58.56,0,0 +2012,3,7,16,17,-18,10,1020,68.39,0,0 +2012,3,7,17,18,-19,9,1021,79.57,0,0 +2012,3,7,18,16,-17,8,1022,86.72,0,0 +2012,3,7,19,14,-16,6,1023,92.53,0,0 +2012,3,7,20,14,-16,4,1024,100.58,0,0 +2012,3,7,21,13,-19,3,1025,108.63,0,0 +2012,3,7,22,12,-19,2,1027,116.68,0,0 +2012,3,7,23,11,-19,1,1028,123.83,0,0 +2012,3,8,0,12,-17,-2,1029,129.64,0,0 +2012,3,8,1,13,-16,-2,1029,136.79,0,0 +2012,3,8,2,11,-17,-2,1030,4.02,0,0 +2012,3,8,3,12,-17,-2,1030,8.94,0,0 +2012,3,8,4,11,-18,-3,1030,14.75,0,0 +2012,3,8,5,12,-19,-3,1031,19.67,0,0 +2012,3,8,6,14,-20,-4,1031,24.59,0,0 +2012,3,8,7,13,-20,-4,1031,29.51,0,0 +2012,3,8,8,13,-20,-4,1032,34.43,0,0 +2012,3,8,9,16,-20,-2,1032,39.35,0,0 +2012,3,8,10,21,-20,0,1032,4.92,0,0 +2012,3,8,11,20,-21,1,1032,4.92,0,0 +2012,3,8,12,18,-22,2,1032,8.94,0,0 +2012,3,8,13,18,-23,3,1030,4.02,0,0 +2012,3,8,14,19,-23,2,1029,4.02,0,0 +2012,3,8,15,12,-23,3,1028,7.15,0,0 +2012,3,8,16,19,-23,2,1028,10.28,0,0 +2012,3,8,17,16,-22,2,1028,13.41,0,0 +2012,3,8,18,17,-22,2,1028,0.89,0,0 +2012,3,8,19,17,-21,2,1028,1.79,0,0 +2012,3,8,20,18,-21,2,1029,5.81,0,0 +2012,3,8,21,31,-20,0,1029,1.79,0,0 +2012,3,8,22,37,-17,-2,1029,0.89,0,0 +2012,3,8,23,26,-15,-3,1029,0.45,0,0 +2012,3,9,0,18,-14,-4,1029,0.89,0,0 +2012,3,9,1,73,-14,-5,1030,2.68,0,0 +2012,3,9,2,102,-16,-4,1029,4.47,0,0 +2012,3,9,3,112,-13,-4,1030,6.26,0,0 +2012,3,9,4,93,-9,-5,1029,0.89,0,0 +2012,3,9,5,89,-8,-6,1029,1.79,0,0 +2012,3,9,6,93,-8,-6,1029,0.89,0,0 +2012,3,9,7,120,-8,-7,1030,0.89,0,0 +2012,3,9,8,119,-7,-4,1030,0.89,0,0 +2012,3,9,9,112,-7,-2,1030,2.68,0,0 +2012,3,9,10,127,-6,0,1030,4.47,0,0 +2012,3,9,11,133,-6,1,1029,1.79,0,0 +2012,3,9,12,140,-8,3,1028,3.13,0,0 +2012,3,9,13,112,-10,5,1026,8.05,0,0 +2012,3,9,14,82,-12,5,1025,12.97,0,0 +2012,3,9,15,73,-14,6,1024,16.99,0,0 +2012,3,9,16,77,-12,6,1023,21.01,0,0 +2012,3,9,17,73,-14,6,1022,25.93,0,0 +2012,3,9,18,68,-15,4,1022,29.95,0,0 +2012,3,9,19,67,-14,2,1022,33.97,0,0 +2012,3,9,20,69,-14,2,1022,37.99,0,0 +2012,3,9,21,67,-14,2,1023,39.78,0,0 +2012,3,9,22,67,-14,3,1023,42.91,0,0 +2012,3,9,23,77,-10,1,1023,46.04,0,0 +2012,3,10,0,89,-8,-1,1023,47.83,0,0 +2012,3,10,1,90,-7,-2,1023,50.96,0,0 +2012,3,10,2,93,-7,-1,1023,52.75,0,0 +2012,3,10,3,103,-7,-2,1023,0.45,0,0 +2012,3,10,4,89,-7,-2,1023,1.79,0,0 +2012,3,10,5,93,-8,-4,1023,3.58,0,0 +2012,3,10,6,85,-8,-4,1023,5.37,0,0 +2012,3,10,7,32,-8,-5,1024,8.5,0,0 +2012,3,10,8,28,-14,1,1025,15.65,0,0 +2012,3,10,9,26,-17,4,1025,21.46,0,0 +2012,3,10,10,24,-16,5,1025,30.4,0,0 +2012,3,10,11,18,-17,6,1025,37.55,0,0 +2012,3,10,12,21,-18,7,1025,47.38,0,0 +2012,3,10,13,27,-19,6,1025,59.45,0,0 +2012,3,10,14,22,-22,6,1025,70.63,0,0 +2012,3,10,15,20,-23,5,1026,83.59,0,0 +2012,3,10,16,14,-24,4,1027,95.66,0,0 +2012,3,10,17,20,-24,3,1028,107.73,0,0 +2012,3,10,18,10,-24,1,1029,117.56,0,0 +2012,3,10,19,10,-23,-1,1030,126.5,0,0 +2012,3,10,20,9,-22,-2,1032,134.55,0,0 +2012,3,10,21,7,-22,-2,1033,144.38,0,0 +2012,3,10,22,7,-22,-2,1034,153.32,0,0 +2012,3,10,23,5,-22,-4,1035,158.24,0,0 +2012,3,11,0,4,-19,-3,1035,165.39,0,0 +2012,3,11,1,7,-19,-3,1036,173.44,0,0 +2012,3,11,2,6,-20,-4,1035,182.38,0,0 +2012,3,11,3,7,-21,-4,1035,190.43,0,0 +2012,3,11,4,17,-24,-4,1035,197.58,0,0 +2012,3,11,5,12,-23,-5,1036,204.73,0,0 +2012,3,11,6,11,-22,-5,1036,211.88,0,0 +2012,3,11,7,18,-21,-5,1036,215.9,0,0 +2012,3,11,8,12,-21,-4,1037,221.71,0,0 +2012,3,11,9,29,-21,-2,1037,228.86,0,0 +2012,3,11,10,18,-21,-1,1036,236.91,0,0 +2012,3,11,11,18,-24,0,1035,244.96,0,0 +2012,3,11,12,13,-23,2,1034,249.88,0,0 +2012,3,11,13,14,-24,4,1032,255.69,0,0 +2012,3,11,14,16,-23,5,1030,261.5,0,0 +2012,3,11,15,17,-23,6,1030,267.31,0,0 +2012,3,11,16,20,-23,6,1029,273.12,0,0 +2012,3,11,17,18,-23,6,1028,278.93,0,0 +2012,3,11,18,26,-23,4,1028,4.92,0,0 +2012,3,11,19,30,-23,4,1029,9.84,0,0 +2012,3,11,20,30,-22,3,1029,12.97,0,0 +2012,3,11,21,45,-22,3,1029,18.78,0,0 +2012,3,11,22,50,-21,2,1030,22.8,0,0 +2012,3,11,23,64,-20,-2,1030,1.79,0,0 +2012,3,12,0,72,-17,-4,1030,0.45,0,0 +2012,3,12,1,77,-16,-4,1030,0.89,0,0 +2012,3,12,2,84,-18,-6,1030,0.89,0,0 +2012,3,12,3,81,-16,-6,1030,1.79,0,0 +2012,3,12,4,86,-16,-6,1029,3.58,0,0 +2012,3,12,5,78,-15,-6,1030,5.37,0,0 +2012,3,12,6,79,-16,-7,1029,7.16,0,0 +2012,3,12,7,69,-15,-9,1029,8.95,0,0 +2012,3,12,8,95,-14,-1,1030,10.74,0,0 +2012,3,12,9,49,-17,1,1030,1.79,0,0 +2012,3,12,10,46,-18,4,1029,1.79,0,0 +2012,3,12,11,47,-20,6,1028,1.79,0,0 +2012,3,12,12,28,-20,7,1027,1.79,0,0 +2012,3,12,13,33,-20,8,1026,3.58,0,0 +2012,3,12,14,41,-19,9,1025,3.13,0,0 +2012,3,12,15,79,-19,9,1023,7.15,0,0 +2012,3,12,16,64,-19,9,1023,11.17,0,0 +2012,3,12,17,78,-19,8,1022,16.09,0,0 +2012,3,12,18,86,-17,6,1023,19.22,0,0 +2012,3,12,19,81,-16,5,1023,22.35,0,0 +2012,3,12,20,83,-16,4,1023,23.24,0,0 +2012,3,12,21,89,-14,1,1023,24.13,0,0 +2012,3,12,22,96,-14,0,1024,0.89,0,0 +2012,3,12,23,106,-13,1,1023,1.79,0,0 +2012,3,13,0,106,-13,2,1023,3.58,0,0 +2012,3,13,1,111,-13,0,1023,5.37,0,0 +2012,3,13,2,123,-11,-2,1023,7.16,0,0 +2012,3,13,3,135,-13,-5,1022,3.13,0,0 +2012,3,13,4,142,-12,-5,1022,0.89,0,0 +2012,3,13,5,175,-11,-5,1021,1.34,0,0 +2012,3,13,6,175,-12,-5,1021,1.79,0,0 +2012,3,13,7,172,-10,-5,1021,2.68,0,0 +2012,3,13,8,180,-10,-1,1022,0.89,0,0 +2012,3,13,9,156,-10,2,1022,1.78,0,0 +2012,3,13,10,160,-10,6,1021,2.67,0,0 +2012,3,13,11,157,-11,8,1020,1.79,0,0 +2012,3,13,12,170,-12,10,1019,3.13,0,0 +2012,3,13,13,147,-12,12,1018,3.13,0,0 +2012,3,13,14,127,-14,14,1017,10.28,0,0 +2012,3,13,15,96,-13,14,1016,18.33,0,0 +2012,3,13,16,75,-14,14,1015,25.48,0,0 +2012,3,13,17,68,-13,14,1015,32.63,0,0 +2012,3,13,18,63,-14,12,1015,36.65,0,0 +2012,3,13,19,62,-14,13,1016,39.78,0,0 +2012,3,13,20,81,-14,10,1017,4.02,0,0 +2012,3,13,21,75,-14,5,1019,8.04,0,0 +2012,3,13,22,26,-14,3,1020,12.06,0,0 +2012,3,13,23,13,-16,5,1021,3.13,0,0 +2012,3,14,0,10,-16,5,1021,8.05,0,0 +2012,3,14,1,8,-16,2,1022,4.92,0,0 +2012,3,14,2,12,-18,5,1022,5.81,0,0 +2012,3,14,3,11,-19,4,1023,10.73,0,0 +2012,3,14,4,9,-18,4,1023,16.54,0,0 +2012,3,14,5,7,-18,3,1023,21.46,0,0 +2012,3,14,6,7,-18,3,1024,24.59,0,0 +2012,3,14,7,9,-16,3,1025,27.72,0,0 +2012,3,14,8,17,-17,5,1026,31.74,0,0 +2012,3,14,9,26,-17,7,1027,35.76,0,0 +2012,3,14,10,24,-18,8,1027,37.55,0,0 +2012,3,14,11,15,-18,10,1027,1.79,0,0 +2012,3,14,12,19,-17,11,1027,1.79,0,0 +2012,3,14,13,16,-17,11,1026,3.58,0,0 +2012,3,14,14,26,-18,12,1025,3.13,0,0 +2012,3,14,15,23,-18,12,1024,7.15,0,0 +2012,3,14,16,NA,-17,12,1024,12.07,0,0 +2012,3,14,17,77,-16,11,1024,16.09,0,0 +2012,3,14,18,52,-17,9,1024,20.11,0,0 +2012,3,14,19,70,-11,7,1025,24.13,0,0 +2012,3,14,20,72,-11,6,1025,29.94,0,0 +2012,3,14,21,65,-11,5,1027,34.86,0,0 +2012,3,14,22,59,-9,3,1028,38.88,0,0 +2012,3,14,23,55,-9,2,1028,42.9,0,0 +2012,3,15,0,71,-9,0,1028,46.03,0,0 +2012,3,15,1,62,-8,0,1029,47.82,0,0 +2012,3,15,2,68,-8,-1,1028,49.61,0,0 +2012,3,15,3,54,-8,-2,1027,0.89,0,0 +2012,3,15,4,46,-10,-4,1027,0.89,0,0 +2012,3,15,5,54,-10,-5,1027,2.68,0,0 +2012,3,15,6,59,-8,-4,1028,3.57,0,0 +2012,3,15,7,60,-8,-4,1028,0.45,0,0 +2012,3,15,8,89,-8,0,1028,1.79,0,0 +2012,3,15,9,65,-9,3,1028,0.89,0,0 +2012,3,15,10,75,-11,5,1027,2.68,0,0 +2012,3,15,11,76,-11,7,1026,1.79,0,0 +2012,3,15,12,85,-12,8,1025,3.58,0,0 +2012,3,15,13,84,-12,9,1023,5.37,0,0 +2012,3,15,14,74,-13,10,1022,8.5,0,0 +2012,3,15,15,68,-7,10,1020,12.52,0,0 +2012,3,15,16,114,-6,9,1020,15.65,0,0 +2012,3,15,17,137,-6,9,1019,18.78,0,0 +2012,3,15,18,135,-5,9,1019,20.57,0,0 +2012,3,15,19,148,-4,8,1019,23.7,0,0 +2012,3,15,20,165,-1,8,1019,0.89,0,1 +2012,3,15,21,170,-2,7,1019,3.13,0,0 +2012,3,15,22,183,0,5,1019,4.92,0,0 +2012,3,15,23,176,2,4,1019,8.05,0,1 +2012,3,16,0,180,2,4,1018,11.18,0,2 +2012,3,16,1,187,3,4,1018,14.31,0,3 +2012,3,16,2,180,3,3,1016,17.44,0,4 +2012,3,16,3,181,3,3,1016,20.57,0,0 +2012,3,16,4,183,3,3,1014,22.36,0,0 +2012,3,16,5,186,3,3,1014,1.79,0,0 +2012,3,16,6,196,3,3,1014,1.79,0,0 +2012,3,16,7,222,3,3,1014,3.58,0,0 +2012,3,16,8,232,3,4,1014,0.89,0,0 +2012,3,16,9,263,4,4,1014,1.78,0,0 +2012,3,16,10,286,4,6,1014,1.79,0,0 +2012,3,16,11,293,3,7,1013,0.89,0,0 +2012,3,16,12,328,4,8,1012,3.13,0,0 +2012,3,16,13,335,3,9,1012,1.79,0,0 +2012,3,16,14,345,3,10,1010,1.79,0,0 +2012,3,16,15,360,3,11,1010,3.58,0,0 +2012,3,16,16,320,3,11,1010,6.71,0,0 +2012,3,16,17,272,4,10,1010,9.84,0,0 +2012,3,16,18,315,4,9,1010,12.97,0,0 +2012,3,16,19,318,4,8,1011,14.76,0,0 +2012,3,16,20,346,5,7,1012,16.55,0,0 +2012,3,16,21,334,4,6,1012,0.89,0,0 +2012,3,16,22,302,3,4,1013,0.89,0,0 +2012,3,16,23,310,3,4,1013,0.89,0,0 +2012,3,17,0,280,3,4,1013,0.89,0,0 +2012,3,17,1,250,4,4,1013,1.78,0,0 +2012,3,17,2,283,4,4,1012,0.89,0,0 +2012,3,17,3,268,4,4,1012,0.89,0,0 +2012,3,17,4,214,4,4,1012,0.89,0,0 +2012,3,17,5,201,4,4,1012,0.89,0,0 +2012,3,17,6,210,3,3,1012,1.79,0,0 +2012,3,17,7,185,3,3,1013,3.58,0,0 +2012,3,17,8,188,2,2,1013,6.71,0,0 +2012,3,17,9,175,2,2,1013,9.84,0,0 +2012,3,17,10,182,2,2,1013,12.97,0,0 +2012,3,17,11,191,3,3,1013,16.1,0,0 +2012,3,17,12,173,3,3,1013,20.12,0,0 +2012,3,17,13,184,3,3,1012,24.14,0,0 +2012,3,17,14,182,4,4,1012,29.06,0,0 +2012,3,17,15,181,4,4,1012,33.08,0,0 +2012,3,17,16,195,3,4,1012,38,0,1 +2012,3,17,17,245,3,4,1013,42.02,0,0 +2012,3,17,18,204,3,4,1014,46.04,0,1 +2012,3,17,19,211,3,4,1015,49.17,0,2 +2012,3,17,20,219,3,4,1016,50.96,0,3 +2012,3,17,21,185,3,4,1017,52.75,0,4 +2012,3,17,22,137,1,1,1018,54.54,0,5 +2012,3,17,23,120,0,0,1020,0.89,1,0 +2012,3,18,0,113,0,0,1021,1.34,3,0 +2012,3,18,1,107,0,0,1022,5.81,4,0 +2012,3,18,2,30,-1,-1,1022,10.73,5,0 +2012,3,18,3,21,-2,-1,1023,16.54,6,0 +2012,3,18,4,18,-4,-1,1024,21.46,0,0 +2012,3,18,5,25,-5,-1,1025,24.59,0,0 +2012,3,18,6,21,-5,-1,1025,27.72,0,0 +2012,3,18,7,17,-5,-1,1027,30.85,0,0 +2012,3,18,8,17,-7,-1,1027,35.77,0,0 +2012,3,18,9,16,-7,0,1029,39.79,0,0 +2012,3,18,10,15,-8,1,1029,44.71,0,0 +2012,3,18,11,15,-8,2,1029,48.73,0,0 +2012,3,18,12,21,-5,3,1029,0.89,0,0 +2012,3,18,13,26,-4,4,1028,1.79,0,0 +2012,3,18,14,26,-5,4,1027,1.79,0,0 +2012,3,18,15,39,-5,5,1026,3.13,0,0 +2012,3,18,16,43,-6,4,1027,1.79,0,0 +2012,3,18,17,39,-6,4,1027,3.13,0,0 +2012,3,18,18,65,-6,4,1027,6.26,0,0 +2012,3,18,19,76,-5,3,1027,10.28,0,0 +2012,3,18,20,80,-3,3,1027,4.02,0,0 +2012,3,18,21,86,-3,3,1027,1.79,0,0 +2012,3,18,22,74,-3,3,1028,4.92,0,0 +2012,3,18,23,68,-3,3,1028,0.89,0,0 +2012,3,19,0,69,-2,1,1028,1.79,0,0 +2012,3,19,1,64,-2,1,1028,1.79,0,0 +2012,3,19,2,69,-2,1,1028,2.68,0,0 +2012,3,19,3,75,-1,1,1027,0.89,0,0 +2012,3,19,4,87,-1,0,1027,0.89,0,0 +2012,3,19,5,100,-2,0,1027,1.78,0,0 +2012,3,19,6,97,-2,-1,1027,0.89,0,0 +2012,3,19,7,102,-2,-1,1027,1.78,0,0 +2012,3,19,8,76,-3,2,1028,2.67,0,0 +2012,3,19,9,66,-2,4,1028,0.89,0,0 +2012,3,19,10,86,-5,5,1028,1.78,0,0 +2012,3,19,11,114,-7,6,1027,3.57,0,0 +2012,3,19,12,150,-5,7,1026,4.46,0,0 +2012,3,19,13,143,-3,6,1025,1.79,0,0 +2012,3,19,14,154,-3,5,1024,4.92,0,0 +2012,3,19,15,128,-3,5,1023,8.05,0,0 +2012,3,19,16,114,-2,5,1023,12.07,0,0 +2012,3,19,17,101,-2,5,1023,16.09,0,0 +2012,3,19,18,91,-2,5,1023,20.11,0,0 +2012,3,19,19,100,-2,4,1023,23.24,0,0 +2012,3,19,20,100,-2,4,1023,3.13,0,0 +2012,3,19,21,100,-2,4,1023,3.13,0,0 +2012,3,19,22,95,-2,3,1024,4.92,0,0 +2012,3,19,23,106,-2,3,1024,8.05,0,0 +2012,3,20,0,125,-2,0,1024,0.89,0,0 +2012,3,20,1,137,-2,-1,1024,0.89,0,0 +2012,3,20,2,134,-2,-2,1024,0.89,0,0 +2012,3,20,3,142,-1,-1,1024,0.89,0,0 +2012,3,20,4,172,-1,-1,1024,1.34,0,0 +2012,3,20,5,166,-2,-1,1024,0.89,0,0 +2012,3,20,6,164,-3,-2,1024,2.68,0,0 +2012,3,20,7,162,-2,-1,1025,5.81,0,0 +2012,3,20,8,159,0,1,1026,9.83,0,0 +2012,3,20,9,155,-1,4,1026,12.96,0,0 +2012,3,20,10,166,-2,7,1026,16.09,0,0 +2012,3,20,11,158,-4,8,1026,19.22,0,0 +2012,3,20,12,107,-6,9,1025,22.35,0,0 +2012,3,20,13,101,-7,11,1024,1.79,0,0 +2012,3,20,14,129,-6,11,1023,4.92,0,0 +2012,3,20,15,143,-4,11,1023,8.94,0,0 +2012,3,20,16,104,-4,11,1022,12.07,0,0 +2012,3,20,17,90,-4,10,1023,16.09,0,0 +2012,3,20,18,79,-3,9,1023,20.11,0,0 +2012,3,20,19,82,-3,6,1023,23.24,0,0 +2012,3,20,20,94,-1,7,1023,26.37,0,0 +2012,3,20,21,126,0,7,1023,29.5,0,0 +2012,3,20,22,133,0,6,1023,33.52,0,0 +2012,3,20,23,163,1,5,1023,38.44,0,0 +2012,3,21,0,187,1,5,1023,42.46,0,0 +2012,3,21,1,202,1,4,1024,45.59,0,0 +2012,3,21,2,201,1,3,1023,48.72,0,0 +2012,3,21,3,188,0,2,1022,50.51,0,0 +2012,3,21,4,193,0,1,1022,52.3,0,0 +2012,3,21,5,180,-1,1,1022,54.09,0,0 +2012,3,21,6,193,-2,-1,1022,0.45,0,0 +2012,3,21,7,217,-1,0,1021,1.79,0,0 +2012,3,21,8,252,1,2,1022,1.79,0,0 +2012,3,21,9,252,1,3,1022,1.79,0,0 +2012,3,21,10,262,1,6,1021,0.89,0,0 +2012,3,21,11,280,1,7,1021,1.78,0,0 +2012,3,21,12,308,1,8,1020,2.67,0,0 +2012,3,21,13,327,2,10,1018,3.56,0,0 +2012,3,21,14,336,2,10,1018,1.79,0,0 +2012,3,21,15,259,2,11,1017,5.81,0,0 +2012,3,21,16,241,2,10,1017,4.02,0,0 +2012,3,21,17,338,2,10,1017,7.15,0,0 +2012,3,21,18,338,2,9,1017,8.04,0,0 +2012,3,21,19,347,2,8,1017,1.79,0,0 +2012,3,21,20,355,2,7,1017,3.13,0,0 +2012,3,21,21,356,3,7,1018,0.89,0,0 +2012,3,21,22,393,2,7,1019,3.13,0,0 +2012,3,21,23,398,2,7,1019,0.89,0,0 +2012,3,22,0,407,2,8,1019,0.89,0,0 +2012,3,22,1,378,2,7,1020,2.68,0,0 +2012,3,22,2,246,2,7,1020,0.89,0,0 +2012,3,22,3,260,2,6,1019,3.13,0,0 +2012,3,22,4,354,2,6,1020,0.89,0,0 +2012,3,22,5,269,2,6,1021,1.34,0,0 +2012,3,22,6,306,2,5,1022,1.79,0,0 +2012,3,22,7,306,3,5,1023,1.79,0,0 +2012,3,22,8,282,3,6,1024,2.68,0,0 +2012,3,22,9,212,1,8,1024,4.47,0,0 +2012,3,22,10,116,-1,9,1025,8.49,0,0 +2012,3,22,11,107,-2,9,1024,12.51,0,0 +2012,3,22,12,94,-2,11,1024,16.53,0,0 +2012,3,22,13,77,-5,11,1022,4.92,0,0 +2012,3,22,14,78,-6,11,1022,4.02,0,0 +2012,3,22,15,64,-6,12,1021,8.04,0,0 +2012,3,22,16,69,-6,11,1020,12.06,0,0 +2012,3,22,17,107,-7,10,1020,16.08,0,0 +2012,3,22,18,106,-2,9,1020,21,0,0 +2012,3,22,19,91,-1,9,1020,25.92,0,0 +2012,3,22,20,71,-1,8,1021,30.84,0,0 +2012,3,22,21,57,-3,8,1021,34.86,0,0 +2012,3,22,22,50,-3,6,1020,37.99,0,0 +2012,3,22,23,39,-4,6,1020,41.12,0,0 +2012,3,23,0,37,-5,7,1019,46.04,0,0 +2012,3,23,1,41,-4,6,1017,50.06,0,0 +2012,3,23,2,37,0,4,1017,57.21,0,1 +2012,3,23,3,40,-1,4,1017,61.23,0,2 +2012,3,23,4,50,0,3,1015,63.02,0,3 +2012,3,23,5,54,1,2,1015,64.81,0,0 +2012,3,23,6,56,1,2,1015,66.6,0,0 +2012,3,23,7,67,1,1,1015,1.79,0,0 +2012,3,23,8,81,2,3,1014,1.79,0,0 +2012,3,23,9,79,1,7,1014,3.13,0,0 +2012,3,23,10,18,-6,11,1012,12.07,0,0 +2012,3,23,11,22,-6,12,1012,25.93,0,0 +2012,3,23,12,18,-6,13,1012,38,0,0 +2012,3,23,13,28,-9,14,1013,51.86,0,0 +2012,3,23,14,15,-12,13,1014,68.85,0,0 +2012,3,23,15,15,-14,13,1015,84.05,0,0 +2012,3,23,16,11,-14,12,1015,99.25,0,0 +2012,3,23,17,8,-14,12,1015,114.45,0,0 +2012,3,23,18,10,-14,12,1015,129.65,0,0 +2012,3,23,19,9,-13,10,1016,144.85,0,0 +2012,3,23,20,10,-9,5,1017,0.89,0,0 +2012,3,23,21,24,-8,2,1017,1.78,0,0 +2012,3,23,22,20,-8,2,1017,2.67,0,0 +2012,3,23,23,16,-8,2,1017,3.56,0,0 +2012,3,24,0,14,-8,2,1017,4.45,0,0 +2012,3,24,1,18,-8,3,1018,1.79,0,0 +2012,3,24,2,26,-15,9,1018,14.75,0,0 +2012,3,24,3,27,-15,7,1018,26.82,0,0 +2012,3,24,4,27,-13,6,1018,35.76,0,0 +2012,3,24,5,16,-13,6,1018,42.91,0,0 +2012,3,24,6,12,-13,5,1019,47.83,0,0 +2012,3,24,7,15,-13,7,1019,51.85,0,0 +2012,3,24,8,15,-11,9,1019,57.66,0,0 +2012,3,24,9,13,-11,10,1020,68.84,0,0 +2012,3,24,10,19,-12,12,1020,80.02,0,0 +2012,3,24,11,16,-13,14,1019,92.09,0,0 +2012,3,24,12,20,-13,14,1019,101.92,0,0 +2012,3,24,13,12,-13,15,1019,113.99,0,0 +2012,3,24,14,12,-14,15,1018,126.06,0,0 +2012,3,24,15,12,-13,14,1018,138.13,0,0 +2012,3,24,16,10,-13,14,1017,147.96,0,0 +2012,3,24,17,17,-13,13,1017,156.9,0,0 +2012,3,24,18,8,-12,12,1018,164.95,0,0 +2012,3,24,19,11,-12,10,1018,4.02,0,0 +2012,3,24,20,17,-12,9,1020,8.04,0,0 +2012,3,24,21,21,-12,8,1021,0.89,0,0 +2012,3,24,22,46,-11,7,1021,0.89,0,0 +2012,3,24,23,45,-11,4,1022,0.89,0,0 +2012,3,25,0,31,-8,3,1023,1.78,0,0 +2012,3,25,1,40,-9,3,1023,2.67,0,0 +2012,3,25,2,11,-10,2,1024,1.79,0,0 +2012,3,25,3,13,-11,4,1024,3.58,0,0 +2012,3,25,4,15,-11,4,1024,5.37,0,0 +2012,3,25,5,13,-12,3,1025,0.89,0,0 +2012,3,25,6,16,-12,3,1025,1.78,0,0 +2012,3,25,7,25,-8,2,1026,2.67,0,0 +2012,3,25,8,22,-11,7,1026,1.79,0,0 +2012,3,25,9,21,-12,8,1026,0.89,0,0 +2012,3,25,10,22,-12,10,1026,1.78,0,0 +2012,3,25,11,20,-12,10,1026,2.67,0,0 +2012,3,25,12,21,-13,13,1024,4.02,0,0 +2012,3,25,13,27,-13,14,1023,3.13,0,0 +2012,3,25,14,33,-13,14,1022,6.26,0,0 +2012,3,25,15,35,-11,15,1021,10.28,0,0 +2012,3,25,16,35,-10,15,1020,15.2,0,0 +2012,3,25,17,37,-10,14,1020,20.12,0,0 +2012,3,25,18,50,-9,13,1020,24.14,0,0 +2012,3,25,19,68,-8,11,1020,25.93,0,0 +2012,3,25,20,83,-8,11,1021,27.72,0,0 +2012,3,25,21,93,-6,9,1021,29.51,0,0 +2012,3,25,22,115,-5,7,1021,30.4,0,0 +2012,3,25,23,124,-5,6,1021,0.89,0,0 +2012,3,26,0,118,-5,6,1021,1.79,0,0 +2012,3,26,1,130,-5,5,1021,3.58,0,0 +2012,3,26,2,118,-5,3,1020,0.89,0,0 +2012,3,26,3,126,-5,1,1020,0.89,0,0 +2012,3,26,4,128,-5,3,1020,2.68,0,0 +2012,3,26,5,136,-5,2,1020,4.47,0,0 +2012,3,26,6,152,-5,2,1021,6.26,0,0 +2012,3,26,7,156,-4,2,1021,1.79,0,0 +2012,3,26,8,148,-3,6,1021,2.68,0,0 +2012,3,26,9,129,-5,8,1021,0.89,0,0 +2012,3,26,10,131,-5,10,1021,1.79,0,0 +2012,3,26,11,131,-5,12,1020,3.58,0,0 +2012,3,26,12,133,-4,14,1019,1.79,0,0 +2012,3,26,13,138,-4,16,1018,4.92,0,0 +2012,3,26,14,144,-6,17,1017,3.13,0,0 +2012,3,26,15,148,-4,18,1016,3.13,0,0 +2012,3,26,16,147,-4,18,1015,6.26,0,0 +2012,3,26,17,135,-3,18,1015,9.39,0,0 +2012,3,26,18,134,-3,15,1016,10.28,0,0 +2012,3,26,19,146,-2,13,1016,11.17,0,0 +2012,3,26,20,204,-1,9,1017,0.89,0,0 +2012,3,26,21,238,-2,8,1017,1.78,0,0 +2012,3,26,22,237,-1,7,1018,0.89,0,0 +2012,3,26,23,280,0,8,1018,0.89,0,0 +2012,3,27,0,280,-1,5,1019,3.13,0,0 +2012,3,27,1,283,1,5,1019,4.02,0,0 +2012,3,27,2,250,-2,4,1019,5.81,0,0 +2012,3,27,3,261,-2,4,1019,8.94,0,0 +2012,3,27,4,235,-3,5,1019,12.07,0,0 +2012,3,27,5,161,-3,5,1020,13.86,0,0 +2012,3,27,6,139,-4,2,1020,16.99,0,0 +2012,3,27,7,131,-2,5,1021,20.12,0,0 +2012,3,27,8,152,-3,11,1022,23.25,0,0 +2012,3,27,9,119,-5,15,1022,26.38,0,0 +2012,3,27,10,87,-6,17,1022,29.51,0,0 +2012,3,27,11,72,-8,20,1022,32.64,0,0 +2012,3,27,12,49,-9,22,1021,35.77,0,0 +2012,3,27,13,27,-12,23,1020,40.69,0,0 +2012,3,27,14,26,-11,23,1019,45.61,0,0 +2012,3,27,15,32,-11,24,1019,52.76,0,0 +2012,3,27,16,31,-10,25,1018,58.57,0,0 +2012,3,27,17,34,-10,23,1018,1.79,0,0 +2012,3,27,18,42,-8,21,1019,0.89,0,0 +2012,3,27,19,41,-10,21,1019,4.02,0,0 +2012,3,27,20,56,-6,17,1020,4.91,0,0 +2012,3,27,21,72,-4,13,1021,0.89,0,0 +2012,3,27,22,95,-4,11,1022,1.78,0,0 +2012,3,27,23,131,-4,10,1022,0.89,0,0 +2012,3,28,0,117,-8,16,1023,4.91,0,0 +2012,3,28,1,26,-9,16,1024,8.93,0,0 +2012,3,28,2,16,-8,15,1024,13.85,0,0 +2012,3,28,3,18,-8,14,1024,16.98,0,0 +2012,3,28,4,3,-8,9,1025,20.11,0,0 +2012,3,28,5,5,-8,10,1026,0.45,0,0 +2012,3,28,6,40,-7,5,1026,1.34,0,0 +2012,3,28,7,78,-4,8,1027,1.79,0,0 +2012,3,28,8,97,-5,13,1027,1.79,0,0 +2012,3,28,9,96,-5,15,1028,1.79,0,0 +2012,3,28,10,62,-6,16,1027,5.81,0,0 +2012,3,28,11,61,-5,18,1027,10.73,0,0 +2012,3,28,12,58,-6,19,1026,15.65,0,0 +2012,3,28,13,51,-5,19,1025,20.57,0,0 +2012,3,28,14,59,-4,19,1023,25.49,0,0 +2012,3,28,15,57,-4,19,1022,32.64,0,0 +2012,3,28,16,48,-5,18,1021,38.45,0,0 +2012,3,28,17,48,-4,17,1020,44.26,0,0 +2012,3,28,18,41,-5,16,1020,50.07,0,0 +2012,3,28,19,37,-5,14,1020,54.99,0,0 +2012,3,28,20,32,-5,13,1020,58.12,0,0 +2012,3,28,21,38,-4,12,1020,61.25,0,0 +2012,3,28,22,47,-4,11,1019,65.27,0,0 +2012,3,28,23,45,-3,11,1019,68.4,0,0 +2012,3,29,0,47,-3,11,1017,0.89,0,0 +2012,3,29,1,54,-3,11,1017,1.78,0,0 +2012,3,29,2,66,-3,10,1016,2.67,0,0 +2012,3,29,3,60,-1,10,1015,3.56,0,1 +2012,3,29,4,69,-1,9,1014,0.89,0,2 +2012,3,29,5,77,-2,9,1014,0.89,0,0 +2012,3,29,6,80,-1,8,1013,0.89,0,0 +2012,3,29,7,77,-2,9,1014,1.78,0,0 +2012,3,29,8,77,-1,11,1014,4.92,0,1 +2012,3,29,9,79,-2,14,1014,0.89,0,0 +2012,3,29,10,87,-1,17,1013,1.79,0,0 +2012,3,29,11,78,0,21,1012,4.92,0,0 +2012,3,29,12,64,-4,22,1012,12.07,0,0 +2012,3,29,13,50,-7,23,1011,25.03,0,0 +2012,3,29,14,36,-9,23,1011,36.21,0,0 +2012,3,29,15,30,-9,23,1011,49.17,0,0 +2012,3,29,16,23,-11,22,1011,60.35,0,0 +2012,3,29,17,17,-11,21,1011,72.42,0,0 +2012,3,29,18,16,-13,20,1011,83.6,0,0 +2012,3,29,19,13,-13,17,1013,92.54,0,0 +2012,3,29,20,10,-13,16,1015,102.37,0,0 +2012,3,29,21,8,-13,15,1015,115.33,0,0 +2012,3,29,22,10,-11,13,1018,124.27,0,0 +2012,3,29,23,18,-10,12,1019,136.34,0,0 +2012,3,30,0,15,-11,11,1020,153.33,0,0 +2012,3,30,1,10,-11,10,1021,162.27,0,0 +2012,3,30,2,7,-12,9,1021,171.21,0,0 +2012,3,30,3,10,-12,9,1022,181.04,0,0 +2012,3,30,4,7,-12,8,1022,190.87,0,0 +2012,3,30,5,5,-11,7,1023,198.92,0,0 +2012,3,30,6,7,-11,6,1023,206.07,0,0 +2012,3,30,7,8,-12,7,1024,215.9,0,0 +2012,3,30,8,10,-12,9,1025,223.95,0,0 +2012,3,30,9,11,-11,9,1025,227.97,0,0 +2012,3,30,10,13,-14,10,1024,233.78,0,0 +2012,3,30,11,20,-15,11,1023,245.85,0,0 +2012,3,30,12,22,-14,13,1023,258.81,0,0 +2012,3,30,13,13,-15,13,1022,270.88,0,0 +2012,3,30,14,16,-15,13,1022,279.82,0,0 +2012,3,30,15,8,-17,13,1021,286.97,0,0 +2012,3,30,16,12,-19,12,1020,294.12,0,0 +2012,3,30,17,19,-18,11,1020,303.95,0,0 +2012,3,30,18,13,-17,11,1020,311.1,0,0 +2012,3,30,19,20,-18,11,1019,315.12,0,0 +2012,3,30,20,30,-18,11,1019,318.25,0,0 +2012,3,30,21,32,-10,10,1019,0.89,0,0 +2012,3,30,22,34,-12,10,1018,3.13,0,0 +2012,3,30,23,37,-11,10,1017,3.13,0,0 +2012,3,31,0,40,-10,11,1017,7.15,0,0 +2012,3,31,1,42,-9,8,1017,1.79,0,0 +2012,3,31,2,42,-11,8,1017,5.81,0,0 +2012,3,31,3,33,-16,8,1018,12.96,0,0 +2012,3,31,4,11,-16,7,1018,25.92,0,0 +2012,3,31,5,10,-16,6,1019,34.86,0,0 +2012,3,31,6,10,-16,5,1020,43.8,0,0 +2012,3,31,7,11,-13,5,1021,4.92,0,0 +2012,3,31,8,10,-14,6,1021,10.73,0,0 +2012,3,31,9,17,-16,6,1021,16.54,0,0 +2012,3,31,10,18,-17,8,1022,20.56,0,0 +2012,3,31,11,14,-17,9,1021,25.48,0,0 +2012,3,31,12,21,-17,11,1020,4.02,0,0 +2012,3,31,13,20,-17,11,1020,1.79,0,0 +2012,3,31,14,21,-16,13,1019,5.81,0,0 +2012,3,31,15,24,-16,13,1019,5.81,0,0 +2012,3,31,16,29,-15,13,1018,10.73,0,0 +2012,3,31,17,26,-14,12,1018,14.75,0,0 +2012,3,31,18,27,-13,12,1018,17.88,0,0 +2012,3,31,19,24,-13,9,1018,21.01,0,0 +2012,3,31,20,27,-12,9,1019,24.14,0,0 +2012,3,31,21,54,-12,9,1019,28.16,0,0 +2012,3,31,22,54,-9,7,1019,32.18,0,0 +2012,3,31,23,52,-8,6,1019,33.97,0,0 +2012,4,1,0,39,-8,5,1019,35.76,0,0 +2012,4,1,1,57,-7,5,1019,37.55,0,0 +2012,4,1,2,55,-7,4,1019,40.68,0,0 +2012,4,1,3,71,-7,4,1019,43.81,0,0 +2012,4,1,4,65,-8,4,1019,45.6,0,0 +2012,4,1,5,57,-7,3,1019,0.89,0,0 +2012,4,1,6,54,-4,4,1019,1.79,0,0 +2012,4,1,7,53,-3,4,1019,0.89,0,0 +2012,4,1,8,82,-5,6,1019,1.79,0,0 +2012,4,1,9,90,-7,7,1019,4.92,0,0 +2012,4,1,10,78,-7,9,1019,8.05,0,0 +2012,4,1,11,76,-9,10,1018,12.07,0,0 +2012,4,1,12,79,-7,11,1017,16.99,0,0 +2012,4,1,13,63,-7,12,1015,24.14,0,0 +2012,4,1,14,56,-6,12,1015,29.95,0,0 +2012,4,1,15,49,-7,11,1014,35.76,0,0 +2012,4,1,16,40,-8,11,1014,42.91,0,0 +2012,4,1,17,43,-7,9,1014,50.06,0,0 +2012,4,1,18,38,-7,9,1015,54.98,0,0 +2012,4,1,19,36,-7,8,1015,60.79,0,0 +2012,4,1,20,43,-8,8,1016,64.81,0,0 +2012,4,1,21,47,-9,7,1016,67.94,0,0 +2012,4,1,22,54,-10,7,1016,71.07,0,0 +2012,4,1,23,44,-10,7,1015,72.86,0,0 +2012,4,2,0,28,-10,6,1014,0.89,0,0 +2012,4,2,1,41,-9,6,1014,1.78,0,0 +2012,4,2,2,31,-9,7,1014,1.79,0,0 +2012,4,2,3,35,-8,6,1013,0.89,0,0 +2012,4,2,4,45,-7,6,1013,0.45,0,0 +2012,4,2,5,36,-7,6,1013,0.9,0,0 +2012,4,2,6,28,-6,6,1013,0.89,0,0 +2012,4,2,7,35,-5,6,1014,2.68,0,0 +2012,4,2,8,38,-4,6,1015,4.47,0,0 +2012,4,2,9,49,-5,7,1016,0.89,0,0 +2012,4,2,10,46,-8,9,1016,1.78,0,0 +2012,4,2,11,60,-7,10,1016,1.79,0,0 +2012,4,2,12,52,-7,11,1015,3.58,0,0 +2012,4,2,13,48,-5,11,1015,7.6,0,0 +2012,4,2,14,52,-6,11,1014,10.73,0,0 +2012,4,2,15,55,-6,11,1014,14.75,0,0 +2012,4,2,16,64,-3,11,1014,19.67,0,0 +2012,4,2,17,58,-3,11,1014,24.59,0,0 +2012,4,2,18,55,-3,10,1016,29.51,0,0 +2012,4,2,19,61,-3,10,1017,3.13,0,0 +2012,4,2,20,63,-2,9,1019,4.02,0,0 +2012,4,2,21,12,-6,6,1020,4.92,0,0 +2012,4,2,22,11,-14,10,1020,10.73,0,0 +2012,4,2,23,11,-12,9,1021,18.78,0,0 +2012,4,3,0,11,-10,8,1021,27.72,0,0 +2012,4,3,1,10,-12,7,1022,36.66,0,0 +2012,4,3,2,8,-14,6,1021,42.47,0,0 +2012,4,3,3,7,-14,7,1021,50.52,0,0 +2012,4,3,4,8,-14,5,1020,56.33,0,0 +2012,4,3,5,15,-14,3,1020,61.25,0,0 +2012,4,3,6,17,-13,3,1020,0.89,0,0 +2012,4,3,7,32,-12,5,1020,4.02,0,0 +2012,4,3,8,20,-15,10,1019,1.79,0,0 +2012,4,3,9,24,-14,10,1019,3.13,0,0 +2012,4,3,10,26,-15,12,1018,8.94,0,0 +2012,4,3,11,28,-15,13,1017,14.75,0,0 +2012,4,3,12,34,-14,15,1015,4.92,0,0 +2012,4,3,13,24,-13,17,1013,8.05,0,0 +2012,4,3,14,39,-13,19,1012,8.94,0,0 +2012,4,3,15,35,-12,20,1010,14.75,0,0 +2012,4,3,16,32,-15,21,1009,20.56,0,0 +2012,4,3,17,27,-15,20,1009,28.61,0,0 +2012,4,3,18,10,-16,19,1010,33.53,0,0 +2012,4,3,19,18,-14,18,1010,36.66,0,0 +2012,4,3,20,19,-15,17,1011,40.68,0,0 +2012,4,3,21,15,-16,17,1012,46.49,0,0 +2012,4,3,22,27,-17,16,1011,51.41,0,0 +2012,4,3,23,25,-17,15,1011,0.89,0,0 +2012,4,4,0,18,-13,10,1011,1.79,0,0 +2012,4,4,1,25,-16,14,1010,6.71,0,0 +2012,4,4,2,25,-16,12,1009,8.5,0,0 +2012,4,4,3,9,-16,12,1008,10.29,0,0 +2012,4,4,4,9,-14,9,1008,0.89,0,0 +2012,4,4,5,30,-16,15,1009,7.15,0,0 +2012,4,4,6,19,-15,14,1009,12.96,0,0 +2012,4,4,7,10,-13,15,1010,21.01,0,0 +2012,4,4,8,11,-12,16,1012,25.93,0,0 +2012,4,4,9,32,-11,16,1012,31.74,0,0 +2012,4,4,10,22,-11,18,1013,41.57,0,0 +2012,4,4,11,21,-12,19,1012,49.62,0,0 +2012,4,4,12,21,-12,18,1012,57.67,0,0 +2012,4,4,13,14,-13,19,1012,64.82,0,0 +2012,4,4,14,18,-15,20,1011,76.89,0,0 +2012,4,4,15,22,-15,20,1010,86.72,0,0 +2012,4,4,16,18,-16,19,1010,96.55,0,0 +2012,4,4,17,10,-16,19,1010,104.6,0,0 +2012,4,4,18,11,-15,16,1011,113.54,0,0 +2012,4,4,19,11,-16,14,1012,121.59,0,0 +2012,4,4,20,10,-16,13,1014,128.74,0,0 +2012,4,4,21,11,-16,12,1014,4.02,0,0 +2012,4,4,22,11,-15,12,1015,3.13,0,0 +2012,4,4,23,11,-14,13,1016,8.94,0,0 +2012,4,5,0,19,-14,11,1016,12.07,0,0 +2012,4,5,1,22,-17,12,1016,20.12,0,0 +2012,4,5,2,23,-16,11,1016,27.27,0,0 +2012,4,5,3,11,-15,9,1016,34.42,0,0 +2012,4,5,4,9,-15,9,1016,40.23,0,0 +2012,4,5,5,11,-14,7,1017,46.04,0,0 +2012,4,5,6,12,-14,9,1018,50.96,0,0 +2012,4,5,7,8,-13,10,1018,56.77,0,0 +2012,4,5,8,17,-14,12,1018,61.69,0,0 +2012,4,5,9,14,-13,14,1018,64.82,0,0 +2012,4,5,10,13,-15,14,1017,68.84,0,0 +2012,4,5,11,13,-15,17,1016,75.99,0,0 +2012,4,5,12,31,-14,17,1015,88.06,0,0 +2012,4,5,13,22,-12,19,1015,99.24,0,0 +2012,4,5,14,25,-12,19,1014,109.07,0,0 +2012,4,5,15,13,-12,19,1013,118.9,0,0 +2012,4,5,16,17,-12,18,1013,128.73,0,0 +2012,4,5,17,17,-12,17,1013,140.8,0,0 +2012,4,5,18,13,-12,16,1013,151.98,0,0 +2012,4,5,19,16,-12,15,1014,163.16,0,0 +2012,4,5,20,12,-13,14,1015,171.21,0,0 +2012,4,5,21,13,-12,14,1016,177.02,0,0 +2012,4,5,22,27,-11,13,1017,181.94,0,0 +2012,4,5,23,15,-9,12,1017,189.09,0,0 +2012,4,6,0,15,-8,12,1017,3.13,0,0 +2012,4,6,1,20,-8,12,1017,6.26,0,0 +2012,4,6,2,20,-8,12,1017,9.39,0,0 +2012,4,6,3,17,-6,9,1017,0.89,0,0 +2012,4,6,4,24,-5,8,1017,0.89,0,0 +2012,4,6,5,36,-5,7,1017,0.45,0,0 +2012,4,6,6,37,-5,5,1017,0.9,0,0 +2012,4,6,7,38,-3,8,1018,0.89,0,0 +2012,4,6,8,44,-6,12,1018,1.79,0,0 +2012,4,6,9,51,-8,15,1019,0.89,0,0 +2012,4,6,10,49,-8,19,1018,1.79,0,0 +2012,4,6,11,37,-8,19,1018,5.81,0,0 +2012,4,6,12,37,-8,20,1017,4.02,0,0 +2012,4,6,13,35,-8,21,1016,4.92,0,0 +2012,4,6,14,28,-7,23,1015,8.94,0,0 +2012,4,6,15,24,-7,23,1014,4.02,0,0 +2012,4,6,16,21,-8,23,1014,4.02,0,0 +2012,4,6,17,20,-8,23,1014,7.15,0,0 +2012,4,6,18,32,-3,14,1015,8.05,0,0 +2012,4,6,19,36,-3,10,1017,15.2,0,0 +2012,4,6,20,28,-4,9,1019,20.12,0,0 +2012,4,6,21,18,-5,7,1021,24.14,0,0 +2012,4,6,22,21,-7,6,1022,27.27,0,0 +2012,4,6,23,23,-8,6,1022,29.06,0,0 +2012,4,7,0,18,-8,5,1021,30.85,0,0 +2012,4,7,1,14,-8,3,1021,0.89,0,0 +2012,4,7,2,27,-8,3,1021,0.89,0,0 +2012,4,7,3,42,-8,1,1021,0.89,0,0 +2012,4,7,4,46,-6,2,1020,0.89,0,0 +2012,4,7,5,53,-8,2,1020,0.89,0,0 +2012,4,7,6,52,-8,1,1020,2.68,0,0 +2012,4,7,7,58,-4,4,1020,4.47,0,0 +2012,4,7,8,53,-8,8,1020,6.26,0,0 +2012,4,7,9,56,-9,10,1019,0.89,0,0 +2012,4,7,10,53,-9,11,1018,2.68,0,0 +2012,4,7,11,54,-7,13,1017,4.47,0,0 +2012,4,7,12,63,-7,14,1016,6.26,0,0 +2012,4,7,13,80,-6,16,1014,4.02,0,0 +2012,4,7,14,92,-6,18,1013,8.94,0,0 +2012,4,7,15,81,-4,20,1012,13.86,0,0 +2012,4,7,16,76,-4,20,1011,19.67,0,0 +2012,4,7,17,95,-3,20,1010,23.69,0,0 +2012,4,7,18,87,-2,20,1010,26.82,0,0 +2012,4,7,19,83,-1,16,1010,1.79,0,0 +2012,4,7,20,124,0,15,1011,0.89,0,0 +2012,4,7,21,171,-1,14,1012,1.79,0,0 +2012,4,7,22,151,0,12,1012,0.89,0,0 +2012,4,7,23,147,-1,13,1012,1.79,0,0 +2012,4,8,0,160,-2,12,1013,4.92,0,0 +2012,4,8,1,154,-2,12,1013,6.71,0,0 +2012,4,8,2,123,-1,10,1014,8.5,0,0 +2012,4,8,3,93,-8,10,1014,12.52,0,0 +2012,4,8,4,92,-11,14,1015,17.44,0,0 +2012,4,8,5,49,-14,14,1015,24.59,0,0 +2012,4,8,6,42,-14,13,1016,27.72,0,0 +2012,4,8,7,35,-14,15,1017,33.53,0,0 +2012,4,8,8,33,-16,17,1017,41.58,0,0 +2012,4,8,9,24,-15,18,1017,48.73,0,0 +2012,4,8,10,91,-16,19,1017,54.54,0,0 +2012,4,8,11,42,-15,20,1017,60.35,0,0 +2012,4,8,12,20,-14,22,1016,66.16,0,0 +2012,4,8,13,18,-15,22,1015,71.08,0,0 +2012,4,8,14,23,-14,24,1014,76.89,0,0 +2012,4,8,15,23,-14,24,1013,84.94,0,0 +2012,4,8,16,22,-14,24,1012,90.75,0,0 +2012,4,8,17,66,-13,25,1012,95.67,0,0 +2012,4,8,18,17,-14,24,1012,97.46,0,0 +2012,4,8,19,16,-13,18,1013,4.02,0,0 +2012,4,8,20,27,-12,18,1013,7.15,0,0 +2012,4,8,21,31,-9,17,1013,8.94,0,0 +2012,4,8,22,47,-9,17,1013,12.07,0,0 +2012,4,8,23,49,-7,18,1014,15.2,0,0 +2012,4,9,0,63,-6,14,1014,16.99,0,0 +2012,4,9,1,79,-5,12,1014,18.78,0,0 +2012,4,9,2,80,-5,13,1014,20.57,0,0 +2012,4,9,3,100,-4,10,1014,22.36,0,0 +2012,4,9,4,111,-3,11,1014,25.49,0,0 +2012,4,9,5,148,-2,10,1014,27.28,0,0 +2012,4,9,6,150,-1,10,1015,29.07,0,0 +2012,4,9,7,164,0,10,1015,0.89,0,0 +2012,4,9,8,164,0,13,1016,0.89,0,0 +2012,4,9,9,181,0,16,1016,1.79,0,0 +2012,4,9,10,183,0,18,1015,4.92,0,0 +2012,4,9,11,170,0,21,1014,8.05,0,0 +2012,4,9,12,171,1,23,1012,12.97,0,0 +2012,4,9,13,160,1,24,1012,17.89,0,0 +2012,4,9,14,163,2,25,1011,22.81,0,0 +2012,4,9,15,138,4,25,1010,27.73,0,0 +2012,4,9,16,119,4,27,1009,34.88,0,0 +2012,4,9,17,99,3,26,1008,42.03,0,0 +2012,4,9,18,108,5,25,1009,46.95,0,0 +2012,4,9,19,90,5,24,1009,51.87,0,0 +2012,4,9,20,70,4,22,1010,55.89,0,0 +2012,4,9,21,73,4,22,1010,59.02,0,0 +2012,4,9,22,79,8,18,1011,60.81,0,0 +2012,4,9,23,91,7,18,1011,62.6,0,0 +2012,4,10,0,81,7,18,1011,64.39,0,0 +2012,4,10,1,80,7,17,1010,0.89,0,0 +2012,4,10,2,91,7,18,1010,3.13,0,0 +2012,4,10,3,101,7,17,1009,7.15,0,0 +2012,4,10,4,107,7,16,1009,11.17,0,0 +2012,4,10,5,104,7,15,1009,14.3,0,0 +2012,4,10,6,94,8,14,1009,0.89,0,0 +2012,4,10,7,72,9,14,1009,1.78,0,0 +2012,4,10,8,68,6,20,1009,8.05,0,0 +2012,4,10,9,41,6,21,1009,20.12,0,0 +2012,4,10,10,45,6,21,1010,28.17,0,0 +2012,4,10,11,50,8,21,1010,39.35,0,0 +2012,4,10,12,62,8,21,1009,49.18,0,0 +2012,4,10,13,60,8,22,1008,57.23,0,0 +2012,4,10,14,54,8,22,1007,65.28,0,0 +2012,4,10,15,56,8,22,1007,71.09,0,0 +2012,4,10,16,56,8,21,1006,78.24,0,0 +2012,4,10,17,50,7,20,1006,85.39,0,0 +2012,4,10,18,49,7,20,1007,90.31,0,0 +2012,4,10,19,38,13,15,1008,92.1,0,1 +2012,4,10,20,32,13,15,1010,5.81,0,2 +2012,4,10,21,32,12,14,1011,1.79,0,0 +2012,4,10,22,51,11,13,1011,1.79,0,0 +2012,4,10,23,55,12,13,1012,3.58,0,0 +2012,4,11,0,37,2,14,1013,7.15,0,0 +2012,4,11,1,38,-1,13,1014,7.15,0,0 +2012,4,11,2,13,-4,13,1015,8.05,0,0 +2012,4,11,3,12,-4,12,1016,12.97,0,0 +2012,4,11,4,9,-6,11,1017,18.78,0,0 +2012,4,11,5,6,-7,9,1018,5.81,0,0 +2012,4,11,6,7,-10,9,1019,11.62,0,0 +2012,4,11,7,8,-11,10,1020,18.77,0,0 +2012,4,11,8,10,-10,12,1021,24.58,0,0 +2012,4,11,9,10,-8,13,1020,29.5,0,0 +2012,4,11,10,29,-12,14,1020,34.42,0,0 +2012,4,11,11,15,-13,16,1018,39.34,0,0 +2012,4,11,12,14,-11,17,1017,44.26,0,0 +2012,4,11,13,14,-11,17,1016,49.18,0,0 +2012,4,11,14,14,-11,19,1013,56.33,0,0 +2012,4,11,15,18,-11,19,1013,63.48,0,0 +2012,4,11,16,15,-12,19,1012,71.53,0,0 +2012,4,11,17,9,-13,19,1011,78.68,0,0 +2012,4,11,18,8,-13,19,1010,83.6,0,0 +2012,4,11,19,43,-13,18,1010,4.92,0,0 +2012,4,11,20,22,-5,16,1011,12.07,0,0 +2012,4,11,21,32,-3,16,1011,17.88,0,0 +2012,4,11,22,40,-3,15,1011,21.9,0,0 +2012,4,11,23,39,-2,15,1011,25.92,0,0 +2012,4,12,0,45,-5,14,1011,4.02,0,0 +2012,4,12,1,52,-8,14,1011,8.04,0,0 +2012,4,12,2,51,-11,14,1011,11.17,0,0 +2012,4,12,3,33,-11,14,1010,14.3,0,0 +2012,4,12,4,49,-12,14,1010,17.43,0,0 +2012,4,12,5,46,-12,14,1009,21.45,0,0 +2012,4,12,6,57,-12,13,1010,24.58,0,0 +2012,4,12,7,68,-12,15,1010,29.5,0,0 +2012,4,12,8,47,-12,17,1010,34.42,0,0 +2012,4,12,9,13,-11,18,1010,42.47,0,0 +2012,4,12,10,40,-9,21,1010,50.52,0,0 +2012,4,12,11,22,-8,22,1009,58.57,0,0 +2012,4,12,12,16,-8,21,1008,64.38,0,0 +2012,4,12,13,22,-8,22,1007,4.02,0,0 +2012,4,12,14,13,-7,25,1007,3.13,0,0 +2012,4,12,15,22,-6,25,1006,7.15,0,0 +2012,4,12,16,24,-6,26,1005,11.17,0,0 +2012,4,12,17,18,-5,25,1005,0.89,0,0 +2012,4,12,18,17,-5,23,1005,3.13,0,0 +2012,4,12,19,10,-4,18,1005,6.26,0,0 +2012,4,12,20,19,-4,18,1006,9.39,0,0 +2012,4,12,21,29,-1,18,1007,11.18,0,0 +2012,4,12,22,28,-3,20,1007,14.31,0,0 +2012,4,12,23,33,-2,17,1008,0.89,0,0 +2012,4,13,0,51,-1,14,1009,1.78,0,0 +2012,4,13,1,56,1,12,1009,2.67,0,0 +2012,4,13,2,84,1,10,1009,0.89,0,0 +2012,4,13,3,80,1,10,1009,0.89,0,0 +2012,4,13,4,77,2,8,1008,1.78,0,0 +2012,4,13,5,89,2,7,1008,2.67,0,0 +2012,4,13,6,63,3,7,1009,3.12,0,0 +2012,4,13,7,90,4,12,1009,4.01,0,0 +2012,4,13,8,91,1,15,1010,1.79,0,0 +2012,4,13,9,91,2,18,1010,3.58,0,0 +2012,4,13,10,86,2,21,1009,5.37,0,0 +2012,4,13,11,85,0,23,1009,8.5,0,0 +2012,4,13,12,58,-1,26,1007,13.42,0,0 +2012,4,13,13,46,-3,27,1006,18.34,0,0 +2012,4,13,14,29,-3,27,1005,23.26,0,0 +2012,4,13,15,25,-3,26,1004,30.41,0,0 +2012,4,13,16,22,-3,27,1004,36.22,0,0 +2012,4,13,17,14,-2,25,1004,42.03,0,0 +2012,4,13,18,22,-1,24,1004,49.18,0,0 +2012,4,13,19,19,-1,23,1005,54.1,0,0 +2012,4,13,20,20,0,19,1005,57.23,0,0 +2012,4,13,21,24,-1,23,1006,64.38,0,0 +2012,4,13,22,34,0,22,1006,71.53,0,0 +2012,4,13,23,47,1,22,1006,78.68,0,0 +2012,4,14,0,50,1,21,1006,83.6,0,0 +2012,4,14,1,44,1,18,1007,85.39,0,0 +2012,4,14,2,56,2,17,1007,87.18,0,0 +2012,4,14,3,56,3,12,1007,0.89,0,0 +2012,4,14,4,63,3,11,1007,1.34,0,0 +2012,4,14,5,72,2,8,1007,1.79,0,0 +2012,4,14,6,76,4,10,1008,0.45,0,0 +2012,4,14,7,92,6,13,1008,0.9,0,0 +2012,4,14,8,101,4,18,1008,0.89,0,0 +2012,4,14,9,112,4,21,1008,0.89,0,0 +2012,4,14,10,111,4,23,1007,3.13,0,0 +2012,4,14,11,89,0,26,1007,6.26,0,0 +2012,4,14,12,51,-3,28,1006,10.28,0,0 +2012,4,14,13,49,-5,28,1005,14.3,0,0 +2012,4,14,14,29,-6,30,1004,20.11,0,0 +2012,4,14,15,34,-6,30,1002,25.92,0,0 +2012,4,14,16,36,-5,29,1002,33.97,0,0 +2012,4,14,17,31,-5,28,1002,41.12,0,0 +2012,4,14,18,22,-5,27,1002,50.06,0,0 +2012,4,14,19,22,-3,25,1003,55.87,0,0 +2012,4,14,20,34,-2,24,1003,60.79,0,0 +2012,4,14,21,51,0,24,1003,66.6,0,0 +2012,4,14,22,63,1,22,1003,71.52,0,0 +2012,4,14,23,69,2,20,1003,76.44,0,0 +2012,4,15,0,79,4,19,1003,81.36,0,0 +2012,4,15,1,86,5,18,1004,83.15,0,0 +2012,4,15,2,84,6,17,1004,84.94,0,0 +2012,4,15,3,80,6,16,1004,86.73,0,0 +2012,4,15,4,83,7,14,1005,1.79,0,0 +2012,4,15,5,106,8,14,1005,1.79,0,0 +2012,4,15,6,114,7,11,1005,0.89,0,0 +2012,4,15,7,119,9,14,1006,1.78,0,0 +2012,4,15,8,139,9,18,1007,3.57,0,0 +2012,4,15,9,112,3,20,1008,7.59,0,0 +2012,4,15,10,84,1,22,1008,11.61,0,0 +2012,4,15,11,52,-3,25,1008,17.42,0,0 +2012,4,15,12,35,-3,24,1008,22.34,0,0 +2012,4,15,13,29,-3,26,1008,25.47,0,0 +2012,4,15,14,27,-3,27,1007,4.02,0,0 +2012,4,15,15,28,6,26,1007,4.92,0,0 +2012,4,15,16,24,6,23,1007,10.73,0,0 +2012,4,15,17,16,5,22,1009,15.65,0,0 +2012,4,15,18,115,4,21,1009,20.57,0,0 +2012,4,15,19,93,4,19,1011,25.49,0,0 +2012,4,15,20,75,4,17,1013,30.41,0,0 +2012,4,15,21,66,3,15,1015,34.43,0,0 +2012,4,15,22,68,3,15,1016,38.45,0,0 +2012,4,15,23,73,2,14,1017,41.58,0,0 +2012,4,16,0,56,1,14,1017,45.6,0,0 +2012,4,16,1,60,0,13,1018,47.39,0,0 +2012,4,16,2,20,0,11,1018,0.89,0,0 +2012,4,16,3,9,1,10,1019,1.79,0,0 +2012,4,16,4,8,2,8,1019,4.92,0,0 +2012,4,16,5,7,2,7,1019,0.45,0,0 +2012,4,16,6,22,2,7,1020,1.34,0,0 +2012,4,16,7,17,1,12,1020,1.79,0,0 +2012,4,16,8,15,-7,16,1021,0.89,0,0 +2012,4,16,9,22,-12,18,1021,1.78,0,0 +2012,4,16,10,22,-12,18,1021,3.13,0,0 +2012,4,16,11,22,-11,20,1020,0.89,0,0 +2012,4,16,12,21,-10,20,1019,2.68,0,0 +2012,4,16,13,23,-9,21,1017,5.81,0,0 +2012,4,16,14,23,-10,21,1016,1.79,0,0 +2012,4,16,15,23,-10,22,1014,7.6,0,0 +2012,4,16,16,40,-10,21,1013,12.52,0,0 +2012,4,16,17,47,-7,21,1013,19.67,0,0 +2012,4,16,18,38,-4,20,1013,26.82,0,0 +2012,4,16,19,48,-3,19,1013,31.74,0,0 +2012,4,16,20,56,-2,17,1014,34.87,0,0 +2012,4,16,21,56,-1,16,1014,38.89,0,0 +2012,4,16,22,63,0,15,1014,42.02,0,0 +2012,4,16,23,61,0,15,1014,46.04,0,0 +2012,4,17,0,70,1,15,1013,50.06,0,0 +2012,4,17,1,64,2,13,1013,54.08,0,0 +2012,4,17,2,73,2,12,1013,58.1,0,0 +2012,4,17,3,80,2,12,1012,62.12,0,0 +2012,4,17,4,86,3,11,1012,65.25,0,0 +2012,4,17,5,90,3,10,1012,67.04,0,0 +2012,4,17,6,90,3,10,1012,68.83,0,0 +2012,4,17,7,95,3,11,1012,71.96,0,0 +2012,4,17,8,101,3,13,1012,73.75,0,0 +2012,4,17,9,108,3,16,1012,75.54,0,0 +2012,4,17,10,130,4,17,1011,1.79,0,0 +2012,4,17,11,127,4,20,1010,1.79,0,0 +2012,4,17,12,128,4,21,1009,5.81,0,0 +2012,4,17,13,138,4,23,1007,8.94,0,0 +2012,4,17,14,121,5,25,1005,12.96,0,0 +2012,4,17,15,235,5,26,1005,17.88,0,0 +2012,4,17,16,168,4,25,1004,23.69,0,0 +2012,4,17,17,136,4,25,1004,29.5,0,0 +2012,4,17,18,163,6,24,1004,34.42,0,0 +2012,4,17,19,184,8,24,1004,40.23,0,0 +2012,4,17,20,164,10,23,1005,45.15,0,0 +2012,4,17,21,127,10,22,1006,50.07,0,0 +2012,4,17,22,116,11,20,1006,54.09,0,0 +2012,4,17,23,108,11,20,1006,59.01,0,0 +2012,4,18,0,105,11,19,1006,63.03,0,0 +2012,4,18,1,113,12,18,1006,67.05,0,0 +2012,4,18,2,121,12,18,1006,70.18,0,0 +2012,4,18,3,136,12,17,1006,71.97,0,0 +2012,4,18,4,138,13,16,1006,75.1,0,0 +2012,4,18,5,140,13,15,1007,78.23,0,0 +2012,4,18,6,141,13,16,1007,81.36,0,0 +2012,4,18,7,140,12,17,1008,84.49,0,0 +2012,4,18,8,144,12,17,1008,87.62,0,0 +2012,4,18,9,142,12,18,1009,90.75,0,0 +2012,4,18,10,145,11,18,1009,93.88,0,0 +2012,4,18,11,155,11,19,1009,97.01,0,0 +2012,4,18,12,160,11,19,1009,100.14,0,0 +2012,4,18,13,174,12,20,1008,103.27,0,0 +2012,4,18,14,178,12,20,1008,106.4,0,0 +2012,4,18,15,202,12,21,1007,108.19,0,0 +2012,4,18,16,213,12,22,1007,109.98,0,0 +2012,4,18,17,222,12,22,1007,0.89,0,0 +2012,4,18,18,219,12,20,1007,4.92,0,0 +2012,4,18,19,221,12,18,1008,1.79,0,0 +2012,4,18,20,225,12,17,1008,0.89,0,0 +2012,4,18,21,229,13,18,1010,4.92,0,2 +2012,4,18,22,73,14,15,1011,8.05,0,3 +2012,4,18,23,51,13,15,1012,11.18,0,4 +2012,4,19,0,50,13,14,1011,1.79,0,5 +2012,4,19,1,59,13,14,1010,3.13,0,0 +2012,4,19,2,70,13,14,1011,1.79,0,0 +2012,4,19,3,59,13,14,1010,1.79,0,0 +2012,4,19,4,61,13,14,1011,0.89,0,0 +2012,4,19,5,69,13,13,1011,1.78,0,0 +2012,4,19,6,77,11,12,1011,2.67,0,0 +2012,4,19,7,85,10,13,1012,4.02,0,0 +2012,4,19,8,97,8,13,1013,5.81,0,0 +2012,4,19,9,100,7,13,1013,8.94,0,0 +2012,4,19,10,109,7,13,1013,12.07,0,0 +2012,4,19,11,102,7,14,1013,13.86,0,0 +2012,4,19,12,102,8,14,1013,15.65,0,0 +2012,4,19,13,122,9,14,1012,18.78,0,0 +2012,4,19,14,125,9,15,1012,20.57,0,0 +2012,4,19,15,139,9,15,1012,23.7,0,0 +2012,4,19,16,154,9,15,1011,1.79,0,0 +2012,4,19,17,156,9,16,1011,3.58,0,0 +2012,4,19,18,158,10,15,1011,3.13,0,0 +2012,4,19,19,152,10,14,1012,6.26,0,0 +2012,4,19,20,148,10,14,1013,8.05,0,0 +2012,4,19,21,151,10,14,1013,9.84,0,0 +2012,4,19,22,157,10,14,1013,0.89,0,0 +2012,4,19,23,159,9,15,1013,1.79,0,0 +2012,4,20,0,164,10,14,1014,3.58,0,0 +2012,4,20,1,170,10,14,1014,4.47,0,0 +2012,4,20,2,174,11,14,1014,5.36,0,0 +2012,4,20,3,169,11,15,1014,0.89,0,0 +2012,4,20,4,183,12,14,1014,0.89,0,0 +2012,4,20,5,176,11,14,1015,1.78,0,0 +2012,4,20,6,183,12,14,1015,2.67,0,0 +2012,4,20,7,185,13,15,1016,0.89,0,0 +2012,4,20,8,149,13,15,1016,2.68,0,0 +2012,4,20,9,143,13,16,1017,6.7,0,0 +2012,4,20,10,157,13,15,1017,10.72,0,0 +2012,4,20,11,172,13,14,1017,14.74,0,0 +2012,4,20,12,194,12,14,1017,20.55,0,0 +2012,4,20,13,205,11,13,1017,25.47,0,0 +2012,4,20,14,177,11,12,1017,29.49,0,0 +2012,4,20,15,149,11,12,1017,34.41,0,0 +2012,4,20,16,90,11,12,1017,37.54,0,0 +2012,4,20,17,65,11,12,1017,43.35,0,1 +2012,4,20,18,89,11,12,1017,46.48,0,0 +2012,4,20,19,110,12,12,1017,48.27,0,1 +2012,4,20,20,118,12,12,1018,0.89,0,2 +2012,4,20,21,98,12,12,1018,1.79,0,3 +2012,4,20,22,82,12,12,1018,1.79,0,4 +2012,4,20,23,100,12,12,1017,5.81,0,5 +2012,4,21,0,129,12,12,1017,0.89,0,6 +2012,4,21,1,142,12,13,1016,1.79,0,0 +2012,4,21,2,148,11,12,1015,3.58,0,0 +2012,4,21,3,151,11,12,1014,4.47,0,0 +2012,4,21,4,147,11,12,1013,5.36,0,0 +2012,4,21,5,167,10,11,1013,7.15,0,0 +2012,4,21,6,185,10,11,1013,10.28,0,0 +2012,4,21,7,184,10,12,1013,14.3,0,0 +2012,4,21,8,154,10,12,1013,18.32,0,0 +2012,4,21,9,155,10,13,1013,20.11,0,0 +2012,4,21,10,168,10,13,1013,21.9,0,0 +2012,4,21,11,155,9,13,1013,0.89,0,0 +2012,4,21,12,181,10,13,1011,1.78,0,0 +2012,4,21,13,206,11,14,1011,2.67,0,0 +2012,4,21,14,202,11,14,1010,1.79,0,0 +2012,4,21,15,185,11,16,1009,0.89,0,0 +2012,4,21,16,156,11,16,1008,1.78,0,0 +2012,4,21,17,149,11,16,1007,1.79,0,0 +2012,4,21,18,159,11,16,1007,3.58,0,0 +2012,4,21,19,185,11,15,1007,5.37,0,0 +2012,4,21,20,200,11,14,1007,9.39,0,0 +2012,4,21,21,192,11,14,1008,12.52,0,0 +2012,4,21,22,161,11,14,1007,14.31,0,0 +2012,4,21,23,147,10,14,1007,16.1,0,0 +2012,4,22,0,150,11,14,1007,1.79,0,0 +2012,4,22,1,157,11,13,1006,0.89,0,0 +2012,4,22,2,143,11,13,1006,0.89,0,0 +2012,4,22,3,131,11,13,1005,1.79,0,0 +2012,4,22,4,116,10,13,1004,4.92,0,0 +2012,4,22,5,115,9,13,1004,6.71,0,0 +2012,4,22,6,116,8,13,1004,8.5,0,0 +2012,4,22,7,113,8,13,1005,10.29,0,0 +2012,4,22,8,115,9,13,1005,12.08,0,0 +2012,4,22,9,132,10,14,1005,13.87,0,0 +2012,4,22,10,144,10,16,1005,0.89,0,0 +2012,4,22,11,160,10,18,1004,1.78,0,0 +2012,4,22,12,167,11,19,1003,1.79,0,0 +2012,4,22,13,167,11,20,1003,3.58,0,0 +2012,4,22,14,162,11,21,1002,3.13,0,0 +2012,4,22,15,172,12,22,1001,3.13,0,0 +2012,4,22,16,160,12,22,1001,6.26,0,0 +2012,4,22,17,195,13,22,1001,9.39,0,0 +2012,4,22,18,201,13,20,1001,11.18,0,0 +2012,4,22,19,216,13,18,1001,12.07,0,0 +2012,4,22,20,235,14,17,1002,12.96,0,0 +2012,4,22,21,247,14,18,1003,16.09,0,0 +2012,4,22,22,257,13,17,1003,19.22,0,0 +2012,4,22,23,245,13,17,1003,22.35,0,0 +2012,4,23,0,217,13,16,1004,25.48,0,0 +2012,4,23,1,199,13,16,1004,29.5,0,0 +2012,4,23,2,203,12,16,1004,0.89,0,0 +2012,4,23,3,191,13,16,1003,1.79,0,0 +2012,4,23,4,188,12,16,1003,4.92,0,0 +2012,4,23,5,190,12,16,1003,6.71,0,0 +2012,4,23,6,222,12,16,1003,8.5,0,0 +2012,4,23,7,235,12,16,1004,11.63,0,0 +2012,4,23,8,230,13,17,1004,13.42,0,0 +2012,4,23,9,248,13,17,1004,0.89,0,0 +2012,4,23,10,258,13,18,1004,1.79,0,0 +2012,4,23,11,266,14,19,1004,4.92,0,0 +2012,4,23,12,281,14,20,1003,8.05,0,0 +2012,4,23,13,279,15,21,1003,9.84,0,0 +2012,4,23,14,278,15,23,1002,0.89,0,0 +2012,4,23,15,268,16,24,1002,1.79,0,0 +2012,4,23,16,272,16,24,1001,3.58,0,0 +2012,4,23,17,277,16,24,1001,0.89,0,0 +2012,4,23,18,280,15,22,1001,4.02,0,0 +2012,4,23,19,288,15,21,1001,7.15,0,0 +2012,4,23,20,303,15,19,1001,0.89,0,0 +2012,4,23,21,298,14,20,1002,3.13,0,0 +2012,4,23,22,307,15,18,1002,1.79,0,0 +2012,4,23,23,268,15,20,1003,5.81,0,0 +2012,4,24,0,157,14,19,1004,8.94,0,0 +2012,4,24,1,183,14,18,1003,10.73,0,0 +2012,4,24,2,208,14,17,1003,0.89,0,0 +2012,4,24,3,207,14,16,1003,1.79,0,0 +2012,4,24,4,195,14,16,1002,3.58,0,0 +2012,4,24,5,174,13,16,1003,1.79,0,0 +2012,4,24,6,173,14,16,1004,3.58,0,1 +2012,4,24,7,179,13,17,1004,0.89,0,2 +2012,4,24,8,187,14,17,1004,3.13,0,3 +2012,4,24,9,132,15,16,1006,4.92,0,4 +2012,4,24,10,60,16,16,1005,5.81,0,5 +2012,4,24,11,40,16,17,1006,9.83,0,6 +2012,4,24,12,34,15,16,1006,18.77,0,7 +2012,4,24,13,44,15,15,1006,23.69,0,8 +2012,4,24,14,48,15,16,1007,27.71,0,9 +2012,4,24,15,37,14,15,1006,33.52,0,10 +2012,4,24,16,38,13,14,1006,39.33,0,11 +2012,4,24,17,35,13,14,1006,44.25,0,12 +2012,4,24,18,45,13,14,1006,50.06,0,13 +2012,4,24,19,41,13,14,1007,54.98,0,0 +2012,4,24,20,43,13,14,1008,58.11,0,0 +2012,4,24,21,46,12,14,1009,61.24,0,0 +2012,4,24,22,29,12,14,1008,66.16,0,0 +2012,4,24,23,14,10,13,1008,70.18,0,0 +2012,4,25,0,13,8,13,1007,74.2,0,0 +2012,4,25,1,11,6,11,1007,77.33,0,0 +2012,4,25,2,9,2,12,1006,81.35,0,0 +2012,4,25,3,9,1,11,1007,87.16,0,0 +2012,4,25,4,12,0,9,1007,91.18,0,0 +2012,4,25,5,11,-1,8,1008,95.2,0,0 +2012,4,25,6,11,-1,8,1008,98.33,0,0 +2012,4,25,7,11,-3,14,1008,103.25,0,0 +2012,4,25,8,10,-2,16,1009,108.17,0,0 +2012,4,25,9,11,-4,17,1009,113.09,0,0 +2012,4,25,10,18,-5,18,1009,118.9,0,0 +2012,4,25,11,16,-8,18,1009,126.05,0,0 +2012,4,25,12,15,-8,19,1008,135.88,0,0 +2012,4,25,13,17,-9,20,1008,145.71,0,0 +2012,4,25,14,13,-8,20,1007,155.54,0,0 +2012,4,25,15,15,-8,20,1007,165.37,0,0 +2012,4,25,16,12,-8,20,1007,174.31,0,0 +2012,4,25,17,11,-8,19,1007,183.25,0,0 +2012,4,25,18,8,-8,17,1007,191.3,0,0 +2012,4,25,19,4,-6,16,1008,196.22,0,0 +2012,4,25,20,6,-7,13,1008,198.01,0,0 +2012,4,25,21,37,0,10,1009,0.89,0,0 +2012,4,25,22,69,3,9,1008,1.78,0,0 +2012,4,25,23,120,0,9,1008,2.67,0,0 +2012,4,26,0,45,2,9,1008,3.56,0,0 +2012,4,26,1,34,2,7,1007,1.79,0,0 +2012,4,26,2,28,2,5,1007,0.89,0,0 +2012,4,26,3,35,1,7,1007,1.79,0,0 +2012,4,26,4,62,1,5,1006,0.89,0,0 +2012,4,26,5,81,0,4,1006,0.89,0,0 +2012,4,26,6,75,1,5,1006,2.68,0,0 +2012,4,26,7,50,2,9,1006,0.89,0,0 +2012,4,26,8,51,2,11,1005,1.78,0,0 +2012,4,26,9,48,2,14,1005,2.67,0,0 +2012,4,26,10,53,3,15,1004,1.79,0,0 +2012,4,26,11,65,2,15,1003,3.58,0,0 +2012,4,26,12,88,2,17,1002,5.37,0,0 +2012,4,26,13,83,5,19,1000,3.13,0,0 +2012,4,26,14,76,8,20,1000,3.13,0,0 +2012,4,26,15,76,2,23,999,8.05,0,0 +2012,4,26,16,62,2,24,998,12.07,0,0 +2012,4,26,17,53,2,23,998,16.99,0,0 +2012,4,26,18,65,9,21,997,3.13,0,0 +2012,4,26,19,72,9,17,998,1.79,0,0 +2012,4,26,20,78,9,16,998,0.89,0,0 +2012,4,26,21,144,9,13,997,1.79,0,0 +2012,4,26,22,163,8,15,997,3.58,0,0 +2012,4,26,23,176,9,14,997,0.89,0,0 +2012,4,27,0,143,8,14,997,1.78,0,0 +2012,4,27,1,152,10,16,995,3.13,0,0 +2012,4,27,2,160,10,16,995,4.92,0,0 +2012,4,27,3,148,9,13,995,1.79,0,0 +2012,4,27,4,134,9,14,997,0.89,0,0 +2012,4,27,5,112,9,13,996,1.78,0,0 +2012,4,27,6,108,10,13,996,2.67,0,0 +2012,4,27,7,114,10,15,997,3.56,0,0 +2012,4,27,8,107,11,18,998,4.45,0,0 +2012,4,27,9,126,0,22,998,4.92,0,0 +2012,4,27,10,99,-5,22,999,17.88,0,0 +2012,4,27,11,55,-7,22,1000,33.08,0,0 +2012,4,27,12,55,-10,22,1001,49.17,0,0 +2012,4,27,13,41,-10,23,1001,64.37,0,0 +2012,4,27,14,21,-10,23,1001,79.57,0,0 +2012,4,27,15,NA,-12,23,1001,92.53,0,0 +2012,4,27,16,29,-12,23,1001,105.49,0,0 +2012,4,27,17,27,-12,22,1002,118.45,0,0 +2012,4,27,18,30,-12,21,1003,127.39,0,0 +2012,4,27,19,27,-12,19,1004,135.44,0,0 +2012,4,27,20,30,-12,18,1006,142.59,0,0 +2012,4,27,21,28,-13,16,1008,149.74,0,0 +2012,4,27,22,30,-12,16,1008,158.68,0,0 +2012,4,27,23,39,-11,14,1009,165.83,0,0 +2012,4,28,0,41,-14,14,1009,4.02,0,0 +2012,4,28,1,46,-13,14,1010,8.04,0,0 +2012,4,28,2,72,-12,13,1010,12.06,0,0 +2012,4,28,3,107,-13,13,1010,16.98,0,0 +2012,4,28,4,141,-15,12,1010,22.79,0,0 +2012,4,28,5,135,-14,11,1011,25.92,0,0 +2012,4,28,6,108,-11,11,1012,0.89,0,0 +2012,4,28,7,108,-5,12,1013,1.78,0,0 +2012,4,28,8,98,-9,13,1013,3.13,0,0 +2012,4,28,9,90,-10,15,1014,0.89,0,0 +2012,4,28,10,79,-12,16,1015,1.78,0,0 +2012,4,28,11,70,-10,17,1014,1.79,0,0 +2012,4,28,12,83,-9,18,1013,3.58,0,0 +2012,4,28,13,90,-7,19,1013,7.6,0,0 +2012,4,28,14,106,-7,19,1012,10.73,0,0 +2012,4,28,15,136,-5,19,1011,13.86,0,0 +2012,4,28,16,114,-6,19,1011,17.88,0,0 +2012,4,28,17,98,-7,19,1011,21.01,0,0 +2012,4,28,18,97,-4,18,1011,25.03,0,0 +2012,4,28,19,113,-1,16,1011,28.16,0,0 +2012,4,28,20,160,-1,15,1012,31.29,0,0 +2012,4,28,21,188,-2,14,1013,34.42,0,0 +2012,4,28,22,183,-2,13,1014,37.55,0,0 +2012,4,28,23,182,-1,12,1014,39.34,0,0 +2012,4,29,0,203,-1,12,1014,41.13,0,0 +2012,4,29,1,201,-1,11,1014,42.92,0,0 +2012,4,29,2,195,1,10,1014,43.81,0,0 +2012,4,29,3,190,2,9,1014,1.79,0,0 +2012,4,29,4,194,4,8,1014,0.89,0,0 +2012,4,29,5,184,3,9,1014,0.45,0,0 +2012,4,29,6,224,4,8,1014,1.79,0,0 +2012,4,29,7,198,4,11,1014,3.58,0,0 +2012,4,29,8,198,3,13,1014,6.71,0,0 +2012,4,29,9,205,0,15,1014,8.5,0,0 +2012,4,29,10,205,-3,17,1014,11.63,0,0 +2012,4,29,11,220,-3,19,1013,1.79,0,0 +2012,4,29,12,200,-4,19,1012,3.58,0,0 +2012,4,29,13,190,-1,21,1011,3.13,0,0 +2012,4,29,14,166,-2,21,1011,7.15,0,0 +2012,4,29,15,142,-2,21,1010,10.28,0,0 +2012,4,29,16,141,0,20,1009,13.41,0,0 +2012,4,29,17,127,1,20,1009,17.43,0,0 +2012,4,29,18,120,1,19,1009,19.22,0,0 +2012,4,29,19,130,2,17,1009,21.01,0,0 +2012,4,29,20,176,2,16,1010,22.8,0,0 +2012,4,29,21,162,3,15,1010,24.59,0,0 +2012,4,29,22,170,3,15,1011,25.48,0,0 +2012,4,29,23,168,5,14,1011,27.27,0,0 +2012,4,30,0,121,10,13,1011,30.4,0,0 +2012,4,30,1,112,11,12,1011,32.19,0,0 +2012,4,30,2,107,11,12,1010,33.98,0,0 +2012,4,30,3,108,11,12,1010,0.45,0,0 +2012,4,30,4,111,10,11,1010,0.9,0,0 +2012,4,30,5,115,10,10,1010,0.89,0,0 +2012,4,30,6,122,10,10,1010,0.45,0,0 +2012,4,30,7,144,12,12,1010,1.79,0,0 +2012,4,30,8,166,12,13,1010,3.58,0,0 +2012,4,30,9,312,11,14,1010,5.37,0,0 +2012,4,30,10,174,12,15,1010,7.16,0,0 +2012,4,30,11,183,12,17,1010,1.79,0,0 +2012,4,30,12,179,12,18,1009,3.58,0,0 +2012,4,30,13,184,12,19,1009,5.37,0,0 +2012,4,30,14,168,11,20,1009,1.79,0,0 +2012,4,30,15,151,11,21,1008,1.79,0,0 +2012,4,30,16,155,12,21,1008,4.92,0,0 +2012,4,30,17,162,12,20,1008,1.79,0,0 +2012,4,30,18,189,13,19,1008,0.89,0,0 +2012,4,30,19,204,13,18,1009,0.89,0,0 +2012,4,30,20,227,12,17,1010,1.78,0,0 +2012,4,30,21,247,13,16,1011,2.67,0,0 +2012,4,30,22,256,13,16,1011,3.56,0,0 +2012,4,30,23,286,14,15,1011,4.45,0,0 +2012,5,1,0,267,13,15,1011,0.45,0,0 +2012,5,1,1,257,14,15,1011,0.89,0,0 +2012,5,1,2,259,13,14,1011,1.78,0,0 +2012,5,1,3,243,12,14,1011,2.67,0,0 +2012,5,1,4,210,12,13,1011,4.46,0,0 +2012,5,1,5,204,12,13,1011,6.25,0,0 +2012,5,1,6,215,12,13,1012,8.04,0,0 +2012,5,1,7,227,13,15,1012,9.83,0,0 +2012,5,1,8,186,12,17,1013,0.89,0,0 +2012,5,1,9,173,12,19,1014,1.79,0,0 +2012,5,1,10,163,12,21,1014,3.58,0,0 +2012,5,1,11,192,14,22,1014,8.5,0,0 +2012,5,1,12,280,15,23,1014,12.52,0,0 +2012,5,1,13,298,13,23,1013,17.44,0,0 +2012,5,1,14,199,13,23,1013,22.36,0,0 +2012,5,1,15,199,14,22,1012,26.38,0,0 +2012,5,1,16,156,12,22,1011,31.3,0,0 +2012,5,1,17,137,13,22,1011,35.32,0,0 +2012,5,1,18,78,12,21,1010,38.45,0,0 +2012,5,1,19,85,9,21,1010,41.58,0,0 +2012,5,1,20,112,10,20,1011,43.37,0,0 +2012,5,1,21,121,10,20,1011,45.16,0,0 +2012,5,1,22,78,9,19,1011,46.95,0,0 +2012,5,1,23,80,10,18,1011,48.74,0,0 +2012,5,2,0,74,9,17,1010,0.89,0,0 +2012,5,2,1,75,8,16,1010,1.79,0,0 +2012,5,2,2,72,9,15,1009,3.58,0,0 +2012,5,2,3,78,10,14,1010,0.89,0,0 +2012,5,2,4,91,10,14,1009,0.89,0,0 +2012,5,2,5,100,10,12,1009,1.78,0,0 +2012,5,2,6,115,11,12,1009,0.45,0,0 +2012,5,2,7,119,12,15,1010,0.9,0,0 +2012,5,2,8,120,13,18,1010,1.79,0,0 +2012,5,2,9,119,11,21,1010,1.79,0,0 +2012,5,2,10,124,11,23,1009,3.58,0,0 +2012,5,2,11,127,12,25,1009,1.79,0,0 +2012,5,2,12,118,10,28,1008,3.13,0,0 +2012,5,2,13,91,6,30,1007,3.13,0,0 +2012,5,2,14,85,3,31,1006,7.15,0,0 +2012,5,2,15,106,6,31,1005,4.02,0,0 +2012,5,2,16,90,6,31,1005,8.04,0,0 +2012,5,2,17,72,8,30,1005,12.06,0,0 +2012,5,2,18,68,8,28,1005,16.98,0,0 +2012,5,2,19,98,9,27,1005,21,0,0 +2012,5,2,20,90,10,26,1006,25.02,0,0 +2012,5,2,21,98,11,22,1007,0.89,0,0 +2012,5,2,22,114,12,20,1007,1.78,0,0 +2012,5,2,23,119,13,19,1007,0.89,0,0 +2012,5,3,0,126,12,17,1008,0.89,0,0 +2012,5,3,1,146,12,16,1008,0.89,0,0 +2012,5,3,2,139,12,16,1008,1.78,0,0 +2012,5,3,3,140,12,14,1008,0.89,0,0 +2012,5,3,4,112,11,16,1008,0.89,0,0 +2012,5,3,5,85,10,13,1009,1.79,0,0 +2012,5,3,6,80,10,14,1009,4.92,0,0 +2012,5,3,7,58,10,19,1009,8.94,0,0 +2012,5,3,8,61,9,23,1010,12.07,0,0 +2012,5,3,9,70,8,27,1010,15.2,0,0 +2012,5,3,10,57,9,29,1010,18.33,0,0 +2012,5,3,11,64,7,31,1010,20.12,0,0 +2012,5,3,12,57,1,31,1009,0.89,0,0 +2012,5,3,13,60,2,33,1008,2.68,0,0 +2012,5,3,14,46,1,33,1007,1.79,0,0 +2012,5,3,15,49,3,32,1006,4.92,0,0 +2012,5,3,16,45,4,31,1005,8.94,0,0 +2012,5,3,17,9,5,30,1005,10.73,0,0 +2012,5,3,18,18,8,29,1005,12.52,0,0 +2012,5,3,19,26,8,27,1005,13.41,0,0 +2012,5,3,20,19,8,26,1006,15.2,0,0 +2012,5,3,21,49,10,21,1006,0.89,0,0 +2012,5,3,22,91,10,21,1007,1.78,0,0 +2012,5,3,23,101,11,19,1007,1.79,0,0 +2012,5,4,0,99,11,19,1007,3.58,0,0 +2012,5,4,1,106,12,16,1007,1.79,0,0 +2012,5,4,2,123,11,16,1007,0.89,0,0 +2012,5,4,3,128,11,16,1007,0.89,0,0 +2012,5,4,4,132,12,14,1007,0.89,0,0 +2012,5,4,5,128,11,14,1007,2.68,0,0 +2012,5,4,6,90,12,16,1008,4.47,0,0 +2012,5,4,7,84,13,18,1008,6.26,0,0 +2012,5,4,8,82,14,22,1008,0.89,0,0 +2012,5,4,9,134,14,24,1008,1.79,0,0 +2012,5,4,10,133,13,27,1008,0.89,0,0 +2012,5,4,11,130,14,29,1008,1.78,0,0 +2012,5,4,12,145,15,30,1007,1.79,0,0 +2012,5,4,13,152,11,32,1006,3.58,0,0 +2012,5,4,14,137,12,32,1005,5.37,0,0 +2012,5,4,15,126,12,32,1004,9.39,0,0 +2012,5,4,16,126,11,33,1003,13.41,0,0 +2012,5,4,17,NA,11,32,1003,17.43,0,0 +2012,5,4,18,80,11,30,1003,20.56,0,0 +2012,5,4,19,98,10,28,1004,22.35,0,0 +2012,5,4,20,116,12,28,1005,26.37,0,0 +2012,5,4,21,217,10,25,1006,33.52,0,0 +2012,5,4,22,116,5,23,1009,36.65,0,0 +2012,5,4,23,45,5,22,1009,1.79,0,0 +2012,5,5,0,50,10,18,1010,0.89,0,0 +2012,5,5,1,77,10,18,1010,0.89,0,0 +2012,5,5,2,150,7,17,1010,1.79,0,0 +2012,5,5,3,76,9,17,1010,0.89,0,0 +2012,5,5,4,69,8,15,1010,1.78,0,0 +2012,5,5,5,85,8,14,1011,2.67,0,0 +2012,5,5,6,94,9,15,1011,1.79,0,0 +2012,5,5,7,89,6,21,1012,4.02,0,0 +2012,5,5,8,108,4,24,1013,8.94,0,0 +2012,5,5,9,72,-1,25,1013,14.75,0,0 +2012,5,5,10,107,-3,26,1012,18.77,0,0 +2012,5,5,11,32,0,27,1012,21.9,0,0 +2012,5,5,12,98,-2,28,1011,1.79,0,0 +2012,5,5,13,87,0,29,1010,3.58,0,0 +2012,5,5,14,39,0,30,1009,5.37,0,0 +2012,5,5,15,47,0,30,1008,6.26,0,0 +2012,5,5,16,212,1,30,1006,1.79,0,0 +2012,5,5,17,286,-1,29,1006,7.6,0,0 +2012,5,5,18,205,-1,28,1006,12.52,0,0 +2012,5,5,19,121,4,27,1005,18.33,0,0 +2012,5,5,20,46,5,26,1006,22.35,0,0 +2012,5,5,21,43,5,25,1006,25.48,0,0 +2012,5,5,22,50,6,22,1006,28.61,0,0 +2012,5,5,23,66,8,23,1006,32.63,0,0 +2012,5,6,0,68,7,23,1005,36.65,0,0 +2012,5,6,1,61,8,20,1005,38.44,0,0 +2012,5,6,2,62,10,17,1004,39.33,0,0 +2012,5,6,3,70,10,15,1004,0.89,0,0 +2012,5,6,4,80,9,14,1004,2.68,0,0 +2012,5,6,5,79,9,15,1004,1.79,0,0 +2012,5,6,6,104,9,15,1004,1.79,0,0 +2012,5,6,7,90,10,18,1005,3.58,0,0 +2012,5,6,8,102,9,20,1005,1.79,0,0 +2012,5,6,9,100,10,22,1005,3.58,0,0 +2012,5,6,10,118,9,25,1006,1.79,0,0 +2012,5,6,11,132,9,26,1006,3.13,0,0 +2012,5,6,12,110,8,28,1005,6.26,0,0 +2012,5,6,13,284,7,29,1004,11.18,0,0 +2012,5,6,14,96,10,28,1004,15.2,0,0 +2012,5,6,15,113,10,29,1003,19.22,0,0 +2012,5,6,16,142,10,28,1003,23.24,0,0 +2012,5,6,17,180,11,27,1002,26.37,0,0 +2012,5,6,18,141,9,26,1003,28.16,0,0 +2012,5,6,19,136,11,24,1003,32.18,0,0 +2012,5,6,20,95,10,23,1003,36.2,0,0 +2012,5,6,21,79,8,22,1004,39.33,0,0 +2012,5,6,22,48,4,22,1005,44.25,0,0 +2012,5,6,23,58,2,21,1005,47.38,0,0 +2012,5,7,0,57,5,19,1006,1.79,0,0 +2012,5,7,1,57,4,19,1005,0.89,0,0 +2012,5,7,2,64,5,15,1005,0.89,0,0 +2012,5,7,3,61,6,17,1005,1.78,0,0 +2012,5,7,4,73,7,15,1005,0.45,0,0 +2012,5,7,5,98,8,14,1005,1.79,0,0 +2012,5,7,6,76,8,16,1005,0.89,0,0 +2012,5,7,7,78,7,16,1005,1.78,0,0 +2012,5,7,8,84,7,18,1005,3.13,0,0 +2012,5,7,9,84,7,20,1005,7.15,0,0 +2012,5,7,10,76,9,22,1005,10.28,0,0 +2012,5,7,11,64,4,25,1004,4.02,0,0 +2012,5,7,12,87,5,26,1004,8.04,0,0 +2012,5,7,13,75,6,27,1003,9.83,0,0 +2012,5,7,14,76,7,27,1002,1.79,0,0 +2012,5,7,15,97,6,27,1002,3.13,0,0 +2012,5,7,16,105,6,26,1002,6.26,0,0 +2012,5,7,17,96,7,26,1002,9.39,0,0 +2012,5,7,18,110,6,25,1003,11.18,0,0 +2012,5,7,19,146,5,24,1003,0.89,0,0 +2012,5,7,20,134,6,22,1004,1.79,0,0 +2012,5,7,21,113,4,23,1005,4.92,0,0 +2012,5,7,22,98,4,20,1006,6.71,0,0 +2012,5,7,23,84,-1,21,1006,9.84,0,0 +2012,5,8,0,71,-1,19,1007,11.63,0,0 +2012,5,8,1,65,1,14,1007,0.45,0,0 +2012,5,8,2,59,6,15,1007,0.9,0,0 +2012,5,8,3,77,6,14,1007,0.89,0,0 +2012,5,8,4,78,6,13,1006,0.45,0,0 +2012,5,8,5,76,5,12,1007,1.34,0,0 +2012,5,8,6,99,7,15,1007,1.79,0,0 +2012,5,8,7,91,9,17,1008,3.58,0,0 +2012,5,8,8,135,9,21,1008,5.37,0,0 +2012,5,8,9,120,7,23,1008,7.16,0,0 +2012,5,8,10,112,7,26,1008,8.95,0,0 +2012,5,8,11,109,8,28,1008,10.74,0,0 +2012,5,8,12,100,7,29,1008,12.53,0,0 +2012,5,8,13,81,7,31,1007,1.79,0,0 +2012,5,8,14,83,5,31,1007,3.58,0,0 +2012,5,8,15,73,5,32,1006,4.47,0,0 +2012,5,8,16,73,4,31,1006,1.79,0,0 +2012,5,8,17,71,7,30,1006,5.81,0,0 +2012,5,8,18,71,7,28,1006,8.94,0,0 +2012,5,8,19,57,7,26,1007,12.07,0,0 +2012,5,8,20,69,6,24,1009,15.2,0,0 +2012,5,8,21,80,6,24,1010,16.99,0,0 +2012,5,8,22,84,8,20,1011,18.78,0,0 +2012,5,8,23,77,9,18,1011,19.67,0,0 +2012,5,9,0,71,6,20,1012,22.8,0,0 +2012,5,9,1,95,9,18,1012,25.93,0,0 +2012,5,9,2,136,8,17,1013,27.72,0,0 +2012,5,9,3,105,8,15,1013,0.89,0,0 +2012,5,9,4,99,7,13,1013,1.78,0,0 +2012,5,9,5,105,8,12,1014,2.67,0,0 +2012,5,9,6,108,7,13,1014,0.89,0,0 +2012,5,9,7,125,8,18,1014,0.89,0,0 +2012,5,9,8,166,6,20,1015,1.78,0,0 +2012,5,9,9,154,7,22,1015,2.67,0,0 +2012,5,9,10,124,7,24,1014,1.79,0,0 +2012,5,9,11,146,7,25,1014,3.58,0,0 +2012,5,9,12,146,8,27,1014,6.71,0,0 +2012,5,9,13,99,6,28,1013,11.63,0,0 +2012,5,9,14,75,6,27,1013,17.44,0,0 +2012,5,9,15,65,5,28,1012,23.25,0,0 +2012,5,9,16,124,6,27,1011,28.17,0,0 +2012,5,9,17,102,7,27,1011,33.09,0,0 +2012,5,9,18,79,7,26,1010,38.01,0,0 +2012,5,9,19,82,8,23,1011,39.8,0,0 +2012,5,9,20,91,7,22,1011,41.59,0,0 +2012,5,9,21,96,9,19,1011,42.48,0,0 +2012,5,9,22,131,9,19,1012,0.89,0,0 +2012,5,9,23,125,9,19,1012,0.89,0,0 +2012,5,10,0,148,9,18,1012,0.45,0,0 +2012,5,10,1,142,10,17,1011,1.34,0,0 +2012,5,10,2,153,11,16,1011,0.89,0,0 +2012,5,10,3,163,11,16,1011,0.89,0,0 +2012,5,10,4,166,10,15,1011,1.78,0,0 +2012,5,10,5,184,11,15,1011,0.89,0,0 +2012,5,10,6,312,11,15,1012,0.89,0,0 +2012,5,10,7,195,14,18,1012,1.78,0,0 +2012,5,10,8,168,10,21,1012,1.79,0,0 +2012,5,10,9,166,11,23,1012,1.79,0,0 +2012,5,10,10,164,12,25,1012,3.58,0,0 +2012,5,10,11,160,13,27,1012,0.89,0,0 +2012,5,10,12,166,14,29,1011,1.79,0,0 +2012,5,10,13,177,13,28,1011,4.92,0,0 +2012,5,10,14,165,15,29,1010,8.05,0,0 +2012,5,10,15,171,14,29,1009,11.18,0,0 +2012,5,10,16,172,16,29,1009,14.31,0,0 +2012,5,10,17,164,15,28,1009,17.44,0,0 +2012,5,10,18,161,15,28,1009,20.57,0,0 +2012,5,10,19,164,14,27,1010,23.7,0,0 +2012,5,10,20,152,13,25,1010,25.49,0,0 +2012,5,10,21,204,13,24,1011,27.28,0,0 +2012,5,10,22,209,14,24,1011,29.07,0,0 +2012,5,10,23,228,13,23,1011,30.86,0,0 +2012,5,11,0,247,13,22,1011,31.75,0,0 +2012,5,11,1,265,12,22,1011,0.89,0,0 +2012,5,11,2,263,11,23,1011,4.02,0,0 +2012,5,11,3,207,14,21,1011,8.94,0,1 +2012,5,11,4,153,15,19,1011,13.86,0,2 +2012,5,11,5,152,12,19,1012,16.99,0,0 +2012,5,11,6,141,12,20,1012,20.12,0,0 +2012,5,11,7,144,13,20,1012,23.25,0,0 +2012,5,11,8,153,13,20,1013,25.04,0,0 +2012,5,11,9,137,14,20,1013,0.89,0,1 +2012,5,11,10,128,14,19,1013,0.89,0,2 +2012,5,11,11,126,14,19,1013,0.89,0,3 +2012,5,11,12,123,14,20,1012,1.34,0,4 +2012,5,11,13,116,15,20,1011,1.79,0,5 +2012,5,11,14,122,16,19,1011,1.79,0,6 +2012,5,11,15,142,16,19,1010,4.92,0,7 +2012,5,11,16,143,16,19,1010,8.05,0,0 +2012,5,11,17,144,15,19,1010,9.84,0,0 +2012,5,11,18,152,15,18,1010,11.63,0,0 +2012,5,11,19,153,15,18,1010,0.89,0,0 +2012,5,11,20,148,15,18,1011,0.89,0,1 +2012,5,11,21,149,16,18,1011,2.68,0,2 +2012,5,11,22,137,16,18,1011,0.89,0,0 +2012,5,11,23,138,15,18,1012,1.78,0,1 +2012,5,12,0,138,12,16,1011,1.79,0,2 +2012,5,12,1,152,13,16,1011,2.68,0,3 +2012,5,12,2,143,13,16,1011,0.89,0,4 +2012,5,12,3,139,13,16,1011,1.79,0,5 +2012,5,12,4,124,13,15,1010,0.89,0,6 +2012,5,12,5,123,13,15,1011,1.79,0,7 +2012,5,12,6,131,14,15,1011,3.13,0,8 +2012,5,12,7,128,14,15,1011,0.89,0,9 +2012,5,12,8,141,14,16,1012,1.79,0,10 +2012,5,12,9,149,14,16,1012,0.89,0,11 +2012,5,12,10,153,15,17,1011,1.79,0,12 +2012,5,12,11,135,15,19,1011,3.58,0,0 +2012,5,12,12,56,12,22,1011,1.79,0,0 +2012,5,12,13,51,8,23,1010,3.58,0,0 +2012,5,12,14,38,11,24,1009,6.71,0,0 +2012,5,12,15,28,10,24,1008,4.02,0,0 +2012,5,12,16,29,11,23,1008,9.83,0,0 +2012,5,12,17,78,11,21,1008,15.64,0,0 +2012,5,12,18,87,11,21,1008,19.66,0,0 +2012,5,12,19,80,10,20,1009,24.58,0,0 +2012,5,12,20,83,12,19,1009,28.6,0,0 +2012,5,12,21,85,14,18,1010,32.62,0,0 +2012,5,12,22,124,14,17,1011,36.64,0,0 +2012,5,12,23,122,14,16,1011,41.56,0,0 +2012,5,13,0,130,14,16,1011,45.58,0,0 +2012,5,13,1,121,13,16,1010,49.6,0,0 +2012,5,13,2,138,13,15,1010,51.39,0,0 +2012,5,13,3,157,14,15,1010,53.18,0,0 +2012,5,13,4,164,14,15,1009,54.97,0,0 +2012,5,13,5,170,13,15,1009,58.99,0,0 +2012,5,13,6,189,14,14,1009,63.01,0,0 +2012,5,13,7,186,14,15,1009,67.03,0,0 +2012,5,13,8,185,14,15,1009,71.05,0,0 +2012,5,13,9,193,13,17,1009,76.86,0,0 +2012,5,13,10,212,14,17,1008,81.78,0,0 +2012,5,13,11,191,14,20,1007,87.59,0,0 +2012,5,13,12,194,14,20,1007,93.4,0,0 +2012,5,13,13,124,14,20,1005,99.21,0,0 +2012,5,13,14,103,13,21,1004,106.36,0,0 +2012,5,13,15,100,12,23,1004,111.28,0,0 +2012,5,13,16,66,13,22,1003,115.3,0,1 +2012,5,13,17,55,12,22,1004,119.32,0,2 +2012,5,13,18,84,8,23,1004,122.45,0,0 +2012,5,13,19,78,13,20,1005,4.02,0,0 +2012,5,13,20,84,13,19,1006,8.04,0,0 +2012,5,13,21,164,13,18,1007,5.81,0,0 +2012,5,13,22,94,13,18,1008,3.13,0,0 +2012,5,13,23,99,13,17,1010,7.15,0,0 +2012,5,14,0,96,12,17,1010,11.17,0,0 +2012,5,14,1,83,10,16,1010,14.3,0,0 +2012,5,14,2,70,8,14,1009,0.89,0,0 +2012,5,14,3,70,9,11,1009,1.78,0,0 +2012,5,14,4,69,8,10,1009,2.67,0,0 +2012,5,14,5,69,8,9,1009,3.56,0,0 +2012,5,14,6,71,9,11,1009,4.01,0,0 +2012,5,14,7,61,11,15,1010,4.9,0,0 +2012,5,14,8,31,0,19,1009,1.79,0,0 +2012,5,14,9,26,-1,21,1009,3.13,0,0 +2012,5,14,10,52,-8,23,1009,5.81,0,0 +2012,5,14,11,53,-1,23,1008,5.81,0,0 +2012,5,14,12,52,0,24,1007,9.83,0,0 +2012,5,14,13,50,-4,25,1006,13.85,0,0 +2012,5,14,14,37,-8,26,1006,8.94,0,0 +2012,5,14,15,33,-7,27,1005,18.77,0,0 +2012,5,14,16,30,-7,27,1005,28.6,0,0 +2012,5,14,17,16,-8,26,1005,38.43,0,0 +2012,5,14,18,15,-7,25,1005,45.58,0,0 +2012,5,14,19,14,-6,22,1006,52.73,0,0 +2012,5,14,20,15,-6,20,1007,58.54,0,0 +2012,5,14,21,26,-6,18,1008,62.56,0,0 +2012,5,14,22,37,-4,15,1008,66.58,0,0 +2012,5,14,23,37,-4,15,1008,69.71,0,0 +2012,5,15,0,43,-6,17,1008,73.73,0,0 +2012,5,15,1,44,1,15,1008,0.89,0,0 +2012,5,15,2,36,-2,14,1008,1.79,0,0 +2012,5,15,3,47,-4,16,1007,3.58,0,0 +2012,5,15,4,46,-2,13,1007,5.37,0,0 +2012,5,15,5,51,0,10,1007,0.89,0,0 +2012,5,15,6,51,3,11,1007,1.79,0,0 +2012,5,15,7,41,3,15,1007,0.89,0,0 +2012,5,15,8,37,-1,19,1007,3.13,0,0 +2012,5,15,9,33,-2,22,1007,8.05,0,0 +2012,5,15,10,38,-4,23,1006,16.1,0,0 +2012,5,15,11,42,-5,24,1006,27.28,0,0 +2012,5,15,12,27,-5,26,1004,38.46,0,0 +2012,5,15,13,38,-3,26,1004,48.29,0,0 +2012,5,15,14,37,-3,26,1003,57.23,0,0 +2012,5,15,15,28,-4,27,1002,66.17,0,0 +2012,5,15,16,16,-5,27,1002,78.24,0,0 +2012,5,15,17,22,-4,26,1002,87.18,0,0 +2012,5,15,18,16,-4,25,1002,95.23,0,0 +2012,5,15,19,19,-3,21,1003,99.25,0,0 +2012,5,15,20,12,-1,18,1003,100.14,0,0 +2012,5,15,21,22,0,22,1003,108.19,0,0 +2012,5,15,22,36,1,22,1003,116.24,0,0 +2012,5,15,23,31,1,21,1003,4.02,0,0 +2012,5,16,0,40,1,21,1002,8.94,0,0 +2012,5,16,1,51,2,19,1002,1.79,0,0 +2012,5,16,2,32,2,19,1002,4.92,0,0 +2012,5,16,3,43,3,13,1002,8.94,0,0 +2012,5,16,4,44,3,12,1003,12.96,0,0 +2012,5,16,5,18,1,14,1003,17.88,0,0 +2012,5,16,6,15,0,17,1004,25.93,0,0 +2012,5,16,7,15,-1,19,1005,34.87,0,0 +2012,5,16,8,19,0,20,1005,44.7,0,0 +2012,5,16,9,25,0,21,1005,54.53,0,0 +2012,5,16,10,20,-2,23,1005,64.36,0,0 +2012,5,16,11,22,-2,24,1005,77.32,0,0 +2012,5,16,12,18,-3,26,1004,89.39,0,0 +2012,5,16,13,24,-2,26,1003,99.22,0,0 +2012,5,16,14,22,-1,27,1003,109.05,0,0 +2012,5,16,15,30,0,28,1002,117.99,0,0 +2012,5,16,16,28,-1,28,1001,127.82,0,0 +2012,5,16,17,33,-1,28,1000,136.76,0,0 +2012,5,16,18,25,-1,27,1000,142.57,0,0 +2012,5,16,19,27,-3,27,1000,150.62,0,0 +2012,5,16,20,32,-3,26,1001,158.67,0,0 +2012,5,16,21,38,-4,26,1001,164.48,0,0 +2012,5,16,22,32,-4,25,1001,171.63,0,0 +2012,5,16,23,28,-5,26,1001,178.78,0,0 +2012,5,17,0,38,-5,25,1001,185.93,0,0 +2012,5,17,1,40,0,21,1002,189.06,0,0 +2012,5,17,2,36,-1,17,1002,190.85,0,0 +2012,5,17,3,44,5,17,1003,4.92,0,0 +2012,5,17,4,58,6,16,1004,8.05,0,0 +2012,5,17,5,50,6,16,1005,11.18,0,0 +2012,5,17,6,47,7,16,1006,0.89,0,0 +2012,5,17,7,58,7,18,1007,1.79,0,0 +2012,5,17,8,73,7,20,1008,5.81,0,0 +2012,5,17,9,73,7,21,1008,9.83,0,0 +2012,5,17,10,87,8,23,1008,1.79,0,0 +2012,5,17,11,102,8,25,1008,3.13,0,0 +2012,5,17,12,79,9,25,1007,7.15,0,0 +2012,5,17,13,92,8,27,1007,11.17,0,0 +2012,5,17,14,66,8,27,1006,16.09,0,0 +2012,5,17,15,67,8,29,1005,20.11,0,0 +2012,5,17,16,85,8,29,1004,24.13,0,0 +2012,5,17,17,54,9,28,1004,29.05,0,0 +2012,5,17,18,50,9,26,1004,34.86,0,0 +2012,5,17,19,42,10,24,1005,39.78,0,0 +2012,5,17,20,25,8,22,1006,44.7,0,0 +2012,5,17,21,27,9,20,1007,48.72,0,0 +2012,5,17,22,27,8,19,1007,50.51,0,0 +2012,5,17,23,27,7,18,1008,52.3,0,0 +2012,5,18,0,34,7,17,1008,54.09,0,0 +2012,5,18,1,32,8,15,1008,0.45,0,0 +2012,5,18,2,28,8,14,1008,1.79,0,0 +2012,5,18,3,35,8,15,1008,0.89,0,0 +2012,5,18,4,45,9,13,1007,0.89,0,0 +2012,5,18,5,52,9,13,1008,0.89,0,0 +2012,5,18,6,55,9,14,1008,0.89,0,0 +2012,5,18,7,67,9,18,1009,1.78,0,0 +2012,5,18,8,69,9,19,1009,1.79,0,0 +2012,5,18,9,72,10,21,1009,3.58,0,0 +2012,5,18,10,85,11,22,1008,1.79,0,0 +2012,5,18,11,107,11,24,1008,3.58,0,0 +2012,5,18,12,106,13,27,1007,3.13,0,0 +2012,5,18,13,136,13,28,1006,1.79,0,0 +2012,5,18,14,134,13,29,1005,3.13,0,0 +2012,5,18,15,127,14,31,1004,6.26,0,0 +2012,5,18,16,125,15,31,1004,8.05,0,0 +2012,5,18,17,108,16,31,1004,9.84,0,0 +2012,5,18,18,122,12,30,1004,13.86,0,0 +2012,5,18,19,130,13,30,1005,4.92,0,0 +2012,5,18,20,138,14,28,1005,3.13,0,0 +2012,5,18,21,129,13,28,1005,6.26,0,0 +2012,5,18,22,136,14,26,1005,9.39,0,0 +2012,5,18,23,140,16,23,1006,1.79,0,0 +2012,5,19,0,146,15,24,1006,1.79,0,0 +2012,5,19,1,137,15,22,1006,0.89,0,0 +2012,5,19,2,139,15,21,1006,1.78,0,0 +2012,5,19,3,123,15,21,1006,1.79,0,0 +2012,5,19,4,136,15,20,1006,3.58,0,0 +2012,5,19,5,140,15,20,1007,5.37,0,0 +2012,5,19,6,150,16,20,1007,8.5,0,0 +2012,5,19,7,160,16,21,1008,10.29,0,0 +2012,5,19,8,151,17,23,1009,12.08,0,0 +2012,5,19,9,156,17,24,1009,13.87,0,0 +2012,5,19,10,159,17,25,1009,15.66,0,0 +2012,5,19,11,298,18,26,1009,17.45,0,0 +2012,5,19,12,164,18,28,1009,20.58,0,0 +2012,5,19,13,164,18,29,1008,23.71,0,0 +2012,5,19,14,162,18,30,1008,26.84,0,0 +2012,5,19,15,163,19,30,1007,29.97,0,0 +2012,5,19,16,154,19,30,1007,33.1,0,0 +2012,5,19,17,147,19,29,1007,1.79,0,0 +2012,5,19,18,136,19,29,1008,4.92,0,0 +2012,5,19,19,128,18,28,1008,8.05,0,0 +2012,5,19,20,NA,17,28,1009,1.79,0,0 +2012,5,19,21,NA,17,25,1011,5.81,0,1 +2012,5,19,22,NA,16,23,1012,4.92,0,2 +2012,5,19,23,NA,15,22,1013,9.84,0,0 +2012,5,20,0,NA,15,22,1012,14.76,0,0 +2012,5,20,1,NA,17,19,1013,5.81,0,1 +2012,5,20,2,NA,16,19,1013,0.89,0,0 +2012,5,20,3,NA,17,19,1013,3.13,0,0 +2012,5,20,4,NA,17,18,1013,4.92,0,0 +2012,5,20,5,NA,17,18,1013,0.45,0,0 +2012,5,20,6,NA,17,18,1013,1.34,0,0 +2012,5,20,7,NA,17,19,1014,1.79,0,0 +2012,5,20,8,NA,16,21,1015,1.79,0,0 +2012,5,20,9,NA,15,22,1015,3.58,0,0 +2012,5,20,10,NA,16,21,1015,1.79,0,0 +2012,5,20,11,NA,16,20,1015,4.92,0,1 +2012,5,20,12,NA,18,22,1015,8.05,0,0 +2012,5,20,13,NA,16,22,1015,12.97,0,0 +2012,5,20,14,NA,16,23,1014,16.99,0,0 +2012,5,20,15,NA,15,23,1014,21.01,0,0 +2012,5,20,16,NA,13,23,1014,25.93,0,0 +2012,5,20,17,NA,15,23,1014,30.85,0,0 +2012,5,20,18,NA,14,22,1014,33.98,0,0 +2012,5,20,19,NA,14,21,1014,35.77,0,0 +2012,5,20,20,NA,14,19,1014,0.45,0,0 +2012,5,20,21,NA,16,18,1015,1.34,0,0 +2012,5,20,22,NA,15,18,1015,2.23,0,0 +2012,5,20,23,NA,15,18,1015,1.79,0,0 +2012,5,21,0,NA,15,19,1015,4.92,0,0 +2012,5,21,1,NA,15,18,1014,6.71,0,0 +2012,5,21,2,NA,16,17,1015,8.5,0,0 +2012,5,21,3,NA,16,17,1015,0.89,0,0 +2012,5,21,4,NA,15,17,1015,1.78,0,0 +2012,5,21,5,NA,15,16,1015,2.67,0,0 +2012,5,21,6,NA,16,17,1015,0.89,0,0 +2012,5,21,7,NA,16,18,1015,0.89,0,0 +2012,5,21,8,NA,15,20,1016,1.78,0,0 +2012,5,21,9,NA,14,20,1015,3.13,0,0 +2012,5,21,10,NA,14,21,1016,4.92,0,0 +2012,5,21,11,NA,14,22,1016,1.79,0,0 +2012,5,21,12,137,15,23,1015,1.79,0,0 +2012,5,21,13,157,15,24,1014,3.58,0,0 +2012,5,21,14,164,16,25,1014,5.37,0,0 +2012,5,21,15,167,17,25,1013,0.89,0,0 +2012,5,21,16,165,18,26,1013,3.13,0,0 +2012,5,21,17,136,17,25,1012,6.26,0,0 +2012,5,21,18,123,17,25,1012,10.28,0,0 +2012,5,21,19,116,16,24,1012,13.41,0,0 +2012,5,21,20,119,15,24,1013,18.33,0,0 +2012,5,21,21,135,16,23,1013,20.12,0,0 +2012,5,21,22,155,17,23,1013,24.14,0,0 +2012,5,21,23,168,17,23,1013,27.27,0,0 +2012,5,22,0,170,17,22,1013,3.13,0,0 +2012,5,22,1,145,17,22,1012,1.79,0,0 +2012,5,22,2,93,17,20,1012,0.89,0,0 +2012,5,22,3,86,16,21,1012,0.89,0,0 +2012,5,22,4,75,16,20,1011,2.68,0,0 +2012,5,22,5,90,16,20,1011,4.47,0,0 +2012,5,22,6,102,17,20,1011,6.26,0,0 +2012,5,22,7,NA,17,20,1011,8.05,0,0 +2012,5,22,8,NA,17,21,1012,0.89,0,0 +2012,5,22,9,NA,17,23,1011,1.78,0,0 +2012,5,22,10,NA,16,24,1011,2.67,0,0 +2012,5,22,11,NA,16,25,1011,4.02,0,0 +2012,5,22,12,NA,15,26,1011,7.15,0,0 +2012,5,22,13,NA,14,27,1010,12.07,0,0 +2012,5,22,14,NA,12,28,1009,16.09,0,0 +2012,5,22,15,65,10,28,1008,21.9,0,0 +2012,5,22,16,61,10,28,1008,29.05,0,0 +2012,5,22,17,50,10,28,1007,34.86,0,0 +2012,5,22,18,61,12,27,1007,40.67,0,0 +2012,5,22,19,58,12,26,1007,44.69,0,0 +2012,5,22,20,68,12,25,1007,47.82,0,0 +2012,5,22,21,72,13,24,1008,49.61,0,0 +2012,5,22,22,117,15,25,1009,53.63,0,0 +2012,5,22,23,129,15,24,1009,56.76,0,0 +2012,5,23,0,132,15,24,1009,59.89,0,0 +2012,5,23,1,107,14,21,1010,4.92,0,0 +2012,5,23,2,85,3,21,1010,4.92,0,0 +2012,5,23,3,52,0,20,1011,9.84,0,0 +2012,5,23,4,19,-2,20,1011,14.76,0,0 +2012,5,23,5,15,-2,18,1012,4.02,0,0 +2012,5,23,6,17,0,17,1013,7.15,0,0 +2012,5,23,7,13,2,20,1014,3.13,0,0 +2012,5,23,8,13,1,22,1014,7.15,0,0 +2012,5,23,9,12,0,24,1014,10.28,0,0 +2012,5,23,10,10,-1,24,1013,14.3,0,0 +2012,5,23,11,15,-1,25,1013,17.43,0,0 +2012,5,23,12,12,-3,26,1012,4.02,0,0 +2012,5,23,13,11,-3,27,1012,5.81,0,0 +2012,5,23,14,17,-2,28,1010,7.6,0,0 +2012,5,23,15,16,-2,27,1009,9.39,0,0 +2012,5,23,16,15,-1,27,1009,1.79,0,0 +2012,5,23,17,19,-1,27,1009,7.6,0,0 +2012,5,23,18,23,0,26,1009,12.52,0,0 +2012,5,23,19,34,0,24,1009,16.54,0,0 +2012,5,23,20,30,1,23,1009,18.33,0,0 +2012,5,23,21,35,3,21,1010,21.46,0,0 +2012,5,23,22,40,5,21,1010,0.89,0,0 +2012,5,23,23,49,6,19,1010,1.78,0,0 +2012,5,24,0,51,7,19,1010,1.79,0,0 +2012,5,24,1,47,8,18,1010,0.89,0,0 +2012,5,24,2,51,9,17,1010,1.78,0,0 +2012,5,24,3,65,9,15,1010,1.79,0,0 +2012,5,24,4,80,7,18,1011,3.13,0,0 +2012,5,24,5,63,7,15,1011,3.13,0,0 +2012,5,24,6,52,6,19,1012,6.26,0,0 +2012,5,24,7,48,5,20,1012,10.28,0,0 +2012,5,24,8,33,2,22,1012,3.13,0,0 +2012,5,24,9,57,-1,25,1013,5.81,0,0 +2012,5,24,10,33,-3,26,1013,5.81,0,0 +2012,5,24,11,NA,-4,25,1013,10.73,0,0 +2012,5,24,12,NA,-4,26,1012,4.02,0,0 +2012,5,24,13,10,-4,27,1011,7.15,0,0 +2012,5,24,14,6,-3,26,1011,0.89,0,0 +2012,5,24,15,8,-3,27,1010,1.79,0,0 +2012,5,24,16,6,-2,27,1009,1.79,0,0 +2012,5,24,17,6,-3,27,1008,1.79,0,0 +2012,5,24,18,19,-1,26,1008,3.58,0,0 +2012,5,24,19,17,1,24,1009,4.47,0,0 +2012,5,24,20,16,-1,22,1009,6.26,0,0 +2012,5,24,21,19,4,19,1010,7.15,0,0 +2012,5,24,22,31,3,15,1011,3.13,0,0 +2012,5,24,23,56,3,16,1011,0.89,0,0 +2012,5,25,0,69,7,16,1010,1.78,0,0 +2012,5,25,1,91,4,17,1010,2.67,0,0 +2012,5,25,2,84,4,16,1011,3.56,0,0 +2012,5,25,3,70,7,16,1010,4.45,0,0 +2012,5,25,4,48,6,14,1011,5.34,0,0 +2012,5,25,5,48,6,15,1011,1.79,0,0 +2012,5,25,6,30,5,17,1011,5.81,0,0 +2012,5,25,7,15,4,20,1011,7.6,0,0 +2012,5,25,8,22,5,22,1011,1.79,0,0 +2012,5,25,9,22,4,24,1011,3.58,0,0 +2012,5,25,10,39,4,25,1011,5.37,0,0 +2012,5,25,11,48,4,27,1010,8.5,0,0 +2012,5,25,12,49,4,28,1010,11.63,0,0 +2012,5,25,13,72,5,28,1009,15.65,0,0 +2012,5,25,14,73,5,28,1008,1.79,0,0 +2012,5,25,15,66,5,27,1008,3.13,0,0 +2012,5,25,16,63,6,27,1007,4.92,0,0 +2012,5,25,17,27,3,27,1007,4.02,0,0 +2012,5,25,18,31,6,25,1006,5.81,0,0 +2012,5,25,19,39,6,24,1007,9.83,0,0 +2012,5,25,20,42,8,22,1008,15.64,0,0 +2012,5,25,21,63,10,20,1009,21.45,0,0 +2012,5,25,22,83,12,19,1009,25.47,0,0 +2012,5,25,23,98,12,17,1009,0.89,0,0 +2012,5,26,0,99,12,17,1009,1.79,0,0 +2012,5,26,1,98,12,16,1008,3.58,0,0 +2012,5,26,2,98,12,14,1008,1.79,0,0 +2012,5,26,3,93,12,14,1007,1.79,0,0 +2012,5,26,4,95,12,14,1007,1.79,0,0 +2012,5,26,5,138,10,11,1007,2.68,0,0 +2012,5,26,6,144,13,14,1007,0.89,0,0 +2012,5,26,7,146,13,18,1007,1.78,0,0 +2012,5,26,8,156,13,20,1007,2.67,0,0 +2012,5,26,9,129,13,22,1007,1.79,0,0 +2012,5,26,10,122,13,24,1007,3.58,0,0 +2012,5,26,11,104,12,27,1006,5.37,0,0 +2012,5,26,12,75,12,30,1005,7.16,0,0 +2012,5,26,13,67,11,32,1004,10.29,0,0 +2012,5,26,14,46,7,33,1003,15.21,0,0 +2012,5,26,15,27,6,33,1002,20.13,0,0 +2012,5,26,16,39,5,33,1001,27.28,0,0 +2012,5,26,17,32,4,33,1001,34.43,0,0 +2012,5,26,18,31,3,32,1001,40.24,0,0 +2012,5,26,19,31,3,30,1002,46.05,0,0 +2012,5,26,20,36,5,29,1002,50.97,0,0 +2012,5,26,21,28,5,28,1002,56.78,0,0 +2012,5,26,22,43,6,27,1003,61.7,0,0 +2012,5,26,23,56,7,25,1003,66.62,0,0 +2012,5,27,0,54,8,22,1003,69.75,0,0 +2012,5,27,1,53,10,19,1003,70.64,0,0 +2012,5,27,2,58,10,18,1003,0.89,0,0 +2012,5,27,3,63,9,16,1004,0.89,0,0 +2012,5,27,4,77,9,16,1004,0.89,0,0 +2012,5,27,5,79,9,16,1005,1.78,0,0 +2012,5,27,6,80,10,19,1006,2.67,0,0 +2012,5,27,7,84,11,22,1007,1.79,0,0 +2012,5,27,8,101,12,24,1007,3.58,0,0 +2012,5,27,9,104,14,25,1007,6.71,0,0 +2012,5,27,10,105,13,27,1007,10.73,0,0 +2012,5,27,11,134,14,28,1007,14.75,0,0 +2012,5,27,12,162,14,29,1007,19.67,0,0 +2012,5,27,13,147,16,30,1007,23.69,0,0 +2012,5,27,14,133,15,31,1006,28.61,0,0 +2012,5,27,15,136,15,31,1007,31.74,0,0 +2012,5,27,16,144,16,30,1006,36.66,0,0 +2012,5,27,17,149,16,29,1007,38.45,0,0 +2012,5,27,18,151,15,29,1006,44.26,0,0 +2012,5,27,19,140,15,27,1008,49.18,0,0 +2012,5,27,20,138,15,25,1009,53.2,0,0 +2012,5,27,21,152,15,24,1010,57.22,0,0 +2012,5,27,22,158,13,23,1011,60.35,0,0 +2012,5,27,23,139,13,22,1011,63.48,0,0 +2012,5,28,0,124,13,21,1011,0.89,0,0 +2012,5,28,1,120,13,20,1011,1.79,0,0 +2012,5,28,2,133,12,19,1012,0.89,0,0 +2012,5,28,3,135,12,19,1012,1.79,0,0 +2012,5,28,4,125,12,16,1012,0.45,0,0 +2012,5,28,5,137,11,18,1013,0.89,0,0 +2012,5,28,6,146,14,20,1013,1.79,0,0 +2012,5,28,7,137,13,22,1013,1.79,0,0 +2012,5,28,8,NA,12,24,1014,4.92,0,0 +2012,5,28,9,125,13,25,1014,1.79,0,0 +2012,5,28,10,130,14,27,1013,1.79,0,0 +2012,5,28,11,136,14,28,1013,3.58,0,0 +2012,5,28,12,143,15,29,1012,6.71,0,0 +2012,5,28,13,149,15,31,1011,9.84,0,0 +2012,5,28,14,152,15,31,1011,13.86,0,0 +2012,5,28,15,144,16,32,1010,17.88,0,0 +2012,5,28,16,105,13,33,1009,22.8,0,0 +2012,5,28,17,87,12,33,1009,28.61,0,0 +2012,5,28,18,82,10,32,1009,33.53,0,0 +2012,5,28,19,104,11,31,1010,38.45,0,0 +2012,5,28,20,125,12,28,1010,41.58,0,0 +2012,5,28,21,NA,14,28,1011,43.37,0,0 +2012,5,28,22,150,15,26,1011,0.89,0,0 +2012,5,28,23,134,14,26,1011,1.79,0,0 +2012,5,29,0,94,15,25,1011,3.58,0,0 +2012,5,29,1,89,15,25,1011,5.37,0,0 +2012,5,29,2,97,15,24,1011,7.16,0,0 +2012,5,29,3,94,15,23,1010,8.95,0,0 +2012,5,29,4,96,15,22,1010,12.08,0,0 +2012,5,29,5,101,14,22,1010,15.21,0,0 +2012,5,29,6,86,14,23,1010,19.23,0,0 +2012,5,29,7,94,15,24,1010,22.36,0,0 +2012,5,29,8,102,15,24,1011,25.49,0,0 +2012,5,29,9,96,14,25,1011,30.41,0,0 +2012,5,29,10,99,12,26,1010,36.22,0,0 +2012,5,29,11,104,12,26,1010,40.24,0,0 +2012,5,29,12,99,11,27,1010,44.26,0,0 +2012,5,29,13,93,11,27,1009,49.18,0,0 +2012,5,29,14,105,12,26,1010,56.33,0,0 +2012,5,29,15,76,13,21,1010,5.81,0,1 +2012,5,29,16,33,13,22,1011,9.83,0,2 +2012,5,29,17,26,13,21,1011,16.98,0,3 +2012,5,29,18,18,14,20,1012,22.79,0,4 +2012,5,29,19,15,12,21,1012,27.71,0,0 +2012,5,29,20,15,14,20,1013,32.63,0,0 +2012,5,29,21,22,12,18,1013,4.02,0,0 +2012,5,29,22,26,11,18,1014,8.04,0,0 +2012,5,29,23,15,10,17,1014,12.06,0,0 +2012,5,30,0,11,9,16,1014,15.19,0,0 +2012,5,30,1,8,9,17,1014,19.21,0,0 +2012,5,30,2,16,9,15,1014,21,0,0 +2012,5,30,3,12,10,15,1014,22.79,0,0 +2012,5,30,4,18,10,13,1015,24.58,0,0 +2012,5,30,5,14,9,12,1016,3.13,0,0 +2012,5,30,6,14,9,18,1016,4.92,0,0 +2012,5,30,7,9,9,18,1016,8.94,0,0 +2012,5,30,8,6,9,21,1017,4.02,0,0 +2012,5,30,9,5,7,23,1017,9.83,0,0 +2012,5,30,10,5,6,24,1017,16.98,0,0 +2012,5,30,11,6,6,26,1017,21.9,0,0 +2012,5,30,12,6,5,26,1016,25.03,0,0 +2012,5,30,13,8,5,27,1016,4.92,0,0 +2012,5,30,14,11,4,27,1015,4.02,0,0 +2012,5,30,15,10,4,27,1015,1.79,0,0 +2012,5,30,16,12,3,28,1015,1.79,0,0 +2012,5,30,17,14,3,27,1015,3.13,0,0 +2012,5,30,18,15,2,27,1015,1.79,0,0 +2012,5,30,19,9,2,24,1015,1.79,0,0 +2012,5,30,20,16,3,21,1016,0.89,0,0 +2012,5,30,21,27,4,19,1017,0.89,0,0 +2012,5,30,22,29,6,16,1017,0.45,0,0 +2012,5,30,23,43,7,15,1018,1.34,0,0 +2012,5,31,0,68,7,15,1018,2.23,0,0 +2012,5,31,1,67,5,16,1018,1.79,0,0 +2012,5,31,2,60,6,15,1018,0.89,0,0 +2012,5,31,3,71,9,13,1019,2.68,0,0 +2012,5,31,4,80,10,12,1019,0.89,0,0 +2012,5,31,5,122,9,13,1019,1.78,0,0 +2012,5,31,6,115,8,15,1019,2.23,0,0 +2012,5,31,7,103,7,18,1020,1.79,0,0 +2012,5,31,8,82,9,20,1020,0.89,0,0 +2012,5,31,9,88,12,21,1019,1.79,0,0 +2012,5,31,10,80,13,24,1018,1.79,0,0 +2012,5,31,11,87,12,26,1018,2.68,0,0 +2012,5,31,12,68,9,27,1017,4.02,0,0 +2012,5,31,13,53,8,27,1016,8.94,0,0 +2012,5,31,14,37,8,27,1015,13.86,0,0 +2012,5,31,15,38,6,27,1014,18.78,0,0 +2012,5,31,16,43,6,27,1014,23.7,0,0 +2012,5,31,17,51,7,26,1014,28.62,0,0 +2012,5,31,18,37,13,18,1015,9.83,0,1 +2012,5,31,19,46,14,17,1016,15.64,0,2 +2012,5,31,20,43,14,16,1015,1.79,0,0 +2012,5,31,21,48,14,16,1015,1.79,0,0 +2012,5,31,22,50,14,17,1015,0.89,0,0 +2012,5,31,23,40,14,16,1016,3.13,0,0 +2012,6,1,0,50,15,17,1015,4.92,0,0 +2012,6,1,1,51,15,16,1015,5.81,0,0 +2012,6,1,2,55,15,16,1014,7.6,0,1 +2012,6,1,3,63,15,17,1014,0.89,0,2 +2012,6,1,4,57,15,16,1014,1.78,0,3 +2012,6,1,5,58,14,15,1014,0.89,0,0 +2012,6,1,6,65,14,16,1014,0.89,0,0 +2012,6,1,7,55,13,17,1014,1.79,0,0 +2012,6,1,8,53,13,18,1014,0.89,0,0 +2012,6,1,9,54,12,18,1014,1.79,0,0 +2012,6,1,10,55,13,18,1014,3.58,0,0 +2012,6,1,11,55,12,20,1014,5.37,0,0 +2012,6,1,12,55,10,22,1013,1.79,0,0 +2012,6,1,13,55,11,22,1012,4.92,0,0 +2012,6,1,14,52,13,22,1011,8.05,0,0 +2012,6,1,15,66,13,22,1011,12.07,0,0 +2012,6,1,16,71,14,23,1010,15.2,0,0 +2012,6,1,17,83,14,22,1011,19.22,0,0 +2012,6,1,18,108,14,21,1011,23.24,0,0 +2012,6,1,19,115,15,20,1010,27.26,0,0 +2012,6,1,20,121,16,19,1011,0.89,0,0 +2012,6,1,21,138,17,19,1012,1.79,0,0 +2012,6,1,22,152,17,18,1012,3.58,0,0 +2012,6,1,23,151,17,18,1012,4.47,0,0 +2012,6,2,0,143,17,17,1012,5.36,0,0 +2012,6,2,1,144,16,17,1012,6.25,0,0 +2012,6,2,2,149,16,16,1011,7.14,0,0 +2012,6,2,3,143,17,17,1011,8.93,0,0 +2012,6,2,4,141,16,16,1011,0.45,0,0 +2012,6,2,5,145,16,16,1011,0.89,0,0 +2012,6,2,6,155,17,17,1011,0.89,0,0 +2012,6,2,7,189,17,18,1012,1.79,0,0 +2012,6,2,8,180,18,19,1012,3.58,0,0 +2012,6,2,9,185,17,20,1012,7.6,0,0 +2012,6,2,10,168,16,22,1012,10.73,0,0 +2012,6,2,11,158,16,23,1011,12.52,0,0 +2012,6,2,12,143,15,25,1011,15.65,0,0 +2012,6,2,13,108,15,26,1010,18.78,0,0 +2012,6,2,14,69,13,28,1009,23.7,0,0 +2012,6,2,15,63,11,29,1008,29.51,0,0 +2012,6,2,16,62,11,29,1007,35.32,0,0 +2012,6,2,17,63,10,28,1007,40.24,0,0 +2012,6,2,18,67,11,28,1007,45.16,0,0 +2012,6,2,19,71,11,27,1007,50.08,0,0 +2012,6,2,20,95,12,24,1007,51.87,0,0 +2012,6,2,21,117,14,23,1007,53.66,0,0 +2012,6,2,22,130,15,22,1008,55.45,0,0 +2012,6,2,23,146,15,22,1008,57.24,0,0 +2012,6,3,0,159,16,22,1007,60.37,0,0 +2012,6,3,1,168,15,21,1007,62.16,0,0 +2012,6,3,2,178,15,21,1007,63.95,0,0 +2012,6,3,3,175,16,20,1007,65.74,0,0 +2012,6,3,4,207,18,20,1007,66.63,0,0 +2012,6,3,5,233,19,20,1007,0.89,0,0 +2012,6,3,6,248,19,20,1007,1.79,0,0 +2012,6,3,7,244,19,21,1007,0.89,0,0 +2012,6,3,8,237,19,21,1007,1.78,0,0 +2012,6,3,9,239,19,22,1006,1.79,0,1 +2012,6,3,10,242,20,22,1006,0.45,0,2 +2012,6,3,11,246,20,24,1005,1.34,0,0 +2012,6,3,12,238,20,25,1005,2.23,0,0 +2012,6,3,13,206,20,23,1004,1.79,0,1 +2012,6,3,14,216,21,23,1005,1.79,0,2 +2012,6,3,15,261,21,23,1004,0.89,0,3 +2012,6,3,16,59,19,21,1004,4.92,0,4 +2012,6,3,17,39,19,22,1004,3.13,0,0 +2012,6,3,18,41,20,22,1003,3.13,0,0 +2012,6,3,19,57,19,21,1003,1.79,0,0 +2012,6,3,20,63,19,20,1004,3.58,0,0 +2012,6,3,21,92,18,20,1005,0.89,0,0 +2012,6,3,22,84,17,18,1006,1.78,0,0 +2012,6,3,23,80,17,18,1006,2.23,0,0 +2012,6,4,0,95,16,17,1006,3.12,0,0 +2012,6,4,1,120,17,17,1006,0.89,0,0 +2012,6,4,2,135,16,16,1005,0.89,0,0 +2012,6,4,3,140,16,16,1005,1.78,0,0 +2012,6,4,4,139,15,16,1006,2.67,0,0 +2012,6,4,5,141,17,17,1006,0.89,0,0 +2012,6,4,6,146,17,17,1006,0.89,0,0 +2012,6,4,7,161,18,19,1006,0.89,0,0 +2012,6,4,8,167,18,20,1006,1.78,0,0 +2012,6,4,9,164,19,23,1007,1.79,0,0 +2012,6,4,10,148,18,24,1006,0.89,0,0 +2012,6,4,11,135,18,26,1006,1.79,0,0 +2012,6,4,12,141,18,28,1005,3.13,0,0 +2012,6,4,13,143,17,29,1004,4.02,0,0 +2012,6,4,14,154,17,30,1003,8.94,0,0 +2012,6,4,15,118,16,30,1003,14.75,0,0 +2012,6,4,16,75,15,32,1002,20.56,0,0 +2012,6,4,17,66,12,32,1001,26.37,0,0 +2012,6,4,18,62,12,32,1001,31.29,0,0 +2012,6,4,19,74,13,31,1001,34.42,0,0 +2012,6,4,20,61,12,30,1002,37.55,0,0 +2012,6,4,21,52,14,29,1002,39.34,0,0 +2012,6,4,22,47,14,27,1003,42.47,0,0 +2012,6,4,23,56,15,23,1002,44.26,0,0 +2012,6,5,0,69,16,22,1003,1.79,0,0 +2012,6,5,1,70,16,20,1003,3.58,0,0 +2012,6,5,2,72,16,20,1003,5.37,0,0 +2012,6,5,3,86,16,20,1003,7.16,0,0 +2012,6,5,4,109,16,18,1004,1.79,0,0 +2012,6,5,5,97,15,20,1004,3.58,0,0 +2012,6,5,6,88,16,22,1005,7.6,0,0 +2012,6,5,7,87,15,23,1005,11.62,0,0 +2012,6,5,8,76,13,26,1006,4.02,0,0 +2012,6,5,9,42,10,29,1006,8.94,0,0 +2012,6,5,10,15,7,31,1006,16.09,0,0 +2012,6,5,11,13,7,31,1006,20.11,0,0 +2012,6,5,12,6,7,33,1005,24.13,0,0 +2012,6,5,13,5,6,34,1004,4.02,0,0 +2012,6,5,14,8,7,34,1004,3.13,0,0 +2012,6,5,15,8,7,34,1003,3.13,0,0 +2012,6,5,16,20,12,33,1002,7.15,0,0 +2012,6,5,17,47,11,33,1002,11.17,0,0 +2012,6,5,18,52,12,31,1002,14.3,0,0 +2012,6,5,19,54,12,30,1002,17.43,0,0 +2012,6,5,20,46,11,28,1003,20.56,0,0 +2012,6,5,21,57,16,27,1003,23.69,0,0 +2012,6,5,22,135,16,25,1003,26.82,0,0 +2012,6,5,23,166,17,24,1003,29.95,0,0 +2012,6,6,0,199,17,23,1003,33.08,0,0 +2012,6,6,1,237,16,23,1003,36.21,0,0 +2012,6,6,2,219,16,23,1003,38,0,0 +2012,6,6,3,148,14,22,1002,41.13,0,0 +2012,6,6,4,141,14,22,1002,44.26,0,0 +2012,6,6,5,122,13,21,1002,0.89,0,0 +2012,6,6,6,130,14,21,1002,1.34,0,0 +2012,6,6,7,148,14,23,1002,2.23,0,0 +2012,6,6,8,167,14,23,1002,1.79,0,0 +2012,6,6,9,194,15,24,1002,3.58,0,0 +2012,6,6,10,193,16,26,1002,1.79,0,0 +2012,6,6,11,191,16,28,1001,1.79,0,0 +2012,6,6,12,189,16,29,1001,3.58,0,0 +2012,6,6,13,205,17,29,1000,5.37,0,0 +2012,6,6,14,203,18,30,999,8.5,0,0 +2012,6,6,15,204,18,31,998,11.63,0,0 +2012,6,6,16,212,19,30,999,13.42,0,0 +2012,6,6,17,168,14,28,1000,9.83,0,0 +2012,6,6,18,46,14,27,1000,16.98,0,0 +2012,6,6,19,49,15,25,1000,20.11,0,0 +2012,6,6,20,45,15,24,1001,1.79,0,0 +2012,6,6,21,50,16,22,1001,0.89,0,0 +2012,6,6,22,59,15,23,1002,4.02,0,0 +2012,6,6,23,56,15,22,1001,7.15,0,0 +2012,6,7,0,48,14,22,1001,11.17,0,0 +2012,6,7,1,50,14,20,1001,0.89,0,0 +2012,6,7,2,43,15,20,1000,3.13,0,0 +2012,6,7,3,49,16,20,1000,0.89,0,0 +2012,6,7,4,56,15,20,1000,1.78,0,0 +2012,6,7,5,50,15,19,1001,1.79,0,0 +2012,6,7,6,49,15,20,1001,0.89,0,0 +2012,6,7,7,45,16,23,1001,1.79,0,0 +2012,6,7,8,38,16,24,1001,0.89,0,0 +2012,6,7,9,44,15,26,1001,1.78,0,0 +2012,6,7,10,48,15,28,1001,3.57,0,0 +2012,6,7,11,50,16,30,1000,4.46,0,0 +2012,6,7,12,53,15,31,1000,1.79,0,0 +2012,6,7,13,55,14,32,999,5.81,0,0 +2012,6,7,14,61,13,31,998,8.94,0,0 +2012,6,7,15,87,16,31,998,12.96,0,0 +2012,6,7,16,91,16,32,998,17.88,0,0 +2012,6,7,17,101,16,31,997,21.9,0,0 +2012,6,7,18,98,15,30,997,25.03,0,0 +2012,6,7,19,87,17,29,998,26.82,0,0 +2012,6,7,20,80,17,27,998,0.89,0,0 +2012,6,7,21,108,19,26,999,1.79,0,0 +2012,6,7,22,129,11,26,1000,6.71,0,0 +2012,6,7,23,29,12,25,1000,1.79,0,0 +2012,6,8,0,17,13,24,1000,3.13,0,0 +2012,6,8,1,23,14,23,1001,4.02,0,0 +2012,6,8,2,101,15,22,1001,5.81,0,0 +2012,6,8,3,99,15,21,1001,8.94,0,1 +2012,6,8,4,85,16,20,1001,1.79,0,2 +2012,6,8,5,90,16,21,1002,1.79,0,0 +2012,6,8,6,81,16,21,1002,0.89,0,0 +2012,6,8,7,82,16,21,1002,1.79,0,0 +2012,6,8,8,103,17,23,1002,0.89,0,0 +2012,6,8,9,107,17,26,1002,2.68,0,0 +2012,6,8,10,121,17,27,1001,1.79,0,0 +2012,6,8,11,111,17,28,1001,1.79,0,0 +2012,6,8,12,98,16,30,1000,3.58,0,0 +2012,6,8,13,82,15,32,999,6.71,0,0 +2012,6,8,14,63,13,33,998,10.73,0,0 +2012,6,8,15,56,13,34,998,14.75,0,0 +2012,6,8,16,56,14,34,997,20.56,0,0 +2012,6,8,17,33,14,33,997,27.71,0,0 +2012,6,8,18,36,15,32,997,33.52,0,0 +2012,6,8,19,46,14,30,997,37.54,0,0 +2012,6,8,20,58,14,28,998,39.33,0,0 +2012,6,8,21,74,15,28,999,42.46,0,0 +2012,6,8,22,69,17,26,999,46.48,0,0 +2012,6,8,23,73,18,26,999,49.61,0,0 +2012,6,9,0,71,18,25,999,51.4,0,0 +2012,6,9,1,82,18,24,1000,53.19,0,0 +2012,6,9,2,75,20,23,999,56.32,0,0 +2012,6,9,3,93,20,22,999,59.45,0,0 +2012,6,9,4,110,20,22,999,62.58,0,0 +2012,6,9,5,161,19,21,1000,0.89,0,0 +2012,6,9,6,197,19,22,1000,1.79,0,0 +2012,6,9,7,189,19,22,1000,3.58,0,0 +2012,6,9,8,186,20,24,1000,0.89,0,0 +2012,6,9,9,192,20,25,999,1.79,0,0 +2012,6,9,10,178,20,27,999,3.58,0,0 +2012,6,9,11,181,20,29,998,1.79,0,0 +2012,6,9,12,177,18,30,997,4.92,0,0 +2012,6,9,13,178,18,31,996,4.92,0,0 +2012,6,9,14,140,18,33,994,10.73,0,0 +2012,6,9,15,116,16,35,994,16.54,0,0 +2012,6,9,16,104,14,33,994,22.35,0,0 +2012,6,9,17,66,18,20,997,26.37,0,1 +2012,6,9,18,23,19,20,997,1.79,0,2 +2012,6,9,19,29,19,21,998,0.89,0,0 +2012,6,9,20,33,19,21,998,3.13,0,0 +2012,6,9,21,32,18,20,999,6.26,0,0 +2012,6,9,22,38,18,19,1000,3.13,0,0 +2012,6,9,23,34,18,19,1000,4.02,0,1 +2012,6,10,0,47,18,19,1000,0.89,0,0 +2012,6,10,1,33,18,19,1000,1.79,0,0 +2012,6,10,2,24,18,20,1001,3.13,0,1 +2012,6,10,3,8,11,19,1001,4.02,0,2 +2012,6,10,4,8,10,18,1001,8.04,0,0 +2012,6,10,5,7,11,17,1002,11.17,0,0 +2012,6,10,6,8,13,18,1002,0.89,0,0 +2012,6,10,7,10,12,18,1002,1.78,0,0 +2012,6,10,8,8,10,21,1002,4.02,0,0 +2012,6,10,9,13,6,24,1002,8.94,0,0 +2012,6,10,10,14,4,25,1002,12.07,0,0 +2012,6,10,11,9,3,26,1001,13.86,0,0 +2012,6,10,12,6,3,27,1001,1.79,0,0 +2012,6,10,13,7,4,26,1001,3.13,0,0 +2012,6,10,14,10,3,25,1001,3.13,0,0 +2012,6,10,15,10,0,29,1000,8.05,0,0 +2012,6,10,16,6,-2,29,1000,16.1,0,0 +2012,6,10,17,2,-2,29,999,21.02,0,0 +2012,6,10,18,1,-2,29,999,24.15,0,0 +2012,6,10,19,7,-2,28,998,1.79,0,0 +2012,6,10,20,18,1,26,999,4.02,0,0 +2012,6,10,21,18,5,24,999,8.04,0,0 +2012,6,10,22,20,7,24,999,12.06,0,0 +2012,6,10,23,21,6,24,999,16.98,0,0 +2012,6,11,0,26,5,23,999,25.03,0,0 +2012,6,11,1,27,5,22,999,32.18,0,0 +2012,6,11,2,30,6,21,999,3.13,0,0 +2012,6,11,3,33,8,16,999,0.89,0,0 +2012,6,11,4,34,10,13,999,1.79,0,0 +2012,6,11,5,37,11,14,999,0.89,0,0 +2012,6,11,6,33,12,16,1000,0.89,0,0 +2012,6,11,7,54,12,20,1000,0.89,0,0 +2012,6,11,8,52,10,22,1000,1.79,0,0 +2012,6,11,9,38,9,24,999,0.89,0,0 +2012,6,11,10,31,7,27,998,1.79,0,0 +2012,6,11,11,13,4,30,997,4.92,0,0 +2012,6,11,12,8,0,32,996,8.05,0,0 +2012,6,11,13,15,2,32,996,5.81,0,0 +2012,6,11,14,12,2,32,995,11.62,0,0 +2012,6,11,15,16,2,33,994,15.64,0,0 +2012,6,11,16,9,2,33,994,4.02,0,0 +2012,6,11,17,13,2,33,994,4.02,0,0 +2012,6,11,18,12,1,32,994,4.92,0,0 +2012,6,11,19,12,3,30,994,8.94,0,0 +2012,6,11,20,12,6,25,995,0.89,0,0 +2012,6,11,21,22,10,21,995,0.89,0,0 +2012,6,11,22,68,10,24,996,2.68,0,0 +2012,6,11,23,75,12,21,996,5.81,0,0 +2012,6,12,0,60,12,20,995,0.89,0,0 +2012,6,12,1,75,12,17,995,1.79,0,0 +2012,6,12,2,81,12,18,995,0.89,0,0 +2012,6,12,3,71,11,17,995,1.79,0,0 +2012,6,12,4,44,9,19,996,4.92,0,0 +2012,6,12,5,23,9,16,996,3.13,0,0 +2012,6,12,6,23,9,19,996,6.26,0,0 +2012,6,12,7,24,10,22,997,9.39,0,0 +2012,6,12,8,13,9,25,997,13.41,0,0 +2012,6,12,9,11,9,27,997,16.54,0,0 +2012,6,12,10,7,8,28,996,4.92,0,0 +2012,6,12,11,4,8,28,996,3.13,0,0 +2012,6,12,12,3,8,29,996,4.02,0,0 +2012,6,12,13,4,7,29,996,3.13,0,0 +2012,6,12,14,6,7,30,996,1.79,0,0 +2012,6,12,15,7,8,30,996,1.79,0,0 +2012,6,12,16,6,8,30,996,1.79,0,0 +2012,6,12,17,5,8,30,996,1.79,0,0 +2012,6,12,18,3,8,29,997,3.58,0,0 +2012,6,12,19,15,11,27,997,5.37,0,0 +2012,6,12,20,23,9,25,998,9.39,0,0 +2012,6,12,21,10,9,23,999,14.31,0,0 +2012,6,12,22,11,9,23,999,18.33,0,0 +2012,6,12,23,7,10,22,1000,20.12,0,0 +2012,6,13,0,11,10,21,1000,0.89,0,0 +2012,6,13,1,10,11,19,1000,1.79,0,0 +2012,6,13,2,15,13,20,1000,1.79,0,0 +2012,6,13,3,29,13,20,1000,0.89,0,0 +2012,6,13,4,58,13,19,1001,0.89,0,0 +2012,6,13,5,73,12,19,1000,0.89,0,0 +2012,6,13,6,96,13,19,1001,1.79,0,0 +2012,6,13,7,112,14,21,1000,1.79,0,0 +2012,6,13,8,130,14,22,1001,3.13,0,0 +2012,6,13,9,135,14,24,1001,6.26,0,0 +2012,6,13,10,152,14,23,1000,3.13,0,0 +2012,6,13,11,148,13,24,1000,4.02,0,0 +2012,6,13,12,80,14,26,999,3.13,0,0 +2012,6,13,13,86,13,26,999,4.92,0,0 +2012,6,13,14,99,14,27,998,8.94,0,0 +2012,6,13,15,62,15,26,998,13.86,0,0 +2012,6,13,16,65,14,25,998,17.88,0,0 +2012,6,13,17,61,13,25,999,22.8,0,1 +2012,6,13,18,54,13,25,999,24.59,0,0 +2012,6,13,19,48,13,24,999,3.13,0,0 +2012,6,13,20,60,17,20,1000,4.92,0,1 +2012,6,13,21,40,16,20,1001,10.73,0,3 +2012,6,13,22,36,15,20,1001,16.54,0,0 +2012,6,13,23,17,14,20,1002,22.35,0,0 +2012,6,14,0,17,14,20,1002,27.27,0,0 +2012,6,14,1,15,13,20,1002,33.08,0,0 +2012,6,14,2,12,13,19,1003,37.1,0,0 +2012,6,14,3,11,13,17,1003,3.13,0,0 +2012,6,14,4,13,13,17,1003,6.26,0,0 +2012,6,14,5,13,14,17,1003,10.28,0,0 +2012,6,14,6,16,14,19,1004,12.07,0,0 +2012,6,14,7,10,14,20,1005,16.09,0,0 +2012,6,14,8,14,14,20,1005,20.11,0,0 +2012,6,14,9,8,13,23,1004,23.24,0,0 +2012,6,14,10,5,14,23,1004,3.13,0,0 +2012,6,14,11,6,13,26,1004,3.13,0,0 +2012,6,14,12,7,13,26,1004,3.13,0,0 +2012,6,14,13,17,12,26,1004,4.92,0,0 +2012,6,14,14,8,11,28,1003,3.13,0,0 +2012,6,14,15,8,9,27,1003,4.02,0,0 +2012,6,14,16,7,11,27,1003,3.13,0,0 +2012,6,14,17,5,11,26,1003,3.13,0,0 +2012,6,14,18,6,12,25,1004,6.26,0,0 +2012,6,14,19,11,14,23,1005,3.13,0,0 +2012,6,14,20,10,15,20,1005,6.26,0,0 +2012,6,14,21,8,15,19,1006,1.79,0,0 +2012,6,14,22,7,15,19,1007,3.13,0,0 +2012,6,14,23,7,15,18,1007,6.26,0,0 +2012,6,15,0,11,14,19,1007,8.05,0,0 +2012,6,15,1,11,15,17,1007,0.45,0,0 +2012,6,15,2,21,15,17,1007,0.9,0,0 +2012,6,15,3,15,15,17,1007,1.79,0,0 +2012,6,15,4,9,14,16,1007,3.13,0,0 +2012,6,15,5,14,14,17,1007,1.79,0,0 +2012,6,15,6,16,15,18,1007,0.89,0,0 +2012,6,15,7,11,13,24,1007,4.92,0,0 +2012,6,15,8,12,13,25,1007,9.84,0,0 +2012,6,15,9,9,10,26,1007,14.76,0,0 +2012,6,15,10,6,11,26,1007,19.68,0,0 +2012,6,15,11,4,10,26,1007,4.92,0,0 +2012,6,15,12,12,10,27,1007,1.79,0,0 +2012,6,15,13,16,10,29,1006,4.92,0,0 +2012,6,15,14,6,10,29,1006,8.94,0,0 +2012,6,15,15,6,10,30,1005,4.92,0,0 +2012,6,15,16,6,10,29,1005,8.05,0,0 +2012,6,15,17,9,11,27,1005,11.18,0,0 +2012,6,15,18,NA,11,27,1005,4.92,0,0 +2012,6,15,19,NA,10,27,1005,9.84,0,0 +2012,6,15,20,NA,10,25,1006,15.65,0,0 +2012,6,15,21,NA,10,24,1006,19.67,0,0 +2012,6,15,22,NA,10,24,1006,22.8,0,0 +2012,6,15,23,NA,11,21,1006,25.93,0,0 +2012,6,16,0,16,12,19,1006,29.06,0,0 +2012,6,16,1,11,12,18,1006,30.85,0,0 +2012,6,16,2,31,13,17,1006,32.64,0,0 +2012,6,16,3,34,12,16,1006,35.77,0,0 +2012,6,16,4,31,13,16,1006,37.56,0,0 +2012,6,16,5,76,13,17,1006,39.35,0,0 +2012,6,16,6,37,15,20,1006,42.48,0,0 +2012,6,16,7,25,14,23,1007,44.27,0,0 +2012,6,16,8,16,13,26,1006,0.89,0,0 +2012,6,16,9,15,12,27,1006,1.78,0,0 +2012,6,16,10,15,11,29,1005,3.13,0,0 +2012,6,16,11,12,11,31,1005,0.89,0,0 +2012,6,16,12,11,10,33,1004,2.68,0,0 +2012,6,16,13,12,10,35,1003,3.57,0,0 +2012,6,16,14,12,10,35,1002,3.13,0,0 +2012,6,16,15,16,9,36,1002,1.79,0,0 +2012,6,16,16,17,9,34,1001,4.02,0,0 +2012,6,16,17,16,13,28,1003,8.05,0,1 +2012,6,16,18,21,14,28,1002,1.79,0,0 +2012,6,16,19,25,14,29,1003,0.89,0,0 +2012,6,16,20,29,16,25,1003,1.79,0,0 +2012,6,16,21,37,16,25,1005,4.92,0,0 +2012,6,16,22,38,15,27,1004,4.02,0,1 +2012,6,16,23,30,15,24,1004,4.91,0,0 +2012,6,17,0,35,16,20,1004,0.89,0,0 +2012,6,17,1,53,16,21,1004,1.78,0,0 +2012,6,17,2,58,17,19,1004,3.13,0,0 +2012,6,17,3,49,15,20,1004,0.89,0,0 +2012,6,17,4,43,16,20,1003,1.79,0,0 +2012,6,17,5,42,16,20,1004,1.79,0,0 +2012,6,17,6,38,17,20,1004,2.24,0,0 +2012,6,17,7,46,17,23,1004,1.79,0,0 +2012,6,17,8,49,16,25,1004,1.79,0,0 +2012,6,17,9,54,15,29,1003,1.79,0,0 +2012,6,17,10,65,15,29,1003,1.79,0,0 +2012,6,17,11,87,15,31,1002,4.92,0,0 +2012,6,17,12,111,13,34,1002,0.89,0,0 +2012,6,17,13,104,12,36,1001,5.81,0,0 +2012,6,17,14,127,12,37,1000,10.73,0,0 +2012,6,17,15,113,11,38,1000,16.54,0,0 +2012,6,17,16,103,10,38,999,21.46,0,0 +2012,6,17,17,96,10,37,1000,24.59,0,0 +2012,6,17,18,96,11,37,999,27.72,0,0 +2012,6,17,19,93,11,36,999,32.64,0,0 +2012,6,17,20,96,9,35,999,36.66,0,0 +2012,6,17,21,86,14,29,1000,0.89,0,0 +2012,6,17,22,79,14,27,1001,1.78,0,0 +2012,6,17,23,99,15,25,1001,2.67,0,0 +2012,6,18,0,101,13,29,1001,1.79,0,0 +2012,6,18,1,113,13,28,1001,3.58,0,0 +2012,6,18,2,111,14,27,1001,5.37,0,0 +2012,6,18,3,118,14,27,1000,8.5,0,0 +2012,6,18,4,116,15,25,1001,10.29,0,0 +2012,6,18,5,126,16,24,1001,3.13,0,0 +2012,6,18,6,133,16,24,1004,3.13,0,0 +2012,6,18,7,154,16,25,1003,4.02,0,0 +2012,6,18,8,144,17,26,1004,4.02,0,0 +2012,6,18,9,145,16,28,1003,1.79,0,0 +2012,6,18,10,151,17,29,1003,4.92,0,0 +2012,6,18,11,153,17,30,1002,8.94,0,0 +2012,6,18,12,155,16,29,1003,12.07,0,0 +2012,6,18,13,154,16,29,1002,13.86,0,0 +2012,6,18,14,160,17,31,1002,17.88,0,0 +2012,6,18,15,164,16,32,1001,21.9,0,0 +2012,6,18,16,202,17,31,1001,25.03,0,0 +2012,6,18,17,216,17,30,1001,0.89,0,0 +2012,6,18,18,232,18,29,1000,1.78,0,0 +2012,6,18,19,254,18,29,1002,4.02,0,0 +2012,6,18,20,255,17,28,1001,8.04,0,0 +2012,6,18,21,257,20,26,1003,12.96,0,0 +2012,6,18,22,305,19,25,1003,16.98,0,0 +2012,6,18,23,266,18,25,1003,18.77,0,0 +2012,6,19,0,259,18,24,1002,20.56,0,0 +2012,6,19,1,236,18,23,1003,22.35,0,0 +2012,6,19,2,219,17,23,1002,25.48,0,0 +2012,6,19,3,185,17,22,1002,27.27,0,0 +2012,6,19,4,164,17,22,1002,30.4,0,0 +2012,6,19,5,147,18,21,1002,31.29,0,0 +2012,6,19,6,139,18,21,1002,33.08,0,0 +2012,6,19,7,132,18,22,1002,0.89,0,0 +2012,6,19,8,140,18,24,1003,1.79,0,0 +2012,6,19,9,150,18,25,1002,0.89,0,0 +2012,6,19,10,135,18,26,1002,1.79,0,0 +2012,6,19,11,157,18,28,1001,4.92,0,0 +2012,6,19,12,NA,18,29,1000,3.13,0,0 +2012,6,19,13,161,18,30,1000,3.13,0,0 +2012,6,19,14,166,19,30,999,7.15,0,0 +2012,6,19,15,208,19,30,998,12.07,0,0 +2012,6,19,16,275,20,30,998,16.99,0,0 +2012,6,19,17,259,20,28,998,20.12,0,0 +2012,6,19,18,225,20,28,998,23.25,0,0 +2012,6,19,19,204,20,27,999,25.04,0,0 +2012,6,19,20,228,21,27,1001,26.83,0,1 +2012,6,19,21,139,20,25,1001,30.85,0,2 +2012,6,19,22,117,19,24,1001,32.64,0,3 +2012,6,19,23,160,21,24,1001,36.66,0,4 +2012,6,20,0,277,21,23,1002,40.68,0,0 +2012,6,20,1,296,20,23,1002,44.7,0,0 +2012,6,20,2,287,20,22,1002,0.89,0,0 +2012,6,20,3,338,19,21,1002,1.34,0,0 +2012,6,20,4,289,18,20,1002,0.89,0,0 +2012,6,20,5,237,19,20,1002,0.89,0,0 +2012,6,20,6,NA,19,21,1003,1.34,0,0 +2012,6,20,7,135,20,23,1003,0.89,0,0 +2012,6,20,8,111,18,24,1003,0.45,0,0 +2012,6,20,9,94,18,26,1003,1.34,0,0 +2012,6,20,10,82,18,27,1003,3.13,0,0 +2012,6,20,11,72,18,29,1003,4.02,0,0 +2012,6,20,12,83,18,28,1003,3.13,0,0 +2012,6,20,13,92,18,29,1003,1.79,0,0 +2012,6,20,14,101,19,30,1002,3.13,0,0 +2012,6,20,15,113,18,30,1001,7.15,0,0 +2012,6,20,16,130,18,31,1001,12.07,0,0 +2012,6,20,17,226,18,30,1001,16.09,0,0 +2012,6,20,18,306,18,29,1001,19.22,0,0 +2012,6,20,19,164,19,28,1001,23.24,0,0 +2012,6,20,20,175,19,27,1002,26.37,0,0 +2012,6,20,21,175,19,26,1003,28.16,0,0 +2012,6,20,22,171,19,26,1004,31.29,0,0 +2012,6,20,23,202,20,25,1004,33.08,0,0 +2012,6,21,0,185,20,24,1004,36.21,0,0 +2012,6,21,1,209,21,23,1004,37.1,0,0 +2012,6,21,2,214,21,23,1004,0.89,0,0 +2012,6,21,3,192,20,22,1004,1.78,0,0 +2012,6,21,4,191,20,21,1004,2.67,0,0 +2012,6,21,5,203,20,21,1005,3.56,0,0 +2012,6,21,6,293,19,21,1005,0.89,0,0 +2012,6,21,7,284,20,22,1005,0.89,0,0 +2012,6,21,8,193,19,25,1005,1.78,0,0 +2012,6,21,9,183,20,26,1005,1.79,0,0 +2012,6,21,10,165,18,28,1005,4.92,0,0 +2012,6,21,11,166,18,29,1005,6.71,0,0 +2012,6,21,12,153,18,30,1004,8.5,0,0 +2012,6,21,13,154,18,31,1003,1.79,0,0 +2012,6,21,14,153,18,31,1003,3.58,0,0 +2012,6,21,15,NA,18,32,1002,5.37,0,0 +2012,6,21,16,163,19,31,1001,1.79,0,0 +2012,6,21,17,182,19,31,1001,0.89,0,0 +2012,6,21,18,230,19,30,1002,1.78,0,0 +2012,6,21,19,210,20,29,1001,1.79,0,0 +2012,6,21,20,183,20,28,1003,4.92,0,0 +2012,6,21,21,191,17,25,1004,13.86,0,0 +2012,6,21,22,60,18,23,1005,21.01,0,1 +2012,6,21,23,52,19,21,1004,25.03,0,2 +2012,6,22,0,NA,19,20,1004,28.16,0,3 +2012,6,22,1,65,19,20,1004,33.97,0,0 +2012,6,22,2,71,19,20,1004,0.89,0,1 +2012,6,22,3,83,20,20,1004,1.79,0,2 +2012,6,22,4,84,19,20,1004,4.92,0,0 +2012,6,22,5,88,19,20,1004,0.89,0,0 +2012,6,22,6,86,20,21,1004,2.68,0,0 +2012,6,22,7,87,20,22,1005,0.89,0,0 +2012,6,22,8,85,20,23,1005,3.13,0,0 +2012,6,22,9,80,19,25,1005,4.92,0,0 +2012,6,22,10,61,19,25,1005,8.05,0,0 +2012,6,22,11,85,20,25,1005,11.18,0,0 +2012,6,22,12,110,20,25,1005,14.31,0,0 +2012,6,22,13,125,21,27,1004,18.33,0,0 +2012,6,22,14,156,21,27,1004,21.46,0,0 +2012,6,22,15,160,21,28,1003,25.48,0,0 +2012,6,22,16,165,21,28,1002,29.5,0,0 +2012,6,22,17,145,21,27,1002,33.52,0,0 +2012,6,22,18,148,21,26,1002,37.54,0,0 +2012,6,22,19,109,20,25,1004,40.67,0,0 +2012,6,22,20,98,20,25,1003,43.8,0,0 +2012,6,22,21,105,20,24,1003,47.82,0,0 +2012,6,22,22,111,20,24,1004,50.95,0,0 +2012,6,22,23,111,19,23,1004,54.08,0,0 +2012,6,23,0,127,20,23,1004,0.89,0,0 +2012,6,23,1,138,20,23,1003,1.78,0,0 +2012,6,23,2,146,20,22,1003,2.23,0,0 +2012,6,23,3,164,20,22,1003,2.68,0,0 +2012,6,23,4,173,20,21,1002,1.79,0,0 +2012,6,23,5,181,20,21,1002,0.89,0,0 +2012,6,23,6,187,20,22,1003,1.79,0,0 +2012,6,23,7,182,20,23,1003,3.58,0,0 +2012,6,23,8,159,21,24,1003,6.71,0,0 +2012,6,23,9,140,20,24,1003,8.5,0,0 +2012,6,23,10,136,20,24,1003,11.63,0,0 +2012,6,23,11,138,20,25,1003,13.42,0,0 +2012,6,23,12,142,20,25,1002,17.44,0,0 +2012,6,23,13,152,20,25,1002,20.57,0,0 +2012,6,23,14,165,20,25,1002,23.7,0,0 +2012,6,23,15,167,21,25,1001,0.89,0,0 +2012,6,23,16,173,21,26,1001,1.79,0,0 +2012,6,23,17,178,21,26,1000,0.89,0,0 +2012,6,23,18,190,22,26,1001,1.78,0,0 +2012,6,23,19,189,22,26,1001,1.79,0,0 +2012,6,23,20,205,22,26,1002,0.89,0,0 +2012,6,23,21,218,22,26,1002,1.79,0,0 +2012,6,23,22,224,23,24,1003,6.71,0,1 +2012,6,23,23,101,23,24,1002,0.89,0,2 +2012,6,24,0,186,22,23,1002,1.79,0,0 +2012,6,24,1,249,22,23,1002,0.89,0,0 +2012,6,24,2,NA,22,23,1001,1.79,0,0 +2012,6,24,3,238,21,23,1001,4.92,0,0 +2012,6,24,4,229,21,23,1001,6.71,0,0 +2012,6,24,5,215,21,23,1001,9.84,0,0 +2012,6,24,6,170,21,23,1002,13.86,0,0 +2012,6,24,7,166,21,23,1002,18.78,0,0 +2012,6,24,8,162,21,23,1002,22.8,0,0 +2012,6,24,9,145,21,24,1002,26.82,0,0 +2012,6,24,10,127,20,24,1002,30.84,0,0 +2012,6,24,11,132,20,24,1002,33.97,0,0 +2012,6,24,12,143,21,24,1003,35.76,0,1 +2012,6,24,13,163,22,23,1002,37.55,0,2 +2012,6,24,14,168,22,23,1002,0.89,0,3 +2012,6,24,15,130,23,24,1002,1.78,0,4 +2012,6,24,16,140,22,24,1002,2.67,0,5 +2012,6,24,17,162,23,24,1002,1.79,0,0 +2012,6,24,18,196,22,24,1002,4.92,0,1 +2012,6,24,19,143,22,24,1003,8.05,0,0 +2012,6,24,20,140,22,23,1003,0.89,0,0 +2012,6,24,21,144,22,22,1004,1.79,0,1 +2012,6,24,22,144,21,22,1004,3.58,0,2 +2012,6,24,23,69,21,22,1004,0.89,0,3 +2012,6,25,0,75,21,21,1004,1.78,0,4 +2012,6,25,1,71,21,21,1004,2.23,0,5 +2012,6,25,2,70,21,21,1005,3.12,0,6 +2012,6,25,3,61,21,21,1005,1.79,0,7 +2012,6,25,4,73,20,20,1005,4.92,0,8 +2012,6,25,5,75,20,20,1005,1.79,0,0 +2012,6,25,6,86,20,20,1005,1.79,0,0 +2012,6,25,7,98,21,21,1006,0.89,0,0 +2012,6,25,8,94,21,22,1006,1.79,0,0 +2012,6,25,9,90,21,23,1006,0.89,0,0 +2012,6,25,10,76,21,24,1006,1.79,0,0 +2012,6,25,11,80,20,25,1006,4.92,0,0 +2012,6,25,12,91,21,26,1006,8.94,0,0 +2012,6,25,13,95,21,26,1006,12.07,0,0 +2012,6,25,14,101,21,26,1005,16.09,0,0 +2012,6,25,15,118,21,26,1005,19.22,0,0 +2012,6,25,16,135,20,26,1006,22.35,0,0 +2012,6,25,17,147,20,26,1006,25.48,0,0 +2012,6,25,18,131,21,26,1005,29.5,0,0 +2012,6,25,19,128,21,25,1006,32.63,0,0 +2012,6,25,20,88,21,25,1007,35.76,0,0 +2012,6,25,21,60,21,24,1007,37.55,0,0 +2012,6,25,22,62,22,24,1008,40.68,0,0 +2012,6,25,23,73,21,24,1008,43.81,0,0 +2012,6,26,0,64,21,24,1008,46.94,0,0 +2012,6,26,1,132,21,23,1008,50.96,0,0 +2012,6,26,2,138,20,23,1008,54.98,0,0 +2012,6,26,3,131,20,23,1008,58.11,0,0 +2012,6,26,4,131,19,23,1009,0.89,0,0 +2012,6,26,5,134,20,22,1009,1.78,0,1 +2012,6,26,6,127,20,22,1010,1.79,0,0 +2012,6,26,7,116,20,23,1010,0.89,0,0 +2012,6,26,8,120,20,23,1010,1.79,0,0 +2012,6,26,9,111,20,23,1011,4.92,0,0 +2012,6,26,10,108,20,24,1011,6.71,0,0 +2012,6,26,11,107,21,26,1010,8.5,0,0 +2012,6,26,12,115,21,25,1010,10.29,0,0 +2012,6,26,13,118,21,25,1010,13.42,0,0 +2012,6,26,14,121,22,27,1010,1.79,0,0 +2012,6,26,15,150,22,27,1009,3.13,0,0 +2012,6,26,16,149,21,28,1009,7.15,0,0 +2012,6,26,17,131,22,27,1009,10.28,0,0 +2012,6,26,18,126,22,26,1010,12.07,0,0 +2012,6,26,19,100,21,25,1010,15.2,0,0 +2012,6,26,20,59,21,24,1011,16.99,0,0 +2012,6,26,21,57,21,24,1011,20.12,0,0 +2012,6,26,22,47,21,24,1012,23.25,0,0 +2012,6,26,23,44,21,23,1012,26.38,0,0 +2012,6,27,0,48,20,22,1012,27.27,0,0 +2012,6,27,1,51,20,22,1012,29.06,0,0 +2012,6,27,2,66,20,21,1012,29.95,0,0 +2012,6,27,3,77,19,21,1012,0.89,0,0 +2012,6,27,4,110,20,21,1013,1.78,0,0 +2012,6,27,5,127,20,21,1013,2.23,0,0 +2012,6,27,6,130,21,21,1013,3.12,0,0 +2012,6,27,7,139,20,24,1014,1.79,0,0 +2012,6,27,8,111,19,24,1014,3.58,0,0 +2012,6,27,9,96,19,25,1013,6.71,0,0 +2012,6,27,10,62,18,26,1013,9.84,0,0 +2012,6,27,11,51,18,27,1013,12.97,0,0 +2012,6,27,12,44,18,27,1012,1.79,0,0 +2012,6,27,13,58,18,28,1012,3.13,0,0 +2012,6,27,14,63,18,28,1011,1.79,0,0 +2012,6,27,15,70,18,28,1011,3.13,0,0 +2012,6,27,16,72,18,27,1011,6.26,0,0 +2012,6,27,17,46,18,27,1011,9.39,0,0 +2012,6,27,18,31,18,26,1010,12.52,0,0 +2012,6,27,19,36,18,25,1011,16.54,0,0 +2012,6,27,20,39,18,25,1011,18.33,0,0 +2012,6,27,21,51,19,24,1011,20.12,0,0 +2012,6,27,22,41,18,24,1012,21.91,0,0 +2012,6,27,23,34,19,23,1012,23.7,0,0 +2012,6,28,0,34,19,23,1011,25.49,0,0 +2012,6,28,1,43,19,22,1011,26.38,0,0 +2012,6,28,2,51,19,22,1011,28.17,0,0 +2012,6,28,3,59,19,22,1011,29.96,0,0 +2012,6,28,4,59,19,22,1010,31.75,0,1 +2012,6,28,5,53,19,21,1010,33.54,0,2 +2012,6,28,6,52,19,21,1011,34.43,0,3 +2012,6,28,7,63,20,21,1011,0.89,0,4 +2012,6,28,8,60,20,21,1011,1.79,0,5 +2012,6,28,9,74,19,20,1011,3.58,0,6 +2012,6,28,10,86,20,21,1011,5.37,0,7 +2012,6,28,11,89,20,21,1011,7.16,0,8 +2012,6,28,12,84,20,21,1010,8.95,0,9 +2012,6,28,13,106,20,22,1010,10.74,0,0 +2012,6,28,14,128,20,22,1009,0.89,0,1 +2012,6,28,15,174,20,23,1009,1.79,0,0 +2012,6,28,16,208,21,23,1008,3.58,0,0 +2012,6,28,17,194,21,23,1008,1.79,0,0 +2012,6,28,18,218,21,22,1008,0.89,0,1 +2012,6,28,19,202,21,21,1008,4.02,0,2 +2012,6,28,20,178,21,22,1008,4.91,0,3 +2012,6,28,21,174,21,22,1008,1.79,0,0 +2012,6,28,22,175,21,22,1008,0.89,0,1 +2012,6,28,23,151,21,21,1008,0.89,0,2 +2012,6,29,0,129,21,21,1007,2.68,0,3 +2012,6,29,1,NA,21,21,1007,0.89,0,4 +2012,6,29,2,109,21,21,1006,1.79,0,5 +2012,6,29,3,104,20,21,1005,3.58,0,6 +2012,6,29,4,118,21,21,1005,0.89,0,7 +2012,6,29,5,105,21,21,1005,1.78,0,8 +2012,6,29,6,91,20,20,1005,1.79,0,9 +2012,6,29,7,90,20,21,1005,3.58,0,10 +2012,6,29,8,72,20,21,1006,0.89,0,11 +2012,6,29,9,75,20,21,1005,1.79,0,12 +2012,6,29,10,74,20,21,1005,3.58,0,0 +2012,6,29,11,63,21,21,1005,5.37,0,0 +2012,6,29,12,61,21,22,1004,7.16,0,0 +2012,6,29,13,61,21,23,1004,8.95,0,0 +2012,6,29,14,59,21,24,1004,0.89,0,0 +2012,6,29,15,67,21,24,1003,1.79,0,0 +2012,6,29,16,69,21,24,1003,3.58,0,0 +2012,6,29,17,73,21,24,1003,5.37,0,0 +2012,6,29,18,72,21,24,1003,7.16,0,0 +2012,6,29,19,73,21,24,1004,8.95,0,0 +2012,6,29,20,81,20,24,1004,10.74,0,0 +2012,6,29,21,83,21,23,1004,0.89,0,0 +2012,6,29,22,98,21,23,1004,1.78,0,0 +2012,6,29,23,106,21,22,1004,0.89,0,0 +2012,6,30,0,110,21,22,1004,0.89,0,0 +2012,6,30,1,98,21,22,1004,1.79,0,0 +2012,6,30,2,104,21,22,1004,0.89,0,0 +2012,6,30,3,105,21,23,1004,1.79,0,0 +2012,6,30,4,97,21,22,1004,3.58,0,0 +2012,6,30,5,127,21,22,1004,5.37,0,0 +2012,6,30,6,128,21,22,1004,0.89,0,0 +2012,6,30,7,128,22,23,1004,1.79,0,0 +2012,6,30,8,100,22,25,1004,4.92,0,0 +2012,6,30,9,78,22,27,1004,6.71,0,0 +2012,6,30,10,70,22,29,1004,0.89,0,0 +2012,6,30,11,50,21,29,1004,1.79,0,0 +2012,6,30,12,36,18,32,1003,1.79,0,0 +2012,6,30,13,30,15,33,1003,0.89,0,0 +2012,6,30,14,24,16,34,1002,1.78,0,0 +2012,6,30,15,21,16,34,1002,2.67,0,0 +2012,6,30,16,27,16,34,1001,3.13,0,0 +2012,6,30,17,40,15,33,1001,6.26,0,0 +2012,6,30,18,40,19,31,1001,12.07,0,0 +2012,6,30,19,27,19,29,1002,15.2,0,0 +2012,6,30,20,42,19,29,1002,19.22,0,0 +2012,6,30,21,71,20,28,1002,22.35,0,0 +2012,6,30,22,69,20,27,1002,26.37,0,0 +2012,6,30,23,69,22,27,1002,30.39,0,0 +2012,7,1,0,77,22,26,1002,34.41,0,0 +2012,7,1,1,77,22,26,1002,37.54,0,0 +2012,7,1,2,90,22,24,1001,39.33,0,0 +2012,7,1,3,104,22,24,1001,0.89,0,0 +2012,7,1,4,112,22,23,1001,1.79,0,0 +2012,7,1,5,126,22,23,1001,3.58,0,0 +2012,7,1,6,131,22,23,1001,5.37,0,0 +2012,7,1,7,134,23,24,1001,8.5,0,0 +2012,7,1,8,135,23,25,1000,11.63,0,0 +2012,7,1,9,145,24,27,1000,13.42,0,0 +2012,7,1,10,138,24,28,1000,15.21,0,0 +2012,7,1,11,140,24,30,999,18.34,0,0 +2012,7,1,12,137,24,32,998,21.47,0,0 +2012,7,1,13,129,24,33,998,24.6,0,0 +2012,7,1,14,NA,23,33,997,29.52,0,0 +2012,7,1,15,133,23,34,997,33.54,0,0 +2012,7,1,16,140,23,34,996,37.56,0,0 +2012,7,1,17,121,23,35,996,1.79,0,0 +2012,7,1,18,122,23,35,996,3.13,0,0 +2012,7,1,19,NA,25,32,996,1.79,0,0 +2012,7,1,20,120,25,30,997,4.92,0,0 +2012,7,1,21,192,25,30,998,6.71,0,0 +2012,7,1,22,199,24,29,998,9.84,0,0 +2012,7,1,23,205,25,28,998,3.13,0,0 +2012,7,2,0,200,25,28,999,6.26,0,0 +2012,7,2,1,178,22,27,999,3.13,0,0 +2012,7,2,2,104,17,26,1000,7.15,0,0 +2012,7,2,3,84,16,25,1000,10.28,0,0 +2012,7,2,4,61,13,22,1000,3.13,0,0 +2012,7,2,5,38,14,21,1000,6.26,0,0 +2012,7,2,6,27,16,23,1000,0.89,0,0 +2012,7,2,7,17,15,26,1001,1.34,0,0 +2012,7,2,8,17,14,28,1001,1.79,0,0 +2012,7,2,9,19,13,31,1001,0.89,0,0 +2012,7,2,10,12,14,32,1000,1.79,0,0 +2012,7,2,11,9,15,32,999,1.79,0,0 +2012,7,2,12,13,13,34,998,1.79,0,0 +2012,7,2,13,13,11,34,997,3.13,0,0 +2012,7,2,14,12,12,35,996,4.92,0,0 +2012,7,2,15,19,14,35,996,3.13,0,0 +2012,7,2,16,NA,14,35,996,7.15,0,0 +2012,7,2,17,16,13,34,995,5.81,0,0 +2012,7,2,18,22,14,33,995,8.94,0,0 +2012,7,2,19,21,13,32,996,12.96,0,0 +2012,7,2,20,24,13,32,996,16.09,0,0 +2012,7,2,21,27,17,29,997,17.88,0,0 +2012,7,2,22,22,16,29,997,19.67,0,0 +2012,7,2,23,23,14,30,997,21.46,0,0 +2012,7,3,0,33,17,25,997,0.89,0,0 +2012,7,3,1,45,18,23,996,1.78,0,0 +2012,7,3,2,48,18,23,996,1.79,0,0 +2012,7,3,3,51,18,21,996,4.92,0,0 +2012,7,3,4,45,18,21,996,5.81,0,0 +2012,7,3,5,48,18,20,997,8.94,0,0 +2012,7,3,6,35,19,22,997,12.07,0,0 +2012,7,3,7,NA,20,25,997,16.09,0,0 +2012,7,3,8,29,19,29,997,17.88,0,0 +2012,7,3,9,21,17,30,997,1.79,0,0 +2012,7,3,10,20,15,32,997,3.13,0,0 +2012,7,3,11,16,15,33,997,3.13,0,0 +2012,7,3,12,18,15,34,996,4.92,0,0 +2012,7,3,13,18,16,35,996,6.71,0,0 +2012,7,3,14,15,15,35,995,3.13,0,0 +2012,7,3,15,26,16,35,995,1.79,0,0 +2012,7,3,16,22,15,35,995,1.79,0,0 +2012,7,3,17,20,15,34,995,4.92,0,0 +2012,7,3,18,24,15,33,995,8.05,0,0 +2012,7,3,19,22,16,32,996,9.84,0,0 +2012,7,3,20,24,17,31,996,11.63,0,0 +2012,7,3,21,33,17,30,997,14.76,0,0 +2012,7,3,22,42,19,30,997,19.68,0,0 +2012,7,3,23,103,17,28,998,25.49,0,0 +2012,7,4,0,115,16,27,999,28.62,0,0 +2012,7,4,1,60,16,27,999,30.41,0,0 +2012,7,4,2,45,17,26,1000,32.2,0,0 +2012,7,4,3,60,17,26,1000,35.33,0,0 +2012,7,4,4,69,17,25,999,1.79,0,0 +2012,7,4,5,87,18,24,1000,0.89,0,0 +2012,7,4,6,91,19,24,1000,1.78,0,0 +2012,7,4,7,114,18,26,1001,1.79,0,0 +2012,7,4,8,96,18,27,1001,0.89,0,0 +2012,7,4,9,97,18,28,1001,1.79,0,0 +2012,7,4,10,81,18,29,1001,1.79,0,0 +2012,7,4,11,81,18,30,1001,3.13,0,0 +2012,7,4,12,83,19,31,1001,6.26,0,0 +2012,7,4,13,88,19,31,1000,8.05,0,0 +2012,7,4,14,85,20,31,1000,3.13,0,0 +2012,7,4,15,95,19,31,1000,4.02,0,0 +2012,7,4,16,114,19,31,1000,8.94,0,0 +2012,7,4,17,NA,22,29,1000,13.86,0,0 +2012,7,4,18,106,21,29,1000,18.78,0,0 +2012,7,4,19,105,19,28,1001,22.8,0,0 +2012,7,4,20,98,19,27,1002,26.82,0,0 +2012,7,4,21,89,17,27,1002,30.84,0,0 +2012,7,4,22,83,17,26,1003,34.86,0,0 +2012,7,4,23,68,18,25,1003,39.78,0,0 +2012,7,5,0,77,19,24,1003,42.91,0,0 +2012,7,5,1,75,19,24,1003,46.04,0,0 +2012,7,5,2,78,20,23,1002,1.79,0,0 +2012,7,5,3,86,20,23,1002,4.92,0,0 +2012,7,5,4,94,21,22,1002,3.13,0,1 +2012,7,5,5,93,21,22,1002,0.89,0,2 +2012,7,5,6,85,22,23,1002,1.34,0,0 +2012,7,5,7,77,22,23,1003,1.79,0,0 +2012,7,5,8,73,22,24,1003,0.89,0,1 +2012,7,5,9,105,22,24,1003,1.79,0,0 +2012,7,5,10,89,22,25,1004,4.92,0,1 +2012,7,5,11,93,22,25,1004,8.05,0,0 +2012,7,5,12,97,21,26,1004,9.84,0,0 +2012,7,5,13,92,21,27,1004,1.79,0,0 +2012,7,5,14,96,20,27,1003,3.58,0,0 +2012,7,5,15,109,21,26,1003,6.71,0,0 +2012,7,5,16,109,20,26,1002,10.73,0,0 +2012,7,5,17,110,21,27,1002,13.86,0,0 +2012,7,5,18,132,21,26,1002,16.99,0,0 +2012,7,5,19,149,21,26,1003,20.12,0,0 +2012,7,5,20,133,21,25,1004,21.91,0,0 +2012,7,5,21,124,21,25,1005,23.7,0,0 +2012,7,5,22,122,20,22,1004,4.02,0,1 +2012,7,5,23,32,20,22,1002,3.13,0,2 +2012,7,6,0,34,20,21,1003,7.15,0,0 +2012,7,6,1,46,20,21,1003,0.89,0,0 +2012,7,6,2,47,20,22,1002,1.78,0,0 +2012,7,6,3,53,20,22,1002,2.23,0,0 +2012,7,6,4,55,20,22,1003,0.89,0,0 +2012,7,6,5,52,20,22,1003,2.68,0,0 +2012,7,6,6,52,21,22,1003,3.57,0,0 +2012,7,6,7,56,21,22,1003,5.36,0,0 +2012,7,6,8,59,21,23,1003,0.89,0,0 +2012,7,6,9,90,21,24,1003,1.78,0,0 +2012,7,6,10,112,22,25,1003,1.79,0,0 +2012,7,6,11,123,22,26,1003,0.89,0,0 +2012,7,6,12,133,22,26,1002,1.78,0,0 +2012,7,6,13,155,22,25,1003,1.79,0,1 +2012,7,6,14,153,23,24,1003,1.79,0,2 +2012,7,6,15,159,23,25,1002,2.68,0,0 +2012,7,6,16,168,23,25,1002,1.79,0,0 +2012,7,6,17,179,23,25,1002,1.79,0,0 +2012,7,6,18,199,23,25,1002,3.58,0,0 +2012,7,6,19,184,23,25,1002,5.37,0,0 +2012,7,6,20,194,23,24,1002,7.16,0,0 +2012,7,6,21,188,23,24,1002,8.95,0,1 +2012,7,6,22,189,23,24,1002,0.89,0,0 +2012,7,6,23,179,23,24,1002,1.78,0,0 +2012,7,7,0,184,23,24,1002,2.67,0,0 +2012,7,7,1,177,23,24,1002,1.79,0,1 +2012,7,7,2,180,23,24,1002,0.89,0,2 +2012,7,7,3,180,23,24,1002,1.79,0,0 +2012,7,7,4,183,23,24,1002,0.89,0,0 +2012,7,7,5,187,23,23,1002,0.89,0,0 +2012,7,7,6,177,23,23,1002,1.79,0,0 +2012,7,7,7,179,23,24,1002,0.89,0,0 +2012,7,7,8,183,24,25,1003,1.78,0,0 +2012,7,7,9,173,24,26,1002,3.13,0,0 +2012,7,7,10,175,24,27,1002,4.92,0,0 +2012,7,7,11,168,24,28,1002,8.05,0,0 +2012,7,7,12,178,24,28,1002,11.18,0,0 +2012,7,7,13,157,24,29,1001,12.97,0,0 +2012,7,7,14,110,24,31,1001,16.1,0,0 +2012,7,7,15,97,24,30,1001,20.12,0,0 +2012,7,7,16,90,25,31,1000,24.14,0,0 +2012,7,7,17,103,24,30,1000,28.16,0,0 +2012,7,7,18,105,24,30,1000,32.18,0,0 +2012,7,7,19,134,24,29,1000,35.31,0,0 +2012,7,7,20,210,24,29,1001,38.44,0,0 +2012,7,7,21,232,24,28,1001,41.57,0,1 +2012,7,7,22,167,22,27,1001,44.7,0,2 +2012,7,7,23,163,21,27,1002,47.83,0,0 +2012,7,8,0,171,22,26,1002,0.89,0,0 +2012,7,8,1,152,23,25,1001,1.78,0,0 +2012,7,8,2,136,23,25,1001,2.67,0,0 +2012,7,8,3,149,23,25,1001,3.56,0,0 +2012,7,8,4,163,23,24,1001,0.89,0,0 +2012,7,8,5,169,23,25,1002,0.89,0,0 +2012,7,8,6,177,23,25,1002,1.78,0,0 +2012,7,8,7,184,24,26,1002,2.67,0,0 +2012,7,8,8,186,23,26,1002,1.79,0,0 +2012,7,8,9,185,24,26,1003,1.79,0,0 +2012,7,8,10,174,24,26,1003,3.58,0,0 +2012,7,8,11,152,24,28,1003,6.71,0,0 +2012,7,8,12,145,24,29,1002,9.84,0,0 +2012,7,8,13,135,24,31,1002,0.89,0,0 +2012,7,8,14,120,23,31,1001,1.79,0,0 +2012,7,8,15,130,23,31,1002,0.89,0,0 +2012,7,8,16,139,24,29,1001,4.92,0,0 +2012,7,8,17,113,23,28,1001,8.94,0,0 +2012,7,8,18,113,23,27,1002,13.86,0,0 +2012,7,8,19,119,24,27,1002,17.88,0,0 +2012,7,8,20,122,24,27,1002,19.67,0,0 +2012,7,8,21,125,24,26,1003,21.46,0,0 +2012,7,8,22,115,24,26,1002,24.59,0,0 +2012,7,8,23,109,24,26,1002,26.38,0,1 +2012,7,9,0,115,24,25,1002,29.51,0,2 +2012,7,9,1,103,24,24,1002,32.64,0,3 +2012,7,9,2,101,23,24,1002,34.43,0,5 +2012,7,9,3,95,23,24,1002,37.56,0,7 +2012,7,9,4,95,23,24,1002,0.89,0,8 +2012,7,9,5,92,23,24,1002,0.89,0,9 +2012,7,9,6,110,24,24,1003,1.78,0,10 +2012,7,9,7,122,24,24,1002,0.89,0,0 +2012,7,9,8,127,24,25,1003,1.78,0,0 +2012,7,9,9,122,24,25,1003,2.67,0,0 +2012,7,9,10,110,24,25,1003,3.56,0,0 +2012,7,9,11,104,24,25,1003,1.79,0,0 +2012,7,9,12,103,24,27,1003,3.58,0,0 +2012,7,9,13,107,24,28,1002,6.71,0,0 +2012,7,9,14,106,24,28,1002,9.84,0,0 +2012,7,9,15,89,24,28,1002,12.97,0,0 +2012,7,9,16,96,24,27,1001,16.99,0,0 +2012,7,9,17,90,24,27,1001,21.01,0,0 +2012,7,9,18,92,24,26,1001,24.14,0,1 +2012,7,9,19,94,23,26,1001,25.93,0,2 +2012,7,9,20,73,24,25,1001,29.95,0,0 +2012,7,9,21,72,23,25,1002,33.08,0,0 +2012,7,9,22,47,23,25,1002,0.89,0,0 +2012,7,9,23,60,23,24,1002,1.79,0,0 +2012,7,10,0,62,23,24,1001,3.58,0,0 +2012,7,10,1,66,23,24,1001,5.37,0,0 +2012,7,10,2,73,23,24,1000,7.16,0,0 +2012,7,10,3,61,23,24,1000,0.89,0,0 +2012,7,10,4,52,23,24,999,1.79,0,0 +2012,7,10,5,48,23,24,999,4.92,0,0 +2012,7,10,6,47,23,24,999,6.71,0,0 +2012,7,10,7,52,24,26,998,8.5,0,0 +2012,7,10,8,42,24,27,998,10.29,0,0 +2012,7,10,9,33,25,29,998,13.42,0,0 +2012,7,10,10,35,24,29,997,0.89,0,0 +2012,7,10,11,35,24,30,996,1.78,0,0 +2012,7,10,12,36,25,32,996,2.67,0,0 +2012,7,10,13,30,25,31,995,3.13,0,0 +2012,7,10,14,46,25,32,994,7.15,0,0 +2012,7,10,15,53,25,32,993,10.28,0,0 +2012,7,10,16,63,25,32,992,16.09,0,0 +2012,7,10,17,67,25,31,993,21.9,0,0 +2012,7,10,18,56,25,30,994,29.05,0,0 +2012,7,10,19,56,21,22,995,41.12,0,2 +2012,7,10,20,22,21,22,995,42.91,0,3 +2012,7,10,21,20,21,23,995,1.79,0,0 +2012,7,10,22,33,22,23,996,6.71,0,0 +2012,7,10,23,43,21,22,995,0.89,0,0 +2012,7,11,0,25,21,23,995,1.79,0,0 +2012,7,11,1,33,21,23,995,4.92,0,0 +2012,7,11,2,45,21,23,994,0.89,0,0 +2012,7,11,3,54,21,22,994,1.79,0,0 +2012,7,11,4,49,21,21,994,3.58,0,0 +2012,7,11,5,47,21,21,995,4.47,0,0 +2012,7,11,6,61,21,21,995,0.89,0,0 +2012,7,11,7,79,21,22,995,1.79,0,0 +2012,7,11,8,86,22,22,995,1.79,0,0 +2012,7,11,9,101,22,24,995,1.79,0,0 +2012,7,11,10,96,23,26,995,1.79,0,0 +2012,7,11,11,95,22,27,994,0.89,0,0 +2012,7,11,12,87,23,28,994,3.13,0,0 +2012,7,11,13,65,24,30,993,4.92,0,0 +2012,7,11,14,76,23,29,994,1.79,0,0 +2012,7,11,15,80,21,29,993,1.79,0,0 +2012,7,11,16,87,23,31,993,3.13,0,0 +2012,7,11,17,88,23,31,993,1.79,0,0 +2012,7,11,18,94,24,29,994,4.92,0,0 +2012,7,11,19,61,24,25,995,4.02,0,0 +2012,7,11,20,47,23,24,995,7.15,0,0 +2012,7,11,21,29,22,25,995,10.28,0,0 +2012,7,11,22,24,22,23,996,1.79,0,0 +2012,7,11,23,28,21,23,996,1.79,0,0 +2012,7,12,0,28,21,23,996,0.89,0,0 +2012,7,12,1,20,21,22,996,0.89,0,0 +2012,7,12,2,19,21,23,996,0.89,0,0 +2012,7,12,3,25,21,22,996,1.79,0,0 +2012,7,12,4,19,21,22,997,3.58,0,0 +2012,7,12,5,27,21,21,997,5.37,0,0 +2012,7,12,6,19,21,22,997,7.16,0,0 +2012,7,12,7,24,22,24,998,10.29,0,0 +2012,7,12,8,20,22,26,998,3.13,0,0 +2012,7,12,9,20,22,27,998,4.92,0,0 +2012,7,12,10,55,22,29,998,1.79,0,0 +2012,7,12,11,57,21,29,998,0.89,0,0 +2012,7,12,12,35,21,31,998,1.79,0,0 +2012,7,12,13,35,20,32,997,4.92,0,0 +2012,7,12,14,20,21,29,998,3.13,0,1 +2012,7,12,15,13,22,31,997,0.89,0,0 +2012,7,12,16,9,20,32,997,1.78,0,0 +2012,7,12,17,9,21,32,997,3.13,0,0 +2012,7,12,18,16,19,28,998,5.81,0,0 +2012,7,12,19,14,19,27,999,3.13,0,0 +2012,7,12,20,NA,19,27,999,4.02,0,0 +2012,7,12,21,NA,20,27,1001,5.81,0,0 +2012,7,12,22,17,20,26,1002,1.79,0,0 +2012,7,12,23,16,20,25,1002,3.58,0,0 +2012,7,13,0,29,20,25,1002,5.37,0,0 +2012,7,13,1,26,21,23,1002,6.26,0,0 +2012,7,13,2,18,21,23,1002,0.89,0,0 +2012,7,13,3,17,20,23,1002,1.79,0,0 +2012,7,13,4,31,20,22,1003,3.58,0,0 +2012,7,13,5,25,20,22,1003,4.47,0,0 +2012,7,13,6,33,22,23,1003,1.79,0,0 +2012,7,13,7,25,22,24,1004,1.79,0,0 +2012,7,13,8,22,20,26,1004,4.92,0,0 +2012,7,13,9,15,19,27,1005,4.02,0,0 +2012,7,13,10,10,20,28,1005,7.15,0,0 +2012,7,13,11,8,18,29,1005,8.94,0,0 +2012,7,13,12,7,19,30,1004,0.89,0,0 +2012,7,13,13,11,19,30,1004,1.78,0,0 +2012,7,13,14,14,19,30,1003,2.67,0,0 +2012,7,13,15,17,17,30,1003,3.56,0,0 +2012,7,13,16,28,19,30,1002,5.35,0,0 +2012,7,13,17,34,21,30,1002,3.13,0,0 +2012,7,13,18,33,19,29,1002,6.26,0,0 +2012,7,13,19,36,19,29,1003,10.28,0,0 +2012,7,13,20,38,20,27,1003,12.07,0,0 +2012,7,13,21,41,21,26,1004,0.89,0,0 +2012,7,13,22,42,20,26,1004,1.78,0,0 +2012,7,13,23,38,22,24,1004,0.89,0,0 +2012,7,14,0,46,22,24,1004,1.78,0,0 +2012,7,14,1,47,22,23,1004,3.57,0,0 +2012,7,14,2,61,21,23,1004,0.45,0,0 +2012,7,14,3,57,21,22,1004,1.79,0,0 +2012,7,14,4,54,21,22,1004,1.79,0,0 +2012,7,14,5,48,20,23,1004,0.89,0,0 +2012,7,14,6,38,20,23,1004,1.78,0,0 +2012,7,14,7,25,19,24,1005,2.67,0,0 +2012,7,14,8,23,18,25,1005,3.13,0,0 +2012,7,14,9,21,17,27,1005,7.15,0,0 +2012,7,14,10,19,17,28,1005,10.28,0,0 +2012,7,14,11,17,17,29,1005,1.79,0,0 +2012,7,14,12,16,16,29,1004,3.13,0,0 +2012,7,14,13,14,16,31,1004,6.26,0,0 +2012,7,14,14,15,15,30,1003,8.05,0,0 +2012,7,14,15,15,15,30,1003,1.79,0,0 +2012,7,14,16,19,17,30,1002,3.13,0,0 +2012,7,14,17,15,16,29,1002,4.92,0,0 +2012,7,14,18,19,16,29,1002,6.71,0,0 +2012,7,14,19,33,17,28,1002,9.84,0,0 +2012,7,14,20,34,17,26,1003,11.63,0,0 +2012,7,14,21,31,18,25,1004,12.52,0,0 +2012,7,14,22,39,18,24,1004,14.31,0,0 +2012,7,14,23,53,18,25,1004,16.1,0,0 +2012,7,15,0,63,19,23,1004,0.89,0,0 +2012,7,15,1,60,20,22,1004,1.79,0,0 +2012,7,15,2,63,20,22,1004,0.89,0,0 +2012,7,15,3,59,19,21,1004,0.89,0,0 +2012,7,15,4,48,19,20,1004,1.78,0,0 +2012,7,15,5,52,19,20,1005,3.57,0,0 +2012,7,15,6,54,19,21,1005,1.79,0,0 +2012,7,15,7,50,20,24,1006,3.58,0,0 +2012,7,15,8,32,18,26,1006,3.13,0,0 +2012,7,15,9,29,19,28,1006,4.02,0,0 +2012,7,15,10,31,19,29,1006,7.15,0,0 +2012,7,15,11,29,19,31,1006,8.94,0,0 +2012,7,15,12,32,17,32,1005,1.79,0,0 +2012,7,15,13,29,18,32,1005,0.89,0,0 +2012,7,15,14,25,15,33,1004,2.68,0,0 +2012,7,15,15,25,15,33,1004,4.47,0,0 +2012,7,15,16,26,15,33,1004,1.79,0,0 +2012,7,15,17,25,15,33,1004,3.58,0,0 +2012,7,15,18,27,14,32,1004,5.37,0,0 +2012,7,15,19,32,17,30,1004,8.5,0,0 +2012,7,15,20,38,16,28,1005,10.29,0,0 +2012,7,15,21,41,15,28,1006,14.31,0,0 +2012,7,15,22,53,17,28,1006,17.44,0,0 +2012,7,15,23,58,17,28,1006,20.57,0,0 +2012,7,16,0,60,18,25,1007,23.7,0,0 +2012,7,16,1,84,19,25,1007,26.83,0,0 +2012,7,16,2,84,20,23,1007,28.62,0,0 +2012,7,16,3,86,19,23,1007,30.41,0,0 +2012,7,16,4,63,19,22,1007,32.2,0,0 +2012,7,16,5,68,19,21,1008,0.89,0,0 +2012,7,16,6,99,21,24,1008,1.34,0,0 +2012,7,16,7,130,21,25,1009,1.79,0,0 +2012,7,16,8,131,20,27,1009,0.89,0,0 +2012,7,16,9,126,20,28,1009,1.78,0,0 +2012,7,16,10,112,20,30,1009,2.67,0,0 +2012,7,16,11,90,20,31,1008,1.79,0,0 +2012,7,16,12,85,19,32,1008,4.92,0,0 +2012,7,16,13,76,16,33,1007,1.79,0,0 +2012,7,16,14,71,17,34,1007,3.58,0,0 +2012,7,16,15,62,17,34,1006,4.02,0,0 +2012,7,16,16,51,17,34,1006,7.15,0,0 +2012,7,16,17,45,16,33,1006,11.17,0,0 +2012,7,16,18,52,17,32,1007,16.09,0,0 +2012,7,16,19,46,17,31,1007,20.11,0,0 +2012,7,16,20,50,18,29,1007,21.9,0,0 +2012,7,16,21,56,18,28,1008,25.03,0,0 +2012,7,16,22,55,18,29,1008,28.16,0,0 +2012,7,16,23,53,17,27,1008,29.95,0,0 +2012,7,17,0,60,17,27,1008,31.74,0,0 +2012,7,17,1,55,18,25,1009,0.45,0,0 +2012,7,17,2,61,19,25,1009,1.79,0,0 +2012,7,17,3,61,21,25,1009,4.92,0,0 +2012,7,17,4,80,21,24,1009,8.94,0,0 +2012,7,17,5,114,21,24,1009,12.07,0,0 +2012,7,17,6,110,22,24,1010,13.86,0,0 +2012,7,17,7,94,22,25,1011,15.65,0,0 +2012,7,17,8,108,22,26,1011,18.78,0,0 +2012,7,17,9,95,22,27,1011,21.91,0,0 +2012,7,17,10,92,22,29,1011,25.04,0,0 +2012,7,17,11,85,21,30,1010,28.17,0,0 +2012,7,17,12,79,22,30,1010,31.3,0,0 +2012,7,17,13,77,21,31,1009,34.43,0,0 +2012,7,17,14,76,21,32,1009,38.45,0,0 +2012,7,17,15,NA,21,32,1008,41.58,0,0 +2012,7,17,16,83,20,31,1008,45.6,0,0 +2012,7,17,17,75,20,31,1007,49.62,0,0 +2012,7,17,18,79,20,30,1007,53.64,0,0 +2012,7,17,19,79,20,29,1008,58.56,0,0 +2012,7,17,20,107,21,28,1008,62.58,0,0 +2012,7,17,21,64,21,27,1009,65.71,0,0 +2012,7,17,22,72,21,26,1010,68.84,0,0 +2012,7,17,23,80,21,25,1010,71.97,0,0 +2012,7,18,0,89,22,25,1010,75.1,0,0 +2012,7,18,1,NA,22,24,1010,78.23,0,0 +2012,7,18,2,99,22,24,1010,81.36,0,0 +2012,7,18,3,99,22,24,1010,83.15,0,0 +2012,7,18,4,79,22,23,1010,84.94,0,0 +2012,7,18,5,51,21,23,1010,86.73,0,0 +2012,7,18,6,54,21,22,1010,89.86,0,0 +2012,7,18,7,54,21,23,1010,91.65,0,0 +2012,7,18,8,65,21,24,1010,93.44,0,0 +2012,7,18,9,76,22,26,1010,95.23,0,0 +2012,7,18,10,81,21,27,1009,97.02,0,0 +2012,7,18,11,90,21,28,1009,1.79,0,0 +2012,7,18,12,96,21,29,1008,1.79,0,0 +2012,7,18,13,99,21,29,1007,3.58,0,0 +2012,7,18,14,104,21,30,1007,6.71,0,0 +2012,7,18,15,103,21,30,1006,9.84,0,0 +2012,7,18,16,95,21,30,1006,12.97,0,0 +2012,7,18,17,98,22,30,1005,16.1,0,0 +2012,7,18,18,88,21,30,1005,19.23,0,0 +2012,7,18,19,105,21,29,1005,21.02,0,0 +2012,7,18,20,106,22,28,1006,22.81,0,0 +2012,7,18,21,100,22,27,1006,24.6,0,0 +2012,7,18,22,100,22,27,1007,27.73,0,0 +2012,7,18,23,80,22,26,1007,30.86,0,0 +2012,7,19,0,82,22,26,1006,32.65,0,0 +2012,7,19,1,83,22,25,1007,34.44,0,0 +2012,7,19,2,92,22,25,1007,36.23,0,0 +2012,7,19,3,105,23,24,1006,0.89,0,0 +2012,7,19,4,120,22,24,1006,1.79,0,0 +2012,7,19,5,124,22,24,1007,3.58,0,0 +2012,7,19,6,123,22,24,1007,5.37,0,0 +2012,7,19,7,116,23,24,1007,7.16,0,0 +2012,7,19,8,126,23,25,1007,8.95,0,0 +2012,7,19,9,132,23,26,1007,10.74,0,0 +2012,7,19,10,136,23,27,1007,12.53,0,0 +2012,7,19,11,128,23,28,1007,14.32,0,0 +2012,7,19,12,130,23,28,1006,16.11,0,0 +2012,7,19,13,116,23,29,1006,17.9,0,0 +2012,7,19,14,115,23,30,1005,19.69,0,0 +2012,7,19,15,120,24,30,1004,21.48,0,0 +2012,7,19,16,126,23,31,1004,23.27,0,0 +2012,7,19,17,142,23,30,1004,26.4,0,0 +2012,7,19,18,138,24,30,1003,28.19,0,0 +2012,7,19,19,133,24,29,1004,29.98,0,0 +2012,7,19,20,124,23,28,1004,0.89,0,0 +2012,7,19,21,138,23,28,1005,1.79,0,0 +2012,7,19,22,132,24,27,1004,2.68,0,0 +2012,7,19,23,146,23,28,1004,5.81,0,0 +2012,7,20,0,160,24,27,1005,7.6,0,0 +2012,7,20,1,173,24,27,1004,9.39,0,0 +2012,7,20,2,167,24,27,1004,11.18,0,0 +2012,7,20,3,179,24,26,1004,12.97,0,0 +2012,7,20,4,187,24,25,1005,14.76,0,0 +2012,7,20,5,195,24,25,1005,16.55,0,0 +2012,7,20,6,197,24,25,1005,0.89,0,0 +2012,7,20,7,202,24,26,1005,1.79,0,0 +2012,7,20,8,206,24,27,1006,3.58,0,0 +2012,7,20,9,205,24,28,1006,0.89,0,0 +2012,7,20,10,212,24,28,1005,1.79,0,0 +2012,7,20,11,222,25,30,1005,1.79,0,0 +2012,7,20,12,226,24,31,1005,4.92,0,0 +2012,7,20,13,259,25,32,1004,8.05,0,0 +2012,7,20,14,220,25,32,1004,11.18,0,0 +2012,7,20,15,189,23,32,1003,16.1,0,0 +2012,7,20,16,197,24,32,1003,19.23,0,0 +2012,7,20,17,181,24,32,1003,23.25,0,0 +2012,7,20,18,188,24,31,1002,26.38,0,0 +2012,7,20,19,192,24,30,1003,28.17,0,0 +2012,7,20,20,189,25,29,1004,29.96,0,0 +2012,7,20,21,194,25,29,1004,31.75,0,0 +2012,7,20,22,206,25,28,1004,33.54,0,0 +2012,7,20,23,219,25,28,1004,35.33,0,0 +2012,7,21,0,240,25,28,1004,37.12,0,0 +2012,7,21,1,239,25,27,1004,38.91,0,0 +2012,7,21,2,246,25,26,1004,40.7,0,0 +2012,7,21,3,227,25,26,1004,42.49,0,0 +2012,7,21,4,198,25,26,1003,44.28,0,0 +2012,7,21,5,174,25,26,1003,46.07,0,0 +2012,7,21,6,185,25,26,1004,49.2,0,0 +2012,7,21,7,192,25,27,1004,52.33,0,0 +2012,7,21,8,233,25,27,1004,54.12,0,0 +2012,7,21,9,256,25,28,1004,57.25,0,0 +2012,7,21,10,278,25,28,1004,60.38,0,0 +2012,7,21,11,283,25,28,1004,62.17,0,1 +2012,7,21,12,293,25,28,1003,63.96,0,2 +2012,7,21,13,318,25,27,1002,3.13,0,3 +2012,7,21,14,299,25,27,1001,7.15,0,4 +2012,7,21,15,164,24,24,1001,11.17,0,5 +2012,7,21,16,31,23,24,1000,14.3,0,6 +2012,7,21,17,46,23,24,1000,18.32,0,7 +2012,7,21,18,95,24,24,1000,21.45,0,9 +2012,7,21,19,23,23,23,1001,8.94,0,12 +2012,7,21,20,29,24,24,1000,0.89,0,15 +2012,7,21,21,21,23,23,999,1.79,0,16 +2012,7,21,22,26,23,23,999,0.89,0,17 +2012,7,21,23,23,23,23,999,1.79,0,18 +2012,7,22,0,28,23,23,998,3.13,0,19 +2012,7,22,1,23,23,23,997,6.26,0,20 +2012,7,22,2,13,23,23,997,7.15,0,22 +2012,7,22,3,12,23,23,996,10.28,0,23 +2012,7,22,4,11,23,23,996,14.3,0,0 +2012,7,22,5,12,22,22,996,16.09,0,0 +2012,7,22,6,12,23,23,997,20.11,0,0 +2012,7,22,7,24,23,24,997,3.13,0,0 +2012,7,22,8,24,23,25,997,3.13,0,0 +2012,7,22,9,11,20,29,998,8.94,0,0 +2012,7,22,10,10,18,30,999,14.75,0,0 +2012,7,22,11,9,17,30,999,22.8,0,0 +2012,7,22,12,8,17,32,999,29.95,0,0 +2012,7,22,13,10,16,33,999,38.89,0,0 +2012,7,22,14,9,15,34,998,47.83,0,0 +2012,7,22,15,9,16,34,998,55.88,0,0 +2012,7,22,16,12,17,34,999,60.8,0,0 +2012,7,22,17,8,15,35,999,62.59,0,0 +2012,7,22,18,17,20,32,999,1.79,0,0 +2012,7,22,19,15,23,29,1000,2.68,0,0 +2012,7,22,20,21,23,27,1001,0.45,0,0 +2012,7,22,21,39,23,26,1002,1.34,0,0 +2012,7,22,22,27,19,25,1003,3.13,0,0 +2012,7,22,23,20,18,25,1003,1.79,0,0 +2012,7,23,0,27,19,23,1003,3.13,0,0 +2012,7,23,1,14,18,25,1003,4.92,0,0 +2012,7,23,2,17,18,23,1003,3.13,0,0 +2012,7,23,3,12,19,22,1003,3.13,0,0 +2012,7,23,4,20,19,22,1004,7.15,0,0 +2012,7,23,5,17,18,21,1004,0.89,0,0 +2012,7,23,6,12,19,22,1005,1.79,0,0 +2012,7,23,7,12,19,25,1005,0.89,0,0 +2012,7,23,8,17,20,27,1005,3.13,0,0 +2012,7,23,9,10,19,28,1005,3.13,0,0 +2012,7,23,10,13,19,30,1006,3.13,0,0 +2012,7,23,11,20,19,31,1005,1.79,0,0 +2012,7,23,12,30,21,32,1005,1.79,0,0 +2012,7,23,13,25,22,32,1005,1.79,0,0 +2012,7,23,14,41,23,31,1005,3.58,0,0 +2012,7,23,15,70,23,31,1005,5.37,0,0 +2012,7,23,16,69,22,32,1004,8.5,0,0 +2012,7,23,17,90,23,30,1004,12.52,0,0 +2012,7,23,18,63,22,29,1005,15.65,0,0 +2012,7,23,19,56,21,28,1005,18.78,0,0 +2012,7,23,20,51,21,28,1006,22.8,0,0 +2012,7,23,21,30,20,27,1007,25.93,0,0 +2012,7,23,22,28,20,26,1007,27.72,0,0 +2012,7,23,23,23,21,26,1007,28.61,0,0 +2012,7,24,0,27,23,25,1007,30.4,0,0 +2012,7,24,1,24,22,25,1008,32.19,0,0 +2012,7,24,2,49,21,24,1008,1.79,0,0 +2012,7,24,3,59,21,24,1008,1.79,0,0 +2012,7,24,4,64,21,24,1008,0.89,0,0 +2012,7,24,5,55,21,23,1008,1.78,0,0 +2012,7,24,6,53,21,23,1008,2.67,0,0 +2012,7,24,7,56,22,24,1009,3.56,0,0 +2012,7,24,8,66,21,25,1009,4.45,0,0 +2012,7,24,9,70,21,25,1008,5.34,0,0 +2012,7,24,10,75,21,26,1009,1.79,0,0 +2012,7,24,11,76,22,27,1009,0.89,0,0 +2012,7,24,12,71,22,28,1009,1.78,0,0 +2012,7,24,13,80,22,29,1008,3.57,0,0 +2012,7,24,14,69,23,30,1008,1.79,0,0 +2012,7,24,15,63,21,31,1008,0.89,0,0 +2012,7,24,16,75,21,31,1008,3.13,0,0 +2012,7,24,17,65,22,31,1007,0.89,0,0 +2012,7,24,18,65,21,29,1007,3.13,0,0 +2012,7,24,19,76,22,28,1007,6.26,0,0 +2012,7,24,20,83,22,27,1008,8.05,0,0 +2012,7,24,21,81,24,28,1008,11.18,0,0 +2012,7,24,22,89,23,27,1009,12.97,0,0 +2012,7,24,23,103,23,27,1009,14.76,0,0 +2012,7,25,0,109,23,26,1009,16.55,0,0 +2012,7,25,1,103,23,26,1009,18.34,0,0 +2012,7,25,2,102,23,25,1008,20.13,0,0 +2012,7,25,3,90,23,25,1008,21.92,0,0 +2012,7,25,4,60,23,25,1008,23.71,0,0 +2012,7,25,5,56,23,24,1009,0.89,0,0 +2012,7,25,6,88,23,25,1009,1.79,0,0 +2012,7,25,7,90,24,25,1009,3.58,0,0 +2012,7,25,8,95,24,26,1010,6.71,0,0 +2012,7,25,9,102,24,25,1009,9.84,0,1 +2012,7,25,10,122,25,25,1008,11.63,0,2 +2012,7,25,11,130,25,27,1008,14.76,0,3 +2012,7,25,12,121,24,28,1007,17.89,0,0 +2012,7,25,13,108,24,28,1007,21.02,0,0 +2012,7,25,14,70,23,27,1007,24.15,0,1 +2012,7,25,15,60,25,28,1007,27.28,0,0 +2012,7,25,16,62,25,28,1006,29.07,0,0 +2012,7,25,17,79,25,28,1006,0.89,0,0 +2012,7,25,18,75,24,28,1005,3.13,0,0 +2012,7,25,19,68,24,27,1005,4.92,0,0 +2012,7,25,20,61,25,26,1005,8.05,0,1 +2012,7,25,21,56,25,26,1006,11.18,0,0 +2012,7,25,22,54,25,26,1005,15.2,0,0 +2012,7,25,23,62,25,26,1005,18.33,0,0 +2012,7,26,0,73,25,26,1005,21.46,0,0 +2012,7,26,1,70,24,25,1005,24.59,0,0 +2012,7,26,2,77,24,25,1004,26.38,0,0 +2012,7,26,3,75,25,25,1004,1.79,0,0 +2012,7,26,4,78,24,25,1004,3.13,0,0 +2012,7,26,5,69,24,25,1004,7.15,0,0 +2012,7,26,6,57,24,24,1004,8.94,0,1 +2012,7,26,7,54,24,25,1004,0.89,0,0 +2012,7,26,8,68,25,25,1004,1.79,0,0 +2012,7,26,9,77,25,25,1004,5.81,0,0 +2012,7,26,10,70,24,25,1004,8.94,0,0 +2012,7,26,11,61,24,26,1004,12.07,0,0 +2012,7,26,12,46,25,27,1004,16.09,0,0 +2012,7,26,13,61,24,26,1004,20.11,0,0 +2012,7,26,14,40,24,25,1004,23.24,0,1 +2012,7,26,15,29,25,26,1003,27.26,0,0 +2012,7,26,16,30,24,26,1004,29.05,0,0 +2012,7,26,17,46,24,25,1004,30.84,0,0 +2012,7,26,18,48,24,26,1002,33.97,0,0 +2012,7,26,19,49,24,26,1003,35.76,0,0 +2012,7,26,20,48,24,25,1005,38.89,0,0 +2012,7,26,21,39,24,25,1004,0.89,0,0 +2012,7,26,22,51,24,25,1004,1.79,0,0 +2012,7,26,23,56,23,25,1005,3.58,0,0 +2012,7,27,0,NA,23,25,1005,4.47,0,0 +2012,7,27,1,63,24,25,1005,6.26,0,0 +2012,7,27,2,53,23,25,1005,0.89,0,0 +2012,7,27,3,59,24,24,1005,0.89,0,0 +2012,7,27,4,54,24,25,1005,0.89,0,0 +2012,7,27,5,82,24,25,1005,0.89,0,0 +2012,7,27,6,79,24,25,1005,1.78,0,0 +2012,7,27,7,77,24,25,1005,3.57,0,0 +2012,7,27,8,80,24,26,1006,0.89,0,0 +2012,7,27,9,86,24,27,1006,1.78,0,0 +2012,7,27,10,101,24,27,1006,1.79,0,0 +2012,7,27,11,99,25,26,1006,0.89,0,0 +2012,7,27,12,91,25,26,1006,2.68,0,0 +2012,7,27,13,94,25,27,1006,3.57,0,0 +2012,7,27,14,99,25,28,1006,4.46,0,0 +2012,7,27,15,89,25,28,1006,1.79,0,0 +2012,7,27,16,86,26,28,1005,3.58,0,0 +2012,7,27,17,90,26,28,1005,6.71,0,0 +2012,7,27,18,94,26,28,1005,8.5,0,0 +2012,7,27,19,93,26,27,1005,1.79,0,0 +2012,7,27,20,105,25,27,1006,0.89,0,0 +2012,7,27,21,108,25,27,1006,1.79,0,0 +2012,7,27,22,109,25,26,1006,3.58,0,1 +2012,7,27,23,37,25,25,1006,0.89,0,2 +2012,7,28,0,51,25,26,1006,1.79,0,3 +2012,7,28,1,83,25,26,1005,4.92,0,0 +2012,7,28,2,NA,25,25,1005,1.79,0,0 +2012,7,28,3,88,25,25,1004,1.79,0,1 +2012,7,28,4,89,25,25,1003,3.58,0,0 +2012,7,28,5,98,24,25,1004,6.71,0,0 +2012,7,28,6,96,24,25,1004,9.84,0,0 +2012,7,28,7,94,24,25,1003,13.86,0,0 +2012,7,28,8,116,25,25,1003,16.99,0,0 +2012,7,28,9,130,25,25,1003,18.78,0,0 +2012,7,28,10,126,25,26,1003,20.57,0,0 +2012,7,28,11,128,26,28,1002,23.7,0,0 +2012,7,28,12,111,27,29,1001,26.83,0,0 +2012,7,28,13,108,26,30,1001,31.75,0,0 +2012,7,28,14,103,26,31,1000,35.77,0,0 +2012,7,28,15,110,26,31,1000,41.58,0,0 +2012,7,28,16,124,26,31,999,46.5,0,0 +2012,7,28,17,NA,26,31,999,51.42,0,0 +2012,7,28,18,NA,26,31,998,55.44,0,0 +2012,7,28,19,NA,26,30,998,58.57,0,0 +2012,7,28,20,NA,26,29,999,60.36,0,0 +2012,7,28,21,NA,27,28,998,62.15,0,0 +2012,7,28,22,NA,27,28,999,1.79,0,0 +2012,7,28,23,NA,22,27,999,4.92,0,0 +2012,7,29,0,NA,23,24,998,8.94,0,0 +2012,7,29,1,NA,22,23,998,12.07,0,0 +2012,7,29,2,NA,22,23,998,1.79,0,0 +2012,7,29,3,NA,21,22,998,1.79,0,0 +2012,7,29,4,NA,21,22,999,4.92,0,0 +2012,7,29,5,NA,21,22,999,8.05,0,0 +2012,7,29,6,NA,22,23,999,11.18,0,0 +2012,7,29,7,NA,22,25,999,12.97,0,0 +2012,7,29,8,NA,23,27,999,17.89,0,0 +2012,7,29,9,NA,22,30,1000,22.81,0,0 +2012,7,29,10,NA,21,32,1000,27.73,0,0 +2012,7,29,11,NA,19,33,1000,33.54,0,0 +2012,7,29,12,NA,19,33,999,39.35,0,0 +2012,7,29,13,NA,19,34,999,44.27,0,0 +2012,7,29,14,NA,20,34,999,49.19,0,0 +2012,7,29,15,NA,22,34,999,1.79,0,0 +2012,7,29,16,NA,20,35,998,1.79,0,0 +2012,7,29,17,NA,21,33,998,0.89,0,0 +2012,7,29,18,NA,20,33,999,1.79,0,0 +2012,7,29,19,NA,23,31,999,0.89,0,0 +2012,7,29,20,NA,24,27,1000,0.89,0,0 +2012,7,29,21,NA,24,27,1001,0.89,0,0 +2012,7,29,22,NA,25,27,1001,1.78,0,0 +2012,7,29,23,NA,24,26,1001,2.67,0,0 +2012,7,30,0,NA,22,25,1001,1.79,0,0 +2012,7,30,1,NA,22,26,1001,4.92,0,0 +2012,7,30,2,NA,21,24,1001,6.71,0,0 +2012,7,30,3,NA,21,22,1002,9.84,0,0 +2012,7,30,4,NA,21,23,1003,12.97,0,0 +2012,7,30,5,NA,20,22,1002,16.1,0,0 +2012,7,30,6,NA,19,25,1002,4.92,0,0 +2012,7,30,7,NA,19,26,1003,8.05,0,0 +2012,7,30,8,NA,20,27,1004,11.18,0,0 +2012,7,30,9,NA,19,29,1004,14.31,0,0 +2012,7,30,10,NA,19,29,1005,3.13,0,0 +2012,7,30,11,NA,20,28,1005,6.26,0,0 +2012,7,30,12,NA,21,29,1005,8.05,0,0 +2012,7,30,13,35,21,29,1005,9.84,0,0 +2012,7,30,14,45,22,29,1004,11.63,0,0 +2012,7,30,15,48,22,28,1004,0.89,0,0 +2012,7,30,16,49,24,26,1004,1.79,0,1 +2012,7,30,17,49,25,26,1004,0.89,0,0 +2012,7,30,18,58,24,26,1005,1.78,0,1 +2012,7,30,19,80,24,25,1005,0.89,0,2 +2012,7,30,20,76,24,25,1006,0.45,0,3 +2012,7,30,21,66,22,23,1007,1.34,0,4 +2012,7,30,22,67,22,23,1007,1.79,0,5 +2012,7,30,23,47,22,22,1007,4.92,0,6 +2012,7,31,0,38,22,22,1006,6.71,0,7 +2012,7,31,1,38,22,22,1005,3.13,0,8 +2012,7,31,2,37,20,20,1006,0.89,0,9 +2012,7,31,3,35,20,20,1006,1.78,0,10 +2012,7,31,4,35,20,21,1006,2.67,0,11 +2012,7,31,5,39,21,21,1005,3.56,0,0 +2012,7,31,6,44,21,21,1006,3.13,0,2 +2012,7,31,7,29,21,21,1007,1.79,0,4 +2012,7,31,8,25,21,21,1007,0.89,0,5 +2012,7,31,9,21,20,21,1007,3.13,0,6 +2012,7,31,10,16,20,21,1008,0.89,0,7 +2012,7,31,11,17,18,20,1007,4.92,0,8 +2012,7,31,12,14,19,19,1007,8.05,0,9 +2012,7,31,13,17,19,20,1007,1.79,0,10 +2012,7,31,14,15,20,20,1007,0.89,0,11 +2012,7,31,15,22,20,21,1006,1.79,0,12 +2012,7,31,16,20,20,21,1006,4.92,0,13 +2012,7,31,17,24,20,21,1006,6.71,0,14 +2012,7,31,18,21,20,21,1007,9.84,0,15 +2012,7,31,19,22,20,21,1007,12.97,0,16 +2012,7,31,20,27,21,21,1007,16.1,0,17 +2012,7,31,21,NA,20,21,1008,19.23,0,18 +2012,7,31,22,26,20,20,1008,22.36,0,19 +2012,7,31,23,22,20,20,1008,26.38,0,20 +2012,8,1,0,9,20,20,1008,30.4,0,21 +2012,8,1,1,14,20,20,1008,33.53,0,22 +2012,8,1,2,9,20,20,1007,36.66,0,23 +2012,8,1,3,9,20,20,1007,38.45,0,0 +2012,8,1,4,17,20,20,1007,40.24,0,0 +2012,8,1,5,11,20,20,1007,0.89,0,1 +2012,8,1,6,14,20,20,1007,0.89,0,2 +2012,8,1,7,17,20,20,1008,4.91,0,3 +2012,8,1,8,22,20,20,1008,9.83,0,4 +2012,8,1,9,18,20,20,1008,4.92,0,5 +2012,8,1,10,8,20,20,1008,10.73,0,6 +2012,8,1,11,8,20,20,1008,4.92,0,7 +2012,8,1,12,16,20,21,1008,10.73,0,8 +2012,8,1,13,19,21,21,1008,4.92,0,9 +2012,8,1,14,13,20,22,1007,4.92,0,10 +2012,8,1,15,10,20,22,1007,4.92,0,11 +2012,8,1,16,6,20,22,1006,4.92,0,12 +2012,8,1,17,7,20,23,1006,4.92,0,13 +2012,8,1,18,9,20,23,1006,5.81,0,14 +2012,8,1,19,13,20,22,1006,10.73,0,0 +2012,8,1,20,9,20,23,1007,14.75,0,0 +2012,8,1,21,16,20,22,1007,17.88,0,0 +2012,8,1,22,17,21,22,1007,21.01,0,0 +2012,8,1,23,11,20,21,1007,1.79,0,0 +2012,8,2,0,18,19,20,1007,1.79,0,0 +2012,8,2,1,16,19,20,1007,4.92,0,0 +2012,8,2,2,13,19,20,1007,8.05,0,0 +2012,8,2,3,18,19,20,1007,3.13,0,0 +2012,8,2,4,11,19,21,1007,7.15,0,0 +2012,8,2,5,16,19,20,1007,1.79,0,0 +2012,8,2,6,15,19,20,1007,3.58,0,0 +2012,8,2,7,15,20,23,1008,7.6,0,0 +2012,8,2,8,9,21,25,1008,11.62,0,0 +2012,8,2,9,14,21,27,1008,15.64,0,0 +2012,8,2,10,10,20,27,1009,3.13,0,0 +2012,8,2,11,16,20,28,1008,3.13,0,0 +2012,8,2,12,14,20,29,1008,0.89,0,0 +2012,8,2,13,9,21,30,1007,1.79,0,0 +2012,8,2,14,17,21,30,1007,1.79,0,0 +2012,8,2,15,15,20,30,1007,3.58,0,0 +2012,8,2,16,17,21,31,1007,5.37,0,0 +2012,8,2,17,21,21,30,1006,8.5,0,0 +2012,8,2,18,18,22,29,1006,11.63,0,0 +2012,8,2,19,22,22,27,1007,13.42,0,0 +2012,8,2,20,30,23,26,1008,0.89,0,0 +2012,8,2,21,33,23,24,1009,0.89,0,0 +2012,8,2,22,33,23,24,1009,2.68,0,0 +2012,8,2,23,55,23,25,1009,4.47,0,0 +2012,8,3,0,69,23,25,1009,7.6,0,0 +2012,8,3,1,77,23,24,1009,10.73,0,0 +2012,8,3,2,64,23,24,1009,12.52,0,0 +2012,8,3,3,61,23,23,1009,0.45,0,0 +2012,8,3,4,50,23,23,1009,0.9,0,0 +2012,8,3,5,48,23,23,1009,0.89,0,0 +2012,8,3,6,54,23,23,1009,2.68,0,0 +2012,8,3,7,60,23,24,1009,0.89,0,0 +2012,8,3,8,77,24,25,1009,1.79,0,0 +2012,8,3,9,93,24,27,1008,3.58,0,0 +2012,8,3,10,74,24,28,1008,1.79,0,0 +2012,8,3,11,76,24,29,1008,3.58,0,0 +2012,8,3,12,81,23,29,1008,3.13,0,0 +2012,8,3,13,54,23,30,1007,4.92,0,0 +2012,8,3,14,38,23,31,1006,8.05,0,0 +2012,8,3,15,42,24,30,1006,11.18,0,0 +2012,8,3,16,NA,24,30,1006,12.97,0,0 +2012,8,3,17,50,23,29,1005,16.1,0,0 +2012,8,3,18,51,24,29,1005,17.89,0,0 +2012,8,3,19,51,23,28,1005,21.91,0,0 +2012,8,3,20,49,23,27,1006,25.93,0,0 +2012,8,3,21,33,23,26,1007,27.72,0,0 +2012,8,3,22,31,23,25,1007,31.74,0,0 +2012,8,3,23,31,22,24,1007,33.53,0,0 +2012,8,4,0,28,22,24,1007,35.32,0,0 +2012,8,4,1,23,22,25,1006,0.89,0,0 +2012,8,4,2,30,22,24,1006,3.13,0,0 +2012,8,4,3,28,23,24,1005,6.26,0,0 +2012,8,4,4,29,22,24,1005,9.39,0,0 +2012,8,4,5,39,22,23,1006,1.79,0,0 +2012,8,4,6,36,22,23,1006,1.79,0,0 +2012,8,4,7,36,23,26,1006,4.92,0,0 +2012,8,4,8,30,22,28,1007,8.94,0,0 +2012,8,4,9,19,21,29,1007,3.13,0,0 +2012,8,4,10,16,22,30,1007,8.05,0,0 +2012,8,4,11,18,21,31,1007,12.07,0,0 +2012,8,4,12,18,20,31,1008,15.2,0,0 +2012,8,4,13,17,22,32,1007,16.99,0,0 +2012,8,4,14,6,22,32,1007,3.13,0,0 +2012,8,4,15,22,22,31,1007,6.26,0,0 +2012,8,4,16,15,23,31,1008,9.39,0,0 +2012,8,4,17,24,23,31,1008,12.52,0,0 +2012,8,4,18,20,22,30,1008,16.54,0,0 +2012,8,4,19,24,22,29,1009,19.67,0,0 +2012,8,4,20,31,22,28,1009,21.46,0,0 +2012,8,4,21,25,22,28,1010,24.59,0,0 +2012,8,4,22,44,23,27,1010,27.72,0,0 +2012,8,4,23,29,24,26,1011,29.51,0,0 +2012,8,5,0,32,24,26,1011,0.89,0,0 +2012,8,5,1,43,24,25,1011,0.89,0,0 +2012,8,5,2,54,24,24,1011,2.68,0,0 +2012,8,5,3,46,23,24,1011,0.45,0,0 +2012,8,5,4,47,23,24,1011,1.79,0,0 +2012,8,5,5,44,24,24,1011,2.68,0,0 +2012,8,5,6,52,24,24,1011,3.57,0,0 +2012,8,5,7,53,24,25,1011,4.46,0,0 +2012,8,5,8,49,25,26,1011,1.79,0,0 +2012,8,5,9,51,24,28,1012,1.79,0,0 +2012,8,5,10,56,23,29,1012,3.58,0,0 +2012,8,5,11,53,22,31,1012,1.79,0,0 +2012,8,5,12,28,23,30,1011,1.79,0,0 +2012,8,5,13,22,23,31,1011,3.13,0,0 +2012,8,5,14,29,22,32,1010,4.92,0,0 +2012,8,5,15,39,21,31,1010,8.05,0,0 +2012,8,5,16,29,22,31,1010,11.18,0,0 +2012,8,5,17,23,22,31,1010,14.31,0,0 +2012,8,5,18,24,22,30,1010,17.44,0,0 +2012,8,5,19,53,22,28,1010,20.57,0,0 +2012,8,5,20,69,23,27,1010,22.36,0,0 +2012,8,5,21,69,23,28,1010,25.49,0,0 +2012,8,5,22,84,23,27,1011,28.62,0,0 +2012,8,5,23,79,23,27,1011,30.41,0,0 +2012,8,6,0,73,23,26,1011,31.3,0,0 +2012,8,6,1,67,24,25,1010,0.89,0,0 +2012,8,6,2,74,24,26,1010,1.78,0,0 +2012,8,6,3,82,24,25,1010,2.67,0,0 +2012,8,6,4,83,24,25,1010,0.89,0,0 +2012,8,6,5,95,24,24,1010,2.68,0,0 +2012,8,6,6,95,24,24,1010,0.89,0,0 +2012,8,6,7,82,25,26,1010,1.79,0,0 +2012,8,6,8,85,25,28,1010,0.89,0,0 +2012,8,6,9,81,25,30,1010,1.79,0,0 +2012,8,6,10,78,25,31,1010,1.79,0,0 +2012,8,6,11,73,23,32,1010,0.89,0,0 +2012,8,6,12,61,23,32,1009,1.79,0,0 +2012,8,6,13,63,24,31,1009,5.81,0,0 +2012,8,6,14,55,24,31,1008,8.94,0,0 +2012,8,6,15,50,23,31,1008,12.07,0,0 +2012,8,6,16,46,23,32,1007,15.2,0,0 +2012,8,6,17,46,24,31,1007,18.33,0,0 +2012,8,6,18,47,23,30,1007,21.46,0,0 +2012,8,6,19,44,23,29,1007,23.25,0,0 +2012,8,6,20,37,23,28,1007,25.04,0,0 +2012,8,6,21,44,24,28,1008,26.83,0,0 +2012,8,6,22,38,24,27,1008,28.62,0,0 +2012,8,6,23,45,24,27,1008,30.41,0,0 +2012,8,7,0,57,23,27,1008,32.2,0,0 +2012,8,7,1,52,24,26,1008,0.89,0,0 +2012,8,7,2,59,24,25,1008,0.89,0,0 +2012,8,7,3,67,24,24,1007,1.78,0,0 +2012,8,7,4,67,24,24,1007,0.89,0,0 +2012,8,7,5,75,23,24,1007,0.89,0,0 +2012,8,7,6,73,24,24,1008,0.89,0,0 +2012,8,7,7,79,25,26,1008,1.78,0,0 +2012,8,7,8,74,25,28,1008,2.67,0,0 +2012,8,7,9,67,24,29,1008,4.46,0,0 +2012,8,7,10,56,25,30,1008,1.79,0,0 +2012,8,7,11,66,24,31,1007,3.58,0,0 +2012,8,7,12,63,24,30,1007,6.71,0,0 +2012,8,7,13,67,24,31,1006,3.13,0,0 +2012,8,7,14,63,24,31,1006,4.02,0,0 +2012,8,7,15,58,24,31,1006,8.04,0,0 +2012,8,7,16,57,25,31,1005,12.06,0,0 +2012,8,7,17,55,25,31,1005,15.19,0,0 +2012,8,7,18,61,25,30,1005,19.21,0,0 +2012,8,7,19,68,25,29,1005,21,0,0 +2012,8,7,20,73,24,28,1005,22.79,0,0 +2012,8,7,21,85,24,28,1006,24.58,0,0 +2012,8,7,22,79,25,28,1006,0.89,0,0 +2012,8,7,23,89,25,27,1006,1.79,0,0 +2012,8,8,0,89,25,27,1005,4.92,0,0 +2012,8,8,1,88,25,27,1005,6.71,0,0 +2012,8,8,2,101,25,27,1005,8.5,0,0 +2012,8,8,3,106,25,27,1004,10.29,0,0 +2012,8,8,4,119,25,27,1004,12.08,0,0 +2012,8,8,5,116,25,26,1005,13.87,0,0 +2012,8,8,6,127,25,26,1005,0.89,0,0 +2012,8,8,7,136,25,26,1005,0.89,0,1 +2012,8,8,8,120,25,26,1006,0.89,0,2 +2012,8,8,9,109,26,28,1005,1.78,0,0 +2012,8,8,10,106,26,28,1006,2.67,0,0 +2012,8,8,11,84,26,30,1005,3.56,0,0 +2012,8,8,12,75,26,31,1005,4.45,0,0 +2012,8,8,13,80,26,31,1005,0.89,0,0 +2012,8,8,14,76,26,31,1005,2.68,0,0 +2012,8,8,15,79,27,32,1004,3.13,0,0 +2012,8,8,16,33,26,31,1004,4.02,0,0 +2012,8,8,17,25,20,29,1004,4.91,0,0 +2012,8,8,18,25,22,29,1005,0.89,0,0 +2012,8,8,19,29,24,28,1005,0.89,0,0 +2012,8,8,20,28,23,27,1005,1.79,0,0 +2012,8,8,21,35,23,26,1006,0.45,0,0 +2012,8,8,22,37,24,26,1006,0.9,0,0 +2012,8,8,23,33,24,26,1006,1.79,0,0 +2012,8,9,0,31,24,24,1006,4.92,0,0 +2012,8,9,1,43,23,24,1006,0.89,0,0 +2012,8,9,2,37,21,23,1006,1.79,0,0 +2012,8,9,3,21,21,22,1006,5.81,0,0 +2012,8,9,4,15,21,22,1006,0.89,0,0 +2012,8,9,5,10,20,21,1007,1.78,0,0 +2012,8,9,6,21,21,22,1007,1.79,0,0 +2012,8,9,7,23,23,25,1008,3.58,0,0 +2012,8,9,8,40,23,27,1008,0.89,0,0 +2012,8,9,9,46,24,29,1008,1.78,0,0 +2012,8,9,10,49,23,29,1008,2.67,0,0 +2012,8,9,11,52,19,31,1008,4.46,0,0 +2012,8,9,12,46,19,32,1007,5.35,0,0 +2012,8,9,13,45,19,32,1007,7.14,0,0 +2012,8,9,14,48,21,34,1006,8.93,0,0 +2012,8,9,15,52,22,33,1006,1.79,0,0 +2012,8,9,16,56,21,32,1005,5.81,0,0 +2012,8,9,17,57,21,32,1005,9.83,0,0 +2012,8,9,18,39,20,31,1005,12.96,0,0 +2012,8,9,19,32,21,29,1006,14.75,0,0 +2012,8,9,20,38,21,28,1006,16.54,0,0 +2012,8,9,21,38,22,27,1007,18.33,0,0 +2012,8,9,22,34,21,27,1007,21.46,0,0 +2012,8,9,23,41,22,25,1007,0.89,0,0 +2012,8,10,0,37,23,25,1008,0.89,0,0 +2012,8,10,1,43,23,24,1007,2.68,0,0 +2012,8,10,2,53,23,24,1007,4.47,0,0 +2012,8,10,3,57,23,24,1007,0.89,0,0 +2012,8,10,4,72,23,23,1007,1.78,0,0 +2012,8,10,5,85,23,23,1007,1.79,0,0 +2012,8,10,6,89,23,23,1007,2.68,0,0 +2012,8,10,7,94,24,24,1008,0.89,0,0 +2012,8,10,8,100,24,26,1008,1.78,0,0 +2012,8,10,9,116,24,28,1008,2.67,0,0 +2012,8,10,10,120,23,29,1007,3.56,0,0 +2012,8,10,11,108,24,31,1007,1.79,0,0 +2012,8,10,12,89,21,31,1006,0.89,0,0 +2012,8,10,13,70,21,32,1006,1.79,0,0 +2012,8,10,14,55,20,32,1006,4.92,0,0 +2012,8,10,15,55,20,31,1005,8.05,0,0 +2012,8,10,16,58,20,31,1005,11.18,0,0 +2012,8,10,17,52,20,31,1005,12.97,0,0 +2012,8,10,18,60,20,30,1005,16.1,0,0 +2012,8,10,19,72,21,29,1005,17.89,0,0 +2012,8,10,20,97,22,27,1006,19.68,0,0 +2012,8,10,21,97,19,27,1006,22.81,0,0 +2012,8,10,22,94,19,26,1006,25.94,0,0 +2012,8,10,23,76,20,25,1006,27.73,0,0 +2012,8,11,0,85,22,24,1006,0.89,0,0 +2012,8,11,1,93,22,24,1006,1.79,0,0 +2012,8,11,2,89,22,24,1006,3.58,0,0 +2012,8,11,3,105,22,24,1005,5.37,0,0 +2012,8,11,4,108,22,23,1006,0.89,0,0 +2012,8,11,5,114,22,23,1006,1.79,0,0 +2012,8,11,6,116,22,23,1006,3.58,0,0 +2012,8,11,7,118,22,24,1006,1.79,0,0 +2012,8,11,8,112,22,25,1007,1.79,0,0 +2012,8,11,9,124,22,25,1006,3.58,0,0 +2012,8,11,10,136,23,27,1006,6.71,0,0 +2012,8,11,11,137,23,27,1006,0.89,0,0 +2012,8,11,12,138,20,28,1006,1.78,0,0 +2012,8,11,13,127,21,28,1005,1.79,0,0 +2012,8,11,14,88,22,27,1005,0.89,0,1 +2012,8,11,15,79,21,27,1005,1.78,0,2 +2012,8,11,16,67,21,27,1006,2.67,0,3 +2012,8,11,17,NA,23,26,1005,3.56,0,4 +2012,8,11,18,NA,20,25,1005,3.13,0,5 +2012,8,11,19,NA,19,23,1006,4.92,0,6 +2012,8,11,20,NA,21,23,1006,8.94,0,7 +2012,8,11,21,NA,19,22,1007,13.86,0,8 +2012,8,11,22,NA,19,22,1006,17.88,0,9 +2012,8,11,23,NA,20,21,1006,23.69,0,10 +2012,8,12,0,NA,20,20,1006,28.61,0,11 +2012,8,12,1,NA,20,20,1006,33.53,0,12 +2012,8,12,2,NA,20,20,1006,36.66,0,13 +2012,8,12,3,NA,20,20,1006,39.79,0,14 +2012,8,12,4,NA,20,20,1005,41.58,0,15 +2012,8,12,5,NA,20,20,1006,43.37,0,0 +2012,8,12,6,NA,20,20,1005,46.5,0,0 +2012,8,12,7,NA,20,21,1005,49.63,0,0 +2012,8,12,8,NA,21,22,1005,52.76,0,0 +2012,8,12,9,NA,22,25,1005,1.79,0,0 +2012,8,12,10,NA,22,27,1005,1.79,0,0 +2012,8,12,11,NA,22,27,1005,3.13,0,0 +2012,8,12,12,NA,22,27,1005,6.26,0,0 +2012,8,12,13,NA,22,29,1004,8.05,0,0 +2012,8,12,14,NA,21,29,1004,11.18,0,0 +2012,8,12,15,NA,21,29,1004,15.2,0,0 +2012,8,12,16,NA,22,28,1004,18.33,0,0 +2012,8,12,17,NA,21,28,1004,22.35,0,0 +2012,8,12,18,NA,21,27,1005,25.48,0,0 +2012,8,12,19,NA,21,26,1005,28.61,0,0 +2012,8,12,20,NA,21,25,1006,32.63,0,0 +2012,8,12,21,NA,21,25,1007,37.55,0,0 +2012,8,12,22,NA,20,24,1008,42.47,0,0 +2012,8,12,23,NA,21,22,1009,0.89,0,2 +2012,8,13,0,NA,21,22,1009,1.78,0,3 +2012,8,13,1,NA,21,22,1009,3.13,0,4 +2012,8,13,2,NA,21,22,1009,1.79,0,0 +2012,8,13,3,NA,21,22,1009,3.58,0,0 +2012,8,13,4,NA,21,21,1009,6.71,0,0 +2012,8,13,5,NA,21,21,1009,9.84,0,0 +2012,8,13,6,NA,20,21,1010,12.97,0,0 +2012,8,13,7,NA,21,22,1010,16.1,0,0 +2012,8,13,8,NA,19,24,1010,21.02,0,0 +2012,8,13,9,NA,20,26,1010,25.04,0,0 +2012,8,13,10,NA,20,28,1010,29.06,0,0 +2012,8,13,11,13,16,28,1010,3.13,0,0 +2012,8,13,12,13,13,29,1010,6.26,0,0 +2012,8,13,13,13,12,29,1009,8.05,0,0 +2012,8,13,14,14,12,29,1008,1.79,0,0 +2012,8,13,15,14,14,29,1008,3.58,0,0 +2012,8,13,16,15,14,29,1008,6.71,0,0 +2012,8,13,17,21,14,28,1008,8.5,0,0 +2012,8,13,18,26,16,27,1008,12.52,0,0 +2012,8,13,19,32,16,26,1008,15.65,0,0 +2012,8,13,20,33,18,22,1009,0.89,0,0 +2012,8,13,21,38,18,21,1009,1.79,0,0 +2012,8,13,22,46,19,22,1009,3.58,0,0 +2012,8,13,23,47,19,20,1009,4.47,0,0 +2012,8,14,0,NA,19,21,1008,6.26,0,0 +2012,8,14,1,50,19,20,1008,7.15,0,0 +2012,8,14,2,67,19,20,1008,1.79,0,0 +2012,8,14,3,71,19,20,1008,2.68,0,0 +2012,8,14,4,72,18,19,1008,3.57,0,0 +2012,8,14,5,68,18,19,1007,0.89,0,0 +2012,8,14,6,66,18,19,1008,1.78,0,0 +2012,8,14,7,71,19,20,1007,2.67,0,0 +2012,8,14,8,73,19,21,1008,1.79,0,0 +2012,8,14,9,84,20,22,1007,1.79,0,0 +2012,8,14,10,104,20,24,1007,3.58,0,0 +2012,8,14,11,76,18,26,1007,6.71,0,0 +2012,8,14,12,47,16,25,1007,10.73,0,0 +2012,8,14,13,42,13,24,1006,14.75,0,1 +2012,8,14,14,27,13,24,1006,19.67,0,0 +2012,8,14,15,37,14,24,1006,26.82,0,0 +2012,8,14,16,42,14,23,1006,31.74,0,0 +2012,8,14,17,52,15,23,1005,36.66,0,0 +2012,8,14,18,50,15,22,1006,40.68,0,0 +2012,8,14,19,27,14,21,1006,44.7,0,0 +2012,8,14,20,31,15,21,1006,47.83,0,0 +2012,8,14,21,33,15,20,1006,49.62,0,0 +2012,8,14,22,42,16,20,1006,51.41,0,0 +2012,8,14,23,40,17,20,1006,53.2,0,0 +2012,8,15,0,48,16,19,1006,54.99,0,0 +2012,8,15,1,57,16,18,1006,0.89,0,0 +2012,8,15,2,59,17,18,1005,1.78,0,0 +2012,8,15,3,68,17,17,1005,0.89,0,0 +2012,8,15,4,69,17,17,1005,0.89,0,0 +2012,8,15,5,67,17,17,1005,0.89,0,0 +2012,8,15,6,73,17,17,1006,0.45,0,0 +2012,8,15,7,71,19,19,1006,0.89,0,0 +2012,8,15,8,70,18,22,1006,4.02,0,0 +2012,8,15,9,72,19,24,1007,5.81,0,0 +2012,8,15,10,63,19,25,1007,1.79,0,0 +2012,8,15,11,59,19,27,1007,3.58,0,0 +2012,8,15,12,45,19,29,1007,3.13,0,0 +2012,8,15,13,26,19,30,1006,3.13,0,0 +2012,8,15,14,14,18,30,1006,4.92,0,0 +2012,8,15,15,16,19,32,1006,1.79,0,0 +2012,8,15,16,25,18,31,1006,0.89,0,0 +2012,8,15,17,20,17,32,1007,1.78,0,0 +2012,8,15,18,23,19,28,1007,1.79,0,0 +2012,8,15,19,31,18,28,1007,5.81,0,0 +2012,8,15,20,45,18,27,1008,9.83,0,0 +2012,8,15,21,80,18,25,1009,11.62,0,0 +2012,8,15,22,67,18,26,1009,15.64,0,0 +2012,8,15,23,89,19,23,1010,16.53,0,0 +2012,8,16,0,91,20,22,1010,18.32,0,0 +2012,8,16,1,90,20,21,1010,0.89,0,0 +2012,8,16,2,96,20,22,1010,1.79,0,0 +2012,8,16,3,88,20,21,1010,0.89,0,0 +2012,8,16,4,86,20,21,1010,1.79,0,0 +2012,8,16,5,65,19,20,1011,0.89,0,0 +2012,8,16,6,83,20,20,1012,1.78,0,0 +2012,8,16,7,97,20,22,1012,0.89,0,0 +2012,8,16,8,108,21,24,1012,1.78,0,0 +2012,8,16,9,96,22,26,1012,1.79,0,0 +2012,8,16,10,102,22,28,1012,0.89,0,0 +2012,8,16,11,78,22,29,1012,1.78,0,0 +2012,8,16,12,60,22,29,1011,3.57,0,0 +2012,8,16,13,52,19,31,1010,4.02,0,0 +2012,8,16,14,53,17,30,1010,8.04,0,0 +2012,8,16,15,NA,17,31,1009,12.06,0,0 +2012,8,16,16,NA,19,30,1009,16.98,0,0 +2012,8,16,17,41,18,29,1009,21.9,0,0 +2012,8,16,18,64,19,29,1009,25.92,0,0 +2012,8,16,19,67,19,27,1009,29.94,0,0 +2012,8,16,20,64,19,26,1009,33.07,0,0 +2012,8,16,21,53,19,26,1010,34.86,0,0 +2012,8,16,22,73,19,25,1010,37.99,0,0 +2012,8,16,23,77,19,25,1010,42.91,0,0 +2012,8,17,0,70,18,24,1010,46.04,0,0 +2012,8,17,1,67,18,24,1009,49.17,0,0 +2012,8,17,2,64,19,23,1009,50.96,0,0 +2012,8,17,3,77,20,22,1009,54.09,0,0 +2012,8,17,4,78,20,22,1009,58.11,0,0 +2012,8,17,5,96,20,22,1009,59.9,0,0 +2012,8,17,6,112,20,22,1009,61.69,0,0 +2012,8,17,7,119,21,23,1009,0.89,0,0 +2012,8,17,8,123,21,23,1009,1.79,0,0 +2012,8,17,9,129,22,24,1009,3.58,0,0 +2012,8,17,10,143,22,25,1009,5.37,0,0 +2012,8,17,11,146,23,27,1008,8.5,0,0 +2012,8,17,12,146,23,27,1008,10.29,0,0 +2012,8,17,13,139,23,27,1008,12.08,0,0 +2012,8,17,14,118,23,29,1007,15.21,0,0 +2012,8,17,15,NA,22,29,1006,20.13,0,0 +2012,8,17,16,130,22,28,1006,24.15,0,0 +2012,8,17,17,144,21,28,1006,29.07,0,0 +2012,8,17,18,147,22,27,1006,33.09,0,0 +2012,8,17,19,161,22,26,1006,37.11,0,0 +2012,8,17,20,148,22,26,1006,41.13,0,0 +2012,8,17,21,145,22,26,1007,45.15,0,0 +2012,8,17,22,166,22,26,1007,48.28,0,0 +2012,8,17,23,185,22,25,1007,51.41,0,0 +2012,8,18,0,206,22,25,1007,55.43,0,1 +2012,8,18,1,192,23,24,1006,58.56,0,2 +2012,8,18,2,202,23,24,1007,62.58,0,3 +2012,8,18,3,198,23,24,1007,64.37,0,4 +2012,8,18,4,186,23,23,1006,66.16,0,0 +2012,8,18,5,153,22,23,1007,67.95,0,0 +2012,8,18,6,140,22,23,1007,69.74,0,0 +2012,8,18,7,153,22,23,1007,71.53,0,0 +2012,8,18,8,182,22,24,1007,73.32,0,0 +2012,8,18,9,182,23,25,1007,75.11,0,0 +2012,8,18,10,190,23,26,1007,0.89,0,0 +2012,8,18,11,NA,23,26,1007,1.78,0,0 +2012,8,18,12,NA,23,27,1006,1.79,0,0 +2012,8,18,13,NA,23,27,1006,3.58,0,0 +2012,8,18,14,NA,23,27,1006,5.37,0,0 +2012,8,18,15,NA,23,26,1006,7.16,0,1 +2012,8,18,16,NA,24,26,1006,0.89,0,2 +2012,8,18,17,NA,24,25,1006,1.79,0,3 +2012,8,18,18,NA,22,24,1005,1.79,0,0 +2012,8,18,19,NA,23,23,1005,0.89,0,0 +2012,8,18,20,NA,22,23,1006,1.78,0,0 +2012,8,18,21,NA,22,23,1007,0.89,0,0 +2012,8,18,22,NA,23,23,1007,1.78,0,0 +2012,8,18,23,NA,22,22,1007,0.89,0,0 +2012,8,19,0,NA,22,22,1007,3.13,0,0 +2012,8,19,1,NA,21,21,1006,4.92,0,0 +2012,8,19,2,NA,21,22,1007,1.79,0,0 +2012,8,19,3,NA,21,21,1007,0.89,0,0 +2012,8,19,4,NA,20,20,1007,0.89,0,0 +2012,8,19,5,NA,18,18,1007,0.89,0,0 +2012,8,19,6,NA,19,19,1008,0.89,0,0 +2012,8,19,7,NA,21,21,1008,2.68,0,0 +2012,8,19,8,NA,22,22,1008,0.89,0,0 +2012,8,19,9,NA,19,27,1008,3.13,0,0 +2012,8,19,10,NA,16,28,1008,7.15,0,0 +2012,8,19,11,NA,17,29,1008,8.94,0,0 +2012,8,19,12,NA,16,30,1007,1.79,0,0 +2012,8,19,13,NA,15,31,1006,4.92,0,0 +2012,8,19,14,NA,15,31,1006,1.79,0,0 +2012,8,19,15,NA,17,31,1005,1.79,0,0 +2012,8,19,16,NA,20,30,1005,5.81,0,0 +2012,8,19,17,NA,19,30,1005,9.83,0,0 +2012,8,19,18,NA,20,29,1004,12.96,0,0 +2012,8,19,19,NA,19,28,1004,16.09,0,0 +2012,8,19,20,NA,20,25,1005,17.88,0,0 +2012,8,19,21,NA,22,24,1005,19.67,0,0 +2012,8,19,22,NA,22,23,1006,20.56,0,0 +2012,8,19,23,NA,22,24,1006,22.35,0,0 +2012,8,20,0,NA,23,24,1006,24.14,0,0 +2012,8,20,1,NA,23,24,1005,25.93,0,0 +2012,8,20,2,NA,23,24,1005,29.06,0,0 +2012,8,20,3,NA,23,23,1005,30.85,0,0 +2012,8,20,4,NA,22,23,1005,32.64,0,0 +2012,8,20,5,NA,22,23,1005,0.89,0,0 +2012,8,20,6,NA,22,23,1005,1.79,0,0 +2012,8,20,7,NA,23,23,1005,3.58,0,0 +2012,8,20,8,NA,23,24,1006,5.37,0,0 +2012,8,20,9,NA,23,24,1006,3.13,0,0 +2012,8,20,10,NA,24,27,1006,1.79,0,0 +2012,8,20,11,NA,23,28,1006,0.89,0,0 +2012,8,20,12,NA,24,29,1005,1.79,0,0 +2012,8,20,13,NA,24,30,1005,0.89,0,0 +2012,8,20,14,NA,23,30,1004,1.79,0,0 +2012,8,20,15,NA,23,31,1004,0.89,0,0 +2012,8,20,16,NA,23,29,1004,1.78,0,0 +2012,8,20,17,NA,20,24,1005,1.79,0,1 +2012,8,20,18,NA,21,23,1006,0.89,0,0 +2012,8,20,19,NA,20,24,1006,3.13,0,0 +2012,8,20,20,NA,18,24,1008,6.26,0,0 +2012,8,20,21,NA,15,25,1008,11.18,0,0 +2012,8,20,22,NA,17,23,1009,3.13,0,0 +2012,8,20,23,NA,16,23,1009,6.26,0,0 +2012,8,21,0,NA,14,23,1010,11.18,0,0 +2012,8,21,1,NA,11,23,1010,14.31,0,0 +2012,8,21,2,NA,11,22,1011,17.44,0,0 +2012,8,21,3,NA,11,21,1012,20.57,0,0 +2012,8,21,4,NA,12,20,1012,22.36,0,0 +2012,8,21,5,NA,11,18,1012,25.49,0,0 +2012,8,21,6,NA,10,19,1013,28.62,0,0 +2012,8,21,7,NA,13,22,1014,30.41,0,0 +2012,8,21,8,NA,13,24,1014,33.54,0,0 +2012,8,21,9,NA,12,25,1015,37.56,0,0 +2012,8,21,10,NA,12,26,1015,3.13,0,0 +2012,8,21,11,NA,10,27,1015,6.26,0,0 +2012,8,21,12,NA,9,27,1014,10.28,0,0 +2012,8,21,13,3,7,27,1014,3.13,0,0 +2012,8,21,14,5,10,29,1013,4.92,0,0 +2012,8,21,15,8,8,29,1013,8.05,0,0 +2012,8,21,16,8,9,28,1013,9.84,0,0 +2012,8,21,17,12,7,28,1012,3.13,0,0 +2012,8,21,18,5,10,26,1012,8.05,0,0 +2012,8,21,19,20,11,24,1013,11.18,0,0 +2012,8,21,20,23,13,21,1013,12.97,0,0 +2012,8,21,21,28,15,20,1014,14.76,0,0 +2012,8,21,22,25,15,19,1014,0.89,0,0 +2012,8,21,23,29,15,19,1014,1.78,0,0 +2012,8,22,0,36,15,17,1015,1.79,0,0 +2012,8,22,1,42,15,16,1015,3.13,0,0 +2012,8,22,2,24,14,16,1015,0.89,0,0 +2012,8,22,3,10,13,15,1016,1.79,0,0 +2012,8,22,4,9,12,15,1016,3.58,0,0 +2012,8,22,5,13,12,15,1017,1.79,0,0 +2012,8,22,6,15,12,14,1018,3.13,0,0 +2012,8,22,7,21,11,19,1018,7.15,0,0 +2012,8,22,8,21,8,23,1018,10.28,0,0 +2012,8,22,9,13,7,24,1018,0.89,0,0 +2012,8,22,10,13,5,25,1018,1.78,0,0 +2012,8,22,11,9,5,26,1018,2.67,0,0 +2012,8,22,12,6,3,27,1017,3.56,0,0 +2012,8,22,13,8,1,27,1016,1.79,0,0 +2012,8,22,14,11,4,28,1015,3.13,0,0 +2012,8,22,15,17,3,28,1015,6.26,0,0 +2012,8,22,16,22,2,28,1014,11.18,0,0 +2012,8,22,17,24,5,27,1014,15.2,0,0 +2012,8,22,18,22,6,26,1013,19.22,0,0 +2012,8,22,19,23,7,24,1014,23.24,0,0 +2012,8,22,20,17,10,22,1015,26.37,0,0 +2012,8,22,21,29,13,20,1015,28.16,0,0 +2012,8,22,22,35,15,19,1016,0.89,0,0 +2012,8,22,23,36,13,18,1016,1.79,0,0 +2012,8,23,0,40,13,17,1016,3.58,0,0 +2012,8,23,1,47,13,17,1016,5.37,0,0 +2012,8,23,2,63,14,16,1016,7.16,0,0 +2012,8,23,3,67,13,16,1016,0.89,0,0 +2012,8,23,4,81,13,17,1015,1.79,0,0 +2012,8,23,5,87,12,14,1015,1.79,0,0 +2012,8,23,6,82,13,15,1016,0.89,0,0 +2012,8,23,7,84,14,17,1016,1.78,0,0 +2012,8,23,8,91,14,19,1016,2.67,0,0 +2012,8,23,9,98,15,21,1016,1.79,0,0 +2012,8,23,10,100,15,24,1016,0.89,0,0 +2012,8,23,11,105,16,26,1015,1.78,0,0 +2012,8,23,12,110,15,27,1015,3.13,0,0 +2012,8,23,13,97,16,28,1014,1.79,0,0 +2012,8,23,14,88,17,29,1013,3.13,0,0 +2012,8,23,15,70,15,29,1013,7.15,0,0 +2012,8,23,16,65,16,29,1012,12.07,0,0 +2012,8,23,17,77,15,29,1012,16.09,0,0 +2012,8,23,18,76,16,27,1012,21.01,0,0 +2012,8,23,19,69,15,26,1013,25.93,0,0 +2012,8,23,20,65,16,24,1013,27.72,0,0 +2012,8,23,21,66,17,23,1014,29.51,0,0 +2012,8,23,22,62,16,21,1014,31.3,0,0 +2012,8,23,23,63,17,20,1014,0.89,0,0 +2012,8,24,0,66,17,20,1014,0.89,0,0 +2012,8,24,1,71,17,19,1014,2.68,0,0 +2012,8,24,2,73,17,18,1014,0.89,0,0 +2012,8,24,3,81,17,18,1013,1.78,0,0 +2012,8,24,4,88,17,18,1014,2.67,0,0 +2012,8,24,5,101,16,17,1014,0.89,0,0 +2012,8,24,6,110,17,17,1014,1.78,0,0 +2012,8,24,7,110,18,19,1015,2.67,0,0 +2012,8,24,8,124,18,21,1015,0.89,0,0 +2012,8,24,9,143,18,23,1015,1.78,0,0 +2012,8,24,10,156,18,25,1015,2.67,0,0 +2012,8,24,11,125,16,27,1015,4.46,0,0 +2012,8,24,12,65,11,29,1014,4.02,0,0 +2012,8,24,13,66,11,29,1013,8.04,0,0 +2012,8,24,14,62,10,30,1013,12.06,0,0 +2012,8,24,15,59,10,30,1012,15.19,0,0 +2012,8,24,16,52,11,29,1012,19.21,0,0 +2012,8,24,17,56,11,29,1012,23.23,0,0 +2012,8,24,18,50,12,27,1012,27.25,0,0 +2012,8,24,19,59,13,25,1012,29.04,0,0 +2012,8,24,20,71,14,22,1012,30.83,0,0 +2012,8,24,21,80,16,21,1013,0.89,0,0 +2012,8,24,22,84,17,20,1013,1.79,0,0 +2012,8,24,23,103,16,20,1013,4.92,0,0 +2012,8,25,0,90,16,21,1013,8.05,0,0 +2012,8,25,1,104,16,20,1013,9.84,0,0 +2012,8,25,2,104,16,19,1013,11.63,0,0 +2012,8,25,3,102,16,18,1013,13.42,0,0 +2012,8,25,4,108,16,18,1013,15.21,0,0 +2012,8,25,5,117,16,17,1013,0.89,0,0 +2012,8,25,6,143,16,17,1013,0.89,0,0 +2012,8,25,7,143,17,19,1013,0.89,0,0 +2012,8,25,8,149,17,21,1013,1.78,0,0 +2012,8,25,9,146,18,23,1013,2.67,0,0 +2012,8,25,10,154,18,25,1013,3.56,0,0 +2012,8,25,11,151,17,27,1013,4.45,0,0 +2012,8,25,12,149,17,27,1012,1.79,0,0 +2012,8,25,13,126,17,28,1012,1.79,0,0 +2012,8,25,14,128,15,29,1011,4.02,0,0 +2012,8,25,15,120,14,29,1011,7.15,0,0 +2012,8,25,16,118,15,29,1010,11.17,0,0 +2012,8,25,17,117,16,28,1010,15.19,0,0 +2012,8,25,18,123,16,27,1010,18.32,0,0 +2012,8,25,19,131,17,25,1010,21.45,0,0 +2012,8,25,20,127,17,24,1011,23.24,0,0 +2012,8,25,21,121,17,24,1011,25.03,0,0 +2012,8,25,22,113,17,23,1011,26.82,0,0 +2012,8,25,23,114,19,22,1011,27.71,0,0 +2012,8,26,0,120,18,21,1011,28.6,0,0 +2012,8,26,1,133,18,20,1011,0.89,0,0 +2012,8,26,2,140,18,21,1011,1.79,0,0 +2012,8,26,3,151,18,20,1011,0.89,0,0 +2012,8,26,4,178,18,20,1011,1.78,0,0 +2012,8,26,5,174,18,20,1011,2.67,0,0 +2012,8,26,6,180,18,19,1011,3.56,0,0 +2012,8,26,7,185,18,20,1012,4.45,0,0 +2012,8,26,8,180,19,23,1012,1.79,0,0 +2012,8,26,9,169,19,25,1012,1.79,0,0 +2012,8,26,10,153,18,26,1011,3.58,0,0 +2012,8,26,11,158,18,26,1011,5.37,0,0 +2012,8,26,12,160,19,29,1011,8.5,0,0 +2012,8,26,13,161,17,29,1010,12.52,0,0 +2012,8,26,14,159,18,29,1009,16.54,0,0 +2012,8,26,15,146,17,29,1009,19.67,0,0 +2012,8,26,16,145,19,29,1009,23.69,0,0 +2012,8,26,17,156,19,29,1009,26.82,0,0 +2012,8,26,18,166,19,27,1009,28.61,0,0 +2012,8,26,19,172,19,26,1009,31.74,0,0 +2012,8,26,20,149,20,26,1010,34.87,0,0 +2012,8,26,21,140,20,25,1010,38,0,0 +2012,8,26,22,134,20,25,1010,41.13,0,0 +2012,8,26,23,128,21,24,1010,42.92,0,0 +2012,8,27,0,125,21,24,1010,0.89,0,0 +2012,8,27,1,102,22,24,1010,1.79,0,0 +2012,8,27,2,93,22,24,1010,0.89,0,0 +2012,8,27,3,93,22,24,1010,1.79,0,1 +2012,8,27,4,93,22,23,1010,3.58,0,2 +2012,8,27,5,71,22,23,1010,0.89,0,3 +2012,8,27,6,74,22,23,1010,1.78,0,0 +2012,8,27,7,81,22,23,1010,1.79,0,0 +2012,8,27,8,80,22,23,1010,3.58,0,0 +2012,8,27,9,73,22,24,1010,5.37,0,0 +2012,8,27,10,56,22,25,1010,0.89,0,0 +2012,8,27,11,48,22,26,1010,1.79,0,0 +2012,8,27,12,46,21,28,1009,3.58,0,0 +2012,8,27,13,47,21,28,1009,5.37,0,0 +2012,8,27,14,48,22,29,1009,7.16,0,0 +2012,8,27,15,44,22,29,1008,10.29,0,0 +2012,8,27,16,62,21,29,1008,13.42,0,0 +2012,8,27,17,59,21,28,1008,16.55,0,0 +2012,8,27,18,56,22,27,1008,20.57,0,0 +2012,8,27,19,56,21,25,1008,22.36,0,0 +2012,8,27,20,43,22,25,1009,25.49,0,0 +2012,8,27,21,49,22,24,1009,27.28,0,0 +2012,8,27,22,54,22,24,1009,29.07,0,0 +2012,8,27,23,55,22,24,1008,30.86,0,0 +2012,8,28,0,68,22,24,1008,32.65,0,0 +2012,8,28,1,64,22,23,1008,0.89,0,0 +2012,8,28,2,68,22,23,1007,1.78,0,0 +2012,8,28,3,72,22,23,1007,1.79,0,0 +2012,8,28,4,80,22,22,1006,0.45,0,0 +2012,8,28,5,83,21,21,1006,0.89,0,0 +2012,8,28,6,83,22,22,1006,1.79,0,0 +2012,8,28,7,83,22,22,1006,3.58,0,0 +2012,8,28,8,92,22,24,1006,0.89,0,0 +2012,8,28,9,83,22,26,1005,3.13,0,0 +2012,8,28,10,76,22,28,1004,1.79,0,0 +2012,8,28,11,88,21,29,1003,0.89,0,0 +2012,8,28,12,80,21,31,1002,1.79,0,0 +2012,8,28,13,78,21,32,1001,1.79,0,0 +2012,8,28,14,64,19,33,1000,2.68,0,0 +2012,8,28,15,57,20,33,999,1.79,0,0 +2012,8,28,16,55,19,33,999,1.79,0,0 +2012,8,28,17,56,17,34,998,3.13,0,0 +2012,8,28,18,75,19,32,998,3.13,0,0 +2012,8,28,19,105,21,31,998,7.15,0,0 +2012,8,28,20,115,22,28,999,8.94,0,0 +2012,8,28,21,118,23,26,1000,0.89,0,0 +2012,8,28,22,128,23,25,1000,1.78,0,0 +2012,8,28,23,145,23,25,1000,3.13,0,0 +2012,8,29,0,154,23,26,1000,6.26,0,0 +2012,8,29,1,157,23,24,1001,0.89,0,0 +2012,8,29,2,161,23,24,1001,0.89,0,0 +2012,8,29,3,143,22,23,1002,1.79,0,0 +2012,8,29,4,153,22,23,1002,3.58,0,0 +2012,8,29,5,149,21,23,1003,5.37,0,0 +2012,8,29,6,115,21,22,1004,7.16,0,0 +2012,8,29,7,64,22,23,1004,10.29,0,0 +2012,8,29,8,61,21,27,1005,14.31,0,0 +2012,8,29,9,42,20,30,1006,19.23,0,0 +2012,8,29,10,41,18,31,1006,3.13,0,0 +2012,8,29,11,35,17,33,1006,4.02,0,0 +2012,8,29,12,29,17,34,1006,8.04,0,0 +2012,8,29,13,26,15,34,1005,4.02,0,0 +2012,8,29,14,29,16,35,1005,7.15,0,0 +2012,8,29,15,24,17,35,1005,3.13,0,0 +2012,8,29,16,38,21,33,1005,6.26,0,0 +2012,8,29,17,63,23,31,1005,10.28,0,0 +2012,8,29,18,115,23,31,1005,13.41,0,0 +2012,8,29,19,123,24,29,1006,15.2,0,0 +2012,8,29,20,160,24,28,1007,0.89,0,0 +2012,8,29,21,175,23,27,1008,1.78,0,0 +2012,8,29,22,205,23,27,1008,1.79,0,0 +2012,8,29,23,229,22,26,1009,4.92,0,0 +2012,8,30,0,245,22,25,1009,5.81,0,0 +2012,8,30,1,260,22,24,1009,1.79,0,0 +2012,8,30,2,268,22,24,1009,0.89,0,0 +2012,8,30,3,266,22,24,1009,0.89,0,0 +2012,8,30,4,281,22,23,1009,0.89,0,0 +2012,8,30,5,286,22,23,1009,0.89,0,0 +2012,8,30,6,301,22,23,1009,0.89,0,0 +2012,8,30,7,302,22,25,1010,1.78,0,0 +2012,8,30,8,277,23,26,1010,0.89,0,0 +2012,8,30,9,252,22,28,1010,1.78,0,0 +2012,8,30,10,242,23,29,1010,2.67,0,0 +2012,8,30,11,231,23,30,1010,3.56,0,0 +2012,8,30,12,232,24,31,1009,1.79,0,0 +2012,8,30,13,239,23,32,1008,3.58,0,0 +2012,8,30,14,233,23,33,1008,6.71,0,0 +2012,8,30,15,197,22,33,1007,10.73,0,0 +2012,8,30,16,195,21,33,1008,14.75,0,0 +2012,8,30,17,170,22,32,1008,18.77,0,0 +2012,8,30,18,176,22,31,1008,20.56,0,0 +2012,8,30,19,213,23,29,1008,22.35,0,0 +2012,8,30,20,224,23,27,1009,0.89,0,0 +2012,8,30,21,229,23,27,1009,0.89,0,0 +2012,8,30,22,215,24,26,1009,1.78,0,0 +2012,8,30,23,204,24,26,1009,2.67,0,0 +2012,8,31,0,194,24,25,1010,3.56,0,0 +2012,8,31,1,188,24,26,1010,5.35,0,0 +2012,8,31,2,197,24,25,1010,7.14,0,0 +2012,8,31,3,206,24,25,1010,8.93,0,0 +2012,8,31,4,207,23,25,1010,10.72,0,0 +2012,8,31,5,212,23,24,1010,12.51,0,0 +2012,8,31,6,232,23,24,1011,0.89,0,0 +2012,8,31,7,240,24,25,1011,0.89,0,0 +2012,8,31,8,245,24,26,1011,1.79,0,0 +2012,8,31,9,242,24,27,1011,3.58,0,0 +2012,8,31,10,230,24,29,1012,0.89,0,0 +2012,8,31,11,179,24,30,1011,3.13,0,0 +2012,8,31,12,159,23,31,1011,7.15,0,0 +2012,8,31,13,142,22,32,1011,11.17,0,0 +2012,8,31,14,142,21,32,1010,16.09,0,0 +2012,8,31,15,145,21,32,1009,20.11,0,0 +2012,8,31,16,135,22,32,1009,24.13,0,0 +2012,8,31,17,143,23,31,1009,27.26,0,0 +2012,8,31,18,150,23,30,1009,30.39,0,0 +2012,8,31,19,166,22,29,1010,34.41,0,0 +2012,8,31,20,160,23,27,1010,36.2,0,0 +2012,8,31,21,169,23,26,1011,0.89,0,0 +2012,8,31,22,180,23,26,1011,3.13,0,0 +2012,8,31,23,188,23,27,1011,4.92,0,0 +2012,9,1,0,196,23,26,1011,0.89,0,0 +2012,9,1,1,205,23,25,1011,1.79,0,0 +2012,9,1,2,196,24,25,1011,3.58,0,0 +2012,9,1,3,192,23,25,1011,5.37,0,0 +2012,9,1,4,183,22,23,1011,0.89,0,0 +2012,9,1,5,186,23,24,1010,1.79,0,0 +2012,9,1,6,203,23,23,1011,0.89,0,0 +2012,9,1,7,204,24,25,1011,1.78,0,0 +2012,9,1,8,206,24,25,1011,1.79,0,0 +2012,9,1,9,202,24,27,1011,3.58,0,0 +2012,9,1,10,207,24,28,1011,5.37,0,0 +2012,9,1,11,179,24,30,1011,7.16,0,0 +2012,9,1,12,173,23,30,1010,10.29,0,0 +2012,9,1,13,166,23,30,1010,13.42,0,0 +2012,9,1,14,141,23,29,1009,15.21,0,1 +2012,9,1,15,114,22,25,1009,1.79,0,0 +2012,9,1,16,124,22,23,1009,3.13,0,1 +2012,9,1,17,126,23,24,1009,1.79,0,2 +2012,9,1,18,110,22,23,1009,4.92,0,3 +2012,9,1,19,113,22,23,1009,6.71,0,4 +2012,9,1,20,111,22,23,1009,8.5,0,5 +2012,9,1,21,106,22,23,1009,0.89,0,0 +2012,9,1,22,106,22,23,1008,0.89,0,1 +2012,9,1,23,108,22,24,1008,1.79,0,2 +2012,9,2,0,109,22,23,1007,4.92,0,3 +2012,9,2,1,103,22,23,1006,1.79,0,4 +2012,9,2,2,124,23,23,1006,0.89,0,5 +2012,9,2,3,127,23,23,1006,0.89,0,6 +2012,9,2,4,123,19,20,1007,5.81,0,7 +2012,9,2,5,10,19,20,1007,10.73,0,8 +2012,9,2,6,7,18,19,1007,18.78,0,9 +2012,9,2,7,6,17,19,1008,27.72,0,10 +2012,9,2,8,5,17,19,1008,36.66,0,11 +2012,9,2,9,8,17,19,1008,43.81,0,12 +2012,9,2,10,9,17,19,1009,49.62,0,13 +2012,9,2,11,9,18,20,1009,55.43,0,14 +2012,9,2,12,10,18,22,1008,59.45,0,0 +2012,9,2,13,13,19,23,1008,62.58,0,0 +2012,9,2,14,14,18,23,1008,4.02,0,0 +2012,9,2,15,12,19,23,1008,5.81,0,0 +2012,9,2,16,13,18,23,1008,3.13,0,0 +2012,9,2,17,16,18,23,1009,1.79,0,0 +2012,9,2,18,15,18,22,1009,1.79,0,0 +2012,9,2,19,17,17,20,1010,4.92,0,0 +2012,9,2,20,18,17,20,1010,8.05,0,0 +2012,9,2,21,16,16,20,1011,12.07,0,0 +2012,9,2,22,18,15,18,1011,16.09,0,0 +2012,9,2,23,13,15,19,1011,20.11,0,0 +2012,9,3,0,7,11,20,1012,24.13,0,0 +2012,9,3,1,5,11,18,1012,3.13,0,0 +2012,9,3,2,6,10,18,1012,7.15,0,0 +2012,9,3,3,5,10,18,1012,11.17,0,0 +2012,9,3,4,5,10,18,1012,15.19,0,0 +2012,9,3,5,6,10,17,1012,18.32,0,0 +2012,9,3,6,6,10,16,1013,22.34,0,0 +2012,9,3,7,8,10,19,1013,26.36,0,0 +2012,9,3,8,13,11,21,1013,31.28,0,0 +2012,9,3,9,17,11,22,1014,35.3,0,0 +2012,9,3,10,11,12,23,1013,4.02,0,0 +2012,9,3,11,9,11,24,1013,8.04,0,0 +2012,9,3,12,10,11,25,1012,12.06,0,0 +2012,9,3,13,12,9,27,1012,16.08,0,0 +2012,9,3,14,11,7,26,1012,24.13,0,0 +2012,9,3,15,10,7,26,1012,33.07,0,0 +2012,9,3,16,11,7,25,1012,42.01,0,0 +2012,9,3,17,12,7,25,1012,50.95,0,0 +2012,9,3,18,13,10,23,1013,54.08,0,0 +2012,9,3,19,11,6,21,1014,59,0,0 +2012,9,3,20,8,7,20,1014,63.02,0,0 +2012,9,3,21,9,8,16,1015,66.15,0,0 +2012,9,3,22,11,7,19,1015,70.17,0,0 +2012,9,3,23,11,7,19,1015,74.19,0,0 +2012,9,4,0,10,7,19,1015,78.21,0,0 +2012,9,4,1,12,9,15,1015,80,0,0 +2012,9,4,2,14,10,14,1015,0.89,0,0 +2012,9,4,3,8,10,13,1015,0.89,0,0 +2012,9,4,4,9,9,13,1015,2.68,0,0 +2012,9,4,5,12,9,12,1015,5.81,0,0 +2012,9,4,6,10,9,13,1016,8.94,0,0 +2012,9,4,7,17,11,16,1016,12.07,0,0 +2012,9,4,8,12,10,20,1016,15.2,0,0 +2012,9,4,9,15,8,22,1017,18.33,0,0 +2012,9,4,10,6,5,24,1016,24.14,0,0 +2012,9,4,11,7,4,25,1016,29.06,0,0 +2012,9,4,12,7,5,26,1016,33.08,0,0 +2012,9,4,13,9,5,26,1015,1.79,0,0 +2012,9,4,14,12,4,27,1014,3.58,0,0 +2012,9,4,15,13,3,26,1014,5.37,0,0 +2012,9,4,16,9,6,26,1014,4.02,0,0 +2012,9,4,17,10,8,25,1014,8.04,0,0 +2012,9,4,18,15,9,23,1014,11.17,0,0 +2012,9,4,19,19,10,21,1015,12.96,0,0 +2012,9,4,20,15,11,19,1015,14.75,0,0 +2012,9,4,21,24,11,19,1016,17.88,0,0 +2012,9,4,22,26,12,16,1016,19.67,0,0 +2012,9,4,23,27,12,16,1016,0.89,0,0 +2012,9,5,0,34,12,16,1017,0.89,0,0 +2012,9,5,1,37,12,14,1017,0.89,0,0 +2012,9,5,2,30,12,13,1017,0.89,0,0 +2012,9,5,3,30,13,14,1017,1.78,0,0 +2012,9,5,4,31,12,13,1017,1.79,0,0 +2012,9,5,5,25,12,13,1017,3.58,0,0 +2012,9,5,6,21,12,13,1017,5.37,0,0 +2012,9,5,7,21,13,15,1018,0.89,0,0 +2012,9,5,8,19,13,19,1018,1.79,0,0 +2012,9,5,9,24,12,21,1018,0.89,0,0 +2012,9,5,10,22,12,23,1018,1.78,0,0 +2012,9,5,11,21,12,24,1018,2.67,0,0 +2012,9,5,12,31,12,26,1017,1.79,0,0 +2012,9,5,13,50,11,27,1017,0.89,0,0 +2012,9,5,14,50,10,28,1016,5.81,0,0 +2012,9,5,15,35,9,28,1016,11.62,0,0 +2012,9,5,16,52,10,27,1016,17.43,0,0 +2012,9,5,17,34,8,26,1016,24.58,0,0 +2012,9,5,18,25,9,25,1016,30.39,0,0 +2012,9,5,19,29,11,22,1017,35.31,0,0 +2012,9,5,20,37,12,20,1017,38.44,0,0 +2012,9,5,21,41,14,19,1018,40.23,0,0 +2012,9,5,22,42,13,18,1018,42.02,0,0 +2012,9,5,23,34,13,16,1018,43.81,0,0 +2012,9,6,0,39,13,15,1018,44.7,0,0 +2012,9,6,1,40,13,15,1018,0.89,0,0 +2012,9,6,2,47,13,15,1018,1.78,0,0 +2012,9,6,3,55,13,15,1018,1.79,0,0 +2012,9,6,4,58,13,14,1017,2.68,0,0 +2012,9,6,5,62,13,14,1017,3.57,0,0 +2012,9,6,6,74,13,13,1017,1.79,0,0 +2012,9,6,7,72,14,16,1018,0.45,0,0 +2012,9,6,8,70,15,17,1018,1.79,0,0 +2012,9,6,9,63,14,19,1018,0.89,0,0 +2012,9,6,10,67,14,21,1018,2.68,0,0 +2012,9,6,11,84,14,22,1018,3.57,0,0 +2012,9,6,12,90,15,23,1017,4.46,0,0 +2012,9,6,13,101,15,23,1017,1.79,0,0 +2012,9,6,14,97,15,23,1016,4.92,0,0 +2012,9,6,15,98,15,23,1015,8.05,0,0 +2012,9,6,16,86,15,22,1015,11.18,0,0 +2012,9,6,17,98,15,22,1016,14.31,0,0 +2012,9,6,18,126,15,21,1016,16.1,0,0 +2012,9,6,19,142,16,18,1016,0.89,0,1 +2012,9,6,20,150,17,18,1017,0.89,0,2 +2012,9,6,21,154,17,18,1017,0.89,0,3 +2012,9,6,22,152,17,18,1017,0.89,0,4 +2012,9,6,23,144,18,18,1017,1.78,0,5 +2012,9,7,0,143,18,18,1016,3.13,0,6 +2012,9,7,1,141,18,18,1016,0.89,0,7 +2012,9,7,2,151,18,18,1016,1.79,0,8 +2012,9,7,3,140,18,18,1015,1.79,0,9 +2012,9,7,4,139,18,18,1015,3.58,0,0 +2012,9,7,5,135,18,18,1015,3.13,0,0 +2012,9,7,6,135,18,18,1015,6.26,0,0 +2012,9,7,7,134,18,19,1015,8.05,0,0 +2012,9,7,8,134,18,20,1015,1.79,0,0 +2012,9,7,9,129,18,23,1016,3.58,0,0 +2012,9,7,10,115,18,24,1016,5.37,0,0 +2012,9,7,11,99,18,24,1015,8.5,0,0 +2012,9,7,12,81,17,25,1015,11.63,0,0 +2012,9,7,13,60,18,24,1014,3.13,0,0 +2012,9,7,14,50,18,25,1014,7.15,0,0 +2012,9,7,15,38,17,24,1014,11.17,0,0 +2012,9,7,16,29,18,23,1014,16.09,0,0 +2012,9,7,17,32,17,23,1015,20.11,0,0 +2012,9,7,18,27,17,21,1015,23.24,0,0 +2012,9,7,19,23,17,20,1016,0.89,0,0 +2012,9,7,20,26,17,20,1016,1.79,0,0 +2012,9,7,21,33,17,19,1016,0.89,0,0 +2012,9,7,22,31,18,19,1016,1.78,0,0 +2012,9,7,23,31,18,19,1016,0.89,0,0 +2012,9,8,0,44,18,19,1016,0.89,0,0 +2012,9,8,1,53,18,20,1016,1.78,0,0 +2012,9,8,2,47,18,19,1016,1.79,0,0 +2012,9,8,3,40,18,19,1016,1.79,0,0 +2012,9,8,4,42,18,19,1016,2.68,0,0 +2012,9,8,5,50,17,18,1016,0.89,0,0 +2012,9,8,6,43,16,18,1016,1.79,0,0 +2012,9,8,7,40,16,19,1016,3.58,0,0 +2012,9,8,8,34,17,21,1016,3.13,0,0 +2012,9,8,9,35,17,23,1016,1.79,0,0 +2012,9,8,10,39,17,25,1016,1.79,0,0 +2012,9,8,11,36,17,25,1015,0.89,0,0 +2012,9,8,12,34,17,27,1015,1.78,0,0 +2012,9,8,13,40,18,27,1014,3.57,0,0 +2012,9,8,14,48,17,27,1013,5.36,0,0 +2012,9,8,15,52,18,27,1013,3.13,0,0 +2012,9,8,16,60,17,26,1013,6.26,0,0 +2012,9,8,17,64,17,26,1013,9.39,0,0 +2012,9,8,18,60,17,25,1013,13.41,0,0 +2012,9,8,19,58,18,23,1013,15.2,0,0 +2012,9,8,20,61,17,22,1014,16.99,0,0 +2012,9,8,21,61,18,22,1015,18.78,0,0 +2012,9,8,22,69,18,20,1014,19.67,0,0 +2012,9,8,23,73,18,19,1014,0.89,0,0 +2012,9,9,0,84,18,19,1014,1.78,0,0 +2012,9,9,1,80,18,18,1014,2.23,0,0 +2012,9,9,2,87,17,18,1014,0.89,0,0 +2012,9,9,3,95,17,18,1014,0.89,0,0 +2012,9,9,4,113,17,18,1015,1.78,0,0 +2012,9,9,5,124,18,18,1015,0.89,0,0 +2012,9,9,6,132,18,18,1015,0.89,0,0 +2012,9,9,7,125,18,19,1015,1.79,0,0 +2012,9,9,8,130,19,20,1016,0.89,0,0 +2012,9,9,9,145,19,21,1016,0.89,0,0 +2012,9,9,10,153,19,22,1016,1.78,0,0 +2012,9,9,11,138,20,25,1015,2.67,0,0 +2012,9,9,12,133,19,26,1015,3.56,0,0 +2012,9,9,13,134,19,27,1014,1.79,0,0 +2012,9,9,14,103,19,27,1014,1.79,0,0 +2012,9,9,15,87,19,27,1013,3.13,0,0 +2012,9,9,16,93,18,27,1013,6.26,0,0 +2012,9,9,17,85,18,27,1013,9.39,0,0 +2012,9,9,18,80,19,25,1013,11.18,0,0 +2012,9,9,19,90,19,24,1014,14.31,0,0 +2012,9,9,20,90,19,24,1015,17.44,0,0 +2012,9,9,21,106,19,23,1015,20.57,0,0 +2012,9,9,22,118,18,23,1015,22.36,0,0 +2012,9,9,23,126,19,20,1015,0.89,0,0 +2012,9,10,0,126,19,19,1015,0.89,0,0 +2012,9,10,1,129,19,19,1015,1.78,0,0 +2012,9,10,2,129,19,19,1015,2.67,0,0 +2012,9,10,3,145,19,19,1015,0.89,0,0 +2012,9,10,4,143,19,19,1015,1.79,0,0 +2012,9,10,5,145,19,19,1015,3.58,0,0 +2012,9,10,6,160,19,19,1015,0.89,0,0 +2012,9,10,7,159,19,19,1015,1.79,0,0 +2012,9,10,8,173,19,21,1015,3.58,0,0 +2012,9,10,9,180,20,24,1015,0.89,0,0 +2012,9,10,10,179,20,24,1015,1.78,0,0 +2012,9,10,11,176,20,25,1015,2.67,0,0 +2012,9,10,12,161,20,27,1015,3.56,0,0 +2012,9,10,13,149,21,28,1014,1.79,0,0 +2012,9,10,14,134,18,28,1013,3.13,0,0 +2012,9,10,15,116,18,27,1013,3.13,0,0 +2012,9,10,16,106,18,27,1013,6.26,0,0 +2012,9,10,17,99,18,26,1013,9.39,0,0 +2012,9,10,18,91,18,25,1013,11.18,0,0 +2012,9,10,19,78,19,23,1013,12.97,0,0 +2012,9,10,20,80,19,22,1012,14.76,0,0 +2012,9,10,21,86,20,22,1013,0.89,0,0 +2012,9,10,22,96,19,23,1012,3.13,0,0 +2012,9,10,23,109,19,22,1012,4.92,0,0 +2012,9,11,0,108,18,22,1012,6.71,0,1 +2012,9,11,1,116,19,21,1012,0.89,0,0 +2012,9,11,2,123,19,21,1012,1.78,0,0 +2012,9,11,3,133,19,21,1012,1.79,0,0 +2012,9,11,4,121,19,21,1011,3.58,0,0 +2012,9,11,5,115,19,20,1011,0.89,0,0 +2012,9,11,6,123,19,20,1012,0.89,0,0 +2012,9,11,7,134,19,21,1012,2.68,0,0 +2012,9,11,8,160,20,21,1012,0.89,0,0 +2012,9,11,9,138,18,22,1011,3.13,0,0 +2012,9,11,10,150,19,21,1012,6.26,0,1 +2012,9,11,11,174,20,22,1011,8.05,0,0 +2012,9,11,12,132,18,24,1010,11.18,0,0 +2012,9,11,13,92,18,25,1010,12.97,0,0 +2012,9,11,14,79,18,24,1009,16.99,0,0 +2012,9,11,15,78,17,25,1009,21.91,0,0 +2012,9,11,16,83,18,24,1008,25.93,0,0 +2012,9,11,17,78,18,23,1009,29.95,0,0 +2012,9,11,18,75,18,23,1008,33.08,0,0 +2012,9,11,19,92,18,22,1009,36.21,0,0 +2012,9,11,20,99,18,22,1009,39.34,0,0 +2012,9,11,21,88,18,18,1009,0.89,0,1 +2012,9,11,22,34,18,19,1010,1.78,0,2 +2012,9,11,23,48,18,18,1009,3.13,0,3 +2012,9,12,0,62,18,18,1009,0.89,0,0 +2012,9,12,1,64,18,18,1009,0.89,0,0 +2012,9,12,2,68,17,18,1009,1.34,0,0 +2012,9,12,3,70,16,17,1009,3.13,0,0 +2012,9,12,4,37,11,18,1009,11.18,0,0 +2012,9,12,5,6,11,18,1009,16.99,0,0 +2012,9,12,6,6,11,17,1009,21.01,0,0 +2012,9,12,7,7,11,19,1009,25.93,0,0 +2012,9,12,8,10,11,22,1010,33.08,0,0 +2012,9,12,9,6,10,22,1011,38,0,0 +2012,9,12,10,7,9,22,1011,45.15,0,0 +2012,9,12,11,7,7,23,1012,52.3,0,0 +2012,9,12,12,5,5,24,1011,61.24,0,0 +2012,9,12,13,9,5,24,1011,69.29,0,0 +2012,9,12,14,7,6,25,1010,76.44,0,0 +2012,9,12,15,7,5,24,1010,8.05,0,0 +2012,9,12,16,10,4,23,1011,15.2,0,0 +2012,9,12,17,9,5,23,1011,19.22,0,0 +2012,9,12,18,16,5,21,1012,22.35,0,0 +2012,9,12,19,13,5,19,1012,25.48,0,0 +2012,9,12,20,14,9,17,1013,27.27,0,0 +2012,9,12,21,21,10,15,1013,3.13,0,0 +2012,9,12,22,16,9,17,1014,4.92,0,0 +2012,9,12,23,14,9,16,1014,6.71,0,0 +2012,9,13,0,10,7,15,1014,9.84,0,0 +2012,9,13,1,6,8,14,1014,0.89,0,0 +2012,9,13,2,11,11,13,1014,1.78,0,0 +2012,9,13,3,10,9,12,1013,1.79,0,0 +2012,9,13,4,13,9,12,1013,3.58,0,0 +2012,9,13,5,16,8,12,1013,1.79,0,0 +2012,9,13,6,20,10,11,1013,1.79,0,0 +2012,9,13,7,30,11,14,1013,0.89,0,0 +2012,9,13,8,27,9,18,1014,2.68,0,0 +2012,9,13,9,16,8,20,1014,0.89,0,0 +2012,9,13,10,15,6,22,1013,1.79,0,0 +2012,9,13,11,10,6,23,1013,1.79,0,0 +2012,9,13,12,10,4,24,1012,4.92,0,0 +2012,9,13,13,13,6,25,1011,3.13,0,0 +2012,9,13,14,13,6,25,1011,8.05,0,0 +2012,9,13,15,15,10,24,1011,4.02,0,0 +2012,9,13,16,15,8,23,1011,8.04,0,0 +2012,9,13,17,17,11,23,1012,9.83,0,0 +2012,9,13,18,17,10,21,1012,0.89,0,0 +2012,9,13,19,10,11,19,1013,1.79,0,0 +2012,9,13,20,16,10,18,1014,0.89,0,0 +2012,9,13,21,14,8,20,1014,3.13,0,0 +2012,9,13,22,15,10,15,1015,6.26,0,0 +2012,9,13,23,12,11,14,1015,1.79,0,0 +2012,9,14,0,11,10,13,1015,4.02,0,0 +2012,9,14,1,13,11,13,1015,7.15,0,0 +2012,9,14,2,15,10,13,1015,8.94,0,0 +2012,9,14,3,14,10,11,1015,12.07,0,0 +2012,9,14,4,26,9,10,1016,15.2,0,0 +2012,9,14,5,24,9,10,1016,18.33,0,0 +2012,9,14,6,20,9,10,1017,21.46,0,0 +2012,9,14,7,17,11,14,1017,23.25,0,0 +2012,9,14,8,20,10,17,1018,27.27,0,0 +2012,9,14,9,25,8,20,1018,31.29,0,0 +2012,9,14,10,14,8,22,1018,34.42,0,0 +2012,9,14,11,12,8,24,1018,1.79,0,0 +2012,9,14,12,12,6,25,1018,3.58,0,0 +2012,9,14,13,8,5,25,1017,1.79,0,0 +2012,9,14,14,6,4,25,1017,0.89,0,0 +2012,9,14,15,7,2,26,1016,1.79,0,0 +2012,9,14,16,10,3,26,1016,1.79,0,0 +2012,9,14,17,8,6,25,1016,0.89,0,0 +2012,9,14,18,7,8,22,1016,2.68,0,0 +2012,9,14,19,27,12,17,1016,0.89,0,0 +2012,9,14,20,25,8,18,1017,4.02,0,0 +2012,9,14,21,28,10,17,1018,5.81,0,0 +2012,9,14,22,NA,11,15,1018,0.89,0,0 +2012,9,14,23,30,12,14,1019,0.89,0,0 +2012,9,15,0,23,12,13,1019,1.78,0,0 +2012,9,15,1,53,13,13,1019,2.67,0,0 +2012,9,15,2,70,12,13,1019,0.89,0,0 +2012,9,15,3,56,11,12,1019,3.13,0,0 +2012,9,15,4,28,10,12,1019,6.26,0,0 +2012,9,15,5,20,10,11,1020,9.39,0,0 +2012,9,15,6,24,10,11,1020,13.41,0,0 +2012,9,15,7,20,11,14,1021,17.43,0,0 +2012,9,15,8,24,11,17,1021,21.45,0,0 +2012,9,15,9,29,10,21,1021,24.58,0,0 +2012,9,15,10,29,10,22,1021,27.71,0,0 +2012,9,15,11,24,9,24,1021,29.5,0,0 +2012,9,15,12,26,9,26,1021,0.89,0,0 +2012,9,15,13,26,7,26,1020,1.78,0,0 +2012,9,15,14,29,6,26,1019,1.79,0,0 +2012,9,15,15,32,6,27,1019,1.79,0,0 +2012,9,15,16,26,6,26,1019,3.13,0,0 +2012,9,15,17,26,6,25,1019,6.26,0,0 +2012,9,15,18,24,9,23,1019,8.05,0,0 +2012,9,15,19,28,7,23,1019,11.18,0,0 +2012,9,15,20,37,8,21,1020,14.31,0,0 +2012,9,15,21,43,12,16,1020,16.1,0,0 +2012,9,15,22,45,12,15,1021,1.79,0,0 +2012,9,15,23,52,13,15,1021,0.89,0,0 +2012,9,16,0,68,13,14,1022,1.79,0,0 +2012,9,16,1,77,12,14,1022,2.68,0,0 +2012,9,16,2,79,12,13,1022,3.57,0,0 +2012,9,16,3,78,12,13,1022,0.89,0,0 +2012,9,16,4,77,12,13,1022,0.89,0,0 +2012,9,16,5,NA,11,12,1022,3.13,0,0 +2012,9,16,6,41,11,11,1022,6.26,0,0 +2012,9,16,7,45,12,14,1023,8.05,0,0 +2012,9,16,8,43,13,17,1023,9.84,0,0 +2012,9,16,9,47,13,20,1023,11.63,0,0 +2012,9,16,10,57,11,22,1023,0.89,0,0 +2012,9,16,11,69,12,24,1022,1.78,0,0 +2012,9,16,12,66,11,25,1021,2.67,0,0 +2012,9,16,13,62,11,26,1020,3.56,0,0 +2012,9,16,14,66,8,27,1019,5.35,0,0 +2012,9,16,15,41,6,27,1019,3.13,0,0 +2012,9,16,16,43,7,26,1018,6.26,0,0 +2012,9,16,17,37,8,25,1018,10.28,0,0 +2012,9,16,18,42,7,24,1018,14.3,0,0 +2012,9,16,19,46,7,24,1018,3.13,0,0 +2012,9,16,20,45,10,19,1018,1.79,0,0 +2012,9,16,21,47,13,17,1018,0.89,0,0 +2012,9,16,22,42,13,15,1018,0.89,0,0 +2012,9,16,23,58,13,14,1018,0.89,0,0 +2012,9,17,0,64,12,13,1018,0.89,0,0 +2012,9,17,1,81,12,14,1018,0.89,0,0 +2012,9,17,2,61,12,13,1017,2.68,0,0 +2012,9,17,3,60,11,12,1017,5.81,0,0 +2012,9,17,4,47,11,12,1017,8.94,0,0 +2012,9,17,5,40,11,13,1017,12.07,0,0 +2012,9,17,6,55,11,12,1017,15.2,0,0 +2012,9,17,7,50,12,17,1017,19.22,0,0 +2012,9,17,8,46,12,19,1017,23.24,0,0 +2012,9,17,9,33,12,21,1016,27.26,0,0 +2012,9,17,10,33,11,23,1016,30.39,0,0 +2012,9,17,11,32,10,25,1015,33.52,0,0 +2012,9,17,12,23,5,26,1014,4.02,0,0 +2012,9,17,13,15,5,27,1013,1.79,0,0 +2012,9,17,14,16,6,27,1011,1.79,0,0 +2012,9,17,15,15,7,27,1010,3.58,0,0 +2012,9,17,16,16,7,27,1010,1.79,0,0 +2012,9,17,17,18,8,26,1010,0.89,0,0 +2012,9,17,18,23,8,24,1009,1.79,0,0 +2012,9,17,19,40,7,25,1009,5.81,0,0 +2012,9,17,20,49,8,24,1009,10.73,0,0 +2012,9,17,21,61,10,24,1009,4.02,0,0 +2012,9,17,22,66,13,18,1010,1.79,0,0 +2012,9,17,23,74,13,15,1010,1.79,0,0 +2012,9,18,0,59,13,15,1010,0.89,0,0 +2012,9,18,1,77,14,15,1010,0.89,0,0 +2012,9,18,2,69,11,16,1010,3.13,0,0 +2012,9,18,3,36,11,14,1010,6.26,0,0 +2012,9,18,4,38,10,13,1010,10.28,0,0 +2012,9,18,5,43,10,12,1010,13.41,0,0 +2012,9,18,6,45,9,11,1011,16.54,0,0 +2012,9,18,7,31,11,17,1011,19.67,0,0 +2012,9,18,8,27,10,19,1011,23.69,0,0 +2012,9,18,9,28,9,22,1012,26.82,0,0 +2012,9,18,10,33,9,24,1012,29.95,0,0 +2012,9,18,11,29,7,27,1011,33.08,0,0 +2012,9,18,12,31,6,27,1010,0.89,0,0 +2012,9,18,13,23,6,29,1009,1.78,0,0 +2012,9,18,14,8,4,29,1008,2.67,0,0 +2012,9,18,15,7,2,29,1008,3.13,0,0 +2012,9,18,16,11,2,29,1008,6.26,0,0 +2012,9,18,17,13,1,29,1007,1.79,0,0 +2012,9,18,18,16,9,24,1007,1.79,0,0 +2012,9,18,19,29,13,20,1008,3.58,0,0 +2012,9,18,20,33,6,24,1008,6.71,0,0 +2012,9,18,21,34,6,25,1008,10.73,0,0 +2012,9,18,22,34,7,23,1008,13.86,0,0 +2012,9,18,23,33,8,22,1008,3.13,0,0 +2012,9,19,0,29,11,16,1008,0.89,0,0 +2012,9,19,1,32,12,15,1008,0.89,0,0 +2012,9,19,2,46,12,14,1008,0.89,0,0 +2012,9,19,3,62,12,14,1008,0.45,0,0 +2012,9,19,4,36,11,13,1008,1.79,0,0 +2012,9,19,5,27,11,13,1008,3.58,0,0 +2012,9,19,6,19,12,13,1009,0.89,0,0 +2012,9,19,7,31,12,14,1009,1.78,0,0 +2012,9,19,8,27,12,17,1010,3.13,0,0 +2012,9,19,9,25,12,20,1010,4.92,0,0 +2012,9,19,10,26,9,23,1010,8.05,0,0 +2012,9,19,11,26,7,26,1010,1.79,0,0 +2012,9,19,12,22,7,27,1010,3.13,0,0 +2012,9,19,13,17,5,29,1009,0.89,0,0 +2012,9,19,14,20,4,29,1008,3.13,0,0 +2012,9,19,15,30,3,29,1008,6.26,0,0 +2012,9,19,16,58,4,29,1008,9.39,0,0 +2012,9,19,17,66,6,28,1008,11.18,0,0 +2012,9,19,18,77,9,26,1008,14.31,0,0 +2012,9,19,19,89,12,22,1009,16.1,0,0 +2012,9,19,20,93,14,19,1010,0.89,0,0 +2012,9,19,21,124,14,17,1010,1.78,0,0 +2012,9,19,22,125,15,17,1011,0.89,0,0 +2012,9,19,23,136,15,18,1011,2.68,0,0 +2012,9,20,0,159,14,16,1012,0.89,0,0 +2012,9,20,1,165,15,16,1012,0.89,0,0 +2012,9,20,2,134,14,16,1013,0.89,0,0 +2012,9,20,3,129,14,15,1012,0.89,0,0 +2012,9,20,4,116,14,15,1013,1.79,0,0 +2012,9,20,5,69,13,14,1013,3.58,0,0 +2012,9,20,6,65,13,14,1013,5.37,0,0 +2012,9,20,7,57,13,15,1014,1.79,0,0 +2012,9,20,8,65,14,18,1014,3.13,0,0 +2012,9,20,9,71,15,20,1014,0.89,0,0 +2012,9,20,10,102,15,23,1014,1.78,0,0 +2012,9,20,11,128,16,24,1014,2.67,0,0 +2012,9,20,12,130,16,25,1013,3.56,0,0 +2012,9,20,13,137,16,26,1012,4.45,0,0 +2012,9,20,14,123,16,27,1012,1.79,0,0 +2012,9,20,15,96,16,28,1012,1.79,0,0 +2012,9,20,16,94,12,27,1012,4.92,0,0 +2012,9,20,17,108,13,26,1012,8.05,0,0 +2012,9,20,18,96,14,24,1012,9.84,0,0 +2012,9,20,19,101,16,21,1012,10.73,0,0 +2012,9,20,20,124,17,20,1013,11.62,0,0 +2012,9,20,21,127,16,19,1013,0.89,0,0 +2012,9,20,22,146,16,18,1013,1.78,0,0 +2012,9,20,23,156,16,17,1013,2.67,0,0 +2012,9,21,0,166,15,19,1013,1.79,0,0 +2012,9,21,1,174,15,18,1013,0.89,0,0 +2012,9,21,2,166,15,18,1013,1.79,0,0 +2012,9,21,3,168,15,17,1013,0.89,0,0 +2012,9,21,4,170,15,17,1013,0.89,0,0 +2012,9,21,5,174,15,18,1012,1.79,0,0 +2012,9,21,6,183,15,17,1013,0.89,0,0 +2012,9,21,7,188,16,18,1013,0.89,0,0 +2012,9,21,8,205,16,19,1013,0.89,0,0 +2012,9,21,9,193,17,20,1013,1.78,0,0 +2012,9,21,10,226,18,22,1013,2.67,0,0 +2012,9,21,11,231,18,23,1012,3.56,0,0 +2012,9,21,12,238,18,25,1012,4.45,0,0 +2012,9,21,13,196,17,26,1011,4.02,0,0 +2012,9,21,14,183,16,26,1010,8.04,0,0 +2012,9,21,15,97,10,27,1010,12.96,0,0 +2012,9,21,16,101,12,26,1010,17.88,0,0 +2012,9,21,17,92,15,24,1010,21.01,0,0 +2012,9,21,18,109,16,24,1010,25.93,0,0 +2012,9,21,19,134,16,23,1010,29.06,0,0 +2012,9,21,20,92,14,23,1011,30.85,0,0 +2012,9,21,21,71,13,24,1011,33.98,0,0 +2012,9,21,22,56,12,23,1011,35.77,0,0 +2012,9,21,23,50,11,22,1011,37.56,0,0 +2012,9,22,0,50,15,19,1011,0.89,0,0 +2012,9,22,1,50,16,19,1011,0.89,0,0 +2012,9,22,2,54,16,18,1011,0.89,0,0 +2012,9,22,3,54,16,18,1011,0.89,0,0 +2012,9,22,4,53,17,18,1011,0.89,0,0 +2012,9,22,5,136,17,18,1011,0.89,0,0 +2012,9,22,6,164,17,18,1011,0.89,0,0 +2012,9,22,7,169,17,18,1012,0.89,0,0 +2012,9,22,8,157,17,20,1012,0.89,0,0 +2012,9,22,9,154,17,22,1012,1.79,0,0 +2012,9,22,10,180,17,23,1012,3.58,0,0 +2012,9,22,11,146,16,25,1012,1.79,0,0 +2012,9,22,12,121,16,25,1011,3.13,0,0 +2012,9,22,13,116,15,26,1011,1.79,0,0 +2012,9,22,14,107,14,26,1010,2.68,0,0 +2012,9,22,15,96,15,25,1010,1.79,0,0 +2012,9,22,16,78,14,25,1010,4.92,0,0 +2012,9,22,17,70,15,25,1010,6.71,0,0 +2012,9,22,18,75,15,23,1010,8.5,0,0 +2012,9,22,19,82,16,21,1011,10.29,0,0 +2012,9,22,20,89,16,20,1012,0.89,0,0 +2012,9,22,21,97,16,21,1012,3.13,0,0 +2012,9,22,22,127,15,21,1012,6.26,0,0 +2012,9,22,23,129,15,21,1012,9.39,0,0 +2012,9,23,0,128,15,18,1012,0.89,0,0 +2012,9,23,1,137,16,17,1012,1.79,0,0 +2012,9,23,2,129,15,17,1012,0.89,0,0 +2012,9,23,3,91,15,16,1013,0.89,0,0 +2012,9,23,4,131,15,15,1013,0.89,0,0 +2012,9,23,5,143,15,16,1013,1.78,0,0 +2012,9,23,6,125,15,16,1013,0.89,0,0 +2012,9,23,7,98,15,16,1014,4.02,0,0 +2012,9,23,8,90,15,18,1014,8.04,0,0 +2012,9,23,9,88,15,21,1014,11.17,0,0 +2012,9,23,10,85,15,23,1014,12.96,0,0 +2012,9,23,11,94,16,25,1013,3.13,0,0 +2012,9,23,12,91,15,26,1013,0.89,0,0 +2012,9,23,13,93,14,27,1013,2.68,0,0 +2012,9,23,14,95,14,28,1012,4.47,0,0 +2012,9,23,15,83,13,28,1012,3.13,0,0 +2012,9,23,16,83,14,27,1012,6.26,0,0 +2012,9,23,17,82,16,25,1012,8.05,0,0 +2012,9,23,18,104,16,22,1013,0.89,0,0 +2012,9,23,19,119,17,22,1013,1.78,0,0 +2012,9,23,20,121,17,21,1014,1.79,0,0 +2012,9,23,21,159,15,22,1016,3.13,0,0 +2012,9,23,22,128,14,19,1016,3.13,0,0 +2012,9,23,23,101,14,19,1016,4.02,0,0 +2012,9,24,0,58,14,18,1016,5.81,0,0 +2012,9,24,1,58,14,17,1016,1.79,0,0 +2012,9,24,2,71,14,16,1016,4.92,0,0 +2012,9,24,3,52,14,16,1017,0.45,0,0 +2012,9,24,4,40,14,17,1017,1.34,0,0 +2012,9,24,5,32,14,16,1018,2.23,0,0 +2012,9,24,6,32,15,17,1018,1.79,0,0 +2012,9,24,7,34,15,17,1019,0.89,0,0 +2012,9,24,8,40,15,19,1019,2.68,0,0 +2012,9,24,9,53,15,21,1019,1.79,0,0 +2012,9,24,10,56,15,23,1020,1.79,0,0 +2012,9,24,11,61,15,23,1019,0.89,0,0 +2012,9,24,12,79,16,23,1018,3.13,0,0 +2012,9,24,13,99,16,25,1018,1.79,0,0 +2012,9,24,14,96,16,24,1017,3.58,0,0 +2012,9,24,15,101,16,24,1017,3.13,0,0 +2012,9,24,16,93,16,24,1017,4.92,0,0 +2012,9,24,17,81,15,24,1017,6.71,0,0 +2012,9,24,18,84,17,22,1016,8.5,0,0 +2012,9,24,19,76,17,21,1016,0.89,0,0 +2012,9,24,20,72,15,22,1017,3.13,0,0 +2012,9,24,21,63,15,21,1017,4.92,0,0 +2012,9,24,22,49,14,21,1018,8.05,0,0 +2012,9,24,23,47,15,21,1018,9.84,0,0 +2012,9,25,0,46,15,21,1018,11.63,0,0 +2012,9,25,1,55,15,20,1017,12.52,0,0 +2012,9,25,2,58,16,19,1017,14.31,0,0 +2012,9,25,3,59,16,19,1017,15.2,0,0 +2012,9,25,4,68,16,18,1017,16.09,0,0 +2012,9,25,5,68,16,17,1017,16.98,0,0 +2012,9,25,6,81,16,18,1017,18.77,0,0 +2012,9,25,7,83,16,18,1018,20.56,0,0 +2012,9,25,8,82,16,19,1017,22.35,0,0 +2012,9,25,9,89,16,21,1018,24.14,0,0 +2012,9,25,10,87,17,22,1018,25.93,0,0 +2012,9,25,11,88,17,22,1018,27.72,0,0 +2012,9,25,12,88,17,23,1018,29.51,0,0 +2012,9,25,13,109,18,21,1017,4.02,0,1 +2012,9,25,14,99,18,21,1017,7.15,0,0 +2012,9,25,15,110,18,20,1017,11.17,0,0 +2012,9,25,16,96,18,19,1017,15.19,0,1 +2012,9,25,17,51,17,18,1018,1.79,0,2 +2012,9,25,18,19,14,16,1019,4.92,0,3 +2012,9,25,19,23,14,16,1019,4.92,0,4 +2012,9,25,20,15,14,16,1020,8.94,0,5 +2012,9,25,21,12,14,16,1020,12.07,0,6 +2012,9,25,22,13,14,16,1019,1.79,0,7 +2012,9,25,23,13,14,16,1019,0.89,0,0 +2012,9,26,0,18,14,16,1019,1.79,0,0 +2012,9,26,1,26,14,15,1020,0.89,0,0 +2012,9,26,2,25,14,15,1019,3.13,0,0 +2012,9,26,3,19,13,15,1019,3.13,0,0 +2012,9,26,4,NA,12,13,1020,1.79,0,0 +2012,9,26,5,19,12,13,1020,3.58,0,0 +2012,9,26,6,19,12,12,1020,6.71,0,0 +2012,9,26,7,18,13,15,1021,9.84,0,0 +2012,9,26,8,27,14,18,1021,14.76,0,0 +2012,9,26,9,30,14,19,1021,18.78,0,0 +2012,9,26,10,32,14,21,1021,22.8,0,0 +2012,9,26,11,31,14,22,1021,25.93,0,0 +2012,9,26,12,27,13,23,1020,3.13,0,0 +2012,9,26,13,17,9,26,1019,3.13,0,0 +2012,9,26,14,11,8,26,1019,6.26,0,0 +2012,9,26,15,11,2,27,1019,1.79,0,0 +2012,9,26,16,9,3,27,1018,0.89,0,0 +2012,9,26,17,9,12,24,1018,0.89,0,0 +2012,9,26,18,14,13,22,1018,0.89,0,0 +2012,9,26,19,31,15,19,1018,1.78,0,0 +2012,9,26,20,41,13,19,1018,1.79,0,0 +2012,9,26,21,59,13,17,1018,3.58,0,0 +2012,9,26,22,55,14,16,1018,0.89,0,0 +2012,9,26,23,56,13,15,1018,1.78,0,0 +2012,9,27,0,49,13,15,1018,0.89,0,0 +2012,9,27,1,51,14,15,1018,0.89,0,0 +2012,9,27,2,90,13,13,1018,0.89,0,0 +2012,9,27,3,72,12,13,1017,4.02,0,0 +2012,9,27,4,57,12,13,1017,0.89,0,0 +2012,9,27,5,50,12,12,1017,1.78,0,0 +2012,9,27,6,49,11,12,1017,1.79,0,0 +2012,9,27,7,47,13,14,1017,1.79,0,0 +2012,9,27,8,62,15,15,1017,4.92,0,0 +2012,9,27,9,95,15,16,1017,6.71,0,0 +2012,9,27,10,NA,16,18,1016,9.84,0,0 +2012,9,27,11,NA,15,20,1016,3.13,0,0 +2012,9,27,12,NA,16,21,1015,4.92,0,0 +2012,9,27,13,82,16,19,1016,10.73,0,1 +2012,9,27,14,10,10,16,1016,17.88,0,2 +2012,9,27,15,10,11,16,1016,22.8,0,0 +2012,9,27,16,8,11,16,1016,4.92,0,0 +2012,9,27,17,5,10,17,1016,4.02,0,0 +2012,9,27,18,8,7,17,1016,7.15,0,0 +2012,9,27,19,16,7,17,1016,10.28,0,0 +2012,9,27,20,24,5,18,1016,15.2,0,0 +2012,9,27,21,9,4,17,1017,22.35,0,0 +2012,9,27,22,9,3,16,1017,27.27,0,0 +2012,9,27,23,7,3,17,1017,37.1,0,0 +2012,9,28,0,3,3,16,1018,44.25,0,0 +2012,9,28,1,3,2,16,1018,53.19,0,0 +2012,9,28,2,3,2,16,1018,61.24,0,0 +2012,9,28,3,2,1,16,1018,69.29,0,0 +2012,9,28,4,5,1,16,1019,77.34,0,0 +2012,9,28,5,4,0,15,1018,86.28,0,0 +2012,9,28,6,2,-1,15,1019,96.11,0,0 +2012,9,28,7,2,-3,16,1019,107.29,0,0 +2012,9,28,8,4,-4,18,1019,118.47,0,0 +2012,9,28,9,3,-5,19,1020,129.65,0,0 +2012,9,28,10,0,-5,20,1020,139.48,0,0 +2012,9,28,11,4,-7,22,1019,149.31,0,0 +2012,9,28,12,4,-7,22,1018,160.49,0,0 +2012,9,28,13,5,-7,23,1018,170.32,0,0 +2012,9,28,14,2,-8,24,1017,181.5,0,0 +2012,9,28,15,0,-10,24,1017,192.68,0,0 +2012,9,28,16,1,-9,23,1016,204.75,0,0 +2012,9,28,17,1,-8,23,1016,213.69,0,0 +2012,9,28,18,2,-6,20,1017,219.5,0,0 +2012,9,28,19,3,-5,19,1017,224.42,0,0 +2012,9,28,20,5,-6,19,1018,231.57,0,0 +2012,9,28,21,5,-6,18,1019,236.49,0,0 +2012,9,28,22,5,-5,18,1019,242.3,0,0 +2012,9,28,23,3,-4,18,1019,247.22,0,0 +2012,9,29,0,2,-3,17,1019,252.14,0,0 +2012,9,29,1,7,-1,14,1019,253.93,0,0 +2012,9,29,2,8,1,13,1019,0.89,0,0 +2012,9,29,3,7,-2,15,1019,4.92,0,0 +2012,9,29,4,10,3,9,1019,8.05,0,0 +2012,9,29,5,6,2,9,1019,11.18,0,0 +2012,9,29,6,6,2,8,1019,14.31,0,0 +2012,9,29,7,5,5,12,1020,0.89,0,0 +2012,9,29,8,9,3,17,1020,3.13,0,0 +2012,9,29,9,14,1,19,1020,3.13,0,0 +2012,9,29,10,7,0,21,1020,4.02,0,0 +2012,9,29,11,2,-1,23,1019,7.15,0,0 +2012,9,29,12,5,-2,23,1019,11.17,0,0 +2012,9,29,13,7,-2,23,1018,15.19,0,0 +2012,9,29,14,6,-2,25,1016,18.32,0,0 +2012,9,29,15,4,-2,25,1015,0.89,0,0 +2012,9,29,16,5,-2,25,1015,1.79,0,0 +2012,9,29,17,5,2,23,1015,1.79,0,0 +2012,9,29,18,12,5,19,1015,0.89,0,0 +2012,9,29,19,21,9,14,1016,1.78,0,0 +2012,9,29,20,30,10,14,1017,2.67,0,0 +2012,9,29,21,62,9,14,1018,3.56,0,0 +2012,9,29,22,53,7,15,1018,3.13,0,0 +2012,9,29,23,48,8,11,1018,0.89,0,0 +2012,9,30,0,21,6,12,1018,3.13,0,0 +2012,9,30,1,4,7,12,1019,1.79,0,0 +2012,9,30,2,2,6,11,1019,3.58,0,0 +2012,9,30,3,1,6,9,1018,3.13,0,0 +2012,9,30,4,19,6,10,1019,1.79,0,0 +2012,9,30,5,22,5,8,1019,3.13,0,0 +2012,9,30,6,17,6,8,1019,8.05,0,0 +2012,9,30,7,29,7,11,1020,11.18,0,0 +2012,9,30,8,12,8,15,1020,14.31,0,0 +2012,9,30,9,17,5,18,1020,17.44,0,0 +2012,9,30,10,11,3,21,1020,19.23,0,0 +2012,9,30,11,8,2,22,1019,22.36,0,0 +2012,9,30,12,11,3,24,1018,0.89,0,0 +2012,9,30,13,9,1,26,1017,1.78,0,0 +2012,9,30,14,6,2,26,1016,2.67,0,0 +2012,9,30,15,4,0,25,1015,1.79,0,0 +2012,9,30,16,3,0,26,1015,1.79,0,0 +2012,9,30,17,4,3,24,1014,3.58,0,0 +2012,9,30,18,8,7,20,1014,5.37,0,0 +2012,9,30,19,25,6,19,1015,7.16,0,0 +2012,9,30,20,32,6,18,1015,8.95,0,0 +2012,9,30,21,29,9,14,1016,0.89,0,0 +2012,9,30,22,29,10,13,1016,1.78,0,0 +2012,9,30,23,28,9,12,1016,0.89,0,0 +2012,10,1,0,41,8,11,1016,0.89,0,0 +2012,10,1,1,60,9,11,1016,0.89,0,0 +2012,10,1,2,65,9,10,1016,1.78,0,0 +2012,10,1,3,64,9,10,1016,0.89,0,0 +2012,10,1,4,59,8,9,1016,1.79,0,0 +2012,10,1,5,31,7,8,1017,4.92,0,0 +2012,10,1,6,23,8,9,1017,6.71,0,0 +2012,10,1,7,28,8,9,1017,8.5,0,0 +2012,10,1,8,25,10,14,1017,10.29,0,0 +2012,10,1,9,22,9,18,1018,13.42,0,0 +2012,10,1,10,26,6,20,1018,1.79,0,0 +2012,10,1,11,23,6,21,1017,0.89,0,0 +2012,10,1,12,25,6,23,1017,1.79,0,0 +2012,10,1,13,32,7,25,1016,3.58,0,0 +2012,10,1,14,37,5,25,1015,1.79,0,0 +2012,10,1,15,37,5,26,1014,1.79,0,0 +2012,10,1,16,42,5,25,1014,4.92,0,0 +2012,10,1,17,51,6,24,1014,8.94,0,0 +2012,10,1,18,59,8,21,1015,10.73,0,0 +2012,10,1,19,72,9,20,1015,12.52,0,0 +2012,10,1,20,92,11,16,1015,13.41,0,0 +2012,10,1,21,104,12,14,1016,0.89,0,0 +2012,10,1,22,104,12,14,1016,0.89,0,0 +2012,10,1,23,108,12,14,1016,0.89,0,0 +2012,10,2,0,97,11,12,1016,0.89,0,0 +2012,10,2,1,100,10,10,1016,1.79,0,0 +2012,10,2,2,108,10,11,1016,0.89,0,0 +2012,10,2,3,115,11,12,1016,1.78,0,0 +2012,10,2,4,103,9,10,1016,0.89,0,0 +2012,10,2,5,79,9,10,1016,4.02,0,0 +2012,10,2,6,70,9,10,1016,5.81,0,0 +2012,10,2,7,63,9,10,1016,0.89,0,0 +2012,10,2,8,72,12,14,1016,0.89,0,0 +2012,10,2,9,87,11,17,1016,1.79,0,0 +2012,10,2,10,142,12,18,1016,0.89,0,0 +2012,10,2,11,140,12,20,1015,1.78,0,0 +2012,10,2,12,145,12,22,1014,2.67,0,0 +2012,10,2,13,145,12,23,1013,1.79,0,0 +2012,10,2,14,164,12,24,1012,3.58,0,0 +2012,10,2,15,169,11,25,1012,6.71,0,0 +2012,10,2,16,162,12,24,1011,9.84,0,0 +2012,10,2,17,165,13,23,1011,11.63,0,0 +2012,10,2,18,181,14,19,1011,12.52,0,0 +2012,10,2,19,202,13,16,1012,0.89,0,0 +2012,10,2,20,244,14,16,1013,0.89,0,0 +2012,10,2,21,248,15,16,1013,0.89,0,0 +2012,10,2,22,274,14,17,1012,0.89,0,0 +2012,10,2,23,289,14,17,1012,1.78,0,0 +2012,10,3,0,269,14,15,1012,2.23,0,0 +2012,10,3,1,259,13,13,1012,3.13,0,0 +2012,10,3,2,259,14,15,1012,0.45,0,0 +2012,10,3,3,NA,13,14,1012,1.79,0,0 +2012,10,3,4,252,13,14,1012,0.89,0,0 +2012,10,3,5,235,12,14,1012,1.79,0,0 +2012,10,3,6,24,12,13,1012,4.92,0,0 +2012,10,3,7,16,12,14,1013,8.05,0,0 +2012,10,3,8,13,13,17,1013,12.07,0,0 +2012,10,3,9,11,7,21,1013,15.2,0,0 +2012,10,3,10,10,1,22,1013,21.01,0,0 +2012,10,3,11,9,0,22,1013,29.06,0,0 +2012,10,3,12,10,2,23,1013,34.87,0,0 +2012,10,3,13,8,0,24,1012,40.68,0,0 +2012,10,3,14,9,0,25,1011,44.7,0,0 +2012,10,3,15,12,1,24,1011,3.13,0,0 +2012,10,3,16,9,2,24,1011,4.92,0,0 +2012,10,3,17,10,2,23,1011,0.89,0,0 +2012,10,3,18,15,9,18,1011,1.34,0,0 +2012,10,3,19,20,8,15,1012,2.23,0,0 +2012,10,3,20,16,7,14,1013,1.79,0,0 +2012,10,3,21,17,3,18,1014,5.81,0,0 +2012,10,3,22,10,6,14,1015,7.6,0,0 +2012,10,3,23,8,8,12,1015,0.89,0,0 +2012,10,4,0,9,6,14,1015,1.78,0,0 +2012,10,4,1,10,8,11,1015,2.23,0,0 +2012,10,4,2,8,5,14,1015,4.02,0,0 +2012,10,4,3,8,6,12,1015,0.89,0,0 +2012,10,4,4,7,7,10,1015,0.89,0,0 +2012,10,4,5,11,7,9,1016,0.89,0,0 +2012,10,4,6,11,7,8,1016,1.79,0,0 +2012,10,4,7,13,8,11,1016,1.79,0,0 +2012,10,4,8,13,9,17,1016,0.89,0,0 +2012,10,4,9,13,5,19,1017,1.79,0,0 +2012,10,4,10,13,4,21,1017,4.02,0,0 +2012,10,4,11,10,3,23,1016,9.83,0,0 +2012,10,4,12,8,2,23,1015,17.88,0,0 +2012,10,4,13,11,2,24,1014,23.69,0,0 +2012,10,4,14,12,0,25,1014,28.61,0,0 +2012,10,4,15,13,1,24,1013,33.53,0,0 +2012,10,4,16,13,1,24,1013,4.02,0,0 +2012,10,4,17,19,2,23,1013,4.02,0,0 +2012,10,4,18,19,2,22,1013,3.13,0,0 +2012,10,4,19,16,2,21,1014,4.02,0,0 +2012,10,4,20,19,3,20,1015,7.15,0,0 +2012,10,4,21,16,4,20,1015,3.13,0,0 +2012,10,4,22,16,5,19,1015,3.13,0,0 +2012,10,4,23,19,5,18,1015,6.26,0,0 +2012,10,5,0,27,8,14,1015,0.89,0,0 +2012,10,5,1,37,9,12,1016,1.78,0,0 +2012,10,5,2,20,9,11,1016,2.67,0,0 +2012,10,5,3,17,8,11,1016,0.89,0,0 +2012,10,5,4,16,9,11,1016,0.45,0,0 +2012,10,5,5,19,9,11,1016,0.89,0,0 +2012,10,5,6,17,7,9,1017,4.02,0,0 +2012,10,5,7,27,9,10,1017,7.15,0,0 +2012,10,5,8,24,10,15,1018,10.28,0,0 +2012,10,5,9,30,8,19,1019,13.41,0,0 +2012,10,5,10,19,5,22,1019,15.2,0,0 +2012,10,5,11,23,5,23,1018,18.33,0,0 +2012,10,5,12,18,4,23,1018,3.13,0,0 +2012,10,5,13,14,6,24,1018,3.13,0,0 +2012,10,5,14,16,6,24,1017,1.79,0,0 +2012,10,5,15,16,6,25,1017,3.13,0,0 +2012,10,5,16,23,5,24,1017,6.26,0,0 +2012,10,5,17,43,7,23,1017,9.39,0,0 +2012,10,5,18,48,9,19,1018,11.18,0,0 +2012,10,5,19,68,9,18,1018,12.97,0,0 +2012,10,5,20,90,10,17,1020,14.76,0,0 +2012,10,5,21,126,11,15,1020,0.89,0,0 +2012,10,5,22,151,11,13,1021,1.79,0,0 +2012,10,5,23,161,12,15,1022,0.89,0,0 +2012,10,6,0,189,12,14,1022,1.79,0,0 +2012,10,6,1,196,12,13,1023,0.45,0,0 +2012,10,6,2,193,11,12,1023,1.79,0,0 +2012,10,6,3,173,10,12,1024,3.58,0,0 +2012,10,6,4,171,9,11,1024,4.47,0,0 +2012,10,6,5,147,10,11,1024,5.36,0,0 +2012,10,6,6,95,9,10,1025,7.15,0,0 +2012,10,6,7,77,9,10,1025,10.28,0,0 +2012,10,6,8,68,10,14,1026,13.41,0,0 +2012,10,6,9,61,9,18,1026,16.54,0,0 +2012,10,6,10,43,7,20,1026,18.33,0,0 +2012,10,6,11,28,4,23,1026,0.89,0,0 +2012,10,6,12,36,4,23,1025,1.79,0,0 +2012,10,6,13,28,3,24,1024,1.79,0,0 +2012,10,6,14,29,1,25,1024,2.68,0,0 +2012,10,6,15,28,0,25,1023,3.13,0,0 +2012,10,6,16,20,-1,24,1023,4.92,0,0 +2012,10,6,17,37,2,23,1023,6.71,0,0 +2012,10,6,18,80,7,18,1022,7.6,0,0 +2012,10,6,19,109,8,20,1023,10.73,0,0 +2012,10,6,20,110,9,20,1024,13.86,0,0 +2012,10,6,21,114,11,14,1024,14.75,0,0 +2012,10,6,22,131,11,14,1024,15.64,0,0 +2012,10,6,23,138,11,13,1024,0.89,0,0 +2012,10,7,0,145,11,12,1024,0.89,0,0 +2012,10,7,1,151,11,12,1025,1.78,0,0 +2012,10,7,2,144,10,11,1025,2.67,0,0 +2012,10,7,3,130,10,11,1024,0.45,0,0 +2012,10,7,4,129,9,10,1024,0.89,0,0 +2012,10,7,5,136,9,10,1024,2.68,0,0 +2012,10,7,6,95,8,9,1024,5.81,0,0 +2012,10,7,7,89,9,10,1025,8.94,0,0 +2012,10,7,8,67,10,13,1025,10.73,0,0 +2012,10,7,9,76,11,16,1025,12.52,0,0 +2012,10,7,10,85,11,17,1025,0.89,0,0 +2012,10,7,11,89,12,19,1024,1.78,0,0 +2012,10,7,12,110,12,21,1023,1.79,0,0 +2012,10,7,13,137,13,23,1022,0.89,0,0 +2012,10,7,14,167,11,23,1021,3.13,0,0 +2012,10,7,15,147,12,24,1021,1.79,0,0 +2012,10,7,16,108,12,23,1021,1.79,0,0 +2012,10,7,17,104,11,21,1020,2.68,0,0 +2012,10,7,18,112,11,20,1020,4.47,0,0 +2012,10,7,19,122,12,19,1021,0.89,0,0 +2012,10,7,20,149,13,18,1021,0.89,0,0 +2012,10,7,21,170,13,16,1021,1.78,0,0 +2012,10,7,22,176,13,15,1021,0.89,0,0 +2012,10,7,23,189,12,14,1021,0.89,0,0 +2012,10,8,0,189,13,14,1021,1.79,0,0 +2012,10,8,1,194,12,14,1021,1.79,0,0 +2012,10,8,2,207,11,12,1021,2.68,0,0 +2012,10,8,3,220,11,11,1020,0.89,0,0 +2012,10,8,4,259,11,11,1020,1.78,0,0 +2012,10,8,5,262,10,10,1020,1.79,0,0 +2012,10,8,6,235,11,11,1021,2.68,0,0 +2012,10,8,7,215,11,11,1021,0.45,0,0 +2012,10,8,8,206,13,13,1021,1.34,0,0 +2012,10,8,9,206,13,16,1021,2.23,0,0 +2012,10,8,10,203,13,18,1021,3.12,0,0 +2012,10,8,11,209,13,19,1020,4.01,0,0 +2012,10,8,12,220,13,21,1019,4.9,0,0 +2012,10,8,13,237,14,22,1018,1.79,0,0 +2012,10,8,14,264,13,22,1017,3.58,0,0 +2012,10,8,15,263,13,23,1016,5.37,0,0 +2012,10,8,16,227,12,23,1016,9.39,0,0 +2012,10,8,17,235,13,22,1016,11.18,0,0 +2012,10,8,18,276,13,20,1016,12.97,0,0 +2012,10,8,19,308,14,17,1016,13.86,0,0 +2012,10,8,20,338,14,16,1016,0.89,0,0 +2012,10,8,21,343,14,16,1016,1.78,0,0 +2012,10,8,22,347,14,16,1016,0.89,0,0 +2012,10,8,23,398,14,16,1015,1.79,0,0 +2012,10,9,0,446,14,17,1015,2.68,0,0 +2012,10,9,1,436,14,17,1014,5.81,0,0 +2012,10,9,2,407,14,17,1014,0.89,0,0 +2012,10,9,3,390,15,17,1014,4.02,0,1 +2012,10,9,4,331,14,17,1014,11.17,0,2 +2012,10,9,5,239,14,15,1013,0.89,0,3 +2012,10,9,6,161,14,15,1013,1.78,0,0 +2012,10,9,7,145,14,15,1013,2.67,0,0 +2012,10,9,8,160,14,17,1013,3.13,0,0 +2012,10,9,9,128,14,19,1013,7.15,0,0 +2012,10,9,10,25,10,21,1013,12.96,0,0 +2012,10,9,11,14,6,22,1013,20.11,0,0 +2012,10,9,12,9,4,22,1013,31.29,0,0 +2012,10,9,13,11,2,23,1013,43.36,0,0 +2012,10,9,14,11,2,23,1013,53.19,0,0 +2012,10,9,15,9,0,21,1013,65.26,0,0 +2012,10,9,16,11,-3,21,1014,76.44,0,0 +2012,10,9,17,9,-3,20,1015,89.4,0,0 +2012,10,9,18,6,-3,18,1016,99.23,0,0 +2012,10,9,19,10,-1,18,1017,110.41,0,0 +2012,10,9,20,10,0,17,1018,118.46,0,0 +2012,10,9,21,8,1,16,1019,125.61,0,0 +2012,10,9,22,8,0,15,1020,132.76,0,0 +2012,10,9,23,5,0,15,1020,138.57,0,0 +2012,10,10,0,2,-1,15,1020,144.38,0,0 +2012,10,10,1,9,-1,14,1021,149.3,0,0 +2012,10,10,2,7,0,13,1021,154.22,0,0 +2012,10,10,3,9,0,13,1021,159.14,0,0 +2012,10,10,4,7,-1,12,1021,0.89,0,0 +2012,10,10,5,12,2,8,1022,1.78,0,0 +2012,10,10,6,12,2,7,1022,2.67,0,0 +2012,10,10,7,9,4,9,1022,3.12,0,0 +2012,10,10,8,26,2,14,1023,1.79,0,0 +2012,10,10,9,20,2,15,1023,3.58,0,0 +2012,10,10,10,12,0,18,1023,5.37,0,0 +2012,10,10,11,14,-1,19,1022,8.5,0,0 +2012,10,10,12,10,-3,21,1021,10.29,0,0 +2012,10,10,13,8,-4,21,1020,15.21,0,0 +2012,10,10,14,5,-5,21,1018,20.13,0,0 +2012,10,10,15,5,-6,21,1018,25.05,0,0 +2012,10,10,16,5,-6,21,1018,29.97,0,0 +2012,10,10,17,10,-6,20,1017,33.99,0,0 +2012,10,10,18,15,1,16,1018,0.89,0,0 +2012,10,10,19,19,-1,18,1018,4.02,0,0 +2012,10,10,20,26,-1,17,1018,7.15,0,0 +2012,10,10,21,43,-1,15,1018,11.17,0,0 +2012,10,10,22,51,-2,16,1019,14.3,0,0 +2012,10,10,23,63,5,9,1019,0.89,0,0 +2012,10,11,0,80,6,9,1019,0.89,0,0 +2012,10,11,1,111,5,8,1019,1.78,0,0 +2012,10,11,2,134,5,7,1018,0.89,0,0 +2012,10,11,3,133,5,7,1018,1.78,0,0 +2012,10,11,4,103,4,5,1018,2.67,0,0 +2012,10,11,5,98,4,5,1018,0.89,0,0 +2012,10,11,6,84,4,5,1018,1.79,0,0 +2012,10,11,7,73,5,6,1019,2.68,0,0 +2012,10,11,8,82,6,9,1019,0.89,0,0 +2012,10,11,9,90,6,11,1019,1.79,0,0 +2012,10,11,10,114,6,14,1018,3.58,0,0 +2012,10,11,11,194,5,16,1017,6.71,0,0 +2012,10,11,12,185,4,19,1016,9.84,0,0 +2012,10,11,13,139,1,21,1015,16.99,0,0 +2012,10,11,14,130,1,22,1013,24.14,0,0 +2012,10,11,15,131,2,22,1013,28.16,0,0 +2012,10,11,16,113,2,21,1013,32.18,0,0 +2012,10,11,17,121,2,19,1013,33.97,0,0 +2012,10,11,18,118,7,15,1013,34.86,0,0 +2012,10,11,19,131,8,14,1013,0.89,0,0 +2012,10,11,20,158,8,13,1013,1.78,0,0 +2012,10,11,21,168,7,13,1013,0.89,0,0 +2012,10,11,22,176,7,12,1013,0.89,0,0 +2012,10,11,23,191,6,11,1014,1.79,0,0 +2012,10,12,0,177,6,11,1014,0.89,0,0 +2012,10,12,1,146,6,11,1014,1.78,0,0 +2012,10,12,2,143,6,11,1014,2.67,0,0 +2012,10,12,3,134,6,11,1014,3.56,0,0 +2012,10,12,4,177,6,11,1014,4.45,0,0 +2012,10,12,5,182,6,11,1014,5.34,0,0 +2012,10,12,6,173,6,11,1014,6.23,0,0 +2012,10,12,7,145,6,11,1014,7.12,0,0 +2012,10,12,8,125,6,11,1014,8.01,0,0 +2012,10,12,9,123,6,11,1014,8.9,0,0 +2012,10,12,10,146,6,11,1014,9.79,0,0 +2012,10,12,11,152,6,11,1014,10.68,0,0 +2012,10,12,12,137,6,11,1014,11.57,0,0 +2012,10,12,13,132,6,11,1014,12.46,0,0 +2012,10,12,14,143,6,11,1014,13.35,0,0 +2012,10,12,15,143,6,11,1014,14.24,0,0 +2012,10,12,16,149,6,11,1014,15.13,0,0 +2012,10,12,17,156,6,11,1014,16.02,0,0 +2012,10,12,18,172,6,11,1014,16.91,0,0 +2012,10,12,19,194,6,11,1014,17.8,0,0 +2012,10,12,20,214,6,11,1014,18.69,0,0 +2012,10,12,21,243,6,11,1014,19.58,0,0 +2012,10,12,22,248,6,11,1014,20.47,0,0 +2012,10,12,23,269,6,11,1014,21.36,0,0 +2012,10,13,0,256,6,11,1014,22.25,0,0 +2012,10,13,1,220,6,11,1014,23.14,0,0 +2012,10,13,2,167,6,11,1014,24.03,0,0 +2012,10,13,3,30,6,11,1014,24.92,0,0 +2012,10,13,4,10,6,11,1014,25.81,0,0 +2012,10,13,5,7,6,11,1014,26.7,0,0 +2012,10,13,6,8,6,11,1014,27.59,0,0 +2012,10,13,7,9,6,11,1014,28.48,0,0 +2012,10,13,8,10,6,11,1014,29.37,0,0 +2012,10,13,9,9,6,11,1014,30.26,0,0 +2012,10,13,10,10,6,11,1014,31.15,0,0 +2012,10,13,11,8,6,11,1014,32.04,0,0 +2012,10,13,12,11,6,11,1014,32.93,0,0 +2012,10,13,13,8,6,11,1014,33.82,0,0 +2012,10,13,14,6,6,11,1014,34.71,0,0 +2012,10,13,15,17,6,11,1014,35.6,0,0 +2012,10,13,16,11,6,11,1014,36.49,0,0 +2012,10,13,17,9,6,11,1014,37.38,0,0 +2012,10,13,18,11,6,11,1014,38.27,0,0 +2012,10,13,19,13,6,11,1014,39.16,0,0 +2012,10,13,20,51,6,11,1014,40.05,0,0 +2012,10,13,21,59,6,11,1014,40.94,0,0 +2012,10,13,22,55,6,11,1014,41.83,0,0 +2012,10,13,23,45,7,11,1017,42.72,0,0 +2012,10,14,0,58,7,14,1020,43.61,0,0 +2012,10,14,1,62,7,14,1020,44.5,0,0 +2012,10,14,2,41,7,14,1020,45.39,0,0 +2012,10,14,3,35,7,14,1020,46.28,0,0 +2012,10,14,4,29,7,14,1020,47.17,0,0 +2012,10,14,5,21,7,14,1020,48.06,0,0 +2012,10,14,6,24,7,14,1020,48.95,0,0 +2012,10,14,7,21,7,14,1020,49.84,0,0 +2012,10,14,8,9,7,14,1020,50.73,0,0 +2012,10,14,9,11,7,14,1020,51.62,0,0 +2012,10,14,10,18,7,14,1020,52.51,0,0 +2012,10,14,11,14,7,14,1020,53.4,0,0 +2012,10,14,12,13,7,14,1020,54.29,0,0 +2012,10,14,13,22,7,14,1020,55.18,0,0 +2012,10,14,14,34,7,14,1020,56.07,0,0 +2012,10,14,15,33,7,14,1020,56.96,0,0 +2012,10,14,16,29,7,14,1020,57.85,0,0 +2012,10,14,17,32,7,14,1020,58.74,0,0 +2012,10,14,18,34,7,14,1020,59.63,0,0 +2012,10,14,19,56,7,14,1020,60.52,0,0 +2012,10,14,20,77,7,14,1020,61.41,0,0 +2012,10,14,21,93,7,14,1020,62.3,0,0 +2012,10,14,22,105,7,14,1020,63.19,0,0 +2012,10,14,23,109,7,14,1020,64.08,0,0 +2012,10,15,0,123,7,14,1020,64.97,0,0 +2012,10,15,1,122,7,14,1020,65.86,0,0 +2012,10,15,2,131,7,14,1020,66.75,0,0 +2012,10,15,3,133,7,14,1020,67.64,0,0 +2012,10,15,4,123,7,14,1020,68.53,0,0 +2012,10,15,5,99,7,14,1020,69.42,0,0 +2012,10,15,6,98,7,14,1020,70.31,0,0 +2012,10,15,7,82,7,14,1020,71.2,0,0 +2012,10,15,8,79,7,14,1020,72.09,0,0 +2012,10,15,9,77,7,14,1020,72.98,0,0 +2012,10,15,10,80,7,14,1020,73.87,0,0 +2012,10,15,11,87,7,14,1020,74.76,0,0 +2012,10,15,12,88,7,14,1020,75.65,0,0 +2012,10,15,13,98,7,14,1020,76.54,0,0 +2012,10,15,14,97,7,14,1020,77.43,0,0 +2012,10,15,15,102,7,14,1020,78.32,0,0 +2012,10,15,16,103,7,14,1020,79.21,0,0 +2012,10,15,17,101,7,14,1020,80.1,0,0 +2012,10,15,18,99,7,14,1020,80.99,0,0 +2012,10,15,19,116,7,14,1020,81.88,0,0 +2012,10,15,20,113,7,14,1020,82.77,0,0 +2012,10,15,21,113,7,14,1020,83.66,0,0 +2012,10,15,22,111,7,14,1020,84.55,0,0 +2012,10,15,23,99,7,13,1020,85.44,0,0 +2012,10,16,0,96,9,12,1019,1.79,0,1 +2012,10,16,1,95,10,12,1019,0.89,0,2 +2012,10,16,2,91,11,12,1018,1.79,0,0 +2012,10,16,3,94,11,12,1017,0.89,0,1 +2012,10,16,4,88,11,13,1016,1.34,0,2 +2012,10,16,5,89,12,12,1016,2.23,0,3 +2012,10,16,6,83,11,12,1016,1.79,0,4 +2012,10,16,7,71,12,12,1016,2.68,0,5 +2012,10,16,8,77,12,13,1017,7.6,0,6 +2012,10,16,9,58,10,13,1018,13.41,0,7 +2012,10,16,10,19,8,12,1018,19.22,0,8 +2012,10,16,11,14,7,12,1017,24.14,0,9 +2012,10,16,12,NA,4,15,1017,32.19,0,0 +2012,10,16,13,25,1,17,1016,40.24,0,0 +2012,10,16,14,12,-1,17,1016,51.42,0,0 +2012,10,16,15,7,-2,17,1016,59.47,0,0 +2012,10,16,16,7,-3,17,1017,66.62,0,0 +2012,10,16,17,7,-7,15,1019,77.8,0,0 +2012,10,16,18,9,-7,13,1020,85.85,0,0 +2012,10,16,19,11,-7,12,1022,93.9,0,0 +2012,10,16,20,14,-7,11,1023,99.71,0,0 +2012,10,16,21,11,-5,7,1024,103.73,0,0 +2012,10,16,22,8,-5,7,1024,108.65,0,0 +2012,10,16,23,6,-6,7,1024,113.57,0,0 +2012,10,17,0,7,-6,7,1024,117.59,0,0 +2012,10,17,1,8,-4,6,1024,0.89,0,0 +2012,10,17,2,6,-2,4,1024,1.78,0,0 +2012,10,17,3,4,0,2,1023,2.67,0,0 +2012,10,17,4,5,0,2,1022,3.56,0,0 +2012,10,17,5,6,0,2,1022,4.45,0,0 +2012,10,17,6,13,0,1,1021,5.34,0,0 +2012,10,17,7,15,-1,2,1021,6.23,0,0 +2012,10,17,8,17,-3,9,1022,1.79,0,0 +2012,10,17,9,22,-5,12,1021,4.92,0,0 +2012,10,17,10,23,-9,14,1021,4.92,0,0 +2012,10,17,11,16,-10,16,1020,7.15,0,0 +2012,10,17,12,16,-12,17,1019,16.98,0,0 +2012,10,17,13,17,-11,17,1018,28.16,0,0 +2012,10,17,14,11,-12,18,1017,36.21,0,0 +2012,10,17,15,14,-12,18,1017,45.15,0,0 +2012,10,17,16,13,-11,18,1017,54.09,0,0 +2012,10,17,17,19,-8,17,1016,4.02,0,0 +2012,10,17,18,29,-7,15,1016,8.94,0,0 +2012,10,17,19,45,-7,15,1017,12.96,0,0 +2012,10,17,20,55,-6,15,1017,16.98,0,0 +2012,10,17,21,55,-5,13,1017,20.11,0,0 +2012,10,17,22,59,1,6,1017,0.89,0,0 +2012,10,17,23,70,2,6,1017,1.78,0,0 +2012,10,18,0,82,2,4,1017,2.67,0,0 +2012,10,18,1,85,2,4,1017,3.56,0,0 +2012,10,18,2,90,2,3,1017,1.79,0,0 +2012,10,18,3,82,2,3,1017,0.45,0,0 +2012,10,18,4,90,2,3,1017,0.9,0,0 +2012,10,18,5,90,1,2,1017,1.79,0,0 +2012,10,18,6,71,1,2,1018,2.68,0,0 +2012,10,18,7,56,2,4,1019,4.47,0,0 +2012,10,18,8,34,4,7,1019,8.49,0,0 +2012,10,18,9,38,2,11,1019,11.62,0,0 +2012,10,18,10,47,0,14,1019,3.13,0,0 +2012,10,18,11,46,-1,18,1019,1.79,0,0 +2012,10,18,12,39,-3,20,1018,4.92,0,0 +2012,10,18,13,27,-7,20,1017,8.94,0,0 +2012,10,18,14,17,-6,20,1016,1.79,0,0 +2012,10,18,15,16,-6,20,1016,0.89,0,0 +2012,10,18,16,18,-2,18,1016,1.79,0,0 +2012,10,18,17,27,1,16,1016,2.68,0,0 +2012,10,18,18,37,-2,16,1017,5.81,0,0 +2012,10,18,19,48,1,14,1017,7.6,0,0 +2012,10,18,20,75,5,11,1018,0.89,0,0 +2012,10,18,21,102,7,10,1018,0.89,0,0 +2012,10,18,22,110,7,9,1018,1.78,0,0 +2012,10,18,23,115,5,8,1018,1.79,0,0 +2012,10,19,0,142,4,7,1018,0.89,0,0 +2012,10,19,1,130,5,7,1019,0.45,0,0 +2012,10,19,2,129,5,6,1018,0.89,0,0 +2012,10,19,3,87,4,5,1018,2.68,0,0 +2012,10,19,4,78,4,4,1018,3.57,0,0 +2012,10,19,5,68,4,4,1019,4.46,0,0 +2012,10,19,6,47,4,4,1019,5.35,0,0 +2012,10,19,7,60,4,4,1019,0.89,0,0 +2012,10,19,8,65,5,6,1019,1.79,0,0 +2012,10,19,9,88,6,10,1019,3.58,0,0 +2012,10,19,10,119,5,13,1019,0.89,0,0 +2012,10,19,11,156,5,15,1018,1.79,0,0 +2012,10,19,12,189,4,17,1017,0.89,0,0 +2012,10,19,13,185,4,18,1016,1.79,0,0 +2012,10,19,14,187,4,19,1015,0.89,0,0 +2012,10,19,15,209,2,19,1015,3.13,0,0 +2012,10,19,16,206,4,18,1014,4.92,0,0 +2012,10,19,17,219,6,16,1014,5.81,0,0 +2012,10,19,18,230,8,12,1015,0.45,0,0 +2012,10,19,19,271,8,11,1015,1.34,0,0 +2012,10,19,20,277,8,9,1016,0.89,0,0 +2012,10,19,21,303,6,8,1016,0.89,0,0 +2012,10,19,22,334,7,8,1016,0.89,0,0 +2012,10,19,23,285,7,7,1016,0.89,0,0 +2012,10,20,0,324,7,7,1016,0.89,0,0 +2012,10,20,1,308,6,7,1016,0.89,0,0 +2012,10,20,2,285,6,7,1016,2.68,0,0 +2012,10,20,3,245,5,5,1016,4.47,0,0 +2012,10,20,4,233,4,5,1016,6.26,0,0 +2012,10,20,5,214,4,5,1016,8.05,0,0 +2012,10,20,6,213,5,6,1016,9.84,0,0 +2012,10,20,7,222,5,6,1017,12.97,0,0 +2012,10,20,8,219,7,8,1017,16.1,0,0 +2012,10,20,9,215,8,12,1018,17.89,0,0 +2012,10,20,10,215,7,15,1018,21.02,0,0 +2012,10,20,11,214,7,18,1018,1.79,0,0 +2012,10,20,12,220,4,20,1017,3.58,0,0 +2012,10,20,13,176,4,21,1017,1.79,0,0 +2012,10,20,14,117,3,22,1016,4.92,0,0 +2012,10,20,15,154,5,22,1016,6.71,0,0 +2012,10,20,16,NA,4,21,1017,8.5,0,0 +2012,10,20,17,199,6,18,1017,10.29,0,0 +2012,10,20,18,273,8,14,1018,0.45,0,0 +2012,10,20,19,341,9,13,1020,1.79,0,0 +2012,10,20,20,311,10,15,1021,6.71,0,0 +2012,10,20,21,151,8,14,1021,11.63,0,0 +2012,10,20,22,56,7,13,1023,14.76,0,0 +2012,10,20,23,42,7,11,1023,0.45,0,0 +2012,10,21,0,40,8,11,1023,0.89,0,0 +2012,10,21,1,43,6,12,1023,1.79,0,0 +2012,10,21,2,48,5,12,1023,3.58,0,0 +2012,10,21,3,60,5,12,1023,0.89,0,0 +2012,10,21,4,66,6,12,1023,1.78,0,0 +2012,10,21,5,55,6,12,1023,2.23,0,0 +2012,10,21,6,53,5,12,1023,1.79,0,0 +2012,10,21,7,48,6,12,1023,0.89,0,0 +2012,10,21,8,56,4,12,1023,1.34,0,0 +2012,10,21,9,53,5,12,1023,1.79,0,0 +2012,10,21,10,54,5,12,1023,1.79,0,0 +2012,10,21,11,56,5,12,1022,3.13,0,1 +2012,10,21,12,57,8,11,1021,6.26,0,2 +2012,10,21,13,56,7,11,1020,10.28,0,3 +2012,10,21,14,49,8,10,1019,13.41,0,4 +2012,10,21,15,49,9,10,1018,18.33,0,5 +2012,10,21,16,44,9,9,1018,22.35,0,6 +2012,10,21,17,43,9,10,1019,28.16,0,7 +2012,10,21,18,37,9,10,1019,3.13,0,8 +2012,10,21,19,40,9,9,1019,3.13,0,9 +2012,10,21,20,38,9,9,1019,6.26,0,10 +2012,10,21,21,37,9,10,1018,3.13,0,0 +2012,10,21,22,39,8,9,1018,1.79,0,0 +2012,10,21,23,41,8,9,1018,3.58,0,0 +2012,10,22,0,41,8,9,1018,6.71,0,0 +2012,10,22,1,43,8,9,1017,0.89,0,0 +2012,10,22,2,36,6,6,1017,1.34,0,0 +2012,10,22,3,33,6,6,1016,0.89,0,0 +2012,10,22,4,33,3,11,1016,4.92,0,0 +2012,10,22,5,39,4,10,1017,8.94,0,0 +2012,10,22,6,16,3,10,1017,13.86,0,0 +2012,10,22,7,12,3,10,1016,16.99,0,0 +2012,10,22,8,11,2,12,1017,0.89,0,0 +2012,10,22,9,14,4,14,1017,1.79,0,0 +2012,10,22,10,10,2,15,1018,10.73,0,0 +2012,10,22,11,8,-1,16,1018,22.8,0,0 +2012,10,22,12,14,-2,17,1018,34.87,0,0 +2012,10,22,13,9,-2,18,1018,46.05,0,0 +2012,10,22,14,11,-3,18,1018,57.23,0,0 +2012,10,22,15,15,-3,18,1019,68.41,0,0 +2012,10,22,16,10,-3,17,1019,76.46,0,0 +2012,10,22,17,11,-2,16,1019,81.38,0,0 +2012,10,22,18,15,-2,14,1019,85.4,0,0 +2012,10,22,19,32,3,9,1020,0.89,0,0 +2012,10,22,20,49,4,7,1019,0.89,0,0 +2012,10,22,21,69,5,7,1019,0.89,0,0 +2012,10,22,22,58,4,6,1019,1.78,0,0 +2012,10,22,23,42,4,7,1019,1.79,0,0 +2012,10,23,0,36,4,5,1019,0.89,0,0 +2012,10,23,1,34,4,5,1019,1.78,0,0 +2012,10,23,2,48,4,4,1018,2.23,0,0 +2012,10,23,3,71,2,3,1018,3.12,0,0 +2012,10,23,4,87,3,4,1018,4.01,0,0 +2012,10,23,5,73,3,3,1017,0.89,0,0 +2012,10,23,6,54,2,3,1017,2.68,0,0 +2012,10,23,7,33,3,3,1017,1.79,0,0 +2012,10,23,8,34,5,6,1018,0.89,0,0 +2012,10,23,9,46,5,10,1017,0.89,0,0 +2012,10,23,10,50,4,13,1017,1.78,0,0 +2012,10,23,11,49,1,17,1016,2.67,0,0 +2012,10,23,12,56,0,18,1015,4.46,0,0 +2012,10,23,13,59,0,19,1014,6.25,0,0 +2012,10,23,14,60,-1,20,1013,3.13,0,0 +2012,10,23,15,64,0,20,1013,7.15,0,0 +2012,10,23,16,69,1,19,1012,10.28,0,0 +2012,10,23,17,41,2,17,1012,1.79,0,0 +2012,10,23,18,57,7,13,1013,0.89,0,0 +2012,10,23,19,83,7,11,1013,0.89,0,0 +2012,10,23,20,125,7,9,1013,0.89,0,0 +2012,10,23,21,162,6,8,1013,1.78,0,0 +2012,10,23,22,167,7,8,1013,2.67,0,0 +2012,10,23,23,185,7,7,1013,1.79,0,0 +2012,10,24,0,177,6,7,1013,0.89,0,0 +2012,10,24,1,154,6,7,1013,1.34,0,0 +2012,10,24,2,154,5,6,1013,1.79,0,0 +2012,10,24,3,149,5,6,1012,0.45,0,0 +2012,10,24,4,132,5,5,1012,1.79,0,0 +2012,10,24,5,129,5,5,1012,3.58,0,0 +2012,10,24,6,108,4,5,1012,0.89,0,0 +2012,10,24,7,123,4,4,1012,1.78,0,0 +2012,10,24,8,119,7,8,1012,1.79,0,0 +2012,10,24,9,88,7,12,1012,3.58,0,0 +2012,10,24,10,77,7,16,1012,0.89,0,0 +2012,10,24,11,82,4,18,1012,1.78,0,0 +2012,10,24,12,78,2,22,1011,4.91,0,0 +2012,10,24,13,52,-5,24,1010,8.05,0,0 +2012,10,24,14,42,-5,25,1009,13.86,0,0 +2012,10,24,15,24,-5,24,1010,21.01,0,0 +2012,10,24,16,31,-5,24,1009,28.16,0,0 +2012,10,24,17,94,-3,22,1010,33.08,0,0 +2012,10,24,18,116,3,20,1011,4.92,0,0 +2012,10,24,19,120,4,19,1011,8.94,0,0 +2012,10,24,20,137,6,15,1012,0.89,0,0 +2012,10,24,21,129,8,11,1013,1.78,0,0 +2012,10,24,22,122,7,9,1014,0.89,0,0 +2012,10,24,23,174,8,10,1014,0.89,0,0 +2012,10,25,0,174,6,7,1014,1.79,0,0 +2012,10,25,1,178,7,8,1015,0.89,0,0 +2012,10,25,2,202,7,7,1015,1.78,0,0 +2012,10,25,3,191,6,6,1015,3.13,0,0 +2012,10,25,4,89,5,7,1015,4.92,0,0 +2012,10,25,5,42,5,6,1015,6.71,0,0 +2012,10,25,6,35,5,6,1016,0.89,0,0 +2012,10,25,7,50,5,6,1017,1.78,0,0 +2012,10,25,8,55,6,9,1017,3.13,0,0 +2012,10,25,9,33,7,13,1018,0.89,0,0 +2012,10,25,10,67,6,16,1018,1.78,0,0 +2012,10,25,11,131,7,17,1018,1.79,0,0 +2012,10,25,12,164,7,19,1018,3.58,0,0 +2012,10,25,13,230,7,19,1017,5.37,0,0 +2012,10,25,14,275,8,20,1016,7.16,0,0 +2012,10,25,15,236,8,20,1016,8.95,0,0 +2012,10,25,16,220,9,20,1016,9.84,0,0 +2012,10,25,17,252,11,15,1016,0.89,0,0 +2012,10,25,18,331,11,13,1017,1.34,0,0 +2012,10,25,19,338,11,12,1017,2.23,0,0 +2012,10,25,20,338,10,11,1018,2.68,0,0 +2012,10,25,21,367,9,10,1018,0.89,0,0 +2012,10,25,22,301,10,13,1019,2.68,0,0 +2012,10,25,23,293,10,12,1019,0.89,0,0 +2012,10,26,0,293,11,12,1019,1.78,0,0 +2012,10,26,1,249,12,13,1019,2.67,0,0 +2012,10,26,2,226,12,12,1019,3.56,0,0 +2012,10,26,3,235,12,12,1019,1.79,0,0 +2012,10,26,4,237,12,12,1019,0.89,0,0 +2012,10,26,5,259,12,12,1020,1.79,0,0 +2012,10,26,6,248,12,12,1020,0.89,0,0 +2012,10,26,7,240,12,12,1020,1.78,0,0 +2012,10,26,8,253,12,12,1020,3.13,0,0 +2012,10,26,9,271,12,12,1021,4.92,0,0 +2012,10,26,10,283,11,12,1020,6.71,0,1 +2012,10,26,11,283,12,12,1020,1.79,0,0 +2012,10,26,12,288,12,13,1019,0.89,0,0 +2012,10,26,13,288,11,13,1018,1.79,0,0 +2012,10,26,14,293,12,14,1017,0.89,0,0 +2012,10,26,15,293,12,15,1017,1.78,0,0 +2012,10,26,16,296,11,15,1017,2.67,0,0 +2012,10,26,17,308,12,14,1017,3.12,0,0 +2012,10,26,18,317,11,13,1017,0.89,0,0 +2012,10,26,19,329,12,13,1017,2.68,0,0 +2012,10,26,20,313,12,14,1017,5.81,0,0 +2012,10,26,21,307,12,14,1017,7.6,0,0 +2012,10,26,22,309,12,15,1017,9.39,0,0 +2012,10,26,23,299,12,14,1017,12.52,0,1 +2012,10,27,0,304,12,14,1016,15.65,0,0 +2012,10,27,1,297,11,13,1016,17.44,0,0 +2012,10,27,2,281,11,13,1015,0.89,0,0 +2012,10,27,3,272,11,12,1015,0.89,0,0 +2012,10,27,4,275,10,11,1015,0.45,0,0 +2012,10,27,5,260,8,9,1015,0.89,0,0 +2012,10,27,6,246,9,9,1016,2.68,0,0 +2012,10,27,7,241,8,9,1016,5.81,0,0 +2012,10,27,8,239,10,10,1016,0.89,0,0 +2012,10,27,9,250,12,12,1017,0.45,0,0 +2012,10,27,10,248,10,16,1016,2.24,0,0 +2012,10,27,11,222,9,18,1016,1.79,0,0 +2012,10,27,12,232,6,21,1015,6.71,0,0 +2012,10,27,13,166,2,22,1015,14.76,0,0 +2012,10,27,14,105,2,22,1014,21.91,0,0 +2012,10,27,15,34,0,22,1015,31.74,0,0 +2012,10,27,16,27,-2,22,1016,40.68,0,0 +2012,10,27,17,20,-3,20,1017,45.6,0,0 +2012,10,27,18,15,-3,19,1018,49.62,0,0 +2012,10,27,19,20,-5,17,1019,53.64,0,0 +2012,10,27,20,19,-10,16,1020,60.79,0,0 +2012,10,27,21,6,-11,15,1022,67.94,0,0 +2012,10,27,22,12,-9,13,1023,75.09,0,0 +2012,10,27,23,11,-7,11,1024,76.88,0,0 +2012,10,28,0,11,-5,9,1024,80.01,0,0 +2012,10,28,1,7,-3,9,1025,84.93,0,0 +2012,10,28,2,13,-3,8,1025,88.06,0,0 +2012,10,28,3,7,-2,6,1026,91.19,0,0 +2012,10,28,4,11,-3,9,1026,4.02,0,0 +2012,10,28,5,13,-3,8,1027,8.04,0,0 +2012,10,28,6,13,-2,7,1027,11.17,0,0 +2012,10,28,7,13,-1,8,1028,12.96,0,0 +2012,10,28,8,20,-2,10,1029,16.98,0,0 +2012,10,28,9,18,-4,12,1029,21,0,0 +2012,10,28,10,9,-4,13,1029,25.02,0,0 +2012,10,28,11,8,-4,14,1028,1.79,0,0 +2012,10,28,12,11,-6,15,1027,1.79,0,0 +2012,10,28,13,16,-5,15,1026,3.13,0,0 +2012,10,28,14,23,-4,15,1025,7.15,0,0 +2012,10,28,15,27,-4,15,1025,11.17,0,0 +2012,10,28,16,26,-3,14,1024,15.19,0,0 +2012,10,28,17,38,-1,12,1024,18.32,0,0 +2012,10,28,18,52,-1,11,1024,21.45,0,0 +2012,10,28,19,57,1,11,1025,25.47,0,0 +2012,10,28,20,54,3,11,1025,29.49,0,0 +2012,10,28,21,57,4,10,1025,32.62,0,0 +2012,10,28,22,46,5,10,1025,34.41,0,0 +2012,10,28,23,41,5,10,1025,0.89,0,0 +2012,10,29,0,40,5,9,1025,0.89,0,0 +2012,10,29,1,39,5,9,1025,1.79,0,0 +2012,10,29,2,41,4,9,1024,3.58,0,0 +2012,10,29,3,42,4,9,1024,0.89,0,0 +2012,10,29,4,43,4,9,1024,1.78,0,0 +2012,10,29,5,48,4,9,1023,1.79,0,0 +2012,10,29,6,53,4,9,1023,4.92,0,0 +2012,10,29,7,55,4,10,1023,6.71,0,0 +2012,10,29,8,58,4,10,1024,0.89,0,0 +2012,10,29,9,58,4,10,1024,1.78,0,0 +2012,10,29,10,68,3,11,1024,2.67,0,0 +2012,10,29,11,80,4,12,1023,1.79,0,0 +2012,10,29,12,67,5,14,1022,0.89,0,0 +2012,10,29,13,65,5,15,1022,1.78,0,0 +2012,10,29,14,66,5,15,1021,2.67,0,0 +2012,10,29,15,53,-6,16,1021,8.05,0,0 +2012,10,29,16,9,4,15,1022,1.79,0,0 +2012,10,29,17,10,4,13,1023,3.13,0,0 +2012,10,29,18,11,-4,11,1025,7.15,0,0 +2012,10,29,19,11,-7,10,1027,12.07,0,0 +2012,10,29,20,10,-6,7,1028,16.09,0,0 +2012,10,29,21,9,-7,7,1029,17.88,0,0 +2012,10,29,22,8,-9,9,1029,25.03,0,0 +2012,10,29,23,9,-11,9,1029,30.84,0,0 +2012,10,30,0,8,-14,9,1029,38.89,0,0 +2012,10,30,1,11,-14,7,1029,43.81,0,0 +2012,10,30,2,11,-13,6,1029,47.83,0,0 +2012,10,30,3,10,-10,2,1028,51.85,0,0 +2012,10,30,4,12,-4,1,1027,0.89,0,0 +2012,10,30,5,16,-6,1,1027,1.78,0,0 +2012,10,30,6,17,-4,1,1027,2.23,0,0 +2012,10,30,7,25,-4,0,1026,3.12,0,0 +2012,10,30,8,29,-3,5,1027,1.79,0,0 +2012,10,30,9,27,-7,8,1026,0.89,0,0 +2012,10,30,10,24,-11,11,1025,4.92,0,0 +2012,10,30,11,NA,-12,11,1024,10.73,0,0 +2012,10,30,12,15,-12,12,1022,16.54,0,0 +2012,10,30,13,49,-8,12,1020,21.46,0,0 +2012,10,30,14,37,-8,12,1020,27.27,0,0 +2012,10,30,15,48,-9,13,1018,34.42,0,0 +2012,10,30,16,40,-8,12,1018,40.23,0,0 +2012,10,30,17,48,-5,11,1018,45.15,0,0 +2012,10,30,18,62,-4,11,1018,49.17,0,0 +2012,10,30,19,68,-4,11,1019,4.02,0,0 +2012,10,30,20,88,-8,11,1020,9.83,0,0 +2012,10,30,21,57,-3,6,1021,13.85,0,0 +2012,10,30,22,10,-13,10,1022,21.9,0,0 +2012,10,30,23,8,-12,10,1022,27.71,0,0 +2012,10,31,0,7,-12,9,1022,33.52,0,0 +2012,10,31,1,5,-12,9,1022,41.57,0,0 +2012,10,31,2,6,-8,5,1023,0.89,0,0 +2012,10,31,3,10,-4,2,1022,1.78,0,0 +2012,10,31,4,13,-7,4,1022,1.79,0,0 +2012,10,31,5,11,-4,1,1022,0.89,0,0 +2012,10,31,6,16,-5,2,1022,0.89,0,0 +2012,10,31,7,28,-2,1,1023,1.78,0,0 +2012,10,31,8,34,1,4,1023,0.89,0,0 +2012,10,31,9,15,-7,10,1023,2.68,0,0 +2012,10,31,10,16,-9,11,1023,6.7,0,0 +2012,10,31,11,11,-11,12,1022,4.02,0,0 +2012,10,31,12,10,-12,13,1020,4.02,0,0 +2012,10,31,13,12,-15,14,1019,11.17,0,0 +2012,10,31,14,11,-15,15,1018,16.98,0,0 +2012,10,31,15,10,-14,15,1017,21.9,0,0 +2012,10,31,16,13,-15,16,1017,27.71,0,0 +2012,10,31,17,11,-12,13,1017,0.89,0,0 +2012,10,31,18,38,-3,8,1018,1.78,0,0 +2012,10,31,19,65,-3,7,1019,2.67,0,0 +2012,10,31,20,79,-1,5,1019,3.56,0,0 +2012,10,31,21,78,-1,4,1019,4.45,0,0 +2012,10,31,22,76,0,4,1019,0.89,0,0 +2012,10,31,23,91,-2,1,1019,1.79,0,0 +2012,11,1,0,124,0,2,1019,2.68,0,0 +2012,11,1,1,135,-1,1,1020,0.45,0,0 +2012,11,1,2,121,-1,1,1020,1.34,0,0 +2012,11,1,3,102,-1,1,1020,1.79,0,0 +2012,11,1,4,78,-3,-1,1019,4.92,0,0 +2012,11,1,5,70,-1,0,1019,0.89,0,0 +2012,11,1,6,49,-1,0,1020,1.79,0,0 +2012,11,1,7,26,-1,1,1020,0.89,0,0 +2012,11,1,8,45,0,3,1021,1.79,0,0 +2012,11,1,9,57,0,8,1021,4.92,0,0 +2012,11,1,10,99,-5,12,1020,6.71,0,0 +2012,11,1,11,85,-5,13,1020,1.79,0,0 +2012,11,1,12,77,-5,14,1019,3.58,0,0 +2012,11,1,13,93,-5,15,1018,5.37,0,0 +2012,11,1,14,81,-5,16,1018,3.13,0,0 +2012,11,1,15,64,-5,16,1017,6.26,0,0 +2012,11,1,16,77,-6,15,1017,10.28,0,0 +2012,11,1,17,82,-3,13,1017,12.07,0,0 +2012,11,1,18,90,1,9,1018,0.89,0,0 +2012,11,1,19,115,2,7,1019,0.89,0,0 +2012,11,1,20,130,1,5,1019,2.68,0,0 +2012,11,1,21,134,0,9,1019,5.81,0,0 +2012,11,1,22,173,1,9,1020,10.73,0,0 +2012,11,1,23,172,4,8,1020,13.86,0,0 +2012,11,2,0,157,2,5,1020,0.89,0,0 +2012,11,2,1,141,2,3,1020,3.13,0,0 +2012,11,2,2,124,2,3,1020,6.26,0,0 +2012,11,2,3,124,3,4,1020,0.89,0,0 +2012,11,2,4,126,2,3,1020,1.79,0,0 +2012,11,2,5,136,2,4,1020,3.58,0,0 +2012,11,2,6,139,2,4,1020,4.47,0,0 +2012,11,2,7,144,2,4,1021,6.26,0,0 +2012,11,2,8,148,3,5,1021,9.39,0,0 +2012,11,2,9,141,4,8,1021,11.18,0,0 +2012,11,2,10,123,3,10,1021,14.31,0,0 +2012,11,2,11,134,5,11,1020,1.79,0,0 +2012,11,2,12,147,5,13,1020,0.89,0,0 +2012,11,2,13,170,5,13,1019,3.13,0,0 +2012,11,2,14,175,5,14,1018,4.92,0,0 +2012,11,2,15,181,5,14,1018,6.71,0,0 +2012,11,2,16,164,5,14,1018,0.89,0,0 +2012,11,2,17,176,7,11,1019,1.34,0,0 +2012,11,2,18,190,6,8,1019,0.89,0,0 +2012,11,2,19,204,7,9,1020,0.89,0,0 +2012,11,2,20,196,6,8,1020,1.79,0,0 +2012,11,2,21,214,6,7,1021,0.89,0,0 +2012,11,2,22,208,6,7,1021,0.89,0,0 +2012,11,2,23,209,6,8,1021,0.45,0,0 +2012,11,3,0,228,7,8,1021,1.34,0,0 +2012,11,3,1,212,7,8,1021,1.79,0,0 +2012,11,3,2,206,6,7,1020,0.89,0,0 +2012,11,3,3,199,5,6,1020,1.78,0,0 +2012,11,3,4,210,6,7,1020,0.89,0,0 +2012,11,3,5,194,7,7,1020,0.89,0,0 +2012,11,3,6,196,7,8,1019,0.45,0,0 +2012,11,3,7,194,7,8,1020,0.89,0,0 +2012,11,3,8,194,8,9,1020,0.89,0,0 +2012,11,3,9,192,8,11,1020,4.02,0,0 +2012,11,3,10,199,8,11,1020,8.04,0,0 +2012,11,3,11,205,8,12,1019,12.06,0,0 +2012,11,3,12,208,8,11,1018,13.85,0,1 +2012,11,3,13,207,9,10,1017,0.89,0,2 +2012,11,3,14,190,9,10,1016,1.34,0,3 +2012,11,3,15,162,9,10,1015,3.13,0,4 +2012,11,3,16,156,9,10,1015,8.05,0,5 +2012,11,3,17,154,9,9,1014,12.97,0,6 +2012,11,3,18,14,5,7,1014,21.02,0,7 +2012,11,3,19,12,4,5,1013,30.85,0,9 +2012,11,3,20,9,3,5,1013,42.92,0,10 +2012,11,3,21,10,3,4,1013,51.86,0,11 +2012,11,3,22,7,2,3,1012,63.04,0,12 +2012,11,3,23,12,2,3,1012,72.87,0,13 +2012,11,4,0,14,2,3,1011,80.92,0,14 +2012,11,4,1,11,2,2,1011,88.97,0,15 +2012,11,4,2,10,1,2,1009,101.93,0,17 +2012,11,4,3,7,1,2,1009,113.11,0,18 +2012,11,4,4,6,2,2,1008,124.29,0,19 +2012,11,4,5,8,2,3,1008,128.31,0,20 +2012,11,4,6,8,2,3,1008,134.12,0,21 +2012,11,4,7,11,2,3,1008,138.14,0,22 +2012,11,4,8,14,2,3,1010,139.93,0,23 +2012,11,4,9,15,2,3,1010,143.06,0,24 +2012,11,4,10,27,2,3,1010,147.08,0,25 +2012,11,4,11,22,3,4,1011,150.21,0,26 +2012,11,4,12,27,2,4,1011,158.26,0,27 +2012,11,4,13,29,2,4,1011,167.2,0,28 +2012,11,4,14,27,1,4,1011,178.38,0,29 +2012,11,4,15,26,1,4,1012,189.56,0,30 +2012,11,4,16,23,0,4,1013,200.74,0,31 +2012,11,4,17,25,0,4,1013,208.79,0,32 +2012,11,4,18,25,0,4,1014,217.73,0,0 +2012,11,4,19,30,0,5,1015,226.67,0,1 +2012,11,4,20,27,0,5,1016,235.61,0,0 +2012,11,4,21,29,-1,5,1017,244.55,0,0 +2012,11,4,22,22,-1,4,1017,253.49,0,0 +2012,11,4,23,22,-2,4,1017,261.54,0,0 +2012,11,5,0,25,-2,4,1017,271.37,0,0 +2012,11,5,1,21,-3,5,1018,280.31,0,0 +2012,11,5,2,17,-3,5,1018,289.25,0,0 +2012,11,5,3,17,-4,5,1018,299.08,0,0 +2012,11,5,4,15,-4,4,1017,308.91,0,0 +2012,11,5,5,14,-4,4,1018,316.96,0,0 +2012,11,5,6,19,-4,5,1018,325.9,0,0 +2012,11,5,7,16,-4,5,1019,330.82,0,0 +2012,11,5,8,19,-4,6,1019,337.97,0,0 +2012,11,5,9,22,-4,7,1020,346.02,0,0 +2012,11,5,10,26,-3,9,1020,355.85,0,0 +2012,11,5,11,24,-3,10,1020,361.66,0,0 +2012,11,5,12,28,-4,9,1020,368.81,0,0 +2012,11,5,13,34,-4,10,1020,377.75,0,0 +2012,11,5,14,27,-3,10,1020,387.58,0,0 +2012,11,5,15,33,-3,11,1020,396.52,0,0 +2012,11,5,16,36,-3,10,1020,404.57,0,0 +2012,11,5,17,34,-3,9,1021,411.72,0,0 +2012,11,5,18,36,-3,8,1021,418.87,0,0 +2012,11,5,19,36,-3,8,1022,424.68,0,0 +2012,11,5,20,38,-3,7,1022,431.83,0,0 +2012,11,5,21,40,-3,7,1023,436.75,0,0 +2012,11,5,22,32,-3,6,1023,440.77,0,0 +2012,11,5,23,38,-2,6,1023,446.58,0,0 +2012,11,6,0,39,-3,5,1023,450.6,0,0 +2012,11,6,1,42,-2,2,1023,454.62,0,0 +2012,11,6,2,48,-2,2,1023,1.79,0,0 +2012,11,6,3,54,-3,2,1023,3.13,0,0 +2012,11,6,4,51,-3,3,1022,0.89,0,0 +2012,11,6,5,50,-3,3,1022,3.13,0,0 +2012,11,6,6,49,-3,3,1022,0.89,0,0 +2012,11,6,7,54,-1,1,1023,1.78,0,0 +2012,11,6,8,68,1,3,1023,1.79,0,0 +2012,11,6,9,66,-1,6,1023,4.92,0,0 +2012,11,6,10,65,-2,8,1023,6.71,0,0 +2012,11,6,11,71,-2,10,1022,0.89,0,0 +2012,11,6,12,80,-2,10,1022,1.79,0,0 +2012,11,6,13,77,-2,12,1021,1.79,0,0 +2012,11,6,14,89,-3,12,1020,1.79,0,0 +2012,11,6,15,106,-3,12,1020,4.92,0,0 +2012,11,6,16,106,-2,12,1020,8.05,0,0 +2012,11,6,17,114,0,9,1020,0.89,0,0 +2012,11,6,18,109,0,9,1020,1.79,0,0 +2012,11,6,19,102,2,5,1020,3.58,0,0 +2012,11,6,20,116,2,5,1020,0.89,0,0 +2012,11,6,21,138,1,7,1020,3.13,0,0 +2012,11,6,22,135,2,4,1020,4.92,0,0 +2012,11,6,23,114,0,3,1020,6.71,0,0 +2012,11,7,0,99,1,2,1020,0.89,0,0 +2012,11,7,1,114,1,1,1020,1.34,0,0 +2012,11,7,2,132,1,1,1020,2.23,0,0 +2012,11,7,3,106,0,1,1020,0.89,0,0 +2012,11,7,4,82,0,1,1019,0.89,0,0 +2012,11,7,5,88,-1,0,1019,1.79,0,0 +2012,11,7,6,93,-1,0,1019,3.58,0,0 +2012,11,7,7,91,-1,0,1020,5.37,0,0 +2012,11,7,8,95,1,2,1020,8.5,0,0 +2012,11,7,9,87,1,7,1020,11.63,0,0 +2012,11,7,10,85,-4,12,1019,19.68,0,0 +2012,11,7,11,65,-5,13,1019,24.6,0,0 +2012,11,7,12,48,-6,14,1018,31.75,0,0 +2012,11,7,13,49,-7,14,1018,37.56,0,0 +2012,11,7,14,31,-8,15,1018,45.61,0,0 +2012,11,7,15,32,-8,14,1018,52.76,0,0 +2012,11,7,16,40,-8,13,1018,58.57,0,0 +2012,11,7,17,32,-7,12,1018,63.49,0,0 +2012,11,7,18,31,-7,12,1018,69.3,0,0 +2012,11,7,19,39,-6,11,1019,74.22,0,0 +2012,11,7,20,43,-6,11,1019,77.35,0,0 +2012,11,7,21,50,-7,11,1019,82.27,0,0 +2012,11,7,22,43,-7,10,1019,86.29,0,0 +2012,11,7,23,44,-7,9,1020,89.42,0,0 +2012,11,8,0,40,-6,7,1020,91.21,0,0 +2012,11,8,1,35,-5,7,1019,95.23,0,0 +2012,11,8,2,32,-4,4,1019,97.02,0,0 +2012,11,8,3,27,-3,2,1019,100.15,0,0 +2012,11,8,4,24,-2,2,1019,101.94,0,0 +2012,11,8,5,40,-3,1,1019,103.73,0,0 +2012,11,8,6,42,-2,2,1020,105.52,0,0 +2012,11,8,7,38,-3,0,1020,108.65,0,0 +2012,11,8,8,28,-1,4,1021,110.44,0,0 +2012,11,8,9,27,-2,8,1022,113.57,0,0 +2012,11,8,10,38,-4,10,1021,115.36,0,0 +2012,11,8,11,51,-5,11,1021,0.89,0,0 +2012,11,8,12,42,-5,12,1020,1.78,0,0 +2012,11,8,13,29,-6,13,1019,3.57,0,0 +2012,11,8,14,50,-5,14,1019,5.36,0,0 +2012,11,8,15,60,-5,14,1019,1.79,0,0 +2012,11,8,16,60,-4,12,1019,4.92,0,0 +2012,11,8,17,61,-2,10,1019,0.89,0,0 +2012,11,8,18,69,1,6,1019,1.34,0,0 +2012,11,8,19,79,2,5,1020,2.23,0,0 +2012,11,8,20,94,1,4,1020,2.68,0,0 +2012,11,8,21,102,1,3,1021,0.89,0,0 +2012,11,8,22,106,1,2,1022,0.89,0,0 +2012,11,8,23,102,1,2,1022,0.89,0,0 +2012,11,9,0,119,1,2,1022,2.68,0,0 +2012,11,9,1,114,1,4,1023,4.47,0,0 +2012,11,9,2,135,1,4,1023,0.89,0,0 +2012,11,9,3,116,1,5,1023,1.34,0,0 +2012,11,9,4,107,3,4,1023,0.89,0,0 +2012,11,9,5,96,3,5,1023,1.78,0,0 +2012,11,9,6,98,3,6,1024,2.67,0,0 +2012,11,9,7,104,4,6,1025,0.89,0,0 +2012,11,9,8,118,4,6,1025,1.34,0,0 +2012,11,9,9,134,3,9,1025,2.23,0,0 +2012,11,9,10,128,3,10,1026,3.12,0,0 +2012,11,9,11,103,3,12,1025,4.01,0,0 +2012,11,9,12,91,2,12,1025,5.8,0,0 +2012,11,9,13,95,3,12,1025,1.79,0,0 +2012,11,9,14,71,2,12,1024,3.58,0,0 +2012,11,9,15,71,1,13,1024,5.37,0,0 +2012,11,9,16,44,1,12,1024,0.89,0,0 +2012,11,9,17,62,3,9,1024,0.89,0,0 +2012,11,9,18,67,3,6,1024,1.78,0,0 +2012,11,9,19,105,3,6,1025,0.89,0,0 +2012,11,9,20,116,3,5,1025,0.89,0,0 +2012,11,9,21,143,4,5,1025,1.78,0,0 +2012,11,9,22,160,3,7,1025,3.57,0,0 +2012,11,9,23,127,3,7,1025,4.46,0,0 +2012,11,10,0,132,3,6,1025,5.35,0,0 +2012,11,10,1,116,3,6,1025,0.89,0,0 +2012,11,10,2,160,4,6,1025,0.89,0,0 +2012,11,10,3,122,4,6,1024,0.89,0,0 +2012,11,10,4,122,4,6,1024,1.78,0,0 +2012,11,10,5,130,4,6,1024,0.89,0,0 +2012,11,10,6,108,4,6,1024,0.89,0,0 +2012,11,10,7,112,4,6,1024,1.79,0,0 +2012,11,10,8,121,5,7,1024,3.13,0,1 +2012,11,10,9,110,5,7,1024,6.26,0,2 +2012,11,10,10,108,5,7,1023,1.79,0,3 +2012,11,10,11,83,5,8,1022,3.13,0,4 +2012,11,10,12,72,4,7,1021,4.92,0,5 +2012,11,10,13,62,5,7,1021,8.05,0,6 +2012,11,10,14,57,5,6,1021,12.07,0,7 +2012,11,10,15,46,3,5,1020,16.99,0,8 +2012,11,10,16,48,3,5,1020,22.8,0,9 +2012,11,10,17,31,4,5,1019,0.89,0,10 +2012,11,10,18,26,2,5,1019,7.15,0,11 +2012,11,10,19,22,1,5,1019,16.09,0,0 +2012,11,10,20,19,-1,5,1019,25.03,0,0 +2012,11,10,21,17,-1,4,1018,33.97,0,0 +2012,11,10,22,12,-3,5,1018,43.8,0,0 +2012,11,10,23,12,-5,5,1017,54.98,0,0 +2012,11,11,0,10,-6,4,1016,63.92,0,0 +2012,11,11,1,12,-8,4,1016,73.75,0,0 +2012,11,11,2,10,-10,4,1015,82.69,0,0 +2012,11,11,3,7,-11,3,1015,91.63,0,0 +2012,11,11,4,9,-12,2,1014,100.57,0,0 +2012,11,11,5,11,-12,2,1014,109.51,0,0 +2012,11,11,6,11,-12,2,1014,119.34,0,0 +2012,11,11,7,11,-12,2,1013,125.15,0,0 +2012,11,11,8,9,-12,3,1013,132.3,0,0 +2012,11,11,9,11,-11,4,1012,141.24,0,0 +2012,11,11,10,12,-12,5,1012,154.2,0,0 +2012,11,11,11,17,-11,6,1013,166.27,0,0 +2012,11,11,12,13,-11,6,1013,179.23,0,0 +2012,11,11,13,17,-12,6,1013,191.3,0,0 +2012,11,11,14,15,-12,6,1013,203.37,0,0 +2012,11,11,15,15,-11,6,1014,214.55,0,0 +2012,11,11,16,13,-12,6,1015,223.49,0,0 +2012,11,11,17,12,-11,5,1015,229.3,0,0 +2012,11,11,18,14,-12,4,1017,238.24,0,0 +2012,11,11,19,11,-12,4,1017,248.07,0,0 +2012,11,11,20,9,-12,4,1018,255.22,0,0 +2012,11,11,21,10,-12,3,1018,261.03,0,0 +2012,11,11,22,11,-12,3,1019,266.84,0,0 +2012,11,11,23,13,-12,3,1019,271.76,0,0 +2012,11,12,0,10,-12,3,1019,276.68,0,0 +2012,11,12,1,12,-12,2,1019,278.47,0,0 +2012,11,12,2,12,-12,2,1019,282.49,0,0 +2012,11,12,3,13,-12,2,1019,288.3,0,0 +2012,11,12,4,12,-13,2,1019,294.11,0,0 +2012,11,12,5,13,-13,2,1019,299.92,0,0 +2012,11,12,6,10,-13,2,1019,304.84,0,0 +2012,11,12,7,14,-12,1,1020,307.97,0,0 +2012,11,12,8,25,-14,4,1020,311.99,0,0 +2012,11,12,9,22,-12,4,1020,1.79,0,0 +2012,11,12,10,18,-13,6,1019,4.02,0,0 +2012,11,12,11,26,-13,7,1019,7.15,0,0 +2012,11,12,12,26,-13,8,1018,11.17,0,0 +2012,11,12,13,26,-16,9,1016,18.32,0,0 +2012,11,12,14,24,-16,10,1015,27.26,0,0 +2012,11,12,15,26,-15,9,1015,36.2,0,0 +2012,11,12,16,22,-14,9,1015,45.14,0,0 +2012,11,12,17,23,-12,8,1015,52.29,0,0 +2012,11,12,18,34,-12,7,1016,58.1,0,0 +2012,11,12,19,43,-11,6,1016,62.12,0,0 +2012,11,12,20,45,-10,6,1016,66.14,0,0 +2012,11,12,21,35,-11,6,1017,71.95,0,0 +2012,11,12,22,8,-11,5,1017,77.76,0,0 +2012,11,12,23,6,-11,5,1017,83.57,0,0 +2012,11,13,0,9,-10,4,1017,88.49,0,0 +2012,11,13,1,12,-11,3,1017,92.51,0,0 +2012,11,13,2,11,-15,3,1017,101.45,0,0 +2012,11,13,3,8,-17,3,1017,114.41,0,0 +2012,11,13,4,14,-16,2,1017,125.59,0,0 +2012,11,13,5,10,-17,2,1018,134.53,0,0 +2012,11,13,6,7,-15,1,1018,141.68,0,0 +2012,11,13,7,7,-16,1,1019,148.83,0,0 +2012,11,13,8,10,-16,1,1019,156.88,0,0 +2012,11,13,9,14,-16,2,1020,165.82,0,0 +2012,11,13,10,17,-16,4,1019,172.97,0,0 +2012,11,13,11,15,-17,5,1019,181.91,0,0 +2012,11,13,12,14,-17,5,1018,190.85,0,0 +2012,11,13,13,11,-18,6,1017,202.03,0,0 +2012,11,13,14,12,-18,6,1017,210.97,0,0 +2012,11,13,15,12,-18,6,1017,219.02,0,0 +2012,11,13,16,8,-20,6,1017,227.07,0,0 +2012,11,13,17,7,-18,5,1018,232.88,0,0 +2012,11,13,18,15,-18,3,1019,240.93,0,0 +2012,11,13,19,11,-18,3,1019,249.87,0,0 +2012,11,13,20,18,-20,3,1020,261.05,0,0 +2012,11,13,21,23,-20,3,1020,273.12,0,0 +2012,11,13,22,24,-20,2,1021,284.3,0,0 +2012,11,13,23,24,-19,1,1022,288.32,0,0 +2012,11,14,0,17,-18,2,1022,293.24,0,0 +2012,11,14,1,17,-17,1,1023,298.16,0,0 +2012,11,14,2,17,-17,1,1023,303.97,0,0 +2012,11,14,3,14,-17,0,1023,309.78,0,0 +2012,11,14,4,12,-13,-2,1023,1.79,0,0 +2012,11,14,5,13,-12,-4,1024,4.02,0,0 +2012,11,14,6,16,-12,-4,1024,8.04,0,0 +2012,11,14,7,15,-11,-2,1025,11.17,0,0 +2012,11,14,8,23,-12,0,1026,15.19,0,0 +2012,11,14,9,26,-14,1,1026,4.92,0,0 +2012,11,14,10,25,-14,3,1026,8.94,0,0 +2012,11,14,11,31,-16,4,1026,12.96,0,0 +2012,11,14,12,27,-16,6,1025,16.09,0,0 +2012,11,14,13,18,-16,7,1025,4.02,0,0 +2012,11,14,14,18,-15,8,1024,7.15,0,0 +2012,11,14,15,26,-14,7,1024,3.13,0,0 +2012,11,14,16,19,-13,6,1024,4.92,0,0 +2012,11,14,17,32,-12,4,1024,0.89,0,0 +2012,11,14,18,36,-9,2,1025,1.34,0,0 +2012,11,14,19,49,-7,1,1026,2.23,0,0 +2012,11,14,20,55,-8,1,1026,3.12,0,0 +2012,11,14,21,81,-5,-1,1027,4.01,0,0 +2012,11,14,22,130,-6,-2,1028,0.89,0,0 +2012,11,14,23,144,-6,-2,1028,0.89,0,0 +2012,11,15,0,144,-6,-3,1028,1.34,0,0 +2012,11,15,1,117,-5,-3,1028,0.89,0,0 +2012,11,15,2,162,-5,-3,1029,0.89,0,0 +2012,11,15,3,200,-5,-3,1029,1.34,0,0 +2012,11,15,4,171,-5,-4,1028,0.89,0,0 +2012,11,15,5,145,-6,-4,1029,1.79,0,0 +2012,11,15,6,102,-5,-4,1029,2.68,0,0 +2012,11,15,7,85,-5,-4,1029,0.89,0,0 +2012,11,15,8,103,-4,-3,1030,1.79,0,0 +2012,11,15,9,83,-4,2,1030,0.89,0,0 +2012,11,15,10,112,-5,3,1029,1.78,0,0 +2012,11,15,11,146,-6,4,1029,2.67,0,0 +2012,11,15,12,183,-6,6,1028,1.79,0,0 +2012,11,15,13,191,-6,6,1027,0.89,0,0 +2012,11,15,14,225,-7,7,1026,1.78,0,0 +2012,11,15,15,227,-9,7,1025,2.67,0,0 +2012,11,15,16,145,-9,7,1025,3.13,0,0 +2012,11,15,17,125,-8,5,1025,0.89,0,0 +2012,11,15,18,130,-7,4,1025,1.79,0,0 +2012,11,15,19,122,-8,7,1026,3.58,0,0 +2012,11,15,20,113,-1,4,1026,4.92,0,1 +2012,11,15,21,130,1,2,1025,8.05,0,2 +2012,11,15,22,137,0,1,1025,0.89,0,3 +2012,11,15,23,137,0,0,1025,0.89,1,0 +2012,11,16,0,129,0,0,1025,1.78,2,0 +2012,11,16,1,126,0,0,1024,0.89,0,0 +2012,11,16,2,136,0,1,1024,1.78,1,0 +2012,11,16,3,159,0,0,1023,1.79,0,0 +2012,11,16,4,159,0,0,1023,3.58,0,0 +2012,11,16,5,180,0,0,1023,0.89,0,0 +2012,11,16,6,175,0,0,1023,1.78,0,0 +2012,11,16,7,170,0,0,1023,2.23,0,0 +2012,11,16,8,171,0,1,1023,3.12,0,0 +2012,11,16,9,176,0,1,1023,4.01,0,0 +2012,11,16,10,206,1,1,1023,1.79,0,0 +2012,11,16,11,252,1,2,1022,1.79,0,0 +2012,11,16,12,263,2,5,1021,0.89,0,0 +2012,11,16,13,256,3,6,1020,1.78,0,0 +2012,11,16,14,182,1,8,1020,3.57,0,0 +2012,11,16,15,10,-5,9,1019,7.15,0,0 +2012,11,16,16,13,-5,9,1020,14.3,0,0 +2012,11,16,17,17,-5,8,1020,21.45,0,0 +2012,11,16,18,20,-5,7,1020,28.6,0,0 +2012,11,16,19,25,-4,6,1021,35.75,0,0 +2012,11,16,20,20,-5,6,1021,40.67,0,0 +2012,11,16,21,19,-5,5,1021,44.69,0,0 +2012,11,16,22,23,-7,6,1021,47.82,0,0 +2012,11,16,23,18,-6,6,1020,50.95,0,0 +2012,11,17,0,17,-5,0,1020,54.08,0,0 +2012,11,17,1,20,-5,1,1020,58.1,0,0 +2012,11,17,2,28,-5,-1,1020,59.89,0,0 +2012,11,17,3,27,-5,-2,1020,61.68,0,0 +2012,11,17,4,35,-5,-1,1019,0.45,0,0 +2012,11,17,5,38,-3,-2,1019,0.9,0,0 +2012,11,17,6,56,-5,-1,1020,1.79,0,0 +2012,11,17,7,57,-5,2,1020,3.58,0,0 +2012,11,17,8,75,-3,-1,1020,1.79,0,0 +2012,11,17,9,70,-3,4,1021,0.89,0,0 +2012,11,17,10,50,-5,7,1021,2.68,0,0 +2012,11,17,11,51,-4,7,1021,1.79,0,0 +2012,11,17,12,58,-4,8,1020,3.58,0,0 +2012,11,17,13,56,-6,9,1019,0.89,0,0 +2012,11,17,14,62,-7,10,1019,2.68,0,0 +2012,11,17,15,64,-8,10,1019,1.79,0,0 +2012,11,17,16,72,-7,9,1019,3.58,0,0 +2012,11,17,17,69,-5,7,1019,5.37,0,0 +2012,11,17,18,66,-2,4,1020,6.26,0,0 +2012,11,17,19,57,-2,3,1020,0.89,0,0 +2012,11,17,20,93,-2,2,1020,1.78,0,0 +2012,11,17,21,114,-2,2,1021,2.23,0,0 +2012,11,17,22,135,-3,2,1021,1.79,0,0 +2012,11,17,23,157,-3,2,1021,3.58,0,0 +2012,11,18,0,179,-3,2,1021,5.37,0,0 +2012,11,18,1,171,-3,-1,1020,6.26,0,0 +2012,11,18,2,180,-3,-1,1020,0.89,0,0 +2012,11,18,3,189,-3,-1,1020,0.89,0,0 +2012,11,18,4,219,-2,0,1019,1.78,0,0 +2012,11,18,5,174,-3,0,1019,0.89,0,0 +2012,11,18,6,154,-3,-1,1019,1.78,0,0 +2012,11,18,7,167,-3,-2,1019,0.89,0,0 +2012,11,18,8,164,-2,0,1019,2.68,0,0 +2012,11,18,9,168,-1,3,1019,4.47,0,0 +2012,11,18,10,179,-2,5,1019,0.89,0,0 +2012,11,18,11,164,-2,6,1018,1.78,0,0 +2012,11,18,12,170,-2,6,1017,2.67,0,0 +2012,11,18,13,157,-2,7,1016,3.56,0,0 +2012,11,18,14,156,-2,8,1016,4.45,0,0 +2012,11,18,15,156,-2,8,1016,5.34,0,0 +2012,11,18,16,153,-2,8,1016,1.79,0,0 +2012,11,18,17,152,-6,11,1017,8.94,0,0 +2012,11,18,18,17,-9,10,1018,16.99,0,0 +2012,11,18,19,13,-9,8,1020,29.06,0,0 +2012,11,18,20,11,-9,7,1020,38.89,0,0 +2012,11,18,21,12,-9,6,1022,43.81,0,0 +2012,11,18,22,8,-11,5,1022,48.73,0,0 +2012,11,18,23,7,-12,5,1023,57.67,0,0 +2012,11,19,0,11,-13,4,1023,67.5,0,0 +2012,11,19,1,7,-12,4,1023,76.44,0,0 +2012,11,19,2,9,-13,4,1024,86.27,0,0 +2012,11,19,3,10,-11,3,1025,94.32,0,0 +2012,11,19,4,6,-11,3,1024,103.26,0,0 +2012,11,19,5,12,-11,2,1025,109.07,0,0 +2012,11,19,6,13,-9,1,1025,0.89,0,0 +2012,11,19,7,14,-8,0,1025,4.02,0,0 +2012,11,19,8,22,-9,1,1026,0.89,0,0 +2012,11,19,9,27,-10,3,1027,1.79,0,0 +2012,11,19,10,20,-11,5,1027,5.81,0,0 +2012,11,19,11,22,-12,7,1027,3.13,0,0 +2012,11,19,12,24,-12,8,1025,4.02,0,0 +2012,11,19,13,23,-12,8,1024,0.89,0,0 +2012,11,19,14,22,-12,9,1024,1.78,0,0 +2012,11,19,15,31,-11,9,1023,3.13,0,0 +2012,11,19,16,36,-11,9,1023,7.15,0,0 +2012,11,19,17,36,-10,7,1023,11.17,0,0 +2012,11,19,18,51,-9,7,1024,3.13,0,0 +2012,11,19,19,67,-8,6,1024,4.92,0,0 +2012,11,19,20,NA,-6,3,1024,1.79,0,0 +2012,11,19,21,NA,-4,0,1024,0.89,0,0 +2012,11,19,22,NA,-4,-1,1024,1.79,0,0 +2012,11,19,23,NA,-4,0,1023,0.89,0,0 +2012,11,20,0,NA,-5,-3,1023,0.89,0,0 +2012,11,20,1,NA,-4,-2,1023,1.79,0,0 +2012,11,20,2,NA,-4,-2,1024,2.68,0,0 +2012,11,20,3,NA,-5,-4,1024,4.47,0,0 +2012,11,20,4,NA,-5,-4,1024,5.36,0,0 +2012,11,20,5,NA,-5,-4,1024,7.15,0,0 +2012,11,20,6,NA,-5,-3,1024,8.04,0,0 +2012,11,20,7,NA,-5,-4,1024,9.83,0,0 +2012,11,20,8,NA,-3,2,1025,12.96,0,0 +2012,11,20,9,NA,-4,4,1025,16.98,0,0 +2012,11,20,10,NA,-5,6,1024,21.9,0,0 +2012,11,20,11,NA,-6,7,1024,4.02,0,0 +2012,11,20,12,NA,-7,9,1023,3.13,0,0 +2012,11,20,13,NA,-7,10,1022,0.89,0,0 +2012,11,20,14,NA,-7,11,1022,1.78,0,0 +2012,11,20,15,NA,-8,10,1021,2.67,0,0 +2012,11,20,16,57,-7,9,1021,1.79,0,0 +2012,11,20,17,87,-6,7,1021,2.68,0,0 +2012,11,20,18,110,-3,4,1022,3.57,0,0 +2012,11,20,19,132,-4,2,1022,4.46,0,0 +2012,11,20,20,159,-2,3,1022,0.89,0,0 +2012,11,20,21,134,-2,2,1023,0.89,0,0 +2012,11,20,22,141,-2,1,1022,0.89,0,0 +2012,11,20,23,153,-2,0,1022,0.89,0,0 +2012,11,21,0,149,-3,-1,1022,1.78,0,0 +2012,11,21,1,163,-3,0,1021,2.67,0,0 +2012,11,21,2,164,-3,0,1021,0.89,0,0 +2012,11,21,3,248,-4,-1,1021,1.78,0,0 +2012,11,21,4,334,-4,-3,1020,0.89,0,0 +2012,11,21,5,370,-3,-2,1020,0.45,0,0 +2012,11,21,6,331,-3,-2,1020,1.79,0,0 +2012,11,21,7,293,-4,-3,1020,0.89,0,0 +2012,11,21,8,305,-3,-2,1020,0.89,0,0 +2012,11,21,9,285,-1,3,1019,1.79,0,0 +2012,11,21,10,280,-1,4,1019,3.58,0,0 +2012,11,21,11,235,-2,6,1019,0.89,0,0 +2012,11,21,12,164,-3,8,1018,1.78,0,0 +2012,11,21,13,183,-3,8,1017,2.67,0,0 +2012,11,21,14,196,-4,9,1017,3.56,0,0 +2012,11,21,15,218,-4,9,1016,4.45,0,0 +2012,11,21,16,237,-3,9,1017,5.34,0,0 +2012,11,21,17,249,-2,6,1017,1.79,0,0 +2012,11,21,18,269,-1,3,1018,0.89,0,0 +2012,11,21,19,277,-2,3,1019,1.78,0,0 +2012,11,21,20,286,-1,1,1020,0.89,0,0 +2012,11,21,21,308,-1,1,1020,0.89,0,0 +2012,11,21,22,338,-1,1,1020,1.78,0,0 +2012,11,21,23,350,-1,0,1019,0.89,0,0 +2012,11,22,0,404,0,0,1020,1.34,0,0 +2012,11,22,1,410,-2,-1,1019,2.23,0,0 +2012,11,22,2,418,-2,-1,1019,1.79,0,0 +2012,11,22,3,431,-1,0,1019,0.89,0,0 +2012,11,22,4,359,-1,0,1019,0.89,0,0 +2012,11,22,5,318,-1,1,1019,0.89,0,0 +2012,11,22,6,335,-1,1,1020,1.79,0,0 +2012,11,22,7,236,0,2,1021,3.58,0,0 +2012,11,22,8,226,0,4,1022,6.71,0,0 +2012,11,22,9,226,1,4,1022,0.89,0,0 +2012,11,22,10,190,1,6,1023,3.13,0,0 +2012,11,22,11,84,1,6,1023,7.15,0,0 +2012,11,22,12,33,0,7,1023,1.79,0,0 +2012,11,22,13,38,-2,7,1023,4.92,0,0 +2012,11,22,14,28,-9,8,1023,12.97,0,0 +2012,11,22,15,23,-10,7,1024,21.91,0,0 +2012,11,22,16,19,-11,7,1025,29.96,0,0 +2012,11,22,17,13,-11,6,1026,37.11,0,0 +2012,11,22,18,16,-11,4,1027,42.92,0,0 +2012,11,22,19,13,-16,4,1029,48.73,0,0 +2012,11,22,20,11,-17,4,1030,55.88,0,0 +2012,11,22,21,6,-18,2,1031,63.03,0,0 +2012,11,22,22,6,-20,2,1032,71.08,0,0 +2012,11,22,23,11,-20,1,1031,76.89,0,0 +2012,11,23,0,15,-20,0,1031,82.7,0,0 +2012,11,23,1,16,-16,-3,1031,3.13,0,0 +2012,11,23,2,15,-16,-4,1031,4.02,0,0 +2012,11,23,3,15,-17,-3,1030,4.91,0,0 +2012,11,23,4,12,-15,-7,1029,8.04,0,0 +2012,11,23,5,14,-14,-5,1029,11.17,0,0 +2012,11,23,6,10,-12,-6,1029,1.79,0,0 +2012,11,23,7,21,-12,-6,1029,3.13,0,0 +2012,11,23,8,25,-13,-2,1029,3.13,0,0 +2012,11,23,9,27,-14,1,1029,3.13,0,0 +2012,11,23,10,23,-15,2,1029,6.26,0,0 +2012,11,23,11,27,-16,3,1028,9.39,0,0 +2012,11,23,12,30,-16,4,1026,12.52,0,0 +2012,11,23,13,29,-16,5,1025,15.65,0,0 +2012,11,23,14,39,-16,6,1024,18.78,0,0 +2012,11,23,15,43,-16,6,1023,21.91,0,0 +2012,11,23,16,44,-14,6,1023,26.83,0,0 +2012,11,23,17,49,-13,5,1023,31.75,0,0 +2012,11,23,18,78,-13,4,1023,3.13,0,0 +2012,11,23,19,85,-12,4,1024,4.02,0,0 +2012,11,23,20,94,-11,4,1024,8.04,0,0 +2012,11,23,21,105,-10,3,1024,12.06,0,0 +2012,11,23,22,100,-10,3,1024,15.19,0,0 +2012,11,23,23,95,-10,3,1024,16.98,0,0 +2012,11,24,0,106,-7,-3,1024,0.45,0,0 +2012,11,24,1,116,-7,-3,1023,1.34,0,0 +2012,11,24,2,123,-8,-6,1023,1.79,0,0 +2012,11,24,3,131,-7,-5,1022,0.89,0,0 +2012,11,24,4,155,-8,-5,1022,1.78,0,0 +2012,11,24,5,162,-8,-5,1022,2.23,0,0 +2012,11,24,6,177,-8,-6,1022,0.89,0,0 +2012,11,24,7,148,-8,-7,1022,0.89,0,0 +2012,11,24,8,121,-7,-5,1023,1.79,0,0 +2012,11,24,9,109,-5,-1,1023,3.58,0,0 +2012,11,24,10,90,-6,2,1022,0.89,0,0 +2012,11,24,11,84,-8,4,1022,1.78,0,0 +2012,11,24,12,72,-9,6,1021,2.67,0,0 +2012,11,24,13,83,-9,7,1020,4.46,0,0 +2012,11,24,14,121,-10,7,1019,7.59,0,0 +2012,11,24,15,141,-9,6,1018,3.13,0,0 +2012,11,24,16,118,-10,6,1018,6.26,0,0 +2012,11,24,17,158,-8,4,1018,8.05,0,0 +2012,11,24,18,202,-7,2,1018,9.84,0,0 +2012,11,24,19,204,-6,0,1019,11.63,0,0 +2012,11,24,20,221,-5,0,1020,0.89,0,0 +2012,11,24,21,209,-5,-3,1019,0.89,0,0 +2012,11,24,22,246,-4,-2,1019,0.89,0,0 +2012,11,24,23,315,-5,-3,1020,1.79,0,0 +2012,11,25,0,326,-5,-2,1020,0.89,0,0 +2012,11,25,1,358,-4,-3,1020,1.79,0,0 +2012,11,25,2,445,-4,-3,1019,2.68,0,0 +2012,11,25,3,413,-5,-3,1018,4.47,0,0 +2012,11,25,4,345,-5,-3,1018,6.26,0,0 +2012,11,25,5,329,-4,-1,1018,0.89,0,0 +2012,11,25,6,290,-4,0,1018,1.79,0,0 +2012,11,25,7,248,-4,-1,1019,0.45,0,0 +2012,11,25,8,284,-3,0,1020,1.79,0,0 +2012,11,25,9,247,-4,2,1020,4.92,0,0 +2012,11,25,10,246,-3,2,1020,8.05,0,0 +2012,11,25,11,109,-8,6,1020,13.86,0,0 +2012,11,25,12,33,-13,8,1019,19.67,0,0 +2012,11,25,13,20,-14,7,1019,27.72,0,0 +2012,11,25,14,14,-15,7,1019,34.87,0,0 +2012,11,25,15,11,-16,7,1020,42.02,0,0 +2012,11,25,16,8,-16,7,1020,46.94,0,0 +2012,11,25,17,9,-16,6,1021,51.86,0,0 +2012,11,25,18,17,-16,6,1021,57.67,0,0 +2012,11,25,19,20,-17,4,1022,61.69,0,0 +2012,11,25,20,17,-15,2,1023,0.89,0,0 +2012,11,25,21,13,-14,1,1023,4.02,0,0 +2012,11,25,22,22,-16,3,1023,8.04,0,0 +2012,11,25,23,32,-13,-1,1023,9.83,0,0 +2012,11,26,0,23,-14,0,1023,12.96,0,0 +2012,11,26,1,11,-14,2,1023,17.88,0,0 +2012,11,26,2,19,-15,2,1023,25.03,0,0 +2012,11,26,3,19,-15,2,1022,29.95,0,0 +2012,11,26,4,16,-15,1,1022,33.97,0,0 +2012,11,26,5,13,-12,-1,1022,35.76,0,0 +2012,11,26,6,15,-16,1,1022,38.89,0,0 +2012,11,26,7,15,-16,1,1022,42.91,0,0 +2012,11,26,8,14,-16,2,1023,46.93,0,0 +2012,11,26,9,17,-16,3,1023,50.06,0,0 +2012,11,26,10,16,-17,4,1023,54.08,0,0 +2012,11,26,11,11,-18,5,1023,61.23,0,0 +2012,11,26,12,20,-19,6,1022,68.38,0,0 +2012,11,26,13,17,-20,6,1021,77.32,0,0 +2012,11,26,14,16,-19,7,1020,85.37,0,0 +2012,11,26,15,23,-19,7,1019,92.52,0,0 +2012,11,26,16,NA,-18,7,1020,97.44,0,0 +2012,11,26,17,NA,-17,6,1020,100.57,0,0 +2012,11,26,18,39,-17,5,1020,3.13,0,0 +2012,11,26,19,34,-17,5,1020,7.15,0,0 +2012,11,26,20,44,-16,4,1019,11.17,0,0 +2012,11,26,21,54,-11,-1,1019,12.06,0,0 +2012,11,26,22,69,-10,-1,1019,13.85,0,0 +2012,11,26,23,90,-9,-2,1018,14.74,0,0 +2012,11,27,0,98,-10,-2,1018,16.53,0,0 +2012,11,27,1,120,-10,-2,1018,0.89,0,0 +2012,11,27,2,129,-10,-3,1017,1.79,0,0 +2012,11,27,3,130,-10,-3,1017,2.68,0,0 +2012,11,27,4,142,-10,-3,1016,0.89,0,0 +2012,11,27,5,171,-9,-4,1015,0.89,0,0 +2012,11,27,6,182,-9,-4,1015,1.78,0,0 +2012,11,27,7,184,-10,-6,1014,0.89,0,0 +2012,11,27,8,179,-7,-4,1014,0.89,0,0 +2012,11,27,9,169,-6,-1,1014,1.78,0,0 +2012,11,27,10,181,-6,1,1013,2.67,0,0 +2012,11,27,11,204,-6,2,1013,3.56,0,0 +2012,11,27,12,234,-6,3,1011,4.45,0,0 +2012,11,27,13,230,-6,4,1010,6.24,0,0 +2012,11,27,14,253,-6,4,1008,1.79,0,0 +2012,11,27,15,244,-6,4,1008,3.58,0,0 +2012,11,27,16,241,-6,3,1008,0.89,0,0 +2012,11,27,17,245,-5,0,1008,1.79,0,0 +2012,11,27,18,247,-4,1,1008,0.89,0,0 +2012,11,27,19,276,-4,-1,1009,1.79,0,0 +2012,11,27,20,285,-4,1,1010,4.92,0,0 +2012,11,27,21,161,-5,1,1011,0.89,0,0 +2012,11,27,22,68,-23,7,1012,12.96,0,0 +2012,11,27,23,73,-22,5,1013,26.82,0,0 +2012,11,28,0,63,-22,5,1014,40.68,0,0 +2012,11,28,1,63,-20,4,1014,51.86,0,0 +2012,11,28,2,53,-18,4,1014,63.04,0,0 +2012,11,28,3,37,-16,3,1016,74.22,0,0 +2012,11,28,4,14,-16,1,1017,82.27,0,0 +2012,11,28,5,11,-17,1,1018,92.1,0,0 +2012,11,28,6,9,-17,0,1019,99.25,0,0 +2012,11,28,7,9,-15,-1,1020,105.06,0,0 +2012,11,28,8,10,-17,1,1022,109.08,0,0 +2012,11,28,9,13,-17,1,1023,113.1,0,0 +2012,11,28,10,23,-18,2,1024,122.04,0,0 +2012,11,28,11,16,-18,3,1024,130.09,0,0 +2012,11,28,12,10,-18,4,1023,139.03,0,0 +2012,11,28,13,11,-17,4,1023,144.84,0,0 +2012,11,28,14,11,-18,4,1022,149.76,0,0 +2012,11,28,15,13,-17,4,1022,155.57,0,0 +2012,11,28,16,12,-16,4,1022,161.38,0,0 +2012,11,28,17,19,-16,4,1022,167.19,0,0 +2012,11,28,18,13,-16,3,1023,170.32,0,0 +2012,11,28,19,18,-13,-1,1023,173.45,0,0 +2012,11,28,20,23,-15,2,1024,3.13,0,0 +2012,11,28,21,24,-15,1,1024,1.79,0,0 +2012,11,28,22,36,-11,-3,1024,3.58,0,0 +2012,11,28,23,43,-12,-2,1024,0.89,0,0 +2012,11,29,0,44,-11,-4,1024,1.79,0,0 +2012,11,29,1,45,-10,-6,1024,3.58,0,0 +2012,11,29,2,65,-11,-7,1024,3.13,0,0 +2012,11,29,3,145,-10,-6,1024,0.89,0,0 +2012,11,29,4,131,-11,-6,1024,1.79,0,0 +2012,11,29,5,99,-11,-7,1024,3.58,0,0 +2012,11,29,6,37,-10,-7,1024,0.89,0,0 +2012,11,29,7,97,-9,-6,1025,1.78,0,0 +2012,11,29,8,76,-7,-3,1026,1.79,0,0 +2012,11,29,9,72,-10,0,1026,0.89,0,0 +2012,11,29,10,58,-10,1,1026,1.79,0,0 +2012,11,29,11,40,-14,3,1025,3.58,0,0 +2012,11,29,12,38,-19,4,1024,1.79,0,0 +2012,11,29,13,40,-19,5,1023,1.79,0,0 +2012,11,29,14,42,-19,5,1022,3.58,0,0 +2012,11,29,15,41,-19,5,1022,0.89,0,0 +2012,11,29,16,46,-18,3,1022,1.79,0,0 +2012,11,29,17,49,-17,1,1022,2.68,0,0 +2012,11,29,18,59,-14,0,1023,0.89,0,0 +2012,11,29,19,80,-9,-2,1023,0.89,0,0 +2012,11,29,20,71,-9,-3,1024,1.78,0,0 +2012,11,29,21,81,-9,-4,1024,0.89,0,0 +2012,11,29,22,79,-10,-5,1025,1.79,0,0 +2012,11,29,23,122,-11,-5,1025,3.58,0,0 +2012,11,30,0,167,-11,-5,1025,6.71,0,0 +2012,11,30,1,152,-11,-5,1025,9.84,0,0 +2012,11,30,2,78,-14,-2,1026,4.92,0,0 +2012,11,30,3,42,-14,-2,1026,12.07,0,0 +2012,11,30,4,31,-16,-3,1027,16.99,0,0 +2012,11,30,5,24,-18,-3,1028,22.8,0,0 +2012,11,30,6,17,-19,-4,1029,30.85,0,0 +2012,11,30,7,13,-20,-5,1031,34.87,0,0 +2012,11,30,8,23,-20,-4,1032,40.68,0,0 +2012,11,30,9,11,-21,-4,1033,47.83,0,0 +2012,11,30,10,21,-21,-3,1033,54.98,0,0 +2012,11,30,11,18,-21,-2,1033,59.9,0,0 +2012,11,30,12,14,-23,-1,1033,64.82,0,0 +2012,11,30,13,16,-23,-1,1032,69.74,0,0 +2012,11,30,14,12,-24,-1,1032,73.76,0,0 +2012,11,30,15,9,-24,-1,1032,77.78,0,0 +2012,11,30,16,16,-24,-1,1032,81.8,0,0 +2012,11,30,17,14,-24,-2,1032,1.79,0,0 +2012,11,30,18,32,-21,-3,1033,3.58,0,0 +2012,11,30,19,19,-21,-4,1034,0.89,0,0 +2012,11,30,20,20,-21,-6,1034,1.79,0,0 +2012,11,30,21,25,-18,-6,1035,0.89,0,0 +2012,11,30,22,45,-20,-5,1035,1.78,0,0 +2012,11,30,23,51,-17,-7,1036,2.67,0,0 +2012,12,1,0,41,-16,-8,1035,1.79,0,0 +2012,12,1,1,46,-17,-7,1035,0.89,0,0 +2012,12,1,2,37,-15,-7,1036,1.34,0,0 +2012,12,1,3,48,-15,-9,1035,0.89,0,0 +2012,12,1,4,43,-14,-9,1035,1.78,0,0 +2012,12,1,5,77,-17,-8,1034,2.67,0,0 +2012,12,1,6,53,-16,-8,1034,3.56,0,0 +2012,12,1,7,57,-13,-8,1033,4.45,0,0 +2012,12,1,8,78,-15,-6,1033,0.89,0,0 +2012,12,1,9,84,-16,-5,1033,1.78,0,0 +2012,12,1,10,101,-15,-4,1033,2.67,0,0 +2012,12,1,11,117,-14,-3,1032,1.79,0,0 +2012,12,1,12,118,-14,-2,1030,4.92,0,0 +2012,12,1,13,107,-14,-2,1029,9.84,0,0 +2012,12,1,14,90,-13,-1,1028,13.86,0,0 +2012,12,1,15,78,-11,-1,1028,18.78,0,0 +2012,12,1,16,76,-10,-2,1027,23.7,0,0 +2012,12,1,17,79,-8,-2,1027,26.83,0,0 +2012,12,1,18,93,-8,-4,1026,29.96,0,0 +2012,12,1,19,97,-7,-4,1026,31.75,0,0 +2012,12,1,20,97,-7,-4,1025,33.54,0,0 +2012,12,1,21,105,-6,-5,1025,34.43,0,0 +2012,12,1,22,112,-7,-6,1024,36.22,0,0 +2012,12,1,23,119,-6,-5,1024,38.01,0,0 +2012,12,2,0,128,-7,-6,1024,0.89,0,0 +2012,12,2,1,129,-7,-6,1024,1.78,0,0 +2012,12,2,2,132,-7,-7,1024,0.89,0,0 +2012,12,2,3,151,-7,-7,1023,1.78,0,0 +2012,12,2,4,163,-8,-8,1022,1.79,0,0 +2012,12,2,5,166,-9,-8,1022,3.58,0,0 +2012,12,2,6,155,-7,-7,1021,0.89,0,0 +2012,12,2,7,185,-6,-6,1022,1.78,0,0 +2012,12,2,8,192,-6,-6,1022,0.89,0,0 +2012,12,2,9,232,-6,-6,1022,1.78,1,0 +2012,12,2,10,253,-6,-5,1022,1.79,2,0 +2012,12,2,11,292,-5,-4,1021,1.79,0,0 +2012,12,2,12,294,-5,-3,1021,0.89,0,0 +2012,12,2,13,266,-5,-3,1019,1.78,0,0 +2012,12,2,14,289,-5,-3,1018,1.79,0,0 +2012,12,2,15,305,-5,-3,1018,1.79,0,0 +2012,12,2,16,332,-5,-2,1018,3.58,0,0 +2012,12,2,17,316,-5,-3,1018,5.37,0,0 +2012,12,2,18,263,-6,-3,1018,7.16,0,0 +2012,12,2,19,277,-5,-3,1018,8.95,0,0 +2012,12,2,20,282,-5,-4,1018,9.84,0,0 +2012,12,2,21,259,-5,-4,1018,0.89,0,0 +2012,12,2,22,268,-6,-4,1018,0.89,0,0 +2012,12,2,23,313,-5,-4,1017,1.78,0,0 +2012,12,3,0,322,-7,-6,1017,2.67,0,0 +2012,12,3,1,318,-6,-6,1018,1.79,0,0 +2012,12,3,2,327,-6,-5,1018,2.68,0,0 +2012,12,3,3,315,-9,-8,1018,4.47,0,0 +2012,12,3,4,36,-7,-6,1018,7.6,0,0 +2012,12,3,5,9,-9,-6,1018,9.39,0,0 +2012,12,3,6,12,-10,-3,1019,16.54,0,0 +2012,12,3,7,12,-14,-2,1021,24.59,0,0 +2012,12,3,8,12,-16,-1,1021,33.53,0,0 +2012,12,3,9,9,-18,0,1022,41.58,0,0 +2012,12,3,10,14,-18,1,1022,44.71,0,0 +2012,12,3,11,15,-18,2,1022,51.86,0,0 +2012,12,3,12,20,-19,3,1021,59.01,0,0 +2012,12,3,13,18,-19,2,1021,66.16,0,0 +2012,12,3,14,16,-19,2,1021,73.31,0,0 +2012,12,3,15,14,-19,3,1021,81.36,0,0 +2012,12,3,16,15,-18,2,1022,89.41,0,0 +2012,12,3,17,11,-18,1,1023,96.56,0,0 +2012,12,3,18,14,-18,1,1023,103.71,0,0 +2012,12,3,19,20,-18,0,1024,109.52,0,0 +2012,12,3,20,23,-18,-1,1025,113.54,0,0 +2012,12,3,21,27,-14,-5,1025,0.89,0,0 +2012,12,3,22,38,-12,-6,1024,0.89,0,0 +2012,12,3,23,40,-13,-5,1024,1.34,0,0 +2012,12,4,0,36,-13,-5,1024,1.79,0,0 +2012,12,4,1,54,-13,-5,1024,4.92,0,0 +2012,12,4,2,61,-14,-5,1023,8.05,0,0 +2012,12,4,3,59,-14,-6,1023,0.89,0,0 +2012,12,4,4,71,-13,-7,1022,0.89,0,0 +2012,12,4,5,102,-13,-9,1022,0.89,0,0 +2012,12,4,6,100,-13,-10,1022,1.78,0,0 +2012,12,4,7,99,-12,-9,1022,2.67,0,0 +2012,12,4,8,82,-12,-8,1023,0.89,0,0 +2012,12,4,9,95,-10,-5,1023,1.78,0,0 +2012,12,4,10,71,-10,-3,1023,2.67,0,0 +2012,12,4,11,91,-11,-1,1022,1.79,0,0 +2012,12,4,12,95,-13,0,1021,0.89,0,0 +2012,12,4,13,109,-14,1,1020,1.78,0,0 +2012,12,4,14,113,-14,2,1019,2.67,0,0 +2012,12,4,15,125,-15,3,1019,3.56,0,0 +2012,12,4,16,138,-15,1,1019,1.79,0,0 +2012,12,4,17,128,-12,-1,1019,1.79,0,0 +2012,12,4,18,138,-15,0,1020,3.58,0,0 +2012,12,4,19,149,-11,-4,1021,0.89,0,0 +2012,12,4,20,123,-11,-6,1021,1.79,0,0 +2012,12,4,21,78,-12,-4,1021,1.79,0,0 +2012,12,4,22,50,-13,-3,1022,4.92,0,0 +2012,12,4,23,39,-14,-4,1022,0.89,0,0 +2012,12,5,0,34,-13,-5,1022,1.78,0,0 +2012,12,5,1,35,-13,-8,1022,2.23,0,0 +2012,12,5,2,22,-14,-7,1022,3.12,0,0 +2012,12,5,3,13,-16,-3,1022,4.02,0,0 +2012,12,5,4,9,-16,-2,1022,11.17,0,0 +2012,12,5,5,9,-17,-2,1022,19.22,0,0 +2012,12,5,6,8,-17,-4,1023,24.14,0,0 +2012,12,5,7,7,-19,-3,1024,29.95,0,0 +2012,12,5,8,9,-19,-3,1024,35.76,0,0 +2012,12,5,9,9,-19,-2,1024,41.57,0,0 +2012,12,5,10,9,-21,-3,1025,50.51,0,0 +2012,12,5,11,11,-24,-2,1025,62.58,0,0 +2012,12,5,12,9,-23,-2,1024,74.65,0,0 +2012,12,5,13,15,-24,-2,1024,85.83,0,0 +2012,12,5,14,12,-24,-2,1024,95.66,0,0 +2012,12,5,15,10,-24,-2,1024,106.84,0,0 +2012,12,5,16,7,-24,-3,1025,114.89,0,0 +2012,12,5,17,9,-24,-5,1025,120.7,0,0 +2012,12,5,18,10,-23,-5,1026,126.51,0,0 +2012,12,5,19,9,-22,-6,1026,132.32,0,0 +2012,12,5,20,14,-22,-8,1026,135.45,0,0 +2012,12,5,21,17,-21,-7,1026,1.79,0,0 +2012,12,5,22,25,-20,-8,1026,0.89,0,0 +2012,12,5,23,29,-17,-10,1025,0.89,0,0 +2012,12,6,0,26,-17,-10,1024,1.78,0,0 +2012,12,6,1,44,-18,-9,1024,3.57,0,0 +2012,12,6,2,43,-17,-10,1024,0.89,0,0 +2012,12,6,3,28,-19,-8,1024,3.13,0,0 +2012,12,6,4,47,-18,-9,1023,1.79,0,0 +2012,12,6,5,49,-19,-9,1023,3.58,0,0 +2012,12,6,6,48,-17,-10,1024,0.89,0,0 +2012,12,6,7,39,-16,-9,1023,1.78,0,0 +2012,12,6,8,41,-14,-8,1023,1.79,0,0 +2012,12,6,9,42,-13,-9,1023,0.89,0,0 +2012,12,6,10,99,-14,-6,1023,1.78,0,0 +2012,12,6,11,83,-14,-5,1023,1.79,0,0 +2012,12,6,12,44,-16,-4,1021,3.58,0,0 +2012,12,6,13,44,-17,-3,1020,5.37,0,0 +2012,12,6,14,37,-18,-2,1019,0.89,0,0 +2012,12,6,15,39,-18,-2,1019,3.13,0,0 +2012,12,6,16,54,-18,-3,1019,4.92,0,0 +2012,12,6,17,78,-18,-4,1020,5.81,0,0 +2012,12,6,18,95,-15,-6,1021,6.7,0,0 +2012,12,6,19,106,-16,-6,1022,0.89,0,0 +2012,12,6,20,155,-15,-7,1022,0.45,0,0 +2012,12,6,21,144,-15,-9,1022,0.89,0,0 +2012,12,6,22,137,-15,-9,1023,4.02,0,0 +2012,12,6,23,158,-15,-10,1023,5.81,0,0 +2012,12,7,0,149,-14,-8,1023,7.6,0,0 +2012,12,7,1,149,-14,-7,1024,11.62,0,0 +2012,12,7,2,114,-15,-8,1024,14.75,0,0 +2012,12,7,3,84,-15,-9,1024,19.67,0,0 +2012,12,7,4,61,-15,-10,1024,23.69,0,0 +2012,12,7,5,51,-16,-11,1024,27.71,0,0 +2012,12,7,6,40,-18,-9,1024,29.5,0,0 +2012,12,7,7,31,-18,-11,1024,32.63,0,0 +2012,12,7,8,36,-17,-8,1025,34.42,0,0 +2012,12,7,9,35,-18,-6,1025,0.89,0,0 +2012,12,7,10,44,-18,-5,1025,3.13,0,0 +2012,12,7,11,44,-20,-3,1024,4.92,0,0 +2012,12,7,12,46,-20,-2,1023,0.89,0,0 +2012,12,7,13,50,-21,-1,1022,1.78,0,0 +2012,12,7,14,95,-21,1,1021,2.67,0,0 +2012,12,7,15,85,-22,1,1021,8.94,0,0 +2012,12,7,16,97,-19,0,1022,16.09,0,0 +2012,12,7,17,102,-17,-1,1022,21.9,0,0 +2012,12,7,18,61,-17,-1,1023,27.71,0,0 +2012,12,7,19,16,-21,-2,1024,33.52,0,0 +2012,12,7,20,18,-21,-3,1025,40.67,0,0 +2012,12,7,21,9,-22,-3,1025,49.61,0,0 +2012,12,7,22,9,-24,-5,1026,56.76,0,0 +2012,12,7,23,10,-24,-6,1027,62.57,0,0 +2012,12,8,0,16,-25,-7,1028,72.4,0,0 +2012,12,8,1,13,-27,-8,1029,82.23,0,0 +2012,12,8,2,9,-27,-9,1029,92.06,0,0 +2012,12,8,3,9,-27,-9,1029,100.11,0,0 +2012,12,8,4,8,-27,-10,1029,109.05,0,0 +2012,12,8,5,7,-27,-10,1029,117.1,0,0 +2012,12,8,6,10,-27,-10,1029,125.15,0,0 +2012,12,8,7,11,-27,-10,1029,130.96,0,0 +2012,12,8,8,14,-27,-9,1029,139.01,0,0 +2012,12,8,9,16,-27,-8,1029,147.95,0,0 +2012,12,8,10,16,-28,-7,1029,156,0,0 +2012,12,8,11,14,-27,-6,1029,161.81,0,0 +2012,12,8,12,16,-27,-5,1028,167.62,0,0 +2012,12,8,13,14,-27,-5,1027,177.45,0,0 +2012,12,8,14,17,-27,-4,1026,186.39,0,0 +2012,12,8,15,14,-26,-4,1026,193.54,0,0 +2012,12,8,16,14,-26,-5,1027,202.48,0,0 +2012,12,8,17,13,-25,-7,1026,209.63,0,0 +2012,12,8,18,13,-25,-7,1027,217.68,0,0 +2012,12,8,19,14,-25,-7,1027,225.73,0,0 +2012,12,8,20,15,-25,-7,1027,235.56,0,0 +2012,12,8,21,14,-24,-8,1028,241.37,0,0 +2012,12,8,22,13,-24,-7,1028,249.42,0,0 +2012,12,8,23,16,-25,-7,1028,256.57,0,0 +2012,12,9,0,15,-25,-7,1028,265.51,0,0 +2012,12,9,1,17,-25,-8,1028,272.66,0,0 +2012,12,9,2,13,-25,-8,1029,278.47,0,0 +2012,12,9,3,14,-24,-8,1028,284.28,0,0 +2012,12,9,4,10,-25,-8,1028,291.43,0,0 +2012,12,9,5,13,-25,-8,1027,296.35,0,0 +2012,12,9,6,NA,-24,-10,1027,301.27,0,0 +2012,12,9,7,16,-23,-10,1028,305.29,0,0 +2012,12,9,8,15,-22,-9,1029,309.31,0,0 +2012,12,9,9,15,-23,-6,1029,315.12,0,0 +2012,12,9,10,19,-22,-4,1029,319.14,0,0 +2012,12,9,11,17,-23,-2,1028,327.19,0,0 +2012,12,9,12,18,-24,-1,1027,334.34,0,0 +2012,12,9,13,21,-26,0,1026,341.49,0,0 +2012,12,9,14,20,-26,1,1026,348.64,0,0 +2012,12,9,15,14,-26,1,1025,355.79,0,0 +2012,12,9,16,16,-26,0,1025,362.94,0,0 +2012,12,9,17,21,-23,0,1025,368.75,0,0 +2012,12,9,18,27,-20,-5,1026,0.89,0,0 +2012,12,9,19,28,-18,-8,1026,3.13,0,0 +2012,12,9,20,43,-18,-5,1026,1.79,0,0 +2012,12,9,21,51,-16,-6,1027,3.13,0,0 +2012,12,9,22,48,-16,-4,1027,4.92,0,0 +2012,12,9,23,35,-16,-3,1027,8.94,0,0 +2012,12,10,0,37,-16,-5,1027,3.13,0,0 +2012,12,10,1,30,-16,-6,1027,6.26,0,0 +2012,12,10,2,22,-15,-6,1027,9.39,0,0 +2012,12,10,3,29,-16,-4,1027,12.52,0,0 +2012,12,10,4,21,-16,-5,1027,15.65,0,0 +2012,12,10,5,27,-16,-6,1028,1.79,0,0 +2012,12,10,6,25,-15,-8,1028,0.89,0,0 +2012,12,10,7,38,-16,-10,1029,1.78,0,0 +2012,12,10,8,59,-13,-7,1030,3.13,0,0 +2012,12,10,9,102,-13,-3,1030,6.26,0,0 +2012,12,10,10,78,-14,-2,1030,8.05,0,0 +2012,12,10,11,68,-15,-2,1030,9.84,0,0 +2012,12,10,12,62,-15,-1,1030,1.79,0,0 +2012,12,10,13,75,-14,-1,1029,3.58,0,0 +2012,12,10,14,82,-13,-1,1029,6.71,0,0 +2012,12,10,15,96,-13,-1,1029,8.5,0,0 +2012,12,10,16,92,-13,-1,1029,10.29,0,0 +2012,12,10,17,80,-13,-3,1030,0.89,0,0 +2012,12,10,18,97,-13,-6,1030,1.78,0,0 +2012,12,10,19,104,-12,-5,1031,2.67,0,0 +2012,12,10,20,122,-11,-5,1031,0.89,0,0 +2012,12,10,21,117,-11,-5,1032,0.89,0,0 +2012,12,10,22,120,-11,-6,1032,1.34,0,0 +2012,12,10,23,129,-10,-6,1032,1.79,0,0 +2012,12,11,0,179,-11,-7,1032,2.68,0,0 +2012,12,11,1,165,-11,-8,1032,3.13,0,0 +2012,12,11,2,200,-13,-9,1032,3.58,0,0 +2012,12,11,3,211,-11,-9,1032,4.03,0,0 +2012,12,11,4,146,-12,-9,1032,4.48,0,0 +2012,12,11,5,107,-12,-10,1032,0.89,0,0 +2012,12,11,6,116,-12,-10,1033,1.79,0,0 +2012,12,11,7,125,-12,-9,1033,1.79,0,0 +2012,12,11,8,148,-11,-9,1034,1.79,0,0 +2012,12,11,9,95,-9,-5,1035,4.92,0,0 +2012,12,11,10,101,-9,-4,1035,8.05,0,0 +2012,12,11,11,73,-10,-3,1034,9.84,0,0 +2012,12,11,12,107,-10,-1,1034,11.63,0,0 +2012,12,11,13,114,-12,0,1033,0.89,0,0 +2012,12,11,14,115,-12,-1,1032,1.78,0,0 +2012,12,11,15,111,-12,-1,1031,1.79,0,0 +2012,12,11,16,122,-11,-1,1031,3.58,0,0 +2012,12,11,17,167,-11,-3,1031,5.37,0,0 +2012,12,11,18,179,-11,-4,1032,6.26,0,0 +2012,12,11,19,146,-11,-4,1032,7.15,0,0 +2012,12,11,20,146,-10,-4,1032,8.94,0,0 +2012,12,11,21,139,-11,-6,1032,9.83,0,0 +2012,12,11,22,126,-9,-5,1032,11.62,0,0 +2012,12,11,23,151,-8,-5,1032,12.51,0,0 +2012,12,12,0,154,-8,-5,1032,14.3,0,0 +2012,12,12,1,142,-7,-5,1031,16.09,0,0 +2012,12,12,2,140,-7,-5,1031,17.88,0,0 +2012,12,12,3,180,-7,-5,1031,19.67,0,0 +2012,12,12,4,205,-7,-5,1031,21.46,0,0 +2012,12,12,5,200,-7,-5,1031,23.25,0,0 +2012,12,12,6,184,-6,-5,1031,25.04,0,0 +2012,12,12,7,172,-5,-4,1030,26.83,0,0 +2012,12,12,8,149,-5,-4,1030,28.62,0,0 +2012,12,12,9,154,-5,-4,1030,30.41,0,0 +2012,12,12,10,121,-5,-4,1030,32.2,1,0 +2012,12,12,11,187,-4,-3,1030,35.33,2,0 +2012,12,12,12,139,-4,-3,1029,38.46,3,0 +2012,12,12,13,111,-3,-2,1028,41.59,4,0 +2012,12,12,14,110,-3,-2,1027,44.72,5,0 +2012,12,12,15,121,-3,-2,1027,46.51,6,0 +2012,12,12,16,284,-3,-2,1027,0.89,7,0 +2012,12,12,17,277,-4,-2,1027,1.79,8,0 +2012,12,12,18,289,-4,-2,1027,2.68,0,0 +2012,12,12,19,281,-4,-2,1027,3.57,0,0 +2012,12,12,20,277,-4,-3,1028,1.79,0,0 +2012,12,12,21,287,-4,-3,1028,0.89,0,0 +2012,12,12,22,290,-4,-3,1028,0.45,0,0 +2012,12,12,23,259,-4,-3,1028,0.89,0,0 +2012,12,13,0,268,-4,-3,1028,1.78,0,0 +2012,12,13,1,274,-4,-3,1028,0.89,0,0 +2012,12,13,2,281,-4,-3,1028,0.89,0,0 +2012,12,13,3,255,-4,-3,1028,0.45,0,0 +2012,12,13,4,243,-4,-3,1027,1.79,0,0 +2012,12,13,5,247,-4,-3,1027,3.58,0,0 +2012,12,13,6,258,-4,-3,1027,5.37,0,0 +2012,12,13,7,259,-4,-3,1027,7.16,0,0 +2012,12,13,8,235,-4,-2,1028,8.95,0,0 +2012,12,13,9,214,-4,-2,1028,12.08,0,0 +2012,12,13,10,203,-4,-2,1028,13.87,0,0 +2012,12,13,11,187,-4,-1,1028,15.66,0,0 +2012,12,13,12,185,-4,-1,1027,18.79,0,0 +2012,12,13,13,186,-5,-1,1026,21.92,0,0 +2012,12,13,14,196,-5,-1,1026,23.71,0,0 +2012,12,13,15,204,-5,-1,1026,25.5,0,0 +2012,12,13,16,208,-5,-1,1026,27.29,0,0 +2012,12,13,17,203,-5,-1,1026,30.42,0,0 +2012,12,13,18,197,-5,-1,1026,33.55,0,0 +2012,12,13,19,202,-5,-1,1026,36.68,0,0 +2012,12,13,20,206,-5,-1,1026,39.81,0,0 +2012,12,13,21,212,-5,-1,1026,42.94,1,0 +2012,12,13,22,205,-4,-1,1027,46.07,2,0 +2012,12,13,23,217,-3,-2,1027,49.2,3,0 +2012,12,14,0,205,-3,-2,1026,52.33,4,0 +2012,12,14,1,203,-2,-2,1026,55.46,5,0 +2012,12,14,2,199,-2,-2,1026,59.48,6,0 +2012,12,14,3,201,-2,-2,1025,63.5,7,0 +2012,12,14,4,184,-2,-2,1025,66.63,8,0 +2012,12,14,5,175,-2,-2,1024,70.65,9,0 +2012,12,14,6,191,-2,-2,1024,75.57,10,0 +2012,12,14,7,196,-2,-2,1024,80.49,11,0 +2012,12,14,8,200,-1,-1,1024,85.41,12,0 +2012,12,14,9,219,-1,-1,1024,90.33,13,0 +2012,12,14,10,241,-1,0,1025,94.35,14,0 +2012,12,14,11,240,-1,0,1024,98.37,15,0 +2012,12,14,12,231,-1,0,1024,102.39,16,0 +2012,12,14,13,213,-1,0,1023,107.31,17,0 +2012,12,14,14,210,0,1,1023,109.1,18,0 +2012,12,14,15,197,-1,1,1022,110.89,19,0 +2012,12,14,16,204,0,1,1023,111.78,0,0 +2012,12,14,17,195,0,1,1023,113.57,0,0 +2012,12,14,18,199,-1,1,1023,0.89,0,0 +2012,12,14,19,207,-1,1,1023,0.89,0,0 +2012,12,14,20,211,-1,0,1023,1.78,0,0 +2012,12,14,21,225,0,0,1023,0.45,0,0 +2012,12,14,22,232,0,0,1023,1.34,0,0 +2012,12,14,23,221,0,0,1023,0.89,0,0 +2012,12,15,0,192,0,1,1023,1.78,0,0 +2012,12,15,1,256,0,1,1023,3.57,0,0 +2012,12,15,2,222,0,1,1023,5.36,0,0 +2012,12,15,3,215,0,1,1023,7.15,0,0 +2012,12,15,4,209,0,1,1022,0.89,0,0 +2012,12,15,5,227,0,1,1022,1.79,0,0 +2012,12,15,6,222,0,1,1022,3.58,0,0 +2012,12,15,7,197,0,1,1023,5.37,0,0 +2012,12,15,8,186,-1,-1,1023,0.89,0,0 +2012,12,15,9,172,0,1,1023,0.89,0,0 +2012,12,15,10,226,1,1,1024,0.89,0,0 +2012,12,15,11,324,1,2,1024,1.34,0,0 +2012,12,15,12,276,0,2,1023,2.23,0,0 +2012,12,15,13,305,0,2,1023,3.12,0,0 +2012,12,15,14,322,0,3,1023,4.01,0,0 +2012,12,15,15,284,0,3,1023,3.13,0,0 +2012,12,15,16,271,0,3,1023,0.89,0,0 +2012,12,15,17,260,0,3,1024,3.13,0,0 +2012,12,15,18,290,-1,2,1024,0.89,0,0 +2012,12,15,19,323,-2,-1,1025,1.79,0,0 +2012,12,15,20,348,-2,-2,1026,0.89,0,0 +2012,12,15,21,343,-1,0,1026,1.78,0,0 +2012,12,15,22,309,0,1,1028,1.79,0,0 +2012,12,15,23,299,0,1,1028,3.58,0,0 +2012,12,16,0,335,0,1,1028,0.89,0,0 +2012,12,16,1,284,-1,1,1028,3.13,0,0 +2012,12,16,2,262,0,0,1028,7.15,0,1 +2012,12,16,3,251,0,0,1028,8.94,0,0 +2012,12,16,4,243,0,0,1027,12.96,0,1 +2012,12,16,5,218,-1,0,1027,16.98,0,2 +2012,12,16,6,124,-1,-1,1028,21,0,3 +2012,12,16,7,96,-2,-2,1028,25.02,0,0 +2012,12,16,8,91,-3,-2,1028,29.04,1,0 +2012,12,16,9,76,-4,-2,1028,33.96,2,0 +2012,12,16,10,71,-4,-2,1029,37.09,3,0 +2012,12,16,11,76,-5,-2,1029,40.22,4,0 +2012,12,16,12,69,-5,-2,1028,42.01,5,0 +2012,12,16,13,70,-5,-2,1027,43.8,6,0 +2012,12,16,14,72,-4,-2,1027,0.89,7,0 +2012,12,16,15,78,-5,-2,1026,1.79,8,0 +2012,12,16,16,85,-4,-2,1026,3.58,0,0 +2012,12,16,17,94,-4,-2,1027,0.89,0,0 +2012,12,16,18,101,-4,-2,1027,0.89,0,0 +2012,12,16,19,112,-4,-2,1028,0.89,0,0 +2012,12,16,20,115,-4,-2,1029,0.89,0,0 +2012,12,16,21,138,-3,-2,1029,1.79,0,0 +2012,12,16,22,155,-3,-2,1029,3.13,0,0 +2012,12,16,23,141,-4,-2,1030,6.26,0,0 +2012,12,17,0,147,-3,-2,1030,9.39,0,0 +2012,12,17,1,138,-5,-4,1030,11.18,0,0 +2012,12,17,2,86,-5,-5,1031,0.89,0,0 +2012,12,17,3,28,-6,-5,1031,1.79,0,0 +2012,12,17,4,19,-10,-3,1031,8.94,0,0 +2012,12,17,5,20,-11,-3,1032,16.09,0,0 +2012,12,17,6,21,-12,-4,1033,21.9,0,0 +2012,12,17,7,20,-13,-4,1033,29.95,0,0 +2012,12,17,8,17,-15,-3,1035,41.13,0,0 +2012,12,17,9,16,-15,-3,1036,49.18,0,0 +2012,12,17,10,14,-14,-2,1036,56.33,0,0 +2012,12,17,11,13,-13,-2,1036,63.48,0,0 +2012,12,17,12,18,-14,-1,1036,70.63,0,0 +2012,12,17,13,19,-14,-1,1035,78.68,0,0 +2012,12,17,14,16,-13,-1,1034,86.73,0,0 +2012,12,17,15,19,-13,-1,1034,93.88,0,0 +2012,12,17,16,22,-14,-2,1035,99.69,0,0 +2012,12,17,17,20,-16,-2,1035,107.74,0,0 +2012,12,17,18,22,-17,-3,1036,116.68,0,0 +2012,12,17,19,22,-17,-4,1036,123.83,0,0 +2012,12,17,20,17,-18,-4,1036,130.98,0,0 +2012,12,17,21,20,-17,-5,1037,135.9,0,0 +2012,12,17,22,24,-19,-5,1038,143.05,0,0 +2012,12,17,23,23,-18,-5,1038,151.1,0,0 +2012,12,18,0,17,-18,-6,1038,160.04,0,0 +2012,12,18,1,18,-19,-6,1039,165.85,0,0 +2012,12,18,2,22,-18,-9,1039,169.87,0,0 +2012,12,18,3,16,-18,-9,1039,173,0,0 +2012,12,18,4,18,-19,-9,1038,3.13,0,0 +2012,12,18,5,17,-19,-10,1038,4.02,0,0 +2012,12,18,6,13,-19,-9,1038,3.13,0,0 +2012,12,18,7,21,-19,-9,1039,7.15,0,0 +2012,12,18,8,17,-19,-9,1039,0.45,0,0 +2012,12,18,9,37,-18,-7,1039,3.13,0,0 +2012,12,18,10,24,-18,-4,1038,7.15,0,0 +2012,12,18,11,31,-18,-5,1038,3.13,0,0 +2012,12,18,12,25,-18,-4,1037,6.26,0,0 +2012,12,18,13,29,-16,-3,1035,1.79,0,0 +2012,12,18,14,24,-16,-2,1034,0.89,0,0 +2012,12,18,15,25,-17,-3,1033,1.79,0,0 +2012,12,18,16,35,-16,-4,1033,0.89,0,0 +2012,12,18,17,53,-15,-5,1033,1.79,0,0 +2012,12,18,18,69,-16,-5,1033,0.89,0,0 +2012,12,18,19,62,-14,-6,1032,1.78,0,0 +2012,12,18,20,78,-14,-5,1033,1.79,0,0 +2012,12,18,21,81,-12,-5,1032,3.58,0,0 +2012,12,18,22,90,-12,-5,1032,6.71,0,0 +2012,12,18,23,85,-12,-6,1032,0.89,0,0 +2012,12,19,0,93,-11,-8,1031,0.89,0,0 +2012,12,19,1,114,-11,-7,1031,0.89,0,0 +2012,12,19,2,124,-12,-8,1030,1.78,0,0 +2012,12,19,3,138,-12,-7,1029,3.57,0,0 +2012,12,19,4,166,-12,-7,1029,5.36,0,0 +2012,12,19,5,176,-12,-8,1028,0.89,0,0 +2012,12,19,6,174,-12,-8,1028,1.78,0,0 +2012,12,19,7,199,-12,-7,1028,3.57,0,0 +2012,12,19,8,172,-11,-6,1029,4.46,0,0 +2012,12,19,9,158,-11,-6,1029,5.35,0,0 +2012,12,19,10,142,-10,-5,1029,6.24,0,0 +2012,12,19,11,152,-10,-5,1029,0.89,0,0 +2012,12,19,12,168,-10,-4,1028,1.78,0,0 +2012,12,19,13,186,-10,-3,1028,2.67,0,0 +2012,12,19,14,203,-9,-3,1028,3.56,0,0 +2012,12,19,15,209,-9,-3,1028,4.45,0,0 +2012,12,19,16,215,-9,-4,1028,4.9,0,0 +2012,12,19,17,236,-9,-5,1028,0.89,0,0 +2012,12,19,18,245,-9,-7,1029,0.89,0,0 +2012,12,19,19,258,-9,-6,1030,0.89,0,0 +2012,12,19,20,290,-11,-9,1031,3.13,0,0 +2012,12,19,21,256,-11,-9,1031,6.26,0,0 +2012,12,19,22,290,-11,-9,1031,8.05,0,0 +2012,12,19,23,241,-10,-8,1031,9.84,0,0 +2012,12,20,0,244,-9,-6,1031,12.97,0,0 +2012,12,20,1,244,-12,-10,1031,14.76,0,0 +2012,12,20,2,240,-11,-9,1031,17.89,0,0 +2012,12,20,3,217,-11,-10,1030,19.68,0,0 +2012,12,20,4,244,-12,-10,1030,21.47,0,0 +2012,12,20,5,267,-11,-9,1030,23.26,0,0 +2012,12,20,6,235,-10,-9,1030,25.05,0,0 +2012,12,20,7,NA,-9,-7,1031,0.45,0,0 +2012,12,20,8,244,-10,-8,1031,1.34,0,0 +2012,12,20,9,187,-9,-6,1032,0.89,0,0 +2012,12,20,10,169,-9,-6,1032,1.78,0,0 +2012,12,20,11,175,-7,-4,1032,0.89,0,0 +2012,12,20,12,208,-7,-3,1031,1.78,0,0 +2012,12,20,13,244,-7,-2,1030,2.67,0,0 +2012,12,20,14,251,-7,-3,1030,3.56,0,0 +2012,12,20,15,246,-7,-3,1029,1.79,0,0 +2012,12,20,16,273,-7,-3,1028,3.58,0,0 +2012,12,20,17,308,-7,-3,1028,0.89,0,0 +2012,12,20,18,276,-7,-3,1028,1.79,0,0 +2012,12,20,19,229,-6,-3,1028,4.92,1,0 +2012,12,20,20,218,-5,-4,1029,0.89,2,0 +2012,12,20,21,229,-4,-4,1029,0.89,3,0 +2012,12,20,22,259,-4,-4,1029,0.89,4,0 +2012,12,20,23,314,-5,-4,1028,0.89,5,0 +2012,12,21,0,315,-5,-4,1028,0.89,6,0 +2012,12,21,1,380,-5,-4,1027,0.89,0,0 +2012,12,21,2,355,-5,-4,1027,2.68,0,0 +2012,12,21,3,323,-5,-5,1027,3.57,0,0 +2012,12,21,4,303,-8,-7,1026,5.36,0,0 +2012,12,21,5,289,-8,-8,1026,7.15,0,0 +2012,12,21,6,299,-10,-9,1027,10.28,0,0 +2012,12,21,7,280,-9,-8,1027,13.41,0,0 +2012,12,21,8,NA,-7,-6,1028,17.43,0,0 +2012,12,21,9,80,-6,-5,1029,21.45,0,0 +2012,12,21,10,35,-7,-3,1029,25.47,0,0 +2012,12,21,11,29,-8,0,1029,29.49,0,0 +2012,12,21,12,27,-10,0,1028,4.92,0,0 +2012,12,21,13,18,-12,1,1028,8.05,0,0 +2012,12,21,14,29,-12,1,1028,15.2,0,0 +2012,12,21,15,22,-14,1,1028,20.12,0,0 +2012,12,21,16,19,-13,0,1029,25.93,0,0 +2012,12,21,17,20,-13,-1,1030,31.74,0,0 +2012,12,21,18,23,-15,-1,1031,35.76,0,0 +2012,12,21,19,24,-16,-2,1032,41.57,0,0 +2012,12,21,20,19,-16,-4,1033,46.49,0,0 +2012,12,21,21,19,-15,-6,1034,50.51,0,0 +2012,12,21,22,15,-17,-6,1035,54.53,0,0 +2012,12,21,23,21,-17,-7,1035,58.55,0,0 +2012,12,22,0,16,-18,-7,1035,4.02,0,0 +2012,12,22,1,17,-18,-7,1034,8.04,0,0 +2012,12,22,2,21,-18,-9,1035,3.13,0,0 +2012,12,22,3,13,-18,-8,1035,4.02,0,0 +2012,12,22,4,22,-18,-8,1034,8.94,0,0 +2012,12,22,5,21,-18,-8,1035,12.96,0,0 +2012,12,22,6,19,-18,-8,1035,16.98,0,0 +2012,12,22,7,25,-19,-8,1035,21.9,0,0 +2012,12,22,8,31,-18,-8,1036,25.92,0,0 +2012,12,22,9,23,-19,-7,1036,30.84,0,0 +2012,12,22,10,22,-19,-6,1036,35.76,0,0 +2012,12,22,11,25,-19,-5,1036,39.78,0,0 +2012,12,22,12,36,-16,-3,1035,1.79,0,0 +2012,12,22,13,38,-16,-2,1034,4.92,0,0 +2012,12,22,14,32,-15,-2,1033,8.94,0,0 +2012,12,22,15,32,-16,-2,1033,17.88,0,0 +2012,12,22,16,34,-16,-3,1034,26.82,0,0 +2012,12,22,17,33,-20,-4,1035,34.87,0,0 +2012,12,22,18,30,-20,-5,1037,44.7,0,0 +2012,12,22,19,23,-21,-6,1038,52.75,0,0 +2012,12,22,20,24,-23,-7,1039,62.58,0,0 +2012,12,22,21,21,-22,-8,1039,70.63,0,0 +2012,12,22,22,15,-24,-9,1040,82.7,0,0 +2012,12,22,23,12,-25,-10,1040,96.56,0,0 +2012,12,23,0,12,-25,-10,1040,108.63,0,0 +2012,12,23,1,10,-24,-10,1039,117.57,0,0 +2012,12,23,2,15,-25,-11,1040,124.72,0,0 +2012,12,23,3,13,-25,-11,1039,130.53,0,0 +2012,12,23,4,9,-26,-11,1039,137.68,0,0 +2012,12,23,5,12,-27,-12,1039,146.62,0,0 +2012,12,23,6,NA,-26,-13,1039,150.64,0,0 +2012,12,23,7,NA,-27,-13,1040,158.69,0,0 +2012,12,23,8,NA,-26,-12,1040,167.63,0,0 +2012,12,23,9,NA,-26,-11,1040,174.78,0,0 +2012,12,23,10,NA,-25,-10,1040,182.83,0,0 +2012,12,23,11,NA,-25,-10,1039,192.66,0,0 +2012,12,23,12,NA,-25,-8,1038,199.81,0,0 +2012,12,23,13,NA,-24,-8,1036,205.62,0,0 +2012,12,23,14,NA,-24,-7,1034,211.43,0,0 +2012,12,23,15,NA,-24,-7,1033,218.58,0,0 +2012,12,23,16,NA,-24,-7,1033,222.6,0,0 +2012,12,23,17,NA,-22,-9,1032,226.62,0,0 +2012,12,23,18,NA,-22,-11,1032,1.79,0,0 +2012,12,23,19,NA,-21,-12,1032,0.89,0,0 +2012,12,23,20,NA,-21,-12,1032,1.78,0,0 +2012,12,23,21,NA,-20,-13,1032,2.67,0,0 +2012,12,23,22,NA,-20,-15,1032,0.89,0,0 +2012,12,23,23,NA,-19,-15,1031,0.45,0,0 +2012,12,24,0,NA,-19,-14,1031,0.89,0,0 +2012,12,24,1,NA,-19,-15,1030,0.45,0,0 +2012,12,24,2,NA,-21,-17,1030,0.89,0,0 +2012,12,24,3,NA,-20,-17,1029,0.89,0,0 +2012,12,24,4,NA,-21,-18,1028,1.78,0,0 +2012,12,24,5,NA,-20,-16,1028,0.89,0,0 +2012,12,24,6,NA,-20,-17,1028,2.68,0,0 +2012,12,24,7,NA,-21,-18,1028,5.81,0,0 +2012,12,24,8,NA,-20,-17,1028,7.6,0,0 +2012,12,24,9,NA,-18,-13,1027,9.39,0,0 +2012,12,24,10,NA,-17,-11,1027,11.18,0,0 +2012,12,24,11,NA,-18,-9,1027,12.97,0,0 +2012,12,24,12,NA,-19,-8,1026,14.76,0,0 +2012,12,24,13,NA,-18,-7,1024,1.79,0,0 +2012,12,24,14,NA,-17,-6,1024,1.79,0,0 +2012,12,24,15,NA,-16,-7,1024,3.58,0,0 +2012,12,24,16,NA,-16,-7,1024,5.37,0,0 +2012,12,24,17,NA,-15,-9,1026,4.02,0,0 +2012,12,24,18,NA,-18,-7,1028,8.94,0,0 +2012,12,24,19,NA,-23,-7,1031,17.88,0,0 +2012,12,24,20,NA,-24,-8,1031,25.03,0,0 +2012,12,24,21,NA,-22,-10,1032,30.84,0,0 +2012,12,24,22,NA,-24,-9,1032,38.89,0,0 +2012,12,24,23,NA,-24,-10,1034,42.91,0,0 +2012,12,25,0,NA,-25,-10,1034,48.72,0,0 +2012,12,25,1,NA,-26,-10,1035,55.87,0,0 +2012,12,25,2,NA,-25,-10,1036,61.68,0,0 +2012,12,25,3,NA,-24,-10,1036,70.62,0,0 +2012,12,25,4,NA,-26,-10,1036,79.56,0,0 +2012,12,25,5,NA,-25,-11,1036,85.37,0,0 +2012,12,25,6,NA,-25,-12,1037,4.02,0,0 +2012,12,25,7,NA,-25,-12,1038,8.94,0,0 +2012,12,25,8,NA,-25,-11,1039,12.07,0,0 +2012,12,25,9,NA,-26,-10,1040,16.99,0,0 +2012,12,25,10,NA,-25,-8,1041,20.12,0,0 +2012,12,25,11,NA,-26,-7,1040,23.25,0,0 +2012,12,25,12,NA,-26,-7,1040,26.38,0,0 +2012,12,25,13,NA,-26,-7,1039,1.79,0,0 +2012,12,25,14,NA,-26,-7,1038,0.89,0,0 +2012,12,25,15,NA,-25,-5,1038,3.13,0,0 +2012,12,25,16,NA,-25,-6,1038,1.79,0,0 +2012,12,25,17,NA,-22,-8,1038,2.68,0,0 +2012,12,25,18,NA,-23,-8,1038,3.57,0,0 +2012,12,25,19,NA,-22,-9,1039,0.89,0,0 +2012,12,25,20,NA,-19,-10,1039,0.45,0,0 +2012,12,25,21,NA,-18,-12,1039,1.34,0,0 +2012,12,25,22,NA,-20,-12,1039,2.23,0,0 +2012,12,25,23,NA,-18,-13,1039,0.89,0,0 +2012,12,26,0,NA,-17,-13,1039,0.89,0,0 +2012,12,26,1,NA,-19,-14,1039,0.89,0,0 +2012,12,26,2,NA,-18,-14,1039,1.78,0,0 +2012,12,26,3,NA,-19,-14,1039,0.45,0,0 +2012,12,26,4,NA,-19,-15,1038,1.79,0,0 +2012,12,26,5,NA,-18,-15,1037,0.89,0,0 +2012,12,26,6,NA,-18,-15,1037,1.79,0,0 +2012,12,26,7,NA,-20,-17,1038,4.92,0,0 +2012,12,26,8,NA,-19,-16,1038,0.89,0,0 +2012,12,26,9,NA,-17,-13,1038,2.68,0,0 +2012,12,26,10,NA,-16,-10,1038,0.89,0,0 +2012,12,26,11,NA,-17,-9,1037,1.78,0,0 +2012,12,26,12,NA,-18,-8,1036,2.67,0,0 +2012,12,26,13,NA,-17,-7,1035,3.56,0,0 +2012,12,26,14,NA,-17,-6,1034,4.45,0,0 +2012,12,26,15,NA,-16,-6,1033,5.34,0,0 +2012,12,26,16,NA,-16,-6,1033,0.89,0,0 +2012,12,26,17,NA,-16,-9,1033,1.78,0,0 +2012,12,26,18,NA,-15,-10,1033,0.89,0,0 +2012,12,26,19,NA,-15,-10,1033,1.78,0,0 +2012,12,26,20,NA,-16,-11,1033,0.89,0,0 +2012,12,26,21,NA,-16,-13,1033,1.78,0,0 +2012,12,26,22,NA,-16,-12,1033,0.89,0,0 +2012,12,26,23,NA,-16,-13,1032,1.79,0,0 +2012,12,27,0,NA,-17,-14,1032,4.92,0,0 +2012,12,27,1,NA,-16,-12,1032,6.71,0,0 +2012,12,27,2,NA,-16,-14,1031,8.5,0,0 +2012,12,27,3,NA,-16,-13,1031,10.29,0,0 +2012,12,27,4,NA,-17,-14,1030,12.08,0,0 +2012,12,27,5,NA,-17,-15,1029,15.21,0,0 +2012,12,27,6,NA,-18,-15,1029,18.34,0,0 +2012,12,27,7,NA,-17,-15,1029,20.13,0,0 +2012,12,27,8,NA,-16,-13,1029,23.26,0,0 +2012,12,27,9,NA,-14,-10,1030,26.39,0,0 +2012,12,27,10,NA,-14,-9,1030,29.52,0,0 +2012,12,27,11,NA,-14,-7,1030,32.65,0,0 +2012,12,27,12,NA,-14,-6,1029,34.44,0,0 +2012,12,27,13,NA,-14,-5,1027,0.89,0,0 +2012,12,27,14,NA,-14,-5,1027,1.78,0,0 +2012,12,27,15,NA,-13,-5,1026,2.67,0,0 +2012,12,27,16,NA,-13,-5,1026,0.89,0,0 +2012,12,27,17,NA,-14,-7,1026,1.78,0,0 +2012,12,27,18,NA,-14,-8,1027,0.45,0,0 +2012,12,27,19,NA,-14,-10,1027,1.79,0,0 +2012,12,27,20,NA,-14,-11,1027,3.58,0,0 +2012,12,27,21,NA,-14,-11,1027,0.89,0,0 +2012,12,27,22,NA,-14,-11,1027,0.89,0,0 +2012,12,27,23,NA,-14,-11,1027,0.89,0,0 +2012,12,28,0,NA,-13,-11,1027,4.91,0,0 +2012,12,28,1,NA,-12,-9,1027,5.8,0,0 +2012,12,28,2,NA,-12,-9,1027,7.59,0,0 +2012,12,28,3,NA,-12,-8,1026,10.72,0,0 +2012,12,28,4,NA,-12,-8,1025,12.51,0,0 +2012,12,28,5,NA,-13,-8,1025,16.53,0,0 +2012,12,28,6,NA,-13,-8,1026,19.66,0,0 +2012,12,28,7,NA,-13,-8,1026,23.68,0,0 +2012,12,28,8,NA,-13,-8,1026,26.81,0,0 +2012,12,28,9,NA,-12,-7,1026,29.94,0,0 +2012,12,28,10,NA,-12,-7,1027,33.07,0,0 +2012,12,28,11,NA,-12,-6,1027,34.86,0,0 +2012,12,28,12,NA,-12,-6,1027,36.65,0,0 +2012,12,28,13,205,-12,-6,1026,38.44,0,0 +2012,12,28,14,215,-11,-5,1025,41.57,0,0 +2012,12,28,15,219,-11,-5,1025,43.36,0,0 +2012,12,28,16,217,-11,-4,1025,46.49,0,0 +2012,12,28,17,270,-11,-4,1026,48.28,0,0 +2012,12,28,18,269,-11,-5,1027,51.41,1,0 +2012,12,28,19,254,-7,-5,1027,54.54,2,0 +2012,12,28,20,241,-6,-6,1027,56.33,3,0 +2012,12,28,21,191,-6,-5,1028,59.46,4,0 +2012,12,28,22,249,-6,-6,1028,61.25,5,0 +2012,12,28,23,288,-6,-5,1027,1.79,0,0 +2012,12,29,0,284,-6,-5,1028,4.02,0,0 +2012,12,29,1,248,-7,-6,1029,8.04,0,0 +2012,12,29,2,164,-9,-4,1029,12.96,0,0 +2012,12,29,3,71,-12,-3,1029,20.11,0,0 +2012,12,29,4,16,-14,-4,1029,25.92,0,0 +2012,12,29,5,15,-15,-4,1029,30.84,0,0 +2012,12,29,6,14,-17,-5,1030,36.65,0,0 +2012,12,29,7,16,-19,-6,1031,42.46,0,0 +2012,12,29,8,16,-20,-6,1031,49.61,0,0 +2012,12,29,9,16,-19,-5,1032,53.63,0,0 +2012,12,29,10,17,-19,-4,1032,64.81,0,0 +2012,12,29,11,17,-18,-4,1032,75.99,0,0 +2012,12,29,12,16,-19,-3,1032,85.82,0,0 +2012,12,29,13,15,-20,-4,1031,97.89,0,0 +2012,12,29,14,13,-21,-3,1030,106.83,0,0 +2012,12,29,15,12,-20,-3,1030,115.77,0,0 +2012,12,29,16,11,-20,-3,1030,123.82,0,0 +2012,12,29,17,8,-18,-4,1030,132.76,0,0 +2012,12,29,18,20,-18,-4,1030,139.91,0,0 +2012,12,29,19,14,-21,-5,1031,147.06,0,0 +2012,12,29,20,18,-20,-7,1031,0.89,0,0 +2012,12,29,21,15,-20,-6,1031,3.13,0,0 +2012,12,29,22,13,-20,-6,1031,7.15,0,0 +2012,12,29,23,12,-20,-6,1031,10.28,0,0 +2012,12,30,0,9,-21,-6,1030,16.09,0,0 +2012,12,30,1,11,-21,-6,1029,19.22,0,0 +2012,12,30,2,11,-20,-7,1028,22.35,0,0 +2012,12,30,3,16,-20,-6,1028,26.37,0,0 +2012,12,30,4,11,-20,-5,1027,32.18,0,0 +2012,12,30,5,11,-20,-6,1026,35.31,0,0 +2012,12,30,6,14,-20,-5,1026,41.12,0,0 +2012,12,30,7,14,-20,-5,1026,45.14,0,0 +2012,12,30,8,18,-17,-7,1026,48.27,0,0 +2012,12,30,9,18,-18,-4,1026,52.29,0,0 +2012,12,30,10,16,-18,-3,1026,58.1,0,0 +2012,12,30,11,17,-20,-2,1026,66.15,0,0 +2012,12,30,12,16,-19,-2,1025,74.2,0,0 +2012,12,30,13,16,-19,-1,1024,82.25,0,0 +2012,12,30,14,17,-20,-1,1024,90.3,0,0 +2012,12,30,15,16,-21,-1,1024,98.35,0,0 +2012,12,30,16,16,-20,-1,1024,106.4,0,0 +2012,12,30,17,13,-20,-3,1024,112.21,0,0 +2012,12,30,18,11,-20,-4,1024,115.34,0,0 +2012,12,30,19,19,-20,-5,1024,118.47,0,0 +2012,12,30,20,19,-19,-7,1024,120.26,0,0 +2012,12,30,21,23,-18,-10,1024,0.89,0,0 +2012,12,30,22,26,-17,-11,1024,1.79,0,0 +2012,12,30,23,82,-17,-10,1023,0.89,0,0 +2012,12,31,0,97,-16,-11,1023,1.78,0,0 +2012,12,31,1,95,-17,-13,1022,2.67,0,0 +2012,12,31,2,131,-18,-14,1022,3.56,0,0 +2012,12,31,3,147,-18,-14,1022,1.79,0,0 +2012,12,31,4,153,-18,-14,1021,3.58,0,0 +2012,12,31,5,109,-19,-14,1021,5.37,0,0 +2012,12,31,6,71,-18,-15,1020,7.16,0,0 +2012,12,31,7,65,-18,-14,1020,10.29,0,0 +2012,12,31,8,56,-18,-14,1020,13.42,0,0 +2012,12,31,9,52,-16,-11,1020,15.21,0,0 +2012,12,31,10,53,-16,-9,1020,17,0,0 +2012,12,31,11,51,-15,-5,1019,0.89,0,0 +2012,12,31,12,73,-16,-3,1018,1.78,0,0 +2012,12,31,13,90,-17,-2,1017,2.67,0,0 +2012,12,31,14,87,-16,-1,1016,3.56,0,0 +2012,12,31,15,87,-16,0,1016,1.79,0,0 +2012,12,31,16,54,-17,0,1016,4.92,0,0 +2012,12,31,17,78,-15,0,1016,4.02,0,0 +2012,12,31,18,103,-15,-2,1016,7.15,0,0 +2012,12,31,19,104,-13,-5,1017,0.89,0,0 +2012,12,31,20,131,-14,-6,1017,0.89,0,0 +2012,12,31,21,113,-14,-9,1018,1.79,0,0 +2012,12,31,22,45,-12,-8,1018,0.89,0,0 +2012,12,31,23,39,-10,-6,1018,1.79,0,0 +2013,1,1,0,35,-10,-5,1018,5.81,0,0 +2013,1,1,1,31,-11,-7,1017,9.83,0,0 +2013,1,1,2,32,-11,-7,1017,11.62,0,0 +2013,1,1,3,21,-14,-10,1018,14.75,0,0 +2013,1,1,4,16,-15,-10,1018,0.45,0,0 +2013,1,1,5,15,-15,-12,1019,3.13,0,0 +2013,1,1,6,9,-15,-11,1020,0.89,0,0 +2013,1,1,7,9,-16,-6,1020,4.92,0,0 +2013,1,1,8,7,-18,-6,1021,8.94,0,0 +2013,1,1,9,14,-19,-5,1022,13.86,0,0 +2013,1,1,10,11,-22,-3,1023,21.01,0,0 +2013,1,1,11,9,-22,-3,1024,29.95,0,0 +2013,1,1,12,10,-23,-3,1023,38,0,0 +2013,1,1,13,16,-23,-4,1023,46.05,0,0 +2013,1,1,14,17,-23,-4,1024,54.1,0,0 +2013,1,1,15,15,-25,-5,1025,63.04,0,0 +2013,1,1,16,13,-25,-6,1026,71.09,0,0 +2013,1,1,17,16,-25,-7,1027,79.14,0,0 +2013,1,1,18,15,-27,-8,1028,87.19,0,0 +2013,1,1,19,20,-26,-9,1029,96.13,0,0 +2013,1,1,20,17,-26,-10,1030,103.28,0,0 +2013,1,1,21,16,-27,-10,1031,111.33,0,0 +2013,1,1,22,19,-27,-11,1032,118.48,0,0 +2013,1,1,23,13,-28,-11,1033,126.53,0,0 +2013,1,2,0,19,-28,-12,1033,134.58,0,0 +2013,1,2,1,15,-28,-12,1033,141.73,0,0 +2013,1,2,2,19,-28,-12,1034,148.88,0,0 +2013,1,2,3,16,-28,-12,1035,156.03,0,0 +2013,1,2,4,17,-28,-12,1035,163.18,0,0 +2013,1,2,5,17,-28,-13,1036,168.99,0,0 +2013,1,2,6,17,-28,-13,1036,177.04,0,0 +2013,1,2,7,15,-28,-13,1036,185.09,0,0 +2013,1,2,8,13,-27,-12,1037,194.03,0,0 +2013,1,2,9,15,-27,-11,1039,202.08,0,0 +2013,1,2,10,15,-28,-10,1040,213.26,0,0 +2013,1,2,11,16,-28,-9,1041,223.09,0,0 +2013,1,2,12,13,-29,-9,1041,234.27,0,0 +2013,1,2,13,19,-28,-8,1040,245.45,0,0 +2013,1,2,14,20,-27,-8,1040,256.63,0,0 +2013,1,2,15,19,-27,-8,1040,267.81,0,0 +2013,1,2,16,19,-27,-8,1041,277.64,0,0 +2013,1,2,17,25,-27,-8,1042,287.47,0,0 +2013,1,2,18,22,-26,-10,1043,296.41,0,0 +2013,1,2,19,25,-26,-10,1044,306.24,0,0 +2013,1,2,20,20,-28,-10,1044,315.18,0,0 +2013,1,2,21,19,-27,-10,1045,325.01,0,0 +2013,1,2,22,21,-27,-11,1046,332.16,0,0 +2013,1,2,23,27,-26,-11,1046,341.1,0,0 +2013,1,3,0,23,-27,-11,1046,350.04,0,0 +2013,1,3,1,21,-28,-11,1046,359.87,0,0 +2013,1,3,2,20,-28,-11,1046,367.02,0,0 +2013,1,3,3,18,-28,-12,1046,375.07,0,0 +2013,1,3,4,20,-28,-12,1046,384.01,0,0 +2013,1,3,5,20,-28,-12,1045,393.84,0,0 +2013,1,3,6,13,-27,-13,1045,4.92,0,0 +2013,1,3,7,13,-26,-14,1045,9.84,0,0 +2013,1,3,8,17,-25,-12,1046,1.79,0,0 +2013,1,3,9,22,-26,-10,1046,4.02,0,0 +2013,1,3,10,29,-26,-9,1046,8.04,0,0 +2013,1,3,11,26,-25,-8,1046,4.92,0,0 +2013,1,3,12,26,-25,-7,1044,4.02,0,0 +2013,1,3,13,18,-24,-6,1043,4.02,0,0 +2013,1,3,14,20,-24,-6,1042,3.13,0,0 +2013,1,3,15,26,-24,-5,1041,3.13,0,0 +2013,1,3,16,24,-23,-5,1041,4.92,0,0 +2013,1,3,17,18,-23,-8,1041,0.89,0,0 +2013,1,3,18,25,-20,-9,1041,1.79,0,0 +2013,1,3,19,34,-21,-10,1041,1.79,0,0 +2013,1,3,20,37,-21,-11,1040,3.58,0,0 +2013,1,3,21,36,-21,-11,1040,5.37,0,0 +2013,1,3,22,42,-21,-12,1040,7.16,0,0 +2013,1,3,23,40,-21,-12,1040,10.29,0,0 +2013,1,4,0,37,-21,-14,1039,12.08,0,0 +2013,1,4,1,33,-21,-15,1039,15.21,0,0 +2013,1,4,2,35,-22,-16,1038,18.34,0,0 +2013,1,4,3,58,-22,-17,1038,20.13,0,0 +2013,1,4,4,56,-21,-14,1037,23.26,0,0 +2013,1,4,5,62,-22,-15,1036,26.39,0,0 +2013,1,4,6,37,-23,-16,1036,28.18,0,0 +2013,1,4,7,37,-22,-15,1035,29.97,0,0 +2013,1,4,8,41,-22,-16,1035,31.76,0,0 +2013,1,4,9,41,-20,-11,1035,34.89,0,0 +2013,1,4,10,39,-21,-8,1035,38.02,0,0 +2013,1,4,11,48,-22,-7,1034,0.89,0,0 +2013,1,4,12,59,-21,-5,1032,1.79,0,0 +2013,1,4,13,63,-22,-5,1031,4.92,0,0 +2013,1,4,14,74,-22,-3,1029,1.79,0,0 +2013,1,4,15,69,-23,-3,1029,3.13,0,0 +2013,1,4,16,79,-23,-4,1029,3.13,0,0 +2013,1,4,17,89,-22,-4,1028,4.92,0,0 +2013,1,4,18,94,-21,-9,1028,1.79,0,0 +2013,1,4,19,113,-20,-9,1028,0.45,0,0 +2013,1,4,20,132,-18,-10,1029,0.9,0,0 +2013,1,4,21,186,-18,-12,1029,1.79,0,0 +2013,1,4,22,205,-18,-13,1029,2.68,0,0 +2013,1,4,23,204,-19,-15,1029,1.79,0,0 +2013,1,5,0,198,-18,-14,1029,2.68,0,0 +2013,1,5,1,176,-20,-14,1028,0.89,0,0 +2013,1,5,2,111,-20,-11,1028,3.13,0,0 +2013,1,5,3,140,-22,-14,1028,3.13,0,0 +2013,1,5,4,78,-21,-14,1028,8.05,0,0 +2013,1,5,5,30,-21,-12,1028,12.97,0,0 +2013,1,5,6,31,-22,-10,1028,4.92,0,0 +2013,1,5,7,34,-23,-11,1029,4.02,0,0 +2013,1,5,8,29,-22,-8,1029,8.04,0,0 +2013,1,5,9,40,-23,-7,1030,4.92,0,0 +2013,1,5,10,43,-23,-5,1030,10.73,0,0 +2013,1,5,11,43,-23,-2,1030,4.92,0,0 +2013,1,5,12,39,-22,-3,1029,4.92,0,0 +2013,1,5,13,38,-22,-2,1028,9.84,0,0 +2013,1,5,14,37,-22,-1,1027,13.86,0,0 +2013,1,5,15,37,-22,0,1028,16.99,0,0 +2013,1,5,16,36,-22,-1,1028,18.78,0,0 +2013,1,5,17,41,-21,-3,1028,19.67,0,0 +2013,1,5,18,49,-20,-5,1029,20.56,0,0 +2013,1,5,19,69,-20,-8,1029,1.79,0,0 +2013,1,5,20,86,-19,-10,1029,4.92,0,0 +2013,1,5,21,73,-20,-9,1029,8.05,0,0 +2013,1,5,22,64,-20,-9,1030,11.18,0,0 +2013,1,5,23,72,-18,-9,1030,14.31,0,0 +2013,1,6,0,72,-19,-10,1030,1.79,0,0 +2013,1,6,1,49,-18,-8,1030,4.92,0,0 +2013,1,6,2,48,-19,-10,1030,3.13,0,0 +2013,1,6,3,34,-18,-11,1030,7.15,0,0 +2013,1,6,4,40,-18,-13,1029,10.28,0,0 +2013,1,6,5,39,-18,-13,1029,14.3,0,0 +2013,1,6,6,30,-18,-12,1029,18.32,0,0 +2013,1,6,7,32,-18,-12,1029,22.34,0,0 +2013,1,6,8,41,-18,-11,1029,25.47,0,0 +2013,1,6,9,45,-17,-7,1030,28.6,0,0 +2013,1,6,10,61,-18,-5,1030,31.73,0,0 +2013,1,6,11,50,-19,-3,1029,34.86,0,0 +2013,1,6,12,65,-19,-3,1028,1.79,0,0 +2013,1,6,13,70,-19,-2,1027,1.79,0,0 +2013,1,6,14,78,-19,-1,1026,3.58,0,0 +2013,1,6,15,108,-19,-1,1025,3.13,0,0 +2013,1,6,16,142,-18,-1,1025,6.26,0,0 +2013,1,6,17,193,-18,-1,1025,9.39,0,0 +2013,1,6,18,204,-17,-3,1025,12.52,0,0 +2013,1,6,19,266,-16,-2,1026,15.65,0,0 +2013,1,6,20,335,-16,-6,1025,0.45,0,0 +2013,1,6,21,392,-15,-7,1026,0.89,0,0 +2013,1,6,22,413,-14,-9,1026,0.89,0,0 +2013,1,6,23,339,-14,-10,1026,0.89,0,0 +2013,1,7,0,285,-14,-10,1026,0.45,0,0 +2013,1,7,1,236,-16,-12,1026,1.79,0,0 +2013,1,7,2,173,-17,-13,1025,4.92,0,0 +2013,1,7,3,159,-16,-12,1025,8.05,0,0 +2013,1,7,4,128,-17,-12,1025,9.84,0,0 +2013,1,7,5,99,-16,-12,1025,11.63,0,0 +2013,1,7,6,89,-17,-14,1025,14.76,0,0 +2013,1,7,7,88,-17,-12,1025,16.55,0,0 +2013,1,7,8,84,-16,-11,1025,19.68,0,0 +2013,1,7,9,71,-15,-7,1026,21.47,0,0 +2013,1,7,10,76,-16,-5,1026,23.26,0,0 +2013,1,7,11,78,-16,-5,1026,0.89,0,0 +2013,1,7,12,89,-15,-3,1025,3.13,0,0 +2013,1,7,13,102,-15,-1,1025,1.79,0,0 +2013,1,7,14,113,-18,1,1024,1.79,0,0 +2013,1,7,15,134,-19,1,1024,0.89,0,0 +2013,1,7,16,33,-19,0,1024,1.34,0,0 +2013,1,7,17,133,-16,-3,1024,2.23,0,0 +2013,1,7,18,150,-15,-4,1024,0.89,0,0 +2013,1,7,19,164,-14,-7,1025,0.89,0,0 +2013,1,7,20,194,-14,-7,1026,1.79,0,0 +2013,1,7,21,233,-14,-8,1026,2.68,0,0 +2013,1,7,22,250,-14,-7,1027,0.45,0,0 +2013,1,7,23,299,-13,-8,1027,0.9,0,0 +2013,1,8,0,333,-14,-10,1028,1.79,0,0 +2013,1,8,1,357,-14,-10,1028,0.89,0,0 +2013,1,8,2,390,-13,-8,1029,2.68,0,0 +2013,1,8,3,326,-14,-9,1029,3.57,0,0 +2013,1,8,4,402,-14,-10,1029,4.46,0,0 +2013,1,8,5,365,-15,-13,1029,1.79,0,0 +2013,1,8,6,257,-14,-12,1030,4.92,0,0 +2013,1,8,7,135,-15,-11,1030,6.71,0,0 +2013,1,8,8,159,-13,-10,1032,1.79,0,0 +2013,1,8,9,170,-11,-7,1033,0.89,0,0 +2013,1,8,10,36,-16,-4,1033,8.05,0,0 +2013,1,8,11,19,-17,-3,1033,15.2,0,0 +2013,1,8,12,20,-19,-2,1031,23.25,0,0 +2013,1,8,13,18,-19,-2,1031,33.08,0,0 +2013,1,8,14,19,-20,-2,1031,40.23,0,0 +2013,1,8,15,14,-20,-2,1031,45.15,0,0 +2013,1,8,16,11,-21,-2,1031,52.3,0,0 +2013,1,8,17,16,-22,-3,1031,58.11,0,0 +2013,1,8,18,19,-21,-4,1031,63.03,0,0 +2013,1,8,19,18,-21,-8,1032,67.05,0,0 +2013,1,8,20,21,-22,-10,1032,71.07,0,0 +2013,1,8,21,21,-21,-9,1033,75.09,0,0 +2013,1,8,22,17,-23,-9,1033,78.22,0,0 +2013,1,8,23,19,-23,-7,1034,82.24,0,0 +2013,1,9,0,23,-23,-10,1034,1.79,0,0 +2013,1,9,1,29,-23,-11,1033,3.13,0,0 +2013,1,9,2,22,-22,-9,1033,7.15,0,0 +2013,1,9,3,18,-21,-12,1033,11.17,0,0 +2013,1,9,4,24,-22,-12,1032,14.3,0,0 +2013,1,9,5,22,-21,-12,1032,16.09,0,0 +2013,1,9,6,17,-21,-13,1032,0.89,0,0 +2013,1,9,7,27,-20,-13,1032,1.79,0,0 +2013,1,9,8,44,-20,-15,1032,1.79,0,0 +2013,1,9,9,46,-18,-9,1033,0.89,0,0 +2013,1,9,10,35,-19,-6,1033,1.79,0,0 +2013,1,9,11,48,-20,-4,1032,0.89,0,0 +2013,1,9,12,59,-19,-3,1031,1.79,0,0 +2013,1,9,13,69,-19,-1,1029,0.89,0,0 +2013,1,9,14,72,-23,1,1028,4.92,0,0 +2013,1,9,15,59,-23,1,1028,9.84,0,0 +2013,1,9,16,57,-23,0,1028,13.86,0,0 +2013,1,9,17,67,-18,-1,1028,4.02,0,0 +2013,1,9,18,75,-18,-3,1028,7.15,0,0 +2013,1,9,19,15,-15,-5,1028,10.28,0,0 +2013,1,9,20,38,-15,-4,1029,12.07,0,0 +2013,1,9,21,41,-14,-7,1029,0.89,0,0 +2013,1,9,22,63,-15,-8,1029,0.89,0,0 +2013,1,9,23,234,-15,-11,1029,0.89,0,0 +2013,1,10,0,305,-15,-12,1029,0.89,0,0 +2013,1,10,1,304,-16,-12,1029,1.78,0,0 +2013,1,10,2,303,-17,-13,1029,1.79,0,0 +2013,1,10,3,221,-17,-13,1029,2.68,0,0 +2013,1,10,4,179,-18,-14,1028,5.81,0,0 +2013,1,10,5,162,-18,-14,1028,8.94,0,0 +2013,1,10,6,111,-17,-13,1028,10.73,0,0 +2013,1,10,7,114,-17,-13,1028,13.86,0,0 +2013,1,10,8,81,-16,-12,1029,16.99,0,0 +2013,1,10,9,56,-15,-9,1029,18.78,0,0 +2013,1,10,10,63,-15,-7,1029,0.89,0,0 +2013,1,10,11,79,-15,-6,1029,1.78,0,0 +2013,1,10,12,84,-14,-4,1028,2.67,0,0 +2013,1,10,13,97,-13,-3,1027,1.79,0,0 +2013,1,10,14,111,-11,-3,1027,3.58,0,0 +2013,1,10,15,167,-11,-3,1026,5.37,0,0 +2013,1,10,16,279,-10,-3,1026,7.16,0,0 +2013,1,10,17,395,-9,-5,1026,8.95,0,0 +2013,1,10,18,449,-10,-7,1026,9.84,0,0 +2013,1,10,19,389,-9,-7,1027,10.73,0,0 +2013,1,10,20,421,-9,-7,1027,12.52,0,0 +2013,1,10,21,486,-8,-7,1027,15.65,0,0 +2013,1,10,22,486,-7,-6,1027,0.89,0,0 +2013,1,10,23,488,-7,-6,1027,0.89,0,0 +2013,1,11,0,434,-7,-6,1026,0.89,0,0 +2013,1,11,1,425,-7,-5,1025,1.78,0,0 +2013,1,11,2,418,-7,-5,1026,2.67,0,0 +2013,1,11,3,416,-7,-5,1025,1.79,0,0 +2013,1,11,4,402,-7,-5,1024,2.68,0,0 +2013,1,11,5,429,-7,-5,1024,4.47,0,0 +2013,1,11,6,408,-7,-5,1024,6.26,0,0 +2013,1,11,7,456,-7,-5,1024,8.05,0,0 +2013,1,11,8,434,-7,-5,1025,9.84,0,0 +2013,1,11,9,391,-6,-5,1025,0.89,1,0 +2013,1,11,10,377,-7,-4,1025,3.13,0,0 +2013,1,11,11,377,-7,-3,1024,6.26,0,0 +2013,1,11,12,361,-7,-2,1023,9.39,0,0 +2013,1,11,13,343,-7,-1,1022,0.89,0,0 +2013,1,11,14,356,-7,-1,1022,1.78,0,0 +2013,1,11,15,353,-8,0,1022,2.67,0,0 +2013,1,11,16,340,-9,1,1022,3.56,0,0 +2013,1,11,17,146,-10,1,1023,3.13,0,0 +2013,1,11,18,216,-8,-2,1023,3.13,0,0 +2013,1,11,19,291,-9,-4,1024,4.92,0,0 +2013,1,11,20,325,-11,-7,1025,1.79,0,0 +2013,1,11,21,292,-9,-6,1025,2.68,0,0 +2013,1,11,22,371,-11,-9,1025,3.57,0,0 +2013,1,11,23,369,-8,-5,1025,3.13,0,0 +2013,1,12,0,374,-10,-9,1025,0.89,0,0 +2013,1,12,1,391,-11,-10,1025,1.79,0,0 +2013,1,12,2,442,-13,-12,1025,1.79,0,0 +2013,1,12,3,425,-12,-11,1025,0.89,0,0 +2013,1,12,4,442,-12,-11,1024,0.89,0,0 +2013,1,12,5,287,-12,-11,1024,1.78,0,0 +2013,1,12,6,275,-13,-10,1024,2.67,0,0 +2013,1,12,7,174,-14,-12,1024,3.56,0,0 +2013,1,12,8,278,-15,-13,1024,1.79,0,0 +2013,1,12,9,NA,-12,-10,1024,3.58,0,0 +2013,1,12,10,319,-10,-6,1024,5.37,0,0 +2013,1,12,11,383,-9,-3,1024,0.89,0,0 +2013,1,12,12,472,-9,-3,1022,1.79,0,0 +2013,1,12,13,458,-9,-2,1021,3.58,0,0 +2013,1,12,14,529,-7,-1,1021,5.37,0,0 +2013,1,12,15,802,-7,-1,1021,7.16,0,0 +2013,1,12,16,845,-7,-2,1021,8.95,0,0 +2013,1,12,17,810,-7,-4,1021,9.84,0,0 +2013,1,12,18,776,-8,-6,1021,11.63,0,0 +2013,1,12,19,824,-8,-7,1022,0.89,0,0 +2013,1,12,20,886,-8,-7,1023,1.34,0,0 +2013,1,12,21,852,-9,-8,1023,0.89,0,0 +2013,1,12,22,858,-10,-9,1024,0.89,0,0 +2013,1,12,23,805,-10,-9,1024,1.79,0,0 +2013,1,13,0,744,-9,-8,1024,2.68,0,0 +2013,1,13,1,731,-8,-7,1025,4.47,0,0 +2013,1,13,2,722,-6,-5,1025,0.89,0,0 +2013,1,13,3,684,-6,-5,1025,1.79,0,0 +2013,1,13,4,651,-6,-5,1025,3.58,0,0 +2013,1,13,5,673,-6,-5,1025,0.89,0,0 +2013,1,13,6,584,-6,-4,1025,3.13,0,0 +2013,1,13,7,475,-6,-3,1025,6.26,0,0 +2013,1,13,8,341,-6,-3,1026,9.39,0,0 +2013,1,13,9,288,-6,-2,1026,11.18,0,0 +2013,1,13,10,266,-6,-2,1026,14.31,0,0 +2013,1,13,11,236,-6,-2,1026,16.1,0,0 +2013,1,13,12,295,-6,-1,1025,17.89,0,0 +2013,1,13,13,419,-5,0,1024,1.79,0,0 +2013,1,13,14,NA,-5,0,1023,1.79,0,0 +2013,1,13,15,323,-6,0,1023,4.92,0,0 +2013,1,13,16,326,-6,0,1023,6.71,0,0 +2013,1,13,17,325,-5,-3,1023,7.6,0,0 +2013,1,13,18,334,-6,-4,1023,0.89,0,0 +2013,1,13,19,343,-7,-4,1024,3.13,0,0 +2013,1,13,20,333,-8,-5,1025,0.89,0,0 +2013,1,13,21,311,-7,-3,1024,1.79,0,0 +2013,1,13,22,304,-9,-7,1024,0.45,0,0 +2013,1,13,23,301,-9,-8,1025,0.9,0,0 +2013,1,14,0,309,-10,-8,1024,1.35,0,0 +2013,1,14,1,298,-10,-9,1025,1.8,0,0 +2013,1,14,2,362,-12,-11,1025,3.13,0,0 +2013,1,14,3,523,-10,-10,1025,4.92,0,0 +2013,1,14,4,468,-9,-8,1024,9.84,0,0 +2013,1,14,5,447,-11,-9,1024,11.63,0,0 +2013,1,14,6,328,-10,-9,1024,14.76,0,0 +2013,1,14,7,308,-11,-8,1026,0.89,0,0 +2013,1,14,8,358,-10,-8,1027,1.78,0,0 +2013,1,14,9,296,-7,-5,1028,2.67,0,0 +2013,1,14,10,257,-7,-4,1028,1.79,0,0 +2013,1,14,11,291,-6,-2,1029,0.89,0,0 +2013,1,14,12,335,-6,-2,1029,1.79,0,0 +2013,1,14,13,345,-6,0,1028,1.79,0,0 +2013,1,14,14,307,-6,0,1028,5.81,0,0 +2013,1,14,15,271,-6,0,1029,8.94,0,0 +2013,1,14,16,267,-6,-1,1029,12.96,0,0 +2013,1,14,17,277,-5,-2,1029,16.09,0,0 +2013,1,14,18,278,-5,-1,1030,17.88,0,0 +2013,1,14,19,285,-5,-1,1029,19.67,1,0 +2013,1,14,20,282,-4,-2,1029,22.8,0,0 +2013,1,14,21,217,-6,-3,1029,26.82,1,0 +2013,1,14,22,169,-6,-4,1029,30.84,0,0 +2013,1,14,23,68,-6,-4,1029,34.86,0,0 +2013,1,15,0,151,-7,-4,1029,37.99,0,0 +2013,1,15,1,66,-8,-5,1029,39.78,0,0 +2013,1,15,2,58,-8,-5,1028,42.91,0,0 +2013,1,15,3,61,-8,-5,1028,46.04,0,0 +2013,1,15,4,61,-9,-6,1027,47.83,0,0 +2013,1,15,5,71,-8,-5,1027,50.96,0,0 +2013,1,15,6,79,-8,-6,1027,54.98,1,0 +2013,1,15,7,74,-8,-6,1027,56.77,2,0 +2013,1,15,8,71,-8,-6,1027,58.56,3,0 +2013,1,15,9,80,-7,-6,1027,60.35,4,0 +2013,1,15,10,79,-7,-5,1027,62.14,5,0 +2013,1,15,11,69,-6,-4,1026,63.93,0,0 +2013,1,15,12,90,-6,-4,1025,65.72,0,0 +2013,1,15,13,109,-6,-3,1024,67.51,0,0 +2013,1,15,14,120,-6,-2,1023,0.89,0,0 +2013,1,15,15,NA,-6,-1,1024,1.78,0,0 +2013,1,15,16,132,-6,-2,1024,2.67,0,0 +2013,1,15,17,134,-7,-3,1024,1.79,0,0 +2013,1,15,18,155,-8,-5,1025,2.68,0,0 +2013,1,15,19,166,-8,-6,1025,0.89,0,0 +2013,1,15,20,183,-8,-7,1026,1.34,0,0 +2013,1,15,21,181,-9,-8,1026,1.79,0,0 +2013,1,15,22,201,-7,-6,1027,2.68,0,0 +2013,1,15,23,227,-10,-9,1028,1.79,0,0 +2013,1,16,0,177,-10,-9,1028,3.58,0,0 +2013,1,16,1,189,-10,-8,1028,3.13,0,0 +2013,1,16,2,NA,-12,-5,1029,7.15,0,0 +2013,1,16,3,76,-12,-4,1029,10.28,0,0 +2013,1,16,4,46,-14,-4,1029,16.09,0,0 +2013,1,16,5,38,-14,-4,1030,21.9,0,0 +2013,1,16,6,35,-15,-5,1031,26.82,0,0 +2013,1,16,7,25,-13,-8,1032,1.79,0,0 +2013,1,16,8,82,-11,-10,1034,1.79,0,0 +2013,1,16,9,245,-9,-7,1035,1.79,0,0 +2013,1,16,10,269,-9,-6,1035,0.89,0,0 +2013,1,16,11,240,-10,-3,1034,1.78,0,0 +2013,1,16,12,194,-14,-1,1033,3.13,0,0 +2013,1,16,13,169,-17,0,1032,3.13,0,0 +2013,1,16,14,53,-16,1,1032,3.13,0,0 +2013,1,16,15,39,-16,1,1033,6.26,0,0 +2013,1,16,16,45,-17,0,1034,8.05,0,0 +2013,1,16,17,61,-16,-1,1034,9.84,0,0 +2013,1,16,18,227,-15,-3,1035,3.13,0,0 +2013,1,16,19,219,-15,-5,1036,6.26,0,0 +2013,1,16,20,193,-16,-2,1036,11.18,0,0 +2013,1,16,21,47,-17,-2,1037,16.1,0,0 +2013,1,16,22,26,-17,-4,1037,19.23,0,0 +2013,1,16,23,28,-17,-8,1038,22.36,0,0 +2013,1,17,0,30,-16,-8,1038,1.79,0,0 +2013,1,17,1,25,-15,-7,1038,4.92,0,0 +2013,1,17,2,24,-16,-7,1038,4.92,0,0 +2013,1,17,3,26,-16,-9,1038,0.89,0,0 +2013,1,17,4,19,-16,-8,1038,1.79,0,0 +2013,1,17,5,35,-17,-10,1038,3.13,0,0 +2013,1,17,6,37,-17,-10,1038,4.92,0,0 +2013,1,17,7,34,-16,-8,1039,4.92,0,0 +2013,1,17,8,33,-16,-6,1040,8.94,0,0 +2013,1,17,9,33,-16,-4,1040,12.07,0,0 +2013,1,17,10,34,-16,-3,1040,16.09,0,0 +2013,1,17,11,38,-15,-1,1040,20.11,0,0 +2013,1,17,12,38,-17,0,1038,23.24,0,0 +2013,1,17,13,41,-18,2,1037,1.79,0,0 +2013,1,17,14,37,-18,2,1036,0.89,0,0 +2013,1,17,15,45,-17,3,1035,1.79,0,0 +2013,1,17,16,71,-18,3,1035,4.92,0,0 +2013,1,17,17,92,-16,1,1035,8.05,0,0 +2013,1,17,18,78,-16,0,1036,11.18,0,0 +2013,1,17,19,86,-13,-1,1036,15.2,0,0 +2013,1,17,20,107,-13,-1,1035,16.99,0,0 +2013,1,17,21,167,-12,-1,1035,18.78,0,0 +2013,1,17,22,208,-12,-7,1034,19.67,0,0 +2013,1,17,23,226,-13,-8,1034,0.89,0,0 +2013,1,18,0,285,-13,-9,1033,0.89,0,0 +2013,1,18,1,283,-13,-10,1032,0.89,0,0 +2013,1,18,2,214,-13,-10,1032,0.89,0,0 +2013,1,18,3,193,-13,-9,1032,1.78,0,0 +2013,1,18,4,231,-14,-10,1031,0.89,0,0 +2013,1,18,5,213,-13,-11,1029,0.45,0,0 +2013,1,18,6,180,-14,-11,1029,0.89,0,0 +2013,1,18,7,149,-14,-12,1029,2.68,0,0 +2013,1,18,8,86,-14,-11,1029,4.47,0,0 +2013,1,18,9,82,-12,-8,1029,6.26,0,0 +2013,1,18,10,87,-12,-4,1028,8.05,0,0 +2013,1,18,11,94,-12,-3,1028,9.84,0,0 +2013,1,18,12,122,-11,0,1026,11.63,0,0 +2013,1,18,13,138,-12,1,1025,1.79,0,0 +2013,1,18,14,138,-13,1,1024,1.79,0,0 +2013,1,18,15,176,-14,2,1023,4.92,0,0 +2013,1,18,16,450,-13,2,1023,0.89,0,0 +2013,1,18,17,372,-11,-2,1024,0.89,0,0 +2013,1,18,18,358,-10,-3,1024,0.89,0,0 +2013,1,18,19,448,-11,-3,1025,0.89,0,0 +2013,1,18,20,485,-10,-4,1025,0.45,0,0 +2013,1,18,21,557,-11,-7,1025,1.34,0,0 +2013,1,18,22,573,-10,-6,1026,0.89,0,0 +2013,1,18,23,603,-11,-7,1026,4.02,0,0 +2013,1,19,0,412,-11,-6,1026,4.91,0,0 +2013,1,19,1,300,-11,-3,1026,8.04,0,0 +2013,1,19,2,312,-11,-6,1026,12.06,0,0 +2013,1,19,3,253,-12,-6,1027,0.89,0,0 +2013,1,19,4,285,-12,-6,1027,0.45,0,0 +2013,1,19,5,277,-12,-8,1027,1.79,0,0 +2013,1,19,6,236,-11,-8,1028,0.45,0,0 +2013,1,19,7,157,-11,-9,1028,1.34,0,0 +2013,1,19,8,237,-11,-9,1029,1.79,0,0 +2013,1,19,9,290,-7,-4,1031,0.89,0,0 +2013,1,19,10,371,-9,-1,1031,1.78,0,0 +2013,1,19,11,353,-11,1,1031,3.13,0,0 +2013,1,19,12,227,-10,1,1031,0.89,0,0 +2013,1,19,13,201,-10,2,1030,3.13,0,0 +2013,1,19,14,98,-10,2,1030,4.92,0,0 +2013,1,19,15,76,-12,3,1030,6.71,0,0 +2013,1,19,16,71,-11,2,1030,8.5,0,0 +2013,1,19,17,73,-10,1,1030,9.39,0,0 +2013,1,19,18,85,-8,-1,1031,0.89,0,0 +2013,1,19,19,100,-8,-1,1031,0.89,0,0 +2013,1,19,20,115,-9,0,1031,2.68,0,0 +2013,1,19,21,128,-9,0,1032,4.47,0,0 +2013,1,19,22,141,-10,-1,1032,6.26,0,0 +2013,1,19,23,72,-10,-1,1032,9.39,0,0 +2013,1,20,0,88,-12,-1,1032,12.52,0,0 +2013,1,20,1,91,-12,-2,1032,14.31,0,0 +2013,1,20,2,69,-11,-2,1032,0.89,0,0 +2013,1,20,3,59,-11,-2,1032,1.34,0,0 +2013,1,20,4,53,-11,-3,1032,2.23,0,0 +2013,1,20,5,57,-8,-3,1032,1.79,1,0 +2013,1,20,6,64,-7,-4,1032,0.89,2,0 +2013,1,20,7,78,-6,-4,1032,0.89,3,0 +2013,1,20,8,87,-5,-4,1032,0.89,4,0 +2013,1,20,9,88,-4,-4,1032,1.79,5,0 +2013,1,20,10,80,-4,-3,1032,4.92,6,0 +2013,1,20,11,84,-3,-2,1031,1.79,7,0 +2013,1,20,12,100,-3,-1,1031,1.79,8,0 +2013,1,20,13,129,-4,-1,1030,2.68,0,0 +2013,1,20,14,128,-3,-1,1029,0.89,0,0 +2013,1,20,15,114,-4,-1,1029,1.78,0,0 +2013,1,20,16,110,-4,-1,1029,2.67,0,0 +2013,1,20,17,122,-4,-2,1029,3.56,0,0 +2013,1,20,18,142,-4,-3,1030,0.89,0,0 +2013,1,20,19,150,-4,-3,1030,1.78,0,0 +2013,1,20,20,138,-4,-3,1030,0.89,0,0 +2013,1,20,21,156,-4,-2,1030,1.79,0,0 +2013,1,20,22,145,-4,-3,1030,0.45,0,0 +2013,1,20,23,169,-4,-3,1030,0.9,0,0 +2013,1,21,0,161,-4,-3,1030,1.79,0,0 +2013,1,21,1,160,-4,-3,1029,1.79,0,0 +2013,1,21,2,156,-4,-3,1029,2.68,0,0 +2013,1,21,3,198,-5,-4,1028,3.57,0,0 +2013,1,21,4,188,-4,-4,1028,5.36,0,0 +2013,1,21,5,176,-6,-5,1028,7.15,0,0 +2013,1,21,6,187,-5,-4,1028,8.94,0,0 +2013,1,21,7,154,-6,-5,1029,12.07,0,0 +2013,1,21,8,179,-7,-6,1029,13.86,0,0 +2013,1,21,9,198,-5,-3,1029,17.88,0,0 +2013,1,21,10,159,-4,-2,1030,21.01,0,0 +2013,1,21,11,164,-5,0,1029,3.13,0,0 +2013,1,21,12,156,-6,2,1029,6.26,0,0 +2013,1,21,13,144,-4,3,1028,9.39,0,0 +2013,1,21,14,104,-7,3,1028,12.52,0,0 +2013,1,21,15,88,-7,4,1028,14.31,0,0 +2013,1,21,16,72,-7,3,1028,0.89,0,0 +2013,1,21,17,93,-7,1,1028,0.89,0,0 +2013,1,21,18,98,-6,-1,1029,0.45,0,0 +2013,1,21,19,114,-6,-1,1029,0.89,0,0 +2013,1,21,20,184,-7,-4,1030,0.89,0,0 +2013,1,21,21,162,-8,-5,1030,1.79,0,0 +2013,1,21,22,179,-7,-5,1030,1.79,0,0 +2013,1,21,23,131,-9,-6,1031,5.81,0,0 +2013,1,22,0,104,-6,-4,1031,0.89,0,0 +2013,1,22,1,84,-8,-6,1031,1.78,0,0 +2013,1,22,2,88,-9,-8,1032,0.89,0,0 +2013,1,22,3,60,-8,-8,1031,0.89,0,0 +2013,1,22,4,45,-9,-8,1031,1.78,0,0 +2013,1,22,5,38,-13,-12,1031,0.89,0,0 +2013,1,22,6,37,-13,-12,1031,0.89,0,0 +2013,1,22,7,40,-14,-13,1031,1.78,0,0 +2013,1,22,8,52,-15,-14,1032,2.67,0,0 +2013,1,22,9,46,-9,-9,1032,3.56,0,0 +2013,1,22,10,48,-6,-6,1032,4.45,0,0 +2013,1,22,11,55,-9,-3,1031,3.13,0,0 +2013,1,22,12,46,-7,-2,1030,1.79,0,0 +2013,1,22,13,111,-6,-1,1029,0.89,0,0 +2013,1,22,14,127,-5,-1,1028,3.13,0,0 +2013,1,22,15,326,-5,-3,1027,6.26,0,0 +2013,1,22,16,382,-5,-4,1026,9.39,0,0 +2013,1,22,17,352,-6,-5,1026,12.52,1,0 +2013,1,22,18,366,-6,-5,1026,15.65,2,0 +2013,1,22,19,396,-5,-5,1026,17.44,3,0 +2013,1,22,20,398,-5,-5,1026,19.23,4,0 +2013,1,22,21,374,-5,-5,1025,22.36,5,0 +2013,1,22,22,412,-6,-5,1025,25.49,6,0 +2013,1,22,23,437,-6,-5,1024,0.89,7,0 +2013,1,23,0,453,-6,-6,1024,1.79,8,0 +2013,1,23,1,439,-6,-6,1023,3.58,0,0 +2013,1,23,2,439,-7,-6,1022,4.47,0,0 +2013,1,23,3,437,-7,-6,1022,6.26,0,0 +2013,1,23,4,411,-7,-6,1021,0.89,0,0 +2013,1,23,5,424,-7,-5,1020,1.78,0,0 +2013,1,23,6,417,-6,-5,1020,2.67,0,0 +2013,1,23,7,422,-7,-5,1020,0.89,0,0 +2013,1,23,8,394,-6,-5,1020,0.89,0,0 +2013,1,23,9,388,-6,-5,1019,0.89,0,0 +2013,1,23,10,401,-6,-5,1019,0.89,1,0 +2013,1,23,11,402,-6,-4,1019,1.78,2,0 +2013,1,23,12,396,-6,-4,1018,1.79,0,0 +2013,1,23,13,395,-6,-4,1017,3.58,0,0 +2013,1,23,14,392,-5,-3,1016,5.37,0,0 +2013,1,23,15,359,-5,-3,1016,7.16,0,0 +2013,1,23,16,301,-5,-3,1016,8.95,0,0 +2013,1,23,17,273,-6,-4,1017,9.84,0,0 +2013,1,23,18,297,-7,-6,1017,10.73,0,0 +2013,1,23,19,321,-8,-8,1018,0.89,0,0 +2013,1,23,20,336,-7,-7,1019,0.89,0,0 +2013,1,23,21,399,-8,-8,1020,1.34,0,0 +2013,1,23,22,378,-6,-5,1021,1.79,0,0 +2013,1,23,23,398,-9,-3,1021,6.71,0,0 +2013,1,24,0,255,-13,0,1022,13.86,0,0 +2013,1,24,1,29,-13,-1,1022,19.67,0,0 +2013,1,24,2,17,-14,-2,1023,26.82,0,0 +2013,1,24,3,18,-14,-2,1023,33.97,0,0 +2013,1,24,4,11,-13,-3,1023,39.78,0,0 +2013,1,24,5,16,-14,-4,1023,44.7,0,0 +2013,1,24,6,12,-14,-5,1023,47.83,0,0 +2013,1,24,7,13,-14,-7,1023,0.89,0,0 +2013,1,24,8,14,-14,-1,1023,4.92,0,0 +2013,1,24,9,20,-16,1,1023,12.07,0,0 +2013,1,24,10,29,-17,3,1023,21.01,0,0 +2013,1,24,11,32,-17,3,1023,30.84,0,0 +2013,1,24,12,12,-17,4,1022,40.67,0,0 +2013,1,24,13,16,-18,4,1022,49.61,0,0 +2013,1,24,14,17,-18,5,1021,58.55,0,0 +2013,1,24,15,13,-17,5,1021,64.36,0,0 +2013,1,24,16,16,-17,3,1022,71.51,0,0 +2013,1,24,17,14,-17,0,1023,76.43,0,0 +2013,1,24,18,10,-16,-1,1023,81.35,0,0 +2013,1,24,19,23,-18,-1,1024,4.02,0,0 +2013,1,24,20,30,-19,-3,1024,5.81,0,0 +2013,1,24,21,27,-19,-4,1024,1.79,0,0 +2013,1,24,22,23,-19,-6,1024,1.79,0,0 +2013,1,24,23,27,-21,-5,1025,0.45,0,0 +2013,1,25,0,27,-18,-9,1025,3.13,0,0 +2013,1,25,1,31,-19,-8,1025,0.89,0,0 +2013,1,25,2,47,-18,-10,1026,3.13,0,0 +2013,1,25,3,46,-18,-11,1025,4.92,0,0 +2013,1,25,4,42,-17,-12,1025,0.89,0,0 +2013,1,25,5,47,-17,-11,1024,2.68,0,0 +2013,1,25,6,64,-18,-11,1024,4.47,0,0 +2013,1,25,7,68,-18,-13,1024,3.13,0,0 +2013,1,25,8,48,-18,-9,1025,6.26,0,0 +2013,1,25,9,40,-18,-7,1025,9.39,0,0 +2013,1,25,10,50,-18,-6,1025,12.52,0,0 +2013,1,25,11,55,-18,-4,1025,15.65,0,0 +2013,1,25,12,53,-19,-3,1024,1.79,0,0 +2013,1,25,13,65,-16,-2,1023,1.79,0,0 +2013,1,25,14,71,-15,-1,1022,1.79,0,0 +2013,1,25,15,84,-15,-1,1022,0.89,0,0 +2013,1,25,16,78,-14,-2,1022,1.78,0,0 +2013,1,25,17,73,-14,-4,1022,2.67,0,0 +2013,1,25,18,84,-13,-5,1022,0.89,0,0 +2013,1,25,19,109,-12,-6,1023,0.89,0,0 +2013,1,25,20,130,-12,-7,1023,1.78,0,0 +2013,1,25,21,113,-13,-9,1024,0.89,0,0 +2013,1,25,22,118,-12,-8,1024,0.45,0,0 +2013,1,25,23,124,-12,-9,1025,1.34,0,0 +2013,1,26,0,135,-13,-9,1025,0.89,0,0 +2013,1,26,1,174,-12,-10,1026,0.45,0,0 +2013,1,26,2,161,-15,-12,1026,1.79,0,0 +2013,1,26,3,186,-12,-11,1026,0.89,0,0 +2013,1,26,4,189,-13,-11,1026,0.89,0,0 +2013,1,26,5,169,-15,-13,1027,4.02,0,0 +2013,1,26,6,155,-14,-13,1027,5.81,0,0 +2013,1,26,7,138,-14,-12,1028,7.6,0,0 +2013,1,26,8,126,-12,-10,1028,9.39,0,0 +2013,1,26,9,124,-11,-7,1029,11.18,0,0 +2013,1,26,10,96,-11,-4,1029,0.89,0,0 +2013,1,26,11,94,-11,-3,1029,1.78,0,0 +2013,1,26,12,122,-11,-1,1028,2.67,0,0 +2013,1,26,13,156,-10,0,1027,1.79,0,0 +2013,1,26,14,198,-11,2,1027,1.79,0,0 +2013,1,26,15,162,-10,2,1026,3.58,0,0 +2013,1,26,16,162,-10,2,1026,3.13,0,0 +2013,1,26,17,172,-9,0,1027,6.26,0,0 +2013,1,26,18,195,-8,-2,1028,9.39,0,0 +2013,1,26,19,177,-8,-4,1028,0.89,0,0 +2013,1,26,20,174,-8,-4,1028,1.78,0,0 +2013,1,26,21,165,-8,-5,1029,2.67,0,0 +2013,1,26,22,170,-8,-5,1029,3.56,0,0 +2013,1,26,23,179,-9,-7,1029,4.45,0,0 +2013,1,27,0,229,-8,-5,1030,1.79,0,0 +2013,1,27,1,307,-8,-5,1030,0.89,0,0 +2013,1,27,2,347,-8,-5,1030,1.78,0,0 +2013,1,27,3,318,-10,-7,1029,2.67,0,0 +2013,1,27,4,293,-11,-9,1029,0.89,0,0 +2013,1,27,5,258,-10,-9,1029,0.89,0,0 +2013,1,27,6,284,-11,-10,1029,2.68,0,0 +2013,1,27,7,312,-9,-8,1030,1.79,0,0 +2013,1,27,8,310,-9,-8,1030,3.58,0,0 +2013,1,27,9,261,-7,-7,1030,5.37,0,0 +2013,1,27,10,387,-6,-6,1030,7.16,1,0 +2013,1,27,11,340,-6,-5,1030,0.89,0,0 +2013,1,27,12,306,-5,-5,1029,1.79,0,0 +2013,1,27,13,321,-5,-4,1028,3.58,0,0 +2013,1,27,14,322,-5,-2,1027,0.89,0,0 +2013,1,27,15,313,-5,-1,1027,1.79,0,0 +2013,1,27,16,320,-5,-2,1026,3.58,0,0 +2013,1,27,17,288,-5,-4,1025,4.47,0,0 +2013,1,27,18,300,-5,-3,1025,1.79,0,0 +2013,1,27,19,314,-5,-3,1025,3.58,0,0 +2013,1,27,20,317,-5,-3,1025,0.89,0,0 +2013,1,27,21,329,-5,-3,1025,0.89,0,0 +2013,1,27,22,306,-5,-3,1024,0.89,0,0 +2013,1,27,23,318,-5,-3,1024,0.89,0,0 +2013,1,28,0,340,-5,-3,1024,1.78,0,0 +2013,1,28,1,359,-5,-3,1023,0.89,0,0 +2013,1,28,2,362,-5,-3,1023,0.89,0,0 +2013,1,28,3,369,-5,-4,1022,0.89,0,0 +2013,1,28,4,364,-5,-3,1021,0.89,0,0 +2013,1,28,5,364,-5,-4,1021,0.89,0,0 +2013,1,28,6,357,-5,-4,1021,1.78,0,0 +2013,1,28,7,362,-5,-4,1021,0.89,0,0 +2013,1,28,8,369,-5,-3,1021,1.79,0,0 +2013,1,28,9,348,-5,-3,1021,3.13,0,0 +2013,1,28,10,348,-5,-2,1022,4.92,0,0 +2013,1,28,11,357,-5,0,1022,6.71,0,0 +2013,1,28,12,367,-5,1,1021,8.5,0,0 +2013,1,28,13,384,-5,2,1020,0.89,0,0 +2013,1,28,14,394,-5,3,1019,1.78,0,0 +2013,1,28,15,409,-5,3,1019,1.79,0,0 +2013,1,28,16,496,-5,3,1019,3.58,0,0 +2013,1,28,17,490,-4,1,1020,6.71,0,0 +2013,1,28,18,459,-5,-2,1021,9.84,0,0 +2013,1,28,19,446,-5,-3,1022,11.63,0,0 +2013,1,28,20,444,-4,-1,1022,13.42,0,0 +2013,1,28,21,443,-6,-5,1023,14.31,0,0 +2013,1,28,22,444,-7,-6,1024,0.89,0,0 +2013,1,28,23,510,-5,-4,1024,0.89,0,0 +2013,1,29,0,508,-6,-6,1024,1.78,0,0 +2013,1,29,1,490,-7,-7,1024,0.89,0,0 +2013,1,29,2,472,-6,-6,1024,0.45,0,0 +2013,1,29,3,451,-7,-7,1025,0.9,0,0 +2013,1,29,4,448,-8,-7,1024,1.79,0,0 +2013,1,29,5,NA,-9,-8,1025,1.79,0,0 +2013,1,29,6,526,-7,-6,1025,3.58,0,0 +2013,1,29,7,460,-6,-5,1025,0.89,0,0 +2013,1,29,8,438,-6,-5,1026,0.89,0,0 +2013,1,29,9,425,-5,-5,1026,2.68,0,0 +2013,1,29,10,462,-5,-5,1026,4.47,1,0 +2013,1,29,11,493,-5,-4,1026,6.26,2,0 +2013,1,29,12,484,-4,-4,1025,0.89,3,0 +2013,1,29,13,463,-4,-3,1025,1.79,4,0 +2013,1,29,14,476,-4,-3,1024,3.58,5,0 +2013,1,29,15,445,-4,-3,1025,6.71,6,0 +2013,1,29,16,441,-4,-3,1025,8.5,7,0 +2013,1,29,17,436,-4,-3,1025,11.63,8,0 +2013,1,29,18,411,-4,-3,1026,13.42,9,0 +2013,1,29,19,389,-4,-3,1026,15.21,10,0 +2013,1,29,20,357,-4,-3,1026,17,0,0 +2013,1,29,21,345,-4,-3,1027,20.13,0,0 +2013,1,29,22,335,-4,-3,1027,21.92,0,0 +2013,1,29,23,322,-5,-3,1027,23.71,0,0 +2013,1,30,0,316,-5,-4,1027,25.5,0,0 +2013,1,30,1,315,-5,-4,1027,27.29,0,0 +2013,1,30,2,304,-5,-4,1027,29.08,0,0 +2013,1,30,3,282,-5,-4,1027,30.87,0,0 +2013,1,30,4,260,-5,-4,1027,32.66,0,0 +2013,1,30,5,266,-5,-4,1026,34.45,0,0 +2013,1,30,6,278,-5,-4,1026,0.89,0,0 +2013,1,30,7,273,-5,-4,1026,1.79,0,0 +2013,1,30,8,275,-5,-4,1027,3.58,0,0 +2013,1,30,9,287,-5,-3,1027,5.37,0,0 +2013,1,30,10,302,-5,-3,1027,7.16,0,0 +2013,1,30,11,299,-5,-3,1027,0.89,0,0 +2013,1,30,12,297,-5,-3,1026,1.78,0,0 +2013,1,30,13,286,-4,-3,1025,2.67,0,0 +2013,1,30,14,297,-4,-2,1025,3.56,0,0 +2013,1,30,15,288,-4,-2,1024,1.79,0,0 +2013,1,30,16,281,-4,-2,1024,0.89,0,0 +2013,1,30,17,251,-4,-2,1025,0.89,0,0 +2013,1,30,18,248,-4,-2,1025,2.68,0,0 +2013,1,30,19,222,-4,-2,1026,0.89,0,0 +2013,1,30,20,199,-4,-2,1026,4.02,0,0 +2013,1,30,21,265,-4,-2,1026,5.81,0,0 +2013,1,30,22,288,-4,-2,1026,1.79,0,0 +2013,1,30,23,296,-4,-2,1026,0.89,0,0 +2013,1,31,0,311,-4,-2,1026,0.89,0,0 +2013,1,31,1,276,-4,-2,1026,0.89,0,0 +2013,1,31,2,259,-4,-2,1026,0.89,0,0 +2013,1,31,3,205,-4,-2,1026,1.78,0,0 +2013,1,31,4,181,-4,-2,1025,0.89,0,0 +2013,1,31,5,213,-4,-2,1025,1.79,0,0 +2013,1,31,6,208,-4,-2,1025,0.45,0,0 +2013,1,31,7,206,-3,-2,1025,0.89,0,0 +2013,1,31,8,183,-3,-2,1026,0.89,0,1 +2013,1,31,9,178,-2,-2,1026,0.89,0,2 +2013,1,31,10,178,-2,-1,1026,3.13,0,3 +2013,1,31,11,169,-2,-1,1025,0.89,0,4 +2013,1,31,12,177,-1,-1,1024,2.68,0,5 +2013,1,31,13,174,-1,-1,1023,1.79,1,0 +2013,1,31,14,172,-1,-1,1022,0.89,2,0 +2013,1,31,15,157,-1,-1,1022,1.79,3,0 +2013,1,31,16,143,-1,-1,1022,0.89,0,0 +2013,1,31,17,147,-1,-1,1022,1.78,0,0 +2013,1,31,18,136,-1,0,1022,4.02,0,0 +2013,1,31,19,144,-1,-1,1022,7.15,1,0 +2013,1,31,20,146,-1,0,1022,10.28,2,0 +2013,1,31,21,149,-1,0,1022,12.07,3,0 +2013,1,31,22,129,-1,0,1022,15.2,0,0 +2013,1,31,23,111,-2,-2,1023,18.33,0,0 +2013,2,1,0,123,-4,-4,1022,3.13,0,0 +2013,2,1,1,155,-3,-3,1023,3.13,0,0 +2013,2,1,2,134,-5,-5,1023,7.15,0,0 +2013,2,1,3,98,-5,-5,1023,1.79,0,0 +2013,2,1,4,53,-4,-2,1023,7.15,0,0 +2013,2,1,5,26,-7,-2,1024,14.3,0,0 +2013,2,1,6,15,-7,-2,1025,3.13,0,0 +2013,2,1,7,21,-8,-3,1026,7.15,0,0 +2013,2,1,8,29,-8,-2,1028,11.17,0,0 +2013,2,1,9,14,-10,-2,1029,7.15,0,0 +2013,2,1,10,18,-11,-1,1030,5.81,0,0 +2013,2,1,11,20,-11,1,1030,7.15,0,0 +2013,2,1,12,24,-13,0,1030,12.07,0,0 +2013,2,1,13,22,-14,1,1030,16.09,0,0 +2013,2,1,14,19,-14,2,1030,4.02,0,0 +2013,2,1,15,21,-15,3,1030,9.83,0,0 +2013,2,1,16,15,-15,2,1030,14.75,0,0 +2013,2,1,17,16,-17,0,1030,3.13,0,0 +2013,2,1,18,22,-15,-1,1031,6.26,0,0 +2013,2,1,19,23,-16,-1,1032,9.39,0,0 +2013,2,1,20,27,-16,-2,1033,13.41,0,0 +2013,2,1,21,21,-16,-2,1033,17.43,0,0 +2013,2,1,22,9,-17,-3,1033,22.35,0,0 +2013,2,1,23,14,-17,-4,1033,27.27,0,0 +2013,2,2,0,14,-17,-4,1034,30.4,0,0 +2013,2,2,1,10,-17,-5,1034,32.19,0,0 +2013,2,2,2,9,-17,-6,1034,33.08,0,0 +2013,2,2,3,16,-17,-8,1034,3.13,0,0 +2013,2,2,4,16,-16,-9,1034,6.26,0,0 +2013,2,2,5,15,-16,-11,1034,9.39,0,0 +2013,2,2,6,19,-16,-10,1034,11.18,0,0 +2013,2,2,7,17,-16,-11,1034,14.31,0,0 +2013,2,2,8,38,-15,-8,1034,18.33,0,0 +2013,2,2,9,32,-14,-5,1034,21.46,0,0 +2013,2,2,10,46,-15,-3,1034,23.25,0,0 +2013,2,2,11,50,-15,-1,1034,25.04,0,0 +2013,2,2,12,52,-16,1,1033,0.89,0,0 +2013,2,2,13,55,-16,2,1031,1.78,0,0 +2013,2,2,14,51,-15,3,1030,3.57,0,0 +2013,2,2,15,63,-15,4,1029,1.79,0,0 +2013,2,2,16,55,-15,4,1029,4.92,0,0 +2013,2,2,17,57,-15,2,1029,6.71,0,0 +2013,2,2,18,59,-13,0,1029,8.5,0,0 +2013,2,2,19,66,-12,1,1029,3.13,0,0 +2013,2,2,20,81,-10,-1,1028,3.13,0,0 +2013,2,2,21,84,-10,-2,1029,4.92,0,0 +2013,2,2,22,83,-8,-3,1029,0.89,0,0 +2013,2,2,23,90,-9,-3,1028,1.79,0,0 +2013,2,3,0,114,-9,-4,1027,3.58,0,0 +2013,2,3,1,129,-10,-3,1027,5.37,0,0 +2013,2,3,2,150,-10,-4,1026,6.26,0,0 +2013,2,3,3,161,-9,-4,1026,1.79,0,0 +2013,2,3,4,166,-6,-3,1024,0.89,0,0 +2013,2,3,5,191,-6,-3,1023,1.79,0,0 +2013,2,3,6,135,-4,-3,1023,4.92,1,0 +2013,2,3,7,126,-3,-3,1023,0.89,2,0 +2013,2,3,8,126,-3,-3,1022,1.79,3,0 +2013,2,3,9,138,-3,-3,1022,3.58,4,0 +2013,2,3,10,165,-3,-3,1022,5.37,5,0 +2013,2,3,11,181,-3,-2,1021,7.16,6,0 +2013,2,3,12,194,-3,-2,1020,8.05,7,0 +2013,2,3,13,201,-2,-2,1019,0.89,8,0 +2013,2,3,14,202,-3,-2,1018,1.79,9,0 +2013,2,3,15,223,-2,-2,1017,1.79,10,0 +2013,2,3,16,228,-2,-2,1017,2.68,11,0 +2013,2,3,17,227,-2,-2,1017,3.57,0,0 +2013,2,3,18,227,-2,-2,1017,5.36,0,0 +2013,2,3,19,232,-2,-1,1018,1.79,0,0 +2013,2,3,20,219,-2,-1,1018,4.92,0,0 +2013,2,3,21,239,-4,-3,1018,6.71,0,0 +2013,2,3,22,233,-6,-5,1019,9.84,0,0 +2013,2,3,23,225,-7,-6,1020,12.97,0,0 +2013,2,4,0,215,-7,-7,1020,16.1,0,0 +2013,2,4,1,217,-6,-6,1022,0.89,0,0 +2013,2,4,2,100,-5,-5,1022,3.13,0,0 +2013,2,4,3,25,-5,-3,1024,12.07,0,0 +2013,2,4,4,12,-10,-3,1026,20.12,0,0 +2013,2,4,5,16,-11,-4,1027,28.17,0,0 +2013,2,4,6,14,-13,-6,1028,33.09,0,0 +2013,2,4,7,20,-14,-6,1030,34.88,0,0 +2013,2,4,8,12,-15,-7,1031,38.9,0,0 +2013,2,4,9,18,-15,-4,1032,3.13,0,0 +2013,2,4,10,15,-14,-3,1033,4.02,0,0 +2013,2,4,11,18,-15,-3,1033,4.02,0,0 +2013,2,4,12,21,-13,-2,1032,7.15,0,0 +2013,2,4,13,17,-13,-1,1031,10.28,0,0 +2013,2,4,14,19,-14,0,1029,13.41,0,0 +2013,2,4,15,21,-16,0,1029,16.54,0,0 +2013,2,4,16,22,-15,1,1029,0.89,0,0 +2013,2,4,17,27,-13,-1,1029,0.89,0,0 +2013,2,4,18,29,-13,-2,1029,2.68,0,0 +2013,2,4,19,27,-12,-3,1029,6.7,0,0 +2013,2,4,20,31,-12,-2,1029,9.83,0,0 +2013,2,4,21,32,-12,-3,1029,11.62,0,0 +2013,2,4,22,29,-11,-3,1028,12.51,0,0 +2013,2,4,23,65,-8,-3,1028,15.64,0,0 +2013,2,5,0,94,-6,-3,1027,18.77,0,0 +2013,2,5,1,77,-6,-3,1026,20.56,0,0 +2013,2,5,2,60,-6,-3,1026,23.69,0,0 +2013,2,5,3,52,-6,-3,1026,25.48,0,0 +2013,2,5,4,49,-6,-4,1026,27.27,0,0 +2013,2,5,5,50,-7,-4,1025,29.06,0,0 +2013,2,5,6,57,-7,-4,1024,30.85,0,0 +2013,2,5,7,72,-7,-4,1024,32.64,0,0 +2013,2,5,8,85,-6,-4,1025,34.43,0,0 +2013,2,5,9,89,-5,-3,1025,37.56,1,0 +2013,2,5,10,85,-4,-3,1025,39.35,2,0 +2013,2,5,11,80,-4,-3,1024,41.14,3,0 +2013,2,5,12,82,-4,-2,1024,0.89,0,0 +2013,2,5,13,90,-3,-2,1023,2.68,0,0 +2013,2,5,14,89,-3,-2,1022,1.79,0,0 +2013,2,5,15,90,-4,-1,1022,1.79,0,0 +2013,2,5,16,96,-4,-1,1022,1.79,0,0 +2013,2,5,17,99,-5,-2,1022,4.92,0,0 +2013,2,5,18,105,-6,-3,1022,6.71,0,0 +2013,2,5,19,115,-5,-3,1023,9.84,0,0 +2013,2,5,20,128,-6,-4,1024,11.63,0,0 +2013,2,5,21,136,-7,-6,1024,0.89,0,0 +2013,2,5,22,148,-8,-8,1024,0.89,0,0 +2013,2,5,23,152,-9,-8,1024,0.89,0,0 +2013,2,6,0,156,-8,-7,1024,1.78,0,0 +2013,2,6,1,192,-11,-10,1024,1.79,0,0 +2013,2,6,2,214,-12,-11,1024,1.79,0,0 +2013,2,6,3,234,-9,-8,1024,3.58,0,0 +2013,2,6,4,229,-11,-10,1023,6.71,0,0 +2013,2,6,5,212,-9,-9,1023,0.89,0,0 +2013,2,6,6,208,-12,-11,1023,1.79,0,0 +2013,2,6,7,227,-11,-10,1024,1.79,0,0 +2013,2,6,8,220,-10,-9,1024,0.89,0,0 +2013,2,6,9,203,-5,-4,1025,1.79,0,0 +2013,2,6,10,188,-4,-2,1025,3.58,0,0 +2013,2,6,11,11,-5,2,1024,0.89,0,0 +2013,2,6,12,9,-13,3,1024,5.81,0,0 +2013,2,6,13,9,-15,4,1023,13.86,0,0 +2013,2,6,14,9,-19,3,1023,23.69,0,0 +2013,2,6,15,6,-18,3,1024,32.63,0,0 +2013,2,6,16,10,-20,0,1026,44.7,0,0 +2013,2,6,17,8,-24,-3,1027,57.66,0,0 +2013,2,6,18,5,-24,-5,1029,67.49,0,0 +2013,2,6,19,8,-26,-7,1032,77.32,0,0 +2013,2,6,20,9,-26,-8,1033,88.5,0,0 +2013,2,6,21,12,-26,-8,1033,98.33,0,0 +2013,2,6,22,9,-25,-9,1034,104.14,0,0 +2013,2,6,23,9,-27,-9,1035,113.08,0,0 +2013,2,7,0,9,-28,-10,1036,122.91,0,0 +2013,2,7,1,7,-26,-11,1036,128.72,0,0 +2013,2,7,2,7,-26,-11,1037,134.53,0,0 +2013,2,7,3,7,-26,-13,1037,1.79,0,0 +2013,2,7,4,6,-25,-14,1037,4.02,0,0 +2013,2,7,5,9,-25,-15,1037,3.13,0,0 +2013,2,7,6,8,-27,-14,1039,1.79,0,0 +2013,2,7,7,11,-27,-14,1040,4.92,0,0 +2013,2,7,8,11,-26,-14,1040,4.02,0,0 +2013,2,7,9,8,-26,-11,1040,8.94,0,0 +2013,2,7,10,10,-27,-11,1040,5.81,0,0 +2013,2,7,11,10,-27,-9,1040,9.83,0,0 +2013,2,7,12,15,-26,-8,1040,3.13,0,0 +2013,2,7,13,13,-25,-5,1038,6.26,0,0 +2013,2,7,14,11,-25,-5,1038,3.13,0,0 +2013,2,7,15,10,-23,-5,1037,6.26,0,0 +2013,2,7,16,9,-23,-4,1036,8.05,0,0 +2013,2,7,17,12,-23,-5,1037,9.84,0,0 +2013,2,7,18,18,-22,-9,1037,3.13,0,0 +2013,2,7,19,24,-22,-9,1038,3.13,0,0 +2013,2,7,20,18,-22,-8,1039,6.26,0,0 +2013,2,7,21,18,-23,-8,1040,10.28,0,0 +2013,2,7,22,20,-23,-8,1041,15.2,0,0 +2013,2,7,23,27,-23,-9,1041,20.12,0,0 +2013,2,8,0,15,-24,-9,1040,25.04,0,0 +2013,2,8,1,10,-23,-10,1040,28.17,0,0 +2013,2,8,2,12,-24,-10,1040,31.3,0,0 +2013,2,8,3,9,-24,-12,1040,3.13,0,0 +2013,2,8,4,13,-23,-13,1040,4.92,0,0 +2013,2,8,5,11,-22,-13,1039,6.71,0,0 +2013,2,8,6,10,-21,-13,1039,9.84,0,0 +2013,2,8,7,15,-22,-14,1038,11.63,0,0 +2013,2,8,8,19,-22,-10,1039,13.42,0,0 +2013,2,8,9,26,-22,-8,1039,15.21,0,0 +2013,2,8,10,26,-22,-7,1038,0.89,0,0 +2013,2,8,11,15,-23,-6,1038,1.79,0,0 +2013,2,8,12,26,-22,-5,1037,3.58,0,0 +2013,2,8,13,48,-22,-5,1035,1.79,0,0 +2013,2,8,14,64,-20,-3,1034,2.68,0,0 +2013,2,8,15,56,-19,-3,1033,3.13,0,0 +2013,2,8,16,57,-18,-3,1032,6.26,0,0 +2013,2,8,17,66,-17,-4,1032,10.28,0,0 +2013,2,8,18,106,-15,-5,1032,13.41,0,0 +2013,2,8,19,166,-15,-5,1032,3.13,0,0 +2013,2,8,20,174,-14,-6,1031,4.02,0,0 +2013,2,8,21,152,-13,-8,1031,4.91,0,0 +2013,2,8,22,144,-13,-8,1030,1.79,0,0 +2013,2,8,23,115,-14,-8,1030,3.58,0,0 +2013,2,9,0,108,-14,-8,1030,5.37,0,0 +2013,2,9,1,111,-14,-8,1029,7.16,0,0 +2013,2,9,2,111,-14,-8,1028,8.95,0,0 +2013,2,9,3,125,-14,-9,1028,10.74,0,0 +2013,2,9,4,128,-15,-11,1027,0.89,0,0 +2013,2,9,5,156,-15,-13,1027,1.79,0,0 +2013,2,9,6,168,-15,-12,1026,0.89,0,0 +2013,2,9,7,175,-14,-13,1026,0.89,0,0 +2013,2,9,8,172,-13,-11,1026,1.78,0,0 +2013,2,9,9,192,-13,-8,1026,0.89,0,0 +2013,2,9,10,185,-13,-6,1026,1.79,0,0 +2013,2,9,11,184,-13,-4,1026,1.79,0,0 +2013,2,9,12,172,-14,-2,1025,3.13,0,0 +2013,2,9,13,167,-13,0,1024,1.79,0,0 +2013,2,9,14,155,-14,2,1023,0.89,0,0 +2013,2,9,15,163,-14,2,1022,1.79,0,0 +2013,2,9,16,156,-14,3,1023,0.89,0,0 +2013,2,9,17,161,-13,1,1023,1.79,0,0 +2013,2,9,18,175,-14,1,1024,4.92,0,0 +2013,2,9,19,210,-12,-1,1024,1.79,0,0 +2013,2,9,20,282,-11,-1,1025,3.58,0,0 +2013,2,9,21,428,-12,-5,1026,6.71,0,0 +2013,2,9,22,512,-16,-2,1027,3.13,0,0 +2013,2,9,23,167,-17,-3,1027,7.15,0,0 +2013,2,10,0,71,-18,-3,1028,10.28,0,0 +2013,2,10,1,419,-18,-5,1028,1.79,0,0 +2013,2,10,2,209,-15,-6,1028,3.58,0,0 +2013,2,10,3,185,-15,-6,1028,5.37,0,0 +2013,2,10,4,78,-18,-4,1028,1.79,0,0 +2013,2,10,5,23,-19,-4,1029,4.92,0,0 +2013,2,10,6,17,-19,-4,1029,8.94,0,0 +2013,2,10,7,10,-19,-4,1030,13.86,0,0 +2013,2,10,8,17,-18,-4,1031,18.78,0,0 +2013,2,10,9,20,-18,-3,1032,23.7,0,0 +2013,2,10,10,25,-18,-2,1032,28.62,0,0 +2013,2,10,11,20,-19,-2,1032,33.54,0,0 +2013,2,10,12,16,-19,0,1032,38.46,0,0 +2013,2,10,13,20,-20,0,1031,43.38,0,0 +2013,2,10,14,14,-22,0,1031,46.51,0,0 +2013,2,10,15,13,-23,0,1031,49.64,0,0 +2013,2,10,16,16,-24,1,1031,3.13,0,0 +2013,2,10,17,16,-20,0,1031,6.26,0,0 +2013,2,10,18,14,-19,-2,1031,9.39,0,0 +2013,2,10,19,23,-20,-3,1031,12.52,0,0 +2013,2,10,20,31,-19,-5,1032,14.31,0,0 +2013,2,10,21,58,-19,-5,1033,16.1,0,0 +2013,2,10,22,67,-18,-9,1033,19.23,0,0 +2013,2,10,23,70,-17,-8,1034,0.89,0,0 +2013,2,11,0,55,-19,-8,1034,1.79,0,0 +2013,2,11,1,36,-19,-6,1034,5.81,0,0 +2013,2,11,2,21,-19,-8,1034,7.6,0,0 +2013,2,11,3,20,-19,-9,1034,8.49,0,0 +2013,2,11,4,21,-17,-8,1034,9.38,0,0 +2013,2,11,5,17,-16,-7,1035,1.79,0,0 +2013,2,11,6,41,-17,-8,1034,3.58,0,0 +2013,2,11,7,46,-17,-9,1034,5.37,0,0 +2013,2,11,8,23,-18,-7,1035,8.5,0,0 +2013,2,11,9,24,-18,-6,1035,11.63,0,0 +2013,2,11,10,36,-17,-5,1035,1.79,0,0 +2013,2,11,11,43,-11,-4,1034,0.89,0,0 +2013,2,11,12,59,-11,-3,1034,1.78,0,0 +2013,2,11,13,77,-11,-2,1033,1.79,0,0 +2013,2,11,14,93,-10,-2,1032,3.58,0,0 +2013,2,11,15,103,-10,-1,1031,5.37,0,0 +2013,2,11,16,100,-11,-1,1031,9.39,0,0 +2013,2,11,17,87,-12,-1,1030,12.52,0,0 +2013,2,11,18,107,-12,-2,1030,14.31,0,0 +2013,2,11,19,124,-11,-2,1030,0.89,0,0 +2013,2,11,20,121,-10,-3,1030,1.78,0,0 +2013,2,11,21,113,-9,-4,1031,0.89,0,0 +2013,2,11,22,113,-9,-4,1030,0.89,0,0 +2013,2,11,23,75,-9,-5,1030,0.45,0,0 +2013,2,12,0,67,-9,-5,1029,0.89,0,0 +2013,2,12,1,68,-9,-6,1029,0.45,0,0 +2013,2,12,2,78,-9,-6,1029,1.34,0,0 +2013,2,12,3,145,-9,-4,1029,3.13,1,0 +2013,2,12,4,129,-8,-6,1028,0.89,0,0 +2013,2,12,5,132,-10,-8,1027,1.79,0,0 +2013,2,12,6,130,-10,-8,1027,0.89,0,0 +2013,2,12,7,138,-10,-9,1027,0.89,0,0 +2013,2,12,8,133,-9,-8,1027,1.79,0,0 +2013,2,12,9,70,-7,-3,1028,3.58,0,0 +2013,2,12,10,83,-7,-1,1028,0.89,0,0 +2013,2,12,11,38,-14,3,1027,1.78,0,0 +2013,2,12,12,94,-19,4,1026,4.02,0,0 +2013,2,12,13,78,-19,5,1025,11.17,0,0 +2013,2,12,14,101,-20,6,1024,16.98,0,0 +2013,2,12,15,71,-16,5,1023,7.15,0,0 +2013,2,12,16,71,-17,4,1023,14.3,0,0 +2013,2,12,17,115,-16,3,1023,20.11,0,0 +2013,2,12,18,123,-13,2,1023,25.03,0,0 +2013,2,12,19,163,-12,1,1024,29.05,0,0 +2013,2,12,20,162,-10,1,1024,33.97,0,0 +2013,2,12,21,167,-10,1,1024,37.1,0,0 +2013,2,12,22,183,-8,0,1024,40.23,0,0 +2013,2,12,23,190,-8,-3,1024,0.89,0,0 +2013,2,13,0,197,-9,-5,1024,1.79,0,0 +2013,2,13,1,229,-7,-4,1023,3.58,0,0 +2013,2,13,2,246,-8,-6,1023,0.89,0,0 +2013,2,13,3,263,-7,-6,1023,1.78,0,0 +2013,2,13,4,267,-7,-7,1022,0.89,0,0 +2013,2,13,5,282,-7,-6,1022,0.89,0,0 +2013,2,13,6,290,-8,-7,1022,1.78,0,0 +2013,2,13,7,304,-8,-8,1022,0.89,0,0 +2013,2,13,8,307,-7,-6,1023,0.89,0,0 +2013,2,13,9,326,-6,-5,1023,1.79,0,0 +2013,2,13,10,352,-5,-3,1023,0.89,0,0 +2013,2,13,11,341,-5,0,1023,1.78,0,0 +2013,2,13,12,344,-5,1,1022,1.79,0,0 +2013,2,13,13,365,-6,3,1021,3.58,0,0 +2013,2,13,14,348,-6,4,1020,6.71,0,0 +2013,2,13,15,330,-6,5,1019,9.84,0,0 +2013,2,13,16,334,-6,5,1019,13.86,0,0 +2013,2,13,17,320,-6,4,1019,16.99,0,0 +2013,2,13,18,292,-7,2,1020,20.12,0,0 +2013,2,13,19,312,-6,-2,1021,21.91,0,0 +2013,2,13,20,296,-6,-3,1022,23.7,0,0 +2013,2,13,21,293,-5,-2,1022,25.49,0,0 +2013,2,13,22,344,-6,-4,1023,26.38,0,0 +2013,2,13,23,384,-6,-5,1023,1.79,0,0 +2013,2,14,0,392,-6,-4,1023,5.81,0,0 +2013,2,14,1,374,-7,-2,1023,3.13,0,0 +2013,2,14,2,208,-11,0,1024,6.26,0,0 +2013,2,14,3,143,-12,0,1024,10.28,0,0 +2013,2,14,4,38,-14,0,1025,16.09,0,0 +2013,2,14,5,15,-14,0,1025,21.9,0,0 +2013,2,14,6,9,-13,-1,1025,29.05,0,0 +2013,2,14,7,11,-14,-1,1026,33.97,0,0 +2013,2,14,8,13,-14,-1,1026,39.78,0,0 +2013,2,14,9,13,-15,-1,1027,45.59,0,0 +2013,2,14,10,15,-19,1,1028,51.4,0,0 +2013,2,14,11,NA,-20,1,1028,56.32,0,0 +2013,2,14,12,9,-22,1,1027,61.24,0,0 +2013,2,14,13,7,-23,2,1026,65.26,0,0 +2013,2,14,14,9,-24,2,1026,68.39,0,0 +2013,2,14,15,14,-23,3,1025,70.18,0,0 +2013,2,14,16,18,-23,3,1026,0.89,0,0 +2013,2,14,17,16,-23,3,1026,1.78,0,0 +2013,2,14,18,21,-20,1,1027,2.67,0,0 +2013,2,14,19,22,-16,0,1028,3.56,0,0 +2013,2,14,20,54,-20,2,1028,3.13,0,0 +2013,2,14,21,126,-21,1,1029,1.79,0,0 +2013,2,14,22,237,-17,0,1030,4.02,0,0 +2013,2,14,23,197,-16,-1,1030,7.15,0,0 +2013,2,15,0,48,-16,-4,1031,11.17,0,0 +2013,2,15,1,17,-17,-3,1031,15.19,0,0 +2013,2,15,2,12,-17,-5,1030,19.21,0,0 +2013,2,15,3,15,-18,-4,1030,23.23,0,0 +2013,2,15,4,16,-18,-7,1030,26.36,0,0 +2013,2,15,5,17,-18,-8,1030,28.15,0,0 +2013,2,15,6,14,-18,-10,1030,31.28,0,0 +2013,2,15,7,12,-17,-9,1030,34.41,0,0 +2013,2,15,8,16,-16,-5,1030,37.54,0,0 +2013,2,15,9,16,-17,-2,1031,1.79,0,0 +2013,2,15,10,11,-20,0,1030,0.89,0,0 +2013,2,15,11,26,-22,2,1029,1.78,0,0 +2013,2,15,12,27,-21,3,1029,3.57,0,0 +2013,2,15,13,47,-19,3,1028,3.13,0,0 +2013,2,15,14,75,-14,3,1027,8.05,0,0 +2013,2,15,15,66,-12,3,1026,13.86,0,0 +2013,2,15,16,54,-13,2,1025,19.67,0,0 +2013,2,15,17,59,-12,1,1025,23.69,0,0 +2013,2,15,18,69,-10,-1,1025,27.71,0,0 +2013,2,15,19,63,-10,-1,1025,29.5,0,0 +2013,2,15,20,57,-10,-2,1025,31.29,0,0 +2013,2,15,21,66,-10,-1,1026,35.31,0,0 +2013,2,15,22,75,-10,-3,1026,38.44,0,0 +2013,2,15,23,77,-11,-3,1025,41.57,0,0 +2013,2,16,0,66,-11,-4,1025,43.36,0,0 +2013,2,16,1,87,-11,-4,1025,45.15,0,0 +2013,2,16,2,96,-11,-5,1025,0.89,0,0 +2013,2,16,3,75,-12,-8,1025,0.89,0,0 +2013,2,16,4,80,-10,-6,1024,0.89,0,0 +2013,2,16,5,78,-11,-6,1024,1.78,0,0 +2013,2,16,6,81,-12,-6,1024,0.89,0,0 +2013,2,16,7,86,-11,-5,1024,1.79,0,0 +2013,2,16,8,112,-11,-5,1024,0.89,0,0 +2013,2,16,9,97,-10,-3,1024,1.78,0,0 +2013,2,16,10,118,-9,-1,1024,2.67,0,0 +2013,2,16,11,121,-7,-1,1024,1.79,0,0 +2013,2,16,12,135,-7,1,1023,0.89,0,0 +2013,2,16,13,139,-7,2,1021,1.79,0,0 +2013,2,16,14,144,-7,2,1021,3.58,0,0 +2013,2,16,15,149,-7,3,1020,5.37,0,0 +2013,2,16,16,159,-6,2,1020,8.5,0,0 +2013,2,16,17,167,-6,1,1020,12.52,0,0 +2013,2,16,18,167,-5,1,1020,15.65,0,0 +2013,2,16,19,192,-5,1,1020,18.78,0,0 +2013,2,16,20,208,-5,1,1020,21.91,0,0 +2013,2,16,21,227,-4,1,1020,25.04,0,0 +2013,2,16,22,241,-4,0,1019,26.83,0,0 +2013,2,16,23,259,-4,0,1019,0.89,0,0 +2013,2,17,0,271,-3,0,1019,1.79,0,0 +2013,2,17,1,281,-2,0,1019,2.68,0,0 +2013,2,17,2,296,-2,-1,1018,0.89,0,0 +2013,2,17,3,220,-1,-1,1018,1.78,0,0 +2013,2,17,4,255,-2,-1,1018,2.67,0,0 +2013,2,17,5,253,-3,-3,1017,0.89,0,0 +2013,2,17,6,247,-3,-2,1017,0.45,0,0 +2013,2,17,7,274,-5,-4,1019,0.89,0,0 +2013,2,17,8,276,-5,-4,1019,4.91,0,0 +2013,2,17,9,231,-2,-2,1020,8.04,0,0 +2013,2,17,10,215,-1,0,1020,11.17,0,0 +2013,2,17,11,167,-9,3,1020,1.79,0,0 +2013,2,17,12,74,-16,5,1020,8.05,0,0 +2013,2,17,13,22,-21,6,1019,15.2,0,0 +2013,2,17,14,14,-25,6,1019,22.35,0,0 +2013,2,17,15,13,-23,6,1019,29.5,0,0 +2013,2,17,16,12,-23,4,1020,37.55,0,0 +2013,2,17,17,11,-22,4,1021,43.36,0,0 +2013,2,17,18,10,-21,3,1022,51.41,0,0 +2013,2,17,19,12,-19,2,1024,4.02,0,0 +2013,2,17,20,20,-18,1,1024,4.92,0,0 +2013,2,17,21,8,-18,1,1025,8.94,0,0 +2013,2,17,22,21,-18,1,1026,4.02,0,0 +2013,2,17,23,26,-16,0,1027,7.15,0,0 +2013,2,18,0,19,-16,0,1027,10.28,0,0 +2013,2,18,1,14,-16,-1,1027,12.07,0,0 +2013,2,18,2,14,-15,-2,1027,15.2,0,0 +2013,2,18,3,10,-15,-3,1027,18.33,0,0 +2013,2,18,4,10,-15,-4,1027,4.02,0,0 +2013,2,18,5,11,-15,-4,1028,5.81,0,0 +2013,2,18,6,12,-15,-4,1028,6.7,0,0 +2013,2,18,7,15,-14,-5,1028,1.79,0,0 +2013,2,18,8,13,-13,-3,1028,3.58,0,0 +2013,2,18,9,16,-15,0,1029,0.89,0,0 +2013,2,18,10,15,-18,1,1029,1.79,0,0 +2013,2,18,11,18,-18,2,1028,1.79,0,0 +2013,2,18,12,19,-19,3,1027,3.58,0,0 +2013,2,18,13,23,-21,4,1026,5.37,0,0 +2013,2,18,14,34,-19,4,1025,3.13,0,0 +2013,2,18,15,31,-19,4,1025,7.15,0,0 +2013,2,18,16,38,-15,4,1025,12.07,0,0 +2013,2,18,17,41,-15,3,1025,16.09,0,0 +2013,2,18,18,52,-18,2,1026,4.02,0,0 +2013,2,18,19,52,-20,0,1027,8.94,0,0 +2013,2,18,20,46,-18,0,1028,12.96,0,0 +2013,2,18,21,13,-16,-2,1028,17.88,0,0 +2013,2,18,22,22,-18,-2,1029,23.69,0,0 +2013,2,18,23,9,-20,-4,1030,27.71,0,0 +2013,2,19,0,9,-20,-4,1030,34.86,0,0 +2013,2,19,1,6,-20,-5,1030,40.67,0,0 +2013,2,19,2,5,-20,-7,1030,45.59,0,0 +2013,2,19,3,8,-21,-7,1031,3.13,0,0 +2013,2,19,4,8,-20,-7,1030,4.02,0,0 +2013,2,19,5,12,-20,-8,1030,8.04,0,0 +2013,2,19,6,9,-22,-8,1031,3.13,0,0 +2013,2,19,7,9,-23,-7,1031,8.05,0,0 +2013,2,19,8,11,-22,-6,1032,12.07,0,0 +2013,2,19,9,13,-23,-4,1032,3.13,0,0 +2013,2,19,10,24,-23,-2,1032,1.79,0,0 +2013,2,19,11,14,-24,-1,1032,1.79,0,0 +2013,2,19,12,18,-24,0,1031,1.79,0,0 +2013,2,19,13,30,-26,1,1030,3.13,0,0 +2013,2,19,14,31,-24,1,1029,8.05,0,0 +2013,2,19,15,41,-22,2,1028,13.86,0,0 +2013,2,19,16,39,-21,1,1027,18.78,0,0 +2013,2,19,17,42,-19,1,1027,23.7,0,0 +2013,2,19,18,45,-16,-1,1027,27.72,0,0 +2013,2,19,19,57,-15,-2,1028,30.85,0,0 +2013,2,19,20,62,-14,-1,1028,32.64,0,0 +2013,2,19,21,72,-13,-2,1028,34.43,0,0 +2013,2,19,22,68,-13,-4,1028,36.22,0,0 +2013,2,19,23,90,-12,-5,1028,39.35,0,0 +2013,2,20,0,77,-11,-4,1028,41.14,0,0 +2013,2,20,1,73,-11,-5,1028,42.93,0,0 +2013,2,20,2,73,-11,-6,1027,44.72,0,0 +2013,2,20,3,63,-12,-8,1027,45.61,0,0 +2013,2,20,4,65,-12,-9,1027,0.89,0,0 +2013,2,20,5,81,-12,-9,1027,1.78,0,0 +2013,2,20,6,104,-12,-9,1027,1.79,0,0 +2013,2,20,7,109,-13,-10,1027,0.89,0,0 +2013,2,20,8,140,-11,-8,1028,0.89,0,0 +2013,2,20,9,133,-9,-3,1029,2.68,0,0 +2013,2,20,10,126,-9,-1,1029,4.47,0,0 +2013,2,20,11,129,-11,0,1028,7.6,0,0 +2013,2,20,12,117,-12,2,1028,1.79,0,0 +2013,2,20,13,126,-13,4,1027,1.79,0,0 +2013,2,20,14,107,-18,6,1026,1.79,0,0 +2013,2,20,15,68,-18,6,1025,2.68,0,0 +2013,2,20,16,8,-20,6,1025,3.13,0,0 +2013,2,20,17,12,-20,5,1025,1.79,0,0 +2013,2,20,18,42,-18,2,1026,3.58,0,0 +2013,2,20,19,58,-18,1,1027,0.45,0,0 +2013,2,20,20,67,-20,1,1028,3.13,0,0 +2013,2,20,21,77,-18,-1,1029,4.02,0,0 +2013,2,20,22,68,-14,-3,1029,4.91,0,0 +2013,2,20,23,76,-17,-6,1030,3.13,0,0 +2013,2,21,0,80,-17,-5,1030,4.92,0,0 +2013,2,21,1,67,-15,-6,1030,1.79,0,0 +2013,2,21,2,66,-7,-4,1030,4.92,0,0 +2013,2,21,3,175,-6,-4,1030,6.71,0,0 +2013,2,21,4,159,-6,-5,1030,8.5,0,0 +2013,2,21,5,147,-5,-3,1030,11.63,0,0 +2013,2,21,6,153,-5,-3,1030,13.42,0,0 +2013,2,21,7,153,-5,-3,1031,15.21,0,0 +2013,2,21,8,162,-5,-3,1031,17,0,0 +2013,2,21,9,171,-5,-3,1031,18.79,0,0 +2013,2,21,10,186,-5,-2,1030,0.89,0,0 +2013,2,21,11,191,-5,-2,1030,1.79,0,0 +2013,2,21,12,200,-5,0,1029,3.58,0,0 +2013,2,21,13,212,-5,0,1027.5,0.89,0,0 +2013,2,21,14,222,-5,2,1026,2.68,0,0 +2013,2,21,15,237,-5,3,1025,1.79,0,0 +2013,2,21,16,250,-5,3,1025,3.58,0,0 +2013,2,21,17,264,-5,2,1024,5.37,0,0 +2013,2,21,18,271,-4,1,1025,7.16,0,0 +2013,2,21,19,268,-5,-2,1026,0.89,0,0 +2013,2,21,20,278,-5,-2,1026,0.89,0,0 +2013,2,21,21,285,-6,-4,1026,1.78,0,0 +2013,2,21,22,316,-6,-4,1027,0.45,0,0 +2013,2,21,23,332,-7,-5,1027,1.34,0,0 +2013,2,22,0,339,-6,-5,1027,2.23,0,0 +2013,2,22,1,322,-8,-7,1027,1.79,0,0 +2013,2,22,2,321,-8,-7,1028,5.81,0,0 +2013,2,22,3,266,-8,-6,1028,7.6,0,0 +2013,2,22,4,219,-7,-3,1028,11.62,0,0 +2013,2,22,5,222,-8,-3,1028,15.64,0,0 +2013,2,22,6,259,-8,-2,1029,20.56,0,0 +2013,2,22,7,28,-10,-2,1030,23.69,0,0 +2013,2,22,8,8,-14,0,1031,28.61,0,0 +2013,2,22,9,10,-17,3,1032,33.53,0,0 +2013,2,22,10,9,-22,4,1033,40.68,0,0 +2013,2,22,11,10,-23,5,1033,45.6,0,0 +2013,2,22,12,14,-25,5,1032,4.92,0,0 +2013,2,22,13,11,-23,5,1031,7.15,0,0 +2013,2,22,14,10,-24,7,1030,12.96,0,0 +2013,2,22,15,14,-25,7,1029,16.98,0,0 +2013,2,22,16,10,-25,7,1029,22.79,0,0 +2013,2,22,17,9,-26,7,1029,27.71,0,0 +2013,2,22,18,17,-25,5,1029,31.73,0,0 +2013,2,22,19,30,-23,5,1029,33.52,0,0 +2013,2,22,20,31,-20,0,1029,0.89,0,0 +2013,2,22,21,41,-17,-1,1029,1.78,0,0 +2013,2,22,22,38,-14,-3,1029,2.67,0,0 +2013,2,22,23,49,-14,-3,1029,3.56,0,0 +2013,2,23,0,94,-13,-5,1029,0.89,0,0 +2013,2,23,1,116,-14,-5,1029,0.45,0,0 +2013,2,23,2,114,-13,-6,1029,0.9,0,0 +2013,2,23,3,120,-14,-6,1028,0.89,0,0 +2013,2,23,4,113,-13,-7,1028,2.68,0,0 +2013,2,23,5,90,-13,-7,1028,4.47,0,0 +2013,2,23,6,73,-14,-9,1028,6.26,0,0 +2013,2,23,7,63,-14,-8,1028,8.05,0,0 +2013,2,23,8,54,-12,-2,1029,9.84,0,0 +2013,2,23,9,55,-13,-1,1029,11.63,0,0 +2013,2,23,10,58,-15,2,1029,0.89,0,0 +2013,2,23,11,61,-16,4,1029,1.79,0,0 +2013,2,23,12,76,-16,7,1028,0.89,0,0 +2013,2,23,13,78,-17,9,1027,2.68,0,0 +2013,2,23,14,88,-16,9,1026,3.13,0,0 +2013,2,23,15,116,-17,10,1025,1.79,0,0 +2013,2,23,16,144,-17,10,1025,1.79,0,0 +2013,2,23,17,163,-16,9,1025,3.58,0,0 +2013,2,23,18,164,-14,6,1026,0.89,0,0 +2013,2,23,19,185,-13,5,1027,1.79,0,0 +2013,2,23,20,243,-11,1,1027,0.89,0,0 +2013,2,23,21,256,-10,-1,1027,0.89,0,0 +2013,2,23,22,356,-9,-2,1028,1.79,0,0 +2013,2,23,23,364,-10,1,1028,3.58,0,0 +2013,2,24,0,426,-10,1,1028,0.89,0,0 +2013,2,24,1,397,-8,-3,1028,1.78,0,0 +2013,2,24,2,417,-9,-1,1027,2.67,0,0 +2013,2,24,3,372,-8,-2,1026,3.12,0,0 +2013,2,24,4,318,-10,-5,1027,1.79,0,0 +2013,2,24,5,287,-9,-4,1026,0.89,0,0 +2013,2,24,6,245,-9,-3,1026,1.34,0,0 +2013,2,24,7,266,-9,-4,1026,1.79,0,0 +2013,2,24,8,270,-6,-2,1027,2.68,0,0 +2013,2,24,9,282,-6,0,1027,1.79,0,0 +2013,2,24,10,284,-7,2,1028,0.89,0,0 +2013,2,24,11,319,-7,2,1027,1.79,0,0 +2013,2,24,12,347,-7,3,1027,1.79,0,0 +2013,2,24,13,337,-8,3,1025,3.58,0,0 +2013,2,24,14,320,-8,4,1024,5.37,0,0 +2013,2,24,15,323,-8,4,1024,0.89,0,0 +2013,2,24,16,318,-7,4,1023,1.78,0,0 +2013,2,24,17,313,-7,4,1024,2.67,0,0 +2013,2,24,18,316,-7,3,1024,0.89,0,0 +2013,2,24,19,313,-6,3,1025,0.89,0,0 +2013,2,24,20,404,-7,1,1026,1.78,0,0 +2013,2,24,21,532,-6,1,1026,0.89,0,0 +2013,2,24,22,386,-5,2,1027,2.68,0,0 +2013,2,24,23,383,-5,0,1028,0.89,0,0 +2013,2,25,0,240,-6,0,1028,0.89,0,0 +2013,2,25,1,226,-5,-2,1028,1.34,0,0 +2013,2,25,2,222,-4,-1,1029,1.79,0,0 +2013,2,25,3,202,-4,-1,1028,0.89,0,0 +2013,2,25,4,207,-4,0,1028,1.78,0,0 +2013,2,25,5,200,-4,0,1027,0.89,0,0 +2013,2,25,6,183,-4,1,1028,1.34,0,0 +2013,2,25,7,176,-4,1,1028,3.13,0,0 +2013,2,25,8,153,-5,1,1029,4.92,0,0 +2013,2,25,9,150,-5,1,1030,6.71,0,0 +2013,2,25,10,137,-5,1,1030,8.5,1,0 +2013,2,25,11,111,-4,0,1029,11.63,2,0 +2013,2,25,12,118,-3,0,1029,13.42,3,0 +2013,2,25,13,118,-3,-1,1028,0.89,4,0 +2013,2,25,14,128,-2,-1,1027,1.78,5,0 +2013,2,25,15,143,-2,-1,1026,3.13,0,0 +2013,2,25,16,153,-2,-1,1026,6.26,1,0 +2013,2,25,17,158,-2,-1,1025,8.05,0,0 +2013,2,25,18,153,-3,-1,1025,11.18,0,0 +2013,2,25,19,143,-3,-2,1025,12.97,0,0 +2013,2,25,20,146,-3,-1,1025,16.1,0,0 +2013,2,25,21,144,-4,-1,1025,17.89,0,0 +2013,2,25,22,148,-4,-1,1025,18.78,0,0 +2013,2,25,23,153,-4,0,1025,21.91,0,0 +2013,2,26,0,155,-4,-1,1024,23.7,0,0 +2013,2,26,1,153,-4,-1,1024,25.49,0,0 +2013,2,26,2,153,-6,-4,1023,26.38,0,0 +2013,2,26,3,148,-6,-5,1022,28.17,0,0 +2013,2,26,4,152,-7,-6,1021,29.96,0,0 +2013,2,26,5,163,-7,-6,1021,33.09,0,0 +2013,2,26,6,155,-7,-7,1021,36.22,0,0 +2013,2,26,7,175,-8,-7,1022,38.01,0,0 +2013,2,26,8,143,-5,-4,1022,39.8,0,0 +2013,2,26,9,155,-5,-1,1022,43.82,0,0 +2013,2,26,10,172,-4,2,1022,46.95,0,0 +2013,2,26,11,173,-3,4,1020,50.08,0,0 +2013,2,26,12,190,-3,5,1020,53.21,0,0 +2013,2,26,13,200,-3,7,1019,1.79,0,0 +2013,2,26,14,208,-4,10,1018,1.79,0,0 +2013,2,26,15,198,-5,11,1017,5.81,0,0 +2013,2,26,16,196,-7,11,1017,9.83,0,0 +2013,2,26,17,213,-6,10,1017,12.96,0,0 +2013,2,26,18,206,-5,6,1018,16.09,0,0 +2013,2,26,19,244,-4,5,1018,17.88,0,0 +2013,2,26,20,289,-3,3,1019,19.67,0,0 +2013,2,26,21,341,-3,1,1019,21.46,0,0 +2013,2,26,22,370,-3,1,1019,23.25,0,0 +2013,2,26,23,382,-4,-1,1019,0.89,0,0 +2013,2,27,0,415,-4,-2,1019,0.89,0,0 +2013,2,27,1,414,-5,-3,1019,0.89,0,0 +2013,2,27,2,415,-6,-5,1018,1.78,0,0 +2013,2,27,3,408,-7,-6,1018,1.79,0,0 +2013,2,27,4,414,-4,-4,1018,0.45,0,0 +2013,2,27,5,403,-5,-5,1018,0.89,0,0 +2013,2,27,6,395,-4,-3,1019,1.78,0,0 +2013,2,27,7,394,-5,-5,1019,4.91,0,0 +2013,2,27,8,330,-3,-1,1020,9.83,0,0 +2013,2,27,9,247,-3,3,1020,12.96,0,0 +2013,2,27,10,188,-5,8,1020,17.88,0,0 +2013,2,27,11,129,-10,11,1019,4.92,0,0 +2013,2,27,12,63,-15,14,1018,4.92,0,0 +2013,2,27,13,49,-18,15,1017,9.84,0,0 +2013,2,27,14,32,-17,16,1016,13.86,0,0 +2013,2,27,15,25,-17,15,1015,3.13,0,0 +2013,2,27,16,23,-18,15,1014,1.79,0,0 +2013,2,27,17,27,-17,14,1014,4.92,0,0 +2013,2,27,18,277,-17,11,1013,6.71,0,0 +2013,2,27,19,273,-7,11,1014,11.63,0,0 +2013,2,27,20,241,-6,9,1014,14.76,0,0 +2013,2,27,21,259,-5,7,1014,16.55,0,0 +2013,2,27,22,259,-7,1,1014,18.34,0,0 +2013,2,27,23,265,-4,4,1014,0.89,0,0 +2013,2,28,0,367,-5,3,1013,1.78,0,0 +2013,2,28,1,392,-1,4,1011,4.02,0,0 +2013,2,28,2,400,0,4,1010,8.94,0,0 +2013,2,28,3,441,0,2,1010,10.73,0,0 +2013,2,28,4,466,1,3,1010,12.52,0,0 +2013,2,28,5,485,2,3,1009,15.65,0,0 +2013,2,28,6,525,1,2,1008,17.44,0,0 +2013,2,28,7,502,1,2,1008,19.23,0,0 +2013,2,28,8,449,1,2,1009,1.79,0,0 +2013,2,28,9,469,2,2,1009,0.89,0,0 +2013,2,28,10,510,2,3,1010,1.78,0,0 +2013,2,28,11,229,-2,7,1011,4.02,0,0 +2013,2,28,12,111,-14,10,1012,11.17,0,0 +2013,2,28,13,95,-20,11,1013,23.24,0,0 +2013,2,28,14,89,-18,10,1013,34.42,0,0 +2013,2,28,15,39,-19,9,1014,48.28,0,0 +2013,2,28,16,20,-20,6,1016,60.35,0,0 +2013,2,28,17,17,-19,5,1018,71.53,0,0 +2013,2,28,18,13,-19,4,1020,81.36,0,0 +2013,2,28,19,11,-19,3,1021,90.3,0,0 +2013,2,28,20,12,-20,2,1023,100.13,0,0 +2013,2,28,21,8,-20,1,1026,109.96,0,0 +2013,2,28,22,6,-22,1,1027,122.92,0,0 +2013,2,28,23,9,-23,0,1028,134.99,0,0 +2013,3,1,0,9,-21,0,1028,148.85,0,0 +2013,3,1,1,7,-21,-1,1028,164.94,0,0 +2013,3,1,2,5,-23,-1,1028,180.14,0,0 +2013,3,1,3,6,-26,-2,1029,195.34,0,0 +2013,3,1,4,4,-25,-2,1030,208.3,0,0 +2013,3,1,5,8,-24,-2,1031,217.24,0,0 +2013,3,1,6,9,-23,-3,1032,224.39,0,0 +2013,3,1,7,9,-23,-3,1032,232.44,0,0 +2013,3,1,8,12,-22,-2,1033,4.02,0,0 +2013,3,1,9,10,-26,0,1035,4.02,0,0 +2013,3,1,10,11,-25,0,1035,7.15,0,0 +2013,3,1,11,12,-25,2,1034,12.07,0,0 +2013,3,1,12,11,-24,3,1034,4.02,0,0 +2013,3,1,13,11,-25,4,1033,4.02,0,0 +2013,3,1,14,9,-25,4,1032,4.02,0,0 +2013,3,1,15,9,-24,5,1032,8.04,0,0 +2013,3,1,16,8,-25,6,1032,3.13,0,0 +2013,3,1,17,9,-24,5,1032,8.05,0,0 +2013,3,1,18,10,-21,2,1033,12.97,0,0 +2013,3,1,19,13,-21,2,1034,1.79,0,0 +2013,3,1,20,13,-24,1,1034,6.71,0,0 +2013,3,1,21,17,-22,0,1035,9.84,0,0 +2013,3,1,22,27,-19,0,1036,11.63,0,0 +2013,3,1,23,12,-19,0,1036,14.76,0,0 +2013,3,2,0,10,-18,0,1036,17.89,0,0 +2013,3,2,1,11,-18,-1,1036,1.79,0,0 +2013,3,2,2,8,-18,-2,1037,0.89,0,0 +2013,3,2,3,10,-17,-2,1037,0.45,0,0 +2013,3,2,4,9,-15,-5,1036,1.79,0,0 +2013,3,2,5,10,-16,-3,1036,4.92,0,0 +2013,3,2,6,12,-15,-5,1035,8.05,0,0 +2013,3,2,7,19,-15,-4,1035,9.84,0,0 +2013,3,2,8,18,-16,-2,1035,12.97,0,0 +2013,3,2,9,18,-16,-1,1035,16.99,0,0 +2013,3,2,10,17,-17,0,1035,20.12,0,0 +2013,3,2,11,24,-19,2,1035,0.89,0,0 +2013,3,2,12,33,-19,2,1034,2.68,0,0 +2013,3,2,13,43,-19,3,1033,1.79,0,0 +2013,3,2,14,39,-18,3,1032,3.58,0,0 +2013,3,2,15,42,-19,4,1030,5.37,0,0 +2013,3,2,16,56,-19,4,1030,7.16,0,0 +2013,3,2,17,59,-19,4,1029,8.95,0,0 +2013,3,2,18,67,-17,1,1029,1.79,0,0 +2013,3,2,19,66,-17,1,1029,1.79,0,0 +2013,3,2,20,81,-16,1,1029,3.58,0,0 +2013,3,2,21,100,-16,0,1029,5.37,0,0 +2013,3,2,22,120,-13,0,1029,0.89,0,0 +2013,3,2,23,153,-14,-1,1028,0.89,0,0 +2013,3,3,0,157,-13,-4,1027,0.89,0,0 +2013,3,3,1,161,-12,-4,1026,0.89,0,0 +2013,3,3,2,145,-11,-3,1025,0.89,0,0 +2013,3,3,3,113,-11,-4,1024,2.68,0,0 +2013,3,3,4,119,-11,-5,1023,3.57,0,0 +2013,3,3,5,133,-12,-6,1023,4.46,0,0 +2013,3,3,6,134,-11,-6,1022,5.35,0,0 +2013,3,3,7,138,-10,-5,1022,7.14,0,0 +2013,3,3,8,123,-8,-2,1022,10.27,0,0 +2013,3,3,9,100,-13,3,1022,13.4,0,0 +2013,3,3,10,110,-14,3,1022,15.19,0,0 +2013,3,3,11,117,-14,5,1021,16.98,0,0 +2013,3,3,12,115,-15,10,1019,0.89,0,0 +2013,3,3,13,110,-16,12,1018,3.13,0,0 +2013,3,3,14,38,-18,15,1017,8.05,0,0 +2013,3,3,15,19,-18,16,1016,12.97,0,0 +2013,3,3,16,25,-18,17,1016,16.99,0,0 +2013,3,3,17,50,-17,15,1016,3.13,0,0 +2013,3,3,18,55,-16,11,1017,1.79,0,0 +2013,3,3,19,143,-16,10,1018,3.58,0,0 +2013,3,3,20,162,-13,6,1019,0.89,0,0 +2013,3,3,21,86,-12,5,1019,1.79,0,0 +2013,3,3,22,73,-10,3,1019,0.89,0,0 +2013,3,3,23,86,-10,3,1020,1.78,0,0 +2013,3,4,0,69,-14,9,1021,8.94,0,0 +2013,3,4,1,24,-14,9,1021,16.09,0,0 +2013,3,4,2,5,-13,8,1022,23.24,0,0 +2013,3,4,3,4,-13,4,1023,26.37,0,0 +2013,3,4,4,5,-13,2,1023,29.5,0,0 +2013,3,4,5,7,-12,1,1023,1.79,0,0 +2013,3,4,6,7,-12,0,1024,0.89,0,0 +2013,3,4,7,9,-9,-2,1025,1.78,0,0 +2013,3,4,8,17,-13,7,1026,4.02,0,0 +2013,3,4,9,16,-16,9,1026,3.13,0,0 +2013,3,4,10,16,-17,11,1027,1.79,0,0 +2013,3,4,11,NA,-17,12,1026,4.92,0,0 +2013,3,4,12,14,-17,13,1026,0.89,0,0 +2013,3,4,13,10,-19,14,1024,3.13,0,0 +2013,3,4,14,10,-20,16,1023,7.15,0,0 +2013,3,4,15,13,-19,15,1022,3.13,0,0 +2013,3,4,16,17,-19,15,1022,3.13,0,0 +2013,3,4,17,23,-18,14,1021,8.05,0,0 +2013,3,4,18,25,-18,12,1021,12.07,0,0 +2013,3,4,19,36,-17,10,1022,15.2,0,0 +2013,3,4,20,47,-15,8,1022,18.33,0,0 +2013,3,4,21,54,-12,7,1022,22.35,0,0 +2013,3,4,22,60,-12,7,1022,26.37,0,0 +2013,3,4,23,78,-13,8,1022,31.29,0,0 +2013,3,5,0,80,-12,7,1021,34.42,0,0 +2013,3,5,1,98,-12,4,1021,36.21,0,0 +2013,3,5,2,101,-11,3,1020,0.89,0,0 +2013,3,5,3,115,-11,1,1020,0.89,0,0 +2013,3,5,4,129,-9,-1,1020,2.68,0,0 +2013,3,5,5,116,-9,0,1019,0.89,0,0 +2013,3,5,6,116,-9,-2,1019,1.78,0,0 +2013,3,5,7,126,-8,-3,1019,0.89,0,0 +2013,3,5,8,130,-6,1,1019,0.89,0,0 +2013,3,5,9,150,-8,5,1019,1.78,0,0 +2013,3,5,10,163,-8,8,1018,2.67,0,0 +2013,3,5,11,172,-8,9,1018,3.56,0,0 +2013,3,5,12,192,-9,11,1017,4.45,0,0 +2013,3,5,13,181,-9,12,1015,5.34,0,0 +2013,3,5,14,187,-10,13,1014,7.13,0,0 +2013,3,5,15,204,-9,14,1013,3.13,0,0 +2013,3,5,16,218,-9,14,1013,4.92,0,0 +2013,3,5,17,222,-8,12,1013,1.79,0,0 +2013,3,5,18,208,-7,8,1013,0.45,0,0 +2013,3,5,19,225,-6,5,1014,1.34,0,0 +2013,3,5,20,235,-7,6,1014,2.23,0,0 +2013,3,5,21,263,-6,4,1014,0.89,0,0 +2013,3,5,22,274,-6,4,1014,0.89,0,0 +2013,3,5,23,275,-6,3,1014,1.78,0,0 +2013,3,6,0,302,-6,2,1014,3.13,0,0 +2013,3,6,1,225,-6,3,1014,4.92,0,0 +2013,3,6,2,220,-6,2,1013,6.71,0,0 +2013,3,6,3,207,-6,2,1013,9.84,0,0 +2013,3,6,4,202,-6,2,1013,12.97,0,0 +2013,3,6,5,200,-6,2,1013,16.1,0,0 +2013,3,6,6,206,-5,2,1013,19.23,0,0 +2013,3,6,7,202,-5,3,1014,23.25,0,0 +2013,3,6,8,203,-5,4,1015,26.38,0,0 +2013,3,6,9,196,-6,7,1015,30.4,0,0 +2013,3,6,10,188,-6,8,1015,34.42,0,0 +2013,3,6,11,187,-6,10,1014,38.44,0,0 +2013,3,6,12,204,-6,10,1013,42.46,0,0 +2013,3,6,13,233,-6,10,1012,0.89,0,0 +2013,3,6,14,259,-6,12,1011,1.78,0,0 +2013,3,6,15,267,-6,13,1011,2.67,0,0 +2013,3,6,16,246,-6,14,1011,3.56,0,0 +2013,3,6,17,247,-5,13,1011,4.45,0,0 +2013,3,6,18,257,-4,7,1012,6.24,0,0 +2013,3,6,19,NA,-4,6,1012,7.13,0,0 +2013,3,6,20,257,-3,5,1013,8.02,0,0 +2013,3,6,21,257,-3,5,1014,8.91,0,0 +2013,3,6,22,249,-3,4,1013,9.8,0,0 +2013,3,6,23,245,-4,1,1013,10.69,0,0 +2013,3,7,0,242,-3,2,1013,0.89,0,0 +2013,3,7,1,239,-4,-1,1013,1.79,0,0 +2013,3,7,2,240,-4,-1,1013,0.89,0,0 +2013,3,7,3,238,-4,-1,1012,1.78,0,0 +2013,3,7,4,338,-5,-3,1012,3.57,0,0 +2013,3,7,5,334,-5,-2,1012,0.45,0,0 +2013,3,7,6,344,-5,-2,1012,0.89,0,0 +2013,3,7,7,351,-3,-1,1012,0.45,0,0 +2013,3,7,8,331,-1,2,1012,0.89,0,0 +2013,3,7,9,327,-2,7,1013,0.89,0,0 +2013,3,7,10,306,-2,8,1012,1.78,0,0 +2013,3,7,11,289,-2,13,1010,1.79,0,0 +2013,3,7,12,363,-3,14,1009,4.92,0,0 +2013,3,7,13,306,-3,17,1007,8.94,0,0 +2013,3,7,14,293,-5,18,1006,3.13,0,0 +2013,3,7,15,279,-4,18,1004,3.13,0,0 +2013,3,7,16,230,-4,19,1003,1.79,0,0 +2013,3,7,17,201,-1,16,1002,1.79,0,0 +2013,3,7,18,200,0,14,1002,0.89,0,0 +2013,3,7,19,195,0,11,1003,1.79,0,0 +2013,3,7,20,294,0,8,1003,3.58,0,0 +2013,3,7,21,510,0,9,1003,0.89,0,0 +2013,3,7,22,516,-1,6,1003,1.79,0,0 +2013,3,7,23,469,0,7,1004,0.45,0,0 +2013,3,8,0,450,-1,5,1003,0.89,0,0 +2013,3,8,1,397,-2,4,1004,2.68,0,0 +2013,3,8,2,365,-2,4,1004,5.81,0,0 +2013,3,8,3,364,-2,3,1003,8.94,0,0 +2013,3,8,4,357,-2,2,1004,0.45,0,0 +2013,3,8,5,357,-2,2,1004,1.34,0,0 +2013,3,8,6,350,-3,0,1005,2.23,0,0 +2013,3,8,7,337,-4,0,1005,3.13,0,0 +2013,3,8,8,323,0,7,1006,0.89,0,0 +2013,3,8,9,306,-2,12,1006,1.79,0,0 +2013,3,8,10,234,-2,14,1007,1.79,0,0 +2013,3,8,11,139,-5,20,1006,4.92,0,0 +2013,3,8,12,95,-6,21,1006,8.94,0,0 +2013,3,8,13,189,0,18,1005,16.09,0,0 +2013,3,8,14,410,0,17,1005,24.14,0,0 +2013,3,8,15,216,0,14,1005,29.95,0,0 +2013,3,8,16,145,0,13,1005,38,0,0 +2013,3,8,17,99,-1,11,1006,42.92,0,0 +2013,3,8,18,87,-1,9,1006,46.05,0,0 +2013,3,8,19,93,-2,7,1006,47.84,0,0 +2013,3,8,20,95,-2,8,1006,50.97,0,0 +2013,3,8,21,101,-3,5,1006,52.76,0,0 +2013,3,8,22,97,-4,6,1006,55.89,0,0 +2013,3,8,23,89,-4,4,1005,59.02,0,0 +2013,3,9,0,87,-4,2,1003,0.89,0,0 +2013,3,9,1,105,-4,3,1002,1.78,0,0 +2013,3,9,2,114,-4,1,1001,0.89,0,0 +2013,3,9,3,119,-4,3,1000,0.89,0,0 +2013,3,9,4,128,-3,1,998,1.78,0,0 +2013,3,9,5,129,-3,2,997,2.67,0,0 +2013,3,9,6,122,-4,1,998,1.79,0,0 +2013,3,9,7,122,-3,1,998,4.92,0,0 +2013,3,9,8,124,-2,4,999,8.05,0,0 +2013,3,9,9,136,-3,7,1000,12.07,0,0 +2013,3,9,10,141,-3,13,1002,3.13,0,0 +2013,3,9,11,33,-11,18,1004,16.99,0,0 +2013,3,9,12,42,-16,15,1007,33.08,0,0 +2013,3,9,13,32,-20,14,1009,49.17,0,0 +2013,3,9,14,40,-21,11,1012,64.37,0,0 +2013,3,9,15,32,-22,10,1014,76.44,0,0 +2013,3,9,16,19,-22,10,1015,86.27,0,0 +2013,3,9,17,16,-22,10,1017,94.32,0,0 +2013,3,9,18,13,-22,9,1018,103.26,0,0 +2013,3,9,19,16,-22,7,1022,113.09,0,0 +2013,3,9,20,12,-21,6,1023,122.03,0,0 +2013,3,9,21,10,-21,5,1025,134.1,0,0 +2013,3,9,22,18,-21,5,1026,143.04,0,0 +2013,3,9,23,12,-21,4,1027,148.85,0,0 +2013,3,10,0,12,-19,2,1027,1.79,0,0 +2013,3,10,1,15,-15,0,1027,3.13,0,0 +2013,3,10,2,18,-13,-2,1027,4.92,0,0 +2013,3,10,3,20,-16,1,1027,0.89,0,0 +2013,3,10,4,18,-12,-4,1027,0.89,0,0 +2013,3,10,5,22,-13,-2,1028,0.89,0,0 +2013,3,10,6,27,-10,-4,1028,1.78,0,0 +2013,3,10,7,26,-10,-4,1028,2.67,0,0 +2013,3,10,8,27,-11,1,1029,1.79,0,0 +2013,3,10,9,29,-19,7,1029,5.81,0,0 +2013,3,10,10,27,-19,8,1029,10.73,0,0 +2013,3,10,11,33,-21,10,1028,15.65,0,0 +2013,3,10,12,32,-20,10,1028,19.67,0,0 +2013,3,10,13,33,-22,11,1026,23.69,0,0 +2013,3,10,14,35,-20,12,1025,28.61,0,0 +2013,3,10,15,33,-20,12,1024,33.53,0,0 +2013,3,10,16,37,-19,12,1024,39.34,0,0 +2013,3,10,17,42,-18,11,1024,45.15,0,0 +2013,3,10,18,59,-16,9,1024,50.07,0,0 +2013,3,10,19,67,-15,8,1024,53.2,0,0 +2013,3,10,20,80,-14,6,1025,54.99,0,0 +2013,3,10,21,75,-12,4,1025,56.78,0,0 +2013,3,10,22,87,-12,5,1025,58.57,0,0 +2013,3,10,23,88,-11,2,1025,60.36,0,0 +2013,3,11,0,81,-11,3,1025,62.15,0,0 +2013,3,11,1,106,-10,1,1024,65.28,0,0 +2013,3,11,2,103,-10,1,1024,0.89,0,0 +2013,3,11,3,118,-9,0,1023,1.78,0,0 +2013,3,11,4,111,-8,-1,1023,0.89,0,0 +2013,3,11,5,110,-8,-1,1023,2.68,0,0 +2013,3,11,6,102,-8,-1,1023,0.89,0,0 +2013,3,11,7,94,-6,-1,1023,1.78,0,0 +2013,3,11,8,105,-7,3,1023,1.79,0,0 +2013,3,11,9,122,-8,4,1023,3.58,0,0 +2013,3,11,10,155,-8,6,1022,6.71,0,0 +2013,3,11,11,150,-9,7,1022,8.5,0,0 +2013,3,11,12,156,-9,8,1021,11.63,0,0 +2013,3,11,13,134,-9,9,1019,13.42,0,0 +2013,3,11,14,114,-9,11,1018,19.23,0,0 +2013,3,11,15,114,-8,12,1017,24.15,0,0 +2013,3,11,16,120,-9,12,1016,29.07,0,0 +2013,3,11,17,153,-6,11,1015,33.09,0,0 +2013,3,11,18,148,-4,9,1016,34.88,0,0 +2013,3,11,19,162,-4,9,1016,36.67,0,0 +2013,3,11,20,164,-3,9,1016,37.56,0,0 +2013,3,11,21,158,-3,8,1016,39.35,0,0 +2013,3,11,22,117,-4,9,1016,42.48,0,0 +2013,3,11,23,108,-4,8,1015,44.27,0,0 +2013,3,12,0,114,-4,8,1014,46.06,0,0 +2013,3,12,1,135,-3,6,1013,47.85,0,0 +2013,3,12,2,131,-3,6,1013,0.89,0,0 +2013,3,12,3,135,-2,6,1012,1.78,0,0 +2013,3,12,4,137,-1,5,1012,2.67,0,1 +2013,3,12,5,150,0,4,1012,0.89,0,2 +2013,3,12,6,153,1,3,1012,1.79,0,3 +2013,3,12,7,145,1,3,1012,0.89,0,4 +2013,3,12,8,127,1,3,1012,1.34,0,5 +2013,3,12,9,130,2,3,1013,2.23,0,6 +2013,3,12,10,161,2,3,1012,1.79,0,7 +2013,3,12,11,164,2,4,1012,0.89,0,8 +2013,3,12,12,167,3,4,1012,0.89,0,9 +2013,3,12,13,148,3,5,1012,4.02,0,0 +2013,3,12,14,153,3,5,1013,7.15,0,0 +2013,3,12,15,147,3,6,1013,8.94,0,1 +2013,3,12,16,150,4,6,1013,0.89,0,0 +2013,3,12,17,152,4,7,1014,3.13,0,0 +2013,3,12,18,164,3,6,1015,6.26,0,0 +2013,3,12,19,159,2,5,1017,9.39,0,0 +2013,3,12,20,176,3,5,1019,0.89,0,0 +2013,3,12,21,174,2,3,1020,1.78,0,0 +2013,3,12,22,194,2,6,1021,1.79,0,0 +2013,3,12,23,114,1,3,1022,0.89,0,0 +2013,3,13,0,67,-5,3,1023,3.13,0,0 +2013,3,13,1,23,-6,0,1024,6.26,0,0 +2013,3,13,2,13,-9,1,1025,10.28,0,0 +2013,3,13,3,10,-8,-1,1025,13.41,0,0 +2013,3,13,4,11,-9,-1,1026,16.54,0,0 +2013,3,13,5,17,-11,0,1026,4.02,0,0 +2013,3,13,6,14,-10,-1,1027,7.15,0,0 +2013,3,13,7,16,-10,1,1029,3.13,0,0 +2013,3,13,8,15,-11,2,1030,4.02,0,0 +2013,3,13,9,16,-12,3,1031,8.94,0,0 +2013,3,13,10,16,-15,5,1031,13.86,0,0 +2013,3,13,11,17,-15,5,1032,17.88,0,0 +2013,3,13,12,13,-16,6,1031,21.9,0,0 +2013,3,13,13,10,-18,7,1029,23.69,0,0 +2013,3,13,14,14,-18,8,1029,0.89,0,0 +2013,3,13,15,12,-19,8,1028,2.68,0,0 +2013,3,13,16,15,-18,8,1028,3.57,0,0 +2013,3,13,17,16,-18,8,1028,1.79,0,0 +2013,3,13,18,23,-15,6,1028,4.92,0,0 +2013,3,13,19,37,-12,4,1029,5.81,0,0 +2013,3,13,20,38,-13,4,1029,7.6,0,0 +2013,3,13,21,30,-12,4,1030,9.39,0,0 +2013,3,13,22,34,-9,2,1030,11.18,0,0 +2013,3,13,23,48,-9,2,1030,14.31,0,0 +2013,3,14,0,71,-3,0,1031,18.33,0,0 +2013,3,14,1,82,-4,0,1031,22.35,0,0 +2013,3,14,2,77,-4,-1,1031,24.14,0,0 +2013,3,14,3,83,-4,-1,1031,25.93,0,0 +2013,3,14,4,92,-4,-2,1030,27.72,0,0 +2013,3,14,5,84,-4,-2,1030,29.51,0,0 +2013,3,14,6,84,-4,-1,1030,0.89,0,0 +2013,3,14,7,75,-4,-1,1030,0.89,0,0 +2013,3,14,8,88,-3,0,1031,0.89,0,0 +2013,3,14,9,92,-4,1,1031,1.79,0,0 +2013,3,14,10,92,-5,3,1030,1.79,0,0 +2013,3,14,11,112,-4,4,1029,1.79,0,0 +2013,3,14,12,NA,-4,5,1028,3.58,0,0 +2013,3,14,13,124,-4,7,1027,1.79,0,0 +2013,3,14,14,122,-6,9,1026,3.13,0,0 +2013,3,14,15,109,-6,9,1025,6.26,0,0 +2013,3,14,16,111,-7,10,1024,11.18,0,0 +2013,3,14,17,121,-6,9,1024,16.1,0,0 +2013,3,14,18,128,-5,7,1024,21.02,0,0 +2013,3,14,19,127,-5,5,1024,25.04,0,0 +2013,3,14,20,126,-4,5,1024,25.93,0,0 +2013,3,14,21,136,-4,3,1024,26.82,0,0 +2013,3,14,22,165,-3,3,1024,28.61,0,0 +2013,3,14,23,182,-3,3,1024,30.4,0,0 +2013,3,15,0,182,-3,2,1024,0.89,0,0 +2013,3,15,1,195,-3,1,1023,1.78,0,0 +2013,3,15,2,205,-4,0,1023,0.89,0,0 +2013,3,15,3,217,-5,0,1022,0.89,0,0 +2013,3,15,4,226,-4,-1,1022,2.68,0,0 +2013,3,15,5,220,-3,-1,1022,0.89,0,0 +2013,3,15,6,197,-3,-2,1022,1.78,0,0 +2013,3,15,7,187,-3,-1,1022,1.79,0,0 +2013,3,15,8,174,-1,1,1022,0.89,0,0 +2013,3,15,9,181,-2,3,1022,0.89,0,0 +2013,3,15,10,192,-1,5,1022,0.89,0,0 +2013,3,15,11,216,-2,6,1021,1.79,0,0 +2013,3,15,12,247,-1,8,1020,0.89,0,0 +2013,3,15,13,254,-2,10,1019,1.79,0,0 +2013,3,15,14,271,-2,11,1018,3.13,0,0 +2013,3,15,15,302,-1,11,1017,1.79,0,0 +2013,3,15,16,294,-1,11,1017,3.58,0,0 +2013,3,15,17,280,0,11,1017,1.79,0,0 +2013,3,15,18,294,1,9,1017,1.79,0,0 +2013,3,15,19,302,1,9,1018,2.68,0,0 +2013,3,15,20,324,2,7,1018,3.57,0,0 +2013,3,15,21,315,2,6,1020,4.46,0,0 +2013,3,15,22,328,1,5,1020,0.89,0,0 +2013,3,15,23,355,1,4,1021,1.79,0,0 +2013,3,16,0,405,0,3,1021,4.92,0,0 +2013,3,16,1,347,0,2,1021,8.05,0,0 +2013,3,16,2,346,0,4,1022,12.07,0,0 +2013,3,16,3,342,0,4,1022,13.86,0,0 +2013,3,16,4,327,0,4,1023,1.79,0,0 +2013,3,16,5,332,0,2,1023,3.13,0,0 +2013,3,16,6,328,0,3,1024,6.26,0,0 +2013,3,16,7,NA,0,3,1025,8.05,0,0 +2013,3,16,8,325,0,6,1025,11.18,0,0 +2013,3,16,9,315,0,9,1026,14.31,0,0 +2013,3,16,10,295,0,11,1026,17.44,0,0 +2013,3,16,11,239,-1,14,1026,0.89,0,0 +2013,3,16,12,212,-2,14,1025,2.68,0,0 +2013,3,16,13,214,-3,14,1024,1.79,0,0 +2013,3,16,14,215,-3,15,1023,6.71,0,0 +2013,3,16,15,208,-2,14,1022,10.73,0,0 +2013,3,16,16,229,-2,14,1021,14.75,0,0 +2013,3,16,17,235,-1,13,1021,18.77,0,0 +2013,3,16,18,199,2,12,1020,22.79,0,0 +2013,3,16,19,251,3,10,1020,25.92,0,0 +2013,3,16,20,280,3,10,1020,29.05,0,0 +2013,3,16,21,250,2,8,1020,32.18,0,0 +2013,3,16,22,205,1,7,1020,35.31,0,0 +2013,3,16,23,202,0,6,1019,0.89,0,0 +2013,3,17,0,204,1,6,1018,0.89,0,0 +2013,3,17,1,216,1,5,1018,2.68,0,0 +2013,3,17,2,221,0,5,1017,0.89,0,0 +2013,3,17,3,227,0,4,1016,1.78,0,0 +2013,3,17,4,235,0,5,1014,2.23,0,0 +2013,3,17,5,236,0,4,1014,3.12,0,0 +2013,3,17,6,232,0,4,1013,1.79,0,0 +2013,3,17,7,241,0,3,1012,3.58,0,0 +2013,3,17,8,258,1,5,1011,0.89,0,0 +2013,3,17,9,273,1,6,1011,1.79,0,0 +2013,3,17,10,287,2,8,1010,3.58,0,0 +2013,3,17,11,316,2,10,1009,0.89,0,0 +2013,3,17,12,337,2,11,1008,2.68,0,0 +2013,3,17,13,359,3,13,1006,1.79,0,0 +2013,3,17,14,394,3,15,1004,3.58,0,0 +2013,3,17,15,393,4,15,1003,6.71,0,0 +2013,3,17,16,402,4,16,1002,9.84,0,0 +2013,3,17,17,400,3,15,1002,11.63,0,0 +2013,3,17,18,422,3,11,1001,13.42,0,0 +2013,3,17,19,429,4,11,1001,0.89,0,0 +2013,3,17,20,480,3,7,1002,1.79,0,0 +2013,3,17,21,512,5,8,1002,0.89,0,0 +2013,3,17,22,541,3,7,1002,1.78,0,0 +2013,3,17,23,522,5,8,1001,0.89,0,0 +2013,3,18,0,498,3,8,1002,4.91,0,0 +2013,3,18,1,517,3,8,1002,8.04,0,0 +2013,3,18,2,246,2,5,1002,12.96,0,0 +2013,3,18,3,39,2,7,1002,16.98,0,0 +2013,3,18,4,14,-10,10,1002,25.92,0,0 +2013,3,18,5,15,-9,10,1003,34.86,0,0 +2013,3,18,6,10,-9,9,1004,47.82,0,0 +2013,3,18,7,13,-17,7,1007,59.89,0,0 +2013,3,18,8,9,-16,7,1008,71.07,0,0 +2013,3,18,9,13,-18,7,1009,80.01,0,0 +2013,3,18,10,10,-21,10,1009,85.82,0,0 +2013,3,18,11,7,-20,9,1010,4.92,0,0 +2013,3,18,12,6,-21,9,1010,8.94,0,0 +2013,3,18,13,9,-22,11,1009,1.79,0,0 +2013,3,18,14,10,-22,13,1008,4.02,0,0 +2013,3,18,15,19,-22,12,1007,7.15,0,0 +2013,3,18,16,14,-21,11,1006,4.92,0,0 +2013,3,18,17,22,-20,10,1006,9.84,0,0 +2013,3,18,18,25,-18,9,1006,16.99,0,0 +2013,3,18,19,87,-3,6,1007,25.93,0,0 +2013,3,18,20,49,-4,4,1009,33.98,0,0 +2013,3,18,21,36,-4,3,1010,41.13,0,0 +2013,3,18,22,30,-5,2,1010,46.05,0,0 +2013,3,18,23,35,-5,2,1010,50.07,0,0 +2013,3,19,0,30,-5,1,1011,53.2,0,0 +2013,3,19,1,33,-4,1,1012,56.33,0,0 +2013,3,19,2,34,-3,0,1012,4.02,0,1 +2013,3,19,3,36,-2,-1,1012,7.15,1,0 +2013,3,19,4,25,-3,0,1012,8.94,0,0 +2013,3,19,5,23,-3,0,1011,10.73,0,0 +2013,3,19,6,22,-3,0,1012,11.62,0,0 +2013,3,19,7,34,-3,0,1012,12.51,0,0 +2013,3,19,8,34,-2,2,1013,1.79,0,0 +2013,3,19,9,48,-2,3,1013,3.13,0,0 +2013,3,19,10,59,-3,3,1014,4.02,0,0 +2013,3,19,11,68,-2,4,1013,3.13,0,0 +2013,3,19,12,69,-1,5,1013,6.26,0,1 +2013,3,19,13,69,0,5,1013,4.02,0,0 +2013,3,19,14,69,0,5,1013,3.13,0,0 +2013,3,19,15,72,1,5,1013,7.15,0,0 +2013,3,19,16,63,1,4,1014,11.17,0,0 +2013,3,19,17,57,1,3,1015,15.19,0,1 +2013,3,19,18,41,1,2,1016,18.32,0,2 +2013,3,19,19,60,1,2,1017,19.21,0,3 +2013,3,19,20,69,0,1,1016,0.89,1,0 +2013,3,19,21,100,0,1,1018,1.78,2,0 +2013,3,19,22,105,0,0,1022,1.79,3,0 +2013,3,19,23,83,0,0,1022,5.81,4,0 +2013,3,20,0,75,-1,-1,1022,8.94,5,0 +2013,3,20,1,62,-1,-1,1023,10.73,6,0 +2013,3,20,2,53,-2,-2,1024,12.52,7,0 +2013,3,20,3,54,-2,-2,1025,0.89,8,0 +2013,3,20,4,45,-2,-2,1025,1.79,9,0 +2013,3,20,5,29,-3,-2,1025,2.68,10,0 +2013,3,20,6,28,-3,-3,1025,4.47,0,0 +2013,3,20,7,36,-3,-2,1025,7.6,0,0 +2013,3,20,8,39,-2,-1,1026,8.49,0,0 +2013,3,20,9,48,-1,0,1027,9.38,0,0 +2013,3,20,10,63,-2,0,1027,11.17,0,0 +2013,3,20,11,56,-6,2,1026,3.13,0,0 +2013,3,20,12,56,-10,3,1025,8.05,0,0 +2013,3,20,13,65,-10,4,1024,12.97,0,0 +2013,3,20,14,49,-10,5,1023,16.99,0,0 +2013,3,20,15,53,-8,5,1022,4.02,0,0 +2013,3,20,16,53,-6,6,1021,7.15,0,0 +2013,3,20,17,63,-6,5,1021,11.17,0,0 +2013,3,20,18,70,-5,4,1020,14.3,0,0 +2013,3,20,19,91,-3,3,1020,19.22,0,0 +2013,3,20,20,97,-3,2,1020,23.24,0,0 +2013,3,20,21,102,-3,2,1020,27.26,0,0 +2013,3,20,22,109,-3,1,1020,30.39,0,0 +2013,3,20,23,119,-3,2,1019,32.18,0,0 +2013,3,21,0,118,-3,2,1018,35.31,0,0 +2013,3,21,1,114,-3,2,1018,38.44,0,0 +2013,3,21,2,98,-3,1,1017,41.57,0,0 +2013,3,21,3,91,-4,1,1016,44.7,0,0 +2013,3,21,4,94,-4,0,1015,47.83,0,0 +2013,3,21,5,97,-4,0,1014,49.62,0,0 +2013,3,21,6,108,-4,0,1014,51.41,0,0 +2013,3,21,7,107,-3,0,1013,0.89,0,0 +2013,3,21,8,104,-4,1,1013,3.13,0,0 +2013,3,21,9,105,-4,2,1012,4.92,0,0 +2013,3,21,10,123,-3,4,1011,0.89,0,0 +2013,3,21,11,148,-3,4,1010,1.79,0,0 +2013,3,21,12,164,-3,6,1009,3.58,0,0 +2013,3,21,13,173,-3,7,1008,0.89,0,0 +2013,3,21,14,172,-2,8,1007,2.68,0,0 +2013,3,21,15,183,-2,8,1006,1.79,0,0 +2013,3,21,16,192,-2,8,1006,0.89,0,0 +2013,3,21,17,198,-1,8,1005,3.13,0,0 +2013,3,21,18,210,-1,6,1006,8.05,0,0 +2013,3,21,19,224,-1,5,1007,11.18,0,0 +2013,3,21,20,224,-1,5,1008,1.79,0,0 +2013,3,21,21,224,-1,4,1009,4.02,0,0 +2013,3,21,22,211,-1,5,1011,8.04,0,0 +2013,3,21,23,210,-1,4,1011,11.17,0,0 +2013,3,22,0,209,-1,2,1013,16.09,0,0 +2013,3,22,1,166,-4,2,1014,4.02,0,0 +2013,3,22,2,125,-7,6,1014,8.04,0,0 +2013,3,22,3,94,-7,3,1015,4.02,0,0 +2013,3,22,4,51,-5,2,1015,8.04,0,0 +2013,3,22,5,58,-8,3,1017,1.79,0,0 +2013,3,22,6,51,-10,3,1018,5.81,0,0 +2013,3,22,7,17,-10,3,1019,1.79,0,0 +2013,3,22,8,14,-14,6,1020,0.89,0,0 +2013,3,22,9,18,-13,7,1020,1.79,0,0 +2013,3,22,10,17,-13,8,1020,1.79,0,0 +2013,3,22,11,66,-7,8,1020,4.92,0,0 +2013,3,22,12,63,-9,8,1019,8.94,0,0 +2013,3,22,13,62,-7,9,1018,14.75,0,0 +2013,3,22,14,73,-8,10,1018,19.67,0,0 +2013,3,22,15,76,-6,10,1017,25.48,0,0 +2013,3,22,16,62,-7,10,1016,31.29,0,0 +2013,3,22,17,74,-6,10,1016,37.1,0,0 +2013,3,22,18,82,-5,9,1016,42.91,0,0 +2013,3,22,19,74,-5,7,1017,46.93,0,0 +2013,3,22,20,69,-4,6,1017,51.85,0,0 +2013,3,22,21,63,-4,4,1018,55.87,0,0 +2013,3,22,22,61,-4,4,1018,59.89,0,0 +2013,3,22,23,58,-3,2,1018,64.81,0,0 +2013,3,23,0,54,-3,1,1018,68.83,0,0 +2013,3,23,1,50,-3,0,1017,71.96,0,0 +2013,3,23,2,51,-3,0,1016,73.75,0,0 +2013,3,23,3,51,-3,1,1015,75.54,0,0 +2013,3,23,4,51,-3,1,1015,79.56,0,0 +2013,3,23,5,52,-3,0,1014,82.69,0,0 +2013,3,23,6,56,-3,0,1014,86.71,0,0 +2013,3,23,7,60,-3,1,1014,89.84,0,0 +2013,3,23,8,76,-4,1,1012,94.76,0,0 +2013,3,23,9,97,-4,3,1011,97.89,0,0 +2013,3,23,10,86,-4,3,1011,101.02,0,0 +2013,3,23,11,88,-5,3,1011,1.79,0,0 +2013,3,23,12,91,-5,6,1011,1.79,0,0 +2013,3,23,13,90,-6,5,1010,0.89,0,0 +2013,3,23,14,96,-6,8,1010,1.78,0,0 +2013,3,23,15,94,-5,8,1009,1.79,0,0 +2013,3,23,16,86,-10,13,1010,12.07,0,0 +2013,3,23,17,71,-11,12,1011,25.93,0,0 +2013,3,23,18,18,-11,9,1014,38,0,0 +2013,3,23,19,15,-13,7,1017,47.83,0,0 +2013,3,23,20,11,-13,6,1018,57.66,0,0 +2013,3,23,21,12,-15,5,1020,64.81,0,0 +2013,3,23,22,8,-14,3,1021,69.73,0,0 +2013,3,23,23,5,-15,3,1022,4.92,0,0 +2013,3,24,0,9,-16,2,1023,9.84,0,0 +2013,3,24,1,10,-16,2,1023,15.65,0,0 +2013,3,24,2,9,-15,1,1023,21.46,0,0 +2013,3,24,3,11,-13,0,1024,26.38,0,0 +2013,3,24,4,9,-13,-1,1023,5.81,0,0 +2013,3,24,5,11,-12,-1,1024,3.13,0,0 +2013,3,24,6,13,-12,-1,1024,4.92,0,0 +2013,3,24,7,18,-13,1,1025,1.79,0,0 +2013,3,24,8,18,-17,4,1025,6.71,0,0 +2013,3,24,9,11,-17,4,1025,11.63,0,0 +2013,3,24,10,12,-18,6,1025,15.65,0,0 +2013,3,24,11,12,-17,6,1024,1.79,0,0 +2013,3,24,12,11,-18,8,1023,3.13,0,0 +2013,3,24,13,15,-18,8,1022,6.26,0,0 +2013,3,24,14,13,-19,8,1021,0.89,0,0 +2013,3,24,15,12,-19,9,1019,2.68,0,0 +2013,3,24,16,12,-18,9,1018,4.02,0,0 +2013,3,24,17,15,-17,9,1018,8.04,0,0 +2013,3,24,18,21,-15,8,1018,11.17,0,0 +2013,3,24,19,32,-14,6,1019,14.3,0,0 +2013,3,24,20,50,-14,6,1019,18.32,0,0 +2013,3,24,21,60,-13,5,1020,20.11,0,0 +2013,3,24,22,56,-12,4,1021,23.24,0,0 +2013,3,24,23,58,-10,3,1021,0.89,0,0 +2013,3,25,0,71,-7,2,1022,4.02,0,0 +2013,3,25,1,91,-7,2,1022,7.15,0,0 +2013,3,25,2,88,-5,0,1023,12.07,0,0 +2013,3,25,3,90,-5,0,1023,16.09,0,0 +2013,3,25,4,74,-5,-1,1023,19.22,0,0 +2013,3,25,5,86,-6,-1,1023,0.89,0,0 +2013,3,25,6,103,-6,-1,1024,1.78,0,0 +2013,3,25,7,119,-5,0,1024,2.67,0,0 +2013,3,25,8,121,-5,1,1024,3.56,0,0 +2013,3,25,9,137,-6,3,1024,1.79,0,0 +2013,3,25,10,126,-7,4,1024,1.79,0,0 +2013,3,25,11,112,-8,6,1023,3.58,0,0 +2013,3,25,12,NA,-8,7,1022,3.13,0,0 +2013,3,25,13,137,-8,9,1021,3.13,0,0 +2013,3,25,14,129,-9,10,1020,4.92,0,0 +2013,3,25,15,137,-9,11,1019,3.13,0,0 +2013,3,25,16,123,-9,11,1019,6.26,0,0 +2013,3,25,17,115,-10,11,1018,10.28,0,0 +2013,3,25,18,117,-9,10,1018,13.41,0,0 +2013,3,25,19,135,-9,8,1018,15.2,0,0 +2013,3,25,20,150,-8,7,1019,16.99,0,0 +2013,3,25,21,127,-7,7,1019,18.78,0,0 +2013,3,25,22,102,-7,6,1019,20.57,0,0 +2013,3,25,23,97,-7,4,1018,22.36,0,0 +2013,3,26,0,98,-8,5,1018,25.49,0,0 +2013,3,26,1,110,-8,5,1018,28.62,0,0 +2013,3,26,2,128,-8,4,1017,30.41,0,0 +2013,3,26,3,153,-7,2,1017,32.2,0,0 +2013,3,26,4,174,-6,1,1016,0.45,0,0 +2013,3,26,5,187,-4,0,1016,0.9,0,0 +2013,3,26,6,188,-4,0,1016,0.89,0,0 +2013,3,26,7,196,-3,0,1017,0.89,0,0 +2013,3,26,8,196,0,4,1016,1.79,0,0 +2013,3,26,9,205,0,5,1016,4.92,0,0 +2013,3,26,10,217,0,7,1016,6.71,0,0 +2013,3,26,11,229,0,8,1015,8.5,0,0 +2013,3,26,12,240,0,10,1014,11.63,0,0 +2013,3,26,13,231,-1,12,1012,15.65,0,0 +2013,3,26,14,211,-2,13,1012,19.67,0,0 +2013,3,26,15,210,-3,13,1011,24.59,0,0 +2013,3,26,16,219,-3,13,1010,30.4,0,0 +2013,3,26,17,210,-2,13,1009,34.42,0,0 +2013,3,26,18,223,-1,12,1009,37.55,0,0 +2013,3,26,19,220,0,9,1009,0.89,0,0 +2013,3,26,20,223,1,8,1010,1.79,0,0 +2013,3,26,21,234,1,10,1010,3.58,0,0 +2013,3,26,22,231,1,8,1010,6.71,0,0 +2013,3,26,23,154,3,9,1010,10.73,0,0 +2013,3,27,0,128,3,8,1009,13.86,0,0 +2013,3,27,1,132,2,6,1009,16.99,0,0 +2013,3,27,2,134,3,7,1009,18.78,0,0 +2013,3,27,3,135,3,5,1008,20.57,0,0 +2013,3,27,4,134,4,5,1007,22.36,0,0 +2013,3,27,5,139,3,5,1007,24.15,0,0 +2013,3,27,6,171,4,6,1008,25.94,0,0 +2013,3,27,7,204,4,6,1009,0.89,0,0 +2013,3,27,8,183,4,6,1010,4.92,0,0 +2013,3,27,9,179,4,8,1010,6.71,0,0 +2013,3,27,10,192,5,11,1010,0.89,0,0 +2013,3,27,11,194,4,13,1009,1.79,0,0 +2013,3,27,12,156,2,15,1009,1.79,0,0 +2013,3,27,13,84,-6,18,1008,4.02,0,0 +2013,3,27,14,21,-6,20,1008,8.94,0,0 +2013,3,27,15,16,-8,20,1008,16.09,0,0 +2013,3,27,16,15,-10,19,1008,25.03,0,0 +2013,3,27,17,17,-15,17,1009,36.21,0,0 +2013,3,27,18,26,-18,14,1011,46.04,0,0 +2013,3,27,19,16,-19,12,1013,50.96,0,0 +2013,3,27,20,15,-19,11,1014,56.77,0,0 +2013,3,27,21,15,-19,10,1016,4.92,0,0 +2013,3,27,22,16,-19,9,1018,8.94,0,0 +2013,3,27,23,15,-19,8,1019,12.96,0,0 +2013,3,28,0,16,-18,8,1020,16.98,0,0 +2013,3,28,1,8,-19,7,1020,21.9,0,0 +2013,3,28,2,8,-19,6,1020,25.92,0,0 +2013,3,28,3,7,-17,5,1020,29.05,0,0 +2013,3,28,4,8,-18,5,1020,32.18,0,0 +2013,3,28,5,16,-16,3,1021,0.89,0,0 +2013,3,28,6,17,-13,2,1022,1.79,0,0 +2013,3,28,7,25,-12,4,1022,3.58,0,0 +2013,3,28,8,17,-20,6,1023,0.89,0,0 +2013,3,28,9,13,-20,7,1023,3.13,0,0 +2013,3,28,10,19,-21,8,1023,4.92,0,0 +2013,3,28,11,18,-20,9,1023,0.89,0,0 +2013,3,28,12,15,-14,10,1022,4.92,0,0 +2013,3,28,13,21,-11,10,1022,9.84,0,0 +2013,3,28,14,11,-14,11,1020,14.76,0,0 +2013,3,28,15,21,-16,12,1019,20.57,0,0 +2013,3,28,16,20,-17,13,1019,4.02,0,0 +2013,3,28,17,22,-21,13,1019,1.79,0,0 +2013,3,28,18,17,-18,12,1019,1.79,0,0 +2013,3,28,19,60,-16,10,1019,1.79,0,0 +2013,3,28,20,97,-7,10,1020,5.81,0,0 +2013,3,28,21,120,-4,8,1021,7.6,0,0 +2013,3,28,22,111,-3,9,1022,9.39,0,0 +2013,3,28,23,109,-3,5,1022,11.18,0,0 +2013,3,29,0,110,-3,4,1022,1.79,0,0 +2013,3,29,1,93,-3,1,1022,0.89,0,0 +2013,3,29,2,103,-4,1,1022,1.79,0,0 +2013,3,29,3,100,-4,0,1022,1.79,0,0 +2013,3,29,4,110,-6,2,1022,0.89,0,0 +2013,3,29,5,100,-6,1,1023,0.89,0,0 +2013,3,29,6,93,-5,1,1024,0.45,0,0 +2013,3,29,7,97,-3,2,1024,1.79,0,0 +2013,3,29,8,116,-3,3,1024,3.13,0,0 +2013,3,29,9,127,-4,5,1024,7.15,0,0 +2013,3,29,10,117,-8,9,1023,4.02,0,0 +2013,3,29,11,90,-11,9,1023,4.02,0,0 +2013,3,29,12,78,-14,10,1022,8.94,0,0 +2013,3,29,13,119,-11,10,1021,12.96,0,0 +2013,3,29,14,121,-8,9,1020,16.98,0,0 +2013,3,29,15,108,-9,10,1020,18.77,0,0 +2013,3,29,16,93,-9,10,1020,20.56,0,0 +2013,3,29,17,97,-7,9,1019,3.13,0,0 +2013,3,29,18,100,-6,9,1019,4.92,0,0 +2013,3,29,19,101,-4,7,1020,1.79,0,0 +2013,3,29,20,80,-4,6,1021,3.13,0,0 +2013,3,29,21,75,-5,4,1022,7.15,0,0 +2013,3,29,22,58,-5,4,1022,11.17,0,0 +2013,3,29,23,66,-7,4,1023,14.3,0,0 +2013,3,30,0,60,-6,3,1023,17.43,0,0 +2013,3,30,1,72,-6,3,1023,19.22,0,0 +2013,3,30,2,75,-7,3,1023,22.35,0,0 +2013,3,30,3,82,-8,3,1022,0.45,0,0 +2013,3,30,4,85,-7,3,1022,0.9,0,0 +2013,3,30,5,82,-8,2,1022,1.79,0,0 +2013,3,30,6,81,-7,2,1023,3.58,0,0 +2013,3,30,7,84,-5,1,1023,5.37,0,0 +2013,3,30,8,90,-7,6,1023,8.5,0,0 +2013,3,30,9,93,-7,7,1024,3.13,0,0 +2013,3,30,10,100,-7,7,1024,6.26,0,0 +2013,3,30,11,122,-6,8,1024,9.39,0,0 +2013,3,30,12,90,-6,9,1023,12.52,0,0 +2013,3,30,13,97,-5,10,1022,15.65,0,0 +2013,3,30,14,116,-4,11,1021,19.67,0,0 +2013,3,30,15,110,-2,10,1021,24.59,0,0 +2013,3,30,16,98,-1,9,1020,29.51,0,0 +2013,3,30,17,104,0,7,1020,33.53,0,0 +2013,3,30,18,85,0,7,1021,37.55,0,0 +2013,3,30,19,91,0,6,1021,41.57,0,0 +2013,3,30,20,98,0,5,1022,44.7,0,0 +2013,3,30,21,107,0,4,1023,47.83,0,0 +2013,3,30,22,100,0,4,1023,49.62,0,0 +2013,3,30,23,101,0,3,1024,51.41,0,0 +2013,3,31,0,107,1,4,1024,53.2,0,0 +2013,3,31,1,105,1,4,1024,56.33,0,0 +2013,3,31,2,106,1,4,1023,59.46,0,0 +2013,3,31,3,109,1,4,1023,61.25,0,0 +2013,3,31,4,130,1,3,1022,63.04,0,0 +2013,3,31,5,131,1,3,1022,64.83,0,0 +2013,3,31,6,140,1,4,1022,66.62,0,0 +2013,3,31,7,141,2,4,1022,68.41,0,0 +2013,3,31,8,155,2,4,1022,70.2,0,0 +2013,3,31,9,174,2,4,1022,73.33,0,0 +2013,3,31,10,193,2,4,1022,76.46,0,0 +2013,3,31,11,203,2,5,1021,79.59,0,0 +2013,3,31,12,230,3,7,1020,81.38,0,0 +2013,3,31,13,227,3,6,1018,83.17,0,0 +2013,3,31,14,251,3,6,1017,86.3,0,0 +2013,3,31,15,248,3,7,1016,88.09,0,0 +2013,3,31,16,243,3,7,1015,91.22,0,0 +2013,3,31,17,253,3,7,1015,1.79,0,0 +2013,3,31,18,258,3,7,1015,3.58,0,0 +2013,3,31,19,254,3,5,1015,3.13,0,0 +2013,3,31,20,259,2,6,1015,4.92,0,0 +2013,3,31,21,256,2,5,1015,8.05,0,0 +2013,3,31,22,255,3,6,1015,11.18,0,0 +2013,3,31,23,246,3,7,1015,14.31,0,0 +2013,4,1,0,244,3,7,1015,0.89,0,0 +2013,4,1,1,259,3,6,1015,4.02,0,0 +2013,4,1,2,244,2,6,1014,0.89,0,0 +2013,4,1,3,256,3,7,1014,3.13,0,0 +2013,4,1,4,250,3,7,1014,1.79,0,0 +2013,4,1,5,245,3,4,1013,0.89,0,0 +2013,4,1,6,247,2,3,1014,1.79,0,0 +2013,4,1,7,247,3,6,1014,3.58,0,0 +2013,4,1,8,158,4,8,1014,5.37,0,0 +2013,4,1,9,18,-3,13,1014,5.81,0,0 +2013,4,1,10,15,-4,14,1015,14.75,0,0 +2013,4,1,11,15,-4,14,1016,23.69,0,0 +2013,4,1,12,29,0,15,1016,31.74,0,0 +2013,4,1,13,39,-2,15,1016,8.94,0,0 +2013,4,1,14,37,-4,16,1017,14.75,0,0 +2013,4,1,15,39,-7,15,1017,8.05,0,0 +2013,4,1,16,41,-6,15,1017,5.81,0,0 +2013,4,1,17,38,-7,14,1018,9.83,0,0 +2013,4,1,18,31,-7,14,1019,11.62,0,0 +2013,4,1,19,29,-9,11,1020,4.02,0,0 +2013,4,1,20,20,-10,8,1021,7.15,0,0 +2013,4,1,21,19,-12,10,1022,12.07,0,0 +2013,4,1,22,7,-20,10,1022,16.99,0,0 +2013,4,1,23,7,-18,9,1023,21.91,0,0 +2013,4,2,0,6,-14,6,1023,1.79,0,0 +2013,4,2,1,13,-13,4,1023,3.13,0,0 +2013,4,2,2,20,-12,4,1023,0.89,0,0 +2013,4,2,3,49,-9,1,1023,3.13,0,0 +2013,4,2,4,64,-7,1,1022,0.45,0,0 +2013,4,2,5,96,-6,-1,1022,1.34,0,0 +2013,4,2,6,122,-5,0,1022,1.79,0,0 +2013,4,2,7,133,-4,1,1022,0.89,0,0 +2013,4,2,8,NA,-2,6,1022,1.79,0,0 +2013,4,2,9,175,-3,8,1021,3.58,0,0 +2013,4,2,10,176,-1,10,1021,1.79,0,0 +2013,4,2,11,164,1,13,1019,3.58,0,0 +2013,4,2,12,169,-2,14,1018,4.02,0,0 +2013,4,2,13,155,-3,16,1016,8.04,0,0 +2013,4,2,14,144,-3,17,1015,12.96,0,0 +2013,4,2,15,119,-4,18,1014,17.88,0,0 +2013,4,2,16,113,-5,18,1013,23.69,0,0 +2013,4,2,17,130,-5,18,1012,28.61,0,0 +2013,4,2,18,123,-5,17,1011,32.63,0,0 +2013,4,2,19,108,-3,14,1011,36.65,0,0 +2013,4,2,20,112,-3,14,1011,39.78,0,0 +2013,4,2,21,111,-2,11,1012,41.57,0,0 +2013,4,2,22,122,-1,10,1011,43.36,0,0 +2013,4,2,23,130,-2,11,1011,0.89,0,0 +2013,4,3,0,138,-2,8,1011,1.78,0,0 +2013,4,3,1,149,-1,6,1010,0.89,0,0 +2013,4,3,2,153,-1,5,1010,1.79,0,0 +2013,4,3,3,184,-1,3,1009,0.89,0,0 +2013,4,3,4,179,-1,3,1010,0.89,0,0 +2013,4,3,5,183,-1,3,1010,0.89,0,0 +2013,4,3,6,194,0,4,1010,0.89,0,0 +2013,4,3,7,184,1,4,1011,1.79,0,0 +2013,4,3,8,153,1,10,1012,3.58,0,0 +2013,4,3,9,150,-1,13,1012,3.13,0,0 +2013,4,3,10,126,-1,17,1012,4.02,0,0 +2013,4,3,11,156,0,18,1012,8.04,0,0 +2013,4,3,12,156,0,18,1012,15.19,0,0 +2013,4,3,13,178,2,19,1011,21,0,0 +2013,4,3,14,243,-1,18,1012,28.15,0,0 +2013,4,3,15,122,-1,17,1012,36.2,0,0 +2013,4,3,16,100,-3,16,1012,44.25,0,0 +2013,4,3,17,73,-4,16,1012,50.06,0,0 +2013,4,3,18,60,-4,14,1013,54.98,0,0 +2013,4,3,19,46,-4,12,1014,58.11,0,0 +2013,4,3,20,33,-4,11,1015,59.9,0,0 +2013,4,3,21,32,-4,10,1015,61.69,0,0 +2013,4,3,22,34,-4,9,1016,64.82,0,0 +2013,4,3,23,37,-4,8,1016,66.61,0,0 +2013,4,4,0,38,-3,7,1016,68.4,0,0 +2013,4,4,1,38,-4,9,1016,71.53,0,0 +2013,4,4,2,47,-4,9,1015,73.32,0,0 +2013,4,4,3,61,-4,8,1015,76.45,0,0 +2013,4,4,4,48,-4,9,1015,79.58,0,0 +2013,4,4,5,47,-4,8,1014,81.37,0,0 +2013,4,4,6,56,-3,8,1015,83.16,0,0 +2013,4,4,7,72,-4,9,1015,0.89,0,0 +2013,4,4,8,89,-4,10,1015,1.79,0,0 +2013,4,4,9,92,-5,10,1015,0.89,0,0 +2013,4,4,10,83,-5,10,1016,1.78,0,0 +2013,4,4,11,87,-5,11,1015,2.67,0,0 +2013,4,4,12,89,-5,11,1014,3.56,0,0 +2013,4,4,13,100,-5,11,1013,1.79,0,0 +2013,4,4,14,107,-5,11,1013,5.81,0,0 +2013,4,4,15,98,-1,9,1012,10.73,0,0 +2013,4,4,16,78,4,7,1012,16.54,0,1 +2013,4,4,17,83,5,7,1013,21.46,0,2 +2013,4,4,18,84,5,6,1013,26.38,0,3 +2013,4,4,19,91,5,6,1014,30.4,0,4 +2013,4,4,20,99,5,6,1015,34.42,0,5 +2013,4,4,21,99,5,6,1015,38.44,0,6 +2013,4,4,22,111,5,6,1016,42.46,0,7 +2013,4,4,23,118,5,6,1016,45.59,0,8 +2013,4,5,0,122,5,6,1016,49.61,0,9 +2013,4,5,1,117,5,6,1016,51.4,0,10 +2013,4,5,2,113,6,6,1017,54.53,0,11 +2013,4,5,3,108,5,6,1017,56.32,0,12 +2013,4,5,4,110,5,6,1017,59.45,0,13 +2013,4,5,5,121,5,6,1016,0.89,0,14 +2013,4,5,6,122,5,5,1016,1.79,0,0 +2013,4,5,7,116,5,6,1016,3.58,0,0 +2013,4,5,8,106,6,7,1017,6.71,0,0 +2013,4,5,9,107,7,9,1017,8.5,0,0 +2013,4,5,10,111,7,11,1017,13.42,0,0 +2013,4,5,11,122,4,14,1017,21.47,0,0 +2013,4,5,12,88,-3,14,1017,30.41,0,0 +2013,4,5,13,24,-5,14,1017,39.35,0,0 +2013,4,5,14,9,-5,14,1017,47.4,0,0 +2013,4,5,15,12,-7,13,1017,57.23,0,0 +2013,4,5,16,15,-9,13,1017,67.06,0,0 +2013,4,5,17,11,-6,12,1018,76,0,0 +2013,4,5,18,11,-5,9,1019,84.94,0,0 +2013,4,5,19,9,-10,9,1020,93.88,0,0 +2013,4,5,20,10,-8,7,1022,98.8,0,0 +2013,4,5,21,9,-10,5,1023,104.61,0,0 +2013,4,5,22,8,-9,4,1023,108.63,0,0 +2013,4,5,23,9,-9,3,1023,111.76,0,0 +2013,4,6,0,8,-9,2,1023,1.79,0,0 +2013,4,6,1,7,-9,3,1023,1.79,0,0 +2013,4,6,2,6,-9,2,1022,1.79,0,0 +2013,4,6,3,6,-8,3,1021,4.02,0,0 +2013,4,6,4,6,-7,1,1021,5.81,0,0 +2013,4,6,5,5,-4,-1,1020,0.89,0,0 +2013,4,6,6,12,-5,0,1020,1.79,0,0 +2013,4,6,7,9,-3,3,1020,3.58,0,0 +2013,4,6,8,17,-9,7,1020,6.71,0,0 +2013,4,6,9,11,-11,10,1020,13.86,0,0 +2013,4,6,10,16,-13,11,1020,19.67,0,0 +2013,4,6,11,13,-12,12,1018,27.72,0,0 +2013,4,6,12,10,-11,12,1017,4.02,0,0 +2013,4,6,13,12,-12,13,1016,8.94,0,0 +2013,4,6,14,10,-11,14,1015,3.13,0,0 +2013,4,6,15,15,-12,14,1014,1.79,0,0 +2013,4,6,16,11,-12,14,1013,3.13,0,0 +2013,4,6,17,13,-13,13,1012,6.26,0,0 +2013,4,6,18,11,-12,12,1011,9.39,0,0 +2013,4,6,19,14,-10,9,1011,12.52,0,0 +2013,4,6,20,24,-10,10,1012,16.54,0,0 +2013,4,6,21,33,-9,8,1012,19.67,0,0 +2013,4,6,22,34,-8,7,1012,21.46,0,0 +2013,4,6,23,32,-7,7,1012,23.25,0,0 +2013,4,7,0,36,-8,6,1012,25.04,0,0 +2013,4,7,1,43,-8,6,1011,28.17,0,0 +2013,4,7,2,55,-7,6,1010,31.3,0,0 +2013,4,7,3,57,-3,4,1010,34.43,0,0 +2013,4,7,4,57,-3,3,1010,38.45,0,0 +2013,4,7,5,59,-3,3,1010,41.58,0,0 +2013,4,7,6,104,-3,3,1010,45.6,0,0 +2013,4,7,7,90,-3,4,1011,49.62,0,0 +2013,4,7,8,69,-3,5,1011,54.54,0,0 +2013,4,7,9,67,-2,7,1011,58.56,0,0 +2013,4,7,10,63,-2,9,1010,62.58,0,0 +2013,4,7,11,71,-1,11,1008,66.6,0,0 +2013,4,7,12,77,-1,12,1007,71.52,0,0 +2013,4,7,13,80,-2,13,1006,75.54,0,0 +2013,4,7,14,89,-4,14,1005,80.46,0,0 +2013,4,7,15,106,-1,15,1004,86.27,0,0 +2013,4,7,16,114,-1,14,1003,91.19,0,0 +2013,4,7,17,126,-1,12,1003,96.11,0,0 +2013,4,7,18,97,-1,12,1003,101.03,0,0 +2013,4,7,19,83,-2,11,1005,104.16,0,0 +2013,4,7,20,97,-2,11,1006,105.95,0,0 +2013,4,7,21,90,-2,12,1008,1.79,0,0 +2013,4,7,22,93,4,9,1008,4.92,0,0 +2013,4,7,23,78,3,9,1008,3.13,0,0 +2013,4,8,0,54,2,8,1008,1.79,0,0 +2013,4,8,1,59,2,7,1007,3.13,0,0 +2013,4,8,2,44,1,7,1008,8.05,0,0 +2013,4,8,3,22,1,8,1009,5.81,0,0 +2013,4,8,4,27,-15,9,1010,14.75,0,0 +2013,4,8,5,33,-21,8,1010,24.58,0,0 +2013,4,8,6,29,-22,8,1011,34.41,0,0 +2013,4,8,7,13,-20,7,1012,45.59,0,0 +2013,4,8,8,15,-17,8,1012,56.77,0,0 +2013,4,8,9,17,-17,9,1013,68.84,0,0 +2013,4,8,10,16,-15,10,1013,80.91,0,0 +2013,4,8,11,14,-15,11,1013,92.98,0,0 +2013,4,8,12,14,-15,12,1012,104.16,0,0 +2013,4,8,13,12,-16,12,1012,115.34,0,0 +2013,4,8,14,17,-16,12,1012,126.52,0,0 +2013,4,8,15,13,-16,13,1012,136.35,0,0 +2013,4,8,16,12,-17,12,1012,148.42,0,0 +2013,4,8,17,17,-16,12,1013,159.6,0,0 +2013,4,8,18,12,-16,11,1014,168.54,0,0 +2013,4,8,19,16,-17,10,1014,178.37,0,0 +2013,4,8,20,10,-16,9,1015,187.31,0,0 +2013,4,8,21,15,-16,9,1016,196.25,0,0 +2013,4,8,22,11,-15,8,1016,203.4,0,0 +2013,4,8,23,13,-13,8,1016,212.34,0,0 +2013,4,9,0,12,-13,8,1015,221.28,0,0 +2013,4,9,1,10,-11,7,1016,229.33,0,0 +2013,4,9,2,8,-11,7,1015,239.16,0,0 +2013,4,9,3,6,-11,7,1016,247.21,0,0 +2013,4,9,4,8,-11,6,1017,251.23,0,0 +2013,4,9,5,8,-11,6,1018,260.17,0,0 +2013,4,9,6,9,-10,5,1018,265.09,0,0 +2013,4,9,7,9,-11,7,1019,270.01,0,0 +2013,4,9,8,11,-11,8,1019,278.95,0,0 +2013,4,9,9,10,-12,10,1020,286.1,0,0 +2013,4,9,10,12,-12,10,1021,295.04,0,0 +2013,4,9,11,13,-13,12,1021,302.19,0,0 +2013,4,9,12,13,-13,12,1020,309.34,0,0 +2013,4,9,13,14,-15,13,1020,316.49,0,0 +2013,4,9,14,14,-12,12,1020,321.41,0,0 +2013,4,9,15,14,-11,11,1020,1.79,0,0 +2013,4,9,16,11,-10,11,1019,4.02,0,0 +2013,4,9,17,11,-12,11,1020,12.07,0,0 +2013,4,9,18,12,-11,10,1020,20.12,0,0 +2013,4,9,19,13,-15,9,1021,25.93,0,0 +2013,4,9,20,14,-13,9,1022,33.08,0,0 +2013,4,9,21,22,-13,8,1022,40.23,0,0 +2013,4,9,22,11,-13,7,1023,46.04,0,0 +2013,4,9,23,11,-12,6,1023,51.85,0,0 +2013,4,10,0,10,-11,6,1022,56.77,0,0 +2013,4,10,1,10,-10,6,1022,62.58,0,0 +2013,4,10,2,11,-10,6,1022,68.39,0,0 +2013,4,10,3,9,-11,5,1022,73.31,0,0 +2013,4,10,4,11,-10,5,1021,79.12,0,0 +2013,4,10,5,8,-9,5,1021,84.93,0,0 +2013,4,10,6,9,-9,4,1021,89.85,0,0 +2013,4,10,7,11,-9,6,1021,94.77,0,0 +2013,4,10,8,8,-10,8,1021,102.82,0,0 +2013,4,10,9,12,-10,9,1021,110.87,0,0 +2013,4,10,10,12,-11,11,1021,120.7,0,0 +2013,4,10,11,12,-13,11,1020,129.64,0,0 +2013,4,10,12,10,-15,12,1019,138.58,0,0 +2013,4,10,13,10,-15,12,1018,147.52,0,0 +2013,4,10,14,14,-13,13,1018,157.35,0,0 +2013,4,10,15,17,-12,12,1017,167.18,0,0 +2013,4,10,16,10,-13,13,1016,175.23,0,0 +2013,4,10,17,7,-14,11,1016,182.38,0,0 +2013,4,10,18,6,-14,11,1016,189.53,0,0 +2013,4,10,19,10,-14,10,1017,194.45,0,0 +2013,4,10,20,17,-14,9,1017,197.58,0,0 +2013,4,10,21,17,-15,9,1018,201.6,0,0 +2013,4,10,22,21,-16,9,1018,204.73,0,0 +2013,4,10,23,26,-13,7,1018,207.86,0,0 +2013,4,11,0,21,-13,7,1019,210.99,0,0 +2013,4,11,1,27,-12,7,1019,214.12,0,0 +2013,4,11,2,29,-10,1,1019,218.14,0,0 +2013,4,11,3,41,-11,3,1019,219.93,0,0 +2013,4,11,4,16,-10,2,1019,223.06,0,0 +2013,4,11,5,15,-10,2,1019,224.85,0,0 +2013,4,11,6,13,-11,6,1019,229.77,0,0 +2013,4,11,7,19,-12,8,1020,236.92,0,0 +2013,4,11,8,19,-13,10,1020,245.86,0,0 +2013,4,11,9,18,-14,11,1021,255.69,0,0 +2013,4,11,10,NA,-15,12,1020,263.74,0,0 +2013,4,11,11,10,-15,14,1020,271.79,0,0 +2013,4,11,12,13,-16,15,1019,280.73,0,0 +2013,4,11,13,12,-17,16,1018,288.78,0,0 +2013,4,11,14,14,-16,17,1017,295.93,0,0 +2013,4,11,15,9,-17,18,1016,305.76,0,0 +2013,4,11,16,8,-17,18,1016,313.81,0,0 +2013,4,11,17,6,-18,17,1016,321.86,0,0 +2013,4,11,18,6,-18,16,1016,329.91,0,0 +2013,4,11,19,5,-16,13,1016,1.79,0,0 +2013,4,11,20,11,-12,11,1017,3.58,0,0 +2013,4,11,21,22,-7,8,1017,0.89,0,0 +2013,4,11,22,41,-11,9,1017,1.79,0,0 +2013,4,11,23,49,-13,12,1017,5.81,0,0 +2013,4,12,0,32,-11,9,1017,8.94,0,0 +2013,4,12,1,38,-7,6,1017,0.89,0,0 +2013,4,12,2,41,-8,5,1017,1.78,0,0 +2013,4,12,3,35,-4,4,1016,0.89,0,0 +2013,4,12,4,42,-4,2,1016,0.89,0,0 +2013,4,12,5,55,-6,3,1016,1.34,0,0 +2013,4,12,6,62,-6,1,1016,3.13,0,0 +2013,4,12,7,66,-4,5,1016,0.89,0,0 +2013,4,12,8,64,-7,10,1016,0.89,0,0 +2013,4,12,9,53,-8,12,1016,1.78,0,0 +2013,4,12,10,62,-10,14,1016,1.79,0,0 +2013,4,12,11,53,-12,16,1015,3.58,0,0 +2013,4,12,12,50,-10,18,1014,0.89,0,0 +2013,4,12,13,62,-10,18,1013,1.79,0,0 +2013,4,12,14,74,-9,21,1011,4.92,0,0 +2013,4,12,15,41,-8,23,1009,8.94,0,0 +2013,4,12,16,29,-8,23,1008,13.86,0,0 +2013,4,12,17,45,-6,23,1008,19.67,0,0 +2013,4,12,18,54,-4,21,1008,22.8,0,0 +2013,4,12,19,52,-3,20,1008,1.79,0,0 +2013,4,12,20,51,-1,17,1009,2.68,0,0 +2013,4,12,21,47,-2,18,1008,1.79,0,0 +2013,4,12,22,52,0,20,1007,7.6,0,0 +2013,4,12,23,54,0,18,1006,0.89,0,0 +2013,4,13,0,59,1,11,1006,0.89,0,0 +2013,4,13,1,57,1,11,1005,0.89,0,0 +2013,4,13,2,65,2,15,1004,3.13,0,0 +2013,4,13,3,91,1,8,1003,0.89,0,0 +2013,4,13,4,100,1,8,1002,1.78,0,0 +2013,4,13,5,109,2,7,1002,2.67,0,0 +2013,4,13,6,101,2,5,1002,1.79,0,0 +2013,4,13,7,111,4,9,1002,3.58,0,0 +2013,4,13,8,101,3,13,1002,1.79,0,0 +2013,4,13,9,110,3,17,1002,1.79,0,0 +2013,4,13,10,117,2,19,1002,1.79,0,0 +2013,4,13,11,132,2,23,1002,3.13,0,0 +2013,4,13,12,117,0,25,1002,4.92,0,0 +2013,4,13,13,75,2,26,1002,11.18,0,0 +2013,4,13,14,25,1,24,1002,21.01,0,0 +2013,4,13,15,14,0,26,1003,29.95,0,0 +2013,4,13,16,16,-2,25,1003,38,0,0 +2013,4,13,17,13,-4,23,1004,47.83,0,0 +2013,4,13,18,10,-5,21,1005,57.66,0,0 +2013,4,13,19,10,-6,17,1006,65.71,0,0 +2013,4,13,20,8,-9,17,1007,75.54,0,0 +2013,4,13,21,10,-9,14,1009,81.35,0,0 +2013,4,13,22,10,-13,14,1010,88.5,0,0 +2013,4,13,23,12,-14,13,1011,7.15,0,0 +2013,4,14,0,8,-15,12,1012,15.2,0,0 +2013,4,14,1,6,-16,11,1013,19.22,0,0 +2013,4,14,2,5,-18,10,1013,4.92,0,0 +2013,4,14,3,6,-18,8,1013,1.79,0,0 +2013,4,14,4,7,-14,4,1013,3.13,0,0 +2013,4,14,5,6,-15,5,1014,4.02,0,0 +2013,4,14,6,4,-15,5,1014,7.15,0,0 +2013,4,14,7,6,-17,8,1014,11.17,0,0 +2013,4,14,8,9,-18,10,1015,3.13,0,0 +2013,4,14,9,7,-18,11,1015,1.79,0,0 +2013,4,14,10,10,-18,12,1014,1.79,0,0 +2013,4,14,11,34,-13,14,1013,0.89,0,0 +2013,4,14,12,59,-5,15,1011,3.13,0,0 +2013,4,14,13,53,-5,15,1010,7.15,0,0 +2013,4,14,14,45,-6,16,1009,11.17,0,0 +2013,4,14,15,38,-5,16,1008,16.98,0,0 +2013,4,14,16,39,-6,17,1007,22.79,0,0 +2013,4,14,17,45,-6,16,1006,27.71,0,0 +2013,4,14,18,42,-5,15,1005,32.63,0,0 +2013,4,14,19,43,-4,14,1005,36.65,0,0 +2013,4,14,20,50,-4,14,1005,39.78,0,0 +2013,4,14,21,48,-4,13,1005,42.91,0,0 +2013,4,14,22,56,-3,12,1006,46.04,0,0 +2013,4,14,23,63,-2,10,1006,49.17,0,0 +2013,4,15,0,59,-3,11,1006,52.3,0,0 +2013,4,15,1,53,-2,9,1005,0.89,0,0 +2013,4,15,2,42,-1,8,1005,1.79,0,0 +2013,4,15,3,44,-1,8,1005,3.58,0,0 +2013,4,15,4,50,-1,7,1005,0.89,0,0 +2013,4,15,5,65,-1,7,1004,1.78,0,0 +2013,4,15,6,70,-1,6,1005,0.89,0,0 +2013,4,15,7,73,1,9,1005,0.89,0,0 +2013,4,15,8,71,-2,11,1006,1.79,0,0 +2013,4,15,9,62,-4,13,1005,3.13,0,0 +2013,4,15,10,73,-4,14,1005,1.79,0,0 +2013,4,15,11,73,-3,16,1004,4.92,0,0 +2013,4,15,12,78,-3,17,1003,8.94,0,0 +2013,4,15,13,79,-3,19,1002,12.96,0,0 +2013,4,15,14,79,-1,18,1001,17.88,0,0 +2013,4,15,15,89,1,19,1000,22.8,0,0 +2013,4,15,16,99,2,18,1000,28.61,0,0 +2013,4,15,17,NA,3,17,1000,34.42,0,0 +2013,4,15,18,102,4,15,1000,41.57,0,0 +2013,4,15,19,81,3,13,1001,47.38,0,0 +2013,4,15,20,69,2,12,1002,52.3,0,0 +2013,4,15,21,64,1,11,1003,55.43,0,0 +2013,4,15,22,55,0,10,1003,57.22,0,0 +2013,4,15,23,53,-1,10,1003,59.01,0,0 +2013,4,16,0,50,-1,9,1003,62.14,0,0 +2013,4,16,1,57,0,10,1004,4.02,0,0 +2013,4,16,2,53,0,9,1002,0.45,0,0 +2013,4,16,3,63,0,10,1003,2.24,0,0 +2013,4,16,4,64,1,9,1003,1.79,0,0 +2013,4,16,5,61,0,8,1003,3.58,0,0 +2013,4,16,6,63,1,6,1004,6.71,0,0 +2013,4,16,7,58,2,11,1005,8.5,0,0 +2013,4,16,8,74,1,13,1006,3.13,0,0 +2013,4,16,9,70,2,15,1007,6.26,0,0 +2013,4,16,10,80,1,17,1007,11.18,0,0 +2013,4,16,11,NA,-1,18,1008,16.1,0,0 +2013,4,16,12,70,-3,19,1007,20.12,0,0 +2013,4,16,13,57,-4,21,1007,0.89,0,0 +2013,4,16,14,39,-4,21,1006,2.68,0,0 +2013,4,16,15,36,-3,21,1006,4.47,0,0 +2013,4,16,16,28,-5,21,1005,6.26,0,0 +2013,4,16,17,NA,-3,20,1006,4.02,0,0 +2013,4,16,18,27,0,19,1006,9.83,0,0 +2013,4,16,19,62,-2,15,1007,16.98,0,0 +2013,4,16,20,96,-2,12,1010,21.9,0,0 +2013,4,16,21,81,-2,11,1011,26.82,0,0 +2013,4,16,22,78,-1,10,1012,32.63,0,0 +2013,4,16,23,75,-1,9,1013,37.55,0,0 +2013,4,17,0,57,0,9,1013,41.57,0,0 +2013,4,17,1,48,1,8,1013,44.7,0,0 +2013,4,17,2,52,1,8,1013,47.83,0,0 +2013,4,17,3,47,1,8,1013,49.62,0,0 +2013,4,17,4,37,1,8,1012,0.89,0,0 +2013,4,17,5,27,1,8,1012,1.79,0,0 +2013,4,17,6,28,1,8,1012,4.92,0,0 +2013,4,17,7,34,1,9,1013,8.05,0,0 +2013,4,17,8,45,1,9,1013,9.84,0,0 +2013,4,17,9,51,-1,11,1012,15.65,0,0 +2013,4,17,10,49,-3,12,1011,21.46,0,0 +2013,4,17,11,32,-4,13,1010,27.27,0,0 +2013,4,17,12,41,-4,14,1010,31.29,0,0 +2013,4,17,13,46,-5,15,1009,34.42,0,0 +2013,4,17,14,55,-3,16,1008,37.55,0,0 +2013,4,17,15,63,-5,16,1008,40.68,0,0 +2013,4,17,16,72,-4,16,1007,44.7,0,0 +2013,4,17,17,68,-4,15,1008,47.83,0,0 +2013,4,17,18,60,-5,14,1009,51.85,0,0 +2013,4,17,19,55,-7,13,1010,55.87,0,0 +2013,4,17,20,49,-8,12,1012,59,0,0 +2013,4,17,21,43,-8,12,1014,0.89,0,0 +2013,4,17,22,44,-6,9,1015,0.89,0,0 +2013,4,17,23,46,-5,9,1016,5.81,0,0 +2013,4,18,0,39,-7,11,1017,13.86,0,0 +2013,4,18,1,10,-8,9,1018,7.15,0,0 +2013,4,18,2,13,-10,8,1020,14.3,0,0 +2013,4,18,3,9,-11,7,1021,5.81,0,0 +2013,4,18,4,6,-12,7,1022,9.83,0,0 +2013,4,18,5,8,-12,6,1023,4.02,0,0 +2013,4,18,6,10,-12,5,1024,7.15,0,0 +2013,4,18,7,8,-14,8,1025,11.17,0,0 +2013,4,18,8,9,-16,9,1026,16.98,0,0 +2013,4,18,9,9,-17,10,1026,21.9,0,0 +2013,4,18,10,7,-16,10,1026,29.05,0,0 +2013,4,18,11,8,-17,11,1025,34.86,0,0 +2013,4,18,12,6,-16,12,1024,39.78,0,0 +2013,4,18,13,6,-19,14,1023,7.15,0,0 +2013,4,18,14,4,-18,14,1022,5.81,0,0 +2013,4,18,15,7,-20,14,1021,9.83,0,0 +2013,4,18,16,6,-20,14,1020,13.85,0,0 +2013,4,18,17,7,-20,14,1020,4.92,0,0 +2013,4,18,18,9,-20,13,1020,4.92,0,0 +2013,4,18,19,8,-19,12,1021,8.05,0,0 +2013,4,18,20,16,-16,11,1022,1.79,0,0 +2013,4,18,21,25,-16,11,1023,3.58,0,0 +2013,4,18,22,28,-16,11,1024,0.89,0,0 +2013,4,18,23,23,-16,11,1024,1.79,0,0 +2013,4,19,0,41,-10,9,1024,1.79,0,0 +2013,4,19,1,41,-2,8,1024,7.6,0,0 +2013,4,19,2,56,-1,7,1024,12.52,0,0 +2013,4,19,3,72,-1,6,1025,15.65,0,0 +2013,4,19,4,63,-2,7,1025,18.78,0,0 +2013,4,19,5,65,-4,6,1025,20.57,0,0 +2013,4,19,6,56,-4,6,1025,22.36,0,0 +2013,4,19,7,60,-4,7,1026,24.15,0,0 +2013,4,19,8,51,-4,8,1027,25.94,0,0 +2013,4,19,9,43,-5,8,1026,29.07,0,0 +2013,4,19,10,41,-1,8,1026,33.09,0,0 +2013,4,19,11,43,1,7,1026,40.24,0,0 +2013,4,19,12,28,-1,7,1025,4.02,0,0 +2013,4,19,13,35,-3,8,1025,4.02,0,0 +2013,4,19,14,40,-3,8,1024,8.04,0,0 +2013,4,19,15,41,-3,8,1023,12.06,0,0 +2013,4,19,16,59,-3,8,1022,16.08,0,0 +2013,4,19,17,57,-2,8,1022,21,0,0 +2013,4,19,18,51,-2,7,1022,26.81,0,0 +2013,4,19,19,57,0,7,1022,32.62,0,0 +2013,4,19,20,64,0,6,1023,37.54,0,0 +2013,4,19,21,71,1,5,1023,41.56,0,0 +2013,4,19,22,77,1,5,1023,45.58,0,0 +2013,4,19,23,73,1,5,1023,48.71,0,0 +2013,4,20,0,67,1,5,1023,51.84,0,0 +2013,4,20,1,64,1,4,1023,53.63,0,0 +2013,4,20,2,65,1,3,1023,55.42,0,0 +2013,4,20,3,74,1,3,1023,57.21,0,0 +2013,4,20,4,80,1,2,1022,0.89,0,0 +2013,4,20,5,84,0,1,1023,0.89,0,0 +2013,4,20,6,83,1,1,1023,0.89,0,0 +2013,4,20,7,86,3,4,1023,2.68,0,0 +2013,4,20,8,92,2,6,1023,4.47,0,0 +2013,4,20,9,92,1,8,1023,6.26,0,0 +2013,4,20,10,84,0,10,1023,0.89,0,0 +2013,4,20,11,75,-1,12,1022,2.68,0,0 +2013,4,20,12,78,-3,14,1021,4.47,0,0 +2013,4,20,13,75,-3,15,1020,6.26,0,0 +2013,4,20,14,81,-4,16,1019,10.28,0,0 +2013,4,20,15,70,-2,16,1018,4.92,0,0 +2013,4,20,16,69,-2,16,1017,9.84,0,0 +2013,4,20,17,63,-2,15,1017,15.65,0,0 +2013,4,20,18,75,-2,14,1018,21.46,0,0 +2013,4,20,19,67,-3,13,1018,27.27,0,0 +2013,4,20,20,73,-2,12,1019,31.29,0,0 +2013,4,20,21,81,-2,11,1020,36.21,0,0 +2013,4,20,22,78,-1,11,1020,38,0,0 +2013,4,20,23,91,0,8,1020,0.89,0,0 +2013,4,21,0,105,1,8,1021,1.79,0,0 +2013,4,21,1,117,1,6,1021,4.92,0,0 +2013,4,21,2,118,1,7,1021,0.89,0,0 +2013,4,21,3,125,2,5,1020,0.89,0,0 +2013,4,21,4,123,2,5,1021,1.78,0,0 +2013,4,21,5,132,2,4,1020,2.67,0,0 +2013,4,21,6,126,2,4,1020,0.89,0,0 +2013,4,21,7,136,3,6,1021,1.78,0,0 +2013,4,21,8,151,4,8,1021,0.89,0,0 +2013,4,21,9,143,4,10,1021,1.79,0,0 +2013,4,21,10,153,3,12,1021,3.58,0,0 +2013,4,21,11,156,3,15,1020,6.71,0,0 +2013,4,21,12,164,2,16,1019,10.73,0,0 +2013,4,21,13,147,3,16,1018,15.65,0,0 +2013,4,21,14,134,3,17,1016,19.67,0,0 +2013,4,21,15,138,2,18,1015,25.48,0,0 +2013,4,21,16,140,0,19,1014,31.29,0,0 +2013,4,21,17,142,0,18,1014,37.1,0,0 +2013,4,21,18,149,2,17,1014,42.02,0,0 +2013,4,21,19,137,3,16,1015,46.94,0,0 +2013,4,21,20,126,3,14,1016,50.96,0,0 +2013,4,21,21,102,3,13,1016,54.98,0,0 +2013,4,21,22,89,3,13,1016,58.11,0,0 +2013,4,21,23,89,3,13,1016,63.03,0,0 +2013,4,22,0,104,3,12,1016,67.05,0,0 +2013,4,22,1,114,4,11,1016,71.07,0,0 +2013,4,22,2,120,4,10,1015,74.2,0,0 +2013,4,22,3,123,4,9,1015,75.99,0,0 +2013,4,22,4,128,4,7,1016,0.89,0,0 +2013,4,22,5,138,3,7,1016,1.79,0,0 +2013,4,22,6,147,4,8,1016,4.92,0,0 +2013,4,22,7,151,5,9,1016,6.71,0,0 +2013,4,22,8,160,5,10,1016,9.84,0,0 +2013,4,22,9,165,6,12,1016,11.63,0,0 +2013,4,22,10,175,6,13,1016,1.79,0,0 +2013,4,22,11,195,6,14,1015,3.13,0,0 +2013,4,22,12,201,7,15,1015,7.15,0,0 +2013,4,22,13,214,7,15,1014,11.17,0,0 +2013,4,22,14,236,7,15,1014,16.09,0,0 +2013,4,22,15,218,7,16,1013,20.11,0,0 +2013,4,22,16,228,7,16,1012,25.03,0,0 +2013,4,22,17,190,7,15,1011,29.95,0,0 +2013,4,22,18,158,7,15,1012,33.08,0,0 +2013,4,22,19,166,6,15,1012,34.87,0,0 +2013,4,22,20,171,7,12,1012,0.89,0,0 +2013,4,22,21,183,7,11,1012,1.78,0,0 +2013,4,22,22,197,7,11,1012,1.79,0,0 +2013,4,22,23,188,7,13,1012,4.92,0,0 +2013,4,23,0,152,7,13,1012,8.94,0,0 +2013,4,23,1,135,7,12,1012,12.96,0,0 +2013,4,23,2,135,6,10,1012,16.09,0,0 +2013,4,23,3,134,6,10,1011,17.88,0,0 +2013,4,23,4,140,7,9,1011,21.01,0,0 +2013,4,23,5,146,7,9,1011,22.8,0,0 +2013,4,23,6,175,7,10,1011,25.93,0,0 +2013,4,23,7,195,7,10,1011,29.95,0,0 +2013,4,23,8,203,7,10,1012,31.74,0,0 +2013,4,23,9,201,7,11,1011,1.79,0,0 +2013,4,23,10,214,7,11,1011,1.79,0,0 +2013,4,23,11,214,8,13,1011,4.92,0,0 +2013,4,23,12,229,8,14,1010,1.79,0,0 +2013,4,23,13,247,8,13,1010,4.02,0,1 +2013,4,23,14,250,9,15,1009,0.89,0,0 +2013,4,23,15,258,9,16,1008,4.02,0,0 +2013,4,23,16,260,8,15,1010,4.92,0,0 +2013,4,23,17,275,8,15,1011,8.05,0,0 +2013,4,23,18,258,7,15,1011,0.89,0,0 +2013,4,23,19,215,7,12,1012,1.79,0,0 +2013,4,23,20,200,7,12,1012,0.89,0,0 +2013,4,23,21,190,8,11,1013,1.79,0,0 +2013,4,23,22,217,7,9,1013,0.89,0,0 +2013,4,23,23,236,7,8,1013,1.78,0,0 +2013,4,24,0,224,7,8,1013,2.67,0,0 +2013,4,24,1,241,7,8,1013,1.79,0,0 +2013,4,24,2,217,7,8,1013,0.89,0,0 +2013,4,24,3,179,6,7,1013,2.68,0,0 +2013,4,24,4,154,5,6,1012,4.47,0,0 +2013,4,24,5,125,4,5,1012,7.6,0,0 +2013,4,24,6,148,5,6,1012,10.73,0,0 +2013,4,24,7,165,8,9,1012,13.86,0,0 +2013,4,24,8,125,8,13,1011,15.65,0,0 +2013,4,24,9,87,7,15,1011,0.89,0,0 +2013,4,24,10,85,6,17,1010,1.79,0,0 +2013,4,24,11,73,2,21,1010,3.13,0,0 +2013,4,24,12,77,-2,23,1008,4.92,0,0 +2013,4,24,13,74,-6,24,1008,13.86,0,0 +2013,4,24,14,36,-10,24,1007,22.8,0,0 +2013,4,24,15,15,-11,25,1007,32.63,0,0 +2013,4,24,16,9,-8,24,1006,42.46,0,0 +2013,4,24,17,8,-6,20,1007,49.61,0,0 +2013,4,24,18,11,-3,19,1007,54.53,0,0 +2013,4,24,19,14,-4,18,1008,59.45,0,0 +2013,4,24,20,18,-2,17,1011,8.05,0,0 +2013,4,24,21,14,-5,17,1012,9.83,0,0 +2013,4,24,22,8,-4,15,1013,18.77,0,0 +2013,4,24,23,4,-4,14,1013,25.92,0,0 +2013,4,25,0,6,-5,13,1013,31.73,0,0 +2013,4,25,1,7,-4,12,1013,35.75,0,0 +2013,4,25,2,14,-4,13,1013,40.67,0,0 +2013,4,25,3,12,-4,12,1013,44.69,0,0 +2013,4,25,4,16,-5,12,1012,48.71,0,0 +2013,4,25,5,14,-7,12,1013,53.63,0,0 +2013,4,25,6,15,-8,13,1014,61.68,0,0 +2013,4,25,7,10,-7,15,1014,68.83,0,0 +2013,4,25,8,9,-8,16,1014,76.88,0,0 +2013,4,25,9,8,-8,17,1014,85.82,0,0 +2013,4,25,10,9,-9,19,1014,94.76,0,0 +2013,4,25,11,10,-10,20,1014,104.59,0,0 +2013,4,25,12,10,-9,21,1013,113.53,0,0 +2013,4,25,13,9,-9,22,1013,121.58,0,0 +2013,4,25,14,8,-11,22,1012,131.41,0,0 +2013,4,25,15,9,-12,23,1011,141.24,0,0 +2013,4,25,16,6,-12,23,1011,147.05,0,0 +2013,4,25,17,5,-15,22,1011,156.88,0,0 +2013,4,25,18,7,-16,21,1011,165.82,0,0 +2013,4,25,19,8,-15,19,1011,170.74,0,0 +2013,4,25,20,19,-13,14,1012,173.87,0,0 +2013,4,25,21,22,-10,14,1013,0.89,0,0 +2013,4,25,22,52,-6,12,1013,1.78,0,0 +2013,4,25,23,40,-13,17,1013,1.79,0,0 +2013,4,26,0,95,-7,11,1013,0.89,0,0 +2013,4,26,1,40,-6,10,1013,3.13,0,0 +2013,4,26,2,71,-5,10,1013,1.79,0,0 +2013,4,26,3,64,-3,9,1013,1.79,0,0 +2013,4,26,4,92,-4,10,1012,0.89,0,0 +2013,4,26,5,83,0,7,1012,0.89,0,0 +2013,4,26,6,100,0,7,1012,1.78,0,0 +2013,4,26,7,115,0,11,1012,2.67,0,0 +2013,4,26,8,70,-2,14,1013,3.56,0,0 +2013,4,26,9,83,-4,16,1012,1.79,0,0 +2013,4,26,10,87,-3,19,1012,0.89,0,0 +2013,4,26,11,81,0,20,1011,2.68,0,0 +2013,4,26,12,80,-1,22,1010,3.13,0,0 +2013,4,26,13,80,0,23,1009,7.15,0,0 +2013,4,26,14,78,1,24,1008,8.94,0,0 +2013,4,26,15,70,1,24,1007,12.96,0,0 +2013,4,26,16,66,1,24,1006,16.98,0,0 +2013,4,26,17,74,2,22,1006,21,0,0 +2013,4,26,18,74,1,22,1006,25.92,0,0 +2013,4,26,19,80,1,20,1006,30.84,0,0 +2013,4,26,20,74,0,19,1007,35.76,0,0 +2013,4,26,21,52,-3,17,1008,38.89,0,0 +2013,4,26,22,41,-3,17,1009,43.81,0,0 +2013,4,26,23,38,-2,17,1009,45.6,0,0 +2013,4,27,0,45,-1,16,1009,48.73,0,0 +2013,4,27,1,55,-1,15,1009,1.79,0,0 +2013,4,27,2,63,-1,14,1009,0.89,0,0 +2013,4,27,3,69,1,11,1009,0.89,0,0 +2013,4,27,4,81,1,11,1009,1.79,0,0 +2013,4,27,5,122,1,9,1009,3.58,0,0 +2013,4,27,6,82,2,9,1010,0.89,0,0 +2013,4,27,7,55,3,13,1011,1.78,0,0 +2013,4,27,8,62,-1,18,1011,1.79,0,0 +2013,4,27,9,61,-6,19,1012,4.02,0,0 +2013,4,27,10,46,-2,20,1012,7.15,0,0 +2013,4,27,11,45,0,21,1012,11.17,0,0 +2013,4,27,12,50,-1,22,1011,14.3,0,0 +2013,4,27,13,54,1,23,1010,18.32,0,0 +2013,4,27,14,64,2,24,1010,22.34,0,0 +2013,4,27,15,75,3,24,1009,26.36,0,0 +2013,4,27,16,76,1,24,1009,31.28,0,0 +2013,4,27,17,70,2,23,1009,36.2,0,0 +2013,4,27,18,80,4,22,1009,41.12,0,0 +2013,4,27,19,82,4,20,1010,45.14,0,0 +2013,4,27,20,95,5,19,1011,48.27,0,0 +2013,4,27,21,77,4,17,1012,51.4,0,0 +2013,4,27,22,81,4,16,1012,54.53,0,0 +2013,4,27,23,97,4,15,1013,56.32,0,0 +2013,4,28,0,103,4,15,1013,58.11,0,0 +2013,4,28,1,85,4,13,1012,61.24,0,0 +2013,4,28,2,89,4,13,1011,63.03,0,0 +2013,4,28,3,96,5,13,1011,0.89,0,0 +2013,4,28,4,80,5,11,1010,0.89,0,0 +2013,4,28,5,85,5,12,1010,0.89,0,0 +2013,4,28,6,91,6,11,1010,0.89,0,0 +2013,4,28,7,81,5,13,1010,0.89,0,0 +2013,4,28,8,81,5,14,1010,1.79,0,0 +2013,4,28,9,94,5,16,1010,4.92,0,0 +2013,4,28,10,80,6,15,1010,8.94,0,0 +2013,4,28,11,79,6,15,1010,0.89,0,0 +2013,4,28,12,89,7,17,1009,3.13,0,0 +2013,4,28,13,85,7,18,1009,4.92,0,0 +2013,4,28,14,82,7,18,1008,0.89,0,0 +2013,4,28,15,85,7,18,1007,1.78,0,0 +2013,4,28,16,92,7,18,1007,2.67,0,0 +2013,4,28,17,92,8,19,1007,3.56,0,0 +2013,4,28,18,96,7,19,1008,3.13,0,0 +2013,4,28,19,74,-5,19,1010,12.96,0,0 +2013,4,28,20,16,-9,20,1011,22.79,0,0 +2013,4,28,21,21,-8,19,1012,29.94,0,0 +2013,4,28,22,20,-5,16,1012,1.79,0,0 +2013,4,28,23,17,-10,19,1013,7.15,0,0 +2013,4,29,0,11,-10,19,1014,12.96,0,0 +2013,4,29,1,11,-11,17,1014,16.98,0,0 +2013,4,29,2,14,-11,16,1014,18.77,0,0 +2013,4,29,3,14,-2,11,1014,0.89,0,0 +2013,4,29,4,15,-3,9,1013,1.79,0,0 +2013,4,29,5,17,-1,8,1013,0.89,0,0 +2013,4,29,6,17,1,8,1013,1.78,0,0 +2013,4,29,7,21,1,14,1014,2.67,0,0 +2013,4,29,8,24,-4,20,1014,1.79,0,0 +2013,4,29,9,20,-9,22,1014,3.13,0,0 +2013,4,29,10,17,-9,23,1013,6.26,0,0 +2013,4,29,11,47,-9,25,1012,9.39,0,0 +2013,4,29,12,44,-8,27,1012,14.31,0,0 +2013,4,29,13,39,-11,27,1011,22.36,0,0 +2013,4,29,14,27,-11,28,1009,31.3,0,0 +2013,4,29,15,30,-14,29,1009,42.48,0,0 +2013,4,29,16,11,-14,29,1008,51.42,0,0 +2013,4,29,17,12,-15,28,1008,62.6,0,0 +2013,4,29,18,38,-13,27,1008,71.54,0,0 +2013,4,29,19,74,-15,26,1009,80.48,0,0 +2013,4,29,20,37,-13,25,1010,87.63,0,0 +2013,4,29,21,31,-14,24,1011,93.44,0,0 +2013,4,29,22,32,-14,24,1011,100.59,0,0 +2013,4,29,23,48,-13,23,1011,106.4,0,0 +2013,4,30,0,81,-10,18,1012,110.42,0,0 +2013,4,30,1,36,-11,20,1013,4.92,0,0 +2013,4,30,2,12,-13,18,1014,9.84,0,0 +2013,4,30,3,10,-12,16,1014,11.63,0,0 +2013,4,30,4,11,-7,12,1014,1.79,0,0 +2013,4,30,5,15,-4,10,1014,0.89,0,0 +2013,4,30,6,10,-8,14,1015,3.13,0,0 +2013,4,30,7,11,-11,17,1015,4.92,0,0 +2013,4,30,8,14,-10,19,1016,9.84,0,0 +2013,4,30,9,10,-10,21,1016,13.86,0,0 +2013,4,30,10,9,-10,22,1015,18.78,0,0 +2013,4,30,11,11,-10,24,1015,23.7,0,0 +2013,4,30,12,70,-11,26,1014,27.72,0,0 +2013,4,30,13,11,-9,28,1013,4.02,0,0 +2013,4,30,14,12,-9,28,1012,3.13,0,0 +2013,4,30,15,13,-13,28,1011,4.92,0,0 +2013,4,30,16,16,-11,28,1011,3.13,0,0 +2013,4,30,17,11,-12,27,1011,4.02,0,0 +2013,4,30,18,13,-15,26,1011,3.13,0,0 +2013,4,30,19,22,-10,23,1012,0.89,0,0 +2013,4,30,20,36,-9,20,1013,1.78,0,0 +2013,4,30,21,45,-6,18,1014,2.67,0,0 +2013,4,30,22,40,-10,19,1015,1.79,0,0 +2013,4,30,23,38,-4,15,1016,6.71,0,0 +2013,5,1,0,38,-2,14,1017,10.73,0,0 +2013,5,1,1,81,-2,13,1018,12.52,0,0 +2013,5,1,2,41,-1,10,1018,1.79,0,0 +2013,5,1,3,36,0,9,1018,3.58,0,0 +2013,5,1,4,59,2,8,1018,0.89,0,0 +2013,5,1,5,46,4,8,1018,0.89,0,0 +2013,5,1,6,48,2,8,1018,0.89,0,0 +2013,5,1,7,83,2,13,1018,0.89,0,0 +2013,5,1,8,45,2,17,1018,1.78,0,0 +2013,5,1,9,33,1,18,1018,2.67,0,0 +2013,5,1,10,30,0,19,1017,1.79,0,0 +2013,5,1,11,27,-2,21,1016,3.58,0,0 +2013,5,1,12,24,-6,22,1015,1.79,0,0 +2013,5,1,13,28,-7,23,1014,3.58,0,0 +2013,5,1,14,34,-5,25,1013,1.79,0,0 +2013,5,1,15,33,-5,25,1012,3.13,0,0 +2013,5,1,16,28,-4,24,1011,7.15,0,0 +2013,5,1,17,35,-1,23,1011,12.96,0,0 +2013,5,1,18,38,-1,22,1011,20.11,0,0 +2013,5,1,19,59,0,21,1012,25.92,0,0 +2013,5,1,20,41,-1,21,1012,29.05,0,0 +2013,5,1,21,51,0,17,1012,30.84,0,0 +2013,5,1,22,56,1,16,1013,32.63,0,0 +2013,5,1,23,70,4,15,1013,35.76,0,0 +2013,5,2,0,71,1,16,1013,38.89,0,0 +2013,5,2,1,55,0,16,1013,42.02,0,0 +2013,5,2,2,52,1,15,1013,43.81,0,0 +2013,5,2,3,53,2,14,1013,45.6,0,0 +2013,5,2,4,57,3,14,1013,49.62,0,0 +2013,5,2,5,72,5,14,1013,52.75,0,0 +2013,5,2,6,88,6,13,1014,54.54,0,0 +2013,5,2,7,98,6,14,1014,57.67,0,0 +2013,5,2,8,103,6,16,1015,1.79,0,0 +2013,5,2,9,100,7,17,1014,1.79,0,0 +2013,5,2,10,102,7,19,1014,0.89,0,0 +2013,5,2,11,75,4,21,1013,3.13,0,0 +2013,5,2,12,78,2,22,1012,4.92,0,0 +2013,5,2,13,89,3,23,1012,8.94,0,0 +2013,5,2,14,98,1,24,1011,12.07,0,0 +2013,5,2,15,96,2,24,1010,15.2,0,0 +2013,5,2,16,97,4,23,1009,19.22,0,0 +2013,5,2,17,96,3,23,1008,23.24,0,0 +2013,5,2,18,95,4,21,1009,29.05,0,0 +2013,5,2,19,86,3,20,1009,33.97,0,0 +2013,5,2,20,94,4,19,1010,39.78,0,0 +2013,5,2,21,79,4,18,1011,44.7,0,0 +2013,5,2,22,87,4,17,1012,48.72,0,0 +2013,5,2,23,81,5,17,1013,54.53,0,0 +2013,5,3,0,59,4,17,1013,58.55,0,0 +2013,5,3,1,68,7,13,1015,4.02,0,1 +2013,5,3,2,82,9,12,1016,1.79,0,2 +2013,5,3,3,60,9,13,1016,4.92,0,3 +2013,5,3,4,44,9,12,1016,0.89,0,0 +2013,5,3,5,48,9,12,1017,0.89,0,0 +2013,5,3,6,52,9,13,1017,0.89,0,0 +2013,5,3,7,51,10,14,1018,1.79,0,0 +2013,5,3,8,51,8,16,1018,3.58,0,0 +2013,5,3,9,56,8,17,1018,0.89,0,0 +2013,5,3,10,57,9,19,1018,1.78,0,0 +2013,5,3,11,63,7,19,1018,3.57,0,0 +2013,5,3,12,61,6,20,1018,4.46,0,0 +2013,5,3,13,69,7,21,1017,6.25,0,0 +2013,5,3,14,71,5,22,1016,3.13,0,0 +2013,5,3,15,73,5,22,1016,1.79,0,0 +2013,5,3,16,67,5,22,1015,4.02,0,0 +2013,5,3,17,60,4,22,1015,8.04,0,0 +2013,5,3,18,57,4,21,1015,12.96,0,0 +2013,5,3,19,52,5,19,1015,16.98,0,0 +2013,5,3,20,66,6,19,1016,20.11,0,0 +2013,5,3,21,72,7,16,1016,23.24,0,0 +2013,5,3,22,77,7,17,1016,25.03,0,0 +2013,5,3,23,96,7,16,1017,26.82,0,0 +2013,5,4,0,86,7,15,1017,30.84,0,0 +2013,5,4,1,71,7,15,1017,34.86,0,0 +2013,5,4,2,67,7,14,1017,36.65,0,0 +2013,5,4,3,74,7,14,1017,39.78,0,0 +2013,5,4,4,83,7,13,1017,42.91,0,0 +2013,5,4,5,88,7,13,1017,46.04,0,0 +2013,5,4,6,98,8,12,1018,1.79,0,0 +2013,5,4,7,112,9,14,1018,1.79,0,0 +2013,5,4,8,124,10,16,1018,3.58,0,0 +2013,5,4,9,131,9,18,1018,5.37,0,0 +2013,5,4,10,139,10,19,1018,1.79,0,0 +2013,5,4,11,117,9,23,1017,7.15,0,0 +2013,5,4,12,83,7,23,1017,14.3,0,0 +2013,5,4,13,82,5,24,1016,19.22,0,0 +2013,5,4,14,78,3,24,1015,26.37,0,0 +2013,5,4,15,70,2,25,1015,32.18,0,0 +2013,5,4,16,64,0,25,1014,36.2,0,0 +2013,5,4,17,65,-1,25,1014,42.01,0,0 +2013,5,4,18,54,-1,24,1014,47.82,0,0 +2013,5,4,19,65,1,23,1014,51.84,0,0 +2013,5,4,20,71,3,21,1015,54.97,0,0 +2013,5,4,21,75,4,20,1016,56.76,0,0 +2013,5,4,22,80,9,16,1016,57.65,0,0 +2013,5,4,23,84,8,17,1016,59.44,0,0 +2013,5,5,0,104,8,17,1016,61.23,0,0 +2013,5,5,1,134,8,16,1016,63.02,0,0 +2013,5,5,2,136,9,15,1016,64.81,0,0 +2013,5,5,3,145,9,14,1016,0.89,0,0 +2013,5,5,4,144,8,14,1017,1.34,0,0 +2013,5,5,5,153,9,12,1017,0.89,0,0 +2013,5,5,6,167,9,12,1017,0.89,0,0 +2013,5,5,7,157,10,15,1018,0.89,0,0 +2013,5,5,8,152,8,17,1018,1.79,0,0 +2013,5,5,9,166,10,19,1018,0.89,0,0 +2013,5,5,10,169,10,21,1018,1.78,0,0 +2013,5,5,11,161,11,23,1018,2.67,0,0 +2013,5,5,12,156,12,25,1017,3.56,0,0 +2013,5,5,13,172,12,26,1016,4.45,0,0 +2013,5,5,14,177,11,27,1016,3.13,0,0 +2013,5,5,15,182,11,27,1015,6.26,0,0 +2013,5,5,16,179,12,27,1014,10.28,0,0 +2013,5,5,17,187,13,27,1013,14.3,0,0 +2013,5,5,18,209,13,26,1013,18.32,0,0 +2013,5,5,19,211,13,25,1014,22.34,0,0 +2013,5,5,20,230,14,22,1014,25.47,0,0 +2013,5,5,21,254,15,23,1015,29.49,0,0 +2013,5,5,22,271,15,21,1015,31.28,0,0 +2013,5,5,23,275,15,19,1015,0.89,0,0 +2013,5,6,0,284,15,18,1015,0.89,0,0 +2013,5,6,1,297,15,18,1015,1.78,0,0 +2013,5,6,2,314,15,17,1015,0.89,0,0 +2013,5,6,3,301,14,16,1015,1.78,0,0 +2013,5,6,4,320,14,15,1015,0.89,0,0 +2013,5,6,5,315,14,15,1015,0.89,0,0 +2013,5,6,6,318,14,15,1015,1.34,0,0 +2013,5,6,7,312,16,17,1015,0.89,0,0 +2013,5,6,8,286,15,20,1016,0.89,0,0 +2013,5,6,9,271,16,23,1015,1.78,0,0 +2013,5,6,10,261,15,24,1015,2.67,0,0 +2013,5,6,11,266,16,26,1015,3.56,0,0 +2013,5,6,12,265,16,27,1014,3.13,0,0 +2013,5,6,13,259,14,29,1014,1.79,0,0 +2013,5,6,14,171,11,30,1013,4.02,0,0 +2013,5,6,15,103,9,31,1012,8.04,0,0 +2013,5,6,16,103,8,31,1012,12.96,0,0 +2013,5,6,17,82,7,30,1012,17.88,0,0 +2013,5,6,18,85,7,28,1012,21.9,0,0 +2013,5,6,19,90,8,27,1012,25.92,0,0 +2013,5,6,20,135,10,25,1013,27.71,0,0 +2013,5,6,21,148,11,23,1014,29.5,0,0 +2013,5,6,22,143,14,20,1014,0.89,0,0 +2013,5,6,23,137,14,19,1014,1.34,0,0 +2013,5,7,0,161,14,18,1015,1.79,0,0 +2013,5,7,1,157,14,18,1015,2.68,0,0 +2013,5,7,2,170,14,18,1014,0.89,0,0 +2013,5,7,3,156,13,17,1015,1.78,0,0 +2013,5,7,4,157,13,17,1015,0.89,0,0 +2013,5,7,5,169,13,15,1015,0.89,0,0 +2013,5,7,6,185,13,16,1015,0.89,0,0 +2013,5,7,7,217,14,18,1016,0.89,0,0 +2013,5,7,8,230,14,20,1016,1.78,0,0 +2013,5,7,9,251,16,23,1017,2.67,0,0 +2013,5,7,10,251,16,24,1017,3.56,0,0 +2013,5,7,11,246,16,25,1017,1.79,0,0 +2013,5,7,12,194,15,27,1016,4.92,0,0 +2013,5,7,13,137,14,28,1015,8.05,0,0 +2013,5,7,14,127,13,29,1014,11.18,0,0 +2013,5,7,15,130,11,29,1014,16.1,0,0 +2013,5,7,16,143,12,29,1013,20.12,0,0 +2013,5,7,17,143,13,28,1013,23.25,0,0 +2013,5,7,18,153,12,28,1013,26.38,0,0 +2013,5,7,19,172,13,27,1013,30.4,0,0 +2013,5,7,20,182,12,26,1013,34.42,0,0 +2013,5,7,21,164,9,24,1014,38.44,0,0 +2013,5,7,22,166,11,22,1014,41.57,0,0 +2013,5,7,23,199,11,21,1014,44.7,0,0 +2013,5,8,0,196,11,21,1014,46.49,0,0 +2013,5,8,1,223,10,20,1014,48.28,0,0 +2013,5,8,2,218,10,19,1014,50.07,0,0 +2013,5,8,3,272,11,18,1014,0.89,0,0 +2013,5,8,4,172,10,16,1014,1.34,0,0 +2013,5,8,5,137,10,16,1014,1.79,0,0 +2013,5,8,6,119,10,18,1014,4.92,0,0 +2013,5,8,7,116,9,18,1015,0.89,0,0 +2013,5,8,8,120,10,20,1015,3.13,0,0 +2013,5,8,9,135,9,20,1015,6.26,0,0 +2013,5,8,10,126,10,22,1015,9.39,0,0 +2013,5,8,11,123,10,22,1015,13.41,0,0 +2013,5,8,12,116,11,23,1014,16.54,0,1 +2013,5,8,13,141,12,24,1014,20.56,0,0 +2013,5,8,14,163,13,22,1014,25.48,0,0 +2013,5,8,15,148,13,23,1013,29.5,0,0 +2013,5,8,16,139,14,23,1013,32.63,0,0 +2013,5,8,17,88,13,23,1012,35.76,0,0 +2013,5,8,18,68,13,22,1012,38.89,0,0 +2013,5,8,19,60,12,22,1012,40.68,0,0 +2013,5,8,20,103,12,21,1012,42.47,0,0 +2013,5,8,21,77,12,22,1013,44.26,0,1 +2013,5,8,22,42,10,20,1013,49.18,0,0 +2013,5,8,23,35,10,20,1013,53.2,0,0 +2013,5,9,0,42,10,19,1012,3.13,0,0 +2013,5,9,1,53,10,19,1012,1.79,0,0 +2013,5,9,2,54,11,18,1011,3.58,0,0 +2013,5,9,3,48,12,17,1011,5.37,0,0 +2013,5,9,4,49,12,17,1011,7.16,0,0 +2013,5,9,5,55,12,17,1011,8.95,0,0 +2013,5,9,6,56,12,17,1011,12.08,0,0 +2013,5,9,7,64,12,17,1011,16.1,0,0 +2013,5,9,8,52,11,17,1011,20.12,0,0 +2013,5,9,9,17,11,17,1012,23.25,0,0 +2013,5,9,10,21,12,17,1011,26.38,0,0 +2013,5,9,11,24,12,18,1011,30.4,0,0 +2013,5,9,12,25,11,18,1010,33.53,0,0 +2013,5,9,13,35,12,18,1010,35.32,0,0 +2013,5,9,14,28,11,20,1009,38.45,0,0 +2013,5,9,15,47,12,20,1008,41.58,0,0 +2013,5,9,16,53,12,19,1008,44.71,0,0 +2013,5,9,17,60,12,19,1007,46.5,0,0 +2013,5,9,18,60,12,19,1007,48.29,0,0 +2013,5,9,19,66,12,18,1007,0.89,0,0 +2013,5,9,20,64,12,16,1008,1.79,0,0 +2013,5,9,21,60,12,15,1008,3.58,0,0 +2013,5,9,22,64,12,15,1008,5.37,0,0 +2013,5,9,23,65,12,14,1008,0.89,0,0 +2013,5,10,0,73,11,13,1007,3.13,0,0 +2013,5,10,1,76,10,12,1006,0.89,0,0 +2013,5,10,2,93,10,11,1005,1.78,0,0 +2013,5,10,3,97,10,11,1004,0.89,0,0 +2013,5,10,4,108,7,8,1003,0.89,0,0 +2013,5,10,5,105,9,9,1003,0.89,0,0 +2013,5,10,6,108,9,9,1003,1.78,0,0 +2013,5,10,7,116,11,12,1003,2.67,0,0 +2013,5,10,8,98,13,16,1003,4.46,0,0 +2013,5,10,9,27,1,23,1003,7.15,0,0 +2013,5,10,10,9,-1,25,1002,15.2,0,0 +2013,5,10,11,13,-3,27,1002,27.27,0,0 +2013,5,10,12,16,3,27,1001,4.02,0,0 +2013,5,10,13,14,-2,30,1001,8.94,0,0 +2013,5,10,14,12,-3,30,1000,14.75,0,0 +2013,5,10,15,9,-2,31,999,18.77,0,0 +2013,5,10,16,14,-5,31,998,3.13,0,0 +2013,5,10,17,13,-9,31,998,4.92,0,0 +2013,5,10,18,16,-9,30,997,8.05,0,0 +2013,5,10,19,19,5,25,998,1.79,0,0 +2013,5,10,20,38,-3,27,999,5.81,0,0 +2013,5,10,21,33,-1,26,999,12.96,0,0 +2013,5,10,22,43,1,25,999,18.77,0,0 +2013,5,10,23,46,2,24,999,22.79,0,0 +2013,5,11,0,47,5,19,999,24.58,0,0 +2013,5,11,1,60,7,15,998,26.37,0,0 +2013,5,11,2,52,8,14,997,0.89,0,0 +2013,5,11,3,54,8,13,997,1.78,0,0 +2013,5,11,4,70,7,12,996,2.67,0,0 +2013,5,11,5,89,6,11,996,3.56,0,0 +2013,5,11,6,115,8,10,995,1.79,0,0 +2013,5,11,7,111,8,17,995,0.89,0,0 +2013,5,11,8,106,8,21,995,1.78,0,0 +2013,5,11,9,77,5,23,995,2.67,0,0 +2013,5,11,10,83,3,27,995,3.13,0,0 +2013,5,11,11,58,-3,31,994,8.05,0,0 +2013,5,11,12,26,-6,32,994,13.86,0,0 +2013,5,11,13,27,-5,33,993,17.88,0,0 +2013,5,11,14,25,-14,34,993,26.82,0,0 +2013,5,11,15,17,-17,34,992,33.97,0,0 +2013,5,11,16,25,-14,34,992,42.02,0,0 +2013,5,11,17,22,-9,33,993,46.94,0,0 +2013,5,11,18,55,-7,30,993,50.96,0,0 +2013,5,11,19,26,-8,26,994,54.09,0,0 +2013,5,11,20,37,-7,21,995,57.22,0,0 +2013,5,11,21,38,-4,21,996,1.79,0,0 +2013,5,11,22,48,-8,21,997,2.68,0,0 +2013,5,11,23,31,0,16,997,4.47,0,0 +2013,5,12,0,25,-9,18,997,3.13,0,0 +2013,5,12,1,32,-7,17,998,0.89,0,0 +2013,5,12,2,67,3,14,998,1.78,0,0 +2013,5,12,3,119,0,11,998,0.89,0,0 +2013,5,12,4,122,-1,12,998,1.79,0,0 +2013,5,12,5,52,-4,13,999,1.79,0,0 +2013,5,12,6,36,-4,15,999,0.89,0,0 +2013,5,12,7,53,-5,19,1000,1.79,0,0 +2013,5,12,8,47,-9,21,1000,7.6,0,0 +2013,5,12,9,29,-6,23,1000,4.92,0,0 +2013,5,12,10,24,-6,25,1001,4.02,0,0 +2013,5,12,11,22,-6,26,1001,1.79,0,0 +2013,5,12,12,10,-9,28,1000,1.79,0,0 +2013,5,12,13,12,-12,29,1000,4.92,0,0 +2013,5,12,14,23,-8,29,999,3.13,0,0 +2013,5,12,15,93,-8,30,999,3.13,0,0 +2013,5,12,16,35,-4,28,999,7.15,0,0 +2013,5,12,17,24,-5,28,999,8.94,0,0 +2013,5,12,18,35,-4,28,999,12.07,0,0 +2013,5,12,19,46,-4,26,999,15.2,0,0 +2013,5,12,20,43,-3,24,1000,19.22,0,0 +2013,5,12,21,77,-2,22,1002,22.35,0,0 +2013,5,12,22,79,-2,20,1002,25.48,0,0 +2013,5,12,23,59,-1,19,1002,26.37,0,0 +2013,5,13,0,35,2,16,1003,0.89,0,0 +2013,5,13,1,87,5,15,1003,0.89,0,0 +2013,5,13,2,50,6,17,1003,1.78,0,0 +2013,5,13,3,53,6,14,1002,2.67,0,0 +2013,5,13,4,76,6,13,1003,1.79,0,0 +2013,5,13,5,91,5,12,1002,3.58,0,0 +2013,5,13,6,96,6,15,1003,5.37,0,0 +2013,5,13,7,96,6,17,1004,7.16,0,0 +2013,5,13,8,130,4,20,1004,8.95,0,0 +2013,5,13,9,83,4,21,1004,12.08,0,0 +2013,5,13,10,78,4,23,1003,0.89,0,0 +2013,5,13,11,69,-1,24,1004,1.78,0,0 +2013,5,13,12,65,1,25,1002,1.79,0,0 +2013,5,13,13,68,4,26,1002,1.79,0,0 +2013,5,13,14,72,6,27,1000,3.58,0,0 +2013,5,13,15,75,5,27,1000,9.39,0,0 +2013,5,13,16,90,6,27,1000,13.41,0,0 +2013,5,13,17,103,7,25,999,16.54,0,0 +2013,5,13,18,115,8,24,999,19.67,0,0 +2013,5,13,19,120,9,23,1000,22.8,0,0 +2013,5,13,20,128,11,21,1000,24.59,0,0 +2013,5,13,21,144,12,20,1002,0.89,0,0 +2013,5,13,22,179,13,18,1002,0.89,0,0 +2013,5,13,23,176,13,18,1003,1.78,0,0 +2013,5,14,0,264,13,17,1004,0.89,0,0 +2013,5,14,1,195,13,16,1005,0.89,0,0 +2013,5,14,2,196,12,15,1005,1.79,0,0 +2013,5,14,3,89,-6,23,1004,12.97,0,0 +2013,5,14,4,25,-7,22,1005,20.12,0,0 +2013,5,14,5,50,-5,21,1006,25.93,0,0 +2013,5,14,6,15,-3,21,1006,33.08,0,0 +2013,5,14,7,28,-3,23,1007,40.23,0,0 +2013,5,14,8,17,-1,24,1008,47.38,0,0 +2013,5,14,9,47,-1,24,1008,55.43,0,0 +2013,5,14,10,21,2,26,1009,62.58,0,0 +2013,5,14,11,21,-1,27,1009,65.71,0,0 +2013,5,14,12,93,-2,29,1009,68.84,0,0 +2013,5,14,13,140,-1,29,1008,1.79,0,0 +2013,5,14,14,75,1,31,1007,3.58,0,0 +2013,5,14,15,89,-2,31,1007,5.37,0,0 +2013,5,14,16,23,1,31,1007,6.26,0,0 +2013,5,14,17,19,1,30,1007,7.15,0,0 +2013,5,14,18,38,4,28,1007,1.79,0,0 +2013,5,14,19,44,2,25,1008,3.58,0,0 +2013,5,14,20,37,-1,27,1009,6.71,0,0 +2013,5,14,21,42,0,27,1010,4.02,0,0 +2013,5,14,22,51,7,21,1011,1.79,0,0 +2013,5,14,23,86,7,20,1011,3.58,0,0 +2013,5,15,0,67,7,19,1012,0.89,0,0 +2013,5,15,1,79,4,19,1013,0.89,0,0 +2013,5,15,2,55,7,16,1014,1.78,0,0 +2013,5,15,3,77,7,17,1014,1.79,0,0 +2013,5,15,4,86,8,15,1015,0.89,0,0 +2013,5,15,5,105,8,15,1016,1.79,0,0 +2013,5,15,6,111,8,16,1016,0.89,0,0 +2013,5,15,7,126,7,20,1017,1.79,0,0 +2013,5,15,8,90,5,21,1018,4.92,0,0 +2013,5,15,9,60,5,23,1018,8.05,0,0 +2013,5,15,10,99,6,25,1018,9.84,0,0 +2013,5,15,11,67,5,25,1018,1.79,0,0 +2013,5,15,12,75,4,26,1017,3.58,0,0 +2013,5,15,13,76,4,27,1016,1.79,0,0 +2013,5,15,14,58,4,27,1015,4.92,0,0 +2013,5,15,15,48,5,27,1014,8.94,0,0 +2013,5,15,16,42,5,27,1014,13.86,0,0 +2013,5,15,17,37,6,27,1014,17.88,0,0 +2013,5,15,18,39,7,25,1014,22.8,0,0 +2013,5,15,19,76,8,24,1015,26.82,0,0 +2013,5,15,20,53,8,22,1015,29.95,0,0 +2013,5,15,21,44,8,21,1016,31.74,0,0 +2013,5,15,22,38,8,20,1017,34.87,0,0 +2013,5,15,23,39,10,18,1018,36.66,0,0 +2013,5,16,0,40,9,17,1018,39.79,0,0 +2013,5,16,1,47,9,17,1018,42.92,0,0 +2013,5,16,2,38,9,16,1018,44.71,0,0 +2013,5,16,3,53,9,15,1018,0.89,0,0 +2013,5,16,4,53,8,13,1018,1.78,0,0 +2013,5,16,5,49,9,13,1018,2.23,0,0 +2013,5,16,6,62,10,14,1019,2.68,0,0 +2013,5,16,7,59,10,18,1019,3.57,0,0 +2013,5,16,8,68,10,20,1019,4.46,0,0 +2013,5,16,9,70,10,22,1019,5.35,0,0 +2013,5,16,10,56,9,23,1019,7.14,0,0 +2013,5,16,11,37,8,24,1018,8.93,0,0 +2013,5,16,12,63,9,26,1017,1.79,0,0 +2013,5,16,13,51,8,26,1016,4.92,0,0 +2013,5,16,14,124,8,26,1016,8.05,0,0 +2013,5,16,15,79,8,26,1015,12.97,0,0 +2013,5,16,16,101,9,26,1014,16.99,0,0 +2013,5,16,17,85,9,25,1014,21.01,0,0 +2013,5,16,18,108,8,25,1013,24.14,0,0 +2013,5,16,19,65,8,24,1013,27.27,0,0 +2013,5,16,20,74,12,22,1014,31.29,0,0 +2013,5,16,21,80,11,21,1015,34.42,0,0 +2013,5,16,22,79,11,20,1015,37.55,0,0 +2013,5,16,23,83,11,19,1015,41.57,0,0 +2013,5,17,0,66,11,19,1015,44.7,0,0 +2013,5,17,1,71,12,18,1015,46.49,0,0 +2013,5,17,2,79,11,18,1014,0.89,0,0 +2013,5,17,3,97,11,17,1014,1.79,0,0 +2013,5,17,4,98,11,17,1013,3.58,0,0 +2013,5,17,5,107,11,16,1013,0.89,0,0 +2013,5,17,6,114,11,17,1013,1.78,0,0 +2013,5,17,7,119,11,17,1013,1.79,0,0 +2013,5,17,8,112,12,18,1013,4.92,0,0 +2013,5,17,9,121,12,19,1013,6.71,0,0 +2013,5,17,10,126,11,19,1013,8.5,0,0 +2013,5,17,11,132,12,20,1013,10.29,0,0 +2013,5,17,12,124,12,22,1012,0.89,0,0 +2013,5,17,13,117,13,22,1012,1.79,0,0 +2013,5,17,14,119,12,24,1010,3.58,0,0 +2013,5,17,15,96,13,25,1010,7.6,0,0 +2013,5,17,16,NA,13,24,1009,10.73,0,0 +2013,5,17,17,NA,12,25,1009,14.75,0,0 +2013,5,17,18,NA,12,24,1009,18.77,0,0 +2013,5,17,19,NA,13,23,1009,21.9,0,0 +2013,5,17,20,NA,15,22,1009,25.03,0,0 +2013,5,17,21,NA,14,21,1010,26.82,0,0 +2013,5,17,22,NA,15,20,1010,29.95,0,0 +2013,5,17,23,NA,14,20,1009,31.74,0,0 +2013,5,18,0,NA,15,19,1009,34.87,0,0 +2013,5,18,1,NA,14,19,1009,38,0,0 +2013,5,18,2,NA,14,18,1009,39.79,0,0 +2013,5,18,3,NA,14,18,1008,41.58,0,0 +2013,5,18,4,NA,13,17,1008,0.89,0,0 +2013,5,18,5,NA,13,17,1008,1.79,0,0 +2013,5,18,6,NA,13,17,1008,0.89,0,0 +2013,5,18,7,NA,14,19,1008,3.13,0,0 +2013,5,18,8,150,15,20,1008,7.15,0,0 +2013,5,18,9,154,15,21,1008,11.17,0,0 +2013,5,18,10,162,15,22,1008,16.09,0,0 +2013,5,18,11,150,15,23,1007,21.01,0,0 +2013,5,18,12,104,14,24,1006,26.82,0,0 +2013,5,18,13,90,13,23,1006,32.63,0,0 +2013,5,18,14,73,13,24,1005,37.55,0,0 +2013,5,18,15,83,13,24,1004,43.36,0,0 +2013,5,18,16,87,13,24,1004,48.28,0,0 +2013,5,18,17,86,13,23,1003,53.2,0,0 +2013,5,18,18,72,13,23,1003,57.22,0,0 +2013,5,18,19,68,13,22,1003,63.03,0,0 +2013,5,18,20,54,13,21,1004,67.95,0,0 +2013,5,18,21,32,14,20,1004,72.87,0,0 +2013,5,18,22,29,13,19,1004,77.79,0,0 +2013,5,18,23,29,13,18,1005,4.02,0,0 +2013,5,19,0,37,13,18,1005,1.79,0,0 +2013,5,19,1,46,13,17,1004,4.92,0,0 +2013,5,19,2,56,12,17,1002,8.94,0,0 +2013,5,19,3,58,12,17,1002,12.07,0,0 +2013,5,19,4,61,13,16,1002,15.2,0,0 +2013,5,19,5,66,13,16,1001,19.22,0,0 +2013,5,19,6,67,13,16,1000,23.24,0,0 +2013,5,19,7,70,14,16,1000,27.26,0,0 +2013,5,19,8,73,14,17,999,30.39,0,0 +2013,5,19,9,73,14,18,998,33.52,0,0 +2013,5,19,10,101,14,18,997,35.31,0,0 +2013,5,19,11,102,13,17,999,4.92,0,0 +2013,5,19,12,101,14,22,998,1.79,0,0 +2013,5,19,13,79,4,26,998,5.81,0,0 +2013,5,19,14,49,-18,28,998,17.88,0,0 +2013,5,19,15,36,-16,27,998,29.06,0,0 +2013,5,19,16,44,-16,27,998,42.02,0,0 +2013,5,19,17,29,-16,26,998,55.88,0,0 +2013,5,19,18,22,-13,25,999,67.06,0,0 +2013,5,19,19,57,-10,23,1000,78.24,0,0 +2013,5,19,20,34,-9,22,1002,88.07,0,0 +2013,5,19,21,22,-8,20,1003,95.22,0,0 +2013,5,19,22,22,-9,20,1004,105.05,0,0 +2013,5,19,23,13,-7,18,1004,4.02,0,0 +2013,5,20,0,27,-4,16,1004,0.89,0,0 +2013,5,20,1,25,-1,13,1003,0.89,0,0 +2013,5,20,2,16,0,12,1003,0.89,0,0 +2013,5,20,3,21,-2,13,1003,1.79,0,0 +2013,5,20,4,22,0,10,1003,0.89,0,0 +2013,5,20,5,20,1,10,1003,1.79,0,0 +2013,5,20,6,23,-4,17,1003,4.92,0,0 +2013,5,20,7,29,-5,20,1004,4.02,0,0 +2013,5,20,8,57,-6,22,1004,4.02,0,0 +2013,5,20,9,27,-7,24,1003,8.94,0,0 +2013,5,20,10,40,-7,25,1003,14.75,0,0 +2013,5,20,11,34,-8,26,1002,19.67,0,0 +2013,5,20,12,27,-8,27,1001,25.48,0,0 +2013,5,20,13,62,-7,28,1000,30.4,0,0 +2013,5,20,14,74,-4,29,999,37.55,0,0 +2013,5,20,15,57,-4,29,998,45.6,0,0 +2013,5,20,16,41,-4,29,997,54.54,0,0 +2013,5,20,17,42,-3,27,997,62.59,0,0 +2013,5,20,18,41,-1,26,997,67.51,0,0 +2013,5,20,19,43,-2,26,997,74.66,0,0 +2013,5,20,20,118,0,24,997,78.68,0,0 +2013,5,20,21,107,2,23,997,82.7,0,0 +2013,5,20,22,86,4,21,997,84.49,0,0 +2013,5,20,23,79,4,21,997,87.62,0,0 +2013,5,21,0,76,5,19,997,90.75,0,0 +2013,5,21,1,75,5,20,996,94.77,0,0 +2013,5,21,2,80,6,19,996,96.56,0,0 +2013,5,21,3,87,6,18,996,99.69,0,0 +2013,5,21,4,71,6,18,996,102.82,0,0 +2013,5,21,5,72,7,17,996,0.89,0,0 +2013,5,21,6,85,8,15,997,0.89,0,0 +2013,5,21,7,92,9,19,997,2.68,0,0 +2013,5,21,8,99,10,21,998,1.79,0,0 +2013,5,21,9,101,11,23,998,3.58,0,0 +2013,5,21,10,99,9,25,998,4.02,0,0 +2013,5,21,11,101,7,26,998,1.79,0,0 +2013,5,21,12,105,9,26,998,3.58,0,0 +2013,5,21,13,101,11,27,998,6.71,0,0 +2013,5,21,14,104,12,28,998,10.73,0,0 +2013,5,21,15,105,12,28,998,13.86,0,0 +2013,5,21,16,106,12,28,999,0.89,0,0 +2013,5,21,17,118,11,29,999,1.79,0,0 +2013,5,21,18,109,13,27,1000,3.58,0,0 +2013,5,21,19,124,13,25,1001,4.47,0,0 +2013,5,21,20,144,14,23,1002,6.26,0,0 +2013,5,21,21,153,12,24,1004,10.28,0,0 +2013,5,21,22,129,9,23,1005,13.41,0,0 +2013,5,21,23,112,5,21,1006,15.2,0,0 +2013,5,22,0,65,8,20,1007,0.89,0,0 +2013,5,22,1,61,10,19,1007,1.78,0,0 +2013,5,22,2,53,10,18,1007,2.67,0,0 +2013,5,22,3,61,11,17,1007,3.56,0,0 +2013,5,22,4,62,10,17,1008,4.45,0,0 +2013,5,22,5,69,10,16,1009,4.9,0,0 +2013,5,22,6,77,11,18,1010,0.89,0,0 +2013,5,22,7,85,11,19,1010,0.45,0,0 +2013,5,22,8,89,10,23,1010,1.34,0,0 +2013,5,22,9,98,8,25,1011,1.79,0,0 +2013,5,22,10,98,7,26,1011,3.58,0,0 +2013,5,22,11,79,8,27,1011,7.6,0,0 +2013,5,22,12,86,8,28,1011,11.62,0,0 +2013,5,22,13,83,3,28,1011,17.43,0,0 +2013,5,22,14,79,5,29,1010,22.35,0,0 +2013,5,22,15,65,7,28,1010,27.27,0,0 +2013,5,22,16,57,6,27,1010,31.29,0,0 +2013,5,22,17,48,5,27,1009,35.31,0,0 +2013,5,22,18,40,5,26,1010,40.23,0,0 +2013,5,22,19,39,5,25,1010,44.25,0,0 +2013,5,22,20,29,3,25,1012,48.27,0,0 +2013,5,22,21,36,3,22,1012,50.06,0,0 +2013,5,22,22,35,4,21,1012,51.85,0,0 +2013,5,22,23,38,7,19,1012,52.74,0,0 +2013,5,23,0,38,9,18,1012,53.63,0,0 +2013,5,23,1,29,9,17,1012,0.89,0,0 +2013,5,23,2,54,10,16,1012,1.78,0,0 +2013,5,23,3,59,9,16,1011,0.89,0,0 +2013,5,23,4,70,10,16,1011,2.68,0,0 +2013,5,23,5,93,7,17,1011,0.89,0,0 +2013,5,23,6,94,7,18,1012,1.79,0,0 +2013,5,23,7,50,7,20,1012,3.58,0,0 +2013,5,23,8,51,10,20,1012,1.79,0,0 +2013,5,23,9,55,10,23,1012,0.89,0,0 +2013,5,23,10,45,7,24,1012,1.78,0,0 +2013,5,23,11,61,6,25,1011,1.79,0,0 +2013,5,23,12,71,5,26,1010,1.79,0,0 +2013,5,23,13,77,6,27,1010,1.79,0,0 +2013,5,23,14,81,10,27,1009,4.92,0,0 +2013,5,23,15,104,11,28,1008,8.94,0,0 +2013,5,23,16,123,12,28,1007,12.07,0,0 +2013,5,23,17,149,12,28,1007,15.2,0,0 +2013,5,23,18,97,15,27,1006,16.99,0,0 +2013,5,23,19,89,12,26,1007,18.78,0,0 +2013,5,23,20,91,12,25,1007,20.57,0,0 +2013,5,23,21,103,15,23,1009,1.79,0,0 +2013,5,23,22,132,16,22,1009,0.89,0,0 +2013,5,23,23,148,17,21,1009,0.89,0,0 +2013,5,24,0,164,17,21,1009,0.89,0,0 +2013,5,24,1,205,18,21,1009,0.89,0,0 +2013,5,24,2,215,18,20,1010,1.78,0,0 +2013,5,24,3,233,17,19,1009,3.57,0,0 +2013,5,24,4,249,17,19,1009,5.36,0,0 +2013,5,24,5,233,17,19,1010,0.89,0,0 +2013,5,24,6,231,16,19,1010,3.13,0,0 +2013,5,24,7,201,16,22,1011,6.26,0,0 +2013,5,24,8,173,15,24,1012,3.13,0,0 +2013,5,24,9,131,12,27,1012,6.26,0,0 +2013,5,24,10,90,10,28,1012,11.18,0,0 +2013,5,24,11,59,8,30,1012,16.99,0,0 +2013,5,24,12,39,9,30,1012,21.01,0,0 +2013,5,24,13,43,11,30,1012,22.8,0,0 +2013,5,24,14,58,11,29,1012,25.93,0,0 +2013,5,24,15,89,16,24,1012,4.02,0,0 +2013,5,24,16,146,15,23,1013,7.15,0,0 +2013,5,24,17,76,16,22,1012,8.94,0,0 +2013,5,24,18,56,15,23,1012,0.89,0,0 +2013,5,24,19,54,15,22,1011,1.78,0,0 +2013,5,24,20,47,12,23,1012,4.02,0,0 +2013,5,24,21,46,13,22,1013,1.79,0,0 +2013,5,24,22,29,13,21,1013,0.89,0,0 +2013,5,24,23,35,13,20,1013,1.78,0,0 +2013,5,25,0,35,15,20,1013,0.89,0,0 +2013,5,25,1,37,14,20,1013,0.89,0,0 +2013,5,25,2,42,14,19,1012,0.89,0,0 +2013,5,25,3,55,15,19,1012,0.89,0,0 +2013,5,25,4,69,14,18,1012,1.78,0,0 +2013,5,25,5,74,14,16,1012,0.89,0,0 +2013,5,25,6,59,14,17,1012,0.89,0,0 +2013,5,25,7,44,13,21,1012,4.02,0,0 +2013,5,25,8,32,12,23,1012,7.15,0,0 +2013,5,25,9,34,12,24,1012,0.89,0,0 +2013,5,25,10,43,9,27,1012,1.78,0,0 +2013,5,25,11,53,10,28,1011,1.79,0,0 +2013,5,25,12,65,10,29,1011,1.79,0,0 +2013,5,25,13,77,10,30,1010,3.13,0,0 +2013,5,25,14,82,10,30,1009,7.15,0,0 +2013,5,25,15,76,10,30,1008,11.17,0,0 +2013,5,25,16,83,11,30,1008,14.3,0,0 +2013,5,25,17,86,11,29,1007,19.22,0,0 +2013,5,25,18,77,11,28,1007,23.24,0,0 +2013,5,25,19,72,13,26,1008,25.03,0,0 +2013,5,25,20,76,13,25,1008,28.16,0,0 +2013,5,25,21,77,17,24,1009,32.18,0,0 +2013,5,25,22,66,18,23,1009,35.31,0,0 +2013,5,25,23,67,17,22,1009,38.44,0,0 +2013,5,26,0,86,18,21,1009,40.23,0,0 +2013,5,26,1,103,17,21,1009,0.89,0,0 +2013,5,26,2,108,17,19,1009,1.78,0,0 +2013,5,26,3,120,17,20,1008,2.67,0,0 +2013,5,26,4,118,17,20,1008,3.56,0,0 +2013,5,26,5,120,17,20,1008,4.45,0,0 +2013,5,26,6,129,18,21,1008,1.79,0,0 +2013,5,26,7,115,18,22,1008,0.89,0,0 +2013,5,26,8,109,18,23,1008,1.79,0,0 +2013,5,26,9,101,17,23,1008,3.58,0,0 +2013,5,26,10,114,18,23,1008,5.37,0,0 +2013,5,26,11,108,17,23,1008,1.79,0,0 +2013,5,26,12,117,16,23,1008,1.79,0,0 +2013,5,26,13,133,17,22,1007,4.92,0,1 +2013,5,26,14,140,17,23,1007,8.05,0,2 +2013,5,26,15,143,16,23,1007,12.07,0,0 +2013,5,26,16,131,16,23,1007,16.09,0,0 +2013,5,26,17,124,16,21,1008,20.11,0,0 +2013,5,26,18,80,17,20,1008,25.03,0,0 +2013,5,26,19,76,17,18,1009,29.05,0,1 +2013,5,26,20,73,16,19,1009,33.97,0,0 +2013,5,26,21,67,16,17,1011,37.1,0,1 +2013,5,26,22,57,16,17,1011,41.12,0,2 +2013,5,26,23,50,16,17,1011,44.25,0,3 +2013,5,27,0,47,15,17,1011,46.04,0,0 +2013,5,27,1,56,15,17,1010,1.79,0,0 +2013,5,27,2,60,15,17,1010,0.89,0,0 +2013,5,27,3,66,15,17,1008,0.89,0,1 +2013,5,27,4,68,15,16,1009,3.13,0,0 +2013,5,27,5,67,15,17,1009,4.02,0,0 +2013,5,27,6,64,16,17,1008,0.45,0,0 +2013,5,27,7,55,16,17,1009,1.79,0,1 +2013,5,27,8,63,16,17,1010,3.58,0,2 +2013,5,27,9,60,16,18,1009,0.89,0,0 +2013,5,27,10,65,15,19,1009,1.79,0,0 +2013,5,27,11,66,16,21,1009,1.79,0,0 +2013,5,27,12,72,16,22,1008,1.79,0,0 +2013,5,27,13,71,16,22,1007,2.68,0,0 +2013,5,27,14,75,16,23,1006,1.79,0,0 +2013,5,27,15,80,15,23,1005,1.79,0,0 +2013,5,27,16,86,15,24,1005,3.58,0,0 +2013,5,27,17,86,15,23,1005,1.79,0,0 +2013,5,27,18,91,15,22,1005,0.89,0,0 +2013,5,27,19,100,16,22,1005,3.13,0,0 +2013,5,27,20,100,15,22,1006,6.26,0,0 +2013,5,27,21,111,16,21,1006,1.79,0,0 +2013,5,27,22,89,17,20,1006,5.81,0,0 +2013,5,27,23,94,16,20,1005,10.73,0,0 +2013,5,28,0,92,16,19,1005,13.86,0,0 +2013,5,28,1,94,16,19,1004,15.65,0,0 +2013,5,28,2,89,17,19,1004,18.78,0,0 +2013,5,28,3,92,17,19,1004,21.91,0,0 +2013,5,28,4,100,16,18,1004,23.7,0,0 +2013,5,28,5,95,16,18,1004,0.89,0,0 +2013,5,28,6,101,17,18,1004,1.78,0,1 +2013,5,28,7,99,17,19,1005,1.79,0,2 +2013,5,28,8,117,17,19,1005,1.79,0,0 +2013,5,28,9,122,17,19,1006,3.13,0,1 +2013,5,28,10,120,17,20,1006,7.15,0,2 +2013,5,28,11,65,17,20,1006,10.28,0,3 +2013,5,28,12,59,16,22,1005,12.07,0,0 +2013,5,28,13,NA,15,22,1005,13.86,0,0 +2013,5,28,14,59,16,22,1005,15.65,0,0 +2013,5,28,15,64,15,23,1005,4.02,0,0 +2013,5,28,16,61,15,23,1005,8.94,0,0 +2013,5,28,17,55,16,24,1004,13.86,0,0 +2013,5,28,18,44,15,24,1004,17.88,0,0 +2013,5,28,19,48,15,23,1004,21.01,0,0 +2013,5,28,20,64,15,21,1005,22.8,0,0 +2013,5,28,21,88,16,21,1006,25.93,0,0 +2013,5,28,22,102,16,21,1007,4.92,0,0 +2013,5,28,23,113,9,21,1007,10.73,0,0 +2013,5,29,0,67,-1,22,1008,16.54,0,0 +2013,5,29,1,40,-2,22,1008,23.69,0,0 +2013,5,29,2,18,1,22,1009,30.84,0,0 +2013,5,29,3,13,2,21,1009,36.65,0,0 +2013,5,29,4,9,2,20,1009,45.59,0,0 +2013,5,29,5,13,3,19,1010,53.64,0,0 +2013,5,29,6,13,4,20,1010,59.45,0,0 +2013,5,29,7,10,4,21,1011,65.26,0,0 +2013,5,29,8,13,3,23,1011,71.07,0,0 +2013,5,29,9,13,2,25,1011,76.88,0,0 +2013,5,29,10,12,1,26,1011,84.93,0,0 +2013,5,29,11,9,0,27,1010,89.85,0,0 +2013,5,29,12,15,-2,28,1009,93.87,0,0 +2013,5,29,13,25,-6,30,1008,99.68,0,0 +2013,5,29,14,17,-8,30,1008,105.49,0,0 +2013,5,29,15,17,-9,31,1007,111.3,0,0 +2013,5,29,16,22,-8,31,1007,120.24,0,0 +2013,5,29,17,17,-11,31,1006,128.29,0,0 +2013,5,29,18,18,-9,31,1006,135.44,0,0 +2013,5,29,19,23,-5,29,1006,4.92,0,0 +2013,5,29,20,25,1,27,1007,12.07,0,0 +2013,5,29,21,32,3,26,1008,16.99,0,0 +2013,5,29,22,34,5,25,1008,22.8,0,0 +2013,5,29,23,42,5,24,1008,27.72,0,0 +2013,5,30,0,40,9,20,1008,0.89,0,0 +2013,5,30,1,49,10,17,1008,1.78,0,0 +2013,5,30,2,49,9,15,1008,2.23,0,0 +2013,5,30,3,51,10,15,1008,3.12,0,0 +2013,5,30,4,80,9,13,1008,3.57,0,0 +2013,5,30,5,91,8,14,1009,4.02,0,0 +2013,5,30,6,79,10,16,1009,0.89,0,0 +2013,5,30,7,63,8,20,1009,0.89,0,0 +2013,5,30,8,60,8,23,1009,1.78,0,0 +2013,5,30,9,54,9,25,1009,2.67,0,0 +2013,5,30,10,54,8,27,1008,1.79,0,0 +2013,5,30,11,47,7,30,1007,1.79,0,0 +2013,5,30,12,66,6,32,1006,3.13,0,0 +2013,5,30,13,71,1,34,1005,8.94,0,0 +2013,5,30,14,65,2,35,1004,14.75,0,0 +2013,5,30,15,37,-5,36,1003,22.8,0,0 +2013,5,30,16,42,-5,35,1003,31.74,0,0 +2013,5,30,17,44,-2,35,1002,40.68,0,0 +2013,5,30,18,52,-1,33,1002,48.73,0,0 +2013,5,30,19,62,2,32,1002,55.88,0,0 +2013,5,30,20,65,7,30,1003,63.03,0,0 +2013,5,30,21,63,7,29,1003,71.08,0,0 +2013,5,30,22,70,7,28,1003,79.13,0,0 +2013,5,30,23,59,7,28,1003,84.94,0,0 +2013,5,31,0,52,6,27,1003,90.75,0,0 +2013,5,31,1,46,7,25,1003,95.67,0,0 +2013,5,31,2,44,9,23,1003,98.8,0,0 +2013,5,31,3,50,11,20,1003,100.59,0,0 +2013,5,31,4,62,11,21,1003,102.38,0,0 +2013,5,31,5,73,12,18,1003,0.89,0,0 +2013,5,31,6,83,13,20,1004,1.79,0,0 +2013,5,31,7,87,14,21,1004,4.92,0,0 +2013,5,31,8,89,14,22,1004,8.05,0,0 +2013,5,31,9,86,15,25,1004,11.18,0,0 +2013,5,31,10,117,15,25,1004,14.31,0,0 +2013,5,31,11,96,15,26,1003,18.33,0,0 +2013,5,31,12,105,15,28,1003,22.35,0,0 +2013,5,31,13,98,15,29,1002,26.37,0,0 +2013,5,31,14,97,13,29,1001,31.29,0,0 +2013,5,31,15,104,13,31,1000,35.31,0,0 +2013,5,31,16,101,12,30,1000,41.12,0,0 +2013,5,31,17,105,12,30,1000,46.04,0,0 +2013,5,31,18,113,14,29,1000,51.85,0,0 +2013,5,31,19,101,15,27,1000,55.87,0,0 +2013,5,31,20,108,16,26,1001,59.89,0,0 +2013,5,31,21,110,16,25,1003,0.89,0,0 +2013,5,31,22,110,16,25,1002,4.02,0,0 +2013,5,31,23,109,16,23,1003,0.89,0,0 +2013,6,1,0,105,16,23,1003,1.79,0,0 +2013,6,1,1,121,15,22,1003,3.58,0,0 +2013,6,1,2,141,16,20,1004,0.89,0,0 +2013,6,1,3,130,15,19,1004,0.89,0,0 +2013,6,1,4,146,15,18,1004,1.78,0,0 +2013,6,1,5,151,14,16,1005,0.89,0,0 +2013,6,1,6,143,15,19,1006,1.34,0,0 +2013,6,1,7,133,15,23,1007,3.13,0,0 +2013,6,1,8,124,14,25,1007,3.13,0,0 +2013,6,1,9,93,11,27,1007,4.92,0,0 +2013,6,1,10,53,-2,30,1007,6.71,0,0 +2013,6,1,11,26,-4,30,1007,1.79,0,0 +2013,6,1,12,26,-4,30,1006,1.79,0,0 +2013,6,1,13,36,-3,32,1006,4.92,0,0 +2013,6,1,14,27,-1,31,1006,1.79,0,0 +2013,6,1,15,23,-4,31,1005,3.13,0,0 +2013,6,1,16,23,-3,30,1005,4.92,0,0 +2013,6,1,17,22,-1,30,1006,0.89,0,0 +2013,6,1,18,24,10,27,1007,4.02,0,0 +2013,6,1,19,60,8,26,1007,8.04,0,0 +2013,6,1,20,55,8,25,1007,12.96,0,0 +2013,6,1,21,54,9,24,1007,16.09,0,0 +2013,6,1,22,41,10,22,1008,17.88,0,0 +2013,6,1,23,48,10,21,1008,0.45,0,0 +2013,6,2,0,56,8,20,1008,1.79,0,0 +2013,6,2,1,60,11,18,1008,3.58,0,0 +2013,6,2,2,64,9,19,1007,0.89,0,0 +2013,6,2,3,71,12,19,1007,1.79,0,0 +2013,6,2,4,96,13,18,1007,2.68,0,0 +2013,6,2,5,121,14,18,1007,0.89,0,0 +2013,6,2,6,168,15,19,1007,3.13,0,0 +2013,6,2,7,215,15,19,1006,7.15,0,0 +2013,6,2,8,220,16,20,1006,10.28,0,0 +2013,6,2,9,210,16,21,1007,12.07,0,0 +2013,6,2,10,217,16,22,1006,13.86,0,0 +2013,6,2,11,229,17,22,1005,16.99,0,0 +2013,6,2,12,231,18,24,1005,20.12,0,0 +2013,6,2,13,244,18,24,1004,23.25,0,0 +2013,6,2,14,216,19,25,1003,26.38,0,0 +2013,6,2,15,186,18,25,1002,29.51,0,0 +2013,6,2,16,137,18,26,1002,33.53,0,0 +2013,6,2,17,130,18,26,1002,36.66,0,0 +2013,6,2,18,138,17,25,1001,38.45,0,0 +2013,6,2,19,135,18,25,1002,40.24,0,0 +2013,6,2,20,127,17,24,1002,44.26,0,0 +2013,6,2,21,111,17,24,1003,47.39,0,0 +2013,6,2,22,122,17,23,1004,49.18,0,0 +2013,6,2,23,136,17,22,1004,0.89,0,0 +2013,6,3,0,153,17,22,1003,1.78,0,0 +2013,6,3,1,160,17,20,1003,0.89,0,0 +2013,6,3,2,168,17,19,1003,0.45,0,0 +2013,6,3,3,169,17,18,1003,1.34,0,0 +2013,6,3,4,170,15,17,1003,1.79,0,0 +2013,6,3,5,182,16,17,1003,3.58,0,0 +2013,6,3,6,189,17,19,1004,6.71,0,0 +2013,6,3,7,186,18,22,1005,0.89,0,0 +2013,6,3,8,170,17,24,1005,3.13,0,0 +2013,6,3,9,152,17,26,1005,6.26,0,0 +2013,6,3,10,141,13,29,1006,9.39,0,0 +2013,6,3,11,127,12,30,1005,1.79,0,0 +2013,6,3,12,89,7,32,1005,3.13,0,0 +2013,6,3,13,65,3,33,1004,3.13,0,0 +2013,6,3,14,53,4,34,1004,6.26,0,0 +2013,6,3,15,54,2,34,1004,1.79,0,0 +2013,6,3,16,123,2,34,1004,3.13,0,0 +2013,6,3,17,70,3,34,1004,6.26,0,0 +2013,6,3,18,115,5,32,1004,10.28,0,0 +2013,6,3,19,96,11,31,1005,13.41,0,0 +2013,6,3,20,94,15,26,1005,0.89,0,0 +2013,6,3,21,103,16,24,1006,1.78,0,0 +2013,6,3,22,150,16,23,1007,2.67,0,0 +2013,6,3,23,171,14,22,1007,0.89,0,0 +2013,6,4,0,166,13,22,1007,1.79,0,0 +2013,6,4,1,164,12,23,1008,3.58,0,0 +2013,6,4,2,152,12,23,1008,5.37,0,0 +2013,6,4,3,152,14,23,1009,11.18,0,0 +2013,6,4,4,174,16,22,1009,16.1,0,0 +2013,6,4,5,203,16,22,1010,21.02,0,0 +2013,6,4,6,179,17,23,1010,25.04,0,0 +2013,6,4,7,203,17,23,1012,29.06,0,0 +2013,6,4,8,167,17,23,1012,32.19,0,0 +2013,6,4,9,149,16,23,1013,33.98,0,0 +2013,6,4,10,147,16,22,1013,38,0,0 +2013,6,4,11,137,16,22,1013,41.13,0,0 +2013,6,4,12,125,16,22,1013,42.92,0,0 +2013,6,4,13,124,17,18,1013,4.92,0,1 +2013,6,4,14,143,17,18,1012,4.02,0,0 +2013,6,4,15,113,17,18,1012,1.79,0,0 +2013,6,4,16,96,16,19,1013,6.71,0,0 +2013,6,4,17,86,16,19,1011,1.79,0,0 +2013,6,4,18,71,17,20,1011,0.89,0,0 +2013,6,4,19,77,17,20,1012,3.13,0,0 +2013,6,4,20,86,17,19,1012,1.79,0,0 +2013,6,4,21,103,17,19,1013,3.58,0,0 +2013,6,4,22,101,17,20,1013,1.79,0,1 +2013,6,4,23,89,17,20,1013,4.92,0,2 +2013,6,5,0,76,18,19,1012,8.05,0,3 +2013,6,5,1,78,17,19,1011,0.89,0,4 +2013,6,5,2,74,18,19,1012,1.78,0,5 +2013,6,5,3,69,18,19,1012,1.79,0,6 +2013,6,5,4,77,18,18,1012,0.89,0,7 +2013,6,5,5,78,17,18,1011,3.13,0,8 +2013,6,5,6,93,18,19,1012,0.89,0,0 +2013,6,5,7,109,18,19,1012,1.78,0,1 +2013,6,5,8,114,18,18,1013,1.79,0,2 +2013,6,5,9,132,18,18,1013,3.58,0,3 +2013,6,5,10,140,18,18,1013,5.37,0,4 +2013,6,5,11,142,18,19,1013,7.16,0,0 +2013,6,5,12,161,17,19,1012,8.95,0,0 +2013,6,5,13,178,17,19,1012,12.08,0,0 +2013,6,5,14,184,17,19,1012,16.1,0,0 +2013,6,5,15,182,17,19,1012,17.89,0,0 +2013,6,5,16,180,17,19,1011,19.68,0,0 +2013,6,5,17,190,17,19,1011,21.47,0,0 +2013,6,5,18,207,17,19,1011,0.89,0,0 +2013,6,5,19,225,17,19,1011,1.79,0,0 +2013,6,5,20,196,17,19,1011,3.58,0,0 +2013,6,5,21,192,17,19,1012,0.89,0,0 +2013,6,5,22,178,17,19,1012,1.78,0,0 +2013,6,5,23,173,17,19,1012,1.79,0,0 +2013,6,6,0,175,17,19,1013,0.89,0,0 +2013,6,6,1,163,17,19,1012,0.89,0,0 +2013,6,6,2,164,17,19,1011,0.89,0,0 +2013,6,6,3,165,17,19,1010,1.79,0,0 +2013,6,6,4,176,17,19,1011,3.58,0,0 +2013,6,6,5,166,17,18,1011,6.71,0,0 +2013,6,6,6,164,17,18,1011,7.6,0,1 +2013,6,6,7,134,17,18,1012,8.49,0,2 +2013,6,6,8,104,18,18,1012,11.62,0,3 +2013,6,6,9,99,18,18,1011,13.41,0,4 +2013,6,6,10,106,18,18,1011,0.89,0,5 +2013,6,6,11,129,18,19,1011,1.79,0,6 +2013,6,6,12,141,18,19,1011,0.89,0,7 +2013,6,6,13,158,18,19,1010,0.89,0,8 +2013,6,6,14,165,19,19,1010,0.89,0,9 +2013,6,6,15,140,19,20,1009,1.79,0,10 +2013,6,6,16,148,19,20,1008,0.89,0,0 +2013,6,6,17,173,19,20,1008,1.78,0,0 +2013,6,6,18,207,19,21,1008,1.79,0,0 +2013,6,6,19,200,19,20,1009,4.92,0,0 +2013,6,6,20,166,19,20,1009,8.05,0,0 +2013,6,6,21,162,19,20,1009,9.84,0,0 +2013,6,6,22,158,19,21,1009,12.97,0,0 +2013,6,6,23,156,19,21,1009,14.76,0,0 +2013,6,7,0,146,19,21,1008,17.89,0,0 +2013,6,7,1,159,19,21,1008,1.79,0,0 +2013,6,7,2,157,19,20,1008,0.89,0,0 +2013,6,7,3,165,19,20,1007,0.89,0,0 +2013,6,7,4,145,19,20,1007,1.34,0,0 +2013,6,7,5,148,19,21,1007,1.79,0,0 +2013,6,7,6,100,19,21,1007,3.58,0,0 +2013,6,7,7,94,18,21,1008,6.71,0,0 +2013,6,7,8,103,18,21,1008,11.63,0,0 +2013,6,7,9,117,18,20,1009,0.89,0,1 +2013,6,7,10,122,18,19,1009,3.13,0,2 +2013,6,7,11,129,19,20,1008,4.92,0,3 +2013,6,7,12,136,19,20,1009,0.89,0,4 +2013,6,7,13,136,19,21,1009,1.78,0,5 +2013,6,7,14,138,19,21,1008,3.13,0,0 +2013,6,7,15,150,19,21,1008,4.92,0,0 +2013,6,7,16,161,19,21,1008,0.89,0,0 +2013,6,7,17,162,19,21,1007,1.79,0,0 +2013,6,7,18,141,19,21,1008,2.68,0,0 +2013,6,7,19,134,19,21,1008,1.79,0,0 +2013,6,7,20,137,19,21,1009,3.58,0,0 +2013,6,7,21,140,19,21,1009,5.37,0,0 +2013,6,7,22,142,19,20,1009,0.89,0,0 +2013,6,7,23,137,19,20,1009,1.78,0,0 +2013,6,8,0,139,19,20,1009,0.89,0,0 +2013,6,8,1,139,19,20,1009,2.68,0,0 +2013,6,8,2,138,19,20,1008,4.47,0,1 +2013,6,8,3,139,19,20,1008,0.89,0,2 +2013,6,8,4,135,19,20,1008,1.78,0,0 +2013,6,8,5,134,19,20,1007,0.89,0,0 +2013,6,8,6,139,18,19,1007,0.89,0,0 +2013,6,8,7,140,18,19,1008,1.78,0,0 +2013,6,8,8,147,18,20,1008,2.23,0,1 +2013,6,8,9,161,18,20,1008,1.79,0,0 +2013,6,8,10,191,18,20,1008,0.89,0,0 +2013,6,8,11,212,19,21,1008,1.79,0,0 +2013,6,8,12,220,19,21,1008,3.58,0,0 +2013,6,8,13,221,19,21,1008,5.37,0,0 +2013,6,8,14,206,19,21,1007,7.16,0,0 +2013,6,8,15,189,19,21,1007,0.89,0,0 +2013,6,8,16,187,19,21,1006,1.78,0,0 +2013,6,8,17,187,19,22,1006,2.67,0,0 +2013,6,8,18,191,19,22,1006,1.79,0,0 +2013,6,8,19,191,19,21,1006,4.92,0,0 +2013,6,8,20,196,19,21,1007,6.71,0,1 +2013,6,8,21,194,19,21,1007,9.84,0,0 +2013,6,8,22,184,19,20,1007,0.89,0,0 +2013,6,8,23,182,19,20,1007,0.89,0,0 +2013,6,9,0,172,19,20,1007,0.89,0,0 +2013,6,9,1,169,19,20,1007,3.13,0,0 +2013,6,9,2,169,19,20,1007,0.45,0,0 +2013,6,9,3,146,19,20,1007,1.79,0,0 +2013,6,9,4,142,19,20,1006,0.89,0,0 +2013,6,9,5,130,19,20,1007,1.78,0,0 +2013,6,9,6,153,19,20,1007,2.23,0,0 +2013,6,9,7,110,19,20,1008,1.79,0,0 +2013,6,9,8,21,18,19,1009,3.58,0,1 +2013,6,9,9,14,16,18,1010,9.39,0,2 +2013,6,9,10,13,16,18,1010,17.44,0,3 +2013,6,9,11,36,16,19,1010,23.25,0,4 +2013,6,9,12,14,16,18,1011,29.06,0,5 +2013,6,9,13,15,16,17,1012,34.87,0,6 +2013,6,9,14,16,16,17,1012,39.79,0,7 +2013,6,9,15,18,16,18,1012,43.81,0,8 +2013,6,9,16,16,16,18,1012,49.62,0,9 +2013,6,9,17,12,16,18,1011,53.64,0,10 +2013,6,9,18,17,15,18,1012,58.56,0,11 +2013,6,9,19,16,15,18,1012,61.69,0,12 +2013,6,9,20,22,15,18,1012,65.71,0,13 +2013,6,9,21,24,15,18,1013,69.73,0,0 +2013,6,9,22,27,16,18,1013,0.89,0,1 +2013,6,9,23,29,16,17,1013,1.78,0,2 +2013,6,10,0,33,16,17,1013,2.67,0,3 +2013,6,10,1,33,15,16,1013,3.56,0,0 +2013,6,10,2,32,15,16,1013,4.45,0,1 +2013,6,10,3,28,15,16,1013,0.89,0,0 +2013,6,10,4,31,15,16,1013,0.89,0,0 +2013,6,10,5,28,15,16,1013,0.89,0,0 +2013,6,10,6,38,15,16,1014,0.89,0,0 +2013,6,10,7,44,16,16,1014,1.78,0,0 +2013,6,10,8,45,16,17,1014,2.67,0,0 +2013,6,10,9,47,16,19,1014,1.79,0,0 +2013,6,10,10,48,16,20,1014,1.79,0,0 +2013,6,10,11,27,17,23,1014,1.79,0,0 +2013,6,10,12,29,14,25,1013,2.68,0,0 +2013,6,10,13,25,13,25,1012,4.47,0,0 +2013,6,10,14,22,14,26,1012,5.36,0,0 +2013,6,10,15,18,13,26,1011,6.25,0,0 +2013,6,10,16,16,12,25,1010,4.02,0,0 +2013,6,10,17,18,12,25,1011,5.81,0,0 +2013,6,10,18,21,12,24,1011,8.94,0,0 +2013,6,10,19,35,13,23,1010,12.07,0,0 +2013,6,10,20,33,15,22,1011,15.2,0,0 +2013,6,10,21,34,15,20,1011,16.99,0,0 +2013,6,10,22,41,16,20,1011,18.78,0,0 +2013,6,10,23,40,16,20,1011,20.57,0,0 +2013,6,11,0,40,16,19,1010,22.36,0,0 +2013,6,11,1,37,15,19,1010,0.89,0,0 +2013,6,11,2,42,16,18,1010,1.78,0,0 +2013,6,11,3,48,16,18,1009,1.79,0,0 +2013,6,11,4,51,16,18,1009,1.79,0,0 +2013,6,11,5,62,16,17,1009,0.89,0,0 +2013,6,11,6,64,16,18,1009,1.34,0,0 +2013,6,11,7,87,16,19,1009,1.79,0,0 +2013,6,11,8,86,16,20,1009,3.13,0,0 +2013,6,11,9,75,16,22,1009,3.13,0,0 +2013,6,11,10,70,16,22,1009,3.13,0,0 +2013,6,11,11,69,16,24,1008,6.26,0,0 +2013,6,11,12,81,16,25,1008,9.39,0,0 +2013,6,11,13,96,16,26,1007,13.41,0,0 +2013,6,11,14,107,16,26,1007,18.33,0,0 +2013,6,11,15,86,15,25,1006,22.35,0,0 +2013,6,11,16,98,15,24,1006,25.48,0,1 +2013,6,11,17,67,13,22,1005,3.13,0,0 +2013,6,11,18,20,15,24,1005,3.13,0,0 +2013,6,11,19,27,13,23,1006,4.02,0,0 +2013,6,11,20,41,12,21,1007,8.04,0,0 +2013,6,11,21,72,12,21,1008,12.96,0,0 +2013,6,11,22,55,13,18,1009,16.09,0,1 +2013,6,11,23,24,13,17,1009,1.79,0,0 +2013,6,12,0,22,13,18,1009,3.13,0,0 +2013,6,12,1,24,13,17,1009,0.89,0,0 +2013,6,12,2,24,12,15,1008,1.79,0,0 +2013,6,12,3,30,13,15,1008,0.45,0,0 +2013,6,12,4,33,13,14,1008,1.34,0,0 +2013,6,12,5,47,12,14,1008,2.23,0,0 +2013,6,12,6,61,14,15,1008,0.89,0,0 +2013,6,12,7,76,14,17,1009,0.89,0,0 +2013,6,12,8,59,15,19,1009,1.79,0,0 +2013,6,12,9,55,14,21,1009,0.89,0,0 +2013,6,12,10,50,14,23,1008,4.02,0,0 +2013,6,12,11,55,14,24,1008,3.13,0,0 +2013,6,12,12,65,14,25,1008,7.15,0,0 +2013,6,12,13,60,14,26,1007,10.28,0,0 +2013,6,12,14,65,15,27,1007,13.41,0,0 +2013,6,12,15,72,15,27,1006,17.43,0,0 +2013,6,12,16,83,15,26,1006,20.56,0,0 +2013,6,12,17,87,14,26,1006,24.58,0,0 +2013,6,12,18,83,14,26,1006,27.71,0,0 +2013,6,12,19,90,14,25,1006,30.84,0,0 +2013,6,12,20,101,15,23,1007,32.63,0,0 +2013,6,12,21,113,15,22,1007,35.76,0,0 +2013,6,12,22,101,15,22,1008,38.89,0,0 +2013,6,12,23,104,15,21,1008,39.78,0,0 +2013,6,13,0,93,15,20,1008,41.57,0,0 +2013,6,13,1,98,15,18,1008,0.89,0,0 +2013,6,13,2,110,15,17,1008,1.79,0,0 +2013,6,13,3,108,15,17,1008,0.89,0,0 +2013,6,13,4,111,15,17,1008,0.89,0,0 +2013,6,13,5,106,15,17,1008,1.78,0,0 +2013,6,13,6,109,16,18,1009,1.79,0,0 +2013,6,13,7,111,16,21,1009,1.79,0,0 +2013,6,13,8,112,15,22,1009,4.92,0,0 +2013,6,13,9,112,16,24,1009,1.79,0,0 +2013,6,13,10,110,16,25,1009,3.13,0,0 +2013,6,13,11,121,16,26,1009,4.92,0,0 +2013,6,13,12,125,16,27,1009,8.05,0,0 +2013,6,13,13,130,17,29,1008,11.18,0,0 +2013,6,13,14,134,17,29,1008,15.2,0,0 +2013,6,13,15,124,16,30,1007,20.12,0,0 +2013,6,13,16,109,15,30,1007,25.93,0,0 +2013,6,13,17,78,12,30,1006,29.95,0,0 +2013,6,13,18,90,14,30,1006,33.08,0,0 +2013,6,13,19,103,15,28,1006,34.87,0,0 +2013,6,13,20,103,18,25,1007,0.89,0,0 +2013,6,13,21,122,16,25,1007,3.13,0,0 +2013,6,13,22,97,13,25,1008,7.15,0,0 +2013,6,13,23,75,11,25,1008,11.17,0,0 +2013,6,14,0,80,10,25,1008,14.3,0,0 +2013,6,14,1,81,13,21,1008,16.09,0,0 +2013,6,14,2,70,11,22,1008,17.88,0,0 +2013,6,14,3,77,11,22,1008,21.01,0,0 +2013,6,14,4,80,11,21,1008,24.14,0,0 +2013,6,14,5,82,11,21,1009,27.27,0,0 +2013,6,14,6,83,12,21,1009,30.4,0,0 +2013,6,14,7,91,13,22,1009,33.53,0,0 +2013,6,14,8,94,13,24,1010,37.55,0,0 +2013,6,14,9,105,13,26,1009,40.68,0,0 +2013,6,14,10,115,14,27,1009,43.81,0,0 +2013,6,14,11,130,13,28,1009,46.94,0,0 +2013,6,14,12,143,14,30,1009,50.07,0,0 +2013,6,14,13,132,14,30,1008,54.09,0,0 +2013,6,14,14,119,14,31,1007,59.01,0,0 +2013,6,14,15,111,13,31,1007,63.93,0,0 +2013,6,14,16,127,14,31,1006,68.85,0,0 +2013,6,14,17,126,15,30,1006,73.77,0,0 +2013,6,14,18,132,15,29,1005,76.9,0,0 +2013,6,14,19,127,14,29,1006,80.03,0,0 +2013,6,14,20,131,14,27,1007,84.95,0,0 +2013,6,14,21,117,13,26,1007,88.97,0,0 +2013,6,14,22,115,13,24,1008,92.99,0,0 +2013,6,14,23,105,13,22,1008,94.78,0,0 +2013,6,15,0,103,13,22,1007,97.91,0,0 +2013,6,15,1,107,13,21,1007,1.79,0,0 +2013,6,15,2,101,13,19,1008,3.58,0,0 +2013,6,15,3,100,12,21,1007,3.13,0,0 +2013,6,15,4,69,12,20,1007,0.89,0,0 +2013,6,15,5,70,14,20,1008,1.79,0,0 +2013,6,15,6,69,14,20,1008,0.89,0,0 +2013,6,15,7,78,14,21,1008,3.13,0,0 +2013,6,15,8,93,14,22,1009,6.26,0,0 +2013,6,15,9,95,15,24,1009,8.05,0,0 +2013,6,15,10,108,15,25,1008,9.84,0,0 +2013,6,15,11,123,15,26,1008,11.63,0,0 +2013,6,15,12,129,15,28,1007,0.89,0,0 +2013,6,15,13,132,15,29,1006,1.79,0,0 +2013,6,15,14,141,16,30,1006,1.79,0,0 +2013,6,15,15,151,15,31,1006,1.79,0,0 +2013,6,15,16,144,16,31,1005,3.58,0,0 +2013,6,15,17,140,16,31,1005,6.71,0,0 +2013,6,15,18,148,18,30,1005,8.5,0,0 +2013,6,15,19,168,19,29,1005,10.29,0,0 +2013,6,15,20,197,19,28,1005,12.08,0,0 +2013,6,15,21,203,18,27,1006,15.21,0,0 +2013,6,15,22,157,16,26,1006,17,0,0 +2013,6,15,23,146,15,25,1006,18.79,0,0 +2013,6,16,0,148,16,24,1006,0.89,0,0 +2013,6,16,1,138,16,23,1005,1.78,0,0 +2013,6,16,2,147,16,23,1005,0.89,0,0 +2013,6,16,3,133,15,24,1004,1.79,0,0 +2013,6,16,4,131,16,23,1004,3.58,0,0 +2013,6,16,5,138,17,23,1004,5.37,0,0 +2013,6,16,6,140,17,23,1004,9.39,0,0 +2013,6,16,7,154,18,23,1004,11.18,0,0 +2013,6,16,8,152,18,23,1004,15.2,0,0 +2013,6,16,9,160,19,24,1004,16.99,0,0 +2013,6,16,10,165,19,24,1004,18.78,0,0 +2013,6,16,11,181,20,23,1003,20.57,0,1 +2013,6,16,12,196,20,23,1003,22.36,0,2 +2013,6,16,13,200,20,22,1003,3.13,0,3 +2013,6,16,14,201,20,21,1002,1.79,0,4 +2013,6,16,15,200,20,22,1002,3.58,0,0 +2013,6,16,16,198,20,22,1002,3.13,0,0 +2013,6,16,17,199,20,22,1001,6.26,0,0 +2013,6,16,18,205,20,22,1002,8.05,0,0 +2013,6,16,19,199,20,21,1002,11.18,0,1 +2013,6,16,20,209,20,22,1003,14.31,0,0 +2013,6,16,21,199,19,22,1004,17.44,0,0 +2013,6,16,22,190,20,22,1003,3.13,0,0 +2013,6,16,23,189,19,22,1003,8.05,0,1 +2013,6,17,0,163,19,22,1003,12.97,0,0 +2013,6,17,1,101,18,21,1003,16.99,0,0 +2013,6,17,2,50,17,21,1003,21.01,0,0 +2013,6,17,3,41,17,21,1003,25.03,0,0 +2013,6,17,4,43,17,21,1002,29.05,0,0 +2013,6,17,5,25,17,21,1003,1.79,0,0 +2013,6,17,6,17,17,21,1003,4.92,0,0 +2013,6,17,7,14,17,22,1003,6.71,0,0 +2013,6,17,8,16,17,24,1003,4.02,0,0 +2013,6,17,9,20,16,26,1003,8.04,0,0 +2013,6,17,10,8,16,27,1002,11.17,0,0 +2013,6,17,11,14,14,27,1002,12.96,0,0 +2013,6,17,12,18,15,27,1002,0.89,0,0 +2013,6,17,13,16,16,29,1001,1.79,0,0 +2013,6,17,14,17,14,30,1001,3.58,0,0 +2013,6,17,15,29,15,31,1000,3.13,0,0 +2013,6,17,16,39,15,31,1000,3.13,0,0 +2013,6,17,17,40,16,30,999,6.26,0,0 +2013,6,17,18,41,16,29,999,9.39,0,0 +2013,6,17,19,46,15,28,999,12.52,0,0 +2013,6,17,20,55,17,27,1000,16.54,0,0 +2013,6,17,21,57,17,27,1000,19.67,0,0 +2013,6,17,22,52,16,27,1000,22.8,0,0 +2013,6,17,23,52,15,27,1000,25.93,0,0 +2013,6,18,0,49,17,24,999,0.89,0,0 +2013,6,18,1,54,18,24,999,1.78,0,0 +2013,6,18,2,62,18,23,999,0.89,0,0 +2013,6,18,3,55,18,22,999,0.89,0,0 +2013,6,18,4,68,19,22,998,1.79,0,0 +2013,6,18,5,66,19,22,998,0.89,0,0 +2013,6,18,6,67,20,23,998,0.89,0,0 +2013,6,18,7,80,20,26,998,0.89,0,0 +2013,6,18,8,74,20,27,998,1.78,0,0 +2013,6,18,9,64,17,29,998,2.67,0,0 +2013,6,18,10,23,16,30,998,3.13,0,0 +2013,6,18,11,31,12,32,997,7.15,0,0 +2013,6,18,12,28,12,33,997,12.07,0,0 +2013,6,18,13,48,13,33,996,16.99,0,0 +2013,6,18,14,37,14,33,995,22.8,0,0 +2013,6,18,15,39,13,33,995,28.61,0,0 +2013,6,18,16,33,12,33,994,33.53,0,0 +2013,6,18,17,35,12,33,995,38.45,0,0 +2013,6,18,18,40,12,32,995,42.47,0,0 +2013,6,18,19,45,13,30,996,46.49,0,0 +2013,6,18,20,51,16,28,997,48.28,0,0 +2013,6,18,21,62,17,27,998,1.79,0,0 +2013,6,18,22,66,17,27,1000,1.79,0,0 +2013,6,18,23,75,18,25,1000,4.92,0,0 +2013,6,19,0,86,18,24,1001,6.71,0,0 +2013,6,19,1,76,18,23,1001,0.89,0,0 +2013,6,19,2,70,18,22,1002,1.78,0,0 +2013,6,19,3,69,18,21,1002,0.89,0,0 +2013,6,19,4,78,18,21,1002,0.89,0,0 +2013,6,19,5,86,18,21,1002,0.89,0,0 +2013,6,19,6,85,19,22,1003,1.34,0,0 +2013,6,19,7,89,19,25,1003,1.79,0,0 +2013,6,19,8,94,19,27,1003,4.92,0,0 +2013,6,19,9,67,19,30,1003,6.71,0,0 +2013,6,19,10,47,17,32,1003,9.84,0,0 +2013,6,19,11,34,13,32,1003,0.89,0,0 +2013,6,19,12,21,11,34,1003,4.02,0,0 +2013,6,19,13,22,11,35,1002,5.81,0,0 +2013,6,19,14,18,9,35,1002,1.79,0,0 +2013,6,19,15,20,8,36,1001,3.13,0,0 +2013,6,19,16,16,6,36,1001,1.79,0,0 +2013,6,19,17,16,9,35,1001,1.79,0,0 +2013,6,19,18,21,10,33,1001,3.58,0,0 +2013,6,19,19,36,13,31,1002,4.47,0,0 +2013,6,19,20,44,10,33,1002,7.6,0,0 +2013,6,19,21,48,12,32,1002,3.13,0,0 +2013,6,19,22,56,13,31,1003,1.79,0,0 +2013,6,19,23,59,17,28,1003,0.89,0,0 +2013,6,20,0,64,16,28,1003,1.79,0,0 +2013,6,20,1,58,17,26,1003,1.79,0,0 +2013,6,20,2,95,17,26,1004,1.79,0,0 +2013,6,20,3,108,18,25,1004,0.89,0,0 +2013,6,20,4,105,19,25,1005,0.89,0,0 +2013,6,20,5,128,19,24,1005,1.79,0,0 +2013,6,20,6,124,19,24,1005,3.58,0,0 +2013,6,20,7,95,19,24,1006,5.37,0,0 +2013,6,20,8,41,19,25,1007,6.26,0,0 +2013,6,20,9,37,18,27,1007,1.79,0,0 +2013,6,20,10,39,17,29,1007,4.92,0,0 +2013,6,20,11,NA,15,30,1007,8.94,0,0 +2013,6,20,12,NA,14,31,1007,12.07,0,0 +2013,6,20,13,38,14,31,1007,12.96,0,0 +2013,6,20,14,30,15,31,1007,1.79,0,0 +2013,6,20,15,34,15,31,1007,3.58,0,0 +2013,6,20,16,51,14,30,1007,6.71,0,0 +2013,6,20,17,68,16,30,1007,9.84,0,0 +2013,6,20,18,118,19,28,1008,11.63,0,0 +2013,6,20,19,77,17,27,1008,14.76,0,0 +2013,6,20,20,212,15,26,1008,0.89,0,0 +2013,6,20,21,188,16,26,1009,1.79,0,0 +2013,6,20,22,146,17,27,1009,4.92,0,0 +2013,6,20,23,129,18,26,1009,0.89,0,0 +2013,6,21,0,102,18,24,1009,0.89,0,0 +2013,6,21,1,123,18,24,1009,1.79,0,0 +2013,6,21,2,132,18,23,1009,0.89,0,0 +2013,6,21,3,155,18,22,1009,1.79,0,0 +2013,6,21,4,165,18,21,1009,0.89,0,0 +2013,6,21,5,154,18,21,1009,0.89,0,0 +2013,6,21,6,69,19,22,1010,1.34,0,0 +2013,6,21,7,58,19,24,1010,1.79,0,0 +2013,6,21,8,88,18,26,1010,3.58,0,0 +2013,6,21,9,122,18,28,1010,0.89,0,0 +2013,6,21,10,105,17,30,1010,3.13,0,0 +2013,6,21,11,84,16,31,1010,3.13,0,0 +2013,6,21,12,52,15,32,1009,6.26,0,0 +2013,6,21,13,32,16,32,1009,9.39,0,0 +2013,6,21,14,30,14,32,1008,15.2,0,0 +2013,6,21,15,46,14,31,1008,22.35,0,0 +2013,6,21,16,67,17,29,1008,28.16,0,0 +2013,6,21,17,67,17,28,1008,33.08,0,0 +2013,6,21,18,67,18,28,1008,37.1,0,0 +2013,6,21,19,70,17,27,1008,42.02,0,0 +2013,6,21,20,48,16,26,1008,46.94,0,0 +2013,6,21,21,29,15,26,1008,50.96,0,0 +2013,6,21,22,28,15,25,1009,52.75,0,0 +2013,6,21,23,29,14,24,1009,54.54,0,0 +2013,6,22,0,42,16,24,1009,56.33,0,0 +2013,6,22,1,37,16,23,1009,58.12,0,0 +2013,6,22,2,42,16,23,1008,59.91,0,0 +2013,6,22,3,45,16,23,1008,1.79,0,0 +2013,6,22,4,47,17,22,1008,1.79,0,0 +2013,6,22,5,61,17,22,1008,3.58,0,1 +2013,6,22,6,79,18,21,1009,0.89,0,2 +2013,6,22,7,98,19,21,1009,1.78,0,3 +2013,6,22,8,108,20,21,1010,1.79,0,4 +2013,6,22,9,85,20,21,1010,1.79,0,5 +2013,6,22,10,70,20,21,1010,4.92,0,6 +2013,6,22,11,63,19,21,1009,8.05,0,7 +2013,6,22,12,54,20,21,1009,3.13,0,8 +2013,6,22,13,40,18,22,1008,7.15,0,0 +2013,6,22,14,45,19,22,1008,10.28,0,1 +2013,6,22,15,56,19,22,1007,13.41,0,0 +2013,6,22,16,69,19,22,1007,15.2,0,0 +2013,6,22,17,69,19,23,1007,0.89,0,0 +2013,6,22,18,59,19,22,1007,2.68,0,0 +2013,6,22,19,57,19,22,1007,3.57,0,0 +2013,6,22,20,54,19,21,1007,6.7,0,0 +2013,6,22,21,45,18,21,1007,8.49,0,0 +2013,6,22,22,44,18,21,1008,10.28,0,0 +2013,6,22,23,51,18,20,1007,0.89,0,0 +2013,6,23,0,57,18,20,1007,0.89,0,0 +2013,6,23,1,85,19,20,1006,1.78,0,0 +2013,6,23,2,91,18,20,1006,2.67,0,0 +2013,6,23,3,90,18,20,1006,3.56,0,0 +2013,6,23,4,94,19,20,1006,4.45,0,0 +2013,6,23,5,103,19,20,1005,0.89,0,0 +2013,6,23,6,103,19,20,1005,0.45,0,0 +2013,6,23,7,113,19,22,1005,1.79,0,0 +2013,6,23,8,87,19,24,1005,3.58,0,0 +2013,6,23,9,61,18,25,1005,1.79,0,0 +2013,6,23,10,55,18,27,1004,0.89,0,0 +2013,6,23,11,40,16,28,1004,1.78,0,0 +2013,6,23,12,33,15,31,1003,3.13,0,0 +2013,6,23,13,39,13,30,1003,1.79,0,0 +2013,6,23,14,33,14,31,1002,4.92,0,0 +2013,6,23,15,32,13,31,1001,3.13,0,0 +2013,6,23,16,40,13,31,1001,6.26,0,0 +2013,6,23,17,59,14,31,1000,9.39,0,0 +2013,6,23,18,65,16,30,1001,12.52,0,0 +2013,6,23,19,67,17,29,1001,16.54,0,0 +2013,6,23,20,60,18,27,1001,20.56,0,0 +2013,6,23,21,60,17,27,1002,24.58,0,0 +2013,6,23,22,68,17,27,1003,28.6,0,0 +2013,6,23,23,86,18,24,1003,30.39,0,0 +2013,6,24,0,88,18,24,1003,33.52,0,0 +2013,6,24,1,75,18,24,1003,36.65,0,0 +2013,6,24,2,46,18,23,1003,38.44,0,0 +2013,6,24,3,50,19,22,1002,41.57,0,0 +2013,6,24,4,57,19,21,1003,0.89,0,0 +2013,6,24,5,67,20,21,1003,1.79,0,0 +2013,6,24,6,66,20,21,1003,4.92,0,0 +2013,6,24,7,176,20,22,1003,6.71,0,0 +2013,6,24,8,248,20,24,1004,9.84,0,0 +2013,6,24,9,194,20,26,1004,0.89,0,0 +2013,6,24,10,153,20,27,1002,4.02,0,0 +2013,6,24,11,143,20,27,1003,7.15,0,0 +2013,6,24,12,147,20,28,1003,11.17,0,0 +2013,6,24,13,148,21,28,1002,14.3,0,0 +2013,6,24,14,125,20,29,1002,18.32,0,0 +2013,6,24,15,126,20,29,1001,22.34,0,0 +2013,6,24,16,110,20,29,1000,25.47,0,0 +2013,6,24,17,120,20,29,1000,30.39,0,0 +2013,6,24,18,122,20,29,1000,34.41,0,0 +2013,6,24,19,122,21,28,1000,37.54,0,1 +2013,6,24,20,120,21,22,1001,5.81,0,2 +2013,6,24,21,45,20,22,1000,3.13,0,3 +2013,6,24,22,45,20,22,1002,0.89,0,0 +2013,6,24,23,58,19,21,1002,1.79,0,0 +2013,6,25,0,48,19,21,1002,0.89,0,0 +2013,6,25,1,50,20,21,1002,1.78,0,0 +2013,6,25,2,52,20,21,1002,2.67,0,0 +2013,6,25,3,58,20,21,1003,1.79,0,0 +2013,6,25,4,64,20,21,1002,1.79,0,0 +2013,6,25,5,73,20,21,1001,1.79,0,0 +2013,6,25,6,82,20,22,1001,0.89,0,0 +2013,6,25,7,103,20,22,1001,0.89,0,0 +2013,6,25,8,120,20,22,1001,0.89,0,0 +2013,6,25,9,127,20,22,1001,1.78,0,1 +2013,6,25,10,150,20,23,1001,1.79,0,0 +2013,6,25,11,168,21,23,1001,3.58,0,0 +2013,6,25,12,161,20,23,1001,1.79,0,0 +2013,6,25,13,103,20,23,1000,0.89,0,0 +2013,6,25,14,113,20,24,1000,1.78,0,0 +2013,6,25,15,128,20,24,999,3.13,0,0 +2013,6,25,16,139,21,24,998,4.92,0,0 +2013,6,25,17,162,21,23,998,6.71,0,0 +2013,6,25,18,183,21,23,999,8.5,0,1 +2013,6,25,19,189,21,22,999,5.81,0,2 +2013,6,25,20,166,21,22,998,4.02,0,3 +2013,6,25,21,172,21,22,998,5.81,0,0 +2013,6,25,22,159,21,23,998,7.6,0,0 +2013,6,25,23,164,21,22,1000,9.39,0,0 +2013,6,26,0,171,21,22,999,11.18,0,0 +2013,6,26,1,171,20,22,998,0.89,0,0 +2013,6,26,2,166,21,22,998,1.78,0,0 +2013,6,26,3,158,19,21,998,1.79,0,0 +2013,6,26,4,161,19,21,998,0.89,0,0 +2013,6,26,5,178,20,21,998,1.78,0,0 +2013,6,26,6,186,20,21,998,2.67,0,0 +2013,6,26,7,188,21,22,998,3.56,0,0 +2013,6,26,8,186,21,22,998,4.45,0,0 +2013,6,26,9,189,21,23,998,1.79,0,0 +2013,6,26,10,187,21,25,998,3.58,0,0 +2013,6,26,11,177,21,26,998,6.71,0,0 +2013,6,26,12,172,21,28,997,8.5,0,0 +2013,6,26,13,167,22,29,997,11.63,0,0 +2013,6,26,14,147,21,29,996,14.76,0,0 +2013,6,26,15,111,21,31,995,18.78,0,0 +2013,6,26,16,87,21,31,994,24.59,0,0 +2013,6,26,17,87,22,31,994,29.51,0,0 +2013,6,26,18,86,22,31,994,33.53,0,0 +2013,6,26,19,94,22,30,995,35.32,0,0 +2013,6,26,20,101,22,29,996,38.45,0,0 +2013,6,26,21,119,22,28,997,43.37,0,0 +2013,6,26,22,137,22,27,997,47.39,0,0 +2013,6,26,23,254,20,24,997,3.13,0,0 +2013,6,27,0,277,21,24,998,0.89,0,0 +2013,6,27,1,184,19,23,997,1.79,0,0 +2013,6,27,2,111,19,23,997,4.02,0,0 +2013,6,27,3,93,19,21,997,0.89,0,0 +2013,6,27,4,62,19,22,997,0.89,0,0 +2013,6,27,5,58,19,23,998,5.81,0,0 +2013,6,27,6,64,19,22,998,0.89,0,0 +2013,6,27,7,89,20,24,998,3.13,0,0 +2013,6,27,8,165,20,26,999,6.26,0,0 +2013,6,27,9,183,19,28,998,8.05,0,0 +2013,6,27,10,145,18,28,998,11.18,0,0 +2013,6,27,11,54,17,31,998,14.31,0,0 +2013,6,27,12,40,13,32,998,3.13,0,0 +2013,6,27,13,29,14,33,998,3.13,0,0 +2013,6,27,14,26,13,33,997,1.79,0,0 +2013,6,27,15,29,15,34,997,4.92,0,0 +2013,6,27,16,42,15,33,997,1.79,0,0 +2013,6,27,17,50,16,32,996,3.13,0,0 +2013,6,27,18,56,18,32,996,3.13,0,0 +2013,6,27,19,88,20,30,998,4.92,0,0 +2013,6,27,20,96,20,29,998,0.89,0,0 +2013,6,27,21,103,22,28,998,1.79,0,0 +2013,6,27,22,124,21,28,999,3.58,0,0 +2013,6,27,23,150,22,27,999,0.89,0,0 +2013,6,28,0,184,23,27,1000,3.13,0,0 +2013,6,28,1,248,22,26,1000,6.26,0,0 +2013,6,28,2,339,22,26,1000,10.28,0,0 +2013,6,28,3,342,22,25,1000,0.89,0,0 +2013,6,28,4,292,22,24,1000,0.89,0,0 +2013,6,28,5,277,22,24,1001,1.79,0,0 +2013,6,28,6,265,22,25,1001,4.92,0,0 +2013,6,28,7,257,22,25,1002,8.94,0,0 +2013,6,28,8,240,21,25,1002,13.86,0,0 +2013,6,28,9,243,21,25,1002,17.88,0,0 +2013,6,28,10,229,22,25,1002,21.01,0,0 +2013,6,28,11,247,22,26,1002,24.14,0,0 +2013,6,28,12,230,21,26,1002,27.27,0,0 +2013,6,28,13,242,22,27,1001,30.4,0,0 +2013,6,28,14,297,22,27,1001,34.42,0,0 +2013,6,28,15,369,22,27,1000,37.55,0,0 +2013,6,28,16,428,22,27,999,41.57,0,0 +2013,6,28,17,432,22,27,999,44.7,0,0 +2013,6,28,18,441,22,27,1000,46.49,0,0 +2013,6,28,19,466,23,27,1000,0.89,0,1 +2013,6,28,20,448,23,26,999,3.13,0,0 +2013,6,28,21,406,23,25,1000,0.89,0,0 +2013,6,28,22,267,19,20,1002,1.79,0,1 +2013,6,28,23,37,19,21,1001,1.79,0,2 +2013,6,29,0,25,20,22,1002,3.13,0,0 +2013,6,29,1,34,20,21,1002,0.89,0,0 +2013,6,29,2,52,20,21,1002,1.79,0,0 +2013,6,29,3,68,20,21,1002,1.79,0,0 +2013,6,29,4,68,20,21,1002,0.45,0,0 +2013,6,29,5,72,20,21,1003,0.9,0,0 +2013,6,29,6,70,20,22,1004,1.79,0,0 +2013,6,29,7,63,20,22,1004,0.89,0,1 +2013,6,29,8,65,20,22,1004,1.79,0,0 +2013,6,29,9,63,20,22,1005,3.58,0,0 +2013,6,29,10,85,20,22,1005,1.79,0,0 +2013,6,29,11,103,20,23,1005,3.58,0,0 +2013,6,29,12,105,21,24,1004,3.13,0,0 +2013,6,29,13,113,21,24,1004,6.26,0,0 +2013,6,29,14,126,21,24,1004,0.89,0,0 +2013,6,29,15,122,22,25,1004,1.79,0,0 +2013,6,29,16,133,22,26,1004,3.13,0,0 +2013,6,29,17,122,22,27,1004,4.92,0,0 +2013,6,29,18,120,23,26,1004,1.79,0,0 +2013,6,29,19,125,22,25,1004,3.58,0,0 +2013,6,29,20,137,22,24,1005,0.89,0,0 +2013,6,29,21,146,22,24,1005,1.78,0,0 +2013,6,29,22,164,22,23,1005,1.79,0,0 +2013,6,29,23,164,22,23,1005,0.89,0,0 +2013,6,30,0,186,22,23,1005,1.79,0,0 +2013,6,30,1,183,21,23,1005,0.89,0,0 +2013,6,30,2,185,21,22,1004,0.89,0,0 +2013,6,30,3,189,21,22,1004,0.89,0,0 +2013,6,30,4,189,21,22,1004,0.89,0,0 +2013,6,30,5,194,21,22,1004,1.78,0,0 +2013,6,30,6,194,21,22,1005,0.89,0,0 +2013,6,30,7,190,22,23,1004,4.02,0,0 +2013,6,30,8,236,22,24,1005,5.81,0,0 +2013,6,30,9,250,22,24,1005,7.6,0,0 +2013,6,30,10,270,22,25,1004,10.73,0,0 +2013,6,30,11,244,23,27,1004,14.75,0,0 +2013,6,30,12,178,23,27,1004,17.88,0,0 +2013,6,30,13,167,23,28,1003,19.67,0,0 +2013,6,30,14,156,23,28,1002,22.8,0,0 +2013,6,30,15,151,23,28,1002,26.82,0,0 +2013,6,30,16,155,23,28,1002,28.61,0,0 +2013,6,30,17,145,24,28,1003,0.89,0,0 +2013,6,30,18,149,24,28,1001,1.79,0,0 +2013,6,30,19,146,24,27,1002,4.92,0,0 +2013,6,30,20,176,23,27,1002,1.79,0,0 +2013,6,30,21,215,23,26,1002,3.13,0,0 +2013,6,30,22,101,23,26,1003,0.89,0,0 +2013,6,30,23,112,23,25,1003,1.78,0,0 +2013,7,1,0,155,23,25,1002,2.67,0,0 +2013,7,1,1,144,22,25,1001,3.56,0,0 +2013,7,1,2,141,22,24,1000,3.13,0,0 +2013,7,1,3,156,22,24,1000,0.89,0,1 +2013,7,1,4,147,22,24,1000,0.89,0,0 +2013,7,1,5,154,22,24,1000,0.45,0,0 +2013,7,1,6,153,22,24,1000,1.34,0,0 +2013,7,1,7,152,22,25,1001,1.79,0,0 +2013,7,1,8,144,22,25,1001,0.89,0,0 +2013,7,1,9,147,22,25,1001,3.13,0,0 +2013,7,1,10,143,22,25,1000,4.92,0,0 +2013,7,1,11,150,23,26,1000,0.89,0,0 +2013,7,1,12,160,23,26,1000,0.89,0,0 +2013,7,1,13,172,23,26,999,1.79,0,0 +2013,7,1,14,183,23,26,998,1.79,0,0 +2013,7,1,15,195,23,26,998,4.92,0,0 +2013,7,1,16,191,23,26,998,1.79,0,0 +2013,7,1,17,193,23,25,997,3.58,0,0 +2013,7,1,18,199,22,25,997,3.13,0,0 +2013,7,1,19,156,22,25,997,3.13,0,0 +2013,7,1,20,104,22,24,996,1.79,0,1 +2013,7,1,21,95,22,24,996,3.58,0,2 +2013,7,1,22,69,22,24,996,5.37,0,3 +2013,7,1,23,50,23,24,995,3.13,0,4 +2013,7,2,0,41,21,23,994,4.92,0,5 +2013,7,2,1,32,20,21,995,10.73,0,6 +2013,7,2,2,19,19,21,995,16.54,0,7 +2013,7,2,3,18,19,21,994,19.67,0,0 +2013,7,2,4,14,19,21,994,23.69,0,0 +2013,7,2,5,11,18,20,994,25.48,0,0 +2013,7,2,6,13,19,22,994,28.61,0,0 +2013,7,2,7,18,19,24,995,32.63,0,0 +2013,7,2,8,15,19,27,996,34.42,0,0 +2013,7,2,9,15,19,28,996,37.55,0,0 +2013,7,2,10,16,17,29,996,43.36,0,0 +2013,7,2,11,11,17,31,996,48.28,0,0 +2013,7,2,12,13,15,32,996,55.43,0,0 +2013,7,2,13,15,15,34,996,62.58,0,0 +2013,7,2,14,9,15,34,995,69.73,0,0 +2013,7,2,15,13,15,35,995,75.54,0,0 +2013,7,2,16,14,15,36,994,81.35,0,0 +2013,7,2,17,13,14,35,994,88.5,0,0 +2013,7,2,18,18,14,35,994,5.81,0,0 +2013,7,2,19,19,16,34,994,9.83,0,0 +2013,7,2,20,24,17,33,994,15.64,0,0 +2013,7,2,21,27,18,32,995,21.45,0,0 +2013,7,2,22,23,18,31,995,27.26,0,0 +2013,7,2,23,25,18,30,995,32.18,0,0 +2013,7,3,0,24,19,29,995,35.31,0,0 +2013,7,3,1,31,18,29,995,1.79,0,0 +2013,7,3,2,30,20,25,995,0.89,0,0 +2013,7,3,3,29,20,23,995,1.79,0,0 +2013,7,3,4,37,20,22,995,2.68,0,0 +2013,7,3,5,43,20,22,996,3.57,0,0 +2013,7,3,6,50,21,24,996,1.79,0,0 +2013,7,3,7,56,21,27,996,3.58,0,0 +2013,7,3,8,44,21,29,997,1.79,0,0 +2013,7,3,9,31,19,31,997,4.02,0,0 +2013,7,3,10,30,18,32,996,4.02,0,0 +2013,7,3,11,14,19,33,996,8.04,0,0 +2013,7,3,12,16,16,35,995,5.81,0,0 +2013,7,3,13,25,16,36,995,8.94,0,0 +2013,7,3,14,26,14,36,995,13.86,0,0 +2013,7,3,15,24,15,36,994,17.88,0,0 +2013,7,3,16,23,17,35,994,21.9,0,0 +2013,7,3,17,14,14,35,994,25.92,0,0 +2013,7,3,18,17,15,35,994,29.94,0,0 +2013,7,3,19,20,17,33,994,31.73,0,0 +2013,7,3,20,31,20,29,994,0.89,0,0 +2013,7,3,21,30,20,28,995,1.79,0,0 +2013,7,3,22,47,20,31,995,4.92,0,0 +2013,7,3,23,51,21,27,995,0.89,0,0 +2013,7,4,0,45,19,29,995,3.13,0,0 +2013,7,4,1,69,19,27,995,1.79,0,0 +2013,7,4,2,49,18,25,994,0.89,0,0 +2013,7,4,3,27,19,23,994,4.02,0,0 +2013,7,4,4,27,20,24,994,5.81,0,0 +2013,7,4,5,35,20,23,994,8.94,0,0 +2013,7,4,6,33,20,23,994,12.07,0,0 +2013,7,4,7,19,19,26,994,15.2,0,0 +2013,7,4,8,24,19,29,994,19.22,0,0 +2013,7,4,9,21,19,29,994,25.03,0,0 +2013,7,4,10,18,17,31,994,4.02,0,0 +2013,7,4,11,19,20,33,994,7.15,0,0 +2013,7,4,12,26,18,34,994,3.13,0,0 +2013,7,4,13,17,18,36,993,1.79,0,0 +2013,7,4,14,14,16,36,992,1.79,0,0 +2013,7,4,15,13,15,36,991,3.58,0,0 +2013,7,4,16,17,14,36,991,1.79,0,0 +2013,7,4,17,25,14,31,994,13.86,0,1 +2013,7,4,18,45,19,25,995,17.88,0,2 +2013,7,4,19,29,20,25,993,4.02,0,0 +2013,7,4,20,30,19,24,995,3.13,0,0 +2013,7,4,21,24,19,24,996,7.15,0,0 +2013,7,4,22,28,18,24,996,1.79,0,0 +2013,7,4,23,38,18,23,997,0.45,0,0 +2013,7,5,0,32,18,23,997,1.79,0,0 +2013,7,5,1,25,18,22,996,4.92,0,0 +2013,7,5,2,18,18,23,996,8.94,0,0 +2013,7,5,3,21,18,21,996,12.07,0,0 +2013,7,5,4,24,18,21,996,16.09,0,0 +2013,7,5,5,19,18,22,997,20.11,0,0 +2013,7,5,6,25,18,24,997,21.9,0,0 +2013,7,5,7,26,19,26,998,23.69,0,0 +2013,7,5,8,23,19,28,998,27.71,0,0 +2013,7,5,9,24,20,30,999,30.84,0,0 +2013,7,5,10,25,19,32,999,33.97,0,0 +2013,7,5,11,17,14,32,998,4.02,0,0 +2013,7,5,12,9,16,34,998,1.79,0,0 +2013,7,5,13,12,14,35,998,3.13,0,0 +2013,7,5,14,14,15,35,997,3.13,0,0 +2013,7,5,15,22,16,35,997,3.13,0,0 +2013,7,5,16,20,15,35,997,6.26,0,0 +2013,7,5,17,19,15,35,997,8.05,0,0 +2013,7,5,18,24,16,34,997,11.18,0,0 +2013,7,5,19,24,16,32,998,15.2,0,0 +2013,7,5,20,25,16,30,998,16.99,0,0 +2013,7,5,21,30,16,29,999,18.78,0,0 +2013,7,5,22,35,16,30,1000,21.91,0,0 +2013,7,5,23,35,16,30,1000,25.04,0,0 +2013,7,6,0,39,17,30,1000,28.17,0,0 +2013,7,6,1,38,20,26,1000,31.3,0,0 +2013,7,6,2,32,20,25,1000,33.09,0,0 +2013,7,6,3,37,20,24,1000,34.88,0,0 +2013,7,6,4,60,20,24,1000,0.89,0,0 +2013,7,6,5,66,21,24,1001,1.79,0,0 +2013,7,6,6,74,21,25,1001,3.58,0,0 +2013,7,6,7,93,22,26,1002,5.37,0,0 +2013,7,6,8,117,22,28,1002,0.89,0,0 +2013,7,6,9,119,22,29,1002,1.79,0,0 +2013,7,6,10,112,22,31,1002,1.79,0,0 +2013,7,6,11,57,21,32,1001,3.13,0,0 +2013,7,6,12,48,16,34,1001,8.94,0,0 +2013,7,6,13,44,17,35,1000,13.86,0,0 +2013,7,6,14,42,16,35,1000,18.78,0,0 +2013,7,6,15,43,17,34,999,23.7,0,0 +2013,7,6,16,45,18,35,999,28.62,0,0 +2013,7,6,17,45,18,34,998,33.54,0,0 +2013,7,6,18,64,19,33,999,37.56,0,0 +2013,7,6,19,60,19,33,999,41.58,0,0 +2013,7,6,20,65,19,31,1000,46.5,0,0 +2013,7,6,21,64,19,30,1001,51.42,0,0 +2013,7,6,22,77,19,29,1001,55.44,0,0 +2013,7,6,23,26,18,24,1002,4.92,0,1 +2013,7,7,0,35,19,24,1002,4.92,0,2 +2013,7,7,1,37,20,22,1001,5.81,0,3 +2013,7,7,2,47,21,23,1002,8.94,0,0 +2013,7,7,3,42,21,23,1000,0.89,0,0 +2013,7,7,4,38,20,22,1000,1.79,0,0 +2013,7,7,5,49,20,22,1000,0.89,0,0 +2013,7,7,6,60,21,23,1000,0.89,0,0 +2013,7,7,7,51,21,24,1001,0.45,0,0 +2013,7,7,8,53,22,26,1002,3.13,0,0 +2013,7,7,9,56,22,26,1002,1.79,0,0 +2013,7,7,10,66,23,28,1001,0.89,0,0 +2013,7,7,11,72,23,29,1001,3.13,0,0 +2013,7,7,12,66,22,29,1001,7.15,0,0 +2013,7,7,13,65,21,30,1000,10.28,0,0 +2013,7,7,14,75,23,31,1000,13.41,0,0 +2013,7,7,15,97,23,32,998,17.43,0,0 +2013,7,7,16,100,23,32,998,21.45,0,0 +2013,7,7,17,120,23,32,998,25.47,0,0 +2013,7,7,18,129,24,31,998,29.49,0,0 +2013,7,7,19,130,24,31,998,31.28,0,0 +2013,7,7,20,142,25,30,999,32.17,0,0 +2013,7,7,21,147,25,29,999,33.06,0,0 +2013,7,7,22,164,25,29,999,34.85,0,0 +2013,7,7,23,195,25,29,999,37.98,0,1 +2013,7,8,0,216,22,24,1001,5.81,0,2 +2013,7,8,1,70,22,24,1001,1.79,0,3 +2013,7,8,2,79,22,23,1001,0.89,0,4 +2013,7,8,3,64,22,23,1001,1.78,0,5 +2013,7,8,4,61,22,23,1001,1.79,0,6 +2013,7,8,5,56,22,23,1002,0.89,0,7 +2013,7,8,6,69,22,23,1001,1.78,0,0 +2013,7,8,7,71,23,24,1002,1.79,0,0 +2013,7,8,8,73,23,24,1003,0.89,0,0 +2013,7,8,9,79,23,25,1003,1.79,0,1 +2013,7,8,10,80,23,25,1003,1.79,0,0 +2013,7,8,11,79,23,25,1003,1.79,0,0 +2013,7,8,12,80,23,26,1003,0.89,0,0 +2013,7,8,13,80,23,27,1002,1.78,0,0 +2013,7,8,14,85,23,27,1002,1.79,0,1 +2013,7,8,15,86,23,27,1002,4.92,0,0 +2013,7,8,16,80,23,27,1001,8.05,0,0 +2013,7,8,17,91,23,27,1001,9.84,0,0 +2013,7,8,18,104,23,27,1001,12.97,0,0 +2013,7,8,19,111,23,26,1001,16.1,0,0 +2013,7,8,20,129,23,26,1002,0.89,0,0 +2013,7,8,21,133,23,25,1003,0.89,0,1 +2013,7,8,22,140,23,25,1003,0.89,0,2 +2013,7,8,23,141,24,25,1003,0.89,0,0 +2013,7,9,0,150,24,25,1003,1.79,0,0 +2013,7,9,1,152,24,25,1003,0.89,0,1 +2013,7,9,2,164,24,25,1002,1.79,0,0 +2013,7,9,3,160,24,25,1002,3.58,0,0 +2013,7,9,4,143,24,25,1002,0.89,0,1 +2013,7,9,5,139,23,25,1003,2.68,0,2 +2013,7,9,6,135,23,24,1003,0.89,0,3 +2013,7,9,7,140,24,25,1003,1.34,0,4 +2013,7,9,8,145,23,25,1003,3.13,0,0 +2013,7,9,9,124,23,25,1002,6.26,0,0 +2013,7,9,10,57,23,26,1002,3.13,0,0 +2013,7,9,11,59,23,26,1003,6.26,0,0 +2013,7,9,12,63,23,25,1003,1.79,0,1 +2013,7,9,13,59,22,24,1004,2.68,0,2 +2013,7,9,14,50,23,24,1003,1.79,0,3 +2013,7,9,15,46,21,23,1003,1.79,0,4 +2013,7,9,16,35,22,23,1003,0.89,0,5 +2013,7,9,17,34,22,24,1003,1.78,0,6 +2013,7,9,18,32,22,24,1003,0.89,0,7 +2013,7,9,19,32,22,24,1003,0.89,0,8 +2013,7,9,20,40,22,23,1003,1.78,0,0 +2013,7,9,21,46,22,23,1004,2.67,0,0 +2013,7,9,22,55,22,23,1004,3.56,0,0 +2013,7,9,23,50,22,23,1004,1.79,0,0 +2013,7,10,0,39,22,23,1004,0.89,0,0 +2013,7,10,1,46,22,23,1003,1.34,0,0 +2013,7,10,2,46,22,23,1003,1.79,0,0 +2013,7,10,3,51,21,23,1003,4.92,0,0 +2013,7,10,4,49,21,22,1003,6.71,0,0 +2013,7,10,5,41,21,22,1004,9.84,0,1 +2013,7,10,6,34,21,22,1004,11.63,0,2 +2013,7,10,7,30,21,23,1005,14.76,0,0 +2013,7,10,8,36,21,24,1005,17.89,0,0 +2013,7,10,9,34,21,24,1005,21.02,0,1 +2013,7,10,10,34,21,25,1005,0.89,0,2 +2013,7,10,11,37,21,25,1005,1.79,0,3 +2013,7,10,12,38,21,25,1005,4.92,0,4 +2013,7,10,13,41,22,25,1005,8.05,0,5 +2013,7,10,14,44,21,25,1005,11.18,0,6 +2013,7,10,15,28,21,24,1006,15.2,0,7 +2013,7,10,16,30,21,24,1005,20.12,0,8 +2013,7,10,17,28,22,24,1005,24.14,0,0 +2013,7,10,18,28,21,24,1005,28.16,0,0 +2013,7,10,19,25,21,24,1005,32.18,0,0 +2013,7,10,20,35,21,25,1005,33.97,0,0 +2013,7,10,21,41,21,24,1006,37.99,0,0 +2013,7,10,22,36,21,24,1006,39.78,0,0 +2013,7,10,23,40,21,24,1006,41.57,0,0 +2013,7,11,0,32,21,23,1005,43.36,0,0 +2013,7,11,1,36,21,23,1005,45.15,0,0 +2013,7,11,2,32,20,23,1005,46.94,0,0 +2013,7,11,3,33,21,23,1005,48.73,0,0 +2013,7,11,4,31,21,22,1005,50.52,0,0 +2013,7,11,5,30,21,22,1006,51.41,0,0 +2013,7,11,6,33,21,23,1006,52.3,0,0 +2013,7,11,7,34,21,23,1006,55.43,0,0 +2013,7,11,8,34,21,24,1006,57.22,0,0 +2013,7,11,9,29,21,25,1006,60.35,0,0 +2013,7,11,10,46,21,26,1006,62.14,0,0 +2013,7,11,11,36,21,27,1006,63.93,0,0 +2013,7,11,12,43,21,26,1006,1.79,0,0 +2013,7,11,13,41,20,28,1006,1.79,0,0 +2013,7,11,14,44,19,27,1006,1.79,0,0 +2013,7,11,15,45,19,27,1006,3.13,0,0 +2013,7,11,16,42,19,28,1005,1.79,0,0 +2013,7,11,17,38,19,28,1004,3.58,0,0 +2013,7,11,18,42,19,28,1004,1.79,0,0 +2013,7,11,19,53,20,26,1005,0.89,0,0 +2013,7,11,20,46,20,25,1005,1.79,0,0 +2013,7,11,21,50,20,26,1006,4.92,0,0 +2013,7,11,22,53,19,25,1006,8.05,0,0 +2013,7,11,23,57,19,25,1006,11.18,0,0 +2013,7,12,0,62,20,24,1006,12.97,0,0 +2013,7,12,1,65,20,22,1005,13.86,0,0 +2013,7,12,2,64,20,21,1006,1.79,0,0 +2013,7,12,3,74,20,21,1005,0.89,0,0 +2013,7,12,4,74,20,21,1006,0.89,0,0 +2013,7,12,5,71,20,21,1006,0.89,0,0 +2013,7,12,6,71,21,22,1006,2.68,0,0 +2013,7,12,7,71,22,23,1006,0.89,0,0 +2013,7,12,8,65,22,26,1006,1.79,0,0 +2013,7,12,9,71,22,27,1006,1.79,0,0 +2013,7,12,10,70,21,28,1006,1.79,0,0 +2013,7,12,11,58,20,29,1006,1.79,0,0 +2013,7,12,12,54,22,30,1005,4.92,0,0 +2013,7,12,13,52,22,31,1005,1.79,0,0 +2013,7,12,14,68,22,32,1004,1.79,0,0 +2013,7,12,15,67,23,32,1004,3.13,0,0 +2013,7,12,16,53,23,31,1004,8.94,0,0 +2013,7,12,17,44,22,31,1004,13.86,0,0 +2013,7,12,18,51,23,30,1004,17.88,0,0 +2013,7,12,19,50,23,29,1005,22.8,0,0 +2013,7,12,20,64,23,27,1005,25.93,0,0 +2013,7,12,21,115,23,27,1006,27.72,0,0 +2013,7,12,22,125,23,26,1006,29.51,0,0 +2013,7,12,23,128,23,26,1007,31.3,0,0 +2013,7,13,0,120,22,25,1006,34.43,0,0 +2013,7,13,1,120,22,25,1006,37.56,0,0 +2013,7,13,2,128,22,25,1006,38.45,0,0 +2013,7,13,3,123,22,24,1006,0.89,0,0 +2013,7,13,4,112,22,24,1006,0.89,0,0 +2013,7,13,5,121,22,24,1006,0.89,0,0 +2013,7,13,6,128,23,24,1007,0.89,0,0 +2013,7,13,7,136,23,25,1007,2.68,0,0 +2013,7,13,8,130,23,27,1007,0.89,0,0 +2013,7,13,9,135,23,27,1007,1.79,0,0 +2013,7,13,10,128,23,29,1007,0.89,0,0 +2013,7,13,11,108,22,29,1007,3.13,0,0 +2013,7,13,12,92,22,29,1007,4.92,0,0 +2013,7,13,13,96,22,30,1007,6.71,0,0 +2013,7,13,14,93,22,30,1006,8.5,0,0 +2013,7,13,15,80,22,31,1006,10.29,0,0 +2013,7,13,16,67,23,31,1006,13.42,0,0 +2013,7,13,17,59,22,31,1006,16.55,0,0 +2013,7,13,18,68,22,30,1006,19.68,0,0 +2013,7,13,19,98,22,29,1006,22.81,0,0 +2013,7,13,20,82,23,28,1006,25.94,0,0 +2013,7,13,21,116,22,27,1007,29.96,0,0 +2013,7,13,22,140,22,27,1007,33.09,0,0 +2013,7,13,23,131,21,26,1007,36.22,0,0 +2013,7,14,0,94,21,25,1007,38.01,0,0 +2013,7,14,1,94,21,25,1007,41.14,0,0 +2013,7,14,2,92,21,25,1007,44.27,0,0 +2013,7,14,3,101,21,24,1008,47.4,0,0 +2013,7,14,4,99,22,24,1008,50.53,0,0 +2013,7,14,5,113,22,24,1008,52.32,0,0 +2013,7,14,6,136,22,23,1009,54.11,0,0 +2013,7,14,7,162,21,24,1009,55.9,0,0 +2013,7,14,8,127,22,25,1009,57.69,0,0 +2013,7,14,9,93,22,26,1009,59.48,0,0 +2013,7,14,10,83,22,27,1009,61.27,0,0 +2013,7,14,11,71,22,28,1009,0.89,0,0 +2013,7,14,12,73,21,29,1008,1.79,0,0 +2013,7,14,13,80,21,29,1007,3.58,0,0 +2013,7,14,14,75,21,30,1007,1.79,0,0 +2013,7,14,15,67,22,30,1006,1.79,0,0 +2013,7,14,16,76,21,30,1006,3.58,0,0 +2013,7,14,17,68,20,29,1006,6.71,0,0 +2013,7,14,18,64,20,28,1005,10.73,0,0 +2013,7,14,19,85,21,28,1005,14.75,0,0 +2013,7,14,20,67,22,28,1006,3.13,0,0 +2013,7,14,21,59,21,27,1006,3.13,0,0 +2013,7,14,22,66,21,26,1006,6.26,0,1 +2013,7,14,23,71,22,24,1006,0.89,0,2 +2013,7,15,0,78,23,24,1005,0.89,0,3 +2013,7,15,1,70,23,24,1005,1.79,0,4 +2013,7,15,2,75,22,23,1003,4.92,0,5 +2013,7,15,3,77,22,23,1003,8.05,0,6 +2013,7,15,4,84,22,23,1002,0.89,0,8 +2013,7,15,5,68,22,23,1002,1.79,0,9 +2013,7,15,6,53,23,23,1001,4.92,0,10 +2013,7,15,7,96,23,24,1001,9.84,0,11 +2013,7,15,8,71,23,24,1001,13.86,0,12 +2013,7,15,9,48,23,24,1000,17.88,0,13 +2013,7,15,10,38,23,25,1000,22.8,0,14 +2013,7,15,11,53,24,25,999,28.61,0,15 +2013,7,15,12,66,24,25,998,32.63,0,16 +2013,7,15,13,65,24,25,998,35.76,0,17 +2013,7,15,14,71,24,25,997,38.89,0,18 +2013,7,15,15,59,24,26,997,42.02,0,19 +2013,7,15,16,53,24,25,997,45.15,0,20 +2013,7,15,17,50,23,25,996,4.02,0,21 +2013,7,15,18,45,22,25,995,5.81,0,22 +2013,7,15,19,59,23,24,995,0.89,0,23 +2013,7,15,20,58,23,24,996,1.79,0,24 +2013,7,15,21,70,23,24,996,3.58,0,25 +2013,7,15,22,61,23,24,996,5.37,0,0 +2013,7,15,23,54,23,25,996,7.16,0,0 +2013,7,16,0,56,23,24,996,0.89,0,0 +2013,7,16,1,60,23,24,996,1.78,0,0 +2013,7,16,2,64,22,23,995,0.89,0,0 +2013,7,16,3,66,23,24,996,1.79,0,0 +2013,7,16,4,57,23,23,996,4.92,0,0 +2013,7,16,5,33,20,22,996,9.84,0,0 +2013,7,16,6,16,19,22,997,3.13,0,0 +2013,7,16,7,15,19,25,998,3.13,0,0 +2013,7,16,8,11,18,27,999,7.15,0,0 +2013,7,16,9,12,17,28,999,12.96,0,0 +2013,7,16,10,12,17,29,999,18.77,0,0 +2013,7,16,11,9,14,30,1000,24.58,0,0 +2013,7,16,12,11,13,31,1000,30.39,0,0 +2013,7,16,13,11,12,32,1000,36.2,0,0 +2013,7,16,14,10,13,32,1000,41.12,0,0 +2013,7,16,15,8,14,33,1000,44.25,0,0 +2013,7,16,16,16,13,33,1000,1.79,0,0 +2013,7,16,17,16,14,32,1000,1.79,0,0 +2013,7,16,18,16,13,31,1001,1.79,0,0 +2013,7,16,19,18,16,29,1002,3.58,0,0 +2013,7,16,20,31,19,26,1002,4.47,0,0 +2013,7,16,21,34,16,27,1003,6.26,0,0 +2013,7,16,22,36,17,25,1004,0.89,0,0 +2013,7,16,23,39,16,26,1004,1.79,0,0 +2013,7,17,0,38,18,22,1004,0.89,0,0 +2013,7,17,1,32,18,22,1005,1.78,0,0 +2013,7,17,2,49,18,21,1005,2.67,0,0 +2013,7,17,3,59,18,21,1005,0.89,0,0 +2013,7,17,4,68,18,20,1006,0.89,0,0 +2013,7,17,5,50,18,19,1006,1.79,0,0 +2013,7,17,6,43,19,21,1006,4.92,0,0 +2013,7,17,7,42,18,23,1006,1.79,0,0 +2013,7,17,8,47,19,25,1006,0.89,0,0 +2013,7,17,9,52,20,28,1006,1.78,0,0 +2013,7,17,10,55,20,28,1006,1.79,0,0 +2013,7,17,11,60,20,29,1006,0.89,0,0 +2013,7,17,12,58,21,31,1006,2.68,0,0 +2013,7,17,13,57,18,32,1005,4.92,0,0 +2013,7,17,14,NA,19,33,1005,8.05,0,0 +2013,7,17,15,35,15,32,1004,12.07,0,0 +2013,7,17,16,33,17,32,1003,15.2,0,0 +2013,7,17,17,34,16,32,1002,18.33,0,0 +2013,7,17,18,35,14,31,1003,21.46,0,0 +2013,7,17,19,33,19,29,1003,25.48,0,0 +2013,7,17,20,54,20,28,1004,29.5,0,0 +2013,7,17,21,79,18,28,1004,32.63,0,0 +2013,7,17,22,49,20,27,1005,36.65,0,0 +2013,7,17,23,53,21,26,1005,40.67,0,0 +2013,7,18,0,72,21,26,1005,44.69,0,0 +2013,7,18,1,100,21,25,1005,46.48,0,0 +2013,7,18,2,128,22,24,1005,49.61,0,0 +2013,7,18,3,135,22,24,1004,51.4,0,0 +2013,7,18,4,149,23,24,1005,53.19,0,0 +2013,7,18,5,157,22,24,1005,0.89,0,0 +2013,7,18,6,150,23,24,1005,1.78,0,0 +2013,7,18,7,145,22,25,1005,3.13,0,0 +2013,7,18,8,154,22,25,1005,6.26,0,0 +2013,7,18,9,163,23,25,1005,8.05,0,1 +2013,7,18,10,167,23,26,1005,11.18,0,2 +2013,7,18,11,169,22,26,1005,15.2,0,0 +2013,7,18,12,175,22,26,1005,19.22,0,0 +2013,7,18,13,161,22,26,1004,23.24,0,0 +2013,7,18,14,163,22,27,1004,27.26,0,0 +2013,7,18,15,173,22,27,1004,31.28,0,0 +2013,7,18,16,137,23,27,1003,35.3,0,0 +2013,7,18,17,137,23,27,1003,39.32,0,0 +2013,7,18,18,122,23,27,1003,42.45,0,0 +2013,7,18,19,108,22,26,1003,44.24,0,0 +2013,7,18,20,91,22,26,1003,46.03,0,0 +2013,7,18,21,99,21,25,1004,47.82,0,0 +2013,7,18,22,77,21,25,1004,0.89,0,0 +2013,7,18,23,86,21,25,1004,1.78,0,0 +2013,7,19,0,93,20,24,1003,3.13,0,0 +2013,7,19,1,107,20,24,1003,0.89,0,0 +2013,7,19,2,126,20,23,1002,1.78,0,0 +2013,7,19,3,141,20,23,1002,2.67,0,0 +2013,7,19,4,146,20,23,1002,3.56,0,0 +2013,7,19,5,160,21,23,1002,4.45,0,0 +2013,7,19,6,165,21,23,1002,5.34,0,0 +2013,7,19,7,165,21,24,1002,6.23,0,0 +2013,7,19,8,166,21,26,1003,8.02,0,0 +2013,7,19,9,150,21,26,1002,1.79,0,0 +2013,7,19,10,134,22,27,1002,0.89,0,0 +2013,7,19,11,120,22,28,1002,1.79,0,0 +2013,7,19,12,116,22,29,1002,4.92,0,0 +2013,7,19,13,95,22,30,1002,0.89,0,0 +2013,7,19,14,109,21,30,1001,2.68,0,0 +2013,7,19,15,118,22,29,1001,1.79,0,0 +2013,7,19,16,111,22,31,1001,4.92,0,0 +2013,7,19,17,98,22,31,1000,8.05,0,0 +2013,7,19,18,94,22,29,1001,12.97,0,0 +2013,7,19,19,103,22,29,1001,16.99,0,0 +2013,7,19,20,97,22,27,1001,18.78,0,0 +2013,7,19,21,111,21,27,1002,21.91,0,0 +2013,7,19,22,105,21,26,1003,0.89,0,0 +2013,7,19,23,110,22,25,1003,1.78,0,0 +2013,7,20,0,119,22,24,1003,0.89,0,0 +2013,7,20,1,127,22,23,1003,0.89,0,0 +2013,7,20,2,136,21,23,1002,1.78,0,0 +2013,7,20,3,170,22,23,1003,0.89,0,0 +2013,7,20,4,170,22,23,1003,1.78,0,0 +2013,7,20,5,168,22,23,1004,2.67,0,0 +2013,7,20,6,166,23,24,1004,0.89,0,0 +2013,7,20,7,161,23,25,1004,2.68,0,0 +2013,7,20,8,152,23,28,1005,0.89,0,0 +2013,7,20,9,116,19,29,1005,1.79,0,0 +2013,7,20,10,86,19,31,1005,0.89,0,0 +2013,7,20,11,31,16,32,1004,3.13,0,0 +2013,7,20,12,30,14,32,1004,1.79,0,0 +2013,7,20,13,14,9,33,1004,1.79,0,0 +2013,7,20,14,16,10,33,1003,1.79,0,0 +2013,7,20,15,15,11,33,1003,1.79,0,0 +2013,7,20,16,15,11,34,1003,2.68,0,0 +2013,7,20,17,17,12,31,1002,1.79,0,0 +2013,7,20,18,56,19,31,1003,0.89,0,0 +2013,7,20,19,96,20,29,1002,3.13,0,0 +2013,7,20,20,137,20,28,1003,4.92,0,0 +2013,7,20,21,139,21,28,1004,0.89,0,0 +2013,7,20,22,128,22,27,1004,1.79,0,0 +2013,7,20,23,112,23,27,1004,4.92,0,0 +2013,7,21,0,109,22,26,1005,8.05,0,0 +2013,7,21,1,93,22,25,1005,9.84,0,0 +2013,7,21,2,87,22,25,1004,12.97,0,0 +2013,7,21,3,74,21,24,1004,16.1,0,0 +2013,7,21,4,65,21,24,1004,0.89,0,0 +2013,7,21,5,65,21,24,1005,1.78,0,0 +2013,7,21,6,83,21,24,1005,1.79,0,0 +2013,7,21,7,86,21,24,1005,0.89,0,0 +2013,7,21,8,85,21,26,1005,0.89,0,0 +2013,7,21,9,77,21,27,1005,1.78,0,0 +2013,7,21,10,80,22,29,1005,2.67,0,0 +2013,7,21,11,90,22,30,1005,1.79,0,0 +2013,7,21,12,94,22,30,1005,1.79,0,0 +2013,7,21,13,91,21,30,1005,3.13,0,0 +2013,7,21,14,65,21,32,1004,3.13,0,0 +2013,7,21,15,75,21,32,1003,4.02,0,0 +2013,7,21,16,66,20,31,1003,8.94,0,0 +2013,7,21,17,66,20,31,1003,12.96,0,0 +2013,7,21,18,56,19,31,1004,16.09,0,0 +2013,7,21,19,48,19,29,1004,17.88,0,0 +2013,7,21,20,42,19,28,1004,19.67,0,0 +2013,7,21,21,50,21,27,1005,20.56,0,0 +2013,7,21,22,48,20,24,1005,21.45,0,0 +2013,7,21,23,54,21,24,1005,22.34,0,0 +2013,7,22,0,61,21,24,1005,23.23,0,0 +2013,7,22,1,73,20,25,1005,26.36,0,0 +2013,7,22,2,84,20,23,1004,27.25,0,0 +2013,7,22,3,92,20,22,1005,28.14,0,0 +2013,7,22,4,92,21,23,1005,29.93,0,0 +2013,7,22,5,104,20,22,1005,0.89,0,0 +2013,7,22,6,118,21,23,1005,1.79,0,0 +2013,7,22,7,124,22,25,1004,3.58,0,0 +2013,7,22,8,116,21,27,1005,5.37,0,0 +2013,7,22,9,99,21,28,1005,0.89,0,0 +2013,7,22,10,96,20,28,1004,1.79,0,0 +2013,7,22,11,NA,19,29,1004,3.58,0,0 +2013,7,22,12,NA,20,30,1004,5.37,0,0 +2013,7,22,13,NA,19,30,1004,7.16,0,0 +2013,7,22,14,NA,20,30,1004,10.29,0,0 +2013,7,22,15,NA,19,30,1003,13.42,0,0 +2013,7,22,16,NA,18,30,1002,17.44,0,0 +2013,7,22,17,75,20,29,1002,20.57,0,0 +2013,7,22,18,96,19,28,1001,22.36,0,0 +2013,7,22,19,117,19,28,1002,24.15,0,0 +2013,7,22,20,122,19,26,1003,25.94,0,0 +2013,7,22,21,124,19,26,1003,0.89,0,0 +2013,7,22,22,107,19,26,1003,1.79,0,0 +2013,7,22,23,102,21,26,1003,0.89,0,0 +2013,7,23,0,70,21,25,1002,1.78,0,0 +2013,7,23,1,71,20,25,1002,2.67,0,0 +2013,7,23,2,80,20,26,1002,0.89,0,0 +2013,7,23,3,83,19,25,1002,4.02,0,1 +2013,7,23,4,59,16,25,1001,8.04,0,0 +2013,7,23,5,49,17,24,1002,9.83,0,0 +2013,7,23,6,53,17,23,1002,12.96,0,0 +2013,7,23,7,59,18,23,1002,16.98,0,0 +2013,7,23,8,44,18,25,1002,1.79,0,0 +2013,7,23,9,27,17,26,1001,4.92,0,0 +2013,7,23,10,23,16,28,1001,4.02,0,0 +2013,7,23,11,18,15,29,1000,3.13,0,0 +2013,7,23,12,22,16,30,1000,3.13,0,0 +2013,7,23,13,24,17,31,999,3.13,0,0 +2013,7,23,14,28,17,32,998,3.13,0,0 +2013,7,23,15,26,18,32,997,7.15,0,0 +2013,7,23,16,31,17,31,997,12.07,0,0 +2013,7,23,17,31,17,31,996,16.99,0,0 +2013,7,23,18,33,18,30,997,21.91,0,0 +2013,7,23,19,41,18,29,998,25.93,0,0 +2013,7,23,20,40,18,28,998,29.95,0,0 +2013,7,23,21,46,19,27,999,33.08,0,0 +2013,7,23,22,46,19,25,999,34.87,0,0 +2013,7,23,23,48,19,25,999,36.66,0,0 +2013,7,24,0,58,19,24,999,38.45,0,0 +2013,7,24,1,64,20,23,998,0.89,0,0 +2013,7,24,2,70,20,22,998,0.89,0,0 +2013,7,24,3,75,19,21,999,0.89,0,0 +2013,7,24,4,72,20,22,999,1.78,0,0 +2013,7,24,5,80,19,21,1000,3.57,0,0 +2013,7,24,6,79,20,22,1000,7.59,0,0 +2013,7,24,7,72,20,25,1000,11.61,0,0 +2013,7,24,8,62,20,28,1001,13.4,0,0 +2013,7,24,9,51,19,30,1001,0.89,0,0 +2013,7,24,10,28,19,33,1000,1.78,0,0 +2013,7,24,11,17,17,34,1000,2.67,0,0 +2013,7,24,12,12,13,35,1000,4.92,0,0 +2013,7,24,13,13,11,37,999,10.73,0,0 +2013,7,24,14,12,12,37,999,15.65,0,0 +2013,7,24,15,15,11,37,998,21.46,0,0 +2013,7,24,16,14,12,37,998,28.61,0,0 +2013,7,24,17,17,12,37,998,34.42,0,0 +2013,7,24,18,18,12,37,998,38.44,0,0 +2013,7,24,19,17,12,36,998,42.46,0,0 +2013,7,24,20,17,14,33,999,44.25,0,0 +2013,7,24,21,29,19,28,999,0.89,0,0 +2013,7,24,22,40,18,27,1000,1.79,0,0 +2013,7,24,23,48,18,26,1000,0.89,0,0 +2013,7,25,0,38,19,26,1000,1.78,0,0 +2013,7,25,1,26,20,25,1000,1.79,0,0 +2013,7,25,2,36,19,24,1000,3.13,0,0 +2013,7,25,3,30,18,23,1000,7.15,0,0 +2013,7,25,4,18,18,24,1000,10.28,0,0 +2013,7,25,5,23,17,24,1001,12.07,0,0 +2013,7,25,6,18,17,25,1001,16.09,0,0 +2013,7,25,7,17,17,27,1001,20.11,0,0 +2013,7,25,8,18,17,29,1002,23.24,0,0 +2013,7,25,9,17,17,30,1002,27.26,0,0 +2013,7,25,10,24,16,32,1002,4.02,0,0 +2013,7,25,11,19,14,34,1002,1.79,0,0 +2013,7,25,12,22,14,35,1002,1.79,0,0 +2013,7,25,13,17,14,35,1001,1.79,0,0 +2013,7,25,14,20,14,36,1001,4.02,0,0 +2013,7,25,15,30,15,36,1000,7.15,0,0 +2013,7,25,16,32,15,35,1000,12.07,0,0 +2013,7,25,17,30,14,35,1000,16.09,0,0 +2013,7,25,18,48,17,33,1000,21.01,0,0 +2013,7,25,19,74,19,31,1000,25.03,0,0 +2013,7,25,20,66,19,30,1001,29.05,0,0 +2013,7,25,21,69,17,29,1002,33.97,0,0 +2013,7,25,22,53,18,28,1003,35.76,0,0 +2013,7,25,23,49,17,27,1003,38.89,0,0 +2013,7,26,0,43,17,26,1003,40.68,0,0 +2013,7,26,1,42,18,26,1003,42.47,0,0 +2013,7,26,2,40,19,26,1003,45.6,0,0 +2013,7,26,3,32,18,26,1003,48.73,0,0 +2013,7,26,4,41,18,26,1003,50.52,0,0 +2013,7,26,5,40,19,26,1003,0.89,0,0 +2013,7,26,6,41,18,26,1003,0.89,0,0 +2013,7,26,7,55,17,26,1003,2.68,0,0 +2013,7,26,8,55,18,27,1003,0.89,0,0 +2013,7,26,9,54,17,28,1004,1.78,0,0 +2013,7,26,10,53,19,28,1004,3.13,0,0 +2013,7,26,11,56,18,27,1004,6.26,0,1 +2013,7,26,12,59,19,23,1004,8.05,0,2 +2013,7,26,13,46,21,24,1004,0.89,0,3 +2013,7,26,14,56,20,25,1003,4.92,0,0 +2013,7,26,15,71,20,25,1003,8.94,0,0 +2013,7,26,16,69,21,25,1003,12.96,0,0 +2013,7,26,17,56,21,27,1002,16.09,0,0 +2013,7,26,18,56,21,27,1002,17.88,0,0 +2013,7,26,19,62,20,26,1002,21.9,0,0 +2013,7,26,20,66,20,25,1002,23.69,0,0 +2013,7,26,21,62,21,26,1002,0.89,0,0 +2013,7,26,22,71,21,26,1002,1.79,0,0 +2013,7,26,23,71,21,25,1002,0.89,0,0 +2013,7,27,0,74,21,25,1002,1.79,0,0 +2013,7,27,1,85,22,25,1002,2.68,0,0 +2013,7,27,2,76,21,25,1002,3.13,0,1 +2013,7,27,3,80,22,24,1001,0.89,0,0 +2013,7,27,4,83,21,24,1001,1.79,0,0 +2013,7,27,5,89,22,24,1001,0.89,0,0 +2013,7,27,6,87,22,25,1001,2.68,0,0 +2013,7,27,7,90,22,24,1001,4.47,0,1 +2013,7,27,8,88,22,24,1001,0.89,0,2 +2013,7,27,9,80,22,24,1001,1.78,0,3 +2013,7,27,10,87,22,24,1001,2.67,0,4 +2013,7,27,11,88,22,24,1000,1.79,0,5 +2013,7,27,12,94,22,24,1000,3.58,0,6 +2013,7,27,13,103,23,25,1000,4.47,0,7 +2013,7,27,14,104,23,25,1000,0.89,0,0 +2013,7,27,15,118,23,25,999,0.89,0,1 +2013,7,27,16,119,23,25,999,2.68,0,0 +2013,7,27,17,112,23,25,999,4.47,0,0 +2013,7,27,18,128,23,25,1000,0.89,0,0 +2013,7,27,19,149,23,25,999,1.78,0,0 +2013,7,27,20,145,22,25,1000,2.67,0,0 +2013,7,27,21,164,23,25,1000,3.56,0,0 +2013,7,27,22,156,23,25,1000,1.79,0,0 +2013,7,27,23,179,22,24,1000,0.89,0,0 +2013,7,28,0,171,22,24,999,0.89,0,0 +2013,7,28,1,178,21,23,999,0.45,0,0 +2013,7,28,2,138,22,24,999,3.13,0,0 +2013,7,28,3,141,22,24,999,0.89,0,0 +2013,7,28,4,131,22,24,999,3.13,0,0 +2013,7,28,5,103,21,23,1000,6.26,0,0 +2013,7,28,6,89,21,23,1000,9.39,0,0 +2013,7,28,7,93,23,25,1000,12.52,0,0 +2013,7,28,8,62,21,28,1001,1.79,0,0 +2013,7,28,9,48,21,29,1001,0.89,0,0 +2013,7,28,10,32,19,30,1001,1.79,0,0 +2013,7,28,11,32,18,32,1001,1.79,0,0 +2013,7,28,12,23,16,34,1001,3.13,0,0 +2013,7,28,13,20,16,36,1000,6.26,0,0 +2013,7,28,14,28,15,37,999,4.02,0,0 +2013,7,28,15,20,15,37,999,4.02,0,0 +2013,7,28,16,22,15,38,999,7.15,0,0 +2013,7,28,17,31,15,38,998,10.28,0,0 +2013,7,28,18,25,17,36,998,4.92,0,0 +2013,7,28,19,26,18,34,999,8.94,0,0 +2013,7,28,20,27,19,32,999,12.07,0,0 +2013,7,28,21,30,16,32,1000,16.09,0,0 +2013,7,28,22,33,19,29,1000,17.88,0,0 +2013,7,28,23,33,21,27,1000,0.89,0,0 +2013,7,29,0,36,21,27,1000,1.79,0,0 +2013,7,29,1,38,22,27,1000,1.79,0,0 +2013,7,29,2,49,22,26,1000,3.58,0,0 +2013,7,29,3,91,22,26,1000,0.89,0,0 +2013,7,29,4,79,22,26,999,1.78,0,0 +2013,7,29,5,72,22,26,1000,2.67,0,0 +2013,7,29,6,69,23,26,1000,3.56,0,0 +2013,7,29,7,70,23,28,1000,0.89,0,0 +2013,7,29,8,74,22,28,1000,0.89,0,0 +2013,7,29,9,76,23,29,1000,1.78,0,0 +2013,7,29,10,81,23,29,1000,0.89,0,0 +2013,7,29,11,87,23,29,1001,4.91,0,0 +2013,7,29,12,78,21,31,1001,4.02,0,1 +2013,7,29,13,69,23,30,1001,3.13,0,0 +2013,7,29,14,84,23,31,1000,4.92,0,0 +2013,7,29,15,55,21,32,999,0.89,0,0 +2013,7,29,16,54,22,31,998,1.78,0,0 +2013,7,29,17,53,23,31,998,0.89,0,0 +2013,7,29,18,40,23,30,998,2.68,0,0 +2013,7,29,19,50,23,30,998,4.47,0,0 +2013,7,29,20,60,22,28,999,6.26,0,0 +2013,7,29,21,59,19,29,999,10.28,0,0 +2013,7,29,22,70,21,28,999,12.07,0,0 +2013,7,29,23,98,21,27,1000,13.86,0,0 +2013,7,30,0,106,22,25,999,14.75,0,0 +2013,7,30,1,99,22,24,998,0.89,0,0 +2013,7,30,2,96,21,24,997,1.79,0,0 +2013,7,30,3,115,21,24,997,0.89,0,0 +2013,7,30,4,77,21,24,997,1.79,0,0 +2013,7,30,5,125,21,23,997,0.45,0,0 +2013,7,30,6,128,21,23,998,0.89,0,0 +2013,7,30,7,135,23,25,998,0.89,0,0 +2013,7,30,8,139,22,26,999,1.79,0,0 +2013,7,30,9,142,23,28,999,3.58,0,0 +2013,7,30,10,132,23,30,998,0.89,0,0 +2013,7,30,11,104,23,31,998,1.78,0,0 +2013,7,30,12,114,21,32,997,1.79,0,0 +2013,7,30,13,128,22,33,997,3.58,0,0 +2013,7,30,14,96,21,33,996,3.13,0,0 +2013,7,30,15,69,21,34,996,3.13,0,0 +2013,7,30,16,65,20,34,995,6.26,0,0 +2013,7,30,17,55,20,34,995,11.18,0,0 +2013,7,30,18,51,21,33,995,15.2,0,0 +2013,7,30,19,74,21,31,996,19.22,0,0 +2013,7,30,20,99,22,30,996,22.35,0,0 +2013,7,30,21,99,22,29,997,24.14,0,0 +2013,7,30,22,82,23,28,998,25.93,0,0 +2013,7,30,23,96,23,28,999,27.72,0,0 +2013,7,31,0,76,19,22,1001,8.94,0,2 +2013,7,31,1,18,19,22,1001,10.73,0,3 +2013,7,31,2,19,19,22,999,4.02,0,4 +2013,7,31,3,22,19,23,998,4.02,0,0 +2013,7,31,4,20,19,23,999,7.15,0,0 +2013,7,31,5,20,19,23,999,10.28,0,0 +2013,7,31,6,25,19,22,999,0.45,0,0 +2013,7,31,7,29,20,23,1000,0.89,0,0 +2013,7,31,8,40,20,24,1000,2.68,0,0 +2013,7,31,9,34,20,26,1000,4.47,0,0 +2013,7,31,10,34,21,28,1000,1.79,0,0 +2013,7,31,11,42,19,29,1000,1.79,0,0 +2013,7,31,12,35,19,31,999,3.58,0,0 +2013,7,31,13,47,19,32,999,6.71,0,0 +2013,7,31,14,54,19,32,999,9.84,0,0 +2013,7,31,15,67,22,32,998,13.86,0,0 +2013,7,31,16,69,22,32,998,16.99,0,0 +2013,7,31,17,81,23,32,998,20.12,0,0 +2013,7,31,18,88,22,31,997,23.25,0,0 +2013,7,31,19,106,23,30,998,0.89,0,0 +2013,7,31,20,85,18,24,1000,9.83,0,1 +2013,7,31,21,31,20,23,1002,15.64,0,2 +2013,7,31,22,24,20,23,1003,3.13,0,3 +2013,7,31,23,33,20,22,1002,7.15,0,4 +2013,8,1,0,36,20,22,1001,1.79,0,0 +2013,8,1,1,36,20,22,1001,4.92,0,0 +2013,8,1,2,31,19,21,1001,1.79,0,0 +2013,8,1,3,31,19,21,1001,0.45,0,0 +2013,8,1,4,38,19,21,1001,0.89,0,0 +2013,8,1,5,42,19,21,1001,0.89,0,0 +2013,8,1,6,37,19,22,1002,3.13,0,0 +2013,8,1,7,34,20,23,1002,1.79,0,0 +2013,8,1,8,39,20,24,1003,1.79,0,0 +2013,8,1,9,42,20,25,1004,4.92,0,0 +2013,8,1,10,41,20,24,1004,6.71,0,1 +2013,8,1,11,39,20,25,1004,0.89,0,2 +2013,8,1,12,42,19,24,1005,4.02,0,3 +2013,8,1,13,45,20,23,1004,5.81,0,4 +2013,8,1,14,39,21,24,1004,0.89,0,0 +2013,8,1,15,46,21,24,1003,3.13,0,0 +2013,8,1,16,42,20,24,1004,6.26,0,0 +2013,8,1,17,45,19,26,1003,0.89,0,0 +2013,8,1,18,56,20,26,1002,1.79,0,0 +2013,8,1,19,62,20,25,1003,3.58,0,0 +2013,8,1,20,59,20,23,1004,0.89,0,0 +2013,8,1,21,55,20,22,1005,0.45,0,0 +2013,8,1,22,68,20,22,1006,1.34,0,0 +2013,8,1,23,73,20,22,1006,1.79,0,0 +2013,8,2,0,76,20,22,1005,2.68,0,0 +2013,8,2,1,80,20,22,1005,3.57,0,0 +2013,8,2,2,84,20,22,1005,4.46,0,0 +2013,8,2,3,90,20,21,1004,1.79,0,0 +2013,8,2,4,79,20,22,1005,0.89,0,0 +2013,8,2,5,69,19,21,1005,1.79,0,0 +2013,8,2,6,63,19,21,1005,0.45,0,0 +2013,8,2,7,61,20,23,1006,4.02,0,0 +2013,8,2,8,55,21,24,1006,7.15,0,0 +2013,8,2,9,65,21,26,1006,8.94,0,0 +2013,8,2,10,68,21,27,1006,0.89,0,0 +2013,8,2,11,74,20,29,1006,1.78,0,0 +2013,8,2,12,72,21,30,1006,2.67,0,0 +2013,8,2,13,70,21,30,1006,3.56,0,0 +2013,8,2,14,81,21,30,1006,1.79,0,0 +2013,8,2,15,88,20,31,1005,3.58,0,0 +2013,8,2,16,89,22,30,1004,6.71,0,0 +2013,8,2,17,107,21,30,1005,9.84,0,0 +2013,8,2,18,102,20,29,1005,7.15,0,1 +2013,8,2,19,48,19,27,1004,3.13,0,0 +2013,8,2,20,47,20,28,1004,4.02,0,0 +2013,8,2,21,36,16,26,1005,4.02,0,0 +2013,8,2,22,38,17,23,1004,3.13,0,0 +2013,8,2,23,61,17,26,1006,3.13,0,0 +2013,8,3,0,32,17,26,1006,1.79,0,0 +2013,8,3,1,32,15,26,1006,3.13,0,0 +2013,8,3,2,26,18,22,1005,0.89,0,0 +2013,8,3,3,19,15,25,1005,4.02,0,0 +2013,8,3,4,25,17,22,1003,1.79,0,0 +2013,8,3,5,38,17,23,1003,0.89,0,0 +2013,8,3,6,27,16,23,1003,4.92,0,0 +2013,8,3,7,18,17,23,1004,6.71,0,0 +2013,8,3,8,21,18,26,1004,1.79,0,0 +2013,8,3,9,26,18,29,1004,1.79,0,0 +2013,8,3,10,25,17,29,1004,1.79,0,0 +2013,8,3,11,25,17,30,1004,1.79,0,0 +2013,8,3,12,28,16,31,1004,3.13,0,0 +2013,8,3,13,32,16,32,1003,4.92,0,0 +2013,8,3,14,28,17,32,1002,6.71,0,0 +2013,8,3,15,28,14,33,1002,10.73,0,0 +2013,8,3,16,27,16,32,1002,15.65,0,0 +2013,8,3,17,30,17,32,1001,20.57,0,0 +2013,8,3,18,29,18,31,1001,25.49,0,0 +2013,8,3,19,37,19,28,1001,29.51,0,0 +2013,8,3,20,55,19,27,1001,32.64,0,0 +2013,8,3,21,62,20,27,1003,35.77,0,0 +2013,8,3,22,57,20,26,1003,36.66,0,0 +2013,8,3,23,63,20,27,1003,39.79,0,0 +2013,8,4,0,76,21,26,1002,42.92,0,0 +2013,8,4,1,75,21,26,1002,0.89,0,0 +2013,8,4,2,84,21,25,1001,1.79,0,0 +2013,8,4,3,90,21,24,1001,1.79,0,0 +2013,8,4,4,86,21,23,1001,0.89,0,0 +2013,8,4,5,100,20,23,1001,1.78,0,0 +2013,8,4,6,103,21,23,1001,2.67,0,0 +2013,8,4,7,109,22,25,1001,0.89,0,0 +2013,8,4,8,110,22,26,1001,0.89,0,0 +2013,8,4,9,107,22,28,1002,1.79,0,0 +2013,8,4,10,63,21,30,1001,3.13,0,0 +2013,8,4,11,66,21,30,1000,4.92,0,0 +2013,8,4,12,58,19,31,1000,6.71,0,0 +2013,8,4,13,76,20,32,999,8.5,0,0 +2013,8,4,14,80,21,33,998,10.29,0,0 +2013,8,4,15,75,21,33,997,13.42,0,0 +2013,8,4,16,62,22,33,997,16.55,0,0 +2013,8,4,17,96,23,32,996,20.57,0,0 +2013,8,4,18,122,23,31,996,24.59,0,0 +2013,8,4,19,169,24,31,996,28.61,0,0 +2013,8,4,20,144,19,20,1000,5.81,0,1 +2013,8,4,21,21,19,21,1000,10.73,0,2 +2013,8,4,22,21,19,21,1000,3.13,0,0 +2013,8,4,23,28,19,22,1000,6.26,0,1 +2013,8,5,0,31,19,22,999,0.89,0,2 +2013,8,5,1,38,19,21,999,1.79,0,0 +2013,8,5,2,42,19,21,999,1.79,0,0 +2013,8,5,3,48,19,21,998,1.79,0,0 +2013,8,5,4,54,18,20,998,1.79,0,0 +2013,8,5,5,62,18,20,998,2.68,0,0 +2013,8,5,6,50,18,20,998,4.47,0,0 +2013,8,5,7,55,19,22,998,6.26,0,0 +2013,8,5,8,46,19,23,999,8.05,0,0 +2013,8,5,9,39,19,26,999,9.84,0,0 +2013,8,5,10,35,19,27,998,1.79,0,0 +2013,8,5,11,35,18,28,999,3.58,0,0 +2013,8,5,12,35,19,30,998,1.79,0,0 +2013,8,5,13,41,19,31,998,0.89,0,0 +2013,8,5,14,41,18,32,997,1.78,0,0 +2013,8,5,15,46,20,32,997,1.79,0,0 +2013,8,5,16,54,19,32,997,3.58,0,0 +2013,8,5,17,57,20,32,996,6.71,0,0 +2013,8,5,18,55,21,31,996,9.84,0,0 +2013,8,5,19,43,20,29,997,12.97,0,0 +2013,8,5,20,34,20,27,997,14.76,0,0 +2013,8,5,21,40,21,26,998,16.55,0,0 +2013,8,5,22,39,21,26,998,17.44,0,0 +2013,8,5,23,46,22,25,998,0.89,0,0 +2013,8,6,0,55,22,25,998,2.68,0,0 +2013,8,6,1,69,22,25,999,1.79,0,0 +2013,8,6,2,71,22,25,998,3.58,0,0 +2013,8,6,3,80,22,25,998,5.37,0,0 +2013,8,6,4,86,22,25,999,0.89,0,0 +2013,8,6,5,90,22,25,998,1.79,0,0 +2013,8,6,6,94,22,25,999,3.58,0,0 +2013,8,6,7,116,22,26,999,0.89,0,0 +2013,8,6,8,124,22,26,1000,1.78,0,0 +2013,8,6,9,130,23,27,1000,2.67,0,0 +2013,8,6,10,139,23,27,1000,3.56,0,0 +2013,8,6,11,127,24,29,1000,1.79,0,0 +2013,8,6,12,130,24,30,1000,1.79,0,0 +2013,8,6,13,143,25,32,999,0.89,0,0 +2013,8,6,14,138,25,32,998,1.79,0,0 +2013,8,6,15,130,24,32,998,4.92,0,0 +2013,8,6,16,128,25,32,998,8.94,0,0 +2013,8,6,17,122,24,32,998,12.07,0,0 +2013,8,6,18,128,25,31,998,15.2,0,0 +2013,8,6,19,147,26,31,999,18.33,0,0 +2013,8,6,20,154,26,30,999,21.46,0,0 +2013,8,6,21,168,26,29,1000,23.25,0,0 +2013,8,6,22,169,26,30,1000,0.89,0,0 +2013,8,6,23,177,26,30,1000,3.13,0,0 +2013,8,7,0,151,23,27,1001,5.81,0,0 +2013,8,7,1,36,22,26,1001,4.02,0,0 +2013,8,7,2,40,21,25,1003,9.83,0,1 +2013,8,7,3,33,21,25,1002,3.13,0,0 +2013,8,7,4,37,21,24,1002,0.89,0,0 +2013,8,7,5,48,21,24,1002,3.13,0,0 +2013,8,7,6,43,21,24,1002,0.89,0,0 +2013,8,7,7,45,21,24,1002,1.78,0,1 +2013,8,7,8,43,22,25,1002,0.89,0,0 +2013,8,7,9,39,22,28,1003,3.13,0,0 +2013,8,7,10,37,22,29,1002,6.26,0,0 +2013,8,7,11,43,21,29,1003,1.79,0,0 +2013,8,7,12,34,20,30,1003,4.02,0,0 +2013,8,7,13,24,21,29,1003,8.04,0,0 +2013,8,7,14,27,22,29,1002,11.17,0,0 +2013,8,7,15,26,20,27,1003,19.22,0,1 +2013,8,7,16,19,19,26,1002,26.37,0,2 +2013,8,7,17,18,19,25,1002,30.39,0,3 +2013,8,7,18,23,19,25,1002,35.31,0,4 +2013,8,7,19,23,19,23,1001,39.33,0,0 +2013,8,7,20,32,19,23,1000,42.46,0,1 +2013,8,7,21,23,19,24,1002,45.59,0,0 +2013,8,7,22,23,18,24,1001,48.72,0,0 +2013,8,7,23,16,19,23,1001,51.85,0,0 +2013,8,8,0,21,19,23,1001,53.64,0,0 +2013,8,8,1,18,19,23,1000,55.43,0,0 +2013,8,8,2,20,19,23,1000,1.79,0,0 +2013,8,8,3,20,19,22,1000,1.79,0,0 +2013,8,8,4,17,19,23,1000,2.68,0,0 +2013,8,8,5,19,19,22,1000,4.47,0,0 +2013,8,8,6,21,19,23,1000,0.89,0,0 +2013,8,8,7,22,19,25,1000,1.78,0,0 +2013,8,8,8,29,20,25,1000,1.79,0,0 +2013,8,8,9,41,19,26,1000,1.79,0,0 +2013,8,8,10,33,20,29,1001,0.89,0,0 +2013,8,8,11,27,19,31,1000,2.68,0,0 +2013,8,8,12,35,19,32,1000,3.13,0,0 +2013,8,8,13,36,17,34,999,7.15,0,0 +2013,8,8,14,37,15,34,999,3.13,0,0 +2013,8,8,15,31,17,35,998,4.92,0,0 +2013,8,8,16,34,20,34,998,8.94,0,0 +2013,8,8,17,30,19,34,998,13.86,0,0 +2013,8,8,18,31,18,32,998,18.78,0,0 +2013,8,8,19,50,21,31,999,22.8,0,0 +2013,8,8,20,59,22,29,1000,0.89,0,0 +2013,8,8,21,58,22,31,1001,4.92,0,0 +2013,8,8,22,27,19,28,1000,1.79,0,0 +2013,8,8,23,50,18,25,1000,4.92,0,0 +2013,8,9,0,30,19,24,1000,8.05,0,0 +2013,8,9,1,31,18,22,1000,12.07,0,0 +2013,8,9,2,26,18,22,1000,15.2,0,0 +2013,8,9,3,24,18,21,1000,16.99,0,0 +2013,8,9,4,31,17,21,1000,20.12,0,0 +2013,8,9,5,23,17,20,1001,24.14,0,0 +2013,8,9,6,29,18,22,1001,25.93,0,0 +2013,8,9,7,26,18,24,1001,29.06,0,0 +2013,8,9,8,28,17,27,1002,30.85,0,0 +2013,8,9,9,22,16,29,1002,33.98,0,0 +2013,8,9,10,29,16,30,1001,1.79,0,0 +2013,8,9,11,28,17,33,1001,1.79,0,0 +2013,8,9,12,32,17,33,1001,3.13,0,0 +2013,8,9,13,38,16,35,1000,1.79,0,0 +2013,8,9,14,42,17,35,1000,4.92,0,0 +2013,8,9,15,36,16,37,1000,1.79,0,0 +2013,8,9,16,40,16,37,999,3.13,0,0 +2013,8,9,17,36,20,35,999,8.05,0,0 +2013,8,9,18,41,19,34,999,12.07,0,0 +2013,8,9,19,43,19,33,1000,16.09,0,0 +2013,8,9,20,49,22,30,1001,17.88,0,0 +2013,8,9,21,60,23,27,1001,18.77,0,0 +2013,8,9,22,65,22,28,1002,20.56,0,0 +2013,8,9,23,56,23,27,1002,22.35,0,0 +2013,8,10,0,61,23,27,1002,1.79,0,0 +2013,8,10,1,96,23,26,1002,2.68,0,0 +2013,8,10,2,101,22,25,1003,0.89,0,0 +2013,8,10,3,101,21,24,1002,2.68,0,0 +2013,8,10,4,62,20,24,1003,4.47,0,0 +2013,8,10,5,54,20,24,1003,6.26,0,0 +2013,8,10,6,42,20,24,1004,0.89,0,0 +2013,8,10,7,45,20,26,1005,1.78,0,0 +2013,8,10,8,39,20,26,1005,4.02,0,0 +2013,8,10,9,46,21,27,1006,0.89,0,0 +2013,8,10,10,62,21,28,1006,1.78,0,0 +2013,8,10,11,104,24,30,1005,2.67,0,0 +2013,8,10,12,115,24,32,1005,1.79,0,0 +2013,8,10,13,117,24,33,1004,3.58,0,0 +2013,8,10,14,113,24,34,1004,1.79,0,0 +2013,8,10,15,128,23,35,1004,4.02,0,0 +2013,8,10,16,132,23,34,1004,8.04,0,0 +2013,8,10,17,116,23,34,1004,12.06,0,0 +2013,8,10,18,93,23,33,1004,15.19,0,0 +2013,8,10,19,100,23,31,1004,16.98,0,0 +2013,8,10,20,111,22,30,1005,18.77,0,0 +2013,8,10,21,109,23,28,1006,0.89,0,0 +2013,8,10,22,93,23,28,1006,1.79,0,0 +2013,8,10,23,80,24,27,1006,3.58,0,0 +2013,8,11,0,74,23,26,1006,0.89,0,0 +2013,8,11,1,70,24,27,1006,1.79,0,0 +2013,8,11,2,80,24,27,1006,3.58,0,0 +2013,8,11,3,106,24,27,1007,6.71,0,0 +2013,8,11,4,141,24,27,1006,8.5,0,0 +2013,8,11,5,172,24,27,1006,0.89,0,0 +2013,8,11,6,178,24,27,1006,1.79,0,0 +2013,8,11,7,203,24,27,1007,3.58,0,0 +2013,8,11,8,208,24,28,1007,5.37,0,1 +2013,8,11,9,175,24,27,1007,4.02,0,3 +2013,8,11,10,180,25,27,1007,1.79,0,0 +2013,8,11,11,194,25,29,1007,4.92,0,0 +2013,8,11,12,161,25,31,1006,8.05,0,0 +2013,8,11,13,176,25,32,1005,11.18,0,0 +2013,8,11,14,167,25,32,1004,15.2,0,0 +2013,8,11,15,143,25,31,1003,19.22,0,1 +2013,8,11,16,127,23,30,1004,22.35,0,2 +2013,8,11,17,55,24,27,1004,0.89,0,3 +2013,8,11,18,47,22,25,1004,4.92,0,4 +2013,8,11,19,58,22,26,1005,8.94,0,0 +2013,8,11,20,62,22,25,1006,3.13,0,1 +2013,8,11,21,57,21,26,1006,3.13,0,0 +2013,8,11,22,58,22,24,1005,1.79,0,1 +2013,8,11,23,60,21,25,1004,3.58,0,2 +2013,8,12,0,57,23,25,1004,7.6,0,0 +2013,8,12,1,61,22,24,1005,10.73,0,0 +2013,8,12,2,67,22,25,1005,13.86,0,1 +2013,8,12,3,56,22,24,1005,16.99,0,2 +2013,8,12,4,48,21,24,1004,18.78,0,0 +2013,8,12,5,42,21,24,1004,20.57,0,0 +2013,8,12,6,38,21,24,1004,22.36,0,0 +2013,8,12,7,42,22,24,1004,24.15,0,0 +2013,8,12,8,51,22,24,1005,25.94,0,0 +2013,8,12,9,50,22,25,1006,29.07,0,0 +2013,8,12,10,48,22,26,1006,32.2,0,0 +2013,8,12,11,56,22,28,1005,35.33,0,0 +2013,8,12,12,47,22,29,1005,38.46,0,0 +2013,8,12,13,50,22,29,1005,41.59,0,0 +2013,8,12,14,53,21,29,1005,45.61,0,0 +2013,8,12,15,47,21,30,1004,49.63,0,0 +2013,8,12,16,51,21,30,1004,52.76,0,0 +2013,8,12,17,49,22,30,1003,55.89,0,0 +2013,8,12,18,53,22,29,1004,59.91,0,0 +2013,8,12,19,50,22,28,1004,63.04,0,0 +2013,8,12,20,57,22,28,1004,66.17,0,0 +2013,8,12,21,63,22,27,1005,0.89,0,0 +2013,8,12,22,64,22,27,1005,1.79,0,0 +2013,8,12,23,77,22,26,1005,2.68,0,0 +2013,8,13,0,82,23,26,1005,3.57,0,0 +2013,8,13,1,87,22,25,1005,5.36,0,0 +2013,8,13,2,90,22,25,1005,7.15,0,0 +2013,8,13,3,82,22,25,1006,10.28,0,0 +2013,8,13,4,69,22,25,1006,12.07,0,0 +2013,8,13,5,63,22,25,1006,12.96,0,0 +2013,8,13,6,73,22,25,1006,14.75,0,0 +2013,8,13,7,77,22,26,1006,16.54,0,0 +2013,8,13,8,78,23,27,1006,0.89,0,0 +2013,8,13,9,76,23,28,1005,1.78,0,0 +2013,8,13,10,69,23,30,1005,2.67,0,0 +2013,8,13,11,64,22,30,1005,3.56,0,0 +2013,8,13,12,76,22,30,1004,4.45,0,0 +2013,8,13,13,77,22,31,1004,1.79,0,0 +2013,8,13,14,78,22,32,1003,3.58,0,0 +2013,8,13,15,78,22,32,1003,6.71,0,0 +2013,8,13,16,88,22,32,1002,10.73,0,0 +2013,8,13,17,89,23,31,1003,14.75,0,0 +2013,8,13,18,96,23,30,1003,17.88,0,0 +2013,8,13,19,95,23,29,1003,21.01,0,0 +2013,8,13,20,92,24,28,1004,21.9,0,0 +2013,8,13,21,95,23,28,1005,1.79,0,0 +2013,8,13,22,96,24,28,1005,3.13,0,0 +2013,8,13,23,36,15,26,999,11.18,0,1 +2013,8,14,0,49,19,24,1003,3.13,0,0 +2013,8,14,1,73,20,24,1004,6.26,0,0 +2013,8,14,2,51,20,24,1004,0.89,0,0 +2013,8,14,3,41,20,24,1003,1.78,0,0 +2013,8,14,4,45,21,24,1004,3.13,0,0 +2013,8,14,5,54,21,25,1004,1.79,0,0 +2013,8,14,6,65,21,24,1005,0.89,0,0 +2013,8,14,7,53,22,24,1005,0.89,0,0 +2013,8,14,8,29,21,27,1005,1.79,0,0 +2013,8,14,9,42,22,29,1005,0.89,0,0 +2013,8,14,10,66,22,30,1004,1.79,0,0 +2013,8,14,11,65,23,31,1004,3.58,0,0 +2013,8,14,12,58,23,32,1004,5.37,0,0 +2013,8,14,13,58,22,32,1003,7.16,0,0 +2013,8,14,14,49,23,33,1003,11.18,0,0 +2013,8,14,15,59,23,32,1003,15.2,0,0 +2013,8,14,16,69,24,32,1002,20.12,0,0 +2013,8,14,17,80,23,32,1003,24.14,0,0 +2013,8,14,18,87,24,31,1002,27.27,0,0 +2013,8,14,19,80,24,30,1003,30.4,0,0 +2013,8,14,20,84,24,29,1004,33.53,0,0 +2013,8,14,21,88,24,29,1004,36.66,0,0 +2013,8,14,22,77,24,29,1004,39.79,0,0 +2013,8,14,23,69,24,29,1004,41.58,0,0 +2013,8,15,0,57,24,28,1003,43.37,0,0 +2013,8,15,1,61,24,27,1004,45.16,0,0 +2013,8,15,2,66,23,27,1003,46.95,0,0 +2013,8,15,3,72,23,27,1003,48.74,0,0 +2013,8,15,4,70,24,27,1003,50.53,0,0 +2013,8,15,5,78,24,26,1003,0.89,0,0 +2013,8,15,6,79,24,26,1003,1.79,0,0 +2013,8,15,7,79,24,27,1003,0.89,0,0 +2013,8,15,8,77,24,28,1004,1.78,0,0 +2013,8,15,9,76,24,29,1004,2.67,0,0 +2013,8,15,10,86,24,31,1004,3.56,0,0 +2013,8,15,11,91,24,31,1003,1.79,0,0 +2013,8,15,12,90,23,32,1003,3.58,0,0 +2013,8,15,13,94,23,32,1002,5.37,0,0 +2013,8,15,14,101,23,32,1002,8.5,0,0 +2013,8,15,15,105,24,32,1001,11.63,0,0 +2013,8,15,16,109,24,32,1001,14.76,0,0 +2013,8,15,17,115,24,32,1001,17.89,0,0 +2013,8,15,18,139,25,31,1001,19.68,0,0 +2013,8,15,19,142,25,30,1001,21.47,0,0 +2013,8,15,20,151,26,30,1001,23.26,0,0 +2013,8,15,21,164,26,30,1001,25.05,0,0 +2013,8,15,22,160,26,29,1001,26.84,0,0 +2013,8,15,23,160,26,29,1000,28.63,0,0 +2013,8,16,0,179,26,29,1000,31.76,0,1 +2013,8,16,1,55,23,27,999,37.57,0,0 +2013,8,16,2,60,22,27,998,40.7,0,0 +2013,8,16,3,65,23,26,999,42.49,0,0 +2013,8,16,4,61,23,26,998,0.89,0,0 +2013,8,16,5,60,23,26,997,1.79,0,0 +2013,8,16,6,57,24,27,997,0.89,0,0 +2013,8,16,7,68,24,27,997,1.78,0,1 +2013,8,16,8,75,24,27,997,1.79,0,2 +2013,8,16,9,83,24,27,997,3.58,0,0 +2013,8,16,10,96,25,28,997,0.89,0,0 +2013,8,16,11,111,25,28,997,1.78,0,0 +2013,8,16,12,98,25,30,997,2.67,0,0 +2013,8,16,13,102,26,30,996,1.79,0,0 +2013,8,16,14,117,26,30,996,0.89,0,0 +2013,8,16,15,127,26,30,997,3.13,0,0 +2013,8,16,16,136,26,30,996,1.79,0,0 +2013,8,16,17,140,26,30,996,0.89,0,0 +2013,8,16,18,154,26,30,997,1.78,0,0 +2013,8,16,19,143,26,29,997,1.79,0,0 +2013,8,16,20,145,26,29,997,0.89,0,0 +2013,8,16,21,148,25,28,998,0.89,0,0 +2013,8,16,22,154,26,28,998,1.78,0,1 +2013,8,16,23,150,25,28,998,2.67,0,0 +2013,8,17,0,155,25,28,998,3.56,0,0 +2013,8,17,1,157,26,28,997,1.79,0,0 +2013,8,17,2,145,25,28,998,0.89,0,0 +2013,8,17,3,113,25,27,998,0.45,0,0 +2013,8,17,4,40,24,26,998,1.79,0,0 +2013,8,17,5,24,17,30,999,6.71,0,0 +2013,8,17,6,26,15,29,999,11.63,0,0 +2013,8,17,7,21,14,31,1001,18.78,0,0 +2013,8,17,8,24,12,32,1001,24.59,0,0 +2013,8,17,9,25,13,33,1001,31.74,0,0 +2013,8,17,10,32,12,33,1001,39.79,0,0 +2013,8,17,11,39,12,34,1001,47.84,0,0 +2013,8,17,12,42,10,34,1001,55.89,0,0 +2013,8,17,13,29,11,34,1001,61.7,0,0 +2013,8,17,14,29,11,35,1001,69.75,0,0 +2013,8,17,15,27,11,35,1001,77.8,0,0 +2013,8,17,16,24,11,35,1001,86.74,0,0 +2013,8,17,17,20,10,35,1001,94.79,0,0 +2013,8,17,18,18,10,34,1001,101.94,0,0 +2013,8,17,19,18,9,33,1002,107.75,0,0 +2013,8,17,20,17,10,32,1002,112.67,0,0 +2013,8,17,21,13,13,28,1003,116.69,0,0 +2013,8,17,22,17,16,25,1004,1.79,0,0 +2013,8,17,23,17,14,25,1004,1.79,0,0 +2013,8,18,0,16,14,23,1005,0.89,0,0 +2013,8,18,1,9,14,24,1005,3.13,0,0 +2013,8,18,2,13,14,24,1006,1.79,0,0 +2013,8,18,3,9,15,21,1006,3.13,0,0 +2013,8,18,4,15,16,21,1007,1.79,0,0 +2013,8,18,5,18,15,23,1008,0.45,0,0 +2013,8,18,6,13,16,22,1008,1.79,0,0 +2013,8,18,7,11,17,25,1009,5.81,0,0 +2013,8,18,8,10,14,28,1009,4.02,0,0 +2013,8,18,9,16,15,29,1009,8.04,0,0 +2013,8,18,10,13,14,31,1009,3.13,0,0 +2013,8,18,11,11,14,32,1009,6.26,0,0 +2013,8,18,12,12,12,33,1009,1.79,0,0 +2013,8,18,13,12,13,33,1008,1.79,0,0 +2013,8,18,14,10,13,33,1008,3.13,0,0 +2013,8,18,15,16,13,34,1007,1.79,0,0 +2013,8,18,16,15,13,33,1007,3.13,0,0 +2013,8,18,17,18,13,32,1006,4.92,0,0 +2013,8,18,18,22,14,32,1006,0.89,0,0 +2013,8,18,19,20,14,30,1007,4.02,0,0 +2013,8,18,20,27,14,30,1008,7.15,0,0 +2013,8,18,21,21,14,30,1009,3.13,0,0 +2013,8,18,22,26,17,27,1009,4.02,0,0 +2013,8,18,23,27,17,24,1009,1.79,0,0 +2013,8,19,0,NA,18,23,1010,4.92,0,0 +2013,8,19,1,44,18,23,1010,6.71,0,0 +2013,8,19,2,39,17,21,1010,9.84,0,0 +2013,8,19,3,36,16,22,1010,12.97,0,0 +2013,8,19,4,29,16,21,1010,16.99,0,0 +2013,8,19,5,22,15,19,1010,20.12,0,0 +2013,8,19,6,20,16,20,1011,0.89,0,0 +2013,8,19,7,18,17,23,1011,1.78,0,0 +2013,8,19,8,19,16,27,1011,1.79,0,0 +2013,8,19,9,17,13,28,1012,1.79,0,0 +2013,8,19,10,21,13,30,1012,3.13,0,0 +2013,8,19,11,17,13,32,1011,4.92,0,0 +2013,8,19,12,19,11,32,1011,0.89,0,0 +2013,8,19,13,14,13,33,1010,2.68,0,0 +2013,8,19,14,17,11,34,1009,3.57,0,0 +2013,8,19,15,8,11,33,1009,1.79,0,0 +2013,8,19,16,12,7,34,1008,0.89,0,0 +2013,8,19,17,17,12,33,1008,1.79,0,0 +2013,8,19,18,25,12,31,1008,3.58,0,0 +2013,8,19,19,36,13,29,1008,6.71,0,0 +2013,8,19,20,27,18,28,1009,0.89,0,0 +2013,8,19,21,33,15,27,1009,1.79,0,0 +2013,8,19,22,40,17,25,1009,3.58,0,0 +2013,8,19,23,48,19,24,1010,4.47,0,0 +2013,8,20,0,41,19,24,1010,6.26,0,0 +2013,8,20,1,57,19,23,1010,8.05,0,0 +2013,8,20,2,70,19,23,1009,9.84,0,0 +2013,8,20,3,69,19,24,1009,0.45,0,0 +2013,8,20,4,76,19,24,1009,3.13,0,0 +2013,8,20,5,92,19,23,1010,4.92,0,0 +2013,8,20,6,116,19,23,1010,1.79,0,0 +2013,8,20,7,122,20,24,1010,0.89,0,0 +2013,8,20,8,120,20,26,1011,1.79,0,0 +2013,8,20,9,118,19,27,1011,3.58,0,0 +2013,8,20,10,108,20,29,1010,5.37,0,0 +2013,8,20,11,98,20,29,1010,7.16,0,0 +2013,8,20,12,91,20,29,1009,10.29,0,0 +2013,8,20,13,92,20,29,1008,13.42,0,0 +2013,8,20,14,70,21,28,1008,4.02,0,0 +2013,8,20,15,63,22,27,1007,7.15,0,1 +2013,8,20,16,66,21,27,1007,3.13,0,2 +2013,8,20,17,75,22,26,1007,4.02,0,0 +2013,8,20,18,104,22,25,1007,8.04,0,1 +2013,8,20,19,105,22,24,1007,3.13,0,2 +2013,8,20,20,109,22,25,1008,4.92,0,3 +2013,8,20,21,110,21,25,1009,6.71,0,0 +2013,8,20,22,123,22,24,1008,1.79,0,0 +2013,8,20,23,115,22,24,1008,0.89,0,0 +2013,8,21,0,NA,21,24,1008,1.79,0,1 +2013,8,21,1,118,21,24,1008,0.89,0,0 +2013,8,21,2,117,22,24,1008,1.78,0,0 +2013,8,21,3,111,21,24,1008,1.79,0,0 +2013,8,21,4,113,22,24,1008,0.89,0,0 +2013,8,21,5,120,22,24,1008,0.89,0,0 +2013,8,21,6,118,21,23,1008,1.79,0,0 +2013,8,21,7,140,22,24,1008,0.89,0,0 +2013,8,21,8,117,22,25,1009,1.78,0,0 +2013,8,21,9,118,22,26,1009,2.67,0,0 +2013,8,21,10,121,22,27,1009,3.56,0,0 +2013,8,21,11,109,22,27,1009,1.79,0,0 +2013,8,21,12,112,20,28,1008,4.92,0,0 +2013,8,21,13,101,20,29,1007,8.05,0,0 +2013,8,21,14,95,19,31,1007,11.18,0,0 +2013,8,21,15,85,18,31,1007,14.31,0,0 +2013,8,21,16,69,18,31,1006,18.33,0,0 +2013,8,21,17,61,17,31,1006,21.46,0,0 +2013,8,21,18,72,17,28,1006,24.59,0,0 +2013,8,21,19,74,17,26,1006,26.38,0,0 +2013,8,21,20,73,17,25,1007,28.17,0,0 +2013,8,21,21,90,17,24,1007,29.96,0,0 +2013,8,21,22,93,18,23,1007,31.75,0,0 +2013,8,21,23,90,18,21,1007,32.64,0,0 +2013,8,22,0,106,18,21,1007,33.53,0,0 +2013,8,22,1,97,19,21,1007,35.32,0,0 +2013,8,22,2,98,19,21,1008,0.89,0,0 +2013,8,22,3,116,18,21,1008,0.89,0,0 +2013,8,22,4,144,19,21,1008,0.89,0,0 +2013,8,22,5,NA,19,21,1008,0.45,0,0 +2013,8,22,6,143,18,20,1009,3.13,0,0 +2013,8,22,7,140,20,22,1009,0.89,0,0 +2013,8,22,8,117,19,24,1009,3.13,0,0 +2013,8,22,9,108,19,24,1009,6.26,0,0 +2013,8,22,10,119,19,25,1010,0.89,0,0 +2013,8,22,11,115,20,25,1010,1.79,0,1 +2013,8,22,12,115,21,24,1009,1.79,0,2 +2013,8,22,13,123,20,26,1009,3.58,0,3 +2013,8,22,14,NA,20,26,1008,0.89,0,4 +2013,8,22,15,NA,20,26,1008,1.78,0,0 +2013,8,22,16,127,21,26,1008,1.79,0,0 +2013,8,22,17,139,21,26,1008,4.92,0,0 +2013,8,22,18,128,20,27,1008,6.71,0,0 +2013,8,22,19,105,21,25,1008,0.89,0,0 +2013,8,22,20,117,20,26,1009,1.79,0,0 +2013,8,22,21,123,21,24,1009,0.89,0,0 +2013,8,22,22,141,20,24,1009,1.79,0,0 +2013,8,22,23,121,21,23,1009,0.45,0,0 +2013,8,23,0,110,21,23,1009,1.79,0,0 +2013,8,23,1,116,21,23,1009,2.68,0,0 +2013,8,23,2,148,21,23,1009,4.47,0,0 +2013,8,23,3,150,20,23,1009,7.6,0,0 +2013,8,23,4,114,19,23,1009,10.73,0,0 +2013,8,23,5,89,19,22,1009,14.75,0,0 +2013,8,23,6,72,19,22,1010,18.77,0,0 +2013,8,23,7,97,19,23,1010,21.9,0,0 +2013,8,23,8,77,18,24,1010,25.03,0,0 +2013,8,23,9,77,18,26,1010,26.82,0,0 +2013,8,23,10,68,16,28,1010,30.84,0,0 +2013,8,23,11,59,14,32,1010,32.63,0,0 +2013,8,23,12,35,11,33,1009,1.79,0,0 +2013,8,23,13,26,10,33,1008,5.81,0,0 +2013,8,23,14,5,11,34,1008,7.6,0,0 +2013,8,23,15,12,12,34,1007,9.39,0,0 +2013,8,23,16,15,11,34,1007,11.18,0,0 +2013,8,23,17,18,13,33,1007,1.79,0,0 +2013,8,23,18,18,12,32,1007,0.89,0,0 +2013,8,23,19,19,17,27,1007,1.78,0,0 +2013,8,23,20,23,17,25,1008,2.67,0,0 +2013,8,23,21,36,16,25,1009,3.56,0,0 +2013,8,23,22,43,17,23,1009,4.01,0,0 +2013,8,23,23,39,18,22,1009,0.89,0,0 +2013,8,24,0,44,17,22,1010,2.68,0,0 +2013,8,24,1,73,17,22,1010,1.79,0,0 +2013,8,24,2,85,17,21,1010,0.89,0,0 +2013,8,24,3,81,17,20,1010,1.79,0,0 +2013,8,24,4,64,16,20,1009,3.58,0,0 +2013,8,24,5,54,16,19,1009,5.37,0,0 +2013,8,24,6,38,16,20,1010,8.5,0,0 +2013,8,24,7,27,16,21,1010,11.63,0,0 +2013,8,24,8,31,16,24,1010,14.76,0,0 +2013,8,24,9,31,15,27,1010,17.89,0,0 +2013,8,24,10,22,13,29,1010,21.02,0,0 +2013,8,24,11,22,12,31,1010,24.15,0,0 +2013,8,24,12,20,12,32,1009,1.79,0,0 +2013,8,24,13,22,9,35,1009,3.13,0,0 +2013,8,24,14,11,12,34,1008,3.13,0,0 +2013,8,24,15,11,10,34,1008,6.26,0,0 +2013,8,24,16,11,13,34,1007,3.13,0,0 +2013,8,24,17,18,11,34,1007,4.92,0,0 +2013,8,24,18,17,13,31,1007,6.71,0,0 +2013,8,24,19,39,15,28,1007,8.5,0,0 +2013,8,24,20,50,13,27,1008,10.29,0,0 +2013,8,24,21,47,16,26,1008,12.08,0,0 +2013,8,24,22,49,17,23,1008,0.89,0,0 +2013,8,24,23,52,18,23,1008,1.78,0,0 +2013,8,25,0,70,18,22,1008,1.79,0,0 +2013,8,25,1,95,18,22,1008,3.58,0,0 +2013,8,25,2,101,17,20,1008,1.79,0,0 +2013,8,25,3,59,17,20,1008,3.58,0,0 +2013,8,25,4,54,17,20,1008,5.37,0,0 +2013,8,25,5,47,16,19,1008,7.16,0,0 +2013,8,25,6,28,16,19,1008,8.95,0,0 +2013,8,25,7,31,17,23,1009,10.74,0,0 +2013,8,25,8,26,17,26,1009,0.89,0,0 +2013,8,25,9,29,16,28,1009,1.79,0,0 +2013,8,25,10,27,17,29,1008,0.89,0,0 +2013,8,25,11,29,16,31,1008,1.78,0,0 +2013,8,25,12,27,15,33,1007,1.79,0,0 +2013,8,25,13,33,14,33,1006,3.58,0,0 +2013,8,25,14,31,13,35,1005,5.37,0,0 +2013,8,25,15,44,11,34,1004,9.39,0,0 +2013,8,25,16,38,13,34,1004,15.2,0,0 +2013,8,25,17,37,13,34,1004,21.01,0,0 +2013,8,25,18,42,12,32,1003,25.03,0,0 +2013,8,25,19,37,12,30,1003,29.05,0,0 +2013,8,25,20,45,15,28,1004,33.07,0,0 +2013,8,25,21,41,16,27,1004,36.2,0,0 +2013,8,25,22,50,17,26,1004,0.89,0,0 +2013,8,25,23,49,17,25,1003,1.78,0,0 +2013,8,26,0,51,17,24,1004,2.67,0,0 +2013,8,26,1,53,18,23,1004,1.79,0,0 +2013,8,26,2,62,18,22,1004,0.89,0,0 +2013,8,26,3,65,18,21,1004,3.13,0,0 +2013,8,26,4,74,18,21,1004,0.89,0,0 +2013,8,26,5,74,17,20,1005,3.13,0,0 +2013,8,26,6,57,17,20,1006,7.15,0,0 +2013,8,26,7,50,17,24,1006,10.28,0,0 +2013,8,26,8,50,16,27,1006,4.02,0,0 +2013,8,26,9,37,14,30,1007,7.15,0,0 +2013,8,26,10,23,12,31,1007,11.17,0,0 +2013,8,26,11,16,10,32,1007,14.3,0,0 +2013,8,26,12,13,12,33,1006,3.13,0,0 +2013,8,26,13,16,13,33,1005,4.92,0,0 +2013,8,26,14,30,11,33,1005,8.94,0,0 +2013,8,26,15,30,12,33,1004,12.96,0,0 +2013,8,26,16,38,16,32,1004,17.88,0,0 +2013,8,26,17,75,16,31,1004,21.9,0,0 +2013,8,26,18,121,20,29,1004,26.82,0,0 +2013,8,26,19,98,19,28,1005,31.74,0,0 +2013,8,26,20,84,20,27,1006,36.66,0,0 +2013,8,26,21,71,20,26,1007,41.58,0,0 +2013,8,26,22,55,19,24,1007,46.5,0,0 +2013,8,26,23,50,19,24,1008,49.63,0,0 +2013,8,27,0,59,19,24,1008,51.42,0,0 +2013,8,27,1,57,18,23,1008,54.55,0,0 +2013,8,27,2,51,18,22,1008,57.68,0,0 +2013,8,27,3,47,18,23,1008,60.81,0,0 +2013,8,27,4,51,17,23,1008,63.94,0,0 +2013,8,27,5,51,17,23,1008,67.07,0,0 +2013,8,27,6,51,17,23,1008,70.2,0,0 +2013,8,27,7,56,17,23,1008,71.99,0,0 +2013,8,27,8,57,17,23,1009,73.78,0,0 +2013,8,27,9,59,17,23,1009,76.91,0,0 +2013,8,27,10,60,18,24,1009,80.04,0,0 +2013,8,27,11,62,18,25,1009,83.17,0,0 +2013,8,27,12,65,19,25,1008,86.3,0,0 +2013,8,27,13,60,19,25,1007,1.79,0,0 +2013,8,27,14,65,19,26,1007,3.58,0,0 +2013,8,27,15,67,20,26,1006,1.79,0,0 +2013,8,27,16,61,20,26,1006,5.81,0,0 +2013,8,27,17,54,20,26,1006,9.83,0,0 +2013,8,27,18,50,20,26,1006,12.96,0,0 +2013,8,27,19,48,20,25,1005,16.09,0,0 +2013,8,27,20,46,19,25,1006,21.01,0,0 +2013,8,27,21,55,19,24,1005,25.03,0,1 +2013,8,27,22,44,19,24,1005,29.05,0,0 +2013,8,27,23,38,19,24,1005,32.18,0,1 +2013,8,28,0,33,18,23,1004,36.2,0,2 +2013,8,28,1,37,18,23,1003,40.22,0,0 +2013,8,28,2,39,18,23,1002,45.14,0,0 +2013,8,28,3,40,18,23,1002,48.27,0,0 +2013,8,28,4,49,19,22,1001,52.29,0,1 +2013,8,28,5,57,19,22,1001,54.08,0,2 +2013,8,28,6,54,19,21,1000,55.87,0,3 +2013,8,28,7,60,19,21,1000,1.79,0,4 +2013,8,28,8,68,19,21,1001,2.68,0,5 +2013,8,28,9,69,19,21,1001,0.89,0,0 +2013,8,28,10,75,20,22,1000,0.89,0,0 +2013,8,28,11,84,20,22,1000,1.79,0,0 +2013,8,28,12,103,20,24,999,0.89,0,0 +2013,8,28,13,94,21,26,999,1.78,0,0 +2013,8,28,14,81,20,25,999,2.67,0,0 +2013,8,28,15,75,20,25,998,3.56,0,0 +2013,8,28,16,85,20,25,998,4.45,0,0 +2013,8,28,17,74,20,25,998,5.34,0,0 +2013,8,28,18,75,21,25,998,6.23,0,0 +2013,8,28,19,72,21,24,998,7.12,0,0 +2013,8,28,20,85,21,23,998,1.79,0,0 +2013,8,28,21,101,21,23,998,0.89,0,0 +2013,8,28,22,112,20,23,998,1.78,0,0 +2013,8,28,23,116,20,22,998,2.67,0,0 +2013,8,29,0,124,20,22,998,0.89,0,0 +2013,8,29,1,124,20,22,998,0.89,0,0 +2013,8,29,2,113,20,22,999,1.78,0,0 +2013,8,29,3,118,20,21,998,1.79,0,0 +2013,8,29,4,106,19,20,999,0.89,0,0 +2013,8,29,5,73,18,21,999,3.13,0,0 +2013,8,29,6,16,15,22,1000,8.05,0,0 +2013,8,29,7,20,9,25,1000,16.99,0,0 +2013,8,29,8,14,8,26,1000,25.93,0,0 +2013,8,29,9,16,8,27,1000,35.76,0,0 +2013,8,29,10,12,8,28,1001,43.81,0,0 +2013,8,29,11,9,8,29,1001,53.64,0,0 +2013,8,29,12,11,7,29,1002,65.71,0,0 +2013,8,29,13,9,5,29,1002,77.78,0,0 +2013,8,29,14,10,5,29,1003,88.96,0,0 +2013,8,29,15,9,6,29,1003,98.79,0,0 +2013,8,29,16,7,6,28,1004,109.97,0,0 +2013,8,29,17,10,7,27,1004,119.8,0,0 +2013,8,29,18,8,7,25,1005,127.85,0,0 +2013,8,29,19,12,7,24,1005,135.9,0,0 +2013,8,29,20,15,8,24,1006,141.71,0,0 +2013,8,29,21,12,9,23,1007,146.63,0,0 +2013,8,29,22,16,12,20,1008,149.76,0,0 +2013,8,29,23,14,11,22,1008,5.81,0,0 +2013,8,30,0,14,10,21,1010,11.62,0,0 +2013,8,30,1,8,8,21,1010,16.54,0,0 +2013,8,30,2,10,10,20,1011,21.46,0,1 +2013,8,30,3,10,10,19,1011,3.13,0,0 +2013,8,30,4,9,12,18,1011,4.92,0,1 +2013,8,30,5,6,12,18,1012,6.71,0,0 +2013,8,30,6,10,11,19,1013,9.84,0,0 +2013,8,30,7,13,11,19,1013,12.97,0,0 +2013,8,30,8,13,11,21,1014,14.76,0,0 +2013,8,30,9,16,8,23,1014,18.78,0,0 +2013,8,30,10,13,8,25,1014,22.8,0,0 +2013,8,30,11,10,6,26,1013,27.72,0,0 +2013,8,30,12,9,5,26,1012,31.74,0,0 +2013,8,30,13,NA,5,27,1012,36.66,0,0 +2013,8,30,14,NA,6,28,1011,40.68,0,0 +2013,8,30,15,NA,6,28,1010,4.02,0,0 +2013,8,30,16,NA,4,29,1010,1.79,0,0 +2013,8,30,17,10,5,28,1010,0.89,0,0 +2013,8,30,18,13,6,25,1010,3.13,0,0 +2013,8,30,19,18,9,23,1010,4.92,0,0 +2013,8,30,20,34,12,20,1011,6.71,0,0 +2013,8,30,21,36,9,21,1011,8.5,0,0 +2013,8,30,22,33,8,23,1011,13.42,0,0 +2013,8,30,23,31,11,20,1012,0.89,0,0 +2013,8,31,0,34,12,17,1012,0.89,0,0 +2013,8,31,1,49,12,18,1011,1.79,0,0 +2013,8,31,2,35,11,16,1011,2.68,0,0 +2013,8,31,3,43,11,16,1012,4.47,0,0 +2013,8,31,4,39,11,15,1011,6.26,0,0 +2013,8,31,5,30,10,14,1012,8.05,0,0 +2013,8,31,6,18,10,15,1012,9.84,0,0 +2013,8,31,7,21,12,18,1013,11.63,0,0 +2013,8,31,8,25,10,22,1013,15.65,0,0 +2013,8,31,9,27,9,24,1013,4.02,0,0 +2013,8,31,10,19,7,27,1013,3.13,0,0 +2013,8,31,11,13,7,28,1013,6.26,0,0 +2013,8,31,12,12,6,29,1012,9.39,0,0 +2013,8,31,13,10,4,30,1012,0.89,0,0 +2013,8,31,14,10,3,30,1011,1.79,0,0 +2013,8,31,15,14,2,31,1010,1.79,0,0 +2013,8,31,16,15,2,31,1010,3.13,0,0 +2013,8,31,17,18,7,28,1010,1.79,0,0 +2013,8,31,18,21,10,25,1010,2.68,0,0 +2013,8,31,19,16,10,25,1010,0.89,0,0 +2013,8,31,20,24,12,21,1011,1.79,0,0 +2013,8,31,21,17,14,19,1011,0.89,0,0 +2013,8,31,22,22,14,20,1012,3.13,0,0 +2013,8,31,23,38,13,21,1012,8.94,0,0 +2013,9,1,0,55,13,20,1013,12.96,0,0 +2013,9,1,1,76,12,19,1013,0.89,0,0 +2013,9,1,2,73,13,17,1013,1.78,0,0 +2013,9,1,3,55,12,16,1013,1.79,0,0 +2013,9,1,4,39,12,16,1013,0.89,0,0 +2013,9,1,5,37,12,15,1013,3.13,0,0 +2013,9,1,6,40,12,15,1014,6.26,0,0 +2013,9,1,7,42,14,18,1014,8.05,0,0 +2013,9,1,8,39,13,21,1014,3.13,0,0 +2013,9,1,9,35,12,23,1014,6.26,0,0 +2013,9,1,10,33,12,24,1014,9.39,0,0 +2013,9,1,11,31,13,27,1014,11.18,0,0 +2013,9,1,12,35,11,28,1013,1.79,0,0 +2013,9,1,13,33,11,29,1013,1.79,0,0 +2013,9,1,14,30,10,30,1012,1.79,0,0 +2013,9,1,15,42,9,30,1011,1.79,0,0 +2013,9,1,16,45,10,29,1012,0.89,0,0 +2013,9,1,17,49,11,28,1011,3.13,0,0 +2013,9,1,18,55,13,27,1012,6.26,0,0 +2013,9,1,19,64,15,25,1012,8.05,0,1 +2013,9,1,20,66,17,23,1013,3.13,0,2 +2013,9,1,21,69,16,23,1014,0.89,0,0 +2013,9,1,22,65,15,23,1015,4.02,0,0 +2013,9,1,23,55,15,21,1015,1.79,0,0 +2013,9,2,0,71,15,19,1014,0.89,0,0 +2013,9,2,1,84,14,18,1014,3.13,0,0 +2013,9,2,2,89,13,19,1015,0.89,0,0 +2013,9,2,3,29,14,18,1015,0.89,0,0 +2013,9,2,4,33,14,17,1015,0.45,0,0 +2013,9,2,5,33,14,17,1016,0.9,0,0 +2013,9,2,6,41,12,16,1016,1.79,0,0 +2013,9,2,7,47,13,18,1017,0.89,0,0 +2013,9,2,8,43,14,20,1017,1.78,0,0 +2013,9,2,9,44,13,23,1017,2.67,0,0 +2013,9,2,10,45,14,25,1017,1.79,0,0 +2013,9,2,11,49,11,25,1017,1.79,0,0 +2013,9,2,12,43,12,26,1016,1.79,0,0 +2013,9,2,13,47,12,27,1016,1.79,0,0 +2013,9,2,14,57,12,28,1015,1.79,0,0 +2013,9,2,15,55,12,28,1014,3.58,0,0 +2013,9,2,16,65,13,27,1014,5.37,0,0 +2013,9,2,17,74,13,27,1014,8.5,0,0 +2013,9,2,18,74,13,25,1014,10.29,0,0 +2013,9,2,19,76,15,23,1014,12.08,0,0 +2013,9,2,20,82,17,21,1015,12.97,0,0 +2013,9,2,21,89,17,21,1016,14.76,0,0 +2013,9,2,22,87,16,21,1016,16.55,0,0 +2013,9,2,23,80,17,21,1016,17.44,0,0 +2013,9,3,0,82,17,19,1016,19.23,0,0 +2013,9,3,1,95,16,19,1016,0.89,0,0 +2013,9,3,2,102,16,18,1016,0.89,0,0 +2013,9,3,3,96,16,18,1016,0.45,0,0 +2013,9,3,4,122,16,18,1016,0.89,0,0 +2013,9,3,5,132,16,18,1016,0.89,0,0 +2013,9,3,6,112,16,19,1017,1.78,0,0 +2013,9,3,7,99,16,19,1017,3.13,0,0 +2013,9,3,8,79,15,21,1017,1.79,0,0 +2013,9,3,9,66,16,22,1017,0.89,0,0 +2013,9,3,10,66,16,24,1018,1.78,0,0 +2013,9,3,11,76,16,26,1017,3.57,0,0 +2013,9,3,12,80,15,26,1017,4.46,0,0 +2013,9,3,13,89,16,27,1016,1.79,0,0 +2013,9,3,14,93,16,28,1015,1.79,0,0 +2013,9,3,15,98,16,28,1014,3.58,0,0 +2013,9,3,16,109,16,27,1014,3.13,0,0 +2013,9,3,17,109,15,26,1014,4.92,0,0 +2013,9,3,18,115,15,25,1014,6.71,0,0 +2013,9,3,19,124,17,23,1015,8.5,0,0 +2013,9,3,20,119,17,22,1015,9.39,0,0 +2013,9,3,21,101,16,23,1016,12.52,0,0 +2013,9,3,22,103,16,23,1016,15.65,0,0 +2013,9,3,23,123,17,22,1016,17.44,0,0 +2013,9,4,0,133,17,21,1016,19.23,0,0 +2013,9,4,1,135,17,21,1016,21.02,0,0 +2013,9,4,2,144,17,21,1016,22.81,0,0 +2013,9,4,3,160,17,21,1016,23.7,0,0 +2013,9,4,4,149,17,20,1016,5.81,0,1 +2013,9,4,5,38,16,18,1016,3.13,0,2 +2013,9,4,6,38,16,19,1016,4.92,0,3 +2013,9,4,7,50,16,19,1017,1.79,0,0 +2013,9,4,8,58,16,19,1017,0.89,0,0 +2013,9,4,9,62,17,20,1017,0.89,0,0 +2013,9,4,10,58,17,22,1017,0.89,0,0 +2013,9,4,11,67,17,22,1017,1.79,0,0 +2013,9,4,12,77,16,22,1016,4.92,0,1 +2013,9,4,13,85,17,21,1016,1.79,0,2 +2013,9,4,14,74,17,21,1015,3.58,0,3 +2013,9,4,15,72,17,22,1015,1.79,0,4 +2013,9,4,16,68,17,22,1015,4.92,0,5 +2013,9,4,17,68,16,21,1015,6.71,0,0 +2013,9,4,18,70,17,21,1015,7.6,0,1 +2013,9,4,19,65,17,20,1015,8.49,0,0 +2013,9,4,20,81,17,20,1015,0.45,0,1 +2013,9,4,21,90,18,20,1015,1.34,0,2 +2013,9,4,22,86,17,20,1015,2.23,0,3 +2013,9,4,23,67,16,18,1015,4.92,0,4 +2013,9,5,0,31,16,18,1015,8.05,0,5 +2013,9,5,1,19,16,18,1015,12.07,0,6 +2013,9,5,2,19,15,18,1015,16.99,0,7 +2013,9,5,3,21,15,18,1015,20.12,0,8 +2013,9,5,4,17,15,17,1014,23.25,0,9 +2013,9,5,5,14,15,17,1014,27.27,0,10 +2013,9,5,6,12,15,17,1014,30.4,0,0 +2013,9,5,7,13,15,17,1014,34.42,0,0 +2013,9,5,8,16,15,20,1014,3.13,0,0 +2013,9,5,9,15,14,22,1014,3.13,0,0 +2013,9,5,10,14,14,23,1015,3.13,0,0 +2013,9,5,11,11,13,24,1014,7.15,0,0 +2013,9,5,12,14,10,25,1014,1.79,0,0 +2013,9,5,13,13,11,26,1013,3.58,0,0 +2013,9,5,14,13,12,26,1013,5.37,0,0 +2013,9,5,15,14,12,26,1012,3.13,0,0 +2013,9,5,16,16,12,26,1012,6.26,0,0 +2013,9,5,17,20,15,24,1012,11.18,0,0 +2013,9,5,18,34,15,22,1013,16.1,0,0 +2013,9,5,19,48,14,20,1014,21.02,0,0 +2013,9,5,20,52,14,19,1015,25.04,0,0 +2013,9,5,21,57,14,19,1016,28.17,0,0 +2013,9,5,22,48,14,18,1016,29.96,0,0 +2013,9,5,23,60,14,17,1016,0.89,0,0 +2013,9,6,0,62,14,18,1016,0.89,0,0 +2013,9,6,1,61,14,18,1016,0.89,0,0 +2013,9,6,2,68,14,18,1016,1.78,0,0 +2013,9,6,3,67,15,18,1016,2.67,0,0 +2013,9,6,4,72,15,18,1016,0.89,0,0 +2013,9,6,5,65,15,18,1016,0.45,0,0 +2013,9,6,6,64,14,18,1016,3.13,0,1 +2013,9,6,7,55,15,18,1017,3.13,0,2 +2013,9,6,8,62,15,19,1017,1.79,0,0 +2013,9,6,9,66,14,21,1017,3.58,0,0 +2013,9,6,10,65,15,22,1017,1.79,0,0 +2013,9,6,11,70,14,23,1017,2.68,0,0 +2013,9,6,12,69,14,24,1016,4.47,0,0 +2013,9,6,13,68,14,25,1016,6.26,0,0 +2013,9,6,14,74,13,25,1015,3.13,0,0 +2013,9,6,15,64,14,26,1014,7.15,0,0 +2013,9,6,16,67,13,26,1014,11.17,0,0 +2013,9,6,17,73,13,25,1014,15.19,0,0 +2013,9,6,18,88,15,23,1014,18.32,0,0 +2013,9,6,19,103,15,21,1015,20.11,0,0 +2013,9,6,20,111,15,21,1015,21.9,0,0 +2013,9,6,21,117,15,21,1016,23.69,0,0 +2013,9,6,22,118,16,20,1016,0.89,0,0 +2013,9,6,23,128,16,19,1017,1.78,0,0 +2013,9,7,0,132,16,19,1016,1.79,0,0 +2013,9,7,1,140,16,18,1016,0.89,0,0 +2013,9,7,2,142,15,18,1016,1.78,0,0 +2013,9,7,3,142,15,18,1016,2.23,0,0 +2013,9,7,4,124,15,17,1016,2.68,0,0 +2013,9,7,5,131,15,17,1016,3.13,0,0 +2013,9,7,6,113,16,18,1017,0.89,0,0 +2013,9,7,7,118,17,19,1017,4.02,0,0 +2013,9,7,8,101,17,21,1018,7.15,0,0 +2013,9,7,9,101,15,23,1018,10.28,0,0 +2013,9,7,10,92,15,25,1018,13.41,0,0 +2013,9,7,11,70,10,26,1018,3.13,0,0 +2013,9,7,12,39,9,27,1017,6.26,0,0 +2013,9,7,13,27,9,27,1016,10.28,0,0 +2013,9,7,14,25,8,28,1016,1.79,0,0 +2013,9,7,15,20,8,29,1015,1.79,0,0 +2013,9,7,16,26,6,28,1015,1.79,0,0 +2013,9,7,17,24,7,28,1015,3.58,0,0 +2013,9,7,18,29,10,25,1015,5.37,0,0 +2013,9,7,19,31,13,20,1015,0.89,0,0 +2013,9,7,20,51,13,21,1016,1.79,0,0 +2013,9,7,21,79,15,19,1017,2.68,0,0 +2013,9,7,22,90,15,19,1017,0.89,0,0 +2013,9,7,23,123,15,18,1017,0.89,0,0 +2013,9,8,0,123,14,18,1017,0.89,0,0 +2013,9,8,1,126,15,19,1017,1.78,0,0 +2013,9,8,2,128,15,18,1017,2.67,0,0 +2013,9,8,3,95,15,17,1017,3.56,0,0 +2013,9,8,4,94,14,17,1017,4.01,0,0 +2013,9,8,5,47,14,17,1017,1.79,0,0 +2013,9,8,6,36,14,18,1017,0.89,0,0 +2013,9,8,7,33,15,18,1017,0.89,0,0 +2013,9,8,8,42,15,19,1018,1.79,0,0 +2013,9,8,9,50,15,21,1018,0.89,0,0 +2013,9,8,10,72,16,23,1018,1.78,0,0 +2013,9,8,11,225,15,24,1018,2.67,0,0 +2013,9,8,12,93,16,24,1017,1.79,0,0 +2013,9,8,13,103,16,25,1016,3.58,0,0 +2013,9,8,14,126,16,25,1015,5.37,0,0 +2013,9,8,15,125,16,26,1015,0.89,0,0 +2013,9,8,16,119,17,25,1014,1.78,0,0 +2013,9,8,17,138,18,25,1014,3.57,0,0 +2013,9,8,18,161,18,24,1014,0.89,0,0 +2013,9,8,19,190,19,23,1014,2.68,0,1 +2013,9,8,20,212,15,23,1015,0.89,0,0 +2013,9,8,21,159,16,21,1015,0.89,0,0 +2013,9,8,22,137,16,22,1015,4.92,0,1 +2013,9,8,23,46,15,20,1015,0.89,0,0 +2013,9,9,0,45,15,20,1014,1.78,0,1 +2013,9,9,1,53,16,19,1015,1.79,0,0 +2013,9,9,2,57,16,19,1014,0.89,0,1 +2013,9,9,3,59,17,20,1014,1.79,0,0 +2013,9,9,4,60,17,19,1013,0.89,0,0 +2013,9,9,5,66,17,19,1014,1.79,0,0 +2013,9,9,6,70,16,19,1014,4.92,0,1 +2013,9,9,7,98,16,19,1014,0.89,0,2 +2013,9,9,8,112,17,19,1014,1.79,0,0 +2013,9,9,9,114,17,20,1014,3.58,0,0 +2013,9,9,10,138,17,20,1014,0.89,0,0 +2013,9,9,11,113,17,22,1014,1.79,0,0 +2013,9,9,12,92,16,24,1013,3.58,0,0 +2013,9,9,13,75,14,25,1012,8.5,0,0 +2013,9,9,14,70,13,26,1011,11.63,0,0 +2013,9,9,15,69,14,25,1011,14.76,0,0 +2013,9,9,16,78,14,25,1010,18.78,0,0 +2013,9,9,17,80,15,24,1010,21.91,0,0 +2013,9,9,18,84,16,23,1010,25.04,0,0 +2013,9,9,19,89,16,21,1011,26.83,0,0 +2013,9,9,20,99,16,21,1012,28.62,0,0 +2013,9,9,21,103,16,21,1012,30.41,0,0 +2013,9,9,22,119,16,20,1012,33.54,0,0 +2013,9,9,23,106,16,21,1012,35.33,0,0 +2013,9,10,0,98,16,20,1012,37.12,0,0 +2013,9,10,1,104,16,20,1011,38.01,0,0 +2013,9,10,2,106,16,19,1011,0.89,0,0 +2013,9,10,3,107,16,19,1011,1.79,0,0 +2013,9,10,4,106,16,18,1011,2.68,0,0 +2013,9,10,5,114,16,18,1011,0.45,0,0 +2013,9,10,6,116,16,18,1011,1.34,0,0 +2013,9,10,7,124,17,19,1012,2.23,0,0 +2013,9,10,8,142,18,21,1013,1.79,0,0 +2013,9,10,9,102,16,24,1013,4.92,0,0 +2013,9,10,10,35,13,26,1013,8.05,0,0 +2013,9,10,11,9,9,28,1013,11.18,0,0 +2013,9,10,12,9,4,29,1013,14.31,0,0 +2013,9,10,13,15,1,29,1012,19.23,0,0 +2013,9,10,14,9,1,29,1011,24.15,0,0 +2013,9,10,15,11,3,30,1011,29.07,0,0 +2013,9,10,16,10,1,29,1011,33.99,0,0 +2013,9,10,17,12,1,29,1011,38.01,0,0 +2013,9,10,18,13,1,28,1011,42.03,0,0 +2013,9,10,19,17,11,22,1011,0.45,0,0 +2013,9,10,20,61,12,20,1012,1.79,0,0 +2013,9,10,21,68,14,18,1013,3.58,0,0 +2013,9,10,22,97,12,18,1013,0.89,0,0 +2013,9,10,23,110,13,18,1014,1.79,0,0 +2013,9,11,0,115,12,18,1014,0.89,0,0 +2013,9,11,1,98,12,16,1014,1.78,0,0 +2013,9,11,2,86,12,16,1014,2.67,0,0 +2013,9,11,3,50,11,15,1014,1.79,0,0 +2013,9,11,4,39,11,15,1014,0.89,0,0 +2013,9,11,5,24,10,14,1015,2.68,0,0 +2013,9,11,6,26,10,13,1015,3.13,0,0 +2013,9,11,7,35,12,15,1015,6.26,0,0 +2013,9,11,8,33,13,19,1016,8.05,0,0 +2013,9,11,9,36,12,21,1016,9.84,0,0 +2013,9,11,10,92,14,23,1016,0.89,0,0 +2013,9,11,11,92,15,24,1016,1.78,0,0 +2013,9,11,12,91,14,25,1015,3.13,0,0 +2013,9,11,13,90,14,27,1014,4.92,0,0 +2013,9,11,14,67,12,29,1013,9.84,0,0 +2013,9,11,15,70,12,29,1013,14.76,0,0 +2013,9,11,16,73,11,29,1012,20.57,0,0 +2013,9,11,17,72,12,28,1012,25.49,0,0 +2013,9,11,18,87,13,26,1012,28.62,0,0 +2013,9,11,19,113,15,25,1013,31.75,0,0 +2013,9,11,20,90,15,24,1013,33.54,0,0 +2013,9,11,21,80,15,23,1014,35.33,0,0 +2013,9,11,22,85,15,22,1014,37.12,0,0 +2013,9,11,23,98,15,19,1014,38.01,0,0 +2013,9,12,0,106,15,19,1014,38.9,0,0 +2013,9,12,1,109,15,19,1014,40.69,0,0 +2013,9,12,2,119,15,19,1013,42.48,0,0 +2013,9,12,3,127,14,18,1013,44.27,0,0 +2013,9,12,4,132,14,17,1013,46.06,0,0 +2013,9,12,5,132,14,17,1013,1.79,0,0 +2013,9,12,6,151,14,17,1014,1.79,0,0 +2013,9,12,7,164,15,18,1014,0.89,0,0 +2013,9,12,8,166,16,20,1014,1.79,0,0 +2013,9,12,9,159,16,22,1014,3.58,0,0 +2013,9,12,10,163,16,24,1014,1.79,0,0 +2013,9,12,11,153,15,25,1013,3.58,0,0 +2013,9,12,12,140,14,27,1012,4.02,0,0 +2013,9,12,13,126,14,28,1011,8.94,0,0 +2013,9,12,14,124,14,27,1010,13.86,0,0 +2013,9,12,15,149,15,27,1010,16.99,0,0 +2013,9,12,16,160,16,27,1009,1.79,0,0 +2013,9,12,17,191,17,24,1009,5.81,0,0 +2013,9,12,18,178,18,24,1008,0.89,0,0 +2013,9,12,19,183,18,23,1008,1.79,0,0 +2013,9,12,20,198,18,23,1009,4.92,0,1 +2013,9,12,21,204,18,22,1009,6.71,0,0 +2013,9,12,22,213,19,22,1009,8.5,0,0 +2013,9,12,23,132,15,21,1008,16.55,0,1 +2013,9,13,0,46,15,19,1008,21.47,0,2 +2013,9,13,1,86,16,19,1009,0.89,0,3 +2013,9,13,2,69,16,19,1008,1.79,0,0 +2013,9,13,3,60,16,18,1008,3.58,0,0 +2013,9,13,4,59,16,19,1007,6.71,0,0 +2013,9,13,5,54,16,18,1007,8.5,0,0 +2013,9,13,6,53,16,19,1007,10.29,0,0 +2013,9,13,7,50,16,19,1007,13.42,0,0 +2013,9,13,8,50,16,19,1007,15.21,0,0 +2013,9,13,9,NA,16,19,1007,18.34,0,0 +2013,9,13,10,NA,16,20,1007,20.13,0,0 +2013,9,13,11,80,17,20,1006,21.92,0,0 +2013,9,13,12,88,17,20,1006,23.71,0,0 +2013,9,13,13,104,17,20,1005,25.5,0,0 +2013,9,13,14,122,18,21,1004,27.29,0,0 +2013,9,13,15,134,18,21,1004,29.08,0,0 +2013,9,13,16,135,18,21,1004,0.89,0,0 +2013,9,13,17,166,18,21,1004,1.79,0,0 +2013,9,13,18,170,18,21,1004,1.79,0,0 +2013,9,13,19,186,18,21,1004,0.89,0,0 +2013,9,13,20,198,18,21,1007,7.15,0,0 +2013,9,13,21,126,18,20,1005,4.02,0,1 +2013,9,13,22,113,18,20,1006,1.79,0,0 +2013,9,13,23,134,18,20,1006,3.13,0,1 +2013,9,14,0,144,18,20,1006,4.02,0,0 +2013,9,14,1,151,18,20,1006,0.45,0,0 +2013,9,14,2,146,18,20,1006,1.79,0,0 +2013,9,14,3,116,18,20,1006,3.58,0,0 +2013,9,14,4,127,18,20,1006,0.89,0,0 +2013,9,14,5,121,18,20,1006,1.79,0,0 +2013,9,14,6,111,18,20,1007,3.58,0,0 +2013,9,14,7,107,18,20,1008,5.37,0,0 +2013,9,14,8,104,19,22,1008,8.5,0,0 +2013,9,14,9,92,19,23,1009,10.29,0,0 +2013,9,14,10,91,19,25,1009,1.79,0,0 +2013,9,14,11,83,17,27,1008,1.79,0,0 +2013,9,14,12,58,16,28,1008,1.79,0,0 +2013,9,14,13,39,5,32,1008,7.15,0,0 +2013,9,14,14,12,3,30,1008,12.07,0,0 +2013,9,14,15,12,1,31,1007,17.88,0,0 +2013,9,14,16,12,-1,31,1007,22.8,0,0 +2013,9,14,17,12,-2,30,1008,25.93,0,0 +2013,9,14,18,17,4,27,1008,1.79,0,0 +2013,9,14,19,32,6,22,1009,3.58,0,0 +2013,9,14,20,27,9,20,1010,5.37,0,0 +2013,9,14,21,37,11,17,1011,6.26,0,0 +2013,9,14,22,31,9,16,1012,3.13,0,0 +2013,9,14,23,66,10,15,1012,4.92,0,0 +2013,9,15,0,52,8,17,1012,8.05,0,0 +2013,9,15,1,17,9,15,1012,11.18,0,0 +2013,9,15,2,20,7,15,1012,15.2,0,0 +2013,9,15,3,19,2,18,1012,3.13,0,0 +2013,9,15,4,13,2,18,1013,4.02,0,0 +2013,9,15,5,16,5,16,1014,8.04,0,0 +2013,9,15,6,8,6,13,1014,11.17,0,0 +2013,9,15,7,10,2,17,1015,4.02,0,0 +2013,9,15,8,15,2,18,1016,7.15,0,0 +2013,9,15,9,10,0,21,1016,11.17,0,0 +2013,9,15,10,12,0,23,1016,1.79,0,0 +2013,9,15,11,16,1,24,1016,2.68,0,0 +2013,9,15,12,22,1,25,1015,1.79,0,0 +2013,9,15,13,29,2,26,1014,0.89,0,0 +2013,9,15,14,28,4,26,1013,3.13,0,0 +2013,9,15,15,26,5,26,1012,4.92,0,0 +2013,9,15,16,28,8,25,1012,8.05,0,0 +2013,9,15,17,29,7,24,1012,11.18,0,0 +2013,9,15,18,28,7,23,1012,12.97,0,0 +2013,9,15,19,32,9,21,1012,14.76,0,0 +2013,9,15,20,42,12,18,1013,0.89,0,0 +2013,9,15,21,45,10,21,1013,1.79,0,0 +2013,9,15,22,39,11,20,1013,3.58,0,0 +2013,9,15,23,40,12,19,1013,5.37,0,0 +2013,9,16,0,32,13,18,1013,1.79,0,0 +2013,9,16,1,26,13,17,1013,0.89,0,0 +2013,9,16,2,29,13,17,1013,1.78,0,0 +2013,9,16,3,35,13,16,1013,2.67,0,0 +2013,9,16,4,64,12,15,1013,3.56,0,0 +2013,9,16,5,60,12,15,1013,0.89,0,0 +2013,9,16,6,75,12,15,1013,1.78,0,0 +2013,9,16,7,88,14,16,1013,0.89,0,0 +2013,9,16,8,60,12,19,1013,1.78,0,0 +2013,9,16,9,62,12,21,1013,2.67,0,0 +2013,9,16,10,66,13,21,1013,1.79,0,0 +2013,9,16,11,62,13,23,1012,0.89,0,0 +2013,9,16,12,54,12,25,1011,2.68,0,0 +2013,9,16,13,58,10,26,1011,4.02,0,0 +2013,9,16,14,56,12,26,1010,7.15,0,0 +2013,9,16,15,37,11,26,1009,10.28,0,0 +2013,9,16,16,37,9,25,1009,13.41,0,0 +2013,9,16,17,46,11,24,1009,15.2,0,0 +2013,9,16,18,50,13,23,1009,16.99,0,0 +2013,9,16,19,57,14,22,1009,18.78,0,0 +2013,9,16,20,64,15,21,1010,20.57,0,0 +2013,9,16,21,70,16,20,1011,0.89,0,0 +2013,9,16,22,70,16,20,1011,1.79,0,0 +2013,9,16,23,113,16,19,1011,0.89,0,0 +2013,9,17,0,113,15,20,1012,0.89,0,0 +2013,9,17,1,130,15,20,1012,0.45,0,0 +2013,9,17,2,117,16,20,1012,1.34,0,0 +2013,9,17,3,117,15,20,1012,0.89,0,0 +2013,9,17,4,98,16,20,1012,1.79,0,0 +2013,9,17,5,90,16,20,1013,3.58,0,0 +2013,9,17,6,87,15,20,1013,0.45,0,0 +2013,9,17,7,85,16,20,1014,1.79,0,1 +2013,9,17,8,84,16,20,1014,3.58,0,0 +2013,9,17,9,84,15,21,1014,6.71,0,0 +2013,9,17,10,85,15,21,1015,0.89,0,1 +2013,9,17,11,94,17,21,1015,1.78,0,2 +2013,9,17,12,91,17,20,1015,1.79,0,3 +2013,9,17,13,97,17,20,1014,3.13,0,4 +2013,9,17,14,94,18,20,1014,0.89,0,5 +2013,9,17,15,93,18,20,1015,1.78,0,6 +2013,9,17,16,95,18,20,1015,2.67,0,7 +2013,9,17,17,93,18,20,1015,1.79,0,0 +2013,9,17,18,102,18,20,1016,0.89,0,0 +2013,9,17,19,108,17,19,1016,1.79,0,0 +2013,9,17,20,114,17,19,1017,0.89,0,0 +2013,9,17,21,179,17,19,1018,1.78,0,0 +2013,9,17,22,117,18,20,1018,2.67,0,0 +2013,9,17,23,116,18,20,1018,3.56,0,0 +2013,9,18,0,118,18,20,1018,1.79,0,0 +2013,9,18,1,121,18,20,1018,3.58,0,0 +2013,9,18,2,132,18,20,1018,5.37,0,0 +2013,9,18,3,128,18,20,1019,6.26,0,0 +2013,9,18,4,126,18,20,1019,0.89,0,0 +2013,9,18,5,113,18,20,1019,1.79,0,1 +2013,9,18,6,106,18,20,1020,0.89,0,2 +2013,9,18,7,98,18,20,1020,1.78,0,0 +2013,9,18,8,104,18,20,1020,2.67,0,1 +2013,9,18,9,110,19,21,1021,1.79,0,0 +2013,9,18,10,126,19,22,1021,1.79,0,0 +2013,9,18,11,151,18,22,1020,4.92,0,0 +2013,9,18,12,148,18,22,1020,8.05,0,0 +2013,9,18,13,139,18,23,1019,9.84,0,0 +2013,9,18,14,105,18,24,1019,0.89,0,0 +2013,9,18,15,106,17,25,1018,3.13,0,0 +2013,9,18,16,95,17,25,1017,4.92,0,0 +2013,9,18,17,90,18,24,1017,1.79,0,0 +2013,9,18,18,90,18,23,1017,0.89,0,0 +2013,9,18,19,110,18,21,1017,0.89,0,0 +2013,9,18,20,119,17,21,1017,1.79,0,0 +2013,9,18,21,121,17,21,1017,4.92,0,0 +2013,9,18,22,116,16,20,1017,5.81,0,0 +2013,9,18,23,106,17,20,1017,7.6,0,0 +2013,9,19,0,113,17,20,1016,9.39,0,0 +2013,9,19,1,113,17,20,1016,10.28,0,0 +2013,9,19,2,123,17,20,1015,0.89,0,0 +2013,9,19,3,118,17,20,1014,1.79,0,0 +2013,9,19,4,114,17,20,1014,3.58,0,0 +2013,9,19,5,131,17,20,1014,0.45,0,0 +2013,9,19,6,128,17,20,1014,1.79,0,0 +2013,9,19,7,128,17,20,1014,3.58,0,0 +2013,9,19,8,120,17,21,1013,0.89,0,0 +2013,9,19,9,125,17,22,1014,1.79,0,0 +2013,9,19,10,126,18,21,1014,3.58,0,1 +2013,9,19,11,124,18,22,1014,0.89,0,2 +2013,9,19,12,129,18,23,1013,1.78,0,0 +2013,9,19,13,108,17,24,1012,1.79,0,0 +2013,9,19,14,109,18,26,1011,1.79,0,0 +2013,9,19,15,103,18,26,1011,3.13,0,0 +2013,9,19,16,112,18,26,1010,7.15,0,0 +2013,9,19,17,124,18,25,1011,11.17,0,0 +2013,9,19,18,126,19,24,1011,14.3,0,0 +2013,9,19,19,121,18,23,1012,17.43,0,0 +2013,9,19,20,128,18,21,1012,0.89,0,0 +2013,9,19,21,129,17,20,1013,1.78,0,0 +2013,9,19,22,140,18,20,1013,2.67,0,0 +2013,9,19,23,138,17,19,1014,0.89,0,0 +2013,9,20,0,153,17,19,1014,0.89,0,0 +2013,9,20,1,156,17,19,1014,3.13,0,0 +2013,9,20,2,153,17,19,1015,4.02,0,0 +2013,9,20,3,147,17,19,1014,7.15,0,0 +2013,9,20,4,131,17,18,1014,10.28,0,0 +2013,9,20,5,114,16,18,1015,14.3,0,0 +2013,9,20,6,98,15,17,1016,18.32,0,0 +2013,9,20,7,87,16,18,1017,21.45,0,0 +2013,9,20,8,59,16,21,1017,24.58,0,0 +2013,9,20,9,38,11,23,1017,4.92,0,0 +2013,9,20,10,29,10,25,1017,8.05,0,0 +2013,9,20,11,15,7,28,1016,5.81,0,0 +2013,9,20,12,7,8,28,1016,4.02,0,0 +2013,9,20,13,12,5,27,1015,7.15,0,0 +2013,9,20,14,12,7,27,1014,1.79,0,0 +2013,9,20,15,12,8,27,1014,3.58,0,0 +2013,9,20,16,16,7,27,1013,5.37,0,0 +2013,9,20,17,16,14,25,1013,6.26,0,0 +2013,9,20,18,29,14,22,1014,0.45,0,0 +2013,9,20,19,49,15,21,1014,0.89,0,0 +2013,9,20,20,54,16,21,1015,1.78,0,0 +2013,9,20,21,75,16,20,1015,3.57,0,0 +2013,9,20,22,71,16,20,1015,1.79,0,0 +2013,9,20,23,94,16,20,1015,0.89,0,0 +2013,9,21,0,89,16,20,1015,0.89,0,0 +2013,9,21,1,77,15,19,1015,0.89,0,0 +2013,9,21,2,74,15,19,1015,1.78,0,0 +2013,9,21,3,75,15,19,1015,0.89,0,0 +2013,9,21,4,63,15,18,1015,0.89,0,0 +2013,9,21,5,53,15,18,1015,1.79,0,0 +2013,9,21,6,34,15,18,1016,0.45,0,0 +2013,9,21,7,42,16,18,1016,1.79,0,0 +2013,9,21,8,41,15,21,1016,3.58,0,0 +2013,9,21,9,39,14,23,1016,0.89,0,0 +2013,9,21,10,46,15,25,1017,1.79,0,0 +2013,9,21,11,51,14,27,1016,1.79,0,0 +2013,9,21,12,54,12,27,1016,3.13,0,0 +2013,9,21,13,56,12,28,1015,6.26,0,0 +2013,9,21,14,59,12,28,1014,8.05,0,0 +2013,9,21,15,63,10,28,1014,12.07,0,0 +2013,9,21,16,38,10,28,1014,16.09,0,0 +2013,9,21,17,37,11,27,1014,17.88,0,0 +2013,9,21,18,61,14,25,1014,21.01,0,0 +2013,9,21,19,83,13,24,1014,25.93,0,0 +2013,9,21,20,48,11,24,1015,30.85,0,0 +2013,9,21,21,36,11,23,1016,34.87,0,0 +2013,9,21,22,32,10,22,1015,38,0,0 +2013,9,21,23,35,13,19,1015,0.89,0,0 +2013,9,22,0,37,13,19,1015,3.13,0,0 +2013,9,22,1,43,13,19,1015,4.92,0,0 +2013,9,22,2,38,14,19,1016,0.89,0,1 +2013,9,22,3,42,15,18,1015,1.78,0,0 +2013,9,22,4,43,15,18,1015,0.89,0,0 +2013,9,22,5,62,15,18,1015,0.89,0,0 +2013,9,22,6,69,15,19,1016,0.89,0,0 +2013,9,22,7,67,16,18,1016,1.79,0,0 +2013,9,22,8,72,16,19,1016,0.89,0,0 +2013,9,22,9,93,16,19,1016,1.78,0,0 +2013,9,22,10,91,16,20,1016,2.67,0,0 +2013,9,22,11,85,15,22,1016,1.79,0,0 +2013,9,22,12,77,15,22,1015,1.79,0,0 +2013,9,22,13,96,16,22,1015,0.89,0,0 +2013,9,22,14,107,15,22,1014,1.78,0,0 +2013,9,22,15,101,15,23,1014,1.79,0,0 +2013,9,22,16,111,16,22,1014,0.89,0,0 +2013,9,22,17,111,16,22,1014,0.89,0,0 +2013,9,22,18,120,17,22,1014,0.89,0,0 +2013,9,22,19,137,17,21,1014,0.89,0,0 +2013,9,22,20,135,17,21,1014,0.89,0,0 +2013,9,22,21,134,17,20,1014,0.89,0,0 +2013,9,22,22,149,17,20,1014,0.89,0,0 +2013,9,22,23,152,17,20,1014,0.89,0,0 +2013,9,23,0,161,16,20,1014,0.89,0,1 +2013,9,23,1,162,17,20,1013,1.78,0,2 +2013,9,23,2,158,18,20,1013,0.89,0,3 +2013,9,23,3,137,17,19,1013,2.68,0,4 +2013,9,23,4,110,17,19,1014,6.7,0,5 +2013,9,23,5,69,13,17,1014,14.75,0,6 +2013,9,23,6,18,10,16,1015,21.9,0,7 +2013,9,23,7,14,10,16,1017,27.71,0,8 +2013,9,23,8,10,9,15,1017,31.73,0,9 +2013,9,23,9,6,7,16,1018,36.65,0,10 +2013,9,23,10,8,7,16,1019,42.46,0,11 +2013,9,23,11,9,6,17,1019,48.27,0,12 +2013,9,23,12,10,7,16,1019,56.32,0,13 +2013,9,23,13,13,7,17,1019,62.13,0,14 +2013,9,23,14,10,4,18,1019,69.28,0,0 +2013,9,23,15,13,3,18,1019,75.09,0,0 +2013,9,23,16,11,3,18,1019,80.01,0,0 +2013,9,23,17,10,6,17,1020,81.8,0,0 +2013,9,23,18,12,5,16,1020,84.93,0,0 +2013,9,23,19,17,7,14,1021,0.89,0,0 +2013,9,23,20,22,9,12,1021,0.89,0,0 +2013,9,23,21,23,8,11,1022,1.79,0,0 +2013,9,23,22,16,7,11,1022,4.92,0,0 +2013,9,23,23,44,7,10,1022,8.94,0,0 +2013,9,24,0,30,7,10,1022,12.07,0,0 +2013,9,24,1,23,6,10,1022,13.86,0,0 +2013,9,24,2,17,6,9,1022,15.65,0,0 +2013,9,24,3,10,6,10,1022,0.89,0,0 +2013,9,24,4,9,5,8,1023,1.79,0,0 +2013,9,24,5,11,4,7,1023,4.92,0,0 +2013,9,24,6,10,5,8,1023,0.89,0,0 +2013,9,24,7,11,2,13,1024,4.02,0,0 +2013,9,24,8,9,3,14,1024,7.15,0,0 +2013,9,24,9,11,1,16,1025,11.17,0,0 +2013,9,24,10,11,2,17,1024,15.19,0,0 +2013,9,24,11,11,0,19,1023,18.32,0,0 +2013,9,24,12,11,-2,20,1022,21.45,0,0 +2013,9,24,13,10,-2,21,1021,24.58,0,0 +2013,9,24,14,7,-2,21,1020,28.6,0,0 +2013,9,24,15,7,-3,23,1019,31.73,0,0 +2013,9,24,16,9,-3,22,1019,35.75,0,0 +2013,9,24,17,11,-4,22,1019,38.88,0,0 +2013,9,24,18,13,3,17,1019,3.13,0,0 +2013,9,24,19,17,5,14,1020,0.89,0,0 +2013,9,24,20,31,6,13,1020,1.78,0,0 +2013,9,24,21,36,6,11,1021,3.13,0,0 +2013,9,24,22,29,5,10,1021,3.13,0,0 +2013,9,24,23,25,2,11,1022,4.92,0,0 +2013,9,25,0,13,2,12,1022,3.13,0,0 +2013,9,25,1,10,1,10,1022,1.79,0,0 +2013,9,25,2,7,2,11,1022,1.79,0,0 +2013,9,25,3,8,2,7,1022,0.89,0,0 +2013,9,25,4,14,-7,15,1022,4.92,0,0 +2013,9,25,5,19,-3,10,1022,8.05,0,0 +2013,9,25,6,30,-1,10,1023,1.79,0,0 +2013,9,25,7,27,3,10,1023,0.89,0,0 +2013,9,25,8,23,-1,14,1023,1.79,0,0 +2013,9,25,9,20,-1,17,1024,0.89,0,0 +2013,9,25,10,16,-5,19,1023,4.02,0,0 +2013,9,25,11,11,-5,21,1022,7.15,0,0 +2013,9,25,12,11,-7,22,1021,12.07,0,0 +2013,9,25,13,10,-8,23,1020,16.99,0,0 +2013,9,25,14,23,-7,25,1019,21.01,0,0 +2013,9,25,15,9,-4,24,1018,4.92,0,0 +2013,9,25,16,8,-5,24,1017,12.07,0,0 +2013,9,25,17,11,-4,23,1016,17.88,0,0 +2013,9,25,18,14,-4,21,1016,23.69,0,0 +2013,9,25,19,19,-2,20,1017,28.61,0,0 +2013,9,25,20,22,-2,19,1018,30.4,0,0 +2013,9,25,21,22,3,15,1018,0.89,0,0 +2013,9,25,22,26,5,13,1018,1.79,0,0 +2013,9,25,23,28,5,10,1018,3.58,0,0 +2013,9,26,0,47,6,10,1018,0.89,0,0 +2013,9,26,1,62,6,9,1018,1.79,0,0 +2013,9,26,2,37,6,9,1018,1.79,0,0 +2013,9,26,3,36,6,9,1018,3.58,0,0 +2013,9,26,4,33,4,8,1018,6.71,0,0 +2013,9,26,5,28,4,7,1018,9.84,0,0 +2013,9,26,6,30,4,8,1018,0.89,0,0 +2013,9,26,7,27,7,15,1019,1.78,0,0 +2013,9,26,8,22,6,15,1019,1.79,0,0 +2013,9,26,9,20,4,17,1020,1.79,0,0 +2013,9,26,10,30,4,19,1020,3.58,0,0 +2013,9,26,11,55,4,21,1018,6.71,0,0 +2013,9,26,12,48,2,22,1018,8.5,0,0 +2013,9,26,13,55,2,23,1017,1.79,0,0 +2013,9,26,14,NA,2,24,1016,3.13,0,0 +2013,9,26,15,66,3,24,1015,6.26,0,0 +2013,9,26,16,64,3,24,1014,9.39,0,0 +2013,9,26,17,53,4,23,1014,11.18,0,0 +2013,9,26,18,47,7,19,1014,12.97,0,0 +2013,9,26,19,51,11,16,1014,13.86,0,0 +2013,9,26,20,69,10,15,1014,0.89,0,0 +2013,9,26,21,107,10,14,1014,1.79,0,0 +2013,9,26,22,114,9,15,1014,3.58,0,0 +2013,9,26,23,138,8,13,1014,0.89,0,0 +2013,9,27,0,187,9,12,1013,1.78,0,0 +2013,9,27,1,184,8,12,1013,2.23,0,0 +2013,9,27,2,146,8,11,1013,0.89,0,0 +2013,9,27,3,139,8,10,1013,0.45,0,0 +2013,9,27,4,143,8,10,1012,1.34,0,0 +2013,9,27,5,129,8,10,1013,1.79,0,0 +2013,9,27,6,126,8,10,1013,0.89,0,0 +2013,9,27,7,137,8,10,1013,0.89,0,0 +2013,9,27,8,113,10,13,1013,1.79,0,0 +2013,9,27,9,127,9,15,1013,0.89,0,0 +2013,9,27,10,148,10,17,1013,1.79,0,0 +2013,9,27,11,159,9,20,1012,1.79,0,0 +2013,9,27,12,161,8,21,1011,3.58,0,0 +2013,9,27,13,166,7,22,1010,6.71,0,0 +2013,9,27,14,202,7,23,1010,9.84,0,0 +2013,9,27,15,229,9,23,1009,12.97,0,0 +2013,9,27,16,229,9,23,1009,14.76,0,0 +2013,9,27,17,254,10,22,1009,16.55,0,0 +2013,9,27,18,259,12,19,1009,17.44,0,0 +2013,9,27,19,281,12,17,1010,19.23,0,0 +2013,9,27,20,292,11,18,1010,21.02,0,0 +2013,9,27,21,298,11,17,1010,0.89,0,0 +2013,9,27,22,266,11,15,1011,1.78,0,0 +2013,9,27,23,250,11,16,1011,3.13,0,0 +2013,9,28,0,211,9,16,1011,4.92,0,0 +2013,9,28,1,207,8,16,1010,8.05,0,0 +2013,9,28,2,209,9,13,1010,0.89,0,0 +2013,9,28,3,210,9,13,1010,1.79,0,0 +2013,9,28,4,217,9,13,1010,3.58,0,0 +2013,9,28,5,226,9,12,1010,4.47,0,0 +2013,9,28,6,232,8,11,1011,0.89,0,0 +2013,9,28,7,238,9,12,1011,0.89,0,0 +2013,9,28,8,253,10,14,1011,0.89,0,0 +2013,9,28,9,269,10,15,1012,1.79,0,0 +2013,9,28,10,272,9,17,1012,3.58,0,0 +2013,9,28,11,273,9,18,1011,5.37,0,0 +2013,9,28,12,265,9,19,1011,8.5,0,0 +2013,9,28,13,236,10,20,1010,11.63,0,0 +2013,9,28,14,213,10,20,1010,1.79,0,0 +2013,9,28,15,221,10,20,1010,3.13,0,0 +2013,9,28,16,234,10,20,1010,4.92,0,0 +2013,9,28,17,237,11,20,1010,0.89,0,0 +2013,9,28,18,246,12,17,1011,1.78,0,0 +2013,9,28,19,262,12,16,1011,2.67,0,0 +2013,9,28,20,274,12,15,1012,3.56,0,0 +2013,9,28,21,283,12,15,1013,1.79,0,0 +2013,9,28,22,272,11,14,1013,0.89,0,0 +2013,9,28,23,286,12,15,1014,1.79,0,0 +2013,9,29,0,289,11,14,1013,0.89,0,0 +2013,9,29,1,280,11,14,1014,4.02,0,0 +2013,9,29,2,286,11,14,1014,5.81,0,0 +2013,9,29,3,284,12,15,1014,7.6,0,0 +2013,9,29,4,259,12,15,1014,9.39,0,0 +2013,9,29,5,233,13,16,1015,11.18,0,0 +2013,9,29,6,226,13,16,1015,12.07,0,0 +2013,9,29,7,232,13,16,1016,12.96,0,0 +2013,9,29,8,228,13,17,1017,14.75,0,0 +2013,9,29,9,221,13,17,1018,16.54,0,0 +2013,9,29,10,211,13,18,1019,19.67,0,0 +2013,9,29,11,204,13,18,1019,21.46,0,0 +2013,9,29,12,207,13,18,1018,24.59,0,0 +2013,9,29,13,228,13,19,1018,26.38,0,0 +2013,9,29,14,222,13,20,1018,29.51,0,0 +2013,9,29,15,210,13,20,1018,32.64,0,0 +2013,9,29,16,212,13,20,1018,35.77,0,0 +2013,9,29,17,219,13,19,1018,38.9,0,0 +2013,9,29,18,213,13,18,1018,0.89,0,0 +2013,9,29,19,221,13,19,1018,3.13,0,0 +2013,9,29,20,208,14,18,1019,4.92,0,0 +2013,9,29,21,207,14,18,1019,0.89,0,0 +2013,9,29,22,208,14,18,1020,1.78,0,0 +2013,9,29,23,210,14,17,1020,2.23,0,0 +2013,9,30,0,210,14,17,1019,3.12,0,0 +2013,9,30,1,214,14,17,1019,3.57,0,0 +2013,9,30,2,221,14,17,1019,4.46,0,0 +2013,9,30,3,226,14,17,1019,5.35,0,0 +2013,9,30,4,228,15,17,1019,1.79,0,0 +2013,9,30,5,202,15,18,1019,0.89,0,0 +2013,9,30,6,122,15,17,1019,0.89,0,0 +2013,9,30,7,100,15,18,1020,2.68,0,0 +2013,9,30,8,98,15,18,1020,3.57,0,0 +2013,9,30,9,120,15,19,1020,5.36,0,0 +2013,9,30,10,127,15,20,1020,8.49,0,0 +2013,9,30,11,128,14,21,1020,11.62,0,0 +2013,9,30,12,125,15,22,1019,14.75,0,0 +2013,9,30,13,114,14,22,1018,18.77,0,0 +2013,9,30,14,102,13,23,1018,21.9,0,0 +2013,9,30,15,103,13,22,1017,25.03,0,0 +2013,9,30,16,84,13,22,1017,26.82,0,0 +2013,9,30,17,92,14,21,1017,0.89,0,0 +2013,9,30,18,93,15,19,1017,1.78,0,0 +2013,9,30,19,114,15,18,1017,2.23,0,0 +2013,9,30,20,164,15,18,1017,1.79,0,0 +2013,9,30,21,200,14,17,1017,2.68,0,0 +2013,9,30,22,208,14,17,1017,0.89,0,0 +2013,9,30,23,188,15,18,1017,1.78,0,0 +2013,10,1,0,173,15,18,1017,0.89,0,0 +2013,10,1,1,170,15,18,1016,0.89,0,0 +2013,10,1,2,169,15,18,1016,1.78,0,0 +2013,10,1,3,152,15,18,1016,2.67,0,0 +2013,10,1,4,147,15,18,1015,0.89,0,0 +2013,10,1,5,136,15,18,1015,0.89,0,0 +2013,10,1,6,145,15,18,1015,0.89,0,1 +2013,10,1,7,134,16,18,1016,3.13,0,2 +2013,10,1,8,131,16,18,1016,4.92,0,3 +2013,10,1,9,115,15,18,1016,8.94,0,4 +2013,10,1,10,36,14,17,1017,3.13,0,5 +2013,10,1,11,23,14,16,1017,3.13,0,6 +2013,10,1,12,27,14,17,1017,3.13,0,7 +2013,10,1,13,23,14,21,1016,5.81,0,0 +2013,10,1,14,16,11,21,1015,4.92,0,0 +2013,10,1,15,10,10,22,1015,8.94,0,0 +2013,10,1,16,12,10,22,1016,12.07,0,0 +2013,10,1,17,12,10,22,1016,13.86,0,0 +2013,10,1,18,13,11,19,1017,0.89,0,0 +2013,10,1,19,26,12,16,1018,1.78,0,0 +2013,10,1,20,64,13,17,1019,3.13,0,0 +2013,10,1,21,50,13,15,1020,4.92,0,0 +2013,10,1,22,34,12,15,1021,8.05,0,0 +2013,10,1,23,36,12,14,1021,0.89,0,0 +2013,10,2,0,34,11,14,1021,1.79,0,0 +2013,10,2,1,36,12,15,1021,4.92,0,0 +2013,10,2,2,37,10,13,1022,1.79,0,0 +2013,10,2,3,37,9,13,1022,2.68,0,0 +2013,10,2,4,24,7,10,1022,1.79,0,0 +2013,10,2,5,11,6,11,1023,0.89,0,0 +2013,10,2,6,9,8,10,1023,0.89,0,0 +2013,10,2,7,11,9,11,1023,4.91,0,0 +2013,10,2,8,13,10,15,1024,6.7,0,0 +2013,10,2,9,8,4,18,1024,11.62,0,0 +2013,10,2,10,7,-1,20,1024,17.43,0,0 +2013,10,2,11,5,-2,20,1023,22.35,0,0 +2013,10,2,12,6,-3,22,1022,25.48,0,0 +2013,10,2,13,10,-2,22,1021,28.61,0,0 +2013,10,2,14,9,-4,23,1020,1.79,0,0 +2013,10,2,15,5,-6,23,1019,3.58,0,0 +2013,10,2,16,8,-4,23,1018,3.13,0,0 +2013,10,2,17,13,-2,21,1018,7.15,0,0 +2013,10,2,18,16,0,19,1017,11.17,0,0 +2013,10,2,19,21,0,19,1017,15.19,0,0 +2013,10,2,20,27,4,15,1018,0.89,0,0 +2013,10,2,21,26,6,13,1018,1.78,0,0 +2013,10,2,22,46,7,12,1018,2.67,0,0 +2013,10,2,23,41,7,12,1018,3.56,0,0 +2013,10,3,0,43,6,10,1018,1.79,0,0 +2013,10,3,1,42,6,10,1018,3.58,0,0 +2013,10,3,2,60,7,10,1018,5.37,0,0 +2013,10,3,3,53,5,9,1018,0.89,0,0 +2013,10,3,4,51,6,9,1018,3.13,0,0 +2013,10,3,5,43,5,8,1018,0.89,0,0 +2013,10,3,6,36,6,8,1019,1.34,0,0 +2013,10,3,7,36,7,9,1019,2.23,0,0 +2013,10,3,8,45,7,13,1020,1.79,0,0 +2013,10,3,9,48,6,16,1021,1.79,0,0 +2013,10,3,10,95,7,17,1021,3.13,0,0 +2013,10,3,11,94,6,19,1020,7.15,0,0 +2013,10,3,12,96,6,19,1019,11.17,0,0 +2013,10,3,13,95,7,20,1018,15.19,0,0 +2013,10,3,14,85,7,21,1017,19.21,0,0 +2013,10,3,15,81,9,21,1017,22.34,0,0 +2013,10,3,16,71,10,21,1016,24.13,0,0 +2013,10,3,17,65,10,20,1016,0.89,0,0 +2013,10,3,18,59,11,19,1016,4.02,0,0 +2013,10,3,19,58,11,17,1016,7.15,0,0 +2013,10,3,20,58,11,17,1017,8.94,0,0 +2013,10,3,21,57,10,16,1017,12.07,0,0 +2013,10,3,22,55,10,15,1017,0.89,0,0 +2013,10,3,23,42,10,14,1016,1.79,0,0 +2013,10,4,0,40,9,13,1016,0.89,0,0 +2013,10,4,1,47,11,14,1016,0.89,0,0 +2013,10,4,2,58,11,14,1015,0.45,0,0 +2013,10,4,3,55,11,14,1015,1.34,0,0 +2013,10,4,4,58,10,13,1014,2.23,0,0 +2013,10,4,5,67,10,12,1014,0.89,0,0 +2013,10,4,6,63,10,12,1014,0.89,0,0 +2013,10,4,7,75,10,12,1014,0.89,0,0 +2013,10,4,8,83,12,14,1015,1.79,0,0 +2013,10,4,9,95,10,16,1015,4.92,0,0 +2013,10,4,10,119,10,17,1015,6.71,0,0 +2013,10,4,11,127,10,19,1014,0.89,0,0 +2013,10,4,12,139,10,19,1014,1.79,0,0 +2013,10,4,13,159,11,21,1013,3.58,0,0 +2013,10,4,14,170,11,21,1012,6.71,0,0 +2013,10,4,15,172,11,21,1012,9.84,0,0 +2013,10,4,16,168,12,21,1012,11.63,0,0 +2013,10,4,17,170,13,20,1012,13.42,0,0 +2013,10,4,18,180,13,19,1012,14.31,0,0 +2013,10,4,19,214,13,17,1013,0.89,0,0 +2013,10,4,20,276,12,15,1014,0.89,0,0 +2013,10,4,21,357,13,15,1014,1.78,0,0 +2013,10,4,22,388,12,15,1015,0.89,0,0 +2013,10,4,23,403,12,14,1015,1.78,0,0 +2013,10,5,0,393,12,14,1015,1.79,0,0 +2013,10,5,1,395,12,14,1016,2.68,0,0 +2013,10,5,2,379,12,13,1016,3.57,0,0 +2013,10,5,3,346,11,13,1016,0.89,0,0 +2013,10,5,4,319,11,13,1016,1.34,0,0 +2013,10,5,5,282,11,13,1017,1.79,0,0 +2013,10,5,6,290,10,11,1018,1.79,0,0 +2013,10,5,7,294,10,12,1018,0.89,0,0 +2013,10,5,8,310,13,14,1019,0.89,0,0 +2013,10,5,9,322,13,16,1020,0.89,0,0 +2013,10,5,10,308,12,19,1020,1.78,0,0 +2013,10,5,11,301,11,20,1020,1.79,0,0 +2013,10,5,12,296,11,21,1020,3.58,0,0 +2013,10,5,13,277,11,22,1019,5.37,0,0 +2013,10,5,14,269,12,23,1019,9.39,0,0 +2013,10,5,15,250,12,24,1019,12.52,0,0 +2013,10,5,16,229,12,24,1020,14.31,0,0 +2013,10,5,17,242,12,22,1020,16.1,0,0 +2013,10,5,18,255,13,19,1020,0.89,0,0 +2013,10,5,19,316,14,18,1021,0.89,0,0 +2013,10,5,20,342,13,16,1022,0.89,0,0 +2013,10,5,21,337,13,16,1022,0.45,0,0 +2013,10,5,22,308,13,15,1023,0.89,0,0 +2013,10,5,23,324,12,14,1023,1.78,0,0 +2013,10,6,0,353,12,15,1023,0.89,0,0 +2013,10,6,1,365,12,14,1023,0.89,0,0 +2013,10,6,2,320,12,14,1023,1.34,0,0 +2013,10,6,3,278,12,13,1023,1.79,0,0 +2013,10,6,4,244,11,13,1023,2.68,0,0 +2013,10,6,5,251,11,12,1023,0.89,0,0 +2013,10,6,6,252,10,12,1023,0.89,0,0 +2013,10,6,7,197,12,14,1024,0.89,0,0 +2013,10,6,8,260,12,14,1024,0.89,0,0 +2013,10,6,9,229,13,15,1024,1.78,0,1 +2013,10,6,10,143,14,15,1024,2.67,0,0 +2013,10,6,11,147,14,16,1024,3.56,0,0 +2013,10,6,12,152,14,17,1024,1.79,0,0 +2013,10,6,13,171,14,20,1022,1.79,0,0 +2013,10,6,14,168,14,20,1022,2.68,0,0 +2013,10,6,15,183,14,21,1021,3.57,0,0 +2013,10,6,16,179,14,21,1020,4.46,0,0 +2013,10,6,17,185,14,21,1020,4.91,0,0 +2013,10,6,18,177,14,19,1020,4.02,0,0 +2013,10,6,19,188,13,17,1020,0.89,0,0 +2013,10,6,20,206,14,16,1021,1.34,0,0 +2013,10,6,21,211,13,16,1021,0.89,0,0 +2013,10,6,22,205,13,15,1021,0.89,0,0 +2013,10,6,23,210,13,15,1021,0.89,0,0 +2013,10,7,0,201,12,14,1021,2.68,0,0 +2013,10,7,1,196,12,14,1021,0.89,0,0 +2013,10,7,2,192,12,14,1021,0.89,0,0 +2013,10,7,3,187,11,13,1020,0.89,0,0 +2013,10,7,4,185,11,13,1021,1.79,0,0 +2013,10,7,5,173,12,13,1021,4.92,0,0 +2013,10,7,6,166,12,13,1022,8.05,0,0 +2013,10,7,7,161,12,14,1022,11.18,0,0 +2013,10,7,8,160,14,16,1023,16.1,0,0 +2013,10,7,9,113,13,19,1023,19.23,0,0 +2013,10,7,10,66,9,22,1024,24.15,0,0 +2013,10,7,11,38,3,23,1024,4.92,0,0 +2013,10,7,12,41,1,25,1023,8.94,0,0 +2013,10,7,13,33,-1,25,1022,12.96,0,0 +2013,10,7,14,23,-1,25,1022,16.98,0,0 +2013,10,7,15,15,-1,25,1022,21,0,0 +2013,10,7,16,15,-1,24,1022,24.13,0,0 +2013,10,7,17,17,4,21,1021,0.89,0,0 +2013,10,7,18,24,5,19,1022,1.34,0,0 +2013,10,7,19,42,9,17,1022,1.79,0,0 +2013,10,7,20,50,7,17,1022,3.58,0,0 +2013,10,7,21,55,10,18,1023,3.13,0,0 +2013,10,7,22,86,11,16,1024,0.89,0,0 +2013,10,7,23,77,12,16,1024,1.78,0,0 +2013,10,8,0,76,12,16,1024,2.67,0,0 +2013,10,8,1,81,12,16,1024,0.89,0,0 +2013,10,8,2,66,12,16,1024,0.89,0,0 +2013,10,8,3,32,12,15,1023,1.78,0,0 +2013,10,8,4,38,8,14,1023,1.79,0,0 +2013,10,8,5,38,7,14,1023,0.89,0,0 +2013,10,8,6,55,7,13,1023,1.78,0,0 +2013,10,8,7,49,8,13,1023,2.23,0,0 +2013,10,8,8,43,8,15,1023,3.12,0,0 +2013,10,8,9,42,8,17,1023,4.01,0,0 +2013,10,8,10,56,7,18,1023,1.79,0,0 +2013,10,8,11,67,8,20,1022,3.13,0,0 +2013,10,8,12,65,7,20,1020,6.26,0,0 +2013,10,8,13,60,6,21,1019,10.28,0,0 +2013,10,8,14,NA,6,22,1017,13.41,0,0 +2013,10,8,15,NA,6,22,1017,17.43,0,0 +2013,10,8,16,NA,6,21,1016,20.56,0,0 +2013,10,8,17,NA,7,20,1015,22.35,0,0 +2013,10,8,18,69,8,18,1015,23.24,0,0 +2013,10,8,19,73,9,16,1015,24.13,0,0 +2013,10,8,20,95,9,15,1015,0.89,0,0 +2013,10,8,21,86,9,14,1015,0.89,0,0 +2013,10,8,22,74,9,14,1015,2.68,0,0 +2013,10,8,23,84,9,14,1014,5.81,0,0 +2013,10,9,0,97,9,15,1014,8.94,0,0 +2013,10,9,1,98,9,13,1013,0.89,0,0 +2013,10,9,2,96,9,13,1013,1.79,0,0 +2013,10,9,3,92,9,13,1012,3.58,0,0 +2013,10,9,4,92,8,11,1012,1.79,0,0 +2013,10,9,5,92,8,11,1012,0.89,0,0 +2013,10,9,6,99,8,10,1012,0.89,0,0 +2013,10,9,7,102,10,12,1013,0.89,0,0 +2013,10,9,8,113,10,13,1013,0.89,0,0 +2013,10,9,9,110,10,14,1013,0.89,0,0 +2013,10,9,10,114,10,14,1013,2.68,0,0 +2013,10,9,11,112,10,15,1012,0.89,0,0 +2013,10,9,12,108,11,16,1011,1.79,0,0 +2013,10,9,13,125,11,18,1010,3.58,0,0 +2013,10,9,14,148,11,19,1009,0.89,0,0 +2013,10,9,15,176,11,20,1009,1.78,0,0 +2013,10,9,16,177,11,20,1009,3.13,0,0 +2013,10,9,17,182,11,18,1009,3.13,0,0 +2013,10,9,18,181,11,15,1009,6.26,0,0 +2013,10,9,19,197,11,15,1009,1.79,0,0 +2013,10,9,20,197,11,15,1009,1.79,0,0 +2013,10,9,21,226,11,15,1009,0.89,0,0 +2013,10,9,22,224,11,14,1008,1.78,0,0 +2013,10,9,23,247,11,14,1008,2.67,0,0 +2013,10,10,0,208,12,15,1008,1.79,0,0 +2013,10,10,1,194,12,15,1007,3.13,0,0 +2013,10,10,2,203,11,14,1006,0.89,0,0 +2013,10,10,3,203,12,15,1007,3.13,0,0 +2013,10,10,4,203,11,14,1006,0.89,0,0 +2013,10,10,5,200,11,13,1007,0.89,0,0 +2013,10,10,6,193,11,14,1007,2.68,0,0 +2013,10,10,7,205,11,14,1007,0.89,0,0 +2013,10,10,8,207,13,16,1008,3.13,0,0 +2013,10,10,9,146,13,18,1009,1.79,0,0 +2013,10,10,10,21,1,20,1010,11.18,0,0 +2013,10,10,11,14,0,21,1010,20.12,0,0 +2013,10,10,12,16,-1,23,1011,32.19,0,0 +2013,10,10,13,19,0,21,1011,43.37,0,0 +2013,10,10,14,16,0,20,1012,56.33,0,0 +2013,10,10,15,11,0,21,1012,66.16,0,0 +2013,10,10,16,12,-2,21,1013,77.34,0,0 +2013,10,10,17,9,-1,20,1013,84.49,0,0 +2013,10,10,18,11,-2,18,1014,91.64,0,0 +2013,10,10,19,9,-3,17,1015,97.45,0,0 +2013,10,10,20,10,-2,14,1015,101.47,0,0 +2013,10,10,21,10,-4,16,1015,107.28,0,0 +2013,10,10,22,8,-3,13,1015,111.3,0,0 +2013,10,10,23,16,-3,13,1015,114.43,0,0 +2013,10,11,0,22,-1,12,1015,1.79,0,0 +2013,10,11,1,23,2,10,1015,2.68,0,0 +2013,10,11,2,27,2,9,1015,0.89,0,0 +2013,10,11,3,19,3,9,1015,0.89,0,0 +2013,10,11,4,19,2,7,1015,1.79,0,0 +2013,10,11,5,21,2,8,1015,1.79,0,0 +2013,10,11,6,25,3,8,1016,0.89,0,0 +2013,10,11,7,30,4,7,1016,0.89,0,0 +2013,10,11,8,46,4,13,1017,0.89,0,0 +2013,10,11,9,51,3,16,1017,1.79,0,0 +2013,10,11,10,45,3,18,1017,0.89,0,0 +2013,10,11,11,39,-2,20,1016,4.92,0,0 +2013,10,11,12,28,-1,22,1015,9.84,0,0 +2013,10,11,13,38,-1,22,1014,3.13,0,0 +2013,10,11,14,47,0,22,1013,1.79,0,0 +2013,10,11,15,46,0,23,1013,4.92,0,0 +2013,10,11,16,50,0,22,1013,8.05,0,0 +2013,10,11,17,64,3,20,1013,0.89,0,0 +2013,10,11,18,78,7,18,1014,1.79,0,0 +2013,10,11,19,111,7,16,1014,3.58,0,0 +2013,10,11,20,102,7,15,1015,5.37,0,0 +2013,10,11,21,149,6,14,1016,0.89,0,0 +2013,10,11,22,158,7,12,1016,1.78,0,0 +2013,10,11,23,107,7,11,1017,0.89,0,0 +2013,10,12,0,100,7,11,1017,1.78,0,0 +2013,10,12,1,92,7,10,1017,0.89,0,0 +2013,10,12,2,72,6,10,1017,2.68,0,0 +2013,10,12,3,66,5,8,1017,5.81,0,0 +2013,10,12,4,57,5,9,1018,7.6,0,0 +2013,10,12,5,63,4,8,1018,9.39,0,0 +2013,10,12,6,75,5,8,1019,12.52,0,0 +2013,10,12,7,70,5,9,1019,15.65,0,0 +2013,10,12,8,63,7,14,1020,17.44,0,0 +2013,10,12,9,57,6,15,1020,21.46,0,0 +2013,10,12,10,49,5,18,1020,24.59,0,0 +2013,10,12,11,51,3,21,1020,27.72,0,0 +2013,10,12,12,52,3,21,1019,3.13,0,0 +2013,10,12,13,47,3,22,1018,4.92,0,0 +2013,10,12,14,50,4,22,1017,1.79,0,0 +2013,10,12,15,64,4,22,1016,3.58,0,0 +2013,10,12,16,82,3,21,1016,0.89,0,0 +2013,10,12,17,84,5,20,1016,1.78,0,0 +2013,10,12,18,59,8,18,1016,2.67,0,0 +2013,10,12,19,65,9,18,1017,3.56,0,0 +2013,10,12,20,81,10,17,1017,4.45,0,0 +2013,10,12,21,106,9,17,1018,5.34,0,0 +2013,10,12,22,98,10,17,1018,0.89,0,0 +2013,10,12,23,92,10,16,1018,1.79,0,1 +2013,10,13,0,96,10,16,1018,3.58,0,2 +2013,10,13,1,91,3,18,1018,3.13,0,0 +2013,10,13,2,48,5,17,1018,0.89,0,0 +2013,10,13,3,51,6,16,1018,4.02,0,0 +2013,10,13,4,38,6,16,1018,1.79,0,0 +2013,10,13,5,33,7,15,1018,3.58,0,0 +2013,10,13,6,34,7,15,1018,5.37,0,0 +2013,10,13,7,39,7,15,1019,7.16,0,0 +2013,10,13,8,45,8,15,1019,8.05,0,0 +2013,10,13,9,46,8,15,1020,9.84,0,0 +2013,10,13,10,44,8,15,1020,10.73,0,0 +2013,10,13,11,46,8,15,1020,0.89,0,0 +2013,10,13,12,57,9,15,1019,1.78,0,0 +2013,10,13,13,57,9,15,1018,0.89,0,0 +2013,10,13,14,61,9,15,1018,0.89,0,0 +2013,10,13,15,63,9,15,1018,1.79,0,0 +2013,10,13,16,76,10,15,1018,3.58,0,0 +2013,10,13,17,73,10,15,1017,4.47,0,0 +2013,10,13,18,70,10,15,1018,0.89,0,0 +2013,10,13,19,55,10,14,1018,1.79,0,0 +2013,10,13,20,59,10,14,1019,0.89,0,0 +2013,10,13,21,59,10,14,1019,1.78,0,0 +2013,10,13,22,72,10,14,1019,0.89,0,0 +2013,10,13,23,73,10,14,1020,0.89,0,1 +2013,10,14,0,86,11,14,1019,1.78,0,0 +2013,10,14,1,77,10,13,1019,1.79,0,1 +2013,10,14,2,86,10,14,1020,1.79,0,2 +2013,10,14,3,100,10,13,1020,1.79,0,0 +2013,10,14,4,103,10,13,1020,2.68,0,0 +2013,10,14,5,88,10,14,1021,3.57,0,0 +2013,10,14,6,80,10,13,1022,5.36,0,0 +2013,10,14,7,48,6,15,1023,4.02,0,0 +2013,10,14,8,39,1,15,1024,11.17,0,0 +2013,10,14,9,20,-2,15,1026,16.98,0,0 +2013,10,14,10,17,-5,15,1026,24.13,0,0 +2013,10,14,11,14,-6,14,1027,32.18,0,0 +2013,10,14,12,13,-5,14,1028,39.33,0,0 +2013,10,14,13,12,-5,14,1027,44.25,0,0 +2013,10,14,14,16,-6,14,1028,51.4,0,0 +2013,10,14,15,11,-7,14,1028,57.21,0,0 +2013,10,14,16,8,-7,14,1028,61.23,0,0 +2013,10,14,17,8,-8,12,1029,64.36,0,0 +2013,10,14,18,16,-7,11,1029,0.89,0,0 +2013,10,14,19,28,-3,8,1030,1.78,0,0 +2013,10,14,20,22,-1,6,1031,2.67,0,0 +2013,10,14,21,27,-1,5,1032,3.56,0,0 +2013,10,14,22,28,-5,6,1033,1.79,0,0 +2013,10,14,23,21,-5,6,1033,3.58,0,0 +2013,10,15,0,26,-5,5,1033,6.71,0,0 +2013,10,15,1,25,-6,4,1033,9.84,0,0 +2013,10,15,2,5,-3,3,1033,0.89,0,0 +2013,10,15,3,7,-2,3,1033,1.79,0,0 +2013,10,15,4,10,-4,1,1033,3.58,0,0 +2013,10,15,5,9,-7,5,1033,5.37,0,0 +2013,10,15,6,8,-5,2,1033,0.89,0,0 +2013,10,15,7,13,-3,2,1033,1.79,0,0 +2013,10,15,8,18,-3,9,1034,3.58,0,0 +2013,10,15,9,13,-9,11,1034,1.79,0,0 +2013,10,15,10,17,-10,12,1033,3.58,0,0 +2013,10,15,11,9,-12,14,1032,1.79,0,0 +2013,10,15,12,9,-12,14,1031,3.58,0,0 +2013,10,15,13,12,-12,16,1029,0.89,0,0 +2013,10,15,14,8,-12,17,1028,2.68,0,0 +2013,10,15,15,15,-12,17,1027,1.79,0,0 +2013,10,15,16,17,-14,17,1027,1.79,0,0 +2013,10,15,17,18,-11,15,1027,4.02,0,0 +2013,10,15,18,28,-8,14,1027,7.15,0,0 +2013,10,15,19,29,-6,12,1027,10.28,0,0 +2013,10,15,20,49,-5,11,1027,12.07,0,0 +2013,10,15,21,54,-3,9,1027,13.86,0,0 +2013,10,15,22,59,-1,6,1027,0.89,0,0 +2013,10,15,23,60,0,5,1027,1.78,0,0 +2013,10,16,0,67,-1,4,1027,2.67,0,0 +2013,10,16,1,59,0,3,1027,1.79,0,0 +2013,10,16,2,71,0,4,1027,0.89,0,0 +2013,10,16,3,69,0,3,1027,0.89,0,0 +2013,10,16,4,64,-2,2,1027,4.02,0,0 +2013,10,16,5,38,-3,3,1027,7.15,0,0 +2013,10,16,6,41,-3,3,1027,11.17,0,0 +2013,10,16,7,51,-2,3,1027,14.3,0,0 +2013,10,16,8,72,0,8,1027,17.43,0,0 +2013,10,16,9,50,-3,12,1027,20.56,0,0 +2013,10,16,10,42,-5,14,1027,23.69,0,0 +2013,10,16,11,46,-7,16,1026,25.48,0,0 +2013,10,16,12,43,-6,19,1025,0.89,0,0 +2013,10,16,13,40,-9,20,1024,4.02,0,0 +2013,10,16,14,29,-10,21,1023,4.91,0,0 +2013,10,16,15,34,-9,21,1023,1.79,0,0 +2013,10,16,16,30,-9,20,1022,3.58,0,0 +2013,10,16,17,26,-8,19,1023,6.71,0,0 +2013,10,16,18,38,-4,15,1023,8.5,0,0 +2013,10,16,19,70,0,11,1024,9.39,0,0 +2013,10,16,20,97,2,10,1024,11.18,0,0 +2013,10,16,21,107,3,9,1024,12.97,0,0 +2013,10,16,22,107,2,11,1025,14.76,0,0 +2013,10,16,23,139,2,8,1025,0.89,0,0 +2013,10,17,0,174,3,8,1025,0.89,0,0 +2013,10,17,1,181,2,7,1025,0.89,0,0 +2013,10,17,2,179,2,6,1026,0.89,0,0 +2013,10,17,3,169,2,6,1026,1.78,0,0 +2013,10,17,4,172,1,5,1026,0.89,0,0 +2013,10,17,5,163,2,5,1026,0.89,0,0 +2013,10,17,6,161,2,4,1027,0.89,0,0 +2013,10,17,7,142,2,4,1027,1.78,0,0 +2013,10,17,8,98,4,7,1028,0.89,0,0 +2013,10,17,9,113,4,10,1028,1.78,0,0 +2013,10,17,10,129,3,12,1028,1.79,0,0 +2013,10,17,11,159,3,14,1028,3.58,0,0 +2013,10,17,12,207,3,16,1027,6.71,0,0 +2013,10,17,13,178,1,17,1026,10.73,0,0 +2013,10,17,14,148,0,18,1025,14.75,0,0 +2013,10,17,15,131,-2,18,1025,19.67,0,0 +2013,10,17,16,95,-1,18,1024,23.69,0,0 +2013,10,17,17,144,0,16,1024,25.48,0,0 +2013,10,17,18,159,2,15,1025,27.27,0,0 +2013,10,17,19,170,4,11,1025,29.06,0,0 +2013,10,17,20,175,5,10,1025,29.95,0,0 +2013,10,17,21,189,4,9,1025,0.89,0,0 +2013,10,17,22,206,4,8,1026,0.89,0,0 +2013,10,17,23,216,4,8,1026,0.89,0,0 +2013,10,18,0,224,3,7,1026,1.78,0,0 +2013,10,18,1,231,3,7,1026,0.89,0,0 +2013,10,18,2,233,3,7,1026,1.79,0,0 +2013,10,18,3,262,3,7,1025,0.89,0,0 +2013,10,18,4,282,3,6,1025,0.45,0,0 +2013,10,18,5,259,3,6,1025,1.34,0,0 +2013,10,18,6,244,2,5,1026,1.79,0,0 +2013,10,18,7,227,3,5,1026,2.24,0,0 +2013,10,18,8,235,5,8,1027,3.13,0,0 +2013,10,18,9,225,4,10,1027,1.79,0,0 +2013,10,18,10,247,4,12,1026,0.89,0,0 +2013,10,18,11,226,4,14,1026,1.78,0,0 +2013,10,18,12,218,4,15,1025,1.79,0,0 +2013,10,18,13,232,3,17,1024,3.58,0,0 +2013,10,18,14,252,4,18,1023,6.71,0,0 +2013,10,18,15,231,3,18,1023,9.84,0,0 +2013,10,18,16,227,4,17,1023,10.73,0,0 +2013,10,18,17,234,6,15,1023,11.62,0,0 +2013,10,18,18,234,7,13,1023,12.51,0,0 +2013,10,18,19,251,6,12,1023,0.89,0,0 +2013,10,18,20,271,7,12,1023,1.78,0,0 +2013,10,18,21,302,7,11,1024,2.23,0,0 +2013,10,18,22,273,7,11,1024,0.89,0,0 +2013,10,18,23,277,7,12,1024,0.89,0,0 +2013,10,19,0,283,7,12,1024,1.79,0,1 +2013,10,19,1,263,7,12,1024,0.89,0,0 +2013,10,19,2,268,7,12,1024,1.79,0,0 +2013,10,19,3,273,7,11,1024,4.92,0,0 +2013,10,19,4,260,6,10,1024,6.71,0,0 +2013,10,19,5,162,6,10,1024,10.73,0,0 +2013,10,19,6,68,5,10,1025,12.52,0,0 +2013,10,19,7,32,2,9,1025,15.65,0,0 +2013,10,19,8,30,-4,14,1026,21.46,0,0 +2013,10,19,9,30,-5,15,1026,28.61,0,0 +2013,10,19,10,17,-5,15,1026,34.42,0,0 +2013,10,19,11,12,-7,16,1026,42.47,0,0 +2013,10,19,12,12,-7,18,1025,50.52,0,0 +2013,10,19,13,20,-7,20,1025,57.67,0,0 +2013,10,19,14,17,-7,19,1024,63.48,0,0 +2013,10,19,15,13,-6,19,1024,71.53,0,0 +2013,10,19,16,12,-5,19,1024,80.47,0,0 +2013,10,19,17,12,-4,17,1025,88.52,0,0 +2013,10,19,18,15,-3,15,1025,94.33,0,0 +2013,10,19,19,13,-4,15,1026,103.27,0,0 +2013,10,19,20,17,-4,13,1026,109.08,0,0 +2013,10,19,21,15,-4,13,1027,116.23,0,0 +2013,10,19,22,13,-4,14,1027,120.25,0,0 +2013,10,19,23,18,-3,12,1026,124.27,0,0 +2013,10,20,0,40,-1,8,1026,0.89,0,0 +2013,10,20,1,29,-2,7,1026,3.13,0,0 +2013,10,20,2,20,-1,7,1026,0.89,0,0 +2013,10,20,3,30,-2,9,1026,3.13,0,0 +2013,10,20,4,21,-2,8,1026,4.92,0,0 +2013,10,20,5,18,-3,10,1026,8.94,0,0 +2013,10,20,6,30,-2,9,1026,12.07,0,0 +2013,10,20,7,29,-2,6,1027,3.13,0,0 +2013,10,20,8,31,-1,11,1027,4.92,0,0 +2013,10,20,9,29,-4,14,1027,3.13,0,0 +2013,10,20,10,24,-5,15,1027,1.79,0,0 +2013,10,20,11,18,-5,17,1026,1.79,0,0 +2013,10,20,12,17,-5,18,1025,3.58,0,0 +2013,10,20,13,11,-5,19,1024,3.13,0,0 +2013,10,20,14,14,-6,20,1023,1.79,0,0 +2013,10,20,15,18,-7,20,1023,0.89,0,0 +2013,10,20,16,16,-4,18,1022,1.79,0,0 +2013,10,20,17,23,-3,16,1022,3.58,0,0 +2013,10,20,18,28,1,13,1023,4.47,0,0 +2013,10,20,19,34,0,12,1023,6.26,0,0 +2013,10,20,20,46,-3,14,1023,9.39,0,0 +2013,10,20,21,46,-4,14,1023,13.41,0,0 +2013,10,20,22,55,-1,11,1023,0.89,0,0 +2013,10,20,23,57,1,8,1023,1.78,0,0 +2013,10,21,0,66,2,8,1023,2.67,0,0 +2013,10,21,1,70,2,7,1024,3.56,0,0 +2013,10,21,2,90,2,5,1024,4.01,0,0 +2013,10,21,3,113,2,6,1023,4.46,0,0 +2013,10,21,4,114,2,5,1023,4.91,0,0 +2013,10,21,5,106,1,4,1023,0.89,0,0 +2013,10,21,6,99,2,5,1023,0.45,0,0 +2013,10,21,7,97,2,6,1023,1.34,0,0 +2013,10,21,8,89,3,8,1024,2.23,0,0 +2013,10,21,9,82,2,10,1024,3.12,0,0 +2013,10,21,10,92,2,13,1024,4.01,0,0 +2013,10,21,11,115,2,15,1024,4.9,0,0 +2013,10,21,12,128,0,17,1023,1.79,0,0 +2013,10,21,13,110,-1,18,1022,4.92,0,0 +2013,10,21,14,99,-3,19,1021,8.94,0,0 +2013,10,21,15,87,-3,19,1021,12.96,0,0 +2013,10,21,16,72,-1,18,1021,16.09,0,0 +2013,10,21,17,79,0,16,1022,16.98,0,0 +2013,10,21,18,69,3,13,1022,0.45,0,0 +2013,10,21,19,77,4,11,1023,0.89,0,0 +2013,10,21,20,93,4,10,1023,0.89,0,0 +2013,10,21,21,122,4,9,1023,1.78,0,0 +2013,10,21,22,121,3,8,1023,0.89,0,0 +2013,10,21,23,134,3,8,1024,0.89,0,0 +2013,10,22,0,146,2,6,1024,0.89,0,0 +2013,10,22,1,149,4,7,1024,1.79,0,0 +2013,10,22,2,143,2,6,1024,0.89,0,0 +2013,10,22,3,141,2,6,1024,1.78,0,0 +2013,10,22,4,166,2,6,1023,0.89,0,0 +2013,10,22,5,187,2,6,1023,0.89,0,0 +2013,10,22,6,193,2,5,1024,1.34,0,0 +2013,10,22,7,184,2,5,1024,2.23,0,0 +2013,10,22,8,174,4,8,1024,1.79,0,0 +2013,10,22,9,182,4,10,1025,0.89,0,0 +2013,10,22,10,187,5,12,1024,1.79,0,0 +2013,10,22,11,184,5,14,1023,4.92,0,0 +2013,10,22,12,166,5,16,1021,8.05,0,0 +2013,10,22,13,151,4,17,1020,12.97,0,0 +2013,10,22,14,144,4,18,1019,17.89,0,0 +2013,10,22,15,NA,4,18,1018,22.81,0,0 +2013,10,22,16,NA,6,17,1018,25.94,0,0 +2013,10,22,17,148,7,16,1018,0.89,0,0 +2013,10,22,18,154,6,14,1020,5.81,0,0 +2013,10,22,19,95,7,13,1020,7.6,0,1 +2013,10,22,20,95,8,11,1020,0.89,0,2 +2013,10,22,21,125,7,11,1021,0.89,0,0 +2013,10,22,22,116,5,8,1021,1.79,0,0 +2013,10,22,23,95,5,9,1020,4.02,0,0 +2013,10,23,0,73,4,8,1021,7.15,0,0 +2013,10,23,1,58,2,6,1021,4.02,0,0 +2013,10,23,2,51,2,7,1022,4.02,0,0 +2013,10,23,3,49,1,7,1022,4.92,0,0 +2013,10,23,4,27,0,6,1022,9.84,0,0 +2013,10,23,5,21,-1,6,1022,4.02,0,0 +2013,10,23,6,21,-2,5,1022,7.15,0,0 +2013,10,23,7,16,-3,3,1023,3.13,0,0 +2013,10,23,8,22,-1,7,1023,4.92,0,0 +2013,10,23,9,27,-4,9,1023,8.05,0,0 +2013,10,23,10,26,-6,11,1023,12.07,0,0 +2013,10,23,11,26,-14,14,1022,19.22,0,0 +2013,10,23,12,18,-14,15,1021,31.29,0,0 +2013,10,23,13,17,-15,15,1020,42.47,0,0 +2013,10,23,14,15,-14,16,1020,50.52,0,0 +2013,10,23,15,20,-16,16,1020,58.57,0,0 +2013,10,23,16,21,-16,16,1020,65.72,0,0 +2013,10,23,17,24,-15,15,1020,70.64,0,0 +2013,10,23,18,27,-13,14,1021,3.13,0,0 +2013,10,23,19,49,-10,9,1022,3.13,0,0 +2013,10,23,20,25,-9,11,1024,8.94,0,0 +2013,10,23,21,18,-9,9,1025,13.86,0,0 +2013,10,23,22,17,-11,8,1026,21.01,0,0 +2013,10,23,23,12,-12,8,1027,29.06,0,0 +2013,10,24,0,11,-12,8,1027,36.21,0,0 +2013,10,24,1,9,-11,7,1027,43.36,0,0 +2013,10,24,2,13,-12,7,1027,52.3,0,0 +2013,10,24,3,12,-12,6,1027,58.11,0,0 +2013,10,24,4,13,-12,5,1027,61.24,0,0 +2013,10,24,5,12,-11,6,1027,66.16,0,0 +2013,10,24,6,14,-10,3,1027,0.89,0,0 +2013,10,24,7,21,-6,2,1028,1.78,0,0 +2013,10,24,8,25,-8,7,1028,2.23,0,0 +2013,10,24,9,18,-10,8,1028,4.02,0,0 +2013,10,24,10,17,-8,12,1027,4.92,0,0 +2013,10,24,11,17,-7,14,1027,10.73,0,0 +2013,10,24,12,17,-7,15,1026,17.88,0,0 +2013,10,24,13,23,-8,16,1025,23.69,0,0 +2013,10,24,14,22,-7,17,1025,29.5,0,0 +2013,10,24,15,22,-5,17,1025,34.42,0,0 +2013,10,24,16,22,-5,16,1025,42.47,0,0 +2013,10,24,17,25,-5,15,1025,49.62,0,0 +2013,10,24,18,33,-5,14,1025,56.77,0,0 +2013,10,24,19,36,-5,13,1026,61.69,0,0 +2013,10,24,20,38,-5,12,1027,67.5,0,0 +2013,10,24,21,38,-4,12,1027,71.52,0,0 +2013,10,24,22,41,-3,9,1028,74.65,0,0 +2013,10,24,23,45,-4,11,1028,78.67,0,0 +2013,10,25,0,37,-4,11,1028,83.59,0,0 +2013,10,25,1,42,-3,10,1028,85.38,0,0 +2013,10,25,2,40,-3,9,1028,88.51,0,0 +2013,10,25,3,41,-2,6,1028,90.3,0,0 +2013,10,25,4,40,-2,5,1028,92.09,0,0 +2013,10,25,5,41,-2,5,1029,93.88,0,0 +2013,10,25,6,36,-2,7,1029,97.01,0,0 +2013,10,25,7,36,-3,9,1029,101.03,0,0 +2013,10,25,8,43,-3,11,1030,105.95,0,0 +2013,10,25,9,43,-4,13,1030,110.87,0,0 +2013,10,25,10,36,-4,14,1029,116.68,0,0 +2013,10,25,11,31,-5,16,1028,121.6,0,0 +2013,10,25,12,26,-7,17,1027,126.52,0,0 +2013,10,25,13,28,-6,18,1026,130.54,0,0 +2013,10,25,14,26,-7,18,1025,3.13,0,0 +2013,10,25,15,25,-7,19,1025,1.79,0,0 +2013,10,25,16,23,-7,17,1025,3.58,0,0 +2013,10,25,17,31,-6,17,1024,1.79,0,0 +2013,10,25,18,36,-1,10,1025,0.89,0,0 +2013,10,25,19,100,-6,15,1025,3.13,0,0 +2013,10,25,20,84,-1,9,1025,4.92,0,0 +2013,10,25,21,73,-2,10,1025,6.71,0,0 +2013,10,25,22,92,0,7,1025,0.89,0,0 +2013,10,25,23,110,-2,4,1025,3.13,0,0 +2013,10,26,0,52,-1,5,1025,4.92,0,0 +2013,10,26,1,34,-3,4,1025,8.05,0,0 +2013,10,26,2,39,-3,4,1025,11.18,0,0 +2013,10,26,3,39,-3,4,1025,14.31,0,0 +2013,10,26,4,42,-3,2,1025,17.44,0,0 +2013,10,26,5,36,-3,2,1024,21.46,0,0 +2013,10,26,6,41,-3,2,1024,24.59,0,0 +2013,10,26,7,39,-3,3,1025,28.61,0,0 +2013,10,26,8,40,-1,7,1025,31.74,0,0 +2013,10,26,9,38,-3,11,1025,34.87,0,0 +2013,10,26,10,38,-5,14,1024,38,0,0 +2013,10,26,11,47,-5,14,1024,41.13,0,0 +2013,10,26,12,44,-7,16,1023,42.92,0,0 +2013,10,26,13,56,-5,16,1021,3.13,0,0 +2013,10,26,14,62,-5,17,1021,3.13,0,0 +2013,10,26,15,50,-5,18,1020,7.15,0,0 +2013,10,26,16,47,-6,17,1019,11.17,0,0 +2013,10,26,17,57,-5,16,1019,14.3,0,0 +2013,10,26,18,71,-5,14,1020,17.43,0,0 +2013,10,26,19,91,1,9,1020,0.89,0,0 +2013,10,26,20,129,1,9,1020,0.89,0,0 +2013,10,26,21,150,1,7,1020,0.89,0,0 +2013,10,26,22,168,1,6,1020,0.89,0,0 +2013,10,26,23,188,1,6,1019,0.89,0,0 +2013,10,27,0,215,0,6,1019,1.78,0,0 +2013,10,27,1,165,1,5,1019,0.89,0,0 +2013,10,27,2,178,1,4,1019,1.78,0,0 +2013,10,27,3,199,1,4,1018,0.45,0,0 +2013,10,27,4,196,0,3,1018,1.34,0,0 +2013,10,27,5,144,0,3,1019,0.89,0,0 +2013,10,27,6,107,0,3,1018,0.89,0,0 +2013,10,27,7,108,0,3,1019,1.78,0,0 +2013,10,27,8,113,2,4,1019,2.67,0,0 +2013,10,27,9,127,4,8,1019,0.45,0,0 +2013,10,27,10,155,1,10,1019,1.79,0,0 +2013,10,27,11,197,0,12,1018,1.79,0,0 +2013,10,27,12,222,1,14,1017,2.68,0,0 +2013,10,27,13,245,1,15,1016,3.57,0,0 +2013,10,27,14,241,1,15,1016,1.79,0,0 +2013,10,27,15,258,1,15,1015,4.92,0,0 +2013,10,27,16,243,2,15,1016,6.71,0,0 +2013,10,27,17,234,3,12,1016,7.6,0,0 +2013,10,27,18,274,4,10,1016,0.45,0,0 +2013,10,27,19,295,4,9,1017,1.34,0,0 +2013,10,27,20,310,4,8,1017,0.89,0,0 +2013,10,27,21,335,4,8,1017,0.89,0,0 +2013,10,27,22,347,4,7,1018,0.45,0,0 +2013,10,27,23,385,3,6,1017,0.89,0,0 +2013,10,28,0,407,3,6,1018,0.89,0,0 +2013,10,28,1,402,3,6,1018,1.78,0,0 +2013,10,28,2,348,3,6,1018,3.57,0,0 +2013,10,28,3,318,2,5,1018,5.36,0,0 +2013,10,28,4,270,2,5,1018,7.15,0,0 +2013,10,28,5,273,2,5,1018,10.28,0,0 +2013,10,28,6,274,1,4,1018,13.41,0,0 +2013,10,28,7,273,2,5,1018,15.2,0,0 +2013,10,28,8,269,3,7,1019,19.22,0,0 +2013,10,28,9,282,4,9,1019,22.35,0,0 +2013,10,28,10,279,3,12,1020,24.14,0,0 +2013,10,28,11,275,2,14,1019,28.16,0,0 +2013,10,28,12,284,2,15,1019,31.29,0,0 +2013,10,28,13,274,2,16,1018,33.08,0,0 +2013,10,28,14,275,2,17,1018,0.89,0,0 +2013,10,28,15,270,3,16,1018,1.79,0,0 +2013,10,28,16,292,4,15,1018,3.58,0,0 +2013,10,28,17,321,5,14,1019,5.37,0,0 +2013,10,28,18,327,6,12,1020,7.16,0,0 +2013,10,28,19,348,6,13,1022,8.95,0,0 +2013,10,28,20,393,6,12,1022,12.08,0,0 +2013,10,28,21,386,6,11,1023,1.79,0,0 +2013,10,28,22,385,6,10,1024,3.58,0,0 +2013,10,28,23,359,6,10,1024,5.37,0,0 +2013,10,29,0,367,5,9,1025,1.79,0,0 +2013,10,29,1,144,5,8,1025,0.89,0,0 +2013,10,29,2,28,3,6,1026,3.13,0,0 +2013,10,29,3,15,3,5,1026,6.26,0,0 +2013,10,29,4,13,2,5,1026,8.05,0,0 +2013,10,29,5,16,2,5,1027,0.89,0,0 +2013,10,29,6,17,0,3,1027,1.78,0,0 +2013,10,29,7,20,1,4,1028,2.67,0,0 +2013,10,29,8,24,-1,10,1029,1.79,0,0 +2013,10,29,9,21,-1,12,1030,3.58,0,0 +2013,10,29,10,14,-3,14,1030,6.71,0,0 +2013,10,29,11,9,-4,15,1029,9.84,0,0 +2013,10,29,12,7,-7,16,1028,12.97,0,0 +2013,10,29,13,10,-8,16,1028,16.1,0,0 +2013,10,29,14,10,-9,16,1027,3.13,0,0 +2013,10,29,15,12,-10,17,1027,4.92,0,0 +2013,10,29,16,12,-9,16,1026,1.79,0,0 +2013,10,29,17,17,-7,13,1027,3.58,0,0 +2013,10,29,18,38,-4,10,1027,5.37,0,0 +2013,10,29,19,35,-3,8,1028,0.89,0,0 +2013,10,29,20,36,-4,8,1028,1.79,0,0 +2013,10,29,21,46,-4,8,1029,3.58,0,0 +2013,10,29,22,52,-2,4,1029,0.89,0,0 +2013,10,29,23,57,-2,4,1029,0.89,0,0 +2013,10,30,0,64,-1,4,1029,0.89,0,0 +2013,10,30,1,103,-1,3,1029,1.78,0,0 +2013,10,30,2,123,-2,2,1029,0.89,0,0 +2013,10,30,3,128,-2,2,1029,1.79,0,0 +2013,10,30,4,135,-2,2,1029,4.92,0,0 +2013,10,30,5,87,-2,1,1029,0.89,0,0 +2013,10,30,6,46,-2,1,1029,0.89,0,0 +2013,10,30,7,37,-2,1,1029,0.89,0,0 +2013,10,30,8,44,0,4,1030,1.79,0,0 +2013,10,30,9,37,-1,7,1030,3.58,0,0 +2013,10,30,10,37,-1,11,1030,0.89,0,0 +2013,10,30,11,37,-2,11,1029,1.78,0,0 +2013,10,30,12,51,-3,14,1028,2.67,0,0 +2013,10,30,13,70,-3,14,1027,1.79,0,0 +2013,10,30,14,74,-2,15,1026,3.58,0,0 +2013,10,30,15,111,-2,15,1026,6.71,0,0 +2013,10,30,16,101,-2,15,1025,9.84,0,0 +2013,10,30,17,103,-2,14,1025,11.63,0,0 +2013,10,30,18,124,1,8,1026,0.89,0,0 +2013,10,30,19,160,2,8,1026,1.78,0,0 +2013,10,30,20,164,2,7,1027,2.67,0,0 +2013,10,30,21,156,2,6,1027,0.89,0,0 +2013,10,30,22,142,2,6,1027,0.45,0,0 +2013,10,30,23,136,1,5,1027,0.89,0,0 +2013,10,31,0,165,2,5,1027,0.89,0,0 +2013,10,31,1,187,2,5,1027,0.45,0,0 +2013,10,31,2,224,1,4,1027,0.89,0,0 +2013,10,31,3,192,0,2,1027,1.79,0,0 +2013,10,31,4,135,0,3,1027,3.58,0,0 +2013,10,31,5,126,0,3,1027,4.47,0,0 +2013,10,31,6,121,-1,2,1027,6.26,0,0 +2013,10,31,7,118,-1,2,1028,10.28,0,0 +2013,10,31,8,118,1,5,1029,12.07,0,0 +2013,10,31,9,118,1,9,1029,15.2,0,0 +2013,10,31,10,112,-1,10,1029,18.33,0,0 +2013,10,31,11,126,-1,13,1028,21.46,0,0 +2013,10,31,12,127,-2,14,1027,23.25,0,0 +2013,10,31,13,122,-1,14,1026,0.89,0,0 +2013,10,31,14,131,-1,15,1026,1.78,0,0 +2013,10,31,15,141,-1,14,1026,3.13,0,0 +2013,10,31,16,151,0,13,1026,4.92,0,0 +2013,10,31,17,149,1,12,1026,5.81,0,0 +2013,10,31,18,174,2,10,1027,6.7,0,0 +2013,10,31,19,233,3,9,1027,0.89,0,0 +2013,10,31,20,218,4,8,1027,1.34,0,0 +2013,10,31,21,211,3,6,1028,0.89,0,0 +2013,10,31,22,229,2,5,1028,0.89,0,0 +2013,10,31,23,280,2,5,1028,0.89,0,0 +2013,11,1,0,274,2,5,1028,0.89,0,0 +2013,11,1,1,289,2,4,1028,1.78,0,0 +2013,11,1,2,311,2,4,1028,0.45,0,0 +2013,11,1,3,299,1,4,1027,0.9,0,0 +2013,11,1,4,308,2,4,1027,0.89,0,0 +2013,11,1,5,297,1,4,1027,1.78,0,0 +2013,11,1,6,233,1,4,1027,1.79,0,0 +2013,11,1,7,178,2,4,1027,0.45,0,0 +2013,11,1,8,167,3,5,1028,1.79,0,0 +2013,11,1,9,139,3,7,1028,2.68,0,0 +2013,11,1,10,145,3,9,1028,0.89,0,0 +2013,11,1,11,166,2,10,1027,1.78,0,0 +2013,11,1,12,189,3,11,1026,2.67,0,0 +2013,11,1,13,190,4,11,1025,1.79,0,0 +2013,11,1,14,254,3,12,1025,3.58,0,0 +2013,11,1,15,266,3,13,1024,6.71,0,0 +2013,11,1,16,241,2,12,1024,8.5,0,0 +2013,11,1,17,206,2,12,1024,12.52,0,0 +2013,11,1,18,177,3,11,1025,15.65,0,1 +2013,11,1,19,180,4,10,1025,0.89,0,0 +2013,11,1,20,175,5,10,1025,1.78,0,0 +2013,11,1,21,188,4,10,1025,1.79,0,0 +2013,11,1,22,209,3,9,1024,0.89,0,0 +2013,11,1,23,205,3,7,1024,1.78,0,0 +2013,11,2,0,196,4,7,1024,2.67,0,0 +2013,11,2,1,195,4,7,1024,3.56,0,0 +2013,11,2,2,205,5,8,1023,4.45,0,0 +2013,11,2,3,213,3,6,1023,5.34,0,0 +2013,11,2,4,223,3,5,1023,5.79,0,0 +2013,11,2,5,231,2,4,1023,6.24,0,0 +2013,11,2,6,238,1,3,1023,7.13,0,0 +2013,11,2,7,242,2,4,1023,7.58,0,0 +2013,11,2,8,247,2,4,1024,0.89,0,0 +2013,11,2,9,290,4,6,1024,1.79,0,0 +2013,11,2,10,297,6,9,1024,0.89,0,0 +2013,11,2,11,323,5,11,1023,1.78,0,0 +2013,11,2,12,356,5,12,1023,1.79,0,0 +2013,11,2,13,388,5,13,1021,3.58,0,0 +2013,11,2,14,329,5,14,1021,6.71,0,0 +2013,11,2,15,310,5,14,1020,9.84,0,0 +2013,11,2,16,292,5,13,1020,12.97,0,0 +2013,11,2,17,309,4,10,1020,14.76,0,0 +2013,11,2,18,285,4,9,1021,17.89,0,0 +2013,11,2,19,284,4,8,1021,19.68,0,0 +2013,11,2,20,331,4,8,1021,20.57,0,0 +2013,11,2,21,320,4,8,1021,0.89,0,0 +2013,11,2,22,325,6,9,1022,3.13,0,0 +2013,11,2,23,316,5,8,1022,0.89,0,0 +2013,11,3,0,334,4,7,1022,0.45,0,0 +2013,11,3,1,326,4,7,1022,1.34,0,0 +2013,11,3,2,314,3,6,1022,2.23,0,0 +2013,11,3,3,212,4,9,1022,4.02,0,0 +2013,11,3,4,52,1,11,1021,11.17,0,0 +2013,11,3,5,22,0,10,1022,15.19,0,0 +2013,11,3,6,12,-2,11,1022,3.13,0,0 +2013,11,3,7,14,-3,12,1022,5.81,0,0 +2013,11,3,8,12,-3,13,1023,12.96,0,0 +2013,11,3,9,13,-3,15,1024,17.88,0,0 +2013,11,3,10,11,-4,17,1024,27.71,0,0 +2013,11,3,11,15,-5,18,1024,35.76,0,0 +2013,11,3,12,14,-7,19,1024,43.81,0,0 +2013,11,3,13,10,-9,20,1024,51.86,0,0 +2013,11,3,14,10,-9,19,1024,60.8,0,0 +2013,11,3,15,10,-9,19,1024,68.85,0,0 +2013,11,3,16,7,-8,18,1024,76,0,0 +2013,11,3,17,6,-9,17,1025,81.81,0,0 +2013,11,3,18,16,-8,16,1025,87.62,0,0 +2013,11,3,19,13,-7,11,1026,1.79,0,0 +2013,11,3,20,13,-4,8,1027,0.89,0,0 +2013,11,3,21,11,-6,8,1027,1.79,0,0 +2013,11,3,22,11,-6,8,1028,3.13,0,0 +2013,11,3,23,6,-4,5,1028,4.92,0,0 +2013,11,4,0,9,-3,4,1029,0.89,0,0 +2013,11,4,1,24,-2,4,1029,1.78,0,0 +2013,11,4,2,19,-3,5,1030,2.67,0,0 +2013,11,4,3,26,-3,3,1029,3.56,0,0 +2013,11,4,4,27,-2,3,1029,1.79,0,0 +2013,11,4,5,44,-4,7,1029,3.58,0,0 +2013,11,4,6,46,-4,3,1030,5.37,0,0 +2013,11,4,7,47,-3,3,1030,0.89,0,0 +2013,11,4,8,48,0,7,1030,1.79,0,0 +2013,11,4,9,41,-4,11,1030,4.92,0,0 +2013,11,4,10,33,-5,14,1030,8.05,0,0 +2013,11,4,11,33,-7,16,1030,0.89,0,0 +2013,11,4,12,32,-6,17,1029,1.79,0,0 +2013,11,4,13,38,-6,17,1027,1.79,0,0 +2013,11,4,14,41,-7,18,1027,4.92,0,0 +2013,11,4,15,25,-7,17,1026,8.05,0,0 +2013,11,4,16,27,-7,17,1026,12.07,0,0 +2013,11,4,17,33,-6,15,1025,16.09,0,0 +2013,11,4,18,46,-4,12,1025,0.89,0,0 +2013,11,4,19,62,-1,9,1026,0.89,0,0 +2013,11,4,20,79,0,8,1025,0.89,0,0 +2013,11,4,21,105,0,6,1026,0.89,0,0 +2013,11,4,22,115,-1,7,1026,0.89,0,0 +2013,11,4,23,129,-1,8,1025,1.79,0,0 +2013,11,5,0,158,-1,6,1025,1.79,0,0 +2013,11,5,1,178,-1,6,1024,2.68,0,0 +2013,11,5,2,227,0,7,1024,0.89,0,0 +2013,11,5,3,223,-1,7,1023,0.89,0,0 +2013,11,5,4,170,0,7,1023,0.89,0,0 +2013,11,5,5,152,0,7,1022,1.79,0,0 +2013,11,5,6,142,0,7,1022,0.89,0,0 +2013,11,5,7,135,1,7,1022,1.78,0,0 +2013,11,5,8,151,1,8,1021,0.89,0,0 +2013,11,5,9,162,0,10,1022,1.79,0,0 +2013,11,5,10,163,0,11,1021,3.58,0,0 +2013,11,5,11,185,0,12,1021,0.89,0,0 +2013,11,5,12,188,0,13,1020,1.78,0,0 +2013,11,5,13,183,0,13,1019,2.67,0,0 +2013,11,5,14,182,0,13,1019,1.79,0,0 +2013,11,5,15,163,-1,13,1018,3.58,0,0 +2013,11,5,16,173,0,13,1018,5.37,0,0 +2013,11,5,17,190,2,11,1018,0.89,0,0 +2013,11,5,18,221,2,8,1019,1.78,0,0 +2013,11,5,19,254,2,8,1019,2.67,0,0 +2013,11,5,20,260,1,6,1019,0.89,0,0 +2013,11,5,21,286,2,5,1019,1.78,0,0 +2013,11,5,22,304,1,5,1019,0.89,0,0 +2013,11,5,23,295,1,5,1019,1.79,0,0 +2013,11,6,0,296,1,4,1018,0.89,0,0 +2013,11,6,1,270,1,4,1018,1.79,0,0 +2013,11,6,2,247,0,3,1018,0.89,0,0 +2013,11,6,3,266,0,3,1017,1.79,0,0 +2013,11,6,4,263,-1,2,1017,4.92,0,0 +2013,11,6,5,239,0,2,1016,6.71,0,0 +2013,11,6,6,238,0,2,1016,0.89,0,0 +2013,11,6,7,206,0,2,1016,1.78,0,0 +2013,11,6,8,183,2,5,1017,4.02,0,0 +2013,11,6,9,159,2,8,1017,7.15,0,0 +2013,11,6,10,162,1,11,1016,8.94,0,0 +2013,11,6,11,173,0,13,1016,1.79,0,0 +2013,11,6,12,149,-12,18,1015,11.18,0,0 +2013,11,6,13,34,-17,18,1015,25.04,0,0 +2013,11,6,14,26,-17,18,1015,34.87,0,0 +2013,11,6,15,18,-17,18,1015,46.05,0,0 +2013,11,6,16,15,-20,16,1016,57.23,0,0 +2013,11,6,17,13,-19,15,1017,70.19,0,0 +2013,11,6,18,12,-17,14,1018,76,0,0 +2013,11,6,19,11,-16,14,1019,87.18,0,0 +2013,11,6,20,12,-15,13,1020,98.36,0,0 +2013,11,6,21,8,-13,11,1021,106.41,0,0 +2013,11,6,22,6,-12,10,1022,111.33,0,0 +2013,11,6,23,8,-12,10,1022,116.25,0,0 +2013,11,7,0,10,-12,10,1022,122.06,0,0 +2013,11,7,1,7,-11,8,1022,125.19,0,0 +2013,11,7,2,7,-12,9,1022,132.34,0,0 +2013,11,7,3,8,-11,8,1022,138.15,0,0 +2013,11,7,4,7,-11,7,1022,142.17,0,0 +2013,11,7,5,8,-11,7,1022,146.19,0,0 +2013,11,7,6,15,-9,5,1022,0.89,0,0 +2013,11,7,7,15,-7,1,1022,0.89,0,0 +2013,11,7,8,27,-7,6,1023,0.89,0,0 +2013,11,7,9,20,-11,11,1023,1.78,0,0 +2013,11,7,10,17,-12,11,1023,3.57,0,0 +2013,11,7,11,18,-15,12,1022,3.13,0,0 +2013,11,7,12,24,-14,14,1021,4.02,0,0 +2013,11,7,13,27,-15,14,1020,8.94,0,0 +2013,11,7,14,38,-15,14,1019,13.86,0,0 +2013,11,7,15,31,-14,14,1019,19.67,0,0 +2013,11,7,16,35,-13,14,1018,23.69,0,0 +2013,11,7,17,51,-11,10,1018,26.82,0,0 +2013,11,7,18,56,-9,10,1019,28.61,0,0 +2013,11,7,19,66,-5,6,1019,0.89,0,0 +2013,11,7,20,89,-5,5,1019,0.89,0,0 +2013,11,7,21,108,-5,5,1020,0.89,0,0 +2013,11,7,22,112,-5,4,1020,1.78,0,0 +2013,11,7,23,118,-6,4,1020,2.67,0,0 +2013,11,8,0,164,-6,2,1020,0.89,0,0 +2013,11,8,1,196,-4,2,1021,0.45,0,0 +2013,11,8,2,188,-4,2,1021,1.34,0,0 +2013,11,8,3,217,-6,0,1021,1.79,0,0 +2013,11,8,4,174,-4,1,1021,3.58,0,0 +2013,11,8,5,167,-4,0,1021,4.47,0,0 +2013,11,8,6,151,-4,0,1021,0.45,0,0 +2013,11,8,7,138,-4,0,1021,0.89,0,0 +2013,11,8,8,121,-3,1,1022,0.89,0,0 +2013,11,8,9,123,-2,6,1022,1.79,0,0 +2013,11,8,10,110,-4,9,1022,3.58,0,0 +2013,11,8,11,109,-5,10,1021,0.89,0,0 +2013,11,8,12,128,-3,12,1020,1.79,0,0 +2013,11,8,13,143,-3,13,1020,0.89,0,0 +2013,11,8,14,159,-4,14,1019,3.13,0,0 +2013,11,8,15,164,-3,14,1019,0.89,0,0 +2013,11,8,16,175,-4,13,1018,0.89,0,0 +2013,11,8,17,177,-2,11,1019,0.89,0,0 +2013,11,8,18,191,-1,7,1019,0.89,0,0 +2013,11,8,19,178,-1,7,1020,0.45,0,0 +2013,11,8,20,195,0,7,1020,0.9,0,0 +2013,11,8,21,181,0,8,1020,1.79,0,0 +2013,11,8,22,169,1,8,1021,2.68,0,0 +2013,11,8,23,173,0,7,1021,3.13,0,0 +2013,11,9,0,178,0,6,1021,4.02,0,0 +2013,11,9,1,170,-1,6,1021,1.79,0,0 +2013,11,9,2,160,-1,5,1021,3.58,0,0 +2013,11,9,3,161,0,5,1021,5.37,0,0 +2013,11,9,4,164,-1,6,1022,0.89,0,0 +2013,11,9,5,171,0,5,1021,1.78,0,0 +2013,11,9,6,178,-1,5,1021,0.89,0,0 +2013,11,9,7,176,0,6,1022,2.68,0,0 +2013,11,9,8,179,0,7,1022,6.7,0,0 +2013,11,9,9,175,0,9,1023,10.72,0,0 +2013,11,9,10,179,-1,11,1022,13.85,0,0 +2013,11,9,11,148,-1,12,1023,16.98,0,0 +2013,11,9,12,118,-3,14,1022,21,0,0 +2013,11,9,13,79,-5,15,1022,3.13,0,0 +2013,11,9,14,115,-7,16,1022,4.92,0,0 +2013,11,9,15,46,-8,15,1022,3.13,0,0 +2013,11,9,16,32,-7,14,1023,1.79,0,0 +2013,11,9,17,20,-7,12,1024,4.92,0,0 +2013,11,9,18,6,-11,13,1025,10.73,0,0 +2013,11,9,19,6,-13,13,1027,20.56,0,0 +2013,11,9,20,7,-13,11,1028,33.52,0,0 +2013,11,9,21,13,-13,10,1030,40.67,0,0 +2013,11,9,22,8,-17,9,1030,53.63,0,0 +2013,11,9,23,6,-22,7,1031,63.46,0,0 +2013,11,10,0,8,-28,6,1032,72.4,0,0 +2013,11,10,1,6,-27,3,1033,81.34,0,0 +2013,11,10,2,3,-31,2,1034,91.17,0,0 +2013,11,10,3,4,-30,1,1034,102.35,0,0 +2013,11,10,4,3,-30,1,1033,110.4,0,0 +2013,11,10,5,3,-28,0,1033,116.21,0,0 +2013,11,10,6,6,-28,1,1032,124.26,0,0 +2013,11,10,7,8,-28,1,1032,130.07,0,0 +2013,11,10,8,9,-28,2,1033,138.12,0,0 +2013,11,10,9,10,-28,3,1032,145.27,0,0 +2013,11,10,10,14,-30,5,1032,152.42,0,0 +2013,11,10,11,9,-31,7,1030,159.57,0,0 +2013,11,10,12,5,-30,8,1028,167.62,0,0 +2013,11,10,13,6,-30,8,1027,177.45,0,0 +2013,11,10,14,5,-27,10,1025,187.28,0,0 +2013,11,10,15,12,-26,10,1024,196.22,0,0 +2013,11,10,16,9,-26,10,1024,204.27,0,0 +2013,11,10,17,17,-24,10,1024,210.08,0,0 +2013,11,10,18,46,-22,9,1025,4.92,0,0 +2013,11,10,19,53,-21,8,1025,8.94,0,0 +2013,11,10,20,72,-19,8,1026,4.92,0,0 +2013,11,10,21,78,-18,3,1027,1.79,0,0 +2013,11,10,22,56,-16,3,1028,3.13,0,0 +2013,11,10,23,16,-15,3,1029,0.89,0,0 +2013,11,11,0,14,-12,1,1030,1.79,0,0 +2013,11,11,1,25,-13,1,1030,3.58,0,0 +2013,11,11,2,20,-13,2,1031,0.89,0,0 +2013,11,11,3,23,-12,1,1031,3.13,0,0 +2013,11,11,4,21,-14,1,1031,4.92,0,0 +2013,11,11,5,24,-10,0,1031,6.71,0,0 +2013,11,11,6,15,-11,-2,1030,8.5,0,0 +2013,11,11,7,17,-11,-1,1031,10.29,0,0 +2013,11,11,8,26,-11,7,1031,0.45,0,0 +2013,11,11,9,23,-12,8,1031,3.13,0,0 +2013,11,11,10,27,-16,11,1031,7.15,0,0 +2013,11,11,11,20,-17,12,1030,10.28,0,0 +2013,11,11,12,14,-16,12,1028,14.3,0,0 +2013,11,11,13,15,-16,13,1027,4.02,0,0 +2013,11,11,14,38,-16,13,1026,8.04,0,0 +2013,11,11,15,38,-14,13,1025,12.96,0,0 +2013,11,11,16,42,-14,12,1025,18.77,0,0 +2013,11,11,17,48,-14,10,1024,23.69,0,0 +2013,11,11,18,52,-13,7,1025,25.48,0,0 +2013,11,11,19,66,-11,7,1024,27.27,0,0 +2013,11,11,20,76,-9,4,1025,0.89,0,0 +2013,11,11,21,81,-8,2,1025,0.89,0,0 +2013,11,11,22,87,-6,2,1025,1.78,0,0 +2013,11,11,23,98,-6,2,1025,0.89,0,0 +2013,11,12,0,103,-7,1,1025,1.79,0,0 +2013,11,12,1,125,-7,0,1025,0.89,0,0 +2013,11,12,2,123,-6,-1,1026,0.89,0,0 +2013,11,12,3,135,-5,0,1025,0.89,0,0 +2013,11,12,4,150,-6,-1,1025,1.79,0,0 +2013,11,12,5,102,-7,-1,1025,5.81,0,0 +2013,11,12,6,89,-7,-1,1025,8.94,0,0 +2013,11,12,7,58,-7,1,1026,12.07,0,0 +2013,11,12,8,45,-6,4,1026,13.86,0,0 +2013,11,12,9,52,-7,8,1026,15.65,0,0 +2013,11,12,10,45,-9,11,1027,1.79,0,0 +2013,11,12,11,38,-9,13,1026,4.92,0,0 +2013,11,12,12,18,-10,15,1025,1.79,0,0 +2013,11,12,13,25,-10,16,1024,1.79,0,0 +2013,11,12,14,24,-10,15,1023,1.79,0,0 +2013,11,12,15,15,-9,15,1023,4.92,0,0 +2013,11,12,16,14,-10,14,1023,0.89,0,0 +2013,11,12,17,17,-9,11,1023,1.78,0,0 +2013,11,12,18,36,-6,7,1023,0.89,0,0 +2013,11,12,19,49,-5,7,1024,2.68,0,0 +2013,11,12,20,55,-7,8,1024,4.47,0,0 +2013,11,12,21,54,-7,8,1025,6.26,0,0 +2013,11,12,22,73,-6,5,1025,8.05,0,0 +2013,11,12,23,82,-5,4,1025,0.89,0,0 +2013,11,13,0,76,-5,4,1025,1.79,0,0 +2013,11,13,1,90,-4,4,1025,3.58,0,0 +2013,11,13,2,106,-4,3,1024,4.47,0,0 +2013,11,13,3,124,-3,3,1024,0.89,0,0 +2013,11,13,4,123,-3,2,1023,1.78,0,0 +2013,11,13,5,128,-2,3,1023,0.89,0,0 +2013,11,13,6,132,-3,1,1023,0.89,0,0 +2013,11,13,7,155,-2,2,1023,0.89,0,0 +2013,11,13,8,163,-2,3,1023,1.78,0,0 +2013,11,13,9,157,-1,5,1023,0.89,0,0 +2013,11,13,10,160,-1,6,1023,1.78,0,0 +2013,11,13,11,185,-2,7,1022,0.89,0,0 +2013,11,13,12,189,-1,8,1021,0.89,0,0 +2013,11,13,13,198,-2,8,1020,1.78,0,0 +2013,11,13,14,216,-2,9,1019,2.67,0,0 +2013,11,13,15,225,-1,9,1019,3.56,0,0 +2013,11,13,16,248,-1,9,1018,0.89,0,0 +2013,11,13,17,270,0,7,1018,0.89,0,0 +2013,11,13,18,296,-1,5,1018,1.79,0,0 +2013,11,13,19,283,0,5,1019,0.45,0,0 +2013,11,13,20,261,0,4,1019,0.9,0,0 +2013,11,13,21,264,0,4,1019,1.79,0,0 +2013,11,13,22,282,-1,4,1019,3.13,0,0 +2013,11,13,23,252,-1,5,1019,4.92,0,0 +2013,11,14,0,252,-2,4,1019,6.71,0,0 +2013,11,14,1,211,-4,2,1019,9.84,0,0 +2013,11,14,2,199,-3,2,1019,12.97,0,0 +2013,11,14,3,235,-3,3,1019,14.76,0,0 +2013,11,14,4,114,-2,2,1019,18.78,0,0 +2013,11,14,5,23,-5,0,1019,20.57,0,0 +2013,11,14,6,18,-5,4,1019,22.36,0,0 +2013,11,14,7,19,-8,4,1019,25.49,0,0 +2013,11,14,8,14,-9,7,1020,1.79,0,0 +2013,11,14,9,23,-11,10,1020,4.92,0,0 +2013,11,14,10,21,-12,11,1020,8.94,0,0 +2013,11,14,11,14,-14,13,1019,14.75,0,0 +2013,11,14,12,24,-15,13,1018,23.69,0,0 +2013,11,14,13,22,-15,14,1018,31.74,0,0 +2013,11,14,14,24,-15,14,1017,39.79,0,0 +2013,11,14,15,27,-16,14,1017,48.73,0,0 +2013,11,14,16,27,-16,14,1017,55.88,0,0 +2013,11,14,17,69,-16,13,1017,59.9,0,0 +2013,11,14,18,64,-12,12,1018,3.13,0,0 +2013,11,14,19,65,-11,11,1018,6.26,0,0 +2013,11,14,20,65,-9,7,1018,0.89,0,0 +2013,11,14,21,76,-10,9,1019,2.68,0,0 +2013,11,14,22,72,-10,10,1019,1.79,0,0 +2013,11,14,23,78,-10,9,1019,1.79,0,0 +2013,11,15,0,81,-6,2,1019,1.79,0,0 +2013,11,15,1,88,-6,2,1019,0.89,0,0 +2013,11,15,2,93,-6,0,1019,0.89,0,0 +2013,11,15,3,106,-6,1,1019,1.78,0,0 +2013,11,15,4,108,-6,-1,1019,0.89,0,0 +2013,11,15,5,115,-6,-2,1019,2.68,0,0 +2013,11,15,6,145,-6,-2,1018,4.47,0,0 +2013,11,15,7,164,-5,-1,1019,0.45,0,0 +2013,11,15,8,272,-5,0,1019,1.79,0,0 +2013,11,15,9,172,-4,6,1019,0.89,0,0 +2013,11,15,10,106,-8,10,1019,1.79,0,0 +2013,11,15,11,70,-12,12,1018,3.58,0,0 +2013,11,15,12,76,-12,14,1017,5.37,0,0 +2013,11,15,13,67,-13,16,1016,1.79,0,0 +2013,11,15,14,69,-13,16,1014,3.58,0,0 +2013,11,15,15,118,-13,16,1015,8.5,0,0 +2013,11,15,16,108,-12,15,1015,12.52,0,0 +2013,11,15,17,84,-12,13,1015,16.54,0,0 +2013,11,15,18,80,-12,13,1015,19.67,0,0 +2013,11,15,19,93,-12,12,1015,22.8,0,0 +2013,11,15,20,93,-7,6,1015,0.89,0,0 +2013,11,15,21,101,-7,4,1016,0.89,0,0 +2013,11,15,22,116,-5,4,1015,1.79,0,0 +2013,11,15,23,187,-7,3,1015,0.89,0,0 +2013,11,16,0,210,-6,4,1015,1.78,0,0 +2013,11,16,1,216,-6,3,1014,1.79,0,0 +2013,11,16,2,214,-5,3,1014,3.58,0,0 +2013,11,16,3,188,-5,4,1014,0.89,0,0 +2013,11,16,4,150,-6,5,1014,3.13,0,0 +2013,11,16,5,71,-8,8,1014,6.26,0,0 +2013,11,16,6,47,-9,10,1015,16.09,0,0 +2013,11,16,7,43,-10,10,1016,25.92,0,0 +2013,11,16,8,32,-10,10,1017,34.86,0,0 +2013,11,16,9,32,-11,11,1018,44.69,0,0 +2013,11,16,10,18,-12,12,1018,56.76,0,0 +2013,11,16,11,14,-13,12,1019,69.72,0,0 +2013,11,16,12,11,-14,13,1018,81.79,0,0 +2013,11,16,13,10,-16,12,1018,95.65,0,0 +2013,11,16,14,13,-17,12,1019,109.51,0,0 +2013,11,16,15,8,-16,11,1020,122.47,0,0 +2013,11,16,16,7,-15,10,1021,134.54,0,0 +2013,11,16,17,6,-15,8,1022,146.61,0,0 +2013,11,16,18,9,-15,8,1023,158.68,0,0 +2013,11,16,19,7,-15,7,1024,170.75,0,0 +2013,11,16,20,5,-15,6,1025,178.8,0,0 +2013,11,16,21,5,-15,6,1025,183.72,0,0 +2013,11,16,22,8,-15,6,1025,189.53,0,0 +2013,11,16,23,11,-14,6,1024,194.45,0,0 +2013,11,17,0,11,-14,3,1024,196.24,0,0 +2013,11,17,1,27,-12,2,1023,0.89,0,0 +2013,11,17,2,37,-9,1,1023,1.34,0,0 +2013,11,17,3,48,-8,-1,1023,1.79,0,0 +2013,11,17,4,21,-8,-2,1022,2.24,0,0 +2013,11,17,5,12,-8,-2,1022,3.13,0,0 +2013,11,17,6,8,-10,1,1023,4.02,0,0 +2013,11,17,7,12,-11,-1,1024,7.15,0,0 +2013,11,17,8,9,-11,2,1025,8.94,0,0 +2013,11,17,9,6,-15,7,1025,12.96,0,0 +2013,11,17,10,8,-16,9,1025,16.09,0,0 +2013,11,17,11,9,-20,9,1025,24.14,0,0 +2013,11,17,12,8,-20,10,1024,35.32,0,0 +2013,11,17,13,9,-21,9,1023,48.28,0,0 +2013,11,17,14,12,-21,10,1023,58.11,0,0 +2013,11,17,15,11,-20,10,1023,67.05,0,0 +2013,11,17,16,9,-19,9,1024,75.1,0,0 +2013,11,17,17,9,-19,8,1025,84.04,0,0 +2013,11,17,18,12,-21,7,1025,95.22,0,0 +2013,11,17,19,10,-20,6,1026,102.37,0,0 +2013,11,17,20,10,-19,5,1027,107.29,0,0 +2013,11,17,21,10,-18,4,1027,112.21,0,0 +2013,11,17,22,9,-17,4,1027,117.13,0,0 +2013,11,17,23,12,-15,4,1027,121.15,0,0 +2013,11,18,0,14,-14,3,1027,126.07,0,0 +2013,11,18,1,13,-15,2,1027,127.86,0,0 +2013,11,18,2,17,-14,2,1027,131.88,0,0 +2013,11,18,3,12,-14,2,1026,135.9,0,0 +2013,11,18,4,10,-16,4,1026,143.95,0,0 +2013,11,18,5,6,-17,4,1026,152.89,0,0 +2013,11,18,6,11,-18,3,1027,160.04,0,0 +2013,11,18,7,7,-18,3,1027,168.09,0,0 +2013,11,18,8,12,-19,4,1028,176.14,0,0 +2013,11,18,9,11,-18,5,1028,185.08,0,0 +2013,11,18,10,9,-19,6,1028,193.13,0,0 +2013,11,18,11,8,-19,7,1028,201.18,0,0 +2013,11,18,12,7,-20,8,1027,209.23,0,0 +2013,11,18,13,14,-20,8,1026,217.28,0,0 +2013,11,18,14,13,-20,10,1025,225.33,0,0 +2013,11,18,15,9,-20,9,1025,234.27,0,0 +2013,11,18,16,9,-21,8,1025,242.32,0,0 +2013,11,18,17,8,-20,5,1025,249.47,0,0 +2013,11,18,18,9,-19,4,1026,254.39,0,0 +2013,11,18,19,20,-19,5,1026,263.33,0,0 +2013,11,18,20,14,-19,5,1027,4.92,0,0 +2013,11,18,21,17,-19,3,1027,10.73,0,0 +2013,11,18,22,10,-18,3,1028,15.65,0,0 +2013,11,18,23,16,-18,2,1028,19.67,0,0 +2013,11,19,0,19,-18,1,1028,23.69,0,0 +2013,11,19,1,14,-18,0,1028,25.48,0,0 +2013,11,19,2,14,-15,0,1027,3.13,0,0 +2013,11,19,3,14,-18,1,1027,4.92,0,0 +2013,11,19,4,12,-15,0,1026,8.05,0,0 +2013,11,19,5,10,-15,-1,1027,9.84,0,0 +2013,11,19,6,15,-15,-1,1026,11.63,0,0 +2013,11,19,7,16,-15,-1,1027,1.79,0,0 +2013,11,19,8,17,-17,0,1027,4.02,0,0 +2013,11,19,9,25,-17,2,1027,8.94,0,0 +2013,11,19,10,22,-18,3,1027,12.96,0,0 +2013,11,19,11,15,-19,5,1026,1.79,0,0 +2013,11,19,12,20,-19,6,1025,3.58,0,0 +2013,11,19,13,20,-20,6,1024,0.89,0,0 +2013,11,19,14,23,-19,7,1023,1.79,0,0 +2013,11,19,15,26,-20,8,1022,0.89,0,0 +2013,11,19,16,NA,-19,7,1022,1.78,0,0 +2013,11,19,17,73,-17,5,1022,0.89,0,0 +2013,11,19,18,71,-15,2,1023,1.78,0,0 +2013,11,19,19,80,-17,5,1023,3.57,0,0 +2013,11,19,20,94,-15,5,1023,6.7,0,0 +2013,11,19,21,94,-14,5,1023,1.79,0,0 +2013,11,19,22,87,-10,-1,1023,3.58,0,0 +2013,11,19,23,93,-13,-1,1023,0.89,0,0 +2013,11,20,0,116,-10,-1,1023,0.89,0,0 +2013,11,20,1,138,-11,-1,1023,1.79,0,0 +2013,11,20,2,130,-10,-3,1023,3.58,0,0 +2013,11,20,3,130,-12,-3,1023,5.37,0,0 +2013,11,20,4,134,-10,-3,1023,9.39,0,0 +2013,11,20,5,71,-11,-3,1023,12.52,0,0 +2013,11,20,6,57,-12,-5,1023,15.65,0,0 +2013,11,20,7,75,-11,-3,1023,19.67,0,0 +2013,11,20,8,62,-13,1,1024,23.69,0,0 +2013,11,20,9,69,-13,4,1024,27.71,0,0 +2013,11,20,10,48,-13,7,1025,31.73,0,0 +2013,11,20,11,43,-13,9,1024,4.02,0,0 +2013,11,20,12,32,-12,10,1023,1.79,0,0 +2013,11,20,13,24,-12,11,1023,3.13,0,0 +2013,11,20,14,20,-13,11,1022,4.92,0,0 +2013,11,20,15,20,-13,11,1022,0.89,0,0 +2013,11,20,16,19,-13,11,1022,1.78,0,0 +2013,11,20,17,30,-12,9,1022,2.67,0,0 +2013,11,20,18,32,-11,7,1023,3.56,0,0 +2013,11,20,19,70,-9,4,1023,4.45,0,0 +2013,11,20,20,89,-11,8,1024,3.13,0,0 +2013,11,20,21,121,-9,1,1024,0.89,0,0 +2013,11,20,22,131,-8,0,1025,1.78,0,0 +2013,11,20,23,144,-8,1,1025,0.89,0,0 +2013,11,21,0,174,-9,0,1026,0.89,0,0 +2013,11,21,1,191,-8,-1,1026,1.78,0,0 +2013,11,21,2,205,-8,-3,1026,3.57,0,0 +2013,11,21,3,197,-7,-2,1026,4.46,0,0 +2013,11,21,4,154,-8,-2,1026,6.25,0,0 +2013,11,21,5,83,-8,-1,1026,7.14,0,0 +2013,11,21,6,103,-8,-2,1026,8.03,0,0 +2013,11,21,7,128,-7,-3,1026,9.82,0,0 +2013,11,21,8,161,-7,-1,1027,0.89,0,0 +2013,11,21,9,86,-7,4,1027,3.13,0,0 +2013,11,21,10,71,-9,7,1027,4.92,0,0 +2013,11,21,11,77,-9,10,1027,6.71,0,0 +2013,11,21,12,63,-11,11,1026,0.89,0,0 +2013,11,21,13,54,-12,11,1025,1.79,0,0 +2013,11,21,14,61,-12,12,1024,3.58,0,0 +2013,11,21,15,43,-12,12,1024,0.89,0,0 +2013,11,21,16,50,-12,11,1024,2.68,0,0 +2013,11,21,17,70,-11,8,1025,0.89,0,0 +2013,11,21,18,158,-9,8,1025,4.02,0,0 +2013,11,21,19,141,-9,9,1025,5.81,0,0 +2013,11,21,20,166,-7,4,1026,0.89,0,0 +2013,11,21,21,206,-7,2,1026,1.78,0,0 +2013,11,21,22,218,-7,1,1027,2.67,0,0 +2013,11,21,23,226,-6,0,1027,0.89,0,0 +2013,11,22,0,232,-7,0,1027,0.45,0,0 +2013,11,22,1,239,-7,-1,1027,1.79,0,0 +2013,11,22,2,230,-7,-1,1027,3.58,0,0 +2013,11,22,3,165,-8,-1,1027,5.37,0,0 +2013,11,22,4,163,-8,-2,1027,8.5,0,0 +2013,11,22,5,128,-8,-2,1026,10.29,0,0 +2013,11,22,6,69,-8,-2,1026,12.08,0,0 +2013,11,22,7,54,-7,0,1027,16.1,0,0 +2013,11,22,8,57,-6,0,1026,20.12,0,0 +2013,11,22,9,58,-8,4,1027,23.25,0,0 +2013,11,22,10,68,-10,7,1026,26.38,0,0 +2013,11,22,11,59,-10,10,1025,28.17,0,0 +2013,11,22,12,63,-11,10,1024,1.79,0,0 +2013,11,22,13,74,-11,11,1023,2.68,0,0 +2013,11,22,14,82,-11,12,1022,4.47,0,0 +2013,11,22,15,125,-10,13,1022,6.26,0,0 +2013,11,22,16,203,-10,11,1021,3.13,0,0 +2013,11,22,17,176,-8,10,1021,7.15,0,0 +2013,11,22,18,185,-8,7,1022,10.28,0,0 +2013,11,22,19,212,-8,8,1022,12.07,0,0 +2013,11,22,20,323,-8,7,1022,13.86,0,0 +2013,11,22,21,347,-5,4,1022,0.89,0,0 +2013,11,22,22,352,-5,3,1022,1.78,0,0 +2013,11,22,23,365,-5,1,1022,2.67,0,0 +2013,11,23,0,383,-6,0,1022,1.79,0,0 +2013,11,23,1,314,-5,0,1022,0.89,0,0 +2013,11,23,2,NA,-5,-1,1021,0.89,0,0 +2013,11,23,3,387,-5,-1,1021,2.68,0,0 +2013,11,23,4,394,-6,-1,1021,5.81,0,0 +2013,11,23,5,280,-7,-2,1021,7.6,0,0 +2013,11,23,6,218,-7,-2,1021,10.73,0,0 +2013,11,23,7,201,-7,-2,1021,0.89,0,0 +2013,11,23,8,163,-6,0,1022,0.89,0,0 +2013,11,23,9,147,-6,4,1022,3.13,0,0 +2013,11,23,10,143,-6,6,1021,0.89,0,0 +2013,11,23,11,153,-8,9,1020,3.13,0,0 +2013,11,23,12,145,-8,12,1019,6.26,0,0 +2013,11,23,13,122,-11,16,1018,3.13,0,0 +2013,11,23,14,107,-11,16,1018,7.15,0,0 +2013,11,23,15,141,-11,16,1018,10.28,0,0 +2013,11,23,16,199,-12,15,1018,14.3,0,0 +2013,11,23,17,222,-7,13,1019,4.02,0,0 +2013,11,23,18,249,-7,12,1019,3.13,0,0 +2013,11,23,19,267,-6,8,1019,0.89,0,0 +2013,11,23,20,271,-6,7,1019,1.78,0,0 +2013,11,23,21,273,-5,6,1019,2.67,0,0 +2013,11,23,22,256,-4,4,1020,3.56,0,0 +2013,11,23,23,218,-4,4,1020,4.01,0,0 +2013,11,24,0,229,-4,3,1020,0.89,0,0 +2013,11,24,1,251,-4,3,1019,0.89,0,0 +2013,11,24,2,303,-4,3,1019,1.78,0,0 +2013,11,24,3,347,-5,3,1018,1.79,0,0 +2013,11,24,4,210,-7,5,1018,0.89,0,0 +2013,11,24,5,98,-6,3,1018,1.79,0,0 +2013,11,24,6,49,-6,4,1017,3.58,0,0 +2013,11,24,7,41,-5,4,1017,7.6,0,0 +2013,11,24,8,47,-7,6,1017,11.62,0,0 +2013,11,24,9,42,-8,7,1017,15.64,0,0 +2013,11,24,10,46,-10,9,1017,18.77,0,0 +2013,11,24,11,46,-10,9,1016,21.9,0,0 +2013,11,24,12,58,-14,10,1015,26.82,0,0 +2013,11,24,13,63,-15,10,1014,32.63,0,0 +2013,11,24,14,69,-15,11,1013,37.55,0,0 +2013,11,24,15,71,-16,10,1012,43.36,0,0 +2013,11,24,16,79,-15,8,1012,3.13,0,0 +2013,11,24,17,78,-14,7,1012,0.89,0,0 +2013,11,24,18,74,-10,4,1012,1.78,0,0 +2013,11,24,19,69,-10,3,1013,1.79,0,0 +2013,11,24,20,68,-14,8,1013,5.81,0,0 +2013,11,24,21,70,-14,8,1013,14.75,0,0 +2013,11,24,22,38,-14,7,1014,25.93,0,0 +2013,11,24,23,23,-13,6,1014,34.87,0,0 +2013,11,25,0,15,-13,6,1015,46.05,0,0 +2013,11,25,1,16,-13,5,1015,53.2,0,0 +2013,11,25,2,24,-14,5,1015,61.25,0,0 +2013,11,25,3,19,-13,4,1015,67.06,0,0 +2013,11,25,4,11,-13,4,1016,72.87,0,0 +2013,11,25,5,18,-14,4,1016,80.92,0,0 +2013,11,25,6,18,-16,3,1017,88.07,0,0 +2013,11,25,7,16,-16,3,1018,96.12,0,0 +2013,11,25,8,17,-17,3,1019,101.93,0,0 +2013,11,25,9,19,-19,4,1020,110.87,0,0 +2013,11,25,10,27,-21,6,1020,119.81,0,0 +2013,11,25,11,13,-18,7,1020,126.96,0,0 +2013,11,25,12,16,-17,6,1018,131.88,0,0 +2013,11,25,13,13,-17,7,1018,137.69,0,0 +2013,11,25,14,17,-19,7,1017,145.74,0,0 +2013,11,25,15,17,-20,7,1017,153.79,0,0 +2013,11,25,16,14,-21,7,1017,162.73,0,0 +2013,11,25,17,11,-21,6,1017,169.88,0,0 +2013,11,25,18,14,-21,6,1018,174.8,0,0 +2013,11,25,19,11,-21,6,1018,177.93,0,0 +2013,11,25,20,96,-17,2,1018,0.89,0,0 +2013,11,25,21,16,-16,1,1018,1.34,0,0 +2013,11,25,22,29,-16,0,1017,2.23,0,0 +2013,11,25,23,59,-12,-1,1017,3.12,0,0 +2013,11,26,0,67,-10,-3,1017,4.01,0,0 +2013,11,26,1,61,-13,-3,1017,4.9,0,0 +2013,11,26,2,97,-13,-3,1017,5.79,0,0 +2013,11,26,3,78,-14,-4,1017,1.79,0,0 +2013,11,26,4,51,-14,-3,1017,0.89,0,0 +2013,11,26,5,32,-13,-5,1017,0.89,0,0 +2013,11,26,6,26,-11,-5,1017,0.89,0,0 +2013,11,26,7,25,-11,-4,1018,0.89,0,0 +2013,11,26,8,21,-13,-3,1019,3.13,0,0 +2013,11,26,9,17,-17,3,1019,6.26,0,0 +2013,11,26,10,16,-20,4,1019,3.13,0,0 +2013,11,26,11,16,-21,6,1019,0.89,0,0 +2013,11,26,12,13,-20,8,1018,4.02,0,0 +2013,11,26,13,17,-22,7,1017,9.83,0,0 +2013,11,26,14,22,-22,7,1017,18.77,0,0 +2013,11,26,15,24,-26,6,1017,28.6,0,0 +2013,11,26,16,16,-26,6,1017,38.43,0,0 +2013,11,26,17,10,-27,5,1018,46.48,0,0 +2013,11,26,18,8,-29,4,1019,54.53,0,0 +2013,11,26,19,19,-28,2,1021,64.36,0,0 +2013,11,26,20,26,-28,1,1022,73.3,0,0 +2013,11,26,21,12,-25,0,1024,84.48,0,0 +2013,11,26,22,8,-24,-2,1025,92.53,0,0 +2013,11,26,23,7,-24,-2,1025,102.36,0,0 +2013,11,27,0,7,-25,-2,1025,112.19,0,0 +2013,11,27,1,5,-25,-3,1025,120.24,0,0 +2013,11,27,2,6,-24,-3,1025,128.29,0,0 +2013,11,27,3,3,-21,-4,1025,134.1,0,0 +2013,11,27,4,5,-23,-3,1025,143.04,0,0 +2013,11,27,5,5,-26,-4,1025,154.22,0,0 +2013,11,27,6,6,-26,-4,1026,164.05,0,0 +2013,11,27,7,7,-26,-4,1027,176.12,0,0 +2013,11,27,8,5,-27,-4,1027,188.19,0,0 +2013,11,27,9,3,-26,-3,1028,198.02,0,0 +2013,11,27,10,5,-28,-2,1028,210.09,0,0 +2013,11,27,11,6,-29,-1,1028,222.16,0,0 +2013,11,27,12,13,-29,0,1027,231.1,0,0 +2013,11,27,13,12,-28,-1,1027,239.15,0,0 +2013,11,27,14,10,-28,0,1027,248.98,0,0 +2013,11,27,15,9,-28,0,1027,257.92,0,0 +2013,11,27,16,12,-29,-1,1027,265.97,0,0 +2013,11,27,17,10,-29,-1,1027,274.02,0,0 +2013,11,27,18,8,-29,-1,1028,281.17,0,0 +2013,11,27,19,10,-29,-2,1028,288.32,0,0 +2013,11,27,20,9,-30,-2,1028,295.47,0,0 +2013,11,27,21,9,-29,-3,1028,302.62,0,0 +2013,11,27,22,7,-29,-3,1029,309.77,0,0 +2013,11,27,23,7,-30,-3,1029,318.71,0,0 +2013,11,28,0,6,-30,-3,1029,329.89,0,0 +2013,11,28,1,5,-29,-3,1029,337.04,0,0 +2013,11,28,2,2,-28,-4,1028,341.06,0,0 +2013,11,28,3,4,-28,-4,1028,345.98,0,0 +2013,11,28,4,4,-28,-4,1028,350.9,0,0 +2013,11,28,5,4,-27,-3,1027,358.05,0,0 +2013,11,28,6,10,-27,-3,1028,365.2,0,0 +2013,11,28,7,11,-26,-3,1028,372.35,0,0 +2013,11,28,8,10,-26,-2,1029,378.16,0,0 +2013,11,28,9,10,-27,0,1028,386.21,0,0 +2013,11,28,10,13,-30,1,1028,394.26,0,0 +2013,11,28,11,9,-32,3,1027,403.2,0,0 +2013,11,28,12,9,-33,4,1026,413.03,0,0 +2013,11,28,13,9,-31,4,1025,421.97,0,0 +2013,11,28,14,23,-30,6,1024,430.02,0,0 +2013,11,28,15,13,-28,5,1024,439.85,0,0 +2013,11,28,16,10,-28,6,1024,447.9,0,0 +2013,11,28,17,10,-24,6,1024,452.82,0,0 +2013,11,28,18,33,-20,6,1024,457.74,0,0 +2013,11,28,19,59,-19,6,1024,460.87,0,0 +2013,11,28,20,91,-19,6,1024,465.79,0,0 +2013,11,28,21,85,-18,5,1024,468.92,0,0 +2013,11,28,22,88,-18,6,1024,474.73,0,0 +2013,11,28,23,94,-18,6,1024,480.54,0,0 +2013,11,29,0,46,-18,4,1024,485.46,0,0 +2013,11,29,1,33,-16,-1,1023,490.38,0,0 +2013,11,29,2,38,-14,-2,1023,0.89,0,0 +2013,11,29,3,25,-16,-3,1023,3.13,0,0 +2013,11,29,4,21,-16,-4,1023,6.26,0,0 +2013,11,29,5,27,-15,-3,1023,8.05,0,0 +2013,11,29,6,19,-14,-3,1022,9.84,0,0 +2013,11,29,7,27,-14,-4,1022,13.86,0,0 +2013,11,29,8,37,-12,-1,1023,0.89,0,0 +2013,11,29,9,65,-14,5,1023,1.78,0,0 +2013,11,29,10,63,-18,8,1022,3.13,0,0 +2013,11,29,11,39,-18,10,1021,6.26,0,0 +2013,11,29,12,37,-17,10,1020,9.39,0,0 +2013,11,29,13,33,-17,12,1018,13.41,0,0 +2013,11,29,14,32,-17,13,1018,4.92,0,0 +2013,11,29,15,77,-18,12,1017,8.94,0,0 +2013,11,29,16,74,-18,11,1016,12.96,0,0 +2013,11,29,17,56,-18,10,1017,16.09,0,0 +2013,11,29,18,122,-17,10,1017,21.01,0,0 +2013,11,29,19,128,-15,8,1017,4.92,0,0 +2013,11,29,20,105,-14,8,1017,4.02,0,0 +2013,11,29,21,94,-14,8,1017,8.04,0,0 +2013,11,29,22,90,-14,7,1017,1.79,0,0 +2013,11,29,23,90,-12,2,1017,0.89,0,0 +2013,11,30,0,97,-12,0,1017,1.78,0,0 +2013,11,30,1,109,-13,1,1017,2.67,0,0 +2013,11,30,2,95,-13,2,1018,4.92,0,0 +2013,11,30,3,61,-10,-1,1019,8.94,0,0 +2013,11,30,4,34,-10,-1,1019,12.96,0,0 +2013,11,30,5,38,-11,0,1020,17.88,0,0 +2013,11,30,6,40,-12,0,1021,21.9,0,0 +2013,11,30,7,31,-12,-1,1022,25.03,0,0 +2013,11,30,8,22,-14,3,1023,30.84,0,0 +2013,11,30,9,24,-15,6,1024,3.13,0,0 +2013,11,30,10,25,-18,8,1024,4.02,0,0 +2013,11,30,11,20,-17,10,1025,9.83,0,0 +2013,11,30,12,15,-17,10,1024,12.96,0,0 +2013,11,30,13,12,-17,11,1024,16.98,0,0 +2013,11,30,14,14,-18,10,1023,3.13,0,0 +2013,11,30,15,16,-19,10,1023,4.92,0,0 +2013,11,30,16,20,-20,9,1024,1.79,0,0 +2013,11,30,17,24,-19,4,1024,0.89,0,0 +2013,11,30,18,23,-19,4,1025,1.78,0,0 +2013,11,30,19,29,-17,1,1025,1.79,0,0 +2013,11,30,20,35,-18,3,1026,0.89,0,0 +2013,11,30,21,56,-17,1,1026,1.78,0,0 +2013,11,30,22,36,-13,0,1026,0.89,0,0 +2013,11,30,23,26,-18,4,1026,0.89,0,0 +2013,12,1,0,28,-13,-2,1026,0.89,0,0 +2013,12,1,1,28,-14,-3,1025,3.13,0,0 +2013,12,1,2,36,-13,-3,1025,0.89,0,0 +2013,12,1,3,46,-13,-4,1025,0.89,0,0 +2013,12,1,4,57,-13,-3,1025,1.78,0,0 +2013,12,1,5,60,-14,-4,1025,2.67,0,0 +2013,12,1,6,90,-13,-6,1025,0.89,0,0 +2013,12,1,7,99,-14,-3,1025,0.89,0,0 +2013,12,1,8,125,-13,-2,1025,3.13,0,0 +2013,12,1,9,101,-14,1,1026,0.89,0,0 +2013,12,1,10,93,-16,7,1025,1.79,0,0 +2013,12,1,11,69,-16,9,1025,0.89,0,0 +2013,12,1,12,43,-16,9,1023,1.79,0,0 +2013,12,1,13,38,-17,10,1022,4.92,0,0 +2013,12,1,14,44,-17,11,1021,3.13,0,0 +2013,12,1,15,62,-18,12,1021,3.13,0,0 +2013,12,1,16,83,-17,11,1020,3.13,0,0 +2013,12,1,17,80,-15,9,1020,6.26,0,0 +2013,12,1,18,63,-14,8,1021,9.39,0,0 +2013,12,1,19,102,-13,8,1021,13.41,0,0 +2013,12,1,20,130,-12,4,1021,15.2,0,0 +2013,12,1,21,154,-11,1,1021,0.89,0,0 +2013,12,1,22,135,-10,2,1021,1.79,0,0 +2013,12,1,23,134,-10,-2,1021,0.89,0,0 +2013,12,2,0,161,-10,-3,1021,1.78,0,0 +2013,12,2,1,219,-10,-3,1020,1.79,0,0 +2013,12,2,2,202,-9,-3,1020,3.58,0,0 +2013,12,2,3,138,-11,-4,1020,6.71,0,0 +2013,12,2,4,107,-12,-2,1019,8.5,0,0 +2013,12,2,5,84,-11,-5,1019,11.63,0,0 +2013,12,2,6,97,-11,-4,1019,0.89,0,0 +2013,12,2,7,109,-12,-4,1019,3.13,0,0 +2013,12,2,8,140,-11,0,1019,6.26,0,0 +2013,12,2,9,69,-12,2,1020,9.39,0,0 +2013,12,2,10,76,-13,5,1019,11.18,0,0 +2013,12,2,11,77,-15,7,1019,1.79,0,0 +2013,12,2,12,87,-16,10,1018,1.79,0,0 +2013,12,2,13,96,-15,10,1018,0.89,0,0 +2013,12,2,14,88,-15,12,1017,1.78,0,0 +2013,12,2,15,71,-15,11,1017,2.67,0,0 +2013,12,2,16,93,-16,11,1017,1.79,0,0 +2013,12,2,17,117,-13,6,1017,0.89,0,0 +2013,12,2,18,138,-11,4,1018,1.34,0,0 +2013,12,2,19,190,-11,1,1018,0.89,0,0 +2013,12,2,20,197,-12,1,1019,0.89,0,0 +2013,12,2,21,213,-11,-1,1020,1.79,0,0 +2013,12,2,22,211,-12,1,1020,0.89,0,0 +2013,12,2,23,226,-11,1,1020,1.78,0,0 +2013,12,3,0,233,-11,2,1021,4.02,0,0 +2013,12,3,1,225,-11,-1,1020,7.15,0,0 +2013,12,3,2,159,-11,-3,1021,11.17,0,0 +2013,12,3,3,115,-11,-3,1021,14.3,0,0 +2013,12,3,4,87,-11,-3,1021,17.43,0,0 +2013,12,3,5,73,-11,-2,1021,20.56,0,0 +2013,12,3,6,60,-11,-3,1021,23.69,0,0 +2013,12,3,7,74,-11,-2,1022,0.45,0,0 +2013,12,3,8,82,-12,0,1023,1.79,0,0 +2013,12,3,9,73,-10,7,1024,3.13,0,0 +2013,12,3,10,50,-14,10,1024,3.13,0,0 +2013,12,3,11,56,-14,11,1024,1.79,0,0 +2013,12,3,12,48,-15,13,1023,4.92,0,0 +2013,12,3,13,33,-14,14,1022,8.05,0,0 +2013,12,3,14,30,-14,14,1021,9.84,0,0 +2013,12,3,15,28,-14,15,1021,13.86,0,0 +2013,12,3,16,30,-15,14,1021,17.88,0,0 +2013,12,3,17,33,-16,13,1021,21.9,0,0 +2013,12,3,18,46,-16,12,1021,25.03,0,0 +2013,12,3,19,74,-15,11,1021,28.16,0,0 +2013,12,3,20,136,-15,11,1021,1.79,0,0 +2013,12,3,21,152,-14,10,1021,1.79,0,0 +2013,12,3,22,159,-11,10,1021,3.13,0,0 +2013,12,3,23,164,-12,6,1021,1.79,0,0 +2013,12,4,0,167,-10,3,1021,2.68,0,0 +2013,12,4,1,180,-9,-1,1021,1.79,0,0 +2013,12,4,2,187,-10,-1,1021,4.92,0,0 +2013,12,4,3,163,-10,-1,1021,0.45,0,0 +2013,12,4,4,185,-9,-3,1020,0.9,0,0 +2013,12,4,5,159,-8,-2,1020,1.79,0,0 +2013,12,4,6,139,-8,-2,1020,2.68,0,0 +2013,12,4,7,151,-9,-3,1020,1.79,0,0 +2013,12,4,8,113,-8,-1,1021,4.92,0,0 +2013,12,4,9,137,-9,3,1021,0.89,0,0 +2013,12,4,10,92,-11,8,1021,4.02,0,0 +2013,12,4,11,71,-12,11,1020,3.13,0,0 +2013,12,4,12,73,-13,11,1019,6.26,0,0 +2013,12,4,13,68,-13,12,1018,8.05,0,0 +2013,12,4,14,47,-13,14,1018,11.18,0,0 +2013,12,4,15,41,-13,14,1018,12.97,0,0 +2013,12,4,16,43,-12,12,1018,16.1,0,0 +2013,12,4,17,56,-12,9,1018,0.89,0,0 +2013,12,4,18,69,-11,5,1018,1.78,0,0 +2013,12,4,19,83,-11,5,1019,0.89,0,0 +2013,12,4,20,130,-9,5,1019,0.45,0,0 +2013,12,4,21,175,-9,3,1020,1.34,0,0 +2013,12,4,22,155,-9,4,1020,1.79,0,0 +2013,12,4,23,106,-9,4,1020,3.58,0,0 +2013,12,5,0,83,-9,4,1021,0.89,0,0 +2013,12,5,1,122,-8,3,1021,1.78,0,0 +2013,12,5,2,162,-8,2,1022,1.79,0,0 +2013,12,5,3,142,-8,2,1022,1.79,0,0 +2013,12,5,4,70,-12,6,1022,7.6,0,0 +2013,12,5,5,33,-13,3,1023,1.79,0,0 +2013,12,5,6,25,-11,2,1024,4.92,0,0 +2013,12,5,7,39,-11,4,1025,4.92,0,0 +2013,12,5,8,33,-12,4,1026,4.02,0,0 +2013,12,5,9,27,-14,6,1027,7.15,0,0 +2013,12,5,10,25,-17,8,1027,10.28,0,0 +2013,12,5,11,12,-18,9,1027,4.02,0,0 +2013,12,5,12,9,-17,9,1026,3.13,0,0 +2013,12,5,13,10,-17,9,1025,3.13,0,0 +2013,12,5,14,11,-17,10,1024,6.26,0,0 +2013,12,5,15,11,-17,10,1023,0.89,0,0 +2013,12,5,16,13,-18,8,1023,1.79,0,0 +2013,12,5,17,17,-16,6,1023,2.68,0,0 +2013,12,5,18,32,-15,3,1023,0.89,0,0 +2013,12,5,19,62,-15,3,1023,1.79,0,0 +2013,12,5,20,42,-16,4,1023,3.58,0,0 +2013,12,5,21,59,-15,3,1022,5.37,0,0 +2013,12,5,22,66,-14,0,1022,7.16,0,0 +2013,12,5,23,77,-11,-1,1022,0.89,0,0 +2013,12,6,0,84,-12,-2,1022,1.78,0,0 +2013,12,6,1,91,-11,-2,1021,2.67,0,0 +2013,12,6,2,112,-11,-4,1021,1.79,0,0 +2013,12,6,3,130,-11,-3,1021,0.89,0,0 +2013,12,6,4,139,-12,-4,1021,0.89,0,0 +2013,12,6,5,155,-11,-5,1020,0.89,0,0 +2013,12,6,6,163,-11,-4,1020,1.79,0,0 +2013,12,6,7,141,-11,-5,1020,0.89,0,0 +2013,12,6,8,182,-11,-5,1021,1.79,0,0 +2013,12,6,9,138,-10,2,1021,0.89,0,0 +2013,12,6,10,119,-11,3,1021,1.78,0,0 +2013,12,6,11,135,-11,5,1020,2.67,0,0 +2013,12,6,12,159,-12,7,1019,1.79,0,0 +2013,12,6,13,122,-12,9,1018,3.58,0,0 +2013,12,6,14,81,-12,10,1017,0.89,0,0 +2013,12,6,15,167,-12,10,1017,1.78,0,0 +2013,12,6,16,246,-9,9,1017,1.79,0,0 +2013,12,6,17,224,-8,8,1018,5.81,0,0 +2013,12,6,18,115,-6,5,1018,0.89,0,0 +2013,12,6,19,124,-6,3,1019,1.78,0,0 +2013,12,6,20,152,-7,-1,1019,1.79,0,0 +2013,12,6,21,170,-5,1,1019,0.45,0,0 +2013,12,6,22,269,-5,-1,1020,0.89,0,0 +2013,12,6,23,294,-6,-2,1020,0.89,0,0 +2013,12,7,0,330,-6,-2,1020,1.78,0,0 +2013,12,7,1,407,-7,-3,1019,0.89,0,0 +2013,12,7,2,430,-6,-3,1020,0.45,0,0 +2013,12,7,3,415,-7,-3,1020,0.9,0,0 +2013,12,7,4,343,-7,-4,1020,1.79,0,0 +2013,12,7,5,320,-8,-4,1020,3.58,0,0 +2013,12,7,6,244,-8,-4,1020,0.89,0,0 +2013,12,7,7,197,-8,-4,1020,1.78,0,0 +2013,12,7,8,172,-8,-4,1021,1.79,0,0 +2013,12,7,9,156,-7,-1,1021,3.58,0,0 +2013,12,7,10,166,-7,2,1022,0.89,0,0 +2013,12,7,11,203,-7,4,1021,1.78,0,0 +2013,12,7,12,277,-5,5,1020,1.79,0,0 +2013,12,7,13,290,-4,5,1020,4.92,0,0 +2013,12,7,14,366,-4,6,1019,6.71,0,0 +2013,12,7,15,392,-3,6,1018,8.5,0,0 +2013,12,7,16,421,-3,5,1019,0.89,0,0 +2013,12,7,17,450,-4,2,1019,1.78,0,0 +2013,12,7,18,443,-4,0,1019,0.89,0,0 +2013,12,7,19,429,-4,0,1019,1.78,0,0 +2013,12,7,20,439,-3,0,1020,0.45,0,0 +2013,12,7,21,460,-2,1,1020,0.9,0,0 +2013,12,7,22,480,-4,-1,1020,1.79,0,0 +2013,12,7,23,424,-3,1,1020,2.68,0,0 +2013,12,8,0,440,-2,2,1020,3.57,0,0 +2013,12,8,1,403,-1,3,1020,1.79,0,0 +2013,12,8,2,403,-1,3,1020,2.68,0,0 +2013,12,8,3,389,-1,3,1020,0.45,0,0 +2013,12,8,4,432,-1,3,1020,1.79,0,0 +2013,12,8,5,432,-1,3,1020,0.45,0,0 +2013,12,8,6,435,-1,4,1020,1.79,0,0 +2013,12,8,7,403,-2,2,1020,4.92,0,0 +2013,12,8,8,379,-2,2,1020,0.89,0,0 +2013,12,8,9,346,-2,3,1021,1.79,0,0 +2013,12,8,10,354,-2,5,1021,1.79,0,0 +2013,12,8,11,343,-3,6,1020,3.58,0,0 +2013,12,8,12,266,-3,7,1019,5.37,0,0 +2013,12,8,13,206,-6,9,1019,9.39,0,0 +2013,12,8,14,184,-9,11,1018,4.92,0,0 +2013,12,8,15,47,-11,10,1018,9.84,0,0 +2013,12,8,16,13,-16,10,1019,17.89,0,0 +2013,12,8,17,15,-15,8,1020,26.83,0,0 +2013,12,8,18,15,-15,7,1021,36.66,0,0 +2013,12,8,19,17,-16,6,1022,47.84,0,0 +2013,12,8,20,11,-16,5,1023,60.8,0,0 +2013,12,8,21,15,-16,4,1024,71.98,0,0 +2013,12,8,22,12,-16,3,1024,80.92,0,0 +2013,12,8,23,14,-16,3,1024,88.07,0,0 +2013,12,9,0,12,-16,2,1025,95.22,0,0 +2013,12,9,1,8,-16,2,1025,102.37,0,0 +2013,12,9,2,8,-16,1,1025,108.18,0,0 +2013,12,9,3,6,-14,-1,1025,113.1,0,0 +2013,12,9,4,7,-16,2,1025,121.15,0,0 +2013,12,9,5,8,-17,2,1025,130.98,0,0 +2013,12,9,6,8,-17,1,1025,139.92,0,0 +2013,12,9,7,8,-17,0,1026,147.07,0,0 +2013,12,9,8,11,-17,1,1026,154.22,0,0 +2013,12,9,9,12,-17,3,1026,163.16,0,0 +2013,12,9,10,11,-18,4,1026,172.1,0,0 +2013,12,9,11,11,-18,5,1025,181.93,0,0 +2013,12,9,12,9,-20,6,1024,189.98,0,0 +2013,12,9,13,17,-19,7,1023,198.03,0,0 +2013,12,9,14,12,-19,6,1022,205.18,0,0 +2013,12,9,15,12,-19,6,1022,213.23,0,0 +2013,12,9,16,10,-19,6,1022,220.38,0,0 +2013,12,9,17,14,-19,5,1022,225.3,0,0 +2013,12,9,18,12,-18,2,1022,1.79,0,0 +2013,12,9,19,19,-14,0,1023,2.68,0,0 +2013,12,9,20,20,-12,-2,1023,0.89,0,0 +2013,12,9,21,55,-14,-3,1022,1.78,0,0 +2013,12,9,22,68,-14,-2,1022,2.67,0,0 +2013,12,9,23,58,-14,-3,1022,1.79,0,0 +2013,12,10,0,63,-14,-3,1022,2.68,0,0 +2013,12,10,1,52,-13,-5,1021,0.89,0,0 +2013,12,10,2,44,-13,-6,1021,1.79,0,0 +2013,12,10,3,60,-14,-6,1021,3.13,0,0 +2013,12,10,4,64,-12,-4,1020,4.92,0,0 +2013,12,10,5,44,-13,-5,1019,6.71,0,0 +2013,12,10,6,38,-12,-4,1019,7.6,0,0 +2013,12,10,7,41,-12,-4,1019,9.39,0,0 +2013,12,10,8,29,-12,-3,1019,0.45,0,0 +2013,12,10,9,32,-11,-2,1019,1.79,0,0 +2013,12,10,10,41,-12,0,1019,0.89,0,0 +2013,12,10,11,53,-14,1,1018,1.78,0,0 +2013,12,10,12,24,-18,5,1017,8.05,0,0 +2013,12,10,13,16,-19,6,1016,20.12,0,0 +2013,12,10,14,14,-19,6,1017,31.3,0,0 +2013,12,10,15,8,-22,6,1017,43.37,0,0 +2013,12,10,16,9,-22,5,1018,56.33,0,0 +2013,12,10,17,8,-22,4,1019,69.29,0,0 +2013,12,10,18,10,-22,3,1021,82.25,0,0 +2013,12,10,19,13,-21,2,1022,93.43,0,0 +2013,12,10,20,11,-20,2,1022,102.37,0,0 +2013,12,10,21,7,-21,1,1022,112.2,0,0 +2013,12,10,22,14,-20,1,1022,121.14,0,0 +2013,12,10,23,13,-21,1,1023,132.32,0,0 +2013,12,11,0,9,-20,0,1023,142.15,0,0 +2013,12,11,1,8,-19,0,1023,149.3,0,0 +2013,12,11,2,6,-20,-1,1023,156.45,0,0 +2013,12,11,3,6,-20,-2,1023,161.37,0,0 +2013,12,11,4,5,-20,-2,1022,164.5,0,0 +2013,12,11,5,11,-18,-4,1022,1.79,0,0 +2013,12,11,6,9,-17,-5,1022,3.58,0,0 +2013,12,11,7,8,-17,-5,1022,5.37,0,0 +2013,12,11,8,23,-15,-1,1022,7.16,0,0 +2013,12,11,9,10,-18,1,1021,8.95,0,0 +2013,12,11,10,16,-21,3,1021,14.76,0,0 +2013,12,11,11,30,-21,4,1020,20.57,0,0 +2013,12,11,12,35,-22,5,1019,25.49,0,0 +2013,12,11,13,33,-21,6,1017,9.83,0,0 +2013,12,11,14,33,-22,7,1016,17.88,0,0 +2013,12,11,15,17,-22,7,1016,25.03,0,0 +2013,12,11,16,29,-21,7,1016,33.08,0,0 +2013,12,11,17,31,-21,6,1016,38,0,0 +2013,12,11,18,34,-19,3,1017,0.89,0,0 +2013,12,11,19,40,-18,3,1017,0.89,0,0 +2013,12,11,20,75,-20,3,1017,1.79,0,0 +2013,12,11,21,55,-19,2,1017,3.58,0,0 +2013,12,11,22,76,-19,1,1017,0.89,0,0 +2013,12,11,23,89,-17,-1,1017,1.79,0,0 +2013,12,12,0,60,-18,-3,1017,3.58,0,0 +2013,12,12,1,47,-16,-3,1016,0.89,0,0 +2013,12,12,2,53,-15,-4,1016,1.34,0,0 +2013,12,12,3,61,-15,-6,1016,1.79,0,0 +2013,12,12,4,48,-15,-7,1017,4.92,0,0 +2013,12,12,5,14,-15,-7,1018,8.05,0,0 +2013,12,12,6,39,-14,-3,1019,0.89,0,0 +2013,12,12,7,23,-19,-1,1020,8.94,0,0 +2013,12,12,8,10,-24,-1,1021,20.12,0,0 +2013,12,12,9,11,-26,0,1022,31.3,0,0 +2013,12,12,10,10,-29,0,1023,44.26,0,0 +2013,12,12,11,9,-28,0,1024,54.09,0,0 +2013,12,12,12,12,-28,1,1023,63.92,0,0 +2013,12,12,13,8,-32,1,1023,75.99,0,0 +2013,12,12,14,6,-33,1,1023,88.06,0,0 +2013,12,12,15,13,-32,0,1023,97,0,0 +2013,12,12,16,10,-29,-1,1023,105.94,0,0 +2013,12,12,17,8,-29,-3,1024,113.09,0,0 +2013,12,12,18,9,-28,-4,1024,117.11,0,0 +2013,12,12,19,16,-27,-4,1025,1.79,0,0 +2013,12,12,20,23,-26,-5,1025,0.89,0,0 +2013,12,12,21,12,-24,-6,1025,1.78,0,0 +2013,12,12,22,16,-24,-6,1025,3.13,0,0 +2013,12,12,23,38,-21,-8,1025,4.02,0,0 +2013,12,13,0,49,-22,-8,1025,0.89,0,0 +2013,12,13,1,45,-23,-6,1025,3.13,0,0 +2013,12,13,2,48,-21,-7,1025,4.92,0,0 +2013,12,13,3,45,-21,-5,1026,6.71,0,0 +2013,12,13,4,39,-20,-8,1026,0.89,0,0 +2013,12,13,5,43,-21,-9,1026,1.79,0,0 +2013,12,13,6,51,-21,-10,1027,3.58,0,0 +2013,12,13,7,88,-20,-10,1027,4.47,0,0 +2013,12,13,8,92,-19,-7,1028,0.89,0,0 +2013,12,13,9,85,-19,-3,1028,1.34,0,0 +2013,12,13,10,87,-20,-1,1028,2.23,0,0 +2013,12,13,11,30,-25,0,1027,3.12,0,0 +2013,12,13,12,57,-26,1,1026,4.91,0,0 +2013,12,13,13,58,-25,1,1025,1.79,0,0 +2013,12,13,14,49,-25,3,1024,4.92,0,0 +2013,12,13,15,52,-25,2,1023,8.94,0,0 +2013,12,13,16,79,-24,2,1024,12.07,0,0 +2013,12,13,17,88,-23,1,1024,0.89,0,0 +2013,12,13,18,114,-20,-4,1024,1.79,0,0 +2013,12,13,19,144,-21,-3,1024,4.92,0,0 +2013,12,13,20,101,-20,-3,1025,6.71,0,0 +2013,12,13,21,66,-20,-4,1025,0.89,0,0 +2013,12,13,22,68,-20,-6,1025,1.79,0,0 +2013,12,13,23,69,-19,-7,1026,5.81,0,0 +2013,12,14,0,71,-18,-7,1026,7.6,0,0 +2013,12,14,1,123,-18,-8,1026,10.73,0,0 +2013,12,14,2,118,-17,-9,1026,13.86,0,0 +2013,12,14,3,91,-18,-8,1027,16.99,0,0 +2013,12,14,4,69,-21,-7,1027,18.78,0,0 +2013,12,14,5,53,-19,-7,1027,0.89,0,0 +2013,12,14,6,51,-19,-8,1028,4.02,0,0 +2013,12,14,7,43,-21,-10,1029,7.15,0,0 +2013,12,14,8,53,-20,-8,1029,8.94,0,0 +2013,12,14,9,41,-20,-3,1030,1.79,0,0 +2013,12,14,10,28,-21,-1,1031,3.13,0,0 +2013,12,14,11,25,-27,1,1030,7.15,0,0 +2013,12,14,12,26,-28,3,1029,11.17,0,0 +2013,12,14,13,21,-29,3,1028,15.19,0,0 +2013,12,14,14,17,-28,4,1028,18.32,0,0 +2013,12,14,15,23,-29,5,1028,0.89,0,0 +2013,12,14,16,28,-27,4,1028,2.68,0,0 +2013,12,14,17,117,-28,1,1029,0.89,0,0 +2013,12,14,18,125,-24,-2,1029,1.78,0,0 +2013,12,14,19,122,-22,-3,1030,2.67,0,0 +2013,12,14,20,92,-20,-4,1030,0.89,0,0 +2013,12,14,21,51,-21,-2,1031,1.78,0,0 +2013,12,14,22,61,-21,-1,1031,3.13,0,0 +2013,12,14,23,80,-20,-3,1032,1.79,0,0 +2013,12,15,0,52,-19,-3,1032,1.79,0,0 +2013,12,15,1,39,-20,-3,1032,0.89,0,0 +2013,12,15,2,48,-19,-4,1033,0.45,0,0 +2013,12,15,3,69,-19,-5,1033,4.92,0,0 +2013,12,15,4,42,-20,-5,1033,9.84,0,0 +2013,12,15,5,16,-21,-5,1033,13.86,0,0 +2013,12,15,6,16,-23,-2,1033,5.81,0,0 +2013,12,15,7,15,-22,-3,1033,10.73,0,0 +2013,12,15,8,22,-22,-2,1034,13.86,0,0 +2013,12,15,9,27,-22,0,1035,0.89,0,0 +2013,12,15,10,24,-23,1,1036,1.79,0,0 +2013,12,15,11,39,-24,3,1035,3.58,0,0 +2013,12,15,12,44,-25,4,1034,0.89,0,0 +2013,12,15,13,25,-25,4,1033,1.78,0,0 +2013,12,15,14,29,-25,5,1032,3.57,0,0 +2013,12,15,15,38,-24,5,1032,4.46,0,0 +2013,12,15,16,48,-23,4,1032,3.13,0,0 +2013,12,15,17,60,-22,1,1033,0.89,0,0 +2013,12,15,18,67,-20,0,1033,1.78,0,0 +2013,12,15,19,60,-16,-2,1034,0.89,0,0 +2013,12,15,20,79,-16,-3,1033,0.45,0,0 +2013,12,15,21,86,-16,-3,1034,0.9,0,0 +2013,12,15,22,98,-16,-5,1034,1.79,0,0 +2013,12,15,23,163,-16,-4,1034,0.89,0,0 +2013,12,16,0,271,-14,-5,1034,0.89,0,0 +2013,12,16,1,331,-16,-6,1033,1.34,0,0 +2013,12,16,2,291,-16,-5,1033,1.79,0,0 +2013,12,16,3,262,-14,-6,1033,0.89,0,0 +2013,12,16,4,201,-16,-7,1032,2.68,0,0 +2013,12,16,5,90,-15,-8,1032,4.47,0,0 +2013,12,16,6,73,-16,-8,1031,6.26,0,0 +2013,12,16,7,66,-15,-7,1031,9.39,0,0 +2013,12,16,8,88,-16,-8,1032,12.52,0,0 +2013,12,16,9,82,-13,-6,1032,0.89,0,0 +2013,12,16,10,84,-14,-3,1033,1.78,0,0 +2013,12,16,11,89,-14,-1,1032,2.67,0,0 +2013,12,16,12,117,-18,1,1031,3.12,0,0 +2013,12,16,13,135,-18,1,1030,4.01,0,0 +2013,12,16,14,143,-18,2,1029,4.9,0,0 +2013,12,16,15,156,-17,2,1029,0.89,0,0 +2013,12,16,16,174,-16,1,1029,1.78,0,0 +2013,12,16,17,202,-15,-1,1029,0.89,0,0 +2013,12,16,18,194,-14,-2,1029,0.89,0,0 +2013,12,16,19,210,-13,-2,1030,0.89,0,0 +2013,12,16,20,247,-11,-2,1030,1.78,0,0 +2013,12,16,21,284,-12,-1,1031,0.89,0,0 +2013,12,16,22,281,-11,-1,1031,0.89,0,0 +2013,12,16,23,291,-12,-1,1031,0.89,0,0 +2013,12,17,0,310,-12,-2,1031,1.79,0,0 +2013,12,17,1,280,-13,-3,1031,3.58,0,0 +2013,12,17,2,302,-12,-3,1031,5.37,0,0 +2013,12,17,3,259,-12,-2,1032,7.16,0,0 +2013,12,17,4,198,-12,-1,1031,10.29,0,0 +2013,12,17,5,192,-12,-2,1031,12.08,0,0 +2013,12,17,6,127,-12,-1,1032,0.89,0,0 +2013,12,17,7,89,-16,0,1032,5.81,0,0 +2013,12,17,8,40,-16,0,1033,10.73,0,0 +2013,12,17,9,24,-16,0,1034,16.54,0,0 +2013,12,17,10,26,-19,0,1034,23.69,0,0 +2013,12,17,11,20,-19,1,1034,29.5,0,0 +2013,12,17,12,19,-19,2,1033,4.92,0,0 +2013,12,17,13,24,-19,1,1033,3.13,0,0 +2013,12,17,14,27,-19,2,1032,6.26,0,0 +2013,12,17,15,30,-20,2,1032,9.39,0,0 +2013,12,17,16,26,-20,1,1032,12.52,0,0 +2013,12,17,17,28,-18,0,1033,14.31,0,0 +2013,12,17,18,39,-18,-3,1034,16.1,0,0 +2013,12,17,19,30,-20,-1,1035,20.12,0,0 +2013,12,17,20,27,-21,-2,1035,24.14,0,0 +2013,12,17,21,17,-21,-3,1036,28.16,0,0 +2013,12,17,22,13,-21,-4,1037,33.08,0,0 +2013,12,17,23,11,-21,-5,1037,38,0,0 +2013,12,18,0,12,-21,-5,1037,42.92,0,0 +2013,12,18,1,12,-21,-6,1037,46.94,0,0 +2013,12,18,2,10,-20,-6,1036,51.86,0,0 +2013,12,18,3,11,-20,-7,1036,55.88,0,0 +2013,12,18,4,13,-20,-7,1036,59.01,0,0 +2013,12,18,5,12,-22,-10,1035,3.13,0,0 +2013,12,18,6,12,-20,-11,1035,6.26,0,0 +2013,12,18,7,27,-20,-10,1035,1.79,0,0 +2013,12,18,8,12,-20,-6,1035,7.15,0,0 +2013,12,18,9,19,-20,-4,1035,11.17,0,0 +2013,12,18,10,18,-20,-3,1035,14.3,0,0 +2013,12,18,11,17,-20,-1,1034,18.32,0,0 +2013,12,18,12,19,-23,1,1032,23.24,0,0 +2013,12,18,13,20,-23,0,1031,1.79,0,0 +2013,12,18,14,13,-20,0,1030,1.79,0,0 +2013,12,18,15,55,-23,0,1029,3.58,0,0 +2013,12,18,16,59,-20,-1,1029,6.71,0,0 +2013,12,18,17,78,-20,-3,1029,8.5,0,0 +2013,12,18,18,91,-19,-4,1029,10.29,0,0 +2013,12,18,19,97,-19,-5,1029,12.08,0,0 +2013,12,18,20,97,-19,-5,1029,0.89,0,0 +2013,12,18,21,123,-18,-8,1030,1.79,0,0 +2013,12,18,22,162,-17,-7,1030,0.89,0,0 +2013,12,18,23,214,-18,-7,1030,1.79,0,0 +2013,12,19,0,160,-18,-9,1030,5.81,0,0 +2013,12,19,1,41,-18,-7,1030,7.6,0,0 +2013,12,19,2,18,-18,-10,1030,9.39,0,0 +2013,12,19,3,11,-19,-8,1030,3.13,0,0 +2013,12,19,4,8,-20,-10,1030,1.79,0,0 +2013,12,19,5,9,-19,-11,1030,4.92,0,0 +2013,12,19,6,8,-20,-10,1030,8.94,0,0 +2013,12,19,7,10,-19,-8,1031,12.07,0,0 +2013,12,19,8,16,-19,-7,1031,4.02,0,0 +2013,12,19,9,23,-19,-6,1032,8.04,0,0 +2013,12,19,10,26,-19,-4,1032,12.06,0,0 +2013,12,19,11,31,-20,-3,1032,16.08,0,0 +2013,12,19,12,31,-20,-1,1031,19.21,0,0 +2013,12,19,13,27,-20,-1,1030,22.34,0,0 +2013,12,19,14,23,-20,0,1029,25.47,0,0 +2013,12,19,15,27,-22,0,1030,27.26,0,0 +2013,12,19,16,33,-21,0,1030,29.05,0,0 +2013,12,19,17,30,-21,-2,1030,0.89,0,0 +2013,12,19,18,43,-20,-4,1031,0.89,0,0 +2013,12,19,19,48,-19,-1,1031,1.79,0,0 +2013,12,19,20,80,-19,-4,1032,3.58,0,0 +2013,12,19,21,93,-17,-6,1032,1.79,0,0 +2013,12,19,22,95,-18,-8,1032,3.58,0,0 +2013,12,19,23,107,-18,-9,1032,6.71,0,0 +2013,12,20,0,152,-19,-8,1032,9.84,0,0 +2013,12,20,1,117,-18,-5,1033,3.13,0,0 +2013,12,20,2,40,-19,-6,1033,6.26,0,0 +2013,12,20,3,23,-19,-10,1033,3.13,0,0 +2013,12,20,4,17,-18,-10,1033,6.26,0,0 +2013,12,20,5,25,-18,-7,1033,9.39,0,0 +2013,12,20,6,29,-18,-9,1033,12.52,0,0 +2013,12,20,7,36,-18,-11,1034,14.31,0,0 +2013,12,20,8,40,-17,-10,1035,16.1,0,0 +2013,12,20,9,24,-15,-3,1035,19.23,0,0 +2013,12,20,10,29,-17,-1,1035,4.02,0,0 +2013,12,20,11,34,-20,1,1036,0.89,0,0 +2013,12,20,12,17,-20,1,1035,3.13,0,0 +2013,12,20,13,17,-21,1,1034,7.15,0,0 +2013,12,20,14,15,-21,2,1034,1.79,0,0 +2013,12,20,15,20,-21,2,1034,3.13,0,0 +2013,12,20,16,24,-21,1,1034,4.92,0,0 +2013,12,20,17,NA,-19,-1,1034,0.89,0,0 +2013,12,20,18,NA,-18,-3,1035,0.89,0,0 +2013,12,20,19,50,-16,0,1035,2.68,0,0 +2013,12,20,20,57,-16,-5,1036,1.79,0,0 +2013,12,20,21,38,-18,-5,1036,3.13,0,0 +2013,12,20,22,68,-17,-2,1036,4.02,0,0 +2013,12,20,23,46,-19,-4,1037,5.81,0,0 +2013,12,21,0,37,-19,-4,1037,6.7,0,0 +2013,12,21,1,28,-19,-5,1037,9.83,0,0 +2013,12,21,2,22,-19,-3,1037,13.85,0,0 +2013,12,21,3,22,-19,-4,1037,17.87,0,0 +2013,12,21,4,17,-19,-5,1037,21,0,0 +2013,12,21,5,21,-19,-9,1036,3.13,0,0 +2013,12,21,6,19,-18,-8,1036,6.26,0,0 +2013,12,21,7,28,-18,-8,1036,8.05,0,0 +2013,12,21,8,26,-16,-8,1037,12.07,0,0 +2013,12,21,9,34,-16,-3,1037,16.09,0,0 +2013,12,21,10,41,-18,-1,1037,4.02,0,0 +2013,12,21,11,42,-20,0,1037,1.79,0,0 +2013,12,21,12,42,-21,1,1035,1.79,0,0 +2013,12,21,13,36,-21,2,1034,1.79,0,0 +2013,12,21,14,34,-21,2,1033,1.79,0,0 +2013,12,21,15,45,-21,4,1033,3.58,0,0 +2013,12,21,16,89,-21,2,1033,1.79,0,0 +2013,12,21,17,121,-19,1,1033,3.58,0,0 +2013,12,21,18,128,-16,-1,1033,6.71,0,0 +2013,12,21,19,158,-16,-1,1033,8.5,0,0 +2013,12,21,20,242,-16,-2,1034,10.29,0,0 +2013,12,21,21,247,-15,-2,1034,0.89,0,0 +2013,12,21,22,242,-15,-5,1034,1.78,0,0 +2013,12,21,23,240,-15,-6,1034,0.89,0,0 +2013,12,22,0,267,-15,-8,1034,0.89,0,0 +2013,12,22,1,177,-14,-7,1034,0.45,0,0 +2013,12,22,2,148,-14,-7,1034,1.79,0,0 +2013,12,22,3,178,-15,-7,1034,3.58,0,0 +2013,12,22,4,165,-16,-8,1034,4.47,0,0 +2013,12,22,5,155,-16,-9,1033,6.26,0,0 +2013,12,22,6,96,-16,-8,1033,8.05,0,0 +2013,12,22,7,70,-16,-8,1033,9.84,0,0 +2013,12,22,8,62,-17,-9,1034,11.63,0,0 +2013,12,22,9,65,-14,-7,1034,13.42,0,0 +2013,12,22,10,52,-14,-4,1034,0.89,0,0 +2013,12,22,11,80,-15,-2,1034,1.78,0,0 +2013,12,22,12,112,-15,0,1032,2.67,0,0 +2013,12,22,13,144,-17,1,1031,3.56,0,0 +2013,12,22,14,139,-17,2,1031,4.45,0,0 +2013,12,22,15,138,-17,1,1030,1.79,0,0 +2013,12,22,16,130,-16,0,1030,3.58,0,0 +2013,12,22,17,118,-14,-2,1031,4.47,0,0 +2013,12,22,18,315,-13,-3,1031,5.36,0,0 +2013,12,22,19,317,-12,-4,1031,0.89,0,0 +2013,12,22,20,325,-12,-5,1031,0.89,0,0 +2013,12,22,21,348,-12,-6,1031,0.45,0,0 +2013,12,22,22,336,-12,-6,1031,0.9,0,0 +2013,12,22,23,334,-13,-7,1031,0.89,0,0 +2013,12,23,0,345,-14,-8,1031,1.78,0,0 +2013,12,23,1,332,-13,-8,1031,3.13,0,0 +2013,12,23,2,337,-15,-8,1031,4.92,0,0 +2013,12,23,3,384,-15,-9,1031,6.71,0,0 +2013,12,23,4,240,-16,-9,1031,9.84,0,0 +2013,12,23,5,139,-14,-7,1031,12.97,0,0 +2013,12,23,6,111,-17,-10,1031,16.1,0,0 +2013,12,23,7,104,-16,-9,1031,20.12,0,0 +2013,12,23,8,124,-15,-6,1031,24.14,0,0 +2013,12,23,9,93,-13,-2,1032,27.27,0,0 +2013,12,23,10,98,-17,3,1033,4.02,0,0 +2013,12,23,11,66,-16,4,1032,8.94,0,0 +2013,12,23,12,47,-17,4,1032,13.86,0,0 +2013,12,23,13,34,-17,5,1031,17.88,0,0 +2013,12,23,14,36,-18,6,1031,21.01,0,0 +2013,12,23,15,36,-18,6,1031,24.14,0,0 +2013,12,23,16,33,-19,5,1031,25.93,0,0 +2013,12,23,17,29,-18,1,1032,1.79,0,0 +2013,12,23,18,105,-17,1,1033,4.92,0,0 +2013,12,23,19,121,-16,-1,1033,6.71,0,0 +2013,12,23,20,168,-14,-2,1034,0.89,0,0 +2013,12,23,21,202,-14,-4,1035,1.78,0,0 +2013,12,23,22,196,-13,-4,1035,2.67,0,0 +2013,12,23,23,216,-13,-6,1035,3.56,0,0 +2013,12,24,0,247,-14,-6,1036,0.89,0,0 +2013,12,24,1,273,-14,-6,1036,0.89,0,0 +2013,12,24,2,334,-15,-9,1036,0.89,0,0 +2013,12,24,3,417,-14,-8,1036,0.45,0,0 +2013,12,24,4,457,-15,-9,1036,0.89,0,0 +2013,12,24,5,426,-14,-8,1035,0.45,0,0 +2013,12,24,6,377,-14,-9,1035,1.79,0,0 +2013,12,24,7,346,-14,-9,1036,0.89,0,0 +2013,12,24,8,332,-13,-9,1036,1.79,0,0 +2013,12,24,9,282,-12,-6,1037,1.79,0,0 +2013,12,24,10,211,-11,-4,1036,3.58,0,0 +2013,12,24,11,201,-11,-1,1036,0.89,0,0 +2013,12,24,12,235,-10,0,1035,1.78,0,0 +2013,12,24,13,283,-11,0,1034,2.67,0,0 +2013,12,24,14,313,-10,1,1033,3.56,0,0 +2013,12,24,15,337,-10,1,1032,4.45,0,0 +2013,12,24,16,362,-11,0,1032,5.34,0,0 +2013,12,24,17,333,-10,-2,1031,0.89,0,0 +2013,12,24,18,312,-10,-4,1032,1.79,0,0 +2013,12,24,19,309,-10,-4,1032,0.89,0,0 +2013,12,24,20,332,-10,-4,1032,1.79,0,0 +2013,12,24,21,294,-9,-4,1031,0.89,0,0 +2013,12,24,22,302,-9,-4,1031,1.78,0,0 +2013,12,24,23,267,-9,-5,1031,0.45,0,0 +2013,12,25,0,267,-10,-6,1031,1.79,0,0 +2013,12,25,1,297,-11,-7,1030,3.58,0,0 +2013,12,25,2,329,-11,-7,1029,5.37,0,0 +2013,12,25,3,326,-10,-7,1029,7.16,0,0 +2013,12,25,4,305,-10,-7,1028,8.95,0,0 +2013,12,25,5,281,-10,-7,1028,10.74,0,0 +2013,12,25,6,254,-12,-8,1028,13.87,0,0 +2013,12,25,7,247,-12,-8,1028,15.66,0,0 +2013,12,25,8,265,-12,-8,1029,18.79,0,0 +2013,12,25,9,262,-10,-6,1029,22.81,0,0 +2013,12,25,10,267,-8,-3,1029,24.6,0,0 +2013,12,25,11,282,-10,-1,1028,27.73,0,0 +2013,12,25,12,295,-11,2,1026,30.86,0,0 +2013,12,25,13,286,-11,4,1025,33.99,0,0 +2013,12,25,14,84,-14,7,1024,38.01,0,0 +2013,12,25,15,58,-15,6,1024,42.03,0,0 +2013,12,25,16,43,-19,6,1025,50.08,0,0 +2013,12,25,17,36,-22,4,1026,59.91,0,0 +2013,12,25,18,26,-23,2,1028,67.06,0,0 +2013,12,25,19,36,-23,1,1030,75.11,0,0 +2013,12,25,20,32,-23,-1,1031,90.31,0,0 +2013,12,25,21,21,-23,-2,1031,104.17,0,0 +2013,12,25,22,18,-24,-3,1032,118.03,0,0 +2013,12,25,23,20,-23,-3,1032,130.99,0,0 +2013,12,26,0,13,-22,-3,1032,143.06,0,0 +2013,12,26,1,15,-22,-4,1032,152.89,0,0 +2013,12,26,2,11,-21,-4,1032,162.72,0,0 +2013,12,26,3,13,-22,-4,1033,172.55,0,0 +2013,12,26,4,16,-22,-5,1034,180.6,0,0 +2013,12,26,5,13,-22,-5,1034,189.54,0,0 +2013,12,26,6,13,-23,-5,1035,196.69,0,0 +2013,12,26,7,11,-23,-5,1035,204.74,0,0 +2013,12,26,8,18,-23,-5,1036,212.79,0,0 +2013,12,26,9,17,-23,-3,1036,219.94,0,0 +2013,12,26,10,16,-23,-2,1036,228.88,0,0 +2013,12,26,11,16,-23,-1,1036,237.82,0,0 +2013,12,26,12,18,-26,1,1035,249,0,0 +2013,12,26,13,15,-26,2,1034,257.94,0,0 +2013,12,26,14,14,-25,2,1033,269.12,0,0 +2013,12,26,15,16,-26,2,1033,278.95,0,0 +2013,12,26,16,13,-25,2,1033,288.78,0,0 +2013,12,26,17,11,-25,1,1034,296.83,0,0 +2013,12,26,18,14,-25,0,1034,306.66,0,0 +2013,12,26,19,17,-23,0,1034,314.71,0,0 +2013,12,26,20,16,-23,-1,1034,322.76,0,0 +2013,12,26,21,13,-23,-1,1034,331.7,0,0 +2013,12,26,22,17,-23,-1,1034,338.85,0,0 +2013,12,26,23,15,-23,-4,1034,343.77,0,0 +2013,12,27,0,13,-22,-5,1034,347.79,0,0 +2013,12,27,1,23,-23,-3,1034,352.71,0,0 +2013,12,27,2,16,-23,-3,1034,358.52,0,0 +2013,12,27,3,18,-23,-2,1034,364.33,0,0 +2013,12,27,4,12,-23,-3,1033,370.14,0,0 +2013,12,27,5,16,-22,-4,1033,374.16,0,0 +2013,12,27,6,9,-22,-4,1033,379.08,0,0 +2013,12,27,7,14,-22,-4,1033,384.89,0,0 +2013,12,27,8,18,-21,-4,1033,392.04,0,0 +2013,12,27,9,24,-22,-2,1033,397.85,0,0 +2013,12,27,10,25,-22,-2,1033,403.66,0,0 +2013,12,27,11,21,-21,-1,1032,409.47,0,0 +2013,12,27,12,23,-21,0,1030,415.28,0,0 +2013,12,27,13,27,-24,0,1029,420.2,0,0 +2013,12,27,14,18,-24,1,1028,425.12,0,0 +2013,12,27,15,15,-23,1,1027,428.25,0,0 +2013,12,27,16,20,-23,0,1027,0.89,0,0 +2013,12,27,17,27,-23,1,1027,1.79,0,0 +2013,12,27,18,30,-19,-2,1027,1.79,0,0 +2013,12,27,19,54,-20,-2,1027,0.89,0,0 +2013,12,27,20,64,-19,-3,1027,0.89,0,0 +2013,12,27,21,126,-18,-4,1027,0.45,0,0 +2013,12,27,22,108,-18,-5,1027,1.79,0,0 +2013,12,27,23,45,-19,-6,1027,1.79,0,0 +2013,12,28,0,30,-20,-6,1027,3.58,0,0 +2013,12,28,1,21,-18,-3,1027,7.6,0,0 +2013,12,28,2,14,-17,-2,1027,13.41,0,0 +2013,12,28,3,12,-17,-3,1027,20.56,0,0 +2013,12,28,4,14,-18,-4,1027,24.58,0,0 +2013,12,28,5,17,-20,-3,1027,31.73,0,0 +2013,12,28,6,13,-20,-4,1027,39.78,0,0 +2013,12,28,7,15,-20,-4,1027,46.93,0,0 +2013,12,28,8,17,-20,-4,1028,52.74,0,0 +2013,12,28,9,21,-21,-3,1029,59.89,0,0 +2013,12,28,10,17,-23,-2,1029,68.83,0,0 +2013,12,28,11,14,-24,-1,1029,77.77,0,0 +2013,12,28,12,11,-23,0,1028,84.92,0,0 +2013,12,28,13,11,-27,1,1026,93.86,0,0 +2013,12,28,14,15,-26,1,1025,101.91,0,0 +2013,12,28,15,21,-27,2,1025,109.06,0,0 +2013,12,28,16,14,-26,2,1025,114.87,0,0 +2013,12,28,17,20,-27,1,1024,119.79,0,0 +2013,12,28,18,22,-27,1,1024,124.71,0,0 +2013,12,28,19,30,-26,1,1025,128.73,0,0 +2013,12,28,20,NA,-26,0,1024,131.86,0,0 +2013,12,28,21,38,-26,0,1024,136.78,0,0 +2013,12,28,22,38,-26,0,1024,139.91,0,0 +2013,12,28,23,48,-26,0,1024,143.04,0,0 +2013,12,29,0,58,-23,-1,1024,4.02,0,0 +2013,12,29,1,64,-24,0,1023,5.81,0,0 +2013,12,29,2,74,-24,-1,1023,9.83,0,0 +2013,12,29,3,77,-24,0,1022,16.98,0,0 +2013,12,29,4,32,-24,0,1022,24.13,0,0 +2013,12,29,5,27,-24,-2,1021,28.15,0,0 +2013,12,29,6,24,-24,-3,1021,32.17,0,0 +2013,12,29,7,33,-23,-4,1021,0.89,0,0 +2013,12,29,8,60,-22,-7,1021,1.78,0,0 +2013,12,29,9,77,-18,-3,1021,1.79,0,0 +2013,12,29,10,84,-22,1,1021,0.89,0,0 +2013,12,29,11,67,-23,4,1020,1.78,0,0 +2013,12,29,12,59,-24,5,1019,1.79,0,0 +2013,12,29,13,42,-25,6,1017,5.81,0,0 +2013,12,29,14,32,-24,7,1016,10.73,0,0 +2013,12,29,15,44,-24,7,1016,15.65,0,0 +2013,12,29,16,68,-25,6,1016,19.67,0,0 +2013,12,29,17,43,-24,4,1016,22.8,0,0 +2013,12,29,18,NA,-24,2,1016,24.59,0,0 +2013,12,29,19,NA,-23,4,1017,27.72,0,0 +2013,12,29,20,NA,-23,1,1017,29.51,0,0 +2013,12,29,21,NA,-23,4,1017,1.79,0,0 +2013,12,29,22,NA,-18,-3,1017,2.68,0,0 +2013,12,29,23,NA,-17,-2,1017,3.13,0,0 +2013,12,30,0,NA,-23,3,1017,4.92,0,0 +2013,12,30,1,NA,-23,2,1017,10.73,0,0 +2013,12,30,2,NA,-24,3,1017,17.88,0,0 +2013,12,30,3,NA,-24,3,1017,25.93,0,0 +2013,12,30,4,NA,-24,1,1016,29.95,0,0 +2013,12,30,5,NA,-24,1,1016,33.97,0,0 +2013,12,30,6,NA,-24,0,1016,35.76,0,0 +2013,12,30,7,NA,-23,3,1016,42.91,0,0 +2013,12,30,8,NA,-23,3,1017,50.06,0,0 +2013,12,30,9,NA,-23,4,1017,57.21,0,0 +2013,12,30,10,NA,-22,5,1017,66.15,0,0 +2013,12,30,11,34,-23,6,1017,75.98,0,0 +2013,12,30,12,25,-22,6,1016,85.81,0,0 +2013,12,30,13,27,-22,7,1015,95.64,0,0 +2013,12,30,14,25,-21,7,1015,104.58,0,0 +2013,12,30,15,25,-23,7,1014,112.63,0,0 +2013,12,30,16,25,-23,7,1014,120.68,0,0 +2013,12,30,17,25,-22,6,1015,126.49,0,0 +2013,12,30,18,36,-22,5,1015,130.51,0,0 +2013,12,30,19,36,-22,6,1015,132.3,0,0 +2013,12,30,20,64,-21,5,1015,3.13,0,0 +2013,12,30,21,67,-20,3,1015,1.79,0,0 +2013,12,30,22,72,-20,3,1014,3.13,0,0 +2013,12,30,23,88,-21,4,1014,4.92,0,0 +2013,12,31,0,84,-18,-2,1013,0.89,0,0 +2013,12,31,1,87,-20,1,1013,1.79,0,0 +2013,12,31,2,107,-18,-2,1012,0.89,0,0 +2013,12,31,3,129,-16,-5,1012,1.79,0,0 +2013,12,31,4,109,-16,-2,1011,3.58,0,0 +2013,12,31,5,113,-15,-5,1011,1.79,0,0 +2013,12,31,6,83,-19,2,1010,3.13,0,0 +2013,12,31,7,71,-20,5,1011,10.28,0,0 +2013,12,31,8,61,-19,5,1012,17.43,0,0 +2013,12,31,9,50,-19,6,1013,27.26,0,0 +2013,12,31,10,48,-18,7,1013,37.09,0,0 +2013,12,31,11,41,-19,7,1013,46.03,0,0 +2013,12,31,12,34,-18,9,1012,57.21,0,0 +2013,12,31,13,28,-18,9,1011,65.26,0,0 +2013,12,31,14,22,-19,9,1011,73.31,0,0 +2013,12,31,15,19,-18,9,1012,83.14,0,0 +2013,12,31,16,16,-18,9,1012,92.97,0,0 +2013,12,31,17,21,-19,8,1012,102.8,0,0 +2013,12,31,18,15,-19,8,1013,109.95,0,0 +2013,12,31,19,22,-19,7,1013,114.87,0,0 +2013,12,31,20,18,-21,7,1014,119.79,0,0 +2013,12,31,21,23,-21,7,1014,125.6,0,0 +2013,12,31,22,20,-21,6,1014,130.52,0,0 +2013,12,31,23,23,-20,7,1014,137.67,0,0 +2014,1,1,0,24,-20,7,1014,143.48,0,0 +2014,1,1,1,53,-20,7,1013,147.5,0,0 +2014,1,1,2,65,-20,6,1013,151.52,0,0 +2014,1,1,3,70,-20,6,1013,153.31,0,0 +2014,1,1,4,79,-18,3,1012,0.89,0,0 +2014,1,1,5,92,-18,4,1012,4.02,0,0 +2014,1,1,6,106,-19,6,1012,8.94,0,0 +2014,1,1,7,75,-19,6,1013,16.09,0,0 +2014,1,1,8,58,-18,6,1013,21.9,0,0 +2014,1,1,9,33,-18,7,1014,26.82,0,0 +2014,1,1,10,51,-18,8,1015,31.74,0,0 +2014,1,1,11,32,-18,9,1015,39.79,0,0 +2014,1,1,12,23,-17,10,1015,48.73,0,0 +2014,1,1,13,28,-18,11,1014,55.88,0,0 +2014,1,1,14,23,-17,11,1014,63.93,0,0 +2014,1,1,15,24,-17,11,1014,71.08,0,0 +2014,1,1,16,26,-17,11,1014,76.89,0,0 +2014,1,1,17,26,-16,10,1015,81.81,0,0 +2014,1,1,18,27,-16,9,1015,84.94,0,0 +2014,1,1,19,43,-16,9,1016,1.79,0,0 +2014,1,1,20,62,-14,3,1017,2.68,0,0 +2014,1,1,21,70,-14,0,1017,1.79,0,0 +2014,1,1,22,81,-12,-1,1018,0.89,0,0 +2014,1,1,23,111,-12,0,1019,1.79,0,0 +2014,1,2,0,144,-13,-2,1019,0.89,0,0 +2014,1,2,1,170,-12,-4,1019,0.89,0,0 +2014,1,2,2,174,-12,-4,1019,1.34,0,0 +2014,1,2,3,174,-12,-4,1019,0.89,0,0 +2014,1,2,4,172,-12,-5,1020,0.89,0,0 +2014,1,2,5,149,-10,-2,1020,1.79,0,0 +2014,1,2,6,166,-7,-2,1020,3.58,0,0 +2014,1,2,7,187,-9,-5,1020,0.89,0,0 +2014,1,2,8,107,-9,-5,1021,3.13,0,0 +2014,1,2,9,114,-7,-2,1021,4.92,0,0 +2014,1,2,10,108,-7,2,1021,6.71,0,0 +2014,1,2,11,102,-8,4,1020,0.89,0,0 +2014,1,2,12,95,-8,5,1019,1.78,0,0 +2014,1,2,13,127,-9,7,1017,2.67,0,0 +2014,1,2,14,125,-9,7,1016,4.46,0,0 +2014,1,2,15,128,-10,7,1016,5.35,0,0 +2014,1,2,16,146,-10,6,1016,5.8,0,0 +2014,1,2,17,165,-9,4,1016,6.69,0,0 +2014,1,2,18,173,-8,3,1016,7.58,0,0 +2014,1,2,19,195,-9,1,1016,8.47,0,0 +2014,1,2,20,239,-8,0,1017,9.36,0,0 +2014,1,2,21,232,-8,0,1017,0.89,0,0 +2014,1,2,22,242,-8,-1,1017,1.79,0,0 +2014,1,2,23,269,-7,-1,1018,4.02,0,0 +2014,1,3,0,264,-9,0,1018,7.15,0,0 +2014,1,3,1,220,-9,1,1018,0.45,0,0 +2014,1,3,2,146,-9,0,1019,4.02,0,0 +2014,1,3,3,34,-9,1,1019,0.89,0,0 +2014,1,3,4,34,-9,-2,1020,3.13,0,0 +2014,1,3,5,35,-9,-1,1020,7.15,0,0 +2014,1,3,6,45,-13,0,1020,0.89,0,0 +2014,1,3,7,43,-13,-1,1022,3.13,0,0 +2014,1,3,8,43,-12,-1,1023,7.15,0,0 +2014,1,3,9,36,-13,4,1024,11.17,0,0 +2014,1,3,10,36,-16,7,1025,14.3,0,0 +2014,1,3,11,23,-16,8,1024,17.43,0,0 +2014,1,3,12,25,-16,8,1023,20.56,0,0 +2014,1,3,13,29,-17,9,1022,22.35,0,0 +2014,1,3,14,26,-18,9,1022,1.79,0,0 +2014,1,3,15,21,-17,8,1022,3.58,0,0 +2014,1,3,16,25,-17,7,1022,1.79,0,0 +2014,1,3,17,31,-17,5,1023,3.58,0,0 +2014,1,3,18,43,-17,3,1023,0.45,0,0 +2014,1,3,19,46,-17,3,1024,1.34,0,0 +2014,1,3,20,50,-16,1,1024,2.23,0,0 +2014,1,3,21,68,-13,-1,1024,2.68,0,0 +2014,1,3,22,60,-11,-1,1025,1.79,0,0 +2014,1,3,23,103,-8,-1,1025,4.92,0,0 +2014,1,4,0,85,-6,-1,1026,6.71,0,0 +2014,1,4,1,86,-6,-2,1025,8.5,0,0 +2014,1,4,2,89,-7,-2,1025,0.89,0,0 +2014,1,4,3,77,-7,-2,1025,1.79,0,0 +2014,1,4,4,77,-9,-5,1025,0.45,0,0 +2014,1,4,5,75,-9,-5,1025,1.34,0,0 +2014,1,4,6,80,-9,-6,1025,1.79,0,0 +2014,1,4,7,86,-7,-4,1025,0.89,0,0 +2014,1,4,8,95,-7,-3,1025,0.89,0,0 +2014,1,4,9,101,-6,-2,1025,0.89,0,0 +2014,1,4,10,132,-6,-1,1025,1.78,0,0 +2014,1,4,11,153,-5,-1,1024,3.57,0,0 +2014,1,4,12,173,-6,0,1023,1.79,0,0 +2014,1,4,13,178,-7,1,1021,0.89,0,0 +2014,1,4,14,176,-7,2,1020,1.79,0,0 +2014,1,4,15,209,-7,2,1020,0.89,0,0 +2014,1,4,16,219,-7,2,1020,1.79,0,0 +2014,1,4,17,224,-7,1,1020,3.58,0,0 +2014,1,4,18,212,-8,-2,1020,6.71,0,0 +2014,1,4,19,221,-8,-3,1021,8.5,0,0 +2014,1,4,20,221,-8,-4,1021,10.29,0,0 +2014,1,4,21,217,-8,-4,1021,0.89,0,0 +2014,1,4,22,203,-8,-4,1021,1.78,0,0 +2014,1,4,23,221,-9,-5,1021,1.79,0,0 +2014,1,5,0,192,-8,-4,1021,3.58,0,0 +2014,1,5,1,183,-10,-6,1022,6.71,0,0 +2014,1,5,2,175,-8,-5,1022,10.73,0,0 +2014,1,5,3,177,-9,-6,1022,14.75,0,0 +2014,1,5,4,183,-10,-7,1023,17.88,0,0 +2014,1,5,5,155,-10,-7,1023,22.8,0,0 +2014,1,5,6,151,-9,-6,1023,26.82,0,0 +2014,1,5,7,137,-10,-7,1024,29.95,0,0 +2014,1,5,8,109,-8,-4,1024,34.87,0,0 +2014,1,5,9,140,-8,-2,1025,39.79,0,0 +2014,1,5,10,117,-8,1,1026,42.92,0,0 +2014,1,5,11,128,-12,5,1026,46.05,0,0 +2014,1,5,12,54,-14,5,1025,3.13,0,0 +2014,1,5,13,34,-14,5,1024,6.26,0,0 +2014,1,5,14,32,-15,6,1024,9.39,0,0 +2014,1,5,15,28,-15,6,1024,12.52,0,0 +2014,1,5,16,29,-15,6,1024,3.13,0,0 +2014,1,5,17,37,-15,3,1024,4.92,0,0 +2014,1,5,18,46,-15,1,1025,6.71,0,0 +2014,1,5,19,54,-12,-1,1025,0.89,0,0 +2014,1,5,20,53,-12,-2,1026,1.78,0,0 +2014,1,5,21,47,-12,0,1026,4.02,0,0 +2014,1,5,22,112,-9,-1,1027,7.15,0,0 +2014,1,5,23,125,-8,-2,1028,8.94,0,0 +2014,1,6,0,128,-7,-3,1028,0.89,0,0 +2014,1,6,1,126,-5,-2,1028,1.78,0,0 +2014,1,6,2,153,-5,-1,1028,2.67,0,0 +2014,1,6,3,157,-5,-1,1028,3.12,1,0 +2014,1,6,4,144,-5,-1,1027,0.89,2,0 +2014,1,6,5,138,-5,-1,1027,0.89,3,0 +2014,1,6,6,128,-5,-1,1027,1.79,0,0 +2014,1,6,7,126,-5,-1,1027,3.58,0,0 +2014,1,6,8,126,-5,-1,1027,5.37,0,0 +2014,1,6,9,159,-6,-1,1028,8.5,1,0 +2014,1,6,10,149,-5,-1,1028,11.63,2,0 +2014,1,6,11,123,-4,0,1027,15.65,0,0 +2014,1,6,12,150,-4,0,1026,18.78,0,0 +2014,1,6,13,146,-5,0,1025,22.8,0,0 +2014,1,6,14,145,-5,0,1024,25.93,0,0 +2014,1,6,15,160,-5,0,1024,27.72,0,0 +2014,1,6,16,148,-5,0,1024,29.51,0,0 +2014,1,6,17,156,-5,0,1024,30.4,0,0 +2014,1,6,18,158,-7,-2,1024,31.29,0,0 +2014,1,6,19,167,-6,-2,1024,0.89,0,0 +2014,1,6,20,171,-5,-1,1025,0.89,0,0 +2014,1,6,21,177,-6,-1,1025,1.78,0,0 +2014,1,6,22,185,-6,-1,1025,0.89,0,0 +2014,1,6,23,201,-6,-1,1024,0.89,0,0 +2014,1,7,0,217,-6,-1,1024,1.34,0,0 +2014,1,7,1,208,-6,-2,1024,0.89,0,0 +2014,1,7,2,204,-7,-4,1024,0.89,0,0 +2014,1,7,3,213,-7,-4,1025,0.89,0,0 +2014,1,7,4,256,-6,-3,1025,2.68,0,0 +2014,1,7,5,211,-6,-3,1024,5.81,0,0 +2014,1,7,6,203,-8,-5,1024,8.94,0,0 +2014,1,7,7,212,-7,-5,1025,12.07,0,0 +2014,1,7,8,205,-6,-3,1026,16.09,0,0 +2014,1,7,9,196,-7,-1,1027,20.11,0,0 +2014,1,7,10,182,-8,1,1028,24.13,0,0 +2014,1,7,11,124,-11,3,1027,4.02,0,0 +2014,1,7,12,102,-14,3,1027,8.94,0,0 +2014,1,7,13,77,-14,5,1026,12.96,0,0 +2014,1,7,14,48,-16,5,1026,16.98,0,0 +2014,1,7,15,38,-16,5,1026,3.13,0,0 +2014,1,7,16,31,-16,5,1027,1.79,0,0 +2014,1,7,17,27,-16,4,1028,1.79,0,0 +2014,1,7,18,33,-16,4,1028,4.92,0,0 +2014,1,7,19,35,-16,-1,1029,8.94,0,0 +2014,1,7,20,29,-19,-1,1030,12.07,0,0 +2014,1,7,21,24,-20,0,1031,16.09,0,0 +2014,1,7,22,26,-25,0,1032,20.11,0,0 +2014,1,7,23,11,-21,-4,1032,24.13,0,0 +2014,1,8,0,14,-22,-3,1032,28.15,0,0 +2014,1,8,1,12,-21,-4,1033,31.28,0,0 +2014,1,8,2,9,-21,-4,1033,34.41,0,0 +2014,1,8,3,11,-20,-4,1033,39.33,0,0 +2014,1,8,4,15,-20,-5,1034,43.35,0,0 +2014,1,8,5,13,-20,-5,1034,1.79,0,0 +2014,1,8,6,15,-21,-6,1034,1.79,0,0 +2014,1,8,7,17,-20,-6,1034,6.71,0,0 +2014,1,8,8,19,-20,-5,1035,4.02,0,0 +2014,1,8,9,18,-21,-5,1036,3.13,0,0 +2014,1,8,10,21,-20,-2,1036,7.15,0,0 +2014,1,8,11,17,-21,-1,1035,1.79,0,0 +2014,1,8,12,22,-28,0,1034,8.94,0,0 +2014,1,8,13,23,-27,1,1033,16.09,0,0 +2014,1,8,14,16,-25,0,1032,21.9,0,0 +2014,1,8,15,22,-26,0,1032,27.71,0,0 +2014,1,8,16,18,-25,0,1032,33.52,0,0 +2014,1,8,17,17,-26,-1,1033,39.33,0,0 +2014,1,8,18,21,-26,-2,1033,44.25,0,0 +2014,1,8,19,23,-25,-2,1033,49.17,0,0 +2014,1,8,20,22,-25,-5,1034,54.09,0,0 +2014,1,8,21,21,-25,-7,1034,58.11,0,0 +2014,1,8,22,23,-23,-8,1034,59.9,0,0 +2014,1,8,23,53,-24,-7,1034,1.79,0,0 +2014,1,9,0,66,-23,-10,1034,0.89,0,0 +2014,1,9,1,52,-23,-10,1033,1.79,0,0 +2014,1,9,2,30,-23,-9,1033,3.58,0,0 +2014,1,9,3,10,-23,-11,1033,6.71,0,0 +2014,1,9,4,10,-23,-8,1033,9.84,0,0 +2014,1,9,5,13,-26,-6,1033,14.76,0,0 +2014,1,9,6,11,-25,-7,1033,18.78,0,0 +2014,1,9,7,14,-25,-9,1033,20.57,0,0 +2014,1,9,8,20,-24,-9,1034,23.7,0,0 +2014,1,9,9,22,-25,-4,1035,27.72,0,0 +2014,1,9,10,23,-26,-3,1035,31.74,0,0 +2014,1,9,11,20,-28,-2,1034,37.55,0,0 +2014,1,9,12,17,-28,0,1032,42.47,0,0 +2014,1,9,13,14,-32,0,1031,48.28,0,0 +2014,1,9,14,10,-33,1,1029,54.09,0,0 +2014,1,9,15,10,-35,1,1029,61.24,0,0 +2014,1,9,16,27,-34,1,1029,67.05,0,0 +2014,1,9,17,44,-26,0,1029,4.92,0,0 +2014,1,9,18,53,-24,-1,1029,8.05,0,0 +2014,1,9,19,59,-23,-2,1030,9.84,0,0 +2014,1,9,20,56,-22,-2,1030,12.97,0,0 +2014,1,9,21,58,-23,-2,1030,14.76,0,0 +2014,1,9,22,66,-24,-6,1031,0.89,0,0 +2014,1,9,23,73,-21,-8,1031,0.89,0,0 +2014,1,10,0,83,-20,-9,1031,0.89,0,0 +2014,1,10,1,81,-20,-9,1031,1.78,0,0 +2014,1,10,2,91,-22,-12,1031,1.79,0,0 +2014,1,10,3,104,-22,-11,1031,3.58,0,0 +2014,1,10,4,88,-22,-12,1031,5.37,0,0 +2014,1,10,5,85,-23,-13,1031,7.16,0,0 +2014,1,10,6,66,-22,-13,1031,10.29,0,0 +2014,1,10,7,47,-23,-13,1031,13.42,0,0 +2014,1,10,8,70,-22,-11,1031,0.89,0,0 +2014,1,10,9,61,-21,-8,1032,1.79,0,0 +2014,1,10,10,47,-22,-5,1032,0.89,0,0 +2014,1,10,11,61,-23,-2,1032,1.78,0,0 +2014,1,10,12,69,-25,0,1031,2.67,0,0 +2014,1,10,13,66,-27,3,1030,1.79,0,0 +2014,1,10,14,63,-28,3,1029,3.58,0,0 +2014,1,10,15,43,-27,4,1029,1.79,0,0 +2014,1,10,16,48,-29,5,1029,4.92,0,0 +2014,1,10,17,67,-26,2,1029,4.02,0,0 +2014,1,10,18,87,-26,1,1030,8.04,0,0 +2014,1,10,19,127,-25,1,1030,12.06,0,0 +2014,1,10,20,126,-20,-3,1031,0.89,0,0 +2014,1,10,21,126,-24,1,1031,1.78,0,0 +2014,1,10,22,106,-19,-5,1032,1.79,0,0 +2014,1,10,23,104,-20,-8,1032,2.68,0,0 +2014,1,11,0,118,-19,-9,1032,0.89,0,0 +2014,1,11,1,141,-19,-9,1032,1.78,0,0 +2014,1,11,2,199,-19,-9,1032,1.79,0,0 +2014,1,11,3,262,-19,-10,1032,2.68,0,0 +2014,1,11,4,234,-21,-10,1032,4.47,0,0 +2014,1,11,5,221,-19,-11,1032,0.89,0,0 +2014,1,11,6,177,-20,-12,1031,0.89,0,0 +2014,1,11,7,135,-20,-11,1031,4.02,0,0 +2014,1,11,8,108,-20,-11,1032,1.79,0,0 +2014,1,11,9,97,-18,-7,1032,0.89,0,0 +2014,1,11,10,87,-20,-3,1032,1.79,0,0 +2014,1,11,11,85,-21,-1,1031,3.58,0,0 +2014,1,11,12,81,-25,1,1030,5.37,0,0 +2014,1,11,13,82,-25,3,1029,1.79,0,0 +2014,1,11,14,81,-25,5,1028,1.79,0,0 +2014,1,11,15,111,-27,5,1027,1.79,0,0 +2014,1,11,16,143,-25,4,1027,4.02,0,0 +2014,1,11,17,153,-22,2,1027,7.15,0,0 +2014,1,11,18,189,-21,2,1028,11.17,0,0 +2014,1,11,19,244,-19,2,1029,14.3,0,0 +2014,1,11,20,273,-18,-4,1029,0.89,0,0 +2014,1,11,21,218,-19,-5,1030,2.68,0,0 +2014,1,11,22,227,-19,-1,1031,6.7,0,0 +2014,1,11,23,95,-19,-2,1032,0.89,0,0 +2014,1,12,0,20,-22,1,1032,8.94,0,0 +2014,1,12,1,NA,-21,-1,1033,16.09,0,0 +2014,1,12,2,NA,-24,-1,1034,24.14,0,0 +2014,1,12,3,NA,-22,-3,1035,29.95,0,0 +2014,1,12,4,NA,-23,-6,1035,33.97,0,0 +2014,1,12,5,NA,-22,-7,1035,37.99,0,0 +2014,1,12,6,12,-24,-6,1036,39.78,0,0 +2014,1,12,7,12,-23,-7,1037,42.91,0,0 +2014,1,12,8,17,-22,-6,1038,0.89,0,0 +2014,1,12,9,33,-22,-2,1039,1.79,0,0 +2014,1,12,10,22,-23,0,1039,3.58,0,0 +2014,1,12,11,20,-26,1,1039,1.79,0,0 +2014,1,12,12,10,-26,1,1038,0.89,0,0 +2014,1,12,13,14,-27,2,1036,4.02,0,0 +2014,1,12,14,12,-27,2,1036,8.04,0,0 +2014,1,12,15,12,-27,4,1035,12.06,0,0 +2014,1,12,16,8,-27,3,1035,16.98,0,0 +2014,1,12,17,19,-27,2,1035,3.13,0,0 +2014,1,12,18,40,-26,1,1035,3.13,0,0 +2014,1,12,19,37,-25,0,1035,7.15,0,0 +2014,1,12,20,42,-22,-2,1035,0.45,0,0 +2014,1,12,21,56,-22,-4,1035,1.34,0,0 +2014,1,12,22,62,-21,-8,1035,3.13,0,0 +2014,1,12,23,57,-22,-9,1035,0.89,0,0 +2014,1,13,0,57,-19,-9,1034,0.89,0,0 +2014,1,13,1,75,-20,-9,1034,1.79,0,0 +2014,1,13,2,96,-20,-9,1034,0.89,0,0 +2014,1,13,3,110,-18,-9,1034,1.79,0,0 +2014,1,13,4,97,-20,-11,1033,3.58,0,0 +2014,1,13,5,105,-20,-11,1033,5.37,0,0 +2014,1,13,6,84,-19,-12,1033,0.89,0,0 +2014,1,13,7,106,-19,-11,1033,1.79,0,0 +2014,1,13,8,112,-19,-11,1033,0.89,0,0 +2014,1,13,9,120,-17,-8,1034,2.68,0,0 +2014,1,13,10,117,-18,-4,1033,0.89,0,0 +2014,1,13,11,118,-18,-1,1033,1.78,0,0 +2014,1,13,12,106,-19,0,1031,2.67,0,0 +2014,1,13,13,96,-23,1,1030,3.56,0,0 +2014,1,13,14,82,-22,2,1029,4.45,0,0 +2014,1,13,15,63,-23,3,1029,1.79,0,0 +2014,1,13,16,79,-23,3,1029,0.89,0,0 +2014,1,13,17,125,-22,0,1029,0.89,0,0 +2014,1,13,18,119,-20,-2,1030,0.89,0,0 +2014,1,13,19,115,-19,-6,1030,1.79,0,0 +2014,1,13,20,131,-16,-6,1031,1.79,0,0 +2014,1,13,21,177,-18,-7,1031,1.79,0,0 +2014,1,13,22,287,-17,-7,1032,0.89,0,0 +2014,1,13,23,299,-19,-8,1032,1.79,0,0 +2014,1,14,0,250,-18,-7,1033,1.79,0,0 +2014,1,14,1,159,-19,-8,1033,3.58,0,0 +2014,1,14,2,124,-20,-9,1033,6.71,0,0 +2014,1,14,3,123,-19,-8,1034,11.63,0,0 +2014,1,14,4,49,-21,-10,1034,15.65,0,0 +2014,1,14,5,36,-19,-9,1034,0.89,0,0 +2014,1,14,6,59,-20,-9,1034,1.78,0,0 +2014,1,14,7,85,-18,-9,1035,2.67,0,0 +2014,1,14,8,170,-18,-11,1035,1.79,0,0 +2014,1,14,9,180,-15,-7,1036,3.58,0,0 +2014,1,14,10,227,-12,-2,1036,5.37,0,0 +2014,1,14,11,199,-20,-1,1036,0.89,0,0 +2014,1,14,12,168,-23,1,1035,1.78,0,0 +2014,1,14,13,93,-24,2,1033,2.67,0,0 +2014,1,14,14,80,-23,3,1033,1.79,0,0 +2014,1,14,15,111,-22,3,1033,1.79,0,0 +2014,1,14,16,133,-19,3,1032,3.13,0,0 +2014,1,14,17,135,-17,1,1033,6.26,0,0 +2014,1,14,18,94,-16,0,1033,9.39,0,0 +2014,1,14,19,103,-17,0,1033,12.52,0,0 +2014,1,14,20,92,-16,1,1033,1.79,0,0 +2014,1,14,21,93,-15,-5,1034,2.68,0,0 +2014,1,14,22,109,-14,-5,1034,3.57,0,0 +2014,1,14,23,122,-14,-6,1034,4.46,0,0 +2014,1,15,0,123,-13,-7,1033,5.35,0,0 +2014,1,15,1,122,-14,-8,1033,0.89,0,0 +2014,1,15,2,172,-15,-7,1034,2.68,0,0 +2014,1,15,3,213,-17,-10,1033,4.47,0,0 +2014,1,15,4,196,-17,-10,1033,6.26,0,0 +2014,1,15,5,158,-18,-10,1033,0.89,0,0 +2014,1,15,6,103,-19,-10,1032,1.78,0,0 +2014,1,15,7,90,-18,-10,1033,3.13,0,0 +2014,1,15,8,70,-18,-7,1033,4.92,0,0 +2014,1,15,9,67,-18,-5,1033,8.05,0,0 +2014,1,15,10,66,-17,-2,1033,12.07,0,0 +2014,1,15,11,52,-18,0,1033,13.86,0,0 +2014,1,15,12,55,-20,2,1032,1.79,0,0 +2014,1,15,13,51,-22,5,1030,2.68,0,0 +2014,1,15,14,47,-22,5,1029,3.57,0,0 +2014,1,15,15,48,-22,5,1029,5.36,0,0 +2014,1,15,16,60,-23,5,1029,6.25,0,0 +2014,1,15,17,88,-23,2,1028,1.79,0,0 +2014,1,15,18,279,-16,2,1029,5.81,0,0 +2014,1,15,19,435,-15,0,1029,8.94,0,0 +2014,1,15,20,489,-13,-2,1029,0.89,0,0 +2014,1,15,21,513,-11,-2,1029,1.78,0,0 +2014,1,15,22,539,-11,-2,1028,1.79,0,0 +2014,1,15,23,567,-10,-2,1029,0.89,0,0 +2014,1,16,0,569,-10,-3,1028,1.79,0,0 +2014,1,16,1,659,-9,-3,1027,0.89,0,0 +2014,1,16,2,648,-9,-3,1027,1.78,0,0 +2014,1,16,3,651,-8,-3,1027,2.67,0,0 +2014,1,16,4,671,-7,-3,1027,1.79,0,0 +2014,1,16,5,623,-7,-3,1026,1.79,0,0 +2014,1,16,6,618,-7,-4,1027,0.89,0,0 +2014,1,16,7,532,-7,-4,1027,1.78,0,0 +2014,1,16,8,477,-8,-5,1028,3.57,0,0 +2014,1,16,9,479,-8,-5,1028,5.36,0,0 +2014,1,16,10,490,-6,-2,1028,0.89,0,0 +2014,1,16,11,476,-11,1,1028,1.79,0,0 +2014,1,16,12,457,-11,2,1027,5.81,0,0 +2014,1,16,13,327,-10,3,1027,8.94,0,0 +2014,1,16,14,161,-11,4,1026,1.79,0,0 +2014,1,16,15,286,-15,5,1027,3.58,0,0 +2014,1,16,16,298,-14,5,1027,5.37,0,0 +2014,1,16,17,310,-11,3,1027,7.16,0,0 +2014,1,16,18,332,-7,0,1029,10.29,0,0 +2014,1,16,19,391,-7,-2,1029,12.08,0,0 +2014,1,16,20,437,-6,-1,1030,13.87,0,0 +2014,1,16,21,422,-5,-1,1031,0.89,0,0 +2014,1,16,22,320,-5,-2,1031,1.79,0,0 +2014,1,16,23,346,-4,-1,1031,3.58,0,0 +2014,1,17,0,351,-4,-1,1031,7.6,0,0 +2014,1,17,1,297,-5,-2,1031,10.73,0,0 +2014,1,17,2,290,-5,-2,1031,13.86,0,0 +2014,1,17,3,250,-7,-2,1031,18.78,1,0 +2014,1,17,4,170,-10,-2,1031,23.7,2,0 +2014,1,17,5,103,-10,-3,1031,29.51,0,0 +2014,1,17,6,79,-10,-3,1031,34.43,0,0 +2014,1,17,7,73,-10,-3,1032,0.89,1,0 +2014,1,17,8,74,-10,-3,1032,1.79,0,0 +2014,1,17,9,77,-10,-3,1033,3.58,0,0 +2014,1,17,10,81,-10,-2,1033,5.37,0,0 +2014,1,17,11,95,-9,0,1033,0.89,0,0 +2014,1,17,12,115,-10,1,1032,1.79,0,0 +2014,1,17,13,77,-11,3,1031,0.89,0,0 +2014,1,17,14,67,-13,5,1030,2.68,0,0 +2014,1,17,15,13,-21,5,1030,7.15,0,0 +2014,1,17,16,12,-21,5,1030,14.3,0,0 +2014,1,17,17,10,-22,4,1030,21.45,0,0 +2014,1,17,18,195,-23,4,1031,28.6,0,0 +2014,1,17,19,238,-22,3,1031,32.62,0,0 +2014,1,17,20,293,-22,3,1031,36.64,0,0 +2014,1,17,21,384,-22,3,1032,40.66,0,0 +2014,1,17,22,381,-18,-1,1032,44.68,0,0 +2014,1,17,23,296,-23,3,1032,49.6,0,0 +2014,1,18,0,38,-22,1,1032,54.52,0,0 +2014,1,18,1,19,-20,-2,1032,57.65,0,0 +2014,1,18,2,22,-21,2,1033,63.46,0,0 +2014,1,18,3,20,-17,-1,1033,68.38,0,0 +2014,1,18,4,14,-17,-4,1033,73.3,0,0 +2014,1,18,5,23,-16,-5,1033,1.79,0,0 +2014,1,18,6,19,-16,-8,1033,3.58,0,0 +2014,1,18,7,22,-17,-6,1034,6.71,0,0 +2014,1,18,8,33,-16,-5,1034,3.13,0,0 +2014,1,18,9,42,-15,-1,1035,6.26,0,0 +2014,1,18,10,40,-19,3,1035,9.39,0,0 +2014,1,18,11,43,-20,4,1035,4.02,0,0 +2014,1,18,12,56,-20,5,1034,0.89,0,0 +2014,1,18,13,57,-21,6,1033,2.68,0,0 +2014,1,18,14,153,-20,6,1032,3.13,0,0 +2014,1,18,15,108,-19,6,1032,7.15,0,0 +2014,1,18,16,89,-18,5,1031,12.07,0,0 +2014,1,18,17,87,-18,3,1031,16.09,0,0 +2014,1,18,18,95,-19,3,1032,17.88,0,0 +2014,1,18,19,111,-16,1,1032,0.89,0,0 +2014,1,18,20,112,-15,1,1031,1.79,0,0 +2014,1,18,21,145,-14,1,1031,5.81,0,0 +2014,1,18,22,184,-12,-2,1032,7.6,0,0 +2014,1,18,23,178,-11,-2,1031,11.62,0,0 +2014,1,19,0,170,-9,-2,1030,15.64,0,0 +2014,1,19,1,161,-8,-2,1030,19.66,0,0 +2014,1,19,2,133,-8,-2,1030,21.45,0,0 +2014,1,19,3,114,-8,-2,1028,24.58,0,0 +2014,1,19,4,112,-7,-2,1027,0.89,0,0 +2014,1,19,5,114,-7,-3,1027,1.78,0,0 +2014,1,19,6,130,-7,-3,1026,1.79,0,0 +2014,1,19,7,133,-7,-2,1026,0.89,0,0 +2014,1,19,8,143,-7,-3,1025,1.78,0,0 +2014,1,19,9,137,-7,-2,1026,2.67,0,0 +2014,1,19,10,143,-6,-2,1026,1.79,0,0 +2014,1,19,11,145,-6,-1,1025,3.58,0,0 +2014,1,19,12,185,-7,1,1023,0.89,0,0 +2014,1,19,13,198,-8,2,1021,2.68,0,0 +2014,1,19,14,205,-9,3,1020,1.79,0,0 +2014,1,19,15,195,-9,3,1020,4.92,0,0 +2014,1,19,16,203,-9,3,1020,6.71,0,0 +2014,1,19,17,209,-9,1,1021,8.5,0,0 +2014,1,19,18,202,-9,1,1022,0.89,0,0 +2014,1,19,19,28,-21,5,1023,12.07,0,0 +2014,1,19,20,26,-21,3,1024,21.9,0,0 +2014,1,19,21,22,-21,2,1025,29.95,0,0 +2014,1,19,22,24,-22,2,1026,38.89,0,0 +2014,1,19,23,16,-21,1,1025,46.04,0,0 +2014,1,20,0,16,-19,-1,1025,50.06,0,0 +2014,1,20,1,17,-20,0,1026,59.89,0,0 +2014,1,20,2,7,-21,-1,1027,69.72,0,0 +2014,1,20,3,9,-23,-1,1027,84.92,0,0 +2014,1,20,4,10,-22,-2,1027,97.88,0,0 +2014,1,20,5,8,-21,-2,1028,109.06,0,0 +2014,1,20,6,7,-21,-3,1028,114.87,0,0 +2014,1,20,7,10,-21,-2,1028,123.81,0,0 +2014,1,20,8,11,-21,-2,1029,135.88,0,0 +2014,1,20,9,11,-21,-1,1029,147.06,0,0 +2014,1,20,10,10,-23,0,1030,159.13,0,0 +2014,1,20,11,7,-24,1,1030,171.2,0,0 +2014,1,20,12,9,-25,2,1030,183.27,0,0 +2014,1,20,13,14,-24,3,1029,194.45,0,0 +2014,1,20,14,13,-24,4,1029,207.41,0,0 +2014,1,20,15,14,-25,3,1029,219.48,0,0 +2014,1,20,16,9,-24,3,1030,230.66,0,0 +2014,1,20,17,14,-24,2,1030,240.49,0,0 +2014,1,20,18,9,-24,1,1032,250.32,0,0 +2014,1,20,19,8,-23,0,1032,258.37,0,0 +2014,1,20,20,10,-20,0,1033,265.52,0,0 +2014,1,20,21,8,-20,-1,1034,272.67,0,0 +2014,1,20,22,9,-20,-1,1034,277.59,0,0 +2014,1,20,23,11,-20,-1,1034,284.74,0,0 +2014,1,21,0,11,-20,-2,1034,291.89,0,0 +2014,1,21,1,10,-20,-3,1034,297.7,0,0 +2014,1,21,2,15,-22,-2,1034,306.64,0,0 +2014,1,21,3,13,-22,-6,1035,310.66,0,0 +2014,1,21,4,16,-22,-4,1035,315.58,0,0 +2014,1,21,5,13,-22,-6,1035,318.71,0,0 +2014,1,21,6,18,-21,-5,1035,323.63,0,0 +2014,1,21,7,18,-22,-5,1035,328.55,0,0 +2014,1,21,8,22,-21,-5,1035,3.13,0,0 +2014,1,21,9,23,-20,-3,1035,6.26,0,0 +2014,1,21,10,24,-21,-1,1035,3.13,0,0 +2014,1,21,11,24,-23,2,1035,6.26,0,0 +2014,1,21,12,23,-24,2,1033,0.89,0,0 +2014,1,21,13,23,-24,4,1032,1.79,0,0 +2014,1,21,14,42,-25,4,1030,3.13,0,0 +2014,1,21,15,54,-24,4,1030,4.02,0,0 +2014,1,21,16,48,-24,3,1029,8.94,0,0 +2014,1,21,17,66,-23,3,1029,12.96,0,0 +2014,1,21,18,56,-24,1,1029,16.09,0,0 +2014,1,21,19,67,-23,1,1029,19.22,0,0 +2014,1,21,20,95,-20,-1,1028,21.01,0,0 +2014,1,21,21,111,-19,-1,1028,22.8,0,0 +2014,1,21,22,112,-18,-4,1028,0.89,0,0 +2014,1,21,23,113,-18,-5,1027,0.89,0,0 +2014,1,22,0,127,-18,-6,1027,1.79,0,0 +2014,1,22,1,137,-19,-6,1026,0.89,0,0 +2014,1,22,2,164,-18,-8,1026,0.89,0,0 +2014,1,22,3,190,-18,-8,1026,2.68,0,0 +2014,1,22,4,175,-18,-9,1025,4.47,0,0 +2014,1,22,5,142,-18,-9,1025,6.26,0,0 +2014,1,22,6,107,-17,-9,1025,8.05,0,0 +2014,1,22,7,101,-19,-10,1025,11.18,0,0 +2014,1,22,8,100,-18,-9,1025,14.31,0,0 +2014,1,22,9,97,-16,-5,1025,0.89,0,0 +2014,1,22,10,101,-18,-2,1025,3.13,0,0 +2014,1,22,11,105,-20,0,1025,4.92,0,0 +2014,1,22,12,117,-20,2,1024,0.89,0,0 +2014,1,22,13,119,-20,3,1023,1.79,0,0 +2014,1,22,14,115,-21,5,1021,0.89,0,0 +2014,1,22,15,125,-22,5,1021,1.78,0,0 +2014,1,22,16,NA,-21,5,1021,1.79,0,0 +2014,1,22,17,NA,-21,4,1021,0.89,0,0 +2014,1,22,18,184,-20,1,1021,1.78,0,0 +2014,1,22,19,210,-16,-2,1021,0.89,0,0 +2014,1,22,20,213,-16,-2,1022,1.78,0,0 +2014,1,22,21,237,-16,-4,1022,3.57,0,0 +2014,1,22,22,258,-17,-5,1022,1.79,0,0 +2014,1,22,23,264,-15,-5,1022,1.79,0,0 +2014,1,23,0,324,-16,-3,1021,3.58,0,0 +2014,1,23,1,344,-16,-6,1021,1.79,0,0 +2014,1,23,2,356,-15,-7,1020,0.45,0,0 +2014,1,23,3,366,-15,-7,1020,0.9,0,0 +2014,1,23,4,350,-15,-7,1020,0.89,0,0 +2014,1,23,5,321,-16,-8,1019,0.45,0,0 +2014,1,23,6,294,-15,-8,1018,0.89,0,0 +2014,1,23,7,239,-16,-8,1018,2.68,0,0 +2014,1,23,8,219,-15,-6,1018,4.47,0,0 +2014,1,23,9,221,-13,-5,1018,0.89,0,0 +2014,1,23,10,245,-14,-2,1017,1.34,0,0 +2014,1,23,11,311,-16,0,1018,1.79,0,0 +2014,1,23,12,240,-15,2,1016,0.89,0,0 +2014,1,23,13,235,-15,2,1015,1.79,0,0 +2014,1,23,14,232,-14,2,1014,0.89,0,0 +2014,1,23,15,238,-14,3,1014,1.78,0,0 +2014,1,23,16,243,-14,3,1013,2.67,0,0 +2014,1,23,17,282,-14,1,1013,0.89,0,0 +2014,1,23,18,310,-12,-1,1014,0.89,0,0 +2014,1,23,19,295,-12,-4,1015,1.78,0,0 +2014,1,23,20,301,-11,-3,1015,0.89,0,0 +2014,1,23,21,325,-12,-5,1015,1.79,0,0 +2014,1,23,22,319,-11,-4,1015,0.89,0,0 +2014,1,23,23,335,-11,-3,1016,2.68,0,0 +2014,1,24,0,301,-11,-3,1015,4.47,0,0 +2014,1,24,1,312,-12,-3,1016,6.26,0,0 +2014,1,24,2,316,-11,-3,1015,8.05,0,0 +2014,1,24,3,296,-11,-3,1014,11.18,0,0 +2014,1,24,4,270,-12,-2,1015,12.97,0,0 +2014,1,24,5,252,-12,-3,1014,16.99,0,0 +2014,1,24,6,234,-12,-3,1014,21.91,0,0 +2014,1,24,7,233,-12,-3,1016,0.89,0,0 +2014,1,24,8,241,-12,-3,1016,1.78,0,0 +2014,1,24,9,231,-12,-1,1017,1.79,0,0 +2014,1,24,10,214,-13,1,1017,3.58,0,0 +2014,1,24,11,210,-16,4,1019,6.71,0,0 +2014,1,24,12,178,-15,5,1018,11.63,0,0 +2014,1,24,13,142,-16,7,1017,15.65,0,0 +2014,1,24,14,122,-21,10,1016,4.92,0,0 +2014,1,24,15,59,-22,10,1017,8.94,0,0 +2014,1,24,16,47,-23,10,1017,13.86,0,0 +2014,1,24,17,40,-24,9,1018,17.88,0,0 +2014,1,24,18,32,-24,9,1019,21.9,0,0 +2014,1,24,19,48,-25,8,1020,25.92,0,0 +2014,1,24,20,32,-24,5,1021,29.05,0,0 +2014,1,24,21,25,-23,4,1021,3.13,0,0 +2014,1,24,22,26,-23,4,1021,1.79,0,0 +2014,1,24,23,43,-21,1,1022,4.02,0,0 +2014,1,25,0,54,-20,1,1023,1.79,0,0 +2014,1,25,1,55,-19,-1,1022,1.79,0,0 +2014,1,25,2,53,-17,-1,1023,0.89,0,0 +2014,1,25,3,60,-17,-1,1023,1.78,0,0 +2014,1,25,4,55,-17,-1,1023,1.79,0,0 +2014,1,25,5,42,-15,-2,1023,3.58,0,0 +2014,1,25,6,36,-19,1,1024,6.71,0,0 +2014,1,25,7,32,-21,1,1025,9.84,0,0 +2014,1,25,8,14,-20,0,1026,12.97,0,0 +2014,1,25,9,15,-21,5,1027,16.99,0,0 +2014,1,25,10,18,-20,6,1028,4.02,0,0 +2014,1,25,11,14,-21,6,1028,8.94,0,0 +2014,1,25,12,15,-22,6,1028,14.75,0,0 +2014,1,25,13,15,-22,6,1027,20.56,0,0 +2014,1,25,14,14,-24,6,1027,26.37,0,0 +2014,1,25,15,19,-24,6,1027,31.29,0,0 +2014,1,25,16,17,-24,5,1027,35.31,0,0 +2014,1,25,17,16,-24,4,1028,0.89,0,0 +2014,1,25,18,14,-23,0,1029,0.89,0,0 +2014,1,25,19,33,-20,-1,1030,0.89,0,0 +2014,1,25,20,45,-22,-2,1031,4.02,0,0 +2014,1,25,21,55,-13,0,1032,3.13,0,0 +2014,1,25,22,71,-7,0,1033,6.26,0,0 +2014,1,25,23,59,-7,-1,1034,10.28,0,0 +2014,1,26,0,42,-8,-2,1034,13.41,0,0 +2014,1,26,1,31,-9,-3,1034,15.2,0,0 +2014,1,26,2,30,-9,-5,1034,0.89,0,0 +2014,1,26,3,39,-8,-3,1034,1.79,0,0 +2014,1,26,4,23,-9,-2,1033,3.58,0,0 +2014,1,26,5,26,-9,-2,1033,5.37,0,0 +2014,1,26,6,25,-10,-3,1033,7.16,0,0 +2014,1,26,7,26,-12,-5,1034,10.29,0,0 +2014,1,26,8,24,-12,-6,1034,12.08,0,0 +2014,1,26,9,29,-11,-3,1033,13.87,0,0 +2014,1,26,10,28,-10,-1,1033,17,0,0 +2014,1,26,11,35,-11,1,1032,21.02,0,0 +2014,1,26,12,35,-11,2,1031,25.94,0,0 +2014,1,26,13,38,-12,2,1029,30.86,0,0 +2014,1,26,14,49,-12,2,1027,34.88,0,0 +2014,1,26,15,65,-12,3,1026,38.9,0,0 +2014,1,26,16,72,-13,3,1024,42.92,0,0 +2014,1,26,17,74,-13,2,1024,44.71,0,0 +2014,1,26,18,80,-13,1,1024,0.89,0,0 +2014,1,26,19,88,-12,0,1024,3.13,0,0 +2014,1,26,20,106,-12,0,1023,6.26,0,0 +2014,1,26,21,113,-10,-1,1023,8.05,0,0 +2014,1,26,22,139,-8,-2,1022,11.18,0,0 +2014,1,26,23,119,-8,-3,1021,12.97,0,0 +2014,1,27,0,122,-8,-3,1020,14.76,0,0 +2014,1,27,1,116,-9,-5,1020,0.45,0,0 +2014,1,27,2,132,-10,-6,1019,0.89,0,0 +2014,1,27,3,120,-10,-6,1019,0.45,0,0 +2014,1,27,4,111,-10,-7,1018,1.79,0,0 +2014,1,27,5,108,-10,-6,1017,3.58,0,0 +2014,1,27,6,110,-11,-7,1017,6.71,0,0 +2014,1,27,7,119,-10,-7,1017,8.5,0,0 +2014,1,27,8,125,-10,-6,1017,10.29,0,0 +2014,1,27,9,143,-8,-4,1017,13.42,0,0 +2014,1,27,10,149,-8,-1,1017,16.55,0,0 +2014,1,27,11,133,-11,2,1018,19.68,0,0 +2014,1,27,12,136,-12,3,1017,22.81,0,0 +2014,1,27,13,149,-12,5,1016,25.94,0,0 +2014,1,27,14,161,-12,7,1016,29.07,0,0 +2014,1,27,15,167,-12,7,1016,30.86,0,0 +2014,1,27,16,167,-12,7,1017,32.65,0,0 +2014,1,27,17,53,-12,6,1018,0.89,0,0 +2014,1,27,18,19,-20,8,1020,7.15,0,0 +2014,1,27,19,32,-27,7,1022,16.09,0,0 +2014,1,27,20,15,-27,7,1023,27.27,0,0 +2014,1,27,21,11,-28,5,1025,36.21,0,0 +2014,1,27,22,10,-28,4,1026,44.26,0,0 +2014,1,27,23,11,-26,2,1027,3.13,0,0 +2014,1,28,0,11,-24,2,1028,4.92,0,0 +2014,1,28,1,9,-21,-1,1028,3.13,0,0 +2014,1,28,2,19,-21,-3,1029,7.15,0,0 +2014,1,28,3,13,-21,-4,1029,10.28,0,0 +2014,1,28,4,13,-21,-5,1029,13.41,0,0 +2014,1,28,5,14,-22,-5,1029,0.89,0,0 +2014,1,28,6,17,-21,-4,1029,3.13,0,0 +2014,1,28,7,16,-22,-3,1030,1.79,0,0 +2014,1,28,8,18,-21,-4,1031,0.89,0,0 +2014,1,28,9,31,-19,0,1031,1.78,0,0 +2014,1,28,10,23,-22,1,1031,1.79,0,0 +2014,1,28,11,15,-16,2,1031,4.92,0,0 +2014,1,28,12,57,-16,3,1030,8.94,0,0 +2014,1,28,13,65,-11,5,1028,14.75,0,0 +2014,1,28,14,64,-12,5,1028,20.56,0,0 +2014,1,28,15,65,-11,5,1027,25.48,0,0 +2014,1,28,16,64,-12,5,1026,30.4,0,0 +2014,1,28,17,81,-13,4,1025,34.42,0,0 +2014,1,28,18,75,-14,1,1025,37.55,0,0 +2014,1,28,19,70,-13,0,1025,40.68,0,0 +2014,1,28,20,71,-10,1,1025,44.7,0,0 +2014,1,28,21,83,-8,-1,1025,48.72,0,0 +2014,1,28,22,74,-8,-2,1024,52.74,0,0 +2014,1,28,23,86,-8,-3,1023,54.53,0,0 +2014,1,29,0,75,-9,-4,1023,56.32,0,0 +2014,1,29,1,72,-9,-5,1022,1.79,0,0 +2014,1,29,2,74,-8,-4,1021,1.79,0,0 +2014,1,29,3,76,-9,-5,1020,0.89,0,0 +2014,1,29,4,84,-10,-6,1019,1.34,0,0 +2014,1,29,5,81,-10,-7,1017,0.89,0,0 +2014,1,29,6,86,-10,-7,1016,0.89,0,0 +2014,1,29,7,84,-10,-7,1016,0.89,0,0 +2014,1,29,8,88,-10,-7,1015,1.79,0,0 +2014,1,29,9,91,-7,-3,1016,0.89,0,0 +2014,1,29,10,102,-7,-2,1015,1.79,0,0 +2014,1,29,11,121,-7,0,1014,3.58,0,0 +2014,1,29,12,132,-8,1,1013,1.79,0,0 +2014,1,29,13,163,-9,2,1012,1.79,0,0 +2014,1,29,14,175,-9,2,1011,0.89,0,0 +2014,1,29,15,190,-9,3,1011,1.78,0,0 +2014,1,29,16,203,-9,3,1011,2.67,0,0 +2014,1,29,17,205,-9,2,1011,3.56,0,0 +2014,1,29,18,217,-8,-1,1011,0.89,0,0 +2014,1,29,19,241,-9,-3,1012,1.78,0,0 +2014,1,29,20,226,-9,-3,1013,1.79,0,0 +2014,1,29,21,225,-9,-3,1013,4.92,0,0 +2014,1,29,22,205,-10,-4,1013,8.05,0,0 +2014,1,29,23,213,-8,-2,1013,0.89,0,0 +2014,1,30,0,229,-10,-3,1014,1.78,0,0 +2014,1,30,1,217,-10,-3,1014,2.67,0,0 +2014,1,30,2,218,-10,-4,1015,1.79,0,0 +2014,1,30,3,128,-10,-2,1015,0.89,0,0 +2014,1,30,4,88,-15,-1,1016,4.92,0,0 +2014,1,30,5,20,-14,-4,1016,8.94,0,0 +2014,1,30,6,16,-21,1,1017,14.75,0,0 +2014,1,30,7,6,-20,0,1018,18.77,0,0 +2014,1,30,8,12,-19,-2,1019,21.9,0,0 +2014,1,30,9,20,-23,4,1020,0.89,0,0 +2014,1,30,10,26,-24,4,1021,4.02,0,0 +2014,1,30,11,16,-26,5,1021,7.15,0,0 +2014,1,30,12,16,-27,7,1020,8.94,0,0 +2014,1,30,13,16,-27,7,1018,3.13,0,0 +2014,1,30,14,12,-26,8,1017,4.92,0,0 +2014,1,30,15,17,-26,8,1017,6.71,0,0 +2014,1,30,16,17,-26,8,1017,10.73,0,0 +2014,1,30,17,25,-24,7,1017,14.75,0,0 +2014,1,30,18,39,-24,4,1018,16.54,0,0 +2014,1,30,19,54,-18,4,1018,21.46,0,0 +2014,1,30,20,123,-7,2,1019,26.38,0,0 +2014,1,30,21,142,-6,0,1020,29.51,0,0 +2014,1,30,22,157,-5,-1,1020,33.53,0,0 +2014,1,30,23,190,-6,-2,1020,36.66,0,0 +2014,1,31,0,137,-7,-1,1021,39.79,0,0 +2014,1,31,1,469,-7,-1,1021,41.58,0,0 +2014,1,31,2,344,-7,-1,1021,44.71,0,0 +2014,1,31,3,281,-7,-2,1021,46.5,0,0 +2014,1,31,4,209,-8,-2,1020,0.89,0,0 +2014,1,31,5,150,-9,-4,1020,0.89,0,0 +2014,1,31,6,138,-9,-4,1020,1.79,0,0 +2014,1,31,7,133,-8,-3,1020,3.58,0,0 +2014,1,31,8,130,-8,-4,1020,5.37,0,0 +2014,1,31,9,143,-6,-1,1020,9.39,0,0 +2014,1,31,10,103,-7,1,1020,13.41,0,0 +2014,1,31,11,81,-7,2,1020,17.43,0,0 +2014,1,31,12,70,-7,2,1019,0.89,0,0 +2014,1,31,13,81,-7,3,1019,1.79,0,0 +2014,1,31,14,117,-6,3,1018,0.89,0,0 +2014,1,31,15,130,-5,3,1017,1.79,0,0 +2014,1,31,16,146,-5,3,1017,3.58,0,0 +2014,1,31,17,144,-6,2,1017,0.89,0,0 +2014,1,31,18,147,-7,2,1018,1.78,0,0 +2014,1,31,19,140,-7,1,1019,2.67,0,0 +2014,1,31,20,148,-5,2,1019,3.56,0,0 +2014,1,31,21,155,-5,2,1019,4.45,0,0 +2014,1,31,22,167,-5,2,1019,4.9,0,0 +2014,1,31,23,180,-4,2,1019,5.79,0,0 +2014,2,1,0,183,-5,2,1019,0.89,0,0 +2014,2,1,1,181,-5,2,1018,0.45,0,0 +2014,2,1,2,172,-6,2,1019,1.79,0,0 +2014,2,1,3,167,-5,2,1018,2.68,0,0 +2014,2,1,4,171,-4,1,1018,4.47,0,0 +2014,2,1,5,179,-4,1,1017,6.26,0,0 +2014,2,1,6,175,-5,1,1017,8.05,0,0 +2014,2,1,7,190,-6,2,1018,11.18,0,0 +2014,2,1,8,189,-6,2,1017,14.31,0,0 +2014,2,1,9,187,-7,3,1018,17.44,0,0 +2014,2,1,10,178,-7,3,1018,20.57,0,0 +2014,2,1,11,184,-7,3,1018,0.89,0,0 +2014,2,1,12,152,-4,4,1017,0.89,0,0 +2014,2,1,13,124,-3,4,1016,1.78,0,0 +2014,2,1,14,163,-3,4,1015,1.79,0,0 +2014,2,1,15,143,-2,4,1015,3.58,0,0 +2014,2,1,16,127,-1,4,1014,5.37,0,0 +2014,2,1,17,109,-1,4,1014,8.5,0,0 +2014,2,1,18,120,-1,4,1014,11.63,0,0 +2014,2,1,19,135,-1,3,1013,14.76,0,0 +2014,2,1,20,118,-1,3,1013,17.89,0,0 +2014,2,1,21,132,-1,2,1013,21.02,0,0 +2014,2,1,22,141,-1,2,1013,25.04,0,0 +2014,2,1,23,144,-1,2,1012,28.17,0,0 +2014,2,2,0,141,-1,2,1012,29.96,0,0 +2014,2,2,1,144,-1,2,1012,33.09,0,0 +2014,2,2,2,159,-1,2,1012,36.22,0,0 +2014,2,2,3,163,-1,2,1011,39.35,0,0 +2014,2,2,4,174,-1,2,1011,41.14,0,0 +2014,2,2,5,164,-2,2,1011,0.89,0,0 +2014,2,2,6,171,-2,1,1012,3.13,0,0 +2014,2,2,7,162,-3,-1,1012,7.15,0,0 +2014,2,2,8,152,-2,2,1013,12.96,0,0 +2014,2,2,9,141,-5,5,1014,20.11,0,0 +2014,2,2,10,66,-10,8,1015,28.16,0,0 +2014,2,2,11,22,-19,10,1015,37.1,0,0 +2014,2,2,12,15,-20,10,1014,49.17,0,0 +2014,2,2,13,13,-21,10,1014,60.35,0,0 +2014,2,2,14,13,-21,10,1014,70.18,0,0 +2014,2,2,15,12,-24,9,1015,81.36,0,0 +2014,2,2,16,13,-24,9,1015,89.41,0,0 +2014,2,2,17,10,-27,6,1017,98.35,0,0 +2014,2,2,18,9,-29,4,1018,104.16,0,0 +2014,2,2,19,8,-33,3,1020,4.92,0,0 +2014,2,2,20,9,-34,3,1020,10.73,0,0 +2014,2,2,21,7,-34,2,1021,14.75,0,0 +2014,2,2,22,9,-33,2,1021,18.77,0,0 +2014,2,2,23,8,-35,1,1022,21.9,0,0 +2014,2,3,0,10,-35,1,1023,26.82,0,0 +2014,2,3,1,8,-34,0,1024,28.61,0,0 +2014,2,3,2,10,-29,0,1024,30.4,0,0 +2014,2,3,3,9,-28,0,1025,0.89,0,0 +2014,2,3,4,6,-21,-1,1026,8.05,0,0 +2014,2,3,5,6,-31,-2,1027,13.86,0,0 +2014,2,3,6,7,-35,-3,1028,22.8,0,0 +2014,2,3,7,5,-33,-4,1028,29.95,0,0 +2014,2,3,8,6,-33,-3,1029,38.89,0,0 +2014,2,3,9,6,-32,-2,1029,46.94,0,0 +2014,2,3,10,4,-32,-1,1029,54.99,0,0 +2014,2,3,11,4,-33,0,1029,63.04,0,0 +2014,2,3,12,6,-36,0,1027,71.09,0,0 +2014,2,3,13,6,-37,1,1026,80.03,0,0 +2014,2,3,14,6,-36,2,1025,88.08,0,0 +2014,2,3,15,4,-39,2,1025,96.13,0,0 +2014,2,3,16,6,-40,2,1025,104.18,0,0 +2014,2,3,17,6,-38,1,1026,113.12,0,0 +2014,2,3,18,6,-33,-1,1027,118.04,0,0 +2014,2,3,19,6,-34,-5,1028,122.06,0,0 +2014,2,3,20,11,-33,-5,1028,126.08,0,0 +2014,2,3,21,11,-33,-3,1029,4.02,0,0 +2014,2,3,22,13,-33,-5,1029,5.81,0,0 +2014,2,3,23,16,-32,-3,1029,4.92,0,0 +2014,2,4,0,3,-32,-5,1029,6.71,0,0 +2014,2,4,1,7,-31,-4,1030,12.52,0,0 +2014,2,4,2,6,-32,-5,1030,19.67,0,0 +2014,2,4,3,6,-32,-5,1030,25.48,0,0 +2014,2,4,4,6,-32,-5,1030,30.4,0,0 +2014,2,4,5,6,-32,-6,1030,35.32,0,0 +2014,2,4,6,7,-32,-6,1030,40.24,0,0 +2014,2,4,7,10,-32,-6,1031,46.05,0,0 +2014,2,4,8,10,-32,-5,1032,51.86,0,0 +2014,2,4,9,9,-32,-3,1032,56.78,0,0 +2014,2,4,10,12,-32,-2,1032,63.93,0,0 +2014,2,4,11,12,-34,-2,1032,71.08,0,0 +2014,2,4,12,11,-33,0,1031,72.87,0,0 +2014,2,4,13,10,-37,1,1030,76,0,0 +2014,2,4,14,6,-38,0,1028,80.02,0,0 +2014,2,4,15,8,-37,1,1028,81.81,0,0 +2014,2,4,16,7,-36,0,1028,3.13,0,0 +2014,2,4,17,18,-33,0,1027,6.26,0,0 +2014,2,4,18,17,-30,-2,1027,0.89,0,0 +2014,2,4,19,50,-27,-4,1028,0.89,0,0 +2014,2,4,20,71,-27,-4,1028,1.78,0,0 +2014,2,4,21,91,-27,-2,1029,3.57,0,0 +2014,2,4,22,109,-27,-2,1029,5.36,0,0 +2014,2,4,23,126,-24,-6,1030,0.89,0,0 +2014,2,5,0,104,-22,-5,1030,1.79,0,0 +2014,2,5,1,80,-25,-5,1029,3.58,0,0 +2014,2,5,2,69,-25,-5,1029,0.89,0,0 +2014,2,5,3,62,-25,-6,1029,1.79,0,0 +2014,2,5,4,73,-24,-7,1029,0.89,0,0 +2014,2,5,5,115,-25,-8,1029,1.34,0,0 +2014,2,5,6,155,-24,-11,1029,2.23,0,0 +2014,2,5,7,148,-22,-11,1030,0.89,0,0 +2014,2,5,8,114,-22,-11,1030,1.78,0,0 +2014,2,5,9,94,-22,-6,1030,0.89,0,0 +2014,2,5,10,117,-20,-4,1030,1.78,0,0 +2014,2,5,11,118,-19,-2,1030,2.67,0,0 +2014,2,5,12,116,-19,1,1029,3.56,0,0 +2014,2,5,13,96,-16,1,1028,4.45,0,0 +2014,2,5,14,96,-14,1,1027,4.02,0,0 +2014,2,5,15,102,-14,1,1027,7.15,0,0 +2014,2,5,16,99,-14,1,1027,8.94,0,0 +2014,2,5,17,104,-13,0,1027,10.73,0,0 +2014,2,5,18,101,-12,-1,1028,11.62,0,0 +2014,2,5,19,92,-12,-2,1028,12.51,0,0 +2014,2,5,20,76,-12,-3,1028,13.4,0,0 +2014,2,5,21,90,-12,-3,1028,14.29,0,0 +2014,2,5,22,111,-11,-4,1029,16.08,0,0 +2014,2,5,23,125,-12,-5,1029,1.79,0,0 +2014,2,6,0,139,-12,-4,1029,2.68,0,0 +2014,2,6,1,151,-12,-5,1029,0.89,0,0 +2014,2,6,2,204,-12,-5,1029,1.79,0,0 +2014,2,6,3,203,-13,-6,1029,0.89,0,0 +2014,2,6,4,200,-14,-7,1029,0.89,0,0 +2014,2,6,5,187,-13,-8,1029,2.68,0,0 +2014,2,6,6,168,-13,-6,1030,0.45,0,0 +2014,2,6,7,158,-13,-7,1030,1.34,0,0 +2014,2,6,8,155,-12,-7,1031,0.89,0,0 +2014,2,6,9,154,-11,-3,1032,1.79,0,0 +2014,2,6,10,146,-12,-1,1032,3.58,0,0 +2014,2,6,11,150,-13,0,1031,5.37,0,0 +2014,2,6,12,136,-15,1,1030,0.89,0,0 +2014,2,6,13,130,-15,1,1029,1.78,0,0 +2014,2,6,14,122,-16,2,1029,2.67,0,0 +2014,2,6,15,125,-16,2,1029,3.56,0,0 +2014,2,6,16,121,-15,1,1030,1.79,0,0 +2014,2,6,17,108,-16,1,1029,3.58,0,0 +2014,2,6,18,102,-16,0,1029,0.89,0,0 +2014,2,6,19,118,-14,-1,1030,1.34,0,0 +2014,2,6,20,130,-14,-1,1030,0.89,0,0 +2014,2,6,21,155,-12,-1,1031,2.68,0,0 +2014,2,6,22,131,-8,-2,1031,4.47,0,0 +2014,2,6,23,93,-6,-1,1031,7.6,0,0 +2014,2,7,0,91,-7,-2,1031,9.39,0,0 +2014,2,7,1,86,-7,-2,1030,12.52,0,0 +2014,2,7,2,101,-7,-2,1030,15.65,0,0 +2014,2,7,3,105,-8,-2,1030,17.44,0,0 +2014,2,7,4,110,-10,-3,1029,19.23,0,0 +2014,2,7,5,112,-11,-3,1029,22.36,0,0 +2014,2,7,6,119,-10,-3,1029,26.38,1,0 +2014,2,7,7,100,-8,-4,1029,30.4,2,0 +2014,2,7,8,105,-7,-5,1029,32.19,3,0 +2014,2,7,9,105,-7,-5,1029,33.98,4,0 +2014,2,7,10,112,-7,-5,1029,35.77,5,0 +2014,2,7,11,107,-7,-4,1029,37.56,6,0 +2014,2,7,12,94,-7,-4,1028,39.35,7,0 +2014,2,7,13,84,-6,-3,1026,41.14,8,0 +2014,2,7,14,89,-7,-4,1026,44.27,9,0 +2014,2,7,15,90,-6,-3,1025,46.06,10,0 +2014,2,7,16,98,-7,-3,1024,47.85,11,0 +2014,2,7,17,100,-6,-3,1024,49.64,12,0 +2014,2,7,18,109,-7,-3,1025,51.43,13,0 +2014,2,7,19,107,-7,-3,1025,54.56,14,0 +2014,2,7,20,107,-7,-3,1025,57.69,15,0 +2014,2,7,21,106,-6,-4,1025,59.48,16,0 +2014,2,7,22,108,-6,-4,1025,61.27,17,0 +2014,2,7,23,109,-6,-4,1025,63.06,18,0 +2014,2,8,0,106,-6,-4,1025,66.19,19,0 +2014,2,8,1,95,-6,-4,1025,69.32,20,0 +2014,2,8,2,94,-6,-4,1025,71.11,21,0 +2014,2,8,3,88,-6,-4,1025,72,22,0 +2014,2,8,4,115,-7,-4,1024,0.89,23,0 +2014,2,8,5,133,-7,-4,1024,1.78,0,0 +2014,2,8,6,132,-6,-4,1024,1.79,0,0 +2014,2,8,7,147,-7,-4,1025,2.68,0,0 +2014,2,8,8,147,-7,-5,1026,3.13,0,0 +2014,2,8,9,146,-7,-5,1027,4.92,0,0 +2014,2,8,10,79,-6,-3,1028,1.79,0,0 +2014,2,8,11,38,-8,0,1028,3.13,0,0 +2014,2,8,12,20,-13,1,1027,7.15,0,0 +2014,2,8,13,14,-14,0,1027,4.02,0,0 +2014,2,8,14,10,-14,2,1027,4.92,0,0 +2014,2,8,15,6,-16,1,1027,3.13,0,0 +2014,2,8,16,9,-16,2,1027,7.15,0,0 +2014,2,8,17,7,-17,0,1028,15.2,0,0 +2014,2,8,18,10,-17,-3,1030,21.01,0,0 +2014,2,8,19,8,-19,-3,1031,29.06,0,0 +2014,2,8,20,7,-19,-5,1033,34.87,0,0 +2014,2,8,21,14,-20,-6,1034,40.68,0,0 +2014,2,8,22,10,-21,-7,1035,46.49,0,0 +2014,2,8,23,6,-21,-8,1036,50.51,0,0 +2014,2,9,0,9,-21,-7,1036,55.43,0,0 +2014,2,9,1,13,-22,-8,1037,61.24,0,0 +2014,2,9,2,9,-22,-8,1037,66.16,0,0 +2014,2,9,3,10,-22,-9,1037,71.08,0,0 +2014,2,9,4,9,-23,-11,1037,74.21,0,0 +2014,2,9,5,10,-22,-12,1037,77.34,0,0 +2014,2,9,6,10,-22,-10,1037,82.26,0,0 +2014,2,9,7,11,-22,-9,1038,88.07,0,0 +2014,2,9,8,12,-21,-10,1038,91.2,0,0 +2014,2,9,9,16,-21,-7,1039,95.22,0,0 +2014,2,9,10,12,-22,-5,1038,100.14,0,0 +2014,2,9,11,17,-22,-4,1038,105.06,0,0 +2014,2,9,12,14,-22,-3,1037,109.08,0,0 +2014,2,9,13,14,-22,-3,1036,113.1,0,0 +2014,2,9,14,12,-21,-1,1035,116.23,0,0 +2014,2,9,15,14,-22,-1,1034,119.36,0,0 +2014,2,9,16,10,-22,-1,1034,0.89,0,0 +2014,2,9,17,11,-22,-1,1034,3.13,0,0 +2014,2,9,18,11,-21,-2,1035,7.15,0,0 +2014,2,9,19,14,-20,-3,1035,12.07,0,0 +2014,2,9,20,15,-20,-4,1036,16.09,0,0 +2014,2,9,21,26,-19,-5,1036,21.01,0,0 +2014,2,9,22,24,-19,-8,1037,22.8,0,0 +2014,2,9,23,18,-21,-9,1037,26.82,0,0 +2014,2,10,0,11,-19,-9,1038,29.95,0,0 +2014,2,10,1,12,-21,-9,1038,33.08,0,0 +2014,2,10,2,9,-20,-9,1038,36.21,0,0 +2014,2,10,3,9,-21,-12,1037,39.34,0,0 +2014,2,10,4,13,-21,-11,1037,42.47,0,0 +2014,2,10,5,9,-21,-12,1037,45.6,0,0 +2014,2,10,6,8,-21,-10,1037,1.79,0,0 +2014,2,10,7,7,-21,-12,1037,1.79,0,0 +2014,2,10,8,13,-18,-9,1038,4.92,0,0 +2014,2,10,9,13,-20,-6,1038,4.92,0,0 +2014,2,10,10,22,-21,-5,1038,8.94,0,0 +2014,2,10,11,10,-22,-3,1037,3.13,0,0 +2014,2,10,12,18,-23,-3,1036,4.92,0,0 +2014,2,10,13,20,-22,-3,1034,1.79,0,0 +2014,2,10,14,20,-21,-2,1033,3.58,0,0 +2014,2,10,15,26,-21,-1,1032,5.37,0,0 +2014,2,10,16,24,-21,-1,1032,8.5,0,0 +2014,2,10,17,25,-19,-3,1032,12.52,0,0 +2014,2,10,18,34,-19,-3,1032,16.54,0,0 +2014,2,10,19,42,-18,-4,1032,19.67,0,0 +2014,2,10,20,47,-18,-4,1032,21.46,0,0 +2014,2,10,21,53,-16,-6,1032,0.89,0,0 +2014,2,10,22,64,-16,-7,1032,1.78,0,0 +2014,2,10,23,65,-15,-8,1032,0.89,0,0 +2014,2,11,0,66,-16,-10,1031,0.89,0,0 +2014,2,11,1,76,-16,-10,1031,1.79,0,0 +2014,2,11,2,83,-15,-10,1030,0.89,0,0 +2014,2,11,3,120,-16,-12,1030,0.45,0,0 +2014,2,11,4,121,-17,-12,1029,0.9,0,0 +2014,2,11,5,111,-16,-12,1029,1.79,0,0 +2014,2,11,6,112,-17,-12,1029,3.58,0,0 +2014,2,11,7,100,-18,-12,1029,5.37,0,0 +2014,2,11,8,84,-17,-11,1030,7.16,0,0 +2014,2,11,9,66,-15,-8,1030,8.95,0,0 +2014,2,11,10,64,-15,-5,1030,10.74,0,0 +2014,2,11,11,73,-16,-3,1029,0.89,0,0 +2014,2,11,12,90,-16,-2,1028,1.78,0,0 +2014,2,11,13,103,-16,-1,1027,1.79,0,0 +2014,2,11,14,126,-17,0,1026,3.13,0,0 +2014,2,11,15,146,-17,1,1026,4.02,0,0 +2014,2,11,16,143,-17,1,1025,8.04,0,0 +2014,2,11,17,135,-17,0,1025,12.06,0,0 +2014,2,11,18,188,-15,-1,1026,13.85,0,0 +2014,2,11,19,221,-13,-2,1027,15.64,0,0 +2014,2,11,20,203,-13,-5,1027,0.89,0,0 +2014,2,11,21,204,-12,-5,1028,1.78,0,0 +2014,2,11,22,184,-13,-7,1028,0.89,0,0 +2014,2,11,23,177,-13,-7,1029,0.89,0,0 +2014,2,12,0,220,-13,-8,1029,0.89,0,0 +2014,2,12,1,268,-14,-10,1029,1.78,0,0 +2014,2,12,2,255,-16,-10,1030,3.13,0,0 +2014,2,12,3,241,-15,-10,1030,6.26,0,0 +2014,2,12,4,181,-14,-9,1030,9.39,0,0 +2014,2,12,5,146,-14,-8,1031,12.52,0,0 +2014,2,12,6,141,-16,-9,1031,15.65,0,0 +2014,2,12,7,136,-14,-7,1032,20.57,0,0 +2014,2,12,8,124,-14,-7,1033,24.59,0,0 +2014,2,12,9,87,-18,-3,1034,3.13,0,0 +2014,2,12,10,66,-18,0,1034,4.92,0,0 +2014,2,12,11,44,-22,1,1034,8.05,0,0 +2014,2,12,12,36,-23,2,1033,11.18,0,0 +2014,2,12,13,39,-22,4,1033,1.79,0,0 +2014,2,12,14,55,-21,4,1032,3.58,0,0 +2014,2,12,15,114,-20,4,1031,1.79,0,0 +2014,2,12,16,100,-20,3,1032,3.13,0,0 +2014,2,12,17,95,-19,2,1032,6.26,0,0 +2014,2,12,18,162,-19,2,1032,9.39,0,0 +2014,2,12,19,237,-15,1,1033,13.41,0,0 +2014,2,12,20,229,-15,0,1034,15.2,0,0 +2014,2,12,21,175,-12,-1,1035,18.33,0,0 +2014,2,12,22,150,-12,-2,1035,21.46,0,0 +2014,2,12,23,145,-9,-3,1036,24.59,0,0 +2014,2,13,0,268,-8,-3,1036,28.61,0,0 +2014,2,13,1,237,-9,-4,1036,32.63,0,0 +2014,2,13,2,208,-10,-4,1037,35.76,0,0 +2014,2,13,3,193,-10,-3,1037,37.55,0,0 +2014,2,13,4,175,-11,-3,1037,40.68,0,0 +2014,2,13,5,160,-11,-3,1037,42.47,0,0 +2014,2,13,6,176,-12,-3,1037,45.6,0,0 +2014,2,13,7,181,-12,-3,1037,48.73,0,0 +2014,2,13,8,185,-13,-3,1037,51.86,0,0 +2014,2,13,9,198,-12,-3,1038,53.65,1,0 +2014,2,13,10,205,-11,-3,1038,57.67,2,0 +2014,2,13,11,206,-11,-2,1037,61.69,0,0 +2014,2,13,12,214,-12,-2,1036,64.82,0,0 +2014,2,13,13,214,-10,0,1035,66.61,0,0 +2014,2,13,14,204,-10,0,1034,68.4,0,0 +2014,2,13,15,208,-10,0,1033,71.53,0,0 +2014,2,13,16,200,-10,0,1032,74.66,0,0 +2014,2,13,17,197,-10,0,1032,76.45,0,0 +2014,2,13,18,220,-11,-1,1032,78.24,0,0 +2014,2,13,19,236,-11,-2,1032,0.89,0,0 +2014,2,13,20,239,-10,-3,1032,1.79,0,0 +2014,2,13,21,250,-10,-3,1032,3.58,0,0 +2014,2,13,22,243,-10,-2,1031,5.37,0,0 +2014,2,13,23,251,-10,-3,1031,6.26,0,0 +2014,2,14,0,263,-10,-4,1031,7.15,0,0 +2014,2,14,1,262,-10,-5,1030,0.89,0,0 +2014,2,14,2,261,-11,-7,1029,0.89,0,0 +2014,2,14,3,276,-11,-7,1029,1.78,0,0 +2014,2,14,4,264,-11,-8,1028,0.89,0,0 +2014,2,14,5,259,-11,-8,1028,0.89,0,0 +2014,2,14,6,250,-11,-8,1028,2.68,0,0 +2014,2,14,7,235,-11,-9,1028,4.47,0,0 +2014,2,14,8,222,-10,-7,1028,0.89,0,0 +2014,2,14,9,206,-8,-4,1028,0.89,0,0 +2014,2,14,10,205,-7,-2,1028,0.89,0,0 +2014,2,14,11,196,-7,-1,1027,1.78,0,0 +2014,2,14,12,218,-8,0,1027,2.67,0,0 +2014,2,14,13,234,-8,1,1025,4.46,0,0 +2014,2,14,14,264,-8,2,1024,5.35,0,0 +2014,2,14,15,272,-9,2,1024,1.79,0,0 +2014,2,14,16,307,-9,3,1023,0.89,0,0 +2014,2,14,17,323,-9,2,1024,1.78,0,0 +2014,2,14,18,311,-8,1,1024,2.23,0,0 +2014,2,14,19,327,-8,-2,1024,2.68,0,0 +2014,2,14,20,362,-7,-2,1024,0.89,0,0 +2014,2,14,21,487,-8,-4,1024,1.79,0,0 +2014,2,14,22,436,-8,-4,1025,0.89,0,0 +2014,2,14,23,436,-8,-4,1025,0.45,0,0 +2014,2,15,0,579,-8,-5,1025,0.89,0,0 +2014,2,15,1,649,-8,-5,1024,0.89,0,0 +2014,2,15,2,457,-9,-6,1024,1.79,0,0 +2014,2,15,3,466,-9,-6,1024,3.58,0,0 +2014,2,15,4,408,-9,-6,1024,5.37,0,0 +2014,2,15,5,344,-8,-5,1024,7.16,0,0 +2014,2,15,6,322,-8,-4,1024,8.95,0,0 +2014,2,15,7,287,-9,-5,1025,10.74,0,0 +2014,2,15,8,319,-9,-4,1025,0.89,0,0 +2014,2,15,9,282,-7,-3,1026,0.45,0,0 +2014,2,15,10,277,-6,0,1026,1.79,0,0 +2014,2,15,11,277,-8,1,1025,1.79,0,0 +2014,2,15,12,279,-8,1,1025,3.58,0,0 +2014,2,15,13,327,-8,3,1024,5.37,0,0 +2014,2,15,14,348,-8,3,1024,7.16,0,0 +2014,2,15,15,418,-8,4,1023,8.95,0,0 +2014,2,15,16,423,-8,4,1023,0.89,0,0 +2014,2,15,17,366,-8,3,1024,1.79,0,0 +2014,2,15,18,338,-8,2,1024,0.89,0,0 +2014,2,15,19,347,-7,-1,1024,1.34,0,0 +2014,2,15,20,336,-7,-2,1024,2.23,0,0 +2014,2,15,21,351,-8,-4,1025,3.12,0,0 +2014,2,15,22,353,-7,-4,1025,4.01,0,0 +2014,2,15,23,375,-8,-4,1025,1.79,0,0 +2014,2,16,0,382,-7,-3,1026,3.58,0,0 +2014,2,16,1,393,-8,-5,1026,6.71,0,0 +2014,2,16,2,388,-8,-4,1026,8.5,0,0 +2014,2,16,3,376,-8,-5,1026,10.29,0,0 +2014,2,16,4,382,-9,-5,1026,14.31,0,0 +2014,2,16,5,362,-8,-4,1027,18.33,0,0 +2014,2,16,6,345,-8,-4,1028,21.46,0,0 +2014,2,16,7,328,-10,-6,1028,24.59,0,0 +2014,2,16,8,323,-9,-4,1029,27.72,0,0 +2014,2,16,9,297,-6,-1,1030,29.51,0,0 +2014,2,16,10,280,-13,4,1030,31.3,0,0 +2014,2,16,11,267,-11,3,1031,1.79,0,0 +2014,2,16,12,259,-15,6,1030,0.89,0,0 +2014,2,16,13,177,-15,7,1030,2.68,0,0 +2014,2,16,14,161,-16,7,1029,1.79,0,0 +2014,2,16,15,120,-15,7,1029,3.58,0,0 +2014,2,16,16,185,-12,6,1029,7.6,0,0 +2014,2,16,17,211,-11,5,1029,9.39,0,0 +2014,2,16,18,169,-8,4,1030,13.41,0,0 +2014,2,16,19,242,-8,3,1030,17.43,0,0 +2014,2,16,20,247,-6,3,1031,20.56,0,0 +2014,2,16,21,238,-6,2,1032,22.35,0,0 +2014,2,16,22,225,-7,1,1033,24.14,0,0 +2014,2,16,23,216,-7,1,1033,25.93,0,0 +2014,2,17,0,165,-7,0,1033,0.89,0,0 +2014,2,17,1,142,-9,0,1033,3.13,0,0 +2014,2,17,2,136,-7,0,1034,4.02,0,0 +2014,2,17,3,117,-8,-2,1034,5.81,0,0 +2014,2,17,4,119,-9,-3,1034,0.89,0,0 +2014,2,17,5,52,-10,-3,1034,0.89,0,0 +2014,2,17,6,46,-9,-3,1034,1.79,0,0 +2014,2,17,7,53,-10,-2,1035,0.89,0,0 +2014,2,17,8,67,-9,-1,1036,0.89,0,0 +2014,2,17,9,70,-10,1,1037,0.89,0,0 +2014,2,17,10,61,-16,3,1037,3.13,0,0 +2014,2,17,11,78,-17,3,1037,7.15,0,0 +2014,2,17,12,88,-17,4,1036,10.28,0,0 +2014,2,17,13,75,-18,5,1035,12.07,0,0 +2014,2,17,14,73,-18,5,1034,15.2,0,0 +2014,2,17,15,79,-19,5,1034,16.99,0,0 +2014,2,17,16,67,-19,5,1034,20.12,0,0 +2014,2,17,17,73,-19,4,1033,23.25,0,0 +2014,2,17,18,71,-18,1,1033,26.38,0,0 +2014,2,17,19,66,-17,1,1034,28.17,0,0 +2014,2,17,20,66,-15,1,1035,33.09,0,0 +2014,2,17,21,75,-14,0,1036,37.11,0,0 +2014,2,17,22,59,-15,-2,1036,41.13,0,0 +2014,2,17,23,41,-13,-1,1036,45.15,0,0 +2014,2,18,0,45,-13,-2,1036,48.28,0,0 +2014,2,18,1,28,-12,-3,1036,49.17,0,0 +2014,2,18,2,27,-12,-3,1035,50.96,0,0 +2014,2,18,3,27,-12,-4,1035,0.89,0,0 +2014,2,18,4,46,-12,-4,1034,1.79,0,0 +2014,2,18,5,65,-13,-4,1035,3.58,0,0 +2014,2,18,6,60,-13,-4,1034,5.37,0,0 +2014,2,18,7,54,-13,-4,1035,7.16,0,0 +2014,2,18,8,63,-12,-3,1035,8.95,0,0 +2014,2,18,9,71,-12,-3,1035,12.97,0,0 +2014,2,18,10,69,-12,-2,1034,16.1,0,0 +2014,2,18,11,76,-11,0,1034,19.23,0,0 +2014,2,18,12,82,-12,1,1033,22.36,0,0 +2014,2,18,13,63,-12,2,1031,26.38,0,0 +2014,2,18,14,63,-12,2,1030,30.4,0,0 +2014,2,18,15,63,-12,2,1029,34.42,0,0 +2014,2,18,16,64,-12,2,1029,37.55,0,0 +2014,2,18,17,NA,-12,2,1028,40.68,0,0 +2014,2,18,18,88,-12,0,1028,42.47,0,0 +2014,2,18,19,80,-11,-1,1029,0.89,0,0 +2014,2,18,20,89,-10,-2,1029,0.89,0,0 +2014,2,18,21,93,-11,-1,1029,1.79,0,0 +2014,2,18,22,104,-11,-1,1030,1.79,0,0 +2014,2,18,23,123,-11,-2,1029,2.68,1,0 +2014,2,19,0,118,-11,-2,1029,1.79,0,0 +2014,2,19,1,116,-11,-3,1029,3.58,0,0 +2014,2,19,2,125,-11,-3,1029,0.89,0,0 +2014,2,19,3,119,-11,-5,1028,1.79,0,0 +2014,2,19,4,120,-11,-5,1028,0.89,0,0 +2014,2,19,5,122,-12,-7,1028,3.13,0,0 +2014,2,19,6,122,-11,-6,1029,0.89,0,0 +2014,2,19,7,112,-12,-8,1029,1.78,0,0 +2014,2,19,8,108,-10,-7,1030,1.79,0,0 +2014,2,19,9,103,-8,0,1030,4.92,0,0 +2014,2,19,10,89,-13,2,1031,8.05,0,0 +2014,2,19,11,58,-15,3,1030,9.84,0,0 +2014,2,19,12,38,-17,4,1029,1.79,0,0 +2014,2,19,13,19,-17,5,1028,1.79,0,0 +2014,2,19,14,22,-17,5,1027,0.89,0,0 +2014,2,19,15,18,-18,6,1027,1.79,0,0 +2014,2,19,16,14,-19,5,1027,3.13,0,0 +2014,2,19,17,16,-18,5,1027,7.15,0,0 +2014,2,19,18,15,-17,2,1027,8.94,0,0 +2014,2,19,19,34,-15,1,1028,12.96,0,0 +2014,2,19,20,39,-11,-1,1029,16.98,0,0 +2014,2,19,21,89,-12,-2,1029,20.11,0,0 +2014,2,19,22,124,-8,0,1030,25.03,0,0 +2014,2,19,23,142,-9,-2,1030,26.82,0,0 +2014,2,20,0,152,-7,-2,1030,29.95,0,0 +2014,2,20,1,156,-7,-3,1030,31.74,0,0 +2014,2,20,2,152,-7,-4,1030,33.53,0,0 +2014,2,20,3,160,-7,-4,1030,35.32,0,0 +2014,2,20,4,173,-6,-3,1030,36.21,0,0 +2014,2,20,5,174,-6,-3,1030,0.89,0,0 +2014,2,20,6,180,-5,-2,1031,0.89,0,0 +2014,2,20,7,185,-5,-2,1031,1.78,0,0 +2014,2,20,8,184,-5,-2,1032,3.57,1,0 +2014,2,20,9,190,-5,-1,1032,5.36,2,0 +2014,2,20,10,196,-6,-1,1032,7.15,0,0 +2014,2,20,11,NA,-6,0,1032,8.94,0,0 +2014,2,20,12,221,-7,0,1031,10.73,0,0 +2014,2,20,13,233,-7,1,1030,13.86,0,0 +2014,2,20,14,240,-7,1,1030,16.99,0,0 +2014,2,20,15,273,-6,1,1030,20.12,0,0 +2014,2,20,16,327,-6,0,1029,23.25,0,0 +2014,2,20,17,347,-5,0,1029,26.38,0,0 +2014,2,20,18,384,-5,-1,1029,28.17,0,0 +2014,2,20,19,392,-6,-3,1029,29.06,0,0 +2014,2,20,20,400,-6,-2,1030,29.95,0,0 +2014,2,20,21,394,-6,-3,1030,0.89,0,0 +2014,2,20,22,448,-6,-3,1030,0.45,0,0 +2014,2,20,23,424,-6,-4,1030,0.89,0,0 +2014,2,21,0,400,-6,-4,1030,0.45,0,0 +2014,2,21,1,398,-7,-4,1030,0.9,0,0 +2014,2,21,2,383,-8,-5,1029,1.35,0,0 +2014,2,21,3,357,-7,-5,1029,1.8,0,0 +2014,2,21,4,367,-7,-5,1029,2.25,0,0 +2014,2,21,5,335,-7,-5,1029,0.89,0,0 +2014,2,21,6,313,-8,-6,1029,0.89,0,0 +2014,2,21,7,328,-6,-4,1029,0.89,0,0 +2014,2,21,8,324,-5,-2,1029,1.79,0,0 +2014,2,21,9,305,-3,0,1029,4.92,0,0 +2014,2,21,10,268,-5,1,1029,6.71,0,0 +2014,2,21,11,247,-5,2,1029,8.5,0,0 +2014,2,21,12,267,-6,2,1028,0.89,0,0 +2014,2,21,13,334,-6,3,1027,1.79,0,0 +2014,2,21,14,328,-6,4,1026,0.89,0,0 +2014,2,21,15,327,-6,4,1026,1.79,0,0 +2014,2,21,16,326,-7,4,1026,1.79,0,0 +2014,2,21,17,309,-7,3,1026,0.89,0,0 +2014,2,21,18,321,-7,1,1027,0.89,0,0 +2014,2,21,19,340,-6,1,1028,0.89,0,0 +2014,2,21,20,346,-7,-2,1028,1.78,0,0 +2014,2,21,21,330,-6,-1,1029,0.89,0,0 +2014,2,21,22,340,-7,-2,1029,3.13,0,0 +2014,2,21,23,353,-6,-1,1029,0.89,0,0 +2014,2,22,0,384,-5,-1,1029,0.89,0,0 +2014,2,22,1,383,-6,-2,1029,2.68,0,0 +2014,2,22,2,399,-5,-1,1029,5.81,0,0 +2014,2,22,3,340,-5,0,1029,6.7,0,0 +2014,2,22,4,327,-6,0,1029,7.59,0,0 +2014,2,22,5,295,-5,1,1029,9.38,0,0 +2014,2,22,6,296,-6,1,1029,11.17,0,0 +2014,2,22,7,333,-6,1,1030,14.3,0,0 +2014,2,22,8,336,-6,2,1030,16.09,0,0 +2014,2,22,9,350,-6,3,1031,19.22,0,0 +2014,2,22,10,305,-5,3,1031,21.01,0,0 +2014,2,22,11,283,-5,5,1031,0.89,0,0 +2014,2,22,12,323,-5,5,1030,1.79,0,0 +2014,2,22,13,377,-5,6,1029,1.79,0,0 +2014,2,22,14,357,-4,6,1029,1.79,0,0 +2014,2,22,15,360,-4,6,1029,4.92,0,0 +2014,2,22,16,350,-4,6,1029,8.05,0,0 +2014,2,22,17,339,-4,5,1029,0.89,0,0 +2014,2,22,18,335,-4,4,1029,0.89,0,0 +2014,2,22,19,341,-4,2,1030,0.89,0,0 +2014,2,22,20,352,-4,3,1031,2.68,0,0 +2014,2,22,21,355,-2,4,1031,5.81,0,0 +2014,2,22,22,327,-3,3,1031,8.94,0,0 +2014,2,22,23,303,-4,2,1032,10.73,0,0 +2014,2,23,0,287,-4,2,1032,12.52,0,0 +2014,2,23,1,279,-5,2,1032,14.31,0,0 +2014,2,23,2,NA,-5,2,1032,16.1,0,0 +2014,2,23,3,264,-5,1,1032,17.89,0,0 +2014,2,23,4,245,-6,1,1031,19.68,0,0 +2014,2,23,5,228,-6,1,1031,0.89,0,0 +2014,2,23,6,233,-6,1,1031,1.78,0,0 +2014,2,23,7,240,-6,1,1032,2.67,0,0 +2014,2,23,8,246,-6,1,1032,3.56,0,0 +2014,2,23,9,246,-6,1,1032,4.45,0,0 +2014,2,23,10,252,-6,2,1032,1.79,0,0 +2014,2,23,11,266,-6,2,1032,0.89,0,0 +2014,2,23,12,270,-6,2,1031,1.78,0,0 +2014,2,23,13,279,-6,2,1030,2.67,0,0 +2014,2,23,14,283,-6,2,1030,3.56,0,0 +2014,2,23,15,316,-6,3,1030,4.45,0,0 +2014,2,23,16,336,-6,3,1029,5.34,0,0 +2014,2,23,17,333,-6,3,1029,0.89,0,0 +2014,2,23,18,348,-5,2,1029,1.78,0,0 +2014,2,23,19,360,-6,2,1030,0.89,0,0 +2014,2,23,20,358,-5,1,1030,1.78,0,0 +2014,2,23,21,358,-5,2,1030,2.67,0,0 +2014,2,23,22,357,-5,2,1030,3.56,0,0 +2014,2,23,23,362,-5,2,1030,4.45,0,0 +2014,2,24,0,348,-5,2,1030,0.89,0,0 +2014,2,24,1,323,-5,2,1029,0.89,0,0 +2014,2,24,2,339,-5,2,1029,0.45,0,0 +2014,2,24,3,339,-5,2,1028,0.89,0,0 +2014,2,24,4,339,-5,2,1028,2.68,0,0 +2014,2,24,5,341,-5,2,1028,4.47,0,0 +2014,2,24,6,344,-5,1,1028,6.26,0,0 +2014,2,24,7,335,-5,1,1028,8.05,0,0 +2014,2,24,8,329,-5,2,1029,0.89,0,0 +2014,2,24,9,323,-5,3,1029,1.79,0,0 +2014,2,24,10,350,-5,4,1029,3.58,0,0 +2014,2,24,11,341,-5,5,1028,1.79,0,0 +2014,2,24,12,347,-5,6,1028,3.58,0,0 +2014,2,24,13,356,-5,7,1027,0.89,0,0 +2014,2,24,14,342,-5,7,1026,1.78,0,0 +2014,2,24,15,366,-5,7,1025,2.67,0,0 +2014,2,24,16,358,-5,7,1025,4.46,0,0 +2014,2,24,17,391,-5,7,1025,5.35,0,0 +2014,2,24,18,425,-5,5,1025,6.24,0,0 +2014,2,24,19,387,-5,4,1026,1.79,0,0 +2014,2,24,20,363,-5,3,1027,3.58,0,0 +2014,2,24,21,343,-5,2,1027,0.89,0,0 +2014,2,24,22,353,-5,0,1027,0.89,0,0 +2014,2,24,23,357,-4,0,1027,1.78,0,0 +2014,2,25,0,371,-4,-1,1027,0.89,0,0 +2014,2,25,1,394,-5,-1,1027,1.34,0,0 +2014,2,25,2,453,-5,-2,1027,0.89,0,0 +2014,2,25,3,453,-5,-2,1027,0.89,0,0 +2014,2,25,4,485,-5,-2,1027,0.89,0,0 +2014,2,25,5,463,-5,-2,1027,1.78,0,0 +2014,2,25,6,463,-5,-2,1027,3.57,0,0 +2014,2,25,7,418,-4,-1,1027,5.36,0,0 +2014,2,25,8,384,-4,0,1028,7.15,0,0 +2014,2,25,9,371,-4,2,1028,10.28,0,0 +2014,2,25,10,369,-4,3,1027,0.89,0,0 +2014,2,25,11,374,-4,5,1027,1.78,0,0 +2014,2,25,12,397,-4,6,1027,2.67,0,0 +2014,2,25,13,397,-4,7,1025,3.56,0,0 +2014,2,25,14,389,-5,8,1024,1.79,0,0 +2014,2,25,15,410,-4,8,1024,3.58,0,0 +2014,2,25,16,436,-4,8,1024,1.79,0,0 +2014,2,25,17,462,-4,8,1024,2.68,0,0 +2014,2,25,18,482,-4,6,1024,3.13,0,0 +2014,2,25,19,501,-3,4,1025,4.02,0,0 +2014,2,25,20,507,-5,3,1025,1.79,0,0 +2014,2,25,21,523,-4,2,1026,1.79,0,0 +2014,2,25,22,545,-4,1,1026,0.89,0,0 +2014,2,25,23,577,-4,1,1025,1.34,0,0 +2014,2,26,0,541,-4,0,1025,2.23,0,0 +2014,2,26,1,532,-4,1,1025,0.89,0,0 +2014,2,26,2,534,-4,1,1025,0.89,0,0 +2014,2,26,3,495,-4,0,1024,2.68,0,0 +2014,2,26,4,483,-4,1,1024,4.47,0,0 +2014,2,26,5,474,-4,1,1023,5.36,0,0 +2014,2,26,6,478,-4,0,1023,0.89,0,0 +2014,2,26,7,496,-4,0,1023,0.89,0,0 +2014,2,26,8,519,-3,1,1024,1.78,0,0 +2014,2,26,9,526,-2,4,1023,2.67,0,0 +2014,2,26,10,512,-1,5,1023,1.79,0,0 +2014,2,26,11,557,0,6,1022,0.89,0,0 +2014,2,26,12,551,0,7,1021,1.78,0,0 +2014,2,26,13,562,0,8,1021,2.67,0,0 +2014,2,26,14,542,-1,9,1020,3.13,0,0 +2014,2,26,15,502,-1,9,1020,0.89,0,0 +2014,2,26,16,525,0,9,1020,1.79,0,0 +2014,2,26,17,446,0,9,1021,4.92,0,1 +2014,2,26,18,188,2,8,1022,10.73,0,2 +2014,2,26,19,111,2,7,1023,15.65,0,3 +2014,2,26,20,82,2,8,1023,19.67,0,0 +2014,2,26,21,58,2,6,1024,3.13,0,1 +2014,2,26,22,43,2,6,1024,3.13,0,0 +2014,2,26,23,27,2,6,1025,6.26,0,0 +2014,2,27,0,31,-9,7,1025,14.31,0,0 +2014,2,27,1,15,-11,5,1026,22.36,0,0 +2014,2,27,2,17,-14,4,1027,28.17,0,0 +2014,2,27,3,17,-15,4,1027,37.11,0,0 +2014,2,27,4,21,-15,3,1027,46.05,0,0 +2014,2,27,5,17,-16,3,1028,54.1,0,0 +2014,2,27,6,13,-17,2,1029,59.91,0,0 +2014,2,27,7,11,-18,2,1030,64.83,0,0 +2014,2,27,8,12,-15,3,1031,67.96,0,0 +2014,2,27,9,10,-18,5,1032,75.11,0,0 +2014,2,27,10,13,-18,5,1032,80.92,0,0 +2014,2,27,11,13,-17,6,1032,84.05,0,0 +2014,2,27,12,8,-18,7,1031,4.02,0,0 +2014,2,27,13,12,-19,8,1030,3.13,0,0 +2014,2,27,14,12,-19,7,1029,3.13,0,0 +2014,2,27,15,14,-19,9,1028,4.92,0,0 +2014,2,27,16,17,-19,8,1028,1.79,0,0 +2014,2,27,17,18,-20,7,1027,3.58,0,0 +2014,2,27,18,17,-19,6,1027,1.79,0,0 +2014,2,27,19,19,-17,2,1028,3.58,0,0 +2014,2,27,20,31,-18,3,1028,5.37,0,0 +2014,2,27,21,45,-16,1,1028,0.89,0,0 +2014,2,27,22,42,-12,0,1028,1.78,0,0 +2014,2,27,23,53,-15,0,1029,2.67,0,0 +2014,2,28,0,42,-12,-2,1028,1.79,0,0 +2014,2,28,1,34,-14,0,1029,3.13,0,0 +2014,2,28,2,130,-6,-1,1029,4.92,0,0 +2014,2,28,3,114,-5,-1,1029,8.05,0,0 +2014,2,28,4,61,-5,-1,1028,9.84,0,0 +2014,2,28,5,48,-6,-2,1028,0.89,0,0 +2014,2,28,6,48,-6,-2,1028,1.78,0,0 +2014,2,28,7,54,-8,-3,1029,2.67,0,0 +2014,2,28,8,57,-5,-1,1029,0.89,0,0 +2014,2,28,9,63,-6,0,1030,0.89,0,0 +2014,2,28,10,81,-6,2,1029,1.79,0,0 +2014,2,28,11,98,-6,2,1029,3.58,0,0 +2014,2,28,12,106,-8,3,1027,7.6,0,0 +2014,2,28,13,97,-8,5,1026,10.73,0,0 +2014,2,28,14,108,-8,5,1025,13.86,0,0 +2014,2,28,15,103,-9,6,1024,0.89,0,0 +2014,2,28,16,96,-9,6,1023,1.78,0,0 +2014,2,28,17,84,-9,6,1023,1.79,0,0 +2014,2,28,18,118,-10,6,1023,3.58,0,0 +2014,2,28,19,116,-7,4,1023,4.02,0,0 +2014,2,28,20,158,-7,2,1024,0.89,0,0 +2014,2,28,21,171,-7,0,1025,1.78,0,0 +2014,2,28,22,179,-7,0,1025,1.79,0,0 +2014,2,28,23,172,-6,0,1025,3.58,0,0 +2014,3,1,0,168,-7,-1,1025,5.37,0,0 +2014,3,1,1,188,-6,-1,1025,0.89,0,0 +2014,3,1,2,206,-8,-3,1025,3.13,0,0 +2014,3,1,3,210,-6,-2,1024,1.79,0,0 +2014,3,1,4,180,-7,-3,1024,1.79,0,0 +2014,3,1,5,181,-6,-1,1025,4.92,0,0 +2014,3,1,6,149,-7,-1,1025,8.05,0,0 +2014,3,1,7,100,-6,0,1025,12.07,0,0 +2014,3,1,8,83,-8,2,1026,16.09,0,0 +2014,3,1,9,45,-9,4,1026,20.11,0,0 +2014,3,1,10,29,-18,7,1026,25.03,0,0 +2014,3,1,11,15,-21,8,1025,30.84,0,0 +2014,3,1,12,14,-23,10,1025,36.65,0,0 +2014,3,1,13,17,-23,10,1023,39.78,0,0 +2014,3,1,14,12,-24,11,1022,42.91,0,0 +2014,3,1,15,10,-24,11,1021,1.79,0,0 +2014,3,1,16,10,-27,12,1020,7.15,0,0 +2014,3,1,17,14,-25,12,1020,15.2,0,0 +2014,3,1,18,29,-23,10,1020,4.02,0,0 +2014,3,1,19,51,-20,9,1021,8.04,0,0 +2014,3,1,20,48,-20,8,1021,12.06,0,0 +2014,3,1,21,74,-18,8,1021,16.08,0,0 +2014,3,1,22,93,-17,8,1022,21,0,0 +2014,3,1,23,112,-16,4,1022,24.13,0,0 +2014,3,2,0,125,-7,1,1023,27.26,0,0 +2014,3,2,1,133,-6,1,1022,30.39,0,0 +2014,3,2,2,150,-6,0,1023,32.18,0,0 +2014,3,2,3,124,-6,-1,1023,1.79,0,0 +2014,3,2,4,135,-5,-1,1023,1.79,0,0 +2014,3,2,5,143,-6,-1,1023,0.89,0,0 +2014,3,2,6,139,-6,-1,1023,1.79,0,0 +2014,3,2,7,128,-7,-1,1024,3.58,0,0 +2014,3,2,8,134,-7,0,1024,0.89,0,0 +2014,3,2,9,146,-9,1,1024,1.79,0,0 +2014,3,2,10,154,-8,3,1025,1.79,0,0 +2014,3,2,11,157,-9,5,1024,1.79,0,0 +2014,3,2,12,174,-9,6,1023,1.79,0,0 +2014,3,2,13,158,-11,8,1022,0.89,0,0 +2014,3,2,14,151,-12,10,1021,3.13,0,0 +2014,3,2,15,141,-12,10,1020,7.15,0,0 +2014,3,2,16,137,-13,11,1020,10.28,0,0 +2014,3,2,17,144,-12,10,1020,14.3,0,0 +2014,3,2,18,144,-11,8,1020,17.43,0,0 +2014,3,2,19,159,-10,5,1021,0.89,0,0 +2014,3,2,20,166,-9,6,1022,3.13,0,0 +2014,3,2,21,183,-6,6,1023,7.15,0,0 +2014,3,2,22,228,-5,4,1023,8.94,0,0 +2014,3,2,23,257,-4,3,1023,10.73,0,0 +2014,3,3,0,306,-4,2,1023,12.52,0,0 +2014,3,3,1,222,-5,1,1024,0.89,0,0 +2014,3,3,2,193,-5,-1,1024,1.78,0,0 +2014,3,3,3,188,-4,0,1023,1.79,0,0 +2014,3,3,4,182,-6,-2,1023,0.89,0,0 +2014,3,3,5,184,-6,-3,1023,1.34,0,0 +2014,3,3,6,209,-6,-3,1023,2.23,0,0 +2014,3,3,7,225,-5,-3,1022,1.79,0,0 +2014,3,3,8,238,-5,-2,1023,2.68,0,0 +2014,3,3,9,246,-4,0,1023,0.89,0,0 +2014,3,3,10,269,-3,2,1023,1.78,0,0 +2014,3,3,11,284,-5,2,1022,2.67,0,0 +2014,3,3,12,260,-6,3,1022,1.79,0,0 +2014,3,3,13,295,-7,3,1021,1.79,0,0 +2014,3,3,14,322,-7,3,1020,1.79,0,0 +2014,3,3,15,308,-8,3,1020,3.58,0,0 +2014,3,3,16,271,-8,3,1020,1.79,0,0 +2014,3,3,17,240,-8,3,1020,4.92,0,0 +2014,3,3,18,230,-7,3,1020,8.05,0,0 +2014,3,3,19,237,-6,3,1021,11.18,0,0 +2014,3,3,20,242,-6,3,1021,14.31,0,0 +2014,3,3,21,241,-6,3,1022,17.44,0,0 +2014,3,3,22,238,-7,0,1022,20.57,0,0 +2014,3,3,23,255,-8,1,1022,23.7,0,0 +2014,3,4,0,266,-9,1,1023,26.83,0,0 +2014,3,4,1,255,-9,2,1023,29.96,0,0 +2014,3,4,2,232,-7,0,1024,33.09,0,0 +2014,3,4,3,226,-7,0,1024,34.88,0,0 +2014,3,4,4,215,-6,0,1024,36.67,0,0 +2014,3,4,5,75,-6,-1,1025,1.79,0,0 +2014,3,4,6,3,-8,-1,1025,3.13,0,0 +2014,3,4,7,4,-10,-1,1026,4.92,0,0 +2014,3,4,8,7,-13,5,1027,8.05,0,0 +2014,3,4,9,6,-18,7,1027,12.07,0,0 +2014,3,4,10,14,-25,7,1028,21.01,0,0 +2014,3,4,11,13,-24,9,1027,26.82,0,0 +2014,3,4,12,8,-23,8,1027,33.97,0,0 +2014,3,4,13,11,-26,8,1026,41.12,0,0 +2014,3,4,14,13,-26,9,1025,49.17,0,0 +2014,3,4,15,11,-29,9,1025,57.22,0,0 +2014,3,4,16,7,-29,10,1025,65.27,0,0 +2014,3,4,17,8,-30,8,1025,71.08,0,0 +2014,3,4,18,11,-29,7,1026,78.23,0,0 +2014,3,4,19,12,-28,6,1027,84.04,0,0 +2014,3,4,20,13,-27,5,1028,89.85,0,0 +2014,3,4,21,9,-25,1,1029,94.77,0,0 +2014,3,4,22,9,-25,0,1030,99.69,0,0 +2014,3,4,23,7,-23,0,1030,105.5,0,0 +2014,3,5,0,14,-22,-2,1030,110.42,0,0 +2014,3,5,1,10,-21,-4,1030,114.44,0,0 +2014,3,5,2,9,-20,-3,1030,117.57,0,0 +2014,3,5,3,18,-19,-4,1031,119.36,0,0 +2014,3,5,4,14,-17,-6,1030,0.89,0,0 +2014,3,5,5,12,-15,-5,1030,0.89,0,0 +2014,3,5,6,26,-16,-6,1030,0.45,0,0 +2014,3,5,7,30,-16,-4,1031,1.34,0,0 +2014,3,5,8,35,-15,0,1031,1.79,0,0 +2014,3,5,9,27,-22,3,1031,2.68,0,0 +2014,3,5,10,13,-23,4,1030,3.57,0,0 +2014,3,5,11,18,-24,5,1030,3.13,0,0 +2014,3,5,12,27,-25,7,1028,7.15,0,0 +2014,3,5,13,23,-25,8,1027,12.07,0,0 +2014,3,5,14,25,-24,8,1026,17.88,0,0 +2014,3,5,15,27,-24,8,1025,23.69,0,0 +2014,3,5,16,26,-24,8,1024,29.5,0,0 +2014,3,5,17,32,-23,8,1024,36.65,0,0 +2014,3,5,18,39,-22,7,1024,43.8,0,0 +2014,3,5,19,49,-21,6,1025,49.61,0,0 +2014,3,5,20,48,-20,6,1026,55.42,0,0 +2014,3,5,21,49,-22,5,1027,3.13,0,0 +2014,3,5,22,16,-27,4,1028,8.05,0,0 +2014,3,5,23,12,-27,3,1029,12.97,0,0 +2014,3,6,0,12,-29,3,1029,16.99,0,0 +2014,3,6,1,11,-28,2,1030,21.01,0,0 +2014,3,6,2,8,-25,-2,1030,24.14,0,0 +2014,3,6,3,11,-27,0,1030,28.16,0,0 +2014,3,6,4,9,-24,0,1030,29.95,0,0 +2014,3,6,5,9,-22,-3,1031,1.79,0,0 +2014,3,6,6,12,-24,-4,1031,1.79,0,0 +2014,3,6,7,16,-23,-5,1032,3.58,0,0 +2014,3,6,8,14,-24,1,1033,5.37,0,0 +2014,3,6,9,23,-27,2,1033,3.13,0,0 +2014,3,6,10,16,-27,4,1033,6.26,0,0 +2014,3,6,11,10,-28,4,1032,1.79,0,0 +2014,3,6,12,13,-30,6,1031,4.92,0,0 +2014,3,6,13,15,-30,6,1030,3.13,0,0 +2014,3,6,14,12,-29,7,1029,4.02,0,0 +2014,3,6,15,11,-31,7,1028,9.83,0,0 +2014,3,6,16,12,-30,7,1027,17.88,0,0 +2014,3,6,17,12,-30,8,1027,23.69,0,0 +2014,3,6,18,17,-31,7,1027,26.82,0,0 +2014,3,6,19,27,-27,5,1028,4.92,0,0 +2014,3,6,20,29,-26,4,1028,9.84,0,0 +2014,3,6,21,41,-25,4,1028,13.86,0,0 +2014,3,6,22,41,-25,2,1029,16.99,0,0 +2014,3,6,23,53,-17,1,1029,22.8,0,0 +2014,3,7,0,67,-13,1,1029,26.82,0,0 +2014,3,7,1,70,-12,0,1029,29.95,0,0 +2014,3,7,2,84,-10,-1,1030,31.74,0,0 +2014,3,7,3,91,-10,-4,1030,1.79,0,0 +2014,3,7,4,99,-10,-4,1030,3.58,0,0 +2014,3,7,5,97,-10,-5,1031,4.47,0,0 +2014,3,7,6,100,-9,-4,1031,0.45,0,0 +2014,3,7,7,97,-11,-7,1032,1.79,0,0 +2014,3,7,8,86,-10,0,1032,3.58,0,0 +2014,3,7,9,92,-15,2,1032,3.13,0,0 +2014,3,7,10,64,-22,4,1032,7.15,0,0 +2014,3,7,11,40,-26,5,1031,4.92,0,0 +2014,3,7,12,26,-26,7,1031,8.94,0,0 +2014,3,7,13,21,-26,8,1029,4.02,0,0 +2014,3,7,14,14,-26,8,1028,3.13,0,0 +2014,3,7,15,17,-28,8,1028,1.79,0,0 +2014,3,7,16,20,-26,8,1027,3.58,0,0 +2014,3,7,17,31,-26,8,1027,5.37,0,0 +2014,3,7,18,40,-25,6,1027,7.16,0,0 +2014,3,7,19,38,-24,5,1027,8.95,0,0 +2014,3,7,20,41,-23,5,1028,12.97,0,0 +2014,3,7,21,51,-22,3,1028,0.89,0,0 +2014,3,7,22,69,-19,4,1028,1.79,0,0 +2014,3,7,23,77,-19,3,1028,3.58,0,0 +2014,3,8,0,111,-16,2,1028,5.37,0,0 +2014,3,8,1,144,-13,2,1027,7.16,0,0 +2014,3,8,2,133,-12,2,1027,8.95,0,0 +2014,3,8,3,110,-11,1,1026,10.74,0,0 +2014,3,8,4,143,-8,0,1025,12.53,0,0 +2014,3,8,5,136,-6,0,1025,14.32,0,0 +2014,3,8,6,141,-5,0,1025,16.11,0,0 +2014,3,8,7,160,-4,0,1025,17.9,0,0 +2014,3,8,8,159,-4,1,1025,0.89,0,0 +2014,3,8,9,167,-4,1,1025,3.13,0,0 +2014,3,8,10,188,-4,3,1025,0.89,0,0 +2014,3,8,11,196,-5,5,1024,2.68,0,0 +2014,3,8,12,205,-6,6,1023,4.47,0,0 +2014,3,8,13,201,-7,8,1021,3.13,0,0 +2014,3,8,14,201,-8,9,1020,1.79,0,0 +2014,3,8,15,202,-8,10,1019,4.92,0,0 +2014,3,8,16,201,-8,10,1019,9.84,0,0 +2014,3,8,17,187,-8,10,1019,12.97,0,0 +2014,3,8,18,187,-9,8,1020,14.76,0,0 +2014,3,8,19,199,-7,6,1021,0.89,0,0 +2014,3,8,20,206,-8,2,1022,1.79,0,0 +2014,3,8,21,213,-7,3,1023,0.89,0,0 +2014,3,8,22,187,-7,1,1024,1.78,0,0 +2014,3,8,23,218,-6,0,1024,0.89,0,0 +2014,3,9,0,134,-8,-2,1024,1.79,0,0 +2014,3,9,1,131,-9,-1,1025,0.89,0,0 +2014,3,9,2,154,-9,-3,1025,0.89,0,0 +2014,3,9,3,112,-9,-3,1025,0.89,0,0 +2014,3,9,4,55,-11,-3,1025,0.89,0,0 +2014,3,9,5,172,-8,0,1025,4.02,0,0 +2014,3,9,6,143,-8,-2,1026,5.81,0,0 +2014,3,9,7,122,-8,-1,1027,0.89,0,0 +2014,3,9,8,114,-7,0,1028,1.79,0,0 +2014,3,9,9,100,-11,4,1028,0.89,0,0 +2014,3,9,10,119,-11,4,1028,1.79,0,0 +2014,3,9,11,130,-10,6,1028,0.89,0,0 +2014,3,9,12,145,-9,8,1027,4.02,0,0 +2014,3,9,13,149,-11,8,1026,3.13,0,0 +2014,3,9,14,163,-11,10,1025,6.26,0,0 +2014,3,9,15,147,-12,10,1024,9.39,0,0 +2014,3,9,16,133,-12,10,1024,12.52,0,0 +2014,3,9,17,126,-13,9,1024,15.65,0,0 +2014,3,9,18,124,-11,7,1025,17.44,0,0 +2014,3,9,19,122,-12,7,1025,22.36,0,0 +2014,3,9,20,91,-13,5,1026,27.28,0,0 +2014,3,9,21,98,-10,4,1027,32.2,0,0 +2014,3,9,22,91,-6,2,1028,37.12,0,0 +2014,3,9,23,78,-6,0,1028,38.91,0,0 +2014,3,10,0,55,-6,0,1029,40.7,0,0 +2014,3,10,1,40,-7,-1,1028,43.83,0,0 +2014,3,10,2,36,-7,-1,1028,47.85,0,0 +2014,3,10,3,48,-8,-1,1028,50.98,0,0 +2014,3,10,4,54,-8,-2,1027,0.89,0,0 +2014,3,10,5,63,-10,-4,1027,1.78,0,0 +2014,3,10,6,78,-8,-3,1027,0.89,0,0 +2014,3,10,7,74,-7,-2,1027,0.45,0,0 +2014,3,10,8,76,-7,0,1027,0.89,0,0 +2014,3,10,9,81,-8,1,1027,1.79,0,0 +2014,3,10,10,86,-7,3,1026,3.58,0,0 +2014,3,10,11,89,-7,4,1025,5.37,0,0 +2014,3,10,12,96,-7,5,1025,0.89,0,0 +2014,3,10,13,99,-8,6,1023,1.79,0,0 +2014,3,10,14,102,-9,8,1021,1.79,0,0 +2014,3,10,15,111,-9,9,1021,3.58,0,0 +2014,3,10,16,121,-9,9,1020,3.13,0,0 +2014,3,10,17,122,-8,8,1020,6.26,0,0 +2014,3,10,18,135,-7,8,1019,9.39,0,0 +2014,3,10,19,145,-7,6,1019,11.18,0,0 +2014,3,10,20,159,-6,5,1020,0.45,0,0 +2014,3,10,21,183,-5,3,1020,0.89,0,0 +2014,3,10,22,187,-6,2,1020,1.78,0,0 +2014,3,10,23,195,-5,3,1020,0.89,0,0 +2014,3,11,0,224,-5,2,1019,1.34,0,0 +2014,3,11,1,259,-4,1,1019,2.23,0,0 +2014,3,11,2,260,-5,1,1018,2.68,0,0 +2014,3,11,3,257,-5,0,1018,3.13,0,0 +2014,3,11,4,267,-4,-1,1017,4.02,0,0 +2014,3,11,5,260,-5,-1,1017,1.79,0,0 +2014,3,11,6,259,-4,0,1017,0.89,0,0 +2014,3,11,7,270,-5,-1,1017,3.13,0,0 +2014,3,11,8,234,-5,2,1017,4.92,0,0 +2014,3,11,9,245,-5,5,1017,8.05,0,0 +2014,3,11,10,269,-6,8,1017,11.18,0,0 +2014,3,11,11,303,-6,10,1017,15.2,0,0 +2014,3,11,12,272,-5,11,1016,0.89,0,0 +2014,3,11,13,234,-5,12,1016,1.78,0,0 +2014,3,11,14,214,-5,12,1015,1.79,0,0 +2014,3,11,15,225,-5,13,1015,3.58,0,0 +2014,3,11,16,220,-6,14,1015,1.79,0,0 +2014,3,11,17,209,-6,13,1015,0.89,0,0 +2014,3,11,18,202,-7,12,1017,3.13,0,0 +2014,3,11,19,168,-13,13,1018,10.28,0,0 +2014,3,11,20,13,-13,11,1020,17.43,0,0 +2014,3,11,21,18,-11,10,1021,26.37,0,0 +2014,3,11,22,18,-9,9,1023,34.42,0,0 +2014,3,11,23,13,-10,8,1023,5.81,0,0 +2014,3,12,0,10,-11,8,1024,11.62,0,0 +2014,3,12,1,11,-11,7,1025,5.81,0,0 +2014,3,12,2,14,-11,7,1025,9.83,0,0 +2014,3,12,3,13,-10,6,1025,4.02,0,0 +2014,3,12,4,10,-11,6,1025,7.15,0,0 +2014,3,12,5,10,-12,5,1025,11.17,0,0 +2014,3,12,6,10,-14,6,1026,16.98,0,0 +2014,3,12,7,11,-15,5,1027,21.9,0,0 +2014,3,12,8,11,-19,6,1027,27.71,0,0 +2014,3,12,9,11,-21,7,1028,33.52,0,0 +2014,3,12,10,12,-23,7,1028,5.81,0,0 +2014,3,12,11,13,-24,8,1028,11.62,0,0 +2014,3,12,12,11,-23,10,1027,4.02,0,0 +2014,3,12,13,10,-24,11,1026,8.04,0,0 +2014,3,12,14,9,-24,10,1025,1.79,0,0 +2014,3,12,15,13,-26,10,1024,3.58,0,0 +2014,3,12,16,13,-25,10,1023,3.13,0,0 +2014,3,12,17,13,-25,10,1023,0.89,0,0 +2014,3,12,18,15,-24,9,1023,1.79,0,0 +2014,3,12,19,20,-23,7,1024,2.68,0,0 +2014,3,12,20,41,-23,6,1024,3.57,0,0 +2014,3,12,21,35,-18,3,1025,0.89,0,0 +2014,3,12,22,52,-20,2,1025,1.79,0,0 +2014,3,12,23,66,-22,4,1026,3.58,0,0 +2014,3,13,0,36,-22,2,1026,6.71,0,0 +2014,3,13,1,18,-23,2,1026,8.5,0,0 +2014,3,13,2,20,-22,1,1026,10.29,0,0 +2014,3,13,3,20,-22,0,1025,12.08,0,0 +2014,3,13,4,14,-19,0,1025,0.89,0,0 +2014,3,13,5,15,-15,-2,1025,1.78,0,0 +2014,3,13,6,18,-19,-1,1025,0.89,0,0 +2014,3,13,7,21,-15,-2,1025,0.89,0,0 +2014,3,13,8,32,-19,3,1025,1.79,0,0 +2014,3,13,9,21,-23,6,1025,3.58,0,0 +2014,3,13,10,19,-23,8,1024,0.89,0,0 +2014,3,13,11,12,-25,9,1023,3.13,0,0 +2014,3,13,12,15,-24,11,1022,1.79,0,0 +2014,3,13,13,16,-25,12,1020,4.92,0,0 +2014,3,13,14,14,-23,12,1019,3.13,0,0 +2014,3,13,15,18,-23,12,1018,8.05,0,0 +2014,3,13,16,20,-22,13,1017,13.86,0,0 +2014,3,13,17,19,-21,12,1017,18.78,0,0 +2014,3,13,18,28,-21,11,1017,24.59,0,0 +2014,3,13,19,32,-20,11,1017,29.51,0,0 +2014,3,13,20,40,-19,10,1017,34.43,0,0 +2014,3,13,21,39,-18,10,1018,0.89,0,0 +2014,3,13,22,22,-18,8,1019,4.92,0,0 +2014,3,13,23,37,-26,10,1019,12.97,0,0 +2014,3,14,0,24,-25,10,1019,20.12,0,0 +2014,3,14,1,19,-25,9,1020,28.17,0,0 +2014,3,14,2,11,-24,9,1020,33.98,0,0 +2014,3,14,3,13,-23,8,1020,39.79,0,0 +2014,3,14,4,11,-24,7,1020,46.94,0,0 +2014,3,14,5,10,-23,6,1020,52.75,0,0 +2014,3,14,6,7,-24,6,1021,57.67,0,0 +2014,3,14,7,12,-22,4,1022,60.8,0,0 +2014,3,14,8,15,-22,9,1022,63.93,0,0 +2014,3,14,9,12,-23,11,1022,67.06,0,0 +2014,3,14,10,16,-23,12,1022,71.08,0,0 +2014,3,14,11,7,-23,12,1021,72.87,0,0 +2014,3,14,12,12,-22,13,1020,3.13,0,0 +2014,3,14,13,19,-23,14,1019,7.15,0,0 +2014,3,14,14,22,-22,15,1017,4.02,0,0 +2014,3,14,15,22,-22,16,1016,8.94,0,0 +2014,3,14,16,16,-21,17,1015,14.75,0,0 +2014,3,14,17,20,-20,16,1014,20.56,0,0 +2014,3,14,18,30,-20,15,1014,26.37,0,0 +2014,3,14,19,32,-18,12,1014,30.39,0,0 +2014,3,14,20,38,-16,12,1014,35.31,0,0 +2014,3,14,21,52,-17,11,1014,39.33,0,0 +2014,3,14,22,63,-15,11,1013,41.12,0,0 +2014,3,14,23,66,-14,11,1013,45.14,0,0 +2014,3,15,0,72,-13,8,1012,46.93,0,0 +2014,3,15,1,63,-11,7,1012,48.72,0,0 +2014,3,15,2,69,-12,2,1012,0.89,0,0 +2014,3,15,3,78,-12,3,1011,0.89,0,0 +2014,3,15,4,85,-12,1,1011,1.79,0,0 +2014,3,15,5,82,-9,1,1010,0.89,0,0 +2014,3,15,6,87,-9,1,1011,0.89,0,0 +2014,3,15,7,93,-7,1,1011,0.89,0,0 +2014,3,15,8,116,-10,6,1011,0.45,0,0 +2014,3,15,9,99,-12,8,1012,1.34,0,0 +2014,3,15,10,88,-11,11,1012,2.23,0,0 +2014,3,15,11,91,-13,12,1011,3.12,0,0 +2014,3,15,12,109,-12,15,1011,1.79,0,0 +2014,3,15,13,110,-12,17,1010,1.79,0,0 +2014,3,15,14,109,-14,20,1009,1.79,0,0 +2014,3,15,15,123,-14,21,1008,4.92,0,0 +2014,3,15,16,136,-13,22,1008,8.05,0,0 +2014,3,15,17,119,-12,20,1009,11.18,0,0 +2014,3,15,18,116,-11,19,1009,12.97,0,0 +2014,3,15,19,101,-8,16,1010,16.1,0,0 +2014,3,15,20,131,-8,14,1011,17.89,0,0 +2014,3,15,21,168,-7,13,1011,19.68,0,0 +2014,3,15,22,189,-6,14,1013,23.7,0,0 +2014,3,15,23,183,-4,12,1013,27.72,0,0 +2014,3,16,0,205,-7,11,1014,30.85,0,0 +2014,3,16,1,188,-9,10,1015,32.64,0,0 +2014,3,16,2,148,-10,8,1015,0.89,0,0 +2014,3,16,3,137,-10,7,1015,1.78,0,0 +2014,3,16,4,140,-9,6,1015,2.67,0,0 +2014,3,16,5,141,-9,5,1016,3.56,0,0 +2014,3,16,6,121,-11,7,1017,4.45,0,0 +2014,3,16,7,109,-11,7,1017,1.79,0,0 +2014,3,16,8,83,-9,10,1018,0.45,0,0 +2014,3,16,9,78,-11,12,1019,3.13,0,0 +2014,3,16,10,77,-12,13,1018,1.79,0,0 +2014,3,16,11,79,-13,14,1018,5.81,0,0 +2014,3,16,12,73,-14,16,1018,3.13,0,0 +2014,3,16,13,75,-12,16,1016,4.02,0,0 +2014,3,16,14,91,-11,17,1015,8.04,0,0 +2014,3,16,15,104,-9,17,1013,12.96,0,0 +2014,3,16,16,116,-8,17,1012,16.98,0,0 +2014,3,16,17,121,-8,16,1012,21,0,0 +2014,3,16,18,137,-7,15,1012,25.02,0,0 +2014,3,16,19,151,-6,13,1012,28.15,0,0 +2014,3,16,20,159,-6,12,1013,31.28,0,0 +2014,3,16,21,157,-5,11,1013,34.41,0,0 +2014,3,16,22,151,-5,10,1013,37.54,0,0 +2014,3,16,23,150,-5,9,1012,0.89,0,0 +2014,3,17,0,149,-5,8,1012,0.89,0,0 +2014,3,17,1,141,-4,6,1011,0.45,0,0 +2014,3,17,2,144,-4,4,1011,1.79,0,0 +2014,3,17,3,148,-4,6,1010,0.89,0,0 +2014,3,17,4,144,-4,5,1010,1.78,0,0 +2014,3,17,5,150,-4,4,1010,0.89,0,0 +2014,3,17,6,152,-4,4,1010,1.79,0,0 +2014,3,17,7,155,-4,5,1011,3.58,0,0 +2014,3,17,8,165,-4,8,1011,6.71,0,0 +2014,3,17,9,181,-5,10,1012,9.84,0,0 +2014,3,17,10,191,-5,12,1011,12.97,0,0 +2014,3,17,11,124,-10,16,1012,20.12,0,0 +2014,3,17,12,36,-11,19,1011,27.27,0,0 +2014,3,17,13,40,-10,19,1011,35.32,0,0 +2014,3,17,14,49,-10,20,1010,44.26,0,0 +2014,3,17,15,30,-10,21,1010,54.09,0,0 +2014,3,17,16,27,-11,21,1010,63.92,0,0 +2014,3,17,17,27,-11,20,1011,73.75,0,0 +2014,3,17,18,22,-10,18,1012,84.93,0,0 +2014,3,17,19,24,-11,18,1014,93.87,0,0 +2014,3,17,20,21,-11,17,1016,101.02,0,0 +2014,3,17,21,26,-10,16,1017,106.83,0,0 +2014,3,17,22,17,-11,15,1018,112.64,0,0 +2014,3,17,23,23,-11,15,1018,119.79,0,0 +2014,3,18,0,23,-11,12,1019,123.81,0,0 +2014,3,18,1,21,-10,10,1019,129.62,0,0 +2014,3,18,2,21,-13,13,1020,5.81,0,0 +2014,3,18,3,17,-12,11,1021,9.83,0,0 +2014,3,18,4,15,-12,11,1021,12.96,0,0 +2014,3,18,5,13,-12,9,1022,0.89,0,0 +2014,3,18,6,17,-12,8,1022,3.13,0,0 +2014,3,18,7,14,-10,6,1023,6.26,0,0 +2014,3,18,8,20,-12,13,1024,10.28,0,0 +2014,3,18,9,18,-12,14,1024,14.3,0,0 +2014,3,18,10,16,-12,15,1024,3.13,0,0 +2014,3,18,11,27,-8,15,1024,8.94,0,0 +2014,3,18,12,52,-7,15,1024,13.86,0,0 +2014,3,18,13,67,-7,15,1023,18.78,0,0 +2014,3,18,14,76,-6,15,1022,24.59,0,0 +2014,3,18,15,79,-7,14,1022,31.74,0,0 +2014,3,18,16,76,-6,14,1021,37.55,0,0 +2014,3,18,17,66,-6,13,1021,44.7,0,0 +2014,3,18,18,60,-7,12,1021,50.51,0,0 +2014,3,18,19,66,-8,11,1021,55.43,0,0 +2014,3,18,20,61,-9,10,1022,59.45,0,0 +2014,3,18,21,57,-10,9,1022,62.58,0,0 +2014,3,18,22,47,-12,9,1022,66.6,0,0 +2014,3,18,23,30,-12,7,1022,68.39,0,0 +2014,3,19,0,34,-11,7,1022,70.18,0,0 +2014,3,19,1,29,-10,7,1021,73.31,0,0 +2014,3,19,2,47,-8,6,1021,75.1,0,0 +2014,3,19,3,72,-8,5,1021,76.89,0,0 +2014,3,19,4,73,-7,5,1021,1.79,0,0 +2014,3,19,5,70,-8,6,1021,1.79,0,0 +2014,3,19,6,78,-7,6,1022,2.68,0,0 +2014,3,19,7,90,-7,6,1022,3.57,0,0 +2014,3,19,8,92,-7,7,1022,0.89,0,0 +2014,3,19,9,94,-7,8,1022,1.78,0,0 +2014,3,19,10,96,-7,9,1022,3.57,0,0 +2014,3,19,11,77,-8,9,1022,3.13,0,0 +2014,3,19,12,79,-9,10,1022,6.26,0,0 +2014,3,19,13,79,-6,10,1023,11.18,0,0 +2014,3,19,14,74,-3,12,1022,16.1,0,0 +2014,3,19,15,76,-4,13,1022,3.13,0,0 +2014,3,19,16,66,-4,12,1022,6.26,0,0 +2014,3,19,17,44,-4,12,1021,1.79,0,0 +2014,3,19,18,48,-4,11,1022,1.79,0,0 +2014,3,19,19,52,-4,11,1023,4.92,0,0 +2014,3,19,20,20,-4,11,1024,8.94,0,0 +2014,3,19,21,15,-15,10,1026,16.09,0,0 +2014,3,19,22,13,-18,10,1027,23.24,0,0 +2014,3,19,23,14,-18,8,1028,28.16,0,0 +2014,3,20,0,10,-17,6,1029,32.18,0,0 +2014,3,20,1,7,-17,8,1029,35.31,0,0 +2014,3,20,2,5,-17,7,1028,41.12,0,0 +2014,3,20,3,6,-16,6,1028,45.14,0,0 +2014,3,20,4,7,-16,8,1028,50.06,0,0 +2014,3,20,5,10,-16,7,1028,54.98,0,0 +2014,3,20,6,9,-16,7,1028,59.9,0,0 +2014,3,20,7,11,-15,8,1028,64.82,0,0 +2014,3,20,8,9,-16,12,1029,70.63,0,0 +2014,3,20,9,11,-16,12,1029,76.44,0,0 +2014,3,20,10,13,-16,14,1029,83.59,0,0 +2014,3,20,11,11,-17,15,1028,87.61,0,0 +2014,3,20,12,17,-17,16,1027,90.74,0,0 +2014,3,20,13,14,-17,16,1026,3.13,0,0 +2014,3,20,14,13,-17,18,1024,0.89,0,0 +2014,3,20,15,9,-17,18,1024,2.68,0,0 +2014,3,20,16,7,-17,18,1023,3.13,0,0 +2014,3,20,17,9,-17,18,1023,0.89,0,0 +2014,3,20,18,11,-17,16,1023,1.79,0,0 +2014,3,20,19,12,-17,15,1023,0.89,0,0 +2014,3,20,20,18,-14,12,1024,1.78,0,0 +2014,3,20,21,11,-15,10,1024,1.79,0,0 +2014,3,20,22,11,-11,7,1024,1.79,0,0 +2014,3,20,23,7,-13,8,1024,0.89,0,0 +2014,3,21,0,6,-16,12,1024,4.02,0,0 +2014,3,21,1,5,-16,13,1024,8.94,0,0 +2014,3,21,2,7,-17,12,1023,13.86,0,0 +2014,3,21,3,12,-17,10,1023,17.88,0,0 +2014,3,21,4,13,-15,8,1023,19.67,0,0 +2014,3,21,5,17,-15,4,1023,22.8,0,0 +2014,3,21,6,16,-12,5,1023,1.79,0,0 +2014,3,21,7,26,-11,5,1024,0.89,0,0 +2014,3,21,8,18,-12,10,1024,1.78,0,0 +2014,3,21,9,19,-14,14,1024,2.67,0,0 +2014,3,21,10,21,-15,16,1023,3.56,0,0 +2014,3,21,11,24,-14,18,1022,3.13,0,0 +2014,3,21,12,20,-17,20,1020,6.26,0,0 +2014,3,21,13,22,-20,21,1019,4.92,0,0 +2014,3,21,14,18,-18,22,1018,1.79,0,0 +2014,3,21,15,19,-18,22,1017,4.02,0,0 +2014,3,21,16,NA,-18,22,1016,8.94,0,0 +2014,3,21,17,28,-16,21,1016,14.75,0,0 +2014,3,21,18,36,-16,19,1016,18.77,0,0 +2014,3,21,19,40,-14,16,1016,22.79,0,0 +2014,3,21,20,49,-14,14,1016,25.92,0,0 +2014,3,21,21,54,-12,15,1016,29.05,0,0 +2014,3,21,22,55,-12,18,1016,36.2,0,0 +2014,3,21,23,67,-11,17,1015,41.12,0,0 +2014,3,22,0,71,-12,16,1015,42.91,0,0 +2014,3,22,1,76,-10,9,1015,0.89,0,0 +2014,3,22,2,72,-10,7,1015,3.13,0,0 +2014,3,22,3,75,-10,5,1015,4.92,0,0 +2014,3,22,4,93,-5,5,1015,6.71,0,0 +2014,3,22,5,89,-7,5,1015,7.6,0,0 +2014,3,22,6,98,-6,3,1016,8.49,0,0 +2014,3,22,7,97,-8,5,1016,10.28,0,0 +2014,3,22,8,109,-11,11,1017,12.07,0,0 +2014,3,22,9,89,-13,14,1017,1.79,0,0 +2014,3,22,10,66,-17,18,1018,4.92,0,0 +2014,3,22,11,72,-18,21,1018,5.81,0,0 +2014,3,22,12,37,-20,22,1017,4.92,0,0 +2014,3,22,13,21,-21,23,1017,4.92,0,0 +2014,3,22,14,17,-22,23,1016,5.81,0,0 +2014,3,22,15,17,-22,23,1017,11.62,0,0 +2014,3,22,16,11,-19,22,1017,17.43,0,0 +2014,3,22,17,16,-22,22,1017,22.35,0,0 +2014,3,22,18,11,-21,21,1018,27.27,0,0 +2014,3,22,19,17,-16,17,1019,0.89,0,0 +2014,3,22,20,31,-14,17,1021,4.02,0,0 +2014,3,22,21,61,-11,16,1022,9.83,0,0 +2014,3,22,22,151,-8,13,1023,13.85,0,0 +2014,3,22,23,163,-10,12,1024,15.64,0,0 +2014,3,23,0,98,-9,8,1025,1.79,0,0 +2014,3,23,1,96,-10,8,1026,0.89,0,0 +2014,3,23,2,119,-7,7,1026,1.78,0,0 +2014,3,23,3,125,-7,6,1026,0.89,0,0 +2014,3,23,4,136,-7,7,1025,0.89,0,0 +2014,3,23,5,147,-4,5,1026,1.78,0,0 +2014,3,23,6,150,-5,5,1026,0.89,0,0 +2014,3,23,7,150,-4,5,1026,1.79,0,0 +2014,3,23,8,173,-5,9,1027,1.79,0,0 +2014,3,23,9,184,-4,13,1027,0.89,0,0 +2014,3,23,10,191,-3,15,1027,1.78,0,0 +2014,3,23,11,166,-5,17,1026,1.79,0,0 +2014,3,23,12,128,-8,18,1025,4.92,0,0 +2014,3,23,13,141,-8,19,1023,8.94,0,0 +2014,3,23,14,141,-7,19,1022,12.96,0,0 +2014,3,23,15,145,-6,19,1021,16.98,0,0 +2014,3,23,16,156,-5,19,1020,21,0,0 +2014,3,23,17,160,-4,18,1019,25.92,0,0 +2014,3,23,18,165,-3,17,1019,29.94,0,0 +2014,3,23,19,155,-2,15,1019,33.07,0,0 +2014,3,23,20,118,-2,14,1019,36.2,0,0 +2014,3,23,21,152,-1,14,1020,40.22,0,0 +2014,3,23,22,214,-1,12,1020,43.35,0,0 +2014,3,23,23,249,0,11,1019,46.48,0,0 +2014,3,24,0,247,-1,10,1019,0.89,0,0 +2014,3,24,1,248,-2,6,1018,0.89,0,0 +2014,3,24,2,253,-1,7,1018,0.89,0,0 +2014,3,24,3,261,-1,6,1017,1.79,0,0 +2014,3,24,4,257,0,6,1017,0.89,0,0 +2014,3,24,5,262,-1,4,1016,0.89,0,0 +2014,3,24,6,270,1,6,1016,0.89,0,0 +2014,3,24,7,262,0,5,1016,0.89,0,0 +2014,3,24,8,260,1,8,1016,0.89,0,0 +2014,3,24,9,271,1,11,1016,0.89,0,0 +2014,3,24,10,281,1,13,1016,1.78,0,0 +2014,3,24,11,280,1,15,1015,2.67,0,0 +2014,3,24,12,270,0,16,1014,4.46,0,0 +2014,3,24,13,277,0,19,1013,6.25,0,0 +2014,3,24,14,249,0,20,1012,9.38,0,0 +2014,3,24,15,242,0,21,1011,13.4,0,0 +2014,3,24,16,192,0,21,1010,4.02,0,0 +2014,3,24,17,167,0,21,1009,8.04,0,0 +2014,3,24,18,168,-1,20,1010,9.83,0,0 +2014,3,24,19,184,0,19,1010,12.96,0,0 +2014,3,24,20,199,0,18,1011,16.98,0,0 +2014,3,24,21,225,2,14,1011,17.87,0,0 +2014,3,24,22,252,2,12,1011,18.76,0,0 +2014,3,24,23,277,3,11,1011,0.45,0,0 +2014,3,25,0,290,2,10,1011,0.89,0,0 +2014,3,25,1,299,2,10,1012,1.79,0,0 +2014,3,25,2,326,1,8,1011,0.89,0,0 +2014,3,25,3,324,1,8,1011,0.89,0,0 +2014,3,25,4,320,0,7,1011,1.78,0,0 +2014,3,25,5,279,0,7,1011,3.57,0,0 +2014,3,25,6,250,0,6,1011,6.7,0,0 +2014,3,25,7,228,0,8,1012,8.49,0,0 +2014,3,25,8,232,0,12,1012,11.62,0,0 +2014,3,25,9,233,-1,15,1012,14.75,0,0 +2014,3,25,10,239,-1,17,1012,0.89,0,0 +2014,3,25,11,235,-1,19,1012,1.78,0,0 +2014,3,25,12,226,1,20,1011,1.79,0,0 +2014,3,25,13,223,1,21,1010,1.79,0,0 +2014,3,25,14,229,0,22,1009,3.58,0,0 +2014,3,25,15,206,1,23,1008,3.13,0,0 +2014,3,25,16,189,0,23,1007,7.15,0,0 +2014,3,25,17,188,1,22,1007,11.17,0,0 +2014,3,25,18,163,2,21,1007,14.3,0,0 +2014,3,25,19,165,3,18,1008,15.19,0,0 +2014,3,25,20,152,4,16,1009,16.08,0,0 +2014,3,25,21,147,5,14,1009,16.97,0,0 +2014,3,25,22,157,4,14,1009,0.89,0,0 +2014,3,25,23,181,4,12,1010,0.89,0,0 +2014,3,26,0,190,4,12,1010,1.78,0,0 +2014,3,26,1,201,4,11,1010,0.89,0,0 +2014,3,26,2,226,3,10,1010,1.78,0,0 +2014,3,26,3,248,4,11,1009,0.89,0,0 +2014,3,26,4,269,4,10,1010,1.78,0,0 +2014,3,26,5,275,4,9,1010,2.67,0,0 +2014,3,26,6,311,5,9,1011,3.56,0,0 +2014,3,26,7,300,5,9,1012,4.45,0,0 +2014,3,26,8,317,7,11,1012,5.34,0,0 +2014,3,26,9,330,7,13,1013,6.23,0,0 +2014,3,26,10,346,7,15,1013,7.12,0,0 +2014,3,26,11,350,8,18,1012,8.01,0,0 +2014,3,26,12,331,8,19,1011,1.79,0,0 +2014,3,26,13,327,7,21,1010,3.58,0,0 +2014,3,26,14,327,6,22,1009,1.79,0,0 +2014,3,26,15,286,6,23,1008,1.79,0,0 +2014,3,26,16,246,6,24,1007,5.81,0,0 +2014,3,26,17,269,6,23,1007,9.83,0,0 +2014,3,26,18,309,7,22,1008,13.85,0,0 +2014,3,26,19,336,8,20,1009,15.64,0,0 +2014,3,26,20,360,8,18,1009,16.53,0,0 +2014,3,26,21,387,8,15,1010,0.89,0,0 +2014,3,26,22,399,7,14,1011,1.34,0,0 +2014,3,26,23,414,7,13,1011,0.89,0,0 +2014,3,27,0,465,7,12,1011,1.78,0,0 +2014,3,27,1,436,7,12,1011,0.89,0,0 +2014,3,27,2,435,7,11,1011,0.89,0,0 +2014,3,27,3,426,7,11,1011,0.45,0,0 +2014,3,27,4,413,6,10,1011,0.89,0,0 +2014,3,27,5,358,6,10,1012,0.89,0,0 +2014,3,27,6,389,7,12,1012,1.78,0,0 +2014,3,27,7,349,8,12,1013,0.45,0,0 +2014,3,27,8,340,8,14,1014,1.34,0,0 +2014,3,27,9,336,9,16,1014,3.13,0,0 +2014,3,27,10,369,8,16,1014,7.15,0,0 +2014,3,27,11,403,8,15,1014,11.17,0,0 +2014,3,27,12,444,5,15,1014,15.19,0,0 +2014,3,27,13,306,1,15,1013,19.21,0,0 +2014,3,27,14,115,0,15,1011,23.23,0,0 +2014,3,27,15,90,0,15,1011,25.02,0,0 +2014,3,27,16,82,0,15,1010,0.89,0,0 +2014,3,27,17,85,0,15,1010,0.89,0,0 +2014,3,27,18,95,1,15,1009,3.13,0,0 +2014,3,27,19,96,3,14,1009,6.26,0,0 +2014,3,27,20,107,3,13,1009,8.05,0,0 +2014,3,27,21,111,3,13,1009,9.84,0,0 +2014,3,27,22,125,3,13,1009,0.89,0,0 +2014,3,27,23,123,2,13,1009,1.79,0,0 +2014,3,28,0,145,2,13,1008,0.89,0,0 +2014,3,28,1,153,4,12,1008,1.78,0,0 +2014,3,28,2,159,4,13,1008,1.79,0,0 +2014,3,28,3,154,4,12,1007,0.89,0,0 +2014,3,28,4,191,5,12,1007,0.89,0,0 +2014,3,28,5,195,5,13,1008,1.78,0,0 +2014,3,28,6,194,7,11,1008,3.57,0,1 +2014,3,28,7,193,8,12,1008,7.59,0,2 +2014,3,28,8,175,9,13,1008,3.13,0,3 +2014,3,28,9,194,8,15,1009,4.02,0,4 +2014,3,28,10,165,9,16,1008,8.94,0,0 +2014,3,28,11,138,8,17,1008,12.96,0,0 +2014,3,28,12,100,6,19,1008,4.02,0,0 +2014,3,28,13,83,5,22,1007,8.04,0,0 +2014,3,28,14,53,4,23,1006,1.79,0,0 +2014,3,28,15,52,4,22,1006,4.02,0,0 +2014,3,28,16,60,5,22,1006,8.04,0,0 +2014,3,28,17,100,6,21,1006,12.06,0,0 +2014,3,28,18,142,6,19,1006,15.19,0,0 +2014,3,28,19,140,7,17,1007,18.32,0,0 +2014,3,28,20,148,7,16,1008,21.45,0,0 +2014,3,28,21,183,7,15,1010,23.24,0,0 +2014,3,28,22,217,8,14,1011,0.89,0,0 +2014,3,28,23,224,8,13,1011,0.89,0,0 +2014,3,29,0,222,8,12,1011,1.78,0,0 +2014,3,29,1,219,8,12,1011,0.89,0,0 +2014,3,29,2,65,6,9,1011,4.92,0,0 +2014,3,29,3,22,6,9,1011,0.89,0,0 +2014,3,29,4,16,4,8,1012,1.79,0,0 +2014,3,29,5,16,4,9,1012,6.71,0,0 +2014,3,29,6,28,2,11,1013,7.6,0,0 +2014,3,29,7,32,3,12,1014,11.62,0,0 +2014,3,29,8,25,1,15,1015,14.75,0,0 +2014,3,29,9,25,0,18,1015,19.67,0,0 +2014,3,29,10,22,0,18,1015,4.92,0,0 +2014,3,29,11,18,-1,20,1015,3.13,0,0 +2014,3,29,12,23,-1,21,1014,4.92,0,0 +2014,3,29,13,23,-1,21,1013,1.79,0,0 +2014,3,29,14,25,-2,22,1012,4.92,0,0 +2014,3,29,15,18,-3,23,1011,6.71,0,0 +2014,3,29,16,23,-3,23,1011,1.79,0,0 +2014,3,29,17,25,-2,22,1011,0.89,0,0 +2014,3,29,18,26,-1,20,1011,1.79,0,0 +2014,3,29,19,27,0,18,1012,3.58,0,0 +2014,3,29,20,36,-2,19,1013,7.6,0,0 +2014,3,29,21,47,-1,19,1013,11.62,0,0 +2014,3,29,22,59,-1,16,1014,0.89,0,0 +2014,3,29,23,50,-1,15,1014,1.79,0,0 +2014,3,30,0,61,2,13,1014,0.89,0,0 +2014,3,30,1,58,3,11,1015,1.78,0,0 +2014,3,30,2,66,1,9,1015,1.79,0,0 +2014,3,30,3,63,1,10,1014,0.45,0,0 +2014,3,30,4,51,1,10,1014,1.79,0,0 +2014,3,30,5,39,0,8,1015,4.92,0,0 +2014,3,30,6,42,1,8,1015,6.71,0,0 +2014,3,30,7,44,1,10,1016,3.13,0,0 +2014,3,30,8,44,-1,15,1016,3.13,0,0 +2014,3,30,9,35,-4,18,1017,6.26,0,0 +2014,3,30,10,30,-4,20,1017,9.39,0,0 +2014,3,30,11,34,-7,21,1017,12.52,0,0 +2014,3,30,12,21,-9,24,1016,1.79,0,0 +2014,3,30,13,14,-10,25,1015,3.58,0,0 +2014,3,30,14,11,-11,26,1014,6.71,0,0 +2014,3,30,15,9,-9,26,1013,8.5,0,0 +2014,3,30,16,12,-9,26,1013,3.13,0,0 +2014,3,30,17,13,-10,25,1013,4.92,0,0 +2014,3,30,18,18,-11,24,1014,8.94,0,0 +2014,3,30,19,21,-10,23,1014,12.07,0,0 +2014,3,30,20,24,-9,21,1015,16.09,0,0 +2014,3,30,21,27,-10,22,1016,20.11,0,0 +2014,3,30,22,39,-8,19,1016,21.9,0,0 +2014,3,30,23,44,-5,19,1016,25.03,0,0 +2014,3,31,0,64,-3,18,1016,28.16,0,0 +2014,3,31,1,106,-1,17,1016,31.29,0,0 +2014,3,31,2,137,-1,17,1015,35.31,0,0 +2014,3,31,3,165,-1,16,1015,37.1,0,0 +2014,3,31,4,153,-1,15,1015,40.23,0,0 +2014,3,31,5,116,1,15,1015,43.36,0,0 +2014,3,31,6,127,2,14,1016,45.15,0,0 +2014,3,31,7,147,3,14,1016,46.94,0,0 +2014,3,31,8,180,4,15,1017,0.89,0,0 +2014,3,31,9,199,4,16,1017,1.78,0,0 +2014,3,31,10,209,5,17,1017,2.67,0,0 +2014,3,31,11,235,5,18,1017,1.79,0,0 +2014,3,31,12,254,5,18,1016,4.92,0,0 +2014,3,31,13,250,5,20,1015,6.71,0,0 +2014,3,31,14,235,3,22,1015,0.89,0,0 +2014,3,31,15,210,2,22,1014,1.78,0,0 +2014,3,31,16,127,1,22,1013,4.02,0,0 +2014,3,31,17,72,0,21,1013,11.17,0,0 +2014,3,31,18,27,0,21,1013,15.19,0,0 +2014,3,31,19,70,7,17,1014,4.92,0,1 +2014,3,31,20,113,7,16,1014,8.94,0,0 +2014,3,31,21,118,8,14,1015,10.73,0,0 +2014,3,31,22,143,7,14,1015,13.86,0,0 +2014,3,31,23,175,8,13,1015,16.99,0,0 +2014,4,1,0,159,7,12,1015,0.89,0,0 +2014,4,1,1,161,7,10,1015,1.78,0,0 +2014,4,1,2,174,6,9,1015,2.23,0,0 +2014,4,1,3,177,6,9,1015,3.12,0,0 +2014,4,1,4,174,5,8,1014,3.57,0,0 +2014,4,1,5,180,5,7,1014,4.02,0,0 +2014,4,1,6,196,3,6,1015,0.89,0,0 +2014,4,1,7,214,6,8,1015,1.78,0,0 +2014,4,1,8,220,9,11,1015,3.57,0,0 +2014,4,1,9,216,7,15,1015,1.79,0,0 +2014,4,1,10,199,6,17,1014,1.79,0,0 +2014,4,1,11,168,5,20,1014,2.68,0,0 +2014,4,1,12,121,3,21,1013,3.57,0,0 +2014,4,1,13,124,0,23,1011,3.13,0,0 +2014,4,1,14,94,-4,24,1010,3.13,0,0 +2014,4,1,15,63,-5,24,1009,4.92,0,0 +2014,4,1,16,52,-6,24,1009,10.73,0,0 +2014,4,1,17,64,-6,24,1008,16.54,0,0 +2014,4,1,18,48,-5,22,1008,20.56,0,0 +2014,4,1,19,69,-4,22,1009,24.58,0,0 +2014,4,1,20,65,-2,19,1009,28.6,0,0 +2014,4,1,21,83,0,18,1010,0.89,0,0 +2014,4,1,22,90,3,15,1009,1.78,0,0 +2014,4,1,23,80,3,15,1010,0.89,0,0 +2014,4,2,0,95,3,15,1010,0.89,0,0 +2014,4,2,1,99,4,14,1010,1.78,0,0 +2014,4,2,2,103,4,13,1010,0.89,0,0 +2014,4,2,3,119,4,12,1010,1.79,0,0 +2014,4,2,4,121,3,10,1010,0.89,0,0 +2014,4,2,5,113,4,11,1010,1.78,0,0 +2014,4,2,6,124,3,9,1011,1.79,0,0 +2014,4,2,7,138,5,10,1012,2.68,0,0 +2014,4,2,8,160,5,15,1013,4.47,0,0 +2014,4,2,9,163,4,17,1014,6.26,0,0 +2014,4,2,10,119,-1,20,1014,11.18,0,0 +2014,4,2,11,80,0,22,1015,16.1,0,0 +2014,4,2,12,84,-2,23,1015,21.02,0,0 +2014,4,2,13,76,-2,23,1015,25.94,0,0 +2014,4,2,14,98,0,23,1014,3.13,0,0 +2014,4,2,15,105,-3,23,1014,8.94,0,0 +2014,4,2,16,86,-1,21,1015,16.09,0,0 +2014,4,2,17,132,0,20,1016,21.9,0,0 +2014,4,2,18,116,-1,18,1017,25.92,0,0 +2014,4,2,19,105,-1,16,1018,29.94,0,0 +2014,4,2,20,97,-2,15,1020,31.73,0,0 +2014,4,2,21,87,0,14,1021,33.52,0,0 +2014,4,2,22,72,1,14,1022,36.65,0,0 +2014,4,2,23,70,-1,13,1022,40.67,0,0 +2014,4,3,0,64,-1,13,1023,44.69,0,0 +2014,4,3,1,55,-2,12,1023,47.82,0,0 +2014,4,3,2,58,-1,11,1024,0.89,0,0 +2014,4,3,3,52,-1,10,1024,3.13,0,0 +2014,4,3,4,52,-2,10,1025,0.89,0,0 +2014,4,3,5,53,-16,11,1025,4.02,0,0 +2014,4,3,6,12,-16,7,1026,1.79,0,0 +2014,4,3,7,15,-16,11,1026,5.81,0,0 +2014,4,3,8,9,-16,14,1025,12.96,0,0 +2014,4,3,9,6,-17,16,1025,21.9,0,0 +2014,4,3,10,9,-19,18,1025,33.08,0,0 +2014,4,3,11,14,-20,19,1024,42.91,0,0 +2014,4,3,12,14,-24,20,1023,50.96,0,0 +2014,4,3,13,11,-27,20,1023,58.11,0,0 +2014,4,3,14,12,-28,19,1022,5.81,0,0 +2014,4,3,15,14,-28,18,1022,12.96,0,0 +2014,4,3,16,9,-29,17,1021,20.11,0,0 +2014,4,3,17,7,-29,17,1021,24.13,0,0 +2014,4,3,18,7,-30,16,1021,28.15,0,0 +2014,4,3,19,19,-28,14,1021,1.79,0,0 +2014,4,3,20,18,-21,12,1022,3.58,0,0 +2014,4,3,21,22,-18,11,1022,5.37,0,0 +2014,4,3,22,20,-20,11,1022,0.89,0,0 +2014,4,3,23,33,-9,10,1022,7.15,0,0 +2014,4,4,0,40,-8,8,1022,10.28,0,0 +2014,4,4,1,35,-9,8,1021,14.3,0,0 +2014,4,4,2,28,-9,7,1020,17.43,0,0 +2014,4,4,3,29,-8,7,1019,19.22,0,0 +2014,4,4,4,32,-6,6,1018,21.01,0,0 +2014,4,4,5,36,-4,5,1016,22.8,0,0 +2014,4,4,6,37,-5,4,1015,1.79,0,0 +2014,4,4,7,42,-5,6,1015,3.58,0,0 +2014,4,4,8,59,-6,9,1014,0.89,0,0 +2014,4,4,9,60,-7,11,1014,2.68,0,0 +2014,4,4,10,53,-7,13,1013,1.79,0,0 +2014,4,4,11,55,-8,16,1012,1.79,0,0 +2014,4,4,12,41,-6,20,1011,3.58,0,0 +2014,4,4,13,31,-10,23,1009,3.13,0,0 +2014,4,4,14,29,-12,25,1009,7.15,0,0 +2014,4,4,15,24,-12,25,1008,11.17,0,0 +2014,4,4,16,13,-17,25,1009,7.15,0,0 +2014,4,4,17,11,-15,22,1010,7.15,0,0 +2014,4,4,18,16,-21,20,1012,14.3,0,0 +2014,4,4,19,14,-18,18,1014,21.45,0,0 +2014,4,4,20,20,-19,17,1017,26.37,0,0 +2014,4,4,21,14,-18,14,1018,28.16,0,0 +2014,4,4,22,31,-7,12,1020,4.02,0,0 +2014,4,4,23,31,-6,12,1021,8.04,0,0 +2014,4,5,0,34,-7,11,1022,1.79,0,0 +2014,4,5,1,33,-6,9,1022,4.02,0,0 +2014,4,5,2,34,-5,8,1023,5.81,0,0 +2014,4,5,3,28,-5,8,1023,0.89,0,0 +2014,4,5,4,32,-5,7,1023,1.78,0,0 +2014,4,5,5,37,-5,4,1024,2.67,0,0 +2014,4,5,6,36,-6,5,1024,3.56,0,0 +2014,4,5,7,49,-5,8,1025,4.45,0,0 +2014,4,5,8,16,-9,13,1025,1.79,0,0 +2014,4,5,9,8,-16,14,1025,6.71,0,0 +2014,4,5,10,6,-18,15,1025,11.63,0,0 +2014,4,5,11,11,-18,16,1024,16.55,0,0 +2014,4,5,12,8,-18,16,1023,21.47,0,0 +2014,4,5,13,4,-17,18,1022,1.79,0,0 +2014,4,5,14,6,-18,18,1021,3.13,0,0 +2014,4,5,15,14,-16,19,1020,3.13,0,0 +2014,4,5,16,10,-18,19,1019,3.13,0,0 +2014,4,5,17,10,-16,19,1018,7.15,0,0 +2014,4,5,18,25,-16,18,1018,10.28,0,0 +2014,4,5,19,30,-12,17,1019,15.2,0,0 +2014,4,5,20,37,-9,16,1020,20.12,0,0 +2014,4,5,21,48,-8,15,1020,24.14,0,0 +2014,4,5,22,48,-7,15,1020,27.27,0,0 +2014,4,5,23,49,-5,11,1021,0.45,0,0 +2014,4,6,0,57,-4,9,1021,0.9,0,0 +2014,4,6,1,58,-2,8,1021,0.89,0,0 +2014,4,6,2,64,-5,5,1020,1.79,0,0 +2014,4,6,3,65,-2,5,1020,0.89,0,0 +2014,4,6,4,61,-3,3,1020,1.79,0,0 +2014,4,6,5,61,-2,4,1020,0.89,0,0 +2014,4,6,6,71,-3,3,1021,0.89,0,0 +2014,4,6,7,102,-1,7,1021,0.89,0,0 +2014,4,6,8,91,-7,11,1021,0.89,0,0 +2014,4,6,9,84,-10,15,1021,1.79,0,0 +2014,4,6,10,51,-13,18,1020,0.89,0,0 +2014,4,6,11,27,-14,21,1019,4.02,0,0 +2014,4,6,12,39,-18,23,1018,4.02,0,0 +2014,4,6,13,33,-16,24,1017,9.83,0,0 +2014,4,6,14,32,-15,25,1015,15.64,0,0 +2014,4,6,15,41,-15,25,1014,21.45,0,0 +2014,4,6,16,40,-15,25,1013,27.26,0,0 +2014,4,6,17,45,-12,24,1013,34.41,0,0 +2014,4,6,18,45,-11,22,1013,40.22,0,0 +2014,4,6,19,59,-8,21,1013,44.24,0,0 +2014,4,6,20,59,-10,20,1014,49.16,0,0 +2014,4,6,21,82,-7,20,1014,54.97,0,0 +2014,4,6,22,58,-5,19,1014,60.78,0,0 +2014,4,6,23,65,-5,18,1014,64.8,0,0 +2014,4,7,0,54,-3,14,1014,0.89,0,0 +2014,4,7,1,55,-2,12,1014,0.89,0,0 +2014,4,7,2,63,-2,11,1014,2.68,0,0 +2014,4,7,3,70,-2,11,1013,0.89,0,0 +2014,4,7,4,74,-2,8,1014,0.89,0,0 +2014,4,7,5,80,0,8,1014,1.78,0,0 +2014,4,7,6,87,0,8,1014,2.67,0,0 +2014,4,7,7,93,1,10,1014,1.79,0,0 +2014,4,7,8,105,1,13,1014,0.89,0,0 +2014,4,7,9,92,1,16,1015,1.78,0,0 +2014,4,7,10,103,0,18,1015,2.67,0,0 +2014,4,7,11,97,-1,20,1014,3.56,0,0 +2014,4,7,12,110,-1,21,1013,1.79,0,0 +2014,4,7,13,92,-3,23,1013,0.89,0,0 +2014,4,7,14,93,-3,24,1012,4.02,0,0 +2014,4,7,15,97,-5,25,1011,3.13,0,0 +2014,4,7,16,95,-5,25,1011,7.15,0,0 +2014,4,7,17,88,-4,24,1011,11.17,0,0 +2014,4,7,18,98,-4,22,1011,14.3,0,0 +2014,4,7,19,105,-2,19,1011,16.09,0,0 +2014,4,7,20,111,1,17,1012,17.88,0,0 +2014,4,7,21,123,1,16,1013,0.89,0,0 +2014,4,7,22,147,2,14,1013,0.89,0,0 +2014,4,7,23,155,2,16,1014,3.13,0,0 +2014,4,8,0,166,3,14,1014,6.26,0,0 +2014,4,8,1,194,4,13,1014,8.05,0,0 +2014,4,8,2,191,4,12,1015,8.94,0,0 +2014,4,8,3,180,3,10,1015,0.89,0,0 +2014,4,8,4,187,4,10,1015,0.45,0,0 +2014,4,8,5,195,4,8,1015,0.89,0,0 +2014,4,8,6,174,4,8,1015,0.89,0,0 +2014,4,8,7,171,4,9,1016,0.89,0,0 +2014,4,8,8,181,4,13,1016,0.89,0,0 +2014,4,8,9,193,4,16,1016,1.78,0,0 +2014,4,8,10,196,3,18,1016,2.67,0,0 +2014,4,8,11,199,4,20,1015,1.79,0,0 +2014,4,8,12,206,4,22,1014,3.13,0,0 +2014,4,8,13,175,3,23,1013,3.13,0,0 +2014,4,8,14,158,0,25,1012,7.15,0,0 +2014,4,8,15,152,-2,25,1011,11.17,0,0 +2014,4,8,16,158,-1,25,1010,16.09,0,0 +2014,4,8,17,139,0,25,1010,21.01,0,0 +2014,4,8,18,138,-1,23,1010,25.03,0,0 +2014,4,8,19,151,2,21,1010,28.16,0,0 +2014,4,8,20,146,3,21,1011,29.95,0,0 +2014,4,8,21,139,3,20,1011,31.74,0,0 +2014,4,8,22,136,3,19,1011,34.87,0,0 +2014,4,8,23,138,4,18,1011,38,0,0 +2014,4,9,0,136,4,18,1010,42.02,0,0 +2014,4,9,1,136,5,17,1010,45.15,0,0 +2014,4,9,2,140,6,16,1010,48.28,0,0 +2014,4,9,3,145,7,16,1009,50.07,0,0 +2014,4,9,4,147,7,15,1009,51.86,0,0 +2014,4,9,5,151,7,14,1009,53.65,0,0 +2014,4,9,6,153,7,13,1010,0.89,0,0 +2014,4,9,7,196,8,14,1010,1.78,0,0 +2014,4,9,8,182,9,16,1010,2.67,0,0 +2014,4,9,9,162,8,19,1010,1.79,0,0 +2014,4,9,10,164,7,22,1009,3.13,0,0 +2014,4,9,11,133,2,26,1009,7.15,0,0 +2014,4,9,12,62,-7,28,1008,4.92,0,0 +2014,4,9,13,60,-9,30,1008,12.07,0,0 +2014,4,9,14,49,-6,30,1008,4.92,0,0 +2014,4,9,15,62,-7,29,1008,12.07,0,0 +2014,4,9,16,56,-8,28,1009,19.22,0,0 +2014,4,9,17,65,-8,27,1010,26.37,0,0 +2014,4,9,18,73,-8,26,1011,30.39,0,0 +2014,4,9,19,80,-1,24,1013,7.15,0,0 +2014,4,9,20,580,-3,22,1015,12.96,0,0 +2014,4,9,21,217,-6,21,1016,17.88,0,0 +2014,4,9,22,159,-4,19,1018,19.67,0,0 +2014,4,9,23,181,-5,19,1019,24.59,0,0 +2014,4,10,0,165,1,16,1020,27.72,0,0 +2014,4,10,1,175,1,15,1021,30.85,0,0 +2014,4,10,2,112,1,14,1021,32.64,0,0 +2014,4,10,3,101,1,14,1021,0.89,0,0 +2014,4,10,4,111,1,14,1022,1.79,0,0 +2014,4,10,5,102,2,13,1021,2.68,0,0 +2014,4,10,6,106,1,14,1022,6.7,0,0 +2014,4,10,7,95,0,14,1023,10.72,0,0 +2014,4,10,8,101,0,15,1023,12.51,0,0 +2014,4,10,9,88,-2,16,1024,15.64,0,0 +2014,4,10,10,125,-3,17,1024,18.77,0,0 +2014,4,10,11,93,-3,17,1024,20.56,0,0 +2014,4,10,12,71,-4,17,1023,22.35,0,0 +2014,4,10,13,74,-4,18,1023,25.48,0,0 +2014,4,10,14,68,-5,18,1022,3.13,0,0 +2014,4,10,15,NA,-7,18,1021,3.13,0,0 +2014,4,10,16,51,-9,18,1020,6.26,0,0 +2014,4,10,17,62,-8,17,1020,9.39,0,0 +2014,4,10,18,64,-7,17,1020,12.52,0,0 +2014,4,10,19,66,-6,15,1020,13.41,0,0 +2014,4,10,20,63,-1,14,1020,0.89,0,0 +2014,4,10,21,65,-2,15,1021,1.78,0,0 +2014,4,10,22,72,0,14,1021,2.67,0,0 +2014,4,10,23,67,-3,15,1021,1.79,0,1 +2014,4,11,0,51,-1,14,1021,3.58,0,2 +2014,4,11,1,67,-2,14,1020,5.37,0,3 +2014,4,11,2,64,-3,14,1020,8.5,0,0 +2014,4,11,3,71,-2,13,1021,10.29,0,0 +2014,4,11,4,76,-1,13,1020,12.08,0,0 +2014,4,11,5,69,-3,13,1020,15.21,0,0 +2014,4,11,6,69,-4,13,1020,18.34,0,0 +2014,4,11,7,76,-4,13,1020,0.89,0,0 +2014,4,11,8,48,-1,13,1021,1.79,0,0 +2014,4,11,9,46,-1,13,1021,3.58,0,0 +2014,4,11,10,47,-1,14,1021,0.89,0,0 +2014,4,11,11,41,-2,14,1021,1.78,0,0 +2014,4,11,12,40,-4,16,1020,1.79,0,0 +2014,4,11,13,43,-5,17,1019,1.79,0,0 +2014,4,11,14,36,-5,17,1018,1.79,0,0 +2014,4,11,15,41,-6,18,1017,3.13,0,0 +2014,4,11,16,39,-6,17,1017,4.92,0,0 +2014,4,11,17,52,-6,17,1016,8.05,0,0 +2014,4,11,18,59,-6,16,1016,9.84,0,0 +2014,4,11,19,60,-5,15,1016,11.63,0,0 +2014,4,11,20,68,-4,14,1017,13.42,0,0 +2014,4,11,21,83,-3,13,1017,15.21,0,0 +2014,4,11,22,103,-3,13,1018,17,0,0 +2014,4,11,23,98,-2,13,1018,18.79,0,0 +2014,4,12,0,78,-2,12,1017,20.58,0,0 +2014,4,12,1,82,-1,10,1017,0.89,0,0 +2014,4,12,2,88,0,8,1017,1.78,0,0 +2014,4,12,3,96,-1,8,1017,0.89,0,0 +2014,4,12,4,118,-1,7,1016,1.78,0,0 +2014,4,12,5,128,1,7,1016,0.45,0,0 +2014,4,12,6,143,0,6,1017,0.89,0,0 +2014,4,12,7,158,2,9,1017,0.89,0,0 +2014,4,12,8,143,-1,12,1017,2.68,0,0 +2014,4,12,9,190,-2,15,1017,1.79,0,0 +2014,4,12,10,151,-3,17,1018,0.89,0,0 +2014,4,12,11,187,-4,19,1017,2.68,0,0 +2014,4,12,12,114,-4,20,1016,3.13,0,0 +2014,4,12,13,88,-7,21,1015,7.15,0,0 +2014,4,12,14,78,-6,21,1015,11.17,0,0 +2014,4,12,15,75,-6,21,1014,14.3,0,0 +2014,4,12,16,89,-5,21,1013,18.32,0,0 +2014,4,12,17,91,-5,21,1014,22.34,0,0 +2014,4,12,18,109,-4,20,1014,0.89,0,0 +2014,4,12,19,119,-2,18,1014,3.13,0,0 +2014,4,12,20,119,-2,17,1015,6.26,0,0 +2014,4,12,21,110,-2,16,1016,9.39,0,0 +2014,4,12,22,120,-2,15,1016,12.52,0,0 +2014,4,12,23,155,0,13,1017,14.31,0,0 +2014,4,13,0,120,1,12,1017,16.1,0,0 +2014,4,13,1,128,1,12,1017,17.89,0,0 +2014,4,13,2,131,2,11,1017,0.89,0,0 +2014,4,13,3,142,2,10,1017,1.78,0,0 +2014,4,13,4,144,2,9,1017,1.79,0,0 +2014,4,13,5,150,1,8,1017,2.68,0,0 +2014,4,13,6,151,2,8,1017,0.89,0,0 +2014,4,13,7,149,4,9,1018,0.89,0,0 +2014,4,13,8,157,5,12,1019,1.79,0,0 +2014,4,13,9,173,5,15,1019,0.89,0,0 +2014,4,13,10,182,5,17,1019,1.78,0,0 +2014,4,13,11,188,4,19,1019,1.79,0,0 +2014,4,13,12,175,3,21,1018,5.81,0,0 +2014,4,13,13,167,2,22,1017,9.83,0,0 +2014,4,13,14,162,2,23,1016,13.85,0,0 +2014,4,13,15,157,2,24,1015,17.87,0,0 +2014,4,13,16,147,2,24,1015,21.89,0,0 +2014,4,13,17,157,3,23,1014,25.91,0,0 +2014,4,13,18,159,4,22,1015,29.04,0,0 +2014,4,13,19,134,4,20,1015,30.83,0,0 +2014,4,13,20,136,5,19,1016,33.96,0,0 +2014,4,13,21,158,5,18,1017,37.98,0,0 +2014,4,13,22,186,7,16,1017,39.77,0,0 +2014,4,13,23,231,7,16,1017,41.56,0,0 +2014,4,14,0,254,8,15,1017,43.35,0,0 +2014,4,14,1,249,7,14,1017,45.14,0,0 +2014,4,14,2,262,7,13,1017,0.89,0,0 +2014,4,14,3,282,7,13,1017,1.79,0,0 +2014,4,14,4,280,7,11,1017,0.89,0,0 +2014,4,14,5,268,7,11,1017,1.78,0,0 +2014,4,14,6,255,6,10,1018,0.89,0,0 +2014,4,14,7,247,7,12,1018,0.89,0,0 +2014,4,14,8,244,7,14,1018,1.79,0,0 +2014,4,14,9,270,8,16,1018,0.89,0,0 +2014,4,14,10,280,8,18,1018,1.78,0,0 +2014,4,14,11,274,9,20,1018,1.79,0,0 +2014,4,14,12,280,9,22,1017,3.58,0,0 +2014,4,14,13,266,8,24,1016,7.6,0,0 +2014,4,14,14,239,8,25,1015,3.13,0,0 +2014,4,14,15,225,8,25,1014,4.02,0,0 +2014,4,14,16,NA,9,26,1013,7.15,0,0 +2014,4,14,17,NA,9,25,1013,10.28,0,0 +2014,4,14,18,224,10,24,1013,14.3,0,0 +2014,4,14,19,246,10,24,1013,17.43,0,0 +2014,4,14,20,252,10,22,1014,19.22,0,0 +2014,4,14,21,274,11,20,1014,21.01,0,0 +2014,4,14,22,187,10,19,1015,24.14,0,0 +2014,4,14,23,80,7,20,1015,27.27,0,0 +2014,4,15,0,56,8,17,1016,0.89,0,0 +2014,4,15,1,55,8,16,1016,3.13,0,0 +2014,4,15,2,55,7,18,1016,4.92,0,0 +2014,4,15,3,57,7,16,1016,8.05,0,0 +2014,4,15,4,67,8,15,1017,9.84,0,0 +2014,4,15,5,69,8,17,1017,12.97,0,0 +2014,4,15,6,78,8,15,1018,14.76,0,0 +2014,4,15,7,83,9,15,1019,0.89,0,0 +2014,4,15,8,105,9,18,1020,1.79,0,0 +2014,4,15,9,112,9,20,1021,5.81,0,0 +2014,4,15,10,104,7,21,1021,9.83,0,0 +2014,4,15,11,103,9,23,1021,14.75,0,0 +2014,4,15,12,93,10,22,1020,21.9,0,0 +2014,4,15,13,106,1,21,1020,27.71,0,0 +2014,4,15,14,53,-2,20,1019,33.52,0,0 +2014,4,15,15,54,-4,20,1019,39.33,0,0 +2014,4,15,16,48,-3,21,1018,44.25,0,0 +2014,4,15,17,38,-2,20,1018,48.27,0,0 +2014,4,15,18,40,-3,18,1018,51.4,0,0 +2014,4,15,19,34,-5,18,1018,54.53,0,0 +2014,4,15,20,47,-6,17,1019,58.55,0,0 +2014,4,15,21,54,-5,16,1019,61.68,0,0 +2014,4,15,22,65,-5,16,1019,64.81,0,0 +2014,4,15,23,41,-2,15,1019,0.89,0,0 +2014,4,16,0,39,-1,14,1018,0.89,0,0 +2014,4,16,1,35,0,14,1018,0.89,0,0 +2014,4,16,2,51,0,13,1017,0.89,0,0 +2014,4,16,3,54,-2,14,1017,1.78,0,0 +2014,4,16,4,69,-1,13,1016,2.23,0,0 +2014,4,16,5,59,2,13,1016,0.89,0,0 +2014,4,16,6,51,1,12,1015,0.89,0,0 +2014,4,16,7,45,2,13,1016,1.78,0,0 +2014,4,16,8,44,2,15,1016,0.89,0,0 +2014,4,16,9,52,0,16,1016,1.78,0,0 +2014,4,16,10,52,-6,18,1016,0.89,0,0 +2014,4,16,11,47,-9,18,1015,1.79,0,0 +2014,4,16,12,51,-6,19,1014,0.89,0,0 +2014,4,16,13,85,-8,21,1014,1.78,0,0 +2014,4,16,14,80,-7,21,1013,3.57,0,0 +2014,4,16,15,62,-7,21,1012,4.46,0,0 +2014,4,16,16,57,-9,20,1011,1.79,0,0 +2014,4,16,17,56,-5,20,1011,3.58,0,0 +2014,4,16,18,67,-7,20,1011,5.37,0,0 +2014,4,16,19,96,-6,19,1010,6.26,0,0 +2014,4,16,20,91,-6,18,1011,7.15,0,0 +2014,4,16,21,84,-4,16,1011,8.94,0,0 +2014,4,16,22,101,-2,15,1011,0.89,0,0 +2014,4,16,23,134,-2,16,1011,1.78,0,0 +2014,4,17,0,127,-2,15,1011,0.89,0,0 +2014,4,17,1,123,1,15,1010,2.68,0,0 +2014,4,17,2,112,-1,15,1010,4.47,0,0 +2014,4,17,3,109,0,15,1010,6.26,0,0 +2014,4,17,4,145,0,14,1010,8.05,0,1 +2014,4,17,5,124,4,12,1011,0.89,0,2 +2014,4,17,6,107,6,11,1011,1.78,0,3 +2014,4,17,7,109,7,11,1012,1.79,0,4 +2014,4,17,8,110,7,11,1013,0.89,0,5 +2014,4,17,9,116,7,12,1013,3.13,0,6 +2014,4,17,10,105,8,12,1014,3.13,0,7 +2014,4,17,11,93,8,12,1014,6.26,0,0 +2014,4,17,12,106,8,13,1014,9.39,0,0 +2014,4,17,13,103,8,15,1013,3.13,0,0 +2014,4,17,14,102,7,17,1012,1.79,0,0 +2014,4,17,15,101,9,17,1012,1.79,0,0 +2014,4,17,16,101,9,17,1012,3.13,0,0 +2014,4,17,17,117,9,17,1012,4.92,0,0 +2014,4,17,18,131,10,16,1013,6.71,0,0 +2014,4,17,19,128,10,16,1013,8.5,0,0 +2014,4,17,20,127,10,16,1014,10.29,0,0 +2014,4,17,21,141,10,16,1015,12.08,0,0 +2014,4,17,22,137,10,15,1016,15.21,0,0 +2014,4,17,23,151,10,15,1017,17,0,0 +2014,4,18,0,152,10,14,1017,18.79,0,0 +2014,4,18,1,148,11,14,1017,20.58,0,0 +2014,4,18,2,151,11,13,1017,21.47,0,0 +2014,4,18,3,156,11,13,1017,23.26,0,0 +2014,4,18,4,143,11,13,1017,0.89,0,0 +2014,4,18,5,141,11,13,1018,0.89,0,0 +2014,4,18,6,140,11,12,1019,1.78,0,0 +2014,4,18,7,150,12,13,1019,0.89,0,0 +2014,4,18,8,162,12,14,1020,1.78,0,0 +2014,4,18,9,171,11,17,1020,2.67,0,0 +2014,4,18,10,179,11,18,1020,3.56,0,0 +2014,4,18,11,193,11,20,1021,5.35,0,0 +2014,4,18,12,170,12,20,1020,3.13,0,0 +2014,4,18,13,167,11,20,1019,6.26,0,0 +2014,4,18,14,158,11,20,1019,9.39,0,0 +2014,4,18,15,158,11,20,1018,12.52,0,0 +2014,4,18,16,176,11,20,1018,16.54,0,0 +2014,4,18,17,162,11,19,1018,20.56,0,0 +2014,4,18,18,153,11,18,1018,25.48,0,0 +2014,4,18,19,129,5,16,1019,31.29,0,0 +2014,4,18,20,92,5,15,1021,34.42,0,0 +2014,4,18,21,83,4,14,1022,37.55,0,0 +2014,4,18,22,74,3,14,1022,41.57,0,0 +2014,4,18,23,86,2,14,1023,44.7,0,0 +2014,4,19,0,83,3,13,1023,46.49,0,0 +2014,4,19,1,84,5,12,1023,0.89,0,0 +2014,4,19,2,84,3,12,1022,1.78,0,0 +2014,4,19,3,77,4,12,1022,2.67,0,0 +2014,4,19,4,75,4,12,1021,3.56,0,0 +2014,4,19,5,64,6,12,1021,0.89,0,0 +2014,4,19,6,59,5,12,1022,1.78,0,0 +2014,4,19,7,59,4,13,1022,3.57,0,0 +2014,4,19,8,72,4,13,1022,5.36,0,0 +2014,4,19,9,69,4,14,1022,7.15,0,0 +2014,4,19,10,69,4,14,1022,8.94,0,1 +2014,4,19,11,56,-1,15,1022,13.86,0,2 +2014,4,19,12,44,-2,14,1022,16.99,0,3 +2014,4,19,13,48,1,14,1022,18.78,0,4 +2014,4,19,14,40,3,14,1021,23.7,0,0 +2014,4,19,15,46,4,13,1020,28.62,0,1 +2014,4,19,16,55,4,14,1019,34.43,0,2 +2014,4,19,17,57,3,14,1019,38.45,0,0 +2014,4,19,18,52,3,14,1018,42.47,0,0 +2014,4,19,19,55,4,12,1018,45.6,0,0 +2014,4,19,20,59,4,11,1018,48.73,0,0 +2014,4,19,21,67,5,11,1019,50.52,0,0 +2014,4,19,22,72,5,9,1019,0.89,0,0 +2014,4,19,23,78,5,9,1019,1.79,0,0 +2014,4,20,0,95,5,8,1019,0.89,0,0 +2014,4,20,1,105,6,8,1019,0.89,0,0 +2014,4,20,2,112,5,8,1018,1.78,0,0 +2014,4,20,3,128,5,7,1018,0.89,0,0 +2014,4,20,4,127,5,7,1018,1.78,0,0 +2014,4,20,5,128,3,5,1018,1.79,0,0 +2014,4,20,6,121,4,6,1018,4.92,0,0 +2014,4,20,7,84,5,9,1018,8.94,0,0 +2014,4,20,8,87,5,13,1018,12.07,0,0 +2014,4,20,9,64,4,15,1018,15.2,0,0 +2014,4,20,10,52,2,16,1018,16.99,0,0 +2014,4,20,11,30,0,17,1018,20.12,0,0 +2014,4,20,12,23,-1,19,1017,21.91,0,0 +2014,4,20,13,22,-2,20,1016,3.13,0,0 +2014,4,20,14,17,-1,22,1015,3.13,0,0 +2014,4,20,15,22,-2,22,1014,4.92,0,0 +2014,4,20,16,33,-2,22,1013,4.02,0,0 +2014,4,20,17,43,-1,22,1013,8.94,0,0 +2014,4,20,18,49,2,20,1013,13.86,0,0 +2014,4,20,19,56,2,19,1013,17.88,0,0 +2014,4,20,20,59,3,19,1014,21.9,0,0 +2014,4,20,21,69,4,19,1015,25.92,0,0 +2014,4,20,22,59,5,18,1015,29.94,0,0 +2014,4,20,23,72,5,14,1016,1.79,0,0 +2014,4,21,0,76,3,11,1016,1.79,0,0 +2014,4,21,1,90,5,10,1016,0.45,0,0 +2014,4,21,2,91,4,9,1016,1.79,0,0 +2014,4,21,3,90,3,11,1016,2.68,0,0 +2014,4,21,4,91,2,9,1016,4.47,0,0 +2014,4,21,5,66,2,10,1016,6.26,0,0 +2014,4,21,6,46,2,10,1017,8.05,0,0 +2014,4,21,7,49,3,12,1018,0.89,0,0 +2014,4,21,8,48,2,15,1018,1.78,0,0 +2014,4,21,9,45,0,18,1018,1.79,0,0 +2014,4,21,10,44,-2,21,1018,1.79,0,0 +2014,4,21,11,39,-5,22,1018,1.79,0,0 +2014,4,21,12,23,-5,24,1017,1.79,0,0 +2014,4,21,13,26,-3,25,1016,1.79,0,0 +2014,4,21,14,24,-2,25,1015,4.92,0,0 +2014,4,21,15,25,-2,25,1013,8.94,0,0 +2014,4,21,16,30,-2,25,1013,14.75,0,0 +2014,4,21,17,44,-2,24,1013,22.8,0,0 +2014,4,21,18,30,-2,23,1013,27.72,0,0 +2014,4,21,19,32,-1,20,1014,30.85,0,0 +2014,4,21,20,36,0,21,1014,34.87,0,0 +2014,4,21,21,43,1,18,1015,0.89,0,0 +2014,4,21,22,66,2,21,1015,3.13,0,0 +2014,4,21,23,68,4,14,1015,0.89,0,0 +2014,4,22,0,73,4,12,1016,1.79,0,0 +2014,4,22,1,90,4,12,1016,3.58,0,0 +2014,4,22,2,106,3,10,1016,1.79,0,0 +2014,4,22,3,102,4,9,1016,2.68,0,0 +2014,4,22,4,66,3,8,1016,3.57,0,0 +2014,4,22,5,65,3,8,1015,5.36,0,0 +2014,4,22,6,52,1,9,1016,7.15,0,0 +2014,4,22,7,68,3,12,1016,8.94,0,0 +2014,4,22,8,104,0,17,1016,12.07,0,0 +2014,4,22,9,84,1,21,1016,13.86,0,0 +2014,4,22,10,42,0,23,1015,15.65,0,0 +2014,4,22,11,36,0,24,1015,1.79,0,0 +2014,4,22,12,56,0,26,1014,3.58,0,0 +2014,4,22,13,51,0,27,1013,4.92,0,0 +2014,4,22,14,170,0,27,1011,9.84,0,0 +2014,4,22,15,87,0,27,1010,13.86,0,0 +2014,4,22,16,55,2,27,1009,19.67,0,0 +2014,4,22,17,107,3,26,1009,24.59,0,0 +2014,4,22,18,105,4,25,1009,30.4,0,0 +2014,4,22,19,66,4,24,1009,35.32,0,0 +2014,4,22,20,73,6,23,1010,40.24,0,0 +2014,4,22,21,76,6,21,1011,43.37,0,0 +2014,4,22,22,60,5,21,1011,46.5,0,0 +2014,4,22,23,64,7,19,1011,48.29,0,0 +2014,4,23,0,73,8,19,1011,52.31,0,0 +2014,4,23,1,81,9,18,1011,56.33,0,0 +2014,4,23,2,88,9,17,1011,60.35,0,0 +2014,4,23,3,98,10,17,1010,64.37,0,0 +2014,4,23,4,99,10,16,1011,66.16,0,0 +2014,4,23,5,99,10,15,1011,67.95,0,0 +2014,4,23,6,108,11,15,1011,71.08,0,0 +2014,4,23,7,129,12,16,1012,72.87,0,0 +2014,4,23,8,136,12,18,1012,76,0,0 +2014,4,23,9,151,12,19,1012,79.13,0,0 +2014,4,23,10,162,13,20,1012,82.26,0,0 +2014,4,23,11,164,13,22,1011,85.39,0,0 +2014,4,23,12,174,13,23,1010,89.41,0,0 +2014,4,23,13,185,13,23,1010,94.33,0,0 +2014,4,23,14,168,13,24,1009,98.35,0,0 +2014,4,23,15,154,12,25,1008,103.27,0,0 +2014,4,23,16,143,10,26,1007,110.42,0,0 +2014,4,23,17,139,11,24,1007,115.34,0,0 +2014,4,23,18,126,11,24,1007,119.36,0,0 +2014,4,23,19,98,13,23,1009,126.51,0,1 +2014,4,23,20,101,10,20,1009,3.13,0,2 +2014,4,23,21,71,10,20,1011,3.13,0,0 +2014,4,23,22,73,9,19,1011,6.26,0,0 +2014,4,23,23,80,9,18,1012,9.39,0,0 +2014,4,24,0,82,11,19,1012,12.52,0,0 +2014,4,24,1,76,11,18,1013,15.65,0,0 +2014,4,24,2,84,10,17,1013,18.78,0,0 +2014,4,24,3,89,11,16,1013,21.91,0,0 +2014,4,24,4,91,11,16,1013,22.8,0,0 +2014,4,24,5,96,11,16,1013,24.59,0,0 +2014,4,24,6,107,11,16,1013,27.72,0,0 +2014,4,24,7,109,11,16,1014,30.85,0,0 +2014,4,24,8,112,11,17,1015,33.98,0,0 +2014,4,24,9,119,11,18,1015,37.11,0,0 +2014,4,24,10,122,11,19,1016,38.9,0,0 +2014,4,24,11,125,11,20,1016,0.89,0,0 +2014,4,24,12,121,12,21,1015,2.68,0,0 +2014,4,24,13,128,13,22,1015,3.13,0,0 +2014,4,24,14,135,13,24,1014,6.26,0,0 +2014,4,24,15,136,13,25,1013,10.28,0,0 +2014,4,24,16,121,12,25,1012,14.3,0,0 +2014,4,24,17,117,12,25,1012,19.22,0,0 +2014,4,24,18,105,11,24,1013,24.14,0,0 +2014,4,24,19,100,12,23,1014,27.27,0,0 +2014,4,24,20,117,12,21,1015,29.06,0,0 +2014,4,24,21,118,13,19,1015,0.89,0,0 +2014,4,24,22,121,11,19,1016,3.13,0,0 +2014,4,24,23,140,10,19,1016,6.26,0,0 +2014,4,25,0,139,9,18,1016,8.05,0,0 +2014,4,25,1,136,9,16,1016,0.89,0,0 +2014,4,25,2,143,9,16,1016,1.79,0,0 +2014,4,25,3,155,9,15,1016,0.89,0,0 +2014,4,25,4,141,9,14,1016,0.89,0,0 +2014,4,25,5,110,9,13,1016,1.78,0,0 +2014,4,25,6,118,9,13,1017,1.79,0,0 +2014,4,25,7,124,10,16,1017,1.79,0,0 +2014,4,25,8,135,10,18,1017,4.92,0,0 +2014,4,25,9,156,10,20,1018,8.05,0,0 +2014,4,25,10,163,10,22,1018,0.89,0,0 +2014,4,25,11,167,11,23,1018,2.68,0,0 +2014,4,25,12,169,11,24,1017,3.57,0,0 +2014,4,25,13,177,10,25,1016,6.7,0,0 +2014,4,25,14,131,10,26,1016,3.13,0,0 +2014,4,25,15,102,8,27,1015,7.15,0,0 +2014,4,25,16,90,7,26,1014,11.17,0,0 +2014,4,25,17,96,7,25,1014,5.81,0,0 +2014,4,25,18,112,6,23,1015,10.73,0,0 +2014,4,25,19,101,8,20,1016,14.75,0,1 +2014,4,25,20,101,6,20,1016,21.9,0,0 +2014,4,25,21,104,7,19,1017,29.95,0,0 +2014,4,25,22,92,11,15,1019,41.13,0,1 +2014,4,25,23,55,11,13,1019,48.28,0,2 +2014,4,26,0,27,11,13,1019,53.2,0,3 +2014,4,26,1,30,11,12,1020,1.79,0,4 +2014,4,26,2,27,9,11,1019,3.13,0,5 +2014,4,26,3,21,10,12,1019,8.05,0,6 +2014,4,26,4,25,8,11,1019,12.97,0,7 +2014,4,26,5,23,8,11,1020,17.89,0,0 +2014,4,26,6,28,7,12,1020,4.02,0,0 +2014,4,26,7,29,8,12,1021,3.13,0,0 +2014,4,26,8,31,8,13,1021,6.26,0,0 +2014,4,26,9,38,8,16,1022,3.13,0,0 +2014,4,26,10,39,5,18,1021,6.26,0,0 +2014,4,26,11,61,4,20,1021,3.13,0,0 +2014,4,26,12,42,1,20,1021,6.26,0,0 +2014,4,26,13,47,1,22,1020,1.79,0,0 +2014,4,26,14,52,2,22,1020,2.68,0,0 +2014,4,26,15,47,3,22,1019,1.79,0,0 +2014,4,26,16,42,3,22,1019,3.58,0,0 +2014,4,26,17,48,9,20,1018,7.6,0,0 +2014,4,26,18,52,9,18,1019,11.62,0,0 +2014,4,26,19,48,8,17,1019,15.64,0,0 +2014,4,26,20,59,8,16,1020,18.77,0,0 +2014,4,26,21,48,9,15,1021,21.9,0,0 +2014,4,26,22,35,9,14,1021,23.69,0,0 +2014,4,26,23,41,10,13,1021,0.89,0,0 +2014,4,27,0,36,10,13,1021,0.89,0,0 +2014,4,27,1,41,10,12,1021,0.89,0,0 +2014,4,27,2,49,10,11,1021,0.89,0,0 +2014,4,27,3,59,10,11,1021,0.89,0,0 +2014,4,27,4,72,9,10,1020,1.79,0,0 +2014,4,27,5,80,9,10,1020,4.92,0,0 +2014,4,27,6,58,9,10,1021,8.94,0,0 +2014,4,27,7,46,10,12,1021,12.96,0,0 +2014,4,27,8,54,8,16,1021,17.88,0,0 +2014,4,27,9,43,4,20,1021,25.03,0,0 +2014,4,27,10,35,1,21,1021,7.15,0,0 +2014,4,27,11,39,1,23,1021,12.96,0,0 +2014,4,27,12,20,1,23,1021,17.88,0,0 +2014,4,27,13,78,0,23,1020,21.9,0,0 +2014,4,27,14,72,-2,24,1019,1.79,0,0 +2014,4,27,15,84,0,24,1018,1.79,0,0 +2014,4,27,16,55,-1,24,1018,1.79,0,0 +2014,4,27,17,77,4,23,1018,4.92,0,0 +2014,4,27,18,135,4,22,1018,8.05,0,0 +2014,4,27,19,76,4,21,1019,12.97,0,0 +2014,4,27,20,75,5,20,1020,16.99,0,0 +2014,4,27,21,98,7,19,1020,21.01,0,0 +2014,4,27,22,116,9,17,1021,24.14,0,0 +2014,4,27,23,106,9,16,1022,28.16,0,0 +2014,4,28,0,48,7,15,1022,31.29,0,0 +2014,4,28,1,37,7,14,1022,0.89,0,0 +2014,4,28,2,39,8,12,1022,0.89,0,0 +2014,4,28,3,38,8,12,1022,0.89,0,0 +2014,4,28,4,64,8,12,1021,1.34,0,0 +2014,4,28,5,73,8,11,1021,1.79,0,0 +2014,4,28,6,73,8,10,1022,3.13,0,0 +2014,4,28,7,80,9,13,1022,7.15,0,0 +2014,4,28,8,86,9,17,1022,11.17,0,0 +2014,4,28,9,106,8,20,1021,15.19,0,0 +2014,4,28,10,132,7,21,1021,3.13,0,0 +2014,4,28,11,101,5,23,1020,0.89,0,0 +2014,4,28,12,68,-2,24,1019,3.13,0,0 +2014,4,28,13,51,0,25,1018,3.13,0,0 +2014,4,28,14,47,1,25,1017,6.26,0,0 +2014,4,28,15,41,0,26,1016,3.13,0,0 +2014,4,28,16,42,0,26,1015,3.13,0,0 +2014,4,28,17,49,0,25,1015,3.13,0,0 +2014,4,28,18,58,2,24,1015,8.94,0,0 +2014,4,28,19,63,3,22,1015,13.86,0,0 +2014,4,28,20,67,3,20,1015,17.88,0,0 +2014,4,28,21,72,5,19,1016,0.89,0,0 +2014,4,28,22,105,7,16,1016,1.78,0,0 +2014,4,28,23,86,8,15,1016,0.89,0,0 +2014,4,29,0,81,8,14,1016,2.68,0,0 +2014,4,29,1,92,6,15,1016,4.47,0,0 +2014,4,29,2,93,6,14,1016,6.26,0,0 +2014,4,29,3,105,5,14,1016,8.05,0,0 +2014,4,29,4,94,5,12,1016,0.89,0,0 +2014,4,29,5,91,5,11,1016,0.45,0,0 +2014,4,29,6,83,7,11,1016,0.89,0,0 +2014,4,29,7,96,7,15,1016,0.89,0,0 +2014,4,29,8,113,4,17,1016,1.78,0,0 +2014,4,29,9,121,4,19,1016,2.67,0,0 +2014,4,29,10,136,5,21,1015,3.56,0,0 +2014,4,29,11,132,4,22,1015,1.79,0,0 +2014,4,29,12,133,5,23,1014,1.79,0,0 +2014,4,29,13,136,6,25,1012,1.79,0,0 +2014,4,29,14,111,5,26,1011,4.92,0,0 +2014,4,29,15,106,4,27,1010,8.05,0,0 +2014,4,29,16,104,4,27,1009,12.97,0,0 +2014,4,29,17,98,3,26,1009,16.99,0,0 +2014,4,29,18,99,5,25,1009,21.91,0,0 +2014,4,29,19,90,6,24,1009,25.93,0,0 +2014,4,29,20,95,7,23,1010,29.95,0,0 +2014,4,29,21,105,8,22,1010,33.08,0,0 +2014,4,29,22,99,8,21,1010,34.87,0,0 +2014,4,29,23,102,8,19,1011,36.66,0,0 +2014,4,30,0,103,9,18,1011,38.45,0,0 +2014,4,30,1,89,9,17,1010,40.24,0,0 +2014,4,30,2,89,10,16,1010,41.13,0,0 +2014,4,30,3,94,9,14,1010,0.89,0,0 +2014,4,30,4,97,9,13,1010,0.89,0,0 +2014,4,30,5,100,9,13,1010,1.78,0,0 +2014,4,30,6,111,9,12,1010,2.67,0,0 +2014,4,30,7,115,10,15,1010,3.56,0,0 +2014,4,30,8,146,10,18,1011,0.89,0,0 +2014,4,30,9,156,11,20,1011,1.79,0,0 +2014,4,30,10,221,11,22,1011,0.89,0,0 +2014,4,30,11,170,11,24,1010,3.13,0,0 +2014,4,30,12,135,10,26,1009,3.13,0,0 +2014,4,30,13,98,9,27,1008,4.02,0,0 +2014,4,30,14,86,9,28,1008,8.04,0,0 +2014,4,30,15,81,9,28,1007,12.96,0,0 +2014,4,30,16,79,9,28,1006,17.88,0,0 +2014,4,30,17,153,9,28,1006,21.9,0,0 +2014,4,30,18,119,8,26,1006,26.82,0,0 +2014,4,30,19,94,9,25,1006,29.95,0,0 +2014,4,30,20,91,10,22,1007,33.08,0,0 +2014,4,30,21,90,10,23,1008,37.1,0,0 +2014,4,30,22,90,10,22,1008,41.12,0,0 +2014,4,30,23,87,11,22,1008,45.14,0,0 +2014,5,1,0,94,11,20,1008,48.27,0,0 +2014,5,1,1,97,11,19,1008,51.4,0,0 +2014,5,1,2,107,12,18,1008,54.53,0,0 +2014,5,1,3,112,13,17,1008,56.32,0,0 +2014,5,1,4,152,12,16,1007,58.11,0,0 +2014,5,1,5,129,12,15,1008,59.9,0,0 +2014,5,1,6,135,13,16,1008,61.69,0,0 +2014,5,1,7,135,14,18,1009,63.48,0,0 +2014,5,1,8,150,14,20,1009,66.61,0,0 +2014,5,1,9,162,14,21,1009,68.4,0,0 +2014,5,1,10,155,15,23,1009,0.89,0,0 +2014,5,1,11,143,15,25,1008,4.92,0,0 +2014,5,1,12,144,15,26,1007,9.84,0,0 +2014,5,1,13,158,15,26,1006,13.86,0,0 +2014,5,1,14,202,14,27,1005,18.78,0,0 +2014,5,1,15,143,13,27,1005,23.7,0,0 +2014,5,1,16,147,12,28,1004,30.85,0,0 +2014,5,1,17,136,6,24,1006,8.94,0,0 +2014,5,1,18,130,4,22,1006,16.09,0,1 +2014,5,1,19,68,8,20,1007,21.9,0,0 +2014,5,1,20,113,4,19,1009,33.97,0,0 +2014,5,1,21,54,4,18,1010,43.8,0,0 +2014,5,1,22,51,4,15,1012,57.66,0,1 +2014,5,1,23,48,1,15,1012,67.49,0,0 +2014,5,2,0,47,1,15,1013,76.43,0,0 +2014,5,2,1,44,0,14,1012,87.61,0,0 +2014,5,2,2,34,0,14,1012,97.44,0,0 +2014,5,2,3,28,-1,13,1012,107.27,0,0 +2014,5,2,4,34,-1,13,1012,116.21,0,0 +2014,5,2,5,34,-2,13,1013,126.04,0,0 +2014,5,2,6,25,-2,13,1014,134.09,0,0 +2014,5,2,7,26,-3,14,1015,145.27,0,0 +2014,5,2,8,39,-5,15,1016,156.45,0,0 +2014,5,2,9,52,-6,17,1017,164.5,0,0 +2014,5,2,10,54,-7,19,1018,171.65,0,0 +2014,5,2,11,50,-8,20,1018,175.67,0,0 +2014,5,2,12,50,-9,21,1017,180.59,0,0 +2014,5,2,13,60,-6,23,1015,187.74,0,0 +2014,5,2,14,59,-6,24,1015,192.66,0,0 +2014,5,2,15,49,-6,22,1015,198.47,0,0 +2014,5,2,16,37,-1,22,1015,207.41,0,0 +2014,5,2,17,32,0,21,1016,216.35,0,0 +2014,5,2,18,24,1,18,1017,226.18,0,0 +2014,5,2,19,17,2,17,1017,231.99,0,0 +2014,5,2,20,21,2,15,1018,0.89,0,0 +2014,5,2,21,27,1,17,1018,1.79,0,0 +2014,5,2,22,27,1,17,1018,4.02,0,0 +2014,5,2,23,42,3,13,1018,0.89,0,0 +2014,5,3,0,42,5,11,1018,1.78,0,0 +2014,5,3,1,55,6,10,1017,0.89,0,0 +2014,5,3,2,48,4,9,1016,1.79,0,0 +2014,5,3,3,40,4,9,1016,2.68,0,0 +2014,5,3,4,42,5,9,1015,0.89,0,0 +2014,5,3,5,39,4,7,1015,0.89,0,0 +2014,5,3,6,64,5,8,1014,0.89,0,0 +2014,5,3,7,88,6,12,1014,1.78,0,0 +2014,5,3,8,104,6,14,1014,2.67,0,0 +2014,5,3,9,122,4,17,1012,4.46,0,0 +2014,5,3,10,167,3,20,1011,3.13,0,0 +2014,5,3,11,116,4,22,1010,8.05,0,0 +2014,5,3,12,121,1,24,1007,16.1,0,0 +2014,5,3,13,113,-1,23,1006,25.04,0,0 +2014,5,3,14,90,2,23,1008,8.05,0,0 +2014,5,3,15,59,0,23,1008,16.1,0,0 +2014,5,3,16,66,-5,23,1008,28.17,0,0 +2014,5,3,17,95,-8,23,1008,38,0,0 +2014,5,3,18,104,-10,20,1010,53.2,0,0 +2014,5,3,19,117,-10,19,1012,65.27,0,0 +2014,5,3,20,96,-7,17,1014,76.45,0,0 +2014,5,3,21,50,-7,16,1016,88.52,0,0 +2014,5,3,22,50,-8,14,1016,99.7,0,0 +2014,5,3,23,48,-5,13,1017,109.53,0,0 +2014,5,4,0,52,-5,12,1017,123.39,0,0 +2014,5,4,1,39,-6,11,1018,133.22,0,0 +2014,5,4,2,35,-6,11,1018,142.16,0,0 +2014,5,4,3,50,-7,10,1018,149.31,0,0 +2014,5,4,4,38,-7,10,1017,155.12,0,0 +2014,5,4,5,41,-6,10,1017,163.17,0,0 +2014,5,4,6,46,-6,10,1018,168.98,0,0 +2014,5,4,7,51,-7,11,1018,174.79,0,0 +2014,5,4,8,51,-9,13,1017,183.73,0,0 +2014,5,4,9,59,-12,15,1017,193.56,0,0 +2014,5,4,10,57,-14,17,1017,205.63,0,0 +2014,5,4,11,56,-13,18,1017,217.7,0,0 +2014,5,4,12,46,-14,19,1016,230.66,0,0 +2014,5,4,13,50,-12,18,1016,242.73,0,0 +2014,5,4,14,52,-12,17,1016,255.69,0,0 +2014,5,4,15,49,-12,18,1017,268.65,0,0 +2014,5,4,16,43,-13,18,1017,281.61,0,0 +2014,5,4,17,43,-11,19,1017,289.66,0,0 +2014,5,4,18,32,-12,16,1017,299.49,0,0 +2014,5,4,19,26,-12,16,1017,305.3,0,0 +2014,5,4,20,31,-10,14,1018,310.22,0,0 +2014,5,4,21,32,-8,11,1019,1.79,0,0 +2014,5,4,22,46,-8,14,1019,1.79,0,0 +2014,5,4,23,98,-9,14,1019,3.13,0,0 +2014,5,5,0,67,-2,8,1018,0.89,0,0 +2014,5,5,1,82,-5,8,1018,1.34,0,0 +2014,5,5,2,65,-2,7,1018,1.79,0,0 +2014,5,5,3,85,-3,6,1018,1.79,0,0 +2014,5,5,4,63,-5,7,1018,0.45,0,0 +2014,5,5,5,48,-4,6,1019,3.13,0,0 +2014,5,5,6,36,-2,8,1020,3.13,0,0 +2014,5,5,7,44,-4,11,1020,6.26,0,0 +2014,5,5,8,49,-7,14,1020,11.18,0,0 +2014,5,5,9,35,-9,16,1020,15.2,0,0 +2014,5,5,10,39,-10,16,1020,3.13,0,0 +2014,5,5,11,52,-11,16,1020,1.79,0,0 +2014,5,5,12,31,-10,18,1018,3.58,0,0 +2014,5,5,13,39,-9,19,1017,5.37,0,0 +2014,5,5,14,36,-9,21,1016,4.92,0,0 +2014,5,5,15,33,-9,21,1015,8.94,0,0 +2014,5,5,16,33,-8,19,1014,5.81,0,0 +2014,5,5,17,38,-8,20,1014,10.73,0,0 +2014,5,5,18,32,-7,19,1014,16.54,0,0 +2014,5,5,19,39,-6,18,1014,20.56,0,0 +2014,5,5,20,38,-4,15,1015,22.35,0,0 +2014,5,5,21,34,-4,14,1015,25.48,0,0 +2014,5,5,22,50,-3,14,1015,28.61,0,0 +2014,5,5,23,59,0,12,1015,0.89,0,0 +2014,5,6,0,59,-1,11,1015,1.79,0,0 +2014,5,6,1,56,0,11,1014,4.92,0,0 +2014,5,6,2,63,1,10,1014,6.71,0,0 +2014,5,6,3,72,0,10,1013,8.5,0,0 +2014,5,6,4,71,1,11,1013,0.89,0,0 +2014,5,6,5,75,1,10,1012,1.78,0,0 +2014,5,6,6,71,0,9,1012,2.67,0,0 +2014,5,6,7,61,0,13,1012,1.79,0,0 +2014,5,6,8,93,2,15,1011,4.92,0,0 +2014,5,6,9,97,3,16,1011,1.79,0,0 +2014,5,6,10,110,4,17,1010,4.92,0,0 +2014,5,6,11,95,3,19,1009,0.89,0,0 +2014,5,6,12,103,4,19,1008,1.79,0,0 +2014,5,6,13,123,5,21,1007,1.79,0,0 +2014,5,6,14,138,4,21,1006,3.13,0,0 +2014,5,6,15,111,5,21,1005,7.15,0,0 +2014,5,6,16,110,6,20,1005,10.28,0,0 +2014,5,6,17,108,6,20,1004,12.07,0,0 +2014,5,6,18,106,6,19,1003,0.89,0,0 +2014,5,6,19,108,7,18,1004,1.78,0,0 +2014,5,6,20,120,8,17,1004,1.79,0,0 +2014,5,6,21,154,8,17,1004,1.79,0,0 +2014,5,6,22,134,7,17,1004,0.89,0,0 +2014,5,6,23,133,8,16,1003,3.13,0,1 +2014,5,7,0,124,8,15,1004,0.89,0,0 +2014,5,7,1,126,8,13,1003,1.78,0,0 +2014,5,7,2,127,8,12,1003,2.23,0,0 +2014,5,7,3,132,9,12,1002,0.89,0,0 +2014,5,7,4,132,8,11,1003,1.78,0,0 +2014,5,7,5,135,8,11,1003,1.79,0,0 +2014,5,7,6,159,8,12,1004,0.89,0,0 +2014,5,7,7,151,8,14,1005,1.79,0,0 +2014,5,7,8,148,8,18,1006,4.92,0,0 +2014,5,7,9,140,4,21,1006,9.84,0,0 +2014,5,7,10,105,3,22,1006,13.86,0,0 +2014,5,7,11,85,-1,23,1007,21.01,0,0 +2014,5,7,12,84,-3,24,1007,25.03,0,0 +2014,5,7,13,114,-4,26,1006,32.18,0,0 +2014,5,7,14,112,-6,27,1006,43.36,0,0 +2014,5,7,15,111,-6,27,1005,52.3,0,0 +2014,5,7,16,36,-7,26,1005,61.24,0,0 +2014,5,7,17,12,-6,26,1006,68.39,0,0 +2014,5,7,18,12,-6,24,1007,72.41,0,0 +2014,5,7,19,15,-4,21,1008,3.13,0,0 +2014,5,7,20,23,-2,20,1009,7.15,0,0 +2014,5,7,21,24,-3,18,1011,1.79,0,0 +2014,5,7,22,16,-2,17,1012,3.58,0,0 +2014,5,7,23,63,8,14,1013,4.47,0,0 +2014,5,8,0,84,9,12,1014,0.89,0,0 +2014,5,8,1,93,9,14,1014,3.13,0,0 +2014,5,8,2,109,10,13,1015,4.92,0,0 +2014,5,8,3,128,9,12,1015,5.81,0,0 +2014,5,8,4,125,9,10,1015,6.7,0,0 +2014,5,8,5,125,9,10,1015,0.89,0,0 +2014,5,8,6,126,9,11,1016,1.78,0,0 +2014,5,8,7,136,11,13,1016,0.89,0,0 +2014,5,8,8,147,11,16,1016,1.78,0,0 +2014,5,8,9,95,10,18,1016,1.79,0,0 +2014,5,8,10,72,10,20,1016,0.89,0,0 +2014,5,8,11,64,9,20,1016,2.68,0,0 +2014,5,8,12,61,7,22,1014,4.02,0,0 +2014,5,8,13,58,5,22,1013,8.04,0,0 +2014,5,8,14,65,5,23,1013,12.06,0,0 +2014,5,8,15,62,5,23,1012,17.87,0,0 +2014,5,8,16,79,5,23,1012,23.68,0,0 +2014,5,8,17,74,5,22,1011,27.7,0,0 +2014,5,8,18,67,5,21,1011,32.62,0,0 +2014,5,8,19,80,5,20,1012,37.54,0,0 +2014,5,8,20,68,4,19,1012,41.56,0,0 +2014,5,8,21,63,5,18,1013,45.58,0,0 +2014,5,8,22,55,5,18,1014,48.71,0,0 +2014,5,8,23,70,6,18,1015,51.84,0,0 +2014,5,9,0,84,6,17,1014,54.97,0,0 +2014,5,9,1,65,6,17,1014,56.76,0,0 +2014,5,9,2,65,6,17,1015,0.89,0,0 +2014,5,9,3,70,7,16,1015,0.89,0,0 +2014,5,9,4,70,6,16,1015,0.89,0,0 +2014,5,9,5,75,5,16,1014,1.78,0,0 +2014,5,9,6,90,6,16,1014,2.67,0,0 +2014,5,9,7,91,5,17,1015,3.56,0,0 +2014,5,9,8,87,4,17,1015,4.92,0,0 +2014,5,9,9,80,6,17,1015,8.94,0,0 +2014,5,9,10,83,7,17,1016,12.96,0,0 +2014,5,9,11,79,7,17,1016,17.88,0,0 +2014,5,9,12,69,7,17,1016,22.8,0,0 +2014,5,9,13,70,7,18,1015,27.72,0,0 +2014,5,9,14,72,7,19,1015,31.74,0,0 +2014,5,9,15,74,7,19,1014,33.53,0,0 +2014,5,9,16,71,4,21,1013,36.66,0,0 +2014,5,9,17,48,3,21,1013,41.58,0,0 +2014,5,9,18,43,4,20,1013,46.5,0,0 +2014,5,9,19,45,4,19,1014,51.42,0,0 +2014,5,9,20,47,4,18,1015,57.23,0,0 +2014,5,9,21,56,5,18,1015,62.15,0,0 +2014,5,9,22,59,5,17,1015,66.17,0,0 +2014,5,9,23,59,5,17,1016,67.96,0,0 +2014,5,10,0,55,5,17,1016,71.09,0,0 +2014,5,10,1,50,5,16,1016,0.89,0,0 +2014,5,10,2,46,5,16,1016,1.79,0,0 +2014,5,10,3,40,5,16,1015,4.92,0,0 +2014,5,10,4,46,5,15,1016,8.05,0,0 +2014,5,10,5,47,5,14,1016,0.89,0,0 +2014,5,10,6,50,6,14,1016,1.78,0,0 +2014,5,10,7,46,7,15,1017,2.67,0,0 +2014,5,10,8,47,6,16,1017,1.79,0,0 +2014,5,10,9,55,7,17,1017,6.71,0,0 +2014,5,10,10,51,7,17,1017,9.84,0,0 +2014,5,10,11,63,7,17,1017,12.97,0,0 +2014,5,10,12,62,7,18,1017,4.02,0,0 +2014,5,10,13,64,8,19,1016,1.79,0,0 +2014,5,10,14,63,7,19,1016,3.58,0,0 +2014,5,10,15,63,7,20,1015,1.79,0,0 +2014,5,10,16,59,7,20,1015,3.58,0,0 +2014,5,10,17,70,6,20,1015,5.37,0,0 +2014,5,10,18,71,8,19,1014,1.79,0,0 +2014,5,10,19,69,5,19,1014,4.92,0,0 +2014,5,10,20,65,3,19,1015,6.71,0,0 +2014,5,10,21,54,1,19,1016,10.73,0,0 +2014,5,10,22,40,3,17,1016,14.75,0,1 +2014,5,10,23,42,6,14,1017,16.54,0,2 +2014,5,11,0,42,9,12,1016,1.79,0,3 +2014,5,11,1,48,9,11,1015,4.92,0,4 +2014,5,11,2,51,9,10,1014,4.92,0,5 +2014,5,11,3,57,9,10,1012,8.94,0,6 +2014,5,11,4,49,9,10,1011,5.81,0,7 +2014,5,11,5,46,9,11,1011,10.73,0,8 +2014,5,11,6,40,10,11,1010,15.65,0,9 +2014,5,11,7,39,10,11,1009,19.67,0,10 +2014,5,11,8,33,10,11,1009,23.69,0,11 +2014,5,11,9,26,10,11,1009,27.71,0,12 +2014,5,11,10,34,10,11,1008,32.63,0,13 +2014,5,11,11,22,10,12,1007,4.92,0,14 +2014,5,11,12,22,10,12,1006,9.84,0,15 +2014,5,11,13,21,10,12,1005,14.76,0,16 +2014,5,11,14,16,10,13,1004,19.68,0,17 +2014,5,11,15,17,10,13,1003,23.7,0,18 +2014,5,11,16,8,10,14,1002,26.83,0,19 +2014,5,11,17,17,9,14,1002,30.85,0,0 +2014,5,11,18,15,9,14,1001,33.98,0,1 +2014,5,11,19,20,9,14,1001,1.79,0,2 +2014,5,11,20,22,10,13,1001,4.92,0,3 +2014,5,11,21,30,10,13,1002,8.05,0,0 +2014,5,11,22,28,10,13,1002,11.18,0,0 +2014,5,11,23,35,10,13,1001,12.97,0,0 +2014,5,12,0,40,10,12,1001,0.89,0,0 +2014,5,12,1,46,10,11,1000,0.89,0,0 +2014,5,12,2,45,10,11,1000,2.68,0,0 +2014,5,12,3,47,10,11,1000,0.89,0,0 +2014,5,12,4,47,9,10,1000,1.34,0,0 +2014,5,12,5,57,8,9,1000,0.89,0,0 +2014,5,12,6,65,10,11,1001,2.68,0,0 +2014,5,12,7,72,12,13,1002,4.47,0,0 +2014,5,12,8,75,12,16,1002,0.89,0,0 +2014,5,12,9,82,11,19,1002,1.78,0,0 +2014,5,12,10,53,10,23,1002,1.79,0,0 +2014,5,12,11,21,5,26,1002,6.71,0,0 +2014,5,12,12,23,3,28,1001,12.52,0,0 +2014,5,12,13,23,6,29,1000,8.05,0,0 +2014,5,12,14,23,5,29,1000,15.2,0,0 +2014,5,12,15,23,6,30,999,23.25,0,0 +2014,5,12,16,30,7,29,999,31.3,0,0 +2014,5,12,17,29,9,29,999,37.11,0,0 +2014,5,12,18,40,9,28,998,44.26,0,0 +2014,5,12,19,52,10,27,999,50.07,0,0 +2014,5,12,20,64,11,26,999,55.88,0,0 +2014,5,12,21,65,12,25,1000,60.8,0,0 +2014,5,12,22,58,11,25,1000,66.61,0,0 +2014,5,12,23,54,9,24,1001,70.63,0,0 +2014,5,13,0,42,7,23,1000,73.76,0,0 +2014,5,13,1,43,9,19,1000,75.55,0,0 +2014,5,13,2,40,10,16,1000,0.89,0,0 +2014,5,13,3,50,9,13,1000,0.89,0,0 +2014,5,13,4,47,10,13,1000,0.45,0,0 +2014,5,13,5,57,11,12,1000,1.34,0,0 +2014,5,13,6,80,12,13,1001,0.89,0,0 +2014,5,13,7,108,14,16,1001,1.78,0,0 +2014,5,13,8,106,12,19,1001,1.79,0,0 +2014,5,13,9,98,12,21,1001,1.79,0,0 +2014,5,13,10,112,12,22,1001,3.58,0,0 +2014,5,13,11,104,12,25,1000,0.89,0,0 +2014,5,13,12,86,12,26,1000,1.79,0,0 +2014,5,13,13,82,10,29,999,3.13,0,0 +2014,5,13,14,52,8,30,999,8.05,0,0 +2014,5,13,15,42,1,30,998,16.99,0,0 +2014,5,13,16,45,-1,31,998,29.06,0,0 +2014,5,13,17,34,0,30,997,38,0,0 +2014,5,13,18,30,-2,29,997,46.94,0,0 +2014,5,13,19,30,-2,28,998,51.86,0,0 +2014,5,13,20,40,-3,27,1000,57.67,0,0 +2014,5,13,21,36,-4,24,1001,64.82,0,0 +2014,5,13,22,29,-5,22,1003,71.97,0,0 +2014,5,13,23,25,-7,22,1003,81.8,0,0 +2014,5,14,0,32,-5,20,1004,91.63,0,0 +2014,5,14,1,38,-5,19,1004,98.78,0,0 +2014,5,14,2,34,-4,18,1004,106.83,0,0 +2014,5,14,3,32,-3,18,1005,114.88,0,0 +2014,5,14,4,34,-2,18,1005,126.95,0,0 +2014,5,14,5,29,-1,18,1006,136.78,0,0 +2014,5,14,6,26,-1,18,1006,144.83,0,0 +2014,5,14,7,23,-2,19,1007,153.77,0,0 +2014,5,14,8,22,-2,19,1007,164.95,0,0 +2014,5,14,9,17,-2,20,1007,176.13,0,0 +2014,5,14,10,11,-1,21,1007,187.31,0,0 +2014,5,14,11,20,-1,22,1007,196.25,0,0 +2014,5,14,12,14,0,20,1007,203.4,0,0 +2014,5,14,13,16,-1,22,1006,210.55,0,0 +2014,5,14,14,14,-1,22,1006,217.7,0,0 +2014,5,14,15,14,-1,22,1005,223.51,0,0 +2014,5,14,16,18,-2,22,1004,4.02,0,0 +2014,5,14,17,12,-3,22,1003,4.91,0,0 +2014,5,14,18,19,-2,22,1003,1.79,0,0 +2014,5,14,19,19,2,18,1003,0.89,0,0 +2014,5,14,20,25,5,14,1004,1.78,0,0 +2014,5,14,21,30,-1,19,1004,4.02,0,0 +2014,5,14,22,25,-2,19,1004,8.04,0,0 +2014,5,14,23,27,2,13,1003,0.89,0,0 +2014,5,15,0,30,4,11,1003,1.78,0,0 +2014,5,15,1,76,5,11,1002,2.67,0,0 +2014,5,15,2,68,5,11,1001,1.79,0,0 +2014,5,15,3,77,6,10,1001,0.89,0,0 +2014,5,15,4,68,5,10,1001,1.78,0,0 +2014,5,15,5,59,4,8,1001,3.13,0,0 +2014,5,15,6,45,6,11,1002,6.26,0,0 +2014,5,15,7,37,5,15,1002,9.39,0,0 +2014,5,15,8,89,4,18,1002,11.18,0,0 +2014,5,15,9,52,3,20,1002,1.79,0,0 +2014,5,15,10,40,5,23,1001,3.58,0,0 +2014,5,15,11,45,0,25,1001,5.37,0,0 +2014,5,15,12,69,0,27,1000,3.13,0,0 +2014,5,15,13,80,1,28,999,7.15,0,0 +2014,5,15,14,96,2,29,999,11.17,0,0 +2014,5,15,15,107,0,29,998,16.98,0,0 +2014,5,15,16,111,1,28,997,22.79,0,0 +2014,5,15,17,121,3,28,998,25.92,0,0 +2014,5,15,18,109,5,27,997,29.05,0,0 +2014,5,15,19,94,3,27,998,33.07,0,0 +2014,5,15,20,95,4,26,999,38.88,0,0 +2014,5,15,21,105,6,24,999,42.01,0,0 +2014,5,15,22,94,7,22,1000,45.14,0,0 +2014,5,15,23,121,6,21,1001,4.02,0,0 +2014,5,16,0,203,4,21,1001,1.79,0,0 +2014,5,16,1,84,5,19,1001,0.89,0,0 +2014,5,16,2,96,6,18,1002,4.02,0,0 +2014,5,16,3,83,7,16,1002,0.89,0,0 +2014,5,16,4,85,8,14,1003,0.89,0,0 +2014,5,16,5,93,7,14,1004,1.79,0,0 +2014,5,16,6,89,9,16,1005,3.13,0,0 +2014,5,16,7,75,5,22,1006,6.26,0,0 +2014,5,16,8,57,3,23,1007,4.02,0,0 +2014,5,16,9,271,2,25,1008,11.17,0,0 +2014,5,16,10,50,4,26,1008,15.19,0,0 +2014,5,16,11,74,4,27,1008,19.21,0,0 +2014,5,16,12,56,3,27,1008,4.02,0,0 +2014,5,16,13,48,1,30,1008,4.02,0,0 +2014,5,16,14,60,2,29,1007,3.13,0,0 +2014,5,16,15,49,1,29,1007,4.92,0,0 +2014,5,16,16,45,2,29,1006,3.13,0,0 +2014,5,16,17,42,3,29,1007,1.79,0,0 +2014,5,16,18,38,1,28,1007,3.13,0,0 +2014,5,16,19,43,5,26,1008,7.15,0,0 +2014,5,16,20,47,6,25,1009,12.07,0,0 +2014,5,16,21,63,5,23,1011,19.22,0,0 +2014,5,16,22,71,4,22,1012,24.14,0,0 +2014,5,16,23,105,6,20,1013,25.93,0,0 +2014,5,17,0,74,6,18,1013,0.89,0,0 +2014,5,17,1,71,6,18,1013,1.78,0,0 +2014,5,17,2,80,5,18,1013,1.79,0,0 +2014,5,17,3,79,6,16,1013,0.45,0,0 +2014,5,17,4,76,7,15,1013,0.89,0,0 +2014,5,17,5,69,8,15,1013,1.78,0,0 +2014,5,17,6,68,8,15,1014,2.67,0,0 +2014,5,17,7,94,9,18,1014,1.79,0,0 +2014,5,17,8,96,7,21,1015,4.92,0,0 +2014,5,17,9,98,5,22,1015,0.89,0,0 +2014,5,17,10,98,6,23,1015,1.79,0,0 +2014,5,17,11,109,7,25,1014,1.79,0,0 +2014,5,17,12,95,5,26,1013,4.02,0,0 +2014,5,17,13,87,5,28,1012,9.83,0,0 +2014,5,17,14,90,5,28,1011,15.64,0,0 +2014,5,17,15,69,5,28,1010,21.45,0,0 +2014,5,17,16,54,5,28,1009,28.6,0,0 +2014,5,17,17,52,5,28,1009,35.75,0,0 +2014,5,17,18,48,5,27,1009,39.77,0,0 +2014,5,17,19,40,5,25,1010,43.79,0,0 +2014,5,17,20,41,5,24,1012,45.58,0,0 +2014,5,17,21,21,7,22,1012,47.37,0,1 +2014,5,17,22,28,7,21,1012,49.16,0,0 +2014,5,17,23,28,6,21,1011,52.29,0,0 +2014,5,18,0,34,6,20,1011,54.08,0,0 +2014,5,18,1,58,7,18,1011,55.87,0,0 +2014,5,18,2,64,7,17,1011,57.66,0,0 +2014,5,18,3,68,7,15,1010,0.89,0,0 +2014,5,18,4,70,8,14,1010,0.89,0,0 +2014,5,18,5,68,8,13,1010,0.89,0,0 +2014,5,18,6,77,8,16,1011,0.89,0,0 +2014,5,18,7,84,8,18,1011,0.89,0,0 +2014,5,18,8,89,9,21,1011,0.89,0,0 +2014,5,18,9,103,8,22,1011,1.79,0,0 +2014,5,18,10,99,8,25,1010,0.89,0,0 +2014,5,18,11,100,9,26,1010,1.78,0,0 +2014,5,18,12,132,9,27,1009,2.67,0,0 +2014,5,18,13,198,10,29,1008,4.02,0,0 +2014,5,18,14,119,10,30,1007,8.94,0,0 +2014,5,18,15,137,10,30,1006,14.75,0,0 +2014,5,18,16,77,10,30,1005,20.56,0,0 +2014,5,18,17,93,11,30,1004,27.71,0,0 +2014,5,18,18,124,12,29,1005,32.63,0,0 +2014,5,18,19,146,14,28,1005,37.55,0,0 +2014,5,18,20,143,13,28,1006,41.57,0,0 +2014,5,18,21,115,13,27,1006,44.7,0,0 +2014,5,18,22,107,13,26,1006,49.62,0,1 +2014,5,18,23,99,13,25,1006,51.41,0,0 +2014,5,19,0,100,13,24,1006,53.2,0,0 +2014,5,19,1,103,14,20,1007,4.92,0,0 +2014,5,19,2,75,14,20,1006,4.02,0,1 +2014,5,19,3,61,15,18,1008,8.94,0,3 +2014,5,19,4,83,16,17,1008,3.13,0,5 +2014,5,19,5,90,16,17,1007,0.89,0,0 +2014,5,19,6,70,16,17,1007,2.68,0,1 +2014,5,19,7,75,17,18,1006,4.47,0,0 +2014,5,19,8,83,16,19,1007,1.79,0,0 +2014,5,19,9,79,17,21,1006,0.89,0,0 +2014,5,19,10,95,16,22,1006,1.79,0,0 +2014,5,19,11,134,16,24,1005,4.92,0,0 +2014,5,19,12,161,16,24,1005,8.05,0,0 +2014,5,19,13,151,16,25,1004,11.18,0,0 +2014,5,19,14,147,16,26,1003,12.97,0,0 +2014,5,19,15,143,15,28,1002,16.99,0,0 +2014,5,19,16,132,15,28,1001,21.01,0,0 +2014,5,19,17,119,14,28,1001,25.03,0,0 +2014,5,19,18,102,14,28,1001,26.82,0,0 +2014,5,19,19,97,16,26,1002,28.61,0,0 +2014,5,19,20,104,16,23,1002,0.89,0,0 +2014,5,19,21,123,16,23,1003,1.79,0,0 +2014,5,19,22,141,16,22,1003,0.89,0,0 +2014,5,19,23,167,16,21,1003,1.78,0,0 +2014,5,20,0,174,16,21,1003,2.67,0,0 +2014,5,20,1,196,16,20,1003,3.56,0,0 +2014,5,20,2,215,16,20,1003,4.01,0,0 +2014,5,20,3,214,16,19,1003,4.9,0,0 +2014,5,20,4,197,15,18,1003,1.79,0,0 +2014,5,20,5,168,14,20,1004,4.92,0,0 +2014,5,20,6,122,12,20,1004,8.94,0,0 +2014,5,20,7,66,10,22,1005,13.86,0,0 +2014,5,20,8,36,8,23,1005,18.78,0,0 +2014,5,20,9,20,8,25,1006,23.7,0,0 +2014,5,20,10,17,8,26,1006,28.62,0,0 +2014,5,20,11,NA,8,28,1005,32.64,0,0 +2014,5,20,12,NA,9,29,1005,3.13,0,0 +2014,5,20,13,18,7,30,1004,0.89,0,0 +2014,5,20,14,14,6,31,1003,2.68,0,0 +2014,5,20,15,16,8,31,1003,1.79,0,0 +2014,5,20,16,18,8,32,1002,1.79,0,0 +2014,5,20,17,20,9,31,1002,4.92,0,0 +2014,5,20,18,22,7,31,1002,8.94,0,0 +2014,5,20,19,22,8,28,1002,12.07,0,0 +2014,5,20,20,45,15,26,1004,16.99,0,0 +2014,5,20,21,121,13,23,1005,21.91,0,0 +2014,5,20,22,122,12,22,1006,26.83,0,0 +2014,5,20,23,114,10,21,1007,28.62,0,0 +2014,5,21,0,106,11,20,1006,31.75,0,0 +2014,5,21,1,62,12,19,1007,0.89,0,0 +2014,5,21,2,84,12,19,1006,1.79,0,0 +2014,5,21,3,79,11,18,1005,0.89,0,0 +2014,5,21,4,80,12,17,1006,1.78,0,0 +2014,5,21,5,85,12,16,1006,0.89,0,0 +2014,5,21,6,81,13,17,1007,0.89,0,0 +2014,5,21,7,79,13,19,1007,1.78,0,0 +2014,5,21,8,80,12,22,1007,3.13,0,0 +2014,5,21,9,79,13,23,1008,6.26,0,0 +2014,5,21,10,85,13,24,1007,0.89,0,0 +2014,5,21,11,85,13,26,1006,2.68,0,0 +2014,5,21,12,81,13,27,1006,4.47,0,0 +2014,5,21,13,90,14,29,1005,6.26,0,0 +2014,5,21,14,97,13,30,1004,3.13,0,0 +2014,5,21,15,103,14,30,1003,6.26,0,0 +2014,5,21,16,115,15,31,1003,10.28,0,0 +2014,5,21,17,117,15,31,1003,16.09,0,0 +2014,5,21,18,130,15,30,1003,19.22,0,0 +2014,5,21,19,132,17,28,1003,21.01,0,0 +2014,5,21,20,136,16,27,1004,22.8,0,0 +2014,5,21,21,150,16,26,1005,24.59,0,0 +2014,5,21,22,128,16,25,1005,26.38,0,0 +2014,5,21,23,144,16,24,1005,28.17,0,0 +2014,5,22,0,146,16,23,1005,31.3,0,0 +2014,5,22,1,152,15,23,1005,33.09,0,0 +2014,5,22,2,165,15,22,1005,34.88,0,0 +2014,5,22,3,178,15,20,1005,0.89,0,0 +2014,5,22,4,196,16,20,1005,3.13,0,0 +2014,5,22,5,215,16,19,1005,0.89,0,0 +2014,5,22,6,242,17,20,1006,0.89,0,0 +2014,5,22,7,249,18,21,1007,0.89,0,0 +2014,5,22,8,258,18,23,1007,1.79,0,0 +2014,5,22,9,259,18,25,1008,0.89,0,0 +2014,5,22,10,239,18,27,1007,1.79,0,0 +2014,5,22,11,224,18,29,1007,4.92,0,0 +2014,5,22,12,182,17,31,1007,9.84,0,0 +2014,5,22,13,111,15,33,1006,13.86,0,0 +2014,5,22,14,59,8,35,1006,21.01,0,0 +2014,5,22,15,43,9,35,1005,28.16,0,0 +2014,5,22,16,50,9,34,1005,36.21,0,0 +2014,5,22,17,54,9,34,1005,43.36,0,0 +2014,5,22,18,38,8,33,1005,49.17,0,0 +2014,5,22,19,40,9,31,1006,53.19,0,0 +2014,5,22,20,56,10,28,1006,56.32,0,0 +2014,5,22,21,64,12,27,1007,58.11,0,0 +2014,5,22,22,65,12,27,1008,61.24,0,0 +2014,5,22,23,63,11,28,1008,64.37,0,0 +2014,5,23,0,73,14,23,1009,66.16,0,0 +2014,5,23,1,74,12,24,1009,69.29,0,0 +2014,5,23,2,84,12,24,1010,72.42,0,0 +2014,5,23,3,80,13,24,1010,75.55,0,0 +2014,5,23,4,96,14,22,1010,77.34,0,0 +2014,5,23,5,109,14,21,1010,1.79,0,0 +2014,5,23,6,110,15,22,1010,1.79,0,0 +2014,5,23,7,102,16,24,1011,0.89,0,0 +2014,5,23,8,105,15,26,1011,1.79,0,0 +2014,5,23,9,105,15,29,1011,3.58,0,0 +2014,5,23,10,94,13,31,1011,9.39,0,0 +2014,5,23,11,92,13,32,1011,16.54,0,0 +2014,5,23,12,85,12,34,1010,22.35,0,0 +2014,5,23,13,61,10,34,1010,29.5,0,0 +2014,5,23,14,54,11,33,1010,36.65,0,0 +2014,5,23,15,46,11,34,1009,43.8,0,0 +2014,5,23,16,46,10,33,1008,48.72,0,0 +2014,5,23,17,56,11,32,1008,53.64,0,0 +2014,5,23,18,57,12,31,1008,56.77,0,0 +2014,5,23,19,59,12,30,1009,59.9,0,0 +2014,5,23,20,61,15,27,1009,0.89,0,0 +2014,5,23,21,76,15,27,1009,3.13,0,0 +2014,5,23,22,87,14,28,1009,8.05,0,0 +2014,5,23,23,99,15,26,1009,11.18,0,0 +2014,5,24,0,104,15,26,1008,15.2,0,0 +2014,5,24,1,92,15,26,1008,18.33,0,0 +2014,5,24,2,89,15,24,1008,1.79,0,0 +2014,5,24,3,85,15,24,1008,1.79,0,0 +2014,5,24,4,99,15,23,1007,4.92,0,1 +2014,5,24,5,98,17,21,1008,8.05,0,3 +2014,5,24,6,84,17,19,1008,9.84,0,5 +2014,5,24,7,83,17,19,1008,12.97,0,6 +2014,5,24,8,102,18,19,1008,14.76,0,7 +2014,5,24,9,84,18,19,1008,17.89,0,0 +2014,5,24,10,91,18,20,1008,19.68,0,0 +2014,5,24,11,104,18,20,1008,22.81,0,1 +2014,5,24,12,95,18,20,1008,24.6,0,0 +2014,5,24,13,88,19,20,1008,27.73,0,0 +2014,5,24,14,78,19,20,1007,29.52,0,0 +2014,5,24,15,84,19,21,1007,1.79,0,0 +2014,5,24,16,99,19,21,1007,3.13,0,0 +2014,5,24,17,93,19,20,1007,6.26,0,0 +2014,5,24,18,79,18,20,1007,8.05,0,0 +2014,5,24,19,79,18,20,1008,11.18,0,0 +2014,5,24,20,79,18,20,1008,14.31,0,0 +2014,5,24,21,91,18,20,1009,16.1,0,0 +2014,5,24,22,88,18,19,1009,17.89,0,0 +2014,5,24,23,92,18,19,1009,19.68,0,0 +2014,5,25,0,95,18,19,1009,21.47,0,0 +2014,5,25,1,107,18,19,1008,0.89,0,0 +2014,5,25,2,123,18,19,1008,1.79,0,0 +2014,5,25,3,117,18,19,1008,3.58,0,0 +2014,5,25,4,140,18,19,1007,4.47,0,0 +2014,5,25,5,150,18,19,1007,0.89,0,0 +2014,5,25,6,150,18,19,1007,1.78,0,0 +2014,5,25,7,132,19,20,1008,1.79,0,0 +2014,5,25,8,160,19,21,1008,0.89,0,0 +2014,5,25,9,158,3,24,1007,8.05,0,0 +2014,5,25,10,157,3,26,1007,16.1,0,0 +2014,5,25,11,113,3,27,1007,27.28,0,0 +2014,5,25,12,51,2,28,1006,38.46,0,0 +2014,5,25,13,13,0,28,1005,53.66,0,0 +2014,5,25,14,12,-1,28,1005,66.62,0,0 +2014,5,25,15,7,-1,28,1005,80.48,0,0 +2014,5,25,16,10,-1,28,1005,92.55,0,0 +2014,5,25,17,8,-1,28,1005,103.73,0,0 +2014,5,25,18,8,0,27,1005,111.78,0,0 +2014,5,25,19,10,1,25,1004,114.91,0,0 +2014,5,25,20,13,3,21,1005,0.89,0,0 +2014,5,25,21,18,0,25,1005,3.13,0,0 +2014,5,25,22,21,7,17,1006,0.89,0,0 +2014,5,25,23,21,9,15,1006,1.78,0,0 +2014,5,26,0,15,8,15,1006,1.79,0,0 +2014,5,26,1,10,7,15,1005,0.89,0,0 +2014,5,26,2,8,7,14,1005,1.79,0,0 +2014,5,26,3,9,6,14,1004,0.89,0,0 +2014,5,26,4,8,7,12,1004,1.78,0,0 +2014,5,26,5,15,7,12,1004,2.67,0,0 +2014,5,26,6,20,9,15,1004,3.56,0,0 +2014,5,26,7,20,11,20,1003,4.45,0,0 +2014,5,26,8,18,4,26,1003,6.24,0,0 +2014,5,26,9,15,4,28,1002,4.02,0,0 +2014,5,26,10,15,1,30,1001,8.04,0,0 +2014,5,26,11,16,1,32,1000,13.85,0,0 +2014,5,26,12,19,3,33,999,18.77,0,0 +2014,5,26,13,23,3,34,998,23.69,0,0 +2014,5,26,14,24,5,34,997,31.74,0,0 +2014,5,26,15,26,5,33,995,39.79,0,0 +2014,5,26,16,27,5,34,994,48.73,0,0 +2014,5,26,17,27,5,33,993,55.88,0,0 +2014,5,26,18,26,0,35,993,7.15,0,0 +2014,5,26,19,23,1,34,993,4.92,0,0 +2014,5,26,20,28,-2,34,994,7.15,0,0 +2014,5,26,21,27,-2,33,995,4.02,0,0 +2014,5,26,22,29,-1,32,995,8.04,0,0 +2014,5,26,23,31,-1,31,995,3.13,0,0 +2014,5,27,0,26,8,21,995,0.89,0,0 +2014,5,27,1,26,0,26,994,3.13,0,0 +2014,5,27,2,23,8,19,994,0.89,0,0 +2014,5,27,3,39,9,18,994,1.78,0,0 +2014,5,27,4,39,-2,27,994,4.92,0,0 +2014,5,27,5,40,5,22,994,8.05,0,0 +2014,5,27,6,40,10,21,994,0.89,0,0 +2014,5,27,7,94,3,30,995,5.81,0,0 +2014,5,27,8,102,5,30,995,15.64,0,0 +2014,5,27,9,100,1,32,995,28.6,0,0 +2014,5,27,10,26,-2,32,995,43.8,0,0 +2014,5,27,11,19,2,31,995,57.66,0,0 +2014,5,27,12,13,3,32,995,67.49,0,0 +2014,5,27,13,17,5,31,995,78.67,0,0 +2014,5,27,14,12,7,28,997,94.76,0,0 +2014,5,27,15,16,10,26,997,102.81,0,0 +2014,5,27,16,12,10,30,996,109.96,0,0 +2014,5,27,17,13,9,30,995,114.88,0,0 +2014,5,27,18,12,8,30,995,118.9,0,0 +2014,5,27,19,14,1,31,997,130.97,0,0 +2014,5,27,20,11,1,28,998,136.78,0,0 +2014,5,27,21,8,5,23,1000,0.89,0,0 +2014,5,27,22,17,4,21,1001,1.34,0,0 +2014,5,27,23,13,5,20,1001,3.13,0,0 +2014,5,28,0,12,6,19,1001,0.89,0,0 +2014,5,28,1,13,3,22,1001,3.13,0,0 +2014,5,28,2,19,6,19,1001,3.13,0,0 +2014,5,28,3,27,6,20,1002,1.79,0,0 +2014,5,28,4,27,9,15,1003,0.89,0,0 +2014,5,28,5,38,9,15,1003,1.78,0,0 +2014,5,28,6,44,12,18,1003,0.89,0,0 +2014,5,28,7,44,11,23,1004,1.78,0,0 +2014,5,28,8,43,10,25,1004,2.67,0,0 +2014,5,28,9,49,9,27,1004,3.56,0,0 +2014,5,28,10,35,9,29,1003,1.79,0,0 +2014,5,28,11,18,7,33,1002,1.79,0,0 +2014,5,28,12,15,4,35,1001,4.02,0,0 +2014,5,28,13,21,5,36,1000,8.04,0,0 +2014,5,28,14,29,5,37,999,12.96,0,0 +2014,5,28,15,21,5,37,998,21.01,0,0 +2014,5,28,16,34,5,37,997,29.95,0,0 +2014,5,28,17,32,5,36,996,38.89,0,0 +2014,5,28,18,24,4,35,996,46.94,0,0 +2014,5,28,19,32,7,34,997,55.88,0,0 +2014,5,28,20,32,9,31,998,57.67,0,0 +2014,5,28,21,34,9,32,999,62.59,0,0 +2014,5,28,22,39,9,31,998,67.51,0,0 +2014,5,28,23,40,8,30,997,72.43,0,0 +2014,5,29,0,44,12,23,997,73.32,0,0 +2014,5,29,1,38,12,23,998,0.89,0,0 +2014,5,29,2,38,13,22,997,1.78,0,0 +2014,5,29,3,42,12,21,997,2.67,0,0 +2014,5,29,4,44,13,20,997,3.12,0,0 +2014,5,29,5,54,12,18,997,4.01,0,0 +2014,5,29,6,62,14,21,997,4.9,0,0 +2014,5,29,7,78,14,25,997,5.79,0,0 +2014,5,29,8,77,12,27,998,6.68,0,0 +2014,5,29,9,75,12,30,998,3.13,0,0 +2014,5,29,10,67,11,33,998,4.92,0,0 +2014,5,29,11,52,11,36,998,1.79,0,0 +2014,5,29,12,41,10,37,998,2.68,0,0 +2014,5,29,13,29,7,39,997,3.57,0,0 +2014,5,29,14,21,7,41,997,5.36,0,0 +2014,5,29,15,19,4,42,996,5.81,0,0 +2014,5,29,16,8,4,39,996,1.79,0,0 +2014,5,29,17,14,6,40,996,0.89,0,0 +2014,5,29,18,30,8,38,997,3.13,0,0 +2014,5,29,19,29,9,35,998,6.26,0,0 +2014,5,29,20,38,10,32,999,0.89,0,0 +2014,5,29,21,63,15,28,1000,2.68,0,0 +2014,5,29,22,63,10,29,1001,1.79,0,0 +2014,5,29,23,63,13,26,1002,3.13,0,0 +2014,5,30,0,79,13,25,1002,0.89,0,0 +2014,5,30,1,71,13,27,1003,1.79,0,0 +2014,5,30,2,96,14,25,1003,1.79,0,0 +2014,5,30,3,102,13,23,1003,1.79,0,0 +2014,5,30,4,113,11,22,1004,3.58,0,0 +2014,5,30,5,73,10,21,1005,6.71,0,0 +2014,5,30,6,67,12,23,1006,8.5,0,0 +2014,5,30,7,67,12,26,1006,10.29,0,0 +2014,5,30,8,88,12,30,1007,0.89,0,0 +2014,5,30,9,85,12,31,1007,1.79,0,0 +2014,5,30,10,88,12,32,1007,5.81,0,0 +2014,5,30,11,104,12,33,1007,9.83,0,0 +2014,5,30,12,100,14,33,1007,14.75,0,0 +2014,5,30,13,99,14,34,1006,19.67,0,0 +2014,5,30,14,100,14,35,1005,23.69,0,0 +2014,5,30,15,107,15,34,1005,27.71,0,0 +2014,5,30,16,104,15,33,1005,33.52,0,0 +2014,5,30,17,90,14,33,1005,37.54,0,0 +2014,5,30,18,100,13,31,1005,41.56,0,0 +2014,5,30,19,99,15,30,1006,44.69,0,0 +2014,5,30,20,95,14,28,1006,47.82,0,0 +2014,5,30,21,97,14,27,1007,50.95,0,0 +2014,5,30,22,90,12,27,1008,54.08,0,0 +2014,5,30,23,76,12,25,1008,55.87,0,0 +2014,5,31,0,72,13,24,1008,56.76,0,0 +2014,5,31,1,81,13,22,1008,57.65,0,0 +2014,5,31,2,84,14,22,1008,0.89,0,0 +2014,5,31,3,90,14,21,1008,1.78,0,0 +2014,5,31,4,96,14,21,1008,2.67,0,0 +2014,5,31,5,115,15,20,1008,0.89,0,0 +2014,5,31,6,115,16,21,1008,1.78,0,0 +2014,5,31,7,108,15,24,1008,0.89,0,0 +2014,5,31,8,125,14,25,1009,1.78,0,0 +2014,5,31,9,123,13,26,1009,2.67,0,0 +2014,5,31,10,127,13,28,1008,1.79,0,0 +2014,5,31,11,132,14,30,1008,1.79,0,0 +2014,5,31,12,125,14,32,1007,1.79,0,0 +2014,5,31,13,119,15,33,1007,1.79,0,0 +2014,5,31,14,127,16,33,1006,0.89,0,0 +2014,5,31,15,139,14,33,1005,3.13,0,0 +2014,5,31,16,134,16,34,1005,6.26,0,0 +2014,5,31,17,131,17,33,1005,9.39,0,0 +2014,5,31,18,135,17,32,1005,11.18,0,0 +2014,5,31,19,137,17,31,1005,12.97,0,0 +2014,5,31,20,143,18,30,1005,13.86,0,0 +2014,5,31,21,140,18,30,1005,16.99,0,0 +2014,5,31,22,86,18,22,1010,30.85,0,1 +2014,5,31,23,83,18,21,1009,8.94,0,2 +2014,6,1,0,83,18,22,1010,14.75,0,0 +2014,6,1,1,68,18,21,1009,0.89,0,0 +2014,6,1,2,53,18,21,1008,4.02,0,0 +2014,6,1,3,52,17,21,1009,7.15,0,0 +2014,6,1,4,46,18,21,1009,1.79,0,0 +2014,6,1,5,49,17,21,1008,4.92,0,0 +2014,6,1,6,59,17,22,1008,9.84,0,0 +2014,6,1,7,52,17,22,1008,14.76,0,0 +2014,6,1,8,43,16,23,1007,20.57,0,0 +2014,6,1,9,46,16,27,1007,25.49,0,0 +2014,6,1,10,40,16,29,1007,28.62,0,0 +2014,6,1,11,32,15,30,1007,31.75,0,0 +2014,6,1,12,29,16,31,1007,3.13,0,0 +2014,6,1,13,33,13,31,1007,4.92,0,0 +2014,6,1,14,37,14,31,1006,6.71,0,0 +2014,6,1,15,37,18,27,1006,4.92,0,0 +2014,6,1,16,62,18,27,1006,9.84,0,0 +2014,6,1,17,80,18,26,1007,13.86,0,0 +2014,6,1,18,62,17,21,1009,19.67,0,1 +2014,6,1,19,53,16,21,1009,21.46,0,2 +2014,6,1,20,59,17,20,1011,4.02,0,3 +2014,6,1,21,51,17,19,1011,7.15,0,0 +2014,6,1,22,60,16,20,1012,3.13,0,0 +2014,6,1,23,59,15,21,1012,1.79,0,0 +2014,6,2,0,55,13,21,1012,3.58,0,0 +2014,6,2,1,49,13,20,1012,0.89,0,0 +2014,6,2,2,52,13,19,1012,1.79,0,0 +2014,6,2,3,55,12,19,1012,3.58,0,0 +2014,6,2,4,24,12,20,1012,5.37,0,0 +2014,6,2,5,21,13,19,1012,0.45,0,0 +2014,6,2,6,18,14,19,1013,3.13,0,0 +2014,6,2,7,25,14,19,1013,1.79,0,0 +2014,6,2,8,25,14,21,1013,3.13,0,0 +2014,6,2,9,18,13,21,1013,6.26,0,0 +2014,6,2,10,18,15,21,1013,1.79,0,0 +2014,6,2,11,24,14,23,1012,0.89,0,0 +2014,6,2,12,20,13,26,1012,2.68,0,0 +2014,6,2,13,31,12,24,1012,1.79,0,0 +2014,6,2,14,31,11,25,1011,1.79,0,0 +2014,6,2,15,28,12,25,1010,4.92,0,0 +2014,6,2,16,31,11,26,1010,0.89,0,0 +2014,6,2,17,28,11,26,1009,1.79,0,0 +2014,6,2,18,34,12,26,1009,0.89,0,0 +2014,6,2,19,32,12,25,1009,3.13,0,0 +2014,6,2,20,44,12,24,1010,3.13,0,0 +2014,6,2,21,46,12,24,1010,6.26,0,0 +2014,6,2,22,43,12,24,1010,10.28,0,0 +2014,6,2,23,48,13,22,1010,13.41,0,0 +2014,6,3,0,57,13,22,1009,16.54,0,0 +2014,6,3,1,53,13,20,1009,19.67,0,0 +2014,6,3,2,64,13,19,1008,20.56,0,0 +2014,6,3,3,52,13,17,1008,22.35,0,0 +2014,6,3,4,37,13,17,1008,26.37,0,0 +2014,6,3,5,35,13,17,1008,0.89,0,0 +2014,6,3,6,39,14,20,1008,4.02,0,0 +2014,6,3,7,31,14,23,1009,7.15,0,0 +2014,6,3,8,29,13,24,1009,10.28,0,0 +2014,6,3,9,28,13,27,1009,13.41,0,0 +2014,6,3,10,32,12,28,1009,15.2,0,0 +2014,6,3,11,33,12,29,1008,0.89,0,0 +2014,6,3,12,25,12,30,1008,1.79,0,0 +2014,6,3,13,30,12,31,1007,4.92,0,0 +2014,6,3,14,28,12,31,1006,8.05,0,0 +2014,6,3,15,36,12,32,1006,12.07,0,0 +2014,6,3,16,43,12,32,1006,16.09,0,0 +2014,6,3,17,43,13,31,1006,20.11,0,0 +2014,6,3,18,54,12,30,1006,23.24,0,0 +2014,6,3,19,56,13,28,1006,26.37,0,0 +2014,6,3,20,45,13,28,1007,28.16,0,0 +2014,6,3,21,48,13,27,1007,29.95,0,0 +2014,6,3,22,57,14,26,1008,34.87,0,0 +2014,6,3,23,50,14,24,1008,38,0,0 +2014,6,4,0,47,14,23,1008,39.79,0,0 +2014,6,4,1,48,15,21,1008,1.79,0,0 +2014,6,4,2,62,15,21,1008,2.68,0,0 +2014,6,4,3,77,15,20,1008,3.57,0,0 +2014,6,4,4,83,16,20,1007,4.46,0,0 +2014,6,4,5,87,16,19,1007,0.89,0,0 +2014,6,4,6,88,17,20,1007,1.78,0,0 +2014,6,4,7,107,17,24,1008,2.67,0,0 +2014,6,4,8,88,16,25,1007,3.56,0,0 +2014,6,4,9,88,15,26,1007,4.45,0,0 +2014,6,4,10,79,14,27,1007,3.13,0,0 +2014,6,4,11,60,14,28,1007,6.26,0,0 +2014,6,4,12,48,14,29,1006,1.79,0,0 +2014,6,4,13,38,13,29,1005,3.13,0,0 +2014,6,4,14,42,13,31,1005,6.26,0,0 +2014,6,4,15,47,13,30,1004,9.39,0,0 +2014,6,4,16,53,13,30,1004,13.41,0,0 +2014,6,4,17,52,14,29,1004,17.43,0,0 +2014,6,4,18,51,14,29,1004,22.35,0,0 +2014,6,4,19,53,14,28,1004,26.37,0,0 +2014,6,4,20,51,15,27,1004,29.5,0,0 +2014,6,4,21,54,14,27,1005,31.29,0,0 +2014,6,4,22,54,14,27,1005,1.79,0,0 +2014,6,4,23,54,15,27,1005,3.13,0,0 +2014,6,5,0,NA,15,26,1005,1.79,0,0 +2014,6,5,1,53,16,24,1005,1.79,0,0 +2014,6,5,2,57,17,23,1005,4.92,0,0 +2014,6,5,3,80,16,22,1005,6.71,0,0 +2014,6,5,4,84,16,21,1005,8.5,0,0 +2014,6,5,5,90,16,20,1006,0.89,0,0 +2014,6,5,6,95,17,21,1006,0.89,0,0 +2014,6,5,7,108,17,22,1006,1.79,0,0 +2014,6,5,8,102,17,25,1006,0.89,0,0 +2014,6,5,9,90,17,27,1006,1.79,0,0 +2014,6,5,10,76,16,29,1006,0.89,0,0 +2014,6,5,11,54,16,30,1005,2.68,0,0 +2014,6,5,12,43,15,31,1005,3.13,0,0 +2014,6,5,13,43,14,33,1004,1.79,0,0 +2014,6,5,14,45,13,34,1003,3.13,0,0 +2014,6,5,15,43,13,34,1003,3.13,0,0 +2014,6,5,16,49,13,33,1002,4.92,0,0 +2014,6,5,17,56,13,34,1002,0.89,0,0 +2014,6,5,18,55,14,33,1002,4.02,0,0 +2014,6,5,19,59,14,31,1002,8.04,0,0 +2014,6,5,20,60,15,31,1002,11.17,0,0 +2014,6,5,21,68,14,30,1003,14.3,0,0 +2014,6,5,22,70,15,30,1003,18.32,0,0 +2014,6,5,23,81,16,26,1003,20.11,0,0 +2014,6,6,0,NA,16,27,1003,23.24,0,0 +2014,6,6,1,90,19,25,1004,27.26,0,0 +2014,6,6,2,108,19,24,1004,29.05,0,0 +2014,6,6,3,109,17,23,1004,32.18,0,0 +2014,6,6,4,106,17,22,1004,0.89,0,0 +2014,6,6,5,96,17,22,1005,1.79,0,0 +2014,6,6,6,82,17,22,1005,0.89,0,0 +2014,6,6,7,87,17,23,1004,1.79,0,0 +2014,6,6,8,101,18,25,1004,3.58,0,0 +2014,6,6,9,99,18,26,1004,6.71,0,0 +2014,6,6,10,104,18,26,1004,9.84,0,0 +2014,6,6,11,101,18,27,1004,0.89,0,0 +2014,6,6,12,88,16,22,1005,11.18,0,1 +2014,6,6,13,9,18,19,1005,16.99,0,2 +2014,6,6,14,9,17,19,1005,24.14,0,3 +2014,6,6,15,6,17,19,1005,29.06,0,4 +2014,6,6,16,6,16,18,1004,4.92,0,5 +2014,6,6,17,7,16,18,1005,5.81,0,6 +2014,6,6,18,7,16,18,1005,3.13,0,7 +2014,6,6,19,9,15,18,1005,7.15,0,0 +2014,6,6,20,10,14,18,1005,3.13,0,0 +2014,6,6,21,12,14,18,1005,3.13,0,0 +2014,6,6,22,10,14,17,1005,1.79,0,0 +2014,6,6,23,17,14,16,1005,4.92,0,0 +2014,6,7,0,NA,14,16,1005,0.89,0,0 +2014,6,7,1,24,14,15,1005,1.78,0,0 +2014,6,7,2,25,13,17,1005,1.79,0,0 +2014,6,7,3,19,12,15,1005,1.79,0,0 +2014,6,7,4,17,11,17,1005,3.13,0,0 +2014,6,7,5,14,11,15,1005,1.79,0,0 +2014,6,7,6,8,12,18,1005,4.92,0,0 +2014,6,7,7,6,12,20,1005,10.73,0,0 +2014,6,7,8,6,11,22,1006,15.65,0,0 +2014,6,7,9,5,10,24,1006,22.8,0,0 +2014,6,7,10,4,10,26,1005,28.61,0,0 +2014,6,7,11,3,9,26,1005,33.53,0,0 +2014,6,7,12,3,9,27,1005,38.45,0,0 +2014,6,7,13,5,9,28,1004,41.58,0,0 +2014,6,7,14,5,8,29,1003,46.5,0,0 +2014,6,7,15,5,9,29,1002,51.42,0,0 +2014,6,7,16,7,8,30,1001,54.55,0,0 +2014,6,7,17,6,10,29,1001,3.13,0,0 +2014,6,7,18,9,10,28,1002,4.02,0,0 +2014,6,7,19,8,11,26,1002,5.81,0,0 +2014,6,7,20,19,11,24,1002,7.6,0,0 +2014,6,7,21,26,14,21,1003,9.39,0,0 +2014,6,7,22,26,13,21,1003,11.18,0,0 +2014,6,7,23,38,13,19,1003,0.89,0,0 +2014,6,8,0,NA,15,19,1003,1.78,0,0 +2014,6,8,1,69,14,18,1003,1.79,0,0 +2014,6,8,2,45,13,18,1003,0.89,0,0 +2014,6,8,3,33,13,17,1003,1.78,0,0 +2014,6,8,4,24,13,17,1003,2.67,0,0 +2014,6,8,5,29,14,17,1003,1.79,0,0 +2014,6,8,6,25,13,18,1003,3.58,0,0 +2014,6,8,7,26,14,19,1003,6.71,0,0 +2014,6,8,8,25,14,21,1003,1.79,0,0 +2014,6,8,9,45,13,23,1003,0.89,0,0 +2014,6,8,10,58,14,25,1002,1.79,0,0 +2014,6,8,11,59,14,27,1002,1.79,0,0 +2014,6,8,12,48,13,28,1001,1.79,0,0 +2014,6,8,13,45,12,30,1000,4.92,0,0 +2014,6,8,14,48,11,30,999,1.79,0,0 +2014,6,8,15,43,14,19,1002,4.92,0,1 +2014,6,8,16,11,14,20,1002,3.13,0,0 +2014,6,8,17,11,14,21,1002,4.02,0,0 +2014,6,8,18,19,15,22,1001,8.94,0,0 +2014,6,8,19,25,15,22,1001,12.96,0,0 +2014,6,8,20,30,15,19,1002,1.79,0,0 +2014,6,8,21,44,14,19,1002,0.89,0,0 +2014,6,8,22,38,15,18,1002,1.78,0,0 +2014,6,8,23,33,14,17,1002,1.79,0,0 +2014,6,9,0,NA,14,17,1002,4.92,0,0 +2014,6,9,1,35,14,16,1003,0.89,0,0 +2014,6,9,2,NA,13,15,1002,3.13,0,0 +2014,6,9,3,NA,13,15,1003,0.89,0,0 +2014,6,9,4,NA,14,16,1004,1.79,0,0 +2014,6,9,5,NA,13,14,1004,0.89,0,0 +2014,6,9,6,NA,15,16,1005,3.13,0,0 +2014,6,9,7,NA,14,19,1005,4.92,0,0 +2014,6,9,8,NA,14,22,1005,6.71,0,0 +2014,6,9,9,NA,14,24,1005,0.89,0,0 +2014,6,9,10,NA,13,25,1005,1.78,0,0 +2014,6,9,11,41,13,26,1005,1.79,0,0 +2014,6,9,12,32,11,28,1005,1.79,0,0 +2014,6,9,13,32,10,29,1004,1.79,0,0 +2014,6,9,14,25,10,30,1003,3.58,0,0 +2014,6,9,15,17,7,30,1003,3.13,0,0 +2014,6,9,16,32,9,29,1003,6.26,0,1 +2014,6,9,17,20,12,27,1003,4.02,0,0 +2014,6,9,18,15,14,25,1004,8.04,0,0 +2014,6,9,19,33,13,22,1005,11.17,0,0 +2014,6,9,20,19,13,22,1006,14.3,0,0 +2014,6,9,21,22,13,22,1007,17.43,0,0 +2014,6,9,22,31,14,21,1008,1.79,0,0 +2014,6,9,23,49,13,21,1008,0.89,0,0 +2014,6,10,0,52,13,20,1008,1.79,0,0 +2014,6,10,1,59,14,18,1008,3.58,0,0 +2014,6,10,2,67,14,19,1008,0.89,0,0 +2014,6,10,3,62,14,19,1009,1.79,0,0 +2014,6,10,4,58,14,19,1008,3.58,0,0 +2014,6,10,5,50,14,18,1008,5.37,0,0 +2014,6,10,6,71,15,18,1009,1.79,0,0 +2014,6,10,7,76,16,19,1009,0.89,0,1 +2014,6,10,8,82,16,20,1009,1.78,0,0 +2014,6,10,9,83,16,22,1009,2.67,0,0 +2014,6,10,10,99,16,22,1009,4.46,0,0 +2014,6,10,11,78,16,25,1009,1.79,0,0 +2014,6,10,12,80,16,26,1008,5.81,0,0 +2014,6,10,13,77,16,25,1008,9.83,0,0 +2014,6,10,14,79,18,23,1008,11.62,0,1 +2014,6,10,15,79,18,23,1008,1.79,0,0 +2014,6,10,16,59,17,24,1008,3.13,0,0 +2014,6,10,17,75,16,23,1008,7.15,0,0 +2014,6,10,18,94,16,23,1008,10.28,0,0 +2014,6,10,19,88,16,22,1008,12.07,0,0 +2014,6,10,20,88,16,21,1009,0.89,0,0 +2014,6,10,21,85,16,20,1010,1.79,0,0 +2014,6,10,22,87,17,20,1010,0.89,0,0 +2014,6,10,23,94,17,19,1011,1.78,0,0 +2014,6,11,0,103,17,19,1010,0.89,0,0 +2014,6,11,1,101,17,19,1010,0.89,0,0 +2014,6,11,2,114,16,18,1010,2.68,0,0 +2014,6,11,3,123,16,18,1011,0.89,0,0 +2014,6,11,4,122,15,17,1011,2.68,0,0 +2014,6,11,5,96,14,17,1011,5.81,0,0 +2014,6,11,6,51,16,19,1011,8.94,0,0 +2014,6,11,7,23,15,21,1012,3.13,0,0 +2014,6,11,8,59,15,22,1012,8.05,0,0 +2014,6,11,9,43,14,24,1012,12.97,0,0 +2014,6,11,10,13,14,26,1012,3.13,0,0 +2014,6,11,11,13,13,27,1012,1.79,0,0 +2014,6,11,12,16,12,27,1011,4.92,0,0 +2014,6,11,13,13,12,29,1010,9.84,0,0 +2014,6,11,14,12,12,30,1009,3.13,0,0 +2014,6,11,15,18,12,30,1008,1.79,0,0 +2014,6,11,16,16,11,30,1007,4.92,0,0 +2014,6,11,17,15,12,30,1007,6.71,0,0 +2014,6,11,18,20,12,30,1007,7.6,0,0 +2014,6,11,19,17,12,29,1007,3.13,0,0 +2014,6,11,20,19,13,27,1007,4.92,0,0 +2014,6,11,21,40,14,26,1008,8.94,0,0 +2014,6,11,22,44,14,25,1008,12.07,0,0 +2014,6,11,23,33,14,24,1008,0.89,0,0 +2014,6,12,0,33,15,22,1008,0.89,0,0 +2014,6,12,1,37,15,20,1008,0.89,0,0 +2014,6,12,2,37,15,20,1008,1.79,0,0 +2014,6,12,3,50,15,19,1008,1.79,0,0 +2014,6,12,4,51,15,19,1008,3.58,0,0 +2014,6,12,5,39,15,20,1007,6.71,0,0 +2014,6,12,6,28,16,21,1007,9.84,0,0 +2014,6,12,7,35,15,24,1007,13.86,0,0 +2014,6,12,8,31,15,26,1008,3.13,0,0 +2014,6,12,9,31,14,29,1007,3.13,0,0 +2014,6,12,10,27,13,30,1007,0.89,0,0 +2014,6,12,11,23,13,33,1006,2.68,0,0 +2014,6,12,12,18,12,33,1006,1.79,0,0 +2014,6,12,13,16,12,35,1005,1.79,0,0 +2014,6,12,14,12,12,35,1004,1.79,0,0 +2014,6,12,15,14,12,36,1003,3.13,0,0 +2014,6,12,16,14,11,35,1003,4.92,0,0 +2014,6,12,17,10,12,34,1003,1.79,0,0 +2014,6,12,18,20,12,33,1003,4.92,0,0 +2014,6,12,19,30,15,31,1004,8.94,0,0 +2014,6,12,20,40,15,29,1005,10.73,0,0 +2014,6,12,21,44,17,28,1006,4.02,0,0 +2014,6,12,22,38,16,28,1006,3.13,0,0 +2014,6,12,23,50,17,26,1007,4.92,0,0 +2014,6,13,0,64,18,25,1007,0.89,0,0 +2014,6,13,1,99,17,24,1007,1.78,0,0 +2014,6,13,2,110,18,24,1007,2.67,0,0 +2014,6,13,3,105,18,22,1007,0.89,0,0 +2014,6,13,4,108,18,22,1007,1.78,0,0 +2014,6,13,5,121,18,21,1007,2.67,0,0 +2014,6,13,6,126,19,22,1008,0.89,0,0 +2014,6,13,7,103,19,24,1008,1.79,0,0 +2014,6,13,8,78,18,27,1009,0.89,0,0 +2014,6,13,9,93,17,28,1008,2.68,0,0 +2014,6,13,10,49,17,29,1008,3.13,0,0 +2014,6,13,11,29,16,31,1008,3.13,0,0 +2014,6,13,12,25,16,31,1007,3.13,0,0 +2014,6,13,13,34,16,31,1007,1.79,0,0 +2014,6,13,14,39,15,32,1006,1.79,0,0 +2014,6,13,15,41,16,32,1005,5.81,0,0 +2014,6,13,16,48,16,32,1005,10.73,0,0 +2014,6,13,17,49,17,19,1008,15.2,0,2 +2014,6,13,18,19,17,19,1007,4.92,0,3 +2014,6,13,19,14,18,20,1007,3.13,0,0 +2014,6,13,20,24,18,20,1008,1.79,0,0 +2014,6,13,21,22,17,21,1008,3.13,0,0 +2014,6,13,22,30,17,21,1008,6.26,0,0 +2014,6,13,23,30,17,20,1008,1.79,0,0 +2014,6,14,0,37,18,19,1008,2.68,0,0 +2014,6,14,1,35,17,19,1007,4.47,0,0 +2014,6,14,2,39,17,19,1007,0.89,0,0 +2014,6,14,3,38,17,18,1007,1.78,0,0 +2014,6,14,4,35,17,19,1008,0.89,0,0 +2014,6,14,5,29,17,18,1008,2.68,0,0 +2014,6,14,6,28,18,19,1008,3.57,0,0 +2014,6,14,7,18,18,21,1008,5.36,0,0 +2014,6,14,8,25,18,23,1008,8.49,0,0 +2014,6,14,9,34,18,25,1008,10.28,0,0 +2014,6,14,10,24,17,26,1008,12.07,0,0 +2014,6,14,11,23,17,28,1007,1.79,0,0 +2014,6,14,12,30,17,29,1007,2.68,0,0 +2014,6,14,13,35,16,30,1006,4.47,0,0 +2014,6,14,14,46,17,31,1005,6.26,0,0 +2014,6,14,15,47,17,32,1005,8.05,0,0 +2014,6,14,16,39,17,31,1004,3.13,0,0 +2014,6,14,17,50,16,32,1004,6.26,0,0 +2014,6,14,18,54,17,31,1004,9.39,0,0 +2014,6,14,19,59,17,30,1004,11.18,0,0 +2014,6,14,20,58,19,28,1005,12.97,0,0 +2014,6,14,21,70,20,28,1005,16.1,0,0 +2014,6,14,22,90,19,26,1005,17.89,0,0 +2014,6,14,23,106,19,26,1005,21.02,0,0 +2014,6,15,0,128,18,25,1006,22.81,0,0 +2014,6,15,1,118,18,23,1006,0.89,0,0 +2014,6,15,2,111,19,23,1006,0.89,0,0 +2014,6,15,3,131,19,23,1005,1.79,0,0 +2014,6,15,4,153,18,23,1005,0.89,0,0 +2014,6,15,5,169,19,22,1005,1.78,0,0 +2014,6,15,6,161,19,23,1005,0.89,0,0 +2014,6,15,7,141,19,25,1005,2.68,0,0 +2014,6,15,8,117,18,27,1006,0.89,0,0 +2014,6,15,9,117,17,28,1005,1.78,0,0 +2014,6,15,10,101,18,29,1005,1.79,0,0 +2014,6,15,11,94,18,30,1005,3.58,0,0 +2014,6,15,12,89,17,32,1004,6.71,0,0 +2014,6,15,13,82,17,32,1003,9.84,0,0 +2014,6,15,14,105,18,31,1003,12.97,0,0 +2014,6,15,15,130,18,32,1002,16.1,0,0 +2014,6,15,16,161,19,31,1002,17.89,0,0 +2014,6,15,17,156,19,31,1002,21.02,0,0 +2014,6,15,18,100,19,30,1002,4.92,0,0 +2014,6,15,19,62,19,29,1002,8.94,0,0 +2014,6,15,20,84,18,23,1005,11.18,0,2 +2014,6,15,21,61,18,21,1004,15.2,0,3 +2014,6,15,22,80,19,21,1004,0.89,0,0 +2014,6,15,23,113,19,23,1004,3.13,0,0 +2014,6,16,0,140,20,22,1004,0.89,0,0 +2014,6,16,1,134,20,21,1004,0.89,0,0 +2014,6,16,2,117,20,22,1004,1.78,0,0 +2014,6,16,3,112,20,21,1003,0.89,0,0 +2014,6,16,4,130,20,21,1003,2.68,0,0 +2014,6,16,5,148,19,21,1003,4.47,0,0 +2014,6,16,6,162,20,22,1003,7.6,0,0 +2014,6,16,7,169,19,22,1003,9.39,0,0 +2014,6,16,8,164,20,24,1003,0.89,0,0 +2014,6,16,9,169,20,25,1003,1.79,0,0 +2014,6,16,10,155,19,26,1004,0.89,0,0 +2014,6,16,11,178,20,26,1003,1.79,0,0 +2014,6,16,12,191,20,26,1003,4.92,0,1 +2014,6,16,13,196,20,27,1001,8.94,0,0 +2014,6,16,14,138,20,28,1001,3.13,0,0 +2014,6,16,15,132,21,29,1000,3.13,0,0 +2014,6,16,16,105,19,29,1000,7.15,0,0 +2014,6,16,17,111,19,30,1000,11.17,0,0 +2014,6,16,18,96,20,28,1000,14.3,0,0 +2014,6,16,19,105,20,28,1000,17.43,0,0 +2014,6,16,20,108,20,26,1001,0.89,0,0 +2014,6,16,21,111,20,26,1001,1.78,0,0 +2014,6,16,22,103,20,26,1001,2.67,0,0 +2014,6,16,23,114,20,26,1001,4.02,0,0 +2014,6,17,0,176,20,26,1001,7.15,0,0 +2014,6,17,1,221,20,26,1001,11.17,0,0 +2014,6,17,2,225,16,21,1003,18.32,0,0 +2014,6,17,3,84,16,19,1002,3.13,0,1 +2014,6,17,4,13,17,19,1003,1.79,0,2 +2014,6,17,5,20,17,19,1003,2.68,0,3 +2014,6,17,6,28,18,19,1004,1.79,0,0 +2014,6,17,7,29,18,20,1003,0.89,0,0 +2014,6,17,8,24,18,20,1003,3.13,0,0 +2014,6,17,9,32,18,21,1003,1.79,0,0 +2014,6,17,10,40,18,22,1003,0.89,0,0 +2014,6,17,11,47,19,23,1003,1.79,0,0 +2014,6,17,12,49,19,24,1002,3.58,0,0 +2014,6,17,13,77,19,25,1002,6.71,0,0 +2014,6,17,14,103,19,26,1002,8.5,0,0 +2014,6,17,15,119,20,27,1001,11.63,0,0 +2014,6,17,16,132,20,26,1001,13.42,0,1 +2014,6,17,17,148,20,24,1001,16.55,0,2 +2014,6,17,18,38,18,20,1001,19.68,0,3 +2014,6,17,19,60,19,20,1002,22.81,0,0 +2014,6,17,20,76,18,20,1002,0.89,0,0 +2014,6,17,21,56,18,19,1003,1.78,0,0 +2014,6,17,22,62,18,19,1003,2.23,0,0 +2014,6,17,23,63,18,19,1004,3.12,0,0 +2014,6,18,0,75,17,19,1004,1.79,0,0 +2014,6,18,1,67,17,19,1003,1.79,0,0 +2014,6,18,2,74,17,18,1003,0.89,0,0 +2014,6,18,3,71,17,18,1004,1.78,0,0 +2014,6,18,4,76,18,19,1003,2.23,0,0 +2014,6,18,5,79,17,18,1004,3.13,0,0 +2014,6,18,6,82,18,19,1004,0.89,0,0 +2014,6,18,7,53,17,20,1004,3.13,0,0 +2014,6,18,8,46,18,21,1004,7.15,0,0 +2014,6,18,9,46,18,25,1004,8.94,0,0 +2014,6,18,10,45,18,26,1004,1.79,0,0 +2014,6,18,11,24,16,28,1003,1.79,0,0 +2014,6,18,12,11,15,29,1003,0.89,0,0 +2014,6,18,13,22,14,29,1002,4.02,0,0 +2014,6,18,14,NA,16,30,1001,5.81,0,0 +2014,6,18,15,47,16,31,1001,3.13,0,0 +2014,6,18,16,NA,16,31,1000,7.15,0,0 +2014,6,18,17,NA,17,30,1000,11.17,0,0 +2014,6,18,18,83,18,29,1000,16.09,0,0 +2014,6,18,19,77,19,28,1001,21.01,0,0 +2014,6,18,20,80,18,26,1001,24.14,0,0 +2014,6,18,21,108,19,26,1003,27.27,0,0 +2014,6,18,22,121,19,25,1003,30.4,0,0 +2014,6,18,23,144,19,24,1003,0.89,0,0 +2014,6,19,0,143,20,22,1002,0.89,0,0 +2014,6,19,1,151,19,22,1003,0.89,0,0 +2014,6,19,2,153,19,21,1003,1.78,0,0 +2014,6,19,3,146,19,21,1003,1.79,0,0 +2014,6,19,4,141,19,20,1003,0.89,0,0 +2014,6,19,5,151,19,20,1003,0.89,0,0 +2014,6,19,6,158,19,21,1004,0.89,0,0 +2014,6,19,7,119,19,23,1005,1.78,0,0 +2014,6,19,8,137,19,24,1005,3.13,0,0 +2014,6,19,9,191,19,25,1005,7.15,0,0 +2014,6,19,10,200,19,26,1005,11.17,0,0 +2014,6,19,11,200,19,26,1005,15.19,0,0 +2014,6,19,12,181,18,26,1005,19.21,0,0 +2014,6,19,13,116,18,27,1005,23.23,0,0 +2014,6,19,14,106,19,27,1004,27.25,0,0 +2014,6,19,15,114,19,26,1005,32.17,0,0 +2014,6,19,16,109,19,26,1005,36.19,0,0 +2014,6,19,17,87,19,25,1004,39.32,0,0 +2014,6,19,18,65,18,25,1004,42.45,0,0 +2014,6,19,19,71,18,24,1005,45.58,0,0 +2014,6,19,20,79,18,24,1005,47.37,0,0 +2014,6,19,21,73,18,23,1006,49.16,0,0 +2014,6,19,22,72,18,23,1006,0.89,0,0 +2014,6,19,23,74,17,23,1006,1.78,0,1 +2014,6,20,0,78,18,22,1006,1.79,0,0 +2014,6,20,1,89,18,21,1007,4.92,0,1 +2014,6,20,2,86,17,20,1007,8.05,0,2 +2014,6,20,3,59,18,20,1007,0.89,0,3 +2014,6,20,4,39,18,19,1007,1.34,0,4 +2014,6,20,5,48,18,19,1007,1.79,0,5 +2014,6,20,6,41,18,19,1007,1.79,0,6 +2014,6,20,7,63,18,19,1008,3.13,0,7 +2014,6,20,8,59,18,20,1008,4.92,0,8 +2014,6,20,9,35,18,20,1008,8.94,0,0 +2014,6,20,10,22,18,22,1008,12.07,0,0 +2014,6,20,11,13,18,25,1007,15.2,0,0 +2014,6,20,12,19,17,27,1007,16.99,0,0 +2014,6,20,13,12,17,26,1007,3.13,0,0 +2014,6,20,14,10,16,27,1006,3.13,0,0 +2014,6,20,15,11,16,28,1006,3.13,0,0 +2014,6,20,16,17,16,28,1005,1.79,0,0 +2014,6,20,17,20,17,27,1005,1.79,0,0 +2014,6,20,18,20,17,27,1006,4.92,0,0 +2014,6,20,19,38,18,25,1007,9.84,0,0 +2014,6,20,20,61,18,23,1007,12.97,0,0 +2014,6,20,21,94,18,23,1008,14.76,0,0 +2014,6,20,22,89,18,22,1008,16.55,0,0 +2014,6,20,23,82,18,22,1009,18.34,0,0 +2014,6,21,0,74,18,21,1009,19.23,0,0 +2014,6,21,1,70,18,20,1009,20.12,0,0 +2014,6,21,2,70,18,20,1009,21.01,0,0 +2014,6,21,3,71,18,20,1009,21.9,0,0 +2014,6,21,4,76,18,20,1008,23.69,0,0 +2014,6,21,5,72,18,20,1009,0.89,0,0 +2014,6,21,6,60,18,20,1009,1.78,0,0 +2014,6,21,7,64,18,21,1009,0.89,0,0 +2014,6,21,8,73,18,21,1010,2.68,0,0 +2014,6,21,9,70,18,22,1010,0.89,0,0 +2014,6,21,10,74,17,24,1010,1.79,0,0 +2014,6,21,11,57,17,24,1009,3.58,0,0 +2014,6,21,12,62,17,26,1009,1.79,0,0 +2014,6,21,13,61,17,27,1008,1.79,0,0 +2014,6,21,14,54,17,27,1007,4.92,0,0 +2014,6,21,15,51,16,28,1007,8.05,0,0 +2014,6,21,16,53,15,28,1007,9.84,0,0 +2014,6,21,17,57,16,27,1006,12.97,0,0 +2014,6,21,18,66,15,27,1007,16.1,0,0 +2014,6,21,19,66,13,24,1008,8.94,0,0 +2014,6,21,20,15,16,20,1008,4.02,0,1 +2014,6,21,21,18,17,19,1008,1.79,0,2 +2014,6,21,22,18,17,19,1009,6.71,0,3 +2014,6,21,23,15,17,19,1009,8.5,0,4 +2014,6,22,0,16,17,19,1009,10.29,0,0 +2014,6,22,1,18,16,18,1009,12.08,0,0 +2014,6,22,2,23,17,18,1009,0.89,0,0 +2014,6,22,3,23,17,19,1009,1.78,0,0 +2014,6,22,4,23,17,18,1009,0.89,0,0 +2014,6,22,5,15,17,18,1009,2.68,0,0 +2014,6,22,6,18,17,19,1009,4.47,0,0 +2014,6,22,7,20,18,20,1009,6.26,0,0 +2014,6,22,8,38,18,23,1010,8.05,0,0 +2014,6,22,9,51,19,24,1010,0.89,0,0 +2014,6,22,10,49,18,25,1010,1.78,0,0 +2014,6,22,11,51,18,26,1009,3.57,0,0 +2014,6,22,12,40,17,27,1009,4.46,0,0 +2014,6,22,13,50,18,27,1008,1.79,0,0 +2014,6,22,14,50,17,28,1007,4.92,0,0 +2014,6,22,15,38,17,28,1007,3.13,0,0 +2014,6,22,16,36,15,29,1006,3.13,0,0 +2014,6,22,17,41,17,28,1007,4.92,0,0 +2014,6,22,18,34,14,25,1008,9.84,0,0 +2014,6,22,19,13,16,22,1008,4.02,0,0 +2014,6,22,20,29,17,21,1009,0.89,0,0 +2014,6,22,21,34,17,20,1010,1.78,0,0 +2014,6,22,22,39,17,20,1010,2.67,0,0 +2014,6,22,23,42,16,20,1009,3.56,0,0 +2014,6,23,0,36,16,20,1009,4.02,0,0 +2014,6,23,1,31,16,19,1009,7.15,0,0 +2014,6,23,2,25,16,18,1009,8.94,0,0 +2014,6,23,3,27,16,18,1009,12.07,0,0 +2014,6,23,4,23,16,18,1009,15.2,0,0 +2014,6,23,5,21,16,18,1009,19.22,0,0 +2014,6,23,6,25,16,20,1010,23.24,0,0 +2014,6,23,7,18,16,22,1010,26.37,0,0 +2014,6,23,8,18,16,24,1010,30.39,0,0 +2014,6,23,9,15,16,26,1009,35.31,0,0 +2014,6,23,10,19,15,28,1009,38.44,0,0 +2014,6,23,11,8,15,29,1009,1.79,0,0 +2014,6,23,12,16,15,30,1008,1.79,0,0 +2014,6,23,13,17,15,31,1008,1.79,0,0 +2014,6,23,14,18,14,30,1007,3.13,0,0 +2014,6,23,15,17,14,31,1006,6.26,0,0 +2014,6,23,16,16,13,31,1006,8.05,0,0 +2014,6,23,17,26,12,31,1005,1.79,0,0 +2014,6,23,18,24,14,30,1005,3.13,0,0 +2014,6,23,19,28,14,29,1005,6.26,0,0 +2014,6,23,20,27,14,26,1005,8.05,0,0 +2014,6,23,21,34,15,25,1006,8.94,0,0 +2014,6,23,22,28,16,25,1006,10.73,0,0 +2014,6,23,23,29,16,25,1006,12.52,0,0 +2014,6,24,0,43,16,22,1006,14.31,0,0 +2014,6,24,1,44,17,22,1006,15.2,0,0 +2014,6,24,2,50,17,20,1005,1.79,0,0 +2014,6,24,3,59,17,21,1005,2.68,0,0 +2014,6,24,4,66,18,20,1005,3.57,0,0 +2014,6,24,5,66,18,19,1005,0.45,0,0 +2014,6,24,6,66,17,20,1006,0.89,0,0 +2014,6,24,7,63,18,23,1006,0.89,0,0 +2014,6,24,8,64,18,25,1006,1.79,0,0 +2014,6,24,9,68,18,28,1006,0.89,0,0 +2014,6,24,10,64,18,29,1005,2.68,0,0 +2014,6,24,11,50,18,30,1005,3.57,0,0 +2014,6,24,12,47,16,30,1004,4.02,0,0 +2014,6,24,13,37,17,30,1004,8.04,0,0 +2014,6,24,14,40,17,31,1003,12.06,0,0 +2014,6,24,15,47,16,31,1003,16.08,0,0 +2014,6,24,16,54,17,31,1002,20.1,0,0 +2014,6,24,17,48,16,30,1002,24.12,0,0 +2014,6,24,18,47,16,30,1002,27.25,0,0 +2014,6,24,19,41,16,30,1002,29.04,0,0 +2014,6,24,20,50,16,28,1003,30.83,0,0 +2014,6,24,21,57,16,27,1003,32.62,0,0 +2014,6,24,22,64,16,27,1004,34.41,0,0 +2014,6,24,23,54,17,26,1003,37.54,0,0 +2014,6,25,0,52,17,26,1003,40.67,0,0 +2014,6,25,1,54,17,25,1003,42.46,0,0 +2014,6,25,2,62,17,24,1003,43.35,0,0 +2014,6,25,3,58,18,23,1002,45.14,0,0 +2014,6,25,4,69,18,23,1002,46.03,0,0 +2014,6,25,5,77,18,23,1002,0.89,0,0 +2014,6,25,6,89,18,23,1002,1.78,0,0 +2014,6,25,7,90,18,24,1003,1.79,0,0 +2014,6,25,8,86,18,25,1003,3.58,0,0 +2014,6,25,9,74,18,26,1003,5.37,0,0 +2014,6,25,10,74,18,26,1004,8.5,0,0 +2014,6,25,11,75,18,26,1004,11.63,0,0 +2014,6,25,12,85,19,27,1004,14.76,0,0 +2014,6,25,13,93,19,27,1004,17.89,0,0 +2014,6,25,14,96,19,27,1003,22.81,0,0 +2014,6,25,15,109,20,26,1003,27.73,0,0 +2014,6,25,16,98,19,26,1002,33.54,0,0 +2014,6,25,17,113,20,25,1003,36.67,0,0 +2014,6,25,18,128,20,24,1003,39.8,0,0 +2014,6,25,19,117,20,24,1003,42.93,0,0 +2014,6,25,20,116,20,24,1003,44.72,0,0 +2014,6,25,21,127,20,23,1004,46.51,0,0 +2014,6,25,22,133,20,24,1004,48.3,0,0 +2014,6,25,23,150,20,23,1004,53.22,0,0 +2014,6,26,0,159,21,23,1004,56.35,0,0 +2014,6,26,1,185,20,23,1004,58.14,0,0 +2014,6,26,2,178,21,23,1003,59.93,0,0 +2014,6,26,3,193,20,23,1003,61.72,0,0 +2014,6,26,4,191,21,23,1003,0.89,0,0 +2014,6,26,5,199,21,23,1003,0.89,0,0 +2014,6,26,6,196,21,23,1003,1.78,0,0 +2014,6,26,7,190,21,23,1003,0.89,0,0 +2014,6,26,8,168,21,24,1004,2.68,0,0 +2014,6,26,9,128,21,27,1003,3.57,0,0 +2014,6,26,10,100,21,28,1003,4.46,0,0 +2014,6,26,11,91,21,29,1003,5.35,0,0 +2014,6,26,12,87,20,31,1003,7.14,0,0 +2014,6,26,13,73,19,33,1002,8.93,0,0 +2014,6,26,14,62,19,33,1001,3.13,0,0 +2014,6,26,15,66,19,33,1001,1.79,0,0 +2014,6,26,16,106,20,33,1001,1.79,0,0 +2014,6,26,17,95,19,31,1001,4.02,0,0 +2014,6,26,18,93,20,28,1001,3.13,0,0 +2014,6,26,19,75,21,28,1002,4.92,0,0 +2014,6,26,20,62,21,27,1003,0.89,0,0 +2014,6,26,21,61,21,26,1004,0.89,0,0 +2014,6,26,22,68,21,26,1005,2.68,0,0 +2014,6,26,23,75,20,25,1005,4.47,0,0 +2014,6,27,0,62,19,23,1005,4.02,0,0 +2014,6,27,1,31,17,23,1005,8.04,0,0 +2014,6,27,2,39,16,23,1005,12.06,0,0 +2014,6,27,3,46,15,22,1005,15.19,0,0 +2014,6,27,4,17,15,22,1005,0.89,0,0 +2014,6,27,5,6,15,21,1007,1.79,0,0 +2014,6,27,6,4,17,23,1007,1.79,0,0 +2014,6,27,7,4,16,27,1008,3.58,0,0 +2014,6,27,8,15,15,29,1008,5.37,0,0 +2014,6,27,9,10,14,30,1008,10.29,0,0 +2014,6,27,10,16,13,31,1008,15.21,0,0 +2014,6,27,11,7,11,32,1007,20.13,0,0 +2014,6,27,12,11,11,32,1007,4.02,0,0 +2014,6,27,13,12,11,34,1006,4.92,0,0 +2014,6,27,14,7,9,34,1006,8.94,0,0 +2014,6,27,15,10,10,34,1005,12.07,0,0 +2014,6,27,16,11,10,34,1005,3.13,0,0 +2014,6,27,17,13,10,33,1005,6.26,0,0 +2014,6,27,18,13,10,32,1005,8.05,0,0 +2014,6,27,19,11,11,31,1005,11.18,0,0 +2014,6,27,20,18,10,30,1006,14.31,0,0 +2014,6,27,21,27,12,30,1006,17.44,0,0 +2014,6,27,22,32,13,29,1006,19.23,0,0 +2014,6,27,23,25,15,24,1006,1.79,0,0 +2014,6,28,0,28,14,24,1007,1.79,0,0 +2014,6,28,1,22,14,27,1006,4.92,0,0 +2014,6,28,2,35,13,23,1006,8.94,0,0 +2014,6,28,3,32,13,21,1006,12.07,0,0 +2014,6,28,4,28,13,18,1006,15.2,0,0 +2014,6,28,5,22,14,19,1007,18.33,0,0 +2014,6,28,6,17,14,22,1007,22.35,0,0 +2014,6,28,7,26,14,25,1007,26.37,0,0 +2014,6,28,8,24,13,28,1007,30.39,0,0 +2014,6,28,9,17,12,30,1006,33.52,0,0 +2014,6,28,10,16,13,33,1006,36.65,0,0 +2014,6,28,11,13,13,35,1006,40.67,0,0 +2014,6,28,12,14,11,36,1005,45.59,0,0 +2014,6,28,13,9,11,36,1005,49.61,0,0 +2014,6,28,14,15,11,37,1004,52.74,0,0 +2014,6,28,15,15,11,38,1004,3.13,0,0 +2014,6,28,16,15,12,36,1003,3.13,0,0 +2014,6,28,17,6,11,35,1003,3.13,0,0 +2014,6,28,18,15,14,34,1003,6.26,0,0 +2014,6,28,19,15,15,33,1003,8.05,0,0 +2014,6,28,20,26,13,32,1004,1.79,0,0 +2014,6,28,21,21,15,29,1005,2.68,0,0 +2014,6,28,22,25,15,28,1005,3.57,0,0 +2014,6,28,23,28,17,25,1005,0.89,0,0 +2014,6,29,0,30,16,25,1006,1.79,0,0 +2014,6,29,1,54,17,25,1006,0.89,0,0 +2014,6,29,2,43,17,23,1006,1.78,0,0 +2014,6,29,3,32,17,22,1006,2.67,0,0 +2014,6,29,4,37,17,21,1006,0.89,0,0 +2014,6,29,5,30,16,21,1006,0.89,0,0 +2014,6,29,6,26,17,24,1007,1.79,0,0 +2014,6,29,7,50,17,26,1007,1.79,0,0 +2014,6,29,8,77,16,29,1007,1.79,0,0 +2014,6,29,9,45,17,28,1007,3.58,0,0 +2014,6,29,10,36,16,30,1007,4.47,0,0 +2014,6,29,11,48,16,33,1007,5.36,0,0 +2014,6,29,12,60,16,33,1006,3.13,0,0 +2014,6,29,13,48,14,33,1006,6.26,0,0 +2014,6,29,14,59,16,33,1005,9.39,0,0 +2014,6,29,15,61,15,34,1005,13.41,0,0 +2014,6,29,16,59,15,35,1004,16.54,0,0 +2014,6,29,17,31,13,34,1004,20.56,0,0 +2014,6,29,18,39,15,33,1004,24.58,0,0 +2014,6,29,19,33,14,32,1004,26.37,0,0 +2014,6,29,20,62,16,30,1004,28.16,0,0 +2014,6,29,21,59,16,29,1005,29.95,0,0 +2014,6,29,22,66,17,27,1006,31.74,0,0 +2014,6,29,23,69,17,28,1006,34.87,0,0 +2014,6,30,0,70,18,25,1006,0.89,0,0 +2014,6,30,1,67,19,25,1006,1.78,0,0 +2014,6,30,2,59,19,25,1006,2.23,0,0 +2014,6,30,3,68,18,24,1006,0.89,0,0 +2014,6,30,4,84,18,23,1006,1.78,0,0 +2014,6,30,5,104,18,23,1006,0.89,0,0 +2014,6,30,6,94,19,25,1007,2.68,0,0 +2014,6,30,7,80,18,27,1007,0.89,0,0 +2014,6,30,8,65,19,28,1007,0.89,0,0 +2014,6,30,9,61,19,28,1007,1.78,0,0 +2014,6,30,10,64,18,30,1007,3.57,0,0 +2014,6,30,11,71,19,31,1007,5.36,0,0 +2014,6,30,12,93,18,33,1006,7.15,0,0 +2014,6,30,13,104,17,34,1005,3.13,0,0 +2014,6,30,14,95,18,35,1005,7.15,0,0 +2014,6,30,15,74,17,35,1004,12.07,0,0 +2014,6,30,16,72,15,34,1004,16.99,0,0 +2014,6,30,17,51,15,34,1004,21.01,0,0 +2014,6,30,18,54,13,34,1003,25.03,0,0 +2014,6,30,19,40,12,33,1004,29.05,0,0 +2014,6,30,20,45,14,32,1004,34.86,0,0 +2014,6,30,21,51,13,31,1005,38.88,0,0 +2014,6,30,22,59,14,30,1005,42.01,0,0 +2014,6,30,23,57,15,28,1005,43.8,0,0 +2014,7,1,0,59,15,28,1005,46.93,0,0 +2014,7,1,1,58,16,26,1005,48.72,0,0 +2014,7,1,2,59,16,27,1005,51.85,0,0 +2014,7,1,3,63,17,26,1005,53.64,0,0 +2014,7,1,4,76,17,24,1005,55.43,0,0 +2014,7,1,5,86,18,24,1005,57.22,0,0 +2014,7,1,6,100,18,25,1005,0.89,0,0 +2014,7,1,7,108,18,26,1006,1.78,0,0 +2014,7,1,8,108,19,27,1006,3.13,0,0 +2014,7,1,9,101,19,29,1006,4.92,0,0 +2014,7,1,10,105,18,30,1006,6.71,0,0 +2014,7,1,11,105,17,31,1006,9.84,0,0 +2014,7,1,12,102,18,31,1005,12.97,0,0 +2014,7,1,13,116,19,31,1004,16.99,0,0 +2014,7,1,14,124,20,31,1004,21.01,0,0 +2014,7,1,15,122,21,31,1003,25.03,0,0 +2014,7,1,16,122,21,31,1003,29.05,0,0 +2014,7,1,17,121,21,31,1002,33.07,0,0 +2014,7,1,18,141,21,31,1002,36.2,0,0 +2014,7,1,19,163,22,30,1002,39.33,0,0 +2014,7,1,20,168,21,28,1002,43.35,0,0 +2014,7,1,21,149,22,28,1002,48.27,0,0 +2014,7,1,22,66,20,21,1005,5.81,0,2 +2014,7,1,23,48,20,21,1005,0.89,0,4 +2014,7,2,0,27,20,21,1005,1.78,0,6 +2014,7,2,1,30,20,21,1004,2.67,0,7 +2014,7,2,2,34,20,21,1004,1.79,0,0 +2014,7,2,3,43,20,21,1004,2.68,0,0 +2014,7,2,4,45,20,21,1003,0.89,0,1 +2014,7,2,5,65,20,21,1003,0.89,0,2 +2014,7,2,6,59,20,21,1004,0.89,0,0 +2014,7,2,7,58,20,21,1004,2.68,0,0 +2014,7,2,8,57,20,22,1004,0.89,0,1 +2014,7,2,9,52,21,22,1004,0.89,0,2 +2014,7,2,10,33,21,23,1004,0.89,0,0 +2014,7,2,11,50,21,24,1004,1.79,0,0 +2014,7,2,12,56,21,24,1003,3.58,0,0 +2014,7,2,13,64,21,25,1003,0.89,0,0 +2014,7,2,14,66,21,25,1002,1.78,0,0 +2014,7,2,15,72,21,26,1002,2.67,0,0 +2014,7,2,16,78,21,26,1002,3.56,0,0 +2014,7,2,17,78,21,26,1001,1.79,0,0 +2014,7,2,18,84,22,25,1001,3.58,0,0 +2014,7,2,19,98,21,24,1002,5.37,0,0 +2014,7,2,20,110,21,24,1002,8.5,0,0 +2014,7,2,21,114,21,24,1003,10.29,0,0 +2014,7,2,22,101,21,23,1003,0.89,0,0 +2014,7,2,23,111,21,23,1003,1.78,0,0 +2014,7,3,0,110,21,23,1001,4.02,0,0 +2014,7,3,1,134,21,23,1001,7.15,0,0 +2014,7,3,2,169,21,23,1001,8.94,0,0 +2014,7,3,3,190,21,23,1001,9.83,0,0 +2014,7,3,4,214,21,23,1001,0.89,0,0 +2014,7,3,5,239,22,23,1002,1.78,0,0 +2014,7,3,6,250,22,23,1002,1.79,0,0 +2014,7,3,7,259,22,23,1002,3.58,0,0 +2014,7,3,8,263,22,24,1002,5.37,0,0 +2014,7,3,9,280,22,25,1002,7.16,0,0 +2014,7,3,10,277,22,26,1002,8.95,0,0 +2014,7,3,11,274,22,27,1001,10.74,0,0 +2014,7,3,12,256,22,28,1001,1.79,0,0 +2014,7,3,13,232,22,28,1001,1.79,0,0 +2014,7,3,14,212,22,28,1000,3.58,0,0 +2014,7,3,15,177,22,29,999,5.37,0,0 +2014,7,3,16,175,22,30,1000,8.5,0,0 +2014,7,3,17,167,22,29,1000,10.29,0,0 +2014,7,3,18,161,22,29,1000,13.42,0,0 +2014,7,3,19,150,23,28,1001,15.21,0,0 +2014,7,3,20,153,22,28,1000,18.34,0,0 +2014,7,3,21,206,23,26,1001,0.89,0,0 +2014,7,3,22,237,23,25,1001,1.78,0,0 +2014,7,3,23,250,23,25,1002,2.67,0,0 +2014,7,4,0,295,23,25,1002,1.79,0,0 +2014,7,4,1,291,23,24,1002,0.89,0,0 +2014,7,4,2,299,23,24,1002,0.89,0,0 +2014,7,4,3,303,23,24,1002,1.78,0,0 +2014,7,4,4,293,22,24,1003,2.67,0,0 +2014,7,4,5,281,22,24,1004,3.56,0,0 +2014,7,4,6,259,23,24,1004,6.69,0,0 +2014,7,4,7,226,23,25,1004,9.82,0,0 +2014,7,4,8,201,22,26,1005,1.79,0,0 +2014,7,4,9,185,22,28,1005,3.13,0,0 +2014,7,4,10,153,23,30,1005,0.89,0,0 +2014,7,4,11,145,23,31,1005,1.78,0,0 +2014,7,4,12,152,22,32,1005,3.57,0,0 +2014,7,4,13,137,23,31,1004,5.36,0,0 +2014,7,4,14,137,23,34,1004,7.15,0,0 +2014,7,4,15,139,23,33,1004,4.02,0,0 +2014,7,4,16,178,23,32,1004,8.04,0,0 +2014,7,4,17,200,24,30,1004,12.06,0,0 +2014,7,4,18,213,23,29,1005,16.98,0,0 +2014,7,4,19,148,22,28,1005,21,0,0 +2014,7,4,20,194,22,27,1005,22.79,0,0 +2014,7,4,21,172,21,26,1007,25.92,0,0 +2014,7,4,22,151,22,26,1007,27.71,0,0 +2014,7,4,23,156,22,25,1007,1.79,0,0 +2014,7,5,0,168,22,25,1007,0.89,0,0 +2014,7,5,1,172,21,24,1007,0.89,0,0 +2014,7,5,2,174,21,23,1007,1.78,0,0 +2014,7,5,3,169,21,24,1007,2.67,0,0 +2014,7,5,4,158,21,23,1007,3.56,0,0 +2014,7,5,5,152,21,23,1007,1.79,0,0 +2014,7,5,6,149,22,23,1007,0.89,0,0 +2014,7,5,7,148,22,24,1008,1.79,0,0 +2014,7,5,8,125,22,25,1008,3.58,0,0 +2014,7,5,9,112,23,26,1008,5.37,0,0 +2014,7,5,10,105,23,27,1008,8.5,0,0 +2014,7,5,11,114,23,29,1008,10.29,0,0 +2014,7,5,12,96,22,29,1007,12.08,0,0 +2014,7,5,13,80,22,30,1007,15.21,0,0 +2014,7,5,14,71,22,31,1006,18.34,0,0 +2014,7,5,15,67,22,31,1006,21.47,0,0 +2014,7,5,16,74,21,31,1005,4.02,0,0 +2014,7,5,17,70,21,31,1005,4.02,0,0 +2014,7,5,18,77,21,30,1005,7.15,0,0 +2014,7,5,19,77,22,29,1005,11.17,0,0 +2014,7,5,20,91,22,28,1005,12.96,0,0 +2014,7,5,21,107,22,27,1006,14.75,0,0 +2014,7,5,22,157,22,27,1006,15.64,0,0 +2014,7,5,23,184,23,26,1006,17.43,0,0 +2014,7,6,0,182,23,26,1006,20.56,0,0 +2014,7,6,1,207,23,26,1005,23.69,0,0 +2014,7,6,2,233,23,25,1005,25.48,0,0 +2014,7,6,3,212,23,25,1005,28.61,0,0 +2014,7,6,4,209,22,24,1005,30.4,0,0 +2014,7,6,5,237,22,24,1005,33.53,0,0 +2014,7,6,6,252,23,24,1006,35.32,0,0 +2014,7,6,7,233,23,25,1006,37.11,0,0 +2014,7,6,8,240,22,25,1006,40.24,0,0 +2014,7,6,9,218,23,26,1006,42.03,0,0 +2014,7,6,10,217,22,27,1006,43.82,0,0 +2014,7,6,11,196,23,28,1006,46.95,0,0 +2014,7,6,12,180,23,29,1005,50.08,0,0 +2014,7,6,13,157,23,30,1005,51.87,0,0 +2014,7,6,14,153,23,30,1004,55,0,0 +2014,7,6,15,153,24,30,1004,58.13,0,0 +2014,7,6,16,150,24,30,1004,62.15,0,0 +2014,7,6,17,158,24,30,1003,66.17,0,0 +2014,7,6,18,168,23,30,1003,69.3,0,0 +2014,7,6,19,167,23,29,1004,72.43,0,0 +2014,7,6,20,184,23,28,1004,74.22,0,0 +2014,7,6,21,194,23,28,1005,76.01,0,0 +2014,7,6,22,181,23,27,1005,79.14,0,0 +2014,7,6,23,153,23,27,1005,80.93,0,0 +2014,7,7,0,139,22,26,1004,82.72,0,0 +2014,7,7,1,133,22,25,1005,84.51,0,0 +2014,7,7,2,130,21,25,1005,86.3,0,0 +2014,7,7,3,116,21,25,1005,0.89,0,0 +2014,7,7,4,114,21,24,1005,1.78,0,0 +2014,7,7,5,99,21,24,1005,1.79,0,0 +2014,7,7,6,94,21,24,1005,3.58,0,0 +2014,7,7,7,84,22,25,1005,6.71,0,0 +2014,7,7,8,80,22,26,1005,9.84,0,0 +2014,7,7,9,80,22,27,1004,12.97,0,0 +2014,7,7,10,88,22,28,1004,14.76,0,0 +2014,7,7,11,103,22,29,1004,16.55,0,0 +2014,7,7,12,132,23,30,1003,19.68,0,0 +2014,7,7,13,151,23,31,1002,22.81,0,0 +2014,7,7,14,165,23,31,1002,27.73,0,0 +2014,7,7,15,182,23,30,1001,32.65,0,0 +2014,7,7,16,191,23,31,1001,35.78,0,0 +2014,7,7,17,193,23,31,1000,40.7,0,0 +2014,7,7,18,192,23,31,1000,43.83,0,0 +2014,7,7,19,189,23,29,1000,46.96,0,0 +2014,7,7,20,188,22,28,1000,50.09,0,0 +2014,7,7,21,223,22,28,1001,53.22,0,0 +2014,7,7,22,239,22,27,1000,56.35,0,0 +2014,7,7,23,235,21,27,1000,58.14,0,0 +2014,7,8,0,196,21,27,1001,0.89,0,0 +2014,7,8,1,180,21,26,1000,1.79,0,0 +2014,7,8,2,192,21,25,1000,0.89,0,0 +2014,7,8,3,187,21,25,1000,1.79,0,0 +2014,7,8,4,164,21,24,1000,0.89,0,0 +2014,7,8,5,156,22,24,1000,1.78,0,0 +2014,7,8,6,163,21,25,1001,0.89,0,0 +2014,7,8,7,144,22,26,1002,1.79,0,0 +2014,7,8,8,137,21,27,1002,3.58,0,0 +2014,7,8,9,131,22,28,1002,5.37,0,0 +2014,7,8,10,115,20,30,1003,4.92,0,0 +2014,7,8,11,68,20,32,1003,3.13,0,0 +2014,7,8,12,26,15,34,1003,5.81,0,0 +2014,7,8,13,16,17,34,1002,10.73,0,0 +2014,7,8,14,15,16,32,1002,4.02,0,0 +2014,7,8,15,10,14,32,1002,3.13,0,0 +2014,7,8,16,13,14,32,1002,3.13,0,0 +2014,7,8,17,11,14,32,1002,1.79,0,0 +2014,7,8,18,8,17,30,1002,0.89,0,0 +2014,7,8,19,10,17,29,1002,1.78,0,0 +2014,7,8,20,16,18,28,1003,2.67,0,0 +2014,7,8,21,49,17,26,1004,0.89,0,0 +2014,7,8,22,45,19,24,1004,1.78,0,0 +2014,7,8,23,42,19,24,1004,2.67,0,0 +2014,7,9,0,55,20,23,1004,0.89,0,0 +2014,7,9,1,53,19,24,1004,1.78,0,0 +2014,7,9,2,85,18,23,1004,1.79,0,0 +2014,7,9,3,37,18,23,1004,3.58,0,0 +2014,7,9,4,19,18,22,1005,6.71,0,0 +2014,7,9,5,21,18,22,1005,8.5,0,0 +2014,7,9,6,16,17,22,1005,11.63,0,0 +2014,7,9,7,17,17,24,1005,14.76,0,0 +2014,7,9,8,17,16,25,1006,17.89,0,0 +2014,7,9,9,16,15,27,1006,22.81,0,0 +2014,7,9,10,11,15,28,1006,3.13,0,0 +2014,7,9,11,11,13,30,1006,7.15,0,0 +2014,7,9,12,14,14,30,1005,3.13,0,0 +2014,7,9,13,9,13,31,1004,1.79,0,0 +2014,7,9,14,8,12,31,1004,3.58,0,0 +2014,7,9,15,10,12,31,1003,0.89,0,0 +2014,7,9,16,14,14,30,1003,1.79,0,0 +2014,7,9,17,14,16,30,1003,0.89,0,0 +2014,7,9,18,22,13,30,1002,3.13,0,0 +2014,7,9,19,32,13,30,1002,7.15,0,0 +2014,7,9,20,36,15,30,1002,8.94,0,0 +2014,7,9,21,29,14,30,1002,12.07,0,0 +2014,7,9,22,31,14,29,1002,13.86,0,0 +2014,7,9,23,35,17,29,1002,1.79,0,0 +2014,7,10,0,34,19,24,1002,2.68,0,0 +2014,7,10,1,28,19,24,1001,0.89,0,0 +2014,7,10,2,52,19,23,1001,0.89,0,0 +2014,7,10,3,70,19,22,1001,0.89,0,0 +2014,7,10,4,52,18,22,1001,0.89,0,0 +2014,7,10,5,39,18,21,1001,2.68,0,0 +2014,7,10,6,31,19,22,1001,4.47,0,0 +2014,7,10,7,25,17,25,1002,7.6,0,0 +2014,7,10,8,25,17,28,1002,10.73,0,0 +2014,7,10,9,28,14,31,1001,12.52,0,0 +2014,7,10,10,28,14,33,1001,14.31,0,0 +2014,7,10,11,25,14,34,1001,1.79,0,0 +2014,7,10,12,15,12,35,1000,1.79,0,0 +2014,7,10,13,17,10,36,999,3.58,0,0 +2014,7,10,14,18,11,37,998,7.6,0,0 +2014,7,10,15,35,11,38,998,11.62,0,0 +2014,7,10,16,17,12,37,998,16.54,0,0 +2014,7,10,17,17,12,36,997,21.46,0,0 +2014,7,10,18,18,13,35,997,26.38,0,0 +2014,7,10,19,24,13,34,997,31.3,0,0 +2014,7,10,20,28,14,32,998,36.22,0,0 +2014,7,10,21,31,15,31,998,41.14,0,0 +2014,7,10,22,40,15,30,998,46.95,0,0 +2014,7,10,23,42,16,29,999,48.74,0,0 +2014,7,11,0,48,16,27,998,49.63,0,0 +2014,7,11,1,52,18,25,998,0.89,0,0 +2014,7,11,2,53,18,23,998,1.78,0,0 +2014,7,11,3,58,18,22,999,0.89,0,0 +2014,7,11,4,58,19,22,999,2.68,0,0 +2014,7,11,5,64,18,23,1000,1.79,0,0 +2014,7,11,6,61,18,23,1000,4.92,0,0 +2014,7,11,7,41,15,28,1000,8.94,0,0 +2014,7,11,8,35,14,29,1001,4.02,0,0 +2014,7,11,9,18,13,31,1001,8.04,0,0 +2014,7,11,10,11,13,32,1001,11.17,0,0 +2014,7,11,11,13,14,33,1000,0.89,0,0 +2014,7,11,12,12,14,34,1000,3.13,0,0 +2014,7,11,13,9,14,35,999,3.13,0,0 +2014,7,11,14,9,14,36,999,4.92,0,0 +2014,7,11,15,16,12,34,999,4.02,0,0 +2014,7,11,16,13,13,35,999,3.13,0,0 +2014,7,11,17,16,13,35,999,0.89,0,0 +2014,7,11,18,9,14,34,999,1.79,0,0 +2014,7,11,19,18,14,32,999,4.92,0,0 +2014,7,11,20,28,16,28,1000,0.89,0,0 +2014,7,11,21,32,17,27,1000,1.78,0,0 +2014,7,11,22,33,17,27,1001,2.67,0,0 +2014,7,11,23,35,18,26,1001,3.56,0,0 +2014,7,12,0,46,14,28,1002,3.13,0,0 +2014,7,12,1,19,14,27,1002,0.89,0,0 +2014,7,12,2,21,16,24,1002,1.79,0,0 +2014,7,12,3,9,13,26,1003,5.81,0,0 +2014,7,12,4,12,11,26,1003,9.83,0,0 +2014,7,12,5,9,12,24,1003,13.85,0,0 +2014,7,12,6,10,12,25,1003,16.98,0,0 +2014,7,12,7,12,13,26,1004,21,0,0 +2014,7,12,8,10,12,27,1004,25.02,0,0 +2014,7,12,9,10,12,28,1004,29.04,0,0 +2014,7,12,10,9,12,30,1004,33.96,0,0 +2014,7,12,11,7,12,31,1004,37.98,0,0 +2014,7,12,12,6,11,32,1003,42,0,0 +2014,7,12,13,NA,11,33,1002,45.13,0,0 +2014,7,12,14,NA,10,33,1001,3.13,0,0 +2014,7,12,15,NA,10,34,1001,7.15,0,0 +2014,7,12,16,NA,10,34,1000,10.28,0,0 +2014,7,12,17,NA,10,34,1000,13.41,0,0 +2014,7,12,18,12,11,33,1000,15.2,0,0 +2014,7,12,19,17,12,31,1000,18.33,0,0 +2014,7,12,20,16,12,29,1001,20.12,0,0 +2014,7,12,21,19,11,30,1002,1.79,0,0 +2014,7,12,22,22,12,31,1002,1.79,0,0 +2014,7,12,23,20,15,27,1003,3.58,0,0 +2014,7,13,0,36,16,24,1003,4.47,0,0 +2014,7,13,1,35,15,23,1002,3.13,0,0 +2014,7,13,2,30,15,25,1002,3.13,0,0 +2014,7,13,3,13,15,22,1003,3.13,0,0 +2014,7,13,4,19,14,21,1003,6.26,0,0 +2014,7,13,5,20,14,22,1003,3.13,0,0 +2014,7,13,6,18,15,25,1003,0.89,0,0 +2014,7,13,7,17,14,26,1004,4.02,0,0 +2014,7,13,8,17,13,28,1004,7.15,0,0 +2014,7,13,9,10,14,29,1004,3.13,0,0 +2014,7,13,10,13,13,32,1003,3.13,0,0 +2014,7,13,11,13,13,34,1003,6.26,0,0 +2014,7,13,12,10,12,34,1002,9.39,0,0 +2014,7,13,13,10,11,35,1001,3.13,0,0 +2014,7,13,14,12,12,36,1001,4.92,0,0 +2014,7,13,15,11,12,36,1000,6.71,0,0 +2014,7,13,16,9,14,31,1001,11.18,0,1 +2014,7,13,17,20,12,32,1000,16.1,0,2 +2014,7,13,18,21,14,30,1000,3.13,0,0 +2014,7,13,19,20,15,30,1001,6.26,0,0 +2014,7,13,20,26,17,26,1001,0.89,0,0 +2014,7,13,21,35,17,25,1002,0.89,0,0 +2014,7,13,22,58,17,24,1002,1.78,0,0 +2014,7,13,23,45,18,23,1002,2.67,0,0 +2014,7,14,0,50,17,23,1003,1.79,0,0 +2014,7,14,1,58,18,22,1002,0.89,0,0 +2014,7,14,2,44,18,22,1002,0.89,0,0 +2014,7,14,3,46,17,21,1002,2.68,0,0 +2014,7,14,4,31,17,21,1002,3.57,0,0 +2014,7,14,5,23,17,20,1003,6.7,0,0 +2014,7,14,6,21,17,22,1003,9.83,0,0 +2014,7,14,7,24,17,24,1003,11.62,0,0 +2014,7,14,8,19,16,27,1003,3.13,0,0 +2014,7,14,9,22,16,29,1003,6.26,0,0 +2014,7,14,10,18,14,31,1003,9.39,0,0 +2014,7,14,11,19,14,33,1003,1.79,0,0 +2014,7,14,12,21,14,34,1002,3.58,0,0 +2014,7,14,13,22,15,35,1002,3.13,0,0 +2014,7,14,14,29,14,35,1002,7.15,0,0 +2014,7,14,15,36,14,36,1001,10.28,0,0 +2014,7,14,16,39,15,36,1001,13.41,0,0 +2014,7,14,17,36,15,36,1000,16.54,0,0 +2014,7,14,18,42,15,35,1001,19.67,0,0 +2014,7,14,19,38,18,32,1001,20.56,0,0 +2014,7,14,20,36,18,29,1001,0.89,0,0 +2014,7,14,21,30,15,31,1002,5.81,0,0 +2014,7,14,22,15,13,29,1002,4.02,0,0 +2014,7,14,23,19,16,28,1002,7.15,0,0 +2014,7,15,0,30,17,28,1003,3.13,0,0 +2014,7,15,1,60,17,27,1005,10.28,0,0 +2014,7,15,2,40,15,26,1004,18.33,0,0 +2014,7,15,3,29,15,26,1004,23.25,0,0 +2014,7,15,4,35,15,25,1004,26.38,0,0 +2014,7,15,5,40,16,24,1004,30.4,0,0 +2014,7,15,6,42,17,23,1004,0.89,0,0 +2014,7,15,7,37,16,25,1004,1.78,0,0 +2014,7,15,8,44,17,26,1004,3.13,0,0 +2014,7,15,9,49,17,27,1004,1.79,0,0 +2014,7,15,10,50,18,29,1004,3.13,0,0 +2014,7,15,11,52,18,30,1004,7.15,0,0 +2014,7,15,12,50,17,31,1004,10.28,0,0 +2014,7,15,13,52,17,31,1004,15.2,0,0 +2014,7,15,14,50,17,31,1003,20.12,0,0 +2014,7,15,15,55,17,32,1003,24.14,0,0 +2014,7,15,16,57,17,32,1002,29.06,0,0 +2014,7,15,17,45,17,32,1002,33.98,0,0 +2014,7,15,18,51,17,31,1002,38.9,0,0 +2014,7,15,19,49,17,30,1002,42.92,0,0 +2014,7,15,20,54,18,30,1003,47.84,0,0 +2014,7,15,21,62,20,28,1003,51.86,0,1 +2014,7,15,22,73,21,26,1003,53.65,0,2 +2014,7,15,23,81,20,27,1004,56.78,0,0 +2014,7,16,0,93,20,27,1004,1.79,0,0 +2014,7,16,1,108,20,24,1003,1.79,0,0 +2014,7,16,2,112,20,24,1003,1.79,0,0 +2014,7,16,3,113,21,24,1003,3.58,0,0 +2014,7,16,4,85,21,24,1003,0.89,0,0 +2014,7,16,5,90,21,24,1003,1.79,0,0 +2014,7,16,6,85,21,24,1004,0.89,0,0 +2014,7,16,7,121,21,25,1004,1.79,0,0 +2014,7,16,8,130,21,26,1004,1.79,0,0 +2014,7,16,9,133,22,27,1004,0.89,0,0 +2014,7,16,10,148,21,27,1005,1.78,0,0 +2014,7,16,11,160,22,27,1004,1.79,0,1 +2014,7,16,12,159,21,27,1005,0.89,0,2 +2014,7,16,13,172,23,26,1003,1.79,0,3 +2014,7,16,14,142,22,29,1002,1.79,0,0 +2014,7,16,15,146,22,30,1002,3.13,0,0 +2014,7,16,16,134,22,31,1002,6.26,0,0 +2014,7,16,17,111,21,28,1002,13.41,0,0 +2014,7,16,18,90,20,27,1002,17.43,0,0 +2014,7,16,19,60,20,26,1003,21.45,0,1 +2014,7,16,20,60,21,24,1004,24.58,0,2 +2014,7,16,21,70,20,24,1005,26.37,0,0 +2014,7,16,22,80,21,23,1005,0.89,0,0 +2014,7,16,23,74,20,22,1006,1.79,0,0 +2014,7,17,0,64,19,22,1004,0.89,0,0 +2014,7,17,1,82,20,23,1006,3.13,0,0 +2014,7,17,2,111,20,23,1005,1.79,0,0 +2014,7,17,3,112,20,23,1005,1.79,0,0 +2014,7,17,4,128,20,23,1005,1.79,0,0 +2014,7,17,5,126,20,23,1006,0.89,0,0 +2014,7,17,6,147,21,23,1006,1.34,0,0 +2014,7,17,7,135,20,24,1006,2.23,0,0 +2014,7,17,8,142,21,24,1006,3.12,0,0 +2014,7,17,9,144,21,26,1006,1.79,0,0 +2014,7,17,10,142,21,26,1006,1.79,0,0 +2014,7,17,11,139,21,28,1006,3.58,0,0 +2014,7,17,12,129,21,28,1006,5.37,0,0 +2014,7,17,13,132,21,29,1005,9.39,0,0 +2014,7,17,14,130,22,28,1005,14.31,0,0 +2014,7,17,15,162,22,28,1005,17.44,0,0 +2014,7,17,16,162,22,28,1005,19.23,0,0 +2014,7,17,17,163,22,28,1005,0.89,0,0 +2014,7,17,18,152,22,28,1005,1.78,0,0 +2014,7,17,19,150,23,28,1006,2.67,0,0 +2014,7,17,20,160,22,26,1006,3.56,0,0 +2014,7,17,21,172,23,26,1007,0.89,0,0 +2014,7,17,22,188,23,26,1007,1.78,0,0 +2014,7,17,23,195,23,26,1007,0.89,0,0 +2014,7,18,0,220,23,26,1006,1.79,0,0 +2014,7,18,1,191,23,25,1006,0.89,0,0 +2014,7,18,2,154,22,26,1006,3.13,0,0 +2014,7,18,3,158,21,25,1007,0.89,0,0 +2014,7,18,4,131,21,25,1007,2.68,0,0 +2014,7,18,5,128,21,24,1007,0.45,0,0 +2014,7,18,6,123,22,25,1007,0.89,0,0 +2014,7,18,7,112,21,25,1008,2.68,0,0 +2014,7,18,8,122,21,26,1008,4.47,0,0 +2014,7,18,9,113,22,28,1008,7.6,0,0 +2014,7,18,10,109,22,29,1008,9.39,0,0 +2014,7,18,11,120,23,30,1008,11.18,0,0 +2014,7,18,12,125,23,31,1007,1.79,0,0 +2014,7,18,13,127,24,32,1007,3.58,0,0 +2014,7,18,14,143,23,33,1006,1.79,0,0 +2014,7,18,15,109,23,34,1006,4.92,0,0 +2014,7,18,16,96,23,34,1006,8.94,0,0 +2014,7,18,17,80,22,34,1005,12.96,0,0 +2014,7,18,18,96,22,34,1005,16.98,0,0 +2014,7,18,19,106,23,32,1006,18.77,0,0 +2014,7,18,20,103,23,32,1006,20.56,0,0 +2014,7,18,21,84,24,30,1007,0.89,0,0 +2014,7,18,22,80,24,29,1007,0.89,0,0 +2014,7,18,23,81,24,29,1007,1.78,0,0 +2014,7,19,0,87,24,28,1007,3.57,0,0 +2014,7,19,1,69,24,28,1007,5.36,0,0 +2014,7,19,2,73,23,28,1008,7.15,0,0 +2014,7,19,3,72,23,26,1008,8.04,0,0 +2014,7,19,4,77,23,27,1008,9.83,0,0 +2014,7,19,5,77,23,27,1008,11.62,0,0 +2014,7,19,6,75,24,27,1009,0.89,0,0 +2014,7,19,7,72,24,28,1009,0.89,0,0 +2014,7,19,8,68,24,30,1009,2.68,0,0 +2014,7,19,9,60,24,32,1010,1.79,0,0 +2014,7,19,10,48,24,34,1009,3.13,0,0 +2014,7,19,11,52,23,34,1009,6.26,0,0 +2014,7,19,12,66,23,36,1009,10.28,0,0 +2014,7,19,13,57,23,35,1008,4.92,0,0 +2014,7,19,14,51,23,36,1008,4.02,0,0 +2014,7,19,15,45,23,36,1007,8.04,0,0 +2014,7,19,16,38,22,37,1006,12.06,0,0 +2014,7,19,17,45,22,36,1006,16.08,0,0 +2014,7,19,18,48,22,35,1006,21,0,0 +2014,7,19,19,53,22,34,1007,25.02,0,0 +2014,7,19,20,64,23,33,1007,28.15,0,0 +2014,7,19,21,72,24,32,1008,32.17,0,0 +2014,7,19,22,81,24,31,1008,33.96,0,0 +2014,7,19,23,66,24,31,1008,35.75,0,0 +2014,7,20,0,41,24,31,1008,39.77,0,1 +2014,7,20,1,46,23,30,1007,1.79,0,2 +2014,7,20,2,60,24,29,1007,1.79,0,0 +2014,7,20,3,57,24,28,1007,3.58,0,0 +2014,7,20,4,65,24,27,1007,6.71,0,0 +2014,7,20,5,64,23,27,1007,0.89,0,0 +2014,7,20,6,62,23,27,1007,1.79,0,0 +2014,7,20,7,55,23,28,1007,4.92,0,0 +2014,7,20,8,52,23,30,1007,8.05,0,0 +2014,7,20,9,55,23,31,1007,9.84,0,0 +2014,7,20,10,60,23,32,1007,12.97,0,0 +2014,7,20,11,52,23,33,1006,16.99,0,0 +2014,7,20,12,55,23,34,1005,21.01,0,0 +2014,7,20,13,67,24,34,1004,25.93,0,0 +2014,7,20,14,85,24,34,1004,30.85,0,0 +2014,7,20,15,87,23,35,1003,35.77,0,0 +2014,7,20,16,79,23,36,1002,41.58,0,0 +2014,7,20,17,60,22,35,1001,48.73,0,0 +2014,7,20,18,69,22,35,1002,54.54,0,0 +2014,7,20,19,59,23,34,1002,58.56,0,0 +2014,7,20,20,53,23,33,1002,61.69,0,0 +2014,7,20,21,55,23,33,1002,65.71,0,0 +2014,7,20,22,52,23,33,1002,69.73,0,0 +2014,7,20,23,48,23,32,1001,72.86,0,0 +2014,7,21,0,56,24,32,1001,74.65,0,0 +2014,7,21,1,59,24,31,1000,76.44,0,0 +2014,7,21,2,60,24,30,1000,79.57,0,0 +2014,7,21,3,55,24,30,999,82.7,0,0 +2014,7,21,4,59,24,30,1000,85.83,0,0 +2014,7,21,5,59,24,30,1000,88.96,0,0 +2014,7,21,6,53,19,30,1001,8.94,0,0 +2014,7,21,7,20,20,26,1002,13.86,0,1 +2014,7,21,8,20,20,27,1002,19.67,0,0 +2014,7,21,9,20,21,27,1003,23.69,0,0 +2014,7,21,10,11,20,28,1003,27.71,0,0 +2014,7,21,11,15,21,30,1002,31.73,0,0 +2014,7,21,12,12,21,32,1002,3.13,0,0 +2014,7,21,13,15,17,32,1002,5.81,0,0 +2014,7,21,14,14,17,34,1002,11.62,0,0 +2014,7,21,15,15,17,35,1001,14.75,0,0 +2014,7,21,16,13,18,34,1002,1.79,0,0 +2014,7,21,17,11,18,35,1002,1.79,0,0 +2014,7,21,18,14,18,33,1002,1.79,0,0 +2014,7,21,19,15,18,32,1002,0.89,0,0 +2014,7,21,20,17,15,32,1003,3.13,0,0 +2014,7,21,21,14,16,31,1004,6.26,0,0 +2014,7,21,22,11,16,29,1004,3.13,0,0 +2014,7,21,23,9,14,30,1004,7.15,0,0 +2014,7,22,0,6,16,29,1004,8.94,0,0 +2014,7,22,1,6,16,27,1005,10.73,0,0 +2014,7,22,2,13,17,27,1005,12.52,0,0 +2014,7,22,3,10,16,27,1005,1.79,0,0 +2014,7,22,4,16,16,27,1006,3.13,0,0 +2014,7,22,5,16,16,26,1006,4.92,0,0 +2014,7,22,6,14,16,27,1006,8.94,0,0 +2014,7,22,7,18,16,27,1006,12.96,0,0 +2014,7,22,8,19,17,27,1007,14.75,0,0 +2014,7,22,9,15,15,29,1007,16.54,0,0 +2014,7,22,10,12,17,28,1007,0.89,0,0 +2014,7,22,11,NA,17,29,1007,1.79,0,0 +2014,7,22,12,NA,16,29,1007,3.13,0,0 +2014,7,22,13,18,17,29,1007,8.05,0,0 +2014,7,22,14,17,16,30,1007,12.07,0,1 +2014,7,22,15,16,17,29,1006,17.88,0,0 +2014,7,22,16,16,17,29,1006,21.01,0,0 +2014,7,22,17,11,17,29,1006,24.14,0,0 +2014,7,22,18,15,16,29,1006,28.16,0,0 +2014,7,22,19,8,17,27,1006,0.89,0,0 +2014,7,22,20,12,16,28,1006,3.13,0,0 +2014,7,22,21,19,16,28,1006,6.26,0,0 +2014,7,22,22,21,15,28,1006,10.28,0,0 +2014,7,22,23,17,15,28,1006,14.3,0,0 +2014,7,23,0,16,15,27,1006,17.43,0,0 +2014,7,23,1,20,15,27,1005,21.45,0,0 +2014,7,23,2,20,15,27,1005,24.58,0,0 +2014,7,23,3,21,16,26,1005,26.37,0,0 +2014,7,23,4,23,17,24,1005,0.45,0,0 +2014,7,23,5,12,18,23,1005,0.9,0,0 +2014,7,23,6,13,18,23,1005,1.35,0,0 +2014,7,23,7,19,16,26,1005,1.79,0,0 +2014,7,23,8,20,19,25,1005,0.89,0,0 +2014,7,23,9,16,18,26,1005,1.79,0,0 +2014,7,23,10,18,16,28,1005,4.92,0,0 +2014,7,23,11,19,16,29,1004,8.94,0,0 +2014,7,23,12,13,15,30,1004,12.07,0,0 +2014,7,23,13,22,15,31,1004,3.13,0,0 +2014,7,23,14,20,14,32,1003,6.26,0,0 +2014,7,23,15,16,13,32,1002,3.13,0,0 +2014,7,23,16,20,14,32,1002,6.26,0,0 +2014,7,23,17,21,14,32,1001,8.05,0,0 +2014,7,23,18,21,14,32,1001,11.18,0,0 +2014,7,23,19,42,18,30,1002,4.02,0,0 +2014,7,23,20,46,18,29,1002,8.04,0,0 +2014,7,23,21,43,19,30,1003,11.17,0,0 +2014,7,23,22,42,19,26,1003,0.89,0,0 +2014,7,23,23,41,20,25,1003,0.89,0,0 +2014,7,24,0,46,21,25,1003,0.89,0,0 +2014,7,24,1,55,21,24,1003,0.89,0,0 +2014,7,24,2,52,21,24,1003,0.45,0,0 +2014,7,24,3,50,21,24,1003,1.34,0,0 +2014,7,24,4,74,20,24,1003,0.89,0,0 +2014,7,24,5,73,20,23,1003,0.89,0,0 +2014,7,24,6,82,20,23,1004,0.89,0,0 +2014,7,24,7,74,20,24,1005,0.89,0,0 +2014,7,24,8,76,20,25,1005,1.34,0,1 +2014,7,24,9,82,21,26,1006,1.79,0,0 +2014,7,24,10,90,21,26,1006,0.89,0,1 +2014,7,24,11,103,21,26,1007,1.79,0,2 +2014,7,24,12,89,20,28,1007,3.13,0,3 +2014,7,24,13,67,21,28,1006,6.26,0,0 +2014,7,24,14,85,20,29,1006,0.89,0,0 +2014,7,24,15,62,20,29,1006,1.78,0,0 +2014,7,24,16,50,19,29,1005,1.79,0,0 +2014,7,24,17,69,20,29,1005,3.58,0,0 +2014,7,24,18,51,19,30,1005,6.71,0,0 +2014,7,24,19,24,18,28,1005,9.84,0,0 +2014,7,24,20,33,18,26,1006,11.63,0,0 +2014,7,24,21,35,19,26,1007,13.42,0,0 +2014,7,24,22,63,20,24,1008,0.89,0,0 +2014,7,24,23,104,19,22,1008,0.89,0,0 +2014,7,25,0,136,19,21,1007,2.68,0,0 +2014,7,25,1,146,20,22,1007,0.89,0,0 +2014,7,25,2,118,19,21,1007,0.89,0,0 +2014,7,25,3,127,19,20,1006,0.89,0,0 +2014,7,25,4,149,19,20,1006,0.89,0,0 +2014,7,25,5,160,18,20,1006,1.79,0,0 +2014,7,25,6,116,19,20,1007,4.92,0,0 +2014,7,25,7,91,19,23,1007,8.05,0,0 +2014,7,25,8,97,20,26,1006,12.07,0,0 +2014,7,25,9,61,18,29,1006,16.09,0,0 +2014,7,25,10,49,18,31,1006,19.22,0,0 +2014,7,25,11,40,18,32,1006,3.13,0,0 +2014,7,25,12,31,17,32,1005,1.79,0,0 +2014,7,25,13,27,17,33,1005,3.13,0,0 +2014,7,25,14,32,16,33,1005,7.15,0,0 +2014,7,25,15,28,17,34,1004,10.28,0,0 +2014,7,25,16,31,17,33,1004,13.41,0,0 +2014,7,25,17,38,16,33,1004,18.33,0,0 +2014,7,25,18,45,15,32,1004,22.35,0,0 +2014,7,25,19,44,15,31,1004,27.27,0,0 +2014,7,25,20,55,14,29,1005,30.4,0,0 +2014,7,25,21,61,15,28,1006,32.19,0,0 +2014,7,25,22,69,15,27,1006,35.32,0,0 +2014,7,25,23,69,16,26,1007,37.11,0,0 +2014,7,26,0,72,16,26,1006,38.9,0,0 +2014,7,26,1,87,18,25,1007,40.69,0,0 +2014,7,26,2,91,18,24,1006,0.89,0,0 +2014,7,26,3,116,19,23,1006,0.89,0,0 +2014,7,26,4,136,18,23,1007,1.79,0,0 +2014,7,26,5,146,18,22,1008,2.68,0,0 +2014,7,26,6,149,19,22,1008,3.57,0,0 +2014,7,26,7,142,20,25,1009,0.89,0,0 +2014,7,26,8,137,20,27,1009,1.78,0,0 +2014,7,26,9,113,20,29,1009,2.67,0,0 +2014,7,26,10,119,20,30,1009,3.56,0,0 +2014,7,26,11,107,20,31,1009,1.79,0,0 +2014,7,26,12,112,20,32,1009,1.79,0,0 +2014,7,26,13,80,19,34,1008,3.58,0,0 +2014,7,26,14,62,18,34,1008,4.02,0,0 +2014,7,26,15,67,17,35,1007,8.04,0,0 +2014,7,26,16,62,16,35,1007,13.85,0,0 +2014,7,26,17,76,16,35,1007,19.66,0,0 +2014,7,26,18,72,16,34,1007,24.58,0,0 +2014,7,26,19,63,16,32,1007,29.5,0,0 +2014,7,26,20,57,16,31,1008,33.52,0,0 +2014,7,26,21,62,17,30,1009,36.65,0,0 +2014,7,26,22,59,16,29,1009,40.67,0,0 +2014,7,26,23,57,15,29,1009,43.8,0,0 +2014,7,27,0,60,16,27,1010,45.59,0,0 +2014,7,27,1,68,16,27,1010,48.72,0,0 +2014,7,27,2,76,16,26,1010,51.85,0,0 +2014,7,27,3,86,16,26,1010,54.98,0,0 +2014,7,27,4,97,17,24,1010,56.77,0,0 +2014,7,27,5,102,17,24,1010,58.56,0,0 +2014,7,27,6,111,18,24,1010,0.89,0,0 +2014,7,27,7,107,18,25,1010,1.78,0,0 +2014,7,27,8,106,18,26,1011,1.79,0,0 +2014,7,27,9,113,18,28,1010,0.89,0,0 +2014,7,27,10,105,18,30,1010,1.78,0,0 +2014,7,27,11,114,19,32,1010,3.57,0,0 +2014,7,27,12,133,18,34,1009,3.13,0,0 +2014,7,27,13,120,17,34,1008,8.05,0,0 +2014,7,27,14,99,16,36,1007,12.97,0,0 +2014,7,27,15,87,17,36,1006,17.89,0,0 +2014,7,27,16,93,18,35,1005,22.81,0,0 +2014,7,27,17,85,17,34,1005,28.62,0,0 +2014,7,27,18,95,18,33,1004,34.43,0,0 +2014,7,27,19,98,18,32,1004,38.45,0,0 +2014,7,27,20,99,19,30,1005,41.58,0,0 +2014,7,27,21,88,19,29,1005,43.37,0,0 +2014,7,27,22,83,19,27,1006,45.16,0,0 +2014,7,27,23,84,19,28,1005,46.95,0,0 +2014,7,28,0,93,20,27,1005,50.08,0,0 +2014,7,28,1,95,20,27,1005,53.21,0,0 +2014,7,28,2,113,21,26,1005,56.34,0,0 +2014,7,28,3,121,21,25,1005,58.13,0,0 +2014,7,28,4,127,21,25,1005,61.26,0,0 +2014,7,28,5,134,21,24,1005,63.05,0,0 +2014,7,28,6,138,21,24,1005,64.84,0,0 +2014,7,28,7,141,21,25,1006,66.63,0,0 +2014,7,28,8,143,21,27,1006,68.42,0,0 +2014,7,28,9,125,22,29,1006,1.79,0,0 +2014,7,28,10,111,21,30,1005,2.68,0,0 +2014,7,28,11,118,20,33,1005,5.81,0,0 +2014,7,28,12,110,19,34,1004,4.02,0,0 +2014,7,28,13,101,18,35,1004,9.83,0,0 +2014,7,28,14,73,17,36,1003,16.98,0,0 +2014,7,28,15,61,17,36,1002,24.13,0,0 +2014,7,28,16,67,17,35,1002,31.28,0,0 +2014,7,28,17,80,17,34,1001,37.09,0,0 +2014,7,28,18,81,17,34,1001,42.9,0,0 +2014,7,28,19,77,20,33,1002,47.82,0,0 +2014,7,28,20,89,19,32,1002,52.74,0,0 +2014,7,28,21,88,20,31,1003,55.87,0,0 +2014,7,28,22,83,20,28,1003,57.66,0,0 +2014,7,28,23,89,20,28,1003,59.45,0,0 +2014,7,29,0,96,21,28,1003,62.58,0,0 +2014,7,29,1,90,21,28,1003,64.37,0,0 +2014,7,29,2,99,21,28,1003,68.39,0,0 +2014,7,29,3,101,21,27,1003,71.52,0,0 +2014,7,29,4,114,21,26,1004,73.31,0,0 +2014,7,29,5,135,21,25,1004,76.44,0,0 +2014,7,29,6,155,21,25,1004,79.57,0,0 +2014,7,29,7,189,21,26,1004,82.7,0,0 +2014,7,29,8,179,21,28,1005,86.72,0,0 +2014,7,29,9,151,22,30,1004,89.85,0,0 +2014,7,29,10,125,22,32,1004,93.87,0,0 +2014,7,29,11,101,21,32,1004,99.68,0,0 +2014,7,29,12,92,19,32,1004,104.6,0,0 +2014,7,29,13,97,19,34,1003,110.41,0,0 +2014,7,29,14,86,19,35,1002,116.22,0,0 +2014,7,29,15,84,18,34,1002,123.37,0,0 +2014,7,29,16,89,18,34,1001,129.18,0,0 +2014,7,29,17,89,20,33,1001,133.2,0,0 +2014,7,29,18,86,20,32,1001,137.22,0,0 +2014,7,29,19,92,20,31,1001,141.24,0,0 +2014,7,29,20,86,21,30,1001,146.16,0,0 +2014,7,29,21,85,20,30,1002,151.08,0,0 +2014,7,29,22,83,22,28,1003,160.02,0,1 +2014,7,29,23,82,23,24,1004,161.81,0,3 +2014,7,30,0,78,22,23,1003,164.94,0,5 +2014,7,30,1,69,22,24,1004,166.73,0,6 +2014,7,30,2,52,21,23,1003,0.89,0,7 +2014,7,30,3,53,22,23,1003,4.02,0,8 +2014,7,30,4,88,22,23,1004,0.89,0,0 +2014,7,30,5,117,22,23,1004,1.79,0,0 +2014,7,30,6,113,22,23,1004,4.92,0,1 +2014,7,30,7,142,22,23,1005,3.13,0,2 +2014,7,30,8,134,22,23,1006,0.89,0,0 +2014,7,30,9,159,22,24,1006,1.79,0,0 +2014,7,30,10,151,21,25,1006,1.79,0,0 +2014,7,30,11,149,21,26,1006,4.92,0,0 +2014,7,30,12,148,21,26,1007,6.71,0,0 +2014,7,30,13,132,21,27,1006,8.5,0,0 +2014,7,30,14,130,22,28,1006,10.29,0,0 +2014,7,30,15,122,22,28,1006,13.42,0,0 +2014,7,30,16,112,22,28,1005,16.55,0,0 +2014,7,30,17,130,21,28,1005,18.34,0,0 +2014,7,30,18,144,22,27,1005,20.13,0,0 +2014,7,30,19,157,22,27,1005,21.92,0,0 +2014,7,30,20,185,22,26,1006,22.81,0,0 +2014,7,30,21,199,22,25,1007,24.6,0,0 +2014,7,30,22,212,22,25,1007,26.39,0,0 +2014,7,30,23,211,22,24,1007,0.89,0,0 +2014,7,31,0,201,22,25,1008,1.78,0,0 +2014,7,31,1,193,22,25,1008,2.67,0,0 +2014,7,31,2,198,23,25,1008,3.13,0,0 +2014,7,31,3,187,23,25,1008,4.92,0,0 +2014,7,31,4,184,22,24,1007,6.71,0,0 +2014,7,31,5,192,23,24,1008,0.89,0,0 +2014,7,31,6,208,23,24,1008,1.79,0,0 +2014,7,31,7,219,23,24,1008,3.58,0,0 +2014,7,31,8,250,23,25,1008,5.37,0,0 +2014,7,31,9,227,23,26,1008,7.16,0,0 +2014,7,31,10,210,23,27,1008,8.95,0,0 +2014,7,31,11,176,22,28,1008,10.74,0,0 +2014,7,31,12,180,22,29,1007,13.87,0,0 +2014,7,31,13,164,22,30,1007,17,0,0 +2014,7,31,14,163,22,31,1006,20.13,0,0 +2014,7,31,15,155,22,31,1006,24.15,0,0 +2014,7,31,16,134,22,31,1005,28.17,0,0 +2014,7,31,17,134,22,30,1005,31.3,0,0 +2014,7,31,18,122,22,30,1004,33.09,0,0 +2014,7,31,19,125,22,29,1005,34.88,0,0 +2014,7,31,20,135,22,28,1005,36.67,0,0 +2014,7,31,21,167,22,27,1006,38.46,0,0 +2014,7,31,22,187,22,27,1007,40.25,0,0 +2014,7,31,23,206,22,26,1006,42.04,0,0 +2014,8,1,0,196,22,26,1007,43.83,0,0 +2014,8,1,1,154,22,26,1007,45.62,0,0 +2014,8,1,2,120,22,25,1006,47.41,0,0 +2014,8,1,3,135,22,25,1006,0.89,0,0 +2014,8,1,4,132,22,24,1006,1.79,0,0 +2014,8,1,5,136,22,24,1006,3.58,0,0 +2014,8,1,6,146,23,24,1007,0.89,0,0 +2014,8,1,7,159,23,25,1007,1.79,0,0 +2014,8,1,8,179,23,26,1007,3.58,0,0 +2014,8,1,9,185,23,26,1008,5.37,0,0 +2014,8,1,10,152,23,28,1007,8.5,0,0 +2014,8,1,11,112,23,29,1007,10.29,0,0 +2014,8,1,12,123,22,31,1007,1.79,0,0 +2014,8,1,13,109,22,32,1006,3.58,0,0 +2014,8,1,14,103,22,32,1006,1.79,0,0 +2014,8,1,15,98,22,32,1005,4.92,0,0 +2014,8,1,16,94,22,32,1005,8.05,0,0 +2014,8,1,17,106,22,32,1005,11.18,0,0 +2014,8,1,18,115,23,31,1005,14.31,0,0 +2014,8,1,19,124,23,30,1005,16.1,0,0 +2014,8,1,20,134,23,29,1005,17.89,0,0 +2014,8,1,21,134,23,28,1006,0.89,0,0 +2014,8,1,22,137,24,27,1006,1.78,0,0 +2014,8,1,23,101,24,26,1006,0.89,0,0 +2014,8,2,0,78,24,26,1005,1.78,0,0 +2014,8,2,1,79,24,26,1005,3.57,0,0 +2014,8,2,2,85,24,26,1005,5.36,0,0 +2014,8,2,3,93,24,26,1005,8.49,0,0 +2014,8,2,4,88,23,25,1005,10.28,0,0 +2014,8,2,5,78,23,25,1005,0.89,0,0 +2014,8,2,6,69,24,25,1005,1.78,0,0 +2014,8,2,7,62,24,26,1005,3.13,0,0 +2014,8,2,8,57,24,27,1005,4.92,0,0 +2014,8,2,9,77,24,28,1005,0.89,0,0 +2014,8,2,10,87,25,29,1004,1.78,0,0 +2014,8,2,11,97,25,31,1004,2.67,0,0 +2014,8,2,12,101,24,32,1003,3.56,0,0 +2014,8,2,13,112,24,33,1002,5.35,0,0 +2014,8,2,14,109,24,33,1002,7.14,0,0 +2014,8,2,15,110,24,34,1001,1.79,0,0 +2014,8,2,16,104,24,34,1001,3.58,0,0 +2014,8,2,17,104,24,34,1001,6.71,0,0 +2014,8,2,18,101,23,33,1000,9.84,0,0 +2014,8,2,19,100,23,32,1001,13.86,0,0 +2014,8,2,20,113,24,30,1001,0.89,0,0 +2014,8,2,21,115,23,31,1002,1.79,0,0 +2014,8,2,22,121,24,28,1001,0.45,0,0 +2014,8,2,23,119,24,27,1002,0.89,0,0 +2014,8,3,0,138,24,27,1001,2.68,0,0 +2014,8,3,1,146,24,27,1001,0.89,0,0 +2014,8,3,2,158,25,26,1001,0.89,0,0 +2014,8,3,3,184,24,26,1001,0.89,0,0 +2014,8,3,4,190,25,26,1001,0.89,0,0 +2014,8,3,5,201,24,26,1001,1.78,0,0 +2014,8,3,6,175,24,26,1001,2.23,0,0 +2014,8,3,7,132,24,27,1002,0.89,0,0 +2014,8,3,8,102,25,29,1002,2.68,0,0 +2014,8,3,9,88,24,31,1002,1.79,0,0 +2014,8,3,10,74,24,32,1001,0.45,0,0 +2014,8,3,11,74,24,34,1001,1.34,0,0 +2014,8,3,12,82,24,34,1001,2.23,0,0 +2014,8,3,13,73,24,34,1000,3.12,0,0 +2014,8,3,14,95,24,34,999,4.02,0,0 +2014,8,3,15,76,24,34,1000,8.04,0,0 +2014,8,3,16,102,24,33,1000,12.96,0,0 +2014,8,3,17,72,23,29,1000,4.92,0,0 +2014,8,3,18,69,23,28,1000,0.89,0,0 +2014,8,3,19,52,24,29,1000,1.78,0,0 +2014,8,3,20,54,24,27,1001,1.79,0,0 +2014,8,3,21,67,23,27,1002,3.58,0,0 +2014,8,3,22,63,23,27,1002,4.47,0,0 +2014,8,3,23,67,23,27,1001,0.45,0,0 +2014,8,4,0,74,24,26,1001,0.89,0,0 +2014,8,4,1,80,24,26,1001,0.89,0,0 +2014,8,4,2,92,24,26,1001,0.89,0,1 +2014,8,4,3,89,24,26,1001,0.89,0,2 +2014,8,4,4,82,24,26,1001,0.89,0,3 +2014,8,4,5,92,24,26,1001,2.68,0,0 +2014,8,4,6,90,24,26,1002,0.89,0,0 +2014,8,4,7,90,22,27,1002,3.13,0,0 +2014,8,4,8,59,20,28,1003,8.05,0,0 +2014,8,4,9,12,19,26,1003,12.97,0,1 +2014,8,4,10,10,19,26,1004,18.78,0,2 +2014,8,4,11,7,18,26,1005,24.59,0,0 +2014,8,4,12,5,19,22,1005,30.4,0,1 +2014,8,4,13,7,19,22,1006,33.53,0,2 +2014,8,4,14,7,19,21,1006,3.13,0,3 +2014,8,4,15,6,19,21,1006,3.13,0,4 +2014,8,4,16,4,19,22,1006,4.92,0,0 +2014,8,4,17,7,19,23,1006,4.02,0,0 +2014,8,4,18,7,18,22,1006,7.15,0,0 +2014,8,4,19,4,18,23,1006,1.79,0,0 +2014,8,4,20,2,18,22,1006,1.79,0,0 +2014,8,4,21,2,18,21,1007,4.92,0,0 +2014,8,4,22,4,18,21,1007,6.71,0,0 +2014,8,4,23,4,18,21,1006,8.5,0,0 +2014,8,5,0,6,18,20,1006,10.29,0,0 +2014,8,5,1,14,17,20,1007,1.79,0,0 +2014,8,5,2,13,18,20,1006,0.89,0,0 +2014,8,5,3,10,18,19,1007,1.79,0,0 +2014,8,5,4,8,18,20,1007,3.58,0,0 +2014,8,5,5,9,17,20,1007,6.71,0,0 +2014,8,5,6,11,18,19,1007,9.84,0,0 +2014,8,5,7,14,18,21,1007,12.97,0,0 +2014,8,5,8,16,18,22,1008,16.1,0,0 +2014,8,5,9,14,19,24,1008,19.23,0,0 +2014,8,5,10,25,18,27,1008,0.89,0,0 +2014,8,5,11,25,19,28,1008,1.78,0,0 +2014,8,5,12,31,17,29,1007,2.67,0,0 +2014,8,5,13,27,17,30,1006,1.79,0,0 +2014,8,5,14,26,14,32,1006,3.58,0,0 +2014,8,5,15,28,15,31,1005,5.37,0,0 +2014,8,5,16,32,16,31,1005,8.5,0,0 +2014,8,5,17,51,18,30,1005,11.63,0,0 +2014,8,5,18,32,19,29,1005,14.76,0,0 +2014,8,5,19,25,20,29,1005,17.89,0,0 +2014,8,5,20,23,19,28,1006,19.68,0,0 +2014,8,5,21,27,21,26,1007,20.57,0,0 +2014,8,5,22,28,21,25,1006,21.46,0,0 +2014,8,5,23,26,21,24,1006,0.89,0,0 +2014,8,6,0,32,21,23,1007,0.89,0,0 +2014,8,6,1,34,21,23,1006,1.79,0,0 +2014,8,6,2,48,21,22,1007,3.58,0,0 +2014,8,6,3,49,21,22,1007,0.89,0,0 +2014,8,6,4,60,20,22,1006,2.68,0,0 +2014,8,6,5,63,19,23,1007,5.81,0,0 +2014,8,6,6,62,19,22,1008,8.94,0,0 +2014,8,6,7,68,19,24,1008,12.96,0,0 +2014,8,6,8,63,19,27,1008,16.09,0,0 +2014,8,6,9,50,16,29,1008,4.02,0,0 +2014,8,6,10,23,16,31,1008,4.02,0,0 +2014,8,6,11,16,14,32,1008,4.02,0,0 +2014,8,6,12,8,12,33,1007,8.94,0,0 +2014,8,6,13,6,12,34,1007,13.86,0,0 +2014,8,6,14,3,11,33,1006,17.88,0,0 +2014,8,6,15,2,10,33,1006,21.01,0,0 +2014,8,6,16,5,12,33,1006,1.79,0,0 +2014,8,6,17,4,11,33,1006,3.58,0,0 +2014,8,6,18,8,10,33,1006,6.71,0,0 +2014,8,6,19,15,18,30,1006,11.63,0,0 +2014,8,6,20,25,19,28,1007,14.76,0,0 +2014,8,6,21,28,20,27,1008,17.89,0,0 +2014,8,6,22,46,20,26,1008,21.02,0,0 +2014,8,6,23,71,19,24,1009,22.81,0,0 +2014,8,7,0,125,19,23,1009,0.89,0,0 +2014,8,7,1,142,19,22,1009,0.89,0,0 +2014,8,7,2,105,19,22,1009,0.89,0,0 +2014,8,7,3,101,19,21,1009,0.89,0,0 +2014,8,7,4,89,19,21,1009,0.89,0,0 +2014,8,7,5,93,18,20,1010,4.02,0,0 +2014,8,7,6,85,18,21,1010,7.15,0,0 +2014,8,7,7,62,18,23,1010,8.94,0,0 +2014,8,7,8,40,17,25,1010,12.96,0,0 +2014,8,7,9,52,17,28,1010,16.09,0,0 +2014,8,7,10,62,16,29,1010,3.13,0,0 +2014,8,7,11,55,14,30,1010,0.89,0,0 +2014,8,7,12,48,13,31,1009,1.79,0,0 +2014,8,7,13,41,15,33,1009,1.79,0,0 +2014,8,7,14,39,15,32,1008,3.13,0,0 +2014,8,7,15,43,15,33,1008,6.26,0,0 +2014,8,7,16,43,14,32,1007,10.28,0,0 +2014,8,7,17,44,15,32,1007,14.3,0,0 +2014,8,7,18,37,16,30,1007,18.32,0,0 +2014,8,7,19,40,18,29,1008,21.45,0,0 +2014,8,7,20,64,17,28,1008,24.58,0,0 +2014,8,7,21,48,17,27,1009,27.71,0,0 +2014,8,7,22,56,17,26,1009,29.5,0,0 +2014,8,7,23,41,17,25,1009,31.29,0,0 +2014,8,8,0,48,17,25,1009,34.42,0,0 +2014,8,8,1,29,18,23,1009,0.89,0,0 +2014,8,8,2,40,18,22,1009,1.79,0,0 +2014,8,8,3,42,18,21,1009,0.89,0,0 +2014,8,8,4,47,18,20,1009,1.78,0,0 +2014,8,8,5,51,18,21,1010,2.67,0,0 +2014,8,8,6,69,18,21,1010,0.89,0,0 +2014,8,8,7,79,19,23,1010,0.89,0,0 +2014,8,8,8,90,19,26,1010,1.78,0,0 +2014,8,8,9,83,17,27,1010,2.67,0,0 +2014,8,8,10,76,18,28,1010,1.79,0,0 +2014,8,8,11,85,18,29,1009,3.58,0,0 +2014,8,8,12,72,17,31,1009,6.71,0,0 +2014,8,8,13,56,15,31,1008,10.73,0,0 +2014,8,8,14,45,14,32,1008,15.65,0,0 +2014,8,8,15,46,13,32,1007,20.57,0,0 +2014,8,8,16,39,14,32,1007,25.49,0,0 +2014,8,8,17,49,14,31,1006,30.41,0,0 +2014,8,8,18,49,15,30,1006,34.43,0,0 +2014,8,8,19,62,16,29,1007,37.56,0,0 +2014,8,8,20,59,16,27,1007,39.35,0,0 +2014,8,8,21,58,17,26,1007,41.14,0,0 +2014,8,8,22,59,18,25,1008,42.93,0,0 +2014,8,8,23,60,18,25,1008,44.72,0,0 +2014,8,9,0,72,18,22,1007,46.51,0,0 +2014,8,9,1,71,18,23,1007,48.3,0,0 +2014,8,9,2,90,18,23,1007,51.43,0,0 +2014,8,9,3,120,18,22,1007,53.22,0,0 +2014,8,9,4,123,18,21,1007,0.89,0,0 +2014,8,9,5,132,18,21,1007,1.34,0,0 +2014,8,9,6,144,19,21,1008,0.89,0,0 +2014,8,9,7,154,20,22,1008,1.79,0,0 +2014,8,9,8,160,20,25,1008,3.58,0,0 +2014,8,9,9,124,20,26,1007,5.37,0,0 +2014,8,9,10,95,18,28,1008,1.79,0,0 +2014,8,9,11,80,19,28,1007,3.58,0,0 +2014,8,9,12,72,19,30,1006,3.13,0,0 +2014,8,9,13,70,18,31,1006,6.26,0,0 +2014,8,9,14,58,16,31,1005,10.28,0,0 +2014,8,9,15,53,17,31,1004,13.41,0,0 +2014,8,9,16,54,16,31,1003,18.33,0,0 +2014,8,9,17,44,15,31,1003,23.25,0,0 +2014,8,9,18,33,17,29,1003,26.38,0,0 +2014,8,9,19,46,16,28,1003,30.4,0,0 +2014,8,9,20,42,17,28,1004,33.53,0,0 +2014,8,9,21,55,18,27,1005,36.66,0,0 +2014,8,9,22,15,18,22,1006,7.15,0,1 +2014,8,9,23,18,18,21,1005,0.89,0,2 +2014,8,10,0,17,19,21,1004,1.79,0,0 +2014,8,10,1,22,18,21,1004,1.79,0,0 +2014,8,10,2,22,18,20,1005,1.79,0,0 +2014,8,10,3,24,18,20,1004,0.89,0,0 +2014,8,10,4,21,17,19,1004,0.89,0,0 +2014,8,10,5,28,18,19,1004,2.68,0,0 +2014,8,10,6,31,18,19,1004,0.89,0,0 +2014,8,10,7,31,19,22,1004,1.79,0,0 +2014,8,10,8,24,19,24,1005,0.89,0,0 +2014,8,10,9,25,19,25,1004,1.79,0,0 +2014,8,10,10,39,18,27,1004,0.89,0,0 +2014,8,10,11,38,18,29,1004,1.78,0,0 +2014,8,10,12,37,17,30,1003,3.13,0,0 +2014,8,10,13,36,17,31,1003,1.79,0,0 +2014,8,10,14,32,17,33,1002,1.79,0,0 +2014,8,10,15,30,16,33,1001,3.58,0,0 +2014,8,10,16,23,16,33,1000,4.02,0,0 +2014,8,10,17,20,18,33,1000,8.94,0,0 +2014,8,10,18,28,17,32,1000,13.86,0,0 +2014,8,10,19,43,18,31,1001,18.78,0,0 +2014,8,10,20,53,19,29,1001,20.57,0,0 +2014,8,10,21,49,19,27,1002,22.36,0,0 +2014,8,10,22,53,20,26,1003,1.79,0,0 +2014,8,10,23,78,20,25,1003,3.58,0,0 +2014,8,11,0,86,19,24,1004,1.79,0,0 +2014,8,11,1,65,20,23,1004,5.81,0,0 +2014,8,11,2,58,19,23,1004,10.73,0,0 +2014,8,11,3,53,18,22,1004,13.86,0,0 +2014,8,11,4,26,18,22,1004,16.99,0,0 +2014,8,11,5,20,18,21,1005,20.12,0,0 +2014,8,11,6,12,18,21,1005,21.91,0,0 +2014,8,11,7,13,18,25,1005,23.7,0,0 +2014,8,11,8,12,16,27,1006,27.72,0,0 +2014,8,11,9,12,15,29,1006,4.02,0,0 +2014,8,11,10,6,11,31,1006,4.92,0,0 +2014,8,11,11,11,13,33,1005,9.84,0,0 +2014,8,11,12,6,11,32,1005,4.02,0,0 +2014,8,11,13,6,11,34,1004,1.79,0,0 +2014,8,11,14,11,11,33,1003,4.02,0,0 +2014,8,11,15,10,10,34,1003,1.79,0,0 +2014,8,11,16,9,10,34,1002,3.58,0,0 +2014,8,11,17,9,11,34,1002,5.37,0,0 +2014,8,11,18,14,12,33,1002,0.89,0,0 +2014,8,11,19,21,16,29,1002,1.34,0,0 +2014,8,11,20,22,16,26,1003,2.23,0,0 +2014,8,11,21,23,12,31,1004,1.79,0,0 +2014,8,11,22,27,14,29,1005,3.13,0,0 +2014,8,11,23,42,15,23,1005,1.79,0,0 +2014,8,12,0,50,17,21,1005,0.45,0,0 +2014,8,12,1,57,17,20,1005,0.9,0,0 +2014,8,12,2,29,14,22,1005,1.79,0,0 +2014,8,12,3,26,13,21,1005,3.58,0,0 +2014,8,12,4,23,12,20,1005,5.37,0,0 +2014,8,12,5,18,10,23,1005,4.02,0,0 +2014,8,12,6,15,12,21,1006,3.13,0,0 +2014,8,12,7,12,11,25,1006,6.26,0,0 +2014,8,12,8,17,10,27,1007,3.13,0,0 +2014,8,12,9,8,11,29,1007,6.26,0,0 +2014,8,12,10,10,11,31,1007,0.89,0,0 +2014,8,12,11,12,12,32,1007,3.13,0,0 +2014,8,12,12,19,13,32,1006,6.26,0,0 +2014,8,12,13,21,12,33,1006,9.39,0,0 +2014,8,12,14,30,13,33,1005,0.89,0,0 +2014,8,12,15,33,13,32,1004,1.79,0,0 +2014,8,12,16,28,12,32,1004,3.58,0,0 +2014,8,12,17,24,13,31,1004,6.71,0,0 +2014,8,12,18,31,16,29,1005,8.5,0,0 +2014,8,12,19,40,17,27,1005,0.89,0,0 +2014,8,12,20,29,18,26,1006,0.89,0,0 +2014,8,12,21,64,17,26,1006,2.68,0,0 +2014,8,12,22,38,16,28,1006,3.13,0,0 +2014,8,12,23,19,15,28,1007,7.15,0,0 +2014,8,13,0,23,18,22,1008,5.81,0,1 +2014,8,13,1,13,18,21,1007,3.13,0,2 +2014,8,13,2,15,17,21,1007,0.89,0,0 +2014,8,13,3,21,18,20,1007,3.13,0,0 +2014,8,13,4,18,18,21,1007,4.02,0,1 +2014,8,13,5,19,18,20,1008,0.89,0,2 +2014,8,13,6,38,18,20,1008,0.89,0,3 +2014,8,13,7,36,19,20,1008,3.13,0,4 +2014,8,13,8,36,19,20,1008,3.13,0,5 +2014,8,13,9,53,18,20,1009,4.92,0,6 +2014,8,13,10,55,18,20,1009,3.13,0,7 +2014,8,13,11,43,18,20,1009,1.79,0,8 +2014,8,13,12,38,18,20,1009,4.92,0,9 +2014,8,13,13,26,18,19,1009,8.05,0,10 +2014,8,13,14,17,18,20,1008,1.79,0,11 +2014,8,13,15,17,18,20,1008,3.13,0,0 +2014,8,13,16,15,18,21,1008,4.92,0,0 +2014,8,13,17,19,18,23,1008,1.79,0,0 +2014,8,13,18,21,18,23,1008,1.79,0,0 +2014,8,13,19,24,18,21,1008,0.89,0,0 +2014,8,13,20,24,18,20,1008,1.78,0,0 +2014,8,13,21,26,18,20,1009,0.89,0,0 +2014,8,13,22,24,17,19,1009,1.78,0,0 +2014,8,13,23,21,17,18,1009,0.89,0,0 +2014,8,14,0,29,17,18,1009,1.79,0,0 +2014,8,14,1,19,16,18,1009,0.89,0,0 +2014,8,14,2,22,16,18,1009,1.79,0,0 +2014,8,14,3,22,16,17,1009,1.79,0,0 +2014,8,14,4,16,16,17,1008,4.92,0,0 +2014,8,14,5,28,15,17,1008,0.89,0,0 +2014,8,14,6,35,16,17,1009,1.34,0,0 +2014,8,14,7,17,17,19,1009,3.13,0,0 +2014,8,14,8,25,17,22,1009,6.26,0,0 +2014,8,14,9,27,17,23,1009,8.05,0,0 +2014,8,14,10,26,18,25,1009,0.89,0,0 +2014,8,14,11,26,17,26,1008,1.78,0,0 +2014,8,14,12,28,18,27,1008,1.79,0,0 +2014,8,14,13,42,17,29,1007,4.92,0,0 +2014,8,14,14,48,17,29,1007,8.94,0,0 +2014,8,14,15,31,18,30,1006,13.86,0,0 +2014,8,14,16,27,17,30,1005,19.67,0,0 +2014,8,14,17,26,17,29,1005,24.59,0,0 +2014,8,14,18,35,16,28,1005,28.61,0,0 +2014,8,14,19,32,17,28,1006,33.53,0,0 +2014,8,14,20,38,18,27,1006,36.66,0,0 +2014,8,14,21,37,16,27,1007,3.13,0,0 +2014,8,14,22,26,16,23,1006,0.89,0,0 +2014,8,14,23,17,17,21,1007,1.78,0,0 +2014,8,15,0,32,17,21,1006,1.79,0,0 +2014,8,15,1,26,18,20,1006,0.45,0,0 +2014,8,15,2,34,18,19,1006,0.89,0,0 +2014,8,15,3,32,17,19,1006,1.79,0,0 +2014,8,15,4,23,17,18,1006,0.89,0,0 +2014,8,15,5,35,17,18,1007,0.89,0,0 +2014,8,15,6,71,17,19,1007,0.89,0,0 +2014,8,15,7,102,19,21,1008,1.78,0,0 +2014,8,15,8,121,19,23,1008,1.79,0,0 +2014,8,15,9,114,19,25,1008,4.92,0,0 +2014,8,15,10,64,19,27,1008,0.89,0,0 +2014,8,15,11,47,17,29,1007,1.79,0,0 +2014,8,15,12,26,15,31,1007,0.89,0,0 +2014,8,15,13,17,14,32,1006,1.78,0,0 +2014,8,15,14,12,13,32,1006,1.79,0,0 +2014,8,15,15,17,14,32,1005,4.92,0,0 +2014,8,15,16,34,14,32,1005,8.05,0,0 +2014,8,15,17,48,15,31,1005,12.07,0,0 +2014,8,15,18,22,17,24,1005,13.86,0,0 +2014,8,15,19,30,18,23,1005,0.89,0,0 +2014,8,15,20,27,19,22,1006,1.78,0,0 +2014,8,15,21,24,19,22,1006,2.67,0,0 +2014,8,15,22,28,19,22,1007,1.79,0,0 +2014,8,15,23,33,19,23,1007,3.58,0,0 +2014,8,16,0,55,19,23,1007,6.71,0,0 +2014,8,16,1,70,18,22,1007,0.89,0,0 +2014,8,16,2,71,19,21,1007,1.78,0,0 +2014,8,16,3,88,18,20,1007,1.79,0,0 +2014,8,16,4,82,18,21,1008,3.58,0,0 +2014,8,16,5,52,17,20,1008,5.37,0,0 +2014,8,16,6,46,17,20,1008,7.16,0,0 +2014,8,16,7,33,19,22,1009,0.45,0,0 +2014,8,16,8,37,19,24,1009,1.79,0,0 +2014,8,16,9,49,18,26,1009,3.58,0,0 +2014,8,16,10,71,17,29,1009,0.89,0,0 +2014,8,16,11,68,18,31,1009,1.78,0,0 +2014,8,16,12,48,18,31,1008,2.67,0,0 +2014,8,16,13,44,19,32,1008,3.56,0,0 +2014,8,16,14,38,18,32,1007,6.69,0,0 +2014,8,16,15,28,16,33,1006,3.13,0,0 +2014,8,16,16,52,16,33,1006,6.26,0,0 +2014,8,16,17,63,17,32,1006,9.39,0,0 +2014,8,16,18,56,18,31,1006,12.52,0,0 +2014,8,16,19,61,19,28,1006,14.31,0,0 +2014,8,16,20,74,19,27,1007,0.89,0,0 +2014,8,16,21,80,19,24,1007,0.89,0,0 +2014,8,16,22,89,20,26,1008,2.68,0,0 +2014,8,16,23,97,20,25,1008,0.89,0,0 +2014,8,17,0,172,22,25,1008,3.13,0,0 +2014,8,17,1,209,21,25,1008,4.92,0,0 +2014,8,17,2,72,21,25,1008,8.05,0,0 +2014,8,17,3,19,18,22,1008,11.18,0,0 +2014,8,17,4,23,17,23,1009,16.1,0,0 +2014,8,17,5,31,17,22,1010,20.12,0,0 +2014,8,17,6,27,17,20,1011,1.79,0,0 +2014,8,17,7,33,18,23,1010,0.89,0,0 +2014,8,17,8,38,17,24,1010,1.79,0,0 +2014,8,17,9,36,18,25,1010,4.92,0,0 +2014,8,17,10,39,18,27,1010,1.79,0,0 +2014,8,17,11,44,17,29,1010,3.58,0,0 +2014,8,17,12,37,17,30,1010,1.79,0,0 +2014,8,17,13,36,17,30,1010,3.13,0,0 +2014,8,17,14,28,16,30,1009,8.05,0,0 +2014,8,17,15,38,16,30,1009,12.97,0,0 +2014,8,17,16,23,16,30,1009,16.1,0,0 +2014,8,17,17,20,16,30,1010,19.23,0,0 +2014,8,17,18,20,17,28,1010,22.36,0,0 +2014,8,17,19,35,18,27,1010,24.15,0,0 +2014,8,17,20,44,18,26,1011,25.94,0,0 +2014,8,17,21,55,18,24,1012,0.89,0,0 +2014,8,17,22,73,19,24,1011,0.89,0,0 +2014,8,17,23,81,19,23,1011,0.89,0,0 +2014,8,18,0,93,19,22,1011,0.89,0,0 +2014,8,18,1,91,19,22,1012,1.78,0,0 +2014,8,18,2,51,19,22,1012,0.89,0,0 +2014,8,18,3,56,18,21,1012,1.78,0,0 +2014,8,18,4,56,17,22,1011,0.45,0,0 +2014,8,18,5,34,17,20,1012,3.13,0,0 +2014,8,18,6,35,17,20,1012,4.92,0,0 +2014,8,18,7,27,17,22,1012,8.94,0,0 +2014,8,18,8,26,17,24,1013,1.79,0,0 +2014,8,18,9,39,17,25,1013,3.58,0,0 +2014,8,18,10,35,18,27,1013,0.89,0,0 +2014,8,18,11,38,19,28,1012,1.79,0,0 +2014,8,18,12,59,19,28,1012,3.58,0,0 +2014,8,18,13,69,19,29,1012,6.71,0,0 +2014,8,18,14,80,18,29,1011,9.84,0,0 +2014,8,18,15,74,19,30,1010,12.97,0,0 +2014,8,18,16,NA,18,30,1010,16.99,0,0 +2014,8,18,17,NA,18,30,1010,21.01,0,0 +2014,8,18,18,65,18,29,1010,24.14,0,0 +2014,8,18,19,71,18,28,1011,28.16,0,0 +2014,8,18,20,71,18,27,1011,31.29,0,0 +2014,8,18,21,74,18,25,1012,33.08,0,0 +2014,8,18,22,57,16,26,1012,37.1,0,0 +2014,8,18,23,60,16,26,1012,40.23,0,0 +2014,8,19,0,74,17,23,1012,42.02,0,0 +2014,8,19,1,61,18,22,1012,42.91,0,0 +2014,8,19,2,66,18,21,1011,0.89,0,0 +2014,8,19,3,74,18,21,1012,1.79,0,0 +2014,8,19,4,88,18,20,1011,0.89,0,0 +2014,8,19,5,94,18,21,1012,0.89,0,0 +2014,8,19,6,97,19,20,1012,0.89,0,0 +2014,8,19,7,111,19,22,1012,0.89,0,0 +2014,8,19,8,107,19,25,1013,3.13,0,0 +2014,8,19,9,123,19,27,1013,4.92,0,0 +2014,8,19,10,97,19,29,1013,0.89,0,0 +2014,8,19,11,93,20,30,1012,1.78,0,0 +2014,8,19,12,78,19,30,1012,3.57,0,0 +2014,8,19,13,69,19,32,1011,4.02,0,0 +2014,8,19,14,61,18,33,1010,7.15,0,0 +2014,8,19,15,55,17,33,1010,11.17,0,0 +2014,8,19,16,48,17,32,1009,16.09,0,0 +2014,8,19,17,13,16,32,1009,20.11,0,0 +2014,8,19,18,59,17,31,1009,24.13,0,0 +2014,8,19,19,74,17,29,1009,28.15,0,0 +2014,8,19,20,80,19,28,1010,32.17,0,0 +2014,8,19,21,84,19,28,1010,35.3,0,0 +2014,8,19,22,78,19,26,1010,37.09,0,0 +2014,8,19,23,70,19,26,1010,38.88,0,0 +2014,8,20,0,73,19,25,1010,40.67,0,0 +2014,8,20,1,71,19,25,1010,42.46,0,0 +2014,8,20,2,68,19,23,1010,44.25,0,0 +2014,8,20,3,88,19,22,1010,46.04,0,0 +2014,8,20,4,99,19,22,1009,46.93,0,0 +2014,8,20,5,104,19,21,1010,48.72,0,0 +2014,8,20,6,106,19,21,1010,49.61,0,0 +2014,8,20,7,115,19,23,1010,0.89,0,0 +2014,8,20,8,112,19,25,1010,0.89,0,0 +2014,8,20,9,114,20,27,1010,1.78,0,0 +2014,8,20,10,107,21,28,1010,1.79,0,0 +2014,8,20,11,99,21,30,1010,1.79,0,0 +2014,8,20,12,81,19,32,1009,4.92,0,0 +2014,8,20,13,69,18,32,1008,9.84,0,0 +2014,8,20,14,69,17,33,1007,14.76,0,0 +2014,8,20,15,77,16,33,1006,20.57,0,0 +2014,8,20,16,NA,17,33,1006,25.49,0,0 +2014,8,20,17,NA,18,32,1006,30.41,0,0 +2014,8,20,18,NA,18,31,1006,34.43,0,0 +2014,8,20,19,NA,18,29,1007,37.56,0,0 +2014,8,20,20,NA,19,28,1007,40.69,0,0 +2014,8,20,21,NA,19,28,1007,42.48,0,0 +2014,8,20,22,NA,19,27,1008,45.61,0,0 +2014,8,20,23,NA,19,27,1008,47.4,0,0 +2014,8,21,0,NA,19,26,1008,50.53,0,0 +2014,8,21,1,99,19,25,1007,52.32,0,0 +2014,8,21,2,112,20,24,1007,0.45,0,0 +2014,8,21,3,119,20,23,1007,1.79,0,0 +2014,8,21,4,122,20,23,1007,2.68,0,0 +2014,8,21,5,133,20,23,1007,0.89,0,0 +2014,8,21,6,133,20,23,1007,0.89,0,0 +2014,8,21,7,140,20,24,1007,0.89,0,0 +2014,8,21,8,153,21,24,1008,1.78,0,1 +2014,8,21,9,173,21,25,1008,2.67,0,0 +2014,8,21,10,169,21,27,1008,3.56,0,1 +2014,8,21,11,163,20,29,1008,4.45,0,0 +2014,8,21,12,138,20,31,1006,1.79,0,0 +2014,8,21,13,141,21,32,1006,3.58,0,0 +2014,8,21,14,137,21,32,1005,5.37,0,0 +2014,8,21,15,134,21,32,1005,10.29,0,0 +2014,8,21,16,123,21,32,1005,14.31,0,0 +2014,8,21,17,113,20,32,1005,18.33,0,0 +2014,8,21,18,115,20,32,1005,20.12,0,0 +2014,8,21,19,123,21,29,1006,21.91,0,0 +2014,8,21,20,145,20,29,1008,5.81,0,0 +2014,8,21,21,69,17,24,1009,4.92,0,0 +2014,8,21,22,19,16,25,1009,9.84,0,0 +2014,8,21,23,26,17,24,1008,4.92,0,0 +2014,8,22,0,22,17,24,1008,9.84,0,0 +2014,8,22,1,23,18,23,1008,1.79,0,0 +2014,8,22,2,21,17,23,1008,4.92,0,0 +2014,8,22,3,23,18,21,1008,0.89,0,0 +2014,8,22,4,18,18,20,1008,1.78,0,0 +2014,8,22,5,24,18,20,1008,1.79,0,0 +2014,8,22,6,30,18,20,1008,3.58,0,0 +2014,8,22,7,19,19,22,1009,5.37,0,0 +2014,8,22,8,23,19,24,1009,1.79,0,0 +2014,8,22,9,19,19,25,1009,0.89,0,0 +2014,8,22,10,22,19,26,1009,1.78,0,0 +2014,8,22,11,31,20,27,1009,2.67,0,0 +2014,8,22,12,42,19,27,1008,3.56,0,0 +2014,8,22,13,41,19,27,1007,1.79,0,0 +2014,8,22,14,56,19,28,1007,4.92,0,0 +2014,8,22,15,72,19,28,1006,0.89,0,0 +2014,8,22,16,76,19,28,1006,3.13,0,0 +2014,8,22,17,87,20,28,1006,0.89,0,0 +2014,8,22,18,91,20,27,1005,1.79,0,0 +2014,8,22,19,97,20,27,1006,3.58,0,0 +2014,8,22,20,110,20,25,1006,0.89,0,0 +2014,8,22,21,124,21,24,1006,0.89,0,0 +2014,8,22,22,123,21,24,1007,0.45,0,0 +2014,8,22,23,110,21,24,1006,0.89,0,0 +2014,8,23,0,115,21,24,1006,2.68,0,0 +2014,8,23,1,120,21,24,1006,3.57,0,0 +2014,8,23,2,149,22,25,1006,6.7,0,1 +2014,8,23,3,142,21,23,1006,0.89,0,0 +2014,8,23,4,142,21,22,1006,1.78,0,0 +2014,8,23,5,128,20,22,1006,2.67,0,0 +2014,8,23,6,120,21,22,1006,0.89,0,0 +2014,8,23,7,138,21,22,1006,0.89,0,0 +2014,8,23,8,134,20,24,1006,1.78,0,0 +2014,8,23,9,153,20,26,1006,2.67,0,0 +2014,8,23,10,133,21,28,1006,3.56,0,0 +2014,8,23,11,134,21,29,1006,5.35,0,0 +2014,8,23,12,116,20,30,1005,1.79,0,0 +2014,8,23,13,106,19,32,1004,1.79,0,0 +2014,8,23,14,87,18,32,1003,2.68,0,0 +2014,8,23,15,83,19,32,1003,3.13,0,0 +2014,8,23,16,108,19,31,1003,6.26,0,0 +2014,8,23,17,95,20,32,1003,9.39,0,0 +2014,8,23,18,104,21,31,1003,11.18,0,0 +2014,8,23,19,118,21,30,1004,14.31,0,0 +2014,8,23,20,133,22,28,1004,16.1,0,0 +2014,8,23,21,151,23,27,1005,0.89,0,0 +2014,8,23,22,171,22,27,1006,1.79,0,0 +2014,8,23,23,35,18,25,1006,7.15,0,0 +2014,8,24,0,26,19,24,1006,11.17,0,1 +2014,8,24,1,20,18,22,1006,1.79,0,2 +2014,8,24,2,25,19,22,1005,4.92,0,0 +2014,8,24,3,31,19,22,1006,8.05,0,0 +2014,8,24,4,35,20,22,1006,0.89,0,0 +2014,8,24,5,39,20,22,1007,3.13,0,0 +2014,8,24,6,45,20,22,1007,4.02,0,0 +2014,8,24,7,29,20,22,1007,0.89,0,0 +2014,8,24,8,3,19,24,1008,4.02,0,0 +2014,8,24,9,6,15,27,1007,8.94,0,0 +2014,8,24,10,7,12,27,1007,14.75,0,0 +2014,8,24,11,5,12,31,1007,17.88,0,0 +2014,8,24,12,4,11,30,1007,0.89,0,0 +2014,8,24,13,8,12,32,1006,4.02,0,0 +2014,8,24,14,6,12,32,1006,5.81,0,0 +2014,8,24,15,10,12,32,1005,6.7,0,0 +2014,8,24,16,7,12,32,1005,8.49,0,0 +2014,8,24,17,6,12,30,1006,3.13,0,0 +2014,8,24,18,7,15,29,1006,6.26,0,0 +2014,8,24,19,12,17,27,1007,8.05,0,0 +2014,8,24,20,17,19,26,1008,11.18,0,0 +2014,8,24,21,46,19,26,1009,12.97,0,0 +2014,8,24,22,44,17,24,1010,14.76,0,0 +2014,8,24,23,38,18,23,1010,0.89,0,0 +2014,8,25,0,25,18,22,1010,0.89,0,0 +2014,8,25,1,16,18,22,1010,0.89,0,0 +2014,8,25,2,27,18,21,1010,1.78,0,0 +2014,8,25,3,24,17,20,1011,1.79,0,0 +2014,8,25,4,27,16,19,1011,3.58,0,0 +2014,8,25,5,19,16,18,1011,5.37,0,0 +2014,8,25,6,15,16,18,1011,8.5,0,0 +2014,8,25,7,16,17,22,1012,11.63,0,0 +2014,8,25,8,17,14,24,1012,3.13,0,0 +2014,8,25,9,17,12,27,1012,3.13,0,0 +2014,8,25,10,10,11,29,1012,8.05,0,0 +2014,8,25,11,7,11,30,1012,4.02,0,0 +2014,8,25,12,7,11,31,1011,4.02,0,0 +2014,8,25,13,9,9,32,1011,8.04,0,0 +2014,8,25,14,7,8,32,1010,1.79,0,0 +2014,8,25,15,9,9,32,1009,1.79,0,0 +2014,8,25,16,6,8,32,1009,3.58,0,0 +2014,8,25,17,6,9,31,1009,5.37,0,0 +2014,8,25,18,8,9,30,1009,7.16,0,0 +2014,8,25,19,11,9,29,1010,11.18,0,0 +2014,8,25,20,18,9,28,1011,15.2,0,0 +2014,8,25,21,21,11,27,1012,19.22,0,0 +2014,8,25,22,30,11,27,1012,23.24,0,0 +2014,8,25,23,26,13,22,1012,1.79,0,0 +2014,8,26,0,26,14,21,1013,0.89,0,0 +2014,8,26,1,41,15,19,1013,1.34,0,0 +2014,8,26,2,35,15,19,1012,1.79,0,0 +2014,8,26,3,39,14,18,1013,1.79,0,0 +2014,8,26,4,40,13,18,1013,3.58,0,0 +2014,8,26,5,23,13,16,1013,6.71,0,0 +2014,8,26,6,27,13,17,1014,9.84,0,0 +2014,8,26,7,15,14,21,1014,13.86,0,0 +2014,8,26,8,19,13,25,1014,15.65,0,0 +2014,8,26,9,19,12,27,1014,18.78,0,0 +2014,8,26,10,17,10,30,1014,20.57,0,0 +2014,8,26,11,17,10,32,1014,0.89,0,0 +2014,8,26,12,13,9,33,1013,1.78,0,0 +2014,8,26,13,10,7,34,1013,1.79,0,0 +2014,8,26,14,6,5,35,1012,1.79,0,0 +2014,8,26,15,8,4,35,1012,4.92,0,0 +2014,8,26,16,10,4,35,1011,6.71,0,0 +2014,8,26,17,6,5,34,1011,7.6,0,0 +2014,8,26,18,13,8,32,1011,1.79,0,0 +2014,8,26,19,19,7,30,1012,5.81,0,0 +2014,8,26,20,27,8,29,1012,8.94,0,0 +2014,8,26,21,31,9,30,1012,12.07,0,0 +2014,8,26,22,29,9,28,1012,15.2,0,0 +2014,8,26,23,39,13,22,1013,1.79,0,0 +2014,8,27,0,57,14,21,1013,1.79,0,0 +2014,8,27,1,71,15,20,1013,0.45,0,0 +2014,8,27,2,68,15,19,1013,1.34,0,0 +2014,8,27,3,79,14,19,1013,1.79,0,0 +2014,8,27,4,89,14,18,1013,2.68,0,0 +2014,8,27,5,45,13,17,1013,3.13,0,0 +2014,8,27,6,31,13,17,1013,1.79,0,0 +2014,8,27,7,21,15,19,1014,4.92,0,0 +2014,8,27,8,27,13,25,1014,8.05,0,0 +2014,8,27,9,24,13,27,1014,1.79,0,0 +2014,8,27,10,30,13,29,1014,0.89,0,0 +2014,8,27,11,25,11,32,1014,1.78,0,0 +2014,8,27,12,38,13,33,1013,1.79,0,0 +2014,8,27,13,43,13,34,1013,3.13,0,0 +2014,8,27,14,48,11,34,1012,4.02,0,0 +2014,8,27,15,48,12,34,1012,8.04,0,0 +2014,8,27,16,47,14,33,1012,12.96,0,0 +2014,8,27,17,63,14,33,1012,17.88,0,0 +2014,8,27,18,62,13,31,1012,21.01,0,0 +2014,8,27,19,67,14,29,1012,22.8,0,0 +2014,8,27,20,73,16,25,1013,0.89,0,0 +2014,8,27,21,82,17,24,1013,1.79,0,0 +2014,8,27,22,94,16,26,1013,3.58,0,0 +2014,8,27,23,93,16,26,1013,5.37,0,0 +2014,8,28,0,89,17,24,1013,6.26,0,0 +2014,8,28,1,90,17,24,1013,8.05,0,0 +2014,8,28,2,100,17,23,1013,8.94,0,0 +2014,8,28,3,103,16,24,1013,0.89,0,0 +2014,8,28,4,108,18,22,1014,0.89,0,0 +2014,8,28,5,91,17,22,1014,1.78,0,0 +2014,8,28,6,83,17,23,1014,0.45,0,0 +2014,8,28,7,69,18,24,1015,1.34,0,0 +2014,8,28,8,64,17,24,1015,2.23,0,0 +2014,8,28,9,67,17,25,1015,1.79,0,0 +2014,8,28,10,62,17,25,1015,0.89,0,0 +2014,8,28,11,68,16,25,1015,4.02,0,0 +2014,8,28,12,66,15,28,1015,7.15,0,0 +2014,8,28,13,60,16,28,1015,1.79,0,0 +2014,8,28,14,73,16,28,1014,2.68,0,0 +2014,8,28,15,80,16,28,1013,3.57,0,1 +2014,8,28,16,80,18,27,1013,4.46,0,2 +2014,8,28,17,84,19,27,1013,1.79,0,3 +2014,8,28,18,98,19,26,1013,3.58,0,4 +2014,8,28,19,99,21,24,1013,4.47,0,5 +2014,8,28,20,129,19,20,1013,7.15,0,6 +2014,8,28,21,125,18,20,1015,4.92,0,0 +2014,8,28,22,74,18,19,1016,3.13,0,0 +2014,8,28,23,58,18,19,1016,1.79,0,1 +2014,8,29,0,79,18,19,1015,1.79,0,0 +2014,8,29,1,77,18,19,1015,1.79,0,0 +2014,8,29,2,80,19,20,1016,4.02,0,1 +2014,8,29,3,85,18,19,1015,0.89,0,2 +2014,8,29,4,74,18,19,1015,1.79,0,0 +2014,8,29,5,78,18,19,1015,1.79,0,0 +2014,8,29,6,90,18,19,1015,1.79,0,0 +2014,8,29,7,89,18,19,1015,2.68,0,0 +2014,8,29,8,68,18,21,1016,0.89,0,0 +2014,8,29,9,71,18,23,1016,1.78,0,0 +2014,8,29,10,66,19,24,1016,1.79,0,0 +2014,8,29,11,59,18,26,1016,0.89,0,0 +2014,8,29,12,49,18,26,1016,2.68,0,0 +2014,8,29,13,62,18,26,1015,3.57,0,0 +2014,8,29,14,74,19,26,1015,1.79,0,0 +2014,8,29,15,81,18,26,1015,0.89,0,0 +2014,8,29,16,85,18,27,1015,1.78,0,0 +2014,8,29,17,88,18,26,1014,1.79,0,0 +2014,8,29,18,87,19,26,1015,2.68,0,0 +2014,8,29,19,95,20,24,1015,3.57,0,0 +2014,8,29,20,121,20,23,1015,0.89,0,0 +2014,8,29,21,139,20,22,1016,1.34,0,0 +2014,8,29,22,132,21,22,1016,1.79,0,0 +2014,8,29,23,111,21,22,1016,2.68,0,0 +2014,8,30,0,107,20,23,1016,3.57,0,0 +2014,8,30,1,102,20,22,1016,1.79,0,0 +2014,8,30,2,125,20,22,1016,0.89,0,0 +2014,8,30,3,174,20,22,1016,1.78,0,0 +2014,8,30,4,151,20,21,1016,2.67,0,0 +2014,8,30,5,154,20,21,1016,1.79,0,0 +2014,8,30,6,163,20,22,1016,3.58,0,0 +2014,8,30,7,148,20,22,1016,0.89,0,1 +2014,8,30,8,152,20,22,1016,1.79,0,0 +2014,8,30,9,151,20,23,1016,0.89,0,0 +2014,8,30,10,160,20,24,1016,1.78,0,0 +2014,8,30,11,173,21,24,1015,1.79,0,0 +2014,8,30,12,196,21,25,1015,3.58,0,0 +2014,8,30,13,181,21,25,1014,5.37,0,0 +2014,8,30,14,183,21,25,1013,3.13,0,0 +2014,8,30,15,130,21,25,1013,0.89,0,1 +2014,8,30,16,158,22,24,1013,1.78,0,0 +2014,8,30,17,164,22,24,1013,1.79,0,0 +2014,8,30,18,149,21,23,1012,1.79,0,0 +2014,8,30,19,141,21,22,1013,0.89,0,0 +2014,8,30,20,143,20,22,1013,0.89,0,0 +2014,8,30,21,143,20,22,1014,2.68,0,0 +2014,8,30,22,138,21,22,1013,0.89,0,0 +2014,8,30,23,102,20,22,1014,3.13,0,1 +2014,8,31,0,100,20,21,1014,1.79,0,2 +2014,8,31,1,107,20,21,1013,0.89,0,3 +2014,8,31,2,131,20,21,1013,1.78,0,0 +2014,8,31,3,139,20,21,1013,0.89,0,0 +2014,8,31,4,121,20,21,1012,2.68,0,0 +2014,8,31,5,97,20,21,1013,1.79,0,0 +2014,8,31,6,86,20,21,1013,3.58,0,0 +2014,8,31,7,85,20,21,1013,0.89,0,0 +2014,8,31,8,79,20,21,1013,1.78,0,0 +2014,8,31,9,80,21,23,1014,1.79,0,0 +2014,8,31,10,105,21,23,1013,3.13,0,0 +2014,8,31,11,61,20,24,1013,4.92,0,0 +2014,8,31,12,58,19,25,1013,0.89,0,0 +2014,8,31,13,55,20,26,1013,1.79,0,0 +2014,8,31,14,65,21,27,1012,4.92,0,0 +2014,8,31,15,78,20,26,1012,9.84,0,0 +2014,8,31,16,79,20,26,1012,13.86,0,0 +2014,8,31,17,71,20,22,1012,4.02,0,1 +2014,8,31,18,72,20,22,1012,1.79,0,2 +2014,8,31,19,75,21,22,1013,1.79,0,0 +2014,8,31,20,93,21,22,1013,0.89,0,0 +2014,8,31,21,99,21,21,1014,2.68,0,0 +2014,8,31,22,104,20,21,1014,3.57,0,0 +2014,8,31,23,104,20,21,1014,0.89,0,0 +2014,9,1,0,103,20,20,1014,1.78,0,0 +2014,9,1,1,96,20,20,1014,0.89,0,0 +2014,9,1,2,95,21,21,1014,0.45,0,0 +2014,9,1,3,87,21,21,1014,0.89,0,0 +2014,9,1,4,96,21,21,1014,0.89,0,0 +2014,9,1,5,102,20,21,1013,0.89,0,0 +2014,9,1,6,101,20,21,1014,0.89,0,0 +2014,9,1,7,106,20,21,1014,0.89,0,0 +2014,9,1,8,109,20,22,1014,1.79,0,0 +2014,9,1,9,123,20,23,1014,3.58,0,0 +2014,9,1,10,132,20,24,1014,5.37,0,0 +2014,9,1,11,139,20,25,1014,7.16,0,0 +2014,9,1,12,144,20,25,1013,8.95,0,0 +2014,9,1,13,153,20,25,1012,10.74,0,0 +2014,9,1,14,146,21,26,1011,12.53,0,1 +2014,9,1,15,163,21,22,1011,4.02,0,3 +2014,9,1,16,95,20,22,1011,4.02,0,4 +2014,9,1,17,149,22,23,1011,0.89,0,0 +2014,9,1,18,157,22,23,1011,0.89,0,0 +2014,9,1,19,163,22,23,1011,1.79,0,2 +2014,9,1,20,175,20,21,1011,1.79,0,3 +2014,9,1,21,84,19,20,1012,8.94,0,5 +2014,9,1,22,25,19,20,1012,3.13,0,0 +2014,9,1,23,21,19,20,1011,4.92,0,0 +2014,9,2,0,36,19,20,1011,0.89,0,0 +2014,9,2,1,50,19,20,1010,1.78,0,1 +2014,9,2,2,59,20,20,1011,2.67,0,2 +2014,9,2,3,49,19,20,1010,0.89,0,3 +2014,9,2,4,30,19,20,1010,1.79,0,4 +2014,9,2,5,28,19,20,1009,1.79,0,5 +2014,9,2,6,27,19,19,1010,1.79,0,6 +2014,9,2,7,21,19,19,1010,3.58,0,7 +2014,9,2,8,21,19,20,1010,1.79,0,8 +2014,9,2,9,25,20,20,1010,1.79,0,9 +2014,9,2,10,34,20,21,1011,3.58,0,0 +2014,9,2,11,37,20,21,1010,5.37,0,1 +2014,9,2,12,39,19,21,1010,0.89,0,0 +2014,9,2,13,36,19,21,1010,1.79,0,0 +2014,9,2,14,19,20,24,1010,3.58,0,0 +2014,9,2,15,23,19,21,1010,1.79,0,0 +2014,9,2,16,24,19,21,1010,1.79,0,1 +2014,9,2,17,25,19,20,1011,0.89,0,2 +2014,9,2,18,12,18,20,1011,3.13,0,3 +2014,9,2,19,9,18,20,1011,3.13,0,0 +2014,9,2,20,8,17,21,1012,3.13,0,0 +2014,9,2,21,10,16,21,1012,4.92,0,0 +2014,9,2,22,6,17,19,1012,1.79,0,0 +2014,9,2,23,11,17,19,1012,3.58,0,0 +2014,9,3,0,10,16,18,1012,0.45,0,0 +2014,9,3,1,9,16,17,1011,3.13,0,0 +2014,9,3,2,9,16,17,1011,6.26,0,0 +2014,9,3,3,12,15,17,1011,0.89,0,0 +2014,9,3,4,9,15,16,1011,1.79,0,0 +2014,9,3,5,7,15,16,1011,4.92,0,0 +2014,9,3,6,6,15,16,1011,8.05,0,0 +2014,9,3,7,14,15,19,1012,12.07,0,0 +2014,9,3,8,11,16,21,1012,15.2,0,0 +2014,9,3,9,17,16,23,1012,18.33,0,0 +2014,9,3,10,16,15,25,1012,21.46,0,0 +2014,9,3,11,13,13,27,1011,0.89,0,0 +2014,9,3,12,8,12,28,1010,2.68,0,0 +2014,9,3,13,12,12,28,1009,1.79,0,0 +2014,9,3,14,10,11,29,1008,3.13,0,0 +2014,9,3,15,12,10,31,1007,0.89,0,0 +2014,9,3,16,11,11,30,1006,4.92,0,0 +2014,9,3,17,16,12,30,1006,10.73,0,0 +2014,9,3,18,12,13,28,1006,14.75,0,0 +2014,9,3,19,11,13,27,1006,19.67,0,0 +2014,9,3,20,15,13,26,1006,24.59,0,0 +2014,9,3,21,21,13,25,1007,26.38,0,0 +2014,9,3,22,19,15,21,1007,0.89,0,0 +2014,9,3,23,23,16,19,1006,1.78,0,0 +2014,9,4,0,23,16,18,1006,1.79,0,0 +2014,9,4,1,51,16,17,1006,2.68,0,0 +2014,9,4,2,38,16,17,1006,3.57,0,0 +2014,9,4,3,47,15,16,1006,0.89,0,0 +2014,9,4,4,50,15,16,1005,1.78,0,0 +2014,9,4,5,54,15,15,1005,0.89,0,0 +2014,9,4,6,53,14,15,1006,2.68,0,0 +2014,9,4,7,56,17,19,1006,4.47,0,0 +2014,9,4,8,81,16,22,1006,6.26,0,0 +2014,9,4,9,48,15,24,1006,0.89,0,0 +2014,9,4,10,44,15,27,1006,1.78,0,0 +2014,9,4,11,27,16,29,1005,1.79,0,0 +2014,9,4,12,31,14,31,1005,1.79,0,0 +2014,9,4,13,45,14,33,1004,3.13,0,0 +2014,9,4,14,33,12,34,1003,7.15,0,0 +2014,9,4,15,17,12,34,1003,12.07,0,0 +2014,9,4,16,15,14,34,1003,16.99,0,0 +2014,9,4,17,27,15,33,1003,21.01,0,0 +2014,9,4,18,35,16,30,1003,25.93,0,0 +2014,9,4,19,31,17,28,1004,29.06,0,0 +2014,9,4,20,38,19,23,1004,29.95,0,0 +2014,9,4,21,60,18,23,1005,0.89,0,0 +2014,9,4,22,49,17,24,1006,1.79,0,0 +2014,9,4,23,52,17,22,1006,4.92,0,0 +2014,9,5,0,60,17,22,1006,8.05,0,0 +2014,9,5,1,61,16,21,1006,11.18,0,0 +2014,9,5,2,64,16,20,1006,0.89,0,0 +2014,9,5,3,74,16,19,1007,0.89,0,0 +2014,9,5,4,80,16,19,1007,0.89,0,0 +2014,9,5,5,76,16,18,1008,1.79,0,0 +2014,9,5,6,70,16,18,1009,3.58,0,0 +2014,9,5,7,73,17,20,1010,4.47,0,0 +2014,9,5,8,86,16,21,1011,6.26,0,0 +2014,9,5,9,93,17,23,1011,0.89,0,0 +2014,9,5,10,96,18,25,1011,3.13,0,0 +2014,9,5,11,109,19,26,1011,6.26,0,0 +2014,9,5,12,109,19,27,1011,9.39,0,0 +2014,9,5,13,114,20,29,1010,11.18,0,0 +2014,9,5,14,90,19,29,1009,14.31,0,0 +2014,9,5,15,74,19,29,1009,17.44,0,0 +2014,9,5,16,75,19,29,1009,20.57,0,0 +2014,9,5,17,68,19,28,1009,24.59,0,0 +2014,9,5,18,70,19,27,1009,27.72,0,0 +2014,9,5,19,88,20,26,1009,30.85,0,0 +2014,9,5,20,143,21,25,1010,33.98,0,0 +2014,9,5,21,155,20,25,1011,37.11,0,0 +2014,9,5,22,147,20,23,1011,38.9,0,0 +2014,9,5,23,143,20,23,1011,42.03,0,0 +2014,9,6,0,148,20,22,1012,0.89,0,0 +2014,9,6,1,157,20,22,1012,0.89,0,0 +2014,9,6,2,152,21,22,1011,1.79,0,0 +2014,9,6,3,152,20,22,1012,3.58,0,0 +2014,9,6,4,147,20,21,1011,5.37,0,0 +2014,9,6,5,143,20,21,1012,6.26,0,0 +2014,9,6,6,139,20,21,1012,8.05,0,0 +2014,9,6,7,142,20,21,1012,0.89,0,0 +2014,9,6,8,139,20,22,1013,0.89,0,0 +2014,9,6,9,133,20,24,1013,0.89,0,0 +2014,9,6,10,137,20,25,1013,1.79,0,0 +2014,9,6,11,138,20,27,1012,0.89,0,0 +2014,9,6,12,138,20,27,1012,1.79,0,0 +2014,9,6,13,132,20,28,1011,3.58,0,0 +2014,9,6,14,135,20,28,1010,6.71,0,0 +2014,9,6,15,127,20,28,1010,9.84,0,0 +2014,9,6,16,109,20,28,1009,13.86,0,0 +2014,9,6,17,94,20,28,1009,15.65,0,0 +2014,9,6,18,100,19,27,1009,17.44,0,0 +2014,9,6,19,110,18,27,1009,20.57,0,0 +2014,9,6,20,123,20,25,1009,23.7,0,0 +2014,9,6,21,132,20,25,1009,27.72,0,0 +2014,9,6,22,138,19,24,1009,30.85,0,0 +2014,9,6,23,151,19,24,1009,33.98,0,0 +2014,9,7,0,162,19,24,1009,35.77,0,0 +2014,9,7,1,155,19,24,1009,37.56,0,0 +2014,9,7,2,143,19,24,1009,39.35,0,0 +2014,9,7,3,145,20,22,1008,40.24,0,0 +2014,9,7,4,147,20,22,1008,42.03,0,0 +2014,9,7,5,154,19,22,1008,43.82,0,1 +2014,9,7,6,163,20,22,1008,0.89,0,2 +2014,9,7,7,170,21,22,1009,1.78,0,0 +2014,9,7,8,174,21,23,1009,1.79,0,0 +2014,9,7,9,196,21,23,1009,0.89,0,0 +2014,9,7,10,207,21,24,1009,1.79,0,1 +2014,9,7,11,198,20,26,1008,3.58,0,0 +2014,9,7,12,176,20,27,1008,5.37,0,0 +2014,9,7,13,111,20,28,1007,8.5,0,0 +2014,9,7,14,85,17,29,1006,12.52,0,0 +2014,9,7,15,79,17,30,1005,16.54,0,0 +2014,9,7,16,100,18,29,1005,19.67,0,0 +2014,9,7,17,105,19,29,1005,22.8,0,0 +2014,9,7,18,110,20,27,1005,25.93,0,0 +2014,9,7,19,123,19,28,1006,27.72,0,0 +2014,9,7,20,124,20,25,1007,0.89,0,0 +2014,9,7,21,129,20,24,1008,1.79,0,0 +2014,9,7,22,140,20,22,1008,2.68,0,0 +2014,9,7,23,156,20,23,1009,5.81,0,0 +2014,9,8,0,104,19,21,1009,8.94,0,0 +2014,9,8,1,2,16,20,1010,13.86,0,0 +2014,9,8,2,2,10,19,1010,16.99,0,0 +2014,9,8,3,6,10,17,1010,20.12,0,0 +2014,9,8,4,5,7,18,1011,0.89,0,0 +2014,9,8,5,5,9,18,1011,3.13,0,0 +2014,9,8,6,6,8,18,1012,6.26,0,0 +2014,9,8,7,3,8,20,1012,9.39,0,0 +2014,9,8,8,3,8,23,1012,13.41,0,0 +2014,9,8,9,7,9,26,1012,17.43,0,0 +2014,9,8,10,7,8,27,1012,1.79,0,0 +2014,9,8,11,3,6,28,1011,4.02,0,0 +2014,9,8,12,3,6,29,1011,7.15,0,0 +2014,9,8,13,4,6,29,1010,1.79,0,0 +2014,9,8,14,6,5,29,1009,1.79,0,0 +2014,9,8,15,6,6,29,1008,1.79,0,0 +2014,9,8,16,4,7,29,1008,1.79,0,0 +2014,9,8,17,10,8,27,1008,3.58,0,0 +2014,9,8,18,15,9,25,1008,6.71,0,0 +2014,9,8,19,12,8,24,1008,10.73,0,0 +2014,9,8,20,14,8,24,1009,13.86,0,0 +2014,9,8,21,20,13,19,1009,0.89,0,0 +2014,9,8,22,19,12,18,1009,1.78,0,0 +2014,9,8,23,22,13,17,1009,2.67,0,0 +2014,9,9,0,30,13,17,1009,3.56,0,0 +2014,9,9,1,37,12,15,1009,1.79,0,0 +2014,9,9,2,39,13,15,1009,2.68,0,0 +2014,9,9,3,45,13,15,1009,0.89,0,0 +2014,9,9,4,42,12,14,1009,1.78,0,0 +2014,9,9,5,39,12,14,1010,0.89,0,0 +2014,9,9,6,25,12,14,1010,1.79,0,0 +2014,9,9,7,23,13,17,1010,3.58,0,0 +2014,9,9,8,17,12,21,1011,5.37,0,0 +2014,9,9,9,27,13,23,1011,7.16,0,0 +2014,9,9,10,19,11,24,1011,1.79,0,0 +2014,9,9,11,22,12,26,1011,0.89,0,0 +2014,9,9,12,31,13,27,1010,1.79,0,0 +2014,9,9,13,43,13,28,1010,3.58,0,0 +2014,9,9,14,54,14,28,1009,1.79,0,0 +2014,9,9,15,45,13,29,1009,3.13,0,0 +2014,9,9,16,44,13,29,1009,6.26,0,0 +2014,9,9,17,35,13,28,1009,9.39,0,0 +2014,9,9,18,41,14,27,1009,11.18,0,0 +2014,9,9,19,46,14,25,1009,14.31,0,0 +2014,9,9,20,47,17,21,1010,15.2,0,0 +2014,9,9,21,61,15,24,1011,18.33,0,0 +2014,9,9,22,80,15,22,1011,21.46,0,0 +2014,9,9,23,52,13,23,1012,24.59,0,0 +2014,9,10,0,41,15,20,1012,26.38,0,0 +2014,9,10,1,46,15,19,1012,1.79,0,0 +2014,9,10,2,44,16,18,1012,3.58,0,0 +2014,9,10,3,54,15,18,1013,0.89,0,0 +2014,9,10,4,59,16,18,1013,1.79,0,0 +2014,9,10,5,79,15,18,1013,3.58,0,0 +2014,9,10,6,115,15,17,1014,0.89,0,0 +2014,9,10,7,96,16,19,1014,0.89,0,0 +2014,9,10,8,108,15,21,1015,1.78,0,0 +2014,9,10,9,112,16,23,1015,2.67,0,0 +2014,9,10,10,118,16,24,1015,3.56,0,0 +2014,9,10,11,92,16,26,1015,1.79,0,0 +2014,9,10,12,74,16,27,1014,3.58,0,0 +2014,9,10,13,50,14,28,1014,6.71,0,0 +2014,9,10,14,36,13,28,1014,10.73,0,0 +2014,9,10,15,30,13,28,1013,14.75,0,0 +2014,9,10,16,38,14,28,1013,18.77,0,0 +2014,9,10,17,36,14,28,1013,21.9,0,0 +2014,9,10,18,43,14,26,1013,25.03,0,0 +2014,9,10,19,48,16,25,1013,26.82,0,0 +2014,9,10,20,57,16,23,1013,28.61,0,0 +2014,9,10,21,65,15,23,1014,30.4,0,0 +2014,9,10,22,83,15,22,1014,0.89,0,0 +2014,9,10,23,95,16,21,1015,1.79,0,0 +2014,9,11,0,82,15,21,1014,3.58,0,0 +2014,9,11,1,61,15,20,1014,5.37,0,0 +2014,9,11,2,73,15,19,1015,0.89,0,0 +2014,9,11,3,77,15,19,1015,1.78,0,0 +2014,9,11,4,86,15,19,1014,2.67,0,0 +2014,9,11,5,81,15,19,1014,3.56,0,0 +2014,9,11,6,81,15,19,1015,4.01,0,0 +2014,9,11,7,89,16,20,1015,4.9,0,0 +2014,9,11,8,91,15,21,1015,5.79,0,0 +2014,9,11,9,94,15,22,1015,1.79,0,0 +2014,9,11,10,108,17,23,1015,3.58,0,0 +2014,9,11,11,101,17,24,1014,5.37,0,0 +2014,9,11,12,93,17,25,1014,0.89,0,0 +2014,9,11,13,88,17,26,1013,1.79,0,0 +2014,9,11,14,84,17,26,1013,3.58,0,0 +2014,9,11,15,91,17,26,1013,5.37,0,0 +2014,9,11,16,92,18,25,1012,0.89,0,0 +2014,9,11,17,88,19,25,1012,1.78,0,0 +2014,9,11,18,116,19,24,1012,1.79,0,1 +2014,9,11,19,120,20,22,1013,3.13,0,2 +2014,9,11,20,124,19,21,1013,8.05,0,0 +2014,9,11,21,113,19,20,1014,0.89,0,1 +2014,9,11,22,104,19,20,1014,3.13,0,0 +2014,9,11,23,106,18,21,1014,4.92,0,1 +2014,9,12,0,121,19,20,1014,6.71,0,0 +2014,9,12,1,124,19,20,1014,0.89,0,0 +2014,9,12,2,135,19,20,1014,1.78,0,0 +2014,9,12,3,147,19,20,1014,2.67,0,0 +2014,9,12,4,82,19,20,1014,1.79,0,1 +2014,9,12,5,60,19,19,1014,0.89,0,2 +2014,9,12,6,60,18,19,1015,1.78,0,3 +2014,9,12,7,52,18,19,1015,2.67,0,0 +2014,9,12,8,54,18,20,1015,0.89,0,0 +2014,9,12,9,59,18,21,1016,2.68,0,0 +2014,9,12,10,59,18,21,1016,3.57,0,0 +2014,9,12,11,74,17,22,1016,0.89,0,0 +2014,9,12,12,68,17,23,1015,2.68,0,0 +2014,9,12,13,65,17,24,1015,3.57,0,0 +2014,9,12,14,68,17,24,1014,4.46,0,0 +2014,9,12,15,45,16,24,1014,1.79,0,0 +2014,9,12,16,36,16,24,1014,4.92,0,0 +2014,9,12,17,63,16,24,1014,6.71,0,0 +2014,9,12,18,54,16,23,1014,8.5,0,0 +2014,9,12,19,48,17,22,1014,10.29,0,0 +2014,9,12,20,57,17,22,1015,12.08,0,0 +2014,9,12,21,58,17,21,1015,13.87,0,0 +2014,9,12,22,63,16,22,1015,15.66,0,0 +2014,9,12,23,51,17,20,1015,0.89,0,0 +2014,9,13,0,52,18,20,1015,0.89,0,0 +2014,9,13,1,50,18,19,1015,1.78,0,0 +2014,9,13,2,54,18,19,1015,0.89,0,0 +2014,9,13,3,62,18,19,1015,1.78,0,0 +2014,9,13,4,64,18,19,1015,0.89,0,0 +2014,9,13,5,108,18,19,1015,1.34,0,1 +2014,9,13,6,95,18,19,1015,0.89,0,0 +2014,9,13,7,93,18,19,1015,1.78,0,0 +2014,9,13,8,94,18,20,1016,0.89,0,0 +2014,9,13,9,96,18,22,1016,1.79,0,0 +2014,9,13,10,51,16,24,1016,3.58,0,0 +2014,9,13,11,60,14,25,1015,6.71,0,0 +2014,9,13,12,50,14,25,1015,9.84,0,0 +2014,9,13,13,46,14,26,1014,13.86,0,0 +2014,9,13,14,39,13,27,1013,17.88,0,0 +2014,9,13,15,38,13,27,1013,22.8,0,0 +2014,9,13,16,47,13,27,1013,27.72,0,0 +2014,9,13,17,46,13,26,1012,31.74,0,0 +2014,9,13,18,48,13,25,1012,34.87,0,0 +2014,9,13,19,53,14,23,1013,38,0,0 +2014,9,13,20,67,13,24,1013,41.13,0,0 +2014,9,13,21,61,14,22,1014,42.92,0,0 +2014,9,13,22,73,16,19,1014,0.89,0,0 +2014,9,13,23,79,16,18,1014,1.78,0,0 +2014,9,14,0,74,17,19,1014,2.67,0,0 +2014,9,14,1,71,16,19,1014,0.89,0,0 +2014,9,14,2,76,16,18,1014,0.89,0,0 +2014,9,14,3,75,16,19,1014,0.89,0,0 +2014,9,14,4,78,17,19,1014,1.79,0,0 +2014,9,14,5,86,16,19,1015,2.68,0,0 +2014,9,14,6,95,16,19,1015,0.89,0,0 +2014,9,14,7,106,16,20,1016,4.02,0,0 +2014,9,14,8,79,16,20,1017,8.04,0,0 +2014,9,14,9,63,11,21,1017,4.92,0,0 +2014,9,14,10,49,8,22,1018,8.94,0,0 +2014,9,14,11,27,5,22,1018,12.96,0,0 +2014,9,14,12,11,3,22,1019,17.88,0,0 +2014,9,14,13,10,4,22,1019,21.01,0,0 +2014,9,14,14,6,6,22,1018,24.14,0,0 +2014,9,14,15,11,5,23,1018,3.13,0,0 +2014,9,14,16,12,6,24,1017,6.26,0,0 +2014,9,14,17,13,7,23,1018,0.89,0,0 +2014,9,14,18,10,11,20,1018,1.78,0,0 +2014,9,14,19,15,12,18,1018,0.89,0,0 +2014,9,14,20,23,12,18,1019,0.89,0,0 +2014,9,14,21,34,11,17,1019,0.89,0,0 +2014,9,14,22,36,13,17,1019,0.45,0,0 +2014,9,14,23,42,12,15,1019,0.89,0,0 +2014,9,15,0,42,12,14,1019,0.89,0,0 +2014,9,15,1,47,12,14,1019,0.89,0,0 +2014,9,15,2,47,12,14,1019,0.89,0,0 +2014,9,15,3,28,10,13,1019,4.02,0,0 +2014,9,15,4,20,10,13,1020,8.04,0,0 +2014,9,15,5,23,9,13,1020,11.17,0,0 +2014,9,15,6,16,9,11,1020,14.3,0,0 +2014,9,15,7,16,10,14,1020,18.32,0,0 +2014,9,15,8,15,9,18,1020,22.34,0,0 +2014,9,15,9,11,8,19,1021,1.79,0,0 +2014,9,15,10,11,5,23,1020,3.13,0,0 +2014,9,15,11,7,5,26,1019,7.15,0,0 +2014,9,15,12,4,-1,26,1019,11.17,0,0 +2014,9,15,13,3,0,28,1018,1.79,0,0 +2014,9,15,14,6,0,29,1017,3.13,0,0 +2014,9,15,15,8,-2,28,1016,7.15,0,0 +2014,9,15,16,6,-1,29,1016,10.28,0,0 +2014,9,15,17,11,0,27,1016,4.02,0,0 +2014,9,15,18,16,2,25,1015,8.04,0,0 +2014,9,15,19,18,4,24,1016,12.96,0,0 +2014,9,15,20,20,3,23,1016,17.88,0,0 +2014,9,15,21,25,4,22,1017,21.9,0,0 +2014,9,15,22,20,4,22,1017,25.03,0,0 +2014,9,15,23,26,8,16,1017,0.89,0,0 +2014,9,16,0,30,8,14,1017,1.79,0,0 +2014,9,16,1,34,9,14,1018,0.89,0,0 +2014,9,16,2,30,10,13,1018,0.89,0,0 +2014,9,16,3,53,10,13,1018,0.89,0,0 +2014,9,16,4,49,10,14,1018,1.79,0,0 +2014,9,16,5,33,8,12,1018,5.81,0,0 +2014,9,16,6,18,9,12,1018,8.94,0,0 +2014,9,16,7,NA,9,15,1019,0.89,0,0 +2014,9,16,8,15,8,18,1019,4.02,0,0 +2014,9,16,9,20,7,19,1019,1.79,0,0 +2014,9,16,10,25,8,20,1019,3.58,0,0 +2014,9,16,11,19,8,20,1019,5.37,0,0 +2014,9,16,12,22,10,20,1018,1.79,0,0 +2014,9,16,13,30,10,21,1018,0.89,0,0 +2014,9,16,14,45,12,21,1018,1.78,0,0 +2014,9,16,15,69,11,21,1017,0.89,0,0 +2014,9,16,16,90,13,20,1018,0.89,0,0 +2014,9,16,17,98,13,20,1018,0.89,0,0 +2014,9,16,18,93,13,19,1018,0.89,0,0 +2014,9,16,19,64,13,18,1018,1.79,0,0 +2014,9,16,20,NA,14,18,1018,3.58,0,0 +2014,9,16,21,56,12,19,1019,6.71,0,0 +2014,9,16,22,56,12,19,1019,9.84,0,0 +2014,9,16,23,68,13,18,1019,11.63,0,0 +2014,9,17,0,56,11,19,1019,13.42,0,0 +2014,9,17,1,30,9,19,1019,15.21,0,0 +2014,9,17,2,25,11,15,1020,0.89,0,0 +2014,9,17,3,16,11,15,1019,0.89,0,0 +2014,9,17,4,10,12,14,1019,0.45,0,0 +2014,9,17,5,13,11,14,1019,1.79,0,0 +2014,9,17,6,19,11,15,1020,4.92,0,0 +2014,9,17,7,25,10,17,1021,8.05,0,0 +2014,9,17,8,22,10,18,1021,0.89,0,0 +2014,9,17,9,18,11,19,1021,2.68,0,0 +2014,9,17,10,19,11,19,1021,1.79,0,0 +2014,9,17,11,29,11,20,1022,0.89,0,0 +2014,9,17,12,44,12,20,1021,1.78,0,0 +2014,9,17,13,75,12,21,1021,2.67,0,0 +2014,9,17,14,60,12,21,1020,4.46,0,0 +2014,9,17,15,60,13,22,1020,3.13,0,0 +2014,9,17,16,60,12,22,1019,4.92,0,0 +2014,9,17,17,38,12,22,1019,8.05,0,0 +2014,9,17,18,35,12,20,1020,9.84,0,0 +2014,9,17,19,32,12,17,1020,11.63,0,0 +2014,9,17,20,41,12,19,1021,14.76,0,0 +2014,9,17,21,49,12,17,1021,0.89,0,0 +2014,9,17,22,49,13,15,1021,1.79,0,0 +2014,9,17,23,55,12,15,1021,0.89,0,0 +2014,9,18,0,56,13,13,1021,0.89,0,0 +2014,9,18,1,62,12,13,1021,1.78,0,0 +2014,9,18,2,80,12,13,1021,0.45,0,0 +2014,9,18,3,99,12,13,1021,1.34,0,0 +2014,9,18,4,104,11,11,1021,3.13,0,0 +2014,9,18,5,99,11,12,1021,7.15,0,0 +2014,9,18,6,97,11,12,1021,10.28,0,0 +2014,9,18,7,108,12,14,1021,13.41,0,0 +2014,9,18,8,99,12,17,1022,16.54,0,0 +2014,9,18,9,119,12,20,1022,19.67,0,0 +2014,9,18,10,93,12,22,1022,21.46,0,0 +2014,9,18,11,78,10,23,1021,0.89,0,0 +2014,9,18,12,67,11,26,1020,1.79,0,0 +2014,9,18,13,69,12,26,1019,0.89,0,0 +2014,9,18,14,51,9,27,1018,3.13,0,0 +2014,9,18,15,41,6,28,1017,6.26,0,0 +2014,9,18,16,34,6,28,1017,8.05,0,0 +2014,9,18,17,21,7,26,1016,9.84,0,0 +2014,9,18,18,27,7,24,1016,11.63,0,0 +2014,9,18,19,34,9,21,1016,13.42,0,0 +2014,9,18,20,37,9,19,1017,15.21,0,0 +2014,9,18,21,51,9,19,1017,17,0,0 +2014,9,18,22,61,9,21,1017,20.13,0,0 +2014,9,18,23,63,10,21,1017,23.26,0,0 +2014,9,19,0,59,12,15,1016,25.05,0,0 +2014,9,19,1,70,12,15,1016,25.94,0,0 +2014,9,19,2,78,11,14,1015,0.89,0,0 +2014,9,19,3,84,11,13,1015,0.89,0,0 +2014,9,19,4,97,11,13,1014,1.79,0,0 +2014,9,19,5,110,10,12,1014,2.68,0,0 +2014,9,19,6,106,11,13,1014,3.57,0,0 +2014,9,19,7,92,11,14,1015,4.46,0,0 +2014,9,19,8,110,13,16,1014,1.79,0,0 +2014,9,19,9,121,15,18,1014,3.58,0,0 +2014,9,19,10,136,15,20,1014,5.37,0,0 +2014,9,19,11,150,15,21,1013,1.79,0,0 +2014,9,19,12,118,14,24,1012,4.92,0,0 +2014,9,19,13,91,14,24,1011,3.13,0,0 +2014,9,19,14,86,14,24,1010,6.26,0,0 +2014,9,19,15,67,14,25,1009,10.28,0,0 +2014,9,19,16,60,15,24,1009,14.3,0,0 +2014,9,19,17,53,14,24,1009,16.09,0,0 +2014,9,19,18,53,15,23,1010,3.13,0,0 +2014,9,19,19,NA,17,19,1010,3.13,0,1 +2014,9,19,20,97,17,19,1010,1.79,0,2 +2014,9,19,21,105,17,19,1011,0.89,0,0 +2014,9,19,22,119,17,18,1011,1.78,0,0 +2014,9,19,23,140,16,18,1010,1.79,0,0 +2014,9,20,0,144,16,18,1010,0.89,0,0 +2014,9,20,1,141,16,18,1010,0.89,0,0 +2014,9,20,2,145,17,18,1010,0.89,0,0 +2014,9,20,3,142,17,18,1009,1.78,0,0 +2014,9,20,4,141,17,18,1009,2.23,0,0 +2014,9,20,5,136,16,17,1009,2.68,0,0 +2014,9,20,6,128,16,17,1010,3.13,0,0 +2014,9,20,7,110,16,17,1010,0.45,0,0 +2014,9,20,8,116,17,19,1011,1.79,0,0 +2014,9,20,9,123,17,20,1011,3.58,0,0 +2014,9,20,10,127,17,22,1011,0.89,0,0 +2014,9,20,11,124,18,23,1011,2.68,0,0 +2014,9,20,12,115,16,24,1010,3.57,0,0 +2014,9,20,13,117,17,26,1010,4.46,0,0 +2014,9,20,14,113,16,27,1009,1.79,0,0 +2014,9,20,15,89,16,27,1008,1.79,0,0 +2014,9,20,16,95,14,27,1008,2.68,0,0 +2014,9,20,17,78,14,26,1008,1.79,0,0 +2014,9,20,18,140,16,23,1009,0.89,0,0 +2014,9,20,19,153,16,22,1010,1.79,0,0 +2014,9,20,20,159,17,20,1011,0.89,0,0 +2014,9,20,21,180,17,19,1012,1.79,0,0 +2014,9,20,22,200,18,20,1012,3.58,0,0 +2014,9,20,23,228,18,20,1013,5.37,0,0 +2014,9,21,0,229,15,21,1014,10.29,0,0 +2014,9,21,1,200,14,20,1014,15.21,0,0 +2014,9,21,2,146,14,20,1015,18.34,0,0 +2014,9,21,3,133,14,19,1015,0.89,0,0 +2014,9,21,4,125,14,19,1016,1.78,0,0 +2014,9,21,5,79,14,19,1016,2.67,0,0 +2014,9,21,6,80,13,18,1017,0.89,0,0 +2014,9,21,7,72,14,19,1017,0.89,0,0 +2014,9,21,8,59,14,19,1018,1.79,0,0 +2014,9,21,9,63,14,20,1018,0.89,0,0 +2014,9,21,10,64,14,21,1019,1.78,0,0 +2014,9,21,11,52,12,22,1018,1.79,0,0 +2014,9,21,12,67,14,23,1018,3.58,0,0 +2014,9,21,13,78,14,24,1017,5.37,0,0 +2014,9,21,14,89,16,24,1017,7.16,0,0 +2014,9,21,15,88,17,25,1016,8.95,0,0 +2014,9,21,16,96,16,25,1016,12.08,0,0 +2014,9,21,17,100,16,24,1016,15.21,0,0 +2014,9,21,18,93,17,23,1016,18.34,0,0 +2014,9,21,19,85,17,21,1017,20.13,0,0 +2014,9,21,20,88,17,20,1018,21.92,0,0 +2014,9,21,21,82,18,21,1018,25.05,0,0 +2014,9,21,22,80,17,21,1018,28.18,0,0 +2014,9,21,23,59,17,21,1019,29.97,0,0 +2014,9,22,0,75,16,21,1019,33.1,0,0 +2014,9,22,1,77,17,20,1019,0.89,0,0 +2014,9,22,2,70,17,20,1019,1.78,0,0 +2014,9,22,3,80,17,20,1019,1.79,0,0 +2014,9,22,4,81,16,20,1019,3.58,0,0 +2014,9,22,5,79,16,20,1018,5.37,0,0 +2014,9,22,6,89,16,20,1019,0.89,0,0 +2014,9,22,7,106,16,20,1019,1.78,0,0 +2014,9,22,8,112,16,20,1019,2.67,0,0 +2014,9,22,9,118,17,20,1020,1.79,0,0 +2014,9,22,10,120,17,21,1020,1.79,0,0 +2014,9,22,11,119,17,21,1019,3.58,0,0 +2014,9,22,12,127,17,21,1019,5.37,0,0 +2014,9,22,13,128,17,21,1018,7.16,0,0 +2014,9,22,14,135,17,21,1017,8.95,0,0 +2014,9,22,15,133,17,21,1017,10.74,0,0 +2014,9,22,16,124,17,21,1016,11.63,0,0 +2014,9,22,17,112,18,20,1017,1.79,0,1 +2014,9,22,18,107,18,20,1017,3.58,0,0 +2014,9,22,19,109,18,20,1018,1.79,0,1 +2014,9,22,20,108,18,19,1018,4.02,0,2 +2014,9,22,21,119,18,19,1018,7.15,0,3 +2014,9,22,22,117,18,19,1018,10.28,0,0 +2014,9,22,23,120,18,19,1018,0.89,0,1 +2014,9,23,0,123,18,19,1018,1.79,0,2 +2014,9,23,1,114,18,19,1018,3.58,0,3 +2014,9,23,2,109,18,19,1018,0.89,0,4 +2014,9,23,3,96,18,19,1017,4.02,0,5 +2014,9,23,4,102,18,19,1017,7.15,0,6 +2014,9,23,5,95,18,18,1017,8.94,0,7 +2014,9,23,6,81,18,18,1018,10.73,0,8 +2014,9,23,7,73,18,18,1018,12.52,0,9 +2014,9,23,8,63,17,18,1018,15.65,0,10 +2014,9,23,9,45,16,18,1019,18.78,0,11 +2014,9,23,10,51,17,18,1019,21.91,0,12 +2014,9,23,11,40,16,18,1018,4.02,0,13 +2014,9,23,12,36,16,18,1018,4.02,0,14 +2014,9,23,13,34,16,18,1018,3.13,0,15 +2014,9,23,14,24,16,18,1017,0.89,0,16 +2014,9,23,15,27,17,18,1017,1.78,0,17 +2014,9,23,16,NA,17,18,1016,2.23,0,18 +2014,9,23,17,38,17,18,1016,1.79,0,19 +2014,9,23,18,42,17,18,1016,0.89,0,20 +2014,9,23,19,NA,18,18,1016,1.78,0,21 +2014,9,23,20,56,17,17,1016,1.79,0,22 +2014,9,23,21,75,17,17,1017,2.68,0,23 +2014,9,23,22,68,16,17,1016,4.47,0,0 +2014,9,23,23,88,16,17,1017,1.79,0,1 +2014,9,24,0,50,16,17,1017,0.89,0,2 +2014,9,24,1,38,16,17,1017,1.79,0,3 +2014,9,24,2,38,16,17,1017,3.58,0,4 +2014,9,24,3,46,16,17,1016,5.37,0,5 +2014,9,24,4,35,16,16,1016,7.16,0,6 +2014,9,24,5,20,16,17,1016,0.89,0,0 +2014,9,24,6,9,16,17,1016,0.89,0,0 +2014,9,24,7,11,17,17,1017,1.79,0,0 +2014,9,24,8,17,16,17,1017,4.92,0,0 +2014,9,24,9,22,16,20,1018,8.05,0,0 +2014,9,24,10,13,17,22,1018,1.79,0,0 +2014,9,24,11,17,16,22,1017,1.79,0,0 +2014,9,24,12,14,15,24,1016,1.79,0,0 +2014,9,24,13,16,14,24,1016,2.68,0,0 +2014,9,24,14,14,14,25,1015,3.57,0,0 +2014,9,24,15,11,14,25,1014,1.79,0,0 +2014,9,24,16,10,13,26,1014,1.79,0,0 +2014,9,24,17,10,15,24,1014,4.02,0,0 +2014,9,24,18,31,15,22,1015,8.04,0,0 +2014,9,24,19,39,16,21,1015,11.17,0,0 +2014,9,24,20,56,16,20,1016,12.96,0,0 +2014,9,24,21,61,16,19,1016,14.75,0,0 +2014,9,24,22,64,16,17,1017,15.64,0,0 +2014,9,24,23,101,16,17,1017,0.89,0,0 +2014,9,25,0,111,15,17,1017,1.79,0,0 +2014,9,25,1,82,16,17,1017,3.58,0,0 +2014,9,25,2,84,15,16,1017,4.47,0,0 +2014,9,25,3,89,15,15,1017,0.89,0,0 +2014,9,25,4,91,15,15,1017,1.34,0,0 +2014,9,25,5,81,14,14,1017,0.89,0,0 +2014,9,25,6,103,15,16,1017,0.45,0,0 +2014,9,25,7,99,16,16,1018,1.34,0,0 +2014,9,25,8,105,17,17,1018,0.89,0,0 +2014,9,25,9,121,17,18,1019,2.68,0,0 +2014,9,25,10,139,17,19,1019,0.89,0,0 +2014,9,25,11,141,17,20,1018,1.79,0,0 +2014,9,25,12,148,17,20,1018,3.58,0,0 +2014,9,25,13,168,17,21,1017,6.71,0,0 +2014,9,25,14,157,17,21,1016,9.84,0,0 +2014,9,25,15,159,17,22,1016,1.79,0,0 +2014,9,25,16,151,17,21,1015,1.79,0,0 +2014,9,25,17,137,17,21,1014,3.58,0,0 +2014,9,25,18,128,18,20,1014,4.47,0,0 +2014,9,25,19,144,17,20,1014,6.26,0,0 +2014,9,25,20,147,17,19,1014,0.89,0,0 +2014,9,25,21,157,17,19,1014,1.79,0,0 +2014,9,25,22,145,17,19,1014,4.92,0,0 +2014,9,25,23,74,16,19,1014,8.05,0,0 +2014,9,26,0,60,16,19,1014,9.84,0,0 +2014,9,26,1,75,16,18,1014,11.63,0,0 +2014,9,26,2,86,16,18,1013,15.65,0,0 +2014,9,26,3,87,16,18,1013,0.89,0,0 +2014,9,26,4,99,16,18,1013,1.79,0,0 +2014,9,26,5,96,16,17,1012,3.58,0,0 +2014,9,26,6,107,16,17,1011,7.6,0,0 +2014,9,26,7,111,16,17,1012,9.39,0,0 +2014,9,26,8,120,16,18,1012,12.52,0,0 +2014,9,26,9,121,16,19,1012,14.31,0,0 +2014,9,26,10,126,16,19,1012,16.1,0,0 +2014,9,26,11,130,16,20,1011,19.23,0,0 +2014,9,26,12,123,16,21,1010,21.02,0,0 +2014,9,26,13,129,16,22,1009,24.15,0,0 +2014,9,26,14,146,17,23,1008,1.79,0,0 +2014,9,26,15,155,17,22,1009,2.68,0,1 +2014,9,26,16,193,17,20,1009,3.13,0,0 +2014,9,26,17,194,18,20,1009,0.89,0,0 +2014,9,26,18,203,17,19,1010,4.02,0,0 +2014,9,26,19,217,17,18,1011,7.15,0,0 +2014,9,26,20,219,17,19,1011,8.94,0,0 +2014,9,26,21,196,17,17,1012,0.89,0,0 +2014,9,26,22,166,17,18,1012,1.79,0,0 +2014,9,26,23,149,16,17,1012,4.92,0,0 +2014,9,27,0,119,15,16,1013,1.79,0,0 +2014,9,27,1,132,14,15,1013,3.58,0,0 +2014,9,27,2,61,14,14,1014,3.13,0,0 +2014,9,27,3,9,13,14,1014,4.92,0,0 +2014,9,27,4,9,12,13,1015,0.89,0,0 +2014,9,27,5,42,11,13,1016,1.79,0,0 +2014,9,27,6,43,11,12,1016,4.92,0,0 +2014,9,27,7,51,12,14,1017,6.71,0,0 +2014,9,27,8,28,12,18,1018,8.5,0,0 +2014,9,27,9,26,10,20,1018,3.13,0,0 +2014,9,27,10,14,9,21,1019,7.15,0,0 +2014,9,27,11,15,9,22,1018,10.28,0,0 +2014,9,27,12,10,9,23,1017,1.79,0,0 +2014,9,27,13,6,8,25,1016,3.58,0,0 +2014,9,27,14,10,7,25,1015,1.79,0,0 +2014,9,27,15,10,7,25,1015,0.89,0,0 +2014,9,27,16,17,8,25,1015,1.79,0,0 +2014,9,27,17,16,8,24,1015,3.58,0,0 +2014,9,27,18,22,13,20,1015,0.89,0,0 +2014,9,27,19,32,13,18,1016,1.78,0,0 +2014,9,27,20,54,13,17,1016,2.23,0,0 +2014,9,27,21,52,13,17,1016,0.89,0,0 +2014,9,27,22,59,14,16,1017,2.68,0,0 +2014,9,27,23,59,13,16,1017,3.57,0,0 +2014,9,28,0,77,13,16,1017,0.89,0,0 +2014,9,28,1,83,13,15,1018,1.78,0,0 +2014,9,28,2,83,12,15,1017,0.89,0,0 +2014,9,28,3,66,11,15,1017,1.78,0,0 +2014,9,28,4,59,12,14,1017,0.89,0,0 +2014,9,28,5,49,12,14,1017,1.78,0,0 +2014,9,28,6,58,12,14,1017,0.89,0,0 +2014,9,28,7,61,12,15,1018,1.78,0,0 +2014,9,28,8,63,12,16,1018,0.89,0,0 +2014,9,28,9,71,13,18,1018,0.89,0,0 +2014,9,28,10,82,14,20,1018,1.78,0,0 +2014,9,28,11,87,14,20,1018,1.79,0,0 +2014,9,28,12,98,15,21,1017,3.58,0,0 +2014,9,28,13,101,16,22,1016,5.37,0,0 +2014,9,28,14,69,15,22,1015,8.5,0,0 +2014,9,28,15,62,14,23,1014,12.52,0,0 +2014,9,28,16,45,15,22,1014,15.65,0,0 +2014,9,28,17,53,15,21,1014,18.78,0,0 +2014,9,28,18,46,15,20,1015,20.57,0,0 +2014,9,28,19,57,15,20,1014,22.36,0,0 +2014,9,28,20,53,16,18,1015,23.25,0,0 +2014,9,28,21,51,17,18,1015,0.89,0,0 +2014,9,28,22,58,16,17,1014,1.78,0,0 +2014,9,28,23,75,16,16,1014,2.67,0,0 +2014,9,29,0,75,15,15,1014,1.79,0,0 +2014,9,29,1,79,15,15,1014,2.68,0,0 +2014,9,29,2,97,15,15,1014,0.89,0,0 +2014,9,29,3,105,13,14,1013,1.78,0,0 +2014,9,29,4,105,14,14,1013,2.67,0,0 +2014,9,29,5,107,14,14,1014,0.89,0,0 +2014,9,29,6,103,14,14,1015,0.89,0,0 +2014,9,29,7,102,15,15,1016,1.79,0,0 +2014,9,29,8,97,15,18,1016,1.79,0,0 +2014,9,29,9,99,15,20,1018,3.13,0,0 +2014,9,29,10,85,10,23,1018,7.15,0,0 +2014,9,29,11,77,9,24,1019,14.3,0,0 +2014,9,29,12,17,7,23,1019,21.45,0,0 +2014,9,29,13,13,6,23,1019,27.26,0,0 +2014,9,29,14,10,6,23,1020,32.18,0,0 +2014,9,29,15,10,4,23,1020,4.02,0,0 +2014,9,29,16,12,3,22,1020,4.92,0,0 +2014,9,29,17,13,1,20,1021,8.05,0,0 +2014,9,29,18,12,0,19,1022,12.07,0,0 +2014,9,29,19,12,0,17,1024,3.13,0,0 +2014,9,29,20,14,6,15,1025,4.92,0,0 +2014,9,29,21,21,5,14,1026,8.05,0,0 +2014,9,29,22,17,4,14,1027,12.97,0,0 +2014,9,29,23,23,2,13,1028,16.1,0,0 +2014,9,30,0,26,4,12,1028,17.89,0,0 +2014,9,30,1,20,3,12,1029,19.68,0,0 +2014,9,30,2,24,4,12,1029,22.81,0,0 +2014,9,30,3,22,3,11,1029,24.6,0,0 +2014,9,30,4,17,2,11,1029,26.39,0,0 +2014,9,30,5,17,2,11,1029,28.18,0,0 +2014,9,30,6,20,2,11,1029,29.97,0,0 +2014,9,30,7,13,1,11,1029,33.1,0,0 +2014,9,30,8,18,2,11,1029,0.89,0,0 +2014,9,30,9,15,2,12,1029,1.78,0,0 +2014,9,30,10,17,1,12,1029,2.67,0,0 +2014,9,30,11,23,1,13,1028,1.79,0,0 +2014,9,30,12,22,0,14,1027,3.58,0,0 +2014,9,30,13,16,0,15,1026,1.79,0,0 +2014,9,30,14,22,0,16,1025,3.13,0,0 +2014,9,30,15,21,0,15,1024,1.79,0,0 +2014,9,30,16,23,0,15,1023,3.13,0,0 +2014,9,30,17,20,1,15,1023,4.92,0,0 +2014,9,30,18,26,3,13,1022,6.71,0,0 +2014,9,30,19,31,3,13,1022,9.84,0,0 +2014,9,30,20,44,5,11,1022,10.73,0,0 +2014,9,30,21,53,5,10,1022,0.89,0,0 +2014,9,30,22,57,6,11,1022,1.78,0,0 +2014,9,30,23,60,5,12,1021,1.79,0,0 +2014,10,1,0,78,6,11,1021,3.58,0,0 +2014,10,1,1,85,6,11,1020,0.89,0,0 +2014,10,1,2,83,6,11,1019,1.79,0,0 +2014,10,1,3,89,6,11,1018,1.79,0,0 +2014,10,1,4,80,7,11,1018,2.68,0,0 +2014,10,1,5,80,7,11,1018,0.89,0,0 +2014,10,1,6,69,8,12,1018,1.78,0,0 +2014,10,1,7,58,7,12,1018,2.67,0,0 +2014,10,1,8,62,8,12,1017,3.56,0,0 +2014,10,1,9,73,9,13,1017,4.45,0,0 +2014,10,1,10,77,8,14,1017,1.79,0,0 +2014,10,1,11,90,9,15,1017,3.13,0,0 +2014,10,1,12,91,8,14,1016,1.79,0,0 +2014,10,1,13,96,9,13,1015,4.92,0,1 +2014,10,1,14,71,10,11,1015,8.05,0,2 +2014,10,1,15,77,10,11,1015,9.84,0,3 +2014,10,1,16,76,10,11,1014,12.97,0,4 +2014,10,1,17,100,10,11,1014,16.1,0,5 +2014,10,1,18,100,10,11,1014,17.89,0,6 +2014,10,1,19,97,11,11,1014,0.89,0,7 +2014,10,1,20,93,10,11,1015,3.13,0,8 +2014,10,1,21,88,11,11,1015,4.92,0,9 +2014,10,1,22,82,10,11,1015,0.89,0,10 +2014,10,1,23,86,11,11,1014,1.79,0,11 +2014,10,2,0,82,11,11,1014,4.92,0,0 +2014,10,2,1,69,10,11,1013,6.71,0,0 +2014,10,2,2,66,9,10,1013,8.5,0,0 +2014,10,2,3,59,10,11,1013,11.63,0,0 +2014,10,2,4,62,10,10,1012,13.42,0,0 +2014,10,2,5,73,10,10,1012,15.21,0,0 +2014,10,2,6,80,9,9,1013,17,0,0 +2014,10,2,7,74,9,10,1013,18.79,0,0 +2014,10,2,8,79,11,12,1013,21.92,0,0 +2014,10,2,9,64,12,15,1013,0.89,0,0 +2014,10,2,10,61,11,17,1013,1.79,0,0 +2014,10,2,11,75,11,18,1013,0.89,0,0 +2014,10,2,12,64,11,20,1012,1.79,0,0 +2014,10,2,13,39,11,21,1012,0.89,0,0 +2014,10,2,14,31,13,20,1011,1.79,0,0 +2014,10,2,15,27,12,21,1011,0.89,0,0 +2014,10,2,16,21,13,20,1012,1.79,0,0 +2014,10,2,17,27,13,20,1012,3.58,0,0 +2014,10,2,18,33,13,17,1013,5.37,0,0 +2014,10,2,19,60,13,16,1014,7.16,0,0 +2014,10,2,20,79,13,14,1014,0.89,0,0 +2014,10,2,21,93,13,15,1015,1.79,0,0 +2014,10,2,22,129,12,15,1016,3.58,0,0 +2014,10,2,23,132,12,14,1016,5.37,0,0 +2014,10,3,0,121,12,14,1016,0.89,0,0 +2014,10,3,1,115,11,12,1017,1.78,0,0 +2014,10,3,2,113,11,11,1017,1.79,0,0 +2014,10,3,3,135,12,13,1017,0.89,0,0 +2014,10,3,4,115,11,13,1017,1.78,0,0 +2014,10,3,5,149,12,14,1017,2.23,0,0 +2014,10,3,6,109,12,14,1017,0.89,0,0 +2014,10,3,7,73,12,13,1018,2.68,0,0 +2014,10,3,8,57,11,14,1019,4.47,0,0 +2014,10,3,9,53,12,14,1019,0.89,0,0 +2014,10,3,10,59,11,15,1019,1.79,0,1 +2014,10,3,11,57,11,15,1019,0.89,0,0 +2014,10,3,12,84,12,16,1018,1.78,0,0 +2014,10,3,13,91,13,16,1017,2.67,0,0 +2014,10,3,14,133,13,17,1017,1.79,0,0 +2014,10,3,15,172,13,18,1016,3.58,0,0 +2014,10,3,16,182,14,18,1016,5.37,0,0 +2014,10,3,17,201,14,17,1016,7.16,0,0 +2014,10,3,18,207,14,16,1017,1.79,0,0 +2014,10,3,19,199,14,16,1017,0.89,0,0 +2014,10,3,20,177,14,16,1017,1.78,0,0 +2014,10,3,21,161,14,16,1018,2.67,0,0 +2014,10,3,22,156,14,16,1018,3.12,0,1 +2014,10,3,23,152,14,15,1018,1.79,0,2 +2014,10,4,0,130,13,15,1017,4.92,0,3 +2014,10,4,1,94,14,15,1017,8.05,0,4 +2014,10,4,2,81,14,15,1017,9.84,0,5 +2014,10,4,3,81,14,15,1017,12.97,0,6 +2014,10,4,4,78,14,15,1017,17.89,0,7 +2014,10,4,5,69,13,14,1017,21.91,0,8 +2014,10,4,6,59,13,14,1018,23.7,0,9 +2014,10,4,7,55,13,14,1018,25.49,0,0 +2014,10,4,8,54,13,14,1019,26.38,0,1 +2014,10,4,9,58,13,14,1019,27.27,0,0 +2014,10,4,10,61,13,14,1020,1.79,0,0 +2014,10,4,11,75,14,15,1019,2.68,0,1 +2014,10,4,12,78,13,15,1019,0.89,0,0 +2014,10,4,13,97,14,15,1019,0.89,0,0 +2014,10,4,14,105,14,16,1018,1.79,0,0 +2014,10,4,15,103,14,16,1019,1.79,0,0 +2014,10,4,16,100,14,16,1019,0.89,0,1 +2014,10,4,17,107,14,15,1019,1.78,0,0 +2014,10,4,18,109,14,15,1020,2.67,0,0 +2014,10,4,19,116,13,14,1020,3.13,0,0 +2014,10,4,20,112,13,14,1021,6.26,0,0 +2014,10,4,21,101,13,14,1022,9.39,0,0 +2014,10,4,22,80,13,14,1023,11.18,0,0 +2014,10,4,23,77,12,13,1023,12.97,0,0 +2014,10,5,0,79,13,13,1023,0.89,0,0 +2014,10,5,1,86,12,13,1023,1.79,0,0 +2014,10,5,2,72,11,12,1023,0.89,0,0 +2014,10,5,3,70,11,11,1022,0.89,0,0 +2014,10,5,4,64,11,11,1023,1.78,0,0 +2014,10,5,5,67,10,10,1023,0.89,0,0 +2014,10,5,6,63,11,11,1024,0.89,0,0 +2014,10,5,7,54,9,12,1024,3.13,0,0 +2014,10,5,8,50,9,15,1025,4.92,0,0 +2014,10,5,9,21,8,17,1025,9.84,0,0 +2014,10,5,10,11,5,18,1025,16.99,0,0 +2014,10,5,11,10,5,20,1025,24.14,0,0 +2014,10,5,12,8,4,22,1024,29.95,0,0 +2014,10,5,13,7,5,20,1024,35.76,0,0 +2014,10,5,14,10,5,21,1024,39.78,0,0 +2014,10,5,15,12,4,22,1023,4.02,0,0 +2014,10,5,16,8,2,20,1024,9.83,0,0 +2014,10,5,17,6,2,19,1024,13.85,0,0 +2014,10,5,18,11,3,17,1024,15.64,0,0 +2014,10,5,19,17,7,13,1025,17.43,0,0 +2014,10,5,20,22,6,12,1026,1.79,0,0 +2014,10,5,21,27,6,10,1026,1.79,0,0 +2014,10,5,22,43,6,11,1027,1.79,0,0 +2014,10,5,23,34,4,13,1027,0.89,0,0 +2014,10,6,0,39,5,7,1027,1.78,0,0 +2014,10,6,1,32,6,8,1027,1.79,0,0 +2014,10,6,2,28,6,7,1027,0.45,0,0 +2014,10,6,3,28,5,10,1027,3.13,0,0 +2014,10,6,4,22,5,7,1026,0.89,0,0 +2014,10,6,5,27,4,6,1026,1.79,0,0 +2014,10,6,6,22,4,6,1027,3.58,0,0 +2014,10,6,7,22,5,7,1027,6.71,0,0 +2014,10,6,8,25,7,11,1027,9.84,0,0 +2014,10,6,9,21,5,15,1027,11.63,0,0 +2014,10,6,10,22,3,16,1027,3.13,0,0 +2014,10,6,11,18,3,19,1026,0.89,0,0 +2014,10,6,12,18,0,20,1025,1.78,0,0 +2014,10,6,13,22,0,21,1024,3.13,0,0 +2014,10,6,14,20,0,21,1023,6.26,0,0 +2014,10,6,15,25,0,21,1022,10.28,0,0 +2014,10,6,16,22,0,20,1021,14.3,0,0 +2014,10,6,17,25,2,19,1021,16.09,0,0 +2014,10,6,18,33,4,17,1021,17.88,0,0 +2014,10,6,19,50,6,14,1021,18.77,0,0 +2014,10,6,20,61,8,13,1021,19.66,0,0 +2014,10,6,21,74,8,12,1021,0.89,0,0 +2014,10,6,22,79,7,12,1021,1.78,0,0 +2014,10,6,23,77,7,11,1021,0.89,0,0 +2014,10,7,0,91,7,11,1021,2.68,0,0 +2014,10,7,1,94,7,11,1020,4.47,0,0 +2014,10,7,2,107,7,10,1020,0.89,0,0 +2014,10,7,3,114,7,9,1020,0.89,0,0 +2014,10,7,4,104,7,8,1020,0.89,0,0 +2014,10,7,5,93,7,8,1020,1.78,0,0 +2014,10,7,6,85,7,8,1020,2.67,0,0 +2014,10,7,7,75,7,8,1021,0.89,0,0 +2014,10,7,8,75,8,11,1021,1.79,0,0 +2014,10,7,9,66,7,13,1022,1.79,0,0 +2014,10,7,10,66,7,15,1021,3.58,0,0 +2014,10,7,11,82,8,16,1021,1.79,0,0 +2014,10,7,12,104,9,18,1020,2.68,0,0 +2014,10,7,13,118,8,19,1019,3.57,0,0 +2014,10,7,14,140,9,20,1019,4.46,0,0 +2014,10,7,15,158,9,21,1018,1.79,0,0 +2014,10,7,16,179,9,20,1018,4.92,0,0 +2014,10,7,17,182,10,19,1018,5.81,0,0 +2014,10,7,18,189,11,16,1018,0.89,0,0 +2014,10,7,19,220,12,14,1019,1.34,0,0 +2014,10,7,20,245,12,13,1019,0.89,0,0 +2014,10,7,21,255,11,12,1019,0.89,0,0 +2014,10,7,22,284,11,12,1020,0.89,0,0 +2014,10,7,23,326,11,12,1020,1.78,0,0 +2014,10,8,0,301,11,12,1020,2.23,0,0 +2014,10,8,1,289,11,12,1020,3.12,0,0 +2014,10,8,2,269,11,12,1020,3.57,0,0 +2014,10,8,3,249,10,12,1020,1.79,0,0 +2014,10,8,4,232,11,12,1019,2.68,0,0 +2014,10,8,5,222,10,11,1020,3.57,0,0 +2014,10,8,6,217,10,12,1019,0.89,0,0 +2014,10,8,7,215,11,13,1020,0.89,0,0 +2014,10,8,8,243,11,13,1020,2.68,0,1 +2014,10,8,9,224,11,14,1021,5.81,0,0 +2014,10,8,10,236,11,16,1021,0.89,0,0 +2014,10,8,11,256,11,17,1020,1.79,0,0 +2014,10,8,12,272,12,17,1019,1.79,0,0 +2014,10,8,13,266,12,19,1019,0.89,0,0 +2014,10,8,14,280,13,19,1018,1.79,0,0 +2014,10,8,15,320,13,20,1017,4.92,0,0 +2014,10,8,16,364,14,20,1017,0.89,0,0 +2014,10,8,17,383,15,18,1017,0.89,0,0 +2014,10,8,18,402,14,16,1017,1.78,0,0 +2014,10,8,19,396,14,15,1018,2.67,0,0 +2014,10,8,20,419,14,15,1018,0.89,0,0 +2014,10,8,21,432,13,14,1019,0.89,0,0 +2014,10,8,22,432,13,14,1019,0.89,0,0 +2014,10,8,23,406,13,13,1019,1.34,0,0 +2014,10,9,0,388,12,14,1019,1.79,0,0 +2014,10,9,1,368,12,13,1019,0.45,0,0 +2014,10,9,2,355,12,12,1019,1.79,0,0 +2014,10,9,3,349,12,12,1018,2.68,0,0 +2014,10,9,4,353,11,12,1019,0.89,0,0 +2014,10,9,5,345,11,12,1019,0.89,0,0 +2014,10,9,6,341,11,11,1019,0.89,0,0 +2014,10,9,7,337,11,12,1020,0.45,0,0 +2014,10,9,8,342,13,13,1020,0.89,0,0 +2014,10,9,9,342,14,15,1021,0.89,0,0 +2014,10,9,10,334,14,17,1021,0.89,0,0 +2014,10,9,11,337,14,19,1021,1.79,0,0 +2014,10,9,12,343,14,20,1020,0.89,0,0 +2014,10,9,13,350,14,21,1019,1.78,0,0 +2014,10,9,14,364,15,22,1019,2.67,0,0 +2014,10,9,15,373,15,22,1018,1.79,0,0 +2014,10,9,16,385,15,22,1018,0.89,0,0 +2014,10,9,17,398,15,21,1018,1.78,0,0 +2014,10,9,18,430,16,18,1019,2.23,0,0 +2014,10,9,19,442,15,16,1019,3.12,0,0 +2014,10,9,20,443,15,15,1020,0.89,0,0 +2014,10,9,21,438,14,15,1020,1.78,0,0 +2014,10,9,22,420,14,15,1021,0.45,0,0 +2014,10,9,23,454,14,14,1021,1.34,0,0 +2014,10,10,0,430,13,14,1021,2.23,0,0 +2014,10,10,1,396,13,14,1022,1.79,0,0 +2014,10,10,2,386,13,14,1022,0.89,0,0 +2014,10,10,3,375,12,13,1022,0.89,0,0 +2014,10,10,4,344,12,13,1022,0.89,0,0 +2014,10,10,5,348,12,12,1022,0.45,0,0 +2014,10,10,6,338,11,12,1022,0.89,0,0 +2014,10,10,7,319,13,13,1023,0.89,0,0 +2014,10,10,8,324,14,14,1024,0.89,0,0 +2014,10,10,9,323,15,16,1024,1.78,0,0 +2014,10,10,10,331,15,18,1024,1.79,0,0 +2014,10,10,11,332,15,19,1024,4.92,0,0 +2014,10,10,12,328,15,21,1023,0.89,0,0 +2014,10,10,13,310,15,22,1022,3.13,0,0 +2014,10,10,14,298,15,22,1021,4.92,0,0 +2014,10,10,15,267,15,22,1021,8.05,0,0 +2014,10,10,16,261,15,22,1020,9.84,0,0 +2014,10,10,17,243,15,20,1020,11.63,0,0 +2014,10,10,18,263,15,18,1020,0.89,0,0 +2014,10,10,19,302,15,17,1020,1.79,0,0 +2014,10,10,20,331,15,18,1021,3.58,0,0 +2014,10,10,21,330,15,17,1021,5.37,0,0 +2014,10,10,22,308,14,15,1021,0.89,0,0 +2014,10,10,23,322,14,15,1021,1.78,0,0 +2014,10,11,0,328,14,15,1021,0.89,0,0 +2014,10,11,1,303,15,15,1021,2.68,0,0 +2014,10,11,2,290,14,14,1021,0.89,0,0 +2014,10,11,3,317,14,15,1021,1.78,0,0 +2014,10,11,4,314,14,15,1020,1.79,0,0 +2014,10,11,5,269,14,14,1020,2.68,0,0 +2014,10,11,6,204,15,15,1021,0.89,0,0 +2014,10,11,7,217,15,15,1021,1.79,0,0 +2014,10,11,8,202,15,15,1021,3.58,0,0 +2014,10,11,9,196,15,15,1022,5.37,0,0 +2014,10,11,10,230,15,16,1022,6.26,0,0 +2014,10,11,11,256,15,17,1021,8.05,0,0 +2014,10,11,12,246,15,18,1021,9.84,0,0 +2014,10,11,13,243,14,19,1020,0.89,0,0 +2014,10,11,14,237,15,20,1020,1.78,0,0 +2014,10,11,15,233,15,20,1020,1.79,0,0 +2014,10,11,16,239,15,20,1021,4.92,0,0 +2014,10,11,17,253,15,19,1021,8.05,0,0 +2014,10,11,18,108,9,20,1022,15.2,0,0 +2014,10,11,19,21,7,19,1024,22.35,0,0 +2014,10,11,20,18,6,19,1025,28.16,0,0 +2014,10,11,21,5,5,18,1026,35.31,0,0 +2014,10,11,22,6,5,17,1026,42.46,0,0 +2014,10,11,23,7,5,17,1027,49.61,0,0 +2014,10,12,0,8,7,16,1028,55.42,0,1 +2014,10,12,1,5,6,15,1028,60.34,0,2 +2014,10,12,2,7,9,13,1028,64.36,0,3 +2014,10,12,3,9,8,13,1029,3.13,0,4 +2014,10,12,4,9,8,13,1029,3.13,0,0 +2014,10,12,5,9,8,13,1029,4.92,0,0 +2014,10,12,6,8,5,14,1030,8.05,0,0 +2014,10,12,7,7,-4,16,1031,16.1,0,0 +2014,10,12,8,4,-2,15,1032,20.12,0,0 +2014,10,12,9,3,1,15,1033,3.13,0,0 +2014,10,12,10,4,-3,16,1033,7.15,0,0 +2014,10,12,11,6,-1,17,1033,10.28,0,0 +2014,10,12,12,3,-3,18,1032,15.2,0,0 +2014,10,12,13,5,-3,18,1031,3.13,0,0 +2014,10,12,14,6,-4,18,1030,8.05,0,0 +2014,10,12,15,6,-4,18,1030,12.07,0,0 +2014,10,12,16,4,-1,16,1030,13.86,0,0 +2014,10,12,17,7,-4,16,1030,18.78,0,0 +2014,10,12,18,6,-4,16,1030,23.7,0,0 +2014,10,12,19,7,-1,14,1030,26.83,0,0 +2014,10,12,20,13,-1,13,1030,29.96,0,0 +2014,10,12,21,8,0,12,1031,33.09,0,0 +2014,10,12,22,10,0,10,1031,37.11,0,0 +2014,10,12,23,10,-2,10,1031,40.24,0,0 +2014,10,13,0,7,-3,8,1031,42.03,0,0 +2014,10,13,1,5,-3,6,1031,45.16,0,0 +2014,10,13,2,3,-3,6,1031,48.29,0,0 +2014,10,13,3,3,-4,5,1030,50.08,0,0 +2014,10,13,4,7,-4,5,1030,51.87,0,0 +2014,10,13,5,9,-2,3,1030,53.66,0,0 +2014,10,13,6,7,-2,3,1030,1.79,0,0 +2014,10,13,7,5,-2,4,1030,4.02,0,0 +2014,10,13,8,5,-2,11,1031,5.81,0,0 +2014,10,13,9,7,-5,12,1031,9.83,0,0 +2014,10,13,10,9,-8,14,1030,13.85,0,0 +2014,10,13,11,8,-8,15,1029,17.87,0,0 +2014,10,13,12,9,-9,16,1027,21,0,0 +2014,10,13,13,9,-10,16,1026,22.79,0,0 +2014,10,13,14,8,-9,17,1025,1.79,0,0 +2014,10,13,15,10,-8,17,1024,2.68,0,0 +2014,10,13,16,10,-8,17,1023,1.79,0,0 +2014,10,13,17,8,-5,15,1023,3.58,0,0 +2014,10,13,18,13,1,11,1023,4.47,0,0 +2014,10,13,19,24,1,9,1023,6.26,0,0 +2014,10,13,20,35,2,7,1023,0.89,0,0 +2014,10,13,21,35,3,7,1024,1.78,0,0 +2014,10,13,22,51,3,6,1023,2.67,0,0 +2014,10,13,23,59,2,5,1023,3.56,0,0 +2014,10,14,0,61,3,5,1023,4.01,0,0 +2014,10,14,1,47,3,5,1023,0.89,0,0 +2014,10,14,2,42,2,4,1023,0.89,0,0 +2014,10,14,3,45,1,4,1022,3.13,0,0 +2014,10,14,4,47,0,4,1022,4.92,0,0 +2014,10,14,5,33,0,3,1022,6.71,0,0 +2014,10,14,6,20,0,4,1022,0.89,0,0 +2014,10,14,7,25,0,4,1022,1.79,0,0 +2014,10,14,8,22,1,8,1022,4.92,0,0 +2014,10,14,9,17,0,11,1022,6.71,0,0 +2014,10,14,10,26,-1,14,1021,0.89,0,0 +2014,10,14,11,28,0,16,1021,1.78,0,0 +2014,10,14,12,31,0,18,1020,3.57,0,0 +2014,10,14,13,46,-1,20,1018,4.02,0,0 +2014,10,14,14,49,0,21,1017,7.15,0,0 +2014,10,14,15,37,0,22,1016,11.17,0,0 +2014,10,14,16,31,-1,21,1016,15.19,0,0 +2014,10,14,17,31,0,20,1015,18.32,0,0 +2014,10,14,18,54,0,19,1016,21.45,0,0 +2014,10,14,19,67,1,18,1016,26.37,0,0 +2014,10,14,20,75,2,16,1016,30.39,0,0 +2014,10,14,21,88,3,17,1015,3.13,0,0 +2014,10,14,22,114,6,10,1015,4.02,0,0 +2014,10,14,23,128,6,9,1015,4.91,0,0 +2014,10,15,0,141,6,8,1015,5.8,0,0 +2014,10,15,1,149,6,8,1014,6.69,0,0 +2014,10,15,2,149,5,6,1014,0.89,0,0 +2014,10,15,3,125,5,6,1013,0.89,0,0 +2014,10,15,4,92,5,6,1013,1.78,0,0 +2014,10,15,5,110,4,5,1013,2.67,0,0 +2014,10,15,6,89,4,5,1013,1.79,0,0 +2014,10,15,7,84,5,7,1013,0.89,0,0 +2014,10,15,8,95,6,11,1014,1.79,0,0 +2014,10,15,9,62,5,13,1014,1.79,0,0 +2014,10,15,10,23,-2,21,1014,9.83,0,0 +2014,10,15,11,11,-2,21,1014,18.77,0,0 +2014,10,15,12,13,-2,21,1014,28.6,0,0 +2014,10,15,13,9,-2,22,1014,37.54,0,0 +2014,10,15,14,12,-5,23,1014,48.72,0,0 +2014,10,15,15,9,-8,23,1014,58.55,0,0 +2014,10,15,16,8,-11,22,1015,67.49,0,0 +2014,10,15,17,9,-9,20,1016,76.43,0,0 +2014,10,15,18,8,-8,17,1017,85.37,0,0 +2014,10,15,19,7,-11,17,1018,94.31,0,0 +2014,10,15,20,16,-9,15,1020,100.12,0,0 +2014,10,15,21,20,-8,13,1021,103.25,0,0 +2014,10,15,22,17,-6,12,1021,1.79,0,0 +2014,10,15,23,20,-3,9,1021,0.89,0,0 +2014,10,16,0,40,-1,7,1022,1.78,0,0 +2014,10,16,1,27,-2,7,1022,1.79,0,0 +2014,10,16,2,27,0,5,1022,0.89,0,0 +2014,10,16,3,29,1,5,1022,1.78,0,0 +2014,10,16,4,31,0,4,1022,2.67,0,0 +2014,10,16,5,37,0,4,1023,3.56,0,0 +2014,10,16,6,36,1,3,1023,4.01,0,0 +2014,10,16,7,43,1,3,1024,4.9,0,0 +2014,10,16,8,44,2,10,1025,5.79,0,0 +2014,10,16,9,44,1,13,1025,6.68,0,0 +2014,10,16,10,25,1,16,1024,7.57,0,0 +2014,10,16,11,17,-5,19,1023,4.92,0,0 +2014,10,16,12,17,-6,20,1022,9.84,0,0 +2014,10,16,13,27,-6,21,1020,16.99,0,0 +2014,10,16,14,29,-6,21,1019,24.14,0,0 +2014,10,16,15,29,-6,22,1018,31.29,0,0 +2014,10,16,16,36,-5,21,1018,38.44,0,0 +2014,10,16,17,59,-4,20,1018,44.25,0,0 +2014,10,16,18,59,-2,15,1018,47.38,0,0 +2014,10,16,19,57,-1,15,1018,50.51,0,0 +2014,10,16,20,55,-1,13,1018,53.64,0,0 +2014,10,16,21,60,0,13,1018,55.43,0,0 +2014,10,16,22,70,3,9,1018,0.89,0,0 +2014,10,16,23,80,3,8,1018,1.79,0,0 +2014,10,17,0,85,3,8,1018,0.89,0,0 +2014,10,17,1,88,3,8,1017,1.78,0,0 +2014,10,17,2,91,2,5,1017,0.89,0,0 +2014,10,17,3,98,4,6,1017,0.45,0,0 +2014,10,17,4,103,4,5,1016,0.89,0,0 +2014,10,17,5,101,3,4,1017,2.68,0,0 +2014,10,17,6,98,3,6,1016,3.57,0,0 +2014,10,17,7,99,4,6,1017,0.89,0,0 +2014,10,17,8,95,5,9,1017,0.89,0,0 +2014,10,17,9,76,3,13,1018,2.68,0,0 +2014,10,17,10,80,3,15,1017,4.47,0,0 +2014,10,17,11,87,3,18,1017,0.89,0,0 +2014,10,17,12,87,4,20,1016,2.68,0,0 +2014,10,17,13,82,3,22,1015,4.47,0,0 +2014,10,17,14,105,3,23,1014,3.13,0,0 +2014,10,17,15,116,4,24,1014,6.26,0,0 +2014,10,17,16,103,4,23,1014,10.28,0,0 +2014,10,17,17,109,5,22,1014,14.3,0,0 +2014,10,17,18,118,7,16,1014,0.89,0,0 +2014,10,17,19,127,8,13,1015,1.79,0,0 +2014,10,17,20,144,8,15,1015,3.58,0,0 +2014,10,17,21,189,8,13,1016,4.47,0,0 +2014,10,17,22,190,7,13,1016,0.89,0,0 +2014,10,17,23,225,8,11,1017,1.78,0,0 +2014,10,18,0,211,8,10,1017,0.89,0,0 +2014,10,18,1,197,7,11,1017,2.68,0,0 +2014,10,18,2,200,7,9,1017,1.79,0,0 +2014,10,18,3,197,6,9,1017,1.79,0,0 +2014,10,18,4,193,6,8,1016,0.89,0,0 +2014,10,18,5,180,6,8,1017,1.79,0,0 +2014,10,18,6,117,6,8,1017,3.58,0,0 +2014,10,18,7,110,5,8,1018,5.37,0,0 +2014,10,18,8,111,7,11,1018,8.5,0,0 +2014,10,18,9,94,7,14,1019,1.79,0,0 +2014,10,18,10,112,7,17,1018,0.89,0,0 +2014,10,18,11,110,8,20,1018,1.78,0,0 +2014,10,18,12,155,8,22,1017,2.67,0,0 +2014,10,18,13,186,8,23,1016,4.46,0,0 +2014,10,18,14,194,8,24,1015,5.35,0,0 +2014,10,18,15,210,8,24,1014,3.13,0,0 +2014,10,18,16,215,8,23,1015,6.26,0,0 +2014,10,18,17,230,10,21,1014,8.05,0,0 +2014,10,18,18,239,12,17,1015,0.89,0,0 +2014,10,18,19,272,12,15,1015,1.78,0,0 +2014,10,18,20,296,11,14,1015,0.89,0,0 +2014,10,18,21,315,11,13,1016,1.78,0,0 +2014,10,18,22,357,11,13,1016,0.89,0,0 +2014,10,18,23,382,12,14,1016,1.34,0,0 +2014,10,19,0,406,11,14,1015,2.23,0,0 +2014,10,19,1,405,11,15,1015,4.02,0,0 +2014,10,19,2,389,12,14,1015,0.89,0,0 +2014,10,19,3,397,12,14,1014,0.89,0,0 +2014,10,19,4,413,12,13,1014,0.89,0,0 +2014,10,19,5,373,12,13,1014,0.89,0,0 +2014,10,19,6,362,12,13,1014,0.89,0,0 +2014,10,19,7,356,11,13,1014,1.78,0,0 +2014,10,19,8,344,12,15,1015,1.79,0,0 +2014,10,19,9,352,12,16,1015,0.89,0,0 +2014,10,19,10,357,12,17,1015,1.79,0,0 +2014,10,19,11,353,12,18,1015,3.58,0,0 +2014,10,19,12,292,11,20,1014,6.71,0,0 +2014,10,19,13,213,11,20,1014,10.73,0,0 +2014,10,19,14,210,12,20,1013,13.86,0,0 +2014,10,19,15,183,12,20,1013,16.99,0,0 +2014,10,19,16,161,12,20,1013,20.12,0,0 +2014,10,19,17,153,12,19,1013,21.91,0,0 +2014,10,19,18,169,13,16,1014,0.89,0,0 +2014,10,19,19,196,13,17,1014,0.89,0,0 +2014,10,19,20,200,13,16,1015,0.89,0,0 +2014,10,19,21,197,13,16,1015,2.68,0,0 +2014,10,19,22,178,13,16,1015,5.81,0,0 +2014,10,19,23,161,13,16,1016,7.6,0,0 +2014,10,20,0,161,13,16,1016,9.39,0,0 +2014,10,20,1,166,13,15,1016,10.28,0,0 +2014,10,20,2,171,13,15,1016,12.07,0,0 +2014,10,20,3,177,12,13,1015,0.89,0,0 +2014,10,20,4,177,12,12,1015,1.78,0,0 +2014,10,20,5,189,11,11,1016,0.89,0,0 +2014,10,20,6,187,11,11,1016,1.78,0,0 +2014,10,20,7,192,12,12,1017,0.89,0,0 +2014,10,20,8,203,12,13,1017,0.89,0,0 +2014,10,20,9,234,14,14,1018,1.78,0,0 +2014,10,20,10,261,12,17,1018,4.02,0,0 +2014,10,20,11,265,13,16,1018,4.92,0,0 +2014,10,20,12,268,13,16,1018,8.94,0,0 +2014,10,20,13,285,13,16,1018,12.96,0,0 +2014,10,20,14,264,14,17,1018,14.75,0,0 +2014,10,20,15,253,13,18,1018,16.54,0,0 +2014,10,20,16,NA,13,17,1018,18.33,0,0 +2014,10,20,17,239,13,17,1018,20.12,0,0 +2014,10,20,18,256,13,15,1020,0.45,0,0 +2014,10,20,19,253,13,15,1021,0.89,0,0 +2014,10,20,20,262,13,16,1022,2.68,0,0 +2014,10,20,21,239,13,15,1022,4.47,0,0 +2014,10,20,22,143,10,15,1023,7.6,0,0 +2014,10,20,23,71,9,14,1023,9.39,0,0 +2014,10,21,0,61,7,14,1024,12.52,0,0 +2014,10,21,1,34,7,13,1024,14.31,0,0 +2014,10,21,2,36,6,13,1025,15.2,0,0 +2014,10,21,3,33,6,12,1024,16.09,0,0 +2014,10,21,4,31,3,12,1024,19.22,0,0 +2014,10,21,5,24,2,12,1024,20.11,0,0 +2014,10,21,6,31,1,12,1024,21.9,0,0 +2014,10,21,7,38,0,12,1025,25.03,0,0 +2014,10,21,8,38,-2,13,1026,28.16,0,0 +2014,10,21,9,38,-3,13,1026,31.29,0,0 +2014,10,21,10,38,-2,13,1026,34.42,0,0 +2014,10,21,11,36,-3,13,1026,37.55,0,0 +2014,10,21,12,38,-4,14,1026,40.68,0,0 +2014,10,21,13,37,-3,14,1025,42.47,0,0 +2014,10,21,14,36,-3,15,1024,1.79,0,0 +2014,10,21,15,35,-4,15,1023,4.02,0,0 +2014,10,21,16,33,-3,15,1023,7.15,0,0 +2014,10,21,17,41,-3,13,1023,8.94,0,0 +2014,10,21,18,38,0,10,1023,0.89,0,0 +2014,10,21,19,51,3,9,1024,1.78,0,0 +2014,10,21,20,67,2,8,1024,0.89,0,0 +2014,10,21,21,84,3,7,1024,2.68,0,0 +2014,10,21,22,91,2,8,1025,4.47,0,0 +2014,10,21,23,97,3,6,1025,0.89,0,0 +2014,10,22,0,60,3,6,1025,0.89,0,0 +2014,10,22,1,50,3,7,1024,1.78,0,0 +2014,10,22,2,47,3,6,1024,2.67,0,0 +2014,10,22,3,52,4,7,1024,0.89,0,0 +2014,10,22,4,73,4,7,1024,0.89,0,0 +2014,10,22,5,111,3,8,1024,1.78,0,0 +2014,10,22,6,104,3,8,1024,2.67,0,0 +2014,10,22,7,90,3,8,1024,3.12,0,0 +2014,10,22,8,84,3,9,1025,4.01,0,0 +2014,10,22,9,79,2,10,1025,4.9,0,0 +2014,10,22,10,83,3,10,1024,5.79,0,0 +2014,10,22,11,92,4,11,1024,6.68,0,0 +2014,10,22,12,104,5,11,1023,1.79,0,0 +2014,10,22,13,100,6,12,1022,3.58,0,0 +2014,10,22,14,101,6,13,1021,6.71,0,0 +2014,10,22,15,86,6,13,1021,9.84,0,0 +2014,10,22,16,85,7,13,1020,11.63,0,0 +2014,10,22,17,83,7,12,1020,13.42,0,0 +2014,10,22,18,97,6,12,1020,1.79,0,0 +2014,10,22,19,117,7,12,1020,3.58,0,0 +2014,10,22,20,124,7,12,1020,0.89,0,0 +2014,10,22,21,127,7,12,1020,1.78,0,0 +2014,10,22,22,132,7,11,1020,2.23,0,0 +2014,10,22,23,130,7,12,1019,2.68,0,0 +2014,10,23,0,126,8,12,1019,0.89,0,0 +2014,10,23,1,119,8,11,1019,0.89,0,0 +2014,10,23,2,108,7,12,1018,0.89,0,0 +2014,10,23,3,108,7,11,1018,0.89,0,0 +2014,10,23,4,102,8,11,1017,0.89,0,0 +2014,10,23,5,110,8,11,1017,1.78,0,0 +2014,10,23,6,113,8,11,1017,0.89,0,0 +2014,10,23,7,108,8,12,1017,1.78,0,0 +2014,10,23,8,116,8,12,1017,1.79,0,0 +2014,10,23,9,130,7,13,1017,3.13,0,0 +2014,10,23,10,134,7,14,1016,1.79,0,0 +2014,10,23,11,136,7,15,1016,0.89,0,0 +2014,10,23,12,145,7,15,1015,1.78,0,0 +2014,10,23,13,149,7,16,1014,2.67,0,0 +2014,10,23,14,133,8,16,1013,3.56,0,0 +2014,10,23,15,143,8,16,1012,1.79,0,0 +2014,10,23,16,149,8,16,1012,3.58,0,0 +2014,10,23,17,164,8,14,1012,5.37,0,0 +2014,10,23,18,170,9,13,1012,0.45,0,0 +2014,10,23,19,174,9,12,1013,1.34,0,0 +2014,10,23,20,191,9,10,1013,2.23,0,0 +2014,10,23,21,196,9,9,1013,0.89,0,0 +2014,10,23,22,195,8,9,1013,1.78,0,0 +2014,10,23,23,214,8,9,1013,0.89,0,0 +2014,10,24,0,227,8,9,1012,0.89,0,0 +2014,10,24,1,217,9,10,1012,0.89,0,0 +2014,10,24,2,195,8,9,1012,1.79,0,0 +2014,10,24,3,183,8,9,1012,3.58,0,0 +2014,10,24,4,179,8,8,1012,5.37,0,0 +2014,10,24,5,191,7,8,1012,7.16,0,0 +2014,10,24,6,178,7,8,1012,8.95,0,0 +2014,10,24,7,183,7,8,1012,10.74,0,0 +2014,10,24,8,185,8,10,1012,12.53,0,0 +2014,10,24,9,192,9,12,1013,15.66,0,0 +2014,10,24,10,199,10,14,1013,18.79,0,0 +2014,10,24,11,231,11,16,1012,0.89,0,0 +2014,10,24,12,253,12,17,1011,1.78,0,0 +2014,10,24,13,281,12,17,1010,2.67,0,0 +2014,10,24,14,294,12,18,1010,1.79,0,0 +2014,10,24,15,317,12,17,1009,3.58,0,0 +2014,10,24,16,318,12,17,1009,5.37,0,0 +2014,10,24,17,323,12,15,1010,6.26,0,0 +2014,10,24,18,339,11,13,1011,0.89,0,0 +2014,10,24,19,341,12,13,1011,0.45,0,0 +2014,10,24,20,346,11,12,1011,0.89,0,0 +2014,10,24,21,356,10,10,1012,0.89,0,0 +2014,10,24,22,383,10,11,1011,0.89,0,0 +2014,10,24,23,363,10,11,1011,0.45,0,0 +2014,10,25,0,381,10,11,1012,0.9,0,0 +2014,10,25,1,352,10,10,1012,1.79,0,0 +2014,10,25,2,319,9,10,1012,3.58,0,0 +2014,10,25,3,310,10,10,1011,0.89,0,0 +2014,10,25,4,331,9,9,1011,1.79,0,0 +2014,10,25,5,332,8,8,1011,3.58,0,0 +2014,10,25,6,315,8,9,1011,5.37,0,0 +2014,10,25,7,312,10,10,1011,7.16,0,0 +2014,10,25,8,310,11,12,1012,10.29,0,0 +2014,10,25,9,324,11,13,1012,12.08,0,0 +2014,10,25,10,330,12,14,1012,13.87,0,0 +2014,10,25,11,324,13,16,1012,0.89,0,0 +2014,10,25,12,331,12,17,1011,1.78,0,0 +2014,10,25,13,346,13,18,1011,1.79,0,0 +2014,10,25,14,352,13,19,1010,3.58,0,0 +2014,10,25,15,376,13,19,1010,5.37,0,0 +2014,10,25,16,402,13,19,1010,7.16,0,0 +2014,10,25,17,423,13,17,1010,8.05,0,0 +2014,10,25,18,440,13,15,1011,0.89,0,0 +2014,10,25,19,449,12,13,1011,0.89,0,0 +2014,10,25,20,472,14,15,1012,2.68,0,0 +2014,10,25,21,453,12,13,1012,0.89,0,0 +2014,10,25,22,413,12,12,1013,1.78,0,0 +2014,10,25,23,396,13,13,1014,2.67,0,0 +2014,10,26,0,395,13,13,1014,1.79,0,0 +2014,10,26,1,379,13,13,1015,3.58,0,0 +2014,10,26,2,341,12,12,1016,6.71,0,0 +2014,10,26,3,312,10,10,1016,10.73,0,0 +2014,10,26,4,257,9,11,1017,1.79,0,0 +2014,10,26,5,183,3,14,1019,4.02,0,0 +2014,10,26,6,39,0,14,1020,9.83,0,0 +2014,10,26,7,16,-2,14,1022,7.15,0,0 +2014,10,26,8,14,-4,14,1025,12.96,0,0 +2014,10,26,9,16,-4,14,1026,20.11,0,0 +2014,10,26,10,10,-8,15,1027,28.16,0,0 +2014,10,26,11,10,-9,16,1027,35.31,0,0 +2014,10,26,12,9,-12,16,1027,43.36,0,0 +2014,10,26,13,7,-11,16,1026,49.17,0,0 +2014,10,26,14,9,-13,17,1026,54.09,0,0 +2014,10,26,15,9,-11,18,1026,58.11,0,0 +2014,10,26,16,10,-14,17,1026,62.13,0,0 +2014,10,26,17,8,-12,16,1026,65.26,0,0 +2014,10,26,18,9,-12,14,1027,68.39,0,0 +2014,10,26,19,17,-13,14,1027,72.41,0,0 +2014,10,26,20,15,-13,13,1028,4.92,0,0 +2014,10,26,21,15,-13,12,1029,4.02,0,0 +2014,10,26,22,12,-12,9,1030,7.15,0,0 +2014,10,26,23,9,-13,10,1031,11.17,0,0 +2014,10,27,0,10,-14,11,1031,15.19,0,0 +2014,10,27,1,9,-15,10,1032,19.21,0,0 +2014,10,27,2,12,-13,9,1032,22.34,0,0 +2014,10,27,3,9,-13,9,1032,25.47,0,0 +2014,10,27,4,13,-11,8,1032,27.26,0,0 +2014,10,27,5,14,-9,6,1032,0.89,0,0 +2014,10,27,6,12,-5,3,1033,1.34,0,0 +2014,10,27,7,24,-5,3,1033,2.23,0,0 +2014,10,27,8,23,-7,8,1034,3.12,0,0 +2014,10,27,9,31,-10,10,1034,4.01,0,0 +2014,10,27,10,57,-9,11,1034,5.8,0,0 +2014,10,27,11,43,-11,13,1034,7.59,0,0 +2014,10,27,12,35,-9,13,1033,3.13,0,0 +2014,10,27,13,31,-10,14,1032,7.15,0,0 +2014,10,27,14,30,-9,14,1031,10.28,0,0 +2014,10,27,15,25,-7,15,1030,14.3,0,0 +2014,10,27,16,33,-5,14,1030,17.43,0,0 +2014,10,27,17,39,-6,13,1029,20.56,0,0 +2014,10,27,18,40,-6,11,1030,23.69,0,0 +2014,10,27,19,60,-6,11,1030,27.71,0,0 +2014,10,27,20,68,-4,10,1030,30.84,0,0 +2014,10,27,21,57,-2,9,1030,33.97,0,0 +2014,10,27,22,51,0,8,1030,35.76,0,0 +2014,10,27,23,26,1,5,1030,0.89,0,0 +2014,10,28,0,50,1,5,1030,1.79,0,0 +2014,10,28,1,42,0,5,1030,0.89,0,0 +2014,10,28,2,43,1,5,1029,0.89,0,0 +2014,10,28,3,74,1,5,1029,0.89,0,0 +2014,10,28,4,87,0,4,1029,0.89,0,0 +2014,10,28,5,78,0,3,1029,1.78,0,0 +2014,10,28,6,77,1,3,1029,0.89,0,0 +2014,10,28,7,91,1,3,1029,1.78,0,0 +2014,10,28,8,99,3,6,1029,2.67,0,0 +2014,10,28,9,92,1,8,1029,0.89,0,0 +2014,10,28,10,83,2,10,1029,1.78,0,0 +2014,10,28,11,94,3,11,1028,2.67,0,0 +2014,10,28,12,90,3,13,1027,4.46,0,0 +2014,10,28,13,68,3,14,1026,6.25,0,0 +2014,10,28,14,60,2,14,1025,3.13,0,0 +2014,10,28,15,53,2,14,1025,3.13,0,0 +2014,10,28,16,51,2,14,1025,1.79,0,0 +2014,10,28,17,52,2,14,1024,4.92,0,0 +2014,10,28,18,51,2,13,1025,8.05,0,0 +2014,10,28,19,47,2,14,1025,11.18,0,0 +2014,10,28,20,47,3,12,1025,0.89,0,0 +2014,10,28,21,59,5,10,1025,0.89,0,0 +2014,10,28,22,59,5,10,1025,1.78,0,0 +2014,10,28,23,74,6,9,1025,2.67,0,0 +2014,10,29,0,93,5,8,1025,0.89,0,0 +2014,10,29,1,101,5,8,1025,0.89,0,0 +2014,10,29,2,111,6,7,1025,1.34,0,0 +2014,10,29,3,139,5,7,1024,2.23,0,0 +2014,10,29,4,129,5,6,1024,0.89,0,0 +2014,10,29,5,127,4,5,1024,1.78,0,0 +2014,10,29,6,114,5,6,1024,3.57,0,0 +2014,10,29,7,98,4,6,1025,5.36,0,0 +2014,10,29,8,102,5,8,1025,7.15,0,0 +2014,10,29,9,106,6,10,1025,8.94,0,0 +2014,10,29,10,111,5,12,1025,10.73,0,0 +2014,10,29,11,106,4,14,1025,0.89,0,0 +2014,10,29,12,105,4,15,1024,1.78,0,0 +2014,10,29,13,108,4,16,1024,2.67,0,0 +2014,10,29,14,107,4,16,1023,4.46,0,0 +2014,10,29,15,113,4,16,1023,5.35,0,0 +2014,10,29,16,126,4,15,1023,1.79,0,0 +2014,10,29,17,141,6,14,1023,0.89,0,0 +2014,10,29,18,158,7,12,1023,0.89,0,0 +2014,10,29,19,170,7,11,1023,1.78,0,0 +2014,10,29,20,176,7,11,1023,2.67,0,0 +2014,10,29,21,183,7,11,1023,3.56,0,0 +2014,10,29,22,193,7,12,1024,0.45,0,0 +2014,10,29,23,215,8,11,1024,0.89,0,0 +2014,10,30,0,215,7,11,1023,1.78,0,1 +2014,10,30,1,210,7,11,1023,2.67,0,0 +2014,10,30,2,214,7,11,1023,3.56,0,0 +2014,10,30,3,214,6,11,1023,0.89,0,0 +2014,10,30,4,272,7,10,1022,1.79,0,0 +2014,10,30,5,198,7,10,1022,1.79,0,0 +2014,10,30,6,176,8,10,1022,0.45,0,0 +2014,10,30,7,159,8,10,1023,0.9,0,0 +2014,10,30,8,158,8,11,1024,1.35,0,0 +2014,10,30,9,137,7,12,1024,1.79,0,1 +2014,10,30,10,156,7,12,1024,2.68,0,0 +2014,10,30,11,161,6,13,1024,0.89,0,0 +2014,10,30,12,163,6,14,1023,1.78,0,0 +2014,10,30,13,178,7,14,1022,0.89,0,0 +2014,10,30,14,180,7,15,1022,0.89,0,0 +2014,10,30,15,179,6,15,1021,1.79,0,0 +2014,10,30,16,182,6,15,1021,0.89,0,0 +2014,10,30,17,192,7,14,1021,1.34,0,0 +2014,10,30,18,199,8,13,1022,1.79,0,0 +2014,10,30,19,208,8,12,1022,2.68,0,0 +2014,10,30,20,212,8,13,1022,1.79,0,0 +2014,10,30,21,193,8,12,1023,0.89,0,0 +2014,10,30,22,205,8,12,1023,1.78,0,0 +2014,10,30,23,206,9,12,1023,2.67,0,1 +2014,10,31,0,212,10,11,1022,0.89,0,0 +2014,10,31,1,216,9,11,1022,0.89,0,0 +2014,10,31,2,232,8,12,1022,0.89,0,0 +2014,10,31,3,223,9,11,1022,0.89,0,0 +2014,10,31,4,229,9,11,1021,0.89,0,0 +2014,10,31,5,206,9,11,1021,0.89,0,0 +2014,10,31,6,194,9,11,1021,0.89,0,0 +2014,10,31,7,211,9,12,1021,0.89,0,0 +2014,10,31,8,225,10,12,1022,1.78,0,0 +2014,10,31,9,169,10,14,1022,0.89,0,0 +2014,10,31,10,144,10,14,1022,2.68,0,0 +2014,10,31,11,150,10,14,1021,3.57,0,1 +2014,10,31,12,144,10,15,1021,1.79,0,0 +2014,10,31,13,143,10,15,1020,3.58,0,1 +2014,10,31,14,143,10,15,1019,5.37,0,2 +2014,10,31,15,145,9,15,1019,7.16,0,0 +2014,10,31,16,152,9,15,1019,8.95,0,0 +2014,10,31,17,165,10,14,1018,0.89,0,0 +2014,10,31,18,176,9,14,1019,1.78,0,0 +2014,10,31,19,209,9,13,1019,2.67,0,0 +2014,10,31,20,224,10,13,1019,3.56,0,0 +2014,10,31,21,200,10,14,1020,1.79,0,0 +2014,10,31,22,188,10,14,1020,4.92,0,0 +2014,10,31,23,184,10,13,1019,6.71,0,0 +2014,11,1,0,180,10,13,1019,1.79,0,0 +2014,11,1,1,189,8,9,1019,0.89,0,0 +2014,11,1,2,215,8,9,1019,1.34,0,0 +2014,11,1,3,180,8,8,1019,1.79,0,0 +2014,11,1,4,81,6,6,1019,0.89,0,0 +2014,11,1,5,78,4,4,1019,1.79,0,0 +2014,11,1,6,24,5,6,1019,0.89,0,0 +2014,11,1,7,26,5,5,1020,0.89,0,0 +2014,11,1,8,29,6,8,1020,1.78,0,0 +2014,11,1,9,22,6,11,1020,2.67,0,0 +2014,11,1,10,14,2,14,1021,4.92,0,0 +2014,11,1,11,11,-2,15,1020,12.97,0,0 +2014,11,1,12,12,-6,15,1020,20.12,0,0 +2014,11,1,13,11,-6,15,1019,25.93,0,0 +2014,11,1,14,13,-7,16,1018,31.74,0,0 +2014,11,1,15,12,-7,16,1018,37.55,0,0 +2014,11,1,16,9,-9,15,1018,44.7,0,0 +2014,11,1,17,10,-8,15,1018,50.51,0,0 +2014,11,1,18,10,-7,14,1018,56.32,0,0 +2014,11,1,19,15,-6,14,1019,61.24,0,0 +2014,11,1,20,18,-5,14,1019,65.26,0,0 +2014,11,1,21,18,-5,13,1020,69.28,0,0 +2014,11,1,22,14,-7,13,1021,77.33,0,0 +2014,11,1,23,16,-8,12,1021,88.51,0,0 +2014,11,2,0,10,-10,11,1022,98.34,0,0 +2014,11,2,1,9,-10,10,1022,106.39,0,0 +2014,11,2,2,6,-11,10,1022,113.54,0,0 +2014,11,2,3,8,-12,9,1022,120.69,0,0 +2014,11,2,4,6,-11,9,1022,130.52,0,0 +2014,11,2,5,3,-11,9,1023,138.57,0,0 +2014,11,2,6,2,-10,8,1023,147.51,0,0 +2014,11,2,7,8,-9,9,1024,153.32,0,0 +2014,11,2,8,8,-8,10,1025,159.13,0,0 +2014,11,2,9,7,-9,11,1026,164.05,0,0 +2014,11,2,10,6,-10,11,1025,171.2,0,0 +2014,11,2,11,8,-10,12,1025,177.01,0,0 +2014,11,2,12,6,-10,14,1024,184.16,0,0 +2014,11,2,13,9,-12,14,1023,193.99,0,0 +2014,11,2,14,8,-12,14,1022,202.93,0,0 +2014,11,2,15,7,-12,15,1022,210.08,0,0 +2014,11,2,16,10,-12,15,1022,218.13,0,0 +2014,11,2,17,10,-12,14,1022,223.94,0,0 +2014,11,2,18,11,-11,13,1022,229.75,0,0 +2014,11,2,19,10,-11,12,1022,234.67,0,0 +2014,11,2,20,7,-11,12,1022,238.69,0,0 +2014,11,2,21,8,-7,8,1023,240.48,0,0 +2014,11,2,22,7,-5,5,1022,0.89,0,0 +2014,11,2,23,10,-4,5,1022,1.78,0,0 +2014,11,3,0,17,-3,3,1022,2.67,0,0 +2014,11,3,1,22,-3,3,1022,3.12,0,0 +2014,11,3,2,28,-5,4,1022,4.91,0,0 +2014,11,3,3,28,-3,3,1022,0.89,0,0 +2014,11,3,4,26,-3,3,1022,0.45,0,0 +2014,11,3,5,25,-4,1,1022,3.13,0,0 +2014,11,3,6,20,-3,0,1022,0.89,0,0 +2014,11,3,7,11,-3,0,1022,1.78,0,0 +2014,11,3,8,28,-1,5,1023,1.79,0,0 +2014,11,3,9,39,-4,10,1023,0.89,0,0 +2014,11,3,10,38,-6,13,1022,1.79,0,0 +2014,11,3,11,14,-5,15,1022,1.79,0,0 +2014,11,3,12,11,-5,17,1021,3.58,0,0 +2014,11,3,13,18,-7,18,1019,3.13,0,0 +2014,11,3,14,8,-8,19,1018,6.26,0,0 +2014,11,3,15,10,-8,20,1018,9.39,0,0 +2014,11,3,16,10,-8,20,1018,12.52,0,0 +2014,11,3,17,11,-8,20,1018,15.65,0,0 +2014,11,3,18,23,-8,20,1018,18.78,0,0 +2014,11,3,19,34,-5,14.66666667,1018,0.89,0,0 +2014,11,3,20,40,1,9.333333333,1018,1.78,0,0 +2014,11,3,21,43,0,4,1017,2.67,0,0 +2014,11,3,22,48,0,4,1017,3.56,0,0 +2014,11,3,23,57,0,4,1017,4.45,0,0 +2014,11,4,0,61,0,4,1017,5.34,0,0 +2014,11,4,1,60,0,4,1017,6.23,0,0 +2014,11,4,2,68,-1,2,1017,1.79,0,0 +2014,11,4,3,76,0,2,1016,2.68,0,0 +2014,11,4,4,72,0,2,1016,0.45,0,0 +2014,11,4,5,80,0,1,1016,0.9,0,0 +2014,11,4,6,80,-1,1,1016,1.79,0,0 +2014,11,4,7,66,0,1,1016,2.68,0,0 +2014,11,4,8,64,1,3,1017,0.89,0,0 +2014,11,4,9,85,0,7,1017,0.89,0,0 +2014,11,4,10,96,-1,10,1017,1.79,0,0 +2014,11,4,11,109,-1,11,1017,3.58,0,0 +2014,11,4,12,118,0,13,1016,5.37,0,0 +2014,11,4,13,141,-1,14,1015,8.5,0,0 +2014,11,4,14,159,-1,15,1014,12.52,0,0 +2014,11,4,15,153,0,15,1014,3.13,0,0 +2014,11,4,16,153,0,15,1013,4.02,0,0 +2014,11,4,17,152,1,12,1013,0.45,0,0 +2014,11,4,18,157,3,8,1014,1.34,0,0 +2014,11,4,19,159,3,7,1015,2.23,0,0 +2014,11,4,20,196,3,6,1015,0.89,0,0 +2014,11,4,21,208,3,5,1015,0.89,0,0 +2014,11,4,22,215,2,5,1016,1.78,0,0 +2014,11,4,23,255,2,4,1016,2.67,0,0 +2014,11,5,0,239,1,4,1016,3.13,0,0 +2014,11,5,1,232,1,4,1016,0.89,0,0 +2014,11,5,2,210,2,3,1017,1.79,0,0 +2014,11,5,3,184,0,3,1016,4.02,0,0 +2014,11,5,4,174,0,4,1016,0.89,0,0 +2014,11,5,5,164,-1,2,1016,1.79,0,0 +2014,11,5,6,147,-1,3,1016,4.92,0,0 +2014,11,5,7,98,-1,3,1017,6.71,0,0 +2014,11,5,8,136,2,5,1018,8.5,0,0 +2014,11,5,9,137,3,10,1018,10.29,0,0 +2014,11,5,10,90,-2,14,1018,0.89,0,0 +2014,11,5,11,14,-4,17,1018,1.79,0,0 +2014,11,5,12,8,-8,19,1017,7.6,0,0 +2014,11,5,13,8,-8,20,1016,14.75,0,0 +2014,11,5,14,11,-9,20,1016,22.8,0,0 +2014,11,5,15,9,-8,20,1016,29.95,0,0 +2014,11,5,16,6,-8,20,1016,37.1,0,0 +2014,11,5,17,6,-8,19,1016,41.12,0,0 +2014,11,5,18,10,-8,18,1017,46.93,0,0 +2014,11,5,19,10,-5,15,1019,50.95,0,0 +2014,11,5,20,7,-7,13,1020,4.02,0,0 +2014,11,5,21,6,-7,14,1022,4.92,0,0 +2014,11,5,22,5,-6,10,1023,8.05,0,0 +2014,11,5,23,6,-7,7,1024,1.79,0,0 +2014,11,6,0,4,-7,7,1025,4.02,0,0 +2014,11,6,1,5,-7,5,1025,8.04,0,0 +2014,11,6,2,4,-8,7,1026,9.83,0,0 +2014,11,6,3,3,-7,4,1026,11.62,0,0 +2014,11,6,4,8,-7,5,1027,3.13,0,0 +2014,11,6,5,9,-8,7,1028,4.92,0,0 +2014,11,6,6,7,-8,7,1029,8.94,0,0 +2014,11,6,7,7,-7,6,1029,13.86,0,0 +2014,11,6,8,9,-6,8,1031,18.78,0,0 +2014,11,6,9,11,-8,9,1032,24.59,0,0 +2014,11,6,10,NA,-8,9,1032,29.51,0,0 +2014,11,6,11,9,-9,10,1032,35.32,0,0 +2014,11,6,12,7,-12,10,1031,41.13,0,0 +2014,11,6,13,4,-14,11,1030,46.05,0,0 +2014,11,6,14,3,-16,11,1029,51.86,0,0 +2014,11,6,15,4,-15,12,1029,55.88,0,0 +2014,11,6,16,7,-17,12,1029,59.01,0,0 +2014,11,6,17,6,-13,9,1030,59.9,0,0 +2014,11,6,18,6,-8,5,1030,0.89,0,0 +2014,11,6,19,33,-7,5,1031,1.78,0,0 +2014,11,6,20,40,-5,4,1031,2.67,0,0 +2014,11,6,21,40,-4,4,1032,0.89,0,0 +2014,11,6,22,48,-5,2,1033,0.89,0,0 +2014,11,6,23,44,-3,2,1033,0.89,0,0 +2014,11,7,0,41,-3,2,1033,1.78,0,0 +2014,11,7,1,44,-4,2,1033,0.89,0,0 +2014,11,7,2,45,-5,3,1032,1.78,0,0 +2014,11,7,3,79,-5,0,1032,2.67,0,0 +2014,11,7,4,76,-4,1,1032,3.56,0,0 +2014,11,7,5,79,-3,0,1032,0.89,0,0 +2014,11,7,6,90,-4,0,1032,0.89,0,0 +2014,11,7,7,73,-4,1,1033,1.78,0,0 +2014,11,7,8,70,-4,1,1033,0.89,0,0 +2014,11,7,9,66,-6,5,1034,0.89,0,0 +2014,11,7,10,59,-7,6,1034,1.78,0,0 +2014,11,7,11,62,-7,7,1033,2.67,0,0 +2014,11,7,12,60,-6,8,1033,3.56,0,0 +2014,11,7,13,41,-5,8,1032,4.45,0,0 +2014,11,7,14,38,-6,9,1031,5.34,0,0 +2014,11,7,15,33,-5,9,1030,1.79,0,0 +2014,11,7,16,18,-4,9,1030,0.89,0,0 +2014,11,7,17,28,-4,8,1030,1.78,0,0 +2014,11,7,18,46,-3,5,1030,2.67,0,0 +2014,11,7,19,51,-4,8,1030,3.13,0,0 +2014,11,7,20,57,-3,8,1030,4.92,0,0 +2014,11,7,21,37,-1,7,1031,1.79,0,0 +2014,11,7,22,35,0,3,1031,2.68,0,0 +2014,11,7,23,41,-1,2,1030,0.89,0,0 +2014,11,8,0,46,-1,1,1030,0.89,0,0 +2014,11,8,1,56,-2,-1,1030,1.79,0,0 +2014,11,8,2,69,-3,-1,1030,3.58,0,0 +2014,11,8,3,79,-1,0,1029,0.89,0,0 +2014,11,8,4,76,-2,0,1029,0.89,0,0 +2014,11,8,5,65,-2,-1,1029,2.68,0,0 +2014,11,8,6,69,-5,-2,1029,5.81,0,0 +2014,11,8,7,74,-4,0,1029,7.6,0,0 +2014,11,8,8,79,-2,3,1030,10.73,0,0 +2014,11,8,9,78,-2,5,1030,13.86,0,0 +2014,11,8,10,87,-3,8,1029,15.65,0,0 +2014,11,8,11,82,-2,9,1029,17.44,0,0 +2014,11,8,12,74,-2,11,1028,1.79,0,0 +2014,11,8,13,77,-4,11,1027,2.68,0,0 +2014,11,8,14,84,-2,12,1026,1.79,0,0 +2014,11,8,15,87,-2,13,1025,3.58,0,0 +2014,11,8,16,91,-2,12,1025,7.6,0,0 +2014,11,8,17,77,-2,11,1025,10.73,0,0 +2014,11,8,18,74,-1,11,1025,1.79,0,0 +2014,11,8,19,84,-1,6,1026,2.68,0,0 +2014,11,8,20,89,-1,3,1026,3.57,0,0 +2014,11,8,21,94,0,2,1027,0.89,0,0 +2014,11,8,22,92,-1,2,1027,0.89,0,0 +2014,11,8,23,97,0,1,1027,0.45,0,0 +2014,11,9,0,92,-2,0,1027,1.79,0,0 +2014,11,9,1,98,-2,-1,1028,3.58,0,0 +2014,11,9,2,116,-1,0,1027,0.89,0,0 +2014,11,9,3,113,-2,-1,1027,1.79,0,0 +2014,11,9,4,101,-4,-2,1027,3.58,0,0 +2014,11,9,5,87,-4,-1,1027,7.6,0,0 +2014,11,9,6,54,-4,-1,1027,9.39,0,0 +2014,11,9,7,50,-5,-1,1027,12.52,0,0 +2014,11,9,8,42,-4,3,1027,16.54,0,0 +2014,11,9,9,38,-3,8,1028,19.67,0,0 +2014,11,9,10,38,-7,11,1027,22.8,0,0 +2014,11,9,11,39,-9,12,1027,24.59,0,0 +2014,11,9,12,33,-9,14,1025,1.79,0,0 +2014,11,9,13,34,-8,16,1024,1.79,0,0 +2014,11,9,14,40,-7,16,1023,3.13,0,0 +2014,11,9,15,48,-5,16,1023,6.26,0,0 +2014,11,9,16,45,-5,15,1023,9.39,0,0 +2014,11,9,17,39,-4,15,1023,11.18,0,0 +2014,11,9,18,44,-3,9,1023,12.97,0,0 +2014,11,9,19,57,-1,6,1023,0.89,0,0 +2014,11,9,20,77,0,5,1024,1.78,0,0 +2014,11,9,21,76,-1,3,1024,2.67,0,0 +2014,11,9,22,98,-1,3,1024,3.12,0,0 +2014,11,9,23,101,-2,1,1024,0.89,0,0 +2014,11,10,0,104,-1,2,1024,1.78,0,0 +2014,11,10,1,106,-1,1,1024,0.89,0,0 +2014,11,10,2,108,-2,-1,1024,1.79,0,0 +2014,11,10,3,98,-4,2,1023,3.58,0,0 +2014,11,10,4,79,-5,1,1023,5.37,0,0 +2014,11,10,5,44,-6,1,1023,7.16,0,0 +2014,11,10,6,33,-6,-1,1023,8.95,0,0 +2014,11,10,7,45,-6,-1,1023,10.74,0,0 +2014,11,10,8,53,-4,5,1024,12.53,0,0 +2014,11,10,9,47,-5,8,1024,14.32,0,0 +2014,11,10,10,42,-6,10,1023,16.11,0,0 +2014,11,10,11,34,-7,12,1023,0.89,0,0 +2014,11,10,12,38,-8,14,1022,1.78,0,0 +2014,11,10,13,41,-5,15,1021,1.79,0,0 +2014,11,10,14,56,-6,15,1020,4.92,0,0 +2014,11,10,15,59,-6,15,1019,8.05,0,0 +2014,11,10,16,68,-6,15,1019,11.18,0,0 +2014,11,10,17,79,-4,13,1019,0.45,0,0 +2014,11,10,18,79,-2,9,1019,1.34,0,0 +2014,11,10,19,111,-1,12,1020,4.02,0,0 +2014,11,10,20,147,1,9,1019,5.81,0,0 +2014,11,10,21,153,0,5,1019,0.89,0,0 +2014,11,10,22,145,0,4,1019,1.78,0,0 +2014,11,10,23,143,1,4,1019,2.67,0,0 +2014,11,11,0,144,1,2,1019,3.12,0,0 +2014,11,11,1,200,0,2,1019,4.01,0,0 +2014,11,11,2,239,0,1,1018,4.9,0,0 +2014,11,11,3,185,-1,2,1018,5.79,0,0 +2014,11,11,4,163,0,2,1018,6.68,0,0 +2014,11,11,5,28,-2,7,1017,3.13,0,0 +2014,11,11,6,12,-2,5,1018,4.92,0,0 +2014,11,11,7,9,-7,11,1018,12.97,0,0 +2014,11,11,8,9,-7,11,1018,18.78,0,0 +2014,11,11,9,11,-7,11,1018,25.93,0,0 +2014,11,11,10,15,-8,12,1018,33.98,0,0 +2014,11,11,11,13,-7,13,1018,42.03,0,0 +2014,11,11,12,14,-7,14,1018,53.21,0,0 +2014,11,11,13,17,-5,13,1017,63.04,0,0 +2014,11,11,14,18,-6,12,1018,71.98,0,0 +2014,11,11,15,16,-7,12,1018,80.92,0,0 +2014,11,11,16,11,-8,12,1018,90.75,0,0 +2014,11,11,17,7,-8,11,1018,98.8,0,0 +2014,11,11,18,9,-8,9,1018,103.72,0,0 +2014,11,11,19,13,-8,11,1019,106.85,0,0 +2014,11,11,20,16,-6,6,1019,108.64,0,0 +2014,11,11,21,8,-10,10,1021,116.69,0,0 +2014,11,11,22,19,-13,9,1022,124.74,0,0 +2014,11,11,23,8,-15,8,1023,133.68,0,0 +2014,11,12,0,9,-14,7,1023,140.83,0,0 +2014,11,12,1,5,-15,7,1023,148.88,0,0 +2014,11,12,2,2,-15,6,1023,156.03,0,0 +2014,11,12,3,2,-16,6,1023,161.84,0,0 +2014,11,12,4,3,-15,4,1023,165.86,0,0 +2014,11,12,5,3,-16,5,1023,173.91,0,0 +2014,11,12,6,6,-16,4,1023,181.96,0,0 +2014,11,12,7,4,-18,3,1024,191.79,0,0 +2014,11,12,8,4,-19,4,1026,202.97,0,0 +2014,11,12,9,5,-20,5,1027,215.04,0,0 +2014,11,12,10,6,-22,6,1027,227.11,0,0 +2014,11,12,11,6,-22,6,1027,240.07,0,0 +2014,11,12,12,5,-22,7,1027,252.14,0,0 +2014,11,12,13,4,-21,8,1026,261.97,0,0 +2014,11,12,14,6,-21,8,1026,271.8,0,0 +2014,11,12,15,5,-20,8,1025,279.85,0,0 +2014,11,12,16,3,-19,8,1025,284.77,0,0 +2014,11,12,17,5,-17,5,1025,288.79,0,0 +2014,11,12,18,7,-18,6,1026,293.71,0,0 +2014,11,12,19,9,-17,5,1026,299.52,0,0 +2014,11,12,20,11,-17,5,1027,306.67,0,0 +2014,11,12,21,12,-15,3,1027,313.82,0,0 +2014,11,12,22,15,-16,3,1028,1.79,0,0 +2014,11,12,23,17,-15,2,1028,4.02,0,0 +2014,11,13,0,12,-16,4,1028,8.04,0,0 +2014,11,13,1,11,-16,3,1028,12.06,0,0 +2014,11,13,2,12,-11,-1,1028,1.79,0,0 +2014,11,13,3,12,-14,0,1028,2.68,0,0 +2014,11,13,4,11,-11,0,1027,1.79,0,0 +2014,11,13,5,13,-8,-2,1027,0.89,0,0 +2014,11,13,6,15,-10,-3,1026,1.78,0,0 +2014,11,13,7,21,-9,-4,1026,0.89,0,0 +2014,11,13,8,21,-7,0,1026,1.78,0,0 +2014,11,13,9,40,-11,5,1027,3.13,0,0 +2014,11,13,10,40,-11,8,1026,4.92,0,0 +2014,11,13,11,20,-13,10,1026,4.92,0,0 +2014,11,13,12,16,-14,11,1025,9.84,0,0 +2014,11,13,13,26,-14,12,1024,17.89,0,0 +2014,11,13,14,25,-15,12,1023,25.04,0,0 +2014,11,13,15,25,-15,12,1022,32.19,0,0 +2014,11,13,16,35,-15,12,1022,39.34,0,0 +2014,11,13,17,NA,-13,12,1022,44.26,0,0 +2014,11,13,18,31,-13,10,1022,4.02,0,0 +2014,11,13,19,30,-12,10,1023,8.94,0,0 +2014,11,13,20,33,-12,8,1023,12.96,0,0 +2014,11,13,21,45,-12,8,1023,16.98,0,0 +2014,11,13,22,53,-11,7,1023,20.11,0,0 +2014,11,13,23,44,-9,2,1024,1.79,0,0 +2014,11,14,0,43,-8,0,1024,4.92,0,0 +2014,11,14,1,52,-9,0,1024,6.71,0,0 +2014,11,14,2,52,-9,-1,1025,8.5,0,0 +2014,11,14,3,52,-9,-1,1025,9.39,0,0 +2014,11,14,4,54,-9,-2,1025,11.18,0,0 +2014,11,14,5,52,-9,-2,1025,12.97,0,0 +2014,11,14,6,49,-9,-3,1025,14.76,0,0 +2014,11,14,7,49,-9,-2,1026,17.89,0,0 +2014,11,14,8,40,-8,1,1027,21.02,0,0 +2014,11,14,9,41,-10,6,1028,1.79,0,0 +2014,11,14,10,24,-11,9,1028,1.79,0,0 +2014,11,14,11,30,-14,10,1028,3.58,0,0 +2014,11,14,12,21,-13,11,1027,5.37,0,0 +2014,11,14,13,22,-12,13,1026,7.16,0,0 +2014,11,14,14,20,-13,14,1026,8.05,0,0 +2014,11,14,15,21,-13,13,1026,1.79,0,0 +2014,11,14,16,21,-13,12,1026,3.13,0,0 +2014,11,14,17,25,-13,9,1026,4.92,0,0 +2014,11,14,18,32,-12,8,1027,8.05,0,0 +2014,11,14,19,35,-9,6,1027,0.89,0,0 +2014,11,14,20,50,-9,4,1028,1.79,0,0 +2014,11,14,21,74,-7,2,1028,2.68,0,0 +2014,11,14,22,64,-8,3,1028,4.47,0,0 +2014,11,14,23,90,-9,3,1028,5.36,0,0 +2014,11,15,0,138,-9,2,1028,1.79,0,0 +2014,11,15,1,182,-5,1,1029,0.45,0,0 +2014,11,15,2,210,-7,1,1029,0.89,0,0 +2014,11,15,3,208,-5,1,1029,0.89,0,0 +2014,11,15,4,152,-5,1,1029,0.89,0,0 +2014,11,15,5,122,-6,0,1029,1.78,0,0 +2014,11,15,6,87,-5,0,1029,3.57,0,0 +2014,11,15,7,62,-5,0,1029,0.89,0,0 +2014,11,15,8,52,-6,0,1029,1.79,0,0 +2014,11,15,9,52,-7,4,1029,0.45,0,0 +2014,11,15,10,59,-7,5,1029,1.34,0,0 +2014,11,15,11,96,-7,8,1028,2.23,0,0 +2014,11,15,12,128,-6,8,1028,3.12,0,0 +2014,11,15,13,141,-6,8,1027,1.79,0,0 +2014,11,15,14,167,-5,8,1026,0.89,0,0 +2014,11,15,15,206,-5,8,1026,0.89,0,0 +2014,11,15,16,227,-4,8,1026,1.79,0,0 +2014,11,15,17,246,-3,7,1026,2.68,0,0 +2014,11,15,18,230,-4,6,1026,4.47,0,0 +2014,11,15,19,239,-4,4,1026,7.6,0,0 +2014,11,15,20,238,-4,5,1026,8.49,0,0 +2014,11,15,21,250,-5,5,1027,10.28,0,0 +2014,11,15,22,261,-4,4,1027,12.07,0,0 +2014,11,15,23,283,-5,4,1027,0.89,0,0 +2014,11,16,0,273,-3,2,1026,1.78,0,0 +2014,11,16,1,281,-3,1,1026,2.67,0,0 +2014,11,16,2,218,-5,2,1026,3.56,0,0 +2014,11,16,3,222,-4,2,1025,1.79,0,0 +2014,11,16,4,206,-4,1,1025,0.89,0,0 +2014,11,16,5,304,-3,1,1025,1.78,0,0 +2014,11,16,6,299,-3,-1,1025,0.89,0,0 +2014,11,16,7,284,-4,-1,1025,0.89,0,0 +2014,11,16,8,266,-4,0,1026,0.89,0,0 +2014,11,16,9,256,-1,5,1026,2.68,0,0 +2014,11,16,10,79,-2,7,1026,5.81,0,0 +2014,11,16,11,38,-3,10,1026,8.94,0,0 +2014,11,16,12,22,-5,11,1025,14.75,0,0 +2014,11,16,13,15,-6,12,1025,21.9,0,0 +2014,11,16,14,21,-6,12,1024,29.95,0,0 +2014,11,16,15,18,-6,12,1024,38,0,0 +2014,11,16,16,19,-5,11,1025,46.05,0,0 +2014,11,16,17,16,-5,10,1025,51.86,0,0 +2014,11,16,18,17,-8,10,1026,59.01,0,0 +2014,11,16,19,15,-8,9,1027,66.16,0,0 +2014,11,16,20,24,-7,9,1028,73.31,0,0 +2014,11,16,21,12,-7,8,1029,79.12,0,0 +2014,11,16,22,19,-7,8,1030,84.04,0,0 +2014,11,16,23,13,-8,7,1030,91.19,0,0 +2014,11,17,0,12,-8,7,1030,97,0,0 +2014,11,17,1,13,-8,6,1030,101.92,0,0 +2014,11,17,2,12,-9,6,1030,107.73,0,0 +2014,11,17,3,13,-9,6,1030,114.88,0,0 +2014,11,17,4,11,-9,5,1030,120.69,0,0 +2014,11,17,5,11,-8,2,1031,0.89,0,0 +2014,11,17,6,9,-9,2,1031,1.78,0,0 +2014,11,17,7,11,-7,-1,1032,1.79,0,0 +2014,11,17,8,17,-6,2,1033,3.58,0,0 +2014,11,17,9,19,-8,8,1033,3.13,0,0 +2014,11,17,10,19,-9,9,1033,7.15,0,0 +2014,11,17,11,16,-9,10,1032,8.94,0,0 +2014,11,17,12,14,-11,11,1031,13.86,0,0 +2014,11,17,13,15,-11,12,1030,18.78,0,0 +2014,11,17,14,15,-11,12,1029,23.7,0,0 +2014,11,17,15,8,-12,12,1029,28.62,0,0 +2014,11,17,16,13,-13,12,1029,33.54,0,0 +2014,11,17,17,16,-13,12,1029,36.67,0,0 +2014,11,17,18,27,-10,7,1030,0.89,0,0 +2014,11,17,19,47,-9,4,1030,1.78,0,0 +2014,11,17,20,54,-6,3,1030,2.67,0,0 +2014,11,17,21,62,-6,2,1030,3.56,0,0 +2014,11,17,22,66,-6,1,1031,0.89,0,0 +2014,11,17,23,72,-7,1,1031,0.89,0,0 +2014,11,18,0,81,-6,0,1030,0.89,0,0 +2014,11,18,1,138,-7,-2,1030,1.78,0,0 +2014,11,18,2,121,-6,-2,1030,3.57,0,0 +2014,11,18,3,78,-5,-1,1030,5.36,0,0 +2014,11,18,4,78,-7,-3,1030,7.15,0,0 +2014,11,18,5,63,-8,-2,1030,10.28,0,0 +2014,11,18,6,45,-8,-2,1030,12.07,0,0 +2014,11,18,7,39,-7,-1,1030,13.86,0,0 +2014,11,18,8,40,-7,1,1031,15.65,0,0 +2014,11,18,9,42,-7,4,1031,18.78,0,0 +2014,11,18,10,41,-8,8,1031,21.91,0,0 +2014,11,18,11,53,-8,10,1031,0.89,0,0 +2014,11,18,12,52,-8,11,1030,2.68,0,0 +2014,11,18,13,61,-8,12,1029,4.47,0,0 +2014,11,18,14,59,-8,13,1028,6.26,0,0 +2014,11,18,15,53,-8,13,1027,1.79,0,0 +2014,11,18,16,59,-8,12,1027,4.92,0,0 +2014,11,18,17,63,-8,10,1027,0.89,0,0 +2014,11,18,18,86,-5,7,1027,0.89,0,0 +2014,11,18,19,106,-5,5,1027,0.89,0,0 +2014,11,18,20,136,-5,3,1028,1.78,0,0 +2014,11,18,21,158,-5,2,1028,0.89,0,0 +2014,11,18,22,189,-5,2,1028,0.89,0,0 +2014,11,18,23,203,-5,0,1027,1.79,0,0 +2014,11,19,0,221,-5,0,1027,0.89,0,0 +2014,11,19,1,257,-5,0,1027,0.89,0,0 +2014,11,19,2,333,-5,0,1026,1.34,0,0 +2014,11,19,3,371,-3,0,1026,1.79,0,0 +2014,11,19,4,354,-5,-2,1026,1.79,0,0 +2014,11,19,5,273,-4,-2,1025,0.89,0,0 +2014,11,19,6,255,-4,-1,1025,1.78,0,0 +2014,11,19,7,165,-3,-1,1025,0.89,0,0 +2014,11,19,8,198,-2,1,1025,0.89,0,0 +2014,11,19,9,275,-2,4,1025,1.79,0,0 +2014,11,19,10,320,-2,4,1025,0.89,0,0 +2014,11,19,11,284,-1,6,1024,1.34,0,0 +2014,11,19,12,346,-1,7,1023,2.23,0,0 +2014,11,19,13,311,-1,8,1021,1.79,0,0 +2014,11,19,14,372,0,8,1020,3.58,0,0 +2014,11,19,15,399,0,9,1020,1.79,0,0 +2014,11,19,16,370,-1,6,1020,0.89,0,0 +2014,11,19,17,327,0,6,1020,0.89,0,0 +2014,11,19,18,346,0,3,1020,1.78,0,0 +2014,11,19,19,351,0,3,1020,2.23,0,0 +2014,11,19,20,362,-1,2,1021,1.79,0,0 +2014,11,19,21,394,-1,1,1021,0.89,0,0 +2014,11,19,22,409,-1,1,1020,1.34,0,0 +2014,11,19,23,384,-2,0,1020,1.79,0,0 +2014,11,20,0,393,0,1,1019,0.89,0,0 +2014,11,20,1,380,-1,1,1019,4.02,0,0 +2014,11,20,2,330,-1,2,1018,5.81,0,0 +2014,11,20,3,324,-1,0,1018,0.89,0,0 +2014,11,20,4,334,-1,0,1017,0.89,0,0 +2014,11,20,5,334,-1,0,1016,0.89,0,0 +2014,11,20,6,368,-2,-1,1016,0.89,0,0 +2014,11,20,7,360,-2,-1,1017,0.89,0,0 +2014,11,20,8,324,-1,-1,1017,1.78,0,0 +2014,11,20,9,299,-1,1,1018,2.67,0,0 +2014,11,20,10,305,1,4,1018,1.79,0,0 +2014,11,20,11,290,1,5,1018,0.89,0,0 +2014,11,20,12,314,0,6,1017,1.79,0,0 +2014,11,20,13,315,0,9,1016,1.79,0,0 +2014,11,20,14,306,0,8,1016,3.13,0,0 +2014,11,20,15,292,0,9,1016,7.15,0,0 +2014,11,20,16,315,0,8,1016,8.94,0,0 +2014,11,20,17,332,0,6,1017,9.83,0,0 +2014,11,20,18,342,0,4,1017,0.89,0,0 +2014,11,20,19,338,0,4,1018,0.89,0,0 +2014,11,20,20,375,0,3,1018,0.89,0,0 +2014,11,20,21,NA,-2,0,1019,0.89,0,0 +2014,11,20,22,NA,-1,0,1020,0.45,0,0 +2014,11,20,23,NA,0,0,1020,1.34,0,0 +2014,11,21,0,NA,-2,-2,1020,1.79,0,0 +2014,11,21,1,NA,-3,-2,1020,3.58,0,0 +2014,11,21,2,NA,-2,-1,1020,6.71,0,0 +2014,11,21,3,NA,-2,-1,1019,8.5,0,0 +2014,11,21,4,NA,-1,1,1019,0.89,0,0 +2014,11,21,5,NA,-2,0,1019,3.13,0,0 +2014,11,21,6,NA,-2,0,1019,7.15,0,0 +2014,11,21,7,NA,-3,0,1020,11.17,0,0 +2014,11,21,8,NA,-3,0,1020,0.89,0,0 +2014,11,21,9,287,-2,2,1021,1.78,0,0 +2014,11,21,10,177,-2,6,1021,1.79,0,0 +2014,11,21,11,133,-3,7,1021,0.89,0,0 +2014,11,21,12,93,-4,10,1020,2.68,0,0 +2014,11,21,13,82,-5,12,1019,3.57,0,0 +2014,11,21,14,86,-5,13,1019,5.36,0,0 +2014,11,21,15,88,-11,15,1019,4.92,0,0 +2014,11,21,16,33,-15,14,1019,10.73,0,0 +2014,11,21,17,16,-15,13,1020,15.65,0,0 +2014,11,21,18,19,-14,12,1021,20.57,0,0 +2014,11,21,19,22,-12,9,1022,3.13,0,0 +2014,11,21,20,31,-11,7,1023,1.79,0,0 +2014,11,21,21,37,-11,6,1024,4.92,0,0 +2014,11,21,22,38,-11,4,1025,6.71,0,0 +2014,11,21,23,30,-12,5,1025,8.5,0,0 +2014,11,22,0,17,-12,4,1025,12.52,0,0 +2014,11,22,1,9,-13,5,1026,15.65,0,0 +2014,11,22,2,9,-10,2,1027,1.79,0,0 +2014,11,22,3,12,-10,0,1027,3.13,0,0 +2014,11,22,4,8,-7,-1,1027,4.92,0,0 +2014,11,22,5,10,-11,0,1027,6.71,0,0 +2014,11,22,6,13,-9,-3,1027,9.84,0,0 +2014,11,22,7,15,-9,-3,1028,11.63,0,0 +2014,11,22,8,29,-8,1,1028,14.76,0,0 +2014,11,22,9,31,-9,6,1029,18.78,0,0 +2014,11,22,10,37,-10,8,1029,21.91,0,0 +2014,11,22,11,36,-11,10,1029,25.04,0,0 +2014,11,22,12,31,-11,11,1028,26.83,0,0 +2014,11,22,13,26,-12,11,1027,1.79,0,0 +2014,11,22,14,28,-11,11,1026,3.58,0,0 +2014,11,22,15,26,-13,11,1025,3.13,0,0 +2014,11,22,16,38,-12,10,1025,6.26,0,0 +2014,11,22,17,48,-11,8,1025,8.05,0,0 +2014,11,22,18,65,-10,6,1025,9.84,0,0 +2014,11,22,19,79,-10,5,1026,11.63,0,0 +2014,11,22,20,101,-5,6,1026,14.76,0,0 +2014,11,22,21,123,0,5,1026,17.89,0,0 +2014,11,22,22,146,1,5,1027,19.68,0,0 +2014,11,22,23,127,0,4,1027,21.47,0,0 +2014,11,23,0,107,0,4,1026,23.26,0,0 +2014,11,23,1,106,0,2,1027,24.15,0,0 +2014,11,23,2,116,0,2,1026,0.45,0,0 +2014,11,23,3,100,0,2,1026,0.89,0,0 +2014,11,23,4,91,-1,1,1025,0.45,0,0 +2014,11,23,5,91,-1,1,1025,0.9,0,0 +2014,11,23,6,99,-1,0,1025,1.79,0,0 +2014,11,23,7,110,-1,1,1026,3.13,0,0 +2014,11,23,8,111,0,3,1026,4.92,0,0 +2014,11,23,9,113,0,5,1026,6.71,0,0 +2014,11,23,10,101,-1,6,1026,10.73,0,0 +2014,11,23,11,101,0,6,1026,0.89,0,0 +2014,11,23,12,111,1,7,1025,2.68,0,0 +2014,11,23,13,114,1,8,1025,0.89,0,0 +2014,11,23,14,119,2,8,1024,1.78,0,0 +2014,11,23,15,141,2,9,1024,1.79,0,0 +2014,11,23,16,149,1,8,1023,3.58,0,0 +2014,11,23,17,134,1,6,1024,0.89,0,0 +2014,11,23,18,143,1,4,1025,1.78,0,0 +2014,11,23,19,174,1,2,1025,2.23,0,0 +2014,11,23,20,182,0,1,1026,1.79,0,0 +2014,11,23,21,181,0,1,1027,2.68,0,0 +2014,11,23,22,186,-1,0,1027,1.79,0,0 +2014,11,23,23,193,1,1,1027,0.45,0,0 +2014,11,24,0,240,-2,-2,1027,1.79,0,0 +2014,11,24,1,289,-1,-1,1027,6.71,0,0 +2014,11,24,2,282,-1,2,1028,8.5,0,0 +2014,11,24,3,188,-3,-2,1028,10.29,0,0 +2014,11,24,4,175,-2,-1,1028,13.42,0,0 +2014,11,24,5,150,-5,-3,1028,16.55,0,0 +2014,11,24,6,116,-6,-2,1028,19.68,0,0 +2014,11,24,7,100,-7,-2,1028,23.7,0,0 +2014,11,24,8,42,-9,3,1028,3.13,0,0 +2014,11,24,9,41,-6,6,1028,3.13,0,0 +2014,11,24,10,30,-10,7,1028,4.92,0,0 +2014,11,24,11,28,-12,9,1028,8.94,0,0 +2014,11,24,12,19,-13,10,1027,10.73,0,0 +2014,11,24,13,22,-13,11,1026,0.89,0,0 +2014,11,24,14,22,-14,12,1025,1.78,0,0 +2014,11,24,15,22,-13,12,1025,3.13,0,0 +2014,11,24,16,31,-10,10,1025,7.15,0,0 +2014,11,24,17,31,-9,7,1026,10.28,0,0 +2014,11,24,18,44,-8,6,1026,13.41,0,0 +2014,11,24,19,46,-7,5,1026,15.2,0,0 +2014,11,24,20,62,-5,2,1026,16.09,0,0 +2014,11,24,21,80,-5,2,1026,0.89,0,0 +2014,11,24,22,129,-5,2,1026,1.78,0,0 +2014,11,24,23,118,-4,2,1026,2.67,0,0 +2014,11,25,0,90,-4,1,1026,0.89,0,0 +2014,11,25,1,78,-4,1,1025,0.89,0,0 +2014,11,25,2,86,-4,-1,1025,1.79,0,0 +2014,11,25,3,111,-3,1,1025,0.89,0,0 +2014,11,25,4,144,-3,-1,1024,1.78,0,0 +2014,11,25,5,121,-4,0,1023,2.67,0,0 +2014,11,25,6,112,-5,-2,1023,0.89,0,0 +2014,11,25,7,111,-4,-1,1023,1.78,0,0 +2014,11,25,8,146,-3,0,1023,2.67,0,0 +2014,11,25,9,159,-3,3,1023,0.89,0,0 +2014,11,25,10,151,-3,4,1023,1.78,0,0 +2014,11,25,11,143,-3,5,1022,3.57,0,0 +2014,11,25,12,157,-1,6,1021,4.46,0,0 +2014,11,25,13,174,-2,7,1019,6.25,0,0 +2014,11,25,14,199,-1,8,1018,7.14,0,0 +2014,11,25,15,192,1,8,1018,4.02,0,0 +2014,11,25,16,189,1,8,1017,8.94,0,0 +2014,11,25,17,159,2,7,1018,12.96,0,0 +2014,11,25,18,205,2,7,1017,14.75,0,0 +2014,11,25,19,230,1,4,1017,0.45,0,0 +2014,11,25,20,280,1,3,1017,1.79,0,0 +2014,11,25,21,266,1,4,1017,3.58,0,0 +2014,11,25,22,311,0,3,1016,5.37,0,0 +2014,11,25,23,342,0,2,1016,7.16,0,0 +2014,11,26,0,323,0,2,1016,8.95,0,0 +2014,11,26,1,299,-1,1,1016,0.89,0,0 +2014,11,26,2,283,-1,0,1015,1.78,0,0 +2014,11,26,3,258,-2,-1,1015,1.79,0,0 +2014,11,26,4,239,-2,-1,1015,0.45,0,0 +2014,11,26,5,236,-2,-2,1015,1.34,0,0 +2014,11,26,6,241,-2,-1,1015,2.23,0,0 +2014,11,26,7,226,-2,-1,1015,0.89,0,0 +2014,11,26,8,215,-2,-2,1015,1.78,0,0 +2014,11,26,9,201,0,1,1016,0.89,0,0 +2014,11,26,10,201,0,3,1017,1.78,0,0 +2014,11,26,11,234,0,5,1016,2.67,0,0 +2014,11,26,12,241,0,6,1016,3.56,0,0 +2014,11,26,13,250,0,7,1015,1.79,0,0 +2014,11,26,14,259,0,8,1015,0.89,0,0 +2014,11,26,15,289,0,7,1015,1.78,0,0 +2014,11,26,16,330,0,7,1016,1.79,0,0 +2014,11,26,17,350,1,6,1016,2.68,0,0 +2014,11,26,18,350,1,5,1018,3.57,0,0 +2014,11,26,19,388,2,5,1018,4.46,0,0 +2014,11,26,20,379,1,4,1019,5.35,0,0 +2014,11,26,21,436,1,3,1020,0.45,0,0 +2014,11,26,22,502,1,2,1020,1.34,0,0 +2014,11,26,23,522,1,2,1020,2.23,0,0 +2014,11,27,0,470,1,2,1021,3.12,0,0 +2014,11,27,1,439,0,2,1022,3.13,0,0 +2014,11,27,2,362,0,2,1022,7.15,0,0 +2014,11,27,3,176,-7,6,1023,4.92,0,0 +2014,11,27,4,98,-10,6,1023,8.05,0,0 +2014,11,27,5,24,-13,7,1023,13.86,0,0 +2014,11,27,6,20,-15,6,1024,18.78,0,0 +2014,11,27,7,11,-16,6,1024,4.92,0,0 +2014,11,27,8,11,-17,6,1025,12.07,0,0 +2014,11,27,9,14,-18,6,1025,7.15,0,0 +2014,11,27,10,14,-18,6,1026,12.07,0,0 +2014,11,27,11,16,-17,6,1025,17.88,0,0 +2014,11,27,12,13,-18,6,1025,5.81,0,0 +2014,11,27,13,13,-18,6,1024,5.81,0,0 +2014,11,27,14,14,-17,7,1024,10.73,0,0 +2014,11,27,15,18,-17,7,1024,14.75,0,0 +2014,11,27,16,17,-16,7,1023,17.88,0,0 +2014,11,27,17,16,-16,6,1023,21.9,0,0 +2014,11,27,18,20,-15,6,1023,23.69,0,0 +2014,11,27,19,30,-13,4,1024,1.79,0,0 +2014,11,27,20,24,-12,5,1024,3.58,0,0 +2014,11,27,21,34,-10,5,1024,5.37,0,0 +2014,11,27,22,37,-11,6,1024,0.89,0,0 +2014,11,27,23,39,-11,6,1024,3.13,0,0 +2014,11,28,0,34,-8,4,1023,4.02,0,0 +2014,11,28,1,39,-8,4,1023,5.81,0,0 +2014,11,28,2,49,-9,3,1022,7.6,0,0 +2014,11,28,3,51,-9,3,1022,8.49,0,0 +2014,11,28,4,52,-8,1,1021,10.28,0,0 +2014,11,28,5,70,-7,1,1021,12.07,0,0 +2014,11,28,6,83,-6,0,1021,15.2,0,0 +2014,11,28,7,92,-6,0,1021,16.99,0,0 +2014,11,28,8,95,-7,1,1022,20.12,0,0 +2014,11,28,9,83,-8,5,1022,23.25,0,0 +2014,11,28,10,66,-8,6,1022,26.38,0,0 +2014,11,28,11,66,-8,7,1022,0.89,0,0 +2014,11,28,12,77,-6,8,1020,1.78,0,0 +2014,11,28,13,74,-5,9,1019,3.57,0,0 +2014,11,28,14,70,-5,10,1019,3.13,0,0 +2014,11,28,15,86,-4,10,1019,6.26,0,0 +2014,11,28,16,90,-4,9,1019,9.39,0,0 +2014,11,28,17,91,-4,8,1019,13.41,0,0 +2014,11,28,18,99,-3,7,1019,17.43,0,0 +2014,11,28,19,129,-2,6,1020,20.56,0,0 +2014,11,28,20,168,-2,4,1020,22.35,0,0 +2014,11,28,21,200,-3,1,1021,24.14,0,0 +2014,11,28,22,214,-1,4,1021,25.93,0,0 +2014,11,28,23,211,-1,3,1021,27.72,0,0 +2014,11,29,0,219,-2,0,1021,28.61,0,0 +2014,11,29,1,237,-2,0,1021,0.89,0,0 +2014,11,29,2,248,-2,-1,1021,1.79,0,0 +2014,11,29,3,254,-3,-1,1022,0.89,0,0 +2014,11,29,4,254,-2,-1,1021,2.68,0,0 +2014,11,29,5,252,-3,-2,1021,3.57,0,0 +2014,11,29,6,277,-3,-2,1021,4.46,0,0 +2014,11,29,7,291,-2,-1,1021,0.89,0,0 +2014,11,29,8,287,-1,1,1021,1.78,0,0 +2014,11,29,9,295,-1,2,1022,1.79,0,0 +2014,11,29,10,287,0,2,1022,0.89,0,0 +2014,11,29,11,325,0,3,1022,1.34,0,0 +2014,11,29,12,301,1,4,1021,2.23,0,0 +2014,11,29,13,311,1,4,1020,3.12,0,0 +2014,11,29,14,369,1,4,1020,0.89,0,0 +2014,11,29,15,380,1,4,1019,0.89,0,0 +2014,11,29,16,393,2,4,1019,0.89,0,0 +2014,11,29,17,344,2,4,1019,3.13,0,0 +2014,11,29,18,343,2,4,1020,0.89,0,1 +2014,11,29,19,343,3,4,1020,1.79,0,0 +2014,11,29,20,323,2,3,1020,3.58,0,0 +2014,11,29,21,328,3,4,1020,5.37,0,0 +2014,11,29,22,312,2,4,1021,6.26,0,0 +2014,11,29,23,264,2,4,1021,8.05,0,0 +2014,11,30,0,242,2,4,1020,0.89,0,0 +2014,11,30,1,254,2,4,1020,0.89,0,0 +2014,11,30,2,204,3,4,1020,1.79,0,0 +2014,11,30,3,194,3,4,1021,3.58,0,0 +2014,11,30,4,227,2,4,1021,6.71,0,0 +2014,11,30,5,242,2,3,1021,9.84,0,0 +2014,11,30,6,235,2,3,1021,11.63,0,0 +2014,11,30,7,168,2,4,1022,14.76,0,0 +2014,11,30,8,20,-3,6,1022,21.91,0,0 +2014,11,30,9,18,-4,6,1022,29.96,0,0 +2014,11,30,10,16,-5,7,1022,38.01,0,0 +2014,11,30,11,24,-11,8,1022,47.84,0,0 +2014,11,30,12,23,-17,8,1022,57.67,0,0 +2014,11,30,13,36,-21,7,1022,67.5,0,0 +2014,11,30,14,43,-22,6,1022,78.68,0,0 +2014,11,30,15,30,-24,5,1022,89.86,0,0 +2014,11,30,16,19,-26,4,1024,101.04,0,0 +2014,11,30,17,14,-26,3,1024,112.22,0,0 +2014,11,30,18,18,-27,1,1026,120.27,0,0 +2014,11,30,19,27,-29,1,1026,129.21,0,0 +2014,11,30,20,31,-31,0,1028,142.17,0,0 +2014,11,30,21,44,-28,-2,1029,152,0,0 +2014,11,30,22,32,-28,-3,1030,164.07,0,0 +2014,11,30,23,33,-26,-3,1030,179.27,0,0 +2014,12,1,0,48,-25,-4,1030,190.45,0,0 +2014,12,1,1,36,-24,-4,1029,200.28,0,0 +2014,12,1,2,25,-22,-4,1030,207.43,0,0 +2014,12,1,3,21,-22,-5,1029,215.48,0,0 +2014,12,1,4,13,-21,-5,1030,220.4,0,0 +2014,12,1,5,13,-22,-5,1031,230.23,0,0 +2014,12,1,6,12,-22,-5,1031,243.19,0,0 +2014,12,1,7,13,-22,-5,1032,255.26,0,0 +2014,12,1,8,12,-22,-5,1032,269.12,0,0 +2014,12,1,9,11,-22,-4,1033,278.95,0,0 +2014,12,1,10,10,-22,-3,1034,290.13,0,0 +2014,12,1,11,12,-22,-2,1033,302.2,0,0 +2014,12,1,12,14,-22,-1,1033,313.38,0,0 +2014,12,1,13,11,-23,0,1032,323.21,0,0 +2014,12,1,14,10,-25,0,1031,332.15,0,0 +2014,12,1,15,9,-23,-1,1031,343.33,0,0 +2014,12,1,16,9,-24,-1,1032,351.38,0,0 +2014,12,1,17,8,-22,-2,1032,359.43,0,0 +2014,12,1,18,9,-23,-3,1032,365.24,0,0 +2014,12,1,19,15,-22,-3,1032,372.39,0,0 +2014,12,1,20,19,-22,-6,1033,376.41,0,0 +2014,12,1,21,8,-21,-5,1033,380.43,0,0 +2014,12,1,22,15,-22,-5,1034,385.35,0,0 +2014,12,1,23,8,-20,-7,1033,388.48,0,0 +2014,12,2,0,15,-20,-8,1033,390.27,0,0 +2014,12,2,1,11,-20,-9,1033,394.29,0,0 +2014,12,2,2,13,-21,-7,1032,3.13,0,0 +2014,12,2,3,11,-21,-7,1031,7.15,0,0 +2014,12,2,4,13,-21,-8,1030,11.17,0,0 +2014,12,2,5,11,-22,-8,1030,14.3,0,0 +2014,12,2,6,12,-22,-10,1030,16.09,0,0 +2014,12,2,7,13,-21,-10,1030,1.79,0,0 +2014,12,2,8,11,-21,-7,1030,4.92,0,0 +2014,12,2,9,26,-20,-6,1031,8.05,0,0 +2014,12,2,10,23,-21,-4,1030,9.84,0,0 +2014,12,2,11,43,-21,-3,1029,0.89,0,0 +2014,12,2,12,52,-19,-1,1028,2.68,0,0 +2014,12,2,13,76,-19,0,1026,4.47,0,0 +2014,12,2,14,66,-22,0,1025,6.26,0,0 +2014,12,2,15,40,-21,0,1024,1.79,0,0 +2014,12,2,16,67,-18,-1,1024,5.81,0,0 +2014,12,2,17,81,-18,0,1024,9.83,0,0 +2014,12,2,18,96,-18,-1,1024,11.62,0,0 +2014,12,2,19,108,-17,-5,1024,0.89,0,0 +2014,12,2,20,99,-18,-4,1025,1.78,0,0 +2014,12,2,21,111,-15,-5,1025,2.67,0,0 +2014,12,2,22,120,-15,-7,1025,0.89,0,0 +2014,12,2,23,125,-17,-9,1025,3.13,0,0 +2014,12,3,0,123,-18,-7,1025,4.92,0,0 +2014,12,3,1,126,-17,-8,1025,8.05,0,0 +2014,12,3,2,111,-17,-8,1025,9.84,0,0 +2014,12,3,3,26,-17,-8,1026,0.89,0,0 +2014,12,3,4,11,-22,-4,1026,4.02,0,0 +2014,12,3,5,13,-24,-4,1026,8.04,0,0 +2014,12,3,6,9,-23,-4,1026,15.19,0,0 +2014,12,3,7,9,-23,-4,1027,20.11,0,0 +2014,12,3,8,13,-23,-4,1028,25.92,0,0 +2014,12,3,9,17,-23,-4,1029,29.94,0,0 +2014,12,3,10,16,-23,-2,1029,37.99,0,0 +2014,12,3,11,11,-23,-1,1028,46.04,0,0 +2014,12,3,12,11,-25,0,1027,51.85,0,0 +2014,12,3,13,17,-26,1,1026,57.66,0,0 +2014,12,3,14,9,-25,1,1025,64.81,0,0 +2014,12,3,15,9,-26,1,1025,73.75,0,0 +2014,12,3,16,11,-24,1,1025,82.69,0,0 +2014,12,3,17,8,-25,0,1025,90.74,0,0 +2014,12,3,18,15,-22,-1,1026,95.66,0,0 +2014,12,3,19,14,-22,-1,1027,100.58,0,0 +2014,12,3,20,9,-22,-2,1028,105.5,0,0 +2014,12,3,21,7,-22,-4,1028,108.63,0,0 +2014,12,3,22,6,-21,-3,1028,112.65,0,0 +2014,12,3,23,8,-21,-2,1029,118.46,0,0 +2014,12,4,0,11,-23,-3,1029,125.61,0,0 +2014,12,4,1,9,-22,-3,1028,130.53,0,0 +2014,12,4,2,11,-22,-4,1028,135.45,0,0 +2014,12,4,3,9,-22,-4,1028,138.58,0,0 +2014,12,4,4,8,-22,-4,1027,141.71,0,0 +2014,12,4,5,5,-22,-4,1027,145.73,0,0 +2014,12,4,6,6,-21,-6,1027,1.79,0,0 +2014,12,4,7,9,-21,-3,1027,4.02,0,0 +2014,12,4,8,6,-21,-3,1028,8.94,0,0 +2014,12,4,9,7,-21,-2,1028,14.75,0,0 +2014,12,4,10,7,-21,-1,1028,20.56,0,0 +2014,12,4,11,6,-21,-1,1027,29.5,0,0 +2014,12,4,12,9,-24,0,1027,37.55,0,0 +2014,12,4,13,11,-24,1,1026,46.49,0,0 +2014,12,4,14,10,-24,1,1026,56.32,0,0 +2014,12,4,15,10,-24,2,1026,64.37,0,0 +2014,12,4,16,8,-24,1,1026,70.18,0,0 +2014,12,4,17,7,-23,0,1026,75.1,0,0 +2014,12,4,18,10,-22,-1,1027,80.02,0,0 +2014,12,4,19,8,-22,-4,1027,84.94,0,0 +2014,12,4,20,17,-23,-2,1028,4.02,0,0 +2014,12,4,21,9,-22,-3,1028,5.81,0,0 +2014,12,4,22,18,-24,-2,1029,15.64,0,0 +2014,12,4,23,10,-23,-3,1029,24.58,0,0 +2014,12,5,0,6,-22,-3,1029,33.52,0,0 +2014,12,5,1,6,-22,-5,1029,38.44,0,0 +2014,12,5,2,9,-22,-7,1028,41.57,0,0 +2014,12,5,3,8,-22,-6,1029,44.7,0,0 +2014,12,5,4,7,-21,-5,1029,3.13,0,0 +2014,12,5,5,8,-21,-6,1029,0.89,0,0 +2014,12,5,6,14,-21,-9,1029,3.13,0,0 +2014,12,5,7,12,-21,-7,1029,3.13,0,0 +2014,12,5,8,25,-19,-5,1029,5.81,0,0 +2014,12,5,9,11,-20,-2,1029,12.96,0,0 +2014,12,5,10,16,-20,-1,1029,16.98,0,0 +2014,12,5,11,17,-23,2,1029,21.9,0,0 +2014,12,5,12,13,-23,2,1027,29.05,0,0 +2014,12,5,13,9,-23,3,1026,36.2,0,0 +2014,12,5,14,NA,-22,4,1025,41.12,0,0 +2014,12,5,15,NA,-22,3,1025,1.79,0,0 +2014,12,5,16,11,-21,2,1025,1.79,0,0 +2014,12,5,17,11,-22,0,1025,1.79,0,0 +2014,12,5,18,32,-18,-2,1025,0.89,0,0 +2014,12,5,19,97,-19,0,1025,2.68,0,0 +2014,12,5,20,115,-15,-6,1025,0.89,0,0 +2014,12,5,21,130,-15,-6,1026,1.79,0,0 +2014,12,5,22,138,-16,-6,1026,1.79,0,0 +2014,12,5,23,158,-16,-6,1026,3.58,0,0 +2014,12,6,0,140,-15,-7,1026,0.45,0,0 +2014,12,6,1,150,-15,-8,1027,1.79,0,0 +2014,12,6,2,149,-16,-8,1027,0.89,0,0 +2014,12,6,3,109,-15,-9,1026,1.79,0,0 +2014,12,6,4,84,-15,-7,1026,0.45,0,0 +2014,12,6,5,60,-16,-8,1026,0.89,0,0 +2014,12,6,6,38,-17,-8,1027,2.68,0,0 +2014,12,6,7,32,-17,-9,1027,4.47,0,0 +2014,12,6,8,44,-15,-6,1027,6.26,0,0 +2014,12,6,9,31,-16,-1,1028,4.02,0,0 +2014,12,6,10,31,-19,0,1028,8.04,0,0 +2014,12,6,11,30,-20,2,1028,9.83,0,0 +2014,12,6,12,16,-20,4,1026,1.79,0,0 +2014,12,6,13,17,-19,4,1025,3.58,0,0 +2014,12,6,14,19,-19,4,1025,1.79,0,0 +2014,12,6,15,33,-20,3,1025,3.58,0,0 +2014,12,6,16,71,-18,2,1025,5.37,0,0 +2014,12,6,17,66,-17,2,1025,7.16,0,0 +2014,12,6,18,61,-17,2,1026,8.95,0,0 +2014,12,6,19,83,-17,1,1026,10.74,0,0 +2014,12,6,20,122,-15,1,1026,13.87,0,0 +2014,12,6,21,119,-14,0,1026,0.89,0,0 +2014,12,6,22,95,-12,-1,1026,0.89,0,0 +2014,12,6,23,93,-13,0,1027,0.89,0,0 +2014,12,7,0,102,-13,-1,1027,1.78,0,0 +2014,12,7,1,174,-12,-1,1026,2.67,0,0 +2014,12,7,2,137,-11,-1,1026,3.56,0,0 +2014,12,7,3,80,-9,-1,1026,0.89,0,0 +2014,12,7,4,63,-9,-2,1025,0.89,0,0 +2014,12,7,5,72,-7,-1,1025,0.89,0,0 +2014,12,7,6,59,-6,-3,1025,0.89,0,0 +2014,12,7,7,59,-5,-3,1025,1.79,0,0 +2014,12,7,8,72,-5,-2,1026,3.58,0,0 +2014,12,7,9,79,-3,0,1026,5.37,0,0 +2014,12,7,10,77,-4,1,1026,1.79,0,0 +2014,12,7,11,96,-16,3,1026,4.92,0,0 +2014,12,7,12,54,-19,4,1026,12.97,0,0 +2014,12,7,13,34,-19,5,1025,21.91,0,0 +2014,12,7,14,18,-20,5,1025,29.06,0,0 +2014,12,7,15,22,-19,5,1025,36.21,0,0 +2014,12,7,16,21,-17,4,1026,43.36,0,0 +2014,12,7,17,15,-16,3,1027,49.17,0,0 +2014,12,7,18,13,-16,3,1028,54.09,0,0 +2014,12,7,19,11,-16,2,1028,59.01,0,0 +2014,12,7,20,17,-17,1,1030,63.03,0,0 +2014,12,7,21,18,-18,1,1030,71.08,0,0 +2014,12,7,22,16,-17,0,1031,76,0,0 +2014,12,7,23,17,-15,0,1032,80.02,0,0 +2014,12,8,0,18,-15,-4,1032,83.15,0,0 +2014,12,8,1,16,-14,-5,1032,1.79,0,0 +2014,12,8,2,15,-14,-2,1032,1.79,0,0 +2014,12,8,3,17,-14,-6,1032,4.92,0,0 +2014,12,8,4,19,-15,-6,1032,8.05,0,0 +2014,12,8,5,15,-15,-7,1032,11.18,0,0 +2014,12,8,6,22,-15,-8,1033,12.97,0,0 +2014,12,8,7,20,-15,-9,1033,16.1,0,0 +2014,12,8,8,21,-13,-4,1034,0.89,0,0 +2014,12,8,9,32,-13,-1,1035,1.79,0,0 +2014,12,8,10,20,-14,1,1035,3.58,0,0 +2014,12,8,11,31,-15,2,1035,0.89,0,0 +2014,12,8,12,40,-14,3,1035,2.68,0,0 +2014,12,8,13,102,-14,3,1034,4.47,0,0 +2014,12,8,14,117,-14,4,1033,6.26,0,0 +2014,12,8,15,100,-13,4,1033,3.13,0,0 +2014,12,8,16,NA,-13,3,1033,1.79,0,0 +2014,12,8,17,136,-13,1,1034,2.68,0,0 +2014,12,8,18,NA,-11,-2,1034,0.89,0,0 +2014,12,8,19,NA,-11,-2,1035,1.78,0,0 +2014,12,8,20,NA,-11,-4,1036,2.67,0,0 +2014,12,8,21,NA,-11,-5,1036,3.56,0,0 +2014,12,8,22,NA,-11,-5,1036,0.89,0,0 +2014,12,8,23,145,-11,-3,1037,0.89,0,0 +2014,12,9,0,180,-11,-4,1037,1.79,0,0 +2014,12,9,1,NA,-11,-4,1037,0.89,0,0 +2014,12,9,2,NA,-10,-5,1036,0.89,0,0 +2014,12,9,3,NA,-10,-6,1037,0.89,0,0 +2014,12,9,4,NA,-10,-7,1036,1.78,0,0 +2014,12,9,5,NA,-11,-6,1036,2.67,0,0 +2014,12,9,6,NA,-11,-7,1036,3.56,0,0 +2014,12,9,7,NA,-11,-8,1036,4.45,0,0 +2014,12,9,8,NA,-9,-6,1036,0.89,0,0 +2014,12,9,9,NA,-8,-5,1037,1.79,0,0 +2014,12,9,10,NA,-8,-4,1037,0.89,0,0 +2014,12,9,11,NA,-8,-3,1036,1.79,0,0 +2014,12,9,12,339,-7,-1,1035,0.89,0,0 +2014,12,9,13,355,-7,0,1033,1.78,0,0 +2014,12,9,14,388,-8,0,1032,1.79,0,0 +2014,12,9,15,390,-8,0,1031,0.89,0,0 +2014,12,9,16,375,-8,0,1031,1.78,0,0 +2014,12,9,17,367,-8,0,1031,0.89,0,0 +2014,12,9,18,352,-6,-1,1031,1.79,0,0 +2014,12,9,19,351,-8,0,1031,4.92,0,0 +2014,12,9,20,356,-7,-1,1030,6.71,0,0 +2014,12,9,21,367,-7,-1,1030,8.5,0,0 +2014,12,9,22,361,-7,-1,1030,0.89,0,0 +2014,12,9,23,350,-7,-1,1030,0.89,0,0 +2014,12,10,0,311,-6,-1,1029,0.89,1,0 +2014,12,10,1,319,-5,-1,1029,0.89,0,0 +2014,12,10,2,331,-5,-2,1028,1.78,0,0 +2014,12,10,3,303,-5,-3,1028,2.67,0,0 +2014,12,10,4,301,-7,-5,1027,0.89,0,0 +2014,12,10,5,306,-7,-6,1027,0.89,0,0 +2014,12,10,6,311,-8,-7,1026,1.78,0,0 +2014,12,10,7,318,-5,-5,1026,2.67,0,0 +2014,12,10,8,325,-5,-4,1027,3.13,0,0 +2014,12,10,9,152,-4,-4,1027,1.79,0,0 +2014,12,10,10,65,-9,3,1027,4.02,0,0 +2014,12,10,11,57,-15,4,1027,13.85,0,0 +2014,12,10,12,63,-17,5,1026,23.68,0,0 +2014,12,10,13,62,-17,5,1025,32.62,0,0 +2014,12,10,14,45,-19,5,1026,43.8,0,0 +2014,12,10,15,19,-19,5,1026,53.63,0,0 +2014,12,10,16,21,-18,4,1027,61.68,0,0 +2014,12,10,17,13,-18,2,1028,70.62,0,0 +2014,12,10,18,31,-18,1,1029,76.43,0,0 +2014,12,10,19,17,-18,0,1030,82.24,0,0 +2014,12,10,20,14,-16,0,1031,90.29,0,0 +2014,12,10,21,13,-16,0,1031,99.23,0,0 +2014,12,10,22,16,-16,-1,1032,108.17,0,0 +2014,12,10,23,18,-16,-1,1033,116.22,0,0 +2014,12,11,0,18,-16,-1,1033,124.27,0,0 +2014,12,11,1,20,-16,-1,1033,134.1,0,0 +2014,12,11,2,17,-16,-2,1033,142.15,0,0 +2014,12,11,3,9,-16,-3,1034,146.17,0,0 +2014,12,11,4,16,-16,-3,1034,151.09,0,0 +2014,12,11,5,14,-16,-3,1034,156.01,0,0 +2014,12,11,6,7,-16,-3,1033,163.16,0,0 +2014,12,11,7,15,-16,-3,1033,168.97,0,0 +2014,12,11,8,11,-16,-6,1034,173.89,0,0 +2014,12,11,9,13,-16,-2,1035,3.13,0,0 +2014,12,11,10,16,-16,-1,1034,4.92,0,0 +2014,12,11,11,14,-17,1,1033,3.13,0,0 +2014,12,11,12,14,-18,3,1031,8.05,0,0 +2014,12,11,13,14,-18,4,1030,12.97,0,0 +2014,12,11,14,14,-19,4,1029,18.78,0,0 +2014,12,11,15,12,-19,4,1028,22.8,0,0 +2014,12,11,16,29,-19,4,1028,4.02,0,0 +2014,12,11,17,40,-17,2,1028,3.13,0,0 +2014,12,11,18,92,-16,1,1028,5.81,0,0 +2014,12,11,19,76,-15,2,1028,10.73,0,0 +2014,12,11,20,81,-15,2,1029,4.92,0,0 +2014,12,11,21,32,-21,2,1029,16.1,0,0 +2014,12,11,22,9,-21,0,1030,23.25,0,0 +2014,12,11,23,12,-18,0,1030,29.06,0,0 +2014,12,12,0,8,-17,-1,1030,34.87,0,0 +2014,12,12,1,8,-17,-1,1029,42.02,0,0 +2014,12,12,2,8,-18,-2,1029,49.17,0,0 +2014,12,12,3,9,-18,-2,1029,52.3,0,0 +2014,12,12,4,12,-18,-3,1029,56.32,0,0 +2014,12,12,5,14,-19,-3,1029,59.45,0,0 +2014,12,12,6,9,-19,-3,1029,63.47,0,0 +2014,12,12,7,7,-19,-2,1029,68.39,0,0 +2014,12,12,8,8,-18,-2,1029,74.2,0,0 +2014,12,12,9,12,-18,-1,1029,78.22,0,0 +2014,12,12,10,13,-20,1,1029,85.37,0,0 +2014,12,12,11,9,-21,1,1028,95.2,0,0 +2014,12,12,12,17,-22,2,1027,103.25,0,0 +2014,12,12,13,18,-21,2,1026,112.19,0,0 +2014,12,12,14,14,-21,3,1026,120.24,0,0 +2014,12,12,15,10,-21,3,1026,126.05,0,0 +2014,12,12,16,12,-21,3,1026,131.86,0,0 +2014,12,12,17,9,-21,3,1026,136.78,0,0 +2014,12,12,18,14,-21,0,1027,0.89,0,0 +2014,12,12,19,17,-18,-1,1027,1.78,0,0 +2014,12,12,20,21,-17,-3,1028,2.67,0,0 +2014,12,12,21,47,-16,-3,1028,0.89,0,0 +2014,12,12,22,18,-15,-5,1028,0.89,0,0 +2014,12,12,23,15,-15,-5,1028,1.78,0,0 +2014,12,13,0,12,-16,-7,1029,2.67,0,0 +2014,12,13,1,16,-16,-6,1029,3.56,0,0 +2014,12,13,2,15,-15,-8,1029,0.89,0,0 +2014,12,13,3,13,-15,-9,1029,0.89,0,0 +2014,12,13,4,16,-14,-8,1029,0.89,0,0 +2014,12,13,5,16,-15,-8,1028,0.89,0,0 +2014,12,13,6,10,-15,-9,1029,1.34,0,0 +2014,12,13,7,13,-14,-10,1029,1.79,0,0 +2014,12,13,8,33,-15,-9,1029,3.13,0,0 +2014,12,13,9,37,-13,-3,1030,0.89,0,0 +2014,12,13,10,36,-16,0,1030,1.79,0,0 +2014,12,13,11,53,-14,0,1030,1.79,0,0 +2014,12,13,12,58,-15,2,1029,0.89,0,0 +2014,12,13,13,46,-16,3,1028,3.13,0,0 +2014,12,13,14,43,-19,5,1027,0.89,0,0 +2014,12,13,15,41,-19,5,1027,4.02,0,0 +2014,12,13,16,44,-19,4,1026,8.04,0,0 +2014,12,13,17,72,-18,3,1026,9.83,0,0 +2014,12,13,18,80,-18,1,1026,11.62,0,0 +2014,12,13,19,86,-17,2,1027,13.41,0,0 +2014,12,13,20,82,-15,-3,1027,0.89,0,0 +2014,12,13,21,86,-14,-6,1027,0.89,0,0 +2014,12,13,22,98,-14,-4,1027,0.89,0,0 +2014,12,13,23,112,-14,-5,1028,1.78,0,0 +2014,12,14,0,126,-13,-7,1027,2.67,0,0 +2014,12,14,1,192,-13,-7,1027,3.12,0,0 +2014,12,14,2,212,-13,-7,1027,0.89,0,0 +2014,12,14,3,144,-14,-8,1027,1.79,0,0 +2014,12,14,4,107,-14,-9,1027,3.58,0,0 +2014,12,14,5,86,-14,-8,1027,5.37,0,0 +2014,12,14,6,90,-15,-9,1027,7.16,0,0 +2014,12,14,7,95,-15,-10,1027,10.29,0,0 +2014,12,14,8,71,-13,-7,1028,15.21,0,0 +2014,12,14,9,76,-13,-2,1028,1.79,0,0 +2014,12,14,10,75,-16,1,1028,1.79,0,0 +2014,12,14,11,58,-18,4,1027,4.92,0,0 +2014,12,14,12,53,-18,5,1027,1.79,0,0 +2014,12,14,13,59,-18,6,1025,2.68,0,0 +2014,12,14,14,49,-19,6,1024,3.57,0,0 +2014,12,14,15,44,-19,6,1024,1.79,0,0 +2014,12,14,16,50,-19,5,1024,3.58,0,0 +2014,12,14,17,68,-16,3,1024,5.37,0,0 +2014,12,14,18,110,-15,1,1025,7.16,0,0 +2014,12,14,19,129,-13,0,1025,8.95,0,0 +2014,12,14,20,123,-14,0,1025,0.45,0,0 +2014,12,14,21,109,-12,-2,1025,0.89,0,0 +2014,12,14,22,132,-12,0,1025,2.68,0,0 +2014,12,14,23,160,-11,0,1025,4.47,0,0 +2014,12,15,0,167,-11,0,1026,0.89,0,0 +2014,12,15,1,257,-9,0,1025,1.78,0,0 +2014,12,15,2,291,-10,-2,1025,0.89,0,0 +2014,12,15,3,305,-10,-4,1025,1.79,0,0 +2014,12,15,4,307,-9,-4,1025,0.89,0,0 +2014,12,15,5,293,-11,-5,1024,0.89,0,0 +2014,12,15,6,269,-10,-4,1025,0.89,0,0 +2014,12,15,7,47,-16,1,1025,9.83,0,0 +2014,12,15,8,24,-16,1,1027,18.77,0,0 +2014,12,15,9,17,-16,2,1027,28.6,0,0 +2014,12,15,10,12,-16,2,1027,38.43,0,0 +2014,12,15,11,9,-17,3,1027,49.61,0,0 +2014,12,15,12,10,-17,4,1027,57.66,0,0 +2014,12,15,13,9,-19,4,1027,69.73,0,0 +2014,12,15,14,11,-21,3,1027,82.69,0,0 +2014,12,15,15,8,-21,2,1028,94.76,0,0 +2014,12,15,16,13,-22,0,1029,105.94,0,0 +2014,12,15,17,12,-20,-1,1031,118.01,0,0 +2014,12,15,18,10,-21,-2,1032,129.19,0,0 +2014,12,15,19,9,-21,-3,1033,141.26,0,0 +2014,12,15,20,18,-21,-3,1034,152.44,0,0 +2014,12,15,21,10,-21,-4,1034,160.49,0,0 +2014,12,15,22,11,-21,-4,1034,168.54,0,0 +2014,12,15,23,11,-21,-4,1034,175.69,0,0 +2014,12,16,0,6,-21,-5,1034,179.71,0,0 +2014,12,16,1,6,-20,-4,1033,185.52,0,0 +2014,12,16,2,6,-20,-5,1033,189.54,0,0 +2014,12,16,3,5,-19,-5,1032,194.46,0,0 +2014,12,16,4,10,-19,-5,1032,197.59,0,0 +2014,12,16,5,9,-19,-5,1032,201.61,0,0 +2014,12,16,6,11,-18,-5,1031,205.63,0,0 +2014,12,16,7,11,-19,-4,1031,211.44,0,0 +2014,12,16,8,8,-18,-4,1032,215.46,0,0 +2014,12,16,9,12,-18,-3,1032,222.61,0,0 +2014,12,16,10,15,-19,-2,1032,231.55,0,0 +2014,12,16,11,11,-19,-2,1031,240.49,0,0 +2014,12,16,12,9,-19,-1,1031,250.32,0,0 +2014,12,16,13,25,-20,0,1030,261.5,0,0 +2014,12,16,14,23,-20,0,1030,271.33,0,0 +2014,12,16,15,13,-21,-1,1030,280.27,0,0 +2014,12,16,16,8,-20,-1,1030,286.08,0,0 +2014,12,16,17,10,-20,-3,1030,290.1,0,0 +2014,12,16,18,11,-20,-3,1031,294.12,0,0 +2014,12,16,19,12,-21,-5,1031,298.14,0,0 +2014,12,16,20,14,-19,-5,1031,303.06,0,0 +2014,12,16,21,14,-20,-5,1031,307.08,0,0 +2014,12,16,22,10,-20,-3,1031,310.21,0,0 +2014,12,16,23,8,-18,-3,1030,313.34,0,0 +2014,12,17,0,9,-17,-3,1030,318.26,0,0 +2014,12,17,1,9,-18,-3,1029,321.39,0,0 +2014,12,17,2,8,-16,-7,1029,0.89,0,0 +2014,12,17,3,17,-16,-8,1029,1.78,0,0 +2014,12,17,4,30,-16,-7,1028,5.8,0,0 +2014,12,17,5,43,-18,-6,1028,9.82,0,0 +2014,12,17,6,57,-17,-7,1028,11.61,0,0 +2014,12,17,7,65,-18,-8,1028,0.89,0,0 +2014,12,17,8,80,-16,-9,1029,1.78,0,0 +2014,12,17,9,88,-15,-6,1029,2.67,0,0 +2014,12,17,10,96,-16,-3,1030,1.79,0,0 +2014,12,17,11,121,-16,-1,1030,3.58,0,0 +2014,12,17,12,152,-17,0,1029,0.89,0,0 +2014,12,17,13,172,-17,2,1028,1.79,0,0 +2014,12,17,14,128,-16,4,1027,3.58,0,0 +2014,12,17,15,77,-17,5,1027,7.6,0,0 +2014,12,17,16,65,-16,4,1027,9.39,0,0 +2014,12,17,17,79,-16,1,1028,0.89,0,0 +2014,12,17,18,93,-14,0,1028,1.79,0,0 +2014,12,17,19,104,-15,-2,1029,3.58,0,0 +2014,12,17,20,202,-15,-3,1029,0.89,0,0 +2014,12,17,21,245,-14,-4,1030,1.78,0,0 +2014,12,17,22,225,-14,-3,1030,2.67,0,0 +2014,12,17,23,180,-14,-6,1030,1.79,0,0 +2014,12,18,0,180,-13,-6,1030,1.79,0,0 +2014,12,18,1,170,-13,-7,1030,0.89,0,0 +2014,12,18,2,160,-13,-7,1030,2.68,0,0 +2014,12,18,3,146,-14,-9,1031,4.47,0,0 +2014,12,18,4,125,-14,-8,1030,7.6,0,0 +2014,12,18,5,130,-13,-6,1031,8.49,0,0 +2014,12,18,6,138,-14,-7,1031,10.28,0,0 +2014,12,18,7,147,-14,-9,1031,12.07,0,0 +2014,12,18,8,135,-13,-7,1031,13.86,0,0 +2014,12,18,9,136,-12,-4,1031,16.99,0,0 +2014,12,18,10,124,-12,-2,1031,1.79,0,0 +2014,12,18,11,135,-12,0,1031,1.79,0,0 +2014,12,18,12,132,-10,1,1029,1.79,0,0 +2014,12,18,13,133,-9,2,1028,1.79,0,0 +2014,12,18,14,142,-7,2,1027,3.58,0,0 +2014,12,18,15,163,-7,2,1026,5.37,0,0 +2014,12,18,16,166,-7,1,1026,7.16,0,0 +2014,12,18,17,189,-6,-1,1026,0.89,0,0 +2014,12,18,18,202,-6,-1,1025,1.78,0,0 +2014,12,18,19,228,-6,-1,1026,2.67,0,0 +2014,12,18,20,240,-7,-2,1025,3.12,0,0 +2014,12,18,21,235,-7,-4,1025,0.89,0,0 +2014,12,18,22,252,-7,-5,1024,1.79,0,0 +2014,12,18,23,254,-6,-4,1024,0.89,0,0 +2014,12,19,0,274,-7,-5,1024,0.89,0,0 +2014,12,19,1,253,-8,-5,1023,2.68,0,0 +2014,12,19,2,248,-8,-6,1022,5.81,0,0 +2014,12,19,3,219,-8,-5,1022,7.6,0,0 +2014,12,19,4,199,-9,-6,1022,9.39,0,0 +2014,12,19,5,198,-8,-6,1022,10.28,0,0 +2014,12,19,6,204,-8,-5,1022,15.2,0,0 +2014,12,19,7,66,-12,0,1023,22.35,0,0 +2014,12,19,8,22,-15,1,1023,30.4,0,0 +2014,12,19,9,19,-15,3,1024,39.34,0,0 +2014,12,19,10,16,-15,3,1025,46.49,0,0 +2014,12,19,11,11,-15,5,1024,55.43,0,0 +2014,12,19,12,17,-16,5,1024,64.37,0,0 +2014,12,19,13,15,-17,5,1023,75.55,0,0 +2014,12,19,14,14,-17,5,1023,86.73,0,0 +2014,12,19,15,12,-17,4,1024,98.8,0,0 +2014,12,19,16,7,-19,3,1025,110.87,0,0 +2014,12,19,17,7,-20,2,1026,120.7,0,0 +2014,12,19,18,15,-20,1,1027,129.64,0,0 +2014,12,19,19,7,-20,0,1028,140.82,0,0 +2014,12,19,20,10,-18,-1,1029,150.65,0,0 +2014,12,19,21,8,-18,-1,1030,159.59,0,0 +2014,12,19,22,12,-18,-2,1030,169.42,0,0 +2014,12,19,23,11,-18,-2,1031,176.57,0,0 +2014,12,20,0,9,-18,-3,1031,183.72,0,0 +2014,12,20,1,14,-18,-3,1031,189.53,0,0 +2014,12,20,2,8,-18,-3,1031,194.45,0,0 +2014,12,20,3,12,-18,-4,1031,200.26,0,0 +2014,12,20,4,11,-18,-4,1031,207.41,0,0 +2014,12,20,5,12,-19,-5,1030,212.33,0,0 +2014,12,20,6,15,-19,-7,1030,215.46,0,0 +2014,12,20,7,12,-18,-4,1030,220.38,0,0 +2014,12,20,8,NA,-18,-4,1031,225.3,0,0 +2014,12,20,9,NA,-17,-4,1031,228.43,0,0 +2014,12,20,10,NA,-18,-2,1031,233.35,0,0 +2014,12,20,11,NA,-17,-1,1031,239.16,0,0 +2014,12,20,12,NA,-18,0,1030,244.97,0,0 +2014,12,20,13,NA,-19,1,1029,249.89,0,0 +2014,12,20,14,NA,-20,1,1029,257.04,0,0 +2014,12,20,15,NA,-20,2,1028,262.85,0,0 +2014,12,20,16,NA,-21,1,1028,270,0,0 +2014,12,20,17,12,-20,1,1028,274.92,0,0 +2014,12,20,18,14,-19,0,1029,278.94,0,0 +2014,12,20,19,10,-18,0,1030,282.96,0,0 +2014,12,20,20,7,-18,-1,1030,286.98,0,0 +2014,12,20,21,8,-19,-2,1031,294.13,0,0 +2014,12,20,22,6,-19,-3,1032,302.18,0,0 +2014,12,20,23,4,-19,-3,1032,312.01,0,0 +2014,12,21,0,4,-20,-3,1032,321.84,0,0 +2014,12,21,1,7,-20,-4,1032,329.89,0,0 +2014,12,21,2,6,-20,-4,1032,339.72,0,0 +2014,12,21,3,8,-20,-4,1032,349.55,0,0 +2014,12,21,4,7,-20,-5,1031,355.36,0,0 +2014,12,21,5,6,-19,-5,1031,360.28,0,0 +2014,12,21,6,9,-20,-5,1030,365.2,0,0 +2014,12,21,7,9,-19,-5,1030,369.22,0,0 +2014,12,21,8,9,-19,-5,1030,372.35,0,0 +2014,12,21,9,8,-19,-3,1030,375.48,0,0 +2014,12,21,10,11,-20,-2,1030,381.29,0,0 +2014,12,21,11,17,-19,-1,1029,388.44,0,0 +2014,12,21,12,21,-19,0,1027,396.49,0,0 +2014,12,21,13,12,-18,1,1026,406.32,0,0 +2014,12,21,14,20,-18,2,1025,416.15,0,0 +2014,12,21,15,15,-18,2,1025,425.09,0,0 +2014,12,21,16,13,-17,2,1024,433.14,0,0 +2014,12,21,17,14,-17,2,1024,437.16,0,0 +2014,12,21,18,20,-17,1,1024,441.18,0,0 +2014,12,21,19,32,-19,1,1024,4.92,0,0 +2014,12,21,20,33,-19,0,1024,8.05,0,0 +2014,12,21,21,46,-17,-2,1023,9.84,0,0 +2014,12,21,22,53,-16,-1,1023,11.63,0,0 +2014,12,21,23,64,-15,-3,1023,0.89,0,0 +2014,12,22,0,53,-16,-4,1023,1.78,0,0 +2014,12,22,1,72,-16,-5,1023,2.67,0,0 +2014,12,22,2,85,-15,-7,1023,3.56,0,0 +2014,12,22,3,95,-14,-6,1024,4.45,0,0 +2014,12,22,4,98,-15,-8,1024,5.34,0,0 +2014,12,22,5,105,-14,-9,1024,3.13,0,0 +2014,12,22,6,117,-13,-6,1024,3.13,0,0 +2014,12,22,7,117,-13,-8,1024,1.79,0,0 +2014,12,22,8,126,-13,-8,1025,3.58,0,0 +2014,12,22,9,145,-13,-4,1026,5.37,0,0 +2014,12,22,10,136,-13,1,1026,3.13,0,0 +2014,12,22,11,95,-13,4,1025,0.89,0,0 +2014,12,22,12,80,-13,6,1024,1.78,0,0 +2014,12,22,13,51,-14,8,1023,3.57,0,0 +2014,12,22,14,67,-14,9,1022,3.13,0,0 +2014,12,22,15,71,-14,9,1022,7.15,0,0 +2014,12,22,16,63,-14,8,1022,10.28,0,0 +2014,12,22,17,80,-13,7,1022,13.41,0,0 +2014,12,22,18,75,-13,6,1022,17.43,0,0 +2014,12,22,19,87,-12,2,1022,0.89,0,0 +2014,12,22,20,105,-12,-1,1022,1.79,0,0 +2014,12,22,21,125,-12,-4,1022,0.89,0,0 +2014,12,22,22,115,-11,-4,1023,1.78,0,0 +2014,12,22,23,123,-11,-4,1023,2.67,0,0 +2014,12,23,0,142,-12,-5,1022,0.89,0,0 +2014,12,23,1,178,-12,-6,1022,1.78,0,0 +2014,12,23,2,163,-11,-5,1022,3.57,0,0 +2014,12,23,3,162,-12,-7,1022,3.13,0,0 +2014,12,23,4,145,-12,-7,1021,4.92,0,0 +2014,12,23,5,114,-13,-7,1021,6.71,0,0 +2014,12,23,6,84,-12,-5,1021,7.6,0,0 +2014,12,23,7,102,-12,-8,1021,9.39,0,0 +2014,12,23,8,82,-12,-6,1021,12.52,0,0 +2014,12,23,9,88,-10,-3,1021,14.31,0,0 +2014,12,23,10,90,-12,0,1021,16.1,0,0 +2014,12,23,11,88,-11,4,1021,19.23,0,0 +2014,12,23,12,95,-13,6,1020,21.02,0,0 +2014,12,23,13,81,-14,9,1019,24.15,0,0 +2014,12,23,14,89,-15,9,1018,25.94,0,0 +2014,12,23,15,95,-18,11,1018,29.96,0,0 +2014,12,23,16,57,-16,8,1019,31.75,0,0 +2014,12,23,17,62,-14,4,1019,0.89,0,0 +2014,12,23,18,234,-14,2,1020,0.89,0,0 +2014,12,23,19,118,-14,2,1021,1.79,0,0 +2014,12,23,20,108,-16,5,1022,5.81,0,0 +2014,12,23,21,33,-14,-2,1022,7.6,0,0 +2014,12,23,22,21,-13,-1,1023,11.62,0,0 +2014,12,23,23,14,-14,-2,1024,1.79,0,0 +2014,12,24,0,13,-15,2,1025,0.89,0,0 +2014,12,24,1,9,-14,-3,1026,3.13,0,0 +2014,12,24,2,10,-16,0,1027,7.15,0,0 +2014,12,24,3,11,-14,-5,1028,11.17,0,0 +2014,12,24,4,9,-15,-3,1029,15.19,0,0 +2014,12,24,5,15,-14,-5,1030,20.11,0,0 +2014,12,24,6,13,-14,-5,1031,24.13,0,0 +2014,12,24,7,12,-15,-6,1032,27.26,0,0 +2014,12,24,8,11,-13,-2,1033,29.05,0,0 +2014,12,24,9,18,-14,0,1033,1.79,0,0 +2014,12,24,10,17,-14,1,1034,6.71,0,0 +2014,12,24,11,11,-15,3,1034,11.63,0,0 +2014,12,24,12,12,-15,4,1034,4.92,0,0 +2014,12,24,13,14,-15,5,1033,12.07,0,0 +2014,12,24,14,14,-15,5,1032,17.88,0,0 +2014,12,24,15,12,-15,5,1032,25.03,0,0 +2014,12,24,16,16,-15,5,1032,30.84,0,0 +2014,12,24,17,16,-14,4,1032,37.99,0,0 +2014,12,24,18,12,-14,3,1032,45.14,0,0 +2014,12,24,19,20,-14,3,1033,50.06,0,0 +2014,12,24,20,23,-14,2,1033,54.98,0,0 +2014,12,24,21,16,-14,3,1033,59.9,0,0 +2014,12,24,22,17,-14,2,1034,65.71,0,0 +2014,12,24,23,18,-14,2,1034,70.63,0,0 +2014,12,25,0,20,-14,2,1033,75.55,0,0 +2014,12,25,1,22,-14,1,1033,79.57,0,0 +2014,12,25,2,22,-14,1,1034,84.49,0,0 +2014,12,25,3,10,-13,-3,1034,88.51,0,0 +2014,12,25,4,14,-14,-3,1034,91.64,0,0 +2014,12,25,5,14,-14,-7,1034,94.77,0,0 +2014,12,25,6,18,-13,-5,1034,1.79,0,0 +2014,12,25,7,13,-13,-3,1034,3.58,0,0 +2014,12,25,8,18,-13,-3,1034,6.71,0,0 +2014,12,25,9,27,-14,0,1035,10.73,0,0 +2014,12,25,10,38,-14,2,1035,13.86,0,0 +2014,12,25,11,25,-14,5,1035,4.02,0,0 +2014,12,25,12,16,-13,7,1033,7.15,0,0 +2014,12,25,13,16,-14,7,1032,10.28,0,0 +2014,12,25,14,9,-14,6,1031,1.79,0,0 +2014,12,25,15,8,-14,6,1031,3.58,0,0 +2014,12,25,16,18,-14,5,1031,1.79,0,0 +2014,12,25,17,31,-13,3,1032,0.89,0,0 +2014,12,25,18,38,-13,-1,1032,1.78,0,0 +2014,12,25,19,49,-11,-1,1032,2.67,0,0 +2014,12,25,20,52,-11,-2,1032,1.79,0,0 +2014,12,25,21,50,-11,0,1033,0.89,0,0 +2014,12,25,22,51,-12,-3,1033,0.89,0,0 +2014,12,25,23,69,-11,-2,1033,2.68,0,0 +2014,12,26,0,93,-11,-3,1033,4.47,0,0 +2014,12,26,1,94,-11,-3,1034,0.89,0,0 +2014,12,26,2,147,-10,-4,1034,1.78,0,0 +2014,12,26,3,226,-10,-3,1033,0.89,0,0 +2014,12,26,4,179,-11,-6,1033,0.89,0,0 +2014,12,26,5,153,-11,-7,1032,1.78,0,0 +2014,12,26,6,133,-11,-7,1032,2.67,0,0 +2014,12,26,7,127,-11,-6,1032,4.46,0,0 +2014,12,26,8,119,-11,-6,1033,0.45,0,0 +2014,12,26,9,111,-9,-4,1033,0.89,0,0 +2014,12,26,10,118,-9,-2,1033,1.78,0,0 +2014,12,26,11,116,-8,-1,1033,1.79,0,0 +2014,12,26,12,131,-9,0,1032,0.89,0,0 +2014,12,26,13,152,-9,2,1031,1.78,0,0 +2014,12,26,14,163,-9,2,1030,2.67,0,0 +2014,12,26,15,171,-9,2,1030,3.56,0,0 +2014,12,26,16,198,-9,2,1030,4.45,0,0 +2014,12,26,17,234,-9,-1,1030,4.9,0,0 +2014,12,26,18,260,-8,-3,1030,0.89,0,0 +2014,12,26,19,271,-9,-3,1031,0.89,0,0 +2014,12,26,20,244,-8,-3,1031,1.79,0,0 +2014,12,26,21,242,-9,-4,1031,0.89,0,0 +2014,12,26,22,210,-9,-4,1030,1.34,0,0 +2014,12,26,23,194,-10,-5,1030,2.23,0,0 +2014,12,27,0,163,-10,-5,1030,1.79,0,0 +2014,12,27,1,194,-10,-5,1030,3.58,0,0 +2014,12,27,2,187,-10,-5,1030,5.37,0,0 +2014,12,27,3,216,-11,-6,1030,7.16,0,0 +2014,12,27,4,225,-11,-6,1030,8.95,0,0 +2014,12,27,5,199,-11,-7,1029,10.74,0,0 +2014,12,27,6,207,-10,-7,1029,12.53,0,0 +2014,12,27,7,227,-10,-6,1029,13.42,0,0 +2014,12,27,8,259,-11,-7,1029,16.55,0,0 +2014,12,27,9,182,-9,-3,1030,19.68,0,0 +2014,12,27,10,185,-9,-2,1030,22.81,0,0 +2014,12,27,11,191,-10,1,1030,25.94,0,0 +2014,12,27,12,194,-10,3,1029,27.73,0,0 +2014,12,27,13,194,-10,4,1027,0.89,0,0 +2014,12,27,14,192,-10,6,1026,1.78,0,0 +2014,12,27,15,208,-10,5,1026,3.13,0,0 +2014,12,27,16,224,-9,5,1025,6.26,0,0 +2014,12,27,17,249,-9,4,1025,10.28,0,0 +2014,12,27,18,317,-9,4,1026,1.79,0,0 +2014,12,27,19,328,-9,0,1026,2.68,0,0 +2014,12,27,20,349,-9,-3,1026,3.57,0,0 +2014,12,27,21,327,-9,-4,1026,4.46,0,0 +2014,12,27,22,348,-7,-4,1025,5.35,0,0 +2014,12,27,23,363,-9,-5,1025,6.24,0,0 +2014,12,28,0,385,-9,-6,1025,6.69,0,0 +2014,12,28,1,393,-9,-4,1024,1.79,0,0 +2014,12,28,2,388,-8,-3,1024,3.13,0,0 +2014,12,28,3,444,-9,-3,1023,6.26,0,0 +2014,12,28,4,334,-9,-4,1022,9.39,0,0 +2014,12,28,5,284,-9,-4,1022,11.18,0,0 +2014,12,28,6,264,-9,-3,1022,14.31,0,0 +2014,12,28,7,221,-9,-4,1022,17.44,0,0 +2014,12,28,8,160,-9,-4,1022,1.79,0,0 +2014,12,28,9,160,-8,-1,1021,3.58,0,0 +2014,12,28,10,92,-10,4,1021,3.13,0,0 +2014,12,28,11,59,-11,7,1020,7.15,0,0 +2014,12,28,12,69,-12,7,1019,1.79,0,0 +2014,12,28,13,48,-13,8,1018,5.81,0,0 +2014,12,28,14,32,-13,9,1018,12.96,0,0 +2014,12,28,15,33,-14,8,1018,20.11,0,0 +2014,12,28,16,45,-13,8,1018,25.92,0,0 +2014,12,28,17,42,-13,7,1017,29.94,0,0 +2014,12,28,18,41,-13,6,1018,33.96,0,0 +2014,12,28,19,110,-13,6,1017,37.98,0,0 +2014,12,28,20,175,-13,3,1017,0.89,0,0 +2014,12,28,21,293,-13,1,1017,1.78,0,0 +2014,12,28,22,324,-10,2,1017,2.67,0,0 +2014,12,28,23,341,-10,-2,1017,3.12,0,0 +2014,12,29,0,362,-10,-4,1016,4.01,0,0 +2014,12,29,1,372,-10,-5,1016,4.9,0,0 +2014,12,29,2,373,-11,-5,1015,5.79,0,0 +2014,12,29,3,372,-11,-6,1015,6.68,0,0 +2014,12,29,4,318,-12,-7,1014,7.57,0,0 +2014,12,29,5,188,-12,-7,1014,8.46,0,0 +2014,12,29,6,171,-13,-8,1014,1.79,0,0 +2014,12,29,7,136,-12,-7,1014,4.92,0,0 +2014,12,29,8,124,-12,-6,1014,0.89,0,0 +2014,12,29,9,141,-10,-2,1014,1.78,0,0 +2014,12,29,10,132,-11,3,1014,2.67,0,0 +2014,12,29,11,134,-11,5,1014,3.56,0,0 +2014,12,29,12,56,-12,9,1013,3.13,0,0 +2014,12,29,13,48,-13,11,1012,10.28,0,0 +2014,12,29,14,27,-14,11,1011,17.43,0,0 +2014,12,29,15,28,-14,11,1011,25.48,0,0 +2014,12,29,16,20,-15,11,1011,32.63,0,0 +2014,12,29,17,22,-15,10,1012,36.65,0,0 +2014,12,29,18,120,-15,6,1013,0.89,0,0 +2014,12,29,19,109,-14,3,1013,1.78,0,0 +2014,12,29,20,112,-12,1,1014,2.67,0,0 +2014,12,29,21,128,-13,0,1015,3.12,0,0 +2014,12,29,22,164,-12,-3,1015,3.13,0,0 +2014,12,29,23,159,-12,-3,1016,1.79,0,0 +2014,12,30,0,189,-12,-2,1016,0.89,0,0 +2014,12,30,1,97,-13,-2,1016,1.79,0,0 +2014,12,30,2,81,-13,-4,1016,5.81,0,0 +2014,12,30,3,28,-13,-2,1017,10.73,0,0 +2014,12,30,4,25,-13,-5,1017,15.65,0,0 +2014,12,30,5,9,-13,-3,1017,19.67,0,0 +2014,12,30,6,9,-14,-2,1017,22.8,0,0 +2014,12,30,7,13,-13,-3,1018,0.89,0,0 +2014,12,30,8,17,-15,0,1019,1.78,0,0 +2014,12,30,9,16,-13,2,1020,1.79,0,0 +2014,12,30,10,25,-14,5,1020,1.79,0,0 +2014,12,30,11,48,-15,6,1020,1.79,0,0 +2014,12,30,12,49,-15,7,1019,3.58,0,0 +2014,12,30,13,73,-15,6,1018,6.71,0,0 +2014,12,30,14,65,-14,6,1018,9.84,0,0 +2014,12,30,15,55,-14,6,1017,11.63,0,0 +2014,12,30,16,60,-14,6,1018,0.89,0,0 +2014,12,30,17,63,-14,5,1019,1.79,0,0 +2014,12,30,18,79,-13,2,1020,3.58,0,0 +2014,12,30,19,35,-8,6,1021,5.81,0,0 +2014,12,30,20,26,-11,5,1022,12.96,0,0 +2014,12,30,21,20,-12,4,1023,21.9,0,0 +2014,12,30,22,8,-21,2,1025,31.73,0,0 +2014,12,30,23,16,-22,0,1026,38.88,0,0 +2014,12,31,0,10,-19,-1,1027,51.84,0,0 +2014,12,31,1,11,-18,-1,1028,61.67,0,0 +2014,12,31,2,20,-17,-1,1028,70.61,0,0 +2014,12,31,3,9,-17,-1,1029,81.79,0,0 +2014,12,31,4,8,-19,-2,1030,94.75,0,0 +2014,12,31,5,9,-21,-3,1030,109.95,0,0 +2014,12,31,6,8,-23,-4,1032,130.07,0,0 +2014,12,31,7,8,-22,-5,1034,143.03,0,0 +2014,12,31,8,8,-22,-5,1034,150.18,0,0 +2014,12,31,9,8,-22,-3,1034,155.99,0,0 +2014,12,31,10,7,-22,-2,1034,163.14,0,0 +2014,12,31,11,12,-22,-2,1034,170.29,0,0 +2014,12,31,12,17,-22,0,1033,177.44,0,0 +2014,12,31,13,11,-27,0,1032,186.38,0,0 +2014,12,31,14,9,-27,1,1032,196.21,0,0 +2014,12,31,15,11,-26,1,1032,205.15,0,0 +2014,12,31,16,8,-23,0,1032,214.09,0,0 +2014,12,31,17,9,-22,-1,1033,221.24,0,0 +2014,12,31,18,10,-22,-2,1033,226.16,0,0 +2014,12,31,19,8,-23,-2,1034,231.97,0,0 +2014,12,31,20,10,-22,-3,1034,237.78,0,0 +2014,12,31,21,10,-22,-3,1034,242.7,0,0 +2014,12,31,22,8,-22,-4,1034,246.72,0,0 +2014,12,31,23,12,-21,-3,1034,249.85,0,0 diff --git a/02_Week2/Data/1D_intro_examples.dat b/02_Week2/Data/1D_intro_examples.dat new file mode 100644 index 0000000..9c32810 --- /dev/null +++ b/02_Week2/Data/1D_intro_examples.dat @@ -0,0 +1,16 @@ +,x,y,error +0,0.0,-1.5436209437344988,5.05 +1,0.7142857142857143,13.432916677960808,6.709183673469388 +2,1.4285714285714286,45.89572620680016,11.34387755102041 +3,2.142857142857143,158.16068886871972,18.95408163265306 +4,2.857142857142857,212.87264609786016,29.539795918367357 +5,3.5714285714285716,329.9334895359132,43.10102040816327 +6,4.285714285714286,534.1130526818873,59.63775510204082 +7,5.0,859.1286423041538,79.15 +8,5.714285714285714,990.4368610174865,101.63775510204084 +9,6.428571428571429,1090.8860681182641,127.10102040816327 +10,7.142857142857143,1394.499142347062,155.5397959183674 +11,7.857142857142858,1936.4329147709418,186.9540816326531 +12,8.571428571428571,2127.9101253036547,221.3438775510204 +13,9.285714285714286,2338.1868121651173,258.7091836734695 +14,10.0,2871.7093125027823,299.05 diff --git a/Day_4/RandomVariable_Generated_Data.dat b/02_Week2/Data/RandomVariable_Generated_Data.dat similarity index 99% rename from Day_4/RandomVariable_Generated_Data.dat rename to 02_Week2/Data/RandomVariable_Generated_Data.dat index 5d8345c..d20d5b3 100644 --- a/Day_4/RandomVariable_Generated_Data.dat +++ b/02_Week2/Data/RandomVariable_Generated_Data.dat @@ -998,4 +998,4 @@ A B C 6.18966722540205 13.013281318897004 1.0 5.440583162901113 7.825326558217267 1.0 7.166919582884262 13.136039901697007 1.0 -1.1224554875018682 2.6400882921985325 1.0 +1.1224554875018682 2.6400882921985325 1.0 \ No newline at end of file diff --git a/02_Week2/README.md b/02_Week2/README.md new file mode 100644 index 0000000..d631c99 --- /dev/null +++ b/02_Week2/README.md @@ -0,0 +1,53 @@ +# STEM Python Course 2020 + +University of South Carolina STEM Python Course Summer 2020 + + +### Run with Binder + +[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/uofscphysics/STEM_Python_Course/Summer2020) + +### Each day will run from 8:30AM to 12PM ET + +# Day 1 - Probability and Statistics +* Introductions, overview of the week +* Directions for accessing/running the code +* Understanding probability theory with Python +* Simple statistics and data fitting + +[Slides for this day](https://docs.google.com/presentation/d/11_07j09Kxa7lyDdU-eumKqfXzdFePYT2x5q7RT6KwrI/edit?usp=sharing) | +[Recording for this day](https://youtu.be/z4TvJKNgffg) +____ +# Day 2 - MCMC Fitting and Global Optimization Methods +* Introducing MCMC and nested sampling (NS) packages +* Fitting data with MCMC/NS +* Global optimization for multimodal posterior distributions + +[Slides for this day](https://docs.google.com/presentation/d/1WDA-fPV1ouuzx-_FelnE_g2s2J42xSisAOSZvHxSnSU/edit?usp=sharing)| +[Recording for this day](https://youtu.be/8vwLXxAdC9Y) +____ +# Day 3 - Machine Learning: Clustering & Classification +* Introduction to clustering & classification with ML +* K-means clustering +* K-nearest neighbor +* Random Forest + +[Slides for this day](https://docs.google.com/presentation/d/1ao4RWm5l6sdd9JhUNdGprSoV19pnqmxU2qfvju5UE5w/edit?usp=sharing) | +[Recording for this day](https://youtu.be/z-ilieZ5f80) +____ +# Day 4 - Machine Learning: Regression +* Introduction to regression with ML +* Random forest for regression +* Neural Networks + +[Slides for this day](https://docs.google.com/presentation/d/1292IC5cYID3oUPv5c05HIDM07p-4sKBwqmkVHnd7OcI/edit?usp=sharing) | +[Recording for this day](https://youtu.be/pAv6p9qgBwc) +____ +# Day 5 - Instruction Based on Feedback + +This day will be optional. We will solicit feedback during the week on topics you would like to learn more about, and then +spend Friday breaking up participants into groups to investigate the module they are most interested in. This will +run during the same time period as days 1-4. + +[Slides for this day](https://docs.google.com/presentation/d/1WoicaQJMif3kXWEq7zNVqDIzIw0tad3aSmpcN2GoGcM/edit?usp=sharing) | +[Recording for this day](https://youtu.be/ejvIncgsRPc) diff --git a/02_Week2/Workshop_CodeOptimization/A_Profiling.ipynb b/02_Week2/Workshop_CodeOptimization/A_Profiling.ipynb new file mode 100644 index 0000000..be8ddfa --- /dev/null +++ b/02_Week2/Workshop_CodeOptimization/A_Profiling.ipynb @@ -0,0 +1,308 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip3 install line_profiler" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import random\n", + "\n", + "test_size = int(1e6)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Basic timing \n", + "\n", + "There are a few builtin functions in jupyter for doing this `%time`/`%%time` and `%timeit`/`%%timeit`. Using the `%` version will run the function over the line it is in front of while the `%%` version needs to be at the top of the cell and will run over the entire cell.\n", + "\n", + "- `time` is used to find the time for running that code\n", + "- `timeit` is used to run the code multiple times and give the mean result\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%time x = sum([random.random() for i in range(test_size)])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%timeit\n", + "x = sum([random.random() for i in range(test_size)])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tip #1: Use numpy\n", + "\n", + "Now lets run these cells and see the difference in speed" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%time x = np.sum(np.random.normal(size=test_size))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%timeit\n", + "x = np.sum(np.random.normal(size=test_size))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tip #2: Don't reinvent the wheel use numpy/scipy\n", + "\n", + "numpy and scipy are packed with useful functions. Take some time at the begining to find a function that works so you're code will be faster all the time." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def make_square_matrix(N, value=None):\n", + " #define empty matrix\n", + " matrix=[] \n", + " for i in range(N):\n", + " #define empty row\n", + " row=[] \n", + " for j in range(N): \n", + " # append random number or set value to the row\n", + " if value is None:\n", + " row.append(random.random()) \n", + " else:\n", + " row.append(value)\n", + " # append new row to full matrix\n", + " matrix.append(row)\n", + " return matrix\n", + "\n", + "def my_matMul(X,Y):\n", + " # Get size of matrices\n", + " size = len(X)\n", + " # Make a matrix that size with all 0's\n", + " result = make_square_matrix(size,value=0)\n", + " # Loop through rows in X/col in Y\n", + " for i in range(size):\n", + " # Loop through rows in Y/col in X\n", + " for j in range(size):\n", + " # Loop through elements in each row/col and \n", + " for k in range(size):\n", + " # Fill in the resulting matrix with the value at i,j\n", + " result[i][j] += X[i][k] * Y[k][j]\n", + " return result" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# X and Y are pure python arrays\n", + "X = make_square_matrix(200)\n", + "Y = make_square_matrix(200)\n", + "%time myResult = my_matMul(X,Y)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# It may make sense to use numpy to try and speed it up...but it makes it slower\n", + "X = np.array(make_square_matrix(200))\n", + "Y = np.array(make_square_matrix(200))\n", + "%time myResult = my_matMul(X,Y)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time \n", + "# Let's use our pure python arrays with numpys function...better than our original\n", + "X = make_square_matrix(200)\n", + "Y = make_square_matrix(200)\n", + "npResult = np.matmul(X,Y)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Now let's just use numpy for everything\n", + "X = np.random.rand(200,200)\n", + "Y = np.random.rand(200,200)\n", + "%time npResult = np.matmul(X,Y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tip #3 Profile your code\n", + "\n", + "There are tools like line_profiler that will tell you what is the most used and slowest functions in your code." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext line_profiler\n", + "# Loads the line profiler extention into jupyter" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile my_function.py\n", + "# In order for this to work you need to save the functions you want to look at as a file\n", + "# and then load the functions in as a module\n", + "\n", + "def make_square_matrix(N, value=None):\n", + " import random\n", + " #define empty matrix\n", + " matrix=[] \n", + " for i in range(N):\n", + " #define empty row\n", + " row=[] \n", + " for j in range(N): \n", + " # append random number or set value to the row\n", + " if value is None:\n", + " row.append(random.random()) \n", + " else:\n", + " row.append(value)\n", + " # append new row to full matrix\n", + " matrix.append(row)\n", + " return matrix\n", + "\n", + "def my_matMul(X,Y):\n", + " # Get size of matrices\n", + " size = len(X)\n", + " # Make a matrix that size with all 0's\n", + " result = make_square_matrix(size,value=0)\n", + " # Loop through rows in X/col in Y\n", + " for i in range(size):\n", + " # Loop through rows in Y/col in X\n", + " for j in range(size):\n", + " # Loop through elements in each row/col and \n", + " for k in range(size):\n", + " # Fill in the resulting matrix with the value at i,j\n", + " result[i][j] += X[i][k] * Y[k][j]\n", + " return result" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Load the functions in from the file we created above\n", + "from my_function import my_matMul, make_square_matrix" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Run the line profiler\n", + "X = make_square_matrix(200)\n", + "Y = make_square_matrix(200)\n", + "# -T gives the output filename\n", + "# -f is the function to profile\n", + "# The end is how you're going to run your function\n", + "%lprun -T profMatMul -f my_matMul my_matMul(X,Y)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "X = make_square_matrix(200)\n", + "Y = make_square_matrix(200)\n", + "# You can also profile different functions inside of the function you want to run\n", + "%lprun -T profSqaureMatrix -f make_square_matrix my_matMul(X,Y)" + ] + }, + { + "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": "3.8.4" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/02_Week2/Workshop_CodeOptimization/B_Cython_and_numba.ipynb b/02_Week2/Workshop_CodeOptimization/B_Cython_and_numba.ipynb new file mode 100644 index 0000000..36caa7f --- /dev/null +++ b/02_Week2/Workshop_CodeOptimization/B_Cython_and_numba.ipynb @@ -0,0 +1,337 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#!pip3 install cython" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Let's look at our matrix multiplication again\n", + "\n", + "I know I mentioned before to just use numpy but sometimes you're working with a complex function or something that's not as easily sped up with numpy.\n", + "\n", + "Let's assume we have a special matrix multiplication we can't use a generic function for." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def my_matMul(X,Y):\n", + " # Get size of matrices\n", + " size = len(X)\n", + " # Make a matrix that size with all 0's\n", + " result = np.zeros_like(X)\n", + " # Loop through rows in X/col in Y\n", + " for i in range(size):\n", + " # Loop through rows in Y/col in X\n", + " for j in range(size):\n", + " # Loop through elements in each row/col and \n", + " for k in range(size):\n", + " # Fill in the resulting matrix with the value at i,j\n", + " result[i][j] += X[i][k] * Y[k][j]\n", + " return result" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "size = 200\n", + "A = np.random.rand(size,size)\n", + "B = np.random.rand(size,size)\n", + "%time x = my_matMul(A,B) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Let's try cython\n", + "\n", + "From the cython website:\n", + "\n", + "#### [Cython](https://cython.org) gives you the combined power of Python and C to let you\n", + "\n", + "- write Python code that calls back and forth from and to C or C++ code natively at any point.\n", + "- easily tune readable Python code into plain C performance by adding static type declarations, also in Python syntax.\n", + "- use combined source code level debugging to find bugs in your Python, Cython and C code.\n", + "- interact efficiently with large data sets, e.g. using multi-dimensional NumPy arrays.\n", + "- quickly build your applications within the large, mature and widely used CPython ecosystem.\n", + "- integrate natively with existing code and data from legacy, low-level or high-performance libraries and applications.\n", + "\n", + "##### Personally I use cython to make libraries which call C/C++/Fortarn code, which is already written and fast, in python which has really nice plotting and fitting methods.\n", + "\n", + "We can use cython in jupyter by loading the extension and using `%%cython` in any cell that we want to cythonize and compile to c style code." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext Cython" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%cython \n", + "import numpy as np # we need to redelare any libraries inside the cython cell for cython to load them\n", + "\n", + "### This is all I added! ^^^^\n", + "\n", + "def my_matMul_cy(X,Y):\n", + " # Get size of matrices\n", + " size = len(X)\n", + " # Make a matrix that size with all 0's\n", + " result = np.zeros_like(X)\n", + " # Loop through rows in X/col in Y\n", + " for i in range(size):\n", + " # Loop through rows in Y/col in X\n", + " for j in range(size):\n", + " # Loop through elements in each row/col and \n", + " for k in range(size):\n", + " # Fill in the resulting matrix with the value at i,j\n", + " result[i][j] += X[i][k] * Y[k][j]\n", + " return result" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "size = 200\n", + "\n", + "A = np.random.rand(size,size)\n", + "B = np.random.rand(size,size)\n", + "%time x = my_matMul_cy(A,B)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

\n", + "

\n", + "

\n", + "

\n", + "## Let's see if we can make it faster\n", + "\n", + "Cython allows you to give your variables types to help speedup the python code that's already written. You can also use the `-a` option to annotate your cython code which shows how one line of python code get's translated into lines of C code. Even if you don't understand C cython will try to help by marking slower functions in the code as red which you can think how to change them.\n", + "\n", + "We can also turn on and off features of python inside of our code which can help to make it faster. https://cython.readthedocs.io/en/latest/src/userguide/source_files_and_compilation.html#compiler-directives" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%cython -a\n", + "import numpy as np # we need to redelare any libraries inside the cython cell for cython to load them\n", + "\n", + "def my_matMul_cy(X,Y):\n", + " # Get size of matrices\n", + " size = len(X)\n", + " # Make a matrix that size with all 0's\n", + " result = np.zeros_like(X)\n", + " # Loop through rows in X/col in Y\n", + " for i in range(size):\n", + " # Loop through rows in Y/col in X\n", + " for j in range(size):\n", + " # Loop through elements in each row/col and \n", + " for k in range(size):\n", + " # Fill in the resulting matrix with the value at i,j\n", + " result[i][j] += X[i][k] * Y[k][j]\n", + " return result" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "size = 200\n", + "\n", + "A = np.random.rand(size,size)\n", + "B = np.random.rand(size,size)\n", + "%time x = my_matMul_cy(A,B)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

\n", + "

\n", + "

\n", + "### Final Fast solution" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%cython -a\n", + "\n", + "cimport cython\n", + "import numpy as np\n", + "cimport numpy as np\n", + "\n", + "from cython.view cimport array\n", + "\n", + "@cython.boundscheck(False)\n", + "@cython.wraparound(False)\n", + "def mat_mul_cy_fast(double [:,:] X, double [:,:] Y):\n", + " cdef int i,j,k = 0\n", + " cdef int size = X.shape[0]\n", + " cdef double [:,:] result = array(shape=(size,size), itemsize=sizeof(double), format=\"d\")\n", + " result[:,:] = 0.0\n", + " for i in range(size):\n", + " for k in range(size):\n", + " for j in range(size):\n", + " result[i][j] += X[i][k] * Y[k][j]\n", + " return np.asarray(result)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "size = 200\n", + "\n", + "A = np.random.rand(size,size)\n", + "B = np.random.rand(size,size)\n", + "%time x = mat_mul_cy_fast(A,B)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Numba \n", + "\n", + "Just in time compilation for python functions allows for the function to be comiled once when it is used the first time to speed up all further executions of the funtion. Also really easy to get simple speedup of already written code, just add `@jit`. Numba also has support for [cuda](https://numba.pydata.org/numba-doc/latest/cuda/index.html) so can be an easy way to start using gpu's with your work." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from numba import jit\n", + "\n", + "@jit\n", + "def my_matMul_numba(X,Y):\n", + " # Get size of matrices\n", + " size = len(X)\n", + " # Make a matrix that size with all 0's\n", + " result = np.zeros_like(X)\n", + " # Loop through rows in X/col in Y\n", + " for i in range(size):\n", + " # Loop through rows in Y/col in X\n", + " for j in range(size):\n", + " # Loop through elements in each row/col and \n", + " for k in range(size):\n", + " # Fill in the resulting matrix with the value at i,j\n", + " result[i][j] += X[i][k] * Y[k][j]\n", + " return result" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "size = 200\n", + "\n", + "A = np.random.rand(size,size)\n", + "B = np.random.rand(size,size)\n", + "print(\"1st time\")\n", + "%time x = my_matMul_numba(A,B)\n", + "print(\"2nd time\")\n", + "%time x = my_matMul_numba(A,B)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Still beat by numpy...\n", + "size = 200\n", + "\n", + "A = np.random.rand(size,size)\n", + "B = np.random.rand(size,size)\n", + "%time x = np.matmul(A,B)" + ] + }, + { + "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": "3.8.4" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/02_Week2/Workshop_CodeOptimization/C_Advanced_Cython.ipynb b/02_Week2/Workshop_CodeOptimization/C_Advanced_Cython.ipynb new file mode 100644 index 0000000..0bad93f --- /dev/null +++ b/02_Week2/Workshop_CodeOptimization/C_Advanced_Cython.ipynb @@ -0,0 +1,190 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#!pip3 install cython" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext Cython" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "num_steps = 50\n", + "scale = 50\n", + "damping= 0.25\n", + "initial_p = 1\n", + "stop_step = 25" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def wave_propogation(num_steps, scale, damping, initial_P, stop_step):\n", + " # Give types to variables we use to calculate with\n", + " omega = 3.0 / (2.0 * np.pi)\n", + " size_x = 2 * scale + 1\n", + "\n", + " # Setup C style arrays in python\n", + " P = np.zeros((size_x,size_x))\n", + " V = np.zeros((size_x,size_x,4))\n", + "\n", + " P[scale][scale] = initial_P\n", + "\n", + " for step in range(num_steps):\n", + " if(step <= stop_step):\n", + " P[scale][scale] = initial_P * np.sin(omega * step)\n", + " for i in range(size_x):\n", + " for j in range(size_x):\n", + " V[i][j][0] = V[i][j][0] + P[i][j] - P[i - 1][j] if i > 0 else P[i][j]\n", + " V[i][j][1] = V[i][j][1] + P[i][j] - P[i][j + 1] if j < size_x - 1 else P[i][j]\n", + " V[i][j][2] = V[i][j][2] + P[i][j] - P[i + 1][j] if i < size_x - 1 else P[i][j]\n", + " V[i][j][3] = V[i][j][3] + P[i][j] - P[i][j - 1] if j > 0 else P[i][j]\n", + "\n", + " for i in range(size_x):\n", + " for j in range(size_x):\n", + " P[i][j] -= 0.5 * damping * (V[i][j][0]+V[i][j][1]+V[i][j][2]+V[i][j][3])\n", + " return P" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%time final_p = wave_propogation(num_steps, scale, damping, initial_p, stop_step)\n", + "\n", + "plt.figure(figsize=(12,6))\n", + "plt.imshow(final_p)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%cython -a\n", + "\n", + "cimport cython\n", + "from libcpp cimport bool\n", + "\n", + "from cython.view cimport array as cython_array\n", + "from libc.math cimport M_PI as pi\n", + "from libc.math cimport sin as sin\n", + "import numpy as np\n", + "\n", + "\n", + "# These turn off some python features for accessing arrays as python arrays\n", + "@cython.boundscheck(False)\n", + "@cython.wraparound(False)\n", + "# This turns off checks for divide by 0 and will seg fault instead of throw a warning\n", + "@cython.cdivision(True)\n", + "# Gave all the input variables types\n", + "def wave_propogation_cython(int num_steps, int scale,float damping,float initial_P,int stop_step):\n", + " # Give types to variables we use to calculate with\n", + " cdef float omega = 3.0 / (2.0 * pi)\n", + " cdef int size_x = 2 * scale + 1\n", + "\n", + " # Give types to loop iterator variables to make loops C loops\n", + " cdef int i = 0\n", + " cdef int j = 0\n", + " cdef int step = 0\n", + "\n", + " # Setup C style arrays in python\n", + " cdef float [:,:] P = cython_array(shape=(size_x, size_x), itemsize=sizeof(float), format=\"f\")\n", + " P[:,:] = 0.0\n", + " cdef float [:,:,:] V = cython_array(shape=(size_x, size_x, 4), itemsize=sizeof(float), format=\"f\")\n", + " V[:,:,:] = 0.0\n", + "\n", + " P[scale][scale] = initial_P\n", + "\n", + " for step in range(num_steps):\n", + " if(step <= stop_step):\n", + " P[scale][scale] = initial_P * sin(omega * step)\n", + " for i in range(size_x):\n", + " for j in range(size_x):\n", + " V[i][j][0] = V[i][j][0] + P[i][j] - P[i - 1][j] if i > 0 else P[i][j]\n", + " V[i][j][1] = V[i][j][1] + P[i][j] - P[i][j + 1] if j < size_x - 1 else P[i][j]\n", + " V[i][j][2] = V[i][j][2] + P[i][j] - P[i + 1][j] if i < size_x - 1 else P[i][j]\n", + " V[i][j][3] = V[i][j][3] + P[i][j] - P[i][j - 1] if j > 0 else P[i][j]\n", + "\n", + " for i in range(size_x):\n", + " for j in range(size_x):\n", + " P[i][j] -= 0.5 * damping * (V[i][j][0]+V[i][j][1]+V[i][j][2]+V[i][j][3])\n", + " return np.asarray(P)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%time final_p = wave_propogation_cython(num_steps, scale, damping, initial_p, stop_step)\n", + "plt.figure(figsize=(12,6))\n", + "plt.imshow(final_p)\n", + "plt.show()" + ] + }, + { + "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": "3.8.4" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/02_Week2/Workshop_CodeOptimization/D_Using_Fortran.ipynb b/02_Week2/Workshop_CodeOptimization/D_Using_Fortran.ipynb new file mode 100644 index 0000000..d360d18 --- /dev/null +++ b/02_Week2/Workshop_CodeOptimization/D_Using_Fortran.ipynb @@ -0,0 +1,218 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#!pip3 install cython" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "!wget https://raw.githubusercontent.com/uofscphysics/STEM_Python_Course/Summer2020/02_Week2/Workshop_CodeOptimization/wave_propogation.f90\n", + "!wget https://raw.githubusercontent.com/uofscphysics/STEM_Python_Course/Summer2020/02_Week2/Workshop_CodeOptimization/setup.py\n", + "\n", + "!ls" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fortran File\n", + "```Fortran\n", + "SUBROUTINE wave_propogation_fortran(num_steps, scale, damping, initial_P, stop_step, P)\n", + " ! INPUTS\n", + " ! num_steps -> int # Number of itereations to make\n", + " ! scale -> int # Size of box for wave to propogate in\n", + " ! damping -> float # Factor for damping wave\n", + " ! initial_P -> float # Initial height at center\n", + " ! stop_step -> int # Number of steps to stop wave\n", + " ! OUTPUTS:\n", + " ! P -> float(x,y) # Array of output heights\n", + " IMPLICIT NONE\n", + " INTEGER size_x,i,j,k,step\n", + " REAL PI, omega\n", + " INTEGER, INTENT(in) :: num_steps, scale, stop_step !input\n", + " REAL, INTENT(in) :: damping, initial_P !input\n", + " REAL, INTENT(out) :: P(2 * scale + 1,2 * scale + 1) ! output\n", + " REAL, DIMENSION(2 * scale + 1,2 * scale + 1,4) :: V\n", + "\n", + " size_x = 2 * scale + 1\n", + "\n", + " PI = 3.14159\n", + " omega = 3.0 / (2.0 * PI)\n", + "\n", + "! Setup initial matrix all 0's\n", + "! P is pressure matrix\n", + "! V is velocity tensor (matrix of x/y and 4 vector of velocity [up,down,left,right])\n", + " DO k=1,4\n", + " DO j=1,size_x\n", + " DO i=1,size_x\n", + " P(i,j) = 0.0\n", + " V(i,j,k) = 0.0\n", + " END DO\n", + " END DO\n", + " END DO\n", + "\n", + " ! Set initial start height\n", + " P(scale + 1,scale + 1) = initial_P\n", + "\n", + " ! For each step\n", + " DO step = 1, num_steps\n", + " ! If step is less than stopping step then add forcing function\n", + " IF(step <= stop_step) THEN\n", + " P(scale + 1 ,scale + 1) = initial_P * SIN(omega * step)\n", + " ENDIF\n", + "\n", + " ! For each cell in matrix P\n", + " DO j=1,size_x\n", + " DO i=1,size_x\n", + " IF (i == 0) THEN\n", + " CYCLE\n", + " END IF\n", + " ! Calculate veolocity of wave at the points\n", + " V(i,j,1) = MERGE(V(i,j,1) + P(i,j) - P(i - 1,j), P(i,j), i > 1)\n", + " V(i,j,2) = MERGE(V(i,j,2) + P(i,j) - P(i,j + 1), P(i,j), j < size_x - 1)\n", + " V(i,j,3) = MERGE(V(i,j,3) + P(i,j) - P(i + 1,j), P(i,j), i < size_x - 1)\n", + " V(i,j,4) = MERGE(V(i,j,4) + P(i,j) - P(i,j - 1), P(i,j), j > 1)\n", + " END DO\n", + " END DO\n", + "\n", + " DO j=1,size_x\n", + " DO i=1,size_x\n", + " ! Get new pressure from P = P_0 - damping*V\n", + " P(i,j) = P(i,j) - 0.5 * damping * (V(i,j,1) + V(i,j,2) + V(i,j,3) + V(i,j,4))\n", + " END DO\n", + " END DO\n", + " END DO\n", + "\n", + "END SUBROUTINE wave_propogation\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### setup.py\n", + "\n", + "```python\n", + "import numpy\n", + "from numpy.distutils.core import Extension\n", + "from numpy.distutils.core import setup\n", + "\n", + "\n", + "fortranExt = Extension(name=\"wave_propogation\", sources=[\"wave_propogation.f90\"])\n", + "\n", + "\n", + "setup(\n", + " name=\"wave_propogation\",\n", + " description=\"Fortran library to compute wave propogation in a box\",\n", + " ext_modules=[fortranExt],\n", + ")\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!python setup.py build_ext --inplace" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import wave_propogation as wp" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(wp.wave_propogation_fortran.__doc__)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "num_steps = 50\n", + "scale = 50\n", + "damping= 0.25\n", + "initial_p = 1\n", + "stop_step = 25" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%time final_p = wp.wave_propogation_fortran(num_steps, scale, damping, initial_p, stop_step)\n", + "plt.figure(figsize=(12,6))\n", + "plt.imshow(final_p)\n", + "plt.show()" + ] + }, + { + "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": "3.8.4" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/02_Week2/Workshop_CodeOptimization/README.md b/02_Week2/Workshop_CodeOptimization/README.md new file mode 100644 index 0000000..1b6a484 --- /dev/null +++ b/02_Week2/Workshop_CodeOptimization/README.md @@ -0,0 +1,28 @@ +# Table of Contents: + +## A_Profiling.ipynb + +Notebook showing timing and code profiling. + +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/uofscphysics/STEM_Python_Course/blob/Summer2020/02_Week2/Workshop_CodeOptimization/A_Profiling.ipynb) + +_____ +## B_Cython_and_numba.ipynb + +Intro to using cythong in jupyter notebooks. + +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/uofscphysics/STEM_Python_Course/blob/Summer2020/02_Week2/Workshop_CodeOptimization/B_Cython_and_numba.ipynb) + +_____ +## C_Advanced_Cython.ipynb + +More advanced function optimized with cython. + +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/uofscphysics/STEM_Python_Course/blob/Summer2020/02_Week2/Workshop_CodeOptimization/C_Advanced_Cython.ipynb) + +_____ +## D_Using_Fortran.ipynb + +Showing how to use fortran code easily in python + +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/uofscphysics/STEM_Python_Course/blob/Summer2020/02_Week2/Workshop_CodeOptimization/D_Using_Fortran.ipynb) diff --git a/02_Week2/Workshop_CodeOptimization/setup.py b/02_Week2/Workshop_CodeOptimization/setup.py new file mode 100644 index 0000000..e085a79 --- /dev/null +++ b/02_Week2/Workshop_CodeOptimization/setup.py @@ -0,0 +1,13 @@ +import numpy +from numpy.distutils.core import Extension +from numpy.distutils.core import setup + + +fortranExt = Extension(name="wave_propogation", sources=["wave_propogation.f90"]) + + +setup( + name="wave_propogation", + description="Fortran library to compute wave propogation in a box", + ext_modules=[fortranExt], +) diff --git a/02_Week2/Workshop_CodeOptimization/wave_propogation.f90 b/02_Week2/Workshop_CodeOptimization/wave_propogation.f90 new file mode 100644 index 0000000..5955789 --- /dev/null +++ b/02_Week2/Workshop_CodeOptimization/wave_propogation.f90 @@ -0,0 +1,68 @@ + +SUBROUTINE wave_propogation_fortran(num_steps, scale, damping, initial_P, stop_step, P) + ! INPUTS + ! num_steps -> int # Number of itereations to make + ! scale -> int # Size of box for wave to propogate in + ! damping -> float # Factor for damping wave + ! initial_P -> float # Initial height at center + ! stop_step -> int # Number of steps to stop wave + ! OUTPUTS: + ! P -> float(x,y) # Array of output heights + IMPLICIT NONE + INTEGER size_x,i,j,k,step + REAL PI, omega + INTEGER, INTENT(in) :: num_steps, scale, stop_step !input + REAL, INTENT(in) :: damping, initial_P !input + REAL, INTENT(out) :: P(2 * scale + 1,2 * scale + 1) ! output + REAL, DIMENSION(2 * scale + 1,2 * scale + 1,4) :: V + + size_x = 2 * scale + 1 + + PI = 3.14159 + omega = 3.0 / (2.0 * PI) + +! Setup initial matrix all 0's +! P is pressure matrix +! V is velocity tensor (matrix of x/y and 4 vector of velocity [up,down,left,right]) + DO k=1,4 + DO j=1,size_x + DO i=1,size_x + P(i,j) = 0.0 + V(i,j,k) = 0.0 + END DO + END DO + END DO + + ! Set initial start height + P(scale + 1,scale + 1) = initial_P + + ! For each step + DO step = 1, num_steps + ! If step is less than stopping step then add forcing function + IF(step <= stop_step) THEN + P(scale + 1 ,scale + 1) = initial_P * SIN(omega * step) + ENDIF + + ! For each cell in matrix P + DO j=1,size_x + DO i=1,size_x + IF (i == 0) THEN + CYCLE + END IF + ! Calculate veolocity of wave at the points + V(i,j,1) = MERGE(V(i,j,1) + P(i,j) - P(i - 1,j), P(i,j), i > 1) + V(i,j,2) = MERGE(V(i,j,2) + P(i,j) - P(i,j + 1), P(i,j), j < size_x - 1) + V(i,j,3) = MERGE(V(i,j,3) + P(i,j) - P(i + 1,j), P(i,j), i < size_x - 1) + V(i,j,4) = MERGE(V(i,j,4) + P(i,j) - P(i,j - 1), P(i,j), j > 1) + END DO + END DO + + DO j=1,size_x + DO i=1,size_x + ! Get new pressure from P = P_0 - damping*V + P(i,j) = P(i,j) - 0.5 * damping * (V(i,j,1) + V(i,j,2) + V(i,j,3) + V(i,j,4)) + END DO + END DO + END DO + +END SUBROUTINE wave_propogation_fortran \ No newline at end of file diff --git a/02_Week2/Workshop_ModelSelection/Model Selection.pdf b/02_Week2/Workshop_ModelSelection/Model Selection.pdf new file mode 100644 index 0000000..5d28edb Binary files /dev/null and b/02_Week2/Workshop_ModelSelection/Model Selection.pdf differ diff --git a/02_Week2/Workshop_ModelSelection/README.md b/02_Week2/Workshop_ModelSelection/README.md new file mode 100644 index 0000000..54ceb30 --- /dev/null +++ b/02_Week2/Workshop_ModelSelection/README.md @@ -0,0 +1,15 @@ +# Table of Contents: + +## model_selection.ipynb + +Un-filled notebook showing use of AIC and BIC for model comparison. + +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/uofscphysics/STEM_Python_Course/blob/Summer2020/02_Week2/Workshop_ModelSelection/model_selection.ipynb) + +----- +## model_selection_complete.ipynb + +Completed version of the model selection notebook. + +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/uofscphysics/STEM_Python_Course/blob/Summer2020/02_Week2/Workshop_ModelSelection/model_selection_complete.ipynb) + diff --git a/02_Week2/Workshop_ModelSelection/model_selection.ipynb b/02_Week2/Workshop_ModelSelection/model_selection.ipynb new file mode 100644 index 0000000..087a715 --- /dev/null +++ b/02_Week2/Workshop_ModelSelection/model_selection.ipynb @@ -0,0 +1,340 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Model comparison / model selection\n", + "## using the Akaike Information Criterion and Bayesian Information Criterion" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# import all packages we will need in this notebook\n", + "import pandas\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import scipy\n", + "import scipy.optimize" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# read in some data we created for this example (.dat is a generic filename, it's just a text file)\n", + "data_filename='https://raw.githubusercontent.com/uofscphysics/STEM_Python_Course/Summer2020/02_Week2/Data/1D_intro_examples.dat'\n", + "example_data_1D = pandas.read_csv(data_filename,sep=',',header=0)#this file is separated by spaces and its first line contains the names of the columns (header) \n", + "print(example_data_1D.head())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Let's plot the data, with error bars, that we read from file (See Day 2)\n", + "plt.errorbar(example_data_1D['x'], #x,y,and error are the column names\n", + " example_data_1D['y'], \n", + " yerr=example_data_1D['error'],#yerr denotes an error in the y-direction for plotting\n", + " fmt='.') #fmt is \"format\", saying that I want data marked by \"points\"\n", + "plt.xlabel('Days since I left the honey jar out') #set the x-axis label \n", + "plt.ylabel('Number of ants') #set the y-axis label\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#The data were generated with a simple quadratic equation:\n", + "#ax^2+bx+c. \n", + "\n", + "# FILL IN : define three models, a quadratic, cubic and exponential\n", + "\n", + "# def modelA_quadratic\n", + "# \"\"\"A quadratic in x (this happens to be the true model)\"\"\"\n", + "\n", + "#def modelB_cubic\n", + "# \"\"\"A third-order polynomial model.\"\"\"\n", + " \n", + "#def modelC_exponential\n", + "# \"\"\"An exponential model\"\"\"\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# For Model A define a function that returns \n", + "# the negative log likelihood: -ln( p(data|theta,Model) )\n", + "\n", + "#def neg_ln_likelihoodA_quadratic(theta, args):\n", + "# \"\"\" This function accepts an argument \"theta\", which is \n", + "# a list of parameter values [alpha,beta,gamma] for model A.\n", + "# It then calculates a log-likelihood by computing the \n", + "# chi-squared statistic (i.e., assuming gaussian uncertainties), \n", + "# which compares the observations and errors (provided in args) \n", + "# to the model A.\n", + "# \"\"\" \n", + "\n", + "# FILL IN THE FUNCTION DEFINITION HERE" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Repeat for Model B\n", + " \n", + "# def neg_ln_likelihoodB_cubic(theta, args):\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Repeat for Model C\n", + "# def neg_ln_likelihoodC_exponential(theta, args):\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's have a look at what these models look like, using some parameters that are in the right ball park" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Make a function that plots the data (as above) and \n", + "# all three models (given some single set of parameters for each)\n", + "\n", + "\n", + "#def plot_three_models(thetaA, thetaB, thetaC):\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Show us a plot : \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Find the best parameters for each model.\n", + "Use the scipy minimizer to find the minimum -ln(likelihood).\n", + "In this case, this is equivalent to \"maximum likelihood estimation\"\n", + "or \"chi2 minimization\". " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# use scipy.optimize.minimize to find the minimum -ln(likelihood)\n", + "# for each of our three models\n", + "\n", + "# FILL IN THE ARGUMENTS TO EACH CALL OF MINIMIZE()\n", + "\n", + "# neglnlike_min_resultA = scipy.optimize.minimize( )\n", + "\n", + "# neglnlike_min_resultB = scipy.optimize.minimize( )\n", + "\n", + "# neglnlike_min_resultC = scipy.optimize.minimize( )\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Print the results (the minimum -ln(likelihood))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Use the plotting function defined above to show \n", + "# a plot of the three models with their 'best-fit' parameters\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Model comparison : the AIC and BIC " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Akaike information criterion is defined as:\n", + "\n", + "### AIC = 2 k - 2 ln(L)\n", + "\n", + "it balances a model's ability to fit the data (measured by the maximum likelihood value L) against the number of parameters 'k' that the model requires. A smaller value of the AIC indicates a better model (i.e., one that matches the data well, without being unnecessarily complex).\n", + "\n", + "The Bayesian information criterion is very similar. It replaces the 2 in the first term with ln(n), where n is the number of data points. This puts more weight on the first term (which penalizes complexity) when the size of the sample is large. As with the AIC, smaller is better.\n", + "\n", + "### BIC = k ln( n ) - 2 ln( L )\n", + "\n", + "These two metrics are the most commonly used, but many others exist, with subtle differences in their properties. One should take care to apply the appropriate criteria based on the data, the models, and the problem." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Let us define a function that computes the AIC and BIC for each of our three models\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Compute each max likelihood value and report. \n", + "\n", + "# Remember that we have found the minimum\n", + "# of the negative log likelihood for each\n", + "# function. This is reported as the 'fun'\n", + "# entry in our set of results from the \n", + "# scipy.optimize.minimize() function calls.\n", + "\n", + "# The opposite of that minimum is our maximum log likelihood.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compute the AIC and BIC for each model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Collect them into 3-element lists for convenience\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Make a nice pandas DataFrame table \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Compute the AIC and BIC Weights : e^(-0.5*deltaAIC)\n", + "\n", + "\n", + "# Compute the AIC / BIC odds ratios : weight_max / weight_i\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# print the updated pandas table\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### next topic : using the Bayesian evidence (Bayes factors) to compare models considering the entire parameter space" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Reading list\n", + "\n", + "A good broad book on Bayesian data analysis\n", + "* Sivia, D. and Skilling, J. \"Data Analysis: A Bayesian Tutorial\"\n", + "https://books.google.com/books/about/Data_Analysis.html?id=lYMSDAAAQBAJ\n", + "\n", + "Some short \"tutorial\" papers about AIC / BIC: \n", + "\n", + "* Wagenmakers and Farrell 2004\n", + "https://link.springer.com/content/pdf/10.3758/BF03206482.pdf\n", + "\n", + "* Symonds and Moussalli 2010\n", + "http://byrneslab.net/classes/biol607/readings/Symonds_and_Moussalli_2010_behav_ecol.pdf" + ] + } + ], + "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": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/02_Week2/Workshop_ModelSelection/model_selection_complete.ipynb b/02_Week2/Workshop_ModelSelection/model_selection_complete.ipynb new file mode 100644 index 0000000..f5891a5 --- /dev/null +++ b/02_Week2/Workshop_ModelSelection/model_selection_complete.ipynb @@ -0,0 +1,707 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Model comparison / model selection\n", + "## using the Akaike Information Criterion and Bayesian Information Criterion" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# import all packages we will need in this notebook\n", + "import pandas\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import scipy\n", + "import scipy.optimize" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Unnamed: 0 x y error\n", + "0 0 0.000000 -1.543621 5.050000\n", + "1 1 0.714286 13.432917 6.709184\n", + "2 2 1.428571 45.895726 11.343878\n", + "3 3 2.142857 158.160689 18.954082\n", + "4 4 2.857143 212.872646 29.539796\n" + ] + } + ], + "source": [ + "# read in some data we created for this example (.dat is a generic filename, it's just a text file)\n", + "data_filename='https://raw.githubusercontent.com/uofscphysics/STEM_Python_Course/Summer2020/02_Week2/Data/1D_intro_examples.dat'\n", + "example_data_1D = pandas.read_csv(data_filename,sep=',',header=0)#this file is separated by spaces and its first line contains the names of the columns (header) \n", + "print(example_data_1D.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#Let's plot the data, with error bars, that we read from file (See Day 2)\n", + "plt.errorbar(example_data_1D['x'], #x,y,and error are the column names\n", + " example_data_1D['y'], \n", + " yerr=example_data_1D['error'],#yerr denotes an error in the y-direction for plotting\n", + " fmt='.') #fmt is \"format\", saying that I want data marked by \"points\"\n", + "plt.xlabel('Days since I left the honey jar out') #set the x-axis label \n", + "plt.ylabel('Number of ants') #set the y-axis label\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "#The data were generated with a simple quadratic equation:\n", + "#ax^2+bx+c. \n", + "def modelA_quadratic(x, alpha, beta, gamma): \n", + " \"\"\"A quadratic in x (this happens to be the true model)\"\"\"\n", + " return(alpha*x**2 + beta*x + gamma)\n", + "\n", + "def modelB_cubic(x, p0, p1, p2, p3):\n", + " \"\"\"A third-order polynomial model.\"\"\"\n", + " return( p0 + p1*x + p2*x**2 + p3*x**3)\n", + " \n", + "def modelC_exponential(x, floor, scale, expfactor):\n", + " \"\"\"\"\"\"\n", + " return( floor + scale * np.exp( expfactor * x) )\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def neg_ln_likelihoodA_quadratic(theta, args):\n", + " \"\"\" This function accepts an argument \"theta\", which is \n", + " a list of parameter values [alpha,beta,gamma] for model A.\n", + " It then calculates a log-likelihood by computing the \n", + " chi-squared statistic (i.e., assuming gaussian uncertainties), \n", + " which compares the observations and errors (provided in args) \n", + " to the model A.\n", + " \"\"\" \n", + " x, y, yerr = args \n", + " alpha, beta, gamma = theta \n", + " \n", + " model_at_observed_x = modelA_quadratic(x, alpha, beta, gamma) \n", + " inverse_uncertainty2 = 1./yerr**2 \n", + " chisquared = np.sum((y - model_at_observed_x)**2 \n", + " * inverse_uncertainty2 )\n", + " # When all uncertainties are gaussian and uncorrelated, the \n", + " # natural log of the likelihood is ln(likelihood) = -0.5 * chi2 \n", + " # We want -ln(likelihood) so we return 0.5*chi2.\n", + " return (0.5 * chisquared)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def neg_ln_likelihoodB_cubic(theta, args):\n", + " \"\"\" This function accepts an argument \"theta\", which is \n", + " a list of parameter values [p0,p1,p2,p3] for model B \n", + " (the cubic model).\n", + " It then calculates a log-likelihood by computing the \n", + " chi-squared statistic (i.e., assuming gaussian uncertainties), \n", + " which compares the observations and errors (provided in args) \n", + " to the model B.\n", + " \"\"\" \n", + " x, y, yerr = args \n", + "\n", + " # Introducing a short-hand here, to pass a list \n", + " # as distinct arguments to a function, pass *name_of_list\n", + " model_at_observed_x = modelB_cubic(x, *theta) \n", + " \n", + " # that is equivalent to doing: \n", + " # p0, p1, p2, p3 = theta \n", + " # model_at_observed_x = modelB_quartic(x, p0, p1, p2, p3)\n", + "\n", + " inverse_uncertainty2 = 1./yerr**2 \n", + " chisquared = np.sum((y - model_at_observed_x)**2 \n", + " * inverse_uncertainty2 )\n", + " return (0.5 * chisquared)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def neg_ln_likelihoodC_exponential(theta, args):\n", + " \"\"\" This function accepts an argument \"theta\", which is \n", + " a list of parameter values [floor, scale, k] for model C \n", + " (the exponential model).\n", + " It then calculates a log-likelihood by computing the \n", + " chi-squared statistic (i.e., assuming gaussian uncertainties), \n", + " which compares the observations and errors (provided in args) \n", + " to the model C.\n", + " \"\"\" \n", + " x, y, yerr = args \n", + " \n", + " model_at_observed_x = modelC_exponential(x, *theta) \n", + " inverse_uncertainty2 = 1./yerr**2 \n", + " chisquared = np.sum((y - model_at_observed_x)**2 \n", + " * inverse_uncertainty2 )\n", + " return (0.5 * chisquared)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's have a look at what these models look like, using some parameters that are in the right ball park" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_three_models(thetaA, thetaB, thetaC):\n", + " \"\"\" Plot all three models over the data, using \n", + " three parameter sets thetaA, thetaB and thetaC\"\"\"\n", + " # first plot the data\n", + " plt.errorbar(example_data_1D['x'], example_data_1D['y'], \n", + " yerr=example_data_1D['error'],\n", + " fmt='.') \n", + " plt.xlabel('Days since I left the honey jar out')\n", + " plt.ylabel('Number of ants')\n", + "\n", + " # now plot the models\n", + " x_for_plotting = np.arange(-0.02, 10.1, 0.1)\n", + " plt.plot(x_for_plotting, \n", + " modelA_quadratic(x_for_plotting, *thetaA),\n", + " color='b', label='A (quadratic)')\n", + " \n", + " plt.plot(x_for_plotting, \n", + " modelB_cubic(x_for_plotting, *thetaB),\n", + " color='g', label='B (cubic)')\n", + "\n", + " plt.plot(x_for_plotting, \n", + " modelC_exponential(x_for_plotting, *thetaC),\n", + " color='m', label='C (exponential)')\n", + "\n", + " plt.legend()\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_three_models([50, -5, 1], [20, 10, 0.7, 4], [-100,100,0.3])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Find the best parameters for each model.\n", + "Use the scipy minimizer to find the minimum -ln(likelihood).\n", + "In this case, this is equivalent to \"maximum likelihood estimation\"\n", + "or \"chi2 minimization\". \n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# use scipy.optimize.minimize to find the minimum -ln(likelihood)\n", + "# for each of our three models\n", + "\n", + "neglnlike_min_resultA = scipy.optimize.minimize(\n", + " neg_ln_likelihoodA_quadratic, \n", + " x0=[20,-1,-1],\n", + " bounds=None, #[(-100,100),(-100,100),(0,100)], \n", + " args=[example_data_1D['x'],\n", + " example_data_1D['y'],\n", + " example_data_1D['error']])\n", + "\n", + "neglnlike_min_resultB = scipy.optimize.minimize(\n", + " neg_ln_likelihoodB_cubic, \n", + " x0=[20, 10, 0.7, 0.3],\n", + " bounds=None,\n", + " args=[example_data_1D['x'],\n", + " example_data_1D['y'],\n", + " example_data_1D['error']])\n", + "\n", + "neglnlike_min_resultC = scipy.optimize.minimize(\n", + " neg_ln_likelihoodC_exponential, \n", + " x0=[-100, 200, 1],\n", + " bounds=None,\n", + " args=[example_data_1D['x'],\n", + " example_data_1D['y'],\n", + " example_data_1D['error']])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[29.15908744 -0.87102224 -1.77518517]\n", + "[-1.12704349 -5.41696833 31.91586375 -0.296965 ]\n", + "[-190.06197701 176.01227151 0.30124996]\n" + ] + } + ], + "source": [ + "# Print out the results: the parameters that give the\n", + "# minimum -ln(likelihood) for each model\n", + "# (note that the scipy minimizer uses 'x' as the key for \n", + "# the best-fit parameters and 'fun' for the best-fit value\n", + "# of the function being minimized)\n", + "print(neglnlike_min_resultA['x'])\n", + "print(neglnlike_min_resultB['x'])\n", + "print(neglnlike_min_resultC['x'])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEGCAYAAACUzrmNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOzdd1yV1R/A8c9ho6IMwa2IknuTe+e2HKm50jJLG5b+sjSttLShZsOyLCtTy9TSNDNzVKRZmuLek6kIAoLsce/5/XGvCMpyXC7I9/163Rfc85zneb6A8uU8ZymtNUIIIURebKwdgBBCiKJPkoUQQoh8SbIQQgiRL0kWQggh8iXJQgghRL7srB2AJZQvX157e3tbOwwhhChW9u3bF6W19szp2D2ZLLy9vQkICLB2GEIIUawopYJzOyaPoYQQQuRLkoUQQoh8SbIQQgiRr3uyzyIn6enphIWFkZKSYu1QxG1wcnKiatWq2NvbWzsUIUokiyULpZQTsANwNN9njdZ6plJqKdAJiDNXfVxrfVAppYAFQB8gyVy+33ytx4DXzPXf0lovu9V4wsLCcHFxwdvbG9OtRHGhtSY6OpqwsDBq1qxp7XCEKJEs2bJIBbpqrROUUvbATqXUb+ZjL2ut19xQvzfga361AhYBrZRS7sBMwA/QwD6l1Aat9ZVbCSYlJUUSRTGllMLDw4PLly9bOxQhSiyL9VlokwTzW3vzK68lbvsDy83n7QZclVKVgJ7ANq11jDlBbAN63U5MkiiKL/nZCWFdFu3gVkrZKqUOApGYfuH/Zz70tlLqsFLqQ6WUo7msChCa5fQwc1lu5Tfea5xSKkApFSB/gQohSqKIFRFcWn4JS2w9YdFkobU2aK2bAlWBlkqphsA0oC5wP+AOTDVXz+lPR51H+Y33Wqy19tNa+3l65jgBsUhYt24dSilOnjyZa53k5GQ6deqEwWCwaCydO3e+5cmLS5cu5eLFi5nvn3zySY4fP55r/Y0bNzJz5szbjlEIUTCGZAPnXjrHpaWXLNISL5Shs1rrWOAvoJfWOtz8qCkV+AZoaa4WBlTLclpV4GIe5cXSypUrad++PatWrcq1zpIlS3j44YextbUtxMiuyytJ3ZgsvvrqK+rXr59r/b59+7JhwwaSkpLuaoxCiOzCvw4n7VIaNWbUsMj1LZYslFKeSilX8+fOQDfgpLkfAvPopwHAUfMpG4DRyqQ1EKe1Dge2AD2UUm5KKTegh7ms2ElISOCff/7h66+/zjNZrFixgv79+wOmkUATJkygfv369O3blz59+rBmjWlsgLe3N1FRUQAEBATQuXNnAPbs2UPbtm1p1qwZbdu25dSpU4CpxTJs2DAaN27M0KFDSU5OzrxnmTJlmDFjBq1atWLXrl3MmjWL+++/n4YNGzJu3Di01qxZs4aAgABGjhxJ06ZNSU5OztY62bx5M82bN6dJkyY88MADgKmvoXPnzmzcuPHufjOFEJmMqUZC54ZSrn05XDu5WuQelhwNVQlYppSyxZSUftBab1RK/amU8sT0eOkg8LS5/iZMw2bPYho6OwZAax2jlJoN7DXXm6W1jrmTwCZNgoMH7+QKN2vaFD76KO8669evp1evXtx33324u7uzf/9+mjdvnq1OWloa58+f59pCiOvWrePUqVMcOXKEiIgI6tevzxNPPJHnferWrcuOHTuws7Pj999/Z/r06axdu5ZFixZRqlQpDh8+zOHDh7PdOzExkYYNGzJr1iwA6tevz4wZMwAYNWoUGzduZPDgwSxcuJD58+fj5+eX7Z6XL1/mqaeeYseOHdSsWZOYmOs/Ij8/P/7++28eeeSRvL9BQojbcmnZJVLDUqnzdR2LDQaxWLLQWh8GmuVQ3jWX+hp4LpdjS4AldzVAK1i5ciWTJk0CYNiwYaxcufKmZBEVFYWr6/W/DHbs2MHw4cOxtbWlcuXKdO2a47cvm7i4OB577DHOnDmDUor09PTMa73wwgsANG7cmMaNG2eeY2try6BBgzLf+/v7M2/ePJKSkoiJiaFBgwY89NBDud5z9+7ddOzYMXMehLu7e+YxLy+vbI+uhBB3jzHdSMi7Ibi0dMGtu5vF7lNiZnBnlV8LwBKio6P5888/OXr0KEopDAYDSinmzZuX7S8BZ2fnm2aZ5/aXgp2dHUajESDbOa+//jpdunRh3bp1BAUFZT6eyutaTk5OmX0kKSkpPPvsswQEBFCtWjXeeOONfGe+a61zvXZKSgrOzs55ni+EuD0RKyJICUqh9ie1LTrEXNaGKiRr1qxh9OjRBAcHExQURGhoKDVr1mTnzp3Z6rm5uWEwGDJ/OXfs2JFVq1ZhMBgIDw/H398/s663tzf79u0DYO3atZnlcXFxVKliGl28dOnSzPKOHTuyYsUKAI4ePcrhw4dzjPXavcuXL09CQkJmHwmAi4sL8fHxN53Tpk0btm/fTmBgIEC2x1CnT5+mYcOG+XyHhBC3yphhJPitYMo0K4NHXw+L3kuSRSFZuXIlAwcOzFY2aNAgvv/++5vq9ujRIzOJDBw4EF9fXxo1asQzzzxDp06dMuvNnDmTiRMn0qFDh2wjp6ZMmcK0adNo165dtpFNzzzzDAkJCTRu3Jh58+bRsmVLcuLq6spTTz1Fo0aNGDBgAPfff3/msccff5ynn346s4P7Gk9PTxYvXszDDz9MkyZNGDp0aOYxf39/+vbtW9BvlRCigCK+jSDlXAreMy2/OoWyxOQNa/Pz89M3zh84ceIE9erVs1JEt+bAgQN88MEHfPvttzcde/zxx3nwwQcZPHiwFSK7dREREYwYMYI//vjjjq9VnH6GQliaMd3Injp7sHOzo0VAi7uSLJRS+7TWfjkdk5ZFEdSsWTO6dOli8Ul5hSEkJIT333/f2mEIcc+JWB5BSmAK3m8Wzpp3JbKDuzjIbXhs1j6I4iDrIywhxN1hTDP1Vbjc72LxvoprJFkIIUQxc2nZJVKCUvD91LfQFtmUx1BCCFGMGFPNrYqWLrj3ds//hLtEWhZCCFGMXFx8kdSQVOp8ZbnZ2jmRloUQQhQThkQDwW8HU65TOdy6WW62dk6kZSGEEMXEhYUXSI9Ix2etT6FvCCYti0Jka2tL06ZNadKkCc2bN+fff//Nsd6d7GexdOlSJkyYkOOxtm3b5nnuSy+9xJ9//nnL9xRCWF5GXAYhc0Nw7+1OuXblCv3+kiwKkbOzMwcPHuTQoUO8++67TJs2Lcd6ltrPIrfkdM3zzz/PnDlz7uo9hRB3R+gHoWRcyaDmWzWtcv8S+Rhq0uZJHLx0d9cob1qxKR/1KvgKhVevXsXNLednjitWrMi2DMi8efP49ttvsbGxoXfv3syZM4fOnTtnLhUeFRWFn58fQUFBAISGhtKrVy8CAwMZMWJE5k51ZcqUISEhIddr1qhRg+joaC5dukTFihVv8zshhLjb0iLTCPsgjPKDyuPS3OWm40O/2AXA6vFtLBZDiUwW1pKcnEzTpk1JSUkhPDw8x0c+N+5n8dtvv7F+/Xr+++8/SpUqlW2Bvtzs2bOHo0ePUqpUKe6//3769u2bbf+JvK7ZvHlz/vnnn2zLlQshrCv4rWAMyQZ83vaxWgwlMlncSgvgbrr2GApg165djB49OnPJ8mtu3M/i999/Z8yYMZQqVQrIvk9Ebrp3746Hh2lW58MPP8zOnTuzJYu8ril7TwhRtCSfT+bi5xep9EQlStUpZbU4pM/CStq0aUNUVBSXL1/OVn7jfha57ROR214WcPOeFTe+l70nhCg+AmcEomwV3jO9rRqHJAsrOXnyJAaDIbMFcM2N+1n06NGDJUuWkJSUBFzfJyLrXhZZ95sA2LZtGzExMSQnJ7N+/XratWuX7Xhu1wTZe0KIoiThUAKR30dSZWIVHKs4WjUWSRaF6FqfRdOmTRk6dCjLli3LccRT1v0sevXqRb9+/fDz86Np06bMnz8fMA1zXbRoEW3btiUqKirb+e3bt2fUqFE0bdqUQYMG3bRfdm7XTE9P5+zZszfVF0JYx/lp57ErZ0f1qdWtHYrpkYQlXoATsAc4BBwD3jSX1wT+A84AqwEHc7mj+f1Z83HvLNeaZi4/BfTM794tWrTQNzp+/PhNZUXV/v379aOPPlro9/3pp5/0a6+9Vuj3Laji9DMU4k5Fb4vW/vjr4PeC8637yOf/6kc+//eO7wkE6Fx+r1qyZZEKdNVaNwGaAr2UUq2BucCHWmtf4Aow1lx/LHBFa10b+NBcD6VUfWAY0ADoBXymlLq7ExCKGGvtZ5GRkcHkyZML9Z5CiJtpo+b8y+dxrOFIlQlVrB0OYMHHUOZElWB+a29+aaArcO0h+zJggPnz/ub3mI8/oEy9sP2BVVrrVK11IKYWRs77gd5Dnnjiibs+KS8/Q4YMyTYSSwhhHRHfRZBwMAGfd32wdSoafxtbtM9CKWWrlDoIRALbgHNArNY6w1wlDLiWNqsAoQDm43GAR9byHM7Jeq9xSqkApVTAjSOMhBCiuDAkGwh8NRAXPxe8hnpZO5xMFk0WWmuD1ropUBVTayCnDZSvbQKe01hOnUf5jfdarLX201r7eXp63m7IQghhVWEfhZEalkqt+bVQNre2WKDRaHpZQqGMhtJaxwJ/Aa0BV6XUtcmAVYFrM8DCgGoA5uPlgJis5TmcI4QQ94y0iDRC3g3Bo58Hrp1u7ZGw1jBxIjz+OFiiu9NiyUIp5amUcjV/7gx0A04A/sBgc7XHgJ/Nn28wv8d8/E9z7/wGYJhSylEpVRPwxTTKqti5dOkSw4YNo1atWtSvX58+ffpw+vTpm+rdyaqz1hQbG8tnn32W+f7ixYsMHjw4jzMgKCgoc17HkSNHePzxxy0ZohBFWuDrgRiTjdR6r9Ytn3vityosXAheXmCJ7k5LtiwqAf5KqcPAXmCb1nojMBV4USl1FlOfxNfm+l8DHubyF4FXALTWx4AfgOPAZuA5rXXx+i2KaYjywIED6dy5M+fOneP48eO88847RERE3FTXUqvOWtqNyaJy5co3TRjMS6NGjQgLCyMkJMQS4QlRpCUcSiD8q3CqTKhCqftubVmPczu8OLqhOqNHw7x5FgowtzG1xflVFOdZ/PHHH7pDhw4FqtumTRsdGBiY+X7evHnaz89PN2rUSM+YMUNrrfWePXt0o0aNdHJysk5ISND169fXR44c0f7+/rpDhw56wIABul69enr8+PHaYDBorbX+/vvvdcOGDXWDBg30lClTMq9funRpPX36dN24cWPdqlUrfenSJa211pGRkfrhhx/Wfn5+2s/PT+/cuVNrrfXMmTP1mDFjdKdOnXTNmjX1ggULtNZaDx06VDs5OekmTZrol156SQcGBuoGDRporbUODAzU7du3182aNdPNmjXT//zzT2b5tTpaa/3RRx/puXPn5vh9sfbPUAhLMRqN+kCXA/pv9791WkzaLZ27Zo3WShl1pUYxOiklXadmpN52HOQxz6JELiR4ZtIZEg4m5F/xFpRpWgbfj3xzPX706FFatGiR73VuXHV269atnDlzhj179qC1pl+/fuzYsYOOHTvSr18/XnvtNZKTk3n00Udp2LAhf/31F3v27OH48ePUqFGDXr168dNPP9G2bVumTp3Kvn37cHNzo0ePHqxfv54BAwaQmJhI69atefvtt5kyZQpffvklr732GhMnTuR///sf7du3JyQkhJ49e3LixAnAtFyJv78/8fHx1KlTh2eeeYY5c+Zw9OjRzMUSry2ZDqYFCrdt24aTkxNnzpxh+PDhBAQE3PT1+/n5MWfOHKZMmXIL330hirfoDdHE+sdS+5Pa2LvZF/i8P/+EESOgfIuLeA84ySOrfkbbn+fnYT9ja3N3n0yUyGRRlN246uzWrVvZunUrzZo1AyAhIYEzZ87QsWNHZsyYwf3334+TkxMff/xx5jktW7bEx8e0lPHw4cPZuXMn9vb2dO7cmWsjxUaOHMmOHTsYMGAADg4OPPjggwC0aNGCbdu2AabVaY8fP5553atXrxIfHw9A3759cXR0xNHRES8vrxwfp2WVnp7OhAkTOHjwILa2tjn21YCseitKHmOqkbOTz1KqXikqj69c4PMCAqB/f6jV8grJ7Q5yMd5I2InODG7ne9cTBZTQZJFXC8BSGjRoUKDn9zmtOjtt2jTGjx9/U92YmBgSEhJIT08nJSWF0qVLAzmvOmtqYebM3t4+8xxbW1syMkzTYIxGI7t27cpxFVpHx+uLmmU9JzcffvghFSpU4NChQxiNRpycnHKsJ6veipIm9MNQUs6l0HhLY2zsC9aNfOoU9O4N5cvDqJei+GKXEbDBFgdqleltkThlIcFC0rVrV1JTU/nyyy8zy/bu3cv27duz1btx1dmePXuyZMmSzB3uLly4QGRkJADjxo1j9uzZjBw5kqlTp2ZeY8+ePQQGBmI0Glm9ejXt27enVatWbN++naioKAwGAytXrqRTp055xtyjRw8WLlyY+f7a46XcuLi4ZLY8bhQXF0elSpWwsbHh22+/zXWkl6x6K0qS1AupBL8VjEd/D9x75L9XDUBoKHTvDjY2sGWL5nDSNxhJB4w42tvS2scj32vcDkkWhUQpxbp169i2bRu1atWiQYMGvPHGG1SufHOzM+uqsz169GDEiBG0adOGRo0aMXjwYOLj41m+fDl2dnaMGDGCV155hb1792buvNemTRteeeUVGjZsSM2aNRk4cCCVKlXi3XffpUuXLjRp0oTmzZvTv3//PGP++OOPCQgIoHHjxtSvX5/PP/88z/oeHh60a9eOhg0b8vLLL2c79uyzz7Js2TJat27N6dOnM1tBN/L396dv37553keIe8W5qefQGZraH9QuUP3Ll02JIi4ONm/WLDw3kVWn38Wp3HaqupZixZOtaVEj5+2a71huPd/F+VUUR0PdijtZddbf31/37dv3LkdUOFJSUnSrVq10enp6jseL089QiPzE7ozV/vjrc6+eK1D9uDitmzfX2tlZ6x07jHrSb5M0b6Bf3PyiHrLon2K96qy4TdZaddbaQkJCmDNnDnZ2JbIrTZQg2qA58/wZHKo4UGNajXzrJydDv35w+DD8+KNmQ/IUPvrvIya2msj8HvNz3fnybpL/lUXUE088cVvnde7cmc6dO9/dYAqJr68vvr6FP/hAiMJ2YdEFEg4kUH91fWxL5z1yKS0NBg+GHTtgxQrNdoepzP93Ps/d/xwf9vywUBIFlLA+C53HiCBRtMnPTtwr0iLSCHwtELdubngOyXvRU4MBRo+GTZtg0SLNIc9pvPfvezzj9wyf9P6k0BIFlKBk4eTkRHR0tPzSKYa01kRHR+c63FaI4uTclHMYk4z4LvTN85e91vDMM7B6Ncydqwmu/Spz/5nL0y2eZmGfhYWaKKAEPYaqWrUqYWFhyF4XxZOTkxNVq1a1dhhC3JHYv2OJWB5B9WnVKVUn9/WftIbJk+HLL2H6q5rYFq/y7s53Gdd8HJ/2/RQbVfh/55eYZGFvb0/NmjWtHYYQooQyphs589wZHKs7UuPVvDu133gDPvwQnn9BY+g8nbk75zC+xXg+6/uZVRIFlKBkIYQQ1hT2YRiJRxJp+HPDPDu133sPZs2CMU9onB+cxtx/5lo9UYAkCyGEsLjkwGSC3gii/IDylO9XPtd6n34KU6bAkEc07kOnMu/f94pEooAS1MEthBDWoLXmzIQzKFtF7Y9zn6n9zTcwYQI81E9T5fGXeH/Xezzr9yyL+i6yeqIAaVkIIYRFXV57mZhNMdT6sBZO1XIe0bdqFYwdC917aGqMe5GP9nzEhPsn8HHvjwt91FNurJ+uhBDiHpUem87ZF85SplkZqkyokmOddevg0UehfQcjPs89z8IA08zsopQoQFoWQghhMeennictMo1GGxthY2fD0C92AbB6fBsAfv0Vhg4Fv/uN1J74DF8cWMxLbV5iXvd5RSpRgCQLIYSwiNjtsYQvDqfay9Vwae5y0/Ft22DQIGjU2IDvi+P45sgSprWfxttd377lRHEt+ViSxR5DKaWqKaX8lVInlFLHlFITzeVvKKUuKKUOml99spwzTSl1Vil1SinVM0t5L3PZWaXUK5aKWQgh7gZDsoFTT53CyccJ7ze8bzru729aGPC+uhn4vPQY3x1fwusdX7+tRFFYLNmyyAAma633K6VcgH1KqW3mYx9qrednrayUqg8MAxoAlYHflVL3mQ9/CnQHwoC9SqkNWuvjCCFEERQ8O5jkM8k0+b0JtqWyz6m4fMaFB18En9rp1HxpJGtO/chbXd7i1Y6vWinagrFYstBahwPh5s/jlVIngJx7eEz6A6u01qlAoFLqLNDSfOys1vo8gFJqlbmuJAshRJETvy+ekHkhVHyiIm4PZN+IKOpcGf5eWA+fWqlUf2koG879zPzu85ncdrKVoi24QhkNpZTyBpoB/5mLJiilDiulliilrn03qwChWU4LM5flVi6EEEWKMc3IyTEncajgQK33a2U7tns37PikHo7ucVSZPIDNQT/zSe9PikWigEJIFkqpMsBaYJLW+iqwCKgFNMXU8nj/WtUcTtd5lN94n3FKqQClVIAsFiiEsIbgd4JJPJLIfV/ch72rfWb5nj3Qsyc4usfi9HRP/grdwlcPfcWElhOsGO2tsWiyUErZY0oUK7TWPwForSO01gattRH4kuuPmsKAallOrwpczKM8G631Yq21n9baz9Mz7zXihRDibos/GE/I2yFUeLQC5R+8vqRHQAD06AFulWJxGP8AMfoA3z38HWObj7VitLfOkqOhFPA1cEJr/UGW8kpZqg0Ejpo/3wAMU0o5KqVqAr7AHmAv4KuUqqmUcsDUCb7BUnELIcStMqYZOTXmFHYedtRecH1Jj4AA6NYNylWKwmXCA8Tp47RxncWIRiOsGO3tseRoqHbAKOCIUuqguWw6MFwp1RTTo6QgYDyA1vqYUuoHTB3XGcBzWmsDgFJqArAFsAWWaK2PWTBuIYS4JcFvBZNwMIEG6xpg7256/HQtUZStEo7juG6cjTtPO7c5VHJsa+Vob48lR0PtJOf+hk15nPM28HYO5ZvyOk8IIazl6t6rBL8TTIXRFfAcYHoEfi1RuFQLxnZMNy4mhrNpxCY+31p8d3uUtaGEEOI2GZINnBx9EsdKjpmPn/77z5QoynifwjC6PbHpUfw++ne61Oxi5WjvjCz3IYQQtynwtUCSTibReGtj7F3t2bULevUCF99DpA7pgY2Cvx77iyYVm1g71DuWb8tCKTVPKVVWKWWvlPpDKRWllHq0MIITQoii6sqfVwj7MIzKz1bGvbs7//5rGh7rUm8X8YM64+TgwI7Hd2RLFPEp6VyITWZf8BUrRn57CvIYqod5fsSDmIax3ge8bNGohBDCyoZ+sStzldgbpV9J5+RjJ3G+z5la79Vixw5zomi6jZiHuuFVpjw7x+ykTvk6mefsC77CyUvxhF1JZuRXu4tdwihIsrg2s6QPsFJrHWPBeIQQokjTWnP6mdOkXUqj3nf1+GuXLb17Q7nWPxHV/UHu8/Bl55id1HCtke283eejMZqnE6dnGNl9PtoK0d++giSLX5RSJwE/4A+llCeQYtmwhBCiaIr8PpLLqy/j/YY3u6LL8uCD4NZ1CeEdhtCicgv8H/OnQpkKN53X2scDG/P4UHs7G1r7eBRy5HemIMliJtAG8NNapwNJQD+LRiWEEEVQcmAyp589Tdm2ZTncoBr9+oH7g/O54DeWbj7d2DZqG27Objme26KGG3UrulDVzZkVT7amRY2c6xVVBUkWu7TWV65NkNNaJwK/WTYsIYQoWozpRk6MOAFA0Mh6DByi8HhkOhcbvswjDR7hl+G/UNqhdJ7XcHGyp4qrc7FLFJDH0FmlVEVMq7s6K6WacX2CXVmgVCHEJoQQRUbQm0Fc3X2VmAn1GfyCA15jxhNe9UvGNR/HZ30/w9bGNv+LFGN5zbPoCTyOaeG+D7KUx2NatkMIIUqEK39dIeSdEOLaVWTQ52XxfPoRIjx/4tUOrzK7y+wiu7vd3ZRrstBaLwOWKaUGaa3XFmJMQghRZKRFpXHi0ROklHdm2N4KeLzQh8tl/fmo50dMbD3R2uEVmoLM4N6olBoBeGetr7WeZamghBCiSDBqTo4+SUpEOhPsquD0vweIK32Eb/t/y6ONS9bc5IIki5+BOGAfkGrZcIQQouhoss1AzG8xLCjlRsSkBzCWDmfDkA309u1t7dAKXUGSRVWtdS+LRyKEEEVIhTNGWq0z8JeTPdte7ImzSzq/jviD1lVbWzs0qyhIsvhXKdVIa33E4tEIIUQRkBSeRo01Gaxum8ZPHabj6eHI5pH+1POsZ+3QrKYgyaI98LhSKhDTYygFaK11Y4tGJoQQVpCUoHm/20GWD0og3c5AGfUyX/RoQD3P+6wdmlUVJFmUvIdzQohi49pif6vHt7nja129Cu83DiaxUizpdkZQtthiy9kIG3rWv+PLF2v5zuDWWgdrrYOBZExboV57CSHEPePyZXi6RRSdggOJcwxAKwNQPNdxsoR8WxZKqX7A+0BlIBKoAZwAGlg2NCGEKBwhITCkaxzTQ/cS5BWOy8Qo6ge7EZ9qYMGwZndteY670fqxloKsDTUbaA2c1lrXBB4A/rFoVEIIUUhOnICOnaIZG/srtspAwkcJvD/gfco6OxTbdZwsoSDJIl1rHQ3YKKVstNb+QNP8TlJKVVNK+SulTiiljimlJprL3ZVS25RSZ8wf3czlSin1sVLqrFLqsFKqeZZrPWauf0Yp9dhtfq1CCJHNnj3QtncQw50Xc190ZdLmp/HC8BdKxPIdt6ogySJWKVUG2AGsUEotADIKcF4GMFlrXQ9Ty+Q5pVR94BXgD621L/CH+T2YOtJ9za9xwCIwJRdMy6S3AloCM68lGCGEuF1btkCnoQfo4juTnifawER4eMLD1g6ryCpIsuiPaQ+L/wGbgXPAQ/mdpLUO11rvN38ej6mfo4r5esvM1ZYBA7LcZ7k22Q24KqUqYVrQcJvWOkZrfQXYBsgkQSHEbfv+e+gzcRN1OzzDs3+OwqGbA50+6GTtsIq0fDu4zftXABi5/kv+liilvIFmwH9ABa11uNjJmv0AACAASURBVPna4UopL3O1KkBoltPCzGW5ld94j3GYWiRUr179dsIUQpQAH3wAk7//ggo9ZzF7yReUqlWKlmtaomzk0VNeCtKyuCPmR1hrgUla66t5Vc2hTOdRnr1A68Vaaz+ttZ+np+ftBSuEKHbiU9K5EJvMvuAredYzGuGll41M/m0qTj0n8fG6DylnW46mG5tiV64gU85KNosmC6WUPaZEsUJr/ZO5OML8eAnzx0hzeRhQLcvpVYGLeZQLIUq4fcFXOHkpnrAryYz8aneuCSMtDUY8lsz7wUNRbd/ji78W43XBi/qr6lPqPtnLrSByTRZKqT/MH+fezoWVaTjB18AJrXXWzZM2ANdGND2GaVXba+WjzaOiWgNx5sdVW4AeSik3c8d2D3OZEKKE230+GqP5OUN6hpHd56NvqnP1KnQfEMlqx66oBmv5LuQ7qv9bDZ+5Pnj0ksl2BZVX26uSUqoT0E8ptYobHgdd67zOQztgFHBEKXXQXDYdmAP8oJQaC4QAQ8zHNgF9gLOYOtTHmO8To5SaDew115ultY4pyBcnhLi3tfbxwEaBUec80/riReg69ASnWvTF3j2ctU4/47LUhYpPVKTa5Gq5XFXkRGmd88odSqnBwFhMCwkG3HBYa627Wji22+bn56cDAm4MWQhxL+qzYAdXUzJumml9/Dh0GfsHkZ0H4ebixK8N1pH2SBpl25SlydYm2DhYvMu22FFK7dNa++V0LK9tVdcAa5RSr2utZ1ssOiGEuAMuTva4ONlnSxTbt0Pv174kufuz1HKty+au64joEYFTdScarm0oieI2FGTo7Gzz+lAdzUV/aa03WjYsIYS4Pd+vNDDq2ykYu31A5yq9Wdv3O850OYPO0DTa2Ah7D3trh1gs5ZtelVLvAhOB4+bXRHOZEEIUGVrDrDnxjPxlIMZWHzCuyfNsGbme4OHBpASm0HB9Q0rVkZFPt6sgg4v7Ak211kYApdQy4AAwzZKBCSFEQRkNMPK5IFbSD3XfcT7ovpCJbZ7lxKgTxG2Po96Kerh2dLV2mMVaQWeiuALXRiCVs1AsQghxy9KTbdm+LpqYVv1xLJXGz4/+Rs/a3Tn3yjkiV0RS862aVBhRwdphFnsFSRbvAgeUUv6Yhs92RFoVQogiICQENv98gOSOL+DlWJ3tT/9C3fJ1CfskjNC5oVR+ujLVp8vyP3dDQTq4Vyql/gLux5QspmqtL1k6MCGEyMt/ew10fWcqyZ3fxzW9FScmb8Ld2Z3IHyM5O/Es5QeUx3ehryw3fpcU6DGUeSb1BgvHIoQQBfLtj3E8/ssIjE03UU0PpVXV53B3dueK/xVOPHqCsm3KUu/7eihbSRR3i6yeJYQoNrSGl949zQfh/VA1zzGnw2dM7foMAFcDrnK031GcfZ1p9EsjbJ1trRztvUWShRCiWEhNhYcmbWGb61AcXe3ZMOJ3etQx7UGRdCqJI72PYOdhR5MtTbB3l7kUd1ue8yyUUjZKqaOFFYwQQuQkMlJTd+x7bPPqQwVHb068uDczUaSEpHCoxyFQ0GRbExyrOFo52ntTnsnCPLfikFJKhhMIIaxi78EkfF4eSZDvFFqXG8S56f9Q080bgNRLqRzqdoiMuAwab2lMKV+ZdGcpBXkMVQk4ppTaA1zbNQ+tdT+LRSWEEMDXa4IZ9+dAjDUP8ux977Bw2CuZo5vSY9I53OMwqRdTabK1CS7NXKwc7b2tIMniTYtHIYQQWWgNT737B1/HDcXGPZ2lPX7hsbZ9M49nxGVwuNdhkk4l0XhTY8q1lbnCllaQeRbblVI1AF+t9e9KqVKADDMQQlhEUpKmw5QP2e/xMmVt6/L3M+toXOW+zOMZ8Rkc7nOYhAMJNFjbALcH3PK4mrhbCrKQ4FPAGuALc1EVYL0lgxJClEynAxOpOmk4+z0n08B2AKEzdmdLFIZEA0f6HuHqf1epv6o+5fuVt2K0JUtBFnV/DtOud1cBtNZnAC9LBiWEKHlWbTtDgw9bc6XSj4yu/C5HXl9DWafr/RCGJANH+h0h7p846q+oj+cgTytGW/IUpM8iVWuddq1TSSllB+S8vZ4QQtyG5z7+hc/CR2FTxpavOm9mbJfu2Y4bEg0ceegIsX/FUnd5XbyGyt+rha0gyWK7Umo64KyU6g48C/xi2bCEECVBUrKB9q/P5IDL25TNaMGOCWtoUsM7Wx1DooEjDx4hdocpUVR8tKJ1gi3hCvIY6hXgMnAEGA9sAl7L7ySl1BKlVGTWSX1KqTeUUheUUgfNrz5Zjk1TSp1VSp1SSvXMUt7LXHZWKfXKrXxxQoiiZegXuxj6xS4Ajpy7TOUpvTng8jZNDE9y8a2dNyWKjIQMDvc9TOyOWOp9W08ShRUVZDSU0bzh0X+YHj+d0loX5DHUUmAhsPyG8g+11vOzFiil6gPDgAZAZeB3pdS1Xq1Pge5AGLBXKbVBa328APcXQhRRizbuYsL2RzC6Xubpyl+x6KmxN9VJj03nSJ8jXN1zlXor6lFhmOxJYU35JgulVF/gc+AcpiXKayqlxmutf8vrPK31DqWUdwHj6A+s0lqnAoFKqbNAS/Oxs1rr8+ZYVpnrSrIQohgyGjW7Dm/hhwvvYKersarnvwxp3/ymeunR6RzqcYjEI4k0+KEBng9LZ7a1FaTP4n2gi9b6LIBSqhbwK5BnssjDBKXUaCAAmKy1voJpOO7uLHXCzGUAoTeUt8rpokqpccA4gOrVZXUSIYqa8JirbDo0l6RKG6gQ+xD/vbKMGhVuniORGp7K4R6HSTqTRMP1DfHo42GFaMWNCtJnEXktUZidByJv836LgFpAUyAcUyICU4vlRjqP8psLtV6stfbTWvt5espfIUIUJRv2HML7nRYkVfiVChemETZ/fY6JIjkomQMdDpAcmEzjXxtLoihCcm1ZKKUeNn96TCm1CfgB0y/qIcDe27mZ1joiy/W/BDaa34YB1bJUrQpcNH+eW7kQoojTWvPc11+zKPh5lHKnScI31GlRGzvbm/9OTTyeyKHuhzAmG2nyexPKtZYlPIqSvFoWD5lfTkAE0AnojGlk1G3Nr1dKVcrydiBwbaTUBmCYUspRKVUT8AX2YEpKvkqpmkopB0yd4LJjnxDFwJXEBBq8PopFF56i7JUO7B17gDr31c6x7tX/rnKg4wEwQtPtTSVRFEG5tiy01mPu5MJKqZWYkkt5pVQYMBPorJRqiqmFEoRpKC5a62NKqR8wdVxnAM9prQ3m60wAtmBaj2qJ1vrYncQlhLC8bUcO0f/boSQ7n6Flwmz+mj8dZycb4rec5GpKBvuCr9Cihulvzujfojk2+BgOFR1osrUJzrWcrRy9yInKbxSs+S/95wFvsiSXorxEuZ+fnw4ICLB2GEKUOFprJn33OR+f/h8qxZ1p963g7ae6ALAv+ApDPv8XowYnextWPNmaKjtSOfXEKUo3Kk3j3xrjUMHByl9ByaaU2qe19svpWEFGQ60HvsY0a9t4NwMTQtw7ohNj6Tj/KY6zhtKXe/HbuGV0aH59WY7d56Mxmv82Tc8wsnnJOdrNSsC1qysN1zXErqzs8lyUFeSnk6K1/tjikQghiq2NB3cxZPVwUuwv0Dx2Ln/NfwmXMtm7RFv7eGCjwKjBzqio8FUcXsMrUfebutg4FmRgprCmgiSLBUqpmcBWIPVaodZ6v8WiEkIUCwajgXHL57Ek8HVUSjVerb6T2W+2QuUw6L1FDTcauLvg9W8qbfbY0GWUNz7v+KBschohL4qagiSLRsAooCvXH0Np83shRAkVHHOBTgtGEWzjT9mwoWx5/gtaN819FFPqhVRGf6LxCLOnzkJfqjxbJde6ougpSLIYCPhordMsHYwQonj4eucGnv5tDBmk0D7+a35bMIYyZXJvIcQfjOfIg0coF6X57Vk7ukqiKHYK8qDwEOBq6UCEEEVfUnoSvT55hif/6I8xxpv3fPfz98dP5Jkoon6O4kD7AyilWP+yPSGNZFfm4qggLYsKwEml1F6y91kU2aGzQoi779/A/fRdMoJY29NUOPcyf7w6mwZ1HXOtr7Um5N0QAl8NxOV+Fxr+3JCPNkhXZ3FVkGQx0+JRCCGKLIPRwEs/zWfBkdfRSV48Yv87337dFYc8pkQYkg2cevIUkd9H4jXCizpf1cHWWVoUxVlB9rPYXhiBCCGKnsArQXT7dDTnDX/jGDiI74Z+weC+eS/ulxKSwtGBR0k4kEDNd2pS/ZXqqJyGR4lipSD7WcRzfaVXB8AeSNRal7VkYEII69Fa88nf3zJ52/NkGDRNLy5jy/xReHnl/Us/dnssx4Ycw5hqpOGGhpR/sHy246vHt7Fk2MKCCtKycMn6Xik1gOsbEwkh7jFRSVHUnT2CaKdtqPD2vNlkOa+/XTPHuRPXaK0J+yiMcy+fw7m2M41+bkSpOqUKL2hhcbc8v15rvV72whbi3vTDoY08vvYpku1icN47iz1zp9OwQd59DRnxGZx68hSXf7hM+QHlqbu0LnblZOmOe01BHkM9nOWtDeBHLhsQCSGKp6upVxmx/H/8enEJXG6Md+RyWvR2yTdRJB5P5NjgYySdSsJnjg/VplST/ol7VEHS/0NZPs/AtLR4f4tEI4QodJtO/c7wlWO5qsMod3Q66ybO5PMT+8jvb8JL313i9PjT2Ja2pcnWJrg9cFvb3IhioiB9Fne0r4UQomhKSEvgiVVT+DFwEUTfx0OGf1ixtDUuLvD5idzPMyQbODvxLOFfhlOuYznqr6yPY+Xc51uIe0Ne26rOyOM8rbWebYF4hBCF4Pez/jyyYixXdBClDr/I8sffYlC/65sOxaek37RJEUDisUSODT1G0rEkqk+rjvcsb2zsZMXYkiCvlkViDmWlgbGAByDJQohiJj41nqd+mMLq859DdG26XN3Bj1+2xyPL1Il9wVc4eSkeo4aRX+1mxZOtaV7dlfCvwzn7wllsXWxpvKUx7j3crfeFiEKX17aq71/7XCnlAkwExgCrgPdzO08IUXiGfrELKNj8hd9Ob2XEyqeI1aE4HXyRxcNmM2rYzcNbb9yk6N9jkThOvkDU2ijcurlR99u6OFaUx04lTZ59Fkopd+BFYCSwDGiutb5SGIEJIe6OK8lXeGL1ZNYHfwNRdegct5PVi9vi5ZVz/WybFCkbXKZHEH1c4zPXh2ovVZP9J0qoXB82KqXeA/YC8UAjrfUbt5IolFJLlFKRSqmjWcrclVLblFJnzB/dzOVKKfWxUuqsUuqwUqp5lnMeM9c/o5R67La+SiFKqB+PrqPGvAasD1yOc8A0lrc9yJ/Lck8UYN6kqLwLbS848vJyB+qlONJ8V3OqT6kuiaIEy6tlMRnTKrOvAa9mGTutMHVw57fcx1JgIbA8S9krwB9a6znmiX2vAFOB3oCv+dUKWAS0MrdsZnJ9bsc+pdQGad0IkbeL8Rd59Pvn8b/0E1xqQo/kjXy3pDmenvmfG38gnjEfaNwv2lH5mcrUeq8WtqVlEcCSLteWhdbaRmvtrLV20VqXzfJyKci6UFrrHUDMDcX9MT3OwvxxQJby5dpkN+CqlKoE9AS2aa1jzAliG9Dr1r5EIe5d8SnpXIhNZl+w6e8nozaycNdifN6vj3/oJlx2z+HHHnvZsjT/RGFMNxI0O4j9LffjmKjZ+Lw99312nyQKAdzGch93qILWOhxAax2ulLrWGK4ChGapF2Yuy638JkqpccA4gOrVq9/lsIUoem4ctfTO4IrM/PMFjl7dCUGdGeK8mMUrfXEtwNZlCUcTOPnYSRL2J+A1zIuv/WJJzWNDI1HyFJUB0jn9q9R5lN9cqPVirbWf1trPsyBtbSGKuayjllLSDTzx41yOXjqO17/f8MfoP/lhUf6Jwphmak3sa76P1NBUGqxpQP2V9SVRiJsUdssiQilVydyqqAREmsvDgGpZ6lUFLprLO99Q/lchxClEkdfaxwOFESMaTQYpQRV4ofoJ5qz3wtk5//OvBlzl1NhTJB5OxHOoJ76f+OLgmceORqJEK+xksQF4DJhj/vhzlvIJSqlVmDq448wJZQvwzrVRU0APYFohxyxEkRMeH84b/pO56HAAp4TOVAwdyN4ZC2jUKP9zDYkGAmcGEvZRGA4VHGj4c0PK95N9J0TeLJYslFIrMbUKyiulwjCNapoD/KCUGguEAEPM1TcBfYCzQBKmyX9orWOUUrMxDeEFmKW1vrHTXIhi4VYm0OXGYDSwYNdnTN/2GqmGFGx2T8HHawT7VtfDpgAPlaM3RXP62dOkBqdSaVwlfOb6YO9qf9vxiJLDYslCaz08l0MP5FBXA8/lcp0lwJK7GJoQxdKu0F2MWvUs55IOwvnuPGjzKap7HM7lYvNNFKkXUjk76SyX11ymVL1SNP27Ka7tC9DzLYRZUengFuKed+Mw14K6nHiZYd+Ppe2Stpy7dJmKf69m66gt/LLUF+dy6Xmea8wwEvpRKHvq7iF6YzTes73xO+AniULcMtnOSohCkNPifFlXc81JhjGDT3YtYvrvM0gxJGC3dwrT27/O9F/L4GhemimvR1qx22M5M+EMiUcTce/jju8nvjj7FKDnW4gcSLIQohDcuDjf7vPReSaLv4L+YswPLxCUfATOdeOBtI/5alE9vL3zv1dKWArnp5wncmUkjjUcafBTA8oPKC872Ik7IslCiEKQdXE+ezsbWvt45FgvODaYp9e9xOaQNRBbnUqH17Lk5YH06pX/L3pDsoHQ+aGEzAlBGzQ1ZtSg+tTq2JaSGdjizkmyEKIQtKjhRt2KLlxNyWDBsGY3tSoS0hKY/ec8Ptj9HhkZCoc9bzLjgZd5ea4zDvlMfdBaE7k6kvOvnCc1OBXPwZ74zPPBuaY8chJ3jyQLIQqJi5M9Lk722RKFURtZfvA7/vfrNGINF+HoMAaXm8fHy6pRqVL+14z7N46zL54l/r94yjQtQ71l9XDtJJ3X4u6TZCGElewI3sFTa17kdMI+CGtJwws/8vUbbWnZMv9zk04lcX76eaJ+isKhsgN1vqlDxVEVUbbSLyEsQ5KFEIXk2silM9FneG79VLaFrYO4qrgfXM4nT41k+DAb8uuDTg1PJXhWMBe/vIitsy3eb3pTbXI1WRlWWJwkCyEKyeXEy0zbMotvDn+OMd0Rhz2zmd7lRab8WirftZzSr6QTOi+UsAVh6HRN5acr4z3DGwcvWctJFA5JFkJYWFJ6Eu/9/RHv/j2XVGMi6sCTjKr6Bu99V5EKFfI+NyM+g7AFYYS9H0ZGXAZew73wftObUrVv3jtbCEuSZCGEhWQYM/hq3ze8snkmccZwONmPLsY5fDq3HvXq5X2uIdHAhU8vEDIvhIzoDDwe8qDmWzUp07hM4QQvxA0kWQhxl2mtWXN8LZN+eY2LqacgpC31L/7Aomnt6dgx73MzEjK4+OlFQueHkh6Vjnsvd7zf9KZsy3w3pxTCoiRZCHGXaK3Zem4rz69/lTOJ+yCyPpVPruOT5/ozcKDKs/M6Iy6DC59eIPSDUDKiM3Dr6Yb3DG/KtS1XeF+AEHmQZCHEXfB38N88v/5VDsX+DbE1cD+0jLkjR/L4Alvs8vhflhaVxoUFFwj7JAxDnAH33u7UmFGDcq0lSYiiRZKFEHfgv7D/mLRhBrsvb4X4ipQ5sJCZDz7JhHcdcXLK/bzkoGTC3g8j/OtwjClGyj9cnhrTa+DS3KXwghfiFkiyEOI27Lu4j0kbZrIz4ldILI/z/nlM6/YcL24oRenSuZ8Xvz+e0PmhRP4QibJRVHi0AtVerkbpenmcJEQRIMlCiFsQcDGAFze8yd8RGyHZDaf97zC5/fNM+akMZXPpg9ZGTcxvMYR+EErsn7HYuthSdWJVqv6vKk5V82h+CFGESLIQ4gY5bX+6O2w3L22YzT+XN0GSO04H3mZS2wlM/aEsrrksxZSRkEHE8gjCFoSRfDoZhyoO+MzzofK4ytiVk/96oniRf7FC5EJrzY7gHUze8Bb7rvwOSR44HXybF9tNYMqPZSmXSx908rlkLnx6gfAl4RjiDLjc70K97+vhOdgTG3vZnFIUT5IshLjB1eR0Liaeo+7cDzmd+iMkVKDM4feY0vVpJq0pg0sOfdDaoInZHMOFzy4Q81sMylbhOdiTKs9XoWybsrLxkCj2rJIslFJBQDxgADK01n5KKXdgNeANBAGPaK2vKNP/sgVAHyAJeFxrvd8acYt7W4Yxg7l/ruX4JQfAA62H43G6B690HsmzbzpTKocVNlIvpXJp6SXCF4eTEpiCQ0UHarxeg8rjK+NY2bHQvwYhLMWaLYsuWuuoLO9fAf7QWs9RSr1ifj8V6A34ml+tgEXmj0LcFYlpiXy+Zwnv+H9ARur9uOpHUcoWG2XLjOc680K37Kv8aaPmyu9XCP8ynKj1UegMjWtnV3zm+lB+QHl51CTuSUXpMVR/oLP582XAX5iSRX9gudZaA7uVUq5KqUpa63CrRCnuGREJEczxX8gX+z4jWcVAaBt8Ex4lvYYdGo2jvQ3tfK9vf5oSkmJqRSwJJzU4FTt3O6q8UIXK4ypTqo4s7CfubdZKFhrYqpTSwBda68VAhWsJQGsdrpTyMtetAoRmOTfMXJYtWSilxgHjAKpXr27h8EVxdizyGK9v+pANQd9hIA1O9aOj7cu880w72rWDPgt2ZG5/2tSrLBHfR3Dpm0tc+eMKaHDr7katubVMrQhHaUWIksFayaKd1vqiOSFsU0qdzKNuTj2D+qYCU8JZDODn53fTcVGyGbWRX09vZsavH3EwfhukO2N39AmGV5/Im7Pq4Ot7ve6vz3cgbmccl2Zd4t8fj2KIN+Dk7YT3TG8qPFYBZ2/Z21qUPFZJFlrri+aPkUqpdUBLIOLa4yWlVCUg0lw9DKiW5fSqwMVCDVgUW1dTr7Jo1zLe37GQy/o0XK1M2VNvM7H9eCYt8cDd/XrdhKMJRK6IJOL7CFJDUrEtY4vnYE8qjK6AaydXlI2MaBIlV6EnC6VUacBGax1v/rwHMAvYADwGzDF//Nl8ygZgglJqFaaO7TjprxD5ORl1kjc2fcpP55aSbpMAYS2pE/s9M4cMZvAce+ztTfWSzycTuTqSyJWRJB5JBFtw7+GOzzumzmrZrlQIE2u0LCoA68zjzu2A77XWm5VSe4EflFJjgRBgiLn+JkzDZs9iGjo7pvBDFsVBuiGdH4/8zFtbPuNEij9kOGB7cij9PCfwxrMtadbMVC8lOIXwHyO5/ONl4vfEA1C2bVlqf1wbr6FeslWpEDko9GShtT4PNMmhPBp4IIdyDTxXCKGJYio4Nph3tn7Jd8e+JsnmEsRWp3zQO0xoN5YXFnvh5maaVR0y7zKX115PEC5+LvjM9cFrqBdONWSNJiHyUpSGzgpRYOmGdNYc2cicrV9yOGkzaIU615t2DuOZObIPD3SxIelIIpcXBHJ2fRSJhxIBU4Ko+W5NvIZ44VxLOqqFKChJFqJYOR55gnd++4afzi0n2TYCh/gOuEV9wiDfrrw1tw4OJ+OI2nCePU9GkxKUAgrKtS9HrfdrUf7h8jKSSYjbJMlCFHmxKbF8sfMHPv33G0L1bjDYYXu+L21dn+JiadAVYHtcIL92uYTPGYWNkw1u3dyo/mp1yvcrL30QQtwFkixEkZRuSGf9ka28t205+xJ+xmiTCpH18YmdzxTvQXT2huWng/neOwFsIENrQno68dC82rh3d5dRTELcZZIsRJGhtebvwP+Yu2kFv0esIs0uCpI88D71POPUw3TNKEfG7iukRwURrqBBN0dsgQzAwdGGh56ri2cNN2t/GULckyRZCKvSWnPw4jHmbVrFr8Eribc/j3OiK632Pkm/uG74xbpCYDKQSobXFdx7u+Peyx237m44eDrwXZalOVpIohDCYiRZCKs4dPE47236kV+DVpPEORoGN2Lo4SfoENmCapFOKCPYONlQrr0Tbs9Uwr2HO6Ublb5pFvWmiR2t9BUIUbJIshCFQmvN7qBDfLB5LTvO/0LVCFuaBDVl1ump1LtcDTujDdhC2fvL4jrGFbdubpRtWxZbJ+l7EKIokGQhLCbDmMEvh/5h6YZNXDoZS1mjL61OejP+yPvYaVu0Dbi0cMFttCuunV0p16Ecdi7yT1KIokj+Z4q7KibxCt8t/ZPjW07gGpxG/Qu1ecixB/OGpRBpC0HVoWXvCrTvUpmybcpiV1b+CQpRHMj/VHFHUiNS+W/dUfZtOIXdiWRqhlekcaoHjWlPnFMKV7xtONWrDGl2pglyRgWhPZxw7+Ke/8WFEEWGJAtxk33BV9h9PprWPh7ZRhhlxGUQvz+esB2XOLE5GLsTGbjHlQGgsapAoEc4+31j8PArS5+xTfFuVwalFPuCr7D28yiMGuztbGjt45HbrYUQRZQkC5HNvuArDPn8X4waHG1s+LC0N5UPphPxTxSO4dd3hctwjeFYpfOE1FM41/Om1+COPN7jAexy+BfVooYbPz7dNscEJIQoHiRZlHDaqEk+n0zioUQSDiXwc+gFtCdgA+npRv7cGIrfiRhOVzrN6TrnCC5jh32N2vRu15WJvYbi7law0UotarhJkhCiGJNkUYKkRaWReDSRxCPXXwlHEjAmGgEwKo1DncvYPOiEQSuMysCKJqvw71CNXnU781bPl/CuUsrKX4UQwhokWdyD0qLSSDqeROLxRJKOJZH4//buPbjK+s7j+PuTG7dAQghSINAARiNeUEypilpsbau2W2m1q6523HYdd1rt2nY7bruOl3Z2HFt3Ou166Y5LkdaltKtYa3edpVpvuy0IgnITVAgsBGgSRG65k3z3j98vcIgJJwnnQsL3NXPmPOf3POf5fX/POfN8n/M8z/n91jfQsL6Btrq2w8s0DWujekwtb5++js0T17L5Q5vZWlzP0OZzObXgCspLz+ErHzubq2bOzWJLnHMnCk8WA5S1G81bm2nc2EjjyQj5NgAADGNJREFU243heWMjjRsaadt9JCm0DzVqxzSwqXQHGyrXsGnycqrHVbOncA/5B05lAh+latyV3HXebK655GyGD/WvhHPug3zPcAKzDqNlRwtNm5poejc8Gt9pDNObm3i39BAbJ7dTuS2X8oN57C5tYsuEOjaevokNH1rB1skrqR9VD4K8hkmc0j6Ts0bP5QunVXHtRVVUlPldSc653vFkkWUdLR00b22mqTokgObNcXpTE83VzXQ0dxxe1gpEw9gOdo06wPKL9/DCzBIsRxht/HnIXbTmboT2fIY3VjIxdwazS+/kktNm8PkLzmPKuNIsttI5N9ANmGQh6QrgJ0AuMM/MHshySL3S0dpBy/YWXltdz7Lq9zhrbwHTtuXQvKWZpi1NtO5sBTuyfHsB7B/dxq5RB9h2Ri1bRm6jesxbbC9bSd3onVhOWLio5QaK2q9HykEUcGXJ/dxxeSWzK0+lIC8/S611zg1WAyJZSMoFHgE+CdQAKyQ9a2Zvpbqunv6Q1h0zo62+jZbtLTRvbw7P21poqG6mYWsjLdubYXc7m8e388Prm2nLhbx2+OpSGNq4n10j66mZVcOOMZvZNe4ddpbsZE/hHogdq+Y2lzLi0IcZmzeV6SNvYu7YqVRNreDSMyvY3zyMm362nLZDHeTn5XL352f7ranOubQZEMkCmAVsMrNqAEm/Aq4GUposlqyp4W8XrgRygA5KD77O2PdbGHVwGMUHwqPo4DCKD46g5MAIxhwsJL/j6E3YmttKbVEd9UV11E6spXZ6LeunltGaNwuUS2vOIb7/xV/T1L6U4R2nUJQ7ntIh4ykfOYPLxpRxxoRJnFNexozyyRQOGXHMeBfecoH/0c05lxEDJVlMBLYnvK4BPpq4gKRbgVsBJk+e3K9Klq6tR5aL5Yic9hzmrLqEzy47Mn5za24buwv38l7hPjaOr2XPyIPsLWxk36gW9he30VjcgRXlM2rIKIqGjWTMiGmUjTqfCSriybWHaO+AgtwCnr7lYc4vP/6+kfyPbs65TBkoyULdlNlRL8weAx4DqKqqsm6WT+ozsybxy9W7OGRGfq741I1TOPveEoZMGMKQiUPIK8lD6i6U5K65sPent5xz7kQzUJJFDTAp4XUZsDPVlXykopRFt12Ylp26/wpwzg1kAyVZrAAqJE0BdgDXA3+Vjop8p+6ccx80IJKFmR2SdDuwhHDr7HwzW5/lsJxz7qQxIJIFgJk9BzyX7Ticc+5klJN8Eeeccyc7TxbOOeeS8mThnHMuKU8WzjnnkvJk4ZxzLimZ9evPzic0SfXA/x3HKkqB3SkKZ6A42drs7R3cTrb2Qmra/GEzG9vdjEGZLI6XpNfNrCrbcWTSydZmb+/gdrK1F9LfZj8N5ZxzLilPFs4555LyZNG9x7IdQBacbG329g5uJ1t7Ic1t9msWzjnnkvJfFs4555LyZOGccy4pTxZdSLpC0tuSNkn6TrbjSSdJkyS9JGmDpPWS7sh2TJkgKVfSG5L+M9uxZIKkYklPSdoYP+sLsx1TOkn6Zvw+r5O0SNLQbMeUSpLmS6qTtC6hrETS85Lejc8pH5THk0UCSbnAI8CVwHTgBknTsxtVWh0C/t7MzgAuAG4b5O3tdAewIdtBZNBPgP82s0pgBoO47ZImAn8HVJnZWYTxb67PblQptwC4okvZd4A/mFkF8If4OqU8WRxtFrDJzKrNrBX4FXB1lmNKGzPbZWar4vQBwk5kYnajSi9JZcBngHnZjiUTJI0CLgV+BmBmrWa2N7tRpV0eMExSHjCcNAzBnE1m9iqwp0vx1cDP4/TPgbmprteTxdEmAtsTXtcwyHeenSSVA+cBr2U3krT7MXAn0JHtQDJkKlAPPB5Pvc2TNCLbQaWLme0A/hnYBuwC9pnZ77MbVUaMM7NdEA4CgVNSXYEni6Opm7JBf2+xpEJgMfANM9uf7XjSRdJngTozW5ntWDIoD5gJ/NTMzgMaSMMpihNFPFd/NTAFmACMkHRTdqMaHDxZHK0GmJTwuoxB9hO2K0n5hESx0MyeznY8aTYb+JykrYRTjB+X9O/ZDSntaoAaM+v8xfgUIXkMVpcDW8ys3szagKeBi7IcUybUShoPEJ/rUl2BJ4ujrQAqJE2RVEC4MPZslmNKG0kinMveYGY/ynY86WZm3zWzMjMrJ3y2L5rZoD7qNLM/A9slnR6LPgG8lcWQ0m0bcIGk4fH7/QkG8QX9BM8CN8fpm4HfprqCvFSvcCAzs0OSbgeWEO6imG9m67McVjrNBr4ErJX0Ziz7RzN7LosxudT7OrAwHgBVA1/OcjxpY2avSXoKWEW42+8NBlnXH5IWAXOAUkk1wL3AA8B/SPobQsL8Ysrr9e4+nHPOJeOnoZxzziXlycI551xSniycc84l5cnCOedcUp4snHPOJeXJwiUlqV3Sm7Enz9WSviUpo98dSd+XdHka179A0rW9Le+yTGXcPm9IOl/S13pYrjhxnqQ5x9PzbW9iy4R0fzbd1Ffc0zZ26ePJwvVGk5mda2ZnAp8EriLc250xZnaPmb2QyTr7YC7w29idxntATzuy4mPMG7D6+tnEDv6Ox6Dcjic6TxauT8ysDrgVuF1BuaT/kbQqPi4CkPSEpMM99kpaKOlzks6UtDweia+RVJG4/jjWxII4FsFaSd+M5YePoiVtlfS9WN9aSZWxvFDS47FsjaRrYvmnJC2Nyz8Z+8Lqs/ir4RVJKyUtkTRe0lXAN4BbJL1E+HPUtNi+B7usort5hToy1sTC+K/jbuvqIaxLJf1JUnXC9pGkBxO24XWxfI6kl3tbn6RpklYltL9C0gf61ery2dwjaUWs+7GE9b8s6X5JrxC6iE98f4mkZ+JntkzSObH8PknfTlhunUKHl8faxi5dzMwf/jjmAzjYTdn7wDhCF9BDY1kF8Hqc/hjwTJwuArYQegx4CLgxlhcAw7qs93zg+YTXxfF5AXBtnN4KfD1Ofw2YF6d/APw44b2jgVLgVWBELPsH4J5u2nN4/d2VA/nAn4Cxsfw6wj/8Ae4Dvh2ny4F1PWzHo+YR/oW7j9AHWQ6wFLj4WHV1E9uT8b3TCd3rA1wDPE/ohWAc4R+94/tTH/AScG6cvr9zu/e07YCShPIngL+I0y8Dj/awXR4C7o3THwfe7Lpd4+t1cRv2uI39kb6Hd/fh+quzh9584GFJ5wLtwGkAZvaKpEcknQJ8AVhsoTuVpcBdCuNKPG1m73ZZbzUwVdJDwH8BPXUv3dnp4cq4fgidyB0e6MbM3lfoaXY68Md4kFtA2En21enAWcDzcT25hC6wj9dyM6sBUOhypRzY24e6njGzDuAtSeNi2cXAIjNrJ3Qw9wrwEWB/P+qbB3xZ0rcISWRWkvZcJulOwkFECbAe+F2c9+se3nMxIcFhZi9KGiOpKEk9LsM8Wbg+kzSVkBjqCNcuagkjsOUAzQmLPgHcSNiBfwXAzH4p6TXCAERLJN1iZi92viHu4GcAnwZuA/6y871dtMTndo58j8UHu5QX4ZfKDf1r7VHrWW9mqR6StCVhurMtfakr8f3q8pyK+hYTPuMXgZVm9l5PK1YYvvRRwih12yXdByQOadrQ01u7KTNC306Jp8oH1fCoA41fs3B9Imks8K/AwxbODRQBu+LR7ZcIR6WdFhDO52OxQ8aYaKrN7F8IPWWe02X9pUCOmS0G7qZv3Wn/Hrg9YV2jgWXAbEmnxrLhkk7rwzo7vQ2MVRy/WlK+pDO7We4AMLKHdRxrXn/q6smrwHUK13/GEkbKW96f+sysmdCx5k+Bx5PU27kz3x2vC/X2Tq1XCQcVSJoD7LYwrspW4ucvaSZhjAro/XZ0KeTJwvXGsHgxcT3wAmGn/L0471HgZknLCKegDh89mlktoXvoxJ3MdcC6eAqkEvhFl7omAi/H+QuA7/Yhzn8CRscLoauBy8ysHvhrYJGkNYTkUdmHdXa2pZWw8/tBXPebdDNOQjzy/mOM4cHezutPXcfwG2ANsJrwi+BOC12V97e+hYQj/WONOGcWhmv9N2At8Ayhy//euA+oip/PAxzpansxUBK/C18F3okV9Wo7utTyXmdd2kgaTthxzDSzfdmOx/VPvCOpyMzu7mH+74AfmdlLmY3MZZJfs3BpofAnrfmEnYgnigFK0m+AaYS7lLqbP59wMft/MxmXyzz/ZeGccy4pv2bhnHMuKU8WzjnnkvJk4ZxzLilPFs4555LyZOGccy6p/we8P2srgcTWbwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the \"best-fit\" curve for each model\n", + "plot_three_models(neglnlike_min_resultA['x'], \n", + " neglnlike_min_resultB['x'], \n", + " neglnlike_min_resultC['x'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Model comparison : the AIC and BIC " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Akaike information criterion is defined as:\n", + "\n", + "### AIC = 2 k - 2 ln(L)\n", + "\n", + "it balances a model's ability to fit the data (measured by the maximum likelihood value L) against the number of parameters 'k' that the model requires. A smaller value of the AIC indicates a better model (i.e., one that matches the data well, without being unnecessarily complex).\n", + "\n", + "The Bayesian information criterion is very similar. It replaces the 2 in the first term with ln(n), where n is the number of data points. This puts more weight on the first term (which penalizes complexity) when the size of the sample is large. As with the AIC, smaller is better.\n", + "\n", + "### BIC = k ln( n ) - 2 ln( L )\n", + "\n", + "These two metrics are the most commonly used, but many others exist, with subtle differences in their properties. One should take care to apply the appropriate criteria based on the data, the models, and the problem." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# Let us define a function that computes the AIC and BIC for each of our three models\n", + "\n", + "def aic(numparams, lnmaxlikelihood):\n", + " return (2 * numparams - 2 * lnmaxlikelihood)\n", + "\n", + "\n", + "def bic(numparams, numdatapoints, lnmaxlikelihood):\n", + " return (numparams * np.log(numdatapoints) - 2 * lnmaxlikelihood)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-4.751264868757671 -4.6364399987077904 -22.127990628538438\n" + ] + } + ], + "source": [ + "# Compute each max likelihood value and report. \n", + "\n", + "# Remember that we have found the minimum\n", + "# of the negative log likelihood for each\n", + "# function. This is reported as the 'fun'\n", + "# entry in our set of results from the \n", + "# scipy.optimize.minimize() function calls.\n", + "\n", + "# The opposite of that minimum is our maximum log likelihood.\n", + "\n", + "lnlike_max_valueA = -neglnlike_min_resultA['fun']\n", + "lnlike_max_valueB = -neglnlike_min_resultB['fun']\n", + "lnlike_max_valueC = -neglnlike_min_resultC['fun']\n", + "\n", + "print(lnlike_max_valueA, lnlike_max_valueB, lnlike_max_valueC)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compute the AIC and BIC for each model" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[15.502529737515342, 17.27287999741558, 50.255981257076876]\n", + "[17.62668034082197, 20.10508080182442, 52.38013186038351]\n" + ] + } + ], + "source": [ + "aic_list = [\n", + " aic(3, lnlike_max_valueA),\n", + " aic(4, lnlike_max_valueB),\n", + " aic(3, lnlike_max_valueC)]\n", + "\n", + "n = len(example_data_1D)\n", + "bic_list = [\n", + " bic(3, n, lnlike_max_valueA),\n", + " bic(4, n, lnlike_max_valueB),\n", + " bic(3, n, lnlike_max_valueC)]\n", + "\n", + "print(aic_list)\n", + "print(bic_list)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
nameAICBICDeltaAICDeltaBIC
0A(quadratic)15.50253017.6266800.0000000.000000
1B(cubic)17.27288020.1050811.7703502.478400
2C(exp)50.25598152.38013234.75345234.753452
\n", + "
" + ], + "text/plain": [ + " name AIC BIC DeltaAIC DeltaBIC\n", + "0 A(quadratic) 15.502530 17.626680 0.000000 0.000000\n", + "1 B(cubic) 17.272880 20.105081 1.770350 2.478400\n", + "2 C(exp) 50.255981 52.380132 34.753452 34.753452" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Make a nice pandas DataFrame table \n", + "\n", + "modelnames = ['A(quadratic)', 'B(cubic)', 'C(exp)']\n", + "df = pandas.DataFrame({\n", + " 'name':modelnames, \n", + " 'AIC':aic_list, \n", + " 'BIC':bic_list,\n", + " 'DeltaAIC':np.array(aic_list)-np.min(aic_list),\n", + " 'DeltaBIC':np.array(bic_list)-np.min(bic_list),\n", + "})\n", + "\n", + "# Show the table\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# interpreting the AIC / BIC as statistical weight\n", + "\n", + "wAIC = np.exp(-0.5 * df['DeltaAIC'])\n", + "df['wgtAIC'] = wAIC / np.sum(wAIC)\n", + "\n", + "wBIC = np.exp(-0.5 * df['DeltaBIC'])\n", + "df['wgtBIC'] = wBIC / np.sum(wBIC)\n", + "\n", + "\n", + "# Interpreting the AIC / BIC as an odds ratio\n", + "df['oddsAIC'] = np.max(df['wgtAIC']) / df['wgtAIC']\n", + "df['oddsBIC'] = np.max(df['wgtBIC']) / df['wgtBIC']\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
nameAICBICDeltaAICDeltaBICwgtAICwgtBICoddsAICoddsBIC
0A(quadratic)15.5017.630.000.000.710.781.001.00
1B(cubic)17.2720.111.772.480.290.222.423.45
2C(exp)50.2652.3834.7534.750.000.0035205953.5035205953.50
\n", + "
" + ], + "text/plain": [ + " name AIC BIC DeltaAIC DeltaBIC wgtAIC wgtBIC oddsAIC \\\n", + "0 A(quadratic) 15.50 17.63 0.00 0.00 0.71 0.78 1.00 \n", + "1 B(cubic) 17.27 20.11 1.77 2.48 0.29 0.22 2.42 \n", + "2 C(exp) 50.26 52.38 34.75 34.75 0.00 0.00 35205953.50 \n", + "\n", + " oddsBIC \n", + "0 1.00 \n", + "1 3.45 \n", + "2 35205953.50 " + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# set a pandas option so we only display two decimal places\n", + "pandas.options.display.float_format = '{:.2f}'.format\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### next topic : using the Bayesian evidence (Bayes factors) to compare models considering the entire parameter space" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Reading list\n", + "\n", + "A good broad book on Bayesian data analysis\n", + "* Sivia, D. and Skilling, J. \"Data Analysis: A Bayesian Tutorial\"\n", + "https://books.google.com/books/about/Data_Analysis.html?id=lYMSDAAAQBAJ\n", + "\n", + "Some short \"tutorial\" papers about AIC / BIC: \n", + "\n", + "* Wagenmakers and Farrell 2004\n", + "https://link.springer.com/content/pdf/10.3758/BF03206482.pdf\n", + "\n", + "* Symonds and Moussalli 2010\n", + "http://byrneslab.net/classes/biol607/readings/Symonds_and_Moussalli_2010_behav_ecol.pdf" + ] + } + ], + "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": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/02_Week2/Workshop_Packages/README.md b/02_Week2/Workshop_Packages/README.md new file mode 100644 index 0000000..55fdc47 --- /dev/null +++ b/02_Week2/Workshop_Packages/README.md @@ -0,0 +1,3 @@ +#### This simply shows an example of how to turn a file (or multiple files) into an installable python package. You can either download the repository and then enter this folder, or just copy the code into files on your local computer (there isn't much). + +Here the code to turn into a package is housed in the `myCode` folder. If you run the `make_installable.py` file this will become a package (in the current directory) called `stem_package`. The package is installable, and you can look at more examples [here](https://codetopackage.readthedocs.io/en/latest/examples.html) about how to add docs and make the package `pip` installable. diff --git a/02_Week2/Workshop_Packages/make_installable.py b/02_Week2/Workshop_Packages/make_installable.py new file mode 100644 index 0000000..13204ae --- /dev/null +++ b/02_Week2/Workshop_Packages/make_installable.py @@ -0,0 +1,7 @@ +import codetopackage +codetopackage.create_package( + PackageTargetDirectory= 'stem_package', + PackageSourceDirectory= 'myCode', + PackageName= "stem_package", + CreateDocs=False + ) diff --git a/02_Week2/Workshop_Packages/myCode/hello.py b/02_Week2/Workshop_Packages/myCode/hello.py new file mode 100644 index 0000000..db7ae81 --- /dev/null +++ b/02_Week2/Workshop_Packages/myCode/hello.py @@ -0,0 +1,4 @@ +import numpy + +def test_function(): + print('Hello World') diff --git a/02_Week2/Workshop_machine_learning/05_Workshop_machine_learning_complete.ipynb b/02_Week2/Workshop_machine_learning/05_Workshop_machine_learning_complete.ipynb new file mode 100644 index 0000000..aabf37d --- /dev/null +++ b/02_Week2/Workshop_machine_learning/05_Workshop_machine_learning_complete.ipynb @@ -0,0 +1,700 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "05_Workshop_machine_learning_complete", + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "QyKkS9_rKXiQ", + "colab_type": "text" + }, + "source": [ + "## Introduction \n", + "\n", + "In this mini workshop, we are going to learn some advanced features in [scikit-learn](https://scikit-learn.org/stable/user_guide.html) that can improve your undersanding and efficiency of machine learning. \n", + "\n", + "This time, we will use a built-in dataset from scikit-learn as an example for our exercise. The dataset we are using is the [Boston house price](https://scikit-learn.org/stable/datasets/index.html#toy-datasets) dataset for regression modeling. \n", + "\n", + "Let's start the notebook with importing the package and the data. " + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "ciogko39LjXo", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 70 + }, + "outputId": "6a42f29e-6061-4b79-863a-c9536a10bfe2" + }, + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sbn\n", + "from sklearn.datasets import load_boston" + ], + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n", + " import pandas.util.testing as tm\n" + ], + "name": "stderr" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "YAGuiY1sKPYo", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "f1b08f1e-34a6-42b8-8590-7767b4800106" + }, + "source": [ + "X, y = load_boston(return_X_y=True)\n", + "print(X.shape, y.shape)" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "text": [ + "(506, 13) (506,)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gNKe4xEoM53q", + "colab_type": "text" + }, + "source": [ + "It looks like we have 13 features that we can use to create a predictive model for the target/output which is the house price in Boston." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UEzEgwz9NYx0", + "colab_type": "text" + }, + "source": [ + "## Pipeline \n", + "\n", + "In the past two days, we are doing the model development step-by-step with the typical process of:\n", + "\n", + "1. Feature selection/transformation;\n", + "2. Defining the model;\n", + "3. Tunning model hyperparameters;\n", + "\n", + "This is a clear way to start our learning of each individual step. However, the code will look lengthy. In scikit-learn, you can create a model [pipeline](https://scikit-learn.org/stable/modules/compose.html#pipeline-chaining-estimators) to nest all model steps into a sequence and specifying the key words for each step. You can also implement grid search CV directly to your pipeline." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "74HpXTr2OgMH", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 319 + }, + "outputId": "87aa139b-cb78-46e5-f23b-2014c3360518" + }, + "source": [ + "from sklearn.decomposition import PCA\n", + "from sklearn.model_selection import GridSearchCV\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.ensemble import RandomForestRegressor\n", + "from sklearn.model_selection import KFold\n", + "from sklearn.model_selection import train_test_split\n", + "## First, let's review a traditional modeling process\n", + "\n", + "## Step 1. Data transformation - e.g., PCA\n", + "## pcaModel = PCA()\n", + "## pcaData = pcaModel.fit_transform(X)\n", + "\n", + "## Step 2. Defining the model\n", + "## rfModel = RandomForestTRegressor(...)\n", + "\n", + "## Step 3. Gridsearch CV\n", + "## gridsearch_obj = {\n", + "## rfModel,\n", + "## param_grid,\n", + "## cv,\n", + "## ... \n", + "## }\n", + "\n", + "## Step 4. fit the model/grid search\n", + "\n", + "## Step 5. find the best combo & final model\n", + "\n", + "## ...\n", + "\n", + "## Things can be simplified with pipeline by nesting these together\n", + "pca = PCA()\n", + "rfModel = RandomForestRegressor()\n", + "\n", + "## making the pipeline\n", + "pipe = Pipeline(steps=[('reduce_dim', pca), ('regression', rfModel)])\n", + "\n", + "print( pipe )" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Pipeline(memory=None,\n", + " steps=[('reduce_dim',\n", + " PCA(copy=True, iterated_power='auto', n_components=None,\n", + " random_state=None, svd_solver='auto', tol=0.0,\n", + " whiten=False)),\n", + " ('regression',\n", + " RandomForestRegressor(bootstrap=True, ccp_alpha=0.0,\n", + " criterion='mse', max_depth=None,\n", + " max_features='auto', max_leaf_nodes=None,\n", + " max_samples=None,\n", + " min_impurity_decrease=0.0,\n", + " min_impurity_split=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0,\n", + " n_estimators=100, n_jobs=None,\n", + " oob_score=False, random_state=None,\n", + " verbose=0, warm_start=False))],\n", + " verbose=False)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "tBbvlkeKNXF4", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 504 + }, + "outputId": "88ad1531-c5bd-4e1f-cc26-d20c62f458f5" + }, + "source": [ + "## Now, le's say we want to change how many different PCA components we want to include in our final model \n", + "## also, we want to change the number of estimators (trees) in our random forest\n", + "param_grid = {\n", + " 'reduce_dim__n_components': [6, 7, 8, 9, 10, 11, 12],\n", + " 'regression__n_estimators': [50,100,200,300]\n", + "}\n", + "\n", + "## now we can define the gridsearch object that we want (say with 10-fold cross validation)\n", + "gridsearch_obj = GridSearchCV(\n", + " pipe,\n", + " param_grid,\n", + " cv=KFold(n_splits=10),\n", + " n_jobs=-1\n", + ")\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)\n", + "print( X_train.shape, y_train.shape )\n", + "print(gridsearch_obj)" + ], + "execution_count": 25, + "outputs": [ + { + "output_type": "stream", + "text": [ + "(379, 13) (379,)\n", + "GridSearchCV(cv=KFold(n_splits=10, random_state=None, shuffle=False),\n", + " error_score=nan,\n", + " estimator=Pipeline(memory=None,\n", + " steps=[('reduce_dim',\n", + " PCA(copy=True, iterated_power='auto',\n", + " n_components=None,\n", + " random_state=None,\n", + " svd_solver='auto', tol=0.0,\n", + " whiten=False)),\n", + " ('regression',\n", + " RandomForestRegressor(bootstrap=True,\n", + " ccp_alpha=0.0,\n", + " criterion='mse',\n", + " max_depth=None,\n", + " max_fe...\n", + " min_weight_fraction_leaf=0.0,\n", + " n_estimators=100,\n", + " n_jobs=None,\n", + " oob_score=False,\n", + " random_state=None,\n", + " verbose=0,\n", + " warm_start=False))],\n", + " verbose=False),\n", + " iid='deprecated', n_jobs=-1,\n", + " param_grid={'reduce_dim__n_components': [6, 7, 8, 9, 10, 11, 12],\n", + " 'regression__n_estimators': [50, 100, 200, 300]},\n", + " pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n", + " scoring=None, verbose=0)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "R2BoaIOTWetS", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 487 + }, + "outputId": "1c4f7c68-445b-4bae-ae8b-d492a68c05b4" + }, + "source": [ + "## Now we can fit the whole pipeline instead of just doing it step by step\n", + "gridsearch_obj.fit(X_train, y_train)" + ], + "execution_count": 26, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "GridSearchCV(cv=KFold(n_splits=10, random_state=None, shuffle=False),\n", + " error_score=nan,\n", + " estimator=Pipeline(memory=None,\n", + " steps=[('reduce_dim',\n", + " PCA(copy=True, iterated_power='auto',\n", + " n_components=None,\n", + " random_state=None,\n", + " svd_solver='auto', tol=0.0,\n", + " whiten=False)),\n", + " ('regression',\n", + " RandomForestRegressor(bootstrap=True,\n", + " ccp_alpha=0.0,\n", + " criterion='mse',\n", + " max_depth=None,\n", + " max_fe...\n", + " min_weight_fraction_leaf=0.0,\n", + " n_estimators=100,\n", + " n_jobs=None,\n", + " oob_score=False,\n", + " random_state=None,\n", + " verbose=0,\n", + " warm_start=False))],\n", + " verbose=False),\n", + " iid='deprecated', n_jobs=-1,\n", + " param_grid={'reduce_dim__n_components': [6, 7, 8, 9, 10, 11, 12],\n", + " 'regression__n_estimators': [50, 100, 200, 300]},\n", + " pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n", + " scoring=None, verbose=0)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 26 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "ghzS3vOHX8KA", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 50 + }, + "outputId": "544ec855-32e1-4c36-dada-5c2f0fc33d0e" + }, + "source": [ + "## Let's looks at what is the suggestion of the model structure\n", + "print( gridsearch_obj.best_params_)\n", + "print( gridsearch_obj.best_score_)" + ], + "execution_count": 27, + "outputs": [ + { + "output_type": "stream", + "text": [ + "{'reduce_dim__n_components': 11, 'regression__n_estimators': 50}\n", + "0.7767816443489\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "tEzMaVcTfvjf", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 313 + }, + "outputId": "5f78081d-c44e-4ff5-8d52-1248afd8160b" + }, + "source": [ + "## Now we can directly apply the model pipeline from the grid search \n", + "## object to our testing data which will give us the best estimator\n", + "## based on our grid search CV results.\n", + "from sklearn.metrics import r2_score, mean_squared_error\n", + "\n", + "y_predicted = gridsearch_obj.predict(X_test)\n", + "\n", + "r2 = r2_score(y_test, y_predicted)\n", + "rmse = np.sqrt(mean_squared_error(y_test, y_predicted))\n", + "\n", + "print(r2, rmse)\n", + "\n", + "## We can also add the scatter plot between predicted and test data\n", + "plt.scatter(y_test, y_predicted)\n", + "plt.plot([0,55],[0,55],'k--')\n", + "plt.xlim(0,55)\n", + "plt.ylim(0,55)\n", + "plt.xlabel('True Median House Value (1,000 USD)')\n", + "plt.ylabel('Predicted Median House Value (1,000 USD)')" + ], + "execution_count": 36, + "outputs": [ + { + "output_type": "stream", + "text": [ + "0.7102048231329757 4.365109395135025\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Text(0, 0.5, 'Predicted Median House Value (1,000 USD)')" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 36 + }, + { + "output_type": "display_data", + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "wDcmPp82jalS", + "colab_type": "code", + "colab": {} + }, + "source": [ + "### Now it's your turn to try build your own pipeline for building a model \n", + "### for this Boston Housing Price data using neural network.\n", + "### For the hyperparameters, you can change both the number of hidden layers\n", + "### for 1-layer and 2-layer models, as well as the activation function\n", + "\n", + "\n", + "\n", + "\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "52a74KpVjzfe", + "colab_type": "text" + }, + "source": [ + "## Random Search for Hyperparameters\n", + "\n", + "In the previous exercise, we always use the grid search method to find the best model hyperparameters. This is a good method when you have a limited number of hyperparameters and small range of the parameters to tune. However, when we have a large parameter space for searching, the grid search can be really time consuming for large data sets. Sometimes, random search can help you reduce the computational need for that. We are going to use random forest model as our base model again here." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "elGukx_ckc8M", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 319 + }, + "outputId": "22c8c699-9807-4d42-ec7d-91398f0ab1a5" + }, + "source": [ + "## define our base model pipeline\n", + "pipebase = Pipeline(steps=[('reduce_dim', PCA(n_components=11)), \n", + " ('regression', RandomForestRegressor())])\n", + "print( pipebase )" + ], + "execution_count": 37, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Pipeline(memory=None,\n", + " steps=[('reduce_dim',\n", + " PCA(copy=True, iterated_power='auto', n_components=11,\n", + " random_state=None, svd_solver='auto', tol=0.0,\n", + " whiten=False)),\n", + " ('regression',\n", + " RandomForestRegressor(bootstrap=True, ccp_alpha=0.0,\n", + " criterion='mse', max_depth=None,\n", + " max_features='auto', max_leaf_nodes=None,\n", + " max_samples=None,\n", + " min_impurity_decrease=0.0,\n", + " min_impurity_split=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0,\n", + " n_estimators=100, n_jobs=None,\n", + " oob_score=False, random_state=None,\n", + " verbose=0, warm_start=False))],\n", + " verbose=False)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "ZPBDMzSSk_JB", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 272 + }, + "outputId": "83a68b65-60e5-44bc-bd03-c6b58635867b" + }, + "source": [ + "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV\n", + "from time import time\n", + "\n", + "# Utility function to report best scores\n", + "def report(results, n_top=3):\n", + " for i in range(1, n_top + 1):\n", + " candidates = np.flatnonzero(results['rank_test_score'] == i)\n", + " for candidate in candidates:\n", + " print(\"Model with rank: {0}\".format(i))\n", + " print(\"Mean validation score: {0:.3f} (std: {1:.3f})\"\n", + " .format(results['mean_test_score'][candidate],\n", + " results['std_test_score'][candidate]))\n", + " print(\"Parameters: {0}\".format(results['params'][candidate]))\n", + " print(\"\")\n", + "\n", + "## In this situation, we can define a grid for our search\n", + "param_grid = {\n", + " 'regression__n_estimators': np.linspace(10, 100, num=10, dtype='int16'),\n", + " 'regression__max_features': np.linspace(3, 11, num=5, dtype='int16'),\n", + " 'regression__min_samples_leaf': [1,3,5]\n", + "}\n", + "\n", + "print(param_grid)\n", + "\n", + "## Now we can perform our grid search with 5-fold cross validation\n", + "grid_search = GridSearchCV(pipebase, param_grid=param_grid,\n", + " cv=KFold(n_splits=5))\n", + "start = time()\n", + "grid_search.fit(X_train, y_train)\n", + "print(\"GridSearchCV took %.2f seconds for %d candidate parameter settings.\"\n", + " % (time() - start, len(grid_search.cv_results_['params'])))\n", + "report(grid_search.cv_results_)\n", + "\n" + ], + "execution_count": 45, + "outputs": [ + { + "output_type": "stream", + "text": [ + "{'regression__n_estimators': array([ 10, 20, 30, 40, 50, 60, 70, 80, 90, 100], dtype=int16), 'regression__max_features': array([ 3, 5, 7, 9, 11], dtype=int16), 'regression__min_samples_leaf': [1, 3, 5]}\n", + "GridSearchCV took 78.92 seconds for 150 candidate parameter settings.\n", + "Model with rank: 1\n", + "Mean validation score: 0.797 (std: 0.108)\n", + "Parameters: {'regression__max_features': 11, 'regression__min_samples_leaf': 1, 'regression__n_estimators': 60}\n", + "\n", + "Model with rank: 2\n", + "Mean validation score: 0.796 (std: 0.087)\n", + "Parameters: {'regression__max_features': 9, 'regression__min_samples_leaf': 1, 'regression__n_estimators': 50}\n", + "\n", + "Model with rank: 3\n", + "Mean validation score: 0.795 (std: 0.098)\n", + "Parameters: {'regression__max_features': 9, 'regression__min_samples_leaf': 1, 'regression__n_estimators': 100}\n", + "\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "hO2B-HU2nr8g", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 54 + }, + "outputId": "abce2eef-c1d1-40ed-91d3-c517e06f57f2" + }, + "source": [ + "## Now let's see how the random search will take for us\n", + "import scipy.stats as stats\n", + "## here we define the range for search first\n", + "param_dist = {\n", + " 'regression__n_estimators': stats.randint(10,100),\n", + " 'regression__max_features': stats.randint(3,11),\n", + " 'regression__min_samples_leaf': stats.randint(1,5) \n", + "}\n", + "print(param_dist)\n" + ], + "execution_count": 47, + "outputs": [ + { + "output_type": "stream", + "text": [ + "{'regression__n_estimators': , 'regression__max_features': , 'regression__min_samples_leaf': }\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "mvBdl6rjpr8x", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 235 + }, + "outputId": "d16d9f56-cb88-44f6-d53c-91b1be48e490" + }, + "source": [ + "\n", + "## run randomized search\n", + "n_iter_search = 75\n", + "random_search = RandomizedSearchCV(pipebase, param_distributions=param_dist,\n", + " n_iter=n_iter_search)\n", + "start = time()\n", + "random_search.fit(X_train, y_train)\n", + "print(\"RandomizedSearchCV took %.2f seconds for %d candidates\"\n", + " \" parameter settings.\" % ((time() - start), n_iter_search))\n", + "report(random_search.cv_results_)" + ], + "execution_count": 48, + "outputs": [ + { + "output_type": "stream", + "text": [ + "RandomizedSearchCV took 38.93 seconds for 75 candidates parameter settings.\n", + "Model with rank: 1\n", + "Mean validation score: 0.795 (std: 0.085)\n", + "Parameters: {'regression__max_features': 10, 'regression__min_samples_leaf': 1, 'regression__n_estimators': 76}\n", + "\n", + "Model with rank: 2\n", + "Mean validation score: 0.788 (std: 0.097)\n", + "Parameters: {'regression__max_features': 5, 'regression__min_samples_leaf': 1, 'regression__n_estimators': 32}\n", + "\n", + "Model with rank: 3\n", + "Mean validation score: 0.784 (std: 0.101)\n", + "Parameters: {'regression__max_features': 7, 'regression__min_samples_leaf': 1, 'regression__n_estimators': 82}\n", + "\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yeKqF2zQpvAk", + "colab_type": "text" + }, + "source": [ + "## Assessing feature importance \n", + "\n", + "Assuming that we finally find our best random forest model. We want to know which features have higer importance than others. What we can do is to use the [permutation feature importance](https://scikit-learn.org/stable/modules/permutation_importance.html#) functionality in scikit-learn.\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "J1-t7VxZqWTI", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## Now, let's use the model hyperparameter from our random search as the final model\n", + "\n", + "rfModel = RandomForestRegressor(n_estimators=76, min_samples_leaf=1, max_features=10)\n", + "rfModel.fit(X_train, y_train)\n", + "\n", + "house = load_boston()\n", + "## Let's calculate feature importance here\n", + "from sklearn.inspection import permutation_importance\n", + "r = permutation_importance(rfModel, X_test, y_test,\n", + " n_repeats=30,\n", + " random_state=0)\n", + "\n", + "print (r)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "jHO7LMsTrQi4", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## We want to visualize the ranking of individual features\n", + "for i in r.importances_mean.argsort()[::-1]:\n", + " if r.importances_mean[i] - 2 * r.importances_std[i] > 0:\n", + " print(f\"{house.feature_names[i]:<8}\"\n", + " f\"{r.importances_mean[i]:.3f}\"\n", + " f\" +/- {r.importances_std[i]:.3f}\")\n", + " " + ], + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/02_Week2/Workshop_machine_learning/05_Workshop_machine_learning_exercise.ipynb b/02_Week2/Workshop_machine_learning/05_Workshop_machine_learning_exercise.ipynb new file mode 100644 index 0000000..1a41cdd --- /dev/null +++ b/02_Week2/Workshop_machine_learning/05_Workshop_machine_learning_exercise.ipynb @@ -0,0 +1,341 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "05_Workshop_machine_learning_exercise", + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "QyKkS9_rKXiQ", + "colab_type": "text" + }, + "source": [ + "## Introduction \n", + "\n", + "In this mini workshop, we are going to learn some advanced features in [scikit-learn](https://scikit-learn.org/stable/user_guide.html) that can improve your undersanding and efficiency of machine learning. \n", + "\n", + "This time, we will use a built-in dataset from scikit-learn as an example for our exercise. The dataset we are using is the [Boston house price](https://scikit-learn.org/stable/datasets/index.html#toy-datasets) dataset for regression modeling. \n", + "\n", + "Let's start the notebook with importing the package and the data. " + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "ciogko39LjXo", + "colab_type": "code", + "colab": {} + }, + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sbn\n", + "from sklearn.datasets import load_boston" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "YAGuiY1sKPYo", + "colab_type": "code", + "colab": {} + }, + "source": [ + "X, y = load_boston(return_X_y=True)\n", + "print(X.shape, y.shape)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gNKe4xEoM53q", + "colab_type": "text" + }, + "source": [ + "It looks like we have 13 features that we can use to create a predictive model for the target/output which is the house price in Boston." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UEzEgwz9NYx0", + "colab_type": "text" + }, + "source": [ + "## Pipeline \n", + "\n", + "In the past two days, we are doing the model development step-by-step with the typical process of:\n", + "\n", + "1. Feature selection/transformation;\n", + "2. Defining the model;\n", + "3. Tunning model hyperparameters;\n", + "\n", + "This is a clear way to start our learning of each individual step. However, the code will look lengthy. In scikit-learn, you can create a model [pipeline](https://scikit-learn.org/stable/modules/compose.html#pipeline-chaining-estimators) to nest all model steps into a sequence and specifying the key words for each step. You can also implement grid search CV directly to your pipeline." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "74HpXTr2OgMH", + "colab_type": "code", + "colab": {} + }, + "source": [ + "from sklearn.decomposition import PCA\n", + "from sklearn.model_selection import GridSearchCV\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.ensemble import RandomForestRegressor\n", + "from sklearn.model_selection import KFold\n", + "from sklearn.model_selection import train_test_split\n", + "## First, let's review a traditional modeling process\n", + "\n", + "## Step 1. Data transformation - e.g., PCA\n", + "\n", + "## Step 2. Defining the model\n", + "\n", + "## Step 3. Gridsearch CV\n", + "\n", + "\n", + "## Step 4. fit the model/grid search\n", + "\n", + "## Step 5. find the best combo & final model\n", + "\n", + "## ...\n", + "\n", + "## Things can be simplified with pipeline by nesting these together\n", + "\n", + "\n", + "## making the pipeline\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "tBbvlkeKNXF4", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## Now, le's say we want to change how many different PCA components we want to include in our final model \n", + "## also, we want to change the number of estimators (trees) in our random forest\n", + "\n", + "\n", + "## now we can define the gridsearch object that we want (say with 10-fold cross validation)\n", + "\n", + "## Seperating the data for training and testing\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "R2BoaIOTWetS", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## Now we can fit the whole pipeline instead of just doing it step by step\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "ghzS3vOHX8KA", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## Let's looks at what is the suggestion of the model structure\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "tEzMaVcTfvjf", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## Now we can directly apply the model pipeline from the grid search \n", + "## object to our testing data which will give us the best estimator\n", + "## based on our grid search CV results.\n", + "\n", + "\n", + "## We can also add the scatter plot between predicted and test data\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "wDcmPp82jalS", + "colab_type": "code", + "colab": {} + }, + "source": [ + "### Now it's your turn to try build your own pipeline for building a model \n", + "### for this Boston Housing Price data using neural network.\n", + "### For the hyperparameters, you can change both the number of hidden layers\n", + "### for 1-layer and 2-layer models, as well as the activation function\n", + "\n", + "\n", + "\n", + "\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "52a74KpVjzfe", + "colab_type": "text" + }, + "source": [ + "## Random Search for Hyperparameters\n", + "\n", + "In the previous exercise, we always use the grid search method to find the best model hyperparameters. This is a good method when you have a limited number of hyperparameters and small range of the parameters to tune. However, when we have a large parameter space for searching, the grid search can be really time consuming for large data sets. Sometimes, random search can help you reduce the computational need for that. We are going to use random forest model as our base model again here." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "elGukx_ckc8M", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## define our base model pipeline\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "ZPBDMzSSk_JB", + "colab_type": "code", + "colab": {} + }, + "source": [ + "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV\n", + "from time import time\n", + "\n", + "# Utility function to report best scores\n", + "def report(results, n_top=3):\n", + " for i in range(1, n_top + 1):\n", + " candidates = np.flatnonzero(results['rank_test_score'] == i)\n", + " for candidate in candidates:\n", + " print(\"Model with rank: {0}\".format(i))\n", + " print(\"Mean validation score: {0:.3f} (std: {1:.3f})\"\n", + " .format(results['mean_test_score'][candidate],\n", + " results['std_test_score'][candidate]))\n", + " print(\"Parameters: {0}\".format(results['params'][candidate]))\n", + " print(\"\")\n", + "\n", + "## In this situation, we can define a grid for our search\n", + "\n", + "\n", + "## Now we can perform our grid search with 5-fold cross validation\n", + "\n", + "\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "hO2B-HU2nr8g", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## Now let's see how the random search will take for us\n", + "import scipy.stats as stats\n", + "## here we define the range for search first\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "mvBdl6rjpr8x", + "colab_type": "code", + "colab": {} + }, + "source": [ + "\n", + "## run randomized search\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yeKqF2zQpvAk", + "colab_type": "text" + }, + "source": [ + "## Assessing feature importance \n", + "\n", + "Assuming that we finally find our best random forest model. We want to know which features have higer importance than others. What we can do is to use the [permutation feature importance](https://scikit-learn.org/stable/modules/permutation_importance.html#) functionality in scikit-learn.\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "J1-t7VxZqWTI", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## Now, let's use the model hyperparameter from our random search as the final model\n", + "\n", + "\n", + "## Let's calculate feature importance here\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "jHO7LMsTrQi4", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## We want to visualize the ranking of individual features\n", + "\n", + " " + ], + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/02_Week2/Workshop_machine_learning/README.md b/02_Week2/Workshop_machine_learning/README.md new file mode 100644 index 0000000..916538c --- /dev/null +++ b/02_Week2/Workshop_machine_learning/README.md @@ -0,0 +1,15 @@ +# Table of Contents: + +## 05_Workshop_machine_learning_exercise.ipynb + +This is the in-class exercise notebook for implementing random forest for regression. + +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/uofscphysics/STEM_Python_Course/blob/Summer2020/02_Week2/Workshop_machine_learning/05_Workshop_machine_learning_exercise.ipynb) + +_____ +## 05_Workshop_machine_learning_complete.ipynb + +This is the complete notebook for implementing random forest for regression. + +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/uofscphysics/STEM_Python_Course/blob/Summer2020/02_Week2/Workshop_machine_learning/05_Workshop_machine_learning_complete.ipynb) + diff --git a/Day_1/README.md b/Day_1/README.md deleted file mode 100644 index 0f98c11..0000000 --- a/Day_1/README.md +++ /dev/null @@ -1,5 +0,0 @@ -## Day 1 - -* Introduction to python and programming: [Introduction_tutorial](Introduction_tutorial) - -* Introduction to git and unix/linux commands: [Introduction_git](Introduction_git) diff --git a/Day_2/511_lab_E_100k.csv.bz2 b/Day_2/511_lab_E_100k.csv.bz2 deleted file mode 100644 index b6de159..0000000 Binary files a/Day_2/511_lab_E_100k.csv.bz2 and /dev/null differ diff --git a/Day_2/Cheat_Sheets/Chang_LaTeX_sheet.pdf b/Day_2/Cheat_Sheets/Chang_LaTeX_sheet.pdf deleted file mode 100644 index d22f516..0000000 Binary files a/Day_2/Cheat_Sheets/Chang_LaTeX_sheet.pdf and /dev/null differ diff --git a/Day_2/Cheat_Sheets/Numpy_Python_Cheat_Sheet.pdf b/Day_2/Cheat_Sheets/Numpy_Python_Cheat_Sheet.pdf deleted file mode 100644 index 40d67a3..0000000 Binary files a/Day_2/Cheat_Sheets/Numpy_Python_Cheat_Sheet.pdf and /dev/null differ diff --git a/Day_2/Cheat_Sheets/Pandas_Cheat_Sheet.pdf b/Day_2/Cheat_Sheets/Pandas_Cheat_Sheet.pdf deleted file mode 100644 index 48da05d..0000000 Binary files a/Day_2/Cheat_Sheets/Pandas_Cheat_Sheet.pdf and /dev/null differ diff --git a/Day_2/Cheat_Sheets/Python_Matplotlib_Cheat_Sheet.pdf b/Day_2/Cheat_Sheets/Python_Matplotlib_Cheat_Sheet.pdf deleted file mode 100644 index c720626..0000000 Binary files a/Day_2/Cheat_Sheets/Python_Matplotlib_Cheat_Sheet.pdf and /dev/null differ diff --git a/Day_2/Cheat_Sheets/Python_SciPy_Cheat_Sheet_Linear_Algebra.pdf b/Day_2/Cheat_Sheets/Python_SciPy_Cheat_Sheet_Linear_Algebra.pdf deleted file mode 100644 index d6fa822..0000000 Binary files a/Day_2/Cheat_Sheets/Python_SciPy_Cheat_Sheet_Linear_Algebra.pdf and /dev/null differ diff --git a/Day_2/Cheat_Sheets/Sympy.pdf b/Day_2/Cheat_Sheets/Sympy.pdf deleted file mode 100644 index 8c80ffe..0000000 Binary files a/Day_2/Cheat_Sheets/Sympy.pdf and /dev/null differ diff --git a/Day_2/Cheat_Sheets/readme.md b/Day_2/Cheat_Sheets/readme.md deleted file mode 100644 index 768dcd0..0000000 --- a/Day_2/Cheat_Sheets/readme.md +++ /dev/null @@ -1,11 +0,0 @@ -Cheat Sheets -============ - -Contents: - * Latex Cheat sheet - * Numpy Cheat sheet - * Pandas Cheat sheet - * Matplotlib Cheat sheet - * SciPy Cheat sheet - * Sympy Cheat sheet - diff --git a/Day_2/ExtraExamples/Pandas_example.ipynb b/Day_2/ExtraExamples/Pandas_example.ipynb deleted file mode 100644 index 9b7fd15..0000000 --- a/Day_2/ExtraExamples/Pandas_example.ipynb +++ /dev/null @@ -1,245 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "import numpy as np\n", - "\n", - "\n", - "# I realize not everyone will have ROOT installed so i've commented out the root specific parts\n", - "\n", - "#from ROOT import TLorentzVector\n", - "\n", - "#@np.vectorize\n", - "#def q2_calc(px, py, pz):\n", - "# e_beam = TLorentzVector(0, 0, 4.8, 4.8)\n", - "# e_prime = TLorentzVector()\n", - "# e_prime.SetXYZM(px, py, pz, 0.000511)\n", - "# return -(e_beam - e_prime).M2()\n", - "\n", - "#@np.vectorize\n", - "#def W_calc(px, py, pz):\n", - "# e_beam = TLorentzVector(0, 0, 4.8, 4.8)\n", - "# p_targ = TLorentzVector(0, 0, 0.0, 0.93828)\n", - "# e_prime = TLorentzVector()\n", - "# e_prime.SetXYZM(px, py, pz, 0.000511)\n", - "# return (e_beam - e_prime + p_targ).M()\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
pcxcycz
count100000.000000100000.000000100000.000000100000.000000
mean2.809895-0.0092480.0001670.927430
std0.7974050.2764660.2490210.036664
min1.500080-0.677151-0.6760800.726911
25%2.096775-0.248829-0.2341650.910984
50%2.804645-0.096869-0.0027300.937082
75%3.5116350.2305670.2371270.954553
max4.5577700.6726100.6628560.974583
\n", - "
" - ], - "text/plain": [ - " p cx cy cz\n", - "count 100000.000000 100000.000000 100000.000000 100000.000000\n", - "mean 2.809895 -0.009248 0.000167 0.927430\n", - "std 0.797405 0.276466 0.249021 0.036664\n", - "min 1.500080 -0.677151 -0.676080 0.726911\n", - "25% 2.096775 -0.248829 -0.234165 0.910984\n", - "50% 2.804645 -0.096869 -0.002730 0.937082\n", - "75% 3.511635 0.230567 0.237127 0.954553\n", - "max 4.557770 0.672610 0.662856 0.974583" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data = pd.read_csv(\"../511_lab_E_100k.csv.bz2\")\n", - "data.describe()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%time\n", - "\n", - "px = []\n", - "py = []\n", - "pz = []\n", - "#q2 = []\n", - "#w = []\n", - "for index, row in data.iterrows():\n", - " px.append(row.p*row.cx)\n", - " py.append(row.p*row.cy)\n", - " pz.append(row.p*row.cz)\n", - " #q2.append(q2_calc(px[-1],py[-1],pz[-1]))\n", - " #w.append(W_calc(px[-1],py[-1],pz[-1]))\n", - " \n", - "data['px'] = px\n", - "data['py'] = py\n", - "data['pz'] = pz\n", - "#data['q2'] = q2\n", - "#data['w'] = w" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%time \n", - "data['px'] = data['cx'] * data['p']\n", - "data['py'] = data['cy'] * data['p']\n", - "data['pz'] = data['cz'] * data['p']\n", - "#data['q2'] = q2_calc(data['px'],data['py'],data['pz'])\n", - "#data['w'] = W_calc(data['px'],data['py'],data['pz'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot directly from pandas df\n", - "data['pz'].hist(bins=50)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Slices data and puts into new dataframe to only keep values where pz < 1.0\n", - "data_2 = data[data['pz'] < 1.0]\n", - "# Can also plot all of the values at once not just a single plot\n", - "data_2.hist(bins=50,figsize=(20,20))\n", - "plt.show()" - ] - }, - { - "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": "3.7.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Day_2/ExtraExamples/StandardMatplotlib.ipynb b/Day_2/ExtraExamples/StandardMatplotlib.ipynb deleted file mode 100644 index b579085..0000000 --- a/Day_2/ExtraExamples/StandardMatplotlib.ipynb +++ /dev/null @@ -1,966 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "***********\n", - "Usage Guide\n", - "***********\n", - "\n", - "This tutorial covers some basic usage patterns and best-practices to\n", - "help you get started with Matplotlib.\n", - "\n", - "\n", - "General Concepts\n", - "================\n", - "\n", - ":mod:`matplotlib` has an extensive codebase that can be daunting to many\n", - "new users. However, most of matplotlib can be understood with a fairly\n", - "simple conceptual framework and knowledge of a few important points.\n", - "\n", - "Plotting requires action on a range of levels, from the most general\n", - "(e.g., 'contour this 2-D array') to the most specific (e.g., 'color\n", - "this screen pixel red'). The purpose of a plotting package is to assist\n", - "you in visualizing your data as easily as possible, with all the necessary\n", - "control -- that is, by using relatively high-level commands most of\n", - "the time, and still have the ability to use the low-level commands when\n", - "needed.\n", - "\n", - "Therefore, everything in matplotlib is organized in a hierarchy. At the top\n", - "of the hierarchy is the matplotlib \"state-machine environment\" which is\n", - "provided by the :mod:`matplotlib.pyplot` module. At this level, simple\n", - "functions are used to add plot elements (lines, images, text, etc.) to\n", - "the current axes in the current figure.\n", - "\n", - "

Note

Pyplot's state-machine environment behaves similarly to MATLAB and\n", - " should be most familiar to users with MATLAB experience.

\n", - "\n", - "The next level down in the hierarchy is the first level of the object-oriented\n", - "interface, in which pyplot is used only for a few functions such as figure\n", - "creation, and the user explicitly creates and keeps track of the figure\n", - "and axes objects. At this level, the user uses pyplot to create figures,\n", - "and through those figures, one or more axes objects can be created. These\n", - "axes objects are then used for most plotting actions.\n", - "\n", - "For even more control -- which is essential for things like embedding\n", - "matplotlib plots in GUI applications -- the pyplot level may be dropped\n", - "completely, leaving a purely object-oriented approach.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# sphinx_gallery_thumbnail_number = 3\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Parts of a Figure\n", - "=================\n", - "\n", - "![](../../_static/anatomy.png)\n", - "\n", - "\n", - "\n", - ":class:`~matplotlib.figure.Figure`\n", - "----------------------------------\n", - "\n", - "The **whole** figure. The figure keeps\n", - "track of all the child :class:`~matplotlib.axes.Axes`, a smattering of\n", - "'special' artists (titles, figure legends, etc), and the **canvas**.\n", - "(Don't worry too much about the canvas, it is crucial as it is the\n", - "object that actually does the drawing to get you your plot, but as the\n", - "user it is more-or-less invisible to you). A figure can have any\n", - "number of :class:`~matplotlib.axes.Axes`, but to be useful should have\n", - "at least one.\n", - "\n", - "The easiest way to create a new figure is with pyplot:\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure() # an empty figure with no axes\n", - "fig.suptitle('No axes on this figure') # Add a title so we know which it is\n", - "\n", - "fig, ax_lst = plt.subplots(2, 2) # a figure with a 2x2 grid of Axes" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - ":class:`~matplotlib.axes.Axes`\n", - "------------------------------\n", - "\n", - "This is what you think of as 'a plot', it is the region of the image\n", - "with the data space. A given figure\n", - "can contain many Axes, but a given :class:`~matplotlib.axes.Axes`\n", - "object can only be in one :class:`~matplotlib.figure.Figure`. The\n", - "Axes contains two (or three in the case of 3D)\n", - ":class:`~matplotlib.axis.Axis` objects (be aware of the difference\n", - "between **Axes** and **Axis**) which take care of the data limits (the\n", - "data limits can also be controlled via set via the\n", - ":meth:`~matplotlib.axes.Axes.set_xlim` and\n", - ":meth:`~matplotlib.axes.Axes.set_ylim` :class:`Axes` methods). Each\n", - ":class:`Axes` has a title (set via\n", - ":meth:`~matplotlib.axes.Axes.set_title`), an x-label (set via\n", - ":meth:`~matplotlib.axes.Axes.set_xlabel`), and a y-label set via\n", - ":meth:`~matplotlib.axes.Axes.set_ylabel`).\n", - "\n", - "The :class:`Axes` class and it's member functions are the primary entry\n", - "point to working with the OO interface.\n", - "\n", - ":class:`~matplotlib.axis.Axis`\n", - "------------------------------\n", - "\n", - "These are the number-line-like objects. They take\n", - "care of setting the graph limits and generating the ticks (the marks\n", - "on the axis) and ticklabels (strings labeling the ticks). The\n", - "location of the ticks is determined by a\n", - ":class:`~matplotlib.ticker.Locator` object and the ticklabel strings\n", - "are formatted by a :class:`~matplotlib.ticker.Formatter`. The\n", - "combination of the correct :class:`Locator` and :class:`Formatter` gives\n", - "very fine control over the tick locations and labels.\n", - "\n", - ":class:`~matplotlib.artist.Artist`\n", - "----------------------------------\n", - "\n", - "Basically everything you can see on the figure is an artist (even the\n", - ":class:`Figure`, :class:`Axes`, and :class:`Axis` objects). This\n", - "includes :class:`Text` objects, :class:`Line2D` objects,\n", - ":class:`collection` objects, :class:`Patch` objects ... (you get the\n", - "idea). When the figure is rendered, all of the artists are drawn to\n", - "the **canvas**. Most Artists are tied to an Axes; such an Artist\n", - "cannot be shared by multiple Axes, or moved from one to another.\n", - "\n", - "\n", - "Types of inputs to plotting functions\n", - "=====================================\n", - "\n", - "All of plotting functions expect `np.array` or `np.ma.masked_array` as\n", - "input. Classes that are 'array-like' such as `pandas` data objects\n", - "and `np.matrix` may or may not work as intended. It is best to\n", - "convert these to `np.array` objects prior to plotting.\n", - "\n", - "For example, to convert a `pandas.DataFrame` ::\n", - "\n", - " a = pandas.DataFrame(np.random.rand(4,5), columns = list('abcde'))\n", - " a_asarray = a.values\n", - "\n", - "and to convert a `np.matrix` ::\n", - "\n", - " b = np.matrix([[1,2],[3,4]])\n", - " b_asarray = np.asarray(b)\n", - "\n", - "\n", - "Matplotlib, pyplot and pylab: how are they related?\n", - "====================================================\n", - "\n", - "Matplotlib is the whole package and :mod:`matplotlib.pyplot` is a module in\n", - "Matplotlib.\n", - "\n", - "For functions in the pyplot module, there is always a \"current\" figure and\n", - "axes (which is created automatically on request). For example, in the\n", - "following example, the first call to ``plt.plot`` creates the axes, then\n", - "subsequent calls to ``plt.plot`` add additional lines on the same axes, and\n", - "``plt.xlabel``, ``plt.ylabel``, ``plt.title`` and ``plt.legend`` set the\n", - "axes labels and title and add a legend.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "x = np.linspace(0, 2, 100)\n", - "\n", - "plt.plot(x, x, label='linear')\n", - "plt.plot(x, x**2, label='quadratic')\n", - "plt.plot(x, x**3, label='cubic')\n", - "\n", - "plt.xlabel('x label')\n", - "plt.ylabel('y label')\n", - "\n", - "plt.title(\"Simple Plot\")\n", - "\n", - "plt.legend()\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - ":mod:`pylab` is a convenience module that bulk imports\n", - ":mod:`matplotlib.pyplot` (for plotting) and :mod:`numpy`\n", - "(for mathematics and working with arrays) in a single namespace.\n", - "pylab is deprecated and its use is strongly discouraged because\n", - "of namespace pollution. Use pyplot instead.\n", - "\n", - "For non-interactive plotting it is suggested\n", - "to use pyplot to create the figures and then the OO interface for\n", - "plotting.\n", - "\n", - "\n", - "Coding Styles\n", - "==================\n", - "\n", - "When viewing this documentation and examples, you will find different\n", - "coding styles and usage patterns. These styles are perfectly valid\n", - "and have their pros and cons. Just about all of the examples can be\n", - "converted into another style and achieve the same results.\n", - "The only caveat is to avoid mixing the coding styles for your own code.\n", - "\n", - "

Note

Developers for matplotlib have to follow a specific style and guidelines.\n", - " See `developers-guide-index`.

\n", - "\n", - "Of the different styles, there are two that are officially supported.\n", - "Therefore, these are the preferred ways to use matplotlib.\n", - "\n", - "For the pyplot style, the imports at the top of your\n", - "scripts will typically be::\n", - "\n", - " import matplotlib.pyplot as plt\n", - " import numpy as np\n", - "\n", - "Then one calls, for example, np.arange, np.zeros, np.pi, plt.figure,\n", - "plt.plot, plt.show, etc. Use the pyplot interface\n", - "for creating figures, and then use the object methods for the rest:\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "x = np.arange(0, 10, 0.2)\n", - "y = np.sin(x)\n", - "fig, ax = plt.subplots()\n", - "ax.plot(x, y)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So, why all the extra typing instead of the MATLAB-style (which relies\n", - "on global state and a flat namespace)? For very simple things like\n", - "this example, the only advantage is academic: the wordier styles are\n", - "more explicit, more clear as to where things come from and what is\n", - "going on. For more complicated applications, this explicitness and\n", - "clarity becomes increasingly valuable, and the richer and more\n", - "complete object-oriented interface will likely make the program easier\n", - "to write and maintain.\n", - "\n", - "\n", - "Typically one finds oneself making the same plots over and over\n", - "again, but with different data sets, which leads to needing to write\n", - "specialized functions to do the plotting. The recommended function\n", - "signature is something like:\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "def my_plotter(ax, data1, data2, param_dict):\n", - " \"\"\"\n", - " A helper function to make a graph\n", - "\n", - " Parameters\n", - " ----------\n", - " ax : Axes\n", - " The axes to draw to\n", - "\n", - " data1 : array\n", - " The x data\n", - "\n", - " data2 : array\n", - " The y data\n", - "\n", - " param_dict : dict\n", - " Dictionary of kwargs to pass to ax.plot\n", - "\n", - " Returns\n", - " -------\n", - " out : list\n", - " list of artists added\n", - " \"\"\"\n", - " out = ax.plot(data1, data2, **param_dict)\n", - " return out\n", - "\n", - "# which you would then use as:\n", - "\n", - "data1, data2, data3, data4 = np.random.randn(4, 100)\n", - "fig, ax = plt.subplots(1, 1)\n", - "my_plotter(ax, data1, data2, {'marker': 'x'})" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "or if you wanted to have 2 sub-plots:\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAD8CAYAAABjAo9vAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzsnXd4VHX2h987k0nvvRFSSAgklEDoTYoCIoooqAgCNmxrW921/Syru+q6uq6iIjYUsYAFKQqCdBJKQkggjfReSO/JlPv7Y0pmkgkkEA2J930eHjLt3m8m95577jmfc44giiISEhISEgMHWV8vQEJCQkKid5EMu4SEhMQAQzLsEhISEgMMybBLSEhIDDAkwy4hISExwJAMu4SEhMQAQzLsEhJdIAiCtSAIJwRBSBQEIVkQhJf6ek0SEt1BkHTsEhLmEQRBAOxEUWwQBEEBHAEeEUXxWB8vTULiglj09QIkJK5URK3X06B7qND9kzwhiSuePjHs7u7uYmBgYF/sWuJPQHx8fIUoih69sS1BEORAPDAEeE8UxeMXer90bEv8nnT32O4Twx4YGEhcXFxf7FriT4AgCHm9tS1RFNXAaEEQnIEfBUGIFEXxbIf93QvcCxAQECAd2xK/G909tqXkqYRENxBFsQY4AMwz89p6URSjRVGM9vDolRsFCYnLQjLsEhJdIAiCh85TRxAEG2AOkNa3q5KQuDhS8lRComt8gM91cXYZsFkUxR19vCYJiYsiGXYJiS4QRTEJiOrrdUhI9BQpFCMhISExwJAMu8Rls+5gFjFZFSbPxWRVsO5gVh+tSOJy2JpQxJTX9hH01E6mvLaPrQlFfb0kiR4iGXaJy2akvxMPfZVgMO4xWRU89FUCI/2d+nhlEj1la0IRT/9whqKaZkSgqKaZp384Ixn3foYUY5e4bCaHuLN2WRRrNsZz7Qhv9qSUs3ZZFJND3Pt6aRI95I3d6TQr1SbPNSvVvLE7nUVRfn20KomeInnsEr2CSi3S0KLi25OFLJ8QIBn1fkpxTXOPnpe4MpEMu8Rlk1xcy71fxCECyycE8OXx/E4xd4n+ga+zTY+el7gykQy7xGVRVNPM7R8dp0WlYeZQD165cQRrl0WZxNwl+g9Pzh3a6Tkbhdzs8xJXLpJhl7hkapuUrPr0BDXNShQygVcXjwTaY+5JhbV9vEKJnmIcRxcAP2cbXl08Qoqv9zOk5KnEJdGqUnPvxjgyyrVdbe+aFoy3k7Xh9ckh7lKcvZ+T89qCvl6CxCUiGXaJHqPRiDyxJYnjOVXIBHCwVnD/jJC+XpbEZbA1oYg3dqcbkqQ2Culmvj8j/fUkeszru9PYnljMjDAPNCI8ODMEJ1tFXy9L4hLpqF0HUKpFSbvej5EMu0SP+CI2lw8PZrN8YgA1TW34Ollzx6TAvl6WxGVgTruu0oi8sTu9j1YkcblctmEXBGGQIAj7BUFI1Q38faQ3FiZx5fFrcikvbktmzjAvJgS5kVhYy2NXh2GtkPf10iQuA0m7PvDoDY9dBfxVFMVhwETgQUEQhvfCdiWuIBLyq3n4mwRG+Dvz1i2jeGvPOcK87Fk8xr+vlyZxmUja9YHHZRt2URRLRFE8pfu5HkgFJG3UACK3opG7Po/Dy9GaT1ZGs+10MTkVjfxtbjhymdDXy5O4TJ6cOxRLuakpkLTr/ZtejbELghCItn91p4G/giDcKwhCnCAIcefPn+/N3Ur8jlQ2tLLysxMAbFg9HltLOf/7LYNxgS7MHubZx6uT6A0WRfkxLdRUmvp/1w2TtOv9mF4z7IIg2APfA4+KoljX8XVpLmT/o7lNzV2fx1Fa28LHK6MJcrfj0yM5nK9v5an54QiC5K0PFCoaWk0eW1lIeZP+TK8YdkEQFGiN+iZRFH/ojW1K9C1qjcjD3ySQWFjDO7dFMSbAharGNtYdzOaa4V6MHeza10uU6CXqWpScKWqvEvZytGJXcmkfrkjicukNVYwAfAKkiqL41uUvSaKvEUWRl7YnsyeljBcXRjA3whuAtfsyaWpT8bd5Uux1IHEiuwqN2P54fqQPh86dp7FV1XeLkrgsesNjnwKsAGYJgnBa9+/aXtiuRB+x/lA2X8Tmce/0YFZODgSgoKqJjcdyWRo9iCGeDn27QIleJSar0uTxvEhvWlUaDp6TcmH9lctuKSCK4hG0/YIkBgDbEot59Zc0rhvpw1Pzwg3Pv7XnHDJB4NE5YX24OonfA+MunItG+zIu0BU3O0t2nS3l2hE+fbgyiUtFqjzVIc3thGPZlTyxOZHxQa68uXQUMp2UMaW4jq2ni1g9Jcik0ZdE/6eyoZW00nrD4yXRg5DLBK4e7sW+tHJaVeoLfFriSkUy7Dr+7HM7M8rqufeLOALcbPloRbSJKuLfu9NwlBp9DUiOZVeZPB4T4ALA3EhvGlpVxGRWmvuYxBWO1N1Rx+QQd95aOoq7NsSxbEIAPyYU/WnmdpbVtbDqs5NYKeRsWD3OpKFXTFYFB9LP88y14VKjrwFITFYF9lYWNOgSpTaW2gv65BA3HKws2HW2lJnhA79ewbi7pa+zDU/OHdqvdfySx27ESH9nWpRqPjmSw9Jo/z+FUW9oVbH6s5PUNLXx2apx+LvYGl4TRZHXf0mTGn0NYGKzKgn2sOv0vJWFnFnDPNmTWoZKremDlf1xdOxuWVTTzNM/nOnX3S0lw25EWmkdlhbar+STIzkczhjYqgClWsP9X8aTXlbP+8vHEulnGnb65Wyp1OhrAFNa20J2RSMqtVbrOKdDJfG8CG+qGts4mVvdF8v7wzDX3bJZqe7X3S0lw65DH1N/a+korBUylGqRuz+PIyZzYM7tFEWRZ344w+GMCl5dPIIZYabVwEq1hjd2p0uNvgYwsdnaY7ugugmAm8cOMnl9xlAPrCxk7B7gxUoDsbulZNh1JBXWsnZZFAtG+nLnlCAAWlWay1LFdKW0WfXZiT5X4Ly9N4Mt8YU8MjuUpdGDOr2+Oa5AavQ1wInJrMTZVkF9iza+PiHItJrY1tKC6WEe7Dpbisa4gmmAMRC7W0qGXcd9M0IMMfU1M0JwstEmCg9lVPDDqcJL2mZXSpspQ9wMz687mMVHh7NMFDi/p5FfdzCLf+1M5X+/ZbBkrD+PzgnttL+mNhVv75UafQ1kRFEkJquSScFuhudc7Cw7vW9ehDeldS0kFQ3cweRPzh3aaRRgf+9uKRl2MzjZKHhwplbaZ2Uh42/fJXH0EkIyk0PcWXtbFHduOMmSdTHc92U8a2+L4p5pIaxdFsWDm05xIqeKf+1M4/6rgpkc4v6HyCzXH85mpL8T/1o8gtjsyk77kxp9aRnIQ2QKqpopqmkm1OvCVcSzh3liIRPYdXbghmMWRfnxzIJhhsd+zja8unhEv1bFSHLHLrhjUiCfHc3FxlKOhUzgvo3xbLl/EuHejj3aToSfEy1KjSEB9ezWs1w7wpv5kT74ONmwL60cuQD/3JnGf/dkIJcJ/PeW0b+LIudsUS3v/pbBYFdbzpXV88g3pzmWXWki65QafZmgHyJzShAEByBeEIQ9oiim9PXCLhf9XaRCF2brGIbR42xryaQQN3adLeHv84YO2At91CCtfn+UvxM/PTS1j1dz+UgeexdYK+Q8NieM7PON3DouADsrC1Z9epKS2p4lVPamaD2daaHu2FrKsbeSs+5gNte9e4SUEm13Y334sqlNTX2Linu+iGPe24e48f2j/HtXGgVVTYii9k2XGqYprG5i9YaTONtacv1oX1qUGn4+U8LyCQEmFxGp0Vc7A3mITExWJR4OViQU1ACYzbPomRvhTW5lE+fKGv6o5XVia0IRU17bR9BTO5ny2r5elyKW1LYAMFBSCZJhvwCLx/gxxNOeL4/n8fHKaIPmu75F2a3Px2RV8OJ2rXN37/RgPl4ZTVFNC4/MDsXOUs4gF21yxvhYspTLuHG0Hx4OVqSW1PH+gSym/Xs/4//1G0vXxXD353HYWsppU3VfW1zbpOSGtUdpbFHyyOxQ3j+QCUDUIGc+Opxj8N4Kqpr48lgeS8ZKjb46MpCGyOjj65ND3NiXVg7A+C48doBrhnshCPRZOOaP0JnrHTaNODAsu2TYL4CFXMaTc4eSfb6RM0W1fLB8DJnlDdz/5aluGdakwlrunqpV2LjZWWlj7suiOJVfzUcrozn891n4OJr2XmlTa9h6ugh7Kwu+uHMC/7oxEisLGW0qNXF51TS1qXn+p2RGvLibpetiee2XNPamlFHd2GZ2Da0qNfdsjKO2WYlMJuMfO5LR15tknW/g8WtCDYnc/+45hyDAo1eHXt4XN8AYaENkMssbqGhoZXJIe+LU36VrBYinozVjA1z6rEf7H6EzH2geuxRjvwjXDPdiTIAzb+89x4EnZvLaTSN5YksiT/2QxJtLRl0w5njfjBC+PJYHgLu9VnEwOcTdJPQR7uNASV0LD1wVwvsHtCGW0YOc+TW5jF+MPKRWlYbbJwTw8OxQ4vOqic+rJi6vmo8PZ7PuoPZoDPawI3qwC2MHuzB2sCtB7nb8dXMiJ3KqePuW0aw/lG0I/wC8uyyKGWGeRPg6sSeljB9PF7Fmegg+Tv1X5tXbDMQhMvo2vWFGidOLxc7nRXrzys5U8iobGezWuVL19+SP0JmX6g37ALHskmG/CIIg8Pd54dyy/hgbYnK5/6oQimuaeWvPOfydbXj8mgvHoisbtJ60OSkZaE+u/ennSTUyuCsnBXLSp4ovj+ebvHfT8XzK6lqYFOKGm70lPz04hRalmsSCGuLzq4nPrebXlDI2x5nKM51tFXwem2ti1D3sLZkRppUyTg5xZ/2hbKnRVwcG6hCZ2KxK/F1sqNLd5UX6XVwQMDdCa9h3J5dy7/Q/9hjxdbahyIwR702duf4iIYVi/kRMCHZj5lAPPjiQSW2Tkr/MGsKt4wbxzr5MvjmRf8HPVja24myrQCE3/1XPGOqBXCZwWpfEAnj029N8fTIfR2sLbBTahKtejrg3tZyXd6Sy7kAWz/54hlN51Ywd7MIDVw3hk1XjSPi/q9n7+AwifNtP1pomJQn5NSb7rWho4+ekYqC90deDM0P4+mR+nxdPXUEMuCEyGo1IbLY2vv7Tae3f/0KJUz2DXG2J8HXskzj7o3M6hwZ7W2deWqcPxUiG/U/F3+aFU9+q4v2DmQiCwMuLIpkR5sGzW8+yP728y89VNrTh1oW3Dlpv+fpRPlQ3tSdkReDaEb6smhLEJ6uiWX9HNJnl7YqEkX5OTAv14IdTRSz7+DgT/vUbT/9whsMZ51FpRDLLG0gpqePq4V7seWy62f2KwANfJRD98h6WfaTNB44LdCXS98/dvtgYURSPiKIoiKI4UhTF0bp/P/f1ui6HlJI6apuVTA5xZ1ui1rBfKHFqzLwIb07l11CmM4J/FE1tpvH1nujMu6OmEUXREGMfIHZdCsV0l2E+jiwa7ceGo7msmhyIj5MN790+hls+jOXBTafYvGZSpyZaoJ3+7mZvdcFth3k5AsUmz724cLjhc9sSi00O7qSiWqwUMpZNCKBFqaasroWfThfxdYe7h5dviGT5J8dxs7Mk2MPOpJnTiwuHk1neYBLuufH9GOytLAhyt+WuDXEsjfZne1LJn6Z98Z+BWF18fZJR4jTEw75bn50X6c2be87xa3IpK/6gbp9tKg0fGt0t7n50OkO9u6fY0qtp9IlXvZoGMLkoVDcpDWIIyWP/E/L41WFoRJH/7c0AwN7Kgs9WjcPF1pLVG05SUNXU6TOVjW2GxKm53jFfH8/n9V1pnT73W6r2LiC3opG/fZeIhUzgnmlBWOgKSk7mVrMhJpdNx/M5kH6eIHc7w2t6Jr76G5nlDdww2o/8Dmu7dXwAL1wfQbC7HaGe9hx44irevmU0i6K0GvdmpZrPY/M66dwl+jcxWRUEe9iZ9P/pKkzYkSGe9gR72LE7uez3Wl4ntp4uori2hejB2gKiQa7dj6t3V02jj6/bWsoHjCpGMuw9YJCrLbdPGMzmuAJDaMTT0ZoNq8fRqlSzesNJaptMNe6VDa242Wk9b+PeMUq1hr9uPs3TP57ptB+ZAF+fyKdVpd1mq1LDW0tH8eyC4Xy2ehxymYCjtQWH/jaTz+8czz3Tg2lsVaHq4qj89GgOZXWthsfejtZYK+Rsjisgu6KRG0b7siu5lEVRfryyaAQTdf1Dbhrjx5fHO8fcJfonSrWGEzlVTA5x44yu90ugm+1FPtWOIAjMi/AmNruSmibz8treRK0R+eBAFhG+jgS52+Fub4WtZfeDDN1V0+gVMb7ONpLH/mfloVlDsFHI+Y/RVT/Uy4H1d0STX9nEPRvjDHMi39+fSXWTEjcjqeP9M4JZ/dlJJr36G9+fKjIUKRkzJcSdpKJant+aTE5FI49dHcb1o/1YdzALuUzg5RsiqG9V8b+951DIBWwVcpxstft44pqwi8ZMS+tauOmDGJ798SxB7nZ8ejTXEEM/kF7Ol8fzGObjwJtLR7N2WZRJzF2i/5JUWEtjm5rJIe7sSdF63Uu6kTg1Zl6kN2qNyN7UrvNKvcUvZ0vIqWjkwZlDKKxuJqAH3jrQ5XzejmqaEl3OwM/ZZsDE2CXD3kPc7a24Z3owu5JLSchvj1lPDHbjP0tHcSKnir9uTkSjEQl013pDtc3aGN4bu9J4bVc6rSoNFQ1tLBjhw/9ui+q0j2lh7qg1It/GFbBqciAPz9aqAvQef6C7HffNCGFzXCFrNsazP/08ZwprWL9iLEuiB5F9vpFgdzvinpvDiwuHm/094vO0a8+paCTMy57aJiXNbWo2xuYhinCTrge7vqhqe2Lxn1UZM2CI1V2cJwa7sflkge7nnvUDGuHnhK+T9e+ujhFFkff2ZxHiYce8CG/yq5oIcO3+3UWbSoOtmeEw5tQ0JTXNWMgEPBysJI/9z8zd04Jxs7Pk9V1phh4uANeP8uWp+eHsSCrh9V1pBLlrk1JfHstj9D9+5b0DWThZW2BrKee+GcHEZlfyXVxBp+3XGIVznr423PCz3sjetzGexhYVMgHqW1Scyq/mxesjuGqoJw9sOkVTm4p1K8bibm9Fakk91goZB564yrCdGWEeJm1Kj2VXcf+mU0S8sIvfdCXm7+3PNPHSdyeX/SmVMQOJmKxKhvk44mpnaQjb9bSpnSAIzI305lDGeRp1c1J/D/allZNaUscDVw1BpREpqW3utmEXRZEXtiWTVdEIgI1ChkDXaprS2ha8HK1RyAXUAyTILhn2S8DeyoK/zBrCsewqDp4z7Q2yZnowKyYO5sND2fx3zzkAlGqRpjY14wa7AAIfr4zmqfnDmBvhxea4Qmx1RjbUU3sh0FegAhzPrjLxlIPd7alvUfHFsTyTZOkdkwL518+pxOdV8/pNIwnzcqCqsY2tp4u4McqfSqOWAysnD8bG0oKbxrQf4MN9HE0SRw2tKlZ+eoJHv0ngoa8SJGVMP6dFqSY+r5rJIW5UNrTnW+ysei6MmxfhTZtKw4H036cvjiiKrN2fib+LDdeP9qW4phmNqM1xdYcvYvP4+kS+YZbr+8vHkvPaAo4+NcusRLKktgUfJ2sEQZCSp392lk0YzCBXG17flW5ShiwIAi9eH8GcYV78qotj2llZIACn8mu4f2awwUBeN8IXlUbEWZdcDTQq1b5+lC8AD246ZeIpv/pLqqFpWJtuVqUgwD93prAhJpe7pgaxUPdZbQJWw+opgeTovBeZAE9sSWLtsijeXDqaF3ShmoqGVgRB23/+6fnhTAv1QAC2ni6WlDEDgIT8GlpVGpPEqftFZLhdER3oipud5e/WOyY2u5KE/BrWzAhBIZcZFF3d8diPZlbwjx0pzBnmyWBXW7wcrZgeeuH+PaV1LXg7WSMTMLkD789Ihv0SsbSQ8derh5JaUsf2JFMNulwm8K5R7PyhmSH89Zow1KLI23szDCEOT0ftiaXP0h84156Q0p9844JcDUb1QFo5204XY2dpGjsURfjocA5jApx5ar42dKNUa9gYm8fUIe6EeTmQfV6r4gnzcjDxvldPCeL/rhtGeX0rogj/WTKKNTNCuHtaEPZWCh68KkRSxgwAYrMqkMsExge5GobGLI2+tFm2cpnANRFe7Esto6WDnLA3eG9/Jh4OViwZq12fwbBfRMGTU9HIA5tOEeJhx7MLhnMoo4LFY/wvONpRFEWKa5rxdbZBJghSjF1C61UP83HkzV/Pder2aGMpZ6SuYGn9oWzmj/AhKsAZUSMaTqyO09+V6vaDKqeikSGediQV1hrift/EFSACjW1qQj3tOfnsHMYEuBg+E+nnZNAk7zpbSmldC6unBOr2VQXAzWP9O3nfyycONvz8/oEs9iSXasMvt0fx5LxwSRkzAIjJqiTSzwkHa4Whl9AEo7F4PWVuhDeNbepePyYS8qs5mlnJPdOCsNYlPwuqmrCUy/ByMK9yAahrUXL35yeRCfDxHeP4NbkUtUbk5rEXvnjVNClpVWnwdrTWGfZe/XX6DMmwXwYymcDf5g0lv6qpU9UnaKtV9dy14STPLRiOiEBSYS0ajUicztiaw8pCRkltCxUNrSTkV2t7aOsuCJ4OVmy4czxudpa42ikMn/kiNs/Qc+azozkMdrNl5lBtoy/9RSTCt3MCVK+QWDM9mKzzDTz301leXzzScAHQJ22TCgfu3MuBTGOritMFNYY2vbXN2uT8cJ+eJU6NmRzijoOVRa+rY97bn4mzrYLbJ7Q7G/lVTfi72iDrwvNWa0T+8lUCeZVNfLB8LINcbdgSX8iYAOeLVtXqWwloY+xS5akJgiB8KghCuSAIZ3tje/2Jq8I8mBDkyrv7MjqpBCobWxnm48jHK8dRUtvCKztT+Os1YRzOqODL43kcyug6+dSq0vDW0tGA1kjvOltKnW6a/Gerx+HnbMMHB7PYm1rOCwuH8+y12pmNi947ytaEIk7l17ByUiAymWCSA4jo0MmvVaXm/QNZhjDOh8vHUt2oZO3+DOqMBopMDnHnPqnzY7/kZG4VKo3YKXHq4XBpMXbQhiJnD/NkT0oZKnX3h75ciNSSOvamlrN6cpBJUvdiUsfXfknl4LnzvHRDBBOD3UgsrCWzvKFbGn39gA1tjF2QdOwd2ADM66Vt9SsEQeDv88OpaGjj48M5Jq9VNGjbCYwd7ML/bo3idEENJ3KqmBbqzvM/JVPRcOHqPRuFtqvj4YwK7t90CoBPVkYT4evEoXPn+c+v6dww2pdVkwO5Z3owi3Uql0e/PQ3AEl0MtdhonJ+jtcJkH1viCimpbeGROWEIgsDMcE/ev30MKSV1rPz0RLenRfUUc+0V/sRdJH9XYrMqUcgFoge7GnI3NmY03tCzEXTzIr2pblJy4gJ3nj3h/QNZ2FnKWTU50PCcKIrkV3Zt2L+LL+SjwzncMWmwwcvfEleAtULGdSN9LrrPEqOqU5nksZsiiuIhoHf+uv2QMQEuzI3wYv2hLBOPqLKx1dDZcV6kN89fN5xfU8qw76bE7JezpSyNHmTw1P1dbJg9zIvC6iYe+SaBME8HXl08wjAk4T83jzJJFP2q6+mRfV6riOmogmhTafjgQBZRAc5MD22Pu88Z7sW7t40hqbCW1Z+d/F30ysbtFZraVH/qLpK/NzFZlUQFuGBjKeeUrjDNXOK0pyPopod5YK2QsbsXwjE5FY3sTCpm+aTBONm2Ox+1zUrqW1UMculs2OPzqnjmhzNMGeLG/12nVXe1KNVsSyxmfqQPDh2cGHOU1rYglwm421tp724lw94z+ttcyJ7y5NyhNCvVrN2faXiusqHNpLPj6ilB3D01yGQyEsC2h6aY3eaelFLOGMW1f3xAO1jjgU2nUKlF1q0Ya9I7QyYTmB/pbXj81y2JvPlrOqd0FbJ6CaWe7+ILKapp5pHZoZ0m6MyL9OadW6NIKKjhzg0naWrrXeOuj9vftSGOES/s5sFNpySt/O9AbZOSs8W1hvj696e0Rtpc4rSnI+hsLS2YEebB7uSyy5489MGBTBRyGXdPDTZ5vqBKe7fZUcNeVNPMmo3x+Dpb896yMQbRwO7kUupbVBdNmuopqW3By8EKuUzQJk97J6rU5/xhhr2/zYXsKUM8HVgydhCbjuVTUNVEU5uKpja1oU8MaJM8XU0gMy420hcqVTS08a1RZaqHgxUvbU8mqbCWN5eOIsjddERZq0rNsexKw2N3e0ve3ZfJ27pulDPD27/3NpWG9/ZnMmqQMzPCzP89Foz04a2loziZW8Xdn8f1urStuKaFZqUatahV5khGvfc5nlOJKGL4bvWTiMwlTi9lBN28SG9K61pILKzp8j0Xo6immR9OFXHruEGd4v7mNOxNbSru+TyOVqWGj1dG42zbfo59F1+In7MNk7qp+CmpbTb0lJFCMRJmefTqUAQB/rvnnGEknruu+Ki2ScmdG07y0eEcfDs0J3rs29MmnRkzjIZqGPPmr+l8faKAB2eGcE2Ed6fXtyeWUNHQxmerxjHUywELmYy/zBpieN14lun3p7Te+qNmvHVjbhjtx3+WjCI2u5J7vug94x6TVcHfv08E4OaxfmyStPK/CzFZlVgrZIwe5GwSJjQXs+5q1NyFRtDNCvfCQiZcVrHSR4eyAbjXTHJeb9j17Xo1GpEntiSSWlrHO7dFMcSzvTd7UU0zRzIruGmsf5cKmo6U1rbgo/v9JB27hFl8nGxYNTmQH08XGbTqbvaWZJTVc8N7R4jJquDp+eEU15pOoMnSxcC7wttReyF4d18mU4e48/jVnUeCiaLIZ0dzCPW056qhHrx20wjK6ltM+s6s3nCCjLJ6lGqttz7S34mrhl787mnxGH9ev2kkhzMquO/LeEP3yksls7yeuzacRK2B6MEu/GeJ1EXy9yI2q5Jxga5YWsgMiVPArOF7cu7QTklVQYAHrupaDeVko2DyEHd2ny29pKrN8/WtfH0inxuj/PAzcwHJr2rC1c7SEC9/Z18GP58p5Zn5w5gZ7mny3h9PFSKKGAqbLoZ+cpKP7vySWgp0QBCEr4FYYKggCIWCINzVG9vtj9x/VQgOVhY8pZvUkpBfw43vx9DQquareyaa3LIKAoYhHHqMD0r9a7OGtR/A79wWZbaS7mRuNcnFdayRUKDeAAAgAElEQVSaEoggCEQFuLBqciAbj+UZ3tOi1LD4gxge35xIYbX52HpXLI0exKuLR2hno2461akgq7ucr29l1WcnaVZqEIDXbhoBSFr534Pz9a2kl9UbpiXpv9vFY8yPlFsU5ceri0eYGFhRhI3H8qgw8vY7Mi/Cm9zKJtLL6nu8xk+P5qBUa7i/i4tHQVWTIb6+M6mEt/dmcPNYf+6eFmTyPlEU+S6+kInBrt3uKVPbrKRZqTYJxei31d/pLVXMbaIo+oiiqBBF0V8UxU96Y7v9EWdbS+4zOkjX7s8kyN2ObQ9NIb20np/PtN+y3jMtmE9XjTP5/Jb4QsPPejnkV0bj64xvFY0lgxticnCyUeDnZGOQDD5xTbtnH+Rux9YHp+DpYMV23azLWR08notx2/gAXr4hgr2p5fzl61Moe6hfbm5Tc/cXcRRWa2O2t44fZHIrLWnlexd9vkUfX9+ZVALAxKCu48+Lovw4+tQs9j9xFXKZQLi3A7mVjdzyYWyXs06vHu6FINDjYqXaJiUbY/O4doQPwV0UEuk17GeLavnrltOMCXDmnzdGdnJITuZWk1vZxJKx3e8vbyx1BG0oBhgQXrsUivkduMWoMGJuhBdb7ptETZOSf+xIMUnqzAjzYKS/M1OHXDxpuHxiAAB7U9rHkuklg9sSi9mdXMa0UHce35JokAzaWVkYJuQUVDXh52xj0j7gtV/SeqxmWDEpkBcWDmd3chmPfnO628Upao3Io98mkFRYg4OVBdYKGY/OCevRviV6RkxWJQ5WFkT6ahOleo96uO/FK06D3O24McqPnIpG3rh5FKW1LSz9MJbC6s7jHz0crBg32LXHhv3z2FwaWlU8OHOI2ddVag1FNc3YKuTc+0UcrraWrFsxFiuLzhr8LXEF2FnKmT+ic+6pK/STkzp67AMhzi4Z9l6moKqJ2z8+bnh80xh/1BqR5Z8cw1YhN/RuAVBpNKw7mIWLnWk4RiHvHB5ZPnEwfs427DZKUunDF49/exq1RuRwRkUnyWC9TgOv0oikFNexISaXcG8Hlk8M4MND2dy/KZ7mtp7FzFdPCeK5BcPYeaaExzcndquH9as/p7I7uYxFo/2ob1Vx99RgvBy77v0hcfnEZlUwIdgVC7mMKqO2zWFe3RsG/fCsUNS61hdf3j2B6sY2bvnwGHmVnXNCcyO9SSutJ7fiwvkiPY2tKj49msPscE+T1hvGlNS2GAbOVDcpWX9HNJ5m+sU0tqrYeaaEBSN9ejQ6z7idAGC4C5AMu4QJRzMrWLj2CMU1zVhbaL/aN3an8+yPZ6huVNKqUvPeAa3OPcTdjse+TSTC19EQGtFj3AxMnzjddbaUuRHeHM2sNKkGLa5pMShqwjztO0kG9X3YXWwVXPvOYfIqm3j86jBeviHSUDB1y/pYyru4ze6Ku6cF8/d54WxLLObJLRc27htjc/n4SA4rJw2mrK4FVztL1swI7vL9EpdPUU0zuZVNTNIdD8aJU0uL7p32AW62LIn25+sTBXg5WvPVPRNpalOx9MNYw8xfPdcM9wIwcTwuxNcn8qlpUvLgLPPeOkBeZfvdwZtLRxHpZ7547eczJTS1qXs85q+kthmZAB66WhN97moA2HXJsPcGoijy6ZEc7vj0BB72Vmx7aCqhXtqYYUZ5A1tPF3NjlB+CIJBYoD3BSupaWLssii+NkpvQHnLRs/6OsYwPdNUZdi/a1NoBB6IosnZfBk9s0UoGrSxknMyrZmeHFsJ6ntH1kgF9TFTgzqlBfLQimszyBha9d5TUkroe/d73XxXCX68O44eEIp76PslsWGdfWhkvbEtmdrgnM4Z6EJNVyV9mDelWVaDEpRObpY+va0N/Z3WGfW6EV4+28+DMIYiIvLc/k0g/J765dxJqDdy6Ppa00vbjZZCrLZF+jt2SPbYo1aw/lM3kEDeT7qQdeX6btvXUzWP9uXZE1+0BvosvJMjdjujBXW/LHCW6yUkWuuKmlGLtdzTs/3ZdtJ3ClY5k2C+TFqWaJ7Yk8Y8dKUQNcubHB6cQ6G6HSgOWRiGV7UnFNBmFPIZ6OVDXrGR3cnvMfGKwK88tMJ1ReiKnynCb6+5ghZudJTuSinl8cyL/+VU7ocnP2Zq9j8/AQibw+OZEQ0LVuFpUZpRsMpZbzhnuxeY1k9CIcPMHMexP69mQ4r/MDuXh2aFsiS/k2a1nTYz72aJaHvoqgWE+jrx962j+vSudQa42LJsQcIEtSvQGsVmVuNpZMlQXdvktVXucTbhA4tQc/i623DougM1xBRRUNTHU24HNayZiIZNx6/pjJpXR8yK8ScivMcSuu+K7+ELK61t5qIvYOsD+tHJDK4zXFo/o8n35lU0cz6ni5rH+3VZ46SmtbTHE17cmFBkqwrvTTuFKRzLsl0FZXQu3rD/G96cKWRzlR/b5RpJ0csbUkjrDhCPQhleiA9s9ioSCGu778pTJ9rwcrVmyLtbkuVd2pjJP1ybg1+QyogJc2J1cxo8JRQxy0WbzH7t6KINcbVk1OZA2lYbfdBPkYzK1Xtv4QFfW7s/EwVqbtHzuxzMmkq5IPye26i5Id31+ki9ic3v0PTw2J5QHZ4bw9Yl8XtiWrNMHN3PX5ydxtlHw6apx7EkpI620nieuGWo2+SXRe4iiSGxWBZOC3Qx69VP52uMyohuJ0448MDMEQRBYu08bRgz2sGfzmknYW1mw7KNjhsHohuM0pWuvXanW5pVGD3I2yDA7kllez8NfJwDaFtV6j9oc38UXIAhdSzgvRHFtsyG+/sbudJMQKFy4ncKVjmTYL5H4vGque/cIGWX1rFs+lrduGc3a27WDphe/f9TsZ5IKzGu0R/g5EexuR1xutSEW+o8bIgyvl9Y2M9LfiXf3ZbBX53ktGetPmJcD7vaWLBylvU19aNYQHKwtDGPwtulj9wI6dcNInpwbzv7082zXSd/0eDtZs3nNJGaFe/H8T8m8uC2524N9BUHgiWuGsmZ6MBuP5fG375J0zcPUfLJqHE42Ct789Rwj/JxYONL34hv8E9KTrooXI6+yieLaFibqDKdx4nTYJRh2Hycblo0P4LtThYbEaYCbLZvXTMLdwYoVnxznWHYlQzwdCPGwu6A6ZntiMYXVzTw0c4hZD7umqY27Po/DSiHD3d6Kod5dJ3o1GpHvTxUxdYi7SVV1dxBFUVt1qvvcpbRTuJKRDPslsDmugNvWH8NGIefHB6YYPJUIHyfqWlQG7wjgPzePNPzc1oU08KM7oqluajP08Xj22mEmssibPojFRiE3CeUUVjezL72cZRMGGzxgZ1tLHpw5hH1p5cRkVRgMe3xeNeHeDlwz3JtVkwMZ5e/ES9uSqW40bRtsZ2XBhyvGcvfUIDbE5HLNfw+yL63M5D1dtdYVBIGn5oezanIgW+ILSSutZ+2yKIb5OLIxNo+immaemh/e7VLvPxM97ap4MWI6xNeNE6cd2zZ3lwdmhqCQC/zvtwzDc77ONnx770T8nG1Y9dkJDp07z7xIb47nVJlcTPRoNCLvH8gi3NuB2cM611Ao1Roe/OoUJTUtfLhiLGqN5oLFRjFZlRTVNPc4aQpQ16Lt5aT32C+lncKVjGTYe4BSreHFbcn87bskfJ2t+b8Fwxjq7UBjq0rbUOsfv3b6zCs/p150uzaWcqqNSv9XTBrc6YA+nqPtirxmejALRvoQm12JACzvEK9eOTkQR2sLnvuxfeaJWiMyN8Kb9YezkcsEXrtpJLXNSl7Z2XltcpnAc9cN5+VFkeRUNHLPF/Hs0CVku9Nat9WoIvV4ThW1TUrW7s9kepgHU7qh17+S+KMGyPS0q+LFiMmqwMvRimBdkzh94rQ79RJd4elgzaRgV344VWRyV+HpaM03904kyN2euz+Pw0ImQ60RDXeWxuxOLiWzvIEHuvDWX9mRwtHMSv55YyShXg5UNykvOGBjS3wBjtYWBkVOT+ioYX9y7tBOFd02CjlPzu3cvqM/0H3R55+cqsY2Htx0itjsSu6aGsRVQz14+OsEFmZW8POZki6HZhj3atHjbKOgprn9+VEvtV8QHp41xDDr0d3eyqSU28PBitMFNdww2o+dSSX4Otvg2UELbq2Qc/vEwXxwoN2rHuRiw8bYPNberh2wPczHkftmhLB2fyaLonyZZmaK+4qJgwlwtWXNxjj+8nUCx7Ir+flMqUEnv+5gFiP9nUzklc/+eIavTxSwZnow9a0qPjiQxQcHshAE+Pu8fnmCbADWAl/8njvpzTCANr5eyfQwD4Px1FegTghyveQ1bk0oIjZb61wY31XE5VWxP+08xTXNWMgF3tF59Lt1swSM16Wvwl5gRuGy6Xgen8fmcc+0IJZEDyJZp1DpyrDXtSjZdbaUJdH+hvOlO7/DG7vTKa5pxlVXO6L32BdF+bElroCjursdDwcrnr12GIuieh67vxKQPPZukFJcx/VrjxCfX82bS0bx9PxwSmpaEEXtnNGORl2foPJwsDS3OROj3pFrIrypamyjsLqpU3+O8/WtnMyt4pyugtC+QzGGPkzy5DVDTdoAl9W1cv/MYBMjHB3ogqutgmd+PNNlr/UZYR789OBU5ILAl8fyWT4hwLAN40EZoO1o+dWJAiYEufL3eeG8ckMk03XtgEXR/KzVK50/aoBMV7f7tpbyHvfBP1fWQGVjm0li8nCG9m/UcSxiT3hjdzotStNQYrNSzZfH8g0hJKW6vS31b2nlNBgNaDl47jzJxXXcPyOkk2ccm1XJCz8lc9VQD56ar5XlFphp12vMjsQSWlWabrcQ6Bju0td3GPcmCjUq3LpralC/NeogeewXZWdSCU9sScTJRsG3906kqKaZa/57iOyKRqwVna+LK3VhlOTiOs7XX3j0nTmue/fIBV/XiLAhJheAtLJ6xr78K3ZWCpQqDeX1Lfi72PLzmRKTNsBONhb8e1c6u86W4eVoRU2Tkri8alxsFRRUNTP8+d2snhKIQi7DQiZo/8llyGUCRTVNhm19fCSHFqWGcB8H5DKBFRMHc+8X8UQHunAg/TyhnnZ8fud4Qxzdw2jIyHv7M7ssHf+z8+TcoTz9wxmTcIxMgMY2NfP/d5j/LBnFuMDuedv6C+1kM4nT4T6XfnHt7t2Dcb498oXdrFs+lgBXW17flY6vk3UnY5lf2cQDm+IZ7GbLO7dFsT2xmDd2pxvyTUlFNWYLk7bEFxDqad/tiVvmwl0AHx3OZvUUbUMx4wvO4Yzz/bpvkWTYu0CjEXlzTzrv7dcOer5l3CCe+fEsqSV1DPVyYP2KsSQV1rB2f3vIY7CrLU9fO4xnfzxzyfu9YbQvP502LTK6aYw/358qNPv+ykYllY1KFHKBsYNdcLSxRN1hDExlYxueDtYGWZpM0LYTNta2f3Y0FxuFHJVG00n2paepTc36w9mdnj+Qfh4HKwu+uXeS4bY4vbSeHxMKWT0lkOrGNt7YnY5CLnDv9P57snSFIAj3AvcCBAT0XKOvN3b6MIGvsw1PXBNGbbOSf/2SxtIPY7lzShBPzh160bBDTFYlAa62+OtGyRknTr0cL314ta+zjcHY9oT7vow3ebxkXQz+rrYEuNriamvJP3U5qC33TWJfanmnC9zL21OxVViYXBAyy+tJyK/hmWvDu61d7+rCVFLTrrk3Nuwnc6tpblNjY9k/pbmSYTdDXYuSx745zW9p5QS526HSiPz9+zMEuNry9i2jWTjKl91nS0yMOkBpXQsbj+WyI6kEhVzo0kBeCL1RHxPgjL21gkPnzjN6kBNJhTUmAzi+u28SNxtp3u+aGmS4jT2aUcH+9Pbxg9eO8OHgufbHD80cwuO6zo91LUrmvHkQN3srtj00xTBiTK0R+eBgJsN9HAlwtWPOWwdZMXEw44Jc2X22lBO5VZyvbw8VyWQC6WX1TNZ56a/vSsPOyoKHZ4XiYG2BSiPyr5/TkMtk3DXVtOVqf0cUxfXAeoDo6OhOf3Tj2K6vsw1Pzh3ayXNdFOVn9tZ/QrAbD3+dwCdHctifXs6bS0YR1UW1plojcjy70qRKU584HTXIuccFPMaYu6voClc7BTOHenVyRhaP8eN8fSvJRbXsOltqIqed+/Zhw+9gjD6JbPzdbIkvRC4TehQqcbFTUNXYOQRqHAbTOzoTglw5nlPFidyqLqeLXelccTH2vp5en31eW17/m64CM6eikfK6VuZGePHPGyNZFOVHZUMrz/3UWSjRptLwc1Ipng6W3TbqHcfb6TmVX8MhnTH+v5+SO01VMjbqC0f5sDmu0PC9bYkvMHnvjqQSQzMwP2drvjSaVuRoreDlRZGkltTxkZE3LpcJPDQzlFnhXgzxtGf0IGd+OVvK7uRSdp4poVV3gssE+ObeiXywfIwh5n4su5J9aeU8cNUQXOwssZDL+O8to5kf6c3LO1J6XADVn7lcKeMwH0e2PTSV2ycEkH2+kZs+iOH1XWlmh52kFNdR16Iyia8n6ObdTgy+9MQpmPZqFwAHa61PaGmmeKiqUdnJqNtaynlzySg23jWBA0/OZIbR8HRnWwWzwz26rJsw9rZVag0/nCpi5lAPsw3BzJFaUmc4/o2RCZioXvS/SnSgC5YWMg6f67+zma84w94xKfd7Tq/veBHZn17OgncOG0qZXWwVPLdgGAeevIqVkwN55JvT/JxUzK0fHaOxtf3EummMPx+vjEZEW1FaUN39hlo53eiGJ5dpPfiuqKhv47qRPqzZGM+usyWklpgfeGAhE5gQ5NZpWtHcCG/mR3rz9t4Ms+tpaFVxuqCGioZWdiaV8MjsUIOa4M2lo5gY7GboNJlYUMNrv6Th7Wht0slSIZfxzm1RXD1cWwC16Xhep/1cafTGAJmupIyv7Eyh1oxiyhw2lnL+eeMIPlwxFkcbBR8cyGLhu0dMyvmhPb5uXAOxV1eF3NvJa3srC2wUMkNthoD2fLG3Mh+6aGpT8+/d6YiiyHNbz7DP6I6ypknJ4YxKXGzNa+yNverDGRWcr2/l5m4mTSsbWrn78zhUGpGHZoYYLkwCWvmnsdcvl2nNoUIuY3ygqyHp3B+54kIxegOxZmM8w30cSS2tY93tY3+XQcf6i8ja26JILKzl9V1pAFgrZNw/Ywh3Tg00NKvSr+uOT07o3iM3aLZfXhRhokPXs2i0L1tPm2/K1RVyQUDdob2cWkOXxhogNruSWJ2krWObAmNUGhFfZxuTaUX67/Wl6yM4klnBU98n8fU9E5HJBNQake/jC3lxe7JhG4/NCUMjiqSXNTB1iDs3RrVPfJoc4k5Nk5LTBTX8+6aRneLBCrmMtcuiuP/LUzz741ksZAK3jLty+8aIonjb5W6jq9huRUMbo/7xK96O1gz1dtD+89L+P8TT3mwsfW6ENyP9nXjs29Mcy65i0ftHeXDmEB6aOQRLCxkxWZUM8bQ3SGCrTRKnl66IgfY7D/1FqqS2xUR59eaSUTy79ewFQzUfHMjih1OFlNV1nsbUrFRjZSHDRiE32UZHLfmW+AJc7Sy7NSSmTaXh/k2nKKpp5uFZ2vDjE3PDqWtRMvLFX5kaampT5Pq2vRqRqaHuvPZLGmV1Lf2yvfQVZ9hBayBCPe0NRTnPb0vmupE+LBzlS0gXk1YudT9rl0Wx6rOThlFv14304eUbIjv1SNe/38vRiqKaFlxsFQY5l62lBS9tS+n0/nmRPj027E/NH8o/f05DJsCCkT5sT9SW/l/ohBkX6MJ7y8aQWlrPyk9PXHD7a/dnYmUhI9zHkQUjfNBoRGQyAU9Ha569dhhP/XCGzXEFBLrb8fKOFJKL6wj1tKOsrhWFXODbk/kU17ZgaSHrNAtTqdbwxu50wrzsuamLuZNWFnLev30M926M56kfziCXybi5mzMq+yMXSjpG+jkS6ulAemk9sVmVBu9XJkCgu53B0Id7OxDm5cBgNzt8nGzYdPdE1h3M4q0953jntwz2ppTx+k0jOZlbZfJdGidOuwr5dRdzdx7Gyqs395zr8hi9fUIANU1Kdp4pMWvU9dQ2K/nvLaN5eUcKlY1tyAR4dfEIg1dd3djG3pRybp8Y0K3Wwy9tT+ZEThVjB7vw8OxQw/Olhj7spjJTfShGLYpMC3XntV/gSEZFl8fylcwVadhjsioMMWWFXCu/+99vGby9N4PhPo5cN8qHhSN9uz3b8EJMCHLD3c6S4toW7pwSyPMLI7p8b0xWhUH/WqAb7xbu5UBMZkWnuDZ0VgR0xeopgXx2NBeAT47kIhO0srHCKlODMMzHwaznfjK3GkEQOK2Lp949LYiPD+d0ub8395wz/GxnKdd5jI4M1bUa1s9r9XO24Z3bolioq3Rd/dlJKhuVWMgEPr4jmskdKhm/OZFPTkUjn6yMNjuXVY+1Qs76FWO5+/M4nvwuEYVc4IbR/VczfCHMJR2tLGR42FtxtqgOF1tL1i6LIsDVltzKRtJLG0gvrSO9rJ7Ukjp2JZca+oNbWcgI9bJnqJcj4d4OPDwrlPcOZJJSUsfCtVqZ7HijIiS9YQ/xsLvg36M7XEzueKHXNx3P5+xLczmeU3XB2am+zjYsivKjTa3hb98loRG1dyl6fjpdRJu6e9r1jcfy2HQ8HwdrC/5362iTRmIdB2zo0ct01RoY5u2Iu70lhzPOS4a9N9DH1D+4fSyPbT5NsLstGeWNrL0tirK6VrYnFfPvXen8e1c6owY5s3CkDwtG+vS4CZCe4zmVtKg0PDxrCF8ez2fOcC+zYR/9uuYM82JHUgnudpZUNbWRcb6eDTG5Bv2ulYXMpKzemGmh7mbjdnqjDlDfojRsK6GgxuR9xgnUIHc7k3j46g0nSCmuw9vRmnkR3gbDPi7QheKaFh6ZHcrfvk/C0kJG3HNzyChrIL20nvTSOlJL69kcV9ApeTXI1Ybk4lrUGg1DvRy5eaw/m47nc+eUIKaHeZhUnza0qvjfbxmEeztwrqye2cMuXOZtrZDz0R3RrN5wgse+PY1MEFg4auA1CDMnZXxy7lAWjvJl0/E83tidzry3D3PfjGAemDmEIZ4OLBjZrmppalORWd5AWmk950rrSS+r51DG+S7lrw99lUB9i4oFI30MMfiuuij2BEcbC2qbuy6WsrzAcQ9aTTto+yJtPpnPnlTT9tAWMoF7pmvVUvriJIC9qWWG42JLfCERvo6G0X5dqY1isyp5aZs2fPja4pEG6aeeEt1FyLuDYdeHYtQaDTKZwNQh7hzJrDDc1fYnrjjDnlRYayhbnxfhzZb4Aj64fQzpZQ3cNyOEO6cGUVDVxM4zJexIKuaVnam8sjOV8YGuXDfKh/mRPng4dE+vqzfW+v1NDHEzedxxXa8tHsEDm7Qx7OWTBvPpkRw8HawMUkIrCxkvLBzOMz92VsxEBTh3Kxkze5hXe1dGtF6F3sPQaEQGu9pS1dhGra569bqRPuxIKuFskXboQWldi0ExYymXUdmgbS6mj8G3qTTsTSlj8Rh/xg520Y4eO1lAVrm2YtG4jcGx7Cri86pNFD6udgo2xORS26wkwNWGB748xfu3j+FEbhUVDW20qTSMGtR1otcYG0s5n64ax6pPT/Lot6exkAnMv8BAhf5KV1LGOyYFMi/Sm1d/TuOdfZn8eLqIf1wfyUyj+LGtpQUj/Z0Z6W/6nVY1tpFeWs+5snrSSuv5+kT7wPOnfzjD0z+011JsTyxh7GAXwry08fuetE0WRZF/706ntllluJPUIxO0E77umhbMyztSsLSQGUKaoFVW3TZ+EF8e065NELRDXmaHexL8zM8m+1FpRF7ekUpcbjWnC2rwc7ZBrRH56XQRC0f5klJcR3JxHS8u1M4r6Bjz16uNKhtbWbsvE5VG5Lbxg0wuknpKalsQBDrFzuVGHjvAtFAPtp4uJrW0rt9VTl9xht242mv+CG82HsujWakxeX6Qqy33zQjhvhkh5FQ0siOxmO1JxYZ2s5NC3Fg40pd5kd4425ov6wfTiwhgNqmoZ830YO75Is5QMn2urJ6GVhUrJwfyW2oZKSX13D0tyJBEdbe34obRvnxyROs5m+sZY45ticUsmxCApVzGhphcg1EHbUxw64NTeHNPOl8d154s9S3KLjXzbWoN2Tqv/kcjed3jmxOJy6vmfH0re3TDsUf5O7Fh9XhG+DuhVGu4fu1RKhta2fXodPamlvHS9mTmhHvR0KoisaCGb+PaQ0/LdDNeFXKBdSt6lui2tbTg09XjWPnpCf7ydQLvywSuiej+QOL+jqeDNf+9ZTRLov35v61nWb3hJHMjvHh+YQR+F+gs6GpnyaQQNyaFuNGiVPP9qUJmhHmQkF/TKdxR26zksW+1k7bkMoEgo/h9mJc2hj/I1bZTuEap1vD0D2f4Lr6Q2ycEMDbAhcd1E7t8nKwprW3h+tF+3D4hgA8PZmFvZUGTUm2IYT9zbThONpYGwy6K8PHhbEMlaaCbLbm68Xd7H5/O5rhCvjmRT51OmujhYMXe1HKqGtv46oRWRRXgZsvu5FJe2JZsVm308g5twZOFTCBqkHm9f2ltCx72VoaaDT36318/87SxVXvOLnjnCH5d1B9cqQhiHwz4i46OFuPi4i76PrVGZPw/9zIpxI21y8Zc9P3ppfXsSCpme2IxuZVNWMgEpoW6s3CUL1cP97qscWyfHMnh5R0pvLBwOPF51exLK6epTc0dkwazI6mE5RMC2Hgsz2DYv79/Mjd9ENNpO2umB/Phoc7Vm3peWDicVpWGkf5O5FU2mXhed0wazD9uiORMYa0hpiqgHXDwy9lSbBUymnT9PLwcrTjy91mcyKkyGa7taG1hOHGMEQTwcbQmwE1bFVjXrGJXcikj/JyYHe7J+GBXE4O9N7WMA2nlhPs48txW7R3KiokBvLyo62k3F6K+RcnyT06QUlzLhyvGMiu85x372n8XIV4UxehL3sBl0N1j2xxtKg0fH8nmnd8yEBB4dE4od04N6mSAOhKTWcGyj4/z2apxTA/0ZM4AACAASURBVA11560950yawD1+dRhzI7w5V9bu4aeX1pNvFPKwVsgI82pX5gS42vLOvgzOFtXx2JwwHp49hGalmuHP7+ap+eEEu9tx78Z4vr5nIpNC3Pg8JpcXtiXzzm1RhiEZoD0+RbRGOnqwC7+cLcVSLsPSQsbJZ+cw7Pld2vcJ8OqNIzhf32qSA7ocbBRyk+Srnjs+PUFtUxs/PTTV5PmNsbn830/JhvGUm47lY2wdu9reH0l3j+0rzmM3Ri4TmBvpzdaEIlqU6ouWU2uTgEN5/Oowkovr2J5YzI6kEh7fnIilhYyZQz24bqQvs4d59miaeVJhDa/9ksrVw71YNTkQjagt+gH4/lQhH90RzeQQd5IKazmgC8t0NdTX0ab94hLu7UBaqWkydFKIG1WNbTz0VQL/XBRp8lpFfStKtYZIP0eG+ziSUlKHCIaRXk1KDe72lmhEkRAPexRyGZOC3bCzlNPYpmZisCuRvk58rLuLWDzGj5vH+FNW30JeZRP5lU3kVTWxL+28wes7U1TLmaJaHI9aMNjNjgA3Wwa72jLYzZZrR/oYen8D7DxTyvwRPmY9dnPdIGOyKkgqrOW+GSE4WCv44s7xLP/4OPdtPMVHK6P7bdXfpaJVGg3h+lG+vLQ9hVd/SeP7U4W8fEMkE4K7jpPHZFUilwmMC3JFIZfx93nhnCms5UimNvT31p5zHMms4D83jzLJYzS2qsgob+Bcqc7Yl9WxP/08W+JN4/ex2RVUNbZirytKspAJHMo4j52lnLG6OaO3jh/EuoNZJkYdtEZdQNvArlQ3ML1NraFNrTEYddB680/9cOmtOMxhrmoVtDH2YI/OKiF9HD2rvIFj2VV0dHm72t6VyBVt2AGujfThq+P5HDx33iRDfiEEQSDSz4lIPyeemh/OqfwadiQVszOphN3JZdgo5Mwe5snCUb7MCPO44AWjrkXJQ18l4GFvxRs3j0QQBKKMioUemRXK5BB3HvsmwWDUAdYbeeXG3nuskSF8ZHYoD319CuP5G/PePkzuawtMNPN6fj5bSvn6Y6xdNoZbxg3ihW3JdOTDFdHcueGkQd4mkwmEeNqTVFjLsewqjudUMXqQM6cLanC1teykbNHT2Koivayexe9r1z03wpvSuhaSi2rZfbbUROqmJ8DVhtWfnWRp9CBmhHkw2M2WQa62WCvk7TUDutCXcX5Dj5ONgo13jWfZR8e554s4Pl05rpPWuL/RnXYCHfF3seWjO6LZm1LGi9uTuWX9MRaP8eOZa4fhbt85fxSTVcFIfyfsrdpPZ31lqJ4TOVVMf2M/TjYKXrxuODeO9cfOyoLRg5wZbZQTKa5p5rp3j1DV2MbEYFcC3exIL6vnu/hCGnWDXoz7+C/+4Ch+zjb4Odt2mTzVHykJ+TVmX/89MafWKa1tMTsbQJ88TSys7WTUL7Q9c1zK3703ueIN+4RgV1xsFfxypqTbht0YQdA2xxo72IXnFgznZG4V2xOL+eVsKTuSSnCwsuDqCC8WjvJl6hB3k9teURQN5eCb10w0xOuN50ZODfWguKaZX1PKkMsEQj3sSStr98JlgjZ+redIZgWrJgey6XgeO8+U4Git6FTcdNMHMby5ZJTBeFrKBdrUIraWclJK6ljwzmH+cUNkp2QVaCtZa5uVBOv0/ofOnTdpTbrjL1OJ8HXib98l8kVsHisnB5qVjdpZWTAmwIWNd41nxScn8HK05o0lowB4f38mfs42fJ9QyKFzWq9wfKALuZVNCIJWarbxWHtlqbejNQG6KfZ3bYhjUZQvu5PLzCapnW0t2XT3BG776Bh3f3GSz1aN7xVVR1/QVYIP6NZJPme4F1OGuLN2fwbrD2WzN6WMJ+eFs2x8gCEe3NCqIrGwlvtmBJt8Vp/Qv2GULz+fLTHkYGqblTy2JZGDGeeZPMSdxlYVDS0qGtpUJOTXcEJXOyII2m0fz6mivkVl9kIOcLaozpC4vxxcbE3Pg7eWjuK6kb6klNSx6D3tqMnE568xGWbjaG2BUq2hWdm1GqdjS+T6FiX1rapOUkdo99iNJ5VdbHvmuNy/e29wxbUU6IhCLuOa4d7sTS032x+jJ8hlAhOD3fjnjSM48cxsvrhzPPMivdmbUsbqz04y7p97efqHJI5mVqDWiHx9ooCdSSX8P3vnHdbU2f7xz0kgEPYeskEFEQc4wb1n66hdamur1dqhnbZ2793a1raODt/aOmq11r3rFheKIIIge++9IcnvjySHhARctL/2ffu9Li8JmcA593me+/6O58Z2pY9PCz9Yl1Vgb2nKs79eQgXMG+SrV9QBXhgfRJTGVVELa3MTOrtYsTcun4/u6klrXMgoY/inRwEYGeQimjfVNip4ZVI3HCxlPLnxol5Rd7E2w8rMRPTUSSuqZtRnR3lQR7D01p3dxen+M2O6IpHApwfaT+kZ0sWZkE62rDiaTEKe+gTu7W3Hq9vjxKLuZmNOXG4lA/wcmdSjEz09bfVUifmV9ZxLL+V4UjF1TQo2nsvS83ZvDXtLGeseGYCXvQVzfzwvFpt/GjoiGUkuk7JkXBB7nxpKiIctr22LY/qKUyKV8XxaKQqlSu93WVbTKBanUynFRgfr2y7l8sKWWN7aGc9nB5NYfSxV/D1LBOjn44CLtTkhHraMCXblwXAfAjTtC60/zDtTurNh/gC+vK83i0feuCVzqLcdvz8egYsOe6314ub17XGU1zZyLq1lh/vWLvUO9eWJQax5qC+e9hbtFnVjCUgFlfrJSbqQXsckTYDrJirVNjbz/p6EDk3EuhX87VfsoGbHbIrK4uS14uvyo28UJlIJQ7s6M7SrM+9OC+FEUjG7YnPZcSmXjedaGB9yUykLhvi3+Tof70vkTGopC4b4s/Z0usH9gzs7sf2SmpEiCOpe4upjqfg6WaBQqmi9EPJopVRc81A/Xt8ex0+nM5BJJfxwMo2tj0Xw1s54PaZLRV0Tod52nNGk3Kw7m4ncVMLLE4PILK1l3ZlMveADd1s58wb78c2RFB4Z7E+Pdrx4Fo/qzKM/X+DJ9Rc58OwwAOp1Dlxt73TflTycrMxwsjJjSBcn9dfWZprvySisqufrwynMGuDNurOZDAxwbLO4O1mZsX7+AO779gwP/+ccP83rr3dx/SegI5OROrtYsf6RAeyIyeWdXQnc+c1JBgU4ivm6SzbH8ML4IKaGehCX27JDK2kj2UuLXl52TOvdiQ/2XsXDTs7auf3bFP552lvwxo4rdHO3prK+mQfCfcX71mjmNk+MCGDFkRS9VoZUImAvN6FY464YnVnOtBWGxAJd1DQouGf1ab0Qm60Xc3h2TFcWDA2gsVnJqqOGJASZVO1f08nOnBfGBRmskHPLjatOAdJL2vZtEoBZA72Z0MON1KJqssvqyCqrJau0juyyWrLK6sgpq20zSU393n9dMHaHFHZBEMYDXwJS4HuVSvVhR7yuFhEBTtiYm7D7cl6HFXZdmJlIGR3syuhgV+qbFOpVuobWVdekYMjHR5jUQ21p0NPTFkEQcLCUUVrTKBbXn89k0NCsxNxUwr19vVh7Wt2KCHKz5kkNpVClgv883I9Xtl4mqaBa87x0vc/yYLgPH+y9Kt4urKpneKAzP53OoJ+fPaeSS/gxMp1x3V31CntDs5Ko9JadwfCuznx6Ty+crMxYplmV6xb2VcdSCPW2x8FSxvt7EtgwfwCnU0vEYaYuxnZ344FwH346ncHSrbEcii/QWwXOHuDN8+MCsZWbtmkNG5lSzFs741k5O4yIACcGd3FqUzOghYu1ORvnD+S+b88wZ815fp7Xv03L2r8j2rITUAFv74znyZGdxYi2G4EgqBW6I4JceGL9RT1dRG5Fvbjdz61Qv6e1mQk2clOjn0HLSY/JKidGI4TbvDAcRyM9fC3KatVFKyGvivv7t6g/G5oVfHs8lf5+DiwZF0RZTSMbdBZH84eoLaV9l+4GYP0jA0TBlS5tVhcqEKmQulg0sjMqlYo3dsRxLr1lJ3dsyXAOXClgxdFkGmuV5JbX8/r2OGobm5k5wEd8XH4bqtMjiYV8dThZvO3lICe/op4mhQpTqYCnnZxD8YWsP5uJLpHQVCrgYSfHy8GC4GBXPO0tWHk0Re9c0+KvDMa+7cIuCIIU+AYYA2QD5wVB2KFSqQzNU24RMhMJY4LdOBCfT2Oz8oZ8Im4V5qZSdVC0AKtn96GuScHOmDzWnk7n+5NpeDtYMLmnu0EKu3br9eyYrsg1jBuJoO63aQ/QUG87RgS68M2sMGasOo1CqeJUcgkyEwkjA13YdyVfr6gD3PftGb64tzegdpF0tDRjxZGUFlc9zS4A9L07fpzbX/y6XtOyKdbxT9cOM+/o6c7a0xmsPJbC9yfS9IaZuiitaUQmFdgcpc+YGN3NhT1x+UzsaZwNo8XNaAZ04Wpjzob5A7h39RkeXHOO9Y8MMBDr/F1hzE5AS/9bcyqNX6PU+bDzhvjdFEvLxtxUdCDVhXa7r3VCHRjgyKQe7gafQW4q5cUJgbzZyt9o6dbLvDctpE07XK2pWKNCybDAFsbS7xdzyK+s5+MZ6rbi21NCOJNWKn7GnTF5epYR/s6W4vDyZHJxm146usc2qNuNSpU6H1V3V739iUH4OFribG2mt5OsrG/m5d/j2Bmby1t3htDV1Vp8r6zSWs6nl5JVWsfnhwzplVk6dh4u1ua42Jjj5WCBl70FnvbqQu7lIMfF2lyP/19e28h3x1PFv7MWf3UwdkdUyP5AskqlSlWpVI3AL8CUDnhdPUzq6UZVfTOnUv5cK82tF7PZciGbRSM6M7a7G1N6e/D9nL5EvTKGj2f0xNfJsl0e+vt7rvKahtc9JtiVXZfzxPvmaiK4Qr3teX1ysPh9P0dLXNpIt0ktqhFdJzNKanltcrBY1J2sZJx6caTR50Umt/yetAdzkk7/39vBgrv7eIonyGf7k1h+n/HVc2FVPXsu5yERBEbpqCJNJAKf39vbwAbYGBYOCzB47YgApxuKH3O3lbNxwUAkAtz/7RkxPAL+Wq/+m0VrD3MPOzlvTelOD41Ap65JwWcHkxj2yVHWncmgSdF2v7g12trW55TXiYPTYHcb8TNoZx5SQWiz33swvoCxnx9nZ4xx4zptH1wmlTBQQ79sVihZqaGyDtEwmEykEp7SMd3KKa/TG6YfuFIgfr1kXGCbve3WEpvCqgY+P5jE69tb2GCvTOwmKp3VvW3D3+HplFLGfn4c36W7+VITtj3z+7M8+2uM0aIOsOGRAQgCjApy4dTSkWx6NJxP7+7FU6O7cFcfT/r7OeBuK9cr6kqliqc3XaK2UcHTY7ro/d3/av57RxR2D0B3P5Wt+V6HYlBnJ6zNTNirUyg7GilF1by6LY7+fg56bnAAtham3NPXi5/m9uetO40bhZmbSpgW2sIT3n+lgKMa32k3G3PGh7SwenRDihMLqvjptL4/+fL7W1bOp5LVA6TM0lryKlpO6KdGdWlzKz//5yix0OZoDMtisit4YUsMQz8+wuCPjrD6eKp4kVCoVOIWvjU+3Z+IUgVPj+kqBpAA9PG1x9rcVFx9f3s89U8LSfGwk/P2nSHUNSm479vTXM2v/FO9+jsKU0M9OLV0JGkfTuLU0pE8GO7LLwsGMqSLEwqlihGBzvg6WvDqtjjGfn6c3bF53IhosL1tvXZwqmVvTQ31oLOLFYKAaAld06BAKhH4/O5ebHtiED6O6r56eW0TizZG88T6i5S0UrBqWzH9/OzFHcbuy3lklNTy+PDOem24yT070cWlxYl1w9lMxga70sXFin1xLRqPqaEejOvefnv14DNDsdXoP74+kmxw/5w15/BdupvCqrYNxtrDo8P8mT+kJdVrVJALnV2tUKkQL2A3guWHr3E0sYjX7wjmqVFd9f7ufzX3vSMKu7HLrcGRKQjCAkEQogRBiCoquvlkEm0f/EB8wU2tbG4U9U0KntwQjZmJhOX3heq5wekit7yOj/ddNXpffZOS36NbVju6NMf8ynp+PJUutnB6edlhY9729ntoFyeRhaDF79E53Pn1KfH2pweSeGbTJaPPr2lQ8Pr2OF7bFsclHTOxX6OyCXSz5vXJwcweoFbYTe7pjlQi8MGeBOpaUb0UShV74/Lp5m7NllbCFU+d4hIR4MSCof4dGpJSVd9ETFY5v0dns+xAIgcSCnCwkFHdoOC+b89ct0f/d4WlmQk/zOnH9FAPjiQW0dXVmu8e7ItMKuGJDReZ+s2pdnc/oF7pylvpL+SmUsYFtxTJZQeTREOtzNJagxWwQqni04NJ9PayY/fiIUwPayk+uy/nMfbz43pFWMuK0grHlEoVK46k0NXVirHB+sVZKhF4ZkxXve9ND/NkfIgbZ9NKKK1pZFt0DoM+PMyeOONiPlDvMDaey8JGbvxceW9Pgl7sozF0sjVn16LBvD/NuCp69bFUvtNxQz2SWMh+za6ir++NzXSOJhby5R/XmB6mtlj4/0ZHDE+zAV0fTU/AYC93vVzIG8GEEDd+j87hTGoJQ7p0rCrxvd0JJORVsuahvkapUKA+EZ7ZdMkop3fmAG8eHx7AiE+PioPFGB3+uJ2FKe/tSVC7+YW4cX9/b16dHMwLW2L1XsfH0YKMklrWncngjTu669EVdbFn8RAmLj/B3rh85g7yY80pQ5ve5MIakgv1e7GrZocxPsSdyJRiPtp3FVOpwKd39yLU2453diXw5o4rfDSjhYJ5+GqhJlZMILlQ3crRenw8MsSfCxllVNY3UVmn/jemmytzfzzPpJ7uHLladN3C26RQklVaS1pxDalFNaQWV5NSVENacY1+pqqg9gjq4WlLmSbMY/HIzv+4oq6FzETCZ/f0wsXGnFXHUiiubuD3JyLYczmfZQcSmfndWYZ2debF8YFGDajaco08lNBSJBPzqxjx6RGeGRPYJjc7p7yOk9eKCQ9wZNk9vRnW1ZlXfo+juqGZkppGFq67wNTenXjzzu4i42OoprAfSiggsaCKL+7tbdT9cHx3Nz0/mK6uVnjay/nqcDLP/3qJk8ktHvRtQaFSGT22W8PNxpzRwS78diHHYJ7wwvggUbD4sk7QvFaR3RpKFWI7ddqKSKzMTLgrzIOHBvnh42Ch97Nui87hg70JFFQ2YCIRGOjneFvZsh2Fjijs54EugiD4ATnAfcDMDnhdAwzt6oylTMqey/kdWtj3Xs7j5zMZzB/i165HyapjKZxNK+VBDUNEF8mF1XjaW+DraMm1wmrGanYXWlTWNeFkZUZ1QxM7YnL1HBz13mN2Hz7ed5U1p9JZdncvfBwsyCjVZweMDXZF99hp68DvZGtOroYFoC3+1wqqGROs4mxqKQ3NSjrZmnMho4xOGvrXpqgsJBK1B35lfZMYrq1drUELW2HClyeMvq9EgN8u5IiFV6VSUVLTqC7cRdWkFdeQoinimSW1ehdKB0sZ/k6WjAh0xs/JCn9nSwKcLfF2sNSkBKl3AVqb5fYok393CILA0glBuNqY8faueB784Rzfz+nL5J7urDuTwddHkpm0/CRTe3fiubGBBjTE1q6R26Jz2HO5pbCrUBep6/GnZ/9wFmdrM+7o2YkpvTuxZ/EQFv8SLe70tl3SP14TcisJdLXmmyPJIplAF00KJXnl9WSV1eJqYy4eLyM/OyY6r+pG43UE8ivr+e1CDnf18eDI1SIDxadSqeL9PS2K2fOvjMbBUsb4L44b5Am3RnVDM2tPZ7D2dAYWMind3NWWHg3NCrZdyhX1JM1KFW/suILMRPL/bjvQISZggiBMBL5ATXdco1Kp3mvv8bdjlLRoYzSRycWcfXlUm+2Sm0FWaS0Tl5/A39mKzY+Gt8m4ickq566VkQzr6syJ5GIam5U8N6Yruy/ncTW/CkdLGfueHkq/9w4B8NX9oSzS+GasmBXG1bxKdsbmXTfj9JnRXYno7Mjdq05jIZPy0oQgXttuaB3QHrQp638mtDMDFxszNWvA2gxbuSnn0kv48lAyIR42XMwsx8teTlFVg57xmMxEgq+jBf6awu3vbIWfk7qAt+fG2dpmufVtLf6JJmC7YnN5dlMMPo4WrJ3bn052cirqmlh9LIU1p9JQKFXMGuDDopGd26QkDvrwcJsME2MwM5HgaS8npRXDxt/Jkok93EkrqWF3rPGZ1kA/B86kldLXx57BXZzIKlXzunPK6sirqDPQZ2gxo4+nQUvPGLwc5OSUtf06bcHDTs6ppfqEgtrGZp7dFMM+jX/TxzN6ck9fL5b/cY1lB5P4ZEZPpvT24PXtcfxy3jj9EsBObsrUUA/icyuJz6s0Smls6zPcCG7EhuAvNQFTqVR7gD3XfWAHYGKIGztjcjmXVtqmz8mNorFZyZOa4vv1/aFtFvWahmae+iVaQ7dSiVfoHp625FbUcTW/ipKaRj02gW6vemIPdyb2cOcZHXOytpg1nx9K4nP1tYHaRsVNF3Xgpou6lZkJLtZmuNiYiQKnWQO8+SOhUBQfgXrIFJdTwbm0Uj6e0ZO0YnXL5HRKCalFNXpF5UxqKQ6WpmSU1DIi0IXwAEfN6tuKTnbyW0r0uVXK5D8Bk3t2wtHSjAU/RTF9RSRr5/Yn0M2aF8YHMSfCly8OXePnMxlsuZDNgqH+zBvsh6WZ/unbngCmtWTfVm7CG5ODmRbmyeGrhXyw9yrJmpVrfmU93xxNNujJ6+KM5hiLyijjQmYZrtbmeDnI6e/ngJe9HE97Czwd5Dy54SKlNS3v28nOnM0Lw7lbkxnQFsprmm66qIPh7yC7rJb5P10gMb+SEYHOHEkswsfBgnNppXxxKImpvTsxo48ngiAwPcyz3cJeUdekaUk1sC06R88zp73PcCPoaBuCf4TyVBfDA12Qm0rZE5d324X90wOJxGSVs2JWWLsxe2/uuEJmaS0TQtzZfTmPHh62XM6pwNHSjFAve5EyqPVe93dW54WCmteuhSCo2xsHdVo0fxVat4ZAre48/sJwPQ51YWU9wz45SnJhtV5RB7W6UDs/0Pb+LWVS/J2t6Otrj1+1JaHedozr7oa/syUWMhPRwXHuYD9uF8aokREBTv/4oq5FeIAjvy4MZ86ac8xYFcl3D/ZloL8jrjbmfDC9B/MG+/Hp/kSWHUzip9MZPDWqM/f19xb9jRwsZWJ0Y2u0luxX1DXz7OZYpFIJd2rM8DZfyGbZwSSKqhoI87ajm7sN689mGn09LXYtGkwXV+PhHWtOpuoVdYBvDqdgbXZ9++yqNlbDWrTmuGuhyxaKSi/l0Z8v0NisZM1D/SisauBIYhHmplIWrruAt4MF707rIfbEr9cAsLMwZd6P5zmaVGSQNtbWZ7hRtGc/cSuF/W/vFdMacpmUkUEu7IsraPeXez0cvlrAt8dTmT3Qm4ntpPbsjs1j84VsBvg5sv9KPqOCXLi/v3rq7WAl03N61K5Y+/s6iAfmj6fSRYbDdydSmLPmnBh+0VG4p69hJuO3D/QRv3axNuPbB/vy5X299R7TxcWK/Ip6DsUX8N3xVF7aGsuTG6Opa1IYXfVri/ojg/3YOH8g514eRdxb49i5aDBf3hfKukcG8NzYQEI8bMWLxY1y1f+FGt3cbdiq8VF5cM05PXpvZxcrVj3Qh62PR+DvbMlr268wZtkxdsXmolSqCA9o23JBd380qHMLhe+pXy7h99IeZn53lrLaRj6+qyePDvPnan4Vm85nYWnWvlX23B/Pc6GVF5IWXxy6ZvA9hUrFx/uNs8puBI4aeq9KpVZ96kIqCKII6NfzWdz/3Rls5Kb8/sQghge6iKrTTw8kUlzdwNczw/QcMaWS9sthWW0TV3IrxfPNTm6Cuan+c25ViNRWC+1WbQj+cSt2UHvH7L6cR1R6abs+1W0hr6KO536NIcjNmlcnBbf5OPV2KBZPezlJBVV42MtZdm9v1mnEFo6WMtxtzA2m69pBo4lE4LER/sz7MQpzEwlldddPUTKVCDTdwAXrseEBBLlZ89Qvl6hrUrJgqL+eVXBKUQ3eDhZkltZiZirhXFopP7ca+J5OLWHkZ8fE29rB5fjubmI/EtQhCSdeGMFdKyMxkQi8Ornt39m/uH142luwZWEE89ae5/ENF3nzju7MifAV7w/ztmfTgoEcTSzio31XNZTSVDJLaw3i67ToZCenoVnJmGAXPpjeE4VSRXRmGQ+uOUdto4Jz6aWiRF8qEXCyklFQ2UBzQ/vGe4VVDcz87iyBrtb89niEWChVKpXRQBdQLxC0u15QF8PWq9W2UFLTyKrZfVi47gJNChW2OlmsCpWKstpG3t4Zz5pTaQzp4sTX94dha6HeIWjTyE5cK+b1ycFikpMWrYVSUomAQqlCAPr42PH06EAG+Duw4KcoTKUC/3m4Pxkltbdlz5tZUsvbu9oW6d+qDcE/srCPCHTBzETC3rj8my7szQolT/1yiYZmJd/MCmvTi11LbWxoVtKsUFHT2MzP8wZgKzelpLoRC5lUfG6ot70YagAtNMdl9/Tihd9iqW9S3vCBu2JWGG62cjEhqS2sPJrCnb068egwf1YfSzXIef1Ih2ufVVrHPavb7mlamErY/dRQ0cP90Z/1h39Lx6uNxK7kVvLGHf8W9b8C9pYy1j8ykEUbo3ljxxUKq+p5fmyg2DYQBIERQS4M7erM9ks5fHYgSYxfbB2orl1FfncilYJKNYVUKhHo6+tA/Nvjic+t5LXtceLKu5ub9XWZIq2RWFBFyBv7CfO24+0pIW3a/Gqhu0K9kXNDV6K/cN0F8futA7bf2qkukg8P8uWVid30CBbaFujobi48PMhX73mV9U1suaDfXx/g5yBy77UXrC8PXeNIYhHvTA0h1NueUG/7W2qV1DUqWHE0mdXHUzGRCEzu6c6hhALqmwz/breCf1wrBtQCj+GBzuyNy0N5k+2Y5YeTOZdWyrtTQwhwtmrzcauOpXAurRRLMxPyK+v5YHoPMR29tKZBT/GpaDczIAAAIABJREFU247RnQlO6tlJ7w+lxYkXRvD0aLWydaC/A8N1fDcW/RKtF3vXHnbE5LL6mHqVXnQd1d2L49UmXdbmLRcyAbU2oFkJM787w8XMMi5klIriDC3u6NWJbdE5SCUCk3t24l/8NZDLpKyaHcb9/b355kgKS7bEGojzpBL10O/3xyPE7zU0K0XxkrmJRJSzu1ibUVilPzcBCO5kw+ZHw/n4rp5YmEqIy63UuzC0bnm0h4uZ5Uz+6qTooW7WBiGhtddSe3hiRABpH05iTrjP9R+sgYOFTK+oVzc0i6lgn8zohSAIbL2QRZ93DuK7dDc93zwgGvcB/PpoOBvmD2RGH0+xqB9NLOSLP5KYHuohivtuFiqVij2X8xj12VG+OpzM+O5uHH5uOF/PDOPD6S36kdu1IfhHFnZQM00KKhuIzjLe3zOGyORivjp8jRl9PJkeZtiX1uJSVjmfH0xCKhEorWnkwXAfpoW2PL6kplHs9YG696mF9jozJ9yHNafUZkCtFaZDPj4i9h9rGxWi7QCgdyEwvU2zs2k6B0UvLzvGBrtQVd+yOurn68DK2X347bEITKQC01dEctdKw5X95gtZbL+Uy5AuTgY7g3/x58JEKuH9aSE8PboLWy5kM/+nKGqMDBa1bqErZ4XxxIgAVJr1bX2zUjTccrUxF1fsrSGRCNzTzwtbI3TTJoXquouMttBWqtKNoJ+PPZYyKQ2ac+LNO7sz8wYK6vRQDz47mMQn+6+iUqlQqVS8qhEmBblZk1dRz0NrzvLs5tg2h839WilOs0preXrTJQJdrXlPZ+B6M0gqqGLW92d5fP1FbOSmbFowkOX3h4qCyKmhHsikEh4bHnDbNgT/2MI+MsgFmVSiJ8hoD8XVDTy16RL+Tpa8PcW41wuor+xP/RJNs1KFQqki1NvOoA9fWtOot2I3purr4WnLsgPX8LA3F3uNxg4F3XSj1midjqQLDzu5WLh1LVR1oWvrO+u7s2y+kKN3/6WsciJTiunhacuuJ4cYPD/c35E+Pva88nscOeV1TO39/yu6+F+FIAg8PborH0zvwfGkImZ+d8bAx0Xbrw4PcGTJuCCOLRkhrpb7vXeILw4lYWlmQnF1A83tqD21A8bW+DMi763N2u4EO1nJ+M/c/piZSsWLgyAIvDMlhIk92k9Sm9TTXdzlvLs7gc0XstmmEdpdza9i4vITHE1q37JBt3DXNyl4fP1FFEoVq2b3QS5rf6DcGpX1Tby9M54JX54gLqeCt6d0Z9eiwUbbyM1K5XUDP24E/9jCbm1uytCuTuy9fP12jFLTL6+sa+LrmWHtWqS+ueMKGRqlnJOVjBWzwgz47erC3rJyNVacPzuQRF2TguyylhPlRk+OpROCiGgnDq6Hhy055XUM9HfA2dqMmKwKURHXFlq/t3p3reLRny/w0tZYjiQWGjxnWpgHL08MEm+PvY5Z07/4c3F/f29WP9CXq/lV3LUykkwdv/K4nAq8HOSiwMvVxpwDzwwV7//i0DV+OJmGStUyRDSGmx3W3YIcQUR7lMY54b6M+/w4pTWN/HYxm22aRYpU4yjaekWti3lrowjxsOH+/l78cDLNwLbjnXYWdsbw1s4rXM6pYNk9vfF1MgzBbgtKpYpfo7IY+elR/hOZxj19vTi6ZAQPhvsaFVeqVOrgHWP2DDeLf2xhB5gQ4k5uRT0x2e2H5K4+nqqehN8R3G7x2xWbK6ripBKBr+4PM0haUalU5Gne8+vD13h8/QU2njPk+ho7eWQmEuwtrs/hnTvIj0hN6LW7rTn9ffVpbGHedgQ4W/Lu7gT6+zoQn1dJbaNCT/Z/PfT2tqdRoaKhSUFNfTOvarwxdPH69jgS81uGaDXXYUj8iz8fY4Jd2TB/AOV1TUxf2RKRF5tTTk8PfZ96bwcLnK3NmNq7k14PfsjHR9gRk2t0QWTMXKwtSCUCfX3sGalj5dwRMDcRWHE0RRyw1jYqeGnrZbG4V9U3a/yL9HH1nfHi16/8Hqfn2a6Fk5VMzxTvevg1KouN57J4fHgAY4JvfGETk1XO9JWRvLAlFm8HC3Y8MZgPpvdoN1hF++cw+V8v7KODXTGVCuxtxx3uQkYpnx5IZFJPd2b2b7s/p6v0AjUTpK+vPVfzK/k9Opt3dsUz4tOj+L2kFtgmF1bz6YGkG2oFrZwVxokXRpDw9ni9wIFvZoaxa9Fgg8e/vatFbXrk+eH8ujBcz0Z47ekMUopqqKpvZncrG+Nx3V3p4nL9VYWWAeFpLye7vA6BliGwqVTgt8fCCfWy1zNNWq7xsv47e6B3NARBGC8IQqIgCMmCICz9//48AH18HNiyMAIzEyn3fXuanTG5ZJXWGdD3BEGgv68D59PLCPW2Z8eTg8T7Fm+M5s5vTnLymn5LorWHfHvo3smGa4XVHL5quNu7HdQ3q9oU61zJrWDK16dIL6kxaKneeR0mGUBxdSO/XcxBwHAorK2n2jlSXE4Fr22LY1BnR54be2PslJLqBl7cEsvUFafILqvjs7t7sWVhRLvRk1o0K9Utp1tRZbfGP5LuqIWt3JTBnZ3YczmPlyYEGQw0ymsbWbQhWpwwtzXwUChVzPvxvN4q4L09Cby3x7hkWAt3G3PyKg1X5tbmJuJr7X96KIFu1oCaP7/hXKbIew9wseQVnYuJFuvOqHcA00I9uHf1aT2XyOuhNaPlekgtrgVqeXVSN5EiOWuAD318HPh5Xn86v7JXfOzPZzLo5WXL+3uutpm09N+EvyId7FbR2cWKrY9HMGfNOdGTqIeHYfHo62vP7st55JTX4WqjHtK9M6U7VuYmfLo/idk/nGVwZydeHB8kFh9dc7H5a89zMMF44Y7NruCVid0YFujMpcxyXvgt1ujjwNDS4FaQU17HjJWnsZWbsvnRCLwdLNgclS3OF5IKqkXu+fWgAr0di4ednPLaRlGPUlHbxGPrL+BgKWP5faHXLbbNCiXrzmSw7GAStY0K5g3yY/HoLtiYX3+HroWmriP5X+6xazGhhzvZZXXE5ajbEKuOpRCZUoxKpWLJlliKqhuYP8SPDRpptEqlIq24hlXHUrjjq5P4Lt1NwMt7uJpf1eZ7eDnIeSjCl/en9WDpBHXP+Yc5fTn98iijXuPaov7Z3b3o6mpFRW0TKUXVPPyf8zQ2K8WD586vTnEhs+1t4e/ROTdV1HUR6tX+CkGXYgnw7u4EUVlqY662AqhpUCCTSvQoay9tvfyP9EC/Rfwl6WC3Clcbc35dGC7e1h73uuinaeNFpZfiaClDIqipsdNCPTn8/DBemxzMldwK7vj6JIs2RpPRKtC5vdYlqBdAk5ef5JMD7TtI3m5R18LRSsZTo7uw8lgy/d47JBZ1LRRKFQP8biz0XBvb62ApY89TQ8Tzslmh5OlN0eRX1LNiVli7ObAAZ1JLmPzVSd7cGU9PTzv2PT2EVycH31RRV38e9QfqAG/Df/aKHdQeKC9LBPbE5dHD01bM8pzc052D8QW425jz1s54mpUqPtx7fSnzsK7OzBrgjb+zFd4OFgaD0z8S1CtirXKvPVbLJ/sTWbo1Vi/4WRdaL+ogN+s2LyzrHxlAHx97UQylDQQGOPTsUEYvO27wnHHdXYlMKdHbOehiWqgHb03pTvgHfxjtm0dnlfNjZDoTe7gb+GU3KVRtcpP/C2EsHWxA6wcJgrAAWADg7f3XhizYmJsyupsrhxIKWHE0hfomJa9O6iYO4ILcrLGUSYlKL2NKbw+crMxEyqOZiZR5g/24u68n3x1P5fsTaey9nMesAd48ObILcpmUtaczxOB2Lbq4WOkJmBoVynZ1FNNDPdgandPm/a3R09OG2Gzj86LsspaW6X39vJg90IfJX+m3YLR2GCMCnUksqCK3vO1hMagLe3RmC226rLZJLUKa0r3d8PS8ijre253Artg8POzkrJodxrjubrfsx67daVzP2uBG8I8v7HYWMsIDHFl/JoNBAU4k5FXSydZc9Es31iqxtzBlcs9ODOnixAu/xYqKvZcnBtHP14GS6kai0kvZfyWfoqoGiqsbKKlupLi6QTygF/x8weB1dRER4IiHnRxHKzOcrGR8eegaVQ3NrHmoL09uiKa2UYGTlRl7Fg/Gxcace1ad1ktd12LT+Sx6e7UMxd68I5g3Neq6JVti2fHkIL1UJVC3Y0wkAsvu7Y1CqeSZTTF69x++WkhhZT31TUosTCXU6nDnp4d5sC06B6UK0enORCJgZiLhwXAfVh1P5aWtl9n/9NC/RaDAn4wbSgfriBCZ20FSQRXju7vhbmfOmlNpFFbV89k9vTAzkWIilRDmY895zbHlamNOQSuRko25Kc+NDeSBgT4sP3yNdWcz2XwhG6VKRX2Tks/u7sVzm1uOoamhHuyLy9dbLZtIhDbVpu2xcIwhNrsSNxsz8tvg3Gux7VKOHk9+Qoib3rztSGIRH9/Vkzd2xBnNQgV1nKVUEIhK19fDTAv1YPZA44KohmaFOvj9cDIKlYrFo7rw2LCAm6ZBtoZY2DvgtPpbFPZVmjBc3e291hWwtYGUUqkio7SWhLxK0Rf5hGYANPuHswB6zBN3W3PuCvOki6sV5bVNlFQ3UFTdSEFlvUFxfn+P4Yre2swEJ2szHC1lBDi3rFRemhDEd8dTKa5pNDigfB0t2DB/oHg7u6yWj/ZdZfZAb4Z1dRF579/MDMXFxpwT14r0iroA3NvPC097OcsOJhGfV8nKWWF0cbUW02sAojPL+U3H21p3ZdSsVLF4YzQTQgw5vxV1TZxKKWFGmCd9fe1ZqlkBvXlHMBKJwP64fD3vGxOpwHdz+hIR4ESTUsX3J9JY/kcyT43uYvDa/2W4oXSw/09U1DaRWVrLff29eGxYAG425nyw9yol1Y2sfrAPNuam9PN14PNDSVTUNuFqY0ZOGytYFxtz3p3ag7mD/Hhvd4KYb9vajfST/YlIBEQbXKBdC4HTqSU3/XMZK+oeGipmbkUdKpU2irJlJ2CMRPHi1tg27YfNTSQEOFuhVEFURikyqUTcob7fhgjp8NUC3t4ZT3pJLWODXXltcnC7zrA3g5YV+3/J8FTbPmkdoLDs7l5cyirXFPAKEvKqSNBQ+9qCu6059ToT9byKer0AXImg3nppY760eG5MV1xtzHGyluFkZYajlbqYt/aSeX9PAmsj00XTLVOJISsnvUQ/8eibI8kICDwxorNo5+vnZMkAf0ciU4qZ/1OLN0tfH3tisss5FF/A+VdHE+Ztz+Jfornz61N8ML2HgROlrgzazFTChkcGMOuHs+LB3B5jKDqzDLmOe99Xh69RUtNEfz8Hzmm2s4GuVmIKE6jZQnsv57E2Mo3HRwSIlrH/pfjL0sFuFXG56lVzDw9bBEHg0WEBuNiYsWRzLPesOs3auf3p62uPSgUXM8twsTEnup25DoC/sxXDAp3542ohEgE9Qzgt9j09lK6u1iiUKt7YEScO/G8E7a3u28PNhIho0bqo63rOPDc2kNOpJeSW13EurVTPPK316ju9uIa3d8Vz+Goh/s6W/DS3v94iqyOg1HzYjuCx/y0KuzYw4bF1F+neyYao9DKcrGTMXXv+hsz2ZSYSUaXpZmNOfF4ld/bqRC8vO8pqGlh7OoNXJ3VjVDdX7C1k5FXUMeHLE1TVN+NqY8auRUNuWCpfUq22E8irqDeQI/f3teeczpYuMqWY40lFbI7KZuYAbzJLavkxMh2AD6arg3W3Refo2QgsGtWFo1cL+U9kOukltUR0dmL34iEs2hDN05sucT69FGdrM4qqGvR66BYyKb9GZfPIEH++uT+UZ36NoaFZ2SYbwVImJaW4hhQdC+HyumZeHB+Ig6WZWNj3PzPMIKno7SkhzFsbxS/nMnkg3PeGfm//RKhUqmZBEJ4E9tOSDnbzySd/IrTtEF1GzLRQTxwtzVi47gLTV0Sy+oE+mEgEzqWX4mptTklNI00KZZsX5SaFktXHUgn1tmPWAB+e3xxj8JjozDK6ulojlQh6Yr0bwa0U9Y6CCjWbTioRWHkshWaF0sCFUpdHXtvYzNeHk/n+RBqmUoGXJwbxUIRfm6E8twPtir0jeOx/i8IO6uJuaSYVhTlVDc34OlriaKVdQav/d9L0rLWraicrGVZmJqw/m8mr2+II87FnyfhAvbZORGcnYrMrcLIyQ6FU8eymGKrqmzGVCqyY1eem/E9KaxpwsJLppSVpoTtQikwu5smN0fT0sEUiEZjRx5O5P7aszDtrvdBbUcnkplIejPDlP5HpnLxWhJ+TJa425myYP4BPDiSKpl+gZt9ot4/aXcqn+xNZObsPdU1Kntscw8OD/Fh2MMngsxoL8f1kRk+mhXoQ+No+AJ4fqw4JaZ1UNDLIhQF+Dnxx6BrTwjz1PK3/2/BXpoPdCi5n6ytOtRja1ZlNC8J5+MdzzP7hLM1KFVHppaJHUlFVQ5sq09+jc8gpryOnvE5vdf/+tB6iruHF3y5jK5eRW14n6hv+TLx1Z3dWHE2moLKBgf4OPDo0AJmJhL1xeTe1WwDYvXgwTQoVD/3nnKgyB7UVwe7YPBQaf5mdsXm8vzuB/Mp6pod6sHRCEC42xoPuOwLawv5fRXeMTCmmrlHBwxG+2FuYsvqBPhx+fjibF0awcnYf3p3ag6dHd2X2QB/Gh7jT19cBPydLrM1NEQRBM41Wx7y1puLphj2sOJIs9rNfmxxMH5+2p97GUFrTiI25KR+0Ytjc3ceTb2aFiSKHmd+fRQIcTSoCFdy1MpLi6gb6+dpjKzfFTm7K4o3R1DcpRLtcUP9xfR0t8LSXc1xHPGIilfDShG5896B+3KG2J6gNLt4bl8+lrHKmh3kwItBZLOrt+XJocSihgB0xLeG8swa0DI90f4eCIPDSxG6U1DTy7f+IUOnviss5FUb566D2K/rtsQjs5OqZ0/n0MvHrAiOkAlCfh1oJvp+TJY8Nb5lx3d/fC1eblkXQwnUX2vUS70jITaUcWzKC1ycHk1xYw8M/nmfl0RS8dfrbkzRtyrHXUYgu2RzLHwkFfHZ3L73vfzJD7a6oUsF9355h8cZoHCxlbFkYzrJ7e/8pRX1bdA6DPjyM39LdzFgVCXRMj/1vUdi1W/1vZoXxxp3d+WZWGE9uiBaTh24EztZm9Pd1YG+c8fBdUPcYv9CsLqaFevBAG1Pv9lBS0yjuKnTxYLgvEQFOTAhRH1zW5iYUa1o1jQqlSHk8n15GRV0TnV/Zy7n0Uvr42JNWXMOQLuqLkVKlQhAEHK1knLxWpGfTGplSTEpRtVGHu6d0lKkfaS4672vaPXD9qDGAPZfzeeqXSwCMCnLBvh35c28vOyb3dOe7E2ltFol/8edCOzhtrTjVhY+jJVsea7ETWH5YPW9q7fIYl1PBnDXnmPmdmoAwpIsTB54ZKrYFZCYSBEHA3kLG2GBXgjSiu78KL/wWy2PrLjCppzsnXhjBq5O6cSa1RI/wcEcvd6QSAX9nK2YPbJt2ejq1hHd3JzCjVe7qGzr5wmfTSrGVm/LIYD/6+t4YL/5moc05zSmvQ0XL30SXenmr+FsU9vZCim8GE3u4k1RQTXKhISe8qr6Jp3+5hEKpIsjNus2pd3tYdSyF7DLDAY6VmZQQDxsiU4o5pQncaGxWIhHUPX+ATrbmYmtDF1pGj/b/5X9c41B8AU6WMuqalKzXpDVpL349PW0ZrLFh7aVDg7y7r5d4cTidWsLxa8W4WBtfYexaNBiZDqfKy8FwSz6hnbhALZaMC6RZqeSLQ4atnn/x50N3cNoenKzUCVjQku6lvRinF9ewaGM0k786KXqoeNjJWftwf0ylEvZrBqf+ml2lhUzKgfgCkgraFvR1BKYbsaw9kljEmGXH2Hclj3mD/QhvZZT32PqL2MlN2XEph7fvDGH2QG8Dp8TP7+3Fsnt6GXVE3azDMAM1e+yVbXGiR83tQKlUUVrTSFJBFZHJxWy/lKOhYRq2RHfFtr04vVH8LZqjHRVSPD7EjTd2XGHv5XwWjdJfUbyxXR1IbW1uckvWm6BOPdFFoKsViQXVDPBz5HRqCU9uiGbFrDAe/fmCuELOr6ynt5cdvz4aTn2zgk8PqIugn5Ml8wb7ieZb2mn92bRSvbzRN3fGE59XyaGEQvHiF6cZmD040IfnNCfjoA8P883MMGKyyqmsb+a5TZfwc9b3jLm3rxeborJIKapmeJALBzR896zSOiICHPV2Is9vjiGzpIZHhwVg2UYbx8fRktkDfVgbmc7cQX50cf1rV3H/69AOTkM6Xd+HxMvBAl9HC5Gx9caOKyTkVbLlQjamUglPjuhMZxcrnt50iWfGdEUiEUgrrhF93rXmVRc1PffeXnY0KVQGys+OwtboHDzt1Yrvd3cn4G5rTl5FPZX1zTyzKUbUZrw6qRthPvZMXxGJSoVIaPgxMp13p/ZgcGcnFq67yNxBfhxNLOSN7Vf4dWE4OUYWaMbQXqC0QlOsi6sbWv5VqW8XVTdQXN1IsUYHU1rTeMND445Q6f4tCntHwdXGnL4+9uyJy2eRTmti+6Uckd/9+U1ab2oRn1vJlzpDIkuZlGwN/Wr2QB+9XYcuXUluKhWtf3VPgm8f6MMn+xOxNjfh5Isj2RmTw6vbrjA22JUzqSXc3VdtOQrwa1Q2i0d2Fi90Po7qvuKxpJaADoBFGy8yItCFP64WUlzTKLaCQC3EyCqrxctBzi/nslj/yABmrIokpagGf2dLo+2l5YeTWX44mWfHdGVOuK+YHan3niO7sCUqm4/2XeX7Of1u+vf6L24dl3Mq8LSXt9sy00V4gKMeFfeX81nc09eT58cF4mxlxvSVkXjay5nSW52SpV2ty02l2FvIOK1zjFy8DmVSi9kDvW96uKmFk5UZZhq68e+PD+JYUiGf7E8Sk5BAfc67aMgPr08Opqi6gZVHU3h7Vzz7r+RzNq2UYHcbXpoYxNzBvty1MpLxX5wweK/P7+3Fs5tijFpr55TX8e6ueE3xbinkpTWNRll7MqlETfCwNsPN1pwQDxuR+OFoJeNiRhlbLmQbJTAA7TpA3ij+qwo7qFsI7+yKJ624Bj8nS7JKa3n1d/WqeNHIzoy+CetNLQor63lk7Xnx9pAuTpy4VkwPD1uSC6sJD3BkhI51aYUmtFoiwPdz+orsg6d+UZs1PT48gCaFigPxBTw1qgu2clOcNW2TA/EFLB7ZmWfHBvLL+UxqGhTc28+TdWczGRjgSESAE9bmpjhYyjiq8VCfE+7D2tMZ2MpNRVGJLk68MIJjSUW8ui0OT3s5p1NLyCqr5f3pPZi8/CQxrWxMV80Ow8HSTMxJXXYwiW+PpzJ7oA/zBvvpsYgcLGU8NiKAj/clcia1hIG3EC7+L24Nce0MTlujrlHB+XTD3m1KUQ0yqYTTqSVEZ5bzztQQkQa5Ly5f9P5PK67hwTVn232PkE42xOXqWwGsO5N5Xd66rdxUPGd0YW7a4lPUpFBybz9vRga50u+9Q+JjFm2MFvv9ggAvjg8iKb+KP64Wijvf0ppGTl4rxsFSxsggV6M22yuOpCAIhrx38ec4myEWZ097C0K97XRYemZiIXeyMsPG3MSgzatSqTiSWMhnB5K4kluJv7Mlgzs7sTkqy0AVa6wNdbP4W/TYOxJapeWey3k0K5Q8s+kSVQ3NDOnixNOjDXvc10Ndo4L5P0VRVtvEC+PV1p0xWeUsGtmZK7kVdPew0RMx6fb35w/xF2PJTiUXi/35Z8Z0Zfkf17A2M2HuID8ADmnUfQuH+bPubCbfnUihVuPjMiLQla9nhuoNlL0dLET+7bNjA3G1MSPQzZp3pnQ3MBHKKqtlZn9vwv0dxc+wOSqbIDcbZg/0MVh1xOdV0d/PgZMvjhBNoKobmll9PIXBHx3m9e1xZJe1rPzmDvLD3dacD/YkGJhQ/Ys/BxW1TWSU1F7XDrZJoeTnMxkM/eQIyTp03CA3a1bMCuNydgUzVp3mld/jcLY24+4+ajpkfkU9l7LKGRPsSmlNI/F5lW16HslMJFjKpAZFHdQWFddrQSwY6m/0+2dSS4nJVl+MtNYBH+xNQCKoGSzaWZPWZ+mtnfG8vj2OM62UrvmV9Tz843mmfHPKaFEHCHC2YqC/8SHpgiF+JLw9npMvjmTbE4P4fk5fPpjek+fGBjInwpdJPd0Z4O9IgLMVtnJTvaKuUqk4ea2YaSsimftjFFX1zXx2dy8OPD2Ut6eE8MH0nqJFsrPGbGxQl9s32PuvK+yd7OSEetuxNy6Pb46kEJVRhoed/IasN1tDqVTx3OZLxOZU8OV9vUUx0Jt3dmdcdzeUKkjIreSlrbFiwf3yjxaVa22j+vGFVfUi26STrTnJhdXsu5LPw4P9sLUwJTKlmO2XcnGykrF0Qje+nhnKsgPX0HoBnUwuMhgoa9sxnWzNsZWb0r2TLWdSS1GiNnfSxePrL3ImrYSPZ/TEQjNb2HwhC4VSZZQzu/yPa5TWNOJpb8Fvj4WLUWShXnZMCHFj47lMhn9ylOc3x5BSVI25qZRnx3QlJrvCwB/+X/w5uN7gVKlUsf1SDqOXHeO1bXH4OlqwZWG42LYoqKxnYg93fprXn+TCapGZpV2kaP+OxjQQrdHUrMRGrt+m01IPjxtJ5mqNrq7WdHU1Hiy//ozar+jhH8/hu3Q3Wy+qfYyWbInlZLIha+6n0xl6xnX39fMyaquxZFyLv7qt3JRVD/Rhw/wWp0wBcLUxw9laxo+nM9pVcLeFs6kl3PvtGWb/cJbCyno+nN6DP54bxl19PMUEpamhHpxaOpK0DyexcnYYQIdE4/3XtWIAJoa4896eBOJyKpGZSFg5O+yG+5C6WHZQHaTx8sQgxnZ3E02xRnVz5cdT6QgCfHZPL44lFfHkhmiWTghiV2yLcEkuM0GhVPH0L5eoblBvNUM8bMXV+jzNaj02uwJXGzO6aoaPEQFOvDghkDd3qDnCWsaM7kDZR8Pf9dT8P3usVUeIAAAgAElEQVSgN0euFrLiSDIWMqme7UJ5bRPnUkuJGKP23X5jxxUKKhv4MTKdn8+ka9Lr1X1LiaDmw8/6/ixbH4vAQmbCNzPD+OpwMssOJqFQweaFEWy/lMPGc5n8djGbiSHuOFub4WUv5+N9iYwNdkNmImnT7+df3D7aGpyqVCqOJhXx8b5EEvIqCXKz5j8P9WN4oDOCINDPz4HdsXmU1TbR0KxgoL8jXg5yskrr2BadQ2cXK3LL6wz64hNC3LiYWWY0DNtBo8TWxayB3hRXN+gRAdqCrqVGW8gqbRl2zgn3wcXGHGcrM9ECRNcIT3dnYW1uwrZL+qwWuamUz3RshivqmvBbultPsJX24SQAymoaeeSnKJ7YcJFXJwUzb7DfdT9rdGYZyw4mceJaMc7WZrx1Z3fu6+9lsOBqjY70ivmvW7EDDNbZyrwzpTs9Pe3aebRxbL2YzddHkrm3rxfzh6i3ioFu1phKBWzMTTiUUECYtz3jQ9z5YHpPvp4ZyhvbryBB7WduIhEQgK8PJxOZUsJrk4Mxlap7jXvj8nloUMswcu4gP/Iq6umqww0ur21CEODp0V3IKKnVy7cE8HZUD4C1PciRQa5MD/OgoLJB9MGZFtqJzi7qldAXf1wjMb+KBwb6EKZJSnpnVzzmJlJ2LW5JcXp9sjq4OyGvkqd+iUahVPPqF4/qwqrZfbhWUMWjP0cxtbcHJ18cyePDAzieVMSPkelkl9eRWVrL+rMZevTMf9HxMDY4jUov5d7VZ3j4P+epaWjmy/t6s2fxEEYEuYjtgX46grzCygau5leKRVOpgo/3JRodds7o44m5ERm9VBAMrDUAZn539oaK+s3ihfGBvHlnd54Y0Zl7+nkxMsiVnp52eNjJmR7mQf9WXuzfnUgTKcfh/o4kvzeBEy+OMCjQKox70dhbylj/yADGBrvyzq543t0V32bGclxOBfN+PM+0FZFcya3klYndOL5kBHMifK9b1KHFj/3/XXkqCMLdgiBcEQRBKQhC3+s/46/Bt8dbZPf39rt5f+zz6aUs/e0y4f6OvDM1RDwpSqsbsbeQkV9Zz+WcCkZ3axnERgQ48WC4DwoVPBThS4CzFevOZPDlH0lMC/Ug3N+RJoWKw1cLsTIz0Tuw0ktqaFaqCNShC564VkxPD1sm91QzFE4k6zNgtDJ+3YPgtcnB2Ji3bMLmDvLn0LPDRGXruC+O83t0Dsvu6S0+5oFwHz0a5/aYXKw1r3EgvoC3d14R++bjQ9zY+ngEMhMJd68+zYlrRSwZF8TJpSNZMi5Q/Ewf7b3KE+sv/i8Fcvzl0B2cXs2v5JG155mx6jRpJTW8MzWEQ88OY0pvDwNDKV2xzdeHk/UYIu0tFOetjSKj1LDwKW5xpnKri9KP9yVy37dnWHU0WVRsDvrwMM0KJeW1TUQZsb7WMoGW3x+KiVSCk5XZDUfdAZibSlkxqw8PRfjy/ck0Fv8STUNzyzlzraCKx9dfYPJXJzmfXsqScYEcf2EE84f63xStWpugZNIBvr2324qJA6YDq2/7k3QQtl/K0bPyzC6rxdP+xm01M0pqWPBTFJ72clbODtMz+ympacTBUsYfGn+XMcEtTJjIlGI2X1DTEtedzcTF2oyaRgX+zpa8OzVEjyo2J8JHz9sjUTP80bZiKuubuJRVzsJh/gQ4W9LJ1pyT14r1JP7Gtm3xeZWYSCX4O1uSVlxDVb26/bP3qSEM/fgIhVUNPLc5hqkaOhvAHwmFmJlI1buDUV35/FASMqkEG3MTpod58mNkOh72chYMVbdTgtxs2P7EYJ5Yf5FnNsVwNa+KF8YH8cSIzswd5MeCn6M4ca2Yhwf5/lvU/wRsi87ho31Xyauop6iqgRkr1SlcVmYmLBkXyMODfLGQmdCkUFJQWS/mCRRrrKq3XmwR4WyK0g97vlnDPV08NjyAF8cH6QXBAHjayUVasBbmphLR+M7TXm5U9NcaXg5yjj0/gl/OZ/HOrit6u4EcTWZvgQ4j7NVJ3dh2KUdMVgO1e+nz4wKxMTe9afGjVCLwxh3BapLA3qsUVTXw8sRu/OdUGttjcrGUmbB4VBfmDfbDVn5zyUlaaDNPO2LFfluFXaVSJQB/m8AFLbXRzsKUFTPDmPn9WfbF5fPIEONT99aoqGti3toolCr44aF+BsZKpTUNOFrJOJRQgK+jBQHO6jZHa/fDAX6OzNJ4wy+7pzeWZiakFLUwEh4ZrP95kgqqNFJo9cr6TEoJCqWKIV3UfdHBXZzYF5ePQqkSC7m2aGtvi1bH9/TisXUXGRXkwpMbWz7TloURTPjyODWNCrZdapkDJBZUkVNex0A/R/r52uNlLyerrI5GhZKRQS4UVTfw/p6ruNnKubOX+oLgYCnjp3n9eWdXPKuPp5JYUMWX94VyJbeCK7mV4sVtSFfnf4t7B0IrQdeqFWsbFURllCOgFgydvFbMtugciqsbbkrkcn9/L4LcbJCZSPQC3XVhIZMSuXQkn+xPZP1Z/VaNo6WMhcMCePbXSwbP27FoMPeujuRaodpF1MNOzh293FmlMbPr62PPg+E+RrMQdP3Rs0rrGPLxEZaMC8RWbkpdk36vX/eadP6V0Thbm5GQV6VX2H86ncFPpzN4445go/TK60Fri9ysVPHJ/kSmfKPu6y8cFsCjQ/1vaY6nC61t7/98j12bbwranEI1tXFcsBsRnZ0Idrdpd5qt+/wmhZInN1wkrbiaiT3c9Yy5tCitaURuqnagHN3NVbygtbZEiM5q4Qubm2o4wRqxx2PDAwwOgMT8KnwdLURGwolrxVjIpIRpYrmGdHGmsr6Z2OwWvnmaJpuyWXPgaz+DUqVOeJ890EePRePtaMEX9xkPoK5uaKaHhw1PbozmqdFdkGkm9gfj1UZJ/f0ceP7XGD0amalUwttTQnh/Wg9OXitm/BfHeWyduv3y7NhAA3rmv7h9fLI/0agEHSCztJZGhRJ/Z0sm9nBn9kBv0QZAi4gAR1bNDuPJEZ3F780J9+GD6T25q48nv+qs4NfNG8BzY1rowZ/d3YujiUV6Rd1Wrl4XPjLEn2c2XWLrxRzxNbX47UI2705t8Sxafn+ongDH28GChyL8DD4rYBDLmFNex5ItMe0mK116fQzO1macuFbE1mj17kQ779LirZ3xfHGofUfKPu8cNLASyK+o59Vtl/UsNEwkAlN6d7rtog6g/XE7ghVz3cIuCMIhQRDijPy7qVBfQRAWCIIQJQhCVFFR0fWfcAPQBnREphTz9ZFkLmSUITeVMiVUvbKc2MONCxll5FUY3+qJz08u5s0dVzhxrRhzEyl39DLuk1JS3ciV3Eoam5WM0umvLxwWIBb1s6klLDuYJA4tVx5VXzy09qfzh/gTmVLMKh1XxKSCKgJ1Bqcnk4sZ4OcgtoEGdXZCEFrYMQCpRerCrt3Gaj/DwfhCLGVSwjViJl1GyhgdcdYDA330TqZ1ZzP5emYoM/p40d9PfUHZfyUfmVTCdw/0xclaxpw15ww8QmYO8Gb9IwMor22ipqFZvJBo6Zk7Y3L1ftZ/cevIbSdo4tiSEax/ZAB39OpEXkU9G89lkVpcQy9PW966szsXXxvDhvkDGR/irkcuWDAsgPomBY+sPU9sdgXWZibYyk0Z4O+g53z6/p4Elm6N1XvPyrpmJAL8GpXFsaQinKzUxW335Ty6uloxMsiFTw4k4mhlJgZMv7s7nngdvruXJlf49TuCb+h30BaXXgs7Cxk55XUs3hiNq7WZKI7SPqu91bDuPSU1jbz4WyzbonMoqmrg7Z3xDP3kCJvOZ3FvPy9OvzSSfU8PwcnKjHtWnebjfVf1ev634i/zlyYoqVSq0bf9Lvw5uZC6AR0VdU2YSgW+f7CvWGQn9nDn0wNJ7IvL5+FBhjQl7fMf/fmCyFEXBHh562VsLWTYyU2xs1Bb7MplJlQ1NIseMIIAyYXV2FmYYis3xVQqoaS6gcW/ROPjaMkvCwbS771DmEgEHlt3UXzPq/mVYtsG1AKojNJapvRWq82ySmtJK67Rc550sJQR0smWE9eKWKyxSkjTBGRkltaiVKqQSASUShV/JBQwLNDZ6BReG8QNsOVCNhsXDGSqZjupVLUMb+cP9ScypYTCqgYuZZdT36SgtkGBuamUh9ac4/cnBuGqY2E6wN+Rg88OZeZ3Z/lw71WyS2t5Z2oIoM5f1f6s/+L2YCs3pdxIC8HRSsZLW2PZFZtHVX0z7rbmLBjqz11hHnR2MfTv8bRvofU5WclY8NMFzqaV8umMXry9K55R3dTxjboBG1lG+uAq1ErNstpGfp7Xn+9PpHH4aiHF1Y18fm9vAt2sGfv5cZ7fHMPzYwM5+8NZojPLic4sFwuu1nZ3eKALo7u5GOQT3Cx+OZcpRj3KTSUG4iiFUmVU6arb99eioVnJi7/FIhEEGhVK7grzYNHILmIUnrutnK2PRzB9xSlWHG1ZvOSUtwRuG/OYaQv/ddF4t4OIACf+r73zDo+qzP7452bSO6STBiShJvTeQZSmgmKl2HXtuu66dlfFhuzPsioo1rWuKwpKR6T3FkpICCGVhPSQXiYz8/7+mJmbmWQmHSYk9/M8PjKTKe8kd84997znfL/zhwXz5d407h7fy2xqq7efO/0CPdh4ynJgNz5/0egwVuxMYVTPbvQL8qS4spbiqlqKK9WkF1YY/m1+INy28oDZbdPecW9XHS//FocQ8MuxujP3iPBuZrV4gOT8coRAztiNQxcT602fTYjy5bNdKZRV1+LioCK9sAIPJ/3JJq+shkAvZ05mlZBXVmOWmRuprtXy6tp4Iv3d+c89o5j70V45qI/trRcxe/iHY/z0wFgm9/HnhTn9WbIugaUbz5CUV87yRcPwdHbghuV7ufmT/ax/fAIezvpNImO/+vf3jea6D/fw3cEM0goric8uVTpj2ok1sVlUqC1LLxeUq/nt+AVmRgcyf1gIY3r7NBocTMspj/8Yy86z+SydH0OglzMlVbXMGBjIq7+fJresBmcHOwYFe3M4rciijgrAo1MjGdvbR5b8nd7fn4lRetu41+ZG8/iPsZzKKmFkz26yrIG9ofU3zKeuseHFOQPaHNifNdkjsGZgbQzqQ0K9ZUXL+kHdSI1Gx4jwbiy7ebDF8qy+973h77ox8TBraOUae7OfYpW2tjveIElSJjAWWC9J0ua2L6ll7EsuYM3xCzw+LZJVRzMb1HRnRQdxOL2IvEaMBX4yiGydy69gZnQg/759KN/cM4rfHp3Ajqencvzla1j3WF2v9wOTevPNPaP49+1DWTJ3IH+7uo/ZQFCUvztJueUN3utI+kUWjQ4zC3T1O2L2JBUQ4Okkl3KMTIzyRaMTHEgpIqu4ilqtYHJf/Zcn3VBv3xqfi8pOYmpff+rz6c4UMooqee36gQR7u/Dg5LoN3K/vGcktI0I4mFLEB4b64b0TejNjYCAHU4vkNUcHe/G3a/qSUVTJgs8OUKvVsf1MLvd/c4RNcdlM+dcOiqtq8XV3ZM+5ggafVaH1LNucaLEMYW8n8e4tgzn8wnTevWUI4yN9Gw3qJZW1fGvik7v5dC6vXDeAW0eGsSkuB2cHO8qrNfwam8WjUyMJ7+5GNzcHgrytm0x8tTeNHJPv1wtz6soq1w0KYlZ0IO/9cdbMr7e6Voejyo4AE2npsGaaQs8cGMCLc/ozsIde7mJWdKAsLwB6GYP412YQbGXNLg4qfN2dzExEGqOwQi23AIM+sz5xvpiPt5/j9pUHzD67KY2Vzixh7I+3eR+7EGK1ECJECOEkhAgQQsxo84pagGk3irUNu9kxgQhRp1TX0ucbuVhZN4Tx6LRIJvXx4/rBPVg8tidjIvQZ0pyYIFLfms3PD47jj6cmc//EuqsELxcHuVvE9PXP5pbhqLKjp48rWp1gb3IBEyL9GnQaDQ/vhouDij1J+aQYyjDGAJ5epO/T/SM+lxHh3Rp085wvqmT5jnPMGRTEOMMXwPRE9NPh87xxQww9fVz5dFcKFyvU7Esu4HBaUYM1Pzg5ggcm9eZUVinj3vqTu78+QkWNlrzSGh6Y1Jul8wehE1j8rAqtx1qQ0OoENw4LsSqtXJ//7E+j3MR0ZXykD3eN74VOJ9gSn0P/IE+WrI/X7z9Ni6SoUj+7YdzIt7a2Hw/pN15HhHczy2wlSWLJvGjcne3N2pBBX/4w7bM3iuQ1hreLA58sHsGsmCDSCiqYEOnL8oXDCPTSB/EBQZ68M38Qro72PD2jHy4ODUuS1bVaeng5sd3QHnlDI1n1mF7dySisYOTrW+n57Hr6vbSR6H9uYu7He1m2OZGSqlqr1pDWrAetUed52vaU/YruimmOQUdUgAeR/u5sONUwsLfE4KPIMF0X5OWMp3Ndn+rFCjWP/xhLSDcX3ppvbt5hPGgdVBIrFg2zePJIzC0jwt8de5UdcVklFFfWMqlPwyzXyV7F6N7d2Z1UIG+cTozSZ2cZhsnUxNwyi2WYJevisZMkXpjdX77PWGf1cnFgybp44rJKWL5wOGqNvjuosRPe87P7M7a3D/nlagb28OSnB8aw55lpTIzyZemmM0pnzCXAWpBoSfCoqNHw5d5Us/tCDTMeJzKLyS2tITZDv6fy3q1DsLeTKCyvYd3J7EbNH3p4O8u+p8sNeiem+Lo7sWRudIPvlamJ9LGMi6xtwmDC3k7ilesHIoTghdWn0Am9KXxptYZVBpOMt+fHmOmwvHVjDN3qyU0LICGnjN8MUgP1TzjG9wI4kFqEVtS1U1bX6lBrBYvHhnPkxelseGIir8+LbnACcXFQmenRNAfZ89TWpRhbY9qNYqR+JwjA7OhADqYWmuk4t+T5AMfS9bXBW0bUOa/oRcJOUFiu5uMFw8wCPuhlTwHuHtfL6snjbE4ZfQ0CSMb6+vhIy+WLCZG+pBRUsCcp3yD160QPb2fSiyrZatgYrR/YdyTmsSU+l8euijQLAqsNRgY7/j6FAE9nHvn+GEFezjwyNZK9yYVmA0b117wvuYDE3DIenxZJdkk1WqHfvG0vJyyFhjw9o2+bg8ePhzLkvSKj6NZ6QzDdfLpuY/25Wf2J8HPnp8MZ6ARmGb4ljDMVgFXXrjmDgpgzqGG32dncMkqqannsh8azdXs7CV93R+YNDWbN8Sx2JObz9Iy+BHu78Lf/1W3yutab9Jw3NNhMfdVIrVaY1eC/uWcU39wzSp7VaEyRUqsTbEvIw9egxmg8gRhVGoO9XXjrxpgW1dfBtMaubJ42i1kxQfx72zm2nM616BfaHIxTevMNLu8An+9JYduZPF69fmAD38nUggrZAd3Xw7w0YhTzKq2u5YKJRszupHz6B3nKB0x9JvXxg/UJbE/MZ2iYN5IkEd7djYzCCgrKaojydyfcp+4yuEaj5ZXfT9Pb181sKCqvtJq95wp4ZGok3dwcWb5wGDet2M+TPx3n08XD2Xw6h2/3p3PHmDo9G+Oa6w9jjYnwkW+3lxOWQkOMQWLZ5kQuFFfRw9uFp2f0bXbwqK7V8vr6BEDfonrH2HBmvr+bshoNOp3g0136ro6JUb5yR9Z7TfR6ezrbExPsyd5k/RSoMShaY8ncaPlEYuT19QmoJMs6Labv89TVfXhlbTyHUot4dW08w8K8uXNcT1bsTGZrQq7skVBeo0UIQUJ2GdsT89iRmNdAoMwSd3x5qMnHmFK/NDZvaHCLA3l95K4YW0+eXin0C/Sgl68bG05ltzqwG3fNjbv4R9Mv8s6mRGYODOQOk4EMIx9tO4eTvR01Gp2ZDrYpSYae8L4BHlTUaDiaflHWZ7dElL87AZ5O5JbWyHXMMB9XfjyUgZ0k8Zd6utaf704lrbCSb+4ZZSaN8PuJC+gEcovloBBvXr5uAC+uieOzXSn86+bBzP14L6+ti+f/bjF3cm8sK1cC+KWlLcFj9r/1mjABnk68PjeaUpMs+/cTF2SDiWU3DZZLiHmNDAINCPLE3dmesRG+cmD//cQFXr8husGVq5FabcPOk10mLmBDQr1IyC6TtdeNhHRz4ZqBgbyyNp5bPt2Po8qOpfMHcSClkP/bksj1g3swd0gPdicV8PiPeh0XSyqUTeHuZM/CMWEMDfWmT4AH7s72THpnu8WOGWt19bagTJ62EEmSmB0TyP6UQrlW3hIu1ntOcaW+rh7k7czSmwY12OhML6xgzfEsFo4OZ3Sv7lYDe2KO/v4+AR4cSi2iVivMhkcsfQ7j0EhPQ2Ye3t0VIfRne1N3qKziKj7clsSMgQH6TN+E345fICbYy6zzZuHoMOYN6cG7W89SXFnLQ5Mj+OVYJtvr6Wm3pHyl0DH4dn+avC+z95lp2NlJZnomT/6klwJ4cU5/eRMSrFu0ebk4MLWfH4dSi2QPXyO3fLKfHCsZcnx2QyMOeY33jmLNIxNYOr/OeCLIsJb47DKzuYnHpkXi5mTPws8PohP65oF7/6OX/s0oqmxxUH/q6j6kvT2HuFdn8Nys/syMDqK3nzv+Hs68feMgHOqJcqnsJMpqNHy5J9XKK7YOjVYJ7C1mVnQQWp3gj/iWC+bvOKsPbi4OKoQQ/P3nk+SVVfPxgmEWBX8+2qY32xgR7k2kvzvn8soRQlicOHV1VBHs7cLupAIc7e0Y2dOyi4sRo6CZsasl3KQPeIiJPPEb6/Va7i9daz7Rdy6vjFNZJQ0yP0mSePPGGKL83Xniv7HcMiKUPgHuPPfLKbPsTuHKYnVsJi/9dhrQWx4aNxYlScK73rFbvwQ4K6ahQQXA3CE9mGLSUjs7JhBHlR1f3TWS80WVzF+xz8xJzEhCI4E9w9DZZWo8sf+5qxhrsFp83KRjZkNcDuPe3ibfrm/taA2jxLVxr+K/D4wBGtblTZk3NJhlNw2WpX8BQryd6RfowWvr4ls1YWoNY8ZeX5GzNXSZwD6whydh3V0tdsc0hXFoYkpfP77Yk8rWhFyem9Xfos57RmElv8ZmMb2/Py/+dhqVnURptYZNcTkN9MnP5pYRFeCBnZ3E7qR8RvfqbnGjx5QehizGWJMM8tJviHo42csHxJ6kAjacyuGRKZENlC3XxF7ATsKibIKroz3LFw6nqlbLU/87zls3xpBXVs2bhtqswpXFxlPZPGXYWOzt68aMgXWBek1sFiX1TtjP/XrKLFAZRe6MGCUDZg4MNOu1Lq6spV+QB1P7+fPTX8ZSo9Exf8V+jqbXKTDqdIJ3NiVijXe3nDXbhAV9F4/RWNu0Nt/YCaI+C0eHkfj6TNLensNDUyKQJBjRsxsBnk6yV2pTfePzhgZz4PmrSH1rNu/eMpiLlbWytMbffz7R4Kq2tSg19lYgSRKzYgL5YncqJZW18qZgU6g1OnYm6uuAmRer2JqQyzUDArh7fE+Lj/94+zlUdhKvzY0mOb+cv3x7FIBHfjjGrSND5UEk0Af2af38ySmpJimvnJuGh1h8TVOMLWLnDRmOUQcnytDloNboePn3OMJ9XLm/Xs1dpxOsOZ7FhCg/q90Lkf7uvD1/EI//GMvGUzncP6k3n+5MYc6gIHmaUKHjs/1MHo//N1aunT95dR+zkuGyzYkNjJtNpyXXxGbxrkmZ5amro+jt586jP8TSzc2RF1Ybx/ZVnMwskQNwdLAXvz40jju/OsSCzw7y0YJhBHk58/JvcY2ut7BCzcz3dzMrOpDvDqZbnQRtLk9Oj+LhKZFme0tH0i4S6efO8Yxi5gwKavEIvyRJ3DgshHERvjz760l2JOaj0Qke+u4of5kUwaqjma3a2DbSnl0xXSZjB71lnkYn+MNEM6UpDqYWyu1ep7JK8PdwZtlNgy1KFZ8vquSXY5ksGBVGgKcz4yJ8uXtcTwACPZ358dB5xr71J4/+cIx1Jy9QUK6mT4CH3ObYWH3diFEj5vSFEipqNOwz6Lwb9Su+3JtKSn4Fr1w3sEH2fzTjIpkXq7hhaOPdC9cP7sGdY8P5fE8qA4I86e3nxrO/nGqy7U2hY7DvXAF/+e4o/QI96enjSi9fN9mD1Ii1LpQLxVWyPHCZyd97xY4UeaBn77kCjmUUM7pXd6pqtZTXaOQr0TWxWdz+2QHSCvTmMfd/c4RrP9zDWcMkdnSwp/ya9c2js4qr+HxPqllQtzTGD/rgd/PwEItWdQmvzeTJ6X3MgrpGqyM24yICKKvRMK2ff6sDaaCXM1/dNZKl82Nwd7KnulbHB38mkVVcJTsx1b/6aQ5apcbeOgaFeBHs7cLGFhgub403Pwl8tGCo1Wz/4+3nsJMkeSNxX3IB3x3M4PFpkVRrdCy7aRCLxoSzy+CRCnrFxrUn9EbW/QM9Lb6uKSn55UiSvg/3YGqhvL7c0mpySqr5959JTO/vz9R+DWUFVsdm4eKg4poBlmunpjw/pz+DQ715cXUcj0yJ5EJJFW9vVEoyHZ2j6UXc980Revm4ce+EXqQVVvLQ5AizYFFSVWvVwaiHt4tFeeCqWi1bDMfap7tSiA725MPb68TdYoK95ROCMcBpTXrBjUmBqT76gRTL1nl3j+/JnWPDiQ72lBOZ+iweE05RhZov6m1g3jehl0XXojM5ZVSotZRW1eKosmN8pK/sWNSaQCpJEreODGPzXyfJtXtTjFc/LUE+0dhaUuBKQ5IkZkUHsjupoFkbgkIIM1GiIaHeDLUyWn2+qJJVRzO5bVQogV7OFuUK3tp4hqsHBHDw+emyrdnOs/nsPJtPQbmaoxkXZRs6a+tJKajgluGhONnb8enOFC4YOhAyCit5Y0MCGp3g5WsHNniuWqNj/clsrhkY0Kzxcyd7FR8vGIpKJfHZ7hQWjArjuwNdRyKgo9o+NsapzBLu+vIwgZ7OfHvfKL4/mE4PL+cGJYF/GgTq6gc048CTNfkCowJqflkNL187EH+TDcWoAPdG9eItIYEs52vKV3vT+OFQBmcNXWP9gxomPF/vS+NPwxXENQMCiHt1Bn4eTlaF0oyWeWXVGkb37o6bk73sWNSWQBrs7b8lxlAAACAASURBVGLRUQraoBWjZOwtZ1ZMEGqtzkzCtj5GA46E7DKzS9Z+gQ0lUI0s35GMnSTJwkKN9Xu7OKqIDvbC29WBd036xG/+ZD/XvLeLr/emWnR4KShXU1atoX+QB6N6dedgahGSBIvGhHGhpJq1Jy7w0OQIM8U8IzsS8yipqm1R3S+kmyvv3zqExNwySqs19PRx5dlfTlFp5cvTyTDaPu6y9UKaQ2JOGYu/PIiniwPf3TeatIJKDqdd5IFJvc1KEutOXmDN8Qs8MT2KBwzOYvWnJa3JFBjb/uYMCmpgGt3nhY2NDhlZQpIwM48x5dDz0wn3cSXIy5lp/azv7bx362BW3jECdyd73BxVVNRYPrEcNkyOV9VqucpwNduWjN2U9pB7AH3G3h5lGOiCgX1oqDdBXs6NdscYDTi+qqerMTSsYRcM6Gtqq46e55aRIXKXSlP93km5ZfQJ8JBlDrb9bTJL58fg4qjilbXxjH5zK3//+QTHTLJ442VpLz93Wda3h5eLWXeONcW6Ncez8HFzZKIVuQJrTOnrz2NTI1l74gIjenYno6iy0e6GzoIQIkEIcUV80JT8chZ+fhAnezt+vH8MPbxd+Gj7OXzdHbltVN1AXm5pNS+sjmNwqDePTI3kxmH6k/wHtw9l77PT5JO+NfkCo7rkc7P6AfDr0Tr/1NYYLOiE/ir6/VuHNJCpnv/JPpLyyhkW1o2Pt1s2annq6j7cMLSu4cDNyZ4KC/tAQggzk+tp/fTzHu21Wdkecg+glzFor8DeZbpijNjZScyMDuT7gxmU12gsTpAZs+vFXxxCQm8Dp9bqLNatAZZv1/etPzQl0uLP6yOEIDG3jHlDgtmdVECUvzu9/fT/3ToyjLisEr4/mMFvx7NYdTST/kGeLBgdRo3hMre3r5t82ebsYMcOQ7vV7aPCLLZLllTVsjUhjwWjwuQ+5pbwxPQ+HM24yNoTFxjVsztf70tjdkzDrK2rIknSA8ADAGFhrZtsbi3niypZ+PlBhBB8f99YwnxcOZlZzK6z+Twzs598PAgheHrVSWo0Wt67ZTAOKju5lFJf0tqSfMFNw0P4wCD0ZWyhfXtTQ5/SllKl1jJvaDDXDe5BxPMb5PuNA1XrDfthg0K8GmgOLd9xjvf+OCt3obg52lssxWRerJKHliL83OQrWq3RPLqNwbStcg9GdDrRLvV16IIZO+idldQaHdvOWO8/HRfhS58AdwT62jpAN9eGk3hZxVX878h5bhkRSnAzL71ySqspq9YQ7uPKodSiBt0w0cFevHVjDAefv4rX50UjAS+tiZO1Pooq1Jy/qG93LK3WyFcfRmGn+myKy0at0TUqT9oYKjuJD24birerA6mFFXi7OvCPVSeoUje/ntoRaS/bRyHESiHECCHECD+/y9cSmlNSzcLPD1Kp1vLtvaPlSeKPt5/D09meRWPqTjLfHUhn19l8Xpjdn96G/nRPZ3ucHezItaAnbjootPsfU+XkwdjWCJBXZn3C01jeWTQmzKJ0rhE/D/1QlMpOYsnchntDoK+hn8kpw82wKWq0dKyu1Zl1oZRV11osxRwx6ac3tbQ0KhzYt0OWbPr7Mr36aQlaXft0xEAXzNgBhod1w9/DiY2nsq0KF+1LLiC3tIbHp0Xy6a4U3JxUOFjIdlfs0GfrD09tXrYOdeYapVW11Gh0DS5DjXg4O7BoTDgLR4dxIrNEdjwyuqODfiPLaDNmFB2rz+rYLHr7upkNR7UUX3cnPl4wjNtWHsDVUUVaYSX/tyWRF69tnldlR6S9bB9tQUF5DQs/P0BRhZrv7hvNAIPpRFJuGZtP5/L4tEjZ4Solv5w3NiQwqY8fi0wsFyVJIsDTuckR/NWxWZwwZMuhJgNvbk6Wa9rB3i7sfXaafHtEeHdeXXuai5UN941KKtW8s/kMq49lWRXr2hKfy5S+fvzr5sG8uT7BosxuVa2WtMIKgiwkV0fS6szlTU1oZJncdsqS24pOCKvdSi2lS2bsxnLM9sQ8ixuB9TtahoR6U6XWNugIyS6p4n+HM7lpePOzdUCeWsstrcFBJTG6l0+jj5ckiSGh3kT4uTEuwqdB7c4oMWocyzYlq7iKAylFzB0SbLH3viWM6NmdZ2f1k4ekvtibytH0i008S6G9Ka5Us/iLQ2QVV/HlXSPlK0rQb+K7OqpkK0iNVsdf/3cCJ3sVyyzoGvl7OFnM2I1U1GhYuumMLF1hqh/j6+bYIBBZqi3PGxpM7MvX8P6tQ8ykbf953QBcHFUs357cqALjS9cO4Ms7R+Lr7sT84SFW6/lVtTqLNXZjYPdwtmdEz7qutvb0GG0PNDpdq0qlluiSgR302jHVtTp2JOY3+Fn9jhZJ0isr1q/xrdiRjE4IHm6mxZaRxJxy/D2cOJVVwrCwbs1qP9RodWQUVTI41FsWAKvPtjN5/HzkPB/+mSSfhH4/fgGAMB8XM52a1nLvhF7MNIymCwH/WHWC6ha0uF0pdATbR0uUVddy51eHSc4r57M7Rpjtc2QUVvL7iQssHB1GN0MA/nh7MifOF/PGDdFmQlpG/D2dyW+kpLJiRzJ5ZTU8argi9THICmi0OnJKa5gY5dtsHXLTcsUfT02iulZHcVXTHVYTo3zlOvjY3j6orMRhNycVlfWuIEoqazlr0K2Z1MfP7KrbuHnaHqWY9kCra7+rhy5ZigEY1as7Pm6ObDiVbebFCDRQKiyqUNPL183s/pySav576Dw3DQ+Rpz6by9ncMnzcnYjPLuXv1/Rp1nMyL+p9Tnv7usmmGkYGBHlyNrcMjU6/QebqYIdWwOvzovnteBZR/m4sWZfARwuGWnn15iNJEu/cPIgzOaWkFVaSnF/B+1uTeNbQKdFZEEKsBlbbeh2mVKm13Pv1EU5nlbBi0fAGEg8rdiajkiTuM7Qxnsws5t/bkpg7pAfXDrJccgzwcGZHqeW9pvNFlazcncK8IT1kP1Jjxp6UV06NRseNw0Jk+efmUF2r5fuDGazYcY6C8uYprb6xPoH/3DMK0F9tT+3n38D02sVBxfgIH7Ym5CGEkK9M9F1l+sdMq+cFrG3HvvH2QKcT7WJkDV04Y1fZScyIDmTbmbwmM86iCjXd3cyV7z7Zqc/WH2lBbR30f7ykvDI5S2qu/oqx1TGsu6tcY5wY5cu9E3pxLq9cDqzv3TqYqwYEotHpeHrVSc7klJFVXG12BdJWPJ0dWL5wuDxxt3JXcrMV9hRaR41GywPfHuFIehHv3TqkgVNWTkk1vxzN5OYRIQR4OlOl1vLXn47j5+7Ea9dHW33dAE8nKtRai3IRb288g0qSeGZWP1nu2hjYjf3nMcHN27dRa3R8eyCdycu2s2RdPH0DPfjloXFWS5im5ZGdZ/PlzVuA5wwWj57O9mZXCkPDuqMTmEkSHDa0OUqSXsTPlPYU3WoPNEpXTPswOzqISrXWYjnGiE4nuFhZi49JbTG3tJofDmUwf1jLs/XzFyuprtVRWFGDl4tDA+clayTn66fwTPXkX7l+IBOjfFFrdXKdNNDThQ9vH8qh56czPkJfu180JrzdTTAG9PBkyTx9wNAJeHrVCWo0na8k0xGo1ep45PtYdicVsHT+IK6zsOH/2e4UtELIV5VLN50hOb+Cf908uFHBO2N5pn6d/VBqEetPZfPg5AiCvFwoNBx3PoYE52RmCR7O9lbLgkY0Wh3/O3yeqf/awUtr4gjt5soP94/m+/vGMDy8G09c1TAxcnFQMX+Y+VXAmxsS0BjaWCL83BkS6k0PbxdS3potd6G4O+m7ZkxPUkcMe0BDQr3xqSdL3NFq7DohUFmrM7WQLh3Yx/TuTjdXBzbGWdeOKamqRasTZptGK3Yko9W1PFuHuo4YIfQO8c09qFIL9G2GK3enAHDH2HAi/NwZ3csHR5WdvHGaUaTP7BNzy0jI0fuSrjqaeUmkAG4ZEcotI/QDImdzy2UdeoX2Q6sT/PWn42xNyGXJ3IHcbOK5a6SoQs0PBzOYO6QHod1d2Z2Uz9f70rhrXM8mheX8PfXBzjSw63SC19adpoeXMw8YFEKNCUU3N/1J4lRWCTHBXlbLGFqdYE1sFle/t4t//HISX3dH/nPPKH5+cKycZMRfKOUfv5wye16wtzNv3RjD6/NizLL5s7nlsj0lwPxhwZzJKeP0hTrtGVdHfWXZ2BCh1ujkK8n6ZRhoX8ei9kCrZOztg73KjhkDA/kzwXo5Rs5UDJtGeaXV/HgogxuHBlsc3W8KY0cMNL8MA/qBjfDursRm6A/UZ2bqSy8ujipG9OxGcn4F9nYS6YWVFnVqHv0h9pIE99fmRstaHst3JBOXpRhXtxc6neDZX06y7mQ2z83qx+KxPS0+7qu9qVRrtDw8JYKSylqe/vkkEX5uzdr3MMo3m9rgrTqaSVxWKc/M6icLahVVqPFwssfJXkWNRktCdikxFtpndTrBhlPZzHx/F0/+dBxnBxWf3TGCNY+MZ3IfPyRJQgjB13tTZbs+0MtFn351BnufvYp5Q4NxtLeTp6ijDP35pprt1w3ugaPKjl+P1bU+GpsQjC2YcRdKZJs9S8OFGjljb/LXdFkwmsK3Bx3kI9mOWTFBlNdo2JNkOejVry1+sjMFjU7w6LSWZ+sAibl1NnkTWjDen1pQIfcSj+3tY9ZJMzHKj3N55Tja25FeVNmoTk174+ygYsXCYXg42aM1bN5aE0VSaD5CCF5Ze5qfj2byxFVR/MWK9WBpdS1f70tj5sBAIv09eOm3OArKa3jv1iFNmraAvsYOkFemz9jLqmt5Z3Miw8O7mc14FFao6W5Ibs7mlFOrFQwKrmuzFEKwNT6Xaz/cw8PfH0MnBB8vGMb6xyZw9YAAeTOzqELNff85witr4+Xnujmq+GTR8AbdYTePCCHIyxknBzt6+rhSWKFm+Q59Z5e3qyNX9ffnt+NZspeqm6EUY5w+NcoIBHg6MbBHQyExnRzYO0YY1GqVjL3dGBfhg5eLAxuslGOKKvSZTHc3R/LKqvn+YDrzhgQT3kRt0RpnDaWYnj6uza7PV9RoyDG5VH7v1iFmPzcOOFWqtWQUVl52X9Kevm4su1kvZpaQXcqKHW1vq+zKCCF4e9MZvtmfzgOTevPk9Cirj/12fzpl1RoemRrJ7ycu8PuJCzx+VZRFdy9LuDvZ4+qokoeUPt6eTEF5DS9fO8Cs572ookbeZzqZpb9qHBTihRCCXWfzmbd8H/d9c4QKtYb3bh3Mlr9OZs6gILMMdN+5Ama+v4vdSQVmSc07Nw0289814mSv4qEpEcRllXLjsBAkCb7YkyqbzNw4LITCCrVshFOXsRsDu76+Pq2fv8UZjo60ebomNosdiXkk5ZUz/u1tbbbc6/KB3UFlx9UDAvgjPtdipmm6abRyZwq1Wl2rs3W1RidvgrakDGOqSe3qqDIzHAZ9u6PxiiK90LJ+9aVmZnQg90/UD8V8tD2pRfZlCub8+89zfLozhcVjwnluVj+rg2VVai1f7kllSl8/fN2deHH1KYaEerdorqJu+rSa9MIKvtyTyvxhIQwONT8xFJbXdYadyizB29WBrOIqbv30AHd8eYiCshqWzo9h61OTuWFoiFndular451NZ1j4xUHcne359t5RssH7vRN6MWdQQ5tGI7eMCMXfw4m95wq4Z3wv1BodSw0aNVP6+uHj5sivsXoxMjfHulKMEELeOJ1qob4OdaUYWyfsRh37akP8aa1Rhyldto/dlNkxgaw6msne5IIGB0GRoddWJwTfGbJ1a64uTZFWWCEfTM1xSzJi+gd+yELWbWcnMSHSl99PXKC0WkNxpRpvC7o2l5p/zOxHbEYxR9Iv8vSqE6x+eLxFGQYF63y2K4X3tp7lpuEhvHr9wEanhf97OIPCCjUPT4nk6VUnqNUK3rt1SIunF/09nMgrreHNDQnYqyT+MbOhKmFRhZrBhquA/x7Wb2LetvIA/h5OLJk7kFtGhuJk37D0c76oksd+jOX4+WJuHRHKP68fwJJ1CeSUVjOyZ7cm9wGcHVQ8ODmC19bF89CUCLafyWPdyWzuHn9RXy4a0oPvD2RQXKk2K8WkFFRQVKHG0V5vqmEJnTyg1PjvSwhBjUZHTa2Oqlot1bVaqjVaqtRaqmt1+tvyffrbVbVaamq1VGt0hsdpDc/VUWN8ruH/aQWV8rCUEVObwtagBHZgfKQvHk72bDyV3SCwFxo2jb7el4Za0/psHeo6YlR2EmMjGpcRMKLTCT43cYmZXq9/2ciEKH1gB0gvrLRJYHdQ2fHRgmFc++Fu4rJKWbkrpVWdQ12Vbw+k88aGBOYMCmLp/EGNbqSpNTpW7kphVK/unMkpZXdSAUvmRbcq6QjwdJaPnadn9G0woSqEoKhCTXZpNQs+OyDf/+Kc/iwaE261lv/7iQu88OspkPTOY9cO6sG+5AJ+PJSBn4dee6g5J/4Fo8NYviOZlbtSWHbzYOav2MdtK/ej0eq71dRaHf/+8xzT++u/u3/E58p+C2qNjtWxWXXB1yQ4rzLIDj/w7RGCvV3knxsfawzE1RptA3/Y5uLioMLZwQ5nBxUuDiqcHFS4GG57ujjg4qAiOd/yVXZLjTpMUQI7+lre9AEBbInP5Q2tzuxgK6pQY2cn8e3+dOYOCZaV8VpDkqEjZkioN57OzTPTNm3xCunmYtXsw1RILN0gPWALAr2c+eC2oSz+4iAfbE3i6gEBZgbeCpZZdTSTl9bEMb2/P+/fOqTJFrxfj2WSXVLNXyb15s0NCUzu48ei0a2TDDZ2fIV0c7HoIXok/SIanb6WbuS9WwebaaGDvmZdXaulqELNM7+cZF9yIU72diydP4i4rBJeWhMnC4GFeLuw5niWWaBtGFTr7isor6GgvIZ9K/YByNrwxlLpl3tT+dLgn/BHPTvLF9fUGWnbScZgq5I7ZgrKavBzd8LV0Z7ubuaB2NnBTg7I9e9zdlDhZPJv4/+Nz3eyt2uWPtPxt7dZNClpqVGHKW0K7JIkLQOuA9RAMnC3EOKKHEGcHRPE6tgs9icXMqlPXf27qEIte0S2JVsHfW85NL8bprhSzTsmmtfT+wdYPVCCvFyI9HfnXF45GTaqsxsZH+nLU1f34V9bzvL0qpP88uDYdhM36kysic1i2eZE+UvdN8Cdj5qRxWq0OlbsTGZAkCe/xmbh7GBZ4Av09W05SKr12Wd1rbEUoA+aX+1NA/QTxV/vS5MDa0J2mVkwN+Xl307z3h9JJgFYKwdbU2o0Op786XiD+2PPFxNr6DF3UEk426twdjQERXsVLo4qnO31Wa2/hxOh3V0bBOz6PDOzH0s3nWF4eDdZnO67e0fTJ8Bd/9r2KhxUkvx7+uVoJn/7+QQbn5jUqtbl9uLpGX157tdTZraCrTHqMKWtGfsfwHNCCI0kSUuB54Bn2viaNmFilC9ujio2xmUzqY8fn+xMZlCIl+xwdN3gHuSWVvNHfG6ru0uMTu3WZHrrs2xzoplFXv0xckuf4VxeuVX53svJw1MiOZJ+kR2J+XyxJ9Vqu15XxbhhZvplTi2o5N0/Ehka2s2sXlut0VKtrqvX/u/IeTnbNHLPfw7rs1+11qSGqzMzlG6K+OxS4pu56T0+wlcffB3scLLXZ6o/HsqQj9cbhwYzpZ8/zvZ2PPfrKTmzNiXIy5nd/5ja7JP+pzuTeWujdXOP8ppavF0d5OaCSH/3RveyjHVtW2+etpdRhyltCuxCiC0mNw8AN7Xl9WyJs4OKq/oHsPl0Lkvm6mR7PJ1OIEkwMdJXHvppDdUGvWgPJ/tmlUniskr44VAGc2KCWHcyGw9n+yYdiyZG+fLV3jTSLcj3Xm7s7CTeu2UIk5dtZ9nmRKYPCCDCUMbal1zAycySS9Z+eSVgyfhZrdWxclcqkGrxOcYgWj+oj4vwManhNiwjOMu1XfP7nB3sePaXkyTlNbzCU0lw1/hePDQlgqPpF/nLt0dZ++gE/vbzcUK6ufLJ4uHyYwvKa3j65xOUVNUyvX8A79w0yGxS+y/fHrX4eXJKqlt0JbdoTDhLN53B0rnKyd6O1ceycHFQyVcZ06w4nhnRdSBJgXlDg9sUyOvTnjX2e4CfrP3QlvZhzWV2TCC/n7jAodQixkXqh3oWfnaQqAB33tx4pk1CWufyyhECxkT4NHmprdMJHvj2CB5O9swdEsy6k/pN3cNpRY0GxLisUlR2evlWI7YMot3cHHl6Rl9e+u00D313lI1PTOJgamGbTpCdBWsbYxKw4YmJZsHZtF675XQODxgCZZCXM5uenISXS/P2a+qTkl9uMagD+Hk485LBRMU4pOfiaMe5vHJmRde1J+5Oyuep/+mD+mtzB7J4THiDklAPb5d2qSG7OdkzOyaQdSfN/YpdHFTcNDyYbw9kIEnUqTk2Edg1HSiwtzdNni6bYx8mSdILgAb43trr2Mo+rCVM7uOPi4NKHlYaF+HL7aNDOZtbzqLRYa0K6p/sTGZfcoHcETMxypd9yQWNaqOvOpbJheJqtDrBwdRCAMJ8XHn0h9hGXZBG9OyGhEROaTXVtVpZWqAtzkltZfHYntw1ridnc8t55pcTclBvb1GyK43GnO37B3nSy9eNQC9nvF0dcXZQyaP4H2+v0+P5182DWx3UQS+sZQ1T7RhjYM8uqUYn9INJtVodb21MYPEXh/ByceC3R8Zzx9ieFuv87WX2DPD2/MG4ONjpT3TUKTu+MGcATipJDuoSkHWx8StXWSumAwwotTdNBnYhxHQhRLSF/34DkCTpTuBaYKEQrW0K6hi4OKqY1s+fTXG5aHWCfckFbIrT24x9dzCjVVorxpLOdoPsqJujqtFgW1JZy9KN+g2glYtH8N2BdCTg+wPpTQbEcRG+zB+m71RYsi6+wwTRf143gNtGhrLqaFarT5CdjdYEu73nCmVZiXvG97Lan90cdiflszUhD09nyxftpieewnI1ro4qeY/Iy8WBm1bs49OdKdw+Koy1j06Q9YIsMW9oMG/dGNNsQ47GcHey55GpkdRodPz+6ARZ2XFTXA61JjUaATzbxJBPR1N3bE/atG0gSdJM9Jul1wshbF/YbQdmxQRSUF7D1/tS20VIy6jTsvl0Dh5O9ryxofGSzntbz3KxUs2r1w9kfJQvt40MRQCLmym9+8Dk3rg7qfj+YEaHCaL7UwrZEt+2E2RnozXB7qPtSYB+U9DSEFFz0Wh1LFkXT7iPKy9fO6DJE0xRRQ0+7o6cMmiw3/XVYVILKli+cBhv3RgjC4U1RnuYPRu5c1xPPJ3t+eDPJPm+V9eeblB7r9UKXl172urrdObA3tYa+0eAE/CH4RLsgBDiwTavyoZM7euPk70dG05mWxXSammwHBfhy1X9Ath0OofHx0dafX78hVK+2Z/GwtHhRAd7sS+5gN9PZMsBcUyET5PvnVtajaO9isfH92r2cy4lpkqT4yJ8GRPh02GuJGxNSzbMjqYXcSClCHs7ifebKfBljR8OZXA2t5yVi4dzzcBA7FV2jXZkFFaocbZXscZgs9g/yIP3bxvaIp/f9sTD2YF7J/Tmva1nicsqITrYy6JRNmD1flACu1WEEJ1urNDNyZ6pff05lnGRMfVMpsdF+LYqGO1LLuBQWlGjAVoIwT9/j8Pb1ZG/X9O3VQGxIwbRxpQmu3pgbw71e92vGRDQbHMWSxRXqnn3j7OMi/CR22ebOsHsNlE+tbeT+PH+MTafS7hrfE8+35PCh9uS+HTxiFa9htzu2BVr7F2RWTGB5JXVcCzjYptfq7na6KtjszicdpFnZvbFy9WhVdK7l1Out7lcbqXJzoSx1920o2RbYl6bxKHe35pEaVUtL183oMmpSJ1O8NmuFLP7vrhrpM2DOujr/HeP78Xm07kkZJfibWUT2dr9oJfJhY5jZt2e2P4v1AGZ1s8fR3s7NpzKafrBTdCcYFtWXcubG84wONSbm4frHXJaExCVINq5sNTrXl2rY9nmxFa93rm8Mr49kM7to8LoF2h9sxMgv6yGu74+zBv1Omea63F6Obh3fC/cnez5cFsSr1w/EId6AdrBTuKV6wdafb62gzkotSdKYLeAh7MDk6L82BiXLQ8xtJbmBNv3tyZRWFHDkrkDO4xjuoLtsdbr3lpxqCXrEnB1VPHU1X0afdzOs/nM+mAXB1MKzdQXQ7q5mA0e2RovVwfuGteTDady6B/kybKbB5ttRi+7eXCjJSbj8GFz9FyuNBQRMCvMjglka0IuJzKLGRrW7ZK9T2JOGV/vS+O2kWHNNkdQ6Bq012APwPYzeew8m8+Lc/o3MHU2otbo+NeWRFbuSqFPgDvf3zcGZwc73jaM8dtyHsIa907oxVd7U/lwWxIfLRjWom4bjU50yjIMKBm7Va7qH4CDSmJjXNvLMdYQQvDyb3F4ONvzjzYI/ih0TtprsKdWq2PJ+nh6+7pxhxXf1NSCCuav2MfKXSksGhPG749OoG+gh5nGS0xwx0s8urk5cse4nqw/lc25vLKmn2CCVohOuXEKSmC3ipeLAxMifdlwKptLNXf1+4kLHEwt4ukZfenWgS5xFToG7TXY883+dFLyK3jx2v442jf8yv96LJNr/72bjKJKPlk0nNfnxcjtlEajGeiYGTvA/RN74+Kg4sNt55p+sAk6neiU9XVQSjGNMismiO2rTnIqq6TdyyTlNRre3JBAdLAnt43smNo5CranreJQRRVqPth6lkl9/BqYyJRV1/Lyb6dZHZvFqF7def/WIQ3KPEUmGXtb2iwvJd3dHFk8JpzPdqfw+FVRsthcU2g6cWBXMvZGuGZAAPZ2Urt0x9Tnwz+TyC2t4bW50Z324FKwPe/+kUiFWstLc/qbbRKeOF/MtR/u4bfjWTx1dR9+vH+Mxdq9sRTTy9etTbo0l5r7J/XG0d6Oj1uQtXfmjF0J7I3g7erIuEhfNsa1bznmXF4ZX+xJ5ZYRIQy7hBuzCl2bxJwyfjiYweIx4UQZCJG+twAACt5JREFUXKx0OsEnO5OZv2IfGq3gp7+M5fGroqwGuEKDH0FHanO0hK+7E4tGh7PmeBZpBc0zmtEK0SkFwEAJ7E0yOzqQ9MLKZhsQNIV+wvQ0ro4qnpnZuJGvgu2QJGmZJElnJEk6KUnSakmSOt7OYSMIIViyLh5PFweenB4FQF5pNXd+dYi3N57h6gEBbHh8IiN7Nq7xbyzFdNT6uikPTO6t993d3rysXatk7F2XawYGorKT2NhO5ZiNcTnsPVfI32f0tdp2ptAh+AOIFkIMAs6idwe7YtiakMeecwX8dXofvF0d2Z6Yx6wPdnM4rYi3boxh+cJheLk2XVoxlmI6esYO4O/hzILRYayOzTLzJLCGEti7MN3dHBnTu3u7dMdUqjW8vi6e/kGeLBilbJh2ZIQQW4QQGsPNA0BIY4/vSNRotLyxPp4of3duHhHCknXx3P3VYfw8nFj76ARuHxXWrKGcNbFZsvTFkz8db5OUweXiwckRqOwkM916a2h1nVMnBpTA3ixmRQeRUlAh61G3lo+2neNCSTVL5g7sEHobCs3mHmCjrRfRXP6zL420wkoWjQnn5k/288WeVO4cG86aR8bLtfamMOrUGA2qs0uqea4JffOOQICnM7ePDOWXY5mcb8IiUqvTKRl7V2bGwEAkCTacym71a6Tkl/PZ7hRuHBbMiCbqmgqXh/ZyB5Mk6QFJko5IknQkPz//ciy9AWtisxj/9jZ6Pbuetzacwd4Olm46Q1ZxFSsXD+fVudEtkvq1pFNTVatttU7N5eTBKRHYSRLLd1h3KQPQis4pAAZKYG8Wfh5OjOrZnY1xrQvsQgheWRuPs73KTHtDwba0lzuYrW0fTVUgBXr3II3O4In6xCSuGRjY4tdsb52ay0mQlwu3jAxh1dHzFiUZjOh0otNqMymBvZnMGRTE2dzyFo8tA2yJz2XX2XyevLoP/h7Ol2B1Cu3NleQOZim7Bn2GHejVuuOtMU/WK4GHpuitIlbssF5r1+h0SrtjV6euHNOy7pgqtZbX1sbTN8CDO8eGX6LVKVwCPgI80LuDHZck6RNbL8ga1rLo7OJqi/c3h/Y0oLYFwd4u3DQ8lP8dziS7xPLvR6vrnJK9oAT2ZhPg6cyI8G4trrOv2HGOrOIqXlM2TK8ohBCRQohQIcQQw38d1vLxUmTX7WlAbSsenhKBTgg+sVJr14nO2+6oaMW0gFnRQby2Lp6U/HJ6N0OPIr2wgk92pTB3SA9G9/Zp8vEKCq3h6Rl9ee7XU2blmPbIrtuqU2NrQru7Mn9YCD8ePs/DUyMJ8DQvS2mUGrsCwMxo/SZUc6V8X1sbj4OdxPOz+1/KZSl0cTpDdn2peGRqJFqDjEJ9dJ1Yj13J2FtAD28XhoZ5szEum0emNu7j/WdCLn+eyeP52f0aZAoKCu3NlZ5dXyrCfFy5YWgwPxzM4KEpEWbNC1qdohWjYGB2dBBxWaWNjixX12p5dW08kf7u3D2+12VcnYKCQn0enRpJrVbHyp3mxtxancCuk0bATvqxLh115Rjrm6if7kwho6iS164fiIOyYaqgYFN6+roxb0gw3x1Mp8CgVgl6dUf7ThrZO+enuoSEdndlcIiX1e6Y80WVLN9xjjmDghgX6WvxMQoKCpeXR6ZFotbo+GxXXdauVTZPFUyZFRPEicwSMi82LMcsWRePnSTxgrJhqqDQYYjwc+e6wT34Zn+6rDGvr7HbeGGXCCWwt4JZhnLMpnrdMTsS89gSn8tjV0VeMRN6CgpdhcemRVKt0fL5nlTAKNvbOUNg5/xUl5hwHzcG9vA0K8fUaLS88vtpevu6cd+E3jZcnYKCgiUi/T2YExPEN/vSuFihNgwo2XpVl4ZO+rEuPbNjgjiWUSyPK3++O5W0wkpeuX6gRSd4BQUF2/PYtCgq1Fq+2JOqmFlbQ5KkJQbrsOOSJG2RJKlHey2so2NajskqruKjbeeYMTCASX0uv7qfgoJC8+gb6MHsmEC+3pdGSVWtUoqxwjIhxCAhxBBgHfByO6zpimBLfC6h3VzYeCqHN9bHIxDMiQmyOOGmoKDQcXhsWhTlNRryy2pYe+IC49/e1uENRFpKmwK7EMLU4dkNvRR0l2BQiBcF5WoOpRWx4VQO1w3qwStr468I018Fha5MYk4ZphWYrOKqK8IdqiW0+TpEkqQ3JEk6DyykkYy9I7jMtCfjInxZMncgAF4u9mxNyOWjBUMZF6H0risodGSWbU5EVy8FvVLcoZpLk4G9KfswIcQLQohQ9NZhj1p7HVu7zFwKbhoRyuhe3Smp0rB4TLgS1BUUrgCuZHeo5tKkCJgQYnozX+sHYD3wzzat6ApiX3IBSXnlPD4tku8OZjAmwkcJ7goKHZwe3i4WLfM60+xJW7tiokxuXg+cadtyrhz2JRfw6A+xfLRgKE9d05ePFgzl0R9i2ZdcYOulKSgoNMKV7g7VHNoq2/u2JEl9AR2QDnRYl5n25mRmiVlNfVyELx8tGMrJzBIla1dQ6MAY5Y2XbU7kQnEVPbxdeHpG304le9ymwC6EmN9eC7nSeHByRIP7xkX4KkFdQeEKoLPr13fO7nwFBQWFLowS2BUUFBQ6GUpgV1BQUOhkKIFdQUFBoZOhBHYFBQWFToYkxOWXd5EkKR99e+TlxhfoTI3myuexTLgQwibjze1wbNv6b2rL91c+e9M069i2SWC3FZIkHRFCjLD1OtoL5fN0Pmz9O7Dl+yufvf3eWynFKCgoKHQylMCuoKCg0MnoaoF9pa0X0M4on6fzYevfgS3fX/ns7USXqrErKCgodAW6WsauoKCg0OnpcoFdkqRlkiSdMZhwr5YkydvWa2oNkiTNlCQpUZKkc5IkPWvr9bQFSZJCJUnaLklSgiRJpyVJesLWa7IltjxGJUm62fA30EmSdNk6RGx5PEuS9KUkSXmSJMVdzvc1vPclOfa7XGAH/gCihRCDgLPAczZeT4uRJEkFfAzMAgYAt0uSNMC2q2oTGuBvQoj+wBjgkSv887QVWx6jccCNwK7L9YYd4Hj+Gph5Gd/PlEty7He5wC6E2CKE0BhuHgBCbLmeVjIKOCeESBFCqIH/AnNtvKZWI4TIFkIcM/y7DEgAOq+mahPY8hgVQiQIIS63+adNj2chxC6g6HK9X733viTHfpcL7PW4B9ho60W0gmDgvMntTDpJIJQkqScwFDho25V0GK7UY7QldNrjuSW057HfVgelDokkSVuBQAs/ekEI8ZvhMS+gvwz6/nKurZ2QLNx3xbc3SZLkDvwCPCmEKLX1ei4ltjxGm/Pel5lOeTy3hPY+9jtlYG/KgFuSpDuBa4GrxJXZ75kJhJrcDgEu2Ggt7YIkSQ7oD+zvhRC/2no9lxpbHqMtMKi/XHS647klXIpjv8uVYiRJmgk8A1wvhKi09XpayWEgSpKkXpIkOQK3Ab/beE2tRpIkCfgCSBBCvGvr9diaTnKMtoROdTy3hEt17He5ASVJks4BTkCh4a4DQogrzoRbkqTZwPuACvhSCPGGjZfUaiRJmgDsBk6hN0YHeF4IscF2q7IdtjxGJUm6AfgQ8AOKgeNCiBmX4X1tdjxLkvQjMAW9wmIu8E8hxBeX6b0vybHf5QK7goKCQmeny5ViFBQUFDo7SmBXUFBQ6GQogV1BQUGhk6EEdgUFBYVOhhLYFRQUFDoZSmBXUFBQ6GQogV1BQUGhk6EEdgUFBYVOxv8DIFIW8Ybi19MAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, (ax1, ax2) = plt.subplots(1, 2)\n", - "my_plotter(ax1, data1, data2, {'marker': 'x'})\n", - "my_plotter(ax2, data3, data4, {'marker': 'o'})" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Again, for these simple examples this style seems like overkill, however\n", - "once the graphs get slightly more complex it pays off.\n", - "\n", - "\n", - "\n", - "Backends\n", - "========\n", - "\n", - "\n", - "What is a backend?\n", - "------------------\n", - "\n", - "A lot of documentation on the website and in the mailing lists refers\n", - "to the \"backend\" and many new users are confused by this term.\n", - "matplotlib targets many different use cases and output formats. Some\n", - "people use matplotlib interactively from the python shell and have\n", - "plotting windows pop up when they type commands. Some people run\n", - "`Jupyter `_ notebooks and draw inline plots for\n", - "quick data analysis. Others embed matplotlib into graphical user\n", - "interfaces like wxpython or pygtk to build rich applications. Some\n", - "people use matplotlib in batch scripts to generate postscript images\n", - "from numerical simulations, and still others run web application\n", - "servers to dynamically serve up graphs.\n", - "\n", - "To support all of these use cases, matplotlib can target different\n", - "outputs, and each of these capabilities is called a backend; the\n", - "\"frontend\" is the user facing code, i.e., the plotting code, whereas the\n", - "\"backend\" does all the hard work behind-the-scenes to make the figure.\n", - "There are two types of backends: user interface backends (for use in\n", - "pygtk, wxpython, tkinter, qt4, or macosx; also referred to as\n", - "\"interactive backends\") and hardcopy backends to make image files\n", - "(PNG, SVG, PDF, PS; also referred to as \"non-interactive backends\").\n", - "\n", - "There are four ways to configure your backend. If they conflict each other,\n", - "the method mentioned last in the following list will be used, e.g. calling\n", - ":func:`~matplotlib.use()` will override the setting in your ``matplotlibrc``.\n", - "\n", - "\n", - "#. The ``backend`` parameter in your ``matplotlibrc`` file (see\n", - " :doc:`/tutorials/introductory/customizing`)::\n", - "\n", - " backend : WXAgg # use wxpython with antigrain (agg) rendering\n", - "\n", - "#. Setting the :envvar:`MPLBACKEND` environment variable, either for your\n", - " current shell or for a single script. On Unix::\n", - "\n", - " > export MPLBACKEND=module://my_backend\n", - " > python simple_plot.py\n", - "\n", - " > MPLBACKEND=\"module://my_backend\" python simple_plot.py\n", - "\n", - " On Windows, only the former is possible::\n", - "\n", - " > set MPLBACKEND=module://my_backend\n", - " > python simple_plot.py\n", - "\n", - " Setting this environment variable will override the ``backend`` parameter\n", - " in *any* ``matplotlibrc``, even if there is a ``matplotlibrc`` in your\n", - " current working directory. Therefore setting :envvar:`MPLBACKEND`\n", - " globally, e.g. in your ``.bashrc`` or ``.profile``, is discouraged as it\n", - " might lead to counter-intuitive behavior.\n", - "\n", - "#. If your script depends on a specific backend you can use the\n", - " :func:`~matplotlib.use` function::\n", - "\n", - " import matplotlib\n", - " matplotlib.use('PS') # generate postscript output by default\n", - "\n", - " If you use the :func:`~matplotlib.use` function, this must be done before\n", - " importing :mod:`matplotlib.pyplot`. Calling :func:`~matplotlib.use` after\n", - " pyplot has been imported will have no effect. Using\n", - " :func:`~matplotlib.use` will require changes in your code if users want to\n", - " use a different backend. Therefore, you should avoid explicitly calling\n", - " :func:`~matplotlib.use` unless absolutely necessary.\n", - "\n", - "

Note

Backend name specifications are not case-sensitive; e.g., 'GTK3Agg'\n", - " and 'gtk3agg' are equivalent.

\n", - "\n", - "With a typical installation of matplotlib, such as from a\n", - "binary installer or a linux distribution package, a good default\n", - "backend will already be set, allowing both interactive work and\n", - "plotting from scripts, with output to the screen and/or to\n", - "a file, so at least initially you will not need to use any of the\n", - "methods given above.\n", - "\n", - "If, however, you want to write graphical user interfaces, or a web\n", - "application server (`howto-webapp`), or need a better\n", - "understanding of what is going on, read on. To make things a little\n", - "more customizable for graphical user interfaces, matplotlib separates\n", - "the concept of the renderer (the thing that actually does the drawing)\n", - "from the canvas (the place where the drawing goes). The canonical\n", - "renderer for user interfaces is ``Agg`` which uses the `Anti-Grain\n", - "Geometry`_ C++ library to make a raster (pixel) image of the figure.\n", - "All of the user interfaces except ``macosx`` can be used with\n", - "agg rendering, e.g., ``WXAgg``, ``GTK3Agg``, ``QT4Agg``, ``QT5Agg``,\n", - "``TkAgg``. In addition, some of the user interfaces support other rendering\n", - "engines. For example, with GTK+ 3, you can also select Cairo rendering\n", - "(backend ``GTK3Cairo``).\n", - "\n", - "For the rendering engines, one can also distinguish between `vector\n", - "`_ or `raster\n", - "`_ renderers. Vector\n", - "graphics languages issue drawing commands like \"draw a line from this\n", - "point to this point\" and hence are scale free, and raster backends\n", - "generate a pixel representation of the line whose accuracy depends on a\n", - "DPI setting.\n", - "\n", - "Here is a summary of the matplotlib renderers (there is an eponymous\n", - "backend for each; these are *non-interactive backends*, capable of\n", - "writing to a file):\n", - "\n", - "============= ============ ================================================\n", - "Renderer Filetypes Description\n", - "============= ============ ================================================\n", - ":term:`AGG` :term:`png` :term:`raster graphics` -- high quality images\n", - " using the `Anti-Grain Geometry`_ engine\n", - "PS :term:`ps` :term:`vector graphics` -- Postscript_ output\n", - " :term:`eps`\n", - "PDF :term:`pdf` :term:`vector graphics` --\n", - " `Portable Document Format`_\n", - "SVG :term:`svg` :term:`vector graphics` --\n", - " `Scalable Vector Graphics`_\n", - ":term:`Cairo` :term:`png` :term:`raster graphics` and\n", - " :term:`ps` :term:`vector graphics` -- using the\n", - " :term:`pdf` `Cairo graphics`_ library\n", - " :term:`svg`\n", - "============= ============ ================================================\n", - "\n", - "And here are the user interfaces and renderer combinations supported;\n", - "these are *interactive backends*, capable of displaying to the screen\n", - "and of using appropriate renderers from the table above to write to\n", - "a file:\n", - "\n", - "========= ================================================================\n", - "Backend Description\n", - "========= ================================================================\n", - "Qt5Agg Agg rendering in a :term:`Qt5` canvas (requires PyQt5_). This\n", - " backend can be activated in IPython with ``%matplotlib qt5``.\n", - "ipympl Agg rendering embedded in a Jupyter widget. (requires ipympl).\n", - " This backend can be enabled in a Jupyter notebook with\n", - " ``%matplotlib ipympl``.\n", - "GTK3Agg Agg rendering to a :term:`GTK` 3.x canvas (requires PyGObject_,\n", - " and pycairo_ or cairocffi_). This backend can be activated in\n", - " IPython with ``%matplotlib gtk3``.\n", - "macosx Agg rendering into a Cocoa canvas in OSX. This backend can be\n", - " activated in IPython with ``%matplotlib osx``.\n", - "TkAgg Agg rendering to a :term:`Tk` canvas (requires TkInter_). This\n", - " backend can be activated in IPython with ``%matplotlib tk``.\n", - "nbAgg Embed an interactive figure in a Jupyter classic notebook. This\n", - " backend can be enabled in Jupyter notebooks via\n", - " ``%matplotlib notebook``.\n", - "WebAgg On ``show()`` will start a tornado server with an interactive\n", - " figure.\n", - "GTK3Cairo Cairo rendering to a :term:`GTK` 3.x canvas (requires PyGObject_,\n", - " and pycairo_ or cairocffi_).\n", - "Qt4Agg Agg rendering to a :term:`Qt4` canvas (requires PyQt4_ or\n", - " ``pyside``). This backend can be activated in IPython with\n", - " ``%matplotlib qt4``.\n", - "WXAgg Agg rendering to a :term:`wxWidgets` canvas (requires wxPython_ 4).\n", - " This backend can be activated in IPython with ``%matplotlib wx``.\n", - "========= ================================================================\n", - "\n", - "\n", - "ipympl\n", - "------\n", - "\n", - "The Jupyter widget ecosystem is moving too fast to support directly in\n", - "Matplotlib. To install ipympl\n", - "\n", - ".. code-block:: bash\n", - "\n", - " pip install ipympl\n", - " jupyter nbextension enable --py --sys-prefix ipympl\n", - "\n", - "or\n", - "\n", - ".. code-block:: bash\n", - "\n", - " conda install ipympl -c conda-forge\n", - "\n", - "See `jupyter-matplotlib `__\n", - "for more details.\n", - "\n", - "GTK and Cairo\n", - "-------------\n", - "\n", - "`GTK3` backends (*both* `GTK3Agg` and `GTK3Cairo`) depend on Cairo\n", - "(pycairo>=1.11.0 or cairocffi).\n", - "\n", - "How do I select PyQt4 or PySide?\n", - "--------------------------------\n", - "\n", - "The `QT_API` environment variable can be set to either `pyqt` or `pyside`\n", - "to use `PyQt4` or `PySide`, respectively.\n", - "\n", - "Since the default value for the bindings to be used is `PyQt4`,\n", - ":mod:`matplotlib` first tries to import it, if the import fails, it tries to\n", - "import `PySide`.\n", - "\n", - "\n", - "What is interactive mode?\n", - "===================================\n", - "\n", - "Use of an interactive backend (see `what-is-a-backend`)\n", - "permits--but does not by itself require or ensure--plotting\n", - "to the screen. Whether and when plotting to the screen occurs,\n", - "and whether a script or shell session continues after a plot\n", - "is drawn on the screen, depends on the functions and methods\n", - "that are called, and on a state variable that determines whether\n", - "matplotlib is in \"interactive mode\". The default Boolean value is set\n", - "by the :file:`matplotlibrc` file, and may be customized like any other\n", - "configuration parameter (see :doc:`/tutorials/introductory/customizing`). It\n", - "may also be set via :func:`matplotlib.interactive`, and its\n", - "value may be queried via :func:`matplotlib.is_interactive`. Turning\n", - "interactive mode on and off in the middle of a stream of plotting\n", - "commands, whether in a script or in a shell, is rarely needed\n", - "and potentially confusing, so in the following we will assume all\n", - "plotting is done with interactive mode either on or off.\n", - "\n", - "

Note

Major changes related to interactivity, and in particular the\n", - " role and behavior of :func:`~matplotlib.pyplot.show`, were made in the\n", - " transition to matplotlib version 1.0, and bugs were fixed in\n", - " 1.0.1. Here we describe the version 1.0.1 behavior for the\n", - " primary interactive backends, with the partial exception of\n", - " *macosx*.

\n", - "\n", - "Interactive mode may also be turned on via :func:`matplotlib.pyplot.ion`,\n", - "and turned off via :func:`matplotlib.pyplot.ioff`.\n", - "\n", - "

Note

Interactive mode works with suitable backends in ipython and in\n", - " the ordinary python shell, but it does *not* work in the IDLE IDE.\n", - " If the default backend does not support interactivity, an interactive\n", - " backend can be explicitly activated using any of the methods discussed in `What is a backend?`_.

\n", - "\n", - "\n", - "Interactive example\n", - "--------------------\n", - "\n", - "From an ordinary python prompt, or after invoking ipython with no options,\n", - "try this::\n", - "\n", - " import matplotlib.pyplot as plt\n", - " plt.ion()\n", - " plt.plot([1.6, 2.7])\n", - "\n", - "Assuming you are running version 1.0.1 or higher, and you have\n", - "an interactive backend installed and selected by default, you should\n", - "see a plot, and your terminal prompt should also be active; you\n", - "can type additional commands such as::\n", - "\n", - " plt.title(\"interactive test\")\n", - " plt.xlabel(\"index\")\n", - "\n", - "and you will see the plot being updated after each line. Since version 1.5,\n", - "modifying the plot by other means *should* also automatically\n", - "update the display on most backends. Get a reference to the :class:`~matplotlib.axes.Axes` instance,\n", - "and call a method of that instance::\n", - "\n", - " ax = plt.gca()\n", - " ax.plot([3.1, 2.2])\n", - "\n", - "If you are using certain backends (like `macosx`), or an older version\n", - "of matplotlib, you may not see the new line added to the plot immediately.\n", - "In this case, you need to explicitly call :func:`~matplotlib.pyplot.draw`\n", - "in order to update the plot::\n", - "\n", - " plt.draw()\n", - "\n", - "\n", - "Non-interactive example\n", - "-----------------------\n", - "\n", - "Start a fresh session as in the previous example, but now\n", - "turn interactive mode off::\n", - "\n", - " import matplotlib.pyplot as plt\n", - " plt.ioff()\n", - " plt.plot([1.6, 2.7])\n", - "\n", - "Nothing happened--or at least nothing has shown up on the\n", - "screen (unless you are using *macosx* backend, which is\n", - "anomalous). To make the plot appear, you need to do this::\n", - "\n", - " plt.show()\n", - "\n", - "Now you see the plot, but your terminal command line is\n", - "unresponsive; the :func:`show()` command *blocks* the input\n", - "of additional commands until you manually kill the plot\n", - "window.\n", - "\n", - "What good is this--being forced to use a blocking function?\n", - "Suppose you need a script that plots the contents of a file\n", - "to the screen. You want to look at that plot, and then end\n", - "the script. Without some blocking command such as show(), the\n", - "script would flash up the plot and then end immediately,\n", - "leaving nothing on the screen.\n", - "\n", - "In addition, non-interactive mode delays all drawing until\n", - "show() is called; this is more efficient than redrawing\n", - "the plot each time a line in the script adds a new feature.\n", - "\n", - "Prior to version 1.0, show() generally could not be called\n", - "more than once in a single script (although sometimes one\n", - "could get away with it); for version 1.0.1 and above, this\n", - "restriction is lifted, so one can write a script like this::\n", - "\n", - " import numpy as np\n", - " import matplotlib.pyplot as plt\n", - "\n", - " plt.ioff()\n", - " for i in range(3):\n", - " plt.plot(np.random.rand(10))\n", - " plt.show()\n", - "\n", - "which makes three plots, one at a time. I.e. the second plot will show up,\n", - "once the first plot is closed.\n", - "\n", - "Summary\n", - "-------\n", - "\n", - "In interactive mode, pyplot functions automatically draw\n", - "to the screen.\n", - "\n", - "When plotting interactively, if using\n", - "object method calls in addition to pyplot functions, then\n", - "call :func:`~matplotlib.pyplot.draw` whenever you want to\n", - "refresh the plot.\n", - "\n", - "Use non-interactive mode in scripts in which you want to\n", - "generate one or more figures and display them before ending\n", - "or generating a new set of figures. In that case, use\n", - ":func:`~matplotlib.pyplot.show` to display the figure(s) and\n", - "to block execution until you have manually destroyed them.\n", - "\n", - "\n", - "Performance\n", - "===========\n", - "\n", - "Whether exploring data in interactive mode or programmatically\n", - "saving lots of plots, rendering performance can be a painful\n", - "bottleneck in your pipeline. Matplotlib provides a couple\n", - "ways to greatly reduce rendering time at the cost of a slight\n", - "change (to a settable tolerance) in your plot's appearance.\n", - "The methods available to reduce rendering time depend on the\n", - "type of plot that is being created.\n", - "\n", - "Line segment simplification\n", - "---------------------------\n", - "\n", - "For plots that have line segments (e.g. typical line plots,\n", - "outlines of polygons, etc.), rendering performance can be\n", - "controlled by the ``path.simplify`` and\n", - "``path.simplify_threshold`` parameters in your\n", - "``matplotlibrc`` file (see\n", - ":doc:`/tutorials/introductory/customizing` for\n", - "more information about the ``matplotlibrc`` file).\n", - "The ``path.simplify`` parameter is a boolean indicating whether\n", - "or not line segments are simplified at all. The\n", - "``path.simplify_threshold`` parameter controls how much line\n", - "segments are simplified; higher thresholds result in quicker\n", - "rendering.\n", - "\n", - "The following script will first display the data without any\n", - "simplification, and then display the same data with simplification.\n", - "Try interacting with both of them::\n", - "\n", - " import numpy as np\n", - " import matplotlib.pyplot as plt\n", - " import matplotlib as mpl\n", - "\n", - " # Setup, and create the data to plot\n", - " y = np.random.rand(100000)\n", - " y[50000:] *= 2\n", - " y[np.logspace(1, np.log10(50000), 400).astype(int)] = -1\n", - " mpl.rcParams['path.simplify'] = True\n", - "\n", - " mpl.rcParams['path.simplify_threshold'] = 0.0\n", - " plt.plot(y)\n", - " plt.show()\n", - "\n", - " mpl.rcParams['path.simplify_threshold'] = 1.0\n", - " plt.plot(y)\n", - " plt.show()\n", - "\n", - "Matplotlib currently defaults to a conservative simplification\n", - "threshold of ``1/9``. If you want to change your default settings\n", - "to use a different value, you can change your ``matplotlibrc``\n", - "file. Alternatively, you could create a new style for\n", - "interactive plotting (with maximal simplification) and another\n", - "style for publication quality plotting (with minimal\n", - "simplification) and activate them as necessary. See\n", - ":doc:`/tutorials/introductory/customizing` for\n", - "instructions on how to perform these actions.\n", - "\n", - "The simplification works by iteratively merging line segments\n", - "into a single vector until the next line segment's perpendicular\n", - "distance to the vector (measured in display-coordinate space)\n", - "is greater than the ``path.simplify_threshold`` parameter.\n", - "\n", - "

Note

Changes related to how line segments are simplified were made\n", - " in version 2.1. Rendering time will still be improved by these\n", - " parameters prior to 2.1, but rendering time for some kinds of\n", - " data will be vastly improved in versions 2.1 and greater.

\n", - "\n", - "Marker simplification\n", - "---------------------\n", - "\n", - "Markers can also be simplified, albeit less robustly than\n", - "line segments. Marker simplification is only available\n", - "to :class:`~matplotlib.lines.Line2D` objects (through the\n", - "``markevery`` property). Wherever\n", - ":class:`~matplotlib.lines.Line2D` construction parameter\n", - "are passed through, such as\n", - ":func:`matplotlib.pyplot.plot` and\n", - ":meth:`matplotlib.axes.Axes.plot`, the ``markevery``\n", - "parameter can be used::\n", - "\n", - " plt.plot(x, y, markevery=10)\n", - "\n", - "The markevery argument allows for naive subsampling, or an\n", - "attempt at evenly spaced (along the *x* axis) sampling. See the\n", - ":doc:`/gallery/lines_bars_and_markers/markevery_demo`\n", - "for more information.\n", - "\n", - "Splitting lines into smaller chunks\n", - "-----------------------------------\n", - "\n", - "If you are using the Agg backend (see `what-is-a-backend`),\n", - "then you can make use of the ``agg.path.chunksize`` rc parameter.\n", - "This allows you to specify a chunk size, and any lines with\n", - "greater than that many vertices will be split into multiple\n", - "lines, each of which have no more than ``agg.path.chunksize``\n", - "many vertices. (Unless ``agg.path.chunksize`` is zero, in\n", - "which case there is no chunking.) For some kind of data,\n", - "chunking the line up into reasonable sizes can greatly\n", - "decrease rendering time.\n", - "\n", - "The following script will first display the data without any\n", - "chunk size restriction, and then display the same data with\n", - "a chunk size of 10,000. The difference can best be seen when\n", - "the figures are large, try maximizing the GUI and then\n", - "interacting with them::\n", - "\n", - " import numpy as np\n", - " import matplotlib.pyplot as plt\n", - " import matplotlib as mpl\n", - " mpl.rcParams['path.simplify_threshold'] = 1.0\n", - "\n", - " # Setup, and create the data to plot\n", - " y = np.random.rand(100000)\n", - " y[50000:] *= 2\n", - " y[np.logspace(1,np.log10(50000), 400).astype(int)] = -1\n", - " mpl.rcParams['path.simplify'] = True\n", - "\n", - " mpl.rcParams['agg.path.chunksize'] = 0\n", - " plt.plot(y)\n", - " plt.show()\n", - "\n", - " mpl.rcParams['agg.path.chunksize'] = 10000\n", - " plt.plot(y)\n", - " plt.show()\n", - "\n", - "Legends\n", - "-------\n", - "\n", - "The default legend behavior for axes attempts to find the location\n", - "that covers the fewest data points (`loc='best'`). This can be a\n", - "very expensive computation if there are lots of data points. In\n", - "this case, you may want to provide a specific location.\n", - "\n", - "Using the *fast* style\n", - "----------------------\n", - "\n", - "The *fast* style can be used to automatically set\n", - "simplification and chunking parameters to reasonable\n", - "settings to speed up plotting large amounts of data.\n", - "It can be used simply by running::\n", - "\n", - " import matplotlib.style as mplstyle\n", - " mplstyle.use('fast')\n", - "\n", - "It is very light weight, so it plays nicely with other\n", - "styles, just make sure the fast style is applied last\n", - "so that other styles do not overwrite the settings::\n", - "\n", - " mplstyle.use(['dark_background', 'ggplot', 'fast'])\n", - "\n" - ] - } - ], - "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": "3.6.8" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/Day_2/ExtraExamples/artists.ipynb b/Day_2/ExtraExamples/artists.ipynb deleted file mode 100644 index dc6cfad..0000000 --- a/Day_2/ExtraExamples/artists.ipynb +++ /dev/null @@ -1,173 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n# Artist tutorial\n\n\nUsing Artist objects to render on the canvas.\n\nThere are three layers to the matplotlib API.\n\n* the :class:`matplotlib.backend_bases.FigureCanvas` is the area onto which\n the figure is drawn\n* the :class:`matplotlib.backend_bases.Renderer` is\n the object which knows how to draw on the\n :class:`~matplotlib.backend_bases.FigureCanvas`\n* and the :class:`matplotlib.artist.Artist` is the object that knows how to use\n a renderer to paint onto the canvas.\n\nThe :class:`~matplotlib.backend_bases.FigureCanvas` and\n:class:`~matplotlib.backend_bases.Renderer` handle all the details of\ntalking to user interface toolkits like `wxPython\n`_ or drawing languages like PostScript\u00ae, and\nthe ``Artist`` handles all the high level constructs like representing\nand laying out the figure, text, and lines. The typical user will\nspend 95% of their time working with the ``Artists``.\n\nThere are two types of ``Artists``: primitives and containers. The primitives\nrepresent the standard graphical objects we want to paint onto our canvas:\n:class:`~matplotlib.lines.Line2D`, :class:`~matplotlib.patches.Rectangle`,\n:class:`~matplotlib.text.Text`, :class:`~matplotlib.image.AxesImage`, etc., and\nthe containers are places to put them (:class:`~matplotlib.axis.Axis`,\n:class:`~matplotlib.axes.Axes` and :class:`~matplotlib.figure.Figure`). The\nstandard use is to create a :class:`~matplotlib.figure.Figure` instance, use\nthe ``Figure`` to create one or more :class:`~matplotlib.axes.Axes` or\n:class:`~matplotlib.axes.Subplot` instances, and use the ``Axes`` instance\nhelper methods to create the primitives. In the example below, we create a\n``Figure`` instance using :func:`matplotlib.pyplot.figure`, which is a\nconvenience method for instantiating ``Figure`` instances and connecting them\nwith your user interface or drawing toolkit ``FigureCanvas``. As we will\ndiscuss below, this is not necessary -- you can work directly with PostScript,\nPDF Gtk+, or wxPython ``FigureCanvas`` instances, instantiate your ``Figures``\ndirectly and connect them yourselves -- but since we are focusing here on the\n``Artist`` API we'll let :mod:`~matplotlib.pyplot` handle some of those details\nfor us::\n\n import matplotlib.pyplot as plt\n fig = plt.figure()\n ax = fig.add_subplot(2, 1, 1) # two rows, one column, first plot\n\nThe :class:`~matplotlib.axes.Axes` is probably the most important\nclass in the matplotlib API, and the one you will be working with most\nof the time. This is because the ``Axes`` is the plotting area into\nwhich most of the objects go, and the ``Axes`` has many special helper\nmethods (:meth:`~matplotlib.axes.Axes.plot`,\n:meth:`~matplotlib.axes.Axes.text`,\n:meth:`~matplotlib.axes.Axes.hist`,\n:meth:`~matplotlib.axes.Axes.imshow`) to create the most common\ngraphics primitives (:class:`~matplotlib.lines.Line2D`,\n:class:`~matplotlib.text.Text`,\n:class:`~matplotlib.patches.Rectangle`,\n:class:`~matplotlib.image.Image`, respectively). These helper methods\nwill take your data (e.g., ``numpy`` arrays and strings) and create\nprimitive ``Artist`` instances as needed (e.g., ``Line2D``), add them to\nthe relevant containers, and draw them when requested. Most of you\nare probably familiar with the :class:`~matplotlib.axes.Subplot`,\nwhich is just a special case of an ``Axes`` that lives on a regular\nrows by columns grid of ``Subplot`` instances. If you want to create\nan ``Axes`` at an arbitrary location, simply use the\n:meth:`~matplotlib.figure.Figure.add_axes` method which takes a list\nof ``[left, bottom, width, height]`` values in 0-1 relative figure\ncoordinates::\n\n fig2 = plt.figure()\n ax2 = fig2.add_axes([0.15, 0.1, 0.7, 0.3])\n\nContinuing with our example::\n\n import numpy as np\n t = np.arange(0.0, 1.0, 0.01)\n s = np.sin(2*np.pi*t)\n line, = ax.plot(t, s, color='blue', lw=2)\n\nIn this example, ``ax`` is the ``Axes`` instance created by the\n``fig.add_subplot`` call above (remember ``Subplot`` is just a\nsubclass of ``Axes``) and when you call ``ax.plot``, it creates a\n``Line2D`` instance and adds it to the :attr:`Axes.lines\n` list. In the interactive `ipython\n`_ session below, you can see that the\n``Axes.lines`` list is length one and contains the same line that was\nreturned by the ``line, = ax.plot...`` call:\n\n.. sourcecode:: ipython\n\n In [101]: ax.lines[0]\n Out[101]: \n\n In [102]: line\n Out[102]: \n\nIf you make subsequent calls to ``ax.plot`` (and the hold state is \"on\"\nwhich is the default) then additional lines will be added to the list.\nYou can remove lines later simply by calling the list methods; either\nof these will work::\n\n del ax.lines[0]\n ax.lines.remove(line) # one or the other, not both!\n\nThe Axes also has helper methods to configure and decorate the x-axis\nand y-axis tick, tick labels and axis labels::\n\n xtext = ax.set_xlabel('my xdata') # returns a Text instance\n ytext = ax.set_ylabel('my ydata')\n\nWhen you call :meth:`ax.set_xlabel `,\nit passes the information on the :class:`~matplotlib.text.Text`\ninstance of the :class:`~matplotlib.axis.XAxis`. Each ``Axes``\ninstance contains an :class:`~matplotlib.axis.XAxis` and a\n:class:`~matplotlib.axis.YAxis` instance, which handle the layout and\ndrawing of the ticks, tick labels and axis labels.\n\nTry creating the figure below.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import numpy as np\nimport matplotlib.pyplot as plt\n\nfig = plt.figure()\nfig.subplots_adjust(top=0.8)\nax1 = fig.add_subplot(211)\nax1.set_ylabel('volts')\nax1.set_title('a sine wave')\n\nt = np.arange(0.0, 1.0, 0.01)\ns = np.sin(2*np.pi*t)\nline, = ax1.plot(t, s, color='blue', lw=2)\n\n# Fixing random state for reproducibility\nnp.random.seed(19680801)\n\nax2 = fig.add_axes([0.15, 0.1, 0.7, 0.3])\nn, bins, patches = ax2.hist(np.random.randn(1000), 50,\n facecolor='yellow', edgecolor='yellow')\nax2.set_xlabel('time (s)')\n\nplt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\nCustomizing your objects\n========================\n\nEvery element in the figure is represented by a matplotlib\n:class:`~matplotlib.artist.Artist`, and each has an extensive list of\nproperties to configure its appearance. The figure itself contains a\n:class:`~matplotlib.patches.Rectangle` exactly the size of the figure,\nwhich you can use to set the background color and transparency of the\nfigures. Likewise, each :class:`~matplotlib.axes.Axes` bounding box\n(the standard white box with black edges in the typical matplotlib\nplot, has a ``Rectangle`` instance that determines the color,\ntransparency, and other properties of the Axes. These instances are\nstored as member variables :attr:`Figure.patch\n` and :attr:`Axes.patch\n` (\"Patch\" is a name inherited from\nMATLAB, and is a 2D \"patch\" of color on the figure, e.g., rectangles,\ncircles and polygons). Every matplotlib ``Artist`` has the following\nproperties\n\n========== ================================================================================\nProperty Description\n========== ================================================================================\nalpha The transparency - a scalar from 0-1\nanimated A boolean that is used to facilitate animated drawing\naxes The axes that the Artist lives in, possibly None\nclip_box The bounding box that clips the Artist\nclip_on Whether clipping is enabled\nclip_path The path the artist is clipped to\ncontains A picking function to test whether the artist contains the pick point\nfigure The figure instance the artist lives in, possibly None\nlabel A text label (e.g., for auto-labeling)\npicker A python object that controls object picking\ntransform The transformation\nvisible A boolean whether the artist should be drawn\nzorder A number which determines the drawing order\nrasterized Boolean; Turns vectors into raster graphics (for compression & eps transparency)\n========== ================================================================================\n\nEach of the properties is accessed with an old-fashioned setter or\ngetter (yes we know this irritates Pythonistas and we plan to support\ndirect access via properties or traits but it hasn't been done yet).\nFor example, to multiply the current alpha by a half::\n\n a = o.get_alpha()\n o.set_alpha(0.5*a)\n\nIf you want to set a number of properties at once, you can also use\nthe ``set`` method with keyword arguments. For example::\n\n o.set(alpha=0.5, zorder=2)\n\nIf you are working interactively at the python shell, a handy way to\ninspect the ``Artist`` properties is to use the\n:func:`matplotlib.artist.getp` function (simply\n:func:`~matplotlib.pyplot.getp` in pyplot), which lists the properties\nand their values. This works for classes derived from ``Artist`` as\nwell, e.g., ``Figure`` and ``Rectangle``. Here are the ``Figure`` rectangle\nproperties mentioned above:\n\n.. sourcecode:: ipython\n\n In [149]: matplotlib.artist.getp(fig.patch)\n\talpha = 1.0\n\tanimated = False\n\tantialiased or aa = True\n\taxes = None\n\tclip_box = None\n\tclip_on = False\n\tclip_path = None\n\tcontains = None\n\tedgecolor or ec = w\n\tfacecolor or fc = 0.75\n\tfigure = Figure(8.125x6.125)\n\tfill = 1\n\thatch = None\n\theight = 1\n\tlabel =\n\tlinewidth or lw = 1.0\n\tpicker = None\n\ttransform = \n\tverts = ((0, 0), (0, 1), (1, 1), (1, 0))\n\tvisible = True\n\twidth = 1\n\twindow_extent = \n\tx = 0\n\ty = 0\n\tzorder = 1\n\nThe docstrings for all of the classes also contain the ``Artist``\nproperties, so you can consult the interactive \"help\" or the\n`artist-api` for a listing of properties for a given object.\n\n\nObject containers\n=================\n\n\nNow that we know how to inspect and set the properties of a given\nobject we want to configure, we need to know how to get at that object.\nAs mentioned in the introduction, there are two kinds of objects:\nprimitives and containers. The primitives are usually the things you\nwant to configure (the font of a :class:`~matplotlib.text.Text`\ninstance, the width of a :class:`~matplotlib.lines.Line2D`) although\nthe containers also have some properties as well -- for example the\n:class:`~matplotlib.axes.Axes` :class:`~matplotlib.artist.Artist` is a\ncontainer that contains many of the primitives in your plot, but it\nalso has properties like the ``xscale`` to control whether the xaxis\nis 'linear' or 'log'. In this section we'll review where the various\ncontainer objects store the ``Artists`` that you want to get at.\n\n\nFigure container\n----------------\n\nThe top level container ``Artist`` is the\n:class:`matplotlib.figure.Figure`, and it contains everything in the\nfigure. The background of the figure is a\n:class:`~matplotlib.patches.Rectangle` which is stored in\n:attr:`Figure.patch `. As\nyou add subplots (:meth:`~matplotlib.figure.Figure.add_subplot`) and\naxes (:meth:`~matplotlib.figure.Figure.add_axes`) to the figure\nthese will be appended to the :attr:`Figure.axes\n`. These are also returned by the\nmethods that create them:\n\n.. sourcecode:: ipython\n\n In [156]: fig = plt.figure()\n\n In [157]: ax1 = fig.add_subplot(211)\n\n In [158]: ax2 = fig.add_axes([0.1, 0.1, 0.7, 0.3])\n\n In [159]: ax1\n Out[159]: \n\n In [160]: print(fig.axes)\n [, ]\n\nBecause the figure maintains the concept of the \"current axes\" (see\n:meth:`Figure.gca ` and\n:meth:`Figure.sca `) to support the\npylab/pyplot state machine, you should not insert or remove axes\ndirectly from the axes list, but rather use the\n:meth:`~matplotlib.figure.Figure.add_subplot` and\n:meth:`~matplotlib.figure.Figure.add_axes` methods to insert, and the\n:meth:`~matplotlib.figure.Figure.delaxes` method to delete. You are\nfree however, to iterate over the list of axes or index into it to get\naccess to ``Axes`` instances you want to customize. Here is an\nexample which turns all the axes grids on::\n\n for ax in fig.axes:\n ax.grid(True)\n\n\nThe figure also has its own text, lines, patches and images, which you\ncan use to add primitives directly. The default coordinate system for\nthe ``Figure`` will simply be in pixels (which is not usually what you\nwant) but you can control this by setting the transform property of\nthe ``Artist`` you are adding to the figure.\n\n.. TODO: Is that still true?\n\nMore useful is \"figure coordinates\" where (0, 0) is the bottom-left of\nthe figure and (1, 1) is the top-right of the figure which you can\nobtain by setting the ``Artist`` transform to :attr:`fig.transFigure\n`:\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import matplotlib.lines as lines\n\nfig = plt.figure()\n\nl1 = lines.Line2D([0, 1], [0, 1], transform=fig.transFigure, figure=fig)\nl2 = lines.Line2D([0, 1], [1, 0], transform=fig.transFigure, figure=fig)\nfig.lines.extend([l1, l2])\n\nplt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here is a summary of the Artists the figure contains\n\n.. TODO: Add xrefs to this table\n\n================ ===============================================================\nFigure attribute Description\n================ ===============================================================\naxes A list of Axes instances (includes Subplot)\npatch The Rectangle background\nimages A list of FigureImages patches - useful for raw pixel display\nlegends A list of Figure Legend instances (different from Axes.legends)\nlines A list of Figure Line2D instances (rarely used, see Axes.lines)\npatches A list of Figure patches (rarely used, see Axes.patches)\ntexts A list Figure Text instances\n================ ===============================================================\n\n\nAxes container\n--------------\n\nThe :class:`matplotlib.axes.Axes` is the center of the matplotlib\nuniverse -- it contains the vast majority of all the ``Artists`` used\nin a figure with many helper methods to create and add these\n``Artists`` to itself, as well as helper methods to access and\ncustomize the ``Artists`` it contains. Like the\n:class:`~matplotlib.figure.Figure`, it contains a\n:class:`~matplotlib.patches.Patch`\n:attr:`~matplotlib.axes.Axes.patch` which is a\n:class:`~matplotlib.patches.Rectangle` for Cartesian coordinates and a\n:class:`~matplotlib.patches.Circle` for polar coordinates; this patch\ndetermines the shape, background and border of the plotting region::\n\n ax = fig.add_subplot(111)\n rect = ax.patch # a Rectangle instance\n rect.set_facecolor('green')\n\nWhen you call a plotting method, e.g., the canonical\n:meth:`~matplotlib.axes.Axes.plot` and pass in arrays or lists of\nvalues, the method will create a :meth:`matplotlib.lines.Line2D`\ninstance, update the line with all the ``Line2D`` properties passed as\nkeyword arguments, add the line to the :attr:`Axes.lines\n` container, and returns it to you:\n\n.. sourcecode:: ipython\n\n In [213]: x, y = np.random.rand(2, 100)\n\n In [214]: line, = ax.plot(x, y, '-', color='blue', linewidth=2)\n\n``plot`` returns a list of lines because you can pass in multiple x, y\npairs to plot, and we are unpacking the first element of the length\none list into the line variable. The line has been added to the\n``Axes.lines`` list:\n\n.. sourcecode:: ipython\n\n In [229]: print(ax.lines)\n []\n\nSimilarly, methods that create patches, like\n:meth:`~matplotlib.axes.Axes.bar` creates a list of rectangles, will\nadd the patches to the :attr:`Axes.patches\n` list:\n\n.. sourcecode:: ipython\n\n In [233]: n, bins, rectangles = ax.hist(np.random.randn(1000), 50, facecolor='yellow')\n\n In [234]: rectangles\n Out[234]: \n\n In [235]: print(len(ax.patches))\n\nYou should not add objects directly to the ``Axes.lines`` or\n``Axes.patches`` lists unless you know exactly what you are doing,\nbecause the ``Axes`` needs to do a few things when it creates and adds\nan object. It sets the figure and axes property of the ``Artist``, as\nwell as the default ``Axes`` transformation (unless a transformation\nis set). It also inspects the data contained in the ``Artist`` to\nupdate the data structures controlling auto-scaling, so that the view\nlimits can be adjusted to contain the plotted data. You can,\nnonetheless, create objects yourself and add them directly to the\n``Axes`` using helper methods like\n:meth:`~matplotlib.axes.Axes.add_line` and\n:meth:`~matplotlib.axes.Axes.add_patch`. Here is an annotated\ninteractive session illustrating what is going on:\n\n.. sourcecode:: ipython\n\n In [262]: fig, ax = plt.subplots()\n\n # create a rectangle instance\n In [263]: rect = matplotlib.patches.Rectangle( (1,1), width=5, height=12)\n\n # by default the axes instance is None\n In [264]: print(rect.get_axes())\n None\n\n # and the transformation instance is set to the \"identity transform\"\n In [265]: print(rect.get_transform())\n \n\n # now we add the Rectangle to the Axes\n In [266]: ax.add_patch(rect)\n\n # and notice that the ax.add_patch method has set the axes\n # instance\n In [267]: print(rect.get_axes())\n Axes(0.125,0.1;0.775x0.8)\n\n # and the transformation has been set too\n In [268]: print(rect.get_transform())\n \n\n # the default axes transformation is ax.transData\n In [269]: print(ax.transData)\n \n\n # notice that the xlimits of the Axes have not been changed\n In [270]: print(ax.get_xlim())\n (0.0, 1.0)\n\n # but the data limits have been updated to encompass the rectangle\n In [271]: print(ax.dataLim.bounds)\n (1.0, 1.0, 5.0, 12.0)\n\n # we can manually invoke the auto-scaling machinery\n In [272]: ax.autoscale_view()\n\n # and now the xlim are updated to encompass the rectangle\n In [273]: print(ax.get_xlim())\n (1.0, 6.0)\n\n # we have to manually force a figure draw\n In [274]: ax.figure.canvas.draw()\n\n\nThere are many, many ``Axes`` helper methods for creating primitive\n``Artists`` and adding them to their respective containers. The table\nbelow summarizes a small sampling of them, the kinds of ``Artist`` they\ncreate, and where they store them\n\n============================== ==================== =======================\nHelper method Artist Container\n============================== ==================== =======================\nax.annotate - text annotations Annotate ax.texts\nax.bar - bar charts Rectangle ax.patches\nax.errorbar - error bar plots Line2D and Rectangle ax.lines and ax.patches\nax.fill - shared area Polygon ax.patches\nax.hist - histograms Rectangle ax.patches\nax.imshow - image data AxesImage ax.images\nax.legend - axes legends Legend ax.legends\nax.plot - xy plots Line2D ax.lines\nax.scatter - scatter charts PolygonCollection ax.collections\nax.text - text Text ax.texts\n============================== ==================== =======================\n\n\nIn addition to all of these ``Artists``, the ``Axes`` contains two\nimportant ``Artist`` containers: the :class:`~matplotlib.axis.XAxis`\nand :class:`~matplotlib.axis.YAxis`, which handle the drawing of the\nticks and labels. These are stored as instance variables\n:attr:`~matplotlib.axes.Axes.xaxis` and\n:attr:`~matplotlib.axes.Axes.yaxis`. The ``XAxis`` and ``YAxis``\ncontainers will be detailed below, but note that the ``Axes`` contains\nmany helper methods which forward calls on to the\n:class:`~matplotlib.axis.Axis` instances so you often do not need to\nwork with them directly unless you want to. For example, you can set\nthe font color of the ``XAxis`` ticklabels using the ``Axes`` helper\nmethod::\n\n for label in ax.get_xticklabels():\n label.set_color('orange')\n\nBelow is a summary of the Artists that the Axes contains\n\n============== ======================================\nAxes attribute Description\n============== ======================================\nartists A list of Artist instances\npatch Rectangle instance for Axes background\ncollections A list of Collection instances\nimages A list of AxesImage\nlegends A list of Legend instances\nlines A list of Line2D instances\npatches A list of Patch instances\ntexts A list of Text instances\nxaxis matplotlib.axis.XAxis instance\nyaxis matplotlib.axis.YAxis instance\n============== ======================================\n\n\nAxis containers\n---------------\n\nThe :class:`matplotlib.axis.Axis` instances handle the drawing of the\ntick lines, the grid lines, the tick labels and the axis label. You\ncan configure the left and right ticks separately for the y-axis, and\nthe upper and lower ticks separately for the x-axis. The ``Axis``\nalso stores the data and view intervals used in auto-scaling, panning\nand zooming, as well as the :class:`~matplotlib.ticker.Locator` and\n:class:`~matplotlib.ticker.Formatter` instances which control where\nthe ticks are placed and how they are represented as strings.\n\nEach ``Axis`` object contains a :attr:`~matplotlib.axis.Axis.label` attribute\n(this is what :mod:`~matplotlib.pyplot` modifies in calls to\n:func:`~matplotlib.pyplot.xlabel` and :func:`~matplotlib.pyplot.ylabel`) as\nwell as a list of major and minor ticks. The ticks are\n:class:`~matplotlib.axis.XTick` and :class:`~matplotlib.axis.YTick` instances,\nwhich contain the actual line and text primitives that render the ticks and\nticklabels. Because the ticks are dynamically created as needed (e.g., when\npanning and zooming), you should access the lists of major and minor ticks\nthrough their accessor methods :meth:`~matplotlib.axis.Axis.get_major_ticks`\nand :meth:`~matplotlib.axis.Axis.get_minor_ticks`. Although the ticks contain\nall the primitives and will be covered below, ``Axis`` instances have accessor\nmethods that return the tick lines, tick labels, tick locations etc.:\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "fig, ax = plt.subplots()\naxis = ax.xaxis\naxis.get_ticklocs()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "axis.get_ticklabels()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "note there are twice as many ticklines as labels because by\n default there are tick lines at the top and bottom but only tick\n labels below the xaxis; this can be customized\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "axis.get_ticklines()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "by default you get the major ticks back\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "axis.get_ticklines()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "but you can also ask for the minor ticks\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "axis.get_ticklines(minor=True)\n\n# Here is a summary of some of the useful accessor methods of the ``Axis``\n# (these have corresponding setters where useful, such as\n# set_major_formatter)\n#\n# ====================== =========================================================\n# Accessor method Description\n# ====================== =========================================================\n# get_scale The scale of the axis, e.g., 'log' or 'linear'\n# get_view_interval The interval instance of the axis view limits\n# get_data_interval The interval instance of the axis data limits\n# get_gridlines A list of grid lines for the Axis\n# get_label The axis label - a Text instance\n# get_ticklabels A list of Text instances - keyword minor=True|False\n# get_ticklines A list of Line2D instances - keyword minor=True|False\n# get_ticklocs A list of Tick locations - keyword minor=True|False\n# get_major_locator The matplotlib.ticker.Locator instance for major ticks\n# get_major_formatter The matplotlib.ticker.Formatter instance for major ticks\n# get_minor_locator The matplotlib.ticker.Locator instance for minor ticks\n# get_minor_formatter The matplotlib.ticker.Formatter instance for minor ticks\n# get_major_ticks A list of Tick instances for major ticks\n# get_minor_ticks A list of Tick instances for minor ticks\n# grid Turn the grid on or off for the major or minor ticks\n# ====================== =========================================================\n#\n# Here is an example, not recommended for its beauty, which customizes\n# the axes and tick properties\n\n# plt.figure creates a matplotlib.figure.Figure instance\nfig = plt.figure()\nrect = fig.patch # a rectangle instance\nrect.set_facecolor('lightgoldenrodyellow')\n\nax1 = fig.add_axes([0.1, 0.3, 0.4, 0.4])\nrect = ax1.patch\nrect.set_facecolor('lightslategray')\n\n\nfor label in ax1.xaxis.get_ticklabels():\n # label is a Text instance\n label.set_color('red')\n label.set_rotation(45)\n label.set_fontsize(16)\n\nfor line in ax1.yaxis.get_ticklines():\n # line is a Line2D instance\n line.set_color('green')\n line.set_markersize(25)\n line.set_markeredgewidth(3)\n\nplt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\nTick containers\n---------------\n\nThe :class:`matplotlib.axis.Tick` is the final container object in our\ndescent from the :class:`~matplotlib.figure.Figure` to the\n:class:`~matplotlib.axes.Axes` to the :class:`~matplotlib.axis.Axis`\nto the :class:`~matplotlib.axis.Tick`. The ``Tick`` contains the tick\nand grid line instances, as well as the label instances for the upper\nand lower ticks. Each of these is accessible directly as an attribute\nof the ``Tick``.\n\n============== ==========================================================\nTick attribute Description\n============== ==========================================================\ntick1line Line2D instance\ntick2line Line2D instance\ngridline Line2D instance\nlabel1 Text instance\nlabel2 Text instance\n============== ==========================================================\n\nHere is an example which sets the formatter for the right side ticks with\ndollar signs and colors them green on the right side of the yaxis\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import matplotlib.ticker as ticker\n\n# Fixing random state for reproducibility\nnp.random.seed(19680801)\n\nfig, ax = plt.subplots()\nax.plot(100*np.random.rand(20))\n\nformatter = ticker.FormatStrFormatter('$%1.2f')\nax.yaxis.set_major_formatter(formatter)\n\nfor tick in ax.yaxis.get_major_ticks():\n tick.label1.set_visible(False)\n tick.label2.set_visible(True)\n tick.label2.set_color('green')\n\nplt.show()" - ] - } - ], - "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": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/Day_2/ExtraExamples/color_cycle.ipynb b/Day_2/ExtraExamples/color_cycle.ipynb deleted file mode 100644 index 1198dea..0000000 --- a/Day_2/ExtraExamples/color_cycle.ipynb +++ /dev/null @@ -1,133 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n# Styling with cycler\n\n\nDemo of custom property-cycle settings to control colors and other style\nproperties for multi-line plots.\n\n

Note

More complete documentation of the ``cycler`` API can be found\n `here `_.

\n\nThis example demonstrates two different APIs:\n\n1. Setting the default rc parameter specifying the property cycle.\n This affects all subsequent axes (but not axes already created).\n2. Setting the property cycle for a single pair of axes.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "from cycler import cycler\nimport numpy as np\nimport matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First we'll generate some sample data, in this case, four offset sine\ncurves.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "x = np.linspace(0, 2 * np.pi, 50)\noffsets = np.linspace(0, 2 * np.pi, 4, endpoint=False)\nyy = np.transpose([np.sin(x + phi) for phi in offsets])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now ``yy`` has shape\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "print(yy.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So ``yy[:, i]`` will give you the ``i``-th offset sine curve. Let's set the\ndefault prop_cycle using :func:`matplotlib.pyplot.rc`. We'll combine a color\ncycler and a linestyle cycler by adding (``+``) two ``cycler``'s together.\nSee the bottom of this tutorial for more information about combining\ndifferent cyclers.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "default_cycler = (cycler(color=['r', 'g', 'b', 'y']) +\n cycler(linestyle=['-', '--', ':', '-.']))\n\nplt.rc('lines', linewidth=4)\nplt.rc('axes', prop_cycle=default_cycler)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we'll generate a figure with two axes, one on top of the other. On the\nfirst axis, we'll plot with the default cycler. On the second axis, we'll\nset the prop_cycler using :func:`matplotlib.axes.Axes.set_prop_cycle`\nwhich will only set the ``prop_cycle`` for this :mod:`matplotlib.axes.Axes`\ninstance. We'll use a second ``cycler`` that combines a color cycler and a\nlinewidth cycler.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "custom_cycler = (cycler(color=['c', 'm', 'y', 'k']) +\n cycler(lw=[1, 2, 3, 4]))\n\nfig, (ax0, ax1) = plt.subplots(nrows=2)\nax0.plot(yy)\nax0.set_title('Set default color cycle to rgby')\nax1.set_prop_cycle(custom_cycler)\nax1.plot(yy)\nax1.set_title('Set axes color cycle to cmyk')\n\n# Add a bit more space between the two plots.\nfig.subplots_adjust(hspace=0.3)\nplt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Setting ``prop_cycler`` in the ``matplotlibrc`` file or style files\n-------------------------------------------------------------------\n\nRemember, if you want to set a custom ``prop_cycler`` in your\n``.matplotlibrc`` file or a style file (``style.mplstyle``), you can set the\n``axes.prop_cycle`` property:\n\n.. code-block:: python\n\n axes.prop_cycle : cycler(color='bgrcmyk')\n\nCycling through multiple properties\n-----------------------------------\n\nYou can add cyclers:\n\n.. code-block:: python\n\n from cycler import cycler\n cc = (cycler(color=list('rgb')) +\n cycler(linestyle=['-', '--', '-.']))\n for d in cc:\n print(d)\n\nResults in:\n\n.. code-block:: python\n\n {'color': 'r', 'linestyle': '-'}\n {'color': 'g', 'linestyle': '--'}\n {'color': 'b', 'linestyle': '-.'}\n\n\nYou can multiply cyclers:\n\n.. code-block:: python\n\n from cycler import cycler\n cc = (cycler(color=list('rgb')) *\n cycler(linestyle=['-', '--', '-.']))\n for d in cc:\n print(d)\n\nResults in:\n\n.. code-block:: python\n\n {'color': 'r', 'linestyle': '-'}\n {'color': 'r', 'linestyle': '--'}\n {'color': 'r', 'linestyle': '-.'}\n {'color': 'g', 'linestyle': '-'}\n {'color': 'g', 'linestyle': '--'}\n {'color': 'g', 'linestyle': '-.'}\n {'color': 'b', 'linestyle': '-'}\n {'color': 'b', 'linestyle': '--'}\n {'color': 'b', 'linestyle': '-.'}\n\n" - ] - } - ], - "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": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/Day_2/ExtraExamples/customizing.ipynb b/Day_2/ExtraExamples/customizing.ipynb deleted file mode 100644 index 72b7b90..0000000 --- a/Day_2/ExtraExamples/customizing.ipynb +++ /dev/null @@ -1,133 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\nCustomizing Matplotlib with style sheets and rcParams\n=====================================================\n\nTips for customizing the properties and default styles of Matplotlib.\n\nUsing style sheets\n------------------\n\nThe ``style`` package adds support for easy-to-switch plotting \"styles\" with\nthe same parameters as a\n`matplotlib rc ` file (which is read\nat startup to configure matplotlib).\n\nThere are a number of pre-defined styles `provided by Matplotlib`_. For\nexample, there's a pre-defined style called \"ggplot\", which emulates the\naesthetics of ggplot_ (a popular plotting package for R_). To use this style,\njust add:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\nplt.style.use('ggplot')\ndata = np.random.randn(50)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To list all available styles, use:\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "print(plt.style.available)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Defining your own style\n-----------------------\n\nYou can create custom styles and use them by calling ``style.use`` with the\npath or URL to the style sheet. Additionally, if you add your\n``.mplstyle`` file to ``mpl_configdir/stylelib``, you can reuse\nyour custom style sheet with a call to ``style.use()``. By default\n``mpl_configdir`` should be ``~/.config/matplotlib``, but you can check where\nyours is with ``matplotlib.get_configdir()``; you may need to create this\ndirectory. You also can change the directory where matplotlib looks for\nthe stylelib/ folder by setting the MPLCONFIGDIR environment variable,\nsee `locating-matplotlib-config-dir`.\n\nNote that a custom style sheet in ``mpl_configdir/stylelib`` will\noverride a style sheet defined by matplotlib if the styles have the same name.\n\nFor example, you might want to create\n``mpl_configdir/stylelib/presentation.mplstyle`` with the following::\n\n axes.titlesize : 24\n axes.labelsize : 20\n lines.linewidth : 3\n lines.markersize : 10\n xtick.labelsize : 16\n ytick.labelsize : 16\n\nThen, when you want to adapt a plot designed for a paper to one that looks\ngood in a presentation, you can just add::\n\n >>> import matplotlib.pyplot as plt\n >>> plt.style.use('presentation')\n\n\nComposing styles\n----------------\n\nStyle sheets are designed to be composed together. So you can have a style\nsheet that customizes colors and a separate style sheet that alters element\nsizes for presentations. These styles can easily be combined by passing\na list of styles::\n\n >>> import matplotlib.pyplot as plt\n >>> plt.style.use(['dark_background', 'presentation'])\n\nNote that styles further to the right will overwrite values that are already\ndefined by styles on the left.\n\n\nTemporary styling\n-----------------\n\nIf you only want to use a style for a specific block of code but don't want\nto change the global styling, the style package provides a context manager\nfor limiting your changes to a specific scope. To isolate your styling\nchanges, you can write something like the following:\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "with plt.style.context('dark_background'):\n plt.plot(np.sin(np.linspace(0, 2 * np.pi)), 'r-o')\nplt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\nmatplotlib rcParams\n===================\n\n\nDynamic rc settings\n-------------------\n\nYou can also dynamically change the default rc settings in a python script or\ninteractively from the python shell. All of the rc settings are stored in a\ndictionary-like variable called :data:`matplotlib.rcParams`, which is global to\nthe matplotlib package. rcParams can be modified directly, for example:\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "mpl.rcParams['lines.linewidth'] = 2\nmpl.rcParams['lines.color'] = 'r'\nplt.plot(data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Matplotlib also provides a couple of convenience functions for modifying rc\nsettings. The :func:`matplotlib.rc` command can be used to modify multiple\nsettings in a single group at once, using keyword arguments:\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "mpl.rc('lines', linewidth=4, color='g')\nplt.plot(data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The :func:`matplotlib.rcdefaults` command will restore the standard matplotlib\ndefault settings.\n\nThere is some degree of validation when setting the values of rcParams, see\n:mod:`matplotlib.rcsetup` for details.\n\n\nThe :file:`matplotlibrc` file\n-----------------------------\n\nmatplotlib uses :file:`matplotlibrc` configuration files to customize all kinds\nof properties, which we call `rc settings` or `rc parameters`. You can control\nthe defaults of almost every property in matplotlib: figure size and dpi, line\nwidth, color and style, axes, axis and grid properties, text and font\nproperties and so on. matplotlib looks for :file:`matplotlibrc` in four\nlocations, in the following order:\n\n1. :file:`matplotlibrc` in the current working directory, usually used for\n specific customizations that you do not want to apply elsewhere.\n\n2. :file:`$MATPLOTLIBRC` if it is a file, else :file:`$MATPLOTLIBRC/matplotlibrc`.\n\n3. It next looks in a user-specific place, depending on your platform:\n\n - On Linux and FreeBSD, it looks in :file:`.config/matplotlib/matplotlibrc`\n (or `$XDG_CONFIG_HOME/matplotlib/matplotlibrc`) if you've customized\n your environment.\n\n - On other platforms, it looks in :file:`.matplotlib/matplotlibrc`.\n\n See `locating-matplotlib-config-dir`.\n\n4. :file:`{INSTALL}/matplotlib/mpl-data/matplotlibrc`, where\n :file:`{INSTALL}` is something like\n :file:`/usr/lib/python3.7/site-packages` on Linux, and maybe\n :file:`C:\\\\Python37\\\\Lib\\\\site-packages` on Windows. Every time you\n install matplotlib, this file will be overwritten, so if you want\n your customizations to be saved, please move this file to your\n user-specific matplotlib directory.\n\nOnce a :file:`matplotlibrc` file has been found, it will *not* search any of\nthe other paths.\n\nTo display where the currently active :file:`matplotlibrc` file was\nloaded from, one can do the following::\n\n >>> import matplotlib\n >>> matplotlib.matplotlib_fname()\n '/home/foo/.config/matplotlib/matplotlibrc'\n\nSee below for a sample `matplotlibrc file`.\n\n\nA sample matplotlibrc file\n~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n.. literalinclude:: ../../../matplotlibrc.template\n\n\n\n" - ] - } - ], - "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": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/Day_2/ExtraExamples/gridspec.ipynb b/Day_2/ExtraExamples/gridspec.ipynb deleted file mode 100644 index 929537f..0000000 --- a/Day_2/ExtraExamples/gridspec.ipynb +++ /dev/null @@ -1,270 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n# Customizing Figure Layouts Using GridSpec and Other Functions\n\n\nHow to create grid-shaped combinations of axes.\n\n :func:`~matplotlib.pyplot.subplots`\n Perhaps the primary function used to create figures and axes.\n It's also similar to :func:`.matplotlib.pyplot.subplot`,\n but creates and places all axes on the figure at once. See also\n `matplotlib.Figure.subplots`.\n\n :class:`~matplotlib.gridspec.GridSpec`\n Specifies the geometry of the grid that a subplot will be\n placed. The number of rows and number of columns of the grid\n need to be set. Optionally, the subplot layout parameters\n (e.g., left, right, etc.) can be tuned.\n\n :class:`~matplotlib.gridspec.SubplotSpec`\n Specifies the location of the subplot in the given *GridSpec*.\n\n :func:`~matplotlib.pyplot.subplot2grid`\n A helper function that is similar to\n :func:`~matplotlib.pyplot.subplot`,\n but uses 0-based indexing and let subplot to occupy multiple cells.\n This function is not covered in this tutorial.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import matplotlib\nimport matplotlib.pyplot as plt\nimport matplotlib.gridspec as gridspec" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Basic Quickstart Guide\n======================\n\nThese first two examples show how to create a basic 2-by-2 grid using\nboth :func:`~matplotlib.pyplot.subplots` and :mod:`~matplotlib.gridspec`.\n\nUsing :func:`~matplotlib.pyplot.subplots` is quite simple.\nIt returns a :class:`~matplotlib.figure.Figure` instance and an array of\n:class:`~matplotlib.axes.Axes` objects.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "fig1, f1_axes = plt.subplots(ncols=2, nrows=2, constrained_layout=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For a simple use case such as this, :mod:`~matplotlib.gridspec` is\nperhaps overly verbose.\nYou have to create the figure and :class:`~matplotlib.gridspec.GridSpec`\ninstance separately, then pass elements of gridspec instance to the\n:func:`~matplotlib.figure.Figure.add_subplot` method to create the axes\nobjects.\nThe elements of the gridspec are accessed in generally the same manner as\nnumpy arrays.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "fig2 = plt.figure(constrained_layout=True)\nspec2 = gridspec.GridSpec(ncols=2, nrows=2, figure=fig2)\nf2_ax1 = fig2.add_subplot(spec2[0, 0])\nf2_ax2 = fig2.add_subplot(spec2[0, 1])\nf2_ax3 = fig2.add_subplot(spec2[1, 0])\nf2_ax4 = fig2.add_subplot(spec2[1, 1])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The power of gridspec comes in being able to create subplots that span\nrows and columns. Note the\n`Numpy slice `_\nsyntax for selecting the part of the gridspec each subplot will occupy.\n\nNote that we have also used the convenience method `.Figure.add_gridspec`\ninstead of `.gridspec.GridSpec`, potentially saving the user an import,\nand keeping the namespace cleaner.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "fig3 = plt.figure(constrained_layout=True)\ngs = fig3.add_gridspec(3, 3)\nf3_ax1 = fig3.add_subplot(gs[0, :])\nf3_ax1.set_title('gs[0, :]')\nf3_ax2 = fig3.add_subplot(gs[1, :-1])\nf3_ax2.set_title('gs[1, :-1]')\nf3_ax3 = fig3.add_subplot(gs[1:, -1])\nf3_ax3.set_title('gs[1:, -1]')\nf3_ax4 = fig3.add_subplot(gs[-1, 0])\nf3_ax4.set_title('gs[-1, 0]')\nf3_ax5 = fig3.add_subplot(gs[-1, -2])\nf3_ax5.set_title('gs[-1, -2]')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - ":mod:`~matplotlib.gridspec` is also indispensable for creating subplots\nof different widths via a couple of methods.\n\nThe method shown here is similar to the one above and initializes a\nuniform grid specification,\nand then uses numpy indexing and slices to allocate multiple\n\"cells\" for a given subplot.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "fig4 = plt.figure(constrained_layout=True)\nspec4 = fig4.add_gridspec(ncols=2, nrows=2)\nanno_opts = dict(xy=(0.5, 0.5), xycoords='axes fraction',\n va='center', ha='center')\n\nf4_ax1 = fig4.add_subplot(spec4[0, 0])\nf4_ax1.annotate('GridSpec[0, 0]', **anno_opts)\nfig4.add_subplot(spec4[0, 1]).annotate('GridSpec[0, 1:]', **anno_opts)\nfig4.add_subplot(spec4[1, 0]).annotate('GridSpec[1:, 0]', **anno_opts)\nfig4.add_subplot(spec4[1, 1]).annotate('GridSpec[1:, 1:]', **anno_opts)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Another option is to use the ``width_ratios`` and ``height_ratios``\nparameters. These keyword arguments are lists of numbers.\nNote that absolute values are meaningless, only their relative ratios\nmatter. That means that ``width_ratios=[2, 4, 8]`` is equivalent to\n``width_ratios=[1, 2, 4]`` within equally wide figures.\nFor the sake of demonstration, we'll blindly create the axes within\n``for`` loops since we won't need them later.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "fig5 = plt.figure(constrained_layout=True)\nwidths = [2, 3, 1.5]\nheights = [1, 3, 2]\nspec5 = fig5.add_gridspec(ncols=3, nrows=3, width_ratios=widths,\n height_ratios=heights)\nfor row in range(3):\n for col in range(3):\n ax = fig5.add_subplot(spec5[row, col])\n label = 'Width: {}\\nHeight: {}'.format(widths[col], heights[row])\n ax.annotate(label, (0.1, 0.5), xycoords='axes fraction', va='center')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Learning to use ``width_ratios`` and ``height_ratios`` is particularly\nuseful since the top-level function :func:`~matplotlib.pyplot.subplots`\naccepts them within the ``gridspec_kw`` parameter.\nFor that matter, any parameter accepted by\n:class:`~matplotlib.gridspec.GridSpec` can be passed to\n:func:`~matplotlib.pyplot.subplots` via the ``gridspec_kw`` parameter.\nThis example recreates the previous figure without directly using a\ngridspec instance.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "gs_kw = dict(width_ratios=widths, height_ratios=heights)\nfig6, f6_axes = plt.subplots(ncols=3, nrows=3, constrained_layout=True,\n gridspec_kw=gs_kw)\nfor r, row in enumerate(f6_axes):\n for c, ax in enumerate(row):\n label = 'Width: {}\\nHeight: {}'.format(widths[c], heights[r])\n ax.annotate(label, (0.1, 0.5), xycoords='axes fraction', va='center')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The ``subplots`` and ``gridspec`` methods can be combined since it is\nsometimes more convenient to make most of the subplots using ``subplots``\nand then remove some and combine them. Here we create a layout with\nthe bottom two axes in the last column combined.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "fig7, f7_axs = plt.subplots(ncols=3, nrows=3)\ngs = f7_axs[1, 2].get_gridspec()\n# remove the underlying axes\nfor ax in f7_axs[1:, -1]:\n ax.remove()\naxbig = fig7.add_subplot(gs[1:, -1])\naxbig.annotate('Big Axes \\nGridSpec[1:, -1]', (0.1, 0.5),\n xycoords='axes fraction', va='center')\n\nfig7.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fine Adjustments to a Gridspec Layout\n=====================================\n\nWhen a GridSpec is explicitly used, you can adjust the layout\nparameters of subplots that are created from the GridSpec. Note this\noption is not compatible with ``constrained_layout`` or\n`.Figure.tight_layout` which both adjust subplot sizes to fill the\nfigure.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "fig8 = plt.figure(constrained_layout=False)\ngs1 = fig8.add_gridspec(nrows=3, ncols=3, left=0.05, right=0.48, wspace=0.05)\nf8_ax1 = fig8.add_subplot(gs1[:-1, :])\nf8_ax2 = fig8.add_subplot(gs1[-1, :-1])\nf8_ax3 = fig8.add_subplot(gs1[-1, -1])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is similar to :func:`~matplotlib.pyplot.subplots_adjust`, but it only\naffects the subplots that are created from the given GridSpec.\n\nFor example, compare the left and right sides of this figure:\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "fig9 = plt.figure(constrained_layout=False)\ngs1 = fig9.add_gridspec(nrows=3, ncols=3, left=0.05, right=0.48,\n wspace=0.05)\nf9_ax1 = fig9.add_subplot(gs1[:-1, :])\nf9_ax2 = fig9.add_subplot(gs1[-1, :-1])\nf9_ax3 = fig9.add_subplot(gs1[-1, -1])\n\ngs2 = fig9.add_gridspec(nrows=3, ncols=3, left=0.55, right=0.98,\n hspace=0.05)\nf9_ax4 = fig9.add_subplot(gs2[:, :-1])\nf9_ax5 = fig9.add_subplot(gs2[:-1, -1])\nf9_ax6 = fig9.add_subplot(gs2[-1, -1])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "GridSpec using SubplotSpec\n==========================\n\nYou can create GridSpec from the :class:`~matplotlib.gridspec.SubplotSpec`,\nin which case its layout parameters are set to that of the location of\nthe given SubplotSpec.\n\nNote this is also available from the more verbose\n`.gridspec.GridSpecFromSubplotSpec`.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "fig10 = plt.figure(constrained_layout=True)\ngs0 = fig10.add_gridspec(1, 2)\n\ngs00 = gs0[0].subgridspec(2, 3)\ngs01 = gs0[1].subgridspec(3, 2)\n\nfor a in range(2):\n for b in range(3):\n fig10.add_subplot(gs00[a, b])\n fig10.add_subplot(gs01[b, a])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A Complex Nested GridSpec using SubplotSpec\n===========================================\n\nHere's a more sophisticated example of nested GridSpec where we put\na box around each cell of the outer 4x4 grid, by hiding appropriate\nspines in each of the inner 3x3 grids.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import numpy as np\nfrom itertools import product\n\n\ndef squiggle_xy(a, b, c, d, i=np.arange(0.0, 2*np.pi, 0.05)):\n return np.sin(i*a)*np.cos(i*b), np.sin(i*c)*np.cos(i*d)\n\n\nfig11 = plt.figure(figsize=(8, 8), constrained_layout=False)\n\n# gridspec inside gridspec\nouter_grid = fig11.add_gridspec(4, 4, wspace=0.0, hspace=0.0)\n\nfor i in range(16):\n inner_grid = outer_grid[i].subgridspec(3, 3, wspace=0.0, hspace=0.0)\n a, b = int(i/4)+1, i % 4+1\n for j, (c, d) in enumerate(product(range(1, 4), repeat=2)):\n ax = fig11.add_subplot(inner_grid[j])\n ax.plot(*squiggle_xy(a, b, c, d))\n ax.set_xticks([])\n ax.set_yticks([])\n fig11.add_subplot(ax)\n\nall_axes = fig11.get_axes()\n\n# show only the outside spines\nfor ax in all_axes:\n for sp in ax.spines.values():\n sp.set_visible(False)\n if ax.is_first_row():\n ax.spines['top'].set_visible(True)\n if ax.is_last_row():\n ax.spines['bottom'].set_visible(True)\n if ax.is_first_col():\n ax.spines['left'].set_visible(True)\n if ax.is_last_col():\n ax.spines['right'].set_visible(True)\n\nplt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "------------\n\nReferences\n\"\"\"\"\"\"\"\"\"\"\n\nThe usage of the following functions and methods is shown in this example:\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "matplotlib.pyplot.subplots\nmatplotlib.figure.Figure.add_gridspec\nmatplotlib.figure.Figure.add_subplot\nmatplotlib.gridspec.GridSpec\nmatplotlib.gridspec.SubplotSpec.subgridspec\nmatplotlib.gridspec.GridSpecFromSubplotSpec" - ] - } - ], - "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": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/Day_2/ExtraExamples/images.ipynb b/Day_2/ExtraExamples/images.ipynb deleted file mode 100644 index 9023e2b..0000000 --- a/Day_2/ExtraExamples/images.ipynb +++ /dev/null @@ -1,277 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n# Image tutorial\n\n\nA short tutorial on plotting images with Matplotlib.\n\n\nStartup commands\n===================\n\nFirst, let's start IPython. It is a most excellent enhancement to the\nstandard Python prompt, and it ties in especially well with\nMatplotlib. Start IPython either at a shell, or the IPython Notebook now.\n\nWith IPython started, we now need to connect to a GUI event loop. This\ntells IPython where (and how) to display plots. To connect to a GUI\nloop, execute the **%matplotlib** magic at your IPython prompt. There's more\ndetail on exactly what this does at `IPython's documentation on GUI\nevent loops\n`_.\n\nIf you're using IPython Notebook, the same commands are available, but\npeople commonly use a specific argument to the %matplotlib magic:\n\n.. sourcecode:: ipython\n\n In [1]: %matplotlib inline\n\nThis turns on inline plotting, where plot graphics will appear in your\nnotebook. This has important implications for interactivity. For inline plotting, commands in\ncells below the cell that outputs a plot will not affect the plot. For example,\nchanging the color map is not possible from cells below the cell that creates a plot.\nHowever, for other backends, such as Qt5, that open a separate window,\ncells below those that create the plot will change the plot - it is a\nlive object in memory.\n\nThis tutorial will use matplotlib's imperative-style plotting\ninterface, pyplot. This interface maintains global state, and is very\nuseful for quickly and easily experimenting with various plot\nsettings. The alternative is the object-oriented interface, which is also\nvery powerful, and generally more suitable for large application\ndevelopment. If you'd like to learn about the object-oriented\ninterface, a great place to start is our :doc:`Usage guide\n`. For now, let's get on\nwith the imperative-style approach:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\nimport matplotlib.image as mpimg" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\nImporting image data into Numpy arrays\n===============================================\n\nLoading image data is supported by the `Pillow\n`_ library. Natively, Matplotlib\nonly supports PNG images. The commands shown below fall back on Pillow if\nthe native read fails.\n\nThe image used in this example is a PNG file, but keep that Pillow\nrequirement in mind for your own data.\n\nHere's the image we're going to play with:\n\n![](../../_static/stinkbug.png)\n\n\nIt's a 24-bit RGB PNG image (8 bits for each of R, G, B). Depending\non where you get your data, the other kinds of image that you'll most\nlikely encounter are RGBA images, which allow for transparency, or\nsingle-channel grayscale (luminosity) images. You can right click on\nit and choose \"Save image as\" to download it to your computer for the\nrest of this tutorial.\n\nAnd here we go...\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "img = mpimg.imread('../../doc/_static/stinkbug.png')\nprint(img)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note the dtype there - float32. Matplotlib has rescaled the 8 bit\ndata from each channel to floating point data between 0.0 and 1.0. As\na side note, the only datatype that Pillow can work with is uint8.\nMatplotlib plotting can handle float32 and uint8, but image\nreading/writing for any format other than PNG is limited to uint8\ndata. Why 8 bits? Most displays can only render 8 bits per channel\nworth of color gradation. Why can they only render 8 bits/channel?\nBecause that's about all the human eye can see. More here (from a\nphotography standpoint): `Luminous Landscape bit depth tutorial\n`_.\n\nEach inner list represents a pixel. Here, with an RGB image, there\nare 3 values. Since it's a black and white image, R, G, and B are all\nsimilar. An RGBA (where A is alpha, or transparency), has 4 values\nper inner list, and a simple luminance image just has one value (and\nis thus only a 2-D array, not a 3-D array). For RGB and RGBA images,\nmatplotlib supports float32 and uint8 data types. For grayscale,\nmatplotlib supports only float32. If your array data does not meet\none of these descriptions, you need to rescale it.\n\n\nPlotting numpy arrays as images\n===================================\n\nSo, you have your data in a numpy array (either by importing it, or by\ngenerating it). Let's render it. In Matplotlib, this is performed\nusing the :func:`~matplotlib.pyplot.imshow` function. Here we'll grab\nthe plot object. This object gives you an easy way to manipulate the\nplot from the prompt.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "imgplot = plt.imshow(img)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can also plot any numpy array.\n\n\nApplying pseudocolor schemes to image plots\n-------------------------------------------------\n\nPseudocolor can be a useful tool for enhancing contrast and\nvisualizing your data more easily. This is especially useful when\nmaking presentations of your data using projectors - their contrast is\ntypically quite poor.\n\nPseudocolor is only relevant to single-channel, grayscale, luminosity\nimages. We currently have an RGB image. Since R, G, and B are all\nsimilar (see for yourself above or in your data), we can just pick one\nchannel of our data:\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "lum_img = img[:, :, 0]\n\n# This is array slicing. You can read more in the `Numpy tutorial\n# `_.\n\nplt.imshow(lum_img)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, with a luminosity (2D, no color) image, the default colormap (aka lookup table,\nLUT), is applied. The default is called viridis. There are plenty of\nothers to choose from.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "plt.imshow(lum_img, cmap=\"hot\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that you can also change colormaps on existing plot objects using the\n:meth:`~matplotlib.image.Image.set_cmap` method:\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "imgplot = plt.imshow(lum_img)\nimgplot.set_cmap('nipy_spectral')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Note

However, remember that in the IPython notebook with the inline backend,\n you can't make changes to plots that have already been rendered. If you\n create imgplot here in one cell, you cannot call set_cmap() on it in a later\n cell and expect the earlier plot to change. Make sure that you enter these\n commands together in one cell. plt commands will not change plots from earlier\n cells.

\n\nThere are many other colormap schemes available. See the `list and\nimages of the colormaps\n<../colors/colormaps.html>`_.\n\n\nColor scale reference\n------------------------\n\nIt's helpful to have an idea of what value a color represents. We can\ndo that by adding color bars.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "imgplot = plt.imshow(lum_img)\nplt.colorbar()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This adds a colorbar to your existing figure. This won't\nautomatically change if you change you switch to a different\ncolormap - you have to re-create your plot, and add in the colorbar\nagain.\n\n\nExamining a specific data range\n---------------------------------\n\nSometimes you want to enhance the contrast in your image, or expand\nthe contrast in a particular region while sacrificing the detail in\ncolors that don't vary much, or don't matter. A good tool to find\ninteresting regions is the histogram. To create a histogram of our\nimage data, we use the :func:`~matplotlib.pyplot.hist` function.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "plt.hist(lum_img.ravel(), bins=256, range=(0.0, 1.0), fc='k', ec='k')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Most often, the \"interesting\" part of the image is around the peak,\nand you can get extra contrast by clipping the regions above and/or\nbelow the peak. In our histogram, it looks like there's not much\nuseful information in the high end (not many white things in the\nimage). Let's adjust the upper limit, so that we effectively \"zoom in\non\" part of the histogram. We do this by passing the clim argument to\nimshow. You could also do this by calling the\n:meth:`~matplotlib.image.Image.set_clim` method of the image plot\nobject, but make sure that you do so in the same cell as your plot\ncommand when working with the IPython Notebook - it will not change\nplots from earlier cells.\n\nYou can specify the clim in the call to ``plot``.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "imgplot = plt.imshow(lum_img, clim=(0.0, 0.7))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can also specify the clim using the returned object\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "fig = plt.figure()\na = fig.add_subplot(1, 2, 1)\nimgplot = plt.imshow(lum_img)\na.set_title('Before')\nplt.colorbar(ticks=[0.1, 0.3, 0.5, 0.7], orientation='horizontal')\na = fig.add_subplot(1, 2, 2)\nimgplot = plt.imshow(lum_img)\nimgplot.set_clim(0.0, 0.7)\na.set_title('After')\nplt.colorbar(ticks=[0.1, 0.3, 0.5, 0.7], orientation='horizontal')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\nArray Interpolation schemes\n---------------------------\n\nInterpolation calculates what the color or value of a pixel \"should\"\nbe, according to different mathematical schemes. One common place\nthat this happens is when you resize an image. The number of pixels\nchange, but you want the same information. Since pixels are discrete,\nthere's missing space. Interpolation is how you fill that space.\nThis is why your images sometimes come out looking pixelated when you\nblow them up. The effect is more pronounced when the difference\nbetween the original image and the expanded image is greater. Let's\ntake our image and shrink it. We're effectively discarding pixels,\nonly keeping a select few. Now when we plot it, that data gets blown\nup to the size on your screen. The old pixels aren't there anymore,\nand the computer has to draw in pixels to fill that space.\n\nWe'll use the Pillow library that we used to load the image also to resize\nthe image.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "from PIL import Image\n\nimg = Image.open('../../doc/_static/stinkbug.png')\nimg.thumbnail((64, 64), Image.ANTIALIAS) # resizes image in-place\nimgplot = plt.imshow(img)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we have the default interpolation, bilinear, since we did not\ngive :func:`~matplotlib.pyplot.imshow` any interpolation argument.\n\nLet's try some others. Here's \"nearest\", which does no interpolation.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "imgplot = plt.imshow(img, interpolation=\"nearest\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "and bicubic:\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "imgplot = plt.imshow(img, interpolation=\"bicubic\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Bicubic interpolation is often used when blowing up photos - people\ntend to prefer blurry over pixelated.\n\n" - ] - } - ], - "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": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/Day_2/ExtraExamples/livegraphsexamples.ipynb b/Day_2/ExtraExamples/livegraphsexamples.ipynb deleted file mode 100644 index 1e6be17..0000000 --- a/Day_2/ExtraExamples/livegraphsexamples.ipynb +++ /dev/null @@ -1,747 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "x = np.linspace(0, 6*np.pi, 100)\n", - "y = np.sin(x)\n", - "\n", - "# You probably won't need this if you're embedding things in a tkinter plot...\n", - "plt.ion()\n", - "\n", - "fig = plt.figure()\n", - "ax = fig.add_subplot(111)\n", - "line1, = ax.plot(x, y, 'r-') # Returns a tuple of line objects, thus the comma\n", - "\n", - "for phase in np.linspace(0, 90*np.pi, 1000):\n", - " line1.set_ydata(np.sin(x + phase))\n", - " fig.canvas.draw()\n", - " #fig.canvas.flush_events()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "from PyQt5 import QtCore, QtGui\n", - "import pyqtgraph as pg\n", - "import random\n", - "\n", - "class MainWindow(QtGui.QMainWindow):\n", - " def __init__(self, parent=None):\n", - " super(MainWindow, self).__init__(parent)\n", - " self.central_widget = QtGui.QStackedWidget()\n", - " self.setCentralWidget(self.central_widget)\n", - " self.login_widget = LoginWidget(self)\n", - " self.login_widget.button.clicked.connect(self.plotter)\n", - " self.central_widget.addWidget(self.login_widget)\n", - "\n", - " def plotter(self):\n", - " self.data =[0]\n", - " self.curve = self.login_widget.plot.getPlotItem().plot()\n", - "\n", - " self.timer = QtCore.QTimer()\n", - " self.timer.timeout.connect(self.updater)\n", - " self.timer.start(0)\n", - "\n", - " def updater(self):\n", - "\n", - " self.data.append(self.data[-1]+0.2*(0.5-random.random()) )\n", - " self.curve.setData(self.data)\n", - "\n", - "class LoginWidget(QtGui.QWidget):\n", - " def __init__(self, parent=None):\n", - " super(LoginWidget, self).__init__(parent)\n", - " layout = QtGui.QHBoxLayout()\n", - " self.button = QtGui.QPushButton('Start Plotting')\n", - " layout.addWidget(self.button)\n", - " self.plot = pg.PlotWidget()\n", - " layout.addWidget(self.plot)\n", - " self.setLayout(layout)\n", - "\n", - "if __name__ == '__main__':\n", - " app = QtGui.QApplication([])\n", - " window = MainWindow()\n", - " window.show()\n", - " app.exec_()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzt3Xl8lOW5//HPPZM9mUkgC5OQQBICTAIIKARcsS51qUXb2lZcqiBq69L22NP+7GlP9djldO+pW2tFxCpubT0V99attXoIYUnYEiAEyCQQCIHJvs7cvz+SYEQg28w8zzNzvV+vvl4kmcxcHcjXO/f1PNettNYIIYQILzajCxBCCBF4Eu5CCBGGJNyFECIMSbgLIUQYknAXQogwJOEuhBBhSMJdCCHCkIS7EEKEIQl3IYQIQ1FGvXBaWprOzc016uWFEMKSNmzYcFhrnT7U4wwL99zcXNavX2/UywshhCUppfYN53GyLSOEEGFIwl0IIcKQhLsQQoQhCXchhAhDEu5CCBGGJNyFECIMSbgLIUQYknAXQkSEVzcfoOpQq9FlhIyEuxAi7K14v5o7ntnIA2/vMrqUkJFwF0KEtWdKavjRqxXYbYrK+majywkZCXchRNj666Y6vvfXLXxqejrLz8ljd0MbXb0+o8sKCQl3IURYenNbPd/6UzkL8sbzu+vPYFZ2Mj6/jph9dwl3IUTYeX9XA3c9s4lZE5NZceN84qLtuF1OACoOtBhcXWhIuAshwkrp3iPc8sf15Kcn8uTSYpJi+4bf5qYmEBtlo/JAZOy7S7gLIcLGltomlj1RSlZKPE/dvIDkhOhjX4uy25g2wUFlvazchRCj4Pdr9hxuM7qMiLPzYAtfWVlCckI0q5cvIN0R+4nHFGY6IuaKGQl3IQJsTfl+LvjVe2ytazK6lIix93Ab160oIdpuY/XyBWQmx5/wcW6Xk8Ot3Rxq6QxxhaEn4S5EgJXsOYLW8My6GqNLiQj7vR1ct6IEn1+zevkCJqcmnvSx7kwHAJUR0FSVcBciwMo9XgBe2lRHa1evwdWEt4aWLq5fUUJzRw9/XFbM1AmOUz6+sP+KmUjYmpFwFyKAOrp97DjYwtkFqbR1+3i5fL/RJYUtb3s3NzxewoGmTlYtm8/MiclDfs+4xBhczjhZuQshRmbr/iZ8fs3Ss/Jwuxw8K1szQdHa1cuNT5RS3dDGY1+ZxxmTxw/7e92ZDrZHwOWQEu5CBNDAlszsnBSWFE9ic22TNFYDrKPbx82rStla18TD153OOVPTRvT9bpeT3Q2tdPf6g1ShOUi4CxFAmzxeJqbEk+6I5aq5E4mLtkljNYC6e/18bfUG1u09wq+/NJuLiyaM+DkKMx30+DTVh8N7DIGEuxABVO7xMicnBYDk+GiuOC1LGqsB0uvz843nNvHejgb++3OzuHLOxFE9T2HmwBiC8N6akXAXIkAOt3ZRe7TjWLgDLCmeJI3VAPD7Nd/5y2Ze31rPf15RxDXFk0b9XHlpicTYbWHfVJVwFyJABu+3Dzh9Uoo0VsdIa829a7bx4sY67r54Gjefkzem54u22yjISKIizMcQSLgLESDlHi92m2LmROexzymlpLE6BlprfvpGJU+t3cdt5+Vz1wUFAXled6Yj7AeISbgLESBltU1Mm+AgISbqY5+XxuroPfROFY/+o5rrF07insvcKKUC8rxFmU4OtXTR2NoVkOczIwl3IQJAa93fTP3kjTTSWB2dx/+1h1/9fSefnzuR+xfPDFiwA8dmu4fzhEgJdyECYG9jO00dPczOTjnh16WxOjLPl9bww1e2c+kMFz+/+jRstsAFO3w0Yyacr5iRcBciAAaaqXMmnTjcpbE6fGvK93PPi1tYNC2d3y6ZQ5Q98DGVlhRLWlKsrNyFEKdW5vGSEGNnasaJB1dJY3V43tp+kLufL2N+7nh+f/0ZxEbZg/ZahZkOWbkLIU6tzONl5sRk7KfYPpDG6ql9UHWY25/ZyIwsJ4/fOI/4mOAFO/TdzLTrYCu9vvAcQyDhLsQYdff62b6/mbk5J96SGSCN1ZPbsO8Iy59cT15qIquWFuOIix76m8bI7XLQ7fOH7alZEu5CjFFlfTPdPv/Hbl46GWmsftLWuiZueqIUV3IcTy0vZlxiTEhed+CKmXC9mUnCXYgxKjvBnaknI43Vj9t1sIWvrFyHMy6ap5cvIMMRF7LXLshIIsqmwnbfXcJdiDEq83hJd8SSlTx0MElj9SM1je1c/3gJNqV4evkCJqac+NzTYImJ6htDEK53qkq4CzFG5R4vs7NThn2TjTRW4UBTB9euWEtXr5/VyxeQl3byc0+Dye1yhO3lkBLuQoxBU0cPuxvaTnhn6slEemP1cGsX160owdved+7pdNepzz0NJnemkwNNnXjbuw2rIViGFe5KqUuVUjuUUlVKqXtO8PVJSql3lVKblFKblVKXB75UIcxnS23f1sqcnHEj+r5Ibaw2tfdww+Pr2O/tYOVN8zntJHf0hspHs93Db/U+ZLgrpezAw8BlQBGwRClVdNzDvg+8oLWeC1wDPBLoQoUwo/LavmbqrOzhr9whMhurrV293LRqHbsPtfLoDfMozhv+uafBUtj/W0Nlffjtuw9n5V4MVGmtq7XW3cBzwJXHPUYDA3NOk4HIWo6IiLWpxkt+eiLJ8SO7LjvSGqudPT5ueXI9m2ubePDauSyalm50SQCkO2IZnxgTlgd3DCfcJwKeQR/X9n9usPuA65VStcBrwF0BqU4IE9NaUzboWL2RipTGanevn9tXb2TtnkZ+9cXZXDLDZXRJxyil+puqkblyP9ElAPq4j5cAq7TW2cDlwFNKqU88t1LqVqXUeqXU+oaGhpFXK4SJHGjq5HBr16jDPVIaqz94aSvvVB7ix1fN4qq5ozv3NJgKM53sONiCz398rFnbcMK9FsgZ9HE2n9x2uRl4AUBr/X9AHJB2/BNprf+gtZ6ntZ6Xnm6OX8uEGK1jNy+NoSkY7o3V0r1HeK7Uw22L8rl2wejPPQ0mt8tBZ4+fvY3hNYZgOOFeCkxVSuUppWLoa5iuOe4xNcCFAEqpQvrCXZbmIqyVe7zE2G3HZoOPRjg3Vn1+zb0vbSMrOY5vXDjV6HJOauCKmXDbdx8y3LXWvcCdwJtABX1XxWxTSt2vlFrc/7BvAbcopcqBZ4GbtNbh9TuOEMcp83gpynKOaSxtODdWn1lXw/YDzXzvM0WfOHrQTAoykrDbVNjtuw/rOnet9Wta62la6yla6x/3f+4HWus1/X/errU+W2s9W2s9R2v9t2AWLYTRfH7NlrqmUe+3DxaOjdUjbd388s0dnDUllctnmaeBeiJx0Xby0xLDbsaM3KEqxCjsOtRCe7eP2SO4M/VkwrGx+su/7aC1q5f7Fs8I6NmnweLOdIbdjUwS7kKMwrFj9UZ4Z+rJhFNjdUttE8+uq+HGM3OZNsG40QIj4XY5qPN20NzZY3QpASPhLsQolHm8OOOiyE1NCMjzhUtj1e/X3LtmK6mJMXzzYvM2UY9X1N9U3RFGQ8Qk3IUYhTJPE7Nzhj8Jcijh0lh9cVMdG2u8/L9L3ThDcJpSoAxc8RRO++4S7kKMUHt3LzsPtgx5rN5IWb2x2tzZw09fr2TupBS+cHq20eWMiMsZR3J8dFjtu0u4CzFCW+ua8fn1sE5eGgmrN1YfeGsXjW1d3L94JrZTHBRuRuE4hkDCXYgRKh/BsXojZdXG6q6DLaz6cC/XzJ804gmZZlGY6WRHfQv+MBlDIOEuxAiV1XrJHhdPWlJswJ/bio1VrTX3vbyNxNgovn3JdKPLGbXCTAft3T5qjrQbXUpASLgLMUJlNd6grNrBmo3V17fW80FVI9/69DTGJ8YYXc6ouV39YwjCZGtGwl2IEWho6aLO28GcIJ4gZKXGake3jx+9sp3CTCfXFptzMNhwTZvgwKbC51QmCXchRmBz/8lLcyYFL9wHN1bbTN5YfeS9KvY3dfJfi2cQZbd2nMTH2MlNS5SVuxCRqMzjxW5TzMhyDv3gMRhorK4xcWN1X2Mbj/6zmqvmZJniyLxAKHSFzxgCCXchRqDM42XaBEfQpxxaobH6w1e2E21TfPfyQqNLCRi3y0HNkXZLXop6PAl3IYZJa035GI7VGwmzN1bf3XGItyoOcdeFU5ngjDO6nIBxh9EYAgl3IYZpz+E2mjt7mROASZDDYdbGalevj/tf3k5+WiLLzs4zupyAKuwfQxAO++4S7kIMU3lt8G5eOhGzNlYf/9ce9hxu497FM4iJCq8ImZgSjyM2KixmzITX34wQQVTuaSIhxs7UjNCNsTVbY/VAUwcPvVPFp4smsGha+J2DrJTCnekIiyP3JNyFGKZNHi+zJiZjD+HcFLM1Vn/yWiU+v+Y/rygyupSgcbucVNa3YPWTQiXchRiGrl4fFfubQ9JMHcxMjdW11Y28XL6fry6aQs74wMyxN6PCTCetXb3UHu0wupQxkXAXYhgqD7TQ7fOHPNzBHI3VXp+f+9ZsY2JKPF87f4phdYRCuMx2l3AXYhjKgjgJcihmaKw+vXYflfUt/OcVhcRF2w2pIVSmTxi4Ysba++4S7kIMQ7nHS7ojlsxkY67pNrKxeri1i1//fSfnTk3jkhmukL9+qCXGRjE5NcHyl0NKuAsxDGW1fTcvBepYvZEysrH6izd20N7t497PzjDs/3+oFbqclr9iRsJdiCE0tfdQ3dBmyH77AKMaq2UeLy9s8LDsnDwKMpJC9rpGc2c62NPYRnu3ee4vGCkJdyGGsLmuf789iGN+hyPUjVW/X3PvS1tJS4rlrgsKQvKaZuF2OdEadh5sNbqUUZNwF2IIA8fqGX18XKgbq3/eUEt5bRPfvcyNIy466K9nJsfGEFj4ihkJdyGGUObxMiU9keR44wMuVI3Vpo4efvZGJfMmj+NzcycG9bXMKGdcAokxdktfDinhLsQpaK0p8zQZcgnkiYSqsfqbv+/kSHs39y2OnCbqYDabYrrLQYWFL4eUcBfiFPY3dXK4tcvQZupgoWisVtY389TafVy3YBIzJxq7FWUkd6aTygPNlh1DIOEuxCmU1fQfq2eScIfgNla11tz70jYccVF86+LpAX9+Kyl0OWju7OVAU6fRpYyKhLsQp1Be6yXGbsPtCu6xeiMRzMbqK5sPULLnCN++ZDrjEmMC+txWU9h/cIdV990l3IU4hTKPl6Isp+nmlgejsdrW1cuPX61g5kQn18yfFLDntappLmuPITDXv1ghTKTX52dLbZOptmQGBKOx+vC7VdQ3d/Jfi2eEdKyxWTnjoskeFy8rdyHCza5DrXT0+EwZ7oFurO453MZj71fz+dMncsbk8QGoMDwUZjpl5S5EuCk3cBLkcASqsaq15r9e3kZslJ17LnMHqLrwUOhyUN3QSmePz+hSRkzCXYiTKK/1khwfTW6qOQ+mCFRj9e2KQ7y3o4FvXjSVDIcxUy/Nyp3pxK9hlwXHEEi4C3ESm2q8zDZwEuRwjLWx2tnj4/5XtlOQkcSNZ+UGtrgw4O5vqlZYcPyvhLsQJ9De3cvOgy3MMXiezFDG2lhd8X41NUfaue+zM4i2Sxwcb3JqIvHRdkuO/5W/TSFOYGtdM34NcyaZc799wFgaq3XeDh56t4rLZro4Z2pakCq0NrtNMc3lsOQVM8MKd6XUpUqpHUqpKqXUPSd5zJeUUtuVUtuUUs8EtkwhQqvMcxSA0wwe8zsco22s/uTVCgC+95nCYJQVNgpdDirrrTeGYMhwV0rZgYeBy4AiYIlSqui4x0wFvgucrbWeAXwzCLUKETLlniayx8WTlhRrdClDGk1j9YOqw7y65QC3n19A9jhzNozNwu1ycLS9h0MtXUaXMiLDWbkXA1Va62qtdTfwHHDlcY+5BXhYa30UQGt9KLBlChFaZR6vKa9vP5mRNFZ7fH7uW7ONnPHx3HpefgiqszarjiEYTrhPBDyDPq7t/9xg04BpSqkPlFJrlVKXBqpAIUKtoaWLOm+HpcJ9JI3VJz/cy65DrfznZ4qIi7aHoDprG5grVGGxpupwwv1E14Edv/kUBUwFzgeWACuUUp/4yVBK3aqUWq+UWt/Q0DDSWoUICbPfvHQiw22sNrR08du3drFoWjoXF00IYYXWlZwQTVZyHJUWuxxyOOFeC+QM+jgbOP53v1rgJa11j9Z6D7CDvrD/GK31H7TW87TW89LT00dbsxBBVV7rxW5TzMwy92WQxxtOY/Vnb1TS2evj3s8Wmfr6fbPpm+0efiv3UmCqUipPKRUDXAOsOe4xfwU+BaCUSqNvm6Y6kIUKESplHi/TJziIj7HWlsVQjdUN+47y5w213HxOPvnpSQZUaF2FmQ52N7TS1WudMQRDhrvWuhe4E3gTqABe0FpvU0rdr5Ra3P+wN4FGpdR24F3g21rrxmAVLUSw+P2aco/XUlsyg52sserza+5bs40JzljuuqDAoOqsy+1y0uvXVB2yzhiCqOE8SGv9GvDacZ/7waA/a+Du/v8JYVl7G9to7uxlrkXDfXBjdUnxRzPZny/1sKWuid9eM4fE2GH92ItBCjP7Z7sfaGGGRbbr5A5VIQYps2AzdbATNVa97d384s1KivPGs3h2lsEVWlNuaiIxUTZLNVUl3IUYpNzjJTHGTkGGdfekj2+s/upvO2nq6OG+z86QJuooRdltTJ/gsNRsdwl3IQYpq21iVnaypU8iGtxYXb/3CKtL9nHDwskUZZnnHFgrcltsxoyEuxD9unp9VOxvtuyWzGADjdWlT5SSkhDD3RdPN7oky3NnOjnc2k2DRcYQWC7cB861FCLQKg600O3zM8cCw8KGMtBYbenq5TuXTCc5Idrokiyv8NiB2dZYvVsu3B94exdf+P2H/GvXYaNLEWFm4M5Us4/5HQ6lFP/+6el8eV4OX5qXM/Q3iCG5+2fMWOVmJsuF+7Jz8piSnsTyP5ZSUi2X0ovAKfN4yXDE4nKGx1FzFxVN4GdXn4bNwv0DMxmfGMMEZ6xl9t0tF+4pCTE8dXMx2eMSWLaqlI01R40uSYSJgZuX5IoScTJul5MKi1wxY7lwB0hLimX18gWkOWK5ceW6EZ9AI8Txmtp7qD7cZqlJkCL03JkOqg610OPzG13KkCwZ7gATnHE8c8tCnHHR3PB4CTss8l9TYU7ltf377RLu4hSKMp30+DTVDW1GlzIky4Y7wMSUeJ65ZQExUTauW7GW3Q3WmfsgzGWgmTrL5AdiC2N9NNvd/Pvulg536DudfPXyhQBc91gJNY3tBlckrKi81suU9ESccXLJoDi5/PREou2KCgtcDmn5cAcoyEji6eUL6Oz1seSxtdR5O4wuSViI1rr/WL1xRpciTC7abqMgw2GJyyHDItyh79elp29eQHNnD9c9tpaDzZ1GlyQsos7bweHWbubkyJaMGFphpsMSNzKFTbgDzJyYzJPLimlo6eK6FSUcbrXGbcLCWOWevqutwmHsgAi+QpeTg81dHGnrNrqUUwqrcAc4fdI4Hr9pPrVH27l+RQnednP/BQjjlXmOEhNlO9YsE+JU3Mdmu5t79R524Q6wMD+VP9wwj+qGNr6ych3NnT1GlyRMrNzTxIwsJzFRYfnjIALs2BUzJr/8Omz/NZ83LZ1Hrjud7fubWfZE6QnPlBSi1+dnS10Ts8NgWJgIjXRHLGlJsbJyN9JFRRN4YMlcNtYcZfmT6+nssc7htiI0dh5spaPHx9wwGBYmQqcw02H6yyHDOtwBLp+Vya+/NIe1exq57akNljq9XATfwJ2psnIXI+F2Odh5sJVeE48hCPtwh75jx376+Vn8Y2cDdz6zyRJzIURolHu8pCREMzk1wehShIW4XU66e/3sbTTvGIKICHeAL8+fxP1XzuDv2w/yb8+X4fNro0sSJlDm8TI7WyZBipEpzBwYQ2DepmrEhDvAV87M5T8ud/PK5gN8+8/l+CXgI1pbVy87D7bI9e1ixKZkJBJlU6aeMRNldAGhdut5U+js8fPrv+8kLtrOj6+aKau2CLW1rgm/Ru5MFSMWG2VnSnoSlSa+HDLiwh3grgsK6Ojx8bv3dhMbZeMHVxRJwEegMo80U8XoFWY6WLfniNFlnFREhrtSiu9cMp3OHh9PfLCXuGg737lkugR8hCmv9ZIzPp7UpFijSxEW5M508tey/TS195jyAPKIDHfoC/gfXFFEV6+f3723m7goO9+4aKrRZYkQKvc0yfXtYtTcrr4xBBX1zSzMTzW4mk+KqIbq8ZRS/OjKmXzh9Gx+89ZOHv3HbqNLEiFyqKWTOm+HnLwkRm3gihmz3qkasSv3ATab4udXn0ZXr4//fr2S2CgbN52dZ3RZIsgGJkFKuIvRynDEMj4xxrRN1YgPdwC7TfGbL8+hu9fPfS9vJzbazpLiSUaXJYKo3OPFblPMyJIrZcToKKVwuxymHSAW0dsyg0XbbTx47VwWTUvnP/53C/+7qdbokkQQlXm8uF0O4mPsRpciLMztcrKjvtmUN0VKuA8SG2Xn0RvO4Mz8VL71QjmvbTlgdEkiCPx+TXmtV25eEmPmznTQ2eNnnwnHEEi4Hycu2s5jX5nH6ZPG8fVnN/HW9oNGlyQCbE9jGy2dvcyR69vFGBUNNFVNuDUj4X4CibFRrFw6n6IsJ7ev3sg/dzYYXZIIoLKavpuX5shlkGKMCjKSsClMOYZAwv0knHHR/HFZMVMykrj1qfWsrW40uiQRIOW1XhJj+m4fF2Is4qLt5KcnmXKAmIT7KaQkxPD0zcVkj0tg2apSNuw7anRJIgDKPV5mZSdjt8kdyWLs3C4HlSY8uEPCfQipSbE8s3wBGY5Yblq5ji21TUaXJMags8fH9gPN0kwVAVOY6aT2aIfpzmqWcB+GDGccq29ZiDM+mhtWlphyf00MT8WBZnp8mrkS7iJACjP7xhDsMFlTVcJ9mCamxPPsLQuJi7Jz/YoSqg61Gl2SGIXygUmQEu4iQNwuc44hkHAfgUmpCay+ZQFKKa5bsdaU17aKUyvzeMlwxOJyxhldiggTmclxOOOiTHen6rDCXSl1qVJqh1KqSil1zyked7VSSiul5gWuRHOZkp7E6uUL6O71s2xVqakPyBWfVF7bxJwcOVZPBI5SisJMp/VW7kopO/AwcBlQBCxRShWd4HEO4OtASaCLNJvpLgc/+dwsdje08cpmuYvVKrzt3ew53CZbMiLgCjOdVNa3mOrozuGs3IuBKq11tda6G3gOuPIEj/sh8HOgM4D1mdYlM1xMn+DgoXerTPUXKk5uc61MghTB4XY5aO/24TnabnQpxwwn3CcCnkEf1/Z/7hil1FwgR2v9yqmeSCl1q1JqvVJqfUODte/6tNkUd1xQQNWhVl7fWm90OWIYyjxelIJZ2TIJUgSWu38MgZluZhpOuJ9oc/LYUlUpZQN+A3xrqCfSWv9Baz1Paz0vPT19+FWa1GdmZZKfnsiD7+yS1bsFlHu8TElPwhlnviPRhLVNn+BAKUx1M9Nwwr0WyBn0cTawf9DHDmAm8J5Sai+wEFgTzk3VAXab4o7zC6isb+GtChkwZmZa90+ClGFhIgjiY+zkpSaa6h6Y4YR7KTBVKZWnlIoBrgHWDHxRa92ktU7TWudqrXOBtcBirfX6oFRsMlfOyWLS+AQefKcKrWX1bla1Rzs43Notw8JE0LgzHaaaDjlkuGute4E7gTeBCuAFrfU2pdT9SqnFwS7Q7KLsNm4/fwpb6pp4T6ZHmlZ5bf8kSFm5iyBxu5zsa2ynravX6FKAYV7nrrV+TWs9TWs9RWv94/7P/UBrveYEjz0/UlbtAz5/ejYTU+J58O1dsno3qXKPl5goG9P7T6wXItAGDszecdAcq3e5QzUAYqJsfHVRPhtrvHy4W0YDm1GZx8vMLCcxUfJPXgSHu3/hYJZ9d/mXHiBfnJdDhiOWB97eZXQp4ji9Pj9b6prk5iURVNnj4kmKjaLSJJdDSrgHSFy0ndsWTaFkzxHW7TlidDlikJ0HW+ns8cvNSyKolFKmmu0u4R5A1xZPIi0phgffkdW7mZT1T4KUcBfB1jdjpsUUvTcJ9wCKj7Gz/Nx83t91mE01cmqTWZR7vKQkRDNpfILRpYgw58500NLVS+3RDqNLkXAPtOsXTiYlIZoH36kyuhTRb+DmJZkEKYLt2Gx3E1zvLuEeYEmxUdx8dh7vVB5ia50cyWe0tq5edh5skS0ZERIDl9qaYfyvhHsQ3Hh2Lo64KNl7N4EtdU34tey3i9BIio1icmqCrNzDlTMumqVn5fLmtoOm6ZxHqoFj9U6TSZAiRNwuhymudZdwD5Jl5+SRGGPnIdl7N1SZx8uk8QmkJsUaXYqIEG6Xkz2NbXR0+wytQ8I9SFISYrjhzFxe3XJADtM2ULnHKzcviZAqzHSiNew0eAyBhHsQLT83j9goG4+8K6t3Ixxq7mR/UyezZUtGhFBhZn9T1eAtWQn3IEpLiuW6BZN5qXw/+xrbjC4n4gzcvDRXxvyKEMoZl0BCjN3wU5kk3IPstvPysdsUj7y72+hSIk55rRe7TTEjS1buInRsNsV0EzRVJdyDLMMZxzXzc/jLxlpqTXR4biQo9zThdjmIi7YbXYqIMIWZTirrjR1DIOEeAl9dNAWl4Pf/kNV7qPj9WpqpwjCFLgdNHT3UN3caVoOEewhkpcRz9RnZvFBaS32TcX/ZkaT6cBstXb1y85IwhLv/4A4jt2Yk3EPka4sK8GnNo/+U1XsolMskSGGg6ccO7jCuqSrhHiKTUhO4as5EnimpoaGly+hywl6Zx0tijJ0p6UlGlyIikDMumuxx8YaOIZBwD6E7PjWFHp+fFe9XG11K2Cuv9XJadgp2m0yCFMZwu5yGDhCTcA+h/PQkrjgti6fW7uNIW7fR5YStzh4fFQeapZkqDFWY6aD6cBudPcaMIZBwD7E7LyigvdvHyn/tMbqUgKo92o633Rz/wdp+oJken2ZOjlzfLozjdjnx+bVh40eiDHnVCDZtgoPLZrp48sO93HJePsnx0UaXNGYf7j7MjSvX0evXuF1OFuSNZ2H+eIrzUhmfGBPyej5qpo4L+WsLMWBgDEHFgWZmTgz9QkPC3QB3XlDA61vrWfXBXr6KdanQAAAND0lEQVRx0VSjyxmTyvpmbvvjBnJTE/ns7CxK9jTyXGkNqz7cC8C0CUksyEtlQf54FuSlku4I/nTGco+XCc5YXMlxQX8tIU5mcmoicdE2w5qqEu4GmJGVzEWFGaz8YA/LzsnFEWfN1fuBpg6WPlFKfIydVcuKmZgSD0ylu9fPljova6uPULLnCC9urOWptfsAyE9PZEFeKgv7wz4YAVzm6TtWTwgj2W2K6ROMG0Mg4W6Quy6YypUPf8BTa/dx+/kFRpczYs2dPSx9opTmjh5e+OqZ/cHeJybKxhmTx3PG5PHc8Sno9fnZur+ZkupG1lY38kr5fp5dVwPA5NQEFg6s7PNTP/Y8o+Ft72ZvYztfmp8zpucRIhDcLid/216P1jrkZ/hKuBtkdk4K501LZ8X7e7jprFwSYqzzV9Hd6+drT2+g6lArK2+aP+Rgrii7jTk5KczJSeG2RVPw+TXb9zdTsqeRtdVHeGNbPc+v9wCQPS7+2DbOwrxUcsbHj+iHory279zaObJyFyZQmOng+fUeGlq6yHCGdpvQOokShr5+QQFX//7/eKakhuXn5htdzrBorbnnL5v5oKqRX1x9GudNSx/xc9htilnZyczKTmb5ufn4/ZrK+hZK9jRSUn2Ed3cc4i8bawHITI5jQV7fqn5B3njy0hJPGfZlNV6Uglkyw12YwLExBPUtEu6RZF7ueM7MT+XRf1Zz/cLJlphe+Ku/7eTFTXXcffE0vjgvMFsfNpuiKMtJUZaTpWfn4fdrqhpa+7Zx9hzhX1WH+WvZfgAyHLHHgn5h/nimpCd9LOzLa70UpCdZto8hwovb9dEVM4tGsRAaCwl3g911YQHXPlbC86Uebjwr1+hyTumZkhoeereKa+bncNcFwesT2GyKaRMcTJvg4IYzc9Fas7uh7djKvmRPIy+X94V9WlIMxXnjj23llHu8fMqdEbTahBiJlIQYMpPjDLlTVcLdYGfmpzJv8jh+/4/dXFOcQ2yUOVfv71Qe5Pt/3cL509P54VUzQ9ocUkpRkJFEQUYS1y2YjNaafY3tg8L+CK9tqT/2eLkzVZjJwGz3UJNwN5hSirsunMqNK9fxlw11XLtgktElfcLmWi93rN5EUZaTh689nWi7sTc2K6XITUskNy2RL8/ve788R9op2XOEnQdb+OxpmYbWJ8RgbpeDf+5soLvXT0xU6H52JNxN4LypaczOTuaR96r44rxsw8NzsJrGdpatKiU1KYaVN80nMdac/2RyxieQMz7B6DKE+AR3ppPe/jEERVnOkL2ueVIkgimluOuCqdQe7eCvm+qMLueYo23d3PTEOnp8mlVLi8lwyB2fQoxUYX9TtbI+tPvuEu4mcWFhBkWZTh55bzc+v3HnLg7o7PGx/I/rqfV2sOLGeRRkyFx0IUYjLy2RmKjQjyGQcDeJvtV7AXsOt/HK5v2G1uLza775XBkba47yP1+ew/zc8YbWI4SVRdltTJuQFPIxBBLuJnLJDBfTJiTx0DtV+A1cvf/o1e28sa2e711eyOWzpDkpxFi5Xc6QH7kn4W4iNpvijk8VsOtQK29sqx/6G4JgxfvVPPHBXpadnWeZu2aFMDu3y8Hh1q6QHrEp4W4yV5yWRX5aIg++U4XWoV29v7J5Pz96tYLLZrr4/mcKQ/raQoSzov4xBDtCuO8+rHBXSl2qlNqhlKpSSt1zgq/frZTarpTarJR6Wyk1OfClRga7TXH7pwqoONDMWxWHQva66/Yc4e7ny5k3eRy/+fIcbHL2qBABM33QGIJQGTLclVJ24GHgMqAIWKKUKjruYZuAeVrr04A/Az8PdKGR5Mo5WeSMj+fBd3aFZPVedaiFW/64nuzx8Tz2lXmWmHEjhJWkJsWS4YilIoSXQw5n5V4MVGmtq7XW3cBzwJWDH6C1fldr3d7/4VogO7BlRpZou43bzy9gc20T/9jZENTXOtTcyY0rS4m2K55cWsw4A47FEyISuDOdVIawqTqccJ8IeAZ9XNv/uZO5GXj9RF9QSt2qlFqvlFrf0BDc0LK6L5yeTVZyXFD33lu7eln2ZClH2rpZedN8ucNTiCAqzHRQdaiVHp8/JK83nHA/0ebrCdNGKXU9MA/4xYm+rrX+g9Z6ntZ6Xnp6aMdfWk1MlI2vnT+FDfuO8n+7GwP+/D0+P3es3kjFgRYeue50TpPDLYQIqkKXk26fn+qGtpC83nDCvRYYPLg7G/jEXTZKqYuA7wGLtdahu94njH1xXg4ZjlgeeGdXQJ9Xa833/3cr/9jZwI+umikjcoUIAXdmaMcQDCfcS4GpSqk8pVQMcA2wZvADlFJzgUfpC/bQXeIR5uKi7dy2aAprq49QuvdIwJ73gbereH69h7suKGBJsfmmUAoRjvLTkoi2q5DdzDRkuGute4E7gTeBCuAFrfU2pdT9SqnF/Q/7BZAE/EkpVaaUWnOSpxMjdG3xJNKSYnjg7cCs3v+03sNv3trJ50+fyN0XTwvIcwohhhYTZaMgwxGylfuw5rdqrV8DXjvucz8Y9OeLAlyX6BcfY2f5ufn89PVKyjxe5ozhIIp/7mzguy9u4ZyCNH76+dNCfhq7EJGu0OXgg92HQ/JacoeqBVy/cDIpCdE8OIbV+7b9TXzt6Q0UZCTxu+tPD+mhAUKIPu5MBwebuzjS1h3015KfcAtIio3i5rPzeLvyEFvrmkb8/XXeDpY+UYozPppVS4vl8GghDFLYP4YgFFszEu4WcePZuTjionjonaoRfV9Tew83rVxHR4+PVUuLcSXLgRtCGMXt6g/3EDRVJdwtwhkXzdKzcnljW/2whw919fq49an17G1s49Ebzjg230IIYYx0Ryyvff1crlsY/KvUJNwtZNk5eSTG2Hno3aFX736/5t//tJmSPUf45Rdnc9aUtBBUKIQYSlGWk9io4M9vknC3kJSEGG44M5dXNu9nd0PrKR/7szcqebl8P//vUjdXzjnVtAghRDiScLeY5efmERtl4+FTrN6f/HAvj/6zmhsWTuari+TADSEikYS7xaQlxXLdgsm8VLafmsb2T3z9ja313PfyNi4qnMB9i2fItexCRCgJdwu67bx87DbFI+99fPW+Yd9RvvHcJmZnp/DgkrnY5cANISKWhLsFZTjjuGZ+Dn/ZWEudtwOA6oZWlj9ZSmZyHI/fOI/4GDlwQ4hIJuFuUV9dNAWA37+3m8OtXdz0RClKKVYtLSY1Kdbg6oQQRhvWbBlhPlkp8Vx9RjbPl3rYWHOUQy2dPHvLQnLTEo0uTQhhArJyt7CvLSrApzUVB5p54Jq5zJ00zuiShBAmISt3C5uUmsBPPjcTZ1w0n57hMrocIYSJSLhb3Jfny2EbQohPkm0ZIYQIQxLuQggRhiTchRAiDEm4CyFEGJJwF0KIMCThLoQQYUjCXQghwpCEuxBChCGltTbmhZVqAPaN8tvTgMMBLMfq5P34OHk/PiLvxceFw/sxWWudPtSDDAv3sVBKrddazzO6DrOQ9+Pj5P34iLwXHxdJ74dsywghRBiScBdCiDBk1XD/g9EFmIy8Hx8n78dH5L34uIh5Pyy55y6EEOLUrLpyF0IIcQqWC3el1KVKqR1KqSql1D1G12MUpVSOUupdpVSFUmqbUuobRtdkBkopu1Jqk1LqFaNrMZpSKkUp9WelVGX/v5Mzja7JKEqpf+v/OdmqlHpWKRVndE3BZqlwV0rZgYeBy4AiYIlSqsjYqgzTC3xLa10ILATuiOD3YrBvABVGF2ESvwXe0Fq7gdlE6PuilJoIfB2Yp7WeCdiBa4ytKvgsFe5AMVClta7WWncDzwFXGlyTIbTWB7TWG/v/3ELfD+5EY6syllIqG/gMsMLoWoymlHIC5wGPA2itu7XWXmOrMlQUEK+UigISgP0G1xN0Vgv3iYBn0Me1RHigASilcoG5QImxlRjuf4DvAH6jCzGBfKABeKJ/m2qFUirR6KKMoLWuA34J1AAHgCat9d+MrSr4rBbu6gSfi+jLfZRSScBfgG9qrZuNrscoSqkrgENa6w1G12ISUcDpwO+01nOBNiAie1RKqXH0/YafB2QBiUqp642tKvisFu61QM6gj7OJgF+vTkYpFU1fsK/WWr9odD0GOxtYrJTaS9923QVKqaeNLclQtUCt1nrgt7k/0xf2kegiYI/WukFr3QO8CJxlcE1BZ7VwLwWmKqXylFIx9DVF1hhckyGUUoq+/dQKrfWvja7HaFrr72qts7XWufT9u3hHax32q7OT0VrXAx6l1PT+T10IbDewJCPVAAuVUgn9PzcXEgHN5SijCxgJrXWvUupO4E36Ot4rtdbbDC7LKGcDNwBblFJl/Z/7D631awbWJMzlLmB1/0KoGlhqcD2G0FqXKKX+DGyk7yqzTUTAnapyh6oQQoQhq23LCCGEGAYJdyGECEMS7kIIEYYk3IUQIgxJuAshRBiScBdCiDAk4S6EEGFIwl0IIcLQ/wcDGOqYWGQWEQAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzt3Xl8nHd16P/Pd2Y02vfRZlvWYo0X2bLjxHYcWwohCYmXxNCSpqSEQsnyu80lhV+5bYHL5UUp3LZwL/zur6S0IUBuISSkQLneEidAAC+JLS+R12gsW/ImabSMltE+mvneP6Qxsq1lJM3MM8t5/2VLj2bOS7aPH53nfM9RWmuEEELEFpPRAQghhAg+Se5CCBGDJLkLIUQMkuQuhBAxSJK7EELEIEnuQggRgyS5CyFEDJLkLoQQMUiSuxBCxCCLUW9ss9l0aWmpUW8vhBBR6dixYx1a67yZrjMsuZeWlnL06FGj3l4IIaKSUupSINdJWUYIIWKQJHchhIhBktyFECIGSXIXQogYJMldCCFikCR3IYSIQZLchRAiBklyF0Gx91QLLT2DRochhBgnyV3MW8+gh2deOs7/2OcwOhQhxDhJ7mLezjvdALx5tpWRUZ/B0QghQJK7CIL68eTeOzTKwYYOg6MRQoAkdxEEjlY3qVYz6YkW9pxqMTocIQSS3EUQ1DvdLC1M5wOVBbxxRkozQkQCSe5i3s47+1hWkM62qqKx0swFKc0IYTRJ7mJeOvqG6ewfwV6QTs1SG2mJFvaelNKMEEaT5C7mxdE69jB1WUE6iRYz96/I542zTjxeKc0IYSRJ7mJe/J0ySwvTANhWVUTPoIdDFzqNDEuIuCfJXcyLw+kmOyWBvLREAO5emielGSEigCR3MS8OZx9LC9JRSgGQlGDmvhX57DvbKqUZIQwkyV3MmdYaR6ubpQXpN3x8W1UR3QMe3pbSjBCGkeQu5qylZwj38ChLC29M7u9bmkeq1cxeOdAkhGEkuYs58z9MXXbTnftYaaaAfWekNCOEUSS5iznzDwxbWpB2y+e2VRXRNeDhnYtSmhHCCJLcxZzVt/aRn55IVor1ls/ds0xKM0IYSZK7mDOH082ym+rtfkkJZu5dUcC+M05GpTQjRNhJchdz4vVpzrfd2ikz0faqQlz9I7xz0RXGyIQQIMldzNEV1wBDHt8tD1MnumdZPilWs4wBFsIAktzFnDiujx2YOrknJZi5d3k++860SmlGiDCT5C7mxJ/c7fm3dspMtL2qCFf/CIcbpTQjRDhJchdzUu/sY1F2MqmJlmmvu2dZPskJUpoRItwkuYs5cbS6p623+yVbzdy7Ip99p6U0I0Q4SXIXs+bx+rjY0TdtvX2i7VVFdPaPcERKM0KEjSR3MWtNHf14vDqgO3eA90tpRoiwk+QuZs0/U8Y+ydiBySRbf9814/XpUIYmhBgXUHJXSm1RStUrpRqUUp+b5POLlVJvKaVOKKVOKqW2BT9UESkcrW5MCpbkBZbcYWzWTEffCIcbZdaMEOEwY3JXSpmB54CtQCXwmFKq8qbLvgi8qrVeC3wE+OdgByoiR73TTaktlaQEc8Bf8/7leSQlmGTWjBBhEsid+wagQWt9UWs9ArwCfPCmazSQMf7rTKA5eCGKSONw9gVcb/dLsVq4d3k+r592SmlGiDAIJLkvBK5M+P3V8Y9N9GXgcaXUVWAv8GxQohMRZ8jj5VJn/7QzZaYyVpoZlq4ZYYjBEW9cteMGktzVJB+7+dbrMeBFrfUiYBvwQ6XULa+tlHpaKXVUKXW0vb199tEKwzW09eHTzCm537s8n6QEE6+dltKMCC+tNVv+1+/48q4zRocSNoEk96tA8YTfL+LWsssTwKsAWuu3gSTAdvMLaa2f11qv01qvy8vLm1vEwlD+sQPLCgN/mOqXYrXw/mX5vHZaumZEeDV29HOpc4BXa6/S5h4yOpywCCS51wJ2pVSZUsrK2APTnTddcxm4D0AptYKx5C635jGo3unGajZRkps6p6/fVlVEu3uYo01SmhHhUzv+923E6+PfDl0yOJrwmDG5a61HgU8B+4BzjHXFnFFKfUUptWP8ss8CTyml6oCXgU9oreXWLAY5Wt2U56WSYJ7bEYl7l+eTaJGuGRFeRxq7yEm18oHKAn50+BIDI6NGhxRyAf0L1Vrv1Vov1Vov0Vp/bfxjX9Ja7xz/9Vmt9Wat9Rqt9W1a6zdCGbQwjsPZN+X2pUCkJlq4Z1ker51uxSelGREmtU0u1pdm8/Td5XQPePjpsatGhxRyckJVBMw95OFa9+CcHqZOtK2qiDb3MEcvdQUpMiGm1tozxGXXAOtLc1hXks1txVl870BjzD/3keQuAna+rQ+YW6fMRPetKMAqpRkRJkfG6+0bynJQSvFUTTmXOgd486zT4MhCS5K7CJijdbxTZp7JPS3Rwj1L83jtdIuUZkTI1Ta6SLWaqSwaO2f54MoCinOS+e7+iwZHFlqS3EXA6p1ukhPMLMpOnvdrbV9dhLN3mGOXpTQjQqu2ycXtJdlYxpsALGYTn9xcxrFLXRyL4dKgJHcRsPPOPpYWpGEyTXaubXb8pZk9J6U0I0KnZ8BDvdPN+tKcGz7+6LpiMpIsvBDDd++S3EXA6p1u7PMsyfilJVp4n5RmRIgdveRCa25J7qmJFj66sYR9Z1q51NlvUHShJcldBMTVP0K7e3je9faJtleNlWaOS2lGhMiRJhcJZsXaxVm3fO4Tm0oxmxTfP9BoQGShJ8ldBMQ/diDQ1XqBuG9F/lhpRrpmRIjUNrpYvShr0vHUBRlJ7FizkFePXqV7YMSA6EJLkrsIyPWZMkG8c09PSuBuex6vnZIDTSL4Bke8nLzac0tJZqIna8oY9Hh56fDlMEYWHpLcRUAcTjcZSRYKMhKD+rrbVxfS2jvEiStSmhHBdeJKF6M+zYay7CmvWVGUQY3dxouHmhge9YYxutCT5C4C4mjtY2lBOkrNv1NmovtWFGA1m9hzsjWorytEbWMXSsEdJVPfuQM8VVNOu3uYne/G1o4hSe5iRlpr6p3uoNbb/TKSErh7qU26ZkLgl2edfOE/ThkdhmFqm1wsK0gnMzlh2utq7DaWF6bzwv5GYmneoSR3MaM29zA9g56g1tsn2lZVREvPECeudIfk9ePVt99q4MeHL3PFNWB0KGE36vVx/HIXG8qmv2sHUErxZE059U43vzvfEYbowkOSu5jR9U6ZECX3+yvHSjMyayZ4rrgGeHf8P8v9MZSwAnWmuZeBEW9AyR1gx5oFFGQk8t3fxc6hJknuYkb1rf7kPvvtS4HISEqgxm7jtVNSmgmWXSfH6scZSRYONMTf3hz/co4N03TKTGS1mPj4plIONHRwtrk3lKGFjSR3MSOH040tzUpuWnA7ZSbaVlVEc88Q716V0kww7K5rYe3iLB5YWcjBhs6YH297s8ONLkpyU8jPSAr4az66oYQUqzlmRhJIchczqnf2hawk43d/ZQEJZsVemTUzbw1tfZxt6eXh1QuosdvoGfRw+lqP0WGFjc+nOdrkmra/fTKZKQk8uq6YnXXNtPQMhii68JHkLqbl82nOO90hT+6ZyQnU2Mc2NMVSx4IRdp9sRqmxyZubK8b21B9oiJ+6+4X2ProGPAGXZCZ6oroMn9a8eKgp+IGFmSR3Ma1r3YMMjHjntVovUNuqirjWPUjd1fi5yww2rTW76prZUJpDQUYStrREKosy2H8+furu/uUc6wN8mDpRcU4KW1cV8ePDl+kbju49q5LcxbRC3Skz0Qf8pRnpmpmz91rdXGjv5+E1C65/rMZu49ilrrhYCg1j82RsaYmU5qbM6eufrCnDPTTKq7VXghxZeElyF9OqH0/u9hB1ykyUmZxAdYWNPSdbpDQzR7vqmjGbFFtXFV7/WLXdhserOdzoMjCy8Klt6uLO8ZV6c7F2cTbrS7P53oFGRr2+IEcXPpLcxbQcrW4WZCaRkTT9Kb9g8ZdmTkppZta01uw62czmCtsNnU3rS3OwWkzsd8R+3f1q1wDXugdZXzr1PJlAPFlTzrXuQV4/E71jMSS5i2nVO/tCMnZgKg9UFkppZo7qrvZwxTXIQ6uLbvh4UoKZDaU5cdHvXjuPevtE968ooMyWynd/dzFqf4qU5C6mNOr1caG9L2RjByaTmZLA5gobe05JaWa2dtc1k2BWPLiy8JbPVdttOJx9OHuHDIgsfI40dpGeaGF5Yca8XsdsUnyyuoy6qz3UNkXnxFJJ7mJKl1wDjIz6wvIwdaJtVUVc7RrkVBz1Zs+Xz6fZfbKF9y3Nn3RQVrW/JTLGRxHUNrm4ozQbcxD2/D5y+yKyUxJ4PkpHEkhyF1NytIavU2aiByoLsJiUbGiahaOXumjtHeLhNUWTfr6yKIPcVGtM97u7+kdoaOub9eGlqSRbzXxsYwm/es/Jhfa+oLxmOElyF1Oqd7pRCiryQ98pM1FWipVNFTb2SmkmYLtPNpOUYOL+FQWTft5kUmyusHGgoSNmv6fX58nMs94+0cfuKiXBbOJ7UbhnVZK7mJLD6aYkJ4Vk6637J0Nte1UhV1yDnL4WG0OcQmnU62PvqRbuW15AaqJlyuuq7Tba3cPX21tjTW2jC6vFxOpFmUF7zbz0RP5w7UJ+duwqnX3DQXvdcJDkLqbkCMNMmak8UFmIWUozAXnnoouOvpEpSzJ+NfbYrrvXNrm4rTiLREtwb0aerCljeNTHj96Jrj2rktzFpIZHvTR29Idl7MBkslOtbFqSK6WZAOyqaybVauaeZfnTXleUmcySvNSYWkjh1z88yunm3jnNk5lJRX469y7P59/ebmLIEz17ViW5i0ldbO/H69PYDbpzB9heVcRl1wBnYmS+diiMjPp4/UwrD6wsJClh5jvWGnseRxo7oypJBeL45S68Pj3v/vapPFlTRmf/CP9x4lpIXj8UJLmLSflnyoSzx/1mD6yU0sxMDjS00zPombEk41ddYWPI4+P4pejs3Z5KbaMLk4LbF2eF5PXvKs9l1cIMvrv/YtQslJHkLiZV3+rGYlKU2VINiyFHSjMz2lXXMj6TJy+g6zcuycViUuyPsZbII00uKhdkkB6iMRlKKZ6qKediez9v1beF5D2CTZK7mJTD2Ud5XipWi7F/RbZVFXGpU0ozkxnyeHnjTCtbVhYG/OeUlmhh7eKsmHqoOjLq48Tl7qD1t09lW1URCzKTouZQU0B/I5RSW5RS9UqpBqXU56a45lGl1Fml1Bml1I+DG6YIN0cYFnQE4sHx0ozMmrnVb+rb6B/x3jDeNxA19jxON/fQ1T8SosjC69S1HoZHfdwZonq7X4LZxJ9tLuNwo4uTUbAOcsbkrpQyA88BW4FK4DGlVOVN19iBzwObtdYrgc+EIFYRJgMjo1x2DUREcs9JtXJXuZRmJrOrrgVbmpWN5bNLatV2G1rDwQuxcffuP7y0LsR37gAf2VBMeqKF7+6P/ENNgdy5bwAatNYXtdYjwCvAB2+65ingOa11F4DWOjqKUmJS551jR60jIbnD2I/DTZ0DnG2R0oxf3/Aov3rPybaqIizm2ZXOVi/MJD3JEjOlmdpGF+V5qdhCuMDdLz0pgY9sKGbvqRaudUf2ntVA/lYsBCauJLk6/rGJlgJLlVIHlVLvKKW2TPZCSqmnlVJHlVJH29tjf/xotPKfYDSqx/1mD64skNLMTX51zsmQx8dDq2dXkgGwmE1sWpLL/vPRP4rA59PUNrlC0t8+lT/bXIYCfhDhIwkCSe6TjVe7+W+EBbAD9wCPAS8opW7pSdJaP6+1Xqe1XpeXF9jTfRF+551uEi0mFufMbU1ZsOWmJbKxPIe9p2R5tt+uuhYKM5JYVzK3pRTV9jyudQ/S2NEf5MjCq97ppndoNOQPUydakJXM9tVFvFJ7hd4hT9jed7YCSe5XgeIJv18ENE9yzf/RWnu01o1APWPJXkShemcf9oK0oIxNDZZtVUU0dvRzriU256LMRs+Ah9862nhodRGmOf4Z1fhHAEd5S2QohoUF4qmacvqGR3nlSOSOJAgkudcCdqVUmVLKCnwE2HnTNb8A3g+glLIxVqaJjn4hcQtHq5ul+ZFRkvF7cGUhJoWUZoB9Z1vxePWsu2QmKslNYVF2MvujvO5+pNFFYUYSi7KTw/q+qxZmcld5Lj842IQnQveszpjctdajwKeAfcA54FWt9Rml1FeUUjvGL9sHdCqlzgJvAX+lte4MVdAidHoGPLT2DoV1tV4gbGmJbJSuGQB2n2xhcU7KvKYfKqWosdt450Jn1C6B1nq83j6PZdjz8dTdZbT0DLHnZGTecAT0mF1rvVdrvVRrvURr/bXxj31Ja71z/Ndaa/2XWutKrXWV1vqVUAYtQsfRZvzYgalsqyriYkd/zI6sDURn3zAHGzp4aHXRvBNajT0P9/AodVHQsz2ZK65BnL3DIZsnM5N7luZTkZ/G8xG6Z1VOqIob+GfKRNqdO8CWVeOlmQi9UwqH10634vXNryTjt2lJLkoRtaWZI/56exgfpk5kMimerC7jbEsvb1+IvEKFJHdxA0erm7RECwsyk4wO5Ra2tETuLMuN6+XZu+qaWZKXyvIg/OeblWJl9cLMqO13P9LYSWZyAvYwbwqb6ENrF2JLs/L8/sh7xCjJXdyg3unGXpBmSA0zENtWF3GhvR+HM/p2Ws6Xs3eII00uHl6zIGh/PtV2GyeudOOO4Ja+qdQ2dbG+NHvOHUPBkJRg5k/vKuU39e3Xf+qNFJLcxQ0czr6IrLf7bRnvmonHMcB7TragNXM6uDSV6oo8vD4dkWWF6bS5h2js6A9rf/tUHt9YQlKCiRci7O5dkru4rqNvGFf/SMSMHZhMXnoiG8py4rIlctfJZiqLMoK6sPz2kiySE8xR1+9+tGlsHr1RD1Mnykm18sgdi/jFiWba3ENGh3OdJHdxnaM1ssYOTGV7VRENbX0R92NwKF1xDXDicjcPBbiUI1CJFjN3ludEXd39SKOL5AQzqxYEbxn2fDxRXY7H5+OHb18yOpTrJLmL6/wthpF85w7w4KpClCJi+4tDwV+GejiIJRm/6gobFzv6I34Q1kS1TS7WLs4yfN+AX5ktlQ+sKOCH71xiYGTU6HAASe5iAofTTXZKArY0q9GhTCs/PYkNpfFVmtlV18xtxVkUh2DeT419bM7TgfPRMcyvd8jDuZbeiKi3T/TU3eV0D3j42bGrRocCSHIXE9S3ji3oiNROmYm2ry7ifFsf5+OgNHOhvY8zzb1B6W2fzNKCNPLTE6Om3/3YpS58OvzzZGayriSb24qzeOFAI94I2LMqyV0AY0e5zzv7Ir7e7rdl5XhpJg7u3nfXtaDU2LOGUFBKUW23cehCZ1Qsf65tdGExKdaGaBn2XPn3rF7qHODNs06jw5HkLsa09AzhHh6N+Hq7X35GEutLYr80o7Vm18lm1pfmUBjCg2U1dhuu/pGoWIhS2+Ri5cJMUqwWo0O5xYMrCyjOSea7EdAWKcldAJG3oCMQ26oKcTj7aGiL3dJMvdNNQ1tfyEoyfpvHRwBHemlmyOOl7koPG0rnNsc+1CxmE5/cXMaxS10cu9RlaCyS3AXw+zbISBv1O52tVUXjXTOtRocSMrvqmjGbFFtXFYb0ffLTk1hemM7+CH+oevJqDyNeHxvKco0OZUqPrismI8li+KEmSe4CGLtDLMhIJDMlwehQAlYwvokoVkszWmt2n2xh05LcsOwHra6wcbSpi8ERb8jfa66uL8Oe4waqcEhNtPDRjSXsO9PK5c4Bw+KQ5C6AsaXY0VJvn2hbVdH10kWsOXWth0udAyHpbZ9Mtd3GiNd3fdpiJDrS6GJpQRrZqZHdrvuJTaWYTYrvHzRuz6okd4HXpznf5o7omTJT2bpqrIMkFu/ed9U1k2BWPLgytCUZvzvLcrGaTRHb7+71aY5d6oq4/vbJFGQksWPNQn5Se4XugRFDYpDkLrjiGmDI44vKO/fCzNgszfh8YyWZ9y3NC1upLNlq5o6S7Ih9qHqupZe+4dGI62+fypM1ZQx6vLx02Jg9q5Lcxe/HDkRRp8xE26qKeK/VzYX22CnNHL/cRUvPUFAnQAaiZqmN91rdtLuHw/q+gTjSOFYuioY7d4AVRRnU2G28eKiJ4dHwP8eQ5C6ud8oYufRgPrZWjZUtYmlD0666ZhItJu6vLAjr+9ZUjI0iOBiBUyJrm1wszEpmQVZ4l2HPx1M15bS7h9n5bnPY31uSu8DR1kdxTjKpiZF3KCQQRZnJ3FGSHTOnVUe9PvacauG+FfmkhfnPZOWCDLJTEiKuNONfhn1nlJRk/GrsNpYXpvPC/sawbw+T5C5wtEbnw9SJ/KWZizFQmjnc6KKjbyTsJRkY2wu6qcLG/vPtEbXKsLGjn46+kYiY3z4bSimerCmn3unmd2H+D1OSe5wbGfVxob0Pe9Qn9/HSTAzcve8+2Uyq1cz7l+Ub8v41FTba3MOcj6D2Un9/e7TU2yfasWYBBRmJfPd34T3UJMk9zjV19jPq01F/516Umczti7PYeyq6T6uOjPp47XQrH6gsINlqNiSGanvkjSI43OgiN9XKkrxUo0OZNavFxMc3lXKgoYOzzeGb3SPJPc7Vt0bHgo5AbKsq4mxLb1R3zRxs6KB7wBPyWTLTWZSdQpktNaL63WubXKwrzY6KcdST+eiGElKsZl44EL67d0nuce68043ZpCiPwjuimz28ZgEpVjN/9e91jIz6jA5nTnbVNZORZLm+QMMo1RU2Dje6IuL72NozxBXXYFSWZPwyUxJ4dF0xO99tprUnPHtWJbnHuXqnm9LcFJISjCkBBFNBRhJff2Q1xy9389/3njM6nFkb8nh546yTLasKDV8fV2O3MTDi5fhlYycbAtfHIUTL4aWpPFFdhk9rXjzUFJb3k+Qe5xxROlNmKg+tXsAnN5fx4qEm/s+714wOZ1Z+U99O3/CooSUZv41LcjGbVEQszq5tdJFqNVNZlGF0KPNSnJPC1lVFvHT4En3Dod+zKsk9jg15vDR19sdUcgf4/LblrC/N5nM/O3X9mUI02HWymdxUK3eVGz/ONiMpgduKs9gfAYeZaptc3F6SjcUc/enqyZoy3EOjvFp7JeTvFf3fLTFnDW19aB1dCzoCkWA28dyf3E5akoU//9Ex3EMeo0Oa0cDIKL8+18bWqsKISWLVFTZOXe02bPAVQPfACPVONxuiuN4+0drF2fzjh6v40NqFIX+vyPhbJAzhcMZOp8zN8jOSeO5PbueSa4C/+veTEXUgZzK/PNfGoMcbtvG+gaix2/BpOHSh07AYjjZ1oTVRd3hpOn+8fjE5YRhZLMk9jtU73VjNJkpzU4wOJSQ2lOXw+a3Lef1MK8+H+QDJbO2qa6YgIzGiOkLWFGeRlmgxtN+9tslFgllxW3FkLcOOBpLc45ij1U15XmrElAFC4YnqMrZVFfKPr7/H2wbegU6nZ9DDb+vbeWj1AkymyOnjTjCb2Fiey4EG4/rdjzS5WL0oKya6ucItdv9Vixk5nH0xV2+/mVKKrz+yhjJbKs++fDxsPcaz8eZZJyNeHw+tLjI6lFvU2G1ccQ1yqbM/7O89OOLl1NWeiPppJpoElNyVUluUUvVKqQal1Oemue4RpZRWSq0LXogiFNxDHq51D8Zkvf1maYkW/vVjdzAw4uWZl45FxMGciXbVNbMoOzkiSw9GjiI4caWLUZ+OukmQkWLG5K6UMgPPAVuBSuAxpVTlJNelA38BHA52kCL4/EOhon2mTKAq8tMj8oCTq3+EAw0dPLxmQUQerS+3pbIwK9mQfvfaxi6UgtsjeBl2JAvkzn0D0KC1vqi1HgFeAT44yXV/B3wdiLyfe8Ut/As6Yr0sM1EkHnB67XQLXp+OqC6ZiZRSVFfYOHShA68vvB1HtU0ulhdmkJkcnjWDsSaQ5L4QmNhxf3X8Y9cppdYCxVrr3UGMTYRQvdNNcoKZhVG01SYYIu2A0+66FsrzUllRFLn/yVbbbfQOjXLyanfY3tPj9XHsUhcbSuWufa4CSe6T/ax4/b9wpZQJ+Bbw2RlfSKmnlVJHlVJH29sjZ+JcPHI43SwtSIuo7oxwiKQDTm29Q7zT2MnDqyOzJOO3ucKGUuGtu59p7mXQ442p/vZwCyS5XwWKJ/x+ETBxIWA6sAr4jVKqCdgI7JzsoarW+nmt9Tqt9bq8PGOn3sW7WJspMxuRcsBpz6kWtIaH10Rel8xEOalWVi7ICGvdvXZ8GXasnEw1QiDJvRawK6XKlFJW4CPATv8ntdY9Wmub1rpUa10KvAPs0FofDUnEYt5c/SO0u4fjqt5+s0g44LSrrpnlhelU5Ef+n0N1RR7HL3eFZeAVjPW3l+SmkJ+RFJb3i0UzJnet9SjwKWAfcA54VWt9Rin1FaXUjlAHKIIvlscOzMbEA06HLoS3G+Rq1wDHL3dHxATIQNTYbYz6NIcvhv4gmM+nOdrkkrv2eQqoz11rvVdrvVRrvURr/bXxj31Ja71zkmvvkbv2yCbJfczEA05/8fKJsB5w2nNybNdrpHbJ3OyOkmwSLaaw1N0vtPfRNeCRevs8yQnVOFTf6iYjyUJBRqLRoRjOqANOu042s6Y4i8VRMtcnKcHMhrIcDoRhBPD15Rxy5z4vktzj0PnxsQOR3KERTuE+4NTY0c/pa708HIHjBqZztz2PhrY+WnoGQ/o+Rxpd5KUnUhIl//FFKknucUZrTb3THfclmZs9tHoBT1SH54DT7rqxZrPtUZbc/aMIQt01U9s4Vm+Xm4/5keQeZ9rcw/QMeuK6U2Yqn9sangNOu042s6E0h6LM6DpAtrwwHVtaYkhLM1e7BmjuGWK9HF6aN0nuccaftOxR0H4XbuE44FTf6sbh7Iv43vbJjI0iyOXA+Q58IRpFUDteb5eHqfMnyT3O/L5TJs3gSCJTqA847T7ZjEnBllXRl9wBqu15dPaPcK61NySvf6Sxi/QkC8sLo3sZdiSQ5B5nHE43trREctOkU2YqoTrgpLVmV10zm5bYyEuPzu9/dUVo6+61TS7WlWRjjrOxGKEgyT3O1Dv7WFYod+0zCcUBp9PXemnqHIjKkoxfYWYS9vy0kNTdXf0jNLTyWN5FAAAT10lEQVT1SUkmSCS5xxGfT3NeOmUCEooDTrtONpNgVjy4sjAIERqn2m7jSKOLIY83qK9bK/3tQSXJPY5c6x5kYMQryT1AwTzg5PNp9pxsocaeR1aKNYhRht/d9jyGR30cbeoK6useaXRhtZioWpQZ1NeNV5Lc44i/U0aSe+CCdcDpxJUurnUPRnVJxu/O8hwSzIr9QV6cXdvk4rbiLBItsgw7GCS5xxFHm3TKzEUwDjjtqmsh0WLi/hUFQY4u/FKsFm5fnB3Uh6r9w6Ocae6VkkwQSXKPI45WNwuzkklPkrVlszWfA05en2bPqRbuXZ4fM9/7GruNM829dPQNB+X1jl/uwuvTbJCHqUEjyT2O1Dv75K59juZzwOlwYyft7mEeipIJkIGoto8t2zkYpK6Z2kYXJlmGHVSS3OPEqNfHhbb43b4UDHM94LSrroUUq5l7l+eHOMLwqVqYSWZyQtBKM0eaXKxckElaoiUoryckuceNps4BRrw+Se7zNNsDTh6vj9dOt/CBygKSrbHzoNBsUmxaksuBho55n+IdGfVx4nI366XeHlSS3OPE+fGxAzIwbP5mc8DpYEMH3QOeqFnKMRvVdhstPUNcaO+f1+ucutbN8KiPDWVSkgkmSe5ztKuumR+9c8noMAJW73SjFFTkS819vmZzwGlXXQvpSRZqltrCGGF41FSM1d0PnJ9fS+SRxrF++XVy5x5UktxnyeP18eWdZ3j25RN88RenqbvSbXRIAXE43ZTmppKUEDulASP5DzgNTnPAacjj5Y0zrWxZWRiTvduLc1MoyU2Z9yiC2iYX5Xmp2GTeUVBJcp+Fzr5hHn/hMC8eauITm0qxpVn5u91ngz45MBTqW93Y5a49qMYOOK2Z8oDTbx3tuIdHo2YJ9lxUV9h456ILj3dup3f9y7DvlBbIoJPkHqDT13rY8e2DvHulm2/98Rq+vGMln31gGUcvdbH3VKvR4U1reNRLU+eA1NtDYPvqoikPOO0+2UJOqpVNS3INii70auw2+oZHeXeOP8HWO930Do3Kw9QQkOQegF+cuMaHv3MIrTU//U+b+IO1iwB4dF0xywvT+YfXzwV9iFIwXWzvx+vT0ikTIpMdcBoYGeWXZ51sXVWIxRy7/8zuWmLDpGC/Y2519+vLOSS5B13s/q0LglGvj6/uPstnfvIua4qz2Pls9Q1DjcwmxRe3V3LFNciLh5qMC3QGDumUCanJDjj96lwbgx5vTJdkADKTE1i9KIv9c6y7H2l0UZSZxKLs6Fo5GA0kuU+hq3+ET/yglhcONPLxu0p46ck7J33gU223cd/yfL7964agHcUOtvpWNwlmRWluqtGhxKybDzjtrGumICMxLu5Ia+w26q500zM4u7WEWmuONLpYL8uwQ0KS+yTONvey47kDHGl08fUPr+ZvP7iKhGl+tP7C9hUMebx8801HGKMMnMPppsyWitUif9yhNPGA05tnnWyrKoqLjULVFTZ8Gt6+0Dmrr7vsGqDNPSzLOUJE/rXfZPfJZj78nUOMjPr4yf+zkUfXF8/4NUvy0nh8YwmvHLk866FS4eBwytiBcPEfcALYEeMlGb+1i7NJsZo5MMsRwEcaZTlHKElyH+f1af7x9ff41I9PULkgg13PVrN2ceAn5j59n530pAS+uieyWiMHRka57BpgmST3sFBK8c1Hb+M/ntk0q78/0cxqMXFXee6s58zUNrnISkmQFt0QkeQO9Ax4+OSLtXznNxd4bMNiXn5qI/npSbN6jexUK39xn5395zv4TX1wlxjMx3lnHwBL5WFq2CQlmOMmsftV2200dQ5wxTUQ8NfUNnWxriQHUxyUrowQ98nd4XTzwecOcOhCB1/7g1X8/R9Wzbk2/bGNJZTZUvnqnrNzPtQRbPX+Thm5cxchVGMfG68Q6GnVNvcQjR39Mk8mhOI6ub9+upU/eO4gfcNeXn5qIx+9s2Rer2e1mPj81uVcaO/n5SOXgxTl/Dha3SRaTBTnpBgdiohhS/LSKMxIYn+Ac2b8+1fjoZvIKHGZ3H0+zTffdPCffnSMioJ0dj9bHbShRR+oLOCu8ly+9aaDnoHZtYaFgqOtD3tBWlx0bQjjKKWotts42NCJ1zfzM6cjjS6SE8ysWijLsEMl7pK7e8jD0z88yv//q/M8cscifvL0RgozZ1dfn45Sii8+tILuQQ//9OvzQXvduXK0uqVTRoRFjd1Gz6CH09d6Zrz2SKOLtYuzpm0xFvMTV9/ZC+19fOi5g7xV387f7ljJNx5ZHZIpiSsXZPLoHcX877ebaOqY36zr+egZ8NDaOyT1dhEWmysCq7v3Dnk419orJZkQCyi5K6W2KKXqlVINSqnPTfL5v1RKnVVKnVRK/UopNb/idQj86pyTD337IF0DHl568k4+vqk0pKfiPvvAUhLMJv7+tVunBYaLo23sYap0yohwsKUlsqIoY8a6+7FLXWiNTIIMsRmTu1LKDDwHbAUqgceUUpU3XXYCWKe1Xg38FPh6sAOdK59P80+/Os+T/3aUxbkp7Hq2mo3loZ/Sl5+RxDP3LGHfGeesT+4Fi/9AlZRlRLjU2G0cu9TFwMjolNfUNrqwmFTctYuGWyB37huABq31Ra31CPAK8MGJF2it39Ja+xtc3wEWBTfMuekbHuWZl47zP9908KHbFvKzP9/EwqzwDSh6sqacBZlJfHXP2YAeMgXbeaebtEQLC4L4TEGI6dTYbXi8msPjp08nU9vkYtXCzJjaKRuJAknuC4ErE35/dfxjU3kCeG0+QQVDU0c/f/jPB3njbCtf3L6Cbz66JuxbiJISzPzN1uWcae7l58evhvW9YazHfWlBmgxlEmGzvjQHq8U05WnVIY+Xuis9bJCSTMgFktwnywyT3oYqpR4H1gHfmOLzTyuljiqljra3h+4U528d7ez49gHa3MP82yfv5MmacsMS3I41C7itOItv7Kunf3jqH1WDTWtNfatbxvyKsEpKMLOhNGfK5F53pZsRr08epoZBIMn9KjBxetYioPnmi5RS9wP/FdihtZ509q3W+nmt9Tqt9bq8vLy5xDstrTX/8tsL/NkPjrAgK5ldn6qm2m7sYmKlFP/toUra3MP8628vhO19O/pG6BrwSL1dhF213Ua9042z99bF4f7lHOtKpN4eaoEk91rArpQqU0pZgY8AOydeoJRaC/wrY4m9LfhhzmxgZJRnXz7BP7z2Hlurivj5M5si5lTmHSXZPLxmAc/vv0hz92BY3tO/oEOSuwi3an9L5CR370eaulhakEZ2qjXcYcWdGZO71noU+BSwDzgHvKq1PqOU+opSasf4Zd8A0oB/V0q9q5TaOcXLhcQV1wAf/s7b7DnVwt9sWc63H1tLitUSzhBm9DdbluHT8I199WF5P0nuwiiVRRnkplpv6Xf3+jTHL3VJvT1MAsqAWuu9wN6bPvalCb++P8hxBexgQwf/+cfH8fk0P/jEeu5Zlm9UKNNalJ3Ck9Vl/PNvLvDxTaXcVpwV0vdzON3kpFqxpckdkggvk0mxqcLGgYYOtNbXn3eda+mlb1iWYYdL1J5Q1VrzvQON/On3j5CfnsjOT1VHbGL3e+b9FdjSEvnq7tDPfK9vlU4ZYZyaChvt7uHrU0lhwnIOuXMPi6hM7kMeL3/5ah1/t/ss96/I5+fPbKbUFvn7QdMSLfyXB5Zy9FIXe0+1hux9tNY4nH0ydkAYxt/IMLHuXtvkYlF2MkWZsgw7HKIuuV/rHuSRfznEf5y4xmc/sJTvfPQO0hIjq74+nT9aV8zywnT+/rVzDHm8IXmP5p4h+oZHsUtyFwZZkJXMkrxU9o8nd/8ybFmpFz5Rl9x/ceIaTR0DvPCn63j2PnvUbXExm8ZaI692DfKDg00heQ//w1TpcRdGqrHncbixk+FRLxc7+unsH5Fl2GEUdcn9z9+3hNc+XcP9lQVGhzJnmyts3L8in+feaqDdPemRgHlx+GfK5EtyF8aprrAx5PFxrKmL2vF6uzxMDZ+oS+4mk4qY/vX5+Py2FQx5vHzrl46gv3a9001hRhKZKQlBf20hArVxSS4Wk2J/QwdHmlzkplpZkhf5z8ZiRdQl91ixJC+NxzeW8MqRy7zX2hvU13Y43dgLZKO8MFZaooW1i7M4cL6D2iYX60tzpHsrjCS5G+gz99tJT0rga3vOBa010uvTnJdOGREhqivyOHWthyuuQam3h5kkdwNlpVj59H129p/v4K364ExtuOIaYHjUJws6RESYONtJOmXCS5K7wR7fWEKZLZWv7TmHx+ub9+v5D43InbuIBGsWZZKeZCHVamZFkfydDCdJ7gazWkx8YdsKLrT38+PDl+f9ev5OGam5i0hgMZv443XF/OHti7DIMuywip7TPzHs/hX5bFqSy7d+ObYxaj5dLvVON8U5yRE3OE3Ery8+dPNWThEO8l9pBFBK8cXtlfQMevinX5+f12s5nG4pyQghJLlHisoFGTx6RzH/++0mGjv65/QaI6M+Lrb3y5hfIYQk90jy2QeXYjWb+Pu95+b09U2d/Yz6tIwdEEJIco8k+elJPPP+Ct446+TQhcl3UE6nvlUWdAghxkhyjzBPVJexMCuZr+4+h9c3u4NNDqcbs0lRLke8hYh7ktwjTFKCmb/esoyzLb387PjVWX2tw+mmNDeFRIs5RNEJIaKFJPcItGPNAtYuzuIb++rpHx4N+Osczj6ptwshAEnuEUmpsZnv7e5h/uW3FwL6miGPl6ZO6ZQRQoyR5B6hbl+czY41C3j+dxdp7h6c8fqGtj60lrEDQogxktwj2F9vWQbA119/b8Zr66+PHZDkLoSQ5B7RFmWn8GRNGb94t5l3r3RPe62jzY3VbKI0N/oXmQgh5k+Se4T783sqsKUl8ne7z047893R6mZJfpoMZxJCAJLcI15aooX/8sBSjl3qYs+plimvczj7WCaTIIUQ4yS5R4E/WlfMiqIM/uG19xjyeG/5vHvIw7XuQVnQIYS4TpJ7FDCbFP9t+wqudg3y/YONt3ze4ewDYGm+JHchxBhJ7lFiU4WN+1cU8M9vXaDdPXzD5877ty/JnbsQYpwk9yjyhW3LGfJ4+eabjhs+Xu90k2I1szAr2aDIhBCRRpJ7FCnPS+Njd5Xwk9rLvNfae/3jDqcbe0E6JpMyMDohRCSR5B5lPn2fnfSkBL66+9z11sj6VumUEULcSJJ7lMlKsfKZ++0caOjgrfo2XP0jdPQNy0wZIcQNJLlHocc3llBuS+Wre85xtnmsPCPJXQgxkST3KJRgNvGFbSu42N7P18ZX8kmnjBBiooCSu1Jqi1KqXinVoJT63CSfT1RK/WT884eVUqXBDlTc6L4V+WyuyOVcSy+ZyQnkpycaHZIQIoLMmNyVUmbgOWArUAk8ppSqvOmyJ4AurXUF8C3gH4MdqLiRUor/uq0SpcbG/ColnTJCiN+zBHDNBqBBa30RQCn1CvBB4OyEaz4IfHn81z8Fvq2UUnq6SVdi3ioXZPC1D1VRmCl37UKIGwWS3BcCVyb8/ipw51TXaK1HlVI9QC7QMfEipdTTwNMAixcvnmPIYqI/uVO+j0KIWwVSc5/s5/2b78gDuQat9fNa63Va63V5eXmBxCeEEGIOAknuV4HiCb9fBDRPdY1SygJkAq5gBCiEEGL2AknutYBdKVWmlLICHwF23nTNTuDj479+BPi11NuFEMI4M9bcx2vonwL2AWbg+1rrM0qprwBHtdY7ge8BP1RKNTB2x/6RUAYthBBieoE8UEVrvRfYe9PHvjTh10PAHwU3NCGEEHMlJ1SFECIGSXIXQogYJMldCCFikDKqqUUp1Q5cmuOX27jpgFSck+/HjeT78XvyvbhRLHw/SrTWMx4UMiy5z4dS6qjWep3RcUQK+X7cSL4fvyffixvF0/dDyjJCCBGDJLkLIUQMitbk/rzRAUQY+X7cSL4fvyffixvFzfcjKmvuQgghphetd+5CCCGmEXXJfaaVf/FCKVWslHpLKXVOKXVGKfVpo2OKBEops1LqhFJqt9GxGE0plaWU+qlS6r3xvyd3GR2TUZRS/+/4v5PTSqmXlVJJRscUalGV3ANc+RcvRoHPaq1XABuB/xzH34uJPg2cMzqICPG/gNe11suBNcTp90UptRD4C2Cd1noVYwMQY364YVQldyas/NNajwD+lX9xR2vdorU+Pv5rN2P/cBcaG5WxlFKLgO3AC0bHYjSlVAZwN2MTW9Faj2itu42NylAWIHl830QKt+6kiDnRltwnW/kX1wkNQClVCqwFDhsbieH+P+CvAZ/RgUSAcqAd+MF4meoFpVSq0UEZQWt9DfgfwGWgBejRWr9hbFShF23JPaB1fvFEKZUG/Az4jNa61+h4jKKUegho01ofMzqWCGEBbge+o7VeC/QDcfmMSimVzdhP+GXAAiBVKfW4sVGFXrQl90BW/sUNpVQCY4n9Ja31z42Ox2CbgR1KqSbGynX3KqV+ZGxIhroKXNVa+3+a+yljyT4e3Q80aq3btdYe4OfAJoNjCrloS+6BrPyLC0opxVg99ZzW+ptGx2M0rfXntdaLtNaljP29+LXWOubvzqaitW4Friillo1/6D7grIEhGekysFEplTL+7+Y+4uDhckCbmCLFVCv/DA7LKJuBjwGnlFLvjn/sC+Nbs4QAeBZ4afxG6CLwZwbHYwit9WGl1E+B44x1mZ0gDk6qyglVIYSIQdFWlhFCCBEASe5CCBGDJLkLIUQMkuQuhBAxSJK7EELEIEnuQggRgyS5CyFEDJLkLoQQMej/AlUsG7B26IW8AAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzt3Xl029d16PvvBsCZoAhOgAYOGkhIlKyJtOMkjm05UizZru3cm6Z2m6TJTet2pe6UrvQlvbe5velN37vJbdLXW3dw06ZphrpOXqwo1uCR8pi4okSJgziIGihxAAHO8wDgvD9IKDRNiRAJ4IcfcD5reS0S/AnYpsitg/3bZx9RSqFpmqYlFovRAWiapmmRp5O7pmlaAtLJXdM0LQHp5K5pmpaAdHLXNE1LQDq5a5qmJSCd3DVN0xKQTu6apmkJSCd3TdO0BGQz6oULCgpUWVmZUS+vaZpmSqdPn+5TShUud51hyb2srIza2lqjXl7TNM2URKQjnOt0WUbTNC0B6eSuaZqWgHRy1zRNS0A6uWuapiUgndw1TdMSkE7umqZpCUgnd03TtASkk7umJair/RO82OQxOgzNIKZL7p7hKZ5+/SL67FdNu7mvPH+ez33/DNP+gNGhaAYwXXJ/tvYaf3GshX9687LRoWha3BoYn+Fkqxd/UHHJN250OJoBTJfcn9y3hUM7XHz1WDMnGnuMDkfT4tLRhh78wbl3t229owZHoxnBdMndYhG++Su72V2cy+8/c5a6q4NGh6RpcedwXRebC7OwWYRWj07uych0yR0gPcXKP36qGmdOOr/xnVqu9k8YHZKmxY2r/ROc7hjkY1XFbCrM0iv3JGXK5A5QkJ3Gtz9zOwGl+PS//AdDEzNGh6RpceG5ui5E4JHd63C7cmjRK/ekZNrkDrC5MJunP1lN58AkT3z3tO4K0JKeUorDZ7u4c2M+63IzcDuz6RycZGzab3RoWoyZOrkD3LExj6//8k7+4/IAf/yjeoJB3SKpJa9zncNc7hvno3vWA1DhtANwQZdmko7pkzvAI7vX84X73fzkbDffeKnN6HA0zTCH67pItVk4eJsLALdrLrnrunvyMewkpkj73L2buTYwwd/UtFOcl8Gv3F5idEiaFlOzgSA/PdfNgW1OctJTACh2ZJKRYtV19ySUMMldRPjzR3fQNTTJnzzXyNo1Gdxdsewxg5qWMN680Ef/+AyPzpdkYK51uMKZrVfuSSghyjIhKVYLf/treykvyuZz3z9Di2fE6JA0LWZ+XNeFIzOFexYtaiqcdlo9YwZFpRklrOQuIgdFpFVE2kXki0t8/Zsicnb+vzYRGYp8qOGxp6fw7c/cTlaalc98+xS9I1NGhaJpMTM6NcuLTR4e2rmOVNu7f63dLjt9Y9P0j00bFJ1mhGWTu4hYgaeAQ0Al8LiIVC68Rin1h0qp3Uqp3cD/AX4cjWDDtXZNBv/86dsZmZzlM98+pdvAtIT3QlMv0/7gu0oyIaGOmbZevXpPJuGs3O8A2pVSl5RSM8AzwCM3uf5x4N8iEdxqbF+3hr/5tb209o7y5A/O4A8EjQ5J06LmcF0XJXmZ7C3Jfc/Xts53zLTqMmVSCSe5rweuLfi8c/6x9xCRUmAj8OrqQ1u9fe4i/vyRHZxs9fHfjzTpMcFaQuodmeKti308umc9IvKerxfa08jNTKFVr9yTSjjdMu/9aYEbZcnHgB8ppZbcKioiTwBPAJSUxKZV8VffV8LVgQn+/rWLlORl8lv3bI7J62parBw5241S8OjudUt+XUSocNp1x0ySCWfl3gkUL/h8A9B9g2sf4yYlGaXU00qpaqVUdWFh7NoU//h+Nw/tXMv/fbyFo/V6TLCWWH5c18Wu4lw2FWbf8Bq3006bZ1S/e00i4ST3U0C5iGwUkVTmEviRxReJiBtwAD+LbIirZ7EI//uXd1Fd6uAPnz3L6Y4Bo0PStIho8YzQ3DPCf1riRupCFS47o9N+uod191iyWDa5K6X8wJPAC0Az8KxSqklEviIiDy+49HHgGRWnS4P0FCtPf6qa9bkZ/MZ3arncp0+n0czvcF03Vovw0M61N70udFO1Te9UTRph9bkrpY4ppSqUUpuVUl+df+zLSqkjC675M6XUe3rg40leVirf/vTtAHzm2//BwLgeE6yZVzCo+MnZLu6pKCQ/O+2m11YUzXfM6Lp70kioHarhKCvI4lu/Xk338BS/+a+1TM3qMcGaOb1zeYCe4akle9sXW5OZgisnXa/ck0jSJXeAqtI8vvnx3ZzuGOSPfnhOjwnWTOm5uk6y02wc2OYM6/oKl10PEEsiSZncAR7cuZYvHdrK0foevvZCq9HhaNotmZoNcLzBw8EdLjJSrWH9ma0uO+2+Mb2hL0kkbXIHeOLuTXzizhL+/rWLfP+dDqPD0bSwvdLsZXTaf/1QjnBUOO3M+IN0DOgzh5NBUid3EeHPfmk7+9yFfPknTdS0eo0OSdPC8lxdF86cNO7clB/2n3E7dcdMMknq5A5gs1r4m1/dy1aXnd/5/hkau4aNDknTbmpgfIaTrV4e2b0eq2WpDeRL21KUjYjumEkWSZ/cAbLSbPzzp29nTUYKn/3OKbqHJo0OSdNu6GhDD/6g4tHd4ZdkADJSrZTlZ9GqV+5JQSf3ec6cdL79mdsZnw7wX/7lFKNTs0aHpGlLeu5MJ26nnW1r7bf8Zyuc2XrlniR0cl9gqyuHv/vEXtq9Y3zu+2eY1V0FWpzp6B/nzNUhPrp36QmQy3E77VzpG9f7O5KATu6LfKi8kL/46G28caGPPz3cqActaXHlcF03IvDwrqUnQC6nwmUnqOCiT4//TXQ6uS/h47cX87v3beGZU9f425MXjQ5H0wBQSnH4bBd3bsxnXW7Gip4j1DGj6+6JTyf3G/j8gQoe2b2Or7/Qyk/OdhkdjqZxrnOYy33jt9TbvlhZQRapVouuuyeBcA7rSEoiwtc+tpOe4Sm+8MN6XDnpvO8Weoo1LdIO13WRarNw8DbXip8jxWphU2GW7nVPAnrlfhNpNitPf7KKDXkZPPHd07pOqRlmNhDkp+e6ObDNSU56yqqey+2y68Oyk4BO7svIzUzlXz59BzaL8Jlvn6JvbNrokLQk9MYFH/3jM6sqyYRUOO10DU3qdt8Ep5N7GEryM/mnT9+Od3SK3/iOHhOsxd5zdd04MlO4u2L1x1NeP7hD191jTinFN15qo8UzEvXX0sk9TLuLc/mrX9nDuc4h/uCZs3pMsBYzo1OzvNjk4aGd60i1rf5XtuJ6x4wuzcRaU/cIf/3KBRo6oz/mRCf3W3Bwh4v/9mAlJ5o8/MWxZqPD0ZLEC029TPuDYR3KEY71uRlkpVr1yt0AJ+eHE97rLor6a4WV3EXkoIi0iki7iCx5lJ6IfFxEzotIk4j8ILJhxo//8sEyPv2BMr715mW++7MrRoejJYHDdV2U5meytyQ3Is9nsQjlTrvudTdATauPnRvWUGi/+bGIkbBschcRK/AUcAioBB4XkcpF15QDXwI+qJTaDvxBFGKNCyLCnz5UyQe35POXL7Xp8owWVZ7hKd662Meju1c2buBGtrrstPaO6h3YMTQ4PkPd1cGYrNohvJX7HUC7UuqSUmoGeAZ4ZNE1vwk8pZQaBFBKJfRgdKtFeHjXOoYmZrnUN250OFoCO3KuC6WIWEkmpMJpZ2B8hr4xfUh8rLx+wUdQwT736m+KhyOc5L4euLbg8875xxaqACpE5C0R+bmIHIxUgPGqqjQPgDMdgwZHoiWy5+q62V2cy8aCrIg+r1t3zMRcTYuXvKxUdm6ITHltOeEk96XeCy5+L2cDyoF7gceBb4nIe/4PROQJEakVkVqfz3erscaVTQVZ5GamUNsxYHQoWoJq8YzQ3DMSkd72xSr0jJmYCgQVr7X5uLei8JYOWFmNcJJ7J1C84PMNQPcS1/xEKTWrlLoMtDKX7N9FKfW0UqpaKVVdWBibtybRYrEIVSUOTuuVuxYlh+u6sVqEh3aujfhzF2SnkpeVqpN7jJzrHGJwYpZ7t8am3g7hJfdTQLmIbBSRVOAx4Miiaw4D+wBEpIC5Ms2lSAYaj/aWOrjoG2dwXNcttcgKBhU/OdvFPRWF5GdHvrNCRHA77XqAWIycbPFiEbi7vCBmr7lscldK+YEngReAZuBZpVSTiHxFRB6ev+wFoF9EzgM1wBeUUv3RCjpeVJc6ADhzVa/etcj6+eV+eoanolKSCXG77FzoHdUdXzFQ0+pjb4mD3MzUmL1mWH3uSqljSqkKpdRmpdRX5x/7slLqyPzHSin1eaVUpVLqNqXUM9EMOl7s3JCLzSK6NKNF3OG6LrLTbOzf5ozaa1Q47YzPBOjSZwZHlXdkioauYfbFsCQDeofqqmSkWtm+Lodandy1CJqaDXC8wcPBHS4yUq1Rex23KxvQHTPRdrJtrnlkX4z620N0cl+lqtI8zl0b0uetahHzSrOX0Wl/VEsy8IuOmRZ9UzWqTrZ6ceakrehA89XQyX2VqkodTPuDNHVHf8qblhyeq+vCmZPGnVE+HMaensL63Ay9co+i2UCQN9r62OcuiugO43Do5L5K1WVzN1V13V2LhIHxGU62enlk9/qY9ENXOLN1O2QUne4YZHTaH7ORAwvp5L5Kzpx01udm6J2qWkQcre/GH1RRL8mEVLjsXPKN67JilNS0eEmxCnfFsAUyRCf3CKgqdVDbMaCHMGmr9lxdF1tddratzYnJ67mddmYCQa7oGUlRUdPq5Y6NeWSnxf64ap3cI6C6zEHvyLRuKdNWpaN/nDNXhyI+JOxmQjNm9GamyOscnKCtdyzmXTIhOrlHwN4SXXfXVu9wXTci8PCudTF7zc2F2VgE2nTdPeJOts61QBpRbwed3CNiq8tOVqpVJ3dtxZRSHD7bxZ0b81mXmxGz101PsVJWkKVX7lFwstVLcV4GmwsjO9EzXDq5R4DNamF3Sa5O7tqKnb02xOW+cT66N3YlmRC3005brz5PNZKmZgO81d5vSAtkiE7uEVJV4qC5Z4Sxab/RoWgmdLiuizSbhYM7XDF/bbfLzpX+cSZnAjF/7UT1zuUBJmcDMR85sJBO7hFSVZZHUMG5a0NGh6KZzGwgyE/re9hf6SQnPSXmr+922lEK2r169R4pNS1e0mwW3h/ljWg3o5N7hOwpyUUEaq/o0ox2a9644GNgfIaP7o59SQbmet1Bd8xE0slWLx/YnE96SvRmAy1HJ/cIyUlPwe20c1qP/9Vu0XN13TgyU7i7wpgDbErzMkm1WfQYggi53DfOlf4JQ0syoJN7RO0tdVDXMUhAz8fWwjQ6NcuLTR4e2rmOVJsxv442q4Xyomw9QCxCXm3xArGfArmYTu4RVF3qYHTazwWv/iXRwnOi0cO0P2hIl8xCbqdd97pHyMlWL1uKsinOyzQ0Dp3cI6iqVG9m0m7N4bNdlOZnsqf4PefJx1SFy45nZIrhiVlD4zC78Wk/71waYJ/b+DOidXKPoJK8TAqyUzmtb6pqYfAMT/H2xX4e3b3esF7oEPf8bPc2/a5zVd6+2M9MIGh4SQbCTO4iclBEWkWkXUS+uMTXPy0iPhE5O//fb0Q+1PgnIlSVOvRNVS0sR851oRQxnSVzI6GOGV13X52aVi/ZaTaqy/KMDmX55C4iVuAp4BBQCTwuIpVLXPrvSqnd8/99K8JxmkZVqYOO/gl8o9NGh6LFuefqutldnMvGAmO2py+0bk069jSbrruvglKKmhYvd20pMOzm+ELhRHAH0K6UuqSUmgGeAR6JbljmVVU69y+2rrtrN9PiGaG5ZyRmc9uXIyJUuOy6130VWntH6RmeYt9W4+vtEF5yXw9cW/B55/xji/1nEakXkR+JSHFEojOhHetzSLVaOKNLM9pNPFfXhc0iPLRzrdGhXFfhtNPWO6rPJVihmhZjp0AuFk5yX+pOz+K//Z8CZUqpncDLwHeWfCKRJ0SkVkRqfT7frUVqEmk2K7dtWEPtlQGjQ9HiVDCo+EldN/dUFJKfnWZ0ONe5ndkMTczqkuIK1bR6qVybgzMn3ehQgPCSeyewcCW+AeheeIFSql8pFfqJ+EegaqknUko9rZSqVkpVFxbGx1uXaKguddDYNcLUrB7EpL3Xzy/34xmZiosbqQu5XXOnP+mbqrdueGKW0x2DcVOSgfCS+ymgXEQ2ikgq8BhwZOEFIrLwveXDQHPkQjSfvaUOZgJBGruGjQ5Fi0OH67rITrOxf5vT6FDepcKZDaDHEKzAG+0+AkHFfQaPHFho2eSulPIDTwIvMJe0n1VKNYnIV0Tk4fnLfk9EmkTkHPB7wKejFbAZ6M1M2o1MzQY43uDh4A4XGanGDZVaSn52GgXZabTqlfstq2nxkZuZwu5ih9GhXBfWqa1KqWPAsUWPfXnBx18CvhTZ0MyrIDuNsvxMndy193il2cvotJ//FGclmRC3K1uv3G9RMKh4rc3L3eWFWC3GbkZbyPhmzAS1t9TB6Y5B3XmgvctzdZ24ctJ5n4Fzvm/G7cyhrXeMoB5+F7bG7mH6xmbiqt4OOrlHTXVpHv3jM3T0TxgdihYnBsZnONnq45Hd6+JqhbeQ25XN5GyAa4P65zZcr7Z4EYG7y3VyTwqhunutLs1o847Wd+MPqrjrklmoYn7GjK67h6+m1cfu4ty4amsFndyjprwoG3u6Tdfdteueq+tiq8vOtrU5RodyQ+WhAWK67h6WvrFp6juH4mJQ2GI6uUeJxSLsLXFwRid3DejoH+fM1aG4XrUDZKfZ2ODIoLVXn6cajtfbfChl/MEcS9HJPYqqSh20eUcZntQzspPd4bpuROCR3euMDmVZW112Wj0jRodhCjWtPgqy09i+Lv7ejenkHkXVpQ6Ugjo9ZyapKaV4rq6T92/KZ+2aDKPDWVaF084l3zgz/qDRocQ1fyDIa61e7nUXYonDG+Q6uUfRruJcrBbRdfckd/baEFf6J+K+JBPidtnxBxWX+8aNDiWu1V0bYmTKH1e7UhfSyT2KstJsbFtr18k9yR2u6yLNZuHgDpfRoYTleseMvql6UzUtXqwW4a7yAqNDWZJO7lFWVeLg7LUh/AH9FjcZzQaC/LS+h/2VTnLSU4wOJyybC7OxWUTX3ZdR0+qjutQRt3+vOrlHWVVZHhMzAT1pL0m9ccHHwPgMH91tjpIMQKrNwsaCLFo9umPmRjzDUzT3jLAvTksyoJN71OkhYsntx2e6cGSmcI87vnYvLqfCZde97jdR0+oF4rMFMkQn9yhbtyYdV056Qu9U9QeCPP70zzne0GN0KHFldGqWl8738ku71pFiNdevmttp5+rABBMzfqNDiUs1LV7W52ZcH5Mcj8z1E2dCIkJVWWJvZvrZpX5+dqmf773TYXQoceVEo4dpf9A0XTILhW6qXtCbmd5j2h/grfY+7nUXIhJ/LZAhOrnHQFWJg66hSXqGJ40OJSqO1s+t2N+5NKA3bC1w+GwXpfmZ7CnONTqUW7bVpWfM3EjtlUHGZwJxXZIBndxjoroscevus4EgJ5o8bCnKxh9UnJyvRSY7z/AUb1/s59Hd6+N6dXcjxXmZpKdYdDvkEmpavKTaLHxgS3yObQ7RyT0Gtq3NIT3FkpDJ/e2L/QxNzPKF+90UZKfy0vleo0OKC0fOdaEUfNSEJRkAq0UoL9I3VZfyaquXOzflk5ka1llHhtHJPQZSrBZ2bchNyOR+tL4be5qNeyoK+fBWJ6+1+vS2dea6ZPaU5FJWkGV0KCtW4bTrsswiHf3jXPKNs88E3U9hJXcROSgirSLSLiJfvMl1HxMRJSLVkQsxMVSXOWjqHkmo7oMZf5AXmno5UOkkPcXKgUono9N+3rncb3RohmrxjNDiGTXtqj1kq8uOd3SawfEZo0OJGydbfUB8t0CGLJvcRcQKPAUcAiqBx0Wkconr7Mwdjv1OpINMBFWlDgJBRX3nsNGhRMxbF/sYnpzlwZ1rAbirvID0FEvSl2aeP9eD1SI8eNtao0NZlQqXHkOwWE2rl40FWaZ4RxbOyv0OoF0pdUkpNQM8AzyyxHV/DnwNmIpgfAljb0ni3VQ9Wt+DPd12fbZGeoqVD5UX8vL53qQ9O1YpxbHGHt63MS/uTua5VW59cMe7TM4E+NnFfu41QUkGwkvu64FrCz7vnH/sOhHZAxQrpZ6PYGwJJTczlS1F2QmT3OdKMh4+UukizWa9/viBSifdw1M0dSfnXJIL3jEu+cY5ZPJVO4AzJ42cdJuuu8/72aU+pv3BuJ0CuVg4yX2pPq7ryzIRsQDfBP5o2ScSeUJEakWk1ufzhR9lgqgqcXC6YzAhTpZ/s93H6JSfh3a+O4l9eGsRIiRtaeZ4gwcRuH+70+hQVk1E2OrK0cl9Xk2Lj4wUK3dszDM6lLCEk9w7geIFn28Auhd8bgd2ACdF5ApwJ3BkqZuqSqmnlVLVSqnqwkJzvLWJpKoyB8OTs1zqM/+uv+fre8hJt/HBLe8ed5qfnUZViSN5k3tjD7eX5lFkTzc6lIiocGXT2juatGW2EKUUNa1ePril4F3vVONZOMn9FFAuIhtFJBV4DDgS+qJSalgpVaCUKlNKlQE/Bx5WStVGJWITS5QhYtP+AC819XL/dheptvf+CB2odHK+Z4SuocTckXsjl3xjtHhGTTO3PRxup53RKT+ekeS+lXbRN0bn4CT7tppnUbpscldK+YEngReAZuBZpVSTiHxFRB6OdoCJZFNBFo7MFGqvmDu5v9HWx+i0/3qXzGIHKudKEi8n2er9eKMHIKGS+/WDO5K8NFPTMldGvtcELZAhYfW5K6WOKaUqlFKblVJfnX/sy0qpI0tce69etS9NRKgqdXDa5GeqHm3oYU1GyntKMiGbCrPZVJiVdKWZE40edhfnsi43/s9JDZfbpTtmAF5t8bLVZWe9if5u9Q7VGKsqzeOSb5wBk24MmZoN8NL5Xg5ud910jO2BSic/v9TPyFRyDBK7NjBBQ9cwD9yWOKt2mOvycuakJfVhM6NTs5y6MmCqVTvo5B5zobq7WUcAv97mY+wmJZmQA9uc84PEkqMr6sR8SebQDvO3QC5W4UzuGTNvtffhDypTjBxYSCf3GNu5YQ0pVjHt4R1HG3pwZKbw/s03n4i3p8RBflbyDBI71tjDjvU5FOdlGh1KxLmddi70jhFIgBbelahp8WFPt7F3fmFmFjq5x1h6ipXt69aYcuU+NRvg5fO9HNxx85IMzE0V/PC2Ik62ehN+kFjP8CR1V4cSctUOc2MIpv1Brg5MGB1KzIVaIO8uLzTdaVrmijZBVJU6ONc5ZLqkd7LVx/hMgAdvWxfW9fu3ORmd8vMflweiHJmxXkjALpmFfnFwR/LtOm7qHsE7Oh3XB2HfiE7uBqgudTDtD9LUba4hYkcbesjLSuXOTeHt0PtQeeH8IDFPlCMz1rFGD26nnc2F8Xue5mpsKcpGBFo95t98d6tCh8/cU2Guejvo5G6IvSbczDQ5E+CV5rmSjC3Mt6cZqVbu2lLIy83ehN3h6Bud5tSVgYRdtQNkptooyctMypuqNa0+dm5YQ6HdfEPgdHI3gDMnnQ2ODFMl95OtXiZmAjx0iwOxDlQW0TU0yfmexHxL/0KTB6XggQQYFHYzFU570o3+HRyfoe7qoOlaIEN0cjdIdamD2o5B06xon2/ooSA79ZaHJt231YkIvHw+Mc9WPdHoYVNBFhXOxCzJhGx12bncN860P2B0KDHz+gUfQYXpWiBDdHI3SFWpA9/oNJ2D8T9/ZWLGz6vN3lsqyYQU2tPYW+LgpebEq7sPjs/ws0v9HLrNZcpDsG9FhdNOIKi46B03OpSYqWnxkp+Vyq4NuUaHsiI6uRukqnRuBWyG0kxNi4/J2fC7ZBbbv81JY9cI3Qk2SOyl870EgiphWyAXSrYxBIGg4rU2H/dUFGKxmPMfbp3cDeJ22clKtZoiuR9t6KYgO23Fc6xDg8ReaU6sDU3HGnsozstg+7oco0OJurL8LFKskjR193OdQwxOzHKvCVsgQ3RyN4jVIuwpccT9TtXxaT+vtnh54DYX1hWuYLYUZbOpIIsXE2i36vDkLG+193Fox9qEL8kApNosbCrIpi1JZsycbPFiEbi7fOnheGagk7uBqkodtHpGGI3j4VqvtniZmg2u+rDn/Qk2SOyV5l5mAyqhWyAXc7vsSTNA7NVWL1WlDnIzU40OZcV0cjdQVamDoIJz1+J3M9PR+h6K7GlUl63uaLEDlU5mA4rX2xJjkNjxRg9r16Sz26Q321bC7bLTNTQZ14uRSPCOTNHYNWLaFsgQndwNtKckFxGo7YjP7flj035qWr08cNvaFZdkQvaWOMhLkEFiY9N+Xmvzcf92l2lvtq1E6OCOC97E3ql6cn4Bsk8nd22l7OkpuJ32uL2p+kpzL9P+4LLjfcNhtQj3bS2ipsXLbMBcM3UWq2mZG4aW6BuXFnPPJ/dEr7ufbPXizElj21q70aGsik7uBqsqdVB3dSgux6kere/BmTN34HUkHKh0MjLl55TJB4mdaPRQkJ12fTZ/stjgyCAz1ZrQdffZQJA32vrY5y4y/Y3ysJK7iBwUkVYRaReRLy7x9d8WkQYROSsib4pIZeRDTUzVZQ7Gpv1x1z88OjXLyTYfD9y2NmKlhw+VF5Bms5i6a2ZyJsCrLV4O7nCuulRlNhaLUJ7gB3fUXhlkdNpvyimQiy2b3EXECjwFHAIqgceXSN4/UErdppTaDXwN+EbEI01QVSXxuZnplea50sNDESjJhGSm2rhrSwEvne81zdiFxV5rm9vQlQwbl5bidmYndHI/2eolxSo3PB/YTMJZud8BtCulLimlZoBngEcWXqCUWjgVKgsw52+uAYrzMii0p8Vdcn++voe1a9LZUxzZ0sOBSiddQ5OmfWt/vHHuJKr3rXBDl9lVOO30jc3QNzZtdChRUdPq5Y6NeWSn2YwOZdXCSe7rgWsLPu+cf+xdROR3ROQicyv334tMeIlPRKgqccRVch+ZmuX1CJdkQu7bVoQIpuyamfYHeKXZy0cqb33GTqJI5DEEnYMTtPWOmb5LJiScn9ClfrvfszJXSj2llNoM/F/Af1vyiUSeEJFaEan1+RKj3zkKrEfhAAAaVElEQVQSqsscXB2YwDs6ZXQoALx8vpeZQGS6ZBYrsqezuzjXlMn9zQt9jE37OXhb8mxcWsx9/VSmxEvuocPczd7fHhJOcu8Eihd8vgHovsn1zwCPLvUFpdTTSqlqpVR1YaE5x2hGQ+jwjng5V/VofQ/rczPYUxydDToHKp00dA3TM2yuQWLHGz3Y0218cLP567ErVZidhiMzJSFX7jUtXkryMtlcmGV0KBERTnI/BZSLyEYRSQUeA44svEBEyhd8+iBwIXIhJr7t63JItVniojQzPDnL6xd8PBDFMbYHts0NEnu52Twz3mcDQV4638uBbU5SbclZkoG5MmKF055wK/ep2QBvXexjn7vQ9C2QIcv+lCql/MCTwAtAM/CsUqpJRL4iIg/PX/akiDSJyFng88CvRy3iBJRms7Jrw5q4GCL20vm5mSkP7lzZeN9wbCnKpiw/k5dNVJr52cV+hidnOZRkG5eW4nbZaesdM23H01LeuTzA1GzQ1FMgFwvrlrBS6hhwbNFjX17w8e9HOK6ks7fUwT+/eZmp2QDpKVbD4jha38363Ax2bVgTtdcQEQ5UOvnO2x2MTftN0ZlwvNFDVqqVD5l4SmCkuF12xqb9dA1NssGRaXQ4EVHT4iXNZuH9m/KNDiVikvf9ZZypLs1jNqBo6DJuiNjwxCxvXOjjoZ3RH2O7f5uTmUCQ11rj/8Z6IKh4scnDfduchv7DGy+ujyFIoLr7yVYvH9icn1B/vzq5x4m9JXM3L42su79w3oM/qKLSJbNYVakDR2YKL5vgAI//uDxA//gMh5JovO/NlDtDHTOJMUDskm+MK/0T3JdAJRnQyT1u5GensbEgy9DkfrR+7mSh29ZHryQTYrNauG+rk1dNMEjseGMP6SkW7jXpQcmRtiYjhbVr0hNm5V6TYC2QITq5x5GqUgdnOgYNuVE1OD7DW+19PHjbuph1CxyoLGJ4cpZTV+J3kFgwqDjR6OHeiiIyU+P/3kCsuF2J0zFzstXLlqJsivMS4/5BiE7ucaSq1EH/+AxX+idi/tovzpdkIjlLZjkfKi8k1Wbh5fPx2xJ55uog3tFpDiXxxqWluJ122n1j+OP8Xddyxqf9vHNpgH0J+K5MJ/c4Uj2/manWgJXs8/U9lOZnxvSw56w0Gx/cnM9LzZ64bas73ugh1WpJuHrsalU47cz4g4YsRCLp7Yv9zASCCTNyYCGd3OPI5sJsctJtnLka27r7wPgMb1/s58HbYn/Y84FKF9cGJmmNw/qtUnMlmQ+VF2BPTzE6nLiSKDNmXm3xkp1mW/UxkvFIJ/c4YrEIe0tjP0TshSYPgRh1ySy2f9vciikeNzTVdw7TNTSpNy4tYUtRNhYx94wZpRQnW73ctaUgIXcdJ97/kclVlzpo6x1jeCJ2hxAfre9hY0EWlWtjV5IJKcpJZ1ecDhI73ujBZpHr4xK0X0hPsVKWn2Xq5N7aO0rP8BT7tiZevR10co8714eIXYvN6r1/bJq3L/ZFdZbMcj5S6eRc5zC9I/ExFRPmVnXHG3v4wJYC1mTqksxSKkx+KlNNS2K2QIbo5B5ndhfnYrUIp6/EJrmfaPIQVPDgbdGbJbOcA5WhQWLxs3pv7hmlo39Cb1y6iQqXnSv940zNBowOZUVqWr1Urs3BmZNudChRoZN7nMlMtVG5Nidmdfej9T1sKsgy9KT38qJsSvIy46o0c7yxB4vMvavQluZ22gkqaPeab6fq8MQspzsGE7oLSif3OFRV6uDstaGo79z0jU7z80v9PBiDWTI3Exok9nZ7P+PTfsPiWOh4o4f3bcwnPzvN6FDilpkP7nij3UcgqBK23g46ucelqlIHk7MBWnqi+0tzvSRjQJfMYgcq5waJvd5m/CCxC72jtHvHeEBvXLqpsvxMUq0WU9bda1p85GamsDvCZwTHE53c41DV/E3V0x3R3cx0tL6bzYVZ16f8Gam61EFuZkpclGaON3oQgfu36+R+Mzarhc1F2XG5R+FmgkHFa21e7i4vxBrhM4LjiU7ucWhdbgbr1qRH9fAO7+gU71we4MGdsZslczM2q4X73EW82uo1fEv7sYYeqkocFCXojbZIcjuzaTNZWaaha5i+sZmErreDTu5xa+/8ELFoOdHoQSliOktmOQcqnQxNzBp6ItXlvnFaPKN641KYKlx2uoenGJmK3b6M1app9SICd1ckbr0ddHKPW9WlDrqHp+geis4h0s/X91BelE1FHJRkQj5UUUiq1WJoaeZ4Yw8AB3ULZFi2hsYQmGj1XtPqY3dxLnlZqUaHElVhJXcROSgirSLSLiJfXOLrnxeR8yJSLyKviEhp5ENNLlWlc7MuotES2TsyxakrA3FxI3Wh7DQbH9iSz8vNvYYNEjvR6GFXcS7rczMMeX2zCS0OzFJ37xubpr5zKCEHhS22bHIXESvwFHAIqAQeF5HKRZfVAdVKqZ3Aj4CvRTrQZLN1rZ2MFGtUkvvxhh6UggfjsPSwf5uTjv4JLhjQO905OEF957DeuHQL1udmkJVqNc3K/fU2H0qhk/u8O4B2pdQlpdQM8AzwyMILlFI1SqnQ7M+fAxsiG2bySbFa2F2cG5XkfrShB7fTfv24tHgS2q1qRGnmRKMHQCf3WyAiVLjsplm5v9ripdCeFtPR1kYJJ7mvB64t+Lxz/rEb+SxwfDVBaXOqSh2c7xlhYiZyG3s8w1OcujIYdyWZEGdOOrs2rDEkuR9v9FC5NofS/KyYv7aZbZ0/lSleZ/KH+Of3UdxbUYglgVsgQ8JJ7kt9F5b8WxSRTwDVwNdv8PUnRKRWRGp9PuM3q8S7qjIHgaDi7LWhiD3nsYa5G4YPxGFJJmT/Nidnrw3hjeEgMc/wFKc7BvXGpRWocNoZnJjFNzZtdCg3VXdtiJEpP/sSvAUyJJzk3gkUL/h8A9C9+CIR2Q/8V+BhpdSSf8tKqaeVUtVKqerCwsRuQ4qEvfO75yLZEnm0oYetLjtbirIj9pyRdmB7aJBY7I7fe6FpriRzcEf8/qMXr0Kb4No88T1jpqbFi9Ui3FVeYHQoMRFOcj8FlIvIRhFJBR4Djiy8QET2AP/AXGKP3wMxTWZNZgrlRdkRq7t3D01yumMwrnrbl+J22inOy4jplMhjDXOtofH8j168qnDFf8fMbCDIC00eqksd5CTJqVrLJnellB94EngBaAaeVUo1ichXROTh+cu+DmQDPxSRsyJy5AZPp92i6rK5k5mCwdXXM81QkoG5m3T7tzl5s70vJoPEfKPTnLoyoDcurVBBdhoF2alx3THz1aPNXPSN86n3lxkdSsyE1eeulDqmlKpQSm1WSn11/rEvK6WOzH+8XynlVErtnv/v4Zs/oxauvSUORqb8XPSt/i3v0YYeKtfmsKkw/lenByqdzPiDvHEh+vdmXjw/N0BNd8msXIXTTkucrtx/WHuNf3n7Cp+9a2PcNhJEg96hGudCB/eudkt+5+AEdVeHTPPDfXtZHmsyUnjpfPSrfCcaPWwsyLq+21K7dRVOOxd6RyPyDjOSzl4b4r8ebuQDm/P50qGtRocTUzq5x7my/EzyslJXXXc/3jB3wzAeNy4tJcVqYZ+7kFdbeqM6SGxwfIa3L/ZzcIdxxwwmArfLzsRMgK4ojctYCe/oFL/93dMU2dP4m1/di82aXOkuuf5vTUhE2Fuy+iFizzf0sGN9DmUF5unhPlDpYnBiljNXI9cKuthLzb0EgooHdJfMqlwfQxAndfcZf5DPfe8MQ5MzPP3J6oSfI7MUndxNoLrMwaW+cfpX2Ed8bWCCc9eGDD0ndSXucYcGiXmi9honGj1scGSwY33i71iMpgrn3H2ceOmY+R8/baK2Y5Cvf2wXlUmwG3UpOrmbQOjwjpWuYENdMmYpyYRkp9m4c3M+L52PziCxkalZ3rjg45AuyayaPT2F9bkZcbFy/8E7V/n+O1f57Xs280u7zLWgiSSd3E3gtvVrSLEKtSs8meloQw87N6yhJD8zwpFF34FKJ1f6JyLSLbTYq81eZgNKb1yKELfLbviRe6c7BvjvRxq5u6KQL9zvNjQWo+nkbgLpKVZ2rF+zorr71f65SYdmW7WH7N82t1X8xSjMmjnW0IMzJ409xbkRf+5kVOG0c9E3FvWD3W/EMzzFb3/vDOtyM/g/j+1J6CP0wqGTu0lUlzo41znMjP/WfnGOmmTj0o2sXZPBbesjP0hsfNrPa20+Du1YmxRDpGJhq8vObEBxuW885q89NRvgt753mvFpP//4qWrWZCbHLtSb0cndJKpKHcz4gzR2D9/Snzva0M2u4lyK88xXkgk5UDk/SGw0coPEalq9TPuD+sSlCDKqY0YpxZd/0si5a0N84+O74up0MSPp5G4Se0tvfYhYR/84jV0jPGTSVXvI/m1OlJqrkUfK8UYPBdmp3D6/SUxbvU2FWVgtEvO6+/d+3sGztZ383n1b9P2TBXRyN4kiezoleZnUXgk/uYdKModMPsZ221o763MzIlaamZoNUNPi5SPbXUlfl42k9BQrZfmZMV25v3Opn//x0/N8eGsRf7C/ImavawY6uZtIVamD01cHw24LPFrfw56SXDY4zFuSgbmNXAcq5waJReLgktfafEzMBPTGpSjY6sqJ2cq9e2iSz33/DCX5mXzzsd363skiOrmbSFWpA9/oNJ2Dy2/xvtw3TlP3iGm7ZBY7UOlk2h/kjQt9q36uE40ecjNTeN8mXZKJtAqnnY6BiYieHraUqdkAv/Xd00z7gzz9yeqkGeN7K3RyN5HQZqZw+t3NMt43XHdszCMn3bbq0sy0P8DL53v5SKWTlCSbNRILblc2SkF7FA84V0rxpR830NA1zF/9ym49g/8G9E+3iVQ47djTbGENEXu+voeqUgfrcjNiEFn0pVgt7NtaxKstXgKrmDz4dns/o9N+DumSTFTEomPmn9+6wnN1XXz+QAX75w9U195LJ3cTsVqE3SW5y95Uvegbo7kncUoyIfu3ORkYn+HM1ZUPUTvW0IM9zcYHtuRHMDItpDQ/izSbJWp197fa+/iLY83cv93Jk/u2ROU1EoVO7iZTVeqgtXeU0anZG15zrD6xSjIh97oLSbEKL6+wNDMbCPJScy/7K52k2awRjk6DuQVIuTObliis3K8NTPDkD86wqSCLv/y4voG6HJ3cTaa6NA+l5g4huJGjDT3cXubAtSY9hpFFnz09hTs35a+47v7zS/0MTczqjUtRVuGM/IyZiRk/T3z3NIGg4h8/VU12mi2iz5+IwkruInJQRFpFpF1EvrjE1+8WkTMi4heRj0U+TC1kV/EaLMINSzPt3lFaPKMJV5IJOVDp5FLf+Ipu2B1v9JCZauWeisIoRKaFuJ12ekemGZqYicjzKaX44x/V0+IZ4a8f32OqMwmMtGxyFxEr8BRwCKgEHheRykWXXQU+Dfwg0gFq72ZPT8Htyrlh3flovQcREvaw5/3b5m6gvdx8a6v3QFDxYpOHfVuLSE/RJZloqpg/rrCtNzIdM//w+iWer+/hj+/fyr3uoog8ZzIIZ+V+B9CulLqklJoBngEeWXiBUuqKUqoeMGYcXJKpLnVQd3Voya6Row3d3F6WhzMnsUoyIety5w7WuNXSzKkrA/SNzeiNSzEQOos2Egd3vNbm43+daOHBnWv57Xs2rfr5kkk4yX09cG3B553zj2kGqSp1MDbtf0+7WVvvKG29YzxkkkOwV2r/Nidnrg7iGw3/ZKrjDT2k2Szc69YlmWhz5aRjT7fR6hlZ1fNc6Rvnd39wBrfTztc/tlMfqHKLwknuS31HV9RoLCJPiEitiNT6fL6VPIXGLzYznV5Umjla34MICX/D8EDl3CCxmpbwBokFg4oTTR7udReSpW/ERZ2I4HbaafOsvCwzPu3nie/WYrEIT3+ymsxU/fd2q8JJ7p1A8YLPNwDdK3kxpdTTSqlqpVR1YaFeQa3UBkcGRfY0Tl/5xU5VpRRHG3p438Y8iuyJWZIJqVybw/rcjLAP8Ki7NkTvyLTeuBRDFS47rb2jKzoeUSnFHz17jnbvGH/z+F5TniAWD8JJ7qeAchHZKCKpwGPAkeiGpd2MiFBd5njXyr2td4x27xgP7kz8MyNFhP3biniz3cfkTGDZ64839JBqtXDfNn0zLlbcTjvDk7N4b6F0FvJUTTsnmjz8yQPbuKu8IArRJYdlk7tSyg88CbwANAPPKqWaROQrIvIwgIjcLiKdwC8D/yAiTdEMWoO9JQ6uDUziHZk7wOJofTcWgYPbE7skE3Kg0sXUbJA3228+SEwpxfFGD3eVF+jhUjHknr+pequbmV5p7uUvX2rj0d3r+OxdG6MRWtIIq89dKXVMKVWhlNqslPrq/GNfVkodmf/4lFJqg1IqSymVr5TaHs2gtQV19465EcDPN/Rw56Z8Cu1pBkcWG3dszMOeZuOl856bXtfQNUzX0GTC34eIN6EZM223kNwv+sb4g2fOsn1dDv/Pf9Y3UFdL71A1qe3r1pBms1DbMUiLZ5RLvnEeTPAumYVSbRbu3VrEK803HyR2vNGDzSJ8RA+Yiqm8rFQK7Wlht0OOTM3ym/9aS6rNwj98slrvRYgAndxNKtVmYdeGXE53DHK0viepSjIhByqd9I/PcPba0hu6lFIcb+jh/Zvzyc1MjXF0mjvMMQTBoOLz/36Wjv4Jnvq1vaxPkEmmRtPJ3cSqyhw0dQ/zk3NdfGBzAfnZyVGSCbmnohCbRW7YNdPiGeVK/4TukjGI2zWX3IPLjGj+q1cu8HKzly8/VMmdm/S0zkjRyd3EqkoczAYU1wYmk6okE7Im4+aDxI43zL2j+ch2XZIxgttpZ2o2yNWBiRtec6LRw1+/coFfrtrAp95fGsPoEp9O7ia2d/6mqtUi3J9kJZmQA5VOLvnGueh774aZ440e7tiYR0GSvaOJFxXLjCFo6x3lj549y67iXP780R36BmqE6eRuYnlZqWx12bm7vIC8rOSsKX94vnd98Yz3du8oF7xjuiRjoPL54++W6pgZnpjliX+tJSPVxj98okrfQI0CvafX5L772feRakvef6M3ODKpXJvDy829/NY9m68/frxhrkVSt0AaJyvNRkle5ntW7oGg4vf/vY6uoUn+7TfvTLhzB+JF8maFBFFoT2NNRnJvzjlQ6eR0xyD9Y7/YDXm80UNVqSNhp2OaRYXT/p4Bd3/5YisnW3382cPbqS7LMyiyxKeTu2Z6ByqdBBW8Mj9IrKN/nPM9IxzSq3bDuV3ZXO4bZ9o/Nybi+fpu/vbkRR6/o4Rfe5++gRpNOrlrprd9XQ7r1qRfr7sfb9QlmXhR4bTjDyou943T3DPCF35YT1Wpgz97ePF5P1qk6Zq7Znoiwv5KJz+s7WRqNsDxhh52bljDBoeeJmi00IyZdy4N8K03L5GTYePvfm2vPqA8BvTKXUsI+7c5mZwN8GztNc51DusumTixqSAbm0X4n0fP0zs8zd9/oooifR8kJnRy1xLCnZvyyU6z8bUTrQC63h4nUm0WNhVmMRtQ/M+P7mBPicPokJKGLstoCSHVZuEedyFH63vYtjaHsoIso0PS5n32ro0MjM/y8eri5S/WIkYndy1hfKTSydH6Hr1qjzO/cnuJ0SEkJV2W0RLGRypdfPaujTx+h04mmqZX7lrCyEi18qcP6RY7TQO9ctc0TUtIYSV3ETkoIq0i0i4iX1zi62ki8u/zX39HRMoiHaimaZoWvmWTu4hYgaeAQ0Al8LiILH7v+1lgUCm1Bfgm8L8iHaimaZoWvnBW7ncA7UqpS0qpGeAZ4JFF1zwCfGf+4x8BHxY9nFnTNM0w4ST39cC1BZ93zj+25DVKKT8wDOjzsjRN0wwSTnJfagW++FDEcK5BRJ4QkVoRqfX5fOHEp2mapq1AOMm9E1i4tWwD0H2ja0TEBqwBBhY/kVLqaaVUtVKqurCwcGURa5qmacsKJ7mfAspFZKOIpAKPAUcWXXME+PX5jz8GvKqUuvmR55qmaVrUSDg5WEQeAP4KsAL/rJT6qoh8BahVSh0RkXTgu8Ae5lbsjymlLi3znD6gY4VxFwB9K/yziUh/P95Nfz9+QX8v3i0Rvh+lSqllSx9hJfd4IyK1Sqlqo+OIF/r78W76+/EL+nvxbsn0/dA7VDVN0xKQTu6apmkJyKzJ/WmjA4gz+vvxbvr78Qv6e/FuSfP9MGXNXdM0Tbs5s67cNU3TtJswXXJfbkJlshCRYhGpEZFmEWkSkd83OqZ4ICJWEakTkeeNjsVoIpIrIj8SkZb5n5P3Gx2TUUTkD+d/TxpF5N/m27cTmqmSe5gTKpOFH/gjpdQ24E7gd5L4e7HQ7wPNRgcRJ/5f4IRSaiuwiyT9vojIeuD3gGql1A7m9us8ZmxU0Weq5E54EyqTglKqRyl1Zv7jUeZ+cRcPdEsqIrIBeBD4ltGxGE1EcoC7gX8CUErNKKWGjI3KUDYgY348SibvHaGScMyW3MOZUJl05g9H2QO8Y2wkhvsr4I+BoNGBxIFNgA/49nyZ6lsikmV0UEZQSnUB/xu4CvQAw0qpF42NKvrMltzDmj6ZTEQkG/j/gD9QSo0YHY9RROQhwKuUOm10LHHCBuwF/k4ptQcYB5LyHpWIOJh7h78RWAdkicgnjI0q+syW3MOZUJk0RCSFucT+faXUj42Ox2AfBB4WkSvMlevuE5HvGRuSoTqBTqVU6N3cj5hL9sloP3BZKeVTSs0CPwY+YHBMUWe25B7OhMqkMH/S1T8BzUqpbxgdj9GUUl9SSm1QSpUx93PxqlIq4VdnN6KU8gDXRMQ9/9CHgfMGhmSkq8CdIpI5/3vzYZLg5rLN6ABuhVLKLyJPAi/wiwmVTQaHZZQPAp8EGkTk7Pxjf6KUOmZgTFp8+V3g+/MLoUvAZwyOxxBKqXdE5EfAGea6zOpIgp2qeoeqpmlaAjJbWUbTNE0Lg07umqZpCUgnd03TtASkk7umaVoC0sld0zQtAenkrmmaloB0ctc0TUtAOrlrmqYloP8faCooFXmVUGUAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzt3Xl8W2eV+P/PkbwvkpJ4t9UmaZxFTqK0hC7AsJZOy5LSQqHpsMww0JnfTIEBOkxbmJaWYcqwzTCQL0OHAv0xtKWEUgKEliktX7aWNm2zOYnl7HZsy85iW94t6/n+ISl1HCeWE8lX9+q8X6+8sOQr61RIx9fPc+45YoxBKaWUs7isDkAppVT6aXJXSikH0uSulFIOpMldKaUcSJO7Uko5kCZ3pZRyIE3uSinlQJrclVLKgTS5K6WUA+VZ9cQVFRVm4cKFVj29UkrZ0gsvvHDUGFM503GWJfeFCxeyZcsWq55eKaVsSUQOpXKcLssopZQDaXJXSikH0uSulFIOpMldKaUcSJO7Uko5kCZ3pZRyIE3uSinlQJrcz1EoHOEPe49aHYZSSk1Lk/s5+uLjLdzy4IvoDFqlVDbS5H6OWsL9nBgap6t/xOpQlFLqNJrcz8HgaJS248MANB/ptzgapZQ6nSb3c9DaPXDy612dmtyVUtlHk/s5CHVFACgpcLOrQ5O7Uir7pJTcReRqEWkRkb0icts0379QRH4tIttF5Dci0pD+ULNHKByhMM/F65ZW0tzZZ3U4Sil1mhmTu4i4gQ3ANUAAWC8igSmHfRn4/40xq4F7gHvTHWg2aQlHaKwuY2W9l7bjw/QNj1sdklJKnSKVM/dLgb3GmP3GmDHgYeDaKccEgF8nvn56mu87SigcYWl1OU11HgD26Lq7UirLpJLc64G2SbfbE/dNtg14Z+Lr64ByEVkw9QeJyM0iskVEtvT09JxLvJbrHRoj3D/KsupyAonk3qzr7kqpLJNKcpdp7pt65c6twOtE5CXgdcARIHrag4y5zxiz1hiztrJyxilRWSkUjlfKLK0up6q8iIqyQq2YUUplnVTG7LUD/km3G4COyQcYYzqA6wFEpAx4pzHGkTuNoXC8UmZpTTkATXUerZhRSmWdVM7cnwcaRWSRiBQANwKbJh8gIhUikvxZtwPfSW+Y2SMUjlBWmEedtwiAQJ2H1u4IY9GYxZEppdTLZkzuxpgocAvwBLAbeMQY0ywi94jIusRhrwdaRCQEVAOfz1C8lmvpirC0ugyR+GpVoNbD+IShtTticWRKKfWyVJZlMMZsBjZPue/OSV9vBDamN7TsY4whFI5w9cqak/c1TdpUbarzWhWaUkqdQq9QnYWegVFODI3TWFV+8r4LF5TqlapKqayjyX0WWhOVMstqXk7ubpewvKZcK2aUUllFk/sstCR6yiytLj/l/qY6L7s7+onFtLe7Uio7aHKfhVA4wvzSAirKCk65P1DnITIapf3EsEWRKaXUqTS5z0JL+NRKmaTkpuoubSKmlMoSmtxTZIyhNTxw2pIMxJdp3C7RNgRKqayhyT1FHX0jDIxGp03uRfluLqos1YoZpVTW0OSeouSAjsmVMpM11Xn1zF0plTU0uaeoJdlTpmr65B6o9dDVP8KxgdG5DEsppaalyT1Foa4INZ4ivCX5034/2f53d6e2IVBKWU+Te4qS05fOJFCbbEOgFTNKKetpck/BRMywt3uAZdNspibNKy2gzlukV6oqpbKCJvcUHD4+xGg0drKH+5kEtLe7UipLaHJPQbLtwNnO3AECdV729QwwPDYxF2GpLDY+of39lbVSavmb65LTl8625g7xdfeYia/Pr/H75iI0lYU2vtDOrT/ahq8kH/+8EhrmFdMwrxj//OTX8f8tKdCPn8ocfXeloCUcwT9/5g/jyTYEHf2a3HNUZ98wd29qZlW9l9UNXtpPDBMKR3hqTzejU6Z1LSgteDnZz3856Sd/IRTluy36r1BOkFJyF5Grga8BbuDbxpgvTPn+BcADgC9xzG2JAR+O0BqOzLgkA9Awr5jyojytmMlRxhhuf3QH0Zhhw02XcMGCkpPfi8UMRwdHaT8xTNvxIdpPDCf+DbGrs5//3RVmbMpSTkVZ4ZQz/vgvAP+8Yup8mvzV2c2Y3EXEDWwA3kx8WPbzIrLJGLNr0mGfIT5+75siEiA+tWlhBuKdc2PRGPt7BrlyRfWMx4oIgVqPVszkqB+/eITftPRw19sDpyR2AJdLqCovoqq8iEsumHfaY2MxQ8/A6KTEH//fthNDbG/v5fGdnYxPnNpSuqq88JTEHz/jj9+u8xVTkKdbarkslTP3S4G9xpj9ACLyMHAtMDm5G8CT+NoLdKQzSCsdODpINGbO2HZgqqY6Lw89d5iJmMHtkpkfoBwh3D/CPT9r5pUL5/GBKxbO+vEul1DtKaLaU8TaaR4+ETOE+0dOJv624y//Anjx8Al+vr2TiUnzBESgxlPEa5ZU8KUbguf+H6ZsK5XkXg+0TbrdDlw25ZjPAr8SkY8ApcCVaYkuC5xsO5DCsgzEyyGHxyc4cHSQJVVn34BVzmCM4dM/2cFoNMYX3xXElYFf6m6XUOeLn5Ffumj+ad+PTsToSiT/5Nn/M/uO8aMX2vmna5ZTUVaY9phUdkvl77bp3qlTRw6tB75njGkA3gJ8X0RO+9kicrOIbBGRLT09PbOP1gKhrghul7C4sjSl45NXqurSTO746dYOntzdza1XLWNRRWrvk3TLc7tomFfC5YsXcMNaPx9/81I+edVSALa391oSk7JWKsm9HfBPut3A6csufw08AmCMeQYoAiqm/iBjzH3GmLXGmLWVlZXnFvEcC4UjLFxQQmFeaptXS6rKKHC7dFM1R3RHRvjsz5q5+AIfH3zNIqvDOcXKei8ugW1t+l7MRakk9+eBRhFZJCIFwI3ApinHHAbeBCAiK4gnd3ucms8gFI6kvN4OUJDnorG6TK9UzQHGGO58rJmhsQm+9K7VWbfHUlqYR2NVOdv0zD0nzZjcjTFR4BbgCWA38aqYZhG5R0TWJQ77JPBhEdkGPAT8pTHG9tOih8cmOHR8KOX19qRAbbwNgQNeAnUWv9jRyePNXXz8yqUsOUMraKsF/V62tfXqezEHpVTnnqhZ3zzlvjsnfb0LeHV6Q7Pe3u4BjJm57cBUTXUefvRCO92RUao9RRmKTlnp2MAod/60mWCDlw//WXYtx0wW9Pt4ZEs7bceHTyvPVM6mhbBn8XLbgVmeudd5AXRpxsHu2tRMZGScL74rSJ47ez9GwYb4ldK6NJN7svddmQVC4QgFbhcLZ3nGs6I2/stAN1Wd6fGdXfx8eycffWPjrPZjrLCsppyCPBfb2jS55xpN7mfREo5wUVXZrM/MyovyuXBBiZZDOtCJwTE+89hOmuo8/O3rL7I6nBnlu12srPPomXsO0uR+FqGuCMtm6AR5JslNVeUsd/+smd6hMb70riD5WbwcM1nQ72PHkT6i2oY4p9jj3WmB/pFxOvpGZhzQcSZNdR4OHhsiMjKe5siUVZ7cFeaxrR383RuWnJyZawfBBh8j4zFauwesDkXNIU3uZ9Aajn8Qlp5jiVvyw7+nSwdmO0Hf0Dh3/GQHy2vKueUNS6wOZ1aCifbTuu6eWzS5n0GyUuZcN8wCtVox4ySf+8Uujg2O8eUbgrbrtrhwQQmeojxdd88x9nqXzqGWrgglBW7qfcXn9PhqTyELSgu0YsYBnm7pZuML7fzt6xazst5rdTizJiIE/T5tQ5BjNLmfQSgcobG6/Jw7/IlIfGC2VszYWv/IOHc8uoPGqjI++qZGq8M5Z8EGHy3hiM73zSGa3M8gFD73SpmkQK2HUNeADku2sXs37ybcP8KXbgim3DwuGwX9PiZiRv+SzCGa3KdxbGCUowNjs+4pM1WgzsPYRIy9WqVgS79vPcpDz7Xx4T9bbPuZuMGG+HLSVt1UzRma3KcRSlbKnGdynzwwW9nLwGiUf/rxdhZXlPLxNy+1OpzzVuUpotZbxPZ2PXPPFZrcp3G+lTJJiyrKKMp30azJ3Xa+8MvddPQN86UbVjtmEHWwwacVMzlEk/s0WsIRvMX5VJWf32gyt0tYXuNhV6eeLdnJH/cd5X+ePcwHX72IV1x4+kg7uwr6fRw6NsSJwTGrQ1FzQJP7NEJdEZZWlyFy/sMXAnXa291Ohsai3PbjHSxcUMKtVy2zOpy0Cvrj6+7bj+jJRi7Q5D6FMYZQOHLe6+1JTXUe+keitJ8YTsvPU5n1xcdbOHx8iH9752qKC5yxHJO0qt6LiF6pmis0uU8R7h+lfySatlauOjDbPp4/eJwHnjnIB664kMsWL7A6nLQrL8rnosoyTe45IqXkLiJXi0iLiOwVkdum+f6/i8jWxL+QiNj23dOS2ExN15n78hoPLtGKmWw3PDbBpzZup2FeMZ+6ernV4WRMclNVlwmdb8bkLiJuYANwDRAA1otIYPIxxpiPG2PWGGPWAF8HHs1EsHMh1JXe5F5c4GZxZZlWzGS5r/5vCweODvJv16+mtDCl6ZO2FPR7OTowRkffiNWhqAxL5cz9UmCvMWa/MWYMeBi49izHryc+JNuWWsIRKsoKmV9akLafGaj1sFuXZbLWC4dOcP/vD3DTZRfwqiUVVoeTUSfH7unSjOOlktzrgbZJt9sT951GRC4EFgFPnX9o1mgNR1hWc35tB6YK1Hk40jusJWhZaGR8gk9t3EaNp4jbr3HuckzS8tpyCtw6di8XpJLcp6sHPNOC3Y3ARmPMtN2JRORmEdkiIlt6enpSjXHOxGKGUHggbUsySckrVfXsPfv8x5Ot7OsZ5AvvXE15Ub7V4WRcYZ6bFXUebUOQA1JJ7u2Af9LtBqDjDMfeyFmWZIwx9xlj1hpj1lZWVqYe5RxpPzHM8PgEy9Kc3LViJjtta+vlvt/u4z1r/bx2afa9HzMl2OBl55E+JmK6qepkqST354FGEVkkIgXEE/imqQeJyDJgHvBMekOcOycrZdI80X5BWSE1niLdVM0io9EJ/nHjNqrKi/j021ZYHc6cCjb4GBybYF+PNrRzshmTuzEmCtwCPAHsBh4xxjSLyD0ism7SoeuBh42Na6ySPWUaq9K75g4vX6mqssM3ntpLKDzAvdevwpMDyzGTJcfu6dKMs6VU82WM2QxsnnLfnVNufzZ9YVkjFI5Q7yvOyNproNbD/w31MDI+4ZhGVHa180gf/+c3+7j+knresLzK6nDm3OKKUsoL89je3su71/pnfoCyJb1CdZKWRE+ZTGiq8zARMyf/OlDWGIvG+MeN25lfWsCdbwvM/AAHcrmEVQ1eHbvncJrcE8YnYuzvGUz7entSQHu7Z4Vv/mYfuzv7+fw7VuIrSd+1DHYT9PvY3dnPyLiO3XMqTe4Jh44NMjYRS3ulTJJ/XgllhXlaMWOh3Z39fOPpVtYF67iqqcbqcCwVbPARjRl9PzqYJveElq70TF86E5dLCNR6tGLGIuMTMf5x4za8xfl8dl2T1eFY7mT7X91UdSxN7gmhcAQRWJKBSpmkQF28DUFM64vn3H2/3c/OI/187tqVaW0tYVc1niKqygvZpmP3HEuTe0IoHGHhgtKMVrIEaj0MjU1w8Nhgxp5DnS4UjvC1J1t566parllVa3U4WUFECPp92obAwTS5J7SEM1cpk3RyU1XXOedMdCJeHVNWlMfd1+pyzGRr/D72Hx2kb3jc6lBUBmhyJ9486uDRwYxtpiY1VpeR5xKtmJlD9//+ANvaevnsuiYqys5vJq7TrG6Ir7vv0KUZR9LkDuzrGSBmoDHDyb0wz01jdbluqs6RfT0DfOV/Q1wVqObtq3U5ZqrV9Yn2v+26NONEmtyB1nC8UiZdo/XOJlDr0WWZOTARM3xq43aK8938y3Ur0zLs3Gm8JfksrijVNgQOpcmd+Hp7vltYuKA0488VqPPQExmlO6KTcDLpe388yAuHTnDX2wNUlRdZHU7WCvp9bNczd0fS5E58tN7iijIK8jL/cjTplaoZd/DoIF96Yg9vWl7FdRdPO1dGJaxu8BLuH6VLx+45jiZ3EpUyc7AkA7BCe7tnVCxm+NSPt5PvdvH561bpcswMtEOkc+V8ch8cjdJ+YpilGbx4aTJvcT4N84p1UzVDvv/sIZ47cJx/fluAGq8ux8wkUOshzyW6qepAOZ/cW7sTbQfm6Mwd4kszuzW5p93Bo4P82+N7eO3SSm54RYPV4dhCUb6b5bXluu7uQDmf3ENd8Ra8ma5xnyxQ6+XAsUEGR6Nz9pxONxqd4JaHXiTf7eIL1+tyzGwEG3xsb+vTthgOk/PJvSUcoSjfhX9+yZw9Z6DOgzGwp0vP3tPl3s172Hmkny/fEKTOV2x1OLYS9PuIjEbZf1TbYjhJSsldRK4WkRYR2Ssit53hmHeLyC4RaRaRB9MbZuaEwhEaq8pxu+buTE8rZtLr8Z1dfO+PB/ngqxfx5kC11eHYzprEpqouzTjLjMldRNzABuAaIACsF5HAlGMagduBVxtjmoB/yECsGdHSFaExwz1lpqr1FuErydeKmTRoOz7EpzZuY3WDl9uuWW51OLZ0UWUZJQVubSLmMKmcuV8K7DXG7DfGjAEPA9dOOebDwAZjzAkAY0x3esPMjN6hMbojo3O63g7xjnxNddrb/XyNT8T4yEMvYQx8Y/0lc3KdghO5XcKqei9btceMo6TyaagH2ibdbk/cN9lSYKmI/EFEnhWRq9MVYCaFwnNfKZMUqPWwpytCdCI258/tFF9+ooWtbb184Z2ruWDB3O2ZONEav4/dHf2MRfX96BSpJPfpFqOnbqvnAY3A64H1wLdFxHfaDxK5WUS2iMiWnp6e2caadi3hua+USQrUeRiLxtjXo5tY5+LpPd1867f7ee/lF/BWbQp23oJ+H2MTMd3kd5BUkns74J90uwHomOaYnxpjxo0xB4AW4sn+FMaY+4wxa40xaysrK8815rQJdUUoL8yj1oKLXZrq4u1Wd3Xqn8Kz1dk3zCce2crymnI+89bAzA9QM0q2/9V1d+dIJbk/DzSKyCIRKQBuBDZNOeYx4A0AIlJBfJlmfzoDzYSWcHwz1Yqa6MUVpRTkubRiZpaiEzE+9tBWRqMxNvzFJRmdnJVL6n3FVJQVsLVNTzacYsbkboyJArcATwC7gUeMMc0ico+IrEsc9gRwTER2AU8D/2iMOZapoNPBGENrODInbX6nk+d2sbxGe7vP1n/+upXnDh7n89et5KLKua1ycjIRIdjg0zYEDpKXykHGmM3A5in33TnpawN8IvHPFnoGRjkxNM5SC9bbk5rqPPxyZxfGGL2iMgV/2HuUrz+9lxte0cB1F2t7gXRb3eDjqZZuIiPjlBflWx2OOk85WzsW6koM6LAwuQdqPfQOjdOp7VZn1BMZ5WMPb+WiyjKdhZohQb8XY2DHEV2acYKcTe7JShkryiCTkgOzdWnm7GIxw8d/uJXIyDgbbrqEkoKU/uBUsxRsSIzd03V3R8jZ5B7qijC/tMDSocnLazyIaBuCmXzz/+7j93uPcve6Jsv2SHLBvNICLlxQom0IHCJ3k3t3hKVz3HZgqtLCPBYtKNVyyLN47sBxvvKrFtYF63jPK/0zP0Cdl9UNPi2HdIicTO7GGEJdEUvX25MC2obgjI4PjvHRh17igvklfF6HXM+JYIOXjr4Ruvt1H8jucjK5H+kdZnBswtL19qRAnYf2E8P0DY9bHUpWMcZw64+2cXxwjG/cdIlWb8yRZIfIbdpnxvZyMrmHLGw7MFWgVtv/Tuf+3x/gqT3dfPqtK1hZ77U6nJzRVOfF7RJdd3eAnEzuLYkyyMYsSO4vtyHQ5J60ta2XL/xyD3/eVM37r7jQ6nBySnGBm6XV5Tow2wFyMrm3hiPUeIrwFlv/p35leSGV5YV65p7QNzzOLQ++SLWniC++M6jr7BZY4/eyra2X+LWJyq5yMrm3hCNZsd6eFKj10Nyha5zGGG778Xa6+kb4+k0X4y2x/pdvLgo2+OgfiXLw2JDVoajzkHPJfSJmaO0eYJnFZZCTNdV52Ns9wGh0wupQLPU/zx7ilzu7+NTVy7jkgnlWh5OzVjfo2D0nyLnkfujYIGPRmKU9ZaYK1HmIxgytieEhuWjnkT4+9/PdvGFZJR96zWKrw8lpS6vLKMp36bq7zeVcck9WymRTcj+5qZqj6+4Do1FuefBF5pXm85V3r8E1h8PK1eny3C5W1Xv1Yiaby8HknqyUyZ5lmQvnl1BS4M7JihljDHc8uoPDx4f4zxsvZn5pgdUhKeLr7s0d/YzrGEjbyrnk3hKOcMH8kqxqPuVyCStydFP1kS1tbNrWwSfevJTLFi+wOhyVsNrvYzQao6UrYnUo6hzlXHIPdUWyakkmqanOw+7OCLFY7pSftXRFuGtTM69ZUsH/9/olVoejJlmT7BCpm6q2lVPJfTQ6wYGjgyyryZ4lmaRArYeB0ShtJ3Kj/GxoLL7OXlaYz1ffE8St6+xZxT+/mHkl+brubmMpJXcRuVpEWkRkr4jcNs33/1JEekRka+Lfh9If6vk7cHSQaMxk5Zl7rvV2/+ymZvb2DPAf71lDVfncDyhXZyciBP0+tmuPGduaMbmLiBvYAFwDBID1IjLdyPkfGmPWJP59O81xpkVyMzUbk/vS6nLcLsmJipmfvNTOI1vaueUNS3hNY4XV4agzWN3gIxSOMDgatToUdQ5SOXO/FNhrjNlvjBkDHgauzWxYmRHqiuB2CYsrS60O5TRF+W6WVJY5vmJmf88An/7JTi5dOJ+PvanR6nDUWazxe4mZ+DUIyn5SSe71QNuk2+2J+6Z6p4hsF5GNIjLtVAURuVlEtojIlp6ennMI9/y0hCMsqiilMM8958+dinhvd+d+kEbGJ/j7B1+iMM/F19avIc+dU1s+trNaN1VtLZVP13Q7XVNLOn4GLDTGrAaeBB6Y7gcZY+4zxqw1xqytrKycXaRpEApnx4COM2mq8xDuH+XowKjVoWTE53+xm92d/Xzl3UFqvcVWh6NmUFFWSL2vWHu721Qqyb0dmHwm3gB0TD7AGHPMGJPMSP8NvCI94aXP8NgEh48PZdXFS1Mle7vvduDSzOYdnXz/2UPc/NrFvHF5tdXhqBSt8evYPbtKJbk/DzSKyCIRKQBuBDZNPkBEaifdXAfsTl+I6bG3ewBjsmNAx5k4tWLm8LEh/mnjdtb4fdx61TKrw1GzEPR7aT8xzDGH/jXpZDMmd2NMFLgFeIJ40n7EGNMsIveIyLrEYR8VkWYR2QZ8FPjLTAV8rlqSPWWyqNXvVL6SAup9xY6qmBmLxvjIQy+CwNfXX0xBnq6z20nwZIdIXZqxm5SuwTfGbAY2T7nvzklf3w7cnt7Q0isUjlCQ5+LC+SVWh3JWTmtD8MXH97CtvY//eu8l+LP8tVenW1nvxSXx6VhvWF5ldThqFnLmNKqlK8KSyrKsr9BoqvOw/+ggQ2P2ry1+cleYb//+AB+44kKuXlk78wNU1iktzKOxqlwrZmwouzNdGoXCEZZm8WZqUqDOgzHYvmFTR+8wt27cRlOdh9vfssLqcNR5COrYPVvKieTePzJOZ99IVq+3JyUrZuy8qRqdiPHRh15iPBrjGzddQlF+dl5XoFIT9Ps4MTRO+4lhq0NRs5ATyb01sZmazZUySQ3zivEU5dn6StV/fzLElkMn+NfrV7GoIvuuBlazk9xU1clM9pITyb2lK3t7ykwlIgTqPLatmPltqIf/85t93PhKP9eume5CZmU3y2rKKchzab27zeREcg+FI5QUuKn32eOqyECtlz1d/UzYrLd7d/8In3hkK41VZdz19iarw1Fpku92sbLOo+WQNpMTyb2lK0JjdbltZnM21XkYGY9x4Kh9BmZPxAz/8MOtDIxG2XDTJRQX6Dq7k6xu8LHjSB9RHbtnGzmR3Fu7IyyzQaVMkh2vVP3GU3v5475j3HPtShptsPylZmeN38fw+ASt3fY54ch1jk/uRwdGOTowZov19qQlVWUUuF22WXffeaSPr/06xDvW1HHDKxqsDkdlQNCf6BCp6+624fjkHkpWytigDDIp3+1iaY09ertHJ2Lc/ugO5pcWcve6lYjYY+lLzc7CBSV4ivK0Q6SNOD+5Jy4GstOZO8Tr3Zs7+rP+wpEHnjnEjiN93PX2AN6SfKvDURmSHLunZ+724fjk3hIewFucT1V5odWhzEpTnZfjg2OE+7O3G9+R3mG+8qsWXr+skret1vYCThds8NESjjA8NmF1KCoFjk/urYkBHXZbLkhuqu7qzM4/g40x3PXTnRgDn7tWl2NyQdDvYyJmHNXYzskcndyNMbSEIyytsU+lTNLyxB5B85HsXHd/ormLJ3d38/E3N2q3xxwRbPAC6Lq7TTg6uXf1jxAZidqi7cBU5UX5LFxQkpWbqv0j49y1qZlArYcPvnqR1eGoOVLlKaLWW6Tr7jbh6OSe7Kxo17rrQJ0nK5P7l59ooScyyr3Xr8r6FsoqvYINPm3/axMpfTJF5GoRaRGRvSJy21mOe5eIGBFZm74Qz12yDNJulTJJgVoPh44N0T8ybnUoJ714+ATff/YQ779i4cnaZ5U7gn4fh44N0Ts0ZnUoagYzJncRcQMbgGuAALBeRALTHFdOfMTen9Id5LkKhQeoLC9kfmmB1aGck6a6+Brnns7s6O0+PhHjjkd3UOMp4tY/11mouUjX3e0jlTP3S4G9xpj9xpgx4GHg2mmO+xzwRWAkjfGdl1CiUsauXm5DkB0fpG//7gB7uiLcva6JssKUJjQqh1nZ4EVEr1S1g1SSez3QNul2e+K+k0TkYsBvjPl5GmM7L7GYSUxfsm9yryovpKKsICvaEBw+NsTXfh3iz5uquaqpxupwlEU8RflcVFmmyd0GUknu0xUwn7xsUkRcwL8Dn5zxB4ncLCJbRGRLT09P6lGeg7YTQ4yMx2wxWu9MRIQVtdZvqhpj+PRjO8hzufjsOm3lm+vim6p9WX/1dK5LJbm3A/5JtxuAjkm3y4GVwG9E5CBwObBpuk1VY8x9xpi1xpi1lZWV5x51CpKVMnYYrXc2gToPoXCEsah1rVY3bevgd61HufWqpdR67dETX2VO0O/MRpZ7AAAS2UlEQVTl6MAoHX1ZswKrppFKcn8eaBSRRSJSANwIbEp+0xjTZ4ypMMYsNMYsBJ4F1hljtmQk4hQlW5M2Vtn3zB3im6rjE4a9FrVa7R0a456f7SLo9/G+KxZaEoPKLsmxe7o0k91mTO7GmChwC/AEsBt4xBjTLCL3iMi6TAd4rlq6ItT7iikvsnczq+TAbKuWZu7dvIfe4XHuvW4VbpsMO1GZtby2nAK3jt3LdimVPBhjNgObp9x35xmOff35h3X+QuGIrdr8nsmiilKK8900d/Txrjnulf6n/cf44ZY2/ua1i09W7ihVmOdmRW25XsyU5Rx5eeH4RIx9PQM02ngzNcntEpbXls95xcxodII7frKDhnnFfOzKxjl9bpX9gn4fO9r7bDfnN5c4MrkfPDrI+ISxdY37ZIFExcxcVif812/2s69nkH95x0pKCrSmXZ0q2OBjcGyCfT06di9bOTK5h8LxN5yda9wnC9R5iIxEaT8xPCfPt69ngA1P7+XtwTpev6xqTp5T2YuO3ct+jkzuLeEILonPInWCZBuCuRiYbYzh0z/ZQVG+izvfdlqXCaUAWFxRSnlhnq67ZzFHJvdQV4SFC0opyndbHUpaLKsuxyWwaw7aEPzohXae3X+c29+ygkqbTa9Sc8flElY1eNnWlh2tMdTpnJncwxFHbKYmFRe4uagy8wOzjw2M8q+bd7P2wnm8Z61/5geonBb0+9jd2c/IuI7dy0aOS+4j4xMcPDbomM3UpECdJ+MVM//yi90Mjka59/pVuLSmXc0g2OAjGjPszsKZA8qByX1fzwAxY/+2A1MFaj109I1wYjAzfbR/19rDT146wt++7iLbDjdRcyvoT7T/1U3VrOS45J4c0OG0M/fkpmomlmZGxif4zGM7WVRRyt+/YUnaf75yphpPEVXlhdrbPUs5Lrm3dA2Q7xYWVpRaHUparahNDMzOwKbqf/66lUPHhvj8O1Y6ZhNaZZ6IEPTr2L1s5bjkHgpHWFxRRr7DZnsuKCukxlOU9nX3PV393Pfb/bzzkgZetaQirT9bOV+wwcv+nkH6hrNnFKSKc1YGJJ7cnbbentSU5oHZsZjhjkd3UF6Ux6ffuiJtP1fljuTFTDt0aSbrOCq5D4zGr+Jc5qAyyMkCdR729QymrfTswecO8+LhXj7z1oBt58wqa62uT1ypqkszWcdRyb01sZnqlLYDUwVqPUzEzMlBJOeju3+Ef3t8D69esoDrL6mf+QFKTcNbks/iilKtmMlCjkruJytlHLssk76Kmbt/tovRaIx/eccqRLSmXZ271Q1ePXPPQo5K7i1dAxTlu/DPK7E6lIxomFdMeWHeeVfMPLUnzC92dPLRNy5hkcOqitTcC/p9hPtH6dKxe1nFUcm9tTtCY1W5Y6+udLmEFed5pergaJR/fqyZxqoybn7tRWmMTuWq5KbqVl2aySopJXcRuVpEWkRkr4jcNs33/1ZEdojIVhH5vYhY0k6wpSvi2PX2pECthz1dkXMekvDv/xviSO8w/3r9KgryHPW7XVkkUOshzyVs16WZrDLjFAYRcQMbgDcD7cDzIrLJGLNr0mEPGmP+K3H8OuCrwNUZiPeMTgyO0R0ZZVmNMytlkgJ1HobG4v1zLqqc3X/rziN9fOcPB1h/6QW8cuH8DEWock1RvpvlWT52LzoR4+tP7aWjd5haXzF13iJqvEXU+Yqp9RbZftbydFIZsXMpsNcYsx9ARB4GrgVOJndjzOR1glJgzmdvhRxeKZPUlJhluqujf1bJPToR4/ZHdzC/tJDbrl6eqfBUjgo2+Ni0tYNYzGTdsuj4RIx/eHgrv9jRSWV5IUcHRpk61Ky8MI9aXxG13mLqEv9bOyn513qLKS6w19XbqST3eqBt0u124LKpB4nI3wOfAAqAN6YlulnIleTeWFVOvlto7ujn7cG6lB/3wDOH2HGkj6+vvxhvifPOUpS1gn4fP/jTYQ6cw1+UmTQWjXHLgy/yq11hPvPWFXzozxYzPhEj3D9CZ98IHb3DdPaN0Nk7TEffCJ19wzR39HF04PQGfb6S/Hjy9xad9ougzltMtbeQwrzs+QWQSnKf7tfwaWfmxpgNwAYRuQn4DPCB036QyM3AzQAXXHDB7CKdQSg8EP/t6y1K68/NNgV5LpZUlc+qHPJI7zBf+VULr19WydtW12YwOpWrgg0vj93LluQ+Mj7B3/3gRZ7a083d65r4wKsWApDvdtEwr4SGs1TVjYxPEO4foaM3nvAn/yI40jvMlkMnpm25UFFWmDjTn3TW70v+QiimuryQvDlqjZJKcm8HJk9uaAA6znL8w8A3p/uGMeY+4D6AtWvXpnXppiXRdiAXarab6jz8pqUnpWONMdz1050YA5+7dmVOvD5q7i2pKqOkwM22tl6uv6TB6nAYGZ/g5u+/wG9DPXz+upX8xWUXzurxRfluLlxQyoULzlwqPDQWpaN3hK6+ETr6hulM/CLo6BvhwNFB/rjvGAOj0VMe4xKoKi/itmuW846LM3vxYCrJ/XmgUUQWAUeAG4GbJh8gIo3GmNbEzbcCrcwhYwyhcIRrVubGWWmg1sPGF9rp7h+hynP2v1Qe39nFk7u7ueMty/HPd2b9v7Ke2yWsqveyNQt6zAyNRfnQA1t4Zv8xvvjO1bz7lZmZKlZSkMeSqrKzzmruHxmns/fl5N+VSP7VM3xu02HG5G6MiYrILcATgBv4jjGmWUTuAbYYYzYBt4jIlcA4cIJplmQyqScySu/QuGN7ykwVSGyqNnf2nzW594+Mc9emZgK1Hj746kVzFZ7KUWv8Pr77h4OMRWOWldkOjEb54PeeZ8vB43z13UGuu9javyI8Rfl4avItuWo+lTN3jDGbgc1T7rtz0tcfS3Ncs9KSI5upSYFJFTNvWFZ1xuO+/EQLPQOj/Pf7187ZOp/KXasbfIxNxNjT1c/qxBr8XOofGeevvvs8W9t6+Y8bL2bdLAoOnMgRn/hQeABw3mi9M/EU5eOfX3zWK1VfPHyC7z97iA9csfDkFYRKZZKVY/f6hsZ53/3Psa2tl2+s18QOTknuXREWlBZQUVZodShzpqnWe8aKmfGJGHc8uoPq8iI+edXSOY5M5ap6XzEVZQVsbZvbdfcTg2P8xf3Psrujn/967yu4ZlVu7L3NxBHJvSXs/LYDUwXqPBw4OnjabjzAt393gD1dEe6+tsmRV96p7CQiBBt8c9qG4NjAKOv/+1lC4QG+9f5XcGWges6eO9vZPrnHYobWcMSxbX7PJFAbX3ffM+Xs/fCxIb726xBXBar586YaK0JTOWx1g4+9PQNERjI/dq87MsKN9z3LwWODfOcDrzzr/lMusn1yP9I7zODYBI05UimT1FSf2FSdlNyNMXz6sR3kuVzcfW2TVaGpHBb0ezEGdhzJ7NJMuD+e2I/0DvPdv7yU1zTq/N+pbJ/cW7sTAzpybFmmxlPEvJJ8mo+8nNw3bevgd61HufWqpdR6iy2MTuWq5JWq2zNY797RO8x7vvUM4b4RHvjgpVxx0YKMPZedpVQKmc1auuKVMo05ltxFhMCkgdm9Q2Pc87NdBP0+3nfFQmuDUzlrXmkBF8wvyVjFTNvxIdb/97P0DY/z/Q9dxiUXzMvI8ziB7c/cQ+EItd4ivMW5t3HYVOelJRxhfCLGvZv30Ds8zr3XrcKdZV35VG4J+n0ZSe6Hjg3ynm89Q2Qkyg80sc/I9sk9FwZ0nEmg1sNYNMZDzx3mh1va+NBrFp28wEkpqwQbvHT0jdAdSd/YvX09A7z7W88wPD7Bgx++zJKLpOzG1sl9ImbY2zPA0hzbTE1KJvJ7fraLhnnFfOzKRosjUirehgBge5rq3VvDEW6871kmYoaHb77i5KB4dXa2Tu6Hjg0yFo3l7Jn74opSCvNcRGOGz71jJSUFtt9CUQ7QVOfF7ZK0TGba3dnPjfc9C8DDN1+ecyXP58PW2SA5oCNX/w/Pc7u4MlCNpyhPa3xV1igucLO0uvy8B2bvPNLHe+//E0V5bh788GUszpI+8XZh6+Te0jWACGdtuel0G266xOoQlDrNGr+XzTu6MMac0wyBbW29vO/+P1FelM+DH77srH3V1fRsvSwTCke4YH6JLkcolWWCDT76hsc5dGxo1o994dAJ3vvtP+EtyeeHf3O5JvZzZOvk3hKO0FiVm0sySmWzZDXLbNfdnztwnPff/ycqygt55G+uOOsoPHV2tk3uo9EJDh4dZFlN7i7JKJWtllaXUZTvmtW6+x/3HeUD33mOGm8RD998uV5lfZ5sm9wPHB0kGjM5WymjVDbLc7tYVe9NuQ3Bb0M9/NV3n8c/v5iHb75iTsbQOV1KyV1ErhaRFhHZKyK3TfP9T4jILhHZLiK/FpHZTaM9By1duV0po1S2W93gY+eRPsYnYmc97qk9YT70wBYWV5bx0Icvp7I8d+YyZNKMyV1E3MAG4BogAKwXkcCUw14C1hpjVgMbgS+mO9CpQuEIeS5hcYUuyyiVjYJ+H6PR2MkTsen8qrmLv/n+CyyrKeehD1/GghwauJNpqZy5XwrsNcbsN8aMAQ8D104+wBjztDEmuS3+LJDxqbQtXQMsrCi1bBCvUurs1sywqbp5Ryd/94MXaarz8j8fugxfScFchud4qWTGeqBt0u32xH1n8tfAL6f7hojcLCJbRGRLT09P6lFOo7U7knNtfpWyE//8YuaV5E/bhuCnW4/wkYdeYo3fx/f/+tKcbPyXaakk9+muQDDTHijyXmAt8KXpvm+Muc8Ys9YYs7aysjL1KKcYGoty+PiQbqYqlcVEhNUNvtPO3H/8Qjsf/+FW1l44jwc+eKmOgsyQVJJ7O+CfdLsB6Jh6kIhcCXwaWGeMGU1PeNPb2z2AMWgZpFJZLuj3EQpHGBqLz/r94fOHuXXjNl51UQXf+6tLKS3UCxAzJZVX9nmgUUQWAUeAG4GbJh8gIhcD3wKuNsZ0pz3KKZIbNHrmrlR2W+P3EjOw80g/LeEI//zYTl63tJJvve8VFOW7rQ7P0WZM7saYqIjcAjwBuIHvGGOaReQeYIsxZhPxZZgy4EeJPhKHjTHrMhV0KByhIM+llyUrleWSV6r+6+bdbG3r5coVVWz4i0sozNPEnmkp/U1kjNkMbJ5y352Tvr4yzXGdVSg8wJLKMp04pFSWqygrpN5XzNa2Xq5uquE/11+sFW5zxJYLXqFwhMsX61Bcpezgg69ZRPuJIe54ywry3ZrY54rtknvf8DidfSO63q6UTfz1axZZHUJOst2v0dZwcjNVK2WUUupMbJfcW8JaKaOUUjOxXXKvLCvkzYFq6n3aDlQppc7EdmvuVzXVcFVTjdVhKKVUVrPdmbtSSqmZaXJXSikH0uSulFIOpMldKaUcSJO7Uko5kCZ3pZRyIE3uSinlQJrclVLKgcSYaSfmZf6JRXqAQ+f48ArgaBrDsTt9PU6lr8fL9LU4lRNejwuNMTPOKbUsuZ8PEdlijFlrdRzZQl+PU+nr8TJ9LU6VS6+HLssopZQDaXJXSikHsmtyv8/qALKMvh6n0tfjZfpanCpnXg9brrkrpZQ6O7ueuSullDoL2yV3EblaRFpEZK+I3GZ1PFYREb+IPC0iu0WkWUQ+ZnVM2UBE3CLykoj83OpYrCYiPhHZKCJ7Eu+TK6yOySoi8vHE52SniDwkIkVWx5RptkruIuIGNgDXAAFgvYgErI3KMlHgk8aYFcDlwN/n8Gsx2ceA3VYHkSW+BjxujFkOBMnR10VE6oGPAmuNMSsBN3CjtVFlnq2SO3ApsNcYs98YMwY8DFxrcUyWMMZ0GmNeTHwdIf7Brbc2KmuJSAPwVuDbVsdiNRHxAK8F7gcwxowZY3qtjcpSeUCxiOQBJUCHxfFknN2Sez3QNul2Ozme0ABEZCFwMfAnayOx3H8AnwJiVgeSBRYDPcB3E8tU3xaRUquDsoIx5gjwZeAw0An0GWN+ZW1UmWe35C7T3JfT5T4iUgb8GPgHY0y/1fFYRUTeBnQbY16wOpYskQdcAnzTGHMxMAjk5B6ViMwj/hf+IqAOKBWR91obVebZLbm3A/5JtxvIgT+vzkRE8okn9h8YYx61Oh6LvRpYJyIHiS/XvVFE/sfakCzVDrQbY5J/zW0knuxz0ZXAAWNMjzFmHHgUeJXFMWWc3ZL780CjiCwSkQLimyKbLI7JEiIixNdTdxtjvmp1PFYzxtxujGkwxiwk/r54yhjj+LOzMzHGdAFtIrIscdebgF0WhmSlw8DlIlKS+Ny8iRzYXM6zOoDZMMZEReQW4AniO97fMcY0WxyWVV4NvA/YISJbE/fdYYzZbGFMKrt8BPhB4kRoP/BXFsdjCWPMn0RkI/Ai8Sqzl8iBK1X1ClWllHIguy3LKKWUSoEmd6WUciBN7kop5UCa3JVSyoE0uSullANpcldKKQfS5K6UUg6kyV0ppRzo/wECOLYonta98AAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzt3Xl4m/d1J/rvAQhwJ8FdJAFKokRSKwFZimJbSbzFjTfJbWLPtWc6nXY6zV3qpm3azji3HTfjNndul9v2TusnTz3pTOd2OnFt1dNIjmInseQsomyLtETJkgiIpBZuAAhQJAFuIIBz/wBAQxQpvCQBvHhfnM/z+IkIvgR/4XL44vzO7xxiZgghhNAXg9oLEEIIkX4S3IUQQockuAshhA5JcBdCCB2S4C6EEDokwV0IIXRIgrsQQuiQBHchhNAhRcGdiB4jIicR9RPRiyu8v4WIThHROSK6QERPpH+pQgghlKJUJ1SJyAjABeBRAMMAzgJ4npkvJ13zKoBzzPxNItoF4AQzb7nb89bW1vKWLXe9RAghxDI9PT0+Zq5LdV2Bguc6CKCfmQcBgIheA/A0gMtJ1zCAivi/KwGMpnrSLVu2oLu7W8GnF0IIkUBEN5RcpyQt0wxgKOnt4fhjyb4O4OeJaBjACQC/tsqivkxE3UTUPT4+rmR9Qggh1kFJcKcVHluey3kewN8ysxXAEwD+jojueG5mfpWZDzDzgbq6lK8qhBBCrJOS4D4MwJb0thV3pl1+GcDrAMDMZwAUAahNxwKFEEKsnZLgfhZAGxFtJSIzgOcAHFt2zU0AjwAAEe1ELLhL3kUIIVSSMrgzcxjACwDeAXAFwOvMfImIXiaiI/HLfgvArxBRL4BvA/hFlkbxQgihGiXVMmDmE4htlCY/9lLSvy8DOJTepQkhhFgvOaEqhBA6pOjOXQghMoWZsRhhhKPR2P9GoghHGYuRKMIRRjgae184En/stvclPub2j1+Mxp8n8fHxj3uqsxFtDeVq/1/OCgnuQoiM+quTV/EP3UPx4HxnoI5Es7c9d25oEv/fvz6Ytc+nJgnuQoiMevOjERAIn9legwKjASYjocAQ/9/b/m1AgYFgMhpgNNDSdQXG2GOJ9632MbHHY4+Z4v9bYCSY4s/xZz9w4dUfD8IfXEBNWaHaX5aMk+AuhMiY+cUIrvtn8MLDbfjqo+2qruWIvQnffG8A3/vYjZ+/d7Oqa8kG2VAVQmRMvzeIKAMdOZDn3rGpHG31ZTjWm7L1lS5IcBdCZIzLEwAAtDeUqbwSgIhw2N6Es9cnMDY1p/ZyMk6CuxAiY5yeAExGwpbaUrWXAgB4qrMRzMB3L4ypvZSMk+AuhMiYq54gttWVwWTMjVDTWleGPc0VOC7BXQgh1s/pDqA9B/LtyY7Ym9A7NIkb/hm1l5JREtyFEBkRmF/EyOQcOjblVnB/srMJAPCWzu/eJbgLITLiqjcIADl3595sKcaBzVU4rvOqGQnuQoiMcLljlTK5UAa53BFHE/rcgaVqHj2S4C6EyAinJ4BikxHWqmK1l3KHx/c0wkDQ9d27BHchREa4PAG0N5TBYFhpUqe66soLcf+2WhzvHYVeR09IcBdCZITTHcy5fHuyw/ZGXPfP4uORabWXkhES3IUQaecPLsAXXMi5Splkj+1uhMlIONY7ovZSMkKCuxAi7Vye3KyUSVZZYsID7XV468IYollsO5wtEtyFEGmXqELJ5Tt3ADhsb8LY1Dx6bt5SeylpJ8FdCJF2Tk8AlcUm1Jfndt/0z+9sQJHJoMuqGQnuQoi0c7kD6GgoB1HuVcokKy0swCM7G3Di4hjCkajay0krRcGdiB4jIicR9RPRiyu8/8+J6Hz8PxcRTaZ/qUIILWBmOD0BtG9Sv82vEoc7m+ALhnBm0K/2UtIqZXAnIiOAVwA8DmAXgOeJaFfyNcz8m8zsYGYHgL8E8GYmFiuEyH3u6XkE5sM5eTJ1JQ921KGssEB3qRkld+4HAfQz8yAzhwC8BuDpu1z/PIBvp2NxQgjtcboTAzq0EdyLTEb8zO4GvP2xGwvhiNrLSRslwb0ZwFDS28Pxx+5ARJsBbAVwcpX3f5mIuomoe3x8fK1rFUJowCfTl7QR3IFYG+Dp+TB+7PKpvZS0URLcV9oRWa0o9DkAR5l5xT9/zPwqMx9g5gN1dXVK1yiE0BCXJ4i68kJUlZrVXopih7bXoqrEpKvUjJLgPgzAlvS2FcBqX4HnkCcpmR9e9uBbPxlUexlC5ByXJ6CZfHuCyWjA43sb8YPLHsyGwmovJy2UBPezANqIaCsRmREL4MeWX0REHQCqAJxJ7xJz01+e6sefft+pu/IpITYiGuV4wzBtBXcgVjUztxjByT6v2ktJi5TBnZnDAF4A8A6AKwBeZ+ZLRPQyER1JuvR5AK+xXlusJZmaW8TF4UnML0aXjlkLIYChW7OYX4yiQyNlkMkObq1GQ0Uhjp3XR2qmQMlFzHwCwIllj7207O2vp29Zue3DaxNItKK4MDyJXU0V6i5IiByhtUqZZEYD4cm9Tfjv79/A9PwiKopMai9pQ+SE6jp0DfhQWGBARVEBeoflvJYQCYlKmTYNBncg1gY4FIni+5c8ai9lwyS4r8OZAT8+taUadpsFvUNTai9HiJzh9ARhrSpGWaGipEDOcdgssFUX45gOqmYkuK+RL7iAPncA92+vgcNmgdMTwFxIPwcfhNiIRE8ZrSIiHO5swul+H/zBBbWXsyES3NfozECs/8T922rRabUgEmVcHpO7dyFC4SgGxoNoz/E2v6kctjchEmV872O32kvZEAnua9Q14Ed5YQH2NFXAbq0EAJyX1IwQuO6fQTjKmr5zB4Adm8qxvb5M8weaJLivUdeAD59urUGB0YD6iiI0VRahd0g2VYXQcqVMMiLCEXsTPrw+AffUvNrLWTcJ7mswfGsWN/yzuH9bzdJjnVYLLkjFjBBweQIwGgitdaVqL2XDnupsBDPw1gXt3r1LcF+DpXz79k+Cu91mwXX/LCZnQ2otS4ic4HQHsKWmBEUmo9pL2bDWujLsaa7A8Qtjai9l3SS4r0HXgB81pebbcoqJvPuFYcm7i/zm8gRyfmbqWhzubELv0CRu+mfVXsq6SHBXiJnRNeDDfdtqbhsdtsdaCSJI3l3ktblQBDcmZjWfb0/2lL0JAHBco6kZCe4KDfpm4JlewP3bam97vKLIhNbaUjmpKvJavzcIZmi+UiZZs6UYBzZXabZqRoK7Ql39sSb+h5Ly7Ql2mwXnh6aQBz3ThALhSDTvNtmdGm87sJrD9ib0uQNLbRW0RIK7Ql0DfjRbitFSXXLH+xw2C3zBBYxpuGxKpM+3zw7hyF+dxsB4/nQMveoJwGw0YEvNnb8fWvbE3kYYCJq8e5fgrkA0yjgz6L8j357QabUAQN7drYmV/eByrOlUz/VbKq8ke5yeALbVl6HAqK+QUldeiPu31eJ476jmXpnr6zuRIZfHpjE5u7hiSgYAdjaWw2QkOakqMBsK4/3BWMnsuTzaZI/1lNFeD3clDtsbcd0/i49HptVeyppIcFcgUd9+X2vtiu8vLDBiZ2OF3LkLnO73IxSOoqKoAOfzJLhPzy9idGpe8z1lVvPY7kaYjKS5qhkJ7gp0DfjQWleKTZVFq15jt1pwYXgK0ai2XrqJ9DrZ50VZYQGeP9gCp3taN/M47+ZqfLNRT5UyySpLTHigvQ7He0c19fstwT2FxUgUH16buK3lwEo6rZUILoQx6MufTTRxO2bGqT4vPttWi09tqUaUgYt5cLjN6Y79zOupxn25w/YmjE3No+emdvZRJLincGF4EjOhCA5tWzklk+CwxTZVZXhH/ro8Ng339Dwe2lEPR0v85yEPUnUuTwClZiOaLcVqLyVjPr+zAUUmg6aqZiS4p9DVH8u339t69zv31roylBXK2L18dqrPCwB4sKMOtWWFsFYV50Xe3ekOoK2hHAbDnZVkelFaWIBHdjTgxMUxhCNRtZejiKLgTkSPEZGTiPqJ6MVVrvlnRHSZiC4R0f9I7zLVc3rAh12NFagqNd/1OqOBsKe5Ar158DJcrOxknxed1krUl8f2Zhw2C87f1H9wd3m0PX1JqcP2JviCIZyJV0PlupTBnYiMAF4B8DiAXQCeJ6Jdy65pA/A1AIeYeTeA38jAWrNufjGCj25MrloCuZzdZsGV0WmEwtr4yy7SZ2ImhHNDk3h4R/3SYw6bBaNT8/BO6/dwmy+4AP9MSLeVMske7KhDWWGBZlIzSu7cDwLoZ+ZBZg4BeA3A08uu+RUArzDzLQBgZm96l6mOnhu3EIpE7+gnsxq71YJQJIo+t7bqYcXGvef0ghm3Bfd98by7nuvdXW59V8okKzIZ8TO7G/D2x24shHN/brKS4N4MYCjp7eH4Y8naAbQT0Wkiep+IHkvXAtV0ut+HAgPhU1urFV1vX9pU1e8vs1jZyT4vassKsaepcumx3U2VKDCQrvPuiZ4y7Zv0eYBpucP2JkzPh/ETl0/tpaSkJLivtEuyvNizAEAbgAcBPA/gW0RkueOJiL5MRN1E1D0+Pr7WtWZd14AfdpsFZYUFiq5vqixCbVmhnFTNM+FIFD92jeOhjrrbNhWLTLHDbXrOu7s8AVSVmFBXVqj2UrLiM9trUVViwjENpGaUBPdhALakt60Alv8/GwbwHWZeZOZrAJyIBfvbMPOrzHyAmQ/U1dWtd81ZMT2/iAvDkynr25MREezWSjmpmmd6btzC9Hz4tpRMgsMWG8MY0dDhl7VwugNobyhfseeSHpmMBjy+txE/uOzJ+QNqSoL7WQBtRLSViMwAngNwbNk1/wTgIQAgolrE0jSD6Vxotn04OIEoA/etIbgDsdRM/3gQwYXc/saL9Dnp9MJkJHym7c69GYfNgplQBP1e/R1uY2a4PEFdTV9S4nBnE+YWIzjZl9tbiymDOzOHAbwA4B0AVwC8zsyXiOhlIjoSv+wdAH4iugzgFIDfYWZt1AutomvAj8ICA+5pqVrTx3VaK8F5cjJRxJy84sXBrdUoLzLd8b7EYabzQ9o52ajU6NQ8ggthXZ9MXcnBrdWoLy/EsfO5nZpRVOfOzCeYuZ2ZtzHzN+KPvcTMx+L/Zmb+KjPvYua9zPxaJhedDV0DPhzYUrXmYb92a/6cTBTA0MQsrnqDeKjjzpQMAGytKdVtE7HEAIt8u3M3GghPdTbhPec4pucX1V7OquSE6gp8wQX0uQOKSyCTVZWa0VJdIhUzeeKUM/bSfKV8OwAYDAS7zYJzOtxUTZRBttfnV3AHYm2AQ5Eovn/Jo/ZSViXBfQWJftxr2UxNZrfFOkQK/TvZ58XW2lK01q1eCrjPZoHLE8CMzvZhnJ4AGioKUVlyZzpK7xw2C6xVxTl9oEmC+wq6BvwoKyzA3ubK1BevwG6txMjkHMYDC2lemcgls6Ewugb8q6ZkEhwtlliHyBF9/cF3eQJ5l29PICIctjfhp/0++IO5+XsuwX0FXf0+fHpr9bpHhiUOM0lJpL51xQdzrJaSSUjsw+gp7x6JMq56gnlxMnU1R+xNiEQZ3/vYrfZSViTBfZmRyTlc98/i/u1rz7cn7G6qgNFAknfXuZNOL0rNRhxMcYK5pqwQLdUlujrMdHNiFgvhaF70lFnNjk3l2F5flrOpGQnuyyRG6q033w4AJeYCtNWXSYdIHUsM5vhMWy3MBal/jRw2i67u3J151FNmNUSEw51N+PD6BNxTudccToL7Ml0DPlSXmjf8Q+uwWdA7PKm5ielCmT53AGNT83hkR4Oi6x02C9zT8zkZBNYjUQbZptOh2EodtjeCGXgrB+erSnBPwszo6vfjvm01Gx480Gm1YHJ2ETcnZtO0OpFLEqcTH9yhrI2G3g4zOT0BtFSXoMSsrO+SXrXWlWFPcwWOXxhTeyl3kOCe5JpvBu7p+Q2lZBLstliljaRm9Olknxd7mz8ZzJHKrsYKmIykm/a/Lnf+Vsosd7izCb1Dk7jpz60bOQnuSbqW8u3r30xNaG8oR5HJIJuqOnRrJoRzN2/hoRRVMsmKTEbs0kmHyIVwBNd8M+jIkza/qTxlbwIAHM+x1IwE9yRdAz40VRZhS03Jhp/LZDRgd5N0iNSjH7nGEWXgkTUEdyCWd784MqX5DpHXfDMIR1nu3OOaLcU4sLkq56pmJLjHRaOMMwN+3LetNm3tS+3W2C+zVgbqCmXejQ/mWOshN0eLBbOhyNJmpFYtVcrkcRnkcoftTehzB3LqeyvBPa7PHcCt2cW05NsT7LZKzC9G4fLor91rvgpHoviR04sHlw3mUMJhi3UY1XpJpMsTQIGB0ForaZmEx/dugoGAt3Lo7l2Ce1zXQGxs1v0Kh2ErkTiZKKkZ/fjo5uSqgzlS2VJTAkuJSfN5d6c7iK21pYrq+/NFfXkR7ttWg2O9ozlT/izfnbiuAT9aa0vRWFmctufcXFOCymKTtP/VkZN9XhQYVh7MkUpsUpf2DzO5PIG8Ppm6miP2Jlz3z+LjkWm1lwJAgjsAYDESxQeD/jVPXUqFiNBprUSvzFTVjVN9scEcFSsM5lDCYbPA5Q1odlLXbCiMmxOzeX0ydTVf2L0JJiPlTNWMBHcAF4anMBOK4NAG+smsxmGzwOkJYC4USftzi+wavjULpyewrpRMgqPFAmbtpuoS4wKlUuZOlhIzPtdWh7d6RxHNgYooCe4AzsTz7fe2pvfOHYidVI1EGZfH5O5d607FT6Wupb59OYfGO0RKpczdHbY3YXRqHj031T+JLMEdsXz7zsYKVJea0/7cdmusXO68pGY072SfF5trStBaW7ru56gqNWNLjXY7RLo8ARQWGNBSvfGzIHr06K4GFJkMOVHznvfBfX4xgu4bt3Aozfn2hPqKIjRWFslJVY2bC0XQNeDHwzvqN3wOItEhMleqKtbC6Qlie30ZjBvsvaRXpYUFeGRHA05cHFP9fEveB/ePbtxCKBxNawnkcnarRbM5VhHTNeDDgoLBHEo4bBZ4AwsY02CHSJc7IJupKRy2N8IXDOH9wQlV16EouBPRY0TkJKJ+Inpxhff/IhGNE9H5+H//Jv1LzYyuAT+MBsKnttx94MJGdNoqcd0/i8nZUMY+h8isk31elCgYzKGEo0Wbh5mmZhfhnp6XMsgUHuyoR1lhAY71jqi6jpTBnYiMAF4B8DiAXQCeJ6JdK1z6D8zsiP/3rTSvM2NOD/jQaa1E+TpL25RwLB1mkry7Fi0N5thei8IC44afb2djOcxGg+aCu8srAzqUKDIZ8TO7G/D2x24shNWrklNy534QQD8zDzJzCMBrAJ7O7LKyIzC/iAvDUziUhi6Qd7PHWgkiSN5do5yeAEan5tOSkgGAwgIjdjVpr0NkolJG7txTO2xvwvR8GD9x+VRbg5Lg3gxgKOnt4fhjy32JiC4Q0VEisqVldRl29voEIlFOaz+ZlVQUmdBaWyq93TXqZBpKIJdLdIhUe9NtLVyeAMoKC9BUqayHfT77zPZaVJWYcEzFqhklwX2lbfHl2/zHAWxh5k4APwTw31Z8IqIvE1E3EXWPj4+vbaUZcLrfD3OBAfdsrsr457JruEIi35284sWe5go0VKQvqDlsFswtRuDMoS6CqTjdAbQ3lKWta6qemYwGPL63ET+47MFsSJ3TyEqC+zCA5DtxK4Db/hwxs5+ZF+Jv/mcA+1d6ImZ+lZkPMPOBujpl48kyqWvAjwObq1Bk2ngeNRW71QJfUJsVEvns1kwIH928hYc70nfXDsSCO6CdTVVmhssTkMNLa3C4swlzi5GlV37ZpiS4nwXQRkRbicgM4DkAx5IvIKLGpDePALiSviVmxsRMCFfGpjOekkmw26RDpBb9+GpsMEc6UzJArKlclYY6RI4HF3BrdlHaDqzBwa3VqC8vVO1AU8rgzsxhAC8AeAexoP06M18iopeJ6Ej8sq8Q0SUi6gXwFQC/mKkFp8uZ+Ei9+zK8mZqws7EcJiPJSVWNOdnnRU2peal9c7oQEew2i2Y6hrrcsZ4yUimjnNFAeLKzEaec45ieX8z651dU587MJ5i5nZm3MfM34o+9xMzH4v/+GjPvZmY7Mz/EzH2ZXHQ6dA34UFZYsNQeINMKC4zY2Vghd+4aEo5E8Z5zHA921K95MIcSDpsFV71BBFT4xV+rxN6AVMqszRF7E0LhKL5/yZP1z523J1TPDPhxcGs1CozZ+xLETqpO5UTHOJHauaFJTM0tpq0EcjmHLdYh8qIGqqhc7gBqSs2oLStUeyma4rBZYK0qViU1k5fBfWxqDoO+mazl2xM6rZUILoQx6JOxe1qQGMzx2fbMpO4Sm6rnNLCp6vQEJN++DkSEw/Ym/LTfh4mZ7J5Qz8vg3tUfy7ffn6V8e0Lil1mGd2jDqT4vPrVl/YM5UrGUmLG1tjTnK2aYGVelUmbdDnc2IRJlnLg4ltXPm5/BfcCPqhITdmT5h7W1rgylZqNmNtHy2cjkHPrcGxvMoYQWOkSOTM5hJhSRO/d12tlYju31ZVlPzeRdcGdmdA34cN+2moxskt2N0UDYa62Uk6oakIlTqStx2CwYDyxgNIfPP7g8iQEdZSqvRJuICIc7m/Dh9Qm4s/h9zrvgft0/i7Gp+aynZBLsNguujE4jFNbOsfN8dKrPi5bqEmyrW/9gDiWWDjPlcL27M14G2SZ37uv2lL0RzMBbWZyvmnfBvSs+Ui/bm6kJdqsFoUgUfe7cmJAu7jS/GEHXgC8tgzlS2dlYAXOBAeeH1B/LthqXJ4DGyqKM7T3kg211ZdjdVIHjF7KXd8+/4N7vx6aKImzdwKi0jbAvbarm7p1avjsz4Mf8YnoGc6RiLjBgd1NFTm+qxnrKyF37Rh2xN6F3aBI3/bNZ+Xx5FdyjUcaZQT/u316jWvOjpsoi1JaZ5aRqDnu3z4MSsxGfbs3cAJdkiQ6RiznYITIciaJ/PCiVMmnwZGesS8vxLKVm8iq4Oz0BTMyEVMu3A/Fj5zocu/f3H9zA4Lj26/djgznGcShNgzmUcNgsmF+MLvVLzyU3JmYRCkflzj0NrFUl2L+5KmtVM3kV3LsGEvXt6uTbEzqtFvSPBxFcUKcVaLr1uafxu//zY/zmP5zX/OlblyeIkcm5rKRkEvbZcnfsnsst05fS6XBnI/rcgaUKpEzKr+De78PW2lI0WYpVXYfdVqmZY+dKHO0eBgD0Dk/hHz8aVnk1G7NUApnmFr93Y6suRnWpOSeDu9MTABGwvV7KINPhic5GGAj4ydXMT2jKm+AejkTxwbUJ3KfyXTuApQ6DejjMtBiJ4p/Oj+ALuxuwr8WCP3rbqYlGWKs51efF7qYKbMritCEiWjrMlGtcngA2V5eg2JydFJXe1ZcX4Ue/8xB++TNbM/658ia4XxyZQnAhrHpKBgCqSs1oqS7RRd79VJ8XvmAI/+yADb9/eDd8wQX81al+tZe1LpOzIXTfmMhqSibBYbNgYDyoSmvYu5FKmfSzVZdk5fPkTXBP5Nvva1U/uAOxkkg99Jh5o2cYdeWFeKC9Dg6bBc/st+K//PQarvlm1F7amv3IlZnBHEokOkReyKGfifnFCK77Z6VSRqPyKLj7sGNTOWpypGWp3VqJkck5jAcWUl+co3zBBZzq8+KL+5qXWif/2y90wGw04Bvfvazy6tbuVJ8X1RkYzKGEfWnsXu4cZhocn0EkynLnrlF5EdznFyPovn5L1RLI5fQwdu+fzo0gHGU8s9+69Fh9RRF+7ZE2/PCKFz9yqT8EXalIlPGeaxwPdtTBmOWeQwBQWWxCa11udYj8pKeMBHctyovg/tHNW1gIR3Foe26kZABgd1MFDKTdk6rMjKM9w7DbLHf0HPmlQ1uwpaYEf/DW5Zw8mLOSczdvYXI2c4M5lMi1DpFOTwAmI2FLjTqnucXG5EVwPzPgh9FAOLg1OycOlSgxF6C9oVyzHSI/HplGnzuAZ5Pu2hMKC4z4vSd3od8bxN+duaHC6tbuZJ8XRgPhs211qq1hn80CXzCE4Vtzqq0hmcsdQGttGcwFeREmdCcvvmtdA37sba5EeY41PnLEByTnyp3aWrzRM4TCAgMO25tWfP8jO+vx2bZa/PkPXfAHc39f4WSfFwc2V6GyWL2fEUeOHWZyeQMyM1XDdB/cgwth9A5N5lRKJqHTasHk7CJuTmSnkVC6zC9G8J3zo/jC7k2rBkMiwktP7cJsKIL/5weuLK9wbUbjgzke2aleSgYAdjSWo7DAkBPBfWYhjKGJOXQ0yOElrVIU3InoMSJyElE/Eb14l+ueISImogPpW+LGnL02gXCUc2ozNcFuqwQAzaVm3r3ixdTcIp49cGdKJllbQzl+4b7N+PaHN3FpNHf/P55yxk6lqplvBwCT0YA9zZU5EdyvemN9gqRSRrtSBnciMgJ4BcDjAHYBeJ6Idq1wXTmArwD4IN2L3IiuAR/MBQbs31yl9lLu0N4Qu1PT2qbqGz1DaKwsUvQH8zceaYel2IT/cPxyzqafTl7xwlZdjG116t+lOmwWfJwDHSKXespIWkazlNy5HwTQz8yDzBwC8BqAp1e47g8A/DGAnJoXdrrfj/0tVSgy5d7x6cSdmpbKId1T8/ixaxxfuseqqGSwssSE3/5CBz68NoETF91ZWOHazC9GcHrAh4c7Mj+YQwmHzYKFcBR9Y+p2iHR6AigyGWCrys5pSpF+SoJ7M4ChpLeH448tIaJ9AGzM/FYa17Zht2ZCuDw2nRMtB1bTaa3ExZEphDVSMvjmuWFEGbfVtqfy3KdasLOxAv/XiSuYC0UyuLq1OzMYG8yhxqnUlThy5DCTyxNAW3151ucMi/RREtxX+u4uvb4mIgOAPwfwWymfiOjLRNRNRN3j45k/4PL+YLzFbw5upiYkenkncpy5jJlxtHsYB7dUY8saJlkZDYTfP7wLI5NzePXHgxlc4dqd6vOi2GTEvTnSlsJaVYzaMjPOqZyqk54y2qckuA8DsCW9bQWQ3G2+HMAeAO8R0XUA9wI4ttKmKjO/yswHmPlAXV3m64lPD/hQajaiU4Xj5EotdYjUQN79o5u3MOibwTMpNlJXcm9rDZ7c24hv/qgfI5O5UcfNzHj3iheHttfmTNouFzpE3prjWjwDAAAZ1klEQVQJwRtYQMcm9fcgxPopCe5nAbQR0VYiMgN4DsCxxDuZeYqZa5l5CzNvAfA+gCPM3J2RFa9B14AfB7dWw2TM3YrPzTUlqCw2aaL979GeYRSbjHhib+O6Pv5rT+wAM/B/f68vzStbn6ve7A/mUMJhs2BwfAZTs+p0iEy0HZA7d21LGfWYOQzgBQDvALgC4HVmvkRELxPRkUwvcL3cU/MYHJ/JyRLIZESETmtlzneInAtFcLx3DE/sbURZYcG6nsNaVYL/9YFtON47ig+vTaR5hWu3NJhjh3qnUleSOMyk1h986SmjD4puaZn5BDO3M/M2Zv5G/LGXmPnYCtc+mBt37bFJJ7kwnCMVu9UCpyeQc5uNyd6+NIbgQjhlbXsq//sD29BYWYT/cPwSIiqP5DvZ58Wuxgo0Vqo7mWu5TlsliNQ7qer0BFBeVIBNFdkbWCLSL3fzFRvUNeCHpcSEXY0Vai8lJbvNgkiUcXksd+/e3+geRkt1CT69wf48xWYjvvbETlwancYb3UOpPyBDpmYX0XPjVs6lZACgosiEbXVlqgV3lzuIjobynCgNFeuny+DOzDgz4Md9rTWaKOWyW2MnVc/naGpmaGIWXQN+PLPfmpZf+MOdjfjUlir8yTtOTM2pk1f+0dVxRKKcMyWQy6nVIZKZ4fRITxk90GVwv+GfxcjkXE7XtyerryhCY2VRzlbMvPnRCIiAL97TnPpiBYgIv394NyZmQ/jLd6+m5TnXKjGYI1FXnmscNgsmZkIYmshuZZE3sICpuUV0yGaq5ukyuCdG6t2/Pbc3U5PZrZacPKkajTKOfjSE+7fVwJrG04p7mivxvxyw4W+7rqM/yzX+kSjjPacXD7SrM5hDicQfnXNZPszkdEuljF7oNLj70FBRiNY1HLRRW6etEtf9s5icDam9lNt8cG0CQxNzeHa/LfXFa/TbX+hAscmIP8zySL7zQ5O4pfJgjlQ6NqnTIfKTMkipcdc63QX3aDSWb79/W62mNoQc1sTYvdzKu7/RM4TywgJ8YfemtD93bVkhfv3zbXjPOY6TfZ60P/9qTvZ5YDQQPteeWyWQyUxGA/aq0CHS6Q6gtqwwZ2YNi/XTXXB3eQPwz4Q0k29P2BPfVM2lvHtwIYzvXXTjKXsTis2ZOcH5C/dtQWtdKf7grSsIhbPTX+dk3zj2qzyYQwmHzYJLo9NZ+7oAgMsblJOpOqG74N7VH8u3a6G+PVms/K00p3q7n7gwhrnFyJqahK2VucCAf//ULlzzzeBvu65l7PMkjE3N4crYdE6nZBIcLRaEwlH0uaez8vmiUcZVj/SU0Qv9BfcBHzbXlKR18y9b7Dk2IPmNniG01pXinpbMVpQ81FGPh3fU4z+924/xQGZH8p3qizWse0QLwX2pQ2R2Xs2NTM5hNhSRShmd0FVwD0ei+GBwIudbDqzGbrXAF1zA2JT6LfGv+WZw9votPLvflpW9i997cifmFyP403ecGf08J/u8sFYVY3t97qcemi3FqC0rxPmb2QnuS5UyUuOuC7oK7h+PTiOwENZcvj3Bbktsqqqfdz/aMwRDGmvbU2mtK8MvHdqC13uGcDFDqan5xQhO9/vw8I7cGMyRSrY7RDrjlTJtGvjDJ1LTVXA/3a+dfjIr2dlYDpORVD+pGoky/rFnBA+016Ehi/1Ffu2RNtSUmvH145cykpp6f9CPucVIzp5KXcm+FgsGfdnpEOnyBNBsKUZ5UW5vNAtldBXczwz4sWNTOWo1WsZVWGDEzsYK1e/cf9rvg3t6Hs9koLb9biqKTPidL3Sg58YtHOsdTf0Ba5QYzHFfjgzmUGIp756Fn4nYgA65a9cL3QT3hXAEZ69PaPauPaHTWomLw1OIqtgx8WjPMCwlJnx+V/bvcJ/db8Pe5kr8xxN9mA2F0/a8zIyTTi8Oba/JmcEcSnRa4x0iM5x3X4xEMTg+I/l2HdFNcD93cxIL4ahmN1MT7FYLAgthDPpmVPn8U7OLeOeSG0/bm1BYkP0gaIiP5HNPz+Ob7w2k7Xn7vUEMTcxpKiUDAOVFJmyvK8v4TNUb/hmEIlGplNER3QT3rn4fDAR8unVjLWnVlngZrtZhpmMXRhEKR/HsgeymZJId2FKNpx1N+OsfD2JoYjYtz7k0mKNDW8EdyE6HSKc71t9Hatz1Qz/BfcCPvVYLKjS+GdRaV4ZSs1G1KTxHu4ewY1M5djep2wf/xcd3wEiE//i9K2l5vpN9XuzYVI4mS24N5lDC0WLBrdlF3EzTH7qVOD0BGAiaKBEVyugiuM8shHF+aFKzJZDJjAbCXmulKidVXZ4Aeoen0ta3fSMaK4vxfzy4DScuupemaq3X1Nwium/cwiM7tXfXDmTnMJPLHcCWmlJN7UeIu9NFcP/w+gTCUcYhjefbE+xWC65kuacIENtILTAQfm5fdmrbU/mVz7Wi2VKMl49fRjiy/q/FT+KDObTQcmAlHQ3lKDYZcS6Dm6ouaTugO7oI7mcG/DAbDdi/uUrtpaSF3WZBKJK9niJArFrizY9G8PCO+pzpCFhkMuJ3n9yJPncA3z67/pF8J694UVViWho8rTUFGe4QOb8YwXW/VMrojS6Ce9eAD/taLBnrXJhtnSp0iPyRcxy+4IKqG6kreXzPJtzbWo0/+75zXb3uI1HGe67xnB7MoYSjxYLLo9NYCKd/iHq/N4goQypldEZRcCeix4jISUT9RPTiCu//34joIhGdJ6KfEtGu9C91ZZOzIVwancYhDU1dSiXWU8Sc1ZOqb/QMobbMjAc7cqvHeWIk39TcIv7ih2sfydc7PImJmRAe3tmQgdVljyP+au7KWCDtz50Y0CGtfvUlZXAnIiOAVwA8DmAXgOdXCN7/g5n3MrMDwB8D+LO0r3QV7w/6wQxdbKYmEFFWx+75gwt494oXP+tohsmYey/mdjZW4J9/ugV/9/6NpUCk1Kk+L4wGwgNtufVHa62WNlVvpr/e3ekJwGw0YHONdiaXidSU/CYfBNDPzIPMHALwGoCnky9g5uTkcCmArB2v7Brwo8RsRKc1Nwcdr1en1YL+8SCCC+k7pbma75wfRTjKOZeSSfbVRztQajbi5eOX11Tv/e4VL/a3VKGyRNslso2VRagvL8xI3v2qJ4jWutKc/MMu1k/Jd7MZQPJu1nD8sdsQ0a8S0QBid+5fWemJiOjLRNRNRN3j4+PrWe8dTvf7cHBrNcwF+vrBtNsqwYyMdUhM9kbPMDqtlejI4Q216lIzvvpoO37a78MPLisbyeeemsflsWnNnUpdSSY7RDrdgZz+3ov1URIRV9qFuuPWiZlfYeZtAP4dgN9b6YmY+VVmPsDMB+rqNv4y2TM9j4HxGV2lZBISr0QyfZjp45EpXBmbxrMZnLaULv/i3s1oqy/DH373iqKNxVPO2KlUrZZALudoseC6fxa3ZtI3RD0wv4iRyTkpg9QhJcF9GEDy63UrgLu17HsNwM9uZFFKnRmIjdTTej+ZlVSXmtFSXZLxvPvRnmGYjQYcsedGbfvdmIwG/P7h3bg5MYu/+WnqkXwn+7xothTrptNhJjpEXvXG2g5IpYz+KAnuZwG0EdFWIjIDeA7AseQLiKgt6c0nAay9rGEdTvf7UFlsws5GdY/KZ0qntRK9GayYWQhH8E/nR/Do7gbN5KQ/01aLR3c14K9O9sMzvfrEKq0N5lCi02pJe4dIlztRKSPBXW9SBndmDgN4AcA7AK4AeJ2ZLxHRy0R0JH7ZC0R0iYjOA/gqgH+VsRV/si50DfhxX2uNpuuX78Zhs2Bkci5jc0XfveLF5OyiJlIyyX7vyZ0IRxh/9Hbfqtd8cG0Cs6GIblIyAFBWWID2+vK05t2dngBKzEY0a7Dnjrg7RbuQzHyCmduZeRszfyP+2EvMfCz+719n5t3M7GDmh5j5UiYXDQBDE3MYmZzD/dv1l29PyPTYvaM9w9hUUYTPaqxMcHNNKX75s1vx5kcjOLdKaeCpPi+KTAbN9/dfzmGzoHc4fR0iXZ4A2urLYNDpDVI+02yJyel4Myk9bqYm7G6qgIEyc1LVOz2P95xefPGeZk2+8vnVh7ajvrwQXz9++Y7BJsyMk31eHNpWq7tGWI4WCyZnF3Hdn54OkU53UDZTdUqzwb1rwI/68kJsq9PHZtlKSswFaG8oz0iHyDfPjSDKwDMaS8kklBUW4N89tgO9Q5P4n+dGbnvfwPgMbk7M6qIEcrlPOkRu/DCTP7gAX3BB8u06pcngzsw4M+DD/dtqdLNZthq7Nb0vw4HY1++N7iHs31yFVg3/cfy5fc2w2yz4o7f7bjvsdbIvVgevx+De3lCOErMxLZuqLo8M6NAzTQZ3lycIXzCkyxLI5ey22MvwoYm5tD3nuaFJDIzPaG4jdTmDgfD1w7vgDSzglVP9S48nBnPocZPQaKC0dYj8pKeMBHc90mRwTwxv0PNmakKiQ2Q6a5uP9gyjyGTAk52NaXtOtexrqcIX72nG3/zkGm74ZzA9v4ju67d0edee4Gix4PLYNOYXN9Yh0ukJoLLYhPry3GjxLNJLo8Hdj5bqElirStReSsZ1bCpHYYEhbZuq84sRHO8dxRN7GlGu8ZGECS8+tgMmI+EPv3sFP3H5EI4yHtFxcN9ns2Axwrg8trF+/y53AB0N5bpPbeYrzQX3cCSK9wf9uq6SSWYyGrCnuTJt5ZDvXHIjMB/GMwe0nZJJVl9RhF99eDt+cNmDv/ihC5YSE/a1aHMwhxKJoSMbybszM5yeANqlza9uaS64XxqdRmA+jPt11L89lU5rJS6OTG1o1FzCG93DsFYV496t+vrj+K8PbUVLdQmueoOaH8yRyqbKImyqKNpQ3t09PY/AfFjaDuiY5oJ7V7yfzH2t+gpOd+OwWTC/GF3qA7JeI5NzOD3gw5fuseru0EqRyYh//1RszMDP7Nqk8moyb6MdIp3xtgNSKaNfBWovYK2OOJrQZClCXR5tAi11iBya3FAfnTd7hsEarm1P5dFdDfjhVx/Atjr9D51wtFjw9iU3/MGFdc28vSplkLqnuTv3ZksxnnbkfgfDdNpSU4KKooINtf9lZhz9aBj3tdbAVq3fjejt9WV5sUGYOMy03p8JpyeA+vJCVJWa07kskUM0F9zzERHBbrNsqEPkh9cmcMM/i2d1tJGaz/Y2V8KwgQ6RLo8M6NA7Ce4aYbda4PQEMBdaX23zGz3DKCsswGN79J+PzgelhbHWFOfWkXePRhkuT0BSMjonwV0j7DYLIlHG5bG1373PLIRx4uIYntzbiBKz5rZZxCocNgt6hybvaJyWytCtWcwvRqVSRuckuGuEPXFSdR2pme9eHMNsKCIpGZ1x2CyYng/jun9mTR+3VCkjaRldk+CuEfUVRWisLFrXYaajPcNorS3F/s36PdiTjxwtiQ6Ra/uZSPSUaauXA0x6JsFdQ2Jj99b2i3zDP4MPr03gS/uteVFFkk/a6stRajauObg7PUHYqotRWigpOj2T4K4hdpsF1/2zmJwNKf6Yoz3DMBDwxXvyq3w0HxgNhL3WtXeIdLkDaK+XlIzeSXDXEIc1MXZPWd49EmX8Y88wPtNWh8ZK/bW/FbE+M1fW0CEyFI5iYDwo+fY8IMFdQ/bEN1WVpma6BnwYnZrXfN92sTpHvEPkpVFlHSKv+2cQjrJUyuQBCe4aUlFkwra6UsVj9472DKOiqACP7mrI8MqEWvatcVNVesrkD0XBnYgeIyInEfUT0YsrvP+rRHSZiC4Q0btEtDn9SxVA7DDT+aHUY/em5hbx9sduPO1o1t2QaPGJhngVldLg7vIEYDQQWvOg/06+SxncicgI4BUAjwPYBeB5Itq17LJzAA4wcyeAowD+ON0LFTF2mwW+4ALGpubvet1bF0axEI7qtkmY+ESsQ6SygdlOdwBbakrkD34eUHLnfhBAPzMPMnMIwGsAnk6+gJlPMfNs/M33AUhEyZDE2L1U9e5vdA+jvaFs6XqhXw6bBUMTc/AHF1JeKz1l8oeS4N4MYCjp7eH4Y6v5ZQDfW+kdRPRlIuomou7x8XHlqxRLdjZWwGSku55U7fcGcH5oEs/ut0ltex5IdIhMlZqZC0VwY2JW8u15QklwXyk6rJjwJaKfB3AAwJ+s9H5mfpWZDzDzgbq6OuWrFEuKTEbsbKy46537Gz3DMBoIP7tPatvzwV5rJYwGShnc+71BMEMqZfKEkuA+DMCW9LYVwOjyi4jo8wB+F8ARZk79+lCsW6e1EheHp1ZsGBWORPHmRyN4qKM+rwaa5LMSc6xDZKrgnmg7IDXu+UFJcD8LoI2IthKRGcBzAI4lX0BE+wD8NWKB3Zv+ZYpkdqsFgYUwBn13Noz68dVxjAcWZCM1zyTG7t2tQ6TLE4C5wIDNOh7WIj6RMrgzcxjACwDeAXAFwOvMfImIXiaiI/HL/gRAGYA3iOg8ER1b5elEGthtn4zdW+6N7mFUl5rx8I76bC9LqGifzYLA/Mp/8BOcngC215WhwCjHW/KBos5BzHwCwIllj72U9O/Pp3ld4i621ZWh1GxE7/AkvpR0hz4xE8IPr3jwL+/dAnOB/ALnk+QOkdtX6fbocgfw6TwaLJ/vJAJokNFA2NNcecdJ1e+cH8FihKVvex7aVleGssKCVevdp+cXMTo1L5UyeUSCu0Y5bBZcGZ1GKBxdeuxozzD2NFdgZ2OFiisTajAaCJ136RB5Nb6Z2rFJerjnCwnuGmW3WRCKRNHnjjWMujw6jUuj03jmHrlrz1cOmwV9Y4EVO0Q63UEA0lMmn0hw16jOZR0i3+gZgtlowNMOqW3PVw6bBeEo4+OROw+4uTwBlJqNaLZI6+d8IcFdo5otxagtM6N3eAqhcBTfOT+Kz++qR1WpWe2lCZXcbeye0x1A+6ZyObGcRyS4axQRodNqQe/QJE72eTAxE8Kz+22pP1DoVn15EZotxTi3QnB3eWT6Ur6R4K5hdqsF/eNB/NfT11FfXojPttWqvSShMofNgvM3bw/uvuAC/DMhOZmaZyS4a1inrRLMwAfXJvBz9zTL4RQBh82Ckck5jAc+6QDiig/okJ4y+UWigYbZ4zNVAUhKRgBYOe/uXOopI2WQ+USCu4ZVl5qxtbYU97RYVj2VKPLLnqZEh8hPDjO5PAFUlZhQVyaN5PKJovYDInf95184gBKzTNURMcVmI3Zsur1DpNMdQHuDVMrkG7lz17jt9WVoktplkcRhs+DCUKwlNDPD5QnK9KU8JMFdCJ1x2GItoQfGgxidmkdwISwnU/OQpGWE0Jl98U3Vc0OTSwNb5M49/0hwF0JnWmvLUF5UgPNDk0uDOeQAU/6R4C6EzhgMBLs1dphpfjGCTRVFqCwxqb0skWWScxdChxw2C5yeAHqHJuVkap6S4C6EDjlsFkSijIHxGXQ0yBmIfCTBXQgdSszZBaSHe76S4C6EDtWVFy71bpdKmfykKLgT0WNE5CSifiJ6cYX3f46IPiKiMBE9k/5lCiHWytFiARGkNUWeSlktQ0RGAK8AeBTAMICzRHSMmS8nXXYTwC8C+O1MLFIIsXa/8tlW7LNZUGKWorh8pOS7fhBAPzMPAgARvQbgaQBLwZ2Zr8ffF13pCYQQ2eewWeBIyr2L/KIkLdMMYCjp7eH4Y0IIIXKUkuC+Uis5Xs8nI6IvE1E3EXWPj4+v5ymEEEIooCS4DwNIngRhBTC6nk/GzK8y8wFmPlBXV7eepxBCCKGAkuB+FkAbEW0lIjOA5wAcy+yyhBBCbETK4M7MYQAvAHgHwBUArzPzJSJ6mYiOAAARfYqIhgE8C+CviehSJhcthBDi7hTVSDHzCQAnlj32UtK/zyKWrhFCCJED5ISqEELokAR3IYTQIWJeV1Xjxj8x0TiAG+v88FoAvjQuR+vk63E7+Xp8Qr4Wt9PD12MzM6csN1QtuG8EEXUz8wG115Er5OtxO/l6fEK+FrfLp6+HpGWEEEKHJLgLIYQOaTW4v6r2AnKMfD1uJ1+PT8jX4nZ58/XQZM5dCCHE3Wn1zl0IIcRdaC64p5oKlS+IyEZEp4joChFdIqJfV3tNuYCIjER0jojeUnstaiMiCxEdJaK++M/JfWqvSS1E9Jvx35OPiejbRFSk9poyTVPBPWkq1OMAdgF4noh2qbsq1YQB/BYz7wRwL4BfzeOvRbJfR6wHkgD+XwBvM/MOAHbk6deFiJoBfAXAAWbeA8CIWANEXdNUcEfSVChmDgFITIXKO8w8xswfxf8dQOwXN6+HqBCRFcCTAL6l9lrURkQVAD4H4G8AgJlDzDyp7qpUVQCgmIgKAJRgnW3LtURrwV2mQq2AiLYA2AfgA3VXorq/APBvAci4R6AVwDiA/xpPU32LiErVXpQamHkEwJ8iNut5DMAUM39f3VVlntaCe9qmQukFEZUB+EcAv8HM02qvRy1E9BQALzP3qL2WHFEA4B4A32TmfQBmAOTlHhURVSH2Cn8rgCYApUT08+quKvO0FtzTNhVKD4jIhFhg/3tmflPt9ajsEIAjRHQdsXTdw0T039VdkqqGAQwzc+LV3FHEgn0++jyAa8w8zsyLAN4EcL/Ka8o4rQV3mQoVR0SEWD71CjP/mdrrURszf42Zrcy8BbGfi5PMrPu7s9UwsxvAEBF1xB96BMBlFZekppsA7iWikvjvzSPIg81lRcM6cgUzh4koMRXKCOC/MHO+Tn06BOBfArhIROfjj/2f8cEqQgDArwH4+/iN0CCAX1J5Papg5g+I6CiAjxCrMjuHPDipKidUhRBCh7SWlhFCCKGABHchhNAhCe5CCKFDEtyFEEKHJLgLIYQOSXAXQggdkuAuhBA6JMFdCCF06P8H7l6g8wx/go4AAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "plt.ion()\n", - "for i in range(50):\n", - " y = np.random.random([10,1])\n", - " plt.plot(y)\n", - " plt.draw()\n", - " plt.pause(0.0001)\n", - " plt.clf()" - ] - }, - { - "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": "3.6.8" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Day_2/ExtraExamples/pyplot.ipynb b/Day_2/ExtraExamples/pyplot.ipynb deleted file mode 100644 index a2d0897..0000000 --- a/Day_2/ExtraExamples/pyplot.ipynb +++ /dev/null @@ -1,230 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n# Pyplot tutorial\n\n\nAn introduction to the pyplot interface.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Intro to pyplot\n===============\n\n:mod:`matplotlib.pyplot` is a collection of command style functions\nthat make matplotlib work like MATLAB.\nEach ``pyplot`` function makes\nsome change to a figure: e.g., creates a figure, creates a plotting area\nin a figure, plots some lines in a plotting area, decorates the plot\nwith labels, etc.\n\nIn :mod:`matplotlib.pyplot` various states are preserved\nacross function calls, so that it keeps track of things like\nthe current figure and plotting area, and the plotting\nfunctions are directed to the current axes (please note that \"axes\" here\nand in most places in the documentation refers to the *axes*\n`part of a figure `\nand not the strict mathematical term for more than one axis).\n\n

Note

the pyplot API is generally less-flexible than the object-oriented API.\n Most of the function calls you see here can also be called as methods\n from an ``Axes`` object. We recommend browsing the tutorials and\n examples to see how this works.

\n\nGenerating visualizations with pyplot is very quick:\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\nplt.plot([1, 2, 3, 4])\nplt.ylabel('some numbers')\nplt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You may be wondering why the x-axis ranges from 0-3 and the y-axis\nfrom 1-4. If you provide a single list or array to the\n:func:`~matplotlib.pyplot.plot` command, matplotlib assumes it is a\nsequence of y values, and automatically generates the x values for\nyou. Since python ranges start with 0, the default x vector has the\nsame length as y but starts with 0. Hence the x data are\n``[0,1,2,3]``.\n\n:func:`~matplotlib.pyplot.plot` is a versatile command, and will take\nan arbitrary number of arguments. For example, to plot x versus y,\nyou can issue the command:\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "plt.plot([1, 2, 3, 4], [1, 4, 9, 16])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Formatting the style of your plot\n---------------------------------\n\nFor every x, y pair of arguments, there is an optional third argument\nwhich is the format string that indicates the color and line type of\nthe plot. The letters and symbols of the format string are from\nMATLAB, and you concatenate a color string with a line style string.\nThe default format string is 'b-', which is a solid blue line. For\nexample, to plot the above with red circles, you would issue\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "plt.plot([1, 2, 3, 4], [1, 4, 9, 16], 'ro')\nplt.axis([0, 6, 0, 20])\nplt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "See the :func:`~matplotlib.pyplot.plot` documentation for a complete\nlist of line styles and format strings. The\n:func:`~matplotlib.pyplot.axis` command in the example above takes a\nlist of ``[xmin, xmax, ymin, ymax]`` and specifies the viewport of the\naxes.\n\nIf matplotlib were limited to working with lists, it would be fairly\nuseless for numeric processing. Generally, you will use `numpy\n`_ arrays. In fact, all sequences are\nconverted to numpy arrays internally. The example below illustrates a\nplotting several lines with different format styles in one command\nusing arrays.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import numpy as np\n\n# evenly sampled time at 200ms intervals\nt = np.arange(0., 5., 0.2)\n\n# red dashes, blue squares and green triangles\nplt.plot(t, t, 'r--', t, t**2, 'bs', t, t**3, 'g^')\nplt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\nPlotting with keyword strings\n=============================\n\nThere are some instances where you have data in a format that lets you\naccess particular variables with strings. For example, with\n:class:`numpy.recarray` or :class:`pandas.DataFrame`.\n\nMatplotlib allows you provide such an object with\nthe ``data`` keyword argument. If provided, then you may generate plots with\nthe strings corresponding to these variables.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "data = {'a': np.arange(50),\n 'c': np.random.randint(0, 50, 50),\n 'd': np.random.randn(50)}\ndata['b'] = data['a'] + 10 * np.random.randn(50)\ndata['d'] = np.abs(data['d']) * 100\n\nplt.scatter('a', 'b', c='c', s='d', data=data)\nplt.xlabel('entry a')\nplt.ylabel('entry b')\nplt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\nPlotting with categorical variables\n===================================\n\nIt is also possible to create a plot using categorical variables.\nMatplotlib allows you to pass categorical variables directly to\nmany plotting functions. For example:\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "names = ['group_a', 'group_b', 'group_c']\nvalues = [1, 10, 100]\n\nplt.figure(figsize=(9, 3))\n\nplt.subplot(131)\nplt.bar(names, values)\nplt.subplot(132)\nplt.scatter(names, values)\nplt.subplot(133)\nplt.plot(names, values)\nplt.suptitle('Categorical Plotting')\nplt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\nControlling line properties\n===========================\n\nLines have many attributes that you can set: linewidth, dash style,\nantialiased, etc; see :class:`matplotlib.lines.Line2D`. There are\nseveral ways to set line properties\n\n* Use keyword args::\n\n plt.plot(x, y, linewidth=2.0)\n\n\n* Use the setter methods of a ``Line2D`` instance. ``plot`` returns a list\n of ``Line2D`` objects; e.g., ``line1, line2 = plot(x1, y1, x2, y2)``. In the code\n below we will suppose that we have only\n one line so that the list returned is of length 1. We use tuple unpacking with\n ``line,`` to get the first element of that list::\n\n line, = plt.plot(x, y, '-')\n line.set_antialiased(False) # turn off antialiasing\n\n* Use the :func:`~matplotlib.pyplot.setp` command. The example below\n uses a MATLAB-style command to set multiple properties\n on a list of lines. ``setp`` works transparently with a list of objects\n or a single object. You can either use python keyword arguments or\n MATLAB-style string/value pairs::\n\n lines = plt.plot(x1, y1, x2, y2)\n # use keyword args\n plt.setp(lines, color='r', linewidth=2.0)\n # or MATLAB style string value pairs\n plt.setp(lines, 'color', 'r', 'linewidth', 2.0)\n\n\nHere are the available :class:`~matplotlib.lines.Line2D` properties.\n\n====================== ==================================================\nProperty Value Type\n====================== ==================================================\nalpha float\nanimated [True | False]\nantialiased or aa [True | False]\nclip_box a matplotlib.transform.Bbox instance\nclip_on [True | False]\nclip_path a Path instance and a Transform instance, a Patch\ncolor or c any matplotlib color\ncontains the hit testing function\ndash_capstyle [``'butt'`` | ``'round'`` | ``'projecting'``]\ndash_joinstyle [``'miter'`` | ``'round'`` | ``'bevel'``]\ndashes sequence of on/off ink in points\ndata (np.array xdata, np.array ydata)\nfigure a matplotlib.figure.Figure instance\nlabel any string\nlinestyle or ls [ ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'steps'`` | ...]\nlinewidth or lw float value in points\nmarker [ ``'+'`` | ``','`` | ``'.'`` | ``'1'`` | ``'2'`` | ``'3'`` | ``'4'`` ]\nmarkeredgecolor or mec any matplotlib color\nmarkeredgewidth or mew float value in points\nmarkerfacecolor or mfc any matplotlib color\nmarkersize or ms float\nmarkevery [ None | integer | (startind, stride) ]\npicker used in interactive line selection\npickradius the line pick selection radius\nsolid_capstyle [``'butt'`` | ``'round'`` | ``'projecting'``]\nsolid_joinstyle [``'miter'`` | ``'round'`` | ``'bevel'``]\ntransform a matplotlib.transforms.Transform instance\nvisible [True | False]\nxdata np.array\nydata np.array\nzorder any number\n====================== ==================================================\n\nTo get a list of settable line properties, call the\n:func:`~matplotlib.pyplot.setp` function with a line or lines\nas argument\n\n.. sourcecode:: ipython\n\n In [69]: lines = plt.plot([1, 2, 3])\n\n In [70]: plt.setp(lines)\n alpha: float\n animated: [True | False]\n antialiased or aa: [True | False]\n ...snip\n\n\n\nWorking with multiple figures and axes\n======================================\n\nMATLAB, and :mod:`~matplotlib.pyplot`, have the concept of the current\nfigure and the current axes. All plotting commands apply to the\ncurrent axes. The function :func:`~matplotlib.pyplot.gca` returns the\ncurrent axes (a :class:`matplotlib.axes.Axes` instance), and\n:func:`~matplotlib.pyplot.gcf` returns the current figure\n(:class:`matplotlib.figure.Figure` instance). Normally, you don't have\nto worry about this, because it is all taken care of behind the\nscenes. Below is a script to create two subplots.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "def f(t):\n return np.exp(-t) * np.cos(2*np.pi*t)\n\nt1 = np.arange(0.0, 5.0, 0.1)\nt2 = np.arange(0.0, 5.0, 0.02)\n\nplt.figure()\nplt.subplot(211)\nplt.plot(t1, f(t1), 'bo', t2, f(t2), 'k')\n\nplt.subplot(212)\nplt.plot(t2, np.cos(2*np.pi*t2), 'r--')\nplt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The :func:`~matplotlib.pyplot.figure` command here is optional because\n``figure(1)`` will be created by default, just as a ``subplot(111)``\nwill be created by default if you don't manually specify any axes. The\n:func:`~matplotlib.pyplot.subplot` command specifies ``numrows,\nnumcols, plot_number`` where ``plot_number`` ranges from 1 to\n``numrows*numcols``. The commas in the ``subplot`` command are\noptional if ``numrows*numcols<10``. So ``subplot(211)`` is identical\nto ``subplot(2, 1, 1)``.\n\nYou can create an arbitrary number of subplots\nand axes. If you want to place an axes manually, i.e., not on a\nrectangular grid, use the :func:`~matplotlib.pyplot.axes` command,\nwhich allows you to specify the location as ``axes([left, bottom,\nwidth, height])`` where all values are in fractional (0 to 1)\ncoordinates. See :doc:`/gallery/subplots_axes_and_figures/axes_demo` for an example of\nplacing axes manually and :doc:`/gallery/subplots_axes_and_figures/subplot_demo` for an\nexample with lots of subplots.\n\n\nYou can create multiple figures by using multiple\n:func:`~matplotlib.pyplot.figure` calls with an increasing figure\nnumber. Of course, each figure can contain as many axes and subplots\nas your heart desires::\n\n import matplotlib.pyplot as plt\n plt.figure(1) # the first figure\n plt.subplot(211) # the first subplot in the first figure\n plt.plot([1, 2, 3])\n plt.subplot(212) # the second subplot in the first figure\n plt.plot([4, 5, 6])\n\n\n plt.figure(2) # a second figure\n plt.plot([4, 5, 6]) # creates a subplot(111) by default\n\n plt.figure(1) # figure 1 current; subplot(212) still current\n plt.subplot(211) # make subplot(211) in figure1 current\n plt.title('Easy as 1, 2, 3') # subplot 211 title\n\nYou can clear the current figure with :func:`~matplotlib.pyplot.clf`\nand the current axes with :func:`~matplotlib.pyplot.cla`. If you find\nit annoying that states (specifically the current image, figure and axes)\nare being maintained for you behind the scenes, don't despair: this is just a thin\nstateful wrapper around an object oriented API, which you can use\ninstead (see :doc:`/tutorials/intermediate/artists`)\n\nIf you are making lots of figures, you need to be aware of one\nmore thing: the memory required for a figure is not completely\nreleased until the figure is explicitly closed with\n:func:`~matplotlib.pyplot.close`. Deleting all references to the\nfigure, and/or using the window manager to kill the window in which\nthe figure appears on the screen, is not enough, because pyplot\nmaintains internal references until :func:`~matplotlib.pyplot.close`\nis called.\n\n\nWorking with text\n=================\n\nThe :func:`~matplotlib.pyplot.text` command can be used to add text in\nan arbitrary location, and the :func:`~matplotlib.pyplot.xlabel`,\n:func:`~matplotlib.pyplot.ylabel` and :func:`~matplotlib.pyplot.title`\nare used to add text in the indicated locations (see :doc:`/tutorials/text/text_intro`\nfor a more detailed example)\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "mu, sigma = 100, 15\nx = mu + sigma * np.random.randn(10000)\n\n# the histogram of the data\nn, bins, patches = plt.hist(x, 50, density=1, facecolor='g', alpha=0.75)\n\n\nplt.xlabel('Smarts')\nplt.ylabel('Probability')\nplt.title('Histogram of IQ')\nplt.text(60, .025, r'$\\mu=100,\\ \\sigma=15$')\nplt.axis([40, 160, 0, 0.03])\nplt.grid(True)\nplt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "All of the :func:`~matplotlib.pyplot.text` commands return an\n:class:`matplotlib.text.Text` instance. Just as with with lines\nabove, you can customize the properties by passing keyword arguments\ninto the text functions or using :func:`~matplotlib.pyplot.setp`::\n\n t = plt.xlabel('my data', fontsize=14, color='red')\n\nThese properties are covered in more detail in :doc:`/tutorials/text/text_props`.\n\n\nUsing mathematical expressions in text\n--------------------------------------\n\nmatplotlib accepts TeX equation expressions in any text expression.\nFor example to write the expression $\\sigma_i=15$ in the title,\nyou can write a TeX expression surrounded by dollar signs::\n\n plt.title(r'$\\sigma_i=15$')\n\nThe ``r`` preceding the title string is important -- it signifies\nthat the string is a *raw* string and not to treat backslashes as\npython escapes. matplotlib has a built-in TeX expression parser and\nlayout engine, and ships its own math fonts -- for details see\n:doc:`/tutorials/text/mathtext`. Thus you can use mathematical text across platforms\nwithout requiring a TeX installation. For those who have LaTeX and\ndvipng installed, you can also use LaTeX to format your text and\nincorporate the output directly into your display figures or saved\npostscript -- see :doc:`/tutorials/text/usetex`.\n\n\nAnnotating text\n---------------\n\nThe uses of the basic :func:`~matplotlib.pyplot.text` command above\nplace text at an arbitrary position on the Axes. A common use for\ntext is to annotate some feature of the plot, and the\n:func:`~matplotlib.pyplot.annotate` method provides helper\nfunctionality to make annotations easy. In an annotation, there are\ntwo points to consider: the location being annotated represented by\nthe argument ``xy`` and the location of the text ``xytext``. Both of\nthese arguments are ``(x,y)`` tuples.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "ax = plt.subplot(111)\n\nt = np.arange(0.0, 5.0, 0.01)\ns = np.cos(2*np.pi*t)\nline, = plt.plot(t, s, lw=2)\n\nplt.annotate('local max', xy=(2, 1), xytext=(3, 1.5),\n arrowprops=dict(facecolor='black', shrink=0.05),\n )\n\nplt.ylim(-2, 2)\nplt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this basic example, both the ``xy`` (arrow tip) and ``xytext``\nlocations (text location) are in data coordinates. There are a\nvariety of other coordinate systems one can choose -- see\n`annotations-tutorial` and `plotting-guide-annotation` for\ndetails. More examples can be found in\n:doc:`/gallery/text_labels_and_annotations/annotation_demo`.\n\n\nLogarithmic and other nonlinear axes\n====================================\n\n:mod:`matplotlib.pyplot` supports not only linear axis scales, but also\nlogarithmic and logit scales. This is commonly used if data spans many orders\nof magnitude. Changing the scale of an axis is easy:\n\n plt.xscale('log')\n\nAn example of four plots with the same data and different scales for the y axis\nis shown below.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "from matplotlib.ticker import NullFormatter # useful for `logit` scale\n\n# Fixing random state for reproducibility\nnp.random.seed(19680801)\n\n# make up some data in the interval ]0, 1[\ny = np.random.normal(loc=0.5, scale=0.4, size=1000)\ny = y[(y > 0) & (y < 1)]\ny.sort()\nx = np.arange(len(y))\n\n# plot with various axes scales\nplt.figure()\n\n# linear\nplt.subplot(221)\nplt.plot(x, y)\nplt.yscale('linear')\nplt.title('linear')\nplt.grid(True)\n\n\n# log\nplt.subplot(222)\nplt.plot(x, y)\nplt.yscale('log')\nplt.title('log')\nplt.grid(True)\n\n\n# symmetric log\nplt.subplot(223)\nplt.plot(x, y - y.mean())\nplt.yscale('symlog', linthreshy=0.01)\nplt.title('symlog')\nplt.grid(True)\n\n# logit\nplt.subplot(224)\nplt.plot(x, y)\nplt.yscale('logit')\nplt.title('logit')\nplt.grid(True)\n# Format the minor tick labels of the y-axis into empty strings with\n# `NullFormatter`, to avoid cumbering the axis with too many labels.\nplt.gca().yaxis.set_minor_formatter(NullFormatter())\n# Adjust the subplot layout, because the logit one may take more space\n# than usual, due to y-tick labels like \"1 - 10^{-3}\"\nplt.subplots_adjust(top=0.92, bottom=0.08, left=0.10, right=0.95, hspace=0.25,\n wspace=0.35)\n\nplt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It is also possible to add your own scale, see `adding-new-scales` for\ndetails.\n\n" - ] - } - ], - "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": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/Day_2/ExtraExamples/readme.md b/Day_2/ExtraExamples/readme.md deleted file mode 100644 index 5864741..0000000 --- a/Day_2/ExtraExamples/readme.md +++ /dev/null @@ -1,20 +0,0 @@ -Extra Examples: -=============== - -Matplotlib: - - * StandardMatplotlib: basics - * artists : making nicer plots - * color_cycle: color manipulation - * customizing: basics customization - * gridspec: grided plots - * images: ploting images - * pyplot: using pyplot - * sample_plots: a bunch of plots from scatter to histogram - * uselatex: using latex with matplot and sympy - -Live Graphs: - * livegraphexamples: a few ways to make live graphs, better if not run with Jupyter notebook - -Pandas: - * Pandas_example diff --git a/Day_2/ExtraExamples/sample_plots.ipynb b/Day_2/ExtraExamples/sample_plots.ipynb deleted file mode 100644 index 5c7e1df..0000000 --- a/Day_2/ExtraExamples/sample_plots.ipynb +++ /dev/null @@ -1,54 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n# Sample plots in Matplotlib\n\n\nHere you'll find a host of example plots with the code that\ngenerated them.\n\n\nLine Plot\n=========\n\nHere's how to create a line plot with text labels using\n:func:`~matplotlib.pyplot.plot`.\n\n.. figure:: ../../gallery/lines_bars_and_markers/images/sphx_glr_simple_plot_001.png\n :target: ../../gallery/lines_bars_and_markers/simple_plot.html\n :align: center\n :scale: 50\n\n Simple Plot\n\n\nMultiple subplots in one figure\n===============================\n\nMultiple axes (i.e. subplots) are created with the\n:func:`~matplotlib.pyplot.subplot` function:\n\n.. figure:: ../../gallery/subplots_axes_and_figures/images/sphx_glr_subplot_001.png\n :target: ../../gallery/subplots_axes_and_figures/subplot.html\n :align: center\n :scale: 50\n\n Subplot\n\n\nImages\n======\n\nMatplotlib can display images (assuming equally spaced\nhorizontal dimensions) using the :func:`~matplotlib.pyplot.imshow` function.\n\n.. figure:: ../../gallery/images_contours_and_fields/images/sphx_glr_image_demo_003.png\n :target: ../../gallery/images_contours_and_fields/image_demo.html\n :align: center\n :scale: 50\n\n Example of using :func:`~matplotlib.pyplot.imshow` to display a CT scan\n\n\n\nContouring and pseudocolor\n==========================\n\nThe :func:`~matplotlib.pyplot.pcolormesh` function can make a colored\nrepresentation of a two-dimensional array, even if the horizontal dimensions\nare unevenly spaced. The\n:func:`~matplotlib.pyplot.contour` function is another way to represent\nthe same data:\n\n.. figure:: ../../gallery/images_contours_and_fields/images/sphx_glr_pcolormesh_levels_001.png\n :target: ../../gallery/images_contours_and_fields/pcolormesh_levels.html\n :align: center\n :scale: 50\n\n Example comparing :func:`~matplotlib.pyplot.pcolormesh` and :func:`~matplotlib.pyplot.contour` for plotting two-dimensional data\n\n\nHistograms\n==========\n\nThe :func:`~matplotlib.pyplot.hist` function automatically generates\nhistograms and returns the bin counts or probabilities:\n\n.. figure:: ../../gallery/statistics/images/sphx_glr_histogram_features_001.png\n :target: ../../gallery/statistics/histogram_features.html\n :align: center\n :scale: 50\n\n Histogram Features\n\n\n\nPaths\n=====\n\nYou can add arbitrary paths in Matplotlib using the\n:mod:`matplotlib.path` module:\n\n.. figure:: ../../gallery/shapes_and_collections/images/sphx_glr_path_patch_001.png\n :target: ../../gallery/shapes_and_collections/path_patch.html\n :align: center\n :scale: 50\n\n Path Patch\n\n\nThree-dimensional plotting\n==========================\n\nThe mplot3d toolkit (see `toolkit_mplot3d-tutorial` and\n`mplot3d-examples-index`) has support for simple 3d graphs\nincluding surface, wireframe, scatter, and bar charts.\n\n.. figure:: ../../gallery/mplot3d/images/sphx_glr_surface3d_001.png\n :target: ../../gallery/mplot3d/surface3d.html\n :align: center\n :scale: 50\n\n Surface3d\n\nThanks to John Porter, Jonathon Taylor, Reinier Heeres, and Ben Root for\nthe `mplot3d` toolkit. This toolkit is included with all standard Matplotlib\ninstalls.\n\n\n\nStreamplot\n==========\n\nThe :meth:`~matplotlib.pyplot.streamplot` function plots the streamlines of\na vector field. In addition to simply plotting the streamlines, it allows you\nto map the colors and/or line widths of streamlines to a separate parameter,\nsuch as the speed or local intensity of the vector field.\n\n.. figure:: ../../gallery/images_contours_and_fields/images/sphx_glr_plot_streamplot_001.png\n :target: ../../gallery/images_contours_and_fields/plot_streamplot.html\n :align: center\n :scale: 50\n\n Streamplot with various plotting options.\n\nThis feature complements the :meth:`~matplotlib.pyplot.quiver` function for\nplotting vector fields. Thanks to Tom Flannaghan and Tony Yu for adding the\nstreamplot function.\n\n\nEllipses\n========\n\nIn support of the `Phoenix `_\nmission to Mars (which used Matplotlib to display ground tracking of\nspacecraft), Michael Droettboom built on work by Charlie Moad to provide\nan extremely accurate 8-spline approximation to elliptical arcs (see\n:class:`~matplotlib.patches.Arc`), which are insensitive to zoom level.\n\n.. figure:: ../../gallery/shapes_and_collections/images/sphx_glr_ellipse_demo_001.png\n :target: ../../gallery/shapes_and_collections/ellipse_demo.html\n :align: center\n :scale: 50\n\n Ellipse Demo\n\n\nBar charts\n==========\n\nUse the :func:`~matplotlib.pyplot.bar` function to make bar charts, which\nincludes customizations such as error bars:\n\n.. figure:: ../../gallery/statistics/images/sphx_glr_barchart_demo_001.png\n :target: ../../gallery/statistics/barchart_demo.html\n :align: center\n :scale: 50\n\n Barchart Demo\n\nYou can also create stacked bars\n(`bar_stacked.py <../../gallery/lines_bars_and_markers/bar_stacked.html>`_),\nor horizontal bar charts\n(`barh.py <../../gallery/lines_bars_and_markers/barh.html>`_).\n\n\n\nPie charts\n==========\n\nThe :func:`~matplotlib.pyplot.pie` function allows you to create pie\ncharts. Optional features include auto-labeling the percentage of area,\nexploding one or more wedges from the center of the pie, and a shadow effect.\nTake a close look at the attached code, which generates this figure in just\na few lines of code.\n\n.. figure:: ../../gallery/pie_and_polar_charts/images/sphx_glr_pie_features_001.png\n :target: ../../gallery/pie_and_polar_charts/pie_features.html\n :align: center\n :scale: 50\n\n Pie Features\n\n\nTables\n======\n\nThe :func:`~matplotlib.pyplot.table` function adds a text table\nto an axes.\n\n.. figure:: ../../gallery/misc/images/sphx_glr_table_demo_001.png\n :target: ../../gallery/misc/table_demo.html\n :align: center\n :scale: 50\n\n Table Demo\n\n\n\n\nScatter plots\n=============\n\nThe :func:`~matplotlib.pyplot.scatter` function makes a scatter plot\nwith (optional) size and color arguments. This example plots changes\nin Google's stock price, with marker sizes reflecting the\ntrading volume and colors varying with time. Here, the\nalpha attribute is used to make semitransparent circle markers.\n\n.. figure:: ../../gallery/lines_bars_and_markers/images/sphx_glr_scatter_demo2_001.png\n :target: ../../gallery/lines_bars_and_markers/scatter_demo2.html\n :align: center\n :scale: 50\n\n Scatter Demo2\n\n\n\nGUI widgets\n===========\n\nMatplotlib has basic GUI widgets that are independent of the graphical\nuser interface you are using, allowing you to write cross GUI figures\nand widgets. See :mod:`matplotlib.widgets` and the\n`widget examples <../../gallery/index.html>`_.\n\n.. figure:: ../../gallery/widgets/images/sphx_glr_slider_demo_001.png\n :target: ../../gallery/widgets/slider_demo.html\n :align: center\n :scale: 50\n\n Slider and radio-button GUI.\n\n\n\nFilled curves\n=============\n\nThe :func:`~matplotlib.pyplot.fill` function lets you\nplot filled curves and polygons:\n\n.. figure:: ../../gallery/lines_bars_and_markers/images/sphx_glr_fill_001.png\n :target: ../../gallery/lines_bars_and_markers/fill.html\n :align: center\n :scale: 50\n\n Fill\n\nThanks to Andrew Straw for adding this function.\n\n\nDate handling\n=============\n\nYou can plot timeseries data with major and minor ticks and custom\ntick formatters for both.\n\n.. figure:: ../../gallery/text_labels_and_annotations/images/sphx_glr_date_001.png\n :target: ../../gallery/text_labels_and_annotations/date.html\n :align: center\n :scale: 50\n\n Date\n\nSee :mod:`matplotlib.ticker` and :mod:`matplotlib.dates` for details and usage.\n\n\n\nLog plots\n=========\n\nThe :func:`~matplotlib.pyplot.semilogx`,\n:func:`~matplotlib.pyplot.semilogy` and\n:func:`~matplotlib.pyplot.loglog` functions simplify the creation of\nlogarithmic plots.\n\n.. figure:: ../../gallery/scales/images/sphx_glr_log_demo_001.png\n :target: ../../gallery/scales/log_demo.html\n :align: center\n :scale: 50\n\n Log Demo\n\nThanks to Andrew Straw, Darren Dale and Gregory Lielens for contributions\nlog-scaling infrastructure.\n\n\nPolar plots\n===========\n\nThe :func:`~matplotlib.pyplot.polar` function generates polar plots.\n\n.. figure:: ../../gallery/pie_and_polar_charts/images/sphx_glr_polar_demo_001.png\n :target: ../../gallery/pie_and_polar_charts/polar_demo.html\n :align: center\n :scale: 50\n\n Polar Demo\n\n\n\nLegends\n=======\n\nThe :func:`~matplotlib.pyplot.legend` function automatically\ngenerates figure legends, with MATLAB-compatible legend-placement\nfunctions.\n\n.. figure:: ../../gallery/text_labels_and_annotations/images/sphx_glr_legend_001.png\n :target: ../../gallery/text_labels_and_annotations/legend.html\n :align: center\n :scale: 50\n\n Legend\n\nThanks to Charles Twardy for input on the legend function.\n\n\nTeX-notation for text objects\n=============================\n\nBelow is a sampling of the many TeX expressions now supported by Matplotlib's\ninternal mathtext engine. The mathtext module provides TeX style mathematical\nexpressions using `FreeType `_\nand the DejaVu, BaKoMa computer modern, or `STIX `_\nfonts. See the :mod:`matplotlib.mathtext` module for additional details.\n\n.. figure:: ../../gallery/text_labels_and_annotations/images/sphx_glr_mathtext_examples_001.png\n :target: ../../gallery/text_labels_and_annotations/mathtext_examples.html\n :align: center\n :scale: 50\n\n Mathtext Examples\n\nMatplotlib's mathtext infrastructure is an independent implementation and\ndoes not require TeX or any external packages installed on your computer. See\nthe tutorial at :doc:`/tutorials/text/mathtext`.\n\n\n\nNative TeX rendering\n====================\n\nAlthough Matplotlib's internal math rendering engine is quite\npowerful, sometimes you need TeX. Matplotlib supports external TeX\nrendering of strings with the *usetex* option.\n\n.. figure:: ../../gallery/text_labels_and_annotations/images/sphx_glr_tex_demo_001.png\n :target: ../../gallery/text_labels_and_annotations/tex_demo.html\n :align: center\n :scale: 50\n\n Tex Demo\n\n\nEEG GUI\n=======\n\nYou can embed Matplotlib into pygtk, wx, Tk, or Qt applications.\nHere is a screenshot of an EEG viewer called `pbrain\n`__.\n\n![](../../_static/eeg_small.png)\n\n\nThe lower axes uses :func:`~matplotlib.pyplot.specgram`\nto plot the spectrogram of one of the EEG channels.\n\nFor examples of how to embed Matplotlib in different toolkits, see:\n\n * :doc:`/gallery/user_interfaces/embedding_in_gtk3_sgskip`\n * :doc:`/gallery/user_interfaces/embedding_in_wx2_sgskip`\n * :doc:`/gallery/user_interfaces/mpl_with_glade3_sgskip`\n * :doc:`/gallery/user_interfaces/embedding_in_qt_sgskip`\n * :doc:`/gallery/user_interfaces/embedding_in_tk_sgskip`\n\nXKCD-style sketch plots\n=======================\n\nJust for fun, Matplotlib supports plotting in the style of `xkcd\n`.\n\n.. figure:: ../../gallery/showcase/images/sphx_glr_xkcd_001.png\n :target: ../../gallery/showcase/xkcd.html\n :align: center\n :scale: 50\n\n xkcd\n\nSubplot example\n===============\n\nMany plot types can be combined in one figure to create\npowerful and flexible representations of data.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\nimport numpy as np\n\nnp.random.seed(19680801)\ndata = np.random.randn(2, 100)\n\nfig, axs = plt.subplots(2, 2, figsize=(5, 5))\naxs[0, 0].hist(data[0])\naxs[1, 0].scatter(data[0], data[1])\naxs[0, 1].plot(data[0], data[1])\naxs[1, 1].hist2d(data[0], data[1])\n\nplt.show()" - ] - } - ], - "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": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/Day_2/ExtraExamples/uselatex.ipynb b/Day_2/ExtraExamples/uselatex.ipynb deleted file mode 100644 index 9b6ed1f..0000000 --- a/Day_2/ExtraExamples/uselatex.ipynb +++ /dev/null @@ -1,43 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n*************************\nText rendering With LaTeX\n*************************\n\nRendering text with LaTeX in Matplotlib.\n\nMatplotlib has the option to use LaTeX to manage all text layout. This\noption is available with the following backends:\n\n* Agg\n* PS\n* PDF\n\nThe LaTeX option is activated by setting ``text.usetex : True`` in your rc\nsettings. Text handling with matplotlib's LaTeX support is slower than\nmatplotlib's very capable :doc:`mathtext `, but is\nmore flexible, since different LaTeX packages (font packages, math packages,\netc.) can be used. The results can be striking, especially when you take care\nto use the same fonts in your figures as in the main document.\n\nMatplotlib's LaTeX support requires a working LaTeX_ installation, dvipng_\n(which may be included with your LaTeX installation), and Ghostscript_\n(GPL Ghostscript 9.0 or later is required). The executables for these\nexternal dependencies must all be located on your :envvar:`PATH`.\n\nThere are a couple of options to mention, which can be changed using\n:doc:`rc settings `. Here is an example\nmatplotlibrc file::\n\n font.family : serif\n font.serif : Times, Palatino, New Century Schoolbook, Bookman, Computer Modern Roman\n font.sans-serif : Helvetica, Avant Garde, Computer Modern Sans serif\n font.cursive : Zapf Chancery\n font.monospace : Courier, Computer Modern Typewriter\n\n text.usetex : true\n\nThe first valid font in each family is the one that will be loaded. If the\nfonts are not specified, the Computer Modern fonts are used by default. All of\nthe other fonts are Adobe fonts. Times and Palatino each have their own\naccompanying math fonts, while the other Adobe serif fonts make use of the\nComputer Modern math fonts. See the PSNFSS_ documentation for more details.\n\nTo use LaTeX and select Helvetica as the default font, without editing\nmatplotlibrc use::\n\n from matplotlib import rc\n rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})\n ## for Palatino and other serif fonts use:\n #rc('font',**{'family':'serif','serif':['Palatino']})\n rc('text', usetex=True)\n\nHere is the standard example, `tex_demo.py`:\n\n.. figure:: ../../gallery/text_labels_and_annotations/images/sphx_glr_tex_demo_001.png\n :target: ../../gallery/text_labels_and_annotations/tex_demo.html\n :align: center\n :scale: 50\n\n TeX Demo\n\nNote that display math mode (``$$ e=mc^2 $$``) is not supported, but adding the\ncommand ``\\displaystyle``, as in `tex_demo.py`, will produce the same\nresults.\n\n

Note

Certain characters require special escaping in TeX, such as::\n\n # $ % & ~ _ ^ \\ { } \\( \\) \\[ \\]\n\n Therefore, these characters will behave differently depending on\n the rcParam ``text.usetex`` flag.

\n\n\nusetex with unicode\n===================\n\nIt is also possible to use unicode strings with the LaTeX text manager, here is\nan example taken from `tex_demo.py`. The axis labels include Unicode text:\n\n.. figure:: ../../gallery/text_labels_and_annotations/images/sphx_glr_tex_demo_001.png\n :target: ../../gallery/text_labels_and_annotations/tex_demo.html\n :align: center\n :scale: 50\n\n TeX Unicode Demo\n\n\nPostscript options\n==================\n\nIn order to produce encapsulated postscript files that can be embedded in a new\nLaTeX document, the default behavior of matplotlib is to distill the output,\nwhich removes some postscript operators used by LaTeX that are illegal in an\neps file. This step produces results which may be unacceptable to some users,\nbecause the text is coarsely rasterized and converted to bitmaps, which are not\nscalable like standard postscript, and the text is not searchable. One\nworkaround is to set ``ps.distiller.res`` to a higher value (perhaps 6000)\nin your rc settings, which will produce larger files but may look better and\nscale reasonably. A better workaround, which requires Poppler_ or Xpdf_, can be\nactivated by changing the ``ps.usedistiller`` rc setting to ``xpdf``. This\nalternative produces postscript without rasterizing text, so it scales\nproperly, can be edited in Adobe Illustrator, and searched text in pdf\ndocuments.\n\n\nPossible hangups\n================\n\n* On Windows, the :envvar:`PATH` environment variable may need to be modified\n to include the directories containing the latex, dvipng and ghostscript\n executables. See `environment-variables` and\n `setting-windows-environment-variables` for details.\n\n* Using MiKTeX with Computer Modern fonts, if you get odd \\*Agg and PNG\n results, go to MiKTeX/Options and update your format files\n\n* On Ubuntu and Gentoo, the base texlive install does not ship with\n the type1cm package. You may need to install some of the extra\n packages to get all the goodies that come bundled with other latex\n distributions.\n\n* Some progress has been made so matplotlib uses the dvi files\n directly for text layout. This allows latex to be used for text\n layout with the pdf and svg backends, as well as the \\*Agg and PS\n backends. In the future, a latex installation may be the only\n external dependency.\n\n\nTroubleshooting\n===============\n\n* Try deleting your :file:`.matplotlib/tex.cache` directory. If you don't know\n where to find :file:`.matplotlib`, see `locating-matplotlib-config-dir`.\n\n* Make sure LaTeX, dvipng and ghostscript are each working and on your\n :envvar:`PATH`.\n\n* Make sure what you are trying to do is possible in a LaTeX document,\n that your LaTeX syntax is valid and that you are using raw strings\n if necessary to avoid unintended escape sequences.\n\n* Most problems reported on the mailing list have been cleared up by\n upgrading Ghostscript_. If possible, please try upgrading to the\n latest release before reporting problems to the list.\n\n* The ``text.latex.preamble`` rc setting is not officially supported. This\n option provides lots of flexibility, and lots of ways to cause\n problems. Please disable this option before reporting problems to\n the mailing list.\n\n* If you still need help, please see `reporting-problems`\n\n" - ] - } - ], - "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": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/Day_2/Python_Core.ipynb b/Day_2/Python_Core.ipynb deleted file mode 100644 index a777bbb..0000000 --- a/Day_2/Python_Core.ipynb +++ /dev/null @@ -1,1696 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# https://tinyurl.com/python-reflect1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Made by: Bryan Chavez\n", - "Code was pulled from a few places and the references are at the bottom." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: numpy in /opt/virtualenvs/python3/lib/python3.8/site-packages (1.18.4)\n", - "Requirement already satisfied: matplotlib in /opt/virtualenvs/python3/lib/python3.8/site-packages (3.2.1)\n", - "Requirement already satisfied: scipy in /opt/virtualenvs/python3/lib/python3.8/site-packages (1.4.1)\n", - "Requirement already satisfied: cycler>=0.10 in /opt/virtualenvs/python3/lib/python3.8/site-packages (from matplotlib) (0.10.0)\n", - "Requirement already satisfied: python-dateutil>=2.1 in /opt/virtualenvs/python3/lib/python3.8/site-packages (from matplotlib) (2.8.1)\n", - "Requirement already satisfied: kiwisolver>=1.0.1 in /opt/virtualenvs/python3/lib/python3.8/site-packages (from matplotlib) (1.2.0)\n", - "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /opt/virtualenvs/python3/lib/python3.8/site-packages (from matplotlib) (2.4.7)\n", - "Requirement already satisfied: six in /opt/virtualenvs/python3/lib/python3.8/site-packages (from cycler>=0.10->matplotlib) (1.14.0)\n", - "\u001b[33mWARNING: You are using pip version 20.1; however, version 20.1.1 is available.\n", - "You should consider upgrading via the '/opt/virtualenvs/python3/bin/python3 -m pip install --upgrade pip' command.\u001b[0m\n" - ] - } - ], - "source": [ - "!pip install numpy matplotlib scipy" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np #three intial packages(numpy, matplotlib, and scipy)\n", - "import matplotlib.pyplot as plt\n", - "import scipy as sc" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# ***********\n", - "Usage Guide\n", - "***********\n", - "\n", - "This tutorial covers some basic usage patterns and best-practices to\n", - "help you get started with Matplotlib.\n", - "\n", - "\n", - "General Concepts\n", - "================\n", - "\n", - ":mod:`matplotlib` has an extensive codebase that can be daunting to many\n", - "new users. However, most of matplotlib can be understood with a fairly\n", - "simple conceptual framework and knowledge of a few important points.\n", - "\n", - "Plotting requires action on a range of levels, from the most general\n", - "(e.g., 'contour this 2-D array') to the most specific (e.g., 'color\n", - "this screen pixel red'). The purpose of a plotting package is to assist\n", - "you in visualizing your data as easily as possible, with all the necessary\n", - "control -- that is, by using relatively high-level commands most of\n", - "the time, and still have the ability to use the low-level commands when\n", - "needed.\n", - "\n", - "Therefore, everything in matplotlib is organized in a hierarchy. At the top\n", - "of the hierarchy is the matplotlib \"state-machine environment\" which is\n", - "provided by the :mod:`matplotlib.pyplot` module. At this level, simple\n", - "functions are used to add plot elements (lines, images, text, etc.) to\n", - "the current axes in the current figure.\n", - "\n", - "

Note

Pyplot's state-machine environment behaves similarly to MATLAB and\n", - " should be most familiar to users with MATLAB experience.

\n", - "\n", - "The next level down in the hierarchy is the first level of the object-oriented\n", - "interface, in which pyplot is used only for a few functions such as figure\n", - "creation, and the user explicitly creates and keeps track of the figure\n", - "and axes objects. At this level, the user uses pyplot to create figures,\n", - "and through those figures, one or more axes objects can be created. These\n", - "axes objects are then used for most plotting actions.\n", - "\n", - "For even more control -- which is essential for things like embedding\n", - "matplotlib plots in GUI applications -- the pyplot level may be dropped\n", - "completely, leaving a purely object-oriented approach.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Basic MatPlotLib" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0. 0.2 0.4 0.6 0.8 1. 1.2 1.4 1.6 1.8 2. 2.2 2.4 2.6 2.8 3. 3.2 3.4\n", - " 3.6 3.8 4. 4.2 4.4 4.6 4.8 5. 5.2 5.4 5.6 5.8 6. 6.2 6.4 6.6 6.8 7.\n", - " 7.2 7.4 7.6 7.8 8. 8.2 8.4 8.6 8.8 9. 9.2 9.4 9.6 9.8]\n" - ] - } - ], - "source": [ - "x = np.arange(0, 10, 0.2) #start by making an array of data\n", - "print(x)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "sinx = np.sin(x)\n", - "cosx = np.cos(x)\n", - "fig, ax = plt.subplots(figsize=(9,5)) #subplots allows for more plots used latter \n", - "ax.plot(x, sinx) #this makes the plot\n", - "plt.show() #This 'prints' the plot" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Numpy & MatPlotlib" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#Now lets take the data we made with numpy and manipulate it\n", - "#new array of data from sin(x)*cos(x)\n", - "sincos = sinx*cosx #manipulate the arrays with numpy\n", - "fig, ax = plt.subplots(3,1, figsize=(15,10)) #setup three subplots numbered from 0 to 2, figsize define the whole area\n", - "#first plot\n", - "ax[0].plot(x,sinx)\n", - "ax[0].set_xlabel('time')\n", - "ax[0].set_ylabel('sin(x)')\n", - "\n", - "#sin(x)*cos(x)\n", - "ax[1].plot(x,sincos, label = 'sin(x)*cos(x)') #two plots on the same graph\n", - "ax[1].plot(x,sinx, label = 'sin(x)')\n", - "ax[1].legend()\n", - "ax[1].set_xlabel('time')\n", - "ax[1].set_ylabel('sin(x)*cos(x)')\n", - "ax[1].set_title('Plot2')\n", - "#sin(x) vs cos(x)\n", - "ax[2].plot(cosx,sinx)\n", - "ax[2].set_xlabel('cos(x)')\n", - "ax[2].set_ylabel('sin(x)')\n", - "\n", - "fig.tight_layout()\n", - "plt.show() #this will plot each different subplot on its own graph" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(10,5))\n", - "plt.plot(x,sinx, label= 'sin(x)', c = 'g') #plot different plot on same plot with Legend and colors\n", - "plt.plot(x,cosx, label= 'cos(x)', c = 'r') #basic two plots without subplots\n", - "plt.title(\"Simple Plot\")\n", - "plt.xlabel(r'Degrees($\\theta$)')\n", - "plt.ylabel('sin(x), cos(x)')\n", - "plt.legend()\n", - "fig.tight_layout()\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# SciPy\n", - "Now Lets do some Curve Fitting of Scatter Sin(x) With SciPy\n", - "\n", - "Lets make data" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlIAAAEvCAYAAACOiy/xAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAebklEQVR4nO3dcWxc13Xn8d+RaNOCGNdZSC63plS6kvOHzaaJyxqG7aaSYldpq429/mNR1y7iLVABtWxERYooqewFtvYCq7ZoVIgWCiENUKNq3ADOSoDQwJVBcwEHdrZk6qzkdRtRjVrJjmPGjW1RpkYk5+wfQ1KkPBzOvHlv3r3vfT+AAHFIDS80M++de8+555q7CwAAAK1blfcAAAAAYkUgBQAAkBCBFAAAQEIEUgAAAAkRSAEAACREIAUAAJBQVx6/dN26dd7f35/HrwYAAGjJ2NjYj919fb3v5RJI9ff3a3R0NI9fDQAA0BIz+9flvkdqDwAAICECKQAAgIQIpAAAABIikAIAAEiIQAoAACAhAikAAICECKQAAAASIpACAABIiEAKAAAgIQIpAACAhAikymrLltofAACQGIEUAABAQgRSAAAACRFIAQAAJEQgBQAAwhFZDS+BFAAAQEIEUgAAAAkRSAEAACSUWiBlZqvN7B/N7FhazwkAyFhk9ShAaNJckfq8pNdTfD4AAICgpRJImVmfpN+Q9NU0ng8RYlYLACihtFak9kv6oqRqSs8HAAAQvLYDKTPbIeltdx9b4ed2mtmomY1OTEy0+2sBAAByl8aK1J2SPmtmZyQ9K2mbmf31lT/k7ofcfdDdB9evX5/CrwUAAMhX24GUu3/Z3fvcvV/Sb0oadveH2h4ZAABA4OgjVUKTlRlNnK/ozXen9NzYOU1WZvIeEgAAUUo1kHL3EXffkeZzIj3urqdfHNfgU8f1g3cu6OxPpvTE0ZMafOq4nn5xXO6e9xABAIhKV94DQOccHDmtoeFxXZyuqlp19b33I31waVaSNDQ8LknatXVznkMEACAqpPaKpEEvp8nKjA4Mn9LU9OzCY33vX949OTU9q6HhcV0gzQcAQNMIpEri+ZNvaZVZw59ZZdLzr73VoREBABA/UnslMTFZ0aWZqna/dFi7v/31hcfP7KuVtO2/8wENfeohvX2+ktcQAQAlN1mZ0dT5iqZnq3p57Jy2D/SqpzvsUIUVqZJY39Otq7tWaf9dD6p/zzG9smFAktS/55j69xzT/rseVHfXKl3/ke6cRwoAKJuYN0MRSJXE9oFeVVd4I1Zd2n5Lb4dGBABAzZWboW549y19cGlWF6erGhoe18GR03kPcVkEUiXR092lx7bdpDVXrV547Ny1lzvMr7lqtR7dtllrA19CBZAeesohBLFvhiKQKpFHtmzSo9s265qrVmnVKtMb1/Vq7dWrdc1Vq/Tots16ZMumvIcIoANiTqOgeGLfDMXyQ4mYmXZt3azP3dGvqW/9kaZnqnryvgFtv6W3rZWoGIsDgTKjpxxCEvtmKFakSqinu0vre7r1M9et0f239iUOopjVAvGJPY2C4ol9MxSBFBpr0OQz5uJAoKxiT6OgeGLfDEUghUQ6MqttEMQBSGZxGuXMvh26/exJSbU0ypl9O7T7pcOqzFSDTaOgeGLfDBXmqBC8Vma199/a16FRAVjJ4jTK/rse1LN/8yXdfvak+vccW/iZtQGnUVBM85udDgyfWrIZatY9+M1QBFJIJPbiQKCstg/0au+REw1/JuQ0Coopq81QnRD26BAsZrVAnObTKEPD4wup+ZjSKCi2nu4u9fTU7huxZDOokUIisRcHAmVGTzkgPQRSBdHpDsWxFwcCZTafRhl9/B7duG6tNnx0jZ68b0Bjj9+jXVs3y1aofwRwGYFU5PLs5cSsFohbWj3lgDLjUxO5PDsUx1wcCABAGliRilgoHYqjmtXSmwplwPscISro+5JAKmJ0KAYKIoYbTAxjBHIQ8NIBVkIvJwAA8kUgFTF6OQEAkC9SexGjl9MipB1QD+8LABkjkIpYW72cRkZqfwAAQGIEUpHLspdTp5t8AoXCahhQCtRIRS6LXk7uroMjp3Vg+JSeeeeC3KUnjp7U3iMn9Ni2m/TIlk2Zdz6erMxo6nxF07NVvTx2TtsHetUTclsFACiS+UkAmYsVsSJVEGn2crqyyecN776lDy7N6uJ0VUPD4zo4cjrFkS+VZ6d2AABaRSCFJfJu8plnEAcACEBkNbwEUlgizyafeQdxAIDIBFCLSCCFJRY3+Tyzb4duP3tSUq3J55l9O7T7pcOqzFQzafJJp3bkJoCLMYA4tV29a2YbJD0j6acluaRD7v7n7T4v8pFnk086tQM5iCiFAoQojRWpGUlfcPebJd0uaZeZ3ZzC8yIHeTb5XBzE9e85plc2DEiS+vccU/+eY9p/14Pq7lSndlYosBzeGwAWaTuQcvcfuvt35/5+XtLrkm5o93mRj8RNPlMoDqRTO1AABJoomVRrpMysX9InJX0nzedFZ2XZ5LORtjq1N4EGo4heE0EK73OEqMjvy9QCKTPrkfScpN3u/n6d7+80s1EzG52YmPjwEyAY800+Rx+/RzeuW6sNH12jJ+8b0Njj92jX1s2ZNuPMIoijNxVCltYNJsv3eZFvgshWGa6/qQRSZnaVakHUYXf/Zr2fcfdD7j7o7oPr16+v9yMITJpNPpuVRRBHbyqEKO0bTBbv8zLcBJGtMlx/2w6krHZn+0tJr7v7n7U/JCC9II7eVAhVmjeYrN7nZbgJIjtluf6msSJ1p6TflrTNzF6d+/PrKTwv0DZ6U5VXyOmotG8wWbzPy3ITRHbKcv1NY9feS+5u7v5xd//E3J+/S2NwQDMa3TDzbDCKfMSQjkr7BpPF+7wsN0FkpyzX3+yLXoCMuLsOjpzWgeFTeuadC3KXnjh6UnuPnNBj227SI1s25dpgFPm4Mh3V996P9MGl2qrK0PC4JGnX1s2ZjmGyMqOp8xVNz1b18tg5bR/oVc+i9HTazWezeJ/TIBftKsv1lyNiiiSWgx5T6jPTTP0GvanKJe90VLOrYWk3n83ifR5Ug1x0VFpp8bJcfwmkEKVmb5gmZdqbCmHJOx3VbHF22jeYLHqwleUmiMvSTotn3RswFARSiFIrN8y8Goyi8/KsyWhlNSyLG0za7/Oy3ARxWRa7NMtw/eUTgCi1Ur8x35vqc3f0a+pbf6TpmaqevG9A22/p5SZQMHnWZLQS3N9/a9/CDeTA8KklN5hZ90Q3mCze52mPEeGanwhcnK4uPFZvIvDwHf0tvZ+yvP6uVIvYKdxFEKUkN8ye7i719NS+vv/Wvo6PGdnbPtCrvUdONPyZK9NRaV2MWy3OzuoGk+b7nElIebQ6EWhVmu/LZjYaZXkCx5VI7SFK1G+gnlbSUWnXgyQtzs7jBIFWxTBGtKettHiHD6oOrVEsgRSiRP0GltNsTUaSi3Gj3UwE94hZLLs0896ZWw+BFKKVVRFjyB2xsbJmzmts9WLczOoVwT2fnZjFMhHIe2du3d/Xsd8EpCztA45j6IiN5jVKR7V6MW529SrLHUohByl8duIXy0QgxG7pxZ0axWA+pxxyE82QxzYnrSLGEDpiozNaKQxvdTdT2sXZoRXW1sNnpxhi2KUZYrd0VqQAhZl3R3ZaqQdJkkpIszg7tMLaK7X12elwkTIaS3uVPwshpiAJpBCuDh55E2LeHdlp5WIcS5PPvPDZKZ6Qd2mGmIIkkAIUZt4dDbS5ktHKxTjP3UwxBCl8dtBpoXVLDyfMBHIUYt4d2Wq2HiRJk8+0tNrkMw98dtBpoTWKZUUqbeT8oxRi3h3ZarYeJM9UQgy9ffjsIC+hpCAJpNBRoW7hDjHvjs5o5mKcVyohhiCFzw7KjkCKFaSOiKHPTGh5d4Qjr91MsQQpfHZQZkwR0BEx9JkJLe+O8ORx8HUMvX347KDMeIcjc602NMxUE+0U8rhZAstJFKTk1EiXzw4aiqDBcxIEUshcK1u4ufiilGII8At6EwTaRSAVgxiOkmkghi3cAIDWTVZmNHW+ounZql4eO6ftA73qKVk6l2JzZC6GLdwAgObFsIGoUwikkLkYtnCjoDp4zBBQJqGfAdlJBFLIXEe2cHPDBICOiOEMyE4ikGpWyv2mQm1MmRX6zKB0CO5RUDGcAdlJ5aoIC4C76+DIaR0YPqVn3rkgd+mJoye198gJPbbtJj2yZVNmzf3yRJ8ZAItlUqQc+cacoDT4P2QD0VLcwToshsaUWcp9CzeAXJV1MlkkHFS9FKm9DiKvDLSvbGnxoqFIOX5sIFqKQKqDyCsDybHdOn6JJ5OciRqUoM6ADKAWkdReilbK+ZNXBpLLNC1OTU1HcMpBccRwBmSnEEiloNmcP3llIJmgzmtEYkwmA5BSQT4biC5LJbVnZp8xs382s3Ez+1Iaz9kJadVaNJvzJ68MJBNUWjyAVEKsOOWgeHq6u7S+p1s/c90a3X9rX+mCKCmFQMrMVkt6WtKvSbpZ0gNmdnO7z5ulNGstWsn5B5VXRmOt3Cyp38jc4pWMM/t26PazJyXVVjLO7Nuh3S8dVmWmykpGCBp8dphMoojSWJG6TdK4u/+Lu1+S9Kyke1N43sykuWuk1ZkyjSmB1rGSMSfy1TAmkyiiNAKpGySdXfT1ubnHljCznWY2amajExMTV367Y9JuQdDqTHk+rzz6+D26cd1abfjoGj1534DGHr9Hu7Zupn8KiieFFTtWMoqDySSKpmPtD9z9kLsPuvvg+vXrV/4HGUm71iLpTLnZvDI9cwBWMoqEySSKJo1A6g1JGxZ93Tf3WJDSrrXIaqZMzxxgKVYyiqVwRcrUSpZWGu/cf5B0k5ndqFoA9ZuSfiuF581E2i0I5mfKQ8PjC+nCNGbKZT9KBrgS260BhKjtFSl3n5H0qKTnJb0u6Rvu/lq7z5uVLFaQ0p4pc5QMsLzCrWQAiFoqNVLu/nfu/jF33+Tu/yON52xLgyXWJLUWK9UppZ3zD6pnDgAAWFYpp3LNtrZv9ZTynu4u9fTUUoLtHG9Q+O6/EW/fBhCmlY7oArJSyndZs7UWedUpcZQMADSn1QkvkLaOtT8IUaNaizzrlOiZAwDNSbPBMpBEqQOpRvKsU6JnDgDUNKpRLfzGHFoqRIFAahl5n+1Fzxw0hQstCqqZXnpszEEIWNJYRt51SvTMQenMB4RsRoCaq1FdvcqKvTEnZZkV5Jf8M8uK1DJCqVOiZw6Asmk2ZXftNV0cZt0ETsrIFoHUMqhTQjM4CxFIX7MpO0lBTHhDR0F+tgikGqBOCcthhgdkp9ka1fcvzjDhXUHhC/IDUN53VxMyr1MqeV45ZpyF2ByaJCKJVmpU//Mnb5C0coPlsmqlIL+dRtJlxopUE6hTwmLM8FbGih3a0UqNatpHdBVN3jvQy4BACmgRW65XRk0GmjIyUndlPkmNap4T3pBrJRev7lGQnw0CKaBFzPAa68iK3TI3YBRHDDWqMay8hrIDvcjIUQEtyrvHWOioyUAaYuilF0Ot5Pzq3tDw+MLkhoL8dLEilTZmyoUX5Qyvgx3QWbFDmkKtUY2pVjKG1b2YFS6QCjlXjWKgx1hj1GSgDGKqlaQgP1uFCaQS56pZQUICzPCWF+WKHdCiGFdeQ13di11h/hdjyFWjOGKo38gLNRkoA2olMa8QK1Ix5apRLMzw6mPFDkWX9corZSrxKEQgFVOuGkhLyBdaajJQdFnVSsbQUgFLFSKQijFXDSQV04W22RW7kINCYDlZrLzSzDY+hchDFD5XTTE8FilSPaC76+DIaR0YPqVn3rkgd+mJoye198gJPbbtJj2yZROrVwhW2rWS82UqF6erC4/VK1N5+I5+yggCUogVKXYJoSyKVg/I7Bu5SbG3Wlq1kpSpxKkQgRR9fVAWRbrQFi0oBNpFmUqcChNZzOeiDwyfWpKrnnVPZ5cQ6TUEYPGFdve3v77w+Jl9OyRJ++98QEOfeiiKCy1HyQBLFb5MpaAKE0jR1wdlUKQLbZGCQgQi8gnv9oFe7T1youHPUKYSnkKk9hajrw+KrEj1gBwlAyxFmUqcChdIAUVWpAttkYJCIC00s41P+FdbAEtkXg/YrPkdTwnTKRwlA3wYZSrx4VUBItPqhXayMqOp8xVNz1b18tg5bR/oVU8gF+RggkKELfLapyR6urvU01NLa7PZImxhXE0BtGylC20MzS6ZfQOIXVtXKjP7E0n/SdIlSacl/Vd3fzeNgQFoT0wd0Jl9Ax1SwtW9rLVbbH5c0oC7f1zS9yV9uf0hAZEZGQnu4kSzSwDojLYCKXf/e3efvxK/IompJLCcZo+kSOHoiiJ1QAeAkKVZhPA7kv42xecDkFBQzS4DW60DgDStGEiZ2QuS6jVy2evuR+d+Zq+kGUmHGzzPTkk7JWnjxo2JBgugOUXqgA4AIVsxkHL3uxt938welrRD0qfdl++u5+6HJB2SpMHBwcZd+AC0haMmgA5j5bW02qqRMrPPSPqipM+6+wfpDAlAu4rUAR1I22RlRhPnK3rz3Sk9N3ZOk2y6QBva3bU3JOkjko6b2atm9hcpjAlACjhqAljK3fX0i+MafOq4fvDOBZ39yZSeOHpSg08d19MvjqtBUgVYVlvTUXcPowkNgA9J1OyyzWNfgJDF1FsN8Sjmuj43AWABzS6By73VLk5XFx6r11vt4Tv640t5MwHKVbupPQAAgkdvNWQlsrAbAIDWBdVbDYVCIAWgZZOVGU2dr2h6tqqXx85p+0CvemJLh6BUouytRqouCqT2gJh1+Jw/dj0hVtsHelVd4f1JbzUkQSAFoGlX7nq64d239MGlWV2crmpoeFwHR07nPUSgLnqrISsEUgCaMr/raWp6duGxerueLiRtbtjh1TWUD73VkAVCbwBNaWXXE20WEKJEvdWAFfDOATqg2eLskIu42fWEoqC3GtJEag/IULPF2TEUcS/e9dS/55he2TAgSerfc0z9e45p/10Pqju0XU9AiLZsudxEE9EjkAIy1GxxdgxF3Ox6AoAPI5ACMtJscfbb71/Mtog7Jex6AoAPI5ACMtJscfZXXvh+NEdXsOsJAJZi6ghkpNni7B++dzGaIm52PQHAUlz5gIw0eyTFf/ypa7I/uqKJ/kyt7Bhk1xMA1JDaAzLSbHH279/9sVyLuGPYMQigvsnKjCbOV/Tmu1N6buycJnOupSwjAikgI80WZ19/7TW5FnHHsGMQwFJMgMJBIAVkqNni7LyKuDM/9gVAJpgAhYMaKSBDzRZn51XEzbEvQHzmJ0AXp6sLj9WbAD18Rz+bQDqA/2GgA5otzu50ETfHvgDxYQIUFgIpoMSa3VnIsS9AOJgAhYUaKaDEOPYFiA/nXoaFQAooMY59QWmNjDTVXy1ETIDCQiAFlBzHvgBxYQIUFv6XgZLj2Begc1o5QaCR+QnOgeFTSyZAs+5MgDqMqyQASRz7AmTJ3XVw5LQODJ/SM+9ckLv0xNGT2nvkhB7bdpMe2bJJtsJOvMWYAIWD/20AADJ2ZQPNvvd+pA8u1RrhDg2PS5J2bd3c8vMyAcofNVIAAGSIEwSKjUAKQDIR73oCOqmVBpqID6k9AAAyRAPNYiOQAgAgQ5wgUGyppPbM7Atm5ma2Lo3nAwCgKGigWWxtB1JmtkHSr0r6t/aHAwBAsdBAs9jSeNW+IumLko6m8FxAcTVbmE0BN1A4NNAsrrYCKTO7V9Ib7v69VhqJAQBQJjTQLK4VXz0ze0FSvcTtXkl/qFpab0VmtlPSTknauHFjC0MEAKAYaKBZPCsGUu5+d73HzeznJd0oaX41qk/Sd83sNnf/UDMMdz8k6ZAkDQ4ONq66AwAAiEDi9UR3PyHp+vmvzeyMpEF3/3EK4wIAAAgenc0BAAASSq3Czd3703ouAACAGLBVAMBltF4A4sPnNlek9gAAABIikAIAAEiIQAoAACAhAikAAICECKQAAAASIpACAABIiEAKAAAgIQIpAACAhGjICQBAJ9FAs1BYkQIAAEiIQAoAACAhAikAAICECKQAAAASIpACAABIiEAKAAAgIQIpAACAhAikAAAAEiKQAgAASIhACgAAICECKQAAgIQIpAAAABIikAIAAEiIQAoAACAhAikAAICECKQAAAASIpACAABIiEAKAAAgIQIpAACAhAikAABYzpYttT/AMgikAAAAEmo7kDKzx8zsn8zsNTP74zQGBQAAEIOudv6xmW2VdK+kX3D3ipldn86wAAAAwtfuitTvSfqf7l6RJHd/u/0hAQAAxKHdQOpjkn7ZzL5jZv/bzH4pjUEBAADEYMXUnpm9IKm3zrf2zv37/yDpdkm/JOkbZvZz7u51nmenpJ2StHHjxnbGDAAAEIQVAyl3v3u575nZ70n65lzg9H/MrCppnaSJOs9zSNIhSRocHPxQoAUAABCbdlN7RyRtlSQz+5ikqyX9uN1BAQAAxKCtXXuSvibpa2Z2UtIlSZ+rl9YDAAAoorYCKXe/JOmhlMYCAAAQFTqbAwAAJEQgBQAAkBCBFAAAQEIEUgAAAAkRSAEAUMdkZUYT5yt6890pPTd2TpOVmbyHhAARSAEAsIi76+kXxzX41HH94J0LOvuTKT1x9KQGnzqup18cF11+sFi7faQAACiUgyOnNTQ8rovTVVWrrr73fqQPLs1KkoaGxyVJu7ZuznOICAgrUgAAzJmszOjA8ClNTc8uPNb3/uVTz6amZzU0PK4LpPkwh0AKAIA5z598S6vMGv7MKpOef+2tDo0IoSO1BwDAnInJii7NVLX7pcPa/e2vLzx+Zt8OSdL+Ox/Q0Kce0tvnK3kNEYEhkAIAYM76nm5d3bVK++96UPvvelDP/s2XdPvZk+rfc2zhZ9Z2rdL1H+nOcZQICak9AADmbB/oVXWFXXlVl7bf0tuhESF0BFIAAMzp6e7SY9tu0pqrVi88du7a9Qt/X3PVaj26bbPWdpPQQQ3vBAAAFnlkyyZJ0oHhU1q1yvTGdb1ae/Vqzbrr0W2bF74PSJLl0VhscHDQR0dHO/57AQBo1mRlRlN3/rKmZ6p65WvPafstvaxElZSZjbn7YL3v8Y4AAKCOnu4u9fTUisrvv7Uv59EgVNRIAQAAJEQgBQAAkBCBFAAAQEIEUgAAAAkRSAEAACREIAUAAJAQgRQAAEBCBFIAAAAJEUgBAAAkRCAFAACQEIEUAABAQgRSAAAACRFIAQAAJNSV9wAAAAjWyEjeI0DgWJECAABIqK1Aysw+YWavmNmrZjZqZrelNTAAAIDQtbsi9ceS/ru7f0LSf5v7GgAAoBTaDaRc0rVzf/8pSW+2+XwAAADRaLfYfLek583sT1ULyu5of0gAAABxWDGQMrMXJPXW+dZeSZ+W9Pvu/pyZ/RdJfynp7mWeZ6eknZK0cePGxAMGAAAIhbl78n9s9p6k69zdzcwkvefu16707wYHB310dDTx7wUAAOgUMxtz98F632u3RupNSb8y9/dtkk61+XwAAADRaLdG6ncl/bmZdUm6qLnUHQAAQBm0FUi5+0uSfjGlsQAAAESFzuYAAAAJEUgBAAAk1NauvcS/1GxC0r92/BfHa52kH+c9CCzBaxImXpfw8JqEidelNT/r7uvrfSOXQAqtMbPR5bZdIh+8JmHidQkPr0mYeF3SQ2oPAAAgIQIpAACAhAik4nAo7wHgQ3hNwsTrEh5ekzDxuqSEGikAAICEWJECAABIiEAqMmb2BTNzM1uX91jKzsz+xMz+ycz+r5n9LzO7Lu8xlZWZfcbM/tnMxs3sS3mPB5KZbTCzF83s/5nZa2b2+bzHhBozW21m/2hmx/IeSxEQSEXEzDZI+lVJ/5b3WCBJOi5pwN0/Lun7kr6c83hKycxWS3pa0q9JulnSA2Z2c76jgqQZSV9w95sl3S5pF69LMD4v6fW8B1EUBFJx+YqkL0qisC0A7v737j4z9+UrkvryHE+J3SZp3N3/xd0vSXpW0r05j6n03P2H7v7dub+fV+3GfUO+o4KZ9Un6DUlfzXssRUEgFQkzu1fSG+7+vbzHgrp+R9K38h5ESd0g6eyir8+JG3ZQzKxf0iclfSffkUDSftUm5NW8B1IUXXkPAJeZ2QuSeut8a6+kP1QtrYcOavSauPvRuZ/Zq1oa43AnxwbEwMx6JD0nabe7v5/3eMrMzHZIetvdx8xsS97jKQoCqYC4+931Hjezn5d0o6TvmZlUSyF918xuc/e3OjjE0lnuNZlnZg9L2iHp004vkby8IWnDoq/75h5DzszsKtWCqMPu/s28xwPdKemzZvbrkq6RdK2Z/bW7P5TzuKJGH6kImdkZSYPuzoGTOTKzz0j6M0m/4u4TeY+nrMysS7Vi/0+rFkD9g6TfcvfXch1YyVlt1vdXkv7d3XfnPR4sNbci9QfuviPvscSOGikguSFJH5F03MxeNbO/yHtAZTRX8P+opOdVK2j+BkFUEO6U9NuSts19Pl6dWwkBCoUVKQAAgIRYkQIAAEiIQAoAACAhAikAAICECKQAAAASIpACAABIiEAKAAAgIQIpAACAhAikAAAAEvr/lDroJnQzy7sAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Seed the random number generator for reproducibility\n", - "np.random.seed(194)\n", - "\n", - "x_data = np.linspace(-5, 5, num=50) #setup x data\n", - "y_data = 2.9 * np.sin(1.5 * x_data) + np.random.normal(size=50) #setup sin(x) random data\n", - "errors = np.zeros(len(y_data)) + 1\n", - "# And plot it\n", - "import matplotlib.pyplot as plt\n", - "plt.figure(figsize=(10, 5))\n", - "plt.scatter(x_data, y_data, s =80) #scatter plot\n", - "plt.errorbar(x_data, y_data,yerr = errors, fmt = \"r+\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now lets Fit the data" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2.59699159 1.46783601]\n" - ] - } - ], - "source": [ - "from scipy import optimize\n", - "\n", - "def test_func(x, a, b):\n", - " return a * np.sin(b * x) #test function with fix of a*sin(b*x)\n", - "\n", - "params, params_covariance = optimize.curve_fit(test_func, x_data, y_data,\n", - " sigma = errors) #fit of data\n", - "\n", - "print(params)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now lets plot the results" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(10, 5))\n", - "plt.scatter(x_data, y_data, label='Data')\n", - "plt.plot(x_data, test_func(x_data, params[0], params[1]),\n", - " label='Fitted function', c='r')\n", - "plt.errorbar(x_data,y_data,yerr = errors, fmt = \"r+\")\n", - "plt.legend(loc='upper right')\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "SciPy can also do Integration and Derviative and Solve Function" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "np.random.normal?" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ 1 4 9 16]\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "def f1(x):\n", - " return x**2 # x**2 is same as x^2\n", - "\n", - "def f2(x):\n", - " return x**3\n", - "\n", - "x = np.array([1,2,3,4]) #x data to integrate with\n", - "y1 = f1(x)\n", - "print(y1)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "21.166666666666664\n" - ] - } - ], - "source": [ - "from scipy.integrate import simps\n", - "I1 = simps(y1, x)\n", - "print(I1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Sympy" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$\\displaystyle \\int x^{2}\\, dx$" - ], - "text/plain": [ - "Integral(x**2, x)" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sympy import *\n", - "#init_printing()\n", - "x = Symbol('x')\n", - "Integral(x**2,x) #same integration but now visual\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\\int x^{2}\\, dx\n" - ] - } - ], - "source": [ - "print(latex(Integral(x**2,x))) #latex code" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$\\displaystyle \\frac{x^{3}}{3}$" - ], - "text/plain": [ - "x**3/3" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "integrate(x**2,x)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$\\displaystyle \\frac{d}{d x} x^{2}$" - ], - "text/plain": [ - "Derivative(x**2, x)" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Derivative(x**2,x)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$\\displaystyle 2 x$" - ], - "text/plain": [ - "2*x" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "diff(x**2,x)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$\\displaystyle 21$" - ], - "text/plain": [ - "21" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "integrate(x**2,(x,1,4)) #definite integration" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Pickle" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Test_vehicle\n" - ] - } - ], - "source": [ - "#Let's make a class to pickle\n", - "import pickle\n", - "\n", - "class Vehicle:\n", - " def __init__(\n", - " self, \n", - " name = None,\n", - " width = None,\n", - " length = None,\n", - " ):\n", - " self.name = name\n", - " self.width = width\n", - " self.length = length\n", - " \n", - "myVehicle = Vehicle(\n", - " name = 'Test_vehicle',\n", - " width = 10,\n", - " length = 10)\n", - "print(myVehicle.name)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "ename": "TypeError", - "evalue": "write() argument must be str, not Vehicle", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m#We can try to save this as text, it'll fail\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'vehicle.txt'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'w'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmyVehicle\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m: write() argument must be str, not Vehicle" - ] - } - ], - "source": [ - "#We can try to save this as text, it'll fail\n", - "with open('vehicle.txt','w') as f:\n", - " f.write(myVehicle)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "#We can pickle the class though, and get back what we put in\n", - "pickle.dump(myVehicle,open('vehicle.pickle','wb'))" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Test_vehicle\n" - ] - } - ], - "source": [ - "myReadInVehichle = pickle.load(open('vehicle.pickle','rb'))\n", - "print(myReadInVehichle.name)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "# However, the actual file looks like this:\n", - "!cat vehicle.pickle" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# JSON" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "#Pickle is nice, but is not readable outside of python\n", - "pets_info = {\n", - " 'cat':{'color':'black','weight':10,'toys':['string','box','squeaky']},\n", - " 'dog':{'color':'white','weight':40,'toys':['ball','frisbee','squeaky']}\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{\"cat\": {\"color\": \"black\", \"weight\": 10, \"toys\": [\"string\", \"box\", \"squeaky\"]}, \"dog\": {\"color\": \"white\", \"weight\": 40, \"toys\": [\"ball\", \"frisbee\", \"squeaky\"]}}\n" - ] - } - ], - "source": [ - "import json\n", - "object_as_string = json.dumps(pets_info)\n", - "print(object_as_string)\n", - "with open('my_pet_info.json','w') as f:\n", - " f.write(object_as_string)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'cat': {'color': 'black', 'weight': 10, 'toys': ['string', 'box', 'squeaky']}, 'dog': {'color': 'white', 'weight': 40, 'toys': ['ball', 'frisbee', 'squeaky']}}\n" - ] - } - ], - "source": [ - "with open('my_pet_info.json','r') as f:\n", - " string_from_file= f.read()\n", - "object_as_string = json.loads(string_from_file)\n", - "print(object_as_string)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{\"cat\": {\"color\": \"black\", \"weight\": 10, \"toys\": [\"string\", \"box\", \"squeaky\"]}, \"dog\": {\"color\": \"white\", \"weight\": 40, \"toys\": [\"ball\", \"frisbee\", \"squeaky\"]}}" - ] - } - ], - "source": [ - "# Notice how you can read the file, it looks like this:\n", - "!cat my_pet_info.json" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Pandas" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we will look at Pandas" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "ename": "UnicodeDecodeError", - "evalue": "'utf-8' codec can't decode byte 0xe9 in position 15: invalid continuation byte", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mUnicodeDecodeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m#plt.rcParams['figure.figsize'] = (15, 5)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mbroken_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'bikes.csv'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#bike.csv is cycling info from Montreal, that is why it is French\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0;31m# Look at the first 3 rows\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mbroken_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/virtualenvs/python3/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m 674\u001b[0m )\n\u001b[1;32m 675\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 676\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 678\u001b[0m \u001b[0mparser_f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/virtualenvs/python3/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 446\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 447\u001b[0m \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 448\u001b[0;31m \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfp_or_buf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 449\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 450\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/virtualenvs/python3/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m 878\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 879\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 880\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 881\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 882\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/virtualenvs/python3/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m 1112\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"c\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1113\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"c\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1114\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1115\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1116\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"python\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/virtualenvs/python3/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m 1889\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"usecols\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1890\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1891\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1892\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1893\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._get_header\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mUnicodeDecodeError\u001b[0m: 'utf-8' codec can't decode byte 0xe9 in position 15: invalid continuation byte" - ] - } - ], - "source": [ - "import pandas as pd\n", - "\n", - "#pd.set_option('display.mpl_style', 'default') # Make the graphs a bit prettier\n", - "#plt.rcParams['figure.figsize'] = (15, 5)\n", - "\n", - "broken_df = pd.read_csv('bikes.csv') #bike.csv is cycling info from Montreal, that is why it is French\n", - "# Look at the first 3 rows\n", - "broken_df[:3]\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Berri 1Brébeuf (données non disponibles)Côte-Sainte-CatherineMaisonneuve 1Maisonneuve 2du ParcPierre-DupuyRachel1St-Urbain (données non disponibles)
Date
2012-01-0135NaN03851261016NaN
2012-01-0283NaN16815353643NaN
2012-01-03135NaN210424889358NaN
2012-01-04144NaN1116318111861NaN
2012-01-05197NaN2124330971395NaN
2012-01-06146NaN09824486475NaN
2012-01-0798NaN28010853654NaN
2012-01-0895NaN16298641163NaN
2012-01-09244NaN216543219812173NaN
\n", - "
" - ], - "text/plain": [ - " Berri 1 Brébeuf (données non disponibles) Côte-Sainte-Catherine \\\n", - "Date \n", - "2012-01-01 35 NaN 0 \n", - "2012-01-02 83 NaN 1 \n", - "2012-01-03 135 NaN 2 \n", - "2012-01-04 144 NaN 1 \n", - "2012-01-05 197 NaN 2 \n", - "2012-01-06 146 NaN 0 \n", - "2012-01-07 98 NaN 2 \n", - "2012-01-08 95 NaN 1 \n", - "2012-01-09 244 NaN 2 \n", - "\n", - " Maisonneuve 1 Maisonneuve 2 du Parc Pierre-Dupuy Rachel1 \\\n", - "Date \n", - "2012-01-01 38 51 26 10 16 \n", - "2012-01-02 68 153 53 6 43 \n", - "2012-01-03 104 248 89 3 58 \n", - "2012-01-04 116 318 111 8 61 \n", - "2012-01-05 124 330 97 13 95 \n", - "2012-01-06 98 244 86 4 75 \n", - "2012-01-07 80 108 53 6 54 \n", - "2012-01-08 62 98 64 11 63 \n", - "2012-01-09 165 432 198 12 173 \n", - "\n", - " St-Urbain (données non disponibles) \n", - "Date \n", - "2012-01-01 NaN \n", - "2012-01-02 NaN \n", - "2012-01-03 NaN \n", - "2012-01-04 NaN \n", - "2012-01-05 NaN \n", - "2012-01-06 NaN \n", - "2012-01-07 NaN \n", - "2012-01-08 NaN \n", - "2012-01-09 NaN " - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fixed_df = pd.read_csv('bikes.csv', sep=';', encoding='latin1', parse_dates=['Date'], dayfirst=True, index_col='Date')\n", - "fixed_df[:9]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 1.2 Selecting a column" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Date\n", - "2012-01-01 35\n", - "2012-01-02 83\n", - "2012-01-03 135\n", - "2012-01-04 144\n", - "2012-01-05 197\n", - " ... \n", - "2012-11-01 2405\n", - "2012-11-02 1582\n", - "2012-11-03 844\n", - "2012-11-04 966\n", - "2012-11-05 2247\n", - "Name: Berri 1, Length: 310, dtype: int64" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fixed_df['Berri 1']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 1.3 Plotting a column" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Just add `.plot()` to the end! How could it be easier? =)\n", - "\n", - "We can see that, unsurprisingly, not many people are biking in January, February, and March, " - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fixed_df['Berri 1'].plot(figsize=(15,10))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also plot all the columns just as easily. We'll make it a little bigger, too.\n", - "You can see that it's more squished together, but all the bike paths behave basically the same -- if it's a bad day for cyclists, it's a bad day everywhere." - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fixed_df.plot(figsize=(15, 10))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Real physics data in Pandas\n", - "\n", - "Here we will look at real physics data from the CLAS collaboration at JLab. Don't worry about knowing the physics behind this it is more to see how you can load manipulate save the data using pandas and numpy." - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
pcxcycz
02.92318-0.0996480.3049810.947131
12.023050.3574810.0532050.932404
22.385040.3730080.0686950.925282
33.94044-0.161400-0.2449570.956005
\n", - "
" - ], - "text/plain": [ - " p cx cy cz\n", - "0 2.92318 -0.099648 0.304981 0.947131\n", - "1 2.02305 0.357481 0.053205 0.932404\n", - "2 2.38504 0.373008 0.068695 0.925282\n", - "3 3.94044 -0.161400 -0.244957 0.956005" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pandas as pd\n", - "data = pd.read_csv(\"511_lab_E_100k.csv.bz2\")\n", - "data[:4]" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
pcxcycz
count100000.000000100000.000000100000.000000100000.000000
mean2.809895-0.0092480.0001670.927430
std0.7974050.2764660.2490210.036664
min1.500080-0.677151-0.6760800.726911
25%2.096775-0.248829-0.2341650.910984
50%2.804645-0.096869-0.0027300.937082
75%3.5116350.2305670.2371270.954553
max4.5577700.6726100.6628560.974583
\n", - "
" - ], - "text/plain": [ - " p cx cy cz\n", - "count 100000.000000 100000.000000 100000.000000 100000.000000\n", - "mean 2.809895 -0.009248 0.000167 0.927430\n", - "std 0.797405 0.276466 0.249021 0.036664\n", - "min 1.500080 -0.677151 -0.676080 0.726911\n", - "25% 2.096775 -0.248829 -0.234165 0.910984\n", - "50% 2.804645 -0.096869 -0.002730 0.937082\n", - "75% 3.511635 0.230567 0.237127 0.954553\n", - "max 4.557770 0.672610 0.662856 0.974583" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Describe the data\n", - "data.describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "@np.vectorize\n", - "def q2_calc(px, py, pz):\n", - " e_beam = np.array([0, 0, 4.8, 4.8])\n", - " e_prime = np.array([px, py, pz, np.linalg.norm([px, py, pz, 0.000511])])\n", - " temp = e_beam - e_prime\n", - " temp2 = temp[0]*temp[0]+temp[1]*temp[1]+temp[2]*temp[2]-temp[3]*temp[3]\n", - " return temp2\n", - "\n", - "@np.vectorize\n", - "def W_calc(px, py, pz):\n", - " e_beam = np.array([0, 0, 4.8, 4.8])\n", - " e_prime = np.array([px, py, pz, np.linalg.norm([px, py, pz, 0.000511])])\n", - " p_targ = np.array([0, 0, 0.0, 0.93828])\n", - " temp = e_beam - e_prime + p_targ\n", - " temp2 = temp[0]*temp[0]+temp[1]*temp[1]+temp[2]*temp[2]-temp[3]*temp[3]\n", - " return np.sqrt(-temp2)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 57.7 s, sys: 866 ms, total: 58.6 s\n", - "Wall time: 1min 1s\n" - ] - } - ], - "source": [ - "%%time\n", - "# Niave way of working with data:\n", - "# Make some arrays\n", - "px = []\n", - "py = []\n", - "pz = []\n", - "q2 = []\n", - "w = []\n", - "# Loop over all the rows\n", - "for index, row in data.iterrows():\n", - " # Do your calculation and fill the array\n", - " px.append(row.p*row.cx)\n", - " py.append(row.p*row.cy)\n", - " pz.append(row.p*row.cz)\n", - " q2.append(q2_calc(px[-1],py[-1],pz[-1]))\n", - " w.append(W_calc(px[-1],py[-1],pz[-1]))\n", - " \n", - "# Place the arrays into the dataframe\n", - "data['px'] = px\n", - "data['py'] = py\n", - "data['pz'] = pz\n", - "data['q2'] = q2\n", - "data['w'] = w" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 6.49 s, sys: 71.6 ms, total: 6.56 s\n", - "Wall time: 6.91 s\n" - ] - } - ], - "source": [ - "%%time\n", - "# Without looping we can caluclate everything much faster\n", - "data['px'] = data['cx'] * data['p']\n", - "data['py'] = data['cy'] * data['p']\n", - "data['pz'] = data['cz'] * data['p']\n", - "# Using the @np.vectorize dectorator on the functions allows them to work\n", - "# Over all the columns without looping\n", - "data['q2'] = q2_calc(data['px'],data['py'],data['pz'])\n", - "data['w'] = W_calc(data['px'],data['py'],data['pz'])" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Plot pandas data from matplotlib\n", - "# Simple variables names without spaces can be called like this:\n", - "plt.hist2d(data.w, data.q2, bins=200)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Or plot directly from pandas df\n", - "data['w'].hist(bins=100)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
pcxcyczpxpypzq2w
02.92318-0.0996480.3049810.947131-0.2912900.8915142.7686341.4836421.708418
12.023050.3574810.0532050.9324040.7232020.1076361.8863001.3128072.186018
22.385040.3730080.0686950.9252820.8896390.1638402.2068351.7107811.923903
33.94044-0.161400-0.2449570.956005-0.635987-0.9652383.7670801.6642420.910574
43.26964-0.3764180.0563650.924734-1.2307510.1842953.0235472.3624991.178847
\n", - "
" - ], - "text/plain": [ - " p cx cy cz px py pz \\\n", - "0 2.92318 -0.099648 0.304981 0.947131 -0.291290 0.891514 2.768634 \n", - "1 2.02305 0.357481 0.053205 0.932404 0.723202 0.107636 1.886300 \n", - "2 2.38504 0.373008 0.068695 0.925282 0.889639 0.163840 2.206835 \n", - "3 3.94044 -0.161400 -0.244957 0.956005 -0.635987 -0.965238 3.767080 \n", - "4 3.26964 -0.376418 0.056365 0.924734 -1.230751 0.184295 3.023547 \n", - "\n", - " q2 w \n", - "0 1.483642 1.708418 \n", - "1 1.312807 2.186018 \n", - "2 1.710781 1.923903 \n", - "3 1.664242 0.910574 \n", - "4 2.362499 1.178847 " - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To calculate $\\theta = \\arccos\\left(cos_z\\right)$\n", - "\n", - "To calculate $\\phi = \\arctan2\\left(cos_x, cos_y\\right)$" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [], - "source": [ - "# Or use already made numpy functions to do our calculations\n", - "data['theta'] = np.arccos(data.cz)\n", - "data['phi'] = np.arctan2(data.cx, data.cy)" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# We can plot all the data from a dataframe at once\n", - "data.hist(bins=100, figsize=(10,10))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# And make cuts to the data and save as new dataframes\n", - "\n", - "\n", - "data_2 = data[data.q2 < 2.0]\n", - "\n", - "data_2.hist(bins=100, figsize=(10,10))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# References" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[1] https://matplotlib.org/tutorials/index.html\n", - "[2] https://pandas.pydata.org/pandas-docs/stable/getting_started/tutorials.html" - ] - } - ], - "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": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/Day_2/WorkProblems/gravity.csv b/Day_2/WorkProblems/gravity.csv deleted file mode 100644 index 768cd69..0000000 --- a/Day_2/WorkProblems/gravity.csv +++ /dev/null @@ -1,501 +0,0 @@ -x,y,errors --10.0,-20.489388618769908,1.0 --9.959919839679358,23.060026669366465,1.0 --9.919839679358718,-21.08601014044241,1.0 --9.879759519038076,17.32439220059607,1.0 --9.839679358717435,26.905539758540304,1.0 --9.799599198396793,62.599226037301605,1.0 --9.759519038076153,11.210008884296307,1.0 --9.719438877755511,25.86275005348945,1.0 --9.67935871743487,45.50229992617264,1.0 --9.639278557114228,91.38359331578724,1.0 --9.599198396793588,69.96371510309808,1.0 --9.559118236472946,-11.50277340427219,1.0 --9.519038076152304,40.74731180596186,1.0 --9.478957915831664,46.76907113767792,1.0 --9.438877755511022,71.20427553410222,1.0 --9.39879759519038,103.88186285421284,1.0 --9.358717434869739,59.271546552575025,1.0 --9.318637274549099,110.00413815578409,1.0 --9.278557114228457,74.26510034193798,1.0 --9.238476953907815,90.60646538465652,1.0 --9.198396793587175,98.33593919102233,1.0 --9.158316633266534,65.50551414168551,1.0 --9.118236472945892,69.56103040923077,1.0 --9.07815631262525,95.49358203860511,1.0 --9.03807615230461,166.3734037945876,1.0 --8.997995991983968,87.22613966138525,1.0 --8.957915831663327,154.34348308585203,1.0 --8.917835671342685,136.8067723084962,1.0 --8.877755511022045,142.53329403281614,1.0 --8.837675350701403,109.31994673226734,1.0 --8.797595190380761,143.99366014110456,1.0 --8.75751503006012,122.40236885069415,1.0 --8.71743486973948,136.16045957200484,1.0 --8.677354709418838,151.10453620691956,1.0 --8.637274549098196,116.35574189122275,1.0 --8.597194388777556,134.92629403523534,1.0 --8.557114228456914,179.2205172654015,1.0 --8.517034068136272,145.46506405307167,1.0 --8.47695390781563,177.6455239287614,1.0 --8.43687374749499,174.81058967001877,1.0 --8.396793587174349,214.74671696272168,1.0 --8.356713426853707,160.25948015896972,1.0 --8.316633266533067,190.02936266306858,1.0 --8.276553106212425,188.31983168312905,1.0 --8.236472945891784,133.95694114087198,1.0 --8.196392785571142,232.56539633891836,1.0 --8.156312625250502,138.07435886860327,1.0 --8.11623246492986,239.57938862964454,1.0 --8.076152304609218,248.37072537177494,1.0 --8.036072144288577,230.77444161220495,1.0 --7.9959919839679365,217.74309123901395,1.0 --7.955911823647295,233.0314106325457,1.0 --7.915831663326653,196.74637265111411,1.0 --7.875751503006012,244.43303947285176,1.0 --7.835671342685371,225.99753880404003,1.0 --7.7955911823647295,273.1514196883211,1.0 --7.755511022044088,225.54775365333927,1.0 --7.715430861723447,204.90393087588507,1.0 --7.675350701402806,274.3947077984593,1.0 --7.635270541082164,270.89997688739044,1.0 --7.595190380761523,230.35949158004112,1.0 --7.555110220440882,315.96691746735945,1.0 --7.515030060120241,239.63548135485007,1.0 --7.474949899799599,241.3006597859102,1.0 --7.434869739478958,282.76385557619716,1.0 --7.394789579158317,298.8521147359008,1.0 --7.354709418837675,256.4088039369002,1.0 --7.314629258517034,268.74819536814204,1.0 --7.274549098196393,194.4853760739211,1.0 --7.234468937875752,238.2007041152661,1.0 --7.19438877755511,228.63548704428365,1.0 --7.154308617234469,275.00613699589536,1.0 --7.114228456913828,319.7512789924864,1.0 --7.074148296593187,270.54626246659876,1.0 --7.034068136272545,271.3017843754083,1.0 --6.993987975951904,289.1355748628511,1.0 --6.953907815631263,339.81476837010223,1.0 --6.913827655310621,308.5577472110263,1.0 --6.8737474949899795,289.36037857422195,1.0 --6.833667334669339,294.6751486867351,1.0 --6.793587174348698,357.3758454177387,1.0 --6.753507014028056,375.7804560267179,1.0 --6.713426853707415,341.64090446447847,1.0 --6.673346693386774,347.4627065877866,1.0 --6.6332665330661325,335.39870681469745,1.0 --6.593186372745491,352.060854032714,1.0 --6.55310621242485,220.5414899578621,1.0 --6.513026052104209,363.13414802447244,1.0 --6.472945891783567,384.5094573940741,1.0 --6.432865731462925,312.4212406675148,1.0 --6.3927855711422845,350.7080415362037,1.0 --6.352705410821644,308.4704481632972,1.0 --6.312625250501002,348.687572581286,1.0 --6.272545090180361,379.92616352974073,1.0 --6.23246492985972,315.54602044701716,1.0 --6.192384769539078,335.3582561178768,1.0 --6.152304609218437,355.1517474820238,1.0 --6.112224448897796,398.9492864891845,1.0 --6.072144288577155,357.51161103688736,1.0 --6.032064128256513,439.0811274251658,1.0 --5.991983967935872,380.5911925932908,1.0 --5.95190380761523,346.3446946544302,1.0 --5.9118236472945895,377.1276103982034,1.0 --5.871743486973948,364.26871320490005,1.0 --5.831663326653307,394.90729274991037,1.0 --5.791583166332665,403.8248965937156,1.0 --5.751503006012024,422.4805873576758,1.0 --5.711422845691383,325.891406023233,1.0 --5.671342685370742,422.3586065831443,1.0 --5.631262525050101,370.12491843366615,1.0 --5.591182364729459,403.85556414653206,1.0 --5.551102204408818,376.84401170395665,1.0 --5.511022044088176,387.5366415011072,1.0 --5.470941883767535,386.31039292459366,1.0 --5.430861723446894,398.3880864304888,1.0 --5.390781563126253,360.4924967707699,1.0 --5.350701402805611,398.19703681505655,1.0 --5.31062124248497,410.84883838063405,1.0 --5.270541082164329,436.00926285623444,1.0 --5.2304609218436875,448.4467171325054,1.0 --5.190380761523047,457.0754095752119,1.0 --5.150300601202405,418.1948849935629,1.0 --5.110220440881764,436.20596630735184,1.0 --5.070140280561122,412.0338627316615,1.0 --5.030060120240481,414.79754416798835,1.0 --4.98997995991984,423.04744383962395,1.0 --4.949899799599199,403.02651124688066,1.0 --4.909819639278557,466.2612620239382,1.0 --4.869739478957916,399.01028713983777,1.0 --4.829659318637275,422.23435351194513,1.0 --4.789579158316633,447.0938969323266,1.0 --4.7494989979959925,493.4848315117414,1.0 --4.709418837675351,422.6502693975922,1.0 --4.66933867735471,461.3191478266679,1.0 --4.629258517034068,449.02255036329484,1.0 --4.589178356713427,455.03115518229976,1.0 --4.5490981963927855,481.32784978644895,1.0 --4.509018036072145,512.876133265109,1.0 --4.468937875751503,449.49749258657295,1.0 --4.428857715430862,426.9389136711203,1.0 --4.388777555110221,480.73495503116004,1.0 --4.348697394789579,435.331914112731,1.0 --4.308617234468938,524.9840209480845,1.0 --4.268537074148297,434.53674429371034,1.0 --4.228456913827656,486.248479742228,1.0 --4.188376753507014,502.86748912098267,1.0 --4.148296593186373,501.99871043981994,1.0 --4.108216432865731,446.0017210522658,1.0 --4.0681362725450905,542.4454433564372,1.0 --4.028056112224449,479.33144075825004,1.0 --3.987975951903808,495.38840408667704,1.0 --3.947895791583167,479.96620804034603,1.0 --3.907815631262525,497.5200478161765,1.0 --3.8677354709418843,502.0012113301762,1.0 --3.8276553106212425,491.6592266556029,1.0 --3.7875751503006017,502.146730152846,1.0 --3.74749498997996,520.4173138770853,1.0 --3.707414829659319,498.87446279188845,1.0 --3.6673346693386772,494.40728726441597,1.0 --3.6272545090180364,488.89853978201677,1.0 --3.5871743486973955,467.1910258887969,1.0 --3.5470941883767537,469.34049961466343,1.0 --3.507014028056113,518.4123312353919,1.0 --3.466933867735471,524.4761259184154,1.0 --3.42685370741483,488.58281206216043,1.0 --3.3867735470941884,499.86236079638394,1.0 --3.3466933867735476,498.48327302456084,1.0 --3.306613226452906,556.0672398441955,1.0 --3.266533066132265,535.3394416022614,1.0 --3.226452905811623,465.18995640859964,1.0 --3.1863727454909823,567.5147664227637,1.0 --3.1462925851703414,516.6416637324164,1.0 --3.1062124248496996,536.9412575339604,1.0 --3.0661322645290587,509.52804851385395,1.0 --3.026052104208417,568.2849029392268,1.0 --2.985971943887776,495.52857214697855,1.0 --2.9458917835671343,577.0615351933657,1.0 --2.9058116232464934,542.3349295640147,1.0 --2.8657314629258517,530.3400182495776,1.0 --2.825651302605211,558.6586051418897,1.0 --2.785571142284569,526.7204175256057,1.0 --2.745490981963928,562.9292239270382,1.0 --2.7054108216432873,483.4863406467993,1.0 --2.6653306613226455,484.1317635204597,1.0 --2.6252505010020046,570.3833451542691,1.0 --2.585170340681363,543.5079117381221,1.0 --2.545090180360722,542.2016207287422,1.0 --2.50501002004008,506.9085808406561,1.0 --2.4649298597194393,547.8026298700265,1.0 --2.4248496993987976,584.9166443614712,1.0 --2.3847695390781567,579.0358857576516,1.0 --2.344689378757515,559.6411611309156,1.0 --2.304609218436874,560.1130047780836,1.0 --2.264529058116233,584.466125301952,1.0 --2.2244488977955914,576.9052624855723,1.0 --2.1843687374749505,540.9797469649741,1.0 --2.1442885771543088,564.2665518366767,1.0 --2.104208416833668,533.797670937515,1.0 --2.064128256513026,589.2569384155398,1.0 --2.0240480961923852,553.2018650628133,1.0 --1.9839679358717444,541.2424411129026,1.0 --1.9438877755511026,543.8765376670997,1.0 --1.9038076152304608,585.0052553520189,1.0 --1.8637274549098208,567.8982727042818,1.0 --1.823647294589179,574.7893303258284,1.0 --1.7835671342685373,571.8599622424147,1.0 --1.7434869739478955,640.0883237010219,1.0 --1.7034068136272555,550.3409818546164,1.0 --1.6633266533066138,580.6958183555763,1.0 --1.623246492985972,581.6503825401709,1.0 --1.5831663326653302,550.4345071766656,1.0 --1.5430861723446903,557.3850089008101,1.0 --1.5030060120240485,558.286311079409,1.0 --1.4629258517034067,627.969688140178,1.0 --1.4228456913827667,603.5366316959693,1.0 --1.382765531062125,605.1345426782182,1.0 --1.3426853707414832,552.1944531919746,1.0 --1.3026052104208414,526.3984218026079,1.0 --1.2625250501002014,579.1873173584626,1.0 --1.2224448897795597,519.20004427035,1.0 --1.182364729458918,620.0392767793112,1.0 --1.1422845691382761,560.3942377484926,1.0 --1.1022044088176361,613.1539177013952,1.0 --1.0621242484969944,559.7108130024178,1.0 --1.0220440881763526,593.1168166750947,1.0 --0.9819639278557126,531.9900792140121,1.0 --0.9418837675350709,571.9596771599006,1.0 --0.9018036072144291,564.3306413253549,1.0 --0.8617234468937873,609.8420712965799,1.0 --0.8216432865731473,651.8624169833985,1.0 --0.7815631262525056,570.9465030565461,1.0 --0.7414829659318638,555.0765219597585,1.0 --0.701402805611222,515.1297361200802,1.0 --0.661322645290582,591.3295435004104,1.0 --0.6212424849699403,561.1351719116527,1.0 --0.5811623246492985,614.423869065901,1.0 --0.5410821643286585,621.0118233163851,1.0 --0.5010020040080168,568.3162685455388,1.0 --0.460921843687375,618.6401492922996,1.0 --0.4208416833667332,601.7532904207092,1.0 --0.38076152304609323,621.8509100457254,1.0 --0.34068136272545146,567.0924721672791,1.0 --0.3006012024048097,576.0973328890041,1.0 --0.26052104208416793,554.2903078940519,1.0 --0.22044088176352794,583.8744109242641,1.0 --0.18036072144288617,579.4338573323283,1.0 --0.1402805611222444,632.8246821947929,1.0 --0.10020040080160442,635.4746689248271,1.0 --0.06012024048096265,576.6974594061164,1.0 --0.020040080160320883,607.710057985429,1.0 -0.020040080160320883,584.7254151153844,1.0 -0.060120240480960874,630.2689409110671,1.0 -0.10020040080160264,619.0619496688606,1.0 -0.1402805611222444,638.5992841124165,1.0 -0.18036072144288617,578.9725414820749,1.0 -0.22044088176352616,603.5397344019502,1.0 -0.26052104208416793,573.8363077325233,1.0 -0.3006012024048097,625.8765747393433,1.0 -0.3406813627254497,573.3361465089259,1.0 -0.38076152304609145,565.1708239606736,1.0 -0.4208416833667332,569.8796537383082,1.0 -0.460921843687375,595.483796248785,1.0 -0.501002004008015,651.4075456882333,1.0 -0.5410821643286567,642.1063487229887,1.0 -0.5811623246492985,606.4467259372113,1.0 -0.6212424849699403,596.2516247780615,1.0 -0.6613226452905803,613.9604684247012,1.0 -0.701402805611222,651.4002879702113,1.0 -0.7414829659318638,586.847310609129,1.0 -0.7815631262525038,602.1771575640757,1.0 -0.8216432865731456,637.0173874873706,1.0 -0.8617234468937873,617.0114714877491,1.0 -0.9018036072144291,596.7093612711891,1.0 -0.9418837675350691,654.6398939805476,1.0 -0.9819639278557108,673.2876051549274,1.0 -1.0220440881763526,627.5049689691919,1.0 -1.0621242484969944,586.0686032016282,1.0 -1.1022044088176344,552.5246484354778,1.0 -1.1422845691382761,545.2327900724305,1.0 -1.182364729458918,663.8215054525781,1.0 -1.222444889779558,570.5887714538791,1.0 -1.2625250501001997,619.3359100198626,1.0 -1.3026052104208414,569.4249112594136,1.0 -1.3426853707414832,596.7281379622756,1.0 -1.3827655310621232,635.1132156287521,1.0 -1.422845691382765,561.9527502806502,1.0 -1.4629258517034067,608.1547217699256,1.0 -1.5030060120240485,623.0445154413286,1.0 -1.5430861723446885,650.0252699122661,1.0 -1.5831663326653302,647.3275605521322,1.0 -1.623246492985972,653.1863662604899,1.0 -1.663326653306612,580.8995087662108,1.0 -1.7034068136272538,659.5066185796219,1.0 -1.7434869739478955,575.1644292419126,1.0 -1.7835671342685373,612.1304680883878,1.0 -1.8236472945891773,604.7350135462879,1.0 -1.863727454909819,622.3401218762314,1.0 -1.9038076152304608,563.0901239319892,1.0 -1.9438877755511026,613.463235798366,1.0 -1.9839679358717426,570.2487718779452,1.0 -2.0240480961923843,581.749844696922,1.0 -2.064128256513026,589.2873953626588,1.0 -2.104208416833666,589.0425453052727,1.0 -2.144288577154308,587.7375398840353,1.0 -2.1843687374749496,610.2894374575538,1.0 -2.2244488977955914,627.0153790344215,1.0 -2.2645290581162314,596.2753223159344,1.0 -2.304609218436873,592.8735184239929,1.0 -2.344689378757515,615.6867027891608,1.0 -2.3847695390781567,608.7827518939138,1.0 -2.4248496993987967,583.5917583820407,1.0 -2.4649298597194385,558.2931626248334,1.0 -2.50501002004008,599.4684682587335,1.0 -2.54509018036072,557.1008864165111,1.0 -2.585170340681362,608.5576737615606,1.0 -2.6252505010020037,574.5193137045117,1.0 -2.6653306613226455,564.7822323882979,1.0 -2.7054108216432855,577.6783732452391,1.0 -2.7454909819639273,584.5678184626457,1.0 -2.785571142284569,536.1723563162593,1.0 -2.825651302605209,590.4622769968573,1.0 -2.865731462925851,634.2136777954519,1.0 -2.9058116232464926,572.3407467471621,1.0 -2.9458917835671343,612.8288187899777,1.0 -2.9859719438877743,594.3972550416446,1.0 -3.026052104208416,587.2825417965207,1.0 -3.066132264529058,597.4043109575259,1.0 -3.1062124248496996,568.7323167549617,1.0 -3.1462925851703396,623.5845473755027,1.0 -3.1863727454909814,542.880815662141,1.0 -3.226452905811623,625.6338826949953,1.0 -3.266533066132263,603.7790355609538,1.0 -3.306613226452905,575.0432409780763,1.0 -3.3466933867735467,611.3734688161545,1.0 -3.3867735470941884,592.4776940708434,1.0 -3.4268537074148284,615.2294622353485,1.0 -3.46693386773547,591.3260521947052,1.0 -3.507014028056112,623.5546218375364,1.0 -3.5470941883767537,596.2087483372378,1.0 -3.5871743486973937,557.2731069394748,1.0 -3.6272545090180355,540.519439120128,1.0 -3.6673346693386772,572.6892241487838,1.0 -3.7074148296593172,530.9815544621437,1.0 -3.747494989979959,545.7042916063316,1.0 -3.7875751503006008,519.9179533390593,1.0 -3.8276553106212425,523.271849278562,1.0 -3.8677354709418825,588.5511150105142,1.0 -3.9078156312625243,564.2198560930025,1.0 -3.947895791583166,542.796703422272,1.0 -3.987975951903808,583.5525313350945,1.0 -4.028056112224448,625.6734888922269,1.0 -4.06813627254509,581.4189772159275,1.0 -4.108216432865731,531.3045178196344,1.0 -4.148296593186371,550.9803590096884,1.0 -4.188376753507013,519.2571037134686,1.0 -4.228456913827655,559.9033103525271,1.0 -4.268537074148297,542.2861589198197,1.0 -4.308617234468937,561.9960324631082,1.0 -4.348697394789578,475.8342118493801,1.0 -4.38877755511022,557.0035563108733,1.0 -4.428857715430862,550.2682478296821,1.0 -4.468937875751502,586.7435090811722,1.0 -4.509018036072144,528.1258288936061,1.0 -4.5490981963927855,569.5117227999507,1.0 -4.589178356713425,573.5113584071357,1.0 -4.629258517034067,573.0751101309272,1.0 -4.669338677354709,597.6991816184147,1.0 -4.709418837675351,499.11465146991145,1.0 -4.749498997995991,538.0098955078373,1.0 -4.7895791583166325,498.21713932612226,1.0 -4.829659318637274,527.4646004475726,1.0 -4.869739478957916,542.2188681631557,1.0 -4.909819639278556,518.656757080374,1.0 -4.949899799599198,570.4417449802412,1.0 -4.98997995991984,510.81397952206675,1.0 -5.0300601202404795,540.419534525096,1.0 -5.070140280561121,517.8560050369131,1.0 -5.110220440881763,538.7849082779297,1.0 -5.150300601202405,486.55250823893425,1.0 -5.190380761523045,446.3002432335991,1.0 -5.230460921843687,490.62919802939206,1.0 -5.270541082164328,534.5339242093816,1.0 -5.31062124248497,494.6268920925527,1.0 -5.35070140280561,493.62466540978534,1.0 -5.390781563126252,545.6153593895062,1.0 -5.430861723446894,494.7298414261536,1.0 -5.470941883767534,527.1540368062773,1.0 -5.511022044088175,449.8654058360149,1.0 -5.551102204408817,490.6937511702011,1.0 -5.591182364729459,463.15772667860955,1.0 -5.631262525050099,500.1762429957467,1.0 -5.671342685370741,502.71967553324606,1.0 -5.7114228456913825,454.4591511975699,1.0 -5.751503006012024,492.775379646788,1.0 -5.791583166332664,478.6630357842463,1.0 -5.831663326653306,477.6611851669956,1.0 -5.871743486973948,477.91027812611856,1.0 -5.911823647294588,457.2394908021918,1.0 -5.9519038076152295,496.8098981376182,1.0 -5.991983967935871,422.34934100116,1.0 -6.032064128256511,440.61918529520517,1.0 -6.072144288577153,461.4632590247213,1.0 -6.112224448897795,439.9070819311425,1.0 -6.152304609218437,514.2312598339602,1.0 -6.192384769539078,464.3609754339997,1.0 -6.23246492985972,455.4403342878385,1.0 -6.272545090180358,485.13779318592157,1.0 -6.312625250501,456.80415042199354,1.0 -6.352705410821642,435.1115961608541,1.0 -6.392785571142284,477.15901832200893,1.0 -6.432865731462925,451.84669086741746,1.0 -6.472945891783567,469.476405270794,1.0 -6.513026052104209,454.56302930536907,1.0 -6.553106212424851,519.9485951142398,1.0 -6.593186372745489,460.5751272526157,1.0 -6.633266533066131,415.6075358319739,1.0 -6.673346693386772,465.879431091191,1.0 -6.713426853707414,476.7582678516894,1.0 -6.753507014028056,473.98187738449496,1.0 -6.793587174348698,426.1336414378882,1.0 -6.8336673346693395,438.772026714295,1.0 -6.873747494989978,478.7202023147311,1.0 -6.9138276553106195,434.41151214545044,1.0 -6.953907815631261,407.07658201222284,1.0 -6.993987975951903,434.4167834225881,1.0 -7.034068136272545,439.91429030817454,1.0 -7.074148296593187,374.76892223746705,1.0 -7.114228456913828,445.4001675543663,1.0 -7.1543086172344665,435.1429647097014,1.0 -7.194388777555108,426.63363995282504,1.0 -7.23446893787575,475.97097006762476,1.0 -7.274549098196392,440.4283190142582,1.0 -7.314629258517034,419.72550120673714,1.0 -7.354709418837675,403.1533929802443,1.0 -7.394789579158317,386.23198141456,1.0 -7.434869739478959,381.3767056552794,1.0 -7.474949899799597,394.05082740204256,1.0 -7.515030060120239,393.0252898420257,1.0 -7.555110220440881,376.4924409253158,1.0 -7.595190380761522,377.2409143742377,1.0 -7.635270541082164,416.6886362407583,1.0 -7.675350701402806,353.58260364862934,1.0 -7.715430861723448,306.4539200590659,1.0 -7.755511022044086,391.2615483965608,1.0 -7.795591182364728,343.70698624068757,1.0 -7.8356713426853695,423.15330571929167,1.0 -7.875751503006011,333.1613878123013,1.0 -7.915831663326653,353.9207473567396,1.0 -7.955911823647295,334.24507874731114,1.0 -7.9959919839679365,355.539732221485,1.0 -8.036072144288575,364.6591389587572,1.0 -8.076152304609217,373.77902651486056,1.0 -8.116232464929858,387.340563658463,1.0 -8.1563126252505,321.0049135075273,1.0 -8.196392785571142,355.03806270931995,1.0 -8.236472945891784,319.0848675610023,1.0 -8.276553106212425,325.7790064087229,1.0 -8.316633266533067,374.4656278051873,1.0 -8.356713426853705,331.627282213922,1.0 -8.396793587174347,291.2079606002262,1.0 -8.436873747494989,329.6249143176816,1.0 -8.47695390781563,325.2815214601693,1.0 -8.517034068136272,387.25988608272576,1.0 -8.557114228456914,331.633714436275,1.0 -8.597194388777556,328.0349018694278,1.0 -8.637274549098194,353.2395976026789,1.0 -8.677354709418836,361.3954143579786,1.0 -8.717434869739478,294.0901275714286,1.0 -8.75751503006012,239.93350321309646,1.0 -8.797595190380761,298.9788497322105,1.0 -8.837675350701403,317.62539365605653,1.0 -8.877755511022045,296.53893068319854,1.0 -8.917835671342683,304.57884765479827,1.0 -8.957915831663325,353.206377578739,1.0 -8.997995991983966,275.60043452809737,1.0 -9.038076152304608,275.09475521554435,1.0 -9.07815631262525,237.52439067088028,1.0 -9.118236472945892,311.9189754310244,1.0 -9.158316633266534,331.7788058528332,1.0 -9.198396793587175,231.32591549018346,1.0 -9.238476953907814,273.2420136204234,1.0 -9.278557114228455,286.16917243622294,1.0 -9.318637274549097,301.21784588111007,1.0 -9.358717434869739,279.39137307053807,1.0 -9.39879759519038,258.84004488884204,1.0 -9.438877755511022,310.2211131382038,1.0 -9.478957915831664,260.5532975176824,1.0 -9.519038076152302,309.43543695107167,1.0 -9.559118236472944,208.64176947518317,1.0 -9.599198396793586,202.21697609246175,1.0 -9.639278557114228,236.93374535763976,1.0 -9.67935871743487,197.36334811020205,1.0 -9.719438877755511,179.2789051203133,1.0 -9.759519038076153,218.96326363088153,1.0 -9.799599198396791,195.0813270773075,1.0 -9.839679358717433,228.7614428575452,1.0 -9.879759519038075,271.66396765499667,1.0 -9.919839679358716,197.3886927991154,1.0 -9.959919839679358,221.43860929344882,1.0 -10.0,222.81942400272487,1.0 diff --git a/Day_2/WorkProblems/hysteresis.csv b/Day_2/WorkProblems/hysteresis.csv deleted file mode 100644 index f98fa69..0000000 --- a/Day_2/WorkProblems/hysteresis.csv +++ /dev/null @@ -1,501 +0,0 @@ -x,y,errors --10.0,-101.01796099313094,1.0 --9.959919839679358,-78.42451512561877,1.0 --9.919839679358718,-82.58664993706768,1.0 --9.879759519038076,-96.19144602305379,1.0 --9.839679358717435,-69.70484516251426,1.0 --9.799599198396793,-91.44906754900639,1.0 --9.759519038076153,-85.42990491866301,1.0 --9.719438877755511,-93.49336974004943,1.0 --9.67935871743487,-81.64752057273986,1.0 --9.639278557114228,-91.7308082242468,1.0 --9.599198396793588,-99.42738840891546,1.0 --9.559118236472946,-79.16134281215619,1.0 --9.519038076152304,-71.96694539056267,1.0 --9.478957915831664,-87.94436422197606,1.0 --9.438877755511022,-98.98291209525206,1.0 --9.39879759519038,-90.38601692279023,1.0 --9.358717434869739,-90.8299912547348,1.0 --9.318637274549099,-81.60691879238449,1.0 --9.278557114228457,-87.04054805291477,1.0 --9.238476953907815,-86.88025078326423,1.0 --9.198396793587175,-94.1672136886394,1.0 --9.158316633266534,-98.56421078391375,1.0 --9.118236472945892,-115.15152450390991,1.0 --9.07815631262525,-88.96968674158003,1.0 --9.03807615230461,-90.88205619084398,1.0 --8.997995991983968,-95.96085199192437,1.0 --8.957915831663327,-93.49255357879333,1.0 --8.917835671342685,-90.09808964642914,1.0 --8.877755511022045,-100.137742568019,1.0 --8.837675350701403,-93.4099023936268,1.0 --8.797595190380761,-92.39702742364524,1.0 --8.75751503006012,-101.1602248750641,1.0 --8.71743486973948,-83.85067305860697,1.0 --8.677354709418838,-90.16411667669766,1.0 --8.637274549098196,-75.01722320744506,1.0 --8.597194388777556,-81.5019774057154,1.0 --8.557114228456914,-80.17508177039754,1.0 --8.517034068136272,-75.65500993618166,1.0 --8.47695390781563,-85.99945236843728,1.0 --8.43687374749499,-105.51928841553642,1.0 --8.396793587174349,-80.39859261125966,1.0 --8.356713426853707,-100.47371960110007,1.0 --8.316633266533067,-103.28780216710138,1.0 --8.276553106212425,-103.74445784037673,1.0 --8.236472945891784,-89.26586744427628,1.0 --8.196392785571142,-115.38014181282028,1.0 --8.156312625250502,-96.23646017971565,1.0 --8.11623246492986,-95.69911530068914,1.0 --8.076152304609218,-84.04501929393037,1.0 --8.036072144288577,-98.19454520030033,1.0 --7.9959919839679365,-97.05003794103415,1.0 --7.955911823647295,-103.18381285544396,1.0 --7.915831663326653,-73.12009015105649,1.0 --7.875751503006012,-87.56435659846143,1.0 --7.835671342685371,-72.80450460800714,1.0 --7.7955911823647295,-83.997888643342,1.0 --7.755511022044088,-107.69722756351146,1.0 --7.715430861723447,-89.19426148851873,1.0 --7.675350701402806,-89.40401998343428,1.0 --7.635270541082164,-88.35274951507779,1.0 --7.595190380761523,-82.81699566676566,1.0 --7.555110220440882,-90.66614542782843,1.0 --7.515030060120241,-83.08834340614368,1.0 --7.474949899799599,-89.89441655951394,1.0 --7.434869739478958,-86.42909683988917,1.0 --7.394789579158317,-90.12029560158696,1.0 --7.354709418837675,-96.5932580632051,1.0 --7.314629258517034,-91.7455059090635,1.0 --7.274549098196393,-92.40381185042801,1.0 --7.234468937875752,-89.45367315932495,1.0 --7.19438877755511,-79.97515423258498,1.0 --7.154308617234469,-99.4212751813547,1.0 --7.114228456913828,-94.96161472261464,1.0 --7.074148296593187,-104.38560774281967,1.0 --7.034068136272545,-77.94483449364752,1.0 --6.993987975951904,-97.98096570138989,1.0 --6.953907815631263,-91.62976990412587,1.0 --6.913827655310621,-91.89113269607029,1.0 --6.8737474949899795,-89.57116751811462,1.0 --6.833667334669339,-102.08335430488326,1.0 --6.793587174348698,-89.17619902759985,1.0 --6.753507014028056,-79.85204467953844,1.0 --6.713426853707415,-88.1823946335883,1.0 --6.673346693386774,-92.4977565299256,1.0 --6.6332665330661325,-84.08612582805138,1.0 --6.593186372745491,-91.63240059505115,1.0 --6.55310621242485,-86.30342949175817,1.0 --6.513026052104209,-68.80770868562371,1.0 --6.472945891783567,-90.7568238118523,1.0 --6.432865731462925,-79.18700361773867,1.0 --6.3927855711422845,-86.37922369436497,1.0 --6.352705410821644,-104.84904758303009,1.0 --6.312625250501002,-116.70258919862746,1.0 --6.272545090180361,-85.15588206470815,1.0 --6.23246492985972,-87.84653793311622,1.0 --6.192384769539078,-98.67236523097084,1.0 --6.152304609218437,-83.97538995721148,1.0 --6.112224448897796,-81.00967308873274,1.0 --6.072144288577155,-80.83464475628344,1.0 --6.032064128256513,-83.44534722761401,1.0 --5.991983967935872,-82.53474387610987,1.0 --5.95190380761523,-65.8569174973012,1.0 --5.9118236472945895,-71.57238460059924,1.0 --5.871743486973948,-81.78322510089973,1.0 --5.831663326653307,-87.64867499275411,1.0 --5.791583166332665,-102.2716497644408,1.0 --5.751503006012024,-74.97591416607304,1.0 --5.711422845691383,-88.60771275253512,1.0 --5.671342685370742,-97.75550896911582,1.0 --5.631262525050101,-83.51626807798313,1.0 --5.591182364729459,-73.74200701100898,1.0 --5.551102204408818,-84.34307292697406,1.0 --5.511022044088176,-105.46906967617511,1.0 --5.470941883767535,-75.46272092690427,1.0 --5.430861723446894,-101.34617230940442,1.0 --5.390781563126253,-99.98801755801503,1.0 --5.350701402805611,-83.71166543415464,1.0 --5.31062124248497,-84.14788916589443,1.0 --5.270541082164329,-96.86145350860755,1.0 --5.2304609218436875,-89.08077122873212,1.0 --5.190380761523047,-94.97176676989369,1.0 --5.150300601202405,-93.81659828307522,1.0 --5.110220440881764,-90.02106730338768,1.0 --5.070140280561122,-99.12296179964046,1.0 --5.030060120240481,-90.40203925451985,1.0 --4.98997995991984,-81.16986420721092,1.0 --4.949899799599199,-73.7406778870616,1.0 --4.909819639278557,-89.44545166816894,1.0 --4.869739478957916,-97.68827817167504,1.0 --4.829659318637275,-72.89859992784724,1.0 --4.789579158316633,-88.97102346353795,1.0 --4.7494989979959925,-78.19804005212015,1.0 --4.709418837675351,-85.58781331860843,1.0 --4.66933867735471,-79.44414892632301,1.0 --4.629258517034068,-117.35129742349837,1.0 --4.589178356713427,-89.00690506616948,1.0 --4.5490981963927855,-84.73690754751236,1.0 --4.509018036072145,-67.98519640473442,1.0 --4.468937875751503,-89.02893566863774,1.0 --4.428857715430862,-92.23773719633897,1.0 --4.388777555110221,-89.54658006934638,1.0 --4.348697394789579,-89.09559281520768,1.0 --4.308617234468938,-97.36295489351296,1.0 --4.268537074148297,-96.40623325531975,1.0 --4.228456913827656,-91.55657649726524,1.0 --4.188376753507014,-86.7686138431309,1.0 --4.148296593186373,-82.177496901341,1.0 --4.108216432865731,-96.0150836924822,1.0 --4.0681362725450905,-116.99501655197813,1.0 --4.028056112224449,-76.91499380506852,1.0 --3.987975951903808,-93.6868628517977,1.0 --3.947895791583167,-105.91392158950069,1.0 --3.907815631262525,-89.57113538007343,1.0 --3.8677354709418843,-75.88207316426966,1.0 --3.8276553106212425,-87.78893512129228,1.0 --3.7875751503006017,-91.85718244985249,1.0 --3.74749498997996,-94.67028150553969,1.0 --3.707414829659319,-83.59997013622514,1.0 --3.6673346693386772,-104.42041497686088,1.0 --3.6272545090180364,-83.61668323331422,1.0 --3.5871743486973955,-110.92799860399236,1.0 --3.5470941883767537,-94.16147490565032,1.0 --3.507014028056113,-91.09244932892982,1.0 --3.466933867735471,-72.71041755416074,1.0 --3.42685370741483,-79.89356162494406,1.0 --3.3867735470941884,-103.26308094177043,1.0 --3.3466933867735476,-85.61042752310695,1.0 --3.306613226452906,-90.17291024141042,1.0 --3.266533066132265,-85.63180970696864,1.0 --3.226452905811623,-104.58304532618251,1.0 --3.1863727454909823,-82.77365856558946,1.0 --3.1462925851703414,-84.31853239457526,1.0 --3.1062124248496996,-84.69710950234537,1.0 --3.0661322645290587,-77.04442786486348,1.0 --3.026052104208417,-104.41189400201931,1.0 --2.985971943887776,-79.11711949140401,1.0 --2.9458917835671343,-76.99789452957809,1.0 --2.9058116232464934,-82.7209075527213,1.0 --2.8657314629258517,-72.08186272014146,1.0 --2.825651302605211,-86.26655274694544,1.0 --2.785571142284569,-78.94981436738448,1.0 --2.745490981963928,-89.78132329168875,1.0 --2.7054108216432873,-107.20434005679334,1.0 --2.6653306613226455,-92.1027668044599,1.0 --2.6252505010020046,-90.62331913156642,1.0 --2.585170340681363,-94.9161913400633,1.0 --2.545090180360722,-92.11863564327778,1.0 --2.50501002004008,-88.9262651377049,1.0 --2.4649298597194393,-95.1887557181006,1.0 --2.4248496993987976,-88.81292056295614,1.0 --2.3847695390781567,-97.3365599710435,1.0 --2.344689378757515,-71.92779355382285,1.0 --2.304609218436874,-92.95712089495947,1.0 --2.264529058116233,-101.63628817439623,1.0 --2.2244488977955914,-98.9139998668097,1.0 --2.1843687374749505,-84.3040113940637,1.0 --2.1442885771543088,-79.93737056479756,1.0 --2.104208416833668,-105.5948770832862,1.0 --2.064128256513026,-86.57528242243555,1.0 --2.0240480961923852,-80.33024593839379,1.0 --1.9839679358717444,-84.23750472180652,1.0 --1.9438877755511026,-84.28044992539209,1.0 --1.9038076152304608,-98.08312262935343,1.0 --1.8637274549098208,-104.3827717420529,1.0 --1.823647294589179,-78.32257609467709,1.0 --1.7835671342685373,-79.43499945307023,1.0 --1.7434869739478955,-79.29622933002292,1.0 --1.7034068136272555,-91.02352518930536,1.0 --1.6633266533066138,-89.10057694027508,1.0 --1.623246492985972,-87.83704937477192,1.0 --1.5831663326653302,-75.5208660185454,1.0 --1.5430861723446903,-92.08006017814711,1.0 --1.5030060120240485,-103.2658291050263,1.0 --1.4629258517034067,-84.23279285972428,1.0 --1.4228456913827667,-76.90879617103109,1.0 --1.382765531062125,-83.61572197042834,1.0 --1.3426853707414832,-90.50024073473898,1.0 --1.3026052104208414,-82.51701225883903,1.0 --1.2625250501002014,-113.02150144935327,1.0 --1.2224448897795597,-109.05793462456685,1.0 --1.182364729458918,-92.06062060826756,1.0 --1.1422845691382761,-109.30501915342657,1.0 --1.1022044088176361,-82.870211940148,1.0 --1.0621242484969944,-100.01520486140066,1.0 --1.0220440881763526,-70.85352485822429,1.0 --0.9819639278557126,-90.15001647958023,1.0 --0.9418837675350709,-100.24101794354546,1.0 --0.9018036072144291,-107.76414750937178,1.0 --0.8617234468937873,-91.37601056879267,1.0 --0.8216432865731473,-79.51717420446661,1.0 --0.7815631262525056,-76.53776610236608,1.0 --0.7414829659318638,-96.67611172525115,1.0 --0.701402805611222,-91.95043299959917,1.0 --0.661322645290582,-88.02082331209198,1.0 --0.6212424849699403,-75.01743191548042,1.0 --0.5811623246492985,-77.84904066997068,1.0 --0.5410821643286585,-60.74098407298291,1.0 --0.5010020040080168,-55.94963184777768,1.0 --0.460921843687375,-64.41103790375152,1.0 --0.4208416833667332,-28.919198449523236,1.0 --0.38076152304609323,-5.619073269548174,1.0 --0.34068136272545146,-20.999137490765122,1.0 --0.3006012024048097,-2.30452555867223,1.0 --0.26052104208416793,15.320031064520181,1.0 --0.22044088176352794,21.379445835413982,1.0 --0.18036072144288617,40.75556949862721,1.0 --0.1402805611222444,39.854694529485485,1.0 --0.10020040080160442,48.25439121043584,1.0 --0.06012024048096265,60.1319258277791,1.0 --0.020040080160320883,69.12466156763928,1.0 -0.020040080160320883,49.711700554281705,1.0 -0.060120240480960874,62.208069682851416,1.0 -0.10020040080160264,64.74977520478564,1.0 -0.1402805611222444,53.30139219245587,1.0 -0.18036072144288617,59.43921713810121,1.0 -0.22044088176352616,68.29722845358882,1.0 -0.26052104208416793,53.259039004580785,1.0 -0.3006012024048097,63.71328909407788,1.0 -0.3406813627254497,71.61543104732382,1.0 -0.38076152304609145,68.33880871917934,1.0 -0.4208416833667332,95.01014619623336,1.0 -0.460921843687375,81.22444023331018,1.0 -0.501002004008015,69.39150217177507,1.0 -0.5410821643286567,65.7558355261258,1.0 -0.5811623246492985,70.84640062314598,1.0 -0.6212424849699403,80.85533006690311,1.0 -0.6613226452905803,79.72261523288516,1.0 -0.701402805611222,62.424531423810286,1.0 -0.7414829659318638,63.2516698537984,1.0 -0.7815631262525038,82.89816815024334,1.0 -0.8216432865731456,67.69329768708374,1.0 -0.8617234468937873,76.88364120436685,1.0 -0.9018036072144291,51.96524516870126,1.0 -0.9418837675350691,47.962841645151,1.0 -0.9819639278557108,60.70682782969277,1.0 -1.0220440881763526,75.91709995985424,1.0 -1.0621242484969944,79.47345219048862,1.0 -1.1022044088176344,66.69440632643347,1.0 -1.1422845691382761,68.9892577290626,1.0 -1.182364729458918,79.43236601635614,1.0 -1.222444889779558,73.98502708538612,1.0 -1.2625250501001997,68.8027861757665,1.0 -1.3026052104208414,74.1431788976508,1.0 -1.3426853707414832,68.00015534882473,1.0 -1.3827655310621232,63.71311357766851,1.0 -1.422845691382765,66.10673702566372,1.0 -1.4629258517034067,86.14634943204281,1.0 -1.5030060120240485,64.76985771231294,1.0 -1.5430861723446885,65.12987620313194,1.0 -1.5831663326653302,74.60533999719044,1.0 -1.623246492985972,69.01297398887593,1.0 -1.663326653306612,52.28942509980306,1.0 -1.7034068136272538,64.80937955232964,1.0 -1.7434869739478955,72.61301234262044,1.0 -1.7835671342685373,79.08645317610012,1.0 -1.8236472945891773,77.08794683273447,1.0 -1.863727454909819,49.51033133484117,1.0 -1.9038076152304608,65.2197103879629,1.0 -1.9438877755511026,68.66196514901979,1.0 -1.9839679358717426,51.40807894849338,1.0 -2.0240480961923843,74.18410239244602,1.0 -2.064128256513026,66.22831552248525,1.0 -2.104208416833666,61.714024069366815,1.0 -2.144288577154308,70.7686430975059,1.0 -2.1843687374749496,85.40564522481908,1.0 -2.2244488977955914,89.06918217796361,1.0 -2.2645290581162314,73.38944584098847,1.0 -2.304609218436873,72.10425208065206,1.0 -2.344689378757515,58.32923125633273,1.0 -2.3847695390781567,76.9245864831026,1.0 -2.4248496993987967,74.45330565680747,1.0 -2.4649298597194385,60.687098003952215,1.0 -2.50501002004008,60.31106392594291,1.0 -2.54509018036072,65.40050986238377,1.0 -2.585170340681362,75.29868184860027,1.0 -2.6252505010020037,88.2940192378396,1.0 -2.6653306613226455,58.21592237757726,1.0 -2.7054108216432855,76.8389099382587,1.0 -2.7454909819639273,80.43607932810382,1.0 -2.785571142284569,71.24895015517542,1.0 -2.825651302605209,64.58540747106555,1.0 -2.865731462925851,65.34100878858038,1.0 -2.9058116232464926,77.10030138342259,1.0 -2.9458917835671343,59.20078472403918,1.0 -2.9859719438877743,73.70356662143544,1.0 -3.026052104208416,76.98558207242715,1.0 -3.066132264529058,81.54864001878546,1.0 -3.1062124248496996,59.82454947676308,1.0 -3.1462925851703396,81.72321718892431,1.0 -3.1863727454909814,56.52964617795162,1.0 -3.226452905811623,71.25854718500388,1.0 -3.266533066132263,69.58614763796292,1.0 -3.306613226452905,72.20429002442079,1.0 -3.3466933867735467,59.267591228815185,1.0 -3.3867735470941884,58.2338916942274,1.0 -3.4268537074148284,57.34973526079729,1.0 -3.46693386773547,69.58828794930943,1.0 -3.507014028056112,75.72996704624019,1.0 -3.5470941883767537,72.5279496851121,1.0 -3.5871743486973937,71.93114522486543,1.0 -3.6272545090180355,59.91327880763249,1.0 -3.6673346693386772,53.7671090403,1.0 -3.7074148296593172,74.71460138319637,1.0 -3.747494989979959,62.60622928687629,1.0 -3.7875751503006008,62.316508441951186,1.0 -3.8276553106212425,88.51673871026239,1.0 -3.8677354709418825,75.27233093073846,1.0 -3.9078156312625243,80.65948299036799,1.0 -3.947895791583166,71.5342718942546,1.0 -3.987975951903808,74.50514266477589,1.0 -4.028056112224448,69.0891524014624,1.0 -4.06813627254509,67.99984648016729,1.0 -4.108216432865731,69.36952383539295,1.0 -4.148296593186371,61.49107717217597,1.0 -4.188376753507013,76.50428382364439,1.0 -4.228456913827655,63.557404999921694,1.0 -4.268537074148297,80.09035530008222,1.0 -4.308617234468937,76.61952827992526,1.0 -4.348697394789578,78.91762915789418,1.0 -4.38877755511022,76.74095926215104,1.0 -4.428857715430862,88.02048169222857,1.0 -4.468937875751502,64.80309573830903,1.0 -4.509018036072144,73.55512105847234,1.0 -4.5490981963927855,74.91574820871217,1.0 -4.589178356713425,76.61839392698754,1.0 -4.629258517034067,61.81647629729003,1.0 -4.669338677354709,81.37760771536097,1.0 -4.709418837675351,47.24962668228701,1.0 -4.749498997995991,79.90904179555,1.0 -4.7895791583166325,62.15341749299216,1.0 -4.829659318637274,58.21813472301835,1.0 -4.869739478957916,63.94267690385225,1.0 -4.909819639278556,62.51105386039724,1.0 -4.949899799599198,83.886424115898,1.0 -4.98997995991984,73.84245091154406,1.0 -5.0300601202404795,86.53717015524452,1.0 -5.070140280561121,86.4805585339548,1.0 -5.110220440881763,76.1441340356258,1.0 -5.150300601202405,93.79778263144269,1.0 -5.190380761523045,69.03420884743123,1.0 -5.230460921843687,81.06326637209821,1.0 -5.270541082164328,73.41115931352826,1.0 -5.31062124248497,69.1637099127912,1.0 -5.35070140280561,69.99328128096812,1.0 -5.390781563126252,63.04752867615905,1.0 -5.430861723446894,71.11766175980527,1.0 -5.470941883767534,90.25263933379745,1.0 -5.511022044088175,67.66184316347776,1.0 -5.551102204408817,74.69667116612641,1.0 -5.591182364729459,91.60552126054466,1.0 -5.631262525050099,76.38621754183292,1.0 -5.671342685370741,64.0790092446542,1.0 -5.7114228456913825,85.71372545866562,1.0 -5.751503006012024,71.72775845205801,1.0 -5.791583166332664,70.95830528085025,1.0 -5.831663326653306,72.87411613062362,1.0 -5.871743486973948,83.14989999911113,1.0 -5.911823647294588,60.92738857150731,1.0 -5.9519038076152295,71.5155900131674,1.0 -5.991983967935871,42.38982291748513,1.0 -6.032064128256511,65.13098307891246,1.0 -6.072144288577153,65.37157287369061,1.0 -6.112224448897795,81.90639453659712,1.0 -6.152304609218437,87.96475224190078,1.0 -6.192384769539078,84.25642036750374,1.0 -6.23246492985972,92.40456748961145,1.0 -6.272545090180358,74.70134734079866,1.0 -6.312625250501,55.78499097339413,1.0 -6.352705410821642,77.1092790247495,1.0 -6.392785571142284,52.13640593998494,1.0 -6.432865731462925,80.58016118651014,1.0 -6.472945891783567,78.1861805920312,1.0 -6.513026052104209,87.8736908542489,1.0 -6.553106212424851,84.74658158064851,1.0 -6.593186372745489,80.45083857453496,1.0 -6.633266533066131,72.39353711666526,1.0 -6.673346693386772,67.14206032715364,1.0 -6.713426853707414,66.26034753430763,1.0 -6.753507014028056,57.774818895758216,1.0 -6.793587174348698,61.22304819303238,1.0 -6.8336673346693395,78.14493331889818,1.0 -6.873747494989978,51.20175078894536,1.0 -6.9138276553106195,70.92871361120717,1.0 -6.953907815631261,72.7869685785212,1.0 -6.993987975951903,71.88384206033976,1.0 -7.034068136272545,71.22852360036093,1.0 -7.074148296593187,84.46998053405461,1.0 -7.114228456913828,79.42845887453383,1.0 -7.1543086172344665,69.98081203008586,1.0 -7.194388777555108,53.45190957328789,1.0 -7.23446893787575,54.66706662702617,1.0 -7.274549098196392,65.77588066354764,1.0 -7.314629258517034,95.15027655907681,1.0 -7.354709418837675,69.73477957900155,1.0 -7.394789579158317,74.68880014865886,1.0 -7.434869739478959,75.40497394778833,1.0 -7.474949899799597,68.85677208674086,1.0 -7.515030060120239,90.9986594829802,1.0 -7.555110220440881,75.98549302110467,1.0 -7.595190380761522,72.71095238650665,1.0 -7.635270541082164,77.12805572955597,1.0 -7.675350701402806,98.95300650925259,1.0 -7.715430861723448,71.33756397930215,1.0 -7.755511022044086,67.74028492574769,1.0 -7.795591182364728,76.53312120180972,1.0 -7.8356713426853695,67.44482158550642,1.0 -7.875751503006011,55.84441709736454,1.0 -7.915831663326653,72.86206775214843,1.0 -7.955911823647295,68.28223491933372,1.0 -7.9959919839679365,88.5022390875188,1.0 -8.036072144288575,93.32942455619636,1.0 -8.076152304609217,74.01243897662825,1.0 -8.116232464929858,69.59125168158559,1.0 -8.1563126252505,76.88459311204352,1.0 -8.196392785571142,77.1483326249264,1.0 -8.236472945891784,78.35989675926083,1.0 -8.276553106212425,68.93167890089529,1.0 -8.316633266533067,73.4133769982281,1.0 -8.356713426853705,86.8624079108682,1.0 -8.396793587174347,78.79839067341351,1.0 -8.436873747494989,58.987028255548054,1.0 -8.47695390781563,73.4513525472526,1.0 -8.517034068136272,73.85045404788329,1.0 -8.557114228456914,66.20823942527463,1.0 -8.597194388777556,69.4418337146657,1.0 -8.637274549098194,77.5340679323281,1.0 -8.677354709418836,71.57125336364818,1.0 -8.717434869739478,66.18370631003039,1.0 -8.75751503006012,63.48578025964923,1.0 -8.797595190380761,63.35103980722124,1.0 -8.837675350701403,67.22114788164758,1.0 -8.877755511022045,77.45520212657095,1.0 -8.917835671342683,73.76658297886068,1.0 -8.957915831663325,71.30909133757318,1.0 -8.997995991983966,74.24098831373684,1.0 -9.038076152304608,72.8929768843116,1.0 -9.07815631262525,64.27772377113284,1.0 -9.118236472945892,83.93666506108309,1.0 -9.158316633266534,73.72954854360982,1.0 -9.198396793587175,75.97969457120689,1.0 -9.238476953907814,86.1682717527462,1.0 -9.278557114228455,67.90307668280641,1.0 -9.318637274549097,50.606955576994096,1.0 -9.358717434869739,68.23440030932748,1.0 -9.39879759519038,74.72574255515846,1.0 -9.438877755511022,58.34701226026997,1.0 -9.478957915831664,77.90899233947148,1.0 -9.519038076152302,86.39913238320584,1.0 -9.559118236472944,65.80684631501032,1.0 -9.599198396793586,64.6335238539748,1.0 -9.639278557114228,78.2200275318236,1.0 -9.67935871743487,65.71210677467822,1.0 -9.719438877755511,67.08877860166895,1.0 -9.759519038076153,68.29067314371977,1.0 -9.799599198396791,76.29414400579996,1.0 -9.839679358717433,63.724934289043965,1.0 -9.879759519038075,72.97160357952626,1.0 -9.919839679358716,52.479913043182336,1.0 -9.959919839679358,66.33313369078382,1.0 -10.0,67.61012053176742,1.0 diff --git a/Day_2/WorkProblems/lineexp.csv b/Day_2/WorkProblems/lineexp.csv deleted file mode 100644 index cb463cb..0000000 --- a/Day_2/WorkProblems/lineexp.csv +++ /dev/null @@ -1,501 +0,0 @@ -x,y,errors --10.0,-303.8629662608794,1.0 --9.959919839679358,-302.1148876306427,1.0 --9.919839679358718,-302.69302658975136,1.0 --9.879759519038076,-301.06057661481793,1.0 --9.839679358717435,-299.65611642623725,1.0 --9.799599198396793,-299.98731440009817,1.0 --9.759519038076153,-296.0804330894053,1.0 --9.719438877755511,-297.37977152245503,1.0 --9.67935871743487,-294.5308210696867,1.0 --9.639278557114228,-295.1142135919711,1.0 --9.599198396793588,-294.6927672493175,1.0 --9.559118236472946,-292.24698157988183,1.0 --9.519038076152304,-291.52380987452085,1.0 --9.478957915831664,-288.87052323305323,1.0 --9.438877755511022,-286.86797791733585,1.0 --9.39879759519038,-287.23593747284104,1.0 --9.358717434869739,-285.35467358220814,1.0 --9.318637274549099,-283.625158104932,1.0 --9.278557114228457,-281.88064766724665,1.0 --9.238476953907815,-280.859867207578,1.0 --9.198396793587175,-279.9430787535166,1.0 --9.158316633266534,-279.6851527284879,1.0 --9.118236472945892,-278.17010970524865,1.0 --9.07815631262525,-276.5565583108864,1.0 --9.03807615230461,-273.9427655750678,1.0 --8.997995991983968,-273.4162608824843,1.0 --8.957915831663327,-273.96875234173405,1.0 --8.917835671342685,-269.8948541612746,1.0 --8.877755511022045,-271.8903716472507,1.0 --8.837675350701403,-269.189373841196,1.0 --8.797595190380761,-268.59000264729826,1.0 --8.75751503006012,-266.3401324063742,1.0 --8.71743486973948,-265.7931633012019,1.0 --8.677354709418838,-263.23766264138385,1.0 --8.637274549098196,-263.2634881292743,1.0 --8.597194388777556,-261.18825433970227,1.0 --8.557114228456914,-261.0494351460637,1.0 --8.517034068136272,-260.45272411765467,1.0 --8.47695390781563,-260.2200917322738,1.0 --8.43687374749499,-258.71205361241556,1.0 --8.396793587174349,-256.5080508749863,1.0 --8.356713426853707,-254.27729982294625,1.0 --8.316633266533067,-252.88450946441765,1.0 --8.276553106212425,-252.7800663309279,1.0 --8.236472945891784,-251.9940962272785,1.0 --8.196392785571142,-249.26736432928988,1.0 --8.156312625250502,-248.8186577421679,1.0 --8.11623246492986,-247.20006188491888,1.0 --8.076152304609218,-244.6543545141221,1.0 --8.036072144288577,-243.78193129712537,1.0 --7.9959919839679365,-242.70816021384687,1.0 --7.955911823647295,-241.93641592380217,1.0 --7.915831663326653,-240.7016208708392,1.0 --7.875751503006012,-239.44517054549104,1.0 --7.835671342685371,-239.94186969720514,1.0 --7.7955911823647295,-238.57515018682014,1.0 --7.755511022044088,-236.1688218815755,1.0 --7.715430861723447,-235.93720720247063,1.0 --7.675350701402806,-234.01070960623005,1.0 --7.635270541082164,-232.64323582330275,1.0 --7.595190380761523,-231.39952021349794,1.0 --7.555110220440882,-231.70870572792904,1.0 --7.515030060120241,-227.685173252999,1.0 --7.474949899799599,-228.97929243726023,1.0 --7.434869739478958,-225.56701666792978,1.0 --7.394789579158317,-224.72290907387293,1.0 --7.354709418837675,-224.00766252889585,1.0 --7.314629258517034,-222.19948490546057,1.0 --7.274549098196393,-221.69407191548265,1.0 --7.234468937875752,-221.675859289399,1.0 --7.19438877755511,-219.91989698092428,1.0 --7.154308617234469,-218.46313352166896,1.0 --7.114228456913828,-217.89073955808638,1.0 --7.074148296593187,-215.82603721423988,1.0 --7.034068136272545,-212.34953191074084,1.0 --6.993987975951904,-213.15536619489095,1.0 --6.953907815631263,-211.78233726329123,1.0 --6.913827655310621,-212.10259131621987,1.0 --6.8737474949899795,-209.0780084301889,1.0 --6.833667334669339,-209.2032524530754,1.0 --6.793587174348698,-207.92935648105032,1.0 --6.753507014028056,-205.2958121621504,1.0 --6.713426853707415,-207.0166323850872,1.0 --6.673346693386774,-205.87714776278625,1.0 --6.6332665330661325,-203.38846319232363,1.0 --6.593186372745491,-201.07170996457788,1.0 --6.55310621242485,-199.13406815142315,1.0 --6.513026052104209,-200.02059256229316,1.0 --6.472945891783567,-196.78421324861432,1.0 --6.432865731462925,-196.00685370952917,1.0 --6.3927855711422845,-195.99715234016662,1.0 --6.352705410821644,-194.6479343093631,1.0 --6.312625250501002,-192.207614503286,1.0 --6.272545090180361,-191.22600327939693,1.0 --6.23246492985972,-189.60969600017512,1.0 --6.192384769539078,-188.40691840925396,1.0 --6.152304609218437,-186.90815890109408,1.0 --6.112224448897796,-185.52660775825902,1.0 --6.072144288577155,-184.46474202499238,1.0 --6.032064128256513,-184.11877056814149,1.0 --5.991983967935872,-182.59491075500551,1.0 --5.95190380761523,-181.54264683793326,1.0 --5.9118236472945895,-180.98288729857103,1.0 --5.871743486973948,-180.51621242090334,1.0 --5.831663326653307,-178.25670721631639,1.0 --5.791583166332665,-178.09017253254888,1.0 --5.751503006012024,-175.16292909426983,1.0 --5.711422845691383,-174.0992939814534,1.0 --5.671342685370742,-174.85267755946677,1.0 --5.631262525050101,-173.06166146884297,1.0 --5.591182364729459,-169.96797646038067,1.0 --5.551102204408818,-170.09164912902895,1.0 --5.511022044088176,-166.94295845951433,1.0 --5.470941883767535,-167.28965487328264,1.0 --5.430861723446894,-165.27968160807174,1.0 --5.390781563126253,-162.5279381991422,1.0 --5.350701402805611,-163.6942472778322,1.0 --5.31062124248497,-160.13302353091757,1.0 --5.270541082164329,-160.00633899850976,1.0 --5.2304609218436875,-160.07413736772568,1.0 --5.190380761523047,-159.77399811462635,1.0 --5.150300601202405,-156.57735056542387,1.0 --5.110220440881764,-155.33258484029665,1.0 --5.070140280561122,-152.78041551868944,1.0 --5.030060120240481,-151.40132225362365,1.0 --4.98997995991984,-152.82670432316266,1.0 --4.949899799599199,-150.52708325203096,1.0 --4.909819639278557,-148.19141191480892,1.0 --4.869739478957916,-148.2643267046568,1.0 --4.829659318637275,-146.52556658486304,1.0 --4.789579158316633,-144.95900705172363,1.0 --4.7494989979959925,-146.16159932092611,1.0 --4.709418837675351,-143.28512753813135,1.0 --4.66933867735471,-142.9634936344136,1.0 --4.629258517034068,-140.00994457265338,1.0 --4.589178356713427,-139.91806485336792,1.0 --4.5490981963927855,-137.68964678790294,1.0 --4.509018036072145,-138.2589460533983,1.0 --4.468937875751503,-135.48577417651808,1.0 --4.428857715430862,-133.5869736694496,1.0 --4.388777555110221,-133.5443373795922,1.0 --4.348697394789579,-132.08883149908468,1.0 --4.308617234468938,-131.5843549952462,1.0 --4.268537074148297,-130.63955009594324,1.0 --4.228456913827656,-129.91773190856813,1.0 --4.188376753507014,-125.64210422805309,1.0 --4.148296593186373,-127.11673241443884,1.0 --4.108216432865731,-124.94243722525867,1.0 --4.0681362725450905,-125.40965134973351,1.0 --4.028056112224449,-122.86971283992068,1.0 --3.987975951903808,-121.49584423342529,1.0 --3.947895791583167,-119.13393327012649,1.0 --3.907815631262525,-119.54127280274858,1.0 --3.8677354709418843,-118.49267575253755,1.0 --3.8276553106212425,-116.25441673429209,1.0 --3.7875751503006017,-114.21161493528868,1.0 --3.74749498997996,-113.97397567462511,1.0 --3.707414829659319,-110.45278834866133,1.0 --3.6673346693386772,-112.7956594693076,1.0 --3.6272545090180364,-110.83992517153987,1.0 --3.5871743486973955,-107.92975116346541,1.0 --3.5470941883767537,-106.47351375376766,1.0 --3.507014028056113,-106.2846843581184,1.0 --3.466933867735471,-103.87453782326561,1.0 --3.42685370741483,-103.29038280769501,1.0 --3.3867735470941884,-100.49986667406695,1.0 --3.3466933867735476,-100.22104952530147,1.0 --3.306613226452906,-100.39841729990671,1.0 --3.266533066132265,-99.16038392973378,1.0 --3.226452905811623,-97.15589056374263,1.0 --3.1863727454909823,-96.75585732055355,1.0 --3.1462925851703414,-93.98268164880871,1.0 --3.1062124248496996,-92.82744169306045,1.0 --3.0661322645290587,-91.85937273956364,1.0 --3.026052104208417,-89.70799963984531,1.0 --2.985971943887776,-90.47553460881706,1.0 --2.9458917835671343,-88.22487395247946,1.0 --2.9058116232464934,-87.33578990436305,1.0 --2.8657314629258517,-85.58747415257449,1.0 --2.825651302605211,-85.89011375370299,1.0 --2.785571142284569,-84.26191469018815,1.0 --2.745490981963928,-83.17204742334447,1.0 --2.7054108216432873,-82.15661960556527,1.0 --2.6653306613226455,-77.71500035549559,1.0 --2.6252505010020046,-79.07019597908858,1.0 --2.585170340681363,-77.68107611910372,1.0 --2.545090180360722,-76.91352163299003,1.0 --2.50501002004008,-74.20274194971181,1.0 --2.4649298597194393,-74.38240400038522,1.0 --2.4248496993987976,-74.18555141711273,1.0 --2.3847695390781567,-72.02497545369343,1.0 --2.344689378757515,-69.89921835988861,1.0 --2.304609218436874,-67.9818149805464,1.0 --2.264529058116233,-67.23361067405683,1.0 --2.2244488977955914,-67.24047024945482,1.0 --2.1843687374749505,-64.44809176088704,1.0 --2.1442885771543088,-63.78416690118185,1.0 --2.104208416833668,-60.63787831946219,1.0 --2.064128256513026,-62.978913994321566,1.0 --2.0240480961923852,-59.905162893219355,1.0 --1.9839679358717444,-58.18791466754048,1.0 --1.9438877755511026,-57.31086464898583,1.0 --1.9038076152304608,-56.21836445987592,1.0 --1.8637274549098208,-54.495216780864205,1.0 --1.823647294589179,-52.17429584097139,1.0 --1.7835671342685373,-52.61576654232008,1.0 --1.7434869739478955,-52.186731589274615,1.0 --1.7034068136272555,-50.11698361907566,1.0 --1.6633266533066138,-49.38153029042428,1.0 --1.623246492985972,-47.522283600618245,1.0 --1.5831663326653302,-44.88950794380428,1.0 --1.5430861723446903,-45.170625520332024,1.0 --1.5030060120240485,-43.93604434629089,1.0 --1.4629258517034067,-42.93009665929059,1.0 --1.4228456913827667,-41.75349020582691,1.0 --1.382765531062125,-40.01144065525667,1.0 --1.3426853707414832,-40.41860914850671,1.0 --1.3026052104208414,-38.205558348185434,1.0 --1.2625250501002014,-36.09859019614624,1.0 --1.2224448897795597,-34.68155420770554,1.0 --1.182364729458918,-33.00812895957033,1.0 --1.1422845691382761,-32.06781297769572,1.0 --1.1022044088176361,-30.10458246559,1.0 --1.0621242484969944,-31.210209711896727,1.0 --1.0220440881763526,-28.830467861021774,1.0 --0.9819639278557126,-28.838628553793278,1.0 --0.9418837675350709,-26.356832900758178,1.0 --0.9018036072144291,-26.032658326749118,1.0 --0.8617234468937873,-23.857686947941446,1.0 --0.8216432865731473,-21.923594240049002,1.0 --0.7815631262525056,-19.78228640304195,1.0 --0.7414829659318638,-17.6015205194726,1.0 --0.701402805611222,-19.007849995920314,1.0 --0.661322645290582,-16.45593757566107,1.0 --0.6212424849699403,-15.52461523055403,1.0 --0.5811623246492985,-14.924225388711942,1.0 --0.5410821643286585,-12.452186156743885,1.0 --0.5010020040080168,-12.511857290102707,1.0 --0.460921843687375,-10.764753774918892,1.0 --0.4208416833667332,-8.331553118846642,1.0 --0.38076152304609323,-6.870648877465647,1.0 --0.34068136272545146,-6.160772979741641,1.0 --0.3006012024048097,-6.108598403503881,1.0 --0.26052104208416793,-5.558030948375055,1.0 --0.22044088176352794,-3.7207749940604398,1.0 --0.18036072144288617,-0.8265801502779919,1.0 --0.1402805611222444,1.5738321504181314,1.0 --0.10020040080160442,0.9133356007855483,1.0 --0.06012024048096265,1.70074311431337,1.0 --0.020040080160320883,5.004401851285052,1.0 -0.020040080160320883,5.109344006971957,1.0 -0.060120240480960874,7.442260294105781,1.0 -0.10020040080160264,8.300421985184428,1.0 -0.1402805611222444,9.057488905857443,1.0 -0.18036072144288617,9.045664351011768,1.0 -0.22044088176352616,11.865233124334729,1.0 -0.26052104208416793,14.155696018857896,1.0 -0.3006012024048097,13.644118259299091,1.0 -0.3406813627254497,14.301216104037612,1.0 -0.38076152304609145,17.912254177819985,1.0 -0.4208416833667332,16.47425276613429,1.0 -0.460921843687375,19.493512897051005,1.0 -0.501002004008015,21.42999991526811,1.0 -0.5410821643286567,22.96201602702029,1.0 -0.5811623246492985,22.6764641527272,1.0 -0.6212424849699403,25.76345007337707,1.0 -0.6613226452905803,26.358465334788438,1.0 -0.701402805611222,27.783817181083215,1.0 -0.7414829659318638,29.05591301074833,1.0 -0.7815631262525038,30.915458157532036,1.0 -0.8216432865731456,30.54416272614596,1.0 -0.8617234468937873,33.3733274271596,1.0 -0.9018036072144291,33.70873712620503,1.0 -0.9418837675350691,38.18939003938894,1.0 -0.9819639278557108,36.75709854762064,1.0 -1.0220440881763526,38.83065421327668,1.0 -1.0621242484969944,38.7055721504778,1.0 -1.1022044088176344,41.538960961550494,1.0 -1.1422845691382761,43.89295586216467,1.0 -1.182364729458918,43.38435957319454,1.0 -1.222444889779558,44.78211378095733,1.0 -1.2625250501001997,47.30614463569049,1.0 -1.3026052104208414,50.12255044290716,1.0 -1.3426853707414832,48.28923790110679,1.0 -1.3827655310621232,52.24407059963926,1.0 -1.422845691382765,52.82509221955426,1.0 -1.4629258517034067,53.914404975174946,1.0 -1.5030060120240485,54.08389830040797,1.0 -1.5430861723446885,55.97460048604369,1.0 -1.5831663326653302,58.250759956028475,1.0 -1.623246492985972,60.99014401487192,1.0 -1.663326653306612,61.97142798127475,1.0 -1.7034068136272538,63.43489942571784,1.0 -1.7434869739478955,63.34842447428313,1.0 -1.7835671342685373,63.930836179807386,1.0 -1.8236472945891773,67.33439164660882,1.0 -1.863727454909819,68.04227577530294,1.0 -1.9038076152304608,69.21989396374492,1.0 -1.9438877755511026,71.97710087535677,1.0 -1.9839679358717426,71.88373675944982,1.0 -2.0240480961923843,74.13116759819418,1.0 -2.064128256513026,76.74092394581386,1.0 -2.104208416833666,77.794584451222,1.0 -2.144288577154308,79.96225897730966,1.0 -2.1843687374749496,80.6429045198244,1.0 -2.2244488977955914,81.86858420759015,1.0 -2.2645290581162314,82.99397633720089,1.0 -2.304609218436873,84.22617632840058,1.0 -2.344689378757515,86.3268922965741,1.0 -2.3847695390781567,88.66337830583797,1.0 -2.4248496993987967,88.6225281633426,1.0 -2.4649298597194385,92.30573044441378,1.0 -2.50501002004008,93.79900833929246,1.0 -2.54509018036072,93.41617507613086,1.0 -2.585170340681362,95.67312731810921,1.0 -2.6252505010020037,96.83671578946381,1.0 -2.6653306613226455,100.15759846602477,1.0 -2.7054108216432855,100.51328365664621,1.0 -2.7454909819639273,102.20222613364905,1.0 -2.785571142284569,103.31440989901263,1.0 -2.825651302605209,103.55692186079791,1.0 -2.865731462925851,106.54419270440034,1.0 -2.9058116232464926,108.07074456868997,1.0 -2.9458917835671343,109.28008308065249,1.0 -2.9859719438877743,111.92898253410725,1.0 -3.026052104208416,111.81828132118,1.0 -3.066132264529058,115.24365334570129,1.0 -3.1062124248496996,113.31792478224291,1.0 -3.1462925851703396,117.08486187742744,1.0 -3.1863727454909814,117.83297890435009,1.0 -3.226452905811623,119.8023399981772,1.0 -3.266533066132263,122.0125340999298,1.0 -3.306613226452905,125.6222320202394,1.0 -3.3466933867735467,124.25543968716475,1.0 -3.3867735470941884,129.63231709056905,1.0 -3.4268537074148284,130.80330372514263,1.0 -3.46693386773547,131.62086334786014,1.0 -3.507014028056112,133.72079975501873,1.0 -3.5470941883767537,136.20947082456502,1.0 -3.5871743486973937,136.86618920187786,1.0 -3.6272545090180355,138.2541227980156,1.0 -3.6673346693386772,139.36048918033333,1.0 -3.7074148296593172,138.84963967505695,1.0 -3.747494989979959,145.57208889151065,1.0 -3.7875751503006008,145.17262042221336,1.0 -3.8276553106212425,146.41469150982317,1.0 -3.8677354709418825,151.08840082455268,1.0 -3.9078156312625243,151.1111851050439,1.0 -3.947895791583166,152.28396126987127,1.0 -3.987975951903808,154.65046731199487,1.0 -4.028056112224448,155.5929287853853,1.0 -4.06813627254509,158.07910602974158,1.0 -4.108216432865731,160.6564336388528,1.0 -4.148296593186371,163.49221258110362,1.0 -4.188376753507013,163.41272893304156,1.0 -4.228456913827655,166.24268100502482,1.0 -4.268537074148297,168.59248045986797,1.0 -4.308617234468937,170.82637246175793,1.0 -4.348697394789578,172.463296362599,1.0 -4.38877755511022,174.78695587033332,1.0 -4.428857715430862,177.06232990524714,1.0 -4.468937875751502,177.5066554160456,1.0 -4.509018036072144,182.0942187019331,1.0 -4.5490981963927855,182.97460631976307,1.0 -4.589178356713425,183.96514299881014,1.0 -4.629258517034067,185.2970561070712,1.0 -4.669338677354709,189.35566731154313,1.0 -4.709418837675351,190.7520136053527,1.0 -4.749498997995991,193.79008431357101,1.0 -4.7895791583166325,195.82031336635262,1.0 -4.829659318637274,197.43326818921128,1.0 -4.869739478957916,199.99797094773965,1.0 -4.909819639278556,201.2124204883676,1.0 -4.949899799599198,203.34668990800952,1.0 -4.98997995991984,207.36007763211086,1.0 -5.0300601202404795,206.94568579342908,1.0 -5.070140280561121,210.5614040048629,1.0 -5.110220440881763,213.28046986414512,1.0 -5.150300601202405,217.47492989194365,1.0 -5.190380761523045,218.54489181583727,1.0 -5.230460921843687,222.0133241882137,1.0 -5.270541082164328,221.88482899916576,1.0 -5.31062124248497,225.92419786550022,1.0 -5.35070140280561,231.01426328710016,1.0 -5.390781563126252,231.43920520799608,1.0 -5.430861723446894,233.17170283671973,1.0 -5.470941883767534,235.532739521764,1.0 -5.511022044088175,240.70869919785542,1.0 -5.551102204408817,242.3554118297829,1.0 -5.591182364729459,244.36783613774725,1.0 -5.631262525050099,246.29263060824385,1.0 -5.671342685370741,250.41224664636053,1.0 -5.7114228456913825,252.96531103453825,1.0 -5.751503006012024,254.17365512432752,1.0 -5.791583166332664,259.3946202409177,1.0 -5.831663326653306,260.9093110556135,1.0 -5.871743486973948,264.4615687482556,1.0 -5.911823647294588,265.7218643816581,1.0 -5.9519038076152295,268.41862578540844,1.0 -5.991983967935871,274.0075051358916,1.0 -6.032064128256511,273.2279563903943,1.0 -6.072144288577153,279.4088732275747,1.0 -6.112224448897795,281.79304754559917,1.0 -6.152304609218437,285.12854758324374,1.0 -6.192384769539078,287.77658408671033,1.0 -6.23246492985972,291.9866437121934,1.0 -6.272545090180358,295.9041472783095,1.0 -6.312625250501,298.86371228068714,1.0 -6.352705410821642,301.15155624466877,1.0 -6.392785571142284,307.49297495281513,1.0 -6.432865731462925,308.3061647943084,1.0 -6.472945891783567,311.40588661980803,1.0 -6.513026052104209,315.87762997815105,1.0 -6.553106212424851,317.9063084648657,1.0 -6.593186372745489,322.3598934316579,1.0 -6.633266533066131,326.05410486133144,1.0 -6.673346693386772,330.4452925749714,1.0 -6.713426853707414,333.2483376997819,1.0 -6.753507014028056,337.4277219154143,1.0 -6.793587174348698,342.0755161057503,1.0 -6.8336673346693395,347.49147356128174,1.0 -6.873747494989978,348.37716154554124,1.0 -6.9138276553106195,353.44512973274647,1.0 -6.953907815631261,357.2758866617909,1.0 -6.993987975951903,362.0021382943786,1.0 -7.034068136272545,366.4420563131063,1.0 -7.074148296593187,370.95947193805904,1.0 -7.114228456913828,373.5414998984906,1.0 -7.1543086172344665,378.70055263757627,1.0 -7.194388777555108,382.43327108886757,1.0 -7.23446893787575,387.37508670838156,1.0 -7.274549098196392,392.58688718156384,1.0 -7.314629258517034,396.1632230640917,1.0 -7.354709418837675,400.43177513394147,1.0 -7.394789579158317,407.56051958996,1.0 -7.434869739478959,411.5193867105058,1.0 -7.474949899799597,416.9878971370051,1.0 -7.515030060120239,420.8373852099494,1.0 -7.555110220440881,428.45881414991453,1.0 -7.595190380761522,433.6260083215668,1.0 -7.635270541082164,437.83561553379275,1.0 -7.675350701402806,444.7706109317986,1.0 -7.715430861723448,449.01218332305433,1.0 -7.755511022044086,454.4298023779405,1.0 -7.795591182364728,460.7325246898569,1.0 -7.8356713426853695,464.3124530538438,1.0 -7.875751503006011,470.0490631120357,1.0 -7.915831663326653,475.2952882431127,1.0 -7.955911823647295,485.0039754158043,1.0 -7.9959919839679365,488.7366220011732,1.0 -8.036072144288575,496.21810372442053,1.0 -8.076152304609217,501.5588179813344,1.0 -8.116232464929858,507.50011522663294,1.0 -8.1563126252505,515.1370149247354,1.0 -8.196392785571142,519.950607603019,1.0 -8.236472945891784,527.0573702196028,1.0 -8.276553106212425,535.1912979227203,1.0 -8.316633266533067,542.6315282490591,1.0 -8.356713426853705,547.5537070085685,1.0 -8.396793587174347,556.3075815712698,1.0 -8.436873747494989,563.2874533477408,1.0 -8.47695390781563,571.8219324064186,1.0 -8.517034068136272,579.5818690008415,1.0 -8.557114228456914,586.1937795674579,1.0 -8.597194388777556,592.8776546593871,1.0 -8.637274549098194,601.3616986505704,1.0 -8.677354709418836,611.1344391609881,1.0 -8.717434869739478,618.323424657126,1.0 -8.75751503006012,626.1236432161055,1.0 -8.797595190380761,635.0894642659274,1.0 -8.837675350701403,643.5044677688023,1.0 -8.877755511022045,650.7238705889313,1.0 -8.917835671342683,661.6771095949547,1.0 -8.957915831663325,668.2697405098035,1.0 -8.997995991983966,679.2176244128585,1.0 -9.038076152304608,688.7859387750136,1.0 -9.07815631262525,696.891158100356,1.0 -9.118236472945892,708.3104780769281,1.0 -9.158316633266534,717.7620888781203,1.0 -9.198396793587175,727.6984952823752,1.0 -9.238476953907814,738.410867378466,1.0 -9.278557114228455,748.8140329497313,1.0 -9.318637274549097,760.7836897730767,1.0 -9.358717434869739,771.052736473637,1.0 -9.39879759519038,781.4150056541586,1.0 -9.438877755511022,791.709758746361,1.0 -9.478957915831664,806.9486365443069,1.0 -9.519038076152302,815.619957795733,1.0 -9.559118236472944,827.1823860501198,1.0 -9.599198396793586,838.9207581556077,1.0 -9.639278557114228,850.2177487151823,1.0 -9.67935871743487,864.954556821446,1.0 -9.719438877755511,877.6805805636077,1.0 -9.759519038076153,889.8035793924153,1.0 -9.799599198396791,901.589105243628,1.0 -9.839679358717433,916.6206861163665,1.0 -9.879759519038075,930.4089690984596,1.0 -9.919839679358716,943.9937312388038,1.0 -9.959919839679358,957.9016099107141,1.0 -10.0,972.3567072989412,1.0 diff --git a/Day_2/WorkProblems/linesinxandcosx.csv b/Day_2/WorkProblems/linesinxandcosx.csv deleted file mode 100644 index c2654eb..0000000 --- a/Day_2/WorkProblems/linesinxandcosx.csv +++ /dev/null @@ -1,501 +0,0 @@ -x,y,errors --10.0,-100.65590742622346,1.0 --9.959919839679358,-99.20773725228351,1.0 --9.919839679358718,-98.36615957664488,1.0 --9.879759519038076,-98.69133136280828,1.0 --9.839679358717435,-95.40340156693694,1.0 --9.799599198396793,-92.92919257179206,1.0 --9.759519038076153,-94.35993714590921,1.0 --9.719438877755511,-90.26046290063071,1.0 --9.67935871743487,-87.97136975200623,1.0 --9.639278557114228,-88.71330222638281,1.0 --9.599198396793588,-89.30007412405291,1.0 --9.559118236472946,-87.30580927231104,1.0 --9.519038076152304,-86.9268361311907,1.0 --9.478957915831664,-85.93753813000501,1.0 --9.438877755511022,-85.2243677172239,1.0 --9.39879759519038,-84.60475607359682,1.0 --9.358717434869739,-86.35028094182545,1.0 --9.318637274549099,-83.65493181122855,1.0 --9.278557114228457,-82.40498746962804,1.0 --9.238476953907815,-82.92975585943378,1.0 --9.198396793587175,-84.36224671243534,1.0 --9.158316633266534,-82.35000313963039,1.0 --9.118236472945892,-80.59732644052471,1.0 --9.07815631262525,-82.42126341191278,1.0 --9.03807615230461,-80.59074095520256,1.0 --8.997995991983968,-81.5766868599015,1.0 --8.957915831663327,-80.72938468945007,1.0 --8.917835671342685,-80.07414866250912,1.0 --8.877755511022045,-79.34935658137957,1.0 --8.837675350701403,-77.88201049004995,1.0 --8.797595190380761,-79.54308885388095,1.0 --8.75751503006012,-76.32813547935055,1.0 --8.71743486973948,-78.36322220212715,1.0 --8.677354709418838,-80.11739493239662,1.0 --8.637274549098196,-78.95623585207628,1.0 --8.597194388777556,-78.86071226110346,1.0 --8.557114228456914,-78.07883816966743,1.0 --8.517034068136272,-77.87082691108107,1.0 --8.47695390781563,-75.74806655248484,1.0 --8.43687374749499,-76.66650650584005,1.0 --8.396793587174349,-77.33639407246471,1.0 --8.356713426853707,-74.43362172709456,1.0 --8.316633266533067,-75.23769030604075,1.0 --8.276553106212425,-75.65967206819636,1.0 --8.236472945891784,-75.25307058703893,1.0 --8.196392785571142,-72.54588808188636,1.0 --8.156312625250502,-71.11969498608985,1.0 --8.11623246492986,-71.0018785265562,1.0 --8.076152304609218,-71.28856064867864,1.0 --8.036072144288577,-70.00506570000603,1.0 --7.9959919839679365,-69.75630987132214,1.0 --7.955911823647295,-69.50647640254788,1.0 --7.915831663326653,-70.61863926814988,1.0 --7.875751503006012,-68.43308102141474,1.0 --7.835671342685371,-65.69380052923485,1.0 --7.7955911823647295,-66.8386189735735,1.0 --7.755511022044088,-67.48931072323383,1.0 --7.715430861723447,-65.08342356706407,1.0 --7.675350701402806,-65.2916844096101,1.0 --7.635270541082164,-65.44656539473962,1.0 --7.595190380761523,-66.08952332942809,1.0 --7.555110220440882,-66.15634492067568,1.0 --7.515030060120241,-65.14204466332926,1.0 --7.474949899799599,-65.29769642519784,1.0 --7.434869739478958,-65.5830409507577,1.0 --7.394789579158317,-65.29991704587535,1.0 --7.354709418837675,-65.14926107028413,1.0 --7.314629258517034,-67.5575995158594,1.0 --7.274549098196393,-67.95112797957923,1.0 --7.234468937875752,-64.8526370025851,1.0 --7.19438877755511,-67.915785373826,1.0 --7.154308617234469,-68.20589373211511,1.0 --7.114228456913828,-69.01072680520957,1.0 --7.074148296593187,-67.36829986006065,1.0 --7.034068136272545,-68.28941420917755,1.0 --6.993987975951904,-70.48238797192984,1.0 --6.953907815631263,-70.33387826836272,1.0 --6.913827655310621,-71.01888669717975,1.0 --6.8737474949899795,-70.58225351721302,1.0 --6.833667334669339,-72.1597895478875,1.0 --6.793587174348698,-73.56001009549018,1.0 --6.753507014028056,-71.07336640193945,1.0 --6.713426853707415,-72.06582922418106,1.0 --6.673346693386774,-73.1708566359437,1.0 --6.6332665330661325,-73.75291823800006,1.0 --6.593186372745491,-73.39322516450419,1.0 --6.55310621242485,-74.22928676783124,1.0 --6.513026052104209,-72.12244924328776,1.0 --6.472945891783567,-74.99055097303531,1.0 --6.432865731462925,-71.5829268158954,1.0 --6.3927855711422845,-73.11992401772625,1.0 --6.352705410821644,-70.68666160092461,1.0 --6.312625250501002,-70.47869331519654,1.0 --6.272545090180361,-69.3274627689772,1.0 --6.23246492985972,-68.6887528730388,1.0 --6.192384769539078,-69.75244110119365,1.0 --6.152304609218437,-67.62409396618439,1.0 --6.112224448897796,-68.03412346706503,1.0 --6.072144288577155,-66.62692085687391,1.0 --6.032064128256513,-64.8977842410913,1.0 --5.991983967935872,-62.66583533712539,1.0 --5.95190380761523,-61.04195757527982,1.0 --5.9118236472945895,-61.301662935550205,1.0 --5.871743486973948,-58.9384106443595,1.0 --5.831663326653307,-60.584745738158375,1.0 --5.791583166332665,-58.30920073532713,1.0 --5.751503006012024,-56.74257510403929,1.0 --5.711422845691383,-56.85242916410207,1.0 --5.671342685370742,-55.168015704002826,1.0 --5.631262525050101,-55.22772618558806,1.0 --5.591182364729459,-50.5107699065805,1.0 --5.551102204408818,-53.508672160282615,1.0 --5.511022044088176,-53.65821826176501,1.0 --5.470941883767535,-50.883519097563735,1.0 --5.430861723446894,-48.542354000592795,1.0 --5.390781563126253,-49.69168916738205,1.0 --5.350701402805611,-47.698330826975656,1.0 --5.31062124248497,-47.4141081774597,1.0 --5.270541082164329,-46.83944437538376,1.0 --5.2304609218436875,-46.79199310495467,1.0 --5.190380761523047,-48.35977049945551,1.0 --5.150300601202405,-46.468688008406886,1.0 --5.110220440881764,-48.412001219571266,1.0 --5.070140280561122,-43.44154073382155,1.0 --5.030060120240481,-47.200670117902135,1.0 --4.98997995991984,-44.53609613428058,1.0 --4.949899799599199,-43.6888524593354,1.0 --4.909819639278557,-44.47839436695417,1.0 --4.869739478957916,-43.590252281726364,1.0 --4.829659318637275,-43.364151643290846,1.0 --4.789579158316633,-43.16345949013251,1.0 --4.7494989979959925,-44.09597775313376,1.0 --4.709418837675351,-40.3176782374712,1.0 --4.66933867735471,-41.017195468234114,1.0 --4.629258517034068,-41.78800439123411,1.0 --4.589178356713427,-40.609199494835856,1.0 --4.5490981963927855,-40.36724037080524,1.0 --4.509018036072145,-41.45663439309392,1.0 --4.468937875751503,-38.171910153477235,1.0 --4.428857715430862,-39.50872631656106,1.0 --4.388777555110221,-38.60105358410625,1.0 --4.348697394789579,-39.133926247737776,1.0 --4.308617234468938,-37.0207359819415,1.0 --4.268537074148297,-36.03504238187263,1.0 --4.228456913827656,-35.534834598935895,1.0 --4.188376753507014,-33.913178206214056,1.0 --4.148296593186373,-33.975648752866505,1.0 --4.108216432865731,-33.761822096669064,1.0 --4.0681362725450905,-31.38995705939494,1.0 --4.028056112224449,-30.020032487601394,1.0 --3.987975951903808,-30.375328522927536,1.0 --3.947895791583167,-30.49459518992239,1.0 --3.907815631262525,-29.041491254537284,1.0 --3.8677354709418843,-28.577830643893893,1.0 --3.8276553106212425,-27.178199386437377,1.0 --3.7875751503006017,-28.635795266610074,1.0 --3.74749498997996,-25.345447192228427,1.0 --3.707414829659319,-26.181551479796813,1.0 --3.6673346693386772,-26.58050558848703,1.0 --3.6272545090180364,-27.097177817421244,1.0 --3.5871743486973955,-26.148427044795003,1.0 --3.5470941883767537,-26.321350297693847,1.0 --3.507014028056113,-26.61237426685702,1.0 --3.466933867735471,-26.06018936144124,1.0 --3.42685370741483,-26.379631896645037,1.0 --3.3867735470941884,-23.77507467440787,1.0 --3.3466933867735476,-27.003206835395723,1.0 --3.306613226452906,-25.894517334670294,1.0 --3.266533066132265,-25.009145826136844,1.0 --3.226452905811623,-26.3337480775765,1.0 --3.1863727454909823,-27.539791588259657,1.0 --3.1462925851703414,-30.370469752851115,1.0 --3.1062124248496996,-28.271692597635074,1.0 --3.0661322645290587,-30.6462962348684,1.0 --3.026052104208417,-31.29753110030715,1.0 --2.985971943887776,-29.66065634707077,1.0 --2.9458917835671343,-29.8278351290294,1.0 --2.9058116232464934,-32.58472467726558,1.0 --2.8657314629258517,-31.165391497783574,1.0 --2.825651302605211,-31.34481778958705,1.0 --2.785571142284569,-30.670454113391713,1.0 --2.745490981963928,-34.08242398279149,1.0 --2.7054108216432873,-32.418327576167954,1.0 --2.6653306613226455,-34.9559705027297,1.0 --2.6252505010020046,-34.06613642696164,1.0 --2.585170340681363,-33.52192280109545,1.0 --2.545090180360722,-34.429613581933225,1.0 --2.50501002004008,-33.72615382428206,1.0 --2.4649298597194393,-33.61991632359402,1.0 --2.4248496993987976,-33.79327905927288,1.0 --2.3847695390781567,-32.494450386503075,1.0 --2.344689378757515,-33.55269766795808,1.0 --2.304609218436874,-31.725740979813114,1.0 --2.264529058116233,-32.99690406131851,1.0 --2.2244488977955914,-32.38419729908214,1.0 --2.1843687374749505,-31.337661262442598,1.0 --2.1442885771543088,-29.487364694598885,1.0 --2.104208416833668,-28.152334015949414,1.0 --2.064128256513026,-25.980313151402576,1.0 --2.0240480961923852,-27.054628972244377,1.0 --1.9839679358717444,-26.10126259208626,1.0 --1.9438877755511026,-24.775847202492667,1.0 --1.9038076152304608,-24.467065058770913,1.0 --1.8637274549098208,-23.748866690806494,1.0 --1.823647294589179,-22.729004359256784,1.0 --1.7835671342685373,-19.702769489747983,1.0 --1.7434869739478955,-21.100787517722367,1.0 --1.7034068136272555,-17.843224889119853,1.0 --1.6633266533066138,-16.88612636634289,1.0 --1.623246492985972,-17.187894028187905,1.0 --1.5831663326653302,-17.16912848074846,1.0 --1.5430861723446903,-15.336421043782295,1.0 --1.5030060120240485,-15.009214921805135,1.0 --1.4629258517034067,-12.812922275382373,1.0 --1.4228456913827667,-13.097877263238583,1.0 --1.382765531062125,-13.197990456316823,1.0 --1.3426853707414832,-12.504972157221136,1.0 --1.3026052104208414,-12.902654711584551,1.0 --1.2625250501002014,-12.973111422320345,1.0 --1.2224448897795597,-9.130704610381075,1.0 --1.182364729458918,-9.691686776613047,1.0 --1.1422845691382761,-11.274557658441493,1.0 --1.1022044088176361,-8.946400155814453,1.0 --1.0621242484969944,-10.976475222576541,1.0 --1.0220440881763526,-9.712611328820321,1.0 --0.9819639278557126,-9.379836295984392,1.0 --0.9418837675350709,-8.116188543135017,1.0 --0.9018036072144291,-9.535096788704806,1.0 --0.8617234468937873,-7.99089542144066,1.0 --0.8216432865731473,-8.122856730838283,1.0 --0.7815631262525056,-6.317081615000107,1.0 --0.7414829659318638,-7.420476578620175,1.0 --0.701402805611222,-4.884286093559899,1.0 --0.661322645290582,-5.88391553340165,1.0 --0.6212424849699403,-9.869274800930436,1.0 --0.5811623246492985,-3.8383482540825935,1.0 --0.5410821643286585,-5.758872550836287,1.0 --0.5010020040080168,-5.154003574926875,1.0 --0.460921843687375,-3.5510490148235885,1.0 --0.4208416833667332,-0.24753321544999318,1.0 --0.38076152304609323,-2.618815978971372,1.0 --0.34068136272545146,-0.9477995177368047,1.0 --0.3006012024048097,1.553851078528677,1.0 --0.26052104208416793,1.9877910475819929,1.0 --0.22044088176352794,2.9295097491542332,1.0 --0.18036072144288617,3.488227996004487,1.0 --0.1402805611222444,3.4408407620391017,1.0 --0.10020040080160442,3.258352652087363,1.0 --0.06012024048096265,6.764189760964778,1.0 --0.020040080160320883,8.825654590810657,1.0 -0.020040080160320883,10.01692612489408,1.0 -0.060120240480960874,8.664673583984424,1.0 -0.10020040080160264,10.136663820334807,1.0 -0.1402805611222444,10.859710153764592,1.0 -0.18036072144288617,11.736891280745667,1.0 -0.22044088176352616,11.894305802217362,1.0 -0.26052104208416793,11.563651578934849,1.0 -0.3006012024048097,13.200393169384819,1.0 -0.3406813627254497,13.199526697872493,1.0 -0.38076152304609145,13.37217454114554,1.0 -0.4208416833667332,12.212304836751587,1.0 -0.460921843687375,14.009053914218766,1.0 -0.501002004008015,14.458034957630455,1.0 -0.5410821643286567,14.75190031919132,1.0 -0.5811623246492985,14.459796845224,1.0 -0.6212424849699403,13.441866482027125,1.0 -0.6613226452905803,14.582387939550888,1.0 -0.701402805611222,15.47414518430887,1.0 -0.7414829659318638,15.322521314716253,1.0 -0.7815631262525038,14.260266165256382,1.0 -0.8216432865731456,12.047091456245008,1.0 -0.8617234468937873,11.621492864552874,1.0 -0.9018036072144291,11.66869152798425,1.0 -0.9418837675350691,9.534689691462821,1.0 -0.9819639278557108,8.772140419195754,1.0 -1.0220440881763526,9.865322995063666,1.0 -1.0621242484969944,7.64751791001323,1.0 -1.1022044088176344,10.022002084640446,1.0 -1.1422845691382761,7.9110274461670285,1.0 -1.182364729458918,5.064065636138549,1.0 -1.222444889779558,6.563873217700696,1.0 -1.2625250501001997,6.108432727980165,1.0 -1.3026052104208414,6.435159189200759,1.0 -1.3426853707414832,4.9048715166185115,1.0 -1.3827655310621232,5.274981874640608,1.0 -1.422845691382765,5.661171776197015,1.0 -1.4629258517034067,4.83654171507416,1.0 -1.5030060120240485,4.757309768858546,1.0 -1.5430861723446885,5.298604949417255,1.0 -1.5831663326653302,5.146693430724367,1.0 -1.623246492985972,6.679764658324779,1.0 -1.663326653306612,7.456858636036029,1.0 -1.7034068136272538,6.924799918297533,1.0 -1.7434869739478955,8.205013332851742,1.0 -1.7835671342685373,8.667775747363601,1.0 -1.8236472945891773,7.300808740125185,1.0 -1.863727454909819,7.992152633452895,1.0 -1.9038076152304608,9.963154049237898,1.0 -1.9438877755511026,10.797148575443675,1.0 -1.9839679358717426,9.918426339312575,1.0 -2.0240480961923843,13.024700577082022,1.0 -2.064128256513026,12.688298119288303,1.0 -2.104208416833666,12.80619010489583,1.0 -2.144288577154308,15.396730195652948,1.0 -2.1843687374749496,15.62772159453128,1.0 -2.2244488977955914,15.918432473231695,1.0 -2.2645290581162314,17.57458160600389,1.0 -2.304609218436873,18.72815384954411,1.0 -2.344689378757515,19.668505459100626,1.0 -2.3847695390781567,19.284373420002307,1.0 -2.4248496993987967,21.284175427078456,1.0 -2.4649298597194385,21.462287928520244,1.0 -2.50501002004008,21.686972982540414,1.0 -2.54509018036072,24.01122868177702,1.0 -2.585170340681362,21.496882390772726,1.0 -2.6252505010020037,24.475054960861467,1.0 -2.6653306613226455,24.407402372776914,1.0 -2.7054108216432855,23.286508881678937,1.0 -2.7454909819639273,24.784407912310996,1.0 -2.785571142284569,26.12647819314915,1.0 -2.825651302605209,25.633606128741572,1.0 -2.865731462925851,25.351853428542434,1.0 -2.9058116232464926,26.349853164042656,1.0 -2.9458917835671343,26.994480898774572,1.0 -2.9859719438877743,28.266161754847108,1.0 -3.026052104208416,26.827011269073036,1.0 -3.066132264529058,26.071208216724855,1.0 -3.1062124248496996,27.576376009589826,1.0 -3.1462925851703396,28.687664332104116,1.0 -3.1863727454909814,27.726223626883737,1.0 -3.226452905811623,30.23343540356245,1.0 -3.266533066132263,29.267822311987548,1.0 -3.306613226452905,29.48308903767431,1.0 -3.3466933867735467,30.871800022576036,1.0 -3.3867735470941884,30.92534037761088,1.0 -3.4268537074148284,33.089015723290416,1.0 -3.46693386773547,32.22212535406103,1.0 -3.507014028056112,31.978814800192374,1.0 -3.5470941883767537,33.21708580013434,1.0 -3.5871743486973937,35.73135875771658,1.0 -3.6272545090180355,35.18192771836466,1.0 -3.6673346693386772,37.21350066383427,1.0 -3.7074148296593172,38.108388208062536,1.0 -3.747494989979959,39.26614430681934,1.0 -3.7875751503006008,38.815053661679016,1.0 -3.8276553106212425,39.16631717242292,1.0 -3.8677354709418825,41.140693720053136,1.0 -3.9078156312625243,42.34530479694776,1.0 -3.947895791583166,45.39654012673368,1.0 -3.987975951903808,46.80871236506194,1.0 -4.028056112224448,44.093278719136556,1.0 -4.06813627254509,45.57035168317331,1.0 -4.108216432865731,46.22133589440107,1.0 -4.148296593186371,50.66369212320467,1.0 -4.188376753507013,48.68452913620155,1.0 -4.228456913827655,51.28589742015849,1.0 -4.268537074148297,50.80750083683147,1.0 -4.308617234468937,52.50909174470394,1.0 -4.348697394789578,52.49640323793104,1.0 -4.38877755511022,52.52464020189004,1.0 -4.428857715430862,55.403370346964834,1.0 -4.468937875751502,52.96064141924771,1.0 -4.509018036072144,54.793312432259725,1.0 -4.5490981963927855,54.91136528186911,1.0 -4.589178356713425,54.413406102279026,1.0 -4.629258517034067,52.82279848611353,1.0 -4.669338677354709,51.950156169295,1.0 -4.709418837675351,51.91761926706385,1.0 -4.749498997995991,54.41369024125301,1.0 -4.7895791583166325,53.79575004314769,1.0 -4.829659318637274,51.97241278851771,1.0 -4.869739478957916,51.52445535698189,1.0 -4.909819639278556,51.30829516922755,1.0 -4.949899799599198,50.974139209614094,1.0 -4.98997995991984,49.664449621557345,1.0 -5.0300601202404795,48.936838568303045,1.0 -5.070140280561121,51.00081182975106,1.0 -5.110220440881763,47.718181017944865,1.0 -5.150300601202405,46.77514695529662,1.0 -5.190380761523045,48.63879211729986,1.0 -5.230460921843687,46.773404767856334,1.0 -5.270541082164328,45.958204755408524,1.0 -5.31062124248497,46.82897542183546,1.0 -5.35070140280561,44.85269418455798,1.0 -5.390781563126252,45.56834303764164,1.0 -5.430861723446894,44.322994332328605,1.0 -5.470941883767534,45.413199761384625,1.0 -5.511022044088175,43.863041239112654,1.0 -5.551102204408817,44.35859566126134,1.0 -5.591182364729459,45.529377998731036,1.0 -5.631262525050099,46.54441528510241,1.0 -5.671342685370741,46.6740241880836,1.0 -5.7114228456913825,44.91778053208961,1.0 -5.751503006012024,47.29561095394557,1.0 -5.791583166332664,47.25875830340436,1.0 -5.831663326653306,47.57330584231544,1.0 -5.871743486973948,49.424004970041004,1.0 -5.911823647294588,49.00603730037155,1.0 -5.9519038076152295,48.675582311574935,1.0 -5.991983967935871,49.702009379009816,1.0 -6.032064128256511,51.48188080517231,1.0 -6.072144288577153,54.028316346680946,1.0 -6.112224448897795,51.495895748787504,1.0 -6.152304609218437,53.769854420461854,1.0 -6.192384769539078,54.80539210073662,1.0 -6.23246492985972,54.55789661870808,1.0 -6.272545090180358,54.90606277703188,1.0 -6.312625250501,55.01806681659129,1.0 -6.352705410821642,56.308980606142455,1.0 -6.392785571142284,56.900700248290505,1.0 -6.432865731462925,57.76472157826207,1.0 -6.472945891783567,59.17483811803871,1.0 -6.513026052104209,58.9502825107662,1.0 -6.553106212424851,58.36868175318838,1.0 -6.593186372745489,59.127249926142504,1.0 -6.633266533066131,60.41550125400638,1.0 -6.673346693386772,60.122946268002124,1.0 -6.713426853707414,61.97175921356713,1.0 -6.753507014028056,60.90970803614046,1.0 -6.793587174348698,61.59850187172145,1.0 -6.8336673346693395,61.87936712290967,1.0 -6.873747494989978,62.546532380364795,1.0 -6.9138276553106195,61.95218488858711,1.0 -6.953907815631261,61.734908484135154,1.0 -6.993987975951903,61.62640476712413,1.0 -7.034068136272545,64.50672681405253,1.0 -7.074148296593187,63.012820156039474,1.0 -7.114228456913828,64.09811072643528,1.0 -7.1543086172344665,62.67672611265759,1.0 -7.194388777555108,62.85625909718129,1.0 -7.23446893787575,65.84426304205772,1.0 -7.274549098196392,64.0940239532543,1.0 -7.314629258517034,64.22967584493593,1.0 -7.354709418837675,66.52127372499055,1.0 -7.394789579158317,67.47010438824188,1.0 -7.434869739478959,67.66784148781223,1.0 -7.474949899799597,69.30671461351653,1.0 -7.515030060120239,68.50344931141782,1.0 -7.555110220440881,70.03010717463826,1.0 -7.595190380761522,69.65883819119713,1.0 -7.635270541082164,71.17069051827968,1.0 -7.675350701402806,72.93072237867932,1.0 -7.715430861723448,74.17003912159556,1.0 -7.755511022044086,74.19409638089763,1.0 -7.795591182364728,76.52380477472244,1.0 -7.8356713426853695,77.54663638755878,1.0 -7.875751503006011,77.10569888552479,1.0 -7.915831663326653,80.75736933503426,1.0 -7.955911823647295,82.6615813290429,1.0 -7.9959919839679365,83.83500711636594,1.0 -8.036072144288575,82.98861143474033,1.0 -8.076152304609217,85.67867137752604,1.0 -8.116232464929858,84.57625034203262,1.0 -8.1563126252505,85.70495564890761,1.0 -8.196392785571142,86.08686918612305,1.0 -8.236472945891784,89.06990051586982,1.0 -8.276553106212425,89.62128794226595,1.0 -8.316633266533067,90.49595040265528,1.0 -8.356713426853705,89.11948413579704,1.0 -8.396793587174347,92.01871378001339,1.0 -8.436873747494989,91.97542177408452,1.0 -8.47695390781563,91.55527674250371,1.0 -8.517034068136272,92.0754553365536,1.0 -8.557114228456914,91.52910164275035,1.0 -8.597194388777556,92.45027730365187,1.0 -8.637274549098194,91.33446697697401,1.0 -8.677354709418836,93.57387739055137,1.0 -8.717434869739478,91.52229528777025,1.0 -8.75751503006012,93.13867707523563,1.0 -8.797595190380761,90.66900464205676,1.0 -8.837675350701403,92.35523883838175,1.0 -8.877755511022045,92.23190060362916,1.0 -8.917835671342683,90.76665972331755,1.0 -8.957915831663325,90.83544742978458,1.0 -8.997995991983966,91.8377766000374,1.0 -9.038076152304608,87.80501035838535,1.0 -9.07815631262525,88.11235097247528,1.0 -9.118236472945892,87.22486763163995,1.0 -9.158316633266534,90.20705516270274,1.0 -9.198396793587175,86.94324248131345,1.0 -9.238476953907814,88.7188024669311,1.0 -9.278557114228455,87.02900485927557,1.0 -9.318637274549097,86.230509038729,1.0 -9.358717434869739,87.96655065251268,1.0 -9.39879759519038,84.54625959591301,1.0 -9.438877755511022,83.92285587573049,1.0 -9.478957915831664,87.41260322961844,1.0 -9.519038076152302,84.79119262164973,1.0 -9.559118236472944,84.1822016705277,1.0 -9.599198396793586,85.20652183949272,1.0 -9.639278557114228,84.47684911609167,1.0 -9.67935871743487,84.52606107813443,1.0 -9.719438877755511,86.68969003118522,1.0 -9.759519038076153,87.04877283419954,1.0 -9.799599198396791,85.73151818189783,1.0 -9.839679358717433,86.22224123460789,1.0 -9.879759519038075,88.90772597199742,1.0 -9.919839679358716,86.67276009217017,1.0 -9.959919839679358,89.66618202135797,1.0 -10.0,87.94191305980316,1.0 diff --git a/Day_2/WorkProblems/readme.md b/Day_2/WorkProblems/readme.md deleted file mode 100644 index 07ce03f..0000000 --- a/Day_2/WorkProblems/readme.md +++ /dev/null @@ -1,26 +0,0 @@ -Work Problems: -============== -1. Load data files(all csv): - - * files style: xdata, ydata, error - - Order: - * sinx: Asin(Bx) - * sinxandcosx: Asin(Bx) + Ccos(Dx) - * sinxcosx: Asin(Bx)cos(Cx) - * gravity: A + Bx -(C/2)x2 - * sinxandcosx2: (Asin(Bx) +Ccos(Dx))2 - * linesinxandcosx: Asin(Bx) + Ccos(Dx) +Ex - * hysteresis: Atanh(Bx + C) - D - * lineexp: AeBx + Cx - * sinxandabs: Aabs(x) + Bsin(Cx) -2. Plot data and transpose - * Do they equal each other - * fit wiht SciPy - * RePlot - * Integrate with both SciPy and Sympy - * does it equal the same thing? -3. Repeat for the next file(complete as many as possible in any order) - -4. Record fit paramaters and functions into a .csv file - diff --git a/Day_2/WorkProblems/sinx.csv b/Day_2/WorkProblems/sinx.csv deleted file mode 100644 index fb3462f..0000000 --- a/Day_2/WorkProblems/sinx.csv +++ /dev/null @@ -1,501 +0,0 @@ -x,y,errors --10.0,-3.9957809324151854,1.0 --9.959919839679358,-3.638927333968336,1.0 --9.919839679358718,-5.024331069759617,1.0 --9.879759519038076,-5.667125271380683,1.0 --9.839679358717435,-6.600615500533194,1.0 --9.799599198396793,-8.305011047508968,1.0 --9.759519038076153,-6.336818569527769,1.0 --9.719438877755511,-6.336920411737637,1.0 --9.67935871743487,-8.267496674612445,1.0 --9.639278557114228,-7.762757934240638,1.0 --9.599198396793588,-10.185686371340582,1.0 --9.559118236472946,-8.471576952374573,1.0 --9.519038076152304,-8.571090821083498,1.0 --9.478957915831664,-7.65508866761014,1.0 --9.438877755511022,-7.4765232663423316,1.0 --9.39879759519038,-8.584441056967782,1.0 --9.358717434869739,-9.768523379319529,1.0 --9.318637274549099,-9.039800610705345,1.0 --9.278557114228457,-7.037643039853021,1.0 --9.238476953907815,-7.162027296523332,1.0 --9.198396793587175,-8.309535420692697,1.0 --9.158316633266534,-7.863197575165304,1.0 --9.118236472945892,-6.509561653772268,1.0 --9.07815631262525,-7.550465187881291,1.0 --9.03807615230461,-6.460915072912017,1.0 --8.997995991983968,-5.561002076045967,1.0 --8.957915831663327,-6.43046410629449,1.0 --8.917835671342685,-6.91874689885866,1.0 --8.877755511022045,-6.778270602381114,1.0 --8.837675350701403,-6.419372174012158,1.0 --8.797595190380761,-6.776329555701299,1.0 --8.75751503006012,-5.367475043924916,1.0 --8.71743486973948,-3.0566666443202077,1.0 --8.677354709418838,-3.3804012943512443,1.0 --8.637274549098196,-2.742683371575596,1.0 --8.597194388777556,-2.3105219593586743,1.0 --8.557114228456914,-1.865723015735829,1.0 --8.517034068136272,-0.4490819221719011,1.0 --8.47695390781563,-1.2095694316959962,1.0 --8.43687374749499,-4.665330406673457,1.0 --8.396793587174349,1.5957539589660996,1.0 --8.356713426853707,-0.7091519869846974,1.0 --8.316633266533067,1.6685556838584645,1.0 --8.276553106212425,-0.300551133477422,1.0 --8.236472945891784,1.8596004928527972,1.0 --8.196392785571142,3.747649278467506,1.0 --8.156312625250502,2.3404481191433497,1.0 --8.11623246492986,3.3541787456035315,1.0 --8.076152304609218,4.748312820863774,1.0 --8.036072144288577,2.7242097618409487,1.0 --7.9959919839679365,2.7477288274880243,1.0 --7.955911823647295,5.728320813239155,1.0 --7.915831663326653,6.333300937653988,1.0 --7.875751503006012,4.556537853742008,1.0 --7.835671342685371,6.374407637973695,1.0 --7.7955911823647295,5.348374705592835,1.0 --7.755511022044088,6.346341621941321,1.0 --7.715430861723447,7.1688131319894755,1.0 --7.675350701402806,7.194783042953738,1.0 --7.635270541082164,6.701141159949685,1.0 --7.595190380761523,7.155368208963814,1.0 --7.555110220440882,6.109763723484177,1.0 --7.515030060120241,7.8675581807341795,1.0 --7.474949899799599,9.160507570361565,1.0 --7.434869739478958,7.596884267799922,1.0 --7.394789579158317,8.486577036565146,1.0 --7.354709418837675,8.071496154654229,1.0 --7.314629258517034,8.580781685545738,1.0 --7.274549098196393,7.014195952660847,1.0 --7.234468937875752,7.0137906453721035,1.0 --7.19438877755511,6.759220360639927,1.0 --7.154308617234469,8.15872430442109,1.0 --7.114228456913828,8.866717635962353,1.0 --7.074148296593187,6.784537069434222,1.0 --7.034068136272545,7.526185441481293,1.0 --6.993987975951904,8.34788130703362,1.0 --6.953907815631263,8.742157422391134,1.0 --6.913827655310621,5.739168613502935,1.0 --6.8737474949899795,5.705582178276656,1.0 --6.833667334669339,5.405121099853206,1.0 --6.793587174348698,5.14678795199622,1.0 --6.753507014028056,3.2037695149668495,1.0 --6.713426853707415,4.817746315110397,1.0 --6.673346693386774,5.643171345975336,1.0 --6.6332665330661325,3.8601643858807546,1.0 --6.593186372745491,3.885126160273221,1.0 --6.55310621242485,4.291117838230024,1.0 --6.513026052104209,4.223117062201413,1.0 --6.472945891783567,1.8796838179736615,1.0 --6.432865731462925,1.4295745765658545,1.0 --6.3927855711422845,1.835820954159059,1.0 --6.352705410821644,0.4058929908784215,1.0 --6.312625250501002,0.008085976095932534,1.0 --6.272545090180361,-0.2442024971530259,1.0 --6.23246492985972,-1.0073312983843992,1.0 --6.192384769539078,-1.7358794116243375,1.0 --6.152304609218437,-1.1787364558431968,1.0 --6.112224448897796,-1.1482379258503346,1.0 --6.072144288577155,-2.038242156220364,1.0 --6.032064128256513,-3.2477276903915944,1.0 --5.991983967935872,-3.3030260015839437,1.0 --5.95190380761523,-3.990628734938894,1.0 --5.9118236472945895,-4.476297582106171,1.0 --5.871743486973948,-5.158028409580047,1.0 --5.831663326653307,-6.636034645943586,1.0 --5.791583166332665,-5.964071361568923,1.0 --5.751503006012024,-4.261254148586006,1.0 --5.711422845691383,-8.273195663140628,1.0 --5.671342685370742,-8.027482798258623,1.0 --5.631262525050101,-6.948433303432041,1.0 --5.591182364729459,-6.710609553810088,1.0 --5.551102204408818,-7.481234793908582,1.0 --5.511022044088176,-8.440748479831871,1.0 --5.470941883767535,-6.60673133106484,1.0 --5.430861723446894,-7.090302128950135,1.0 --5.390781563126253,-7.966381393215899,1.0 --5.350701402805611,-8.808405704908536,1.0 --5.31062124248497,-7.655117930774208,1.0 --5.270541082164329,-8.605207061631857,1.0 --5.2304609218436875,-8.483609456217438,1.0 --5.190380761523047,-8.922593374221123,1.0 --5.150300601202405,-7.434952066520266,1.0 --5.110220440881764,-8.11025652278912,1.0 --5.070140280561122,-9.583355127303292,1.0 --5.030060120240481,-7.166480413953505,1.0 --4.98997995991984,-7.335023550894208,1.0 --4.949899799599199,-7.579090772208895,1.0 --4.909819639278557,-6.615468441373043,1.0 --4.869739478957916,-8.499854054258435,1.0 --4.829659318637275,-5.553075769042799,1.0 --4.789579158316633,-5.943748558970212,1.0 --4.7494989979959925,-6.228725728935209,1.0 --4.709418837675351,-5.83390533006424,1.0 --4.66933867735471,-4.938024575020901,1.0 --4.629258517034068,-6.015745692216539,1.0 --4.589178356713427,-3.7265288186423593,1.0 --4.5490981963927855,-3.864491270041633,1.0 --4.509018036072145,-4.088419845130036,1.0 --4.468937875751503,-1.7114205048757678,1.0 --4.428857715430862,-2.415111818232897,1.0 --4.388777555110221,-3.467834303451284,1.0 --4.348697394789579,-1.4626687626159303,1.0 --4.308617234468938,-1.2413416663661503,1.0 --4.268537074148297,-2.664553840791709,1.0 --4.228456913827656,0.5637416014875951,1.0 --4.188376753507014,-0.8149345609516773,1.0 --4.148296593186373,1.710224610823321,1.0 --4.108216432865731,0.8948416581395817,1.0 --4.0681362725450905,2.3270532003348574,1.0 --4.028056112224449,2.5013373257884943,1.0 --3.987975951903808,3.6504385401949433,1.0 --3.947895791583167,2.836250277459777,1.0 --3.907815631262525,3.3394588938015697,1.0 --3.8677354709418843,2.562969983766406,1.0 --3.8276553106212425,4.415409981227924,1.0 --3.7875751503006017,4.863941957190921,1.0 --3.74749498997996,4.138696779963984,1.0 --3.707414829659319,5.219071098586154,1.0 --3.6673346693386772,6.741720330749002,1.0 --3.6272545090180364,5.831005949925199,1.0 --3.5871743486973955,7.477378151926235,1.0 --3.5470941883767537,6.694110405747025,1.0 --3.507014028056113,6.5819026408513945,1.0 --3.466933867735471,7.813053379583103,1.0 --3.42685370741483,6.394093243156714,1.0 --3.3867735470941884,8.639560367151997,1.0 --3.3466933867735476,8.046588784934187,1.0 --3.306613226452906,8.475218658950674,1.0 --3.266533066132265,8.523375572576558,1.0 --3.226452905811623,8.242250847121786,1.0 --3.1863727454909823,10.031051555715418,1.0 --3.1462925851703414,8.475100575350107,1.0 --3.1062124248496996,8.395519043799869,1.0 --3.0661322645290587,9.354557926877034,1.0 --3.026052104208417,8.589344860671677,1.0 --2.985971943887776,7.379808176225588,1.0 --2.9458917835671343,7.869954074501667,1.0 --2.9058116232464934,6.830439766639366,1.0 --2.8657314629258517,8.682829640933372,1.0 --2.825651302605211,9.260059723155363,1.0 --2.785571142284569,7.388333392018208,1.0 --2.745490981963928,7.4294998148735285,1.0 --2.7054108216432873,6.3468340525228735,1.0 --2.6653306613226455,7.718225448089922,1.0 --2.6252505010020046,5.6494432078036505,1.0 --2.585170340681363,6.7946249680157,1.0 --2.545090180360722,5.310870512688358,1.0 --2.50501002004008,4.752257767109594,1.0 --2.4649298597194393,3.6849394887064704,1.0 --2.4248496993987976,4.4443842474589506,1.0 --2.3847695390781567,4.830022342289689,1.0 --2.344689378757515,3.542934123702159,1.0 --2.304609218436874,2.4430904326816254,1.0 --2.264529058116233,2.935807064545056,1.0 --2.2244488977955914,2.4599784719399342,1.0 --2.1843687374749505,1.3383040030781022,1.0 --2.1442885771543088,1.9027182807693208,1.0 --2.104208416833668,0.9994615233503755,1.0 --2.064128256513026,-0.9614210236858691,1.0 --2.0240480961923852,0.7210287232370863,1.0 --1.9839679358717444,-0.37215023114049317,1.0 --1.9438877755511026,-2.7439923877712658,1.0 --1.9038076152304608,-2.7759067488092324,1.0 --1.8637274549098208,-1.7891195949315724,1.0 --1.823647294589179,-4.061582566230697,1.0 --1.7835671342685373,-3.9331065430617076,1.0 --1.7434869739478955,-4.48906359482103,1.0 --1.7034068136272555,-4.553531452717307,1.0 --1.6633266533066138,-5.5471999556481055,1.0 --1.623246492985972,-5.884008929325533,1.0 --1.5831663326653302,-5.035705130599378,1.0 --1.5430861723446903,-6.394078359749823,1.0 --1.5030060120240485,-6.990385628314403,1.0 --1.4629258517034067,-6.001464161097674,1.0 --1.4228456913827667,-6.591456971026867,1.0 --1.382765531062125,-6.6221521582445035,1.0 --1.3426853707414832,-7.826581251999698,1.0 --1.3026052104208414,-6.854145343015682,1.0 --1.2625250501002014,-7.098367067660991,1.0 --1.2224448897795597,-9.0142142843875,1.0 --1.182364729458918,-8.221427247648569,1.0 --1.1422845691382761,-7.4930616964758405,1.0 --1.1022044088176361,-8.935944021884202,1.0 --1.0621242484969944,-9.820006070483373,1.0 --1.0220440881763526,-8.068596823682803,1.0 --0.9819639278557126,-8.468341303022838,1.0 --0.9418837675350709,-7.45859101390905,1.0 --0.9018036072144291,-6.842178654583252,1.0 --0.8617234468937873,-8.86501085478377,1.0 --0.8216432865731473,-7.170601475694047,1.0 --0.7815631262525056,-9.064944636836909,1.0 --0.7414829659318638,-7.073000607835519,1.0 --0.701402805611222,-7.446391965021689,1.0 --0.661322645290582,-7.204292761898772,1.0 --0.6212424849699403,-8.637558670145378,1.0 --0.5811623246492985,-5.989696490698553,1.0 --0.5410821643286585,-7.523397267464044,1.0 --0.5010020040080168,-4.599818023667919,1.0 --0.460921843687375,-6.292969890910214,1.0 --0.4208416833667332,-4.262077520418456,1.0 --0.38076152304609323,-3.4460794460034156,1.0 --0.34068136272545146,-3.496866812949735,1.0 --0.3006012024048097,-2.674414462328989,1.0 --0.26052104208416793,-2.8029147027832644,1.0 --0.22044088176352794,-1.9460472884284497,1.0 --0.18036072144288617,-1.6173186360101681,1.0 --0.1402805611222444,-1.5019410304357963,1.0 --0.10020040080160442,-1.4709437555688603,1.0 --0.06012024048096265,-1.867204272104491,1.0 --0.020040080160320883,-2.6795340203607934,1.0 -0.020040080160320883,-0.757889038147968,1.0 -0.060120240480960874,0.04558916882728559,1.0 -0.10020040080160264,2.749396516173218,1.0 -0.1402805611222444,3.5491182753115114,1.0 -0.18036072144288617,2.57656138703644,1.0 -0.22044088176352616,3.736511256142209,1.0 -0.26052104208416793,3.945353773423872,1.0 -0.3006012024048097,3.79177351132466,1.0 -0.3406813627254497,5.614886972704868,1.0 -0.38076152304609145,4.929667297553781,1.0 -0.4208416833667332,7.22350698090787,1.0 -0.460921843687375,3.527276563009542,1.0 -0.501002004008015,5.444364520441894,1.0 -0.5410821643286567,3.438749472804941,1.0 -0.5811623246492985,8.412073653120542,1.0 -0.6212424849699403,6.565596752605489,1.0 -0.6613226452905803,7.860708305195435,1.0 -0.701402805611222,7.755712679462658,1.0 -0.7414829659318638,6.726041032215142,1.0 -0.7815631262525038,7.660250710375481,1.0 -0.8216432865731456,8.949225515898553,1.0 -0.8617234468937873,8.045306533754216,1.0 -0.9018036072144291,9.020368489704806,1.0 -0.9418837675350691,8.799813633823325,1.0 -0.9819639278557108,8.47523251540545,1.0 -1.0220440881763526,7.684416014022551,1.0 -1.0621242484969944,7.605854394592795,1.0 -1.1022044088176344,8.741411045904439,1.0 -1.1422845691382761,7.453818121990997,1.0 -1.182364729458918,7.955833209818349,1.0 -1.222444889779558,8.2390831037482,1.0 -1.2625250501001997,8.430076509305445,1.0 -1.3026052104208414,8.594581340945675,1.0 -1.3426853707414832,6.428300442390215,1.0 -1.3827655310621232,7.782055004952643,1.0 -1.422845691382765,7.56014362496351,1.0 -1.4629258517034067,8.144861089908632,1.0 -1.5030060120240485,5.773017583591079,1.0 -1.5430861723446885,8.145982518768403,1.0 -1.5831663326653302,6.367565231874473,1.0 -1.623246492985972,6.991674759068429,1.0 -1.663326653306612,4.851142909147384,1.0 -1.7034068136272538,6.452725816136513,1.0 -1.7434869739478955,5.129374474022631,1.0 -1.7835671342685373,3.8300645954552195,1.0 -1.8236472945891773,3.160457176491361,1.0 -1.863727454909819,3.424849316524295,1.0 -1.9038076152304608,2.0110464389169906,1.0 -1.9438877755511026,1.9981065703295113,1.0 -1.9839679358717426,1.0635065404938608,1.0 -2.0240480961923843,2.240647543228448,1.0 -2.064128256513026,0.45436128064002634,1.0 -2.104208416833666,-2.483810949173884,1.0 -2.144288577154308,-3.0584119036999056,1.0 -2.1843687374749496,-2.3763863956904183,1.0 -2.2244488977955914,-0.9152854471853437,1.0 -2.2645290581162314,-2.7087815599259515,1.0 -2.304609218436873,-0.411894479344451,1.0 -2.344689378757515,-3.690469740581208,1.0 -2.3847695390781567,-4.758119467627463,1.0 -2.4248496993987967,-1.1799040954143303,1.0 -2.4649298597194385,-5.280079664715702,1.0 -2.50501002004008,-4.746013743570215,1.0 -2.54509018036072,-3.2939804541373734,1.0 -2.585170340681362,-5.123600085165344,1.0 -2.6252505010020037,-7.181113076378516,1.0 -2.6653306613226455,-8.225527230218983,1.0 -2.7054108216432855,-5.888885645727622,1.0 -2.7454909819639273,-6.593773464551369,1.0 -2.785571142284569,-7.449461499423,1.0 -2.825651302605209,-8.12196542037657,1.0 -2.865731462925851,-6.436038128922168,1.0 -2.9058116232464926,-8.949967685556008,1.0 -2.9458917835671343,-8.735649121888107,1.0 -2.9859719438877743,-8.055614096636418,1.0 -3.026052104208416,-8.328902900250622,1.0 -3.066132264529058,-6.712078428342056,1.0 -3.1062124248496996,-9.003159924995138,1.0 -3.1462925851703396,-7.062205296639661,1.0 -3.1863727454909814,-9.371240452981601,1.0 -3.226452905811623,-8.414031868685637,1.0 -3.266533066132263,-9.440563021436157,1.0 -3.306613226452905,-6.28157834797877,1.0 -3.3466933867735467,-8.816092828785765,1.0 -3.3867735470941884,-7.599694172348096,1.0 -3.4268537074148284,-7.827319901521333,1.0 -3.46693386773547,-6.944702996634743,1.0 -3.507014028056112,-7.299191147513161,1.0 -3.5470941883767537,-7.277972037298759,1.0 -3.5871743486973937,-6.044298323157915,1.0 -3.6272545090180355,-5.982925600173162,1.0 -3.6673346693386772,-6.258351897102857,1.0 -3.7074148296593172,-4.611640592851886,1.0 -3.747494989979959,-5.656678132985717,1.0 -3.7875751503006008,-4.437089260026151,1.0 -3.8276553106212425,-4.150421008295866,1.0 -3.8677354709418825,-3.8280734776809795,1.0 -3.9078156312625243,-4.28990834002035,1.0 -3.947895791583166,-1.880113403486894,1.0 -3.987975951903808,-1.383985652393203,1.0 -4.028056112224448,-0.5806972160710542,1.0 -4.06813627254509,-2.7352966403990235,1.0 -4.108216432865731,0.3580957027850682,1.0 -4.148296593186371,-0.7034347102700442,1.0 -4.188376753507013,1.1165230933391896,1.0 -4.228456913827655,0.3524701785988855,1.0 -4.268537074148297,0.8597843550335681,1.0 -4.308617234468937,1.592000278288107,1.0 -4.348697394789578,2.858614174471474,1.0 -4.38877755511022,3.5933807193744327,1.0 -4.428857715430862,2.021393859049828,1.0 -4.468937875751502,3.3531430441777856,1.0 -4.509018036072144,4.358389886488596,1.0 -4.5490981963927855,4.397003103074955,1.0 -4.589178356713425,3.60921425371715,1.0 -4.629258517034067,5.6844161346031346,1.0 -4.669338677354709,3.762942573001941,1.0 -4.709418837675351,4.921288270179062,1.0 -4.749498997995991,6.144791574045181,1.0 -4.7895791583166325,5.615919490480094,1.0 -4.829659318637274,7.303880830675383,1.0 -4.869739478957916,6.7788183608051265,1.0 -4.909819639278556,7.842353140433663,1.0 -4.949899799599198,7.782136274266063,1.0 -4.98997995991984,9.639819057787776,1.0 -5.0300601202404795,7.802978469877302,1.0 -5.070140280561121,9.495167892960906,1.0 -5.110220440881763,7.8938427930803075,1.0 -5.150300601202405,6.8531306763514275,1.0 -5.190380761523045,10.615796037352332,1.0 -5.230460921843687,7.959397864325979,1.0 -5.270541082164328,7.872989564250389,1.0 -5.31062124248497,9.007894490934316,1.0 -5.35070140280561,8.436946254251549,1.0 -5.390781563126252,9.626726913030675,1.0 -5.430861723446894,9.27408713524203,1.0 -5.470941883767534,8.416355072612912,1.0 -5.511022044088175,7.479953447361359,1.0 -5.551102204408817,7.708106457699131,1.0 -5.591182364729459,6.8656703708334,1.0 -5.631262525050099,8.159898029376038,1.0 -5.671342685370741,6.217915401542196,1.0 -5.7114228456913825,6.131678468850101,1.0 -5.751503006012024,6.14898727787419,1.0 -5.791583166332664,5.5840312064274,1.0 -5.831663326653306,6.549892577843683,1.0 -5.871743486973948,5.148059548786753,1.0 -5.911823647294588,3.313112421508362,1.0 -5.9519038076152295,4.2666662626389,1.0 -5.991983967935871,3.8313120323652474,1.0 -6.032064128256511,2.195303325631786,1.0 -6.072144288577153,2.9947467481250656,1.0 -6.112224448897795,2.891434470699438,1.0 -6.152304609218437,2.1486757499728215,1.0 -6.192384769539078,0.6552810214111637,1.0 -6.23246492985972,0.2949505768323907,1.0 -6.272545090180358,-1.2387199095977652,1.0 -6.312625250501,0.5274212479790537,1.0 -6.352705410821642,-1.993468331292466,1.0 -6.392785571142284,-2.9639691038109715,1.0 -6.432865731462925,-0.75548820096817,1.0 -6.472945891783567,-2.460202922489031,1.0 -6.513026052104209,-2.8607725079333344,1.0 -6.553106212424851,-3.4076846718570266,1.0 -6.593186372745489,-2.9428014675932186,1.0 -6.633266533066131,-5.7152856789747855,1.0 -6.673346693386772,-4.578845267462366,1.0 -6.713426853707414,-5.688420507903997,1.0 -6.753507014028056,-5.4928990756344325,1.0 -6.793587174348698,-7.3251405519019865,1.0 -6.8336673346693395,-6.307190173917455,1.0 -6.873747494989978,-5.219885148777224,1.0 -6.9138276553106195,-5.985483961212116,1.0 -6.953907815631261,-7.901373514498715,1.0 -6.993987975951903,-8.30445647005571,1.0 -7.034068136272545,-7.73100016995219,1.0 -7.074148296593187,-7.1603861034202625,1.0 -7.114228456913828,-8.85647729194793,1.0 -7.1543086172344665,-7.867670523652367,1.0 -7.194388777555108,-7.8999951126248185,1.0 -7.23446893787575,-7.612467740936883,1.0 -7.274549098196392,-8.94548198063248,1.0 -7.314629258517034,-7.643877900057861,1.0 -7.354709418837675,-9.414167678796549,1.0 -7.394789579158317,-7.0601739945642885,1.0 -7.434869739478959,-9.141334060755593,1.0 -7.474949899799597,-7.304002694680461,1.0 -7.515030060120239,-6.951593755221195,1.0 -7.555110220440881,-8.141298623190561,1.0 -7.595190380761522,-6.830560693521393,1.0 -7.635270541082164,-9.023386904202436,1.0 -7.675350701402806,-6.86438591544323,1.0 -7.715430861723448,-6.415687778569978,1.0 -7.755511022044086,-6.749651435683981,1.0 -7.795591182364728,-6.759137998765862,1.0 -7.8356713426853695,-6.912502270032461,1.0 -7.875751503006011,-5.981836949694165,1.0 -7.915831663326653,-6.784517115472695,1.0 -7.955911823647295,-5.791389787020789,1.0 -7.9959919839679365,-3.4261114110171866,1.0 -8.036072144288575,-2.4091621615794923,1.0 -8.076152304609217,-5.470887345052164,1.0 -8.116232464929858,-3.9416274538642093,1.0 -8.1563126252505,-2.8471900600717865,1.0 -8.196392785571142,-1.8469249361891773,1.0 -8.236472945891784,-1.398141278239331,1.0 -8.276553106212425,0.3361063213990674,1.0 -8.316633266533067,-0.8542029305857504,1.0 -8.356713426853705,-1.278183859644525,1.0 -8.396793587174347,1.3425327780102567,1.0 -8.436873747494989,1.7406764578643497,1.0 -8.47695390781563,2.1255275297472154,1.0 -8.517034068136272,2.3193087043950227,1.0 -8.557114228456914,4.234502781674992,1.0 -8.597194388777556,1.6602451488935617,1.0 -8.637274549098194,4.391996233511181,1.0 -8.677354709418836,2.010201463632673,1.0 -8.717434869739478,2.7025384394357026,1.0 -8.75751503006012,5.650596942287574,1.0 -8.797595190380761,5.326585461456165,1.0 -8.837675350701403,4.380137000232813,1.0 -8.877755511022045,7.359538447063473,1.0 -8.917835671342683,6.800365721667922,1.0 -8.957915831663325,7.3975390818384295,1.0 -8.997995991983966,6.738982007700631,1.0 -9.038076152304608,6.671125242657692,1.0 -9.07815631262525,8.842312692942475,1.0 -9.118236472945892,7.733117788604992,1.0 -9.158316633266534,6.696263581262942,1.0 -9.198396793587175,9.190023814937618,1.0 -9.238476953907814,10.39873099975393,1.0 -9.278557114228455,8.359958766940863,1.0 -9.318637274549097,6.928237840295628,1.0 -9.358717434869739,10.170130227982202,1.0 -9.39879759519038,9.597895829501809,1.0 -9.438877755511022,7.421099489033455,1.0 -9.478957915831664,7.25163976614896,1.0 -9.519038076152302,8.064546521003049,1.0 -9.559118236472944,7.122331694874807,1.0 -9.599198396793586,6.79691111871275,1.0 -9.639278557114228,8.693635807384885,1.0 -9.67935871743487,10.398569279741409,1.0 -9.719438877755511,5.389749112576408,1.0 -9.759519038076153,8.319313850348115,1.0 -9.799599198396791,7.758610147726053,1.0 -9.839679358717433,6.221971036557955,1.0 -9.879759519038075,6.829669751483637,1.0 -9.919839679358716,6.542424532243599,1.0 -9.959919839679358,6.531426672948761,1.0 -10.0,6.806693947885651,1.0 diff --git a/Day_2/WorkProblems/sinxandabs.csv b/Day_2/WorkProblems/sinxandabs.csv deleted file mode 100644 index 23ad8ad..0000000 --- a/Day_2/WorkProblems/sinxandabs.csv +++ /dev/null @@ -1,501 +0,0 @@ -x,y,errors --10.0,1.6857347086763765,1.0 --9.959919839679358,4.013613699427915,1.0 --9.919839679358718,2.5529792916297893,1.0 --9.879759519038076,3.507401717609016,1.0 --9.839679358717435,4.000886785605449,1.0 --9.799599198396793,4.815675406546204,1.0 --9.759519038076153,7.971104861052875,1.0 --9.719438877755511,8.564745431309857,1.0 --9.67935871743487,9.1931106883172,1.0 --9.639278557114228,9.52056278745853,1.0 --9.599198396793588,9.899564332686028,1.0 --9.559118236472946,11.100908269057072,1.0 --9.519038076152304,12.861553063573957,1.0 --9.478957915831664,11.848889309130266,1.0 --9.438877755511022,10.867069967075462,1.0 --9.39879759519038,12.200050910300593,1.0 --9.358717434869739,12.66445755672576,1.0 --9.318637274549099,11.15382096710966,1.0 --9.278557114228457,11.59173810333844,1.0 --9.238476953907815,12.824223782248755,1.0 --9.198396793587175,9.795142794348473,1.0 --9.158316633266534,10.875119435560851,1.0 --9.118236472945892,8.899921620838288,1.0 --9.07815631262525,8.26281063453338,1.0 --9.03807615230461,7.865953684158161,1.0 --8.997995991983968,7.577945076996276,1.0 --8.957915831663327,6.5072675691181185,1.0 --8.917835671342685,5.200836454890055,1.0 --8.877755511022045,5.114349387089195,1.0 --8.837675350701403,5.393563315812749,1.0 --8.797595190380761,4.859822192491134,1.0 --8.75751503006012,3.6545619885606393,1.0 --8.71743486973948,2.5291066075068835,1.0 --8.677354709418838,2.218984650452404,1.0 --8.637274549098196,1.7417794544289804,1.0 --8.597194388777556,0.41659394766596614,1.0 --8.557114228456914,2.5339065141669574,1.0 --8.517034068136272,0.5052720724283823,1.0 --8.47695390781563,2.118786132116184,1.0 --8.43687374749499,-0.394387064816502,1.0 --8.396793587174349,0.9170975946875346,1.0 --8.356713426853707,-0.9318316301888536,1.0 --8.316633266533067,1.0063137802768618,1.0 --8.276553106212425,-0.30521973713974265,1.0 --8.236472945891784,0.19975473317138565,1.0 --8.196392785571142,-0.19717584238530894,1.0 --8.156312625250502,2.671302010059077,1.0 --8.11623246492986,0.8936957286634039,1.0 --8.076152304609218,0.2410856389764494,1.0 --8.036072144288577,0.8910829924545329,1.0 --7.9959919839679365,4.110363717525511,1.0 --7.955911823647295,-0.7728186162402997,1.0 --7.915831663326653,2.1595887434666277,1.0 --7.875751503006012,1.1710687556938875,1.0 --7.835671342685371,2.3031178622086244,1.0 --7.7955911823647295,3.765580478410845,1.0 --7.755511022044088,1.2534975211847308,1.0 --7.715430861723447,5.2846406034039966,1.0 --7.675350701402806,2.9393734840014774,1.0 --7.635270541082164,2.4855551636355893,1.0 --7.595190380761523,2.741091947588973,1.0 --7.555110220440882,2.933647954608781,1.0 --7.515030060120241,2.8478892865082353,1.0 --7.474949899799599,0.2945009237915419,1.0 --7.434869739478958,1.4494711898013453,1.0 --7.394789579158317,-0.19491266058728118,1.0 --7.354709418837675,-0.020747330625312144,1.0 --7.314629258517034,-1.952241565760322,1.0 --7.274549098196393,-1.9652287055546935,1.0 --7.234468937875752,-1.823856252637708,1.0 --7.19438877755511,-5.049265871013052,1.0 --7.154308617234469,-1.8427178393660832,1.0 --7.114228456913828,-4.0385816285528815,1.0 --7.074148296593187,-6.822512309745342,1.0 --7.034068136272545,-6.1669503604756715,1.0 --6.993987975951904,-7.727144790971328,1.0 --6.953907815631263,-8.25610671412232,1.0 --6.913827655310621,-10.126306865506658,1.0 --6.8737474949899795,-9.628335410520098,1.0 --6.833667334669339,-12.801516424174583,1.0 --6.793587174348698,-11.741462518755599,1.0 --6.753507014028056,-12.939613355449248,1.0 --6.713426853707415,-13.053706740906227,1.0 --6.673346693386774,-14.27314891474202,1.0 --6.6332665330661325,-13.052509911340199,1.0 --6.593186372745491,-11.367124686467964,1.0 --6.55310621242485,-13.87207896536081,1.0 --6.513026052104209,-12.769776794578382,1.0 --6.472945891783567,-11.904725339174512,1.0 --6.432865731462925,-13.136764278827803,1.0 --6.3927855711422845,-11.236714049781042,1.0 --6.352705410821644,-10.558380524289438,1.0 --6.312625250501002,-10.937712052946543,1.0 --6.272545090180361,-9.75255943836789,1.0 --6.23246492985972,-10.448320306893784,1.0 --6.192384769539078,-7.825004480047912,1.0 --6.152304609218437,-7.9975416079807395,1.0 --6.112224448897796,-6.409425744777252,1.0 --6.072144288577155,-5.141545931747437,1.0 --6.032064128256513,-4.988940877777135,1.0 --5.991983967935872,-4.1749689698261925,1.0 --5.95190380761523,-1.6225045243178602,1.0 --5.9118236472945895,-0.23691518402088318,1.0 --5.871743486973948,1.4422500221992247,1.0 --5.831663326653307,0.5074746467968344,1.0 --5.791583166332665,2.418661676574002,1.0 --5.751503006012024,3.3484692660234914,1.0 --5.711422845691383,3.10631025893412,1.0 --5.671342685370742,3.2165614582893793,1.0 --5.631262525050101,4.723024072869765,1.0 --5.591182364729459,4.851933154675596,1.0 --5.551102204408818,5.855285763078102,1.0 --5.511022044088176,7.048261736739326,1.0 --5.470941883767535,7.2794894178458325,1.0 --5.430861723446894,8.303184626687415,1.0 --5.390781563126253,8.224431911389535,1.0 --5.350701402805611,7.3526051609461325,1.0 --5.31062124248497,8.35249757324526,1.0 --5.270541082164329,8.715043079768812,1.0 --5.2304609218436875,6.97499989646195,1.0 --5.190380761523047,8.764356011892582,1.0 --5.150300601202405,5.728787293915461,1.0 --5.110220440881764,8.265624290701139,1.0 --5.070140280561122,5.944821076110989,1.0 --5.030060120240481,6.686150042256749,1.0 --4.98997995991984,4.453732818256995,1.0 --4.949899799599199,6.766312035573879,1.0 --4.909819639278557,4.747467685242633,1.0 --4.869739478957916,3.6753734118460564,1.0 --4.829659318637275,5.18447852866729,1.0 --4.789579158316633,3.7943762622860953,1.0 --4.7494989979959925,2.5769074588137757,1.0 --4.709418837675351,4.116138408082359,1.0 --4.66933867735471,2.4007463385199013,1.0 --4.629258517034068,2.9543771756620543,1.0 --4.589178356713427,1.7129460344025258,1.0 --4.5490981963927855,2.7358713489949458,1.0 --4.509018036072145,1.9716231299228137,1.0 --4.468937875751503,2.0027202684391594,1.0 --4.428857715430862,1.3244383401959594,1.0 --4.388777555110221,3.628932880538506,1.0 --4.348697394789579,0.42520941529001766,1.0 --4.308617234468938,2.8031089079872014,1.0 --4.268537074148297,2.7364165354383108,1.0 --4.228456913827656,4.272708726222519,1.0 --4.188376753507014,5.616078170959657,1.0 --4.148296593186373,4.143508194473371,1.0 --4.108216432865731,5.051741411518203,1.0 --4.0681362725450905,5.4751484589382216,1.0 --4.028056112224449,4.510000948275952,1.0 --3.987975951903808,5.901251451326756,1.0 --3.947895791583167,6.400137438229996,1.0 --3.907815631262525,6.058986299456652,1.0 --3.8677354709418843,7.92902895200586,1.0 --3.8276553106212425,6.060553540262248,1.0 --3.7875751503006017,6.6405194682460635,1.0 --3.74749498997996,9.479992758094195,1.0 --3.707414829659319,8.388424736463694,1.0 --3.6673346693386772,8.112562975250103,1.0 --3.6272545090180364,8.900217090398332,1.0 --3.5871743486973955,8.400619279947868,1.0 --3.5470941883767537,6.531373447289622,1.0 --3.507014028056113,7.681514626971887,1.0 --3.466933867735471,7.123064886641511,1.0 --3.42685370741483,6.295367626345502,1.0 --3.3867735470941884,4.960507509576253,1.0 --3.3466933867735476,5.002422572234223,1.0 --3.306613226452906,4.487800477262426,1.0 --3.266533066132265,3.9601896540983135,1.0 --3.226452905811623,1.9152273193065956,1.0 --3.1863727454909823,0.5969568981734196,1.0 --3.1462925851703414,1.189613501075297,1.0 --3.1062124248496996,-0.1817650872355856,1.0 --3.0661322645290587,-1.283677965851594,1.0 --3.026052104208417,-3.2358224199597276,1.0 --2.985971943887776,-4.511976025076525,1.0 --2.9458917835671343,-5.366333181052176,1.0 --2.9058116232464934,-6.725933316560824,1.0 --2.8657314629258517,-8.083876339678605,1.0 --2.825651302605211,-9.855953003937294,1.0 --2.785571142284569,-8.20501163730394,1.0 --2.745490981963928,-10.403538208148294,1.0 --2.7054108216432873,-10.220820364217797,1.0 --2.6653306613226455,-11.278982059040912,1.0 --2.6252505010020046,-11.555785621400027,1.0 --2.585170340681363,-11.128709709451893,1.0 --2.545090180360722,-14.115780010442817,1.0 --2.50501002004008,-13.039503424638989,1.0 --2.4649298597194393,-13.115132656597018,1.0 --2.4248496993987976,-13.737005015466602,1.0 --2.3847695390781567,-13.706632220238381,1.0 --2.344689378757515,-11.607345257489563,1.0 --2.304609218436874,-13.389660930117918,1.0 --2.264529058116233,-12.750849441332138,1.0 --2.2244488977955914,-11.046837514327745,1.0 --2.1843687374749505,-11.481325459418937,1.0 --2.1442885771543088,-9.621878326364742,1.0 --2.104208416833668,-10.381371392480313,1.0 --2.064128256513026,-8.64754260733544,1.0 --2.0240480961923852,-9.703708962905646,1.0 --1.9839679358717444,-6.811462029836657,1.0 --1.9438877755511026,-4.589184816246273,1.0 --1.9038076152304608,-5.796648585782622,1.0 --1.8637274549098208,-1.9974076809799528,1.0 --1.823647294589179,-4.628315718396862,1.0 --1.7835671342685373,-3.2943684685744223,1.0 --1.7434869739478955,-2.22418954551942,1.0 --1.7034068136272555,-0.896661751074189,1.0 --1.6633266533066138,-0.9353308626425502,1.0 --1.623246492985972,0.6501514282413464,1.0 --1.5831663326653302,-0.8746244851892131,1.0 --1.5430861723446903,3.0805461175863718,1.0 --1.5030060120240485,1.2212451040359116,1.0 --1.4629258517034067,2.9065242808390117,1.0 --1.4228456913827667,2.672804825545158,1.0 --1.382765531062125,4.182998415822822,1.0 --1.3426853707414832,3.774759484418653,1.0 --1.3026052104208414,3.5120069045746662,1.0 --1.2625250501002014,2.0177000537609127,1.0 --1.2224448897795597,0.9941244517749186,1.0 --1.182364729458918,3.9956607735606244,1.0 --1.1422845691382761,1.7648822006662634,1.0 --1.1022044088176361,2.974341244730146,1.0 --1.0621242484969944,-0.23880038576491436,1.0 --1.0220440881763526,0.7128253004636743,1.0 --0.9819639278557126,0.8338080391049101,1.0 --0.9418837675350709,-0.6815624502177473,1.0 --0.9018036072144291,0.3112659093053486,1.0 --0.8617234468937873,-1.135059415264367,1.0 --0.8216432865731473,-0.09265892061799558,1.0 --0.7815631262525056,1.04167196027139,1.0 --0.7414829659318638,1.5048753708268636,1.0 --0.701402805611222,-0.7207488986566587,1.0 --0.661322645290582,-0.5923063867513836,1.0 --0.6212424849699403,0.2953616339437556,1.0 --0.5811623246492985,-1.2527176395486932,1.0 --0.5410821643286585,-0.8564924902260751,1.0 --0.5010020040080168,0.7882046156904949,1.0 --0.460921843687375,0.48174055367823887,1.0 --0.4208416833667332,1.2458009805690164,1.0 --0.38076152304609323,2.614160949884348,1.0 --0.34068136272545146,1.4724045340530028,1.0 --0.3006012024048097,2.77725101554365,1.0 --0.26052104208416793,2.319097271467372,1.0 --0.22044088176352794,5.053305469945468,1.0 --0.18036072144288617,4.942216907638576,1.0 --0.1402805611222444,3.5780486380712437,1.0 --0.10020040080160442,4.098668109532289,1.0 --0.06012024048096265,7.6966986277505995,1.0 --0.020040080160320883,8.44097777833922,1.0 -0.020040080160320883,9.362870136470423,1.0 -0.060120240480960874,7.526196708078163,1.0 -0.10020040080160264,8.270568214986595,1.0 -0.1402805611222444,9.75449185329195,1.0 -0.18036072144288617,10.910855648676323,1.0 -0.22044088176352616,11.896320902103959,1.0 -0.26052104208416793,11.390086282779711,1.0 -0.3006012024048097,11.319724716795626,1.0 -0.3406813627254497,12.473719891636614,1.0 -0.38076152304609145,12.648481680118234,1.0 -0.4208416833667332,13.552338754020399,1.0 -0.460921843687375,12.617490488422131,1.0 -0.501002004008015,11.896849298182632,1.0 -0.5410821643286567,11.57912263053422,1.0 -0.5811623246492985,10.749793491765324,1.0 -0.6212424849699403,9.374918552801706,1.0 -0.6613226452905803,9.178657957514059,1.0 -0.701402805611222,8.53391911763687,1.0 -0.7414829659318638,9.205030183702062,1.0 -0.7815631262525038,7.882179773137894,1.0 -0.8216432865731456,6.5227645279417885,1.0 -0.8617234468937873,5.524846515808931,1.0 -0.9018036072144291,5.520715737999964,1.0 -0.9418837675350691,3.869992663875465,1.0 -0.9819639278557108,0.8374413038287829,1.0 -1.0220440881763526,0.5407978816361908,1.0 -1.0621242484969944,-0.3029057519845745,1.0 -1.1022044088176344,-3.2322180340501445,1.0 -1.1422845691382761,-2.2783269369185133,1.0 -1.182364729458918,-5.697970010248994,1.0 -1.222444889779558,-6.769533250465215,1.0 -1.2625250501001997,-7.340684198340792,1.0 -1.3026052104208414,-7.558371885138308,1.0 -1.3426853707414832,-10.003513449058694,1.0 -1.3827655310621232,-8.155427783266731,1.0 -1.422845691382765,-8.69245681993435,1.0 -1.4629258517034067,-9.869943990628467,1.0 -1.5030060120240485,-10.378285553600966,1.0 -1.5430861723446885,-11.18230711839069,1.0 -1.5831663326653302,-9.988403746738872,1.0 -1.623246492985972,-9.6578873852644,1.0 -1.663326653306612,-10.22794039138045,1.0 -1.7034068136272538,-9.638856458741923,1.0 -1.7434869739478955,-12.011166398111623,1.0 -1.7835671342685373,-11.678210590872089,1.0 -1.8236472945891773,-10.464108194061305,1.0 -1.863727454909819,-9.190945104674297,1.0 -1.9038076152304608,-9.606092125922096,1.0 -1.9438877755511026,-8.023868501763754,1.0 -1.9839679358717426,-6.94808760143074,1.0 -2.0240480961923843,-7.788560449490305,1.0 -2.064128256513026,-5.832009343147087,1.0 -2.104208416833666,-5.894766305517156,1.0 -2.144288577154308,-5.94930734615683,1.0 -2.1843687374749496,-4.728494211770308,1.0 -2.2244488977955914,-5.714779052828128,1.0 -2.2645290581162314,-3.192506716178885,1.0 -2.304609218436873,-5.273613293374376,1.0 -2.344689378757515,-1.2899721318785917,1.0 -2.3847695390781567,-1.565042547179915,1.0 -2.4248496993987967,-1.7969465117344225,1.0 -2.4649298597194385,0.42255332940597645,1.0 -2.50501002004008,-1.1389635527583482,1.0 -2.54509018036072,-3.069846988505496,1.0 -2.585170340681362,-0.09442958871576002,1.0 -2.6252505010020037,-1.0219665180236608,1.0 -2.6653306613226455,-3.6649507337477267,1.0 -2.7054108216432855,-0.7555355195437825,1.0 -2.7454909819639273,-0.2932123030181,1.0 -2.785571142284569,-0.10054438440462032,1.0 -2.825651302605209,-2.0917085038418737,1.0 -2.865731462925851,-0.32505797160875227,1.0 -2.9058116232464926,-2.8630392441042303,1.0 -2.9458917835671343,-4.1311692024170075,1.0 -2.9859719438877743,-3.7951432253170365,1.0 -3.026052104208416,-3.9712845884091155,1.0 -3.066132264529058,-5.965559238139067,1.0 -3.1062124248496996,-4.386828271336863,1.0 -3.1462925851703396,-5.522411202335872,1.0 -3.1863727454909814,-4.784996397565062,1.0 -3.226452905811623,-6.146096563100851,1.0 -3.266533066132263,-5.93466078091802,1.0 -3.306613226452905,-4.3446910799961005,1.0 -3.3466933867735467,-5.837219479939273,1.0 -3.3867735470941884,-5.14427612989239,1.0 -3.4268537074148284,-3.290160292958376,1.0 -3.46693386773547,-4.869678156373776,1.0 -3.507014028056112,-3.224123017454569,1.0 -3.5470941883767537,-3.7738848555473172,1.0 -3.5871743486973937,-3.4630904496311894,1.0 -3.6272545090180355,-3.5681422879602023,1.0 -3.6673346693386772,-0.8145190485280192,1.0 -3.7074148296593172,-1.2415747594431301,1.0 -3.747494989979959,-0.7226987403670484,1.0 -3.7875751503006008,0.9959732431679117,1.0 -3.8276553106212425,0.8247961473893664,1.0 -3.8677354709418825,4.694576786940612,1.0 -3.9078156312625243,3.324325000231044,1.0 -3.947895791583166,5.593871929525455,1.0 -3.987975951903808,5.254279233547044,1.0 -4.028056112224448,7.4624696848745495,1.0 -4.06813627254509,8.338092008680137,1.0 -4.108216432865731,10.026265118223897,1.0 -4.148296593186371,9.481354841989786,1.0 -4.188376753507013,9.793494445254929,1.0 -4.228456913827655,11.451883333823277,1.0 -4.268537074148297,12.300097639156212,1.0 -4.308617234468937,13.09387375373446,1.0 -4.348697394789578,10.353094844871814,1.0 -4.38877755511022,14.306344162976709,1.0 -4.428857715430862,16.27064632054354,1.0 -4.468937875751502,12.585521865521812,1.0 -4.509018036072144,14.595795704386317,1.0 -4.5490981963927855,12.145133873530025,1.0 -4.589178356713425,12.318833540722924,1.0 -4.629258517034067,13.252319042840028,1.0 -4.669338677354709,12.887079523341143,1.0 -4.709418837675351,11.458624024286696,1.0 -4.749498997995991,11.117873158057156,1.0 -4.7895791583166325,13.219757560694589,1.0 -4.829659318637274,11.288250802941745,1.0 -4.869739478957916,11.055770446370255,1.0 -4.909819639278556,9.719100932656424,1.0 -4.949899799599198,8.184778170052379,1.0 -4.98997995991984,5.570310083737072,1.0 -5.0300601202404795,3.916932418095192,1.0 -5.070140280561121,3.1612136199424183,1.0 -5.110220440881763,2.696301660684228,1.0 -5.150300601202405,1.2815131297051812,1.0 -5.190380761523045,0.8365113621792702,1.0 -5.230460921843687,1.6650854231417733,1.0 -5.270541082164328,-0.6922371258814811,1.0 -5.31062124248497,-2.800613169709073,1.0 -5.35070140280561,-2.91795835022064,1.0 -5.390781563126252,-3.345986210178308,1.0 -5.430861723446894,-2.819433036410053,1.0 -5.470941883767534,-3.0830929929282096,1.0 -5.511022044088175,-5.6254346041570145,1.0 -5.551102204408817,-4.112729827940153,1.0 -5.591182364729459,-5.519859322638446,1.0 -5.631262525050099,-6.9126033014494634,1.0 -5.671342685370741,-4.010401467714592,1.0 -5.7114228456913825,-5.524271441579278,1.0 -5.751503006012024,-7.243627526686647,1.0 -5.791583166332664,-4.995827384275748,1.0 -5.831663326653306,-4.788201450335225,1.0 -5.871743486973948,-4.588529782484135,1.0 -5.911823647294588,-4.241303242537801,1.0 -5.9519038076152295,-3.57369168711058,1.0 -5.991983967935871,-3.575279909229851,1.0 -6.032064128256511,-3.3455729678424433,1.0 -6.072144288577153,-3.6722027092652607,1.0 -6.112224448897795,-4.025168127842969,1.0 -6.152304609218437,-3.256687862663131,1.0 -6.192384769539078,0.12579106546542373,1.0 -6.23246492985972,-2.779448268368732,1.0 -6.272545090180358,0.6914950983320387,1.0 -6.312625250501,-0.8132266910548271,1.0 -6.352705410821642,-2.0418867234241835,1.0 -6.392785571142284,-1.2479484935125789,1.0 -6.432865731462925,-1.4389167213829592,1.0 -6.472945891783567,-2.2814010691527193,1.0 -6.513026052104209,-2.185047177308038,1.0 -6.553106212424851,-2.3443094303204894,1.0 -6.593186372745489,-1.7529336231005237,1.0 -6.633266533066131,-3.4473606956244276,1.0 -6.673346693386772,-2.9923809889680153,1.0 -6.713426853707414,-4.246101593593135,1.0 -6.753507014028056,-5.130217300830152,1.0 -6.793587174348698,-4.337501473632818,1.0 -6.8336673346693395,-3.3087715876023034,1.0 -6.873747494989978,-6.0878276174409764,1.0 -6.9138276553106195,-5.8109003916467,1.0 -6.953907815631261,-8.204869851547087,1.0 -6.993987975951903,-9.24549296234525,1.0 -7.034068136272545,-10.078340482558673,1.0 -7.074148296593187,-9.208059725597117,1.0 -7.114228456913828,-8.341965790920709,1.0 -7.1543086172344665,-9.580147484625142,1.0 -7.194388777555108,-9.942165792707883,1.0 -7.23446893787575,-8.796175444719797,1.0 -7.274549098196392,-10.757990592321422,1.0 -7.314629258517034,-11.541921416415548,1.0 -7.354709418837675,-7.171781157443522,1.0 -7.394789579158317,-9.466581104781369,1.0 -7.434869739478959,-9.900193151245123,1.0 -7.474949899799597,-7.883515701002809,1.0 -7.515030060120239,-9.638871756164008,1.0 -7.555110220440881,-9.009504618715662,1.0 -7.595190380761522,-8.116777442103283,1.0 -7.635270541082164,-8.26687925171636,1.0 -7.675350701402806,-7.2650844008558035,1.0 -7.715430861723448,-6.800036439535858,1.0 -7.755511022044086,-6.144275272351635,1.0 -7.795591182364728,-4.54432317409064,1.0 -7.8356713426853695,-4.1272091210891,1.0 -7.875751503006011,-2.316778358363732,1.0 -7.915831663326653,-0.18237989155567813,1.0 -7.955911823647295,1.3857881776926044,1.0 -7.9959919839679365,0.40307996773499766,1.0 -8.036072144288575,0.45216648817576655,1.0 -8.076152304609217,5.432557392014297,1.0 -8.116232464929858,5.079311151164154,1.0 -8.1563126252505,4.008555191824007,1.0 -8.196392785571142,6.455819519918089,1.0 -8.236472945891784,8.555913343695197,1.0 -8.276553106212425,10.178588559816651,1.0 -8.316633266533067,8.55516163718229,1.0 -8.356713426853705,8.519535997457119,1.0 -8.396793587174347,10.713176799373043,1.0 -8.436873747494989,11.46509926599844,1.0 -8.47695390781563,12.216183701314772,1.0 -8.517034068136272,11.842979194102123,1.0 -8.557114228456914,11.580979302586018,1.0 -8.597194388777556,12.657439585428122,1.0 -8.637274549098194,12.023251266266833,1.0 -8.677354709418836,12.923061517867263,1.0 -8.717434869739478,13.097041031318847,1.0 -8.75751503006012,10.623052576402161,1.0 -8.797595190380761,9.748170593858141,1.0 -8.837675350701403,10.576638076157208,1.0 -8.877755511022045,10.544136645652053,1.0 -8.917835671342683,9.038812736140024,1.0 -8.957915831663325,9.16157316288103,1.0 -8.997995991983966,9.240190185179484,1.0 -9.038076152304608,8.021035315682388,1.0 -9.07815631262525,7.29097188037126,1.0 -9.118236472945892,5.067081740267046,1.0 -9.158316633266534,4.857896442283964,1.0 -9.198396793587175,3.622971631217414,1.0 -9.238476953907814,4.126010972188454,1.0 -9.278557114228455,3.6813652861281163,1.0 -9.318637274549097,2.3467866956488725,1.0 -9.358717434869739,0.3460164251619109,1.0 -9.39879759519038,0.6064448858735881,1.0 -9.438877755511022,0.2074343921303725,1.0 -9.478957915831664,0.9236411541640853,1.0 -9.519038076152302,2.10349826719505,1.0 -9.559118236472944,-0.08257756049648494,1.0 -9.599198396793586,-0.1472189606555847,1.0 -9.639278557114228,0.023041867095100588,1.0 -9.67935871743487,0.5509961349999192,1.0 -9.719438877755511,-0.4106205869462889,1.0 -9.759519038076153,-0.7855568992439452,1.0 -9.799599198396791,1.3416748689165017,1.0 -9.839679358717433,0.21270518576283762,1.0 -9.879759519038075,0.9148589565542363,1.0 -9.919839679358716,1.3272712931911994,1.0 -9.959919839679358,0.42758740347525087,1.0 -10.0,0.1247784691085646,1.0 diff --git a/Day_2/WorkProblems/sinxandcosx.csv b/Day_2/WorkProblems/sinxandcosx.csv deleted file mode 100644 index 23ad8ad..0000000 --- a/Day_2/WorkProblems/sinxandcosx.csv +++ /dev/null @@ -1,501 +0,0 @@ -x,y,errors --10.0,1.6857347086763765,1.0 --9.959919839679358,4.013613699427915,1.0 --9.919839679358718,2.5529792916297893,1.0 --9.879759519038076,3.507401717609016,1.0 --9.839679358717435,4.000886785605449,1.0 --9.799599198396793,4.815675406546204,1.0 --9.759519038076153,7.971104861052875,1.0 --9.719438877755511,8.564745431309857,1.0 --9.67935871743487,9.1931106883172,1.0 --9.639278557114228,9.52056278745853,1.0 --9.599198396793588,9.899564332686028,1.0 --9.559118236472946,11.100908269057072,1.0 --9.519038076152304,12.861553063573957,1.0 --9.478957915831664,11.848889309130266,1.0 --9.438877755511022,10.867069967075462,1.0 --9.39879759519038,12.200050910300593,1.0 --9.358717434869739,12.66445755672576,1.0 --9.318637274549099,11.15382096710966,1.0 --9.278557114228457,11.59173810333844,1.0 --9.238476953907815,12.824223782248755,1.0 --9.198396793587175,9.795142794348473,1.0 --9.158316633266534,10.875119435560851,1.0 --9.118236472945892,8.899921620838288,1.0 --9.07815631262525,8.26281063453338,1.0 --9.03807615230461,7.865953684158161,1.0 --8.997995991983968,7.577945076996276,1.0 --8.957915831663327,6.5072675691181185,1.0 --8.917835671342685,5.200836454890055,1.0 --8.877755511022045,5.114349387089195,1.0 --8.837675350701403,5.393563315812749,1.0 --8.797595190380761,4.859822192491134,1.0 --8.75751503006012,3.6545619885606393,1.0 --8.71743486973948,2.5291066075068835,1.0 --8.677354709418838,2.218984650452404,1.0 --8.637274549098196,1.7417794544289804,1.0 --8.597194388777556,0.41659394766596614,1.0 --8.557114228456914,2.5339065141669574,1.0 --8.517034068136272,0.5052720724283823,1.0 --8.47695390781563,2.118786132116184,1.0 --8.43687374749499,-0.394387064816502,1.0 --8.396793587174349,0.9170975946875346,1.0 --8.356713426853707,-0.9318316301888536,1.0 --8.316633266533067,1.0063137802768618,1.0 --8.276553106212425,-0.30521973713974265,1.0 --8.236472945891784,0.19975473317138565,1.0 --8.196392785571142,-0.19717584238530894,1.0 --8.156312625250502,2.671302010059077,1.0 --8.11623246492986,0.8936957286634039,1.0 --8.076152304609218,0.2410856389764494,1.0 --8.036072144288577,0.8910829924545329,1.0 --7.9959919839679365,4.110363717525511,1.0 --7.955911823647295,-0.7728186162402997,1.0 --7.915831663326653,2.1595887434666277,1.0 --7.875751503006012,1.1710687556938875,1.0 --7.835671342685371,2.3031178622086244,1.0 --7.7955911823647295,3.765580478410845,1.0 --7.755511022044088,1.2534975211847308,1.0 --7.715430861723447,5.2846406034039966,1.0 --7.675350701402806,2.9393734840014774,1.0 --7.635270541082164,2.4855551636355893,1.0 --7.595190380761523,2.741091947588973,1.0 --7.555110220440882,2.933647954608781,1.0 --7.515030060120241,2.8478892865082353,1.0 --7.474949899799599,0.2945009237915419,1.0 --7.434869739478958,1.4494711898013453,1.0 --7.394789579158317,-0.19491266058728118,1.0 --7.354709418837675,-0.020747330625312144,1.0 --7.314629258517034,-1.952241565760322,1.0 --7.274549098196393,-1.9652287055546935,1.0 --7.234468937875752,-1.823856252637708,1.0 --7.19438877755511,-5.049265871013052,1.0 --7.154308617234469,-1.8427178393660832,1.0 --7.114228456913828,-4.0385816285528815,1.0 --7.074148296593187,-6.822512309745342,1.0 --7.034068136272545,-6.1669503604756715,1.0 --6.993987975951904,-7.727144790971328,1.0 --6.953907815631263,-8.25610671412232,1.0 --6.913827655310621,-10.126306865506658,1.0 --6.8737474949899795,-9.628335410520098,1.0 --6.833667334669339,-12.801516424174583,1.0 --6.793587174348698,-11.741462518755599,1.0 --6.753507014028056,-12.939613355449248,1.0 --6.713426853707415,-13.053706740906227,1.0 --6.673346693386774,-14.27314891474202,1.0 --6.6332665330661325,-13.052509911340199,1.0 --6.593186372745491,-11.367124686467964,1.0 --6.55310621242485,-13.87207896536081,1.0 --6.513026052104209,-12.769776794578382,1.0 --6.472945891783567,-11.904725339174512,1.0 --6.432865731462925,-13.136764278827803,1.0 --6.3927855711422845,-11.236714049781042,1.0 --6.352705410821644,-10.558380524289438,1.0 --6.312625250501002,-10.937712052946543,1.0 --6.272545090180361,-9.75255943836789,1.0 --6.23246492985972,-10.448320306893784,1.0 --6.192384769539078,-7.825004480047912,1.0 --6.152304609218437,-7.9975416079807395,1.0 --6.112224448897796,-6.409425744777252,1.0 --6.072144288577155,-5.141545931747437,1.0 --6.032064128256513,-4.988940877777135,1.0 --5.991983967935872,-4.1749689698261925,1.0 --5.95190380761523,-1.6225045243178602,1.0 --5.9118236472945895,-0.23691518402088318,1.0 --5.871743486973948,1.4422500221992247,1.0 --5.831663326653307,0.5074746467968344,1.0 --5.791583166332665,2.418661676574002,1.0 --5.751503006012024,3.3484692660234914,1.0 --5.711422845691383,3.10631025893412,1.0 --5.671342685370742,3.2165614582893793,1.0 --5.631262525050101,4.723024072869765,1.0 --5.591182364729459,4.851933154675596,1.0 --5.551102204408818,5.855285763078102,1.0 --5.511022044088176,7.048261736739326,1.0 --5.470941883767535,7.2794894178458325,1.0 --5.430861723446894,8.303184626687415,1.0 --5.390781563126253,8.224431911389535,1.0 --5.350701402805611,7.3526051609461325,1.0 --5.31062124248497,8.35249757324526,1.0 --5.270541082164329,8.715043079768812,1.0 --5.2304609218436875,6.97499989646195,1.0 --5.190380761523047,8.764356011892582,1.0 --5.150300601202405,5.728787293915461,1.0 --5.110220440881764,8.265624290701139,1.0 --5.070140280561122,5.944821076110989,1.0 --5.030060120240481,6.686150042256749,1.0 --4.98997995991984,4.453732818256995,1.0 --4.949899799599199,6.766312035573879,1.0 --4.909819639278557,4.747467685242633,1.0 --4.869739478957916,3.6753734118460564,1.0 --4.829659318637275,5.18447852866729,1.0 --4.789579158316633,3.7943762622860953,1.0 --4.7494989979959925,2.5769074588137757,1.0 --4.709418837675351,4.116138408082359,1.0 --4.66933867735471,2.4007463385199013,1.0 --4.629258517034068,2.9543771756620543,1.0 --4.589178356713427,1.7129460344025258,1.0 --4.5490981963927855,2.7358713489949458,1.0 --4.509018036072145,1.9716231299228137,1.0 --4.468937875751503,2.0027202684391594,1.0 --4.428857715430862,1.3244383401959594,1.0 --4.388777555110221,3.628932880538506,1.0 --4.348697394789579,0.42520941529001766,1.0 --4.308617234468938,2.8031089079872014,1.0 --4.268537074148297,2.7364165354383108,1.0 --4.228456913827656,4.272708726222519,1.0 --4.188376753507014,5.616078170959657,1.0 --4.148296593186373,4.143508194473371,1.0 --4.108216432865731,5.051741411518203,1.0 --4.0681362725450905,5.4751484589382216,1.0 --4.028056112224449,4.510000948275952,1.0 --3.987975951903808,5.901251451326756,1.0 --3.947895791583167,6.400137438229996,1.0 --3.907815631262525,6.058986299456652,1.0 --3.8677354709418843,7.92902895200586,1.0 --3.8276553106212425,6.060553540262248,1.0 --3.7875751503006017,6.6405194682460635,1.0 --3.74749498997996,9.479992758094195,1.0 --3.707414829659319,8.388424736463694,1.0 --3.6673346693386772,8.112562975250103,1.0 --3.6272545090180364,8.900217090398332,1.0 --3.5871743486973955,8.400619279947868,1.0 --3.5470941883767537,6.531373447289622,1.0 --3.507014028056113,7.681514626971887,1.0 --3.466933867735471,7.123064886641511,1.0 --3.42685370741483,6.295367626345502,1.0 --3.3867735470941884,4.960507509576253,1.0 --3.3466933867735476,5.002422572234223,1.0 --3.306613226452906,4.487800477262426,1.0 --3.266533066132265,3.9601896540983135,1.0 --3.226452905811623,1.9152273193065956,1.0 --3.1863727454909823,0.5969568981734196,1.0 --3.1462925851703414,1.189613501075297,1.0 --3.1062124248496996,-0.1817650872355856,1.0 --3.0661322645290587,-1.283677965851594,1.0 --3.026052104208417,-3.2358224199597276,1.0 --2.985971943887776,-4.511976025076525,1.0 --2.9458917835671343,-5.366333181052176,1.0 --2.9058116232464934,-6.725933316560824,1.0 --2.8657314629258517,-8.083876339678605,1.0 --2.825651302605211,-9.855953003937294,1.0 --2.785571142284569,-8.20501163730394,1.0 --2.745490981963928,-10.403538208148294,1.0 --2.7054108216432873,-10.220820364217797,1.0 --2.6653306613226455,-11.278982059040912,1.0 --2.6252505010020046,-11.555785621400027,1.0 --2.585170340681363,-11.128709709451893,1.0 --2.545090180360722,-14.115780010442817,1.0 --2.50501002004008,-13.039503424638989,1.0 --2.4649298597194393,-13.115132656597018,1.0 --2.4248496993987976,-13.737005015466602,1.0 --2.3847695390781567,-13.706632220238381,1.0 --2.344689378757515,-11.607345257489563,1.0 --2.304609218436874,-13.389660930117918,1.0 --2.264529058116233,-12.750849441332138,1.0 --2.2244488977955914,-11.046837514327745,1.0 --2.1843687374749505,-11.481325459418937,1.0 --2.1442885771543088,-9.621878326364742,1.0 --2.104208416833668,-10.381371392480313,1.0 --2.064128256513026,-8.64754260733544,1.0 --2.0240480961923852,-9.703708962905646,1.0 --1.9839679358717444,-6.811462029836657,1.0 --1.9438877755511026,-4.589184816246273,1.0 --1.9038076152304608,-5.796648585782622,1.0 --1.8637274549098208,-1.9974076809799528,1.0 --1.823647294589179,-4.628315718396862,1.0 --1.7835671342685373,-3.2943684685744223,1.0 --1.7434869739478955,-2.22418954551942,1.0 --1.7034068136272555,-0.896661751074189,1.0 --1.6633266533066138,-0.9353308626425502,1.0 --1.623246492985972,0.6501514282413464,1.0 --1.5831663326653302,-0.8746244851892131,1.0 --1.5430861723446903,3.0805461175863718,1.0 --1.5030060120240485,1.2212451040359116,1.0 --1.4629258517034067,2.9065242808390117,1.0 --1.4228456913827667,2.672804825545158,1.0 --1.382765531062125,4.182998415822822,1.0 --1.3426853707414832,3.774759484418653,1.0 --1.3026052104208414,3.5120069045746662,1.0 --1.2625250501002014,2.0177000537609127,1.0 --1.2224448897795597,0.9941244517749186,1.0 --1.182364729458918,3.9956607735606244,1.0 --1.1422845691382761,1.7648822006662634,1.0 --1.1022044088176361,2.974341244730146,1.0 --1.0621242484969944,-0.23880038576491436,1.0 --1.0220440881763526,0.7128253004636743,1.0 --0.9819639278557126,0.8338080391049101,1.0 --0.9418837675350709,-0.6815624502177473,1.0 --0.9018036072144291,0.3112659093053486,1.0 --0.8617234468937873,-1.135059415264367,1.0 --0.8216432865731473,-0.09265892061799558,1.0 --0.7815631262525056,1.04167196027139,1.0 --0.7414829659318638,1.5048753708268636,1.0 --0.701402805611222,-0.7207488986566587,1.0 --0.661322645290582,-0.5923063867513836,1.0 --0.6212424849699403,0.2953616339437556,1.0 --0.5811623246492985,-1.2527176395486932,1.0 --0.5410821643286585,-0.8564924902260751,1.0 --0.5010020040080168,0.7882046156904949,1.0 --0.460921843687375,0.48174055367823887,1.0 --0.4208416833667332,1.2458009805690164,1.0 --0.38076152304609323,2.614160949884348,1.0 --0.34068136272545146,1.4724045340530028,1.0 --0.3006012024048097,2.77725101554365,1.0 --0.26052104208416793,2.319097271467372,1.0 --0.22044088176352794,5.053305469945468,1.0 --0.18036072144288617,4.942216907638576,1.0 --0.1402805611222444,3.5780486380712437,1.0 --0.10020040080160442,4.098668109532289,1.0 --0.06012024048096265,7.6966986277505995,1.0 --0.020040080160320883,8.44097777833922,1.0 -0.020040080160320883,9.362870136470423,1.0 -0.060120240480960874,7.526196708078163,1.0 -0.10020040080160264,8.270568214986595,1.0 -0.1402805611222444,9.75449185329195,1.0 -0.18036072144288617,10.910855648676323,1.0 -0.22044088176352616,11.896320902103959,1.0 -0.26052104208416793,11.390086282779711,1.0 -0.3006012024048097,11.319724716795626,1.0 -0.3406813627254497,12.473719891636614,1.0 -0.38076152304609145,12.648481680118234,1.0 -0.4208416833667332,13.552338754020399,1.0 -0.460921843687375,12.617490488422131,1.0 -0.501002004008015,11.896849298182632,1.0 -0.5410821643286567,11.57912263053422,1.0 -0.5811623246492985,10.749793491765324,1.0 -0.6212424849699403,9.374918552801706,1.0 -0.6613226452905803,9.178657957514059,1.0 -0.701402805611222,8.53391911763687,1.0 -0.7414829659318638,9.205030183702062,1.0 -0.7815631262525038,7.882179773137894,1.0 -0.8216432865731456,6.5227645279417885,1.0 -0.8617234468937873,5.524846515808931,1.0 -0.9018036072144291,5.520715737999964,1.0 -0.9418837675350691,3.869992663875465,1.0 -0.9819639278557108,0.8374413038287829,1.0 -1.0220440881763526,0.5407978816361908,1.0 -1.0621242484969944,-0.3029057519845745,1.0 -1.1022044088176344,-3.2322180340501445,1.0 -1.1422845691382761,-2.2783269369185133,1.0 -1.182364729458918,-5.697970010248994,1.0 -1.222444889779558,-6.769533250465215,1.0 -1.2625250501001997,-7.340684198340792,1.0 -1.3026052104208414,-7.558371885138308,1.0 -1.3426853707414832,-10.003513449058694,1.0 -1.3827655310621232,-8.155427783266731,1.0 -1.422845691382765,-8.69245681993435,1.0 -1.4629258517034067,-9.869943990628467,1.0 -1.5030060120240485,-10.378285553600966,1.0 -1.5430861723446885,-11.18230711839069,1.0 -1.5831663326653302,-9.988403746738872,1.0 -1.623246492985972,-9.6578873852644,1.0 -1.663326653306612,-10.22794039138045,1.0 -1.7034068136272538,-9.638856458741923,1.0 -1.7434869739478955,-12.011166398111623,1.0 -1.7835671342685373,-11.678210590872089,1.0 -1.8236472945891773,-10.464108194061305,1.0 -1.863727454909819,-9.190945104674297,1.0 -1.9038076152304608,-9.606092125922096,1.0 -1.9438877755511026,-8.023868501763754,1.0 -1.9839679358717426,-6.94808760143074,1.0 -2.0240480961923843,-7.788560449490305,1.0 -2.064128256513026,-5.832009343147087,1.0 -2.104208416833666,-5.894766305517156,1.0 -2.144288577154308,-5.94930734615683,1.0 -2.1843687374749496,-4.728494211770308,1.0 -2.2244488977955914,-5.714779052828128,1.0 -2.2645290581162314,-3.192506716178885,1.0 -2.304609218436873,-5.273613293374376,1.0 -2.344689378757515,-1.2899721318785917,1.0 -2.3847695390781567,-1.565042547179915,1.0 -2.4248496993987967,-1.7969465117344225,1.0 -2.4649298597194385,0.42255332940597645,1.0 -2.50501002004008,-1.1389635527583482,1.0 -2.54509018036072,-3.069846988505496,1.0 -2.585170340681362,-0.09442958871576002,1.0 -2.6252505010020037,-1.0219665180236608,1.0 -2.6653306613226455,-3.6649507337477267,1.0 -2.7054108216432855,-0.7555355195437825,1.0 -2.7454909819639273,-0.2932123030181,1.0 -2.785571142284569,-0.10054438440462032,1.0 -2.825651302605209,-2.0917085038418737,1.0 -2.865731462925851,-0.32505797160875227,1.0 -2.9058116232464926,-2.8630392441042303,1.0 -2.9458917835671343,-4.1311692024170075,1.0 -2.9859719438877743,-3.7951432253170365,1.0 -3.026052104208416,-3.9712845884091155,1.0 -3.066132264529058,-5.965559238139067,1.0 -3.1062124248496996,-4.386828271336863,1.0 -3.1462925851703396,-5.522411202335872,1.0 -3.1863727454909814,-4.784996397565062,1.0 -3.226452905811623,-6.146096563100851,1.0 -3.266533066132263,-5.93466078091802,1.0 -3.306613226452905,-4.3446910799961005,1.0 -3.3466933867735467,-5.837219479939273,1.0 -3.3867735470941884,-5.14427612989239,1.0 -3.4268537074148284,-3.290160292958376,1.0 -3.46693386773547,-4.869678156373776,1.0 -3.507014028056112,-3.224123017454569,1.0 -3.5470941883767537,-3.7738848555473172,1.0 -3.5871743486973937,-3.4630904496311894,1.0 -3.6272545090180355,-3.5681422879602023,1.0 -3.6673346693386772,-0.8145190485280192,1.0 -3.7074148296593172,-1.2415747594431301,1.0 -3.747494989979959,-0.7226987403670484,1.0 -3.7875751503006008,0.9959732431679117,1.0 -3.8276553106212425,0.8247961473893664,1.0 -3.8677354709418825,4.694576786940612,1.0 -3.9078156312625243,3.324325000231044,1.0 -3.947895791583166,5.593871929525455,1.0 -3.987975951903808,5.254279233547044,1.0 -4.028056112224448,7.4624696848745495,1.0 -4.06813627254509,8.338092008680137,1.0 -4.108216432865731,10.026265118223897,1.0 -4.148296593186371,9.481354841989786,1.0 -4.188376753507013,9.793494445254929,1.0 -4.228456913827655,11.451883333823277,1.0 -4.268537074148297,12.300097639156212,1.0 -4.308617234468937,13.09387375373446,1.0 -4.348697394789578,10.353094844871814,1.0 -4.38877755511022,14.306344162976709,1.0 -4.428857715430862,16.27064632054354,1.0 -4.468937875751502,12.585521865521812,1.0 -4.509018036072144,14.595795704386317,1.0 -4.5490981963927855,12.145133873530025,1.0 -4.589178356713425,12.318833540722924,1.0 -4.629258517034067,13.252319042840028,1.0 -4.669338677354709,12.887079523341143,1.0 -4.709418837675351,11.458624024286696,1.0 -4.749498997995991,11.117873158057156,1.0 -4.7895791583166325,13.219757560694589,1.0 -4.829659318637274,11.288250802941745,1.0 -4.869739478957916,11.055770446370255,1.0 -4.909819639278556,9.719100932656424,1.0 -4.949899799599198,8.184778170052379,1.0 -4.98997995991984,5.570310083737072,1.0 -5.0300601202404795,3.916932418095192,1.0 -5.070140280561121,3.1612136199424183,1.0 -5.110220440881763,2.696301660684228,1.0 -5.150300601202405,1.2815131297051812,1.0 -5.190380761523045,0.8365113621792702,1.0 -5.230460921843687,1.6650854231417733,1.0 -5.270541082164328,-0.6922371258814811,1.0 -5.31062124248497,-2.800613169709073,1.0 -5.35070140280561,-2.91795835022064,1.0 -5.390781563126252,-3.345986210178308,1.0 -5.430861723446894,-2.819433036410053,1.0 -5.470941883767534,-3.0830929929282096,1.0 -5.511022044088175,-5.6254346041570145,1.0 -5.551102204408817,-4.112729827940153,1.0 -5.591182364729459,-5.519859322638446,1.0 -5.631262525050099,-6.9126033014494634,1.0 -5.671342685370741,-4.010401467714592,1.0 -5.7114228456913825,-5.524271441579278,1.0 -5.751503006012024,-7.243627526686647,1.0 -5.791583166332664,-4.995827384275748,1.0 -5.831663326653306,-4.788201450335225,1.0 -5.871743486973948,-4.588529782484135,1.0 -5.911823647294588,-4.241303242537801,1.0 -5.9519038076152295,-3.57369168711058,1.0 -5.991983967935871,-3.575279909229851,1.0 -6.032064128256511,-3.3455729678424433,1.0 -6.072144288577153,-3.6722027092652607,1.0 -6.112224448897795,-4.025168127842969,1.0 -6.152304609218437,-3.256687862663131,1.0 -6.192384769539078,0.12579106546542373,1.0 -6.23246492985972,-2.779448268368732,1.0 -6.272545090180358,0.6914950983320387,1.0 -6.312625250501,-0.8132266910548271,1.0 -6.352705410821642,-2.0418867234241835,1.0 -6.392785571142284,-1.2479484935125789,1.0 -6.432865731462925,-1.4389167213829592,1.0 -6.472945891783567,-2.2814010691527193,1.0 -6.513026052104209,-2.185047177308038,1.0 -6.553106212424851,-2.3443094303204894,1.0 -6.593186372745489,-1.7529336231005237,1.0 -6.633266533066131,-3.4473606956244276,1.0 -6.673346693386772,-2.9923809889680153,1.0 -6.713426853707414,-4.246101593593135,1.0 -6.753507014028056,-5.130217300830152,1.0 -6.793587174348698,-4.337501473632818,1.0 -6.8336673346693395,-3.3087715876023034,1.0 -6.873747494989978,-6.0878276174409764,1.0 -6.9138276553106195,-5.8109003916467,1.0 -6.953907815631261,-8.204869851547087,1.0 -6.993987975951903,-9.24549296234525,1.0 -7.034068136272545,-10.078340482558673,1.0 -7.074148296593187,-9.208059725597117,1.0 -7.114228456913828,-8.341965790920709,1.0 -7.1543086172344665,-9.580147484625142,1.0 -7.194388777555108,-9.942165792707883,1.0 -7.23446893787575,-8.796175444719797,1.0 -7.274549098196392,-10.757990592321422,1.0 -7.314629258517034,-11.541921416415548,1.0 -7.354709418837675,-7.171781157443522,1.0 -7.394789579158317,-9.466581104781369,1.0 -7.434869739478959,-9.900193151245123,1.0 -7.474949899799597,-7.883515701002809,1.0 -7.515030060120239,-9.638871756164008,1.0 -7.555110220440881,-9.009504618715662,1.0 -7.595190380761522,-8.116777442103283,1.0 -7.635270541082164,-8.26687925171636,1.0 -7.675350701402806,-7.2650844008558035,1.0 -7.715430861723448,-6.800036439535858,1.0 -7.755511022044086,-6.144275272351635,1.0 -7.795591182364728,-4.54432317409064,1.0 -7.8356713426853695,-4.1272091210891,1.0 -7.875751503006011,-2.316778358363732,1.0 -7.915831663326653,-0.18237989155567813,1.0 -7.955911823647295,1.3857881776926044,1.0 -7.9959919839679365,0.40307996773499766,1.0 -8.036072144288575,0.45216648817576655,1.0 -8.076152304609217,5.432557392014297,1.0 -8.116232464929858,5.079311151164154,1.0 -8.1563126252505,4.008555191824007,1.0 -8.196392785571142,6.455819519918089,1.0 -8.236472945891784,8.555913343695197,1.0 -8.276553106212425,10.178588559816651,1.0 -8.316633266533067,8.55516163718229,1.0 -8.356713426853705,8.519535997457119,1.0 -8.396793587174347,10.713176799373043,1.0 -8.436873747494989,11.46509926599844,1.0 -8.47695390781563,12.216183701314772,1.0 -8.517034068136272,11.842979194102123,1.0 -8.557114228456914,11.580979302586018,1.0 -8.597194388777556,12.657439585428122,1.0 -8.637274549098194,12.023251266266833,1.0 -8.677354709418836,12.923061517867263,1.0 -8.717434869739478,13.097041031318847,1.0 -8.75751503006012,10.623052576402161,1.0 -8.797595190380761,9.748170593858141,1.0 -8.837675350701403,10.576638076157208,1.0 -8.877755511022045,10.544136645652053,1.0 -8.917835671342683,9.038812736140024,1.0 -8.957915831663325,9.16157316288103,1.0 -8.997995991983966,9.240190185179484,1.0 -9.038076152304608,8.021035315682388,1.0 -9.07815631262525,7.29097188037126,1.0 -9.118236472945892,5.067081740267046,1.0 -9.158316633266534,4.857896442283964,1.0 -9.198396793587175,3.622971631217414,1.0 -9.238476953907814,4.126010972188454,1.0 -9.278557114228455,3.6813652861281163,1.0 -9.318637274549097,2.3467866956488725,1.0 -9.358717434869739,0.3460164251619109,1.0 -9.39879759519038,0.6064448858735881,1.0 -9.438877755511022,0.2074343921303725,1.0 -9.478957915831664,0.9236411541640853,1.0 -9.519038076152302,2.10349826719505,1.0 -9.559118236472944,-0.08257756049648494,1.0 -9.599198396793586,-0.1472189606555847,1.0 -9.639278557114228,0.023041867095100588,1.0 -9.67935871743487,0.5509961349999192,1.0 -9.719438877755511,-0.4106205869462889,1.0 -9.759519038076153,-0.7855568992439452,1.0 -9.799599198396791,1.3416748689165017,1.0 -9.839679358717433,0.21270518576283762,1.0 -9.879759519038075,0.9148589565542363,1.0 -9.919839679358716,1.3272712931911994,1.0 -9.959919839679358,0.42758740347525087,1.0 -10.0,0.1247784691085646,1.0 diff --git a/Day_2/WorkProblems/sinxandcosx2.csv b/Day_2/WorkProblems/sinxandcosx2.csv deleted file mode 100644 index 32a4a29..0000000 --- a/Day_2/WorkProblems/sinxandcosx2.csv +++ /dev/null @@ -1,501 +0,0 @@ -x,y,errors --10.0,0.8322567484997467,1.0 --9.959919839679358,5.415948560233922,1.0 --9.919839679358718,10.493526969737017,1.0 --9.879759519038076,21.670399321997543,1.0 --9.839679358717435,30.908579011331025,1.0 --9.799599198396793,43.0635444387071,1.0 --9.759519038076153,57.20748971205301,1.0 --9.719438877755511,72.7478228530643,1.0 --9.67935871743487,86.59261067103066,1.0 --9.639278557114228,99.80336147909058,1.0 --9.599198396793588,113.97667784360698,1.0 --9.559118236472946,123.12390171030742,1.0 --9.519038076152304,133.08167998568055,1.0 --9.478957915831664,139.03711510063155,1.0 --9.438877755511022,144.65640028805115,1.0 --9.39879759519038,145.73231257753696,1.0 --9.358717434869739,147.16972385474597,1.0 --9.318637274549099,142.55245751275754,1.0 --9.278557114228457,138.78953394297034,1.0 --9.238476953907815,130.69179037536284,1.0 --9.198396793587175,121.0193377164591,1.0 --9.158316633266534,111.47765381319894,1.0 --9.118236472945892,98.97397482270449,1.0 --9.07815631262525,86.96590694814779,1.0 --9.03807615230461,77.48567411807231,1.0 --8.997995991983968,65.7208689970845,1.0 --8.957915831663327,54.087335500762094,1.0 --8.917835671342685,45.0794659567418,1.0 --8.877755511022045,35.68956309305719,1.0 --8.837675350701403,26.628837398704768,1.0 --8.797595190380761,18.726063589991384,1.0 --8.75751503006012,15.12901034567748,1.0 --8.71743486973948,11.016265114885025,1.0 --8.677354709418838,6.308656674379161,1.0 --8.637274549098196,4.555124666806071,1.0 --8.597194388777556,0.683618721481861,1.0 --8.557114228456914,0.6770177058669566,1.0 --8.517034068136272,1.1770766705333835,1.0 --8.47695390781563,-0.9107462853223613,1.0 --8.43687374749499,-1.3972785592304044,1.0 --8.396793587174349,-0.5735654426148162,1.0 --8.356713426853707,1.5419513386437922,1.0 --8.316633266533067,1.2510495527515348,1.0 --8.276553106212425,0.8620118314046415,1.0 --8.236472945891784,0.022132932329893967,1.0 --8.196392785571142,-1.0495308921019788,1.0 --8.156312625250502,0.4250490829324593,1.0 --8.11623246492986,1.528411103456102,1.0 --8.076152304609218,1.1957822085126,1.0 --8.036072144288577,2.0920024254295684,1.0 --7.9959919839679365,3.3379048784641503,1.0 --7.955911823647295,5.911049384461387,1.0 --7.915831663326653,3.846290976635126,1.0 --7.875751503006012,6.325154806728509,1.0 --7.835671342685371,6.669921181954081,1.0 --7.7955911823647295,7.322654174963052,1.0 --7.755511022044088,9.158761293669144,1.0 --7.715430861723447,7.694080677254936,1.0 --7.675350701402806,9.079849986237004,1.0 --7.635270541082164,7.942118555291353,1.0 --7.595190380761523,6.611017429744896,1.0 --7.555110220440882,2.4744583428481857,1.0 --7.515030060120241,3.625629013442824,1.0 --7.474949899799599,3.2745952187114593,1.0 --7.434869739478958,1.1022286166326887,1.0 --7.394789579158317,-1.083125293909735,1.0 --7.354709418837675,2.3155772810028536,1.0 --7.314629258517034,-1.0700943833695855,1.0 --7.274549098196393,0.5865063258784795,1.0 --7.234468937875752,4.290635140462264,1.0 --7.19438877755511,10.735533895379902,1.0 --7.154308617234469,15.725690331637587,1.0 --7.114228456913828,25.9993607031397,1.0 --7.074148296593187,33.912284117648724,1.0 --7.034068136272545,46.75260213963634,1.0 --6.993987975951904,57.990028460157575,1.0 --6.953907815631263,72.47633783480305,1.0 --6.913827655310621,87.475184885125,1.0 --6.8737474949899795,105.28903257916787,1.0 --6.833667334669339,117.89798406654053,1.0 --6.793587174348698,133.26627111866293,1.0 --6.753507014028056,147.22421448407695,1.0 --6.713426853707415,155.5940923063333,1.0 --6.673346693386774,167.729877962906,1.0 --6.6332665330661325,173.20720980768806,1.0 --6.593186372745491,177.54544030979008,1.0 --6.55310621242485,181.54144319609037,1.0 --6.513026052104209,180.01885843993284,1.0 --6.472945891783567,174.56589704764158,1.0 --6.432865731462925,171.3729650514662,1.0 --6.3927855711422845,159.72324058490273,1.0 --6.352705410821644,145.5440249018155,1.0 --6.312625250501002,132.36310647062928,1.0 --6.272545090180361,115.39781073279396,1.0 --6.23246492985972,99.98796959869075,1.0 --6.192384769539078,83.76933690591319,1.0 --6.152304609218437,68.27919477658455,1.0 --6.112224448897796,52.58162959465498,1.0 --6.072144288577155,38.36259247131271,1.0 --6.032064128256513,24.513120663167868,1.0 --5.991983967935872,15.61220316921596,1.0 --5.95190380761523,9.053678456160215,1.0 --5.9118236472945895,3.057476446822294,1.0 --5.871743486973948,-0.16540892647740624,1.0 --5.831663326653307,-0.5716941643866238,1.0 --5.791583166332665,2.6640157946200436,1.0 --5.751503006012024,7.14698869328021,1.0 --5.711422845691383,10.24273933101893,1.0 --5.671342685370742,16.52573593364177,1.0 --5.631262525050101,25.47512126251228,1.0 --5.591182364729459,32.42119500082487,1.0 --5.551102204408818,39.55906305460302,1.0 --5.511022044088176,46.98323382664419,1.0 --5.470941883767535,53.359456788763836,1.0 --5.430861723446894,56.726714857018536,1.0 --5.390781563126253,59.420495726583425,1.0 --5.350701402805611,61.28754696859096,1.0 --5.31062124248497,64.40133799167135,1.0 --5.270541082164329,63.49947908964552,1.0 --5.2304609218436875,62.53348111718554,1.0 --5.190380761523047,59.35958714538531,1.0 --5.150300601202405,56.14499652747622,1.0 --5.110220440881764,51.14408155025642,1.0 --5.070140280561122,45.444541657646994,1.0 --5.030060120240481,41.47848980107007,1.0 --4.98997995991984,35.103845944830134,1.0 --4.949899799599199,31.972682842750974,1.0 --4.909819639278557,25.399351621691892,1.0 --4.869739478957916,22.05628651463801,1.0 --4.829659318637275,18.15971964193839,1.0 --4.789579158316633,16.00094920819005,1.0 --4.7494989979959925,12.127600176543549,1.0 --4.709418837675351,10.284357149356735,1.0 --4.66933867735471,7.9951194141128505,1.0 --4.629258517034068,7.1406608091888115,1.0 --4.589178356713427,5.009026386808308,1.0 --4.5490981963927855,4.614365846465789,1.0 --4.509018036072145,4.29353576535943,1.0 --4.468937875751503,5.3257263184989725,1.0 --4.428857715430862,6.638576842426161,1.0 --4.388777555110221,6.872913778820205,1.0 --4.348697394789579,8.485953599211582,1.0 --4.308617234468938,9.946646336097514,1.0 --4.268537074148297,11.822394990405432,1.0 --4.228456913827656,14.299206018720904,1.0 --4.188376753507014,18.938741191724397,1.0 --4.148296593186373,21.154445140525976,1.0 --4.108216432865731,24.98735845303841,1.0 --4.0681362725450905,31.38052712658086,1.0 --4.028056112224449,34.43117517013294,1.0 --3.987975951903808,39.99837028056889,1.0 --3.947895791583167,48.13130296674366,1.0 --3.907815631262525,51.941410033512476,1.0 --3.8677354709418843,56.2244599426186,1.0 --3.8276553106212425,60.0086983065495,1.0 --3.7875751503006017,65.01582870131178,1.0 --3.74749498997996,65.75672585891319,1.0 --3.707414829659319,68.75541491358008,1.0 --3.6673346693386772,67.9150070364172,1.0 --3.6272545090180364,65.81036399453792,1.0 --3.5871743486973955,63.97853975044776,1.0 --3.5470941883767537,60.42858051150465,1.0 --3.507014028056113,55.40402044350938,1.0 --3.466933867735471,44.217468611066785,1.0 --3.42685370741483,36.72336615446888,1.0 --3.3867735470941884,30.628100927701553,1.0 --3.3466933867735476,24.716883561265277,1.0 --3.306613226452906,17.088410705186828,1.0 --3.266533066132265,8.327913312549772,1.0 --3.226452905811623,4.358101532019336,1.0 --3.1863727454909823,2.6672526141028707,1.0 --3.1462925851703414,-1.6912649056104325,1.0 --3.1062124248496996,1.3909006052817872,1.0 --3.0661322645290587,2.9181215709283723,1.0 --3.026052104208417,10.642123250484127,1.0 --2.985971943887776,15.715913738258113,1.0 --2.9458917835671343,29.760115778152404,1.0 --2.9058116232464934,39.513242757151936,1.0 --2.8657314629258517,56.494636292509924,1.0 --2.825651302605211,72.45646380962387,1.0 --2.785571142284569,90.3114420647532,1.0 --2.745490981963928,104.77555336448,1.0 --2.7054108216432873,122.33514264442104,1.0 --2.6653306613226455,135.5115615406085,1.0 --2.6252505010020046,150.34920612394757,1.0 --2.585170340681363,158.7597432925803,1.0 --2.545090180360722,171.4244191127748,1.0 --2.50501002004008,176.28552550162584,1.0 --2.4649298597194393,178.97037983521827,1.0 --2.4248496993987976,180.4935793802711,1.0 --2.3847695390781567,174.98958881311074,1.0 --2.344689378757515,172.08569771432025,1.0 --2.304609218436874,162.99170021378293,1.0 --2.264529058116233,151.62885147154853,1.0 --2.2244488977955914,137.28851981399578,1.0 --2.1843687374749505,124.04099566673587,1.0 --2.1442885771543088,110.51164253396078,1.0 --2.104208416833668,94.08237241386914,1.0 --2.064128256513026,79.68449800687124,1.0 --2.0240480961923852,65.37082471680752,1.0 --1.9839679358717444,53.61578984492961,1.0 --1.9438877755511026,41.58346774876635,1.0 --1.9038076152304608,32.09155297576791,1.0 --1.8637274549098208,21.532500569716742,1.0 --1.823647294589179,14.26665805245818,1.0 --1.7835671342685373,9.1166734083496,1.0 --1.7434869739478955,3.625703043526896,1.0 --1.7034068136272555,4.324810895610808,1.0 --1.6633266533066138,0.9246101294742939,1.0 --1.623246492985972,-0.07852803961580891,1.0 --1.5831663326653302,0.7564282831779188,1.0 --1.5430861723446903,2.2695769143050213,1.0 --1.5030060120240485,1.293151790184861,1.0 --1.4629258517034067,3.9003342386546356,1.0 --1.4228456913827667,5.770563283991999,1.0 --1.382765531062125,6.525207978488934,1.0 --1.3426853707414832,7.6565208119261445,1.0 --1.3026052104208414,5.593163961589303,1.0 --1.2625250501002014,6.035434299241689,1.0 --1.2224448897795597,7.0107112094099175,1.0 --1.182364729458918,4.079285907049973,1.0 --1.1422845691382761,5.929493571373483,1.0 --1.1022044088176361,3.787840825057581,1.0 --1.0621242484969944,2.022527341567498,1.0 --1.0220440881763526,4.178441116815211,1.0 --0.9819639278557126,-0.7305293782680276,1.0 --0.9418837675350709,0.74237458929938,1.0 --0.9018036072144291,1.056896374574265,1.0 --0.8617234468937873,1.55637080832563,1.0 --0.8216432865731473,1.291694848854189,1.0 --0.7815631262525056,0.3985347295974574,1.0 --0.7414829659318638,-0.256373299332995,1.0 --0.701402805611222,0.20671254068852385,1.0 --0.661322645290582,-0.15095403242929772,1.0 --0.6212424849699403,-0.426189664216447,1.0 --0.5811623246492985,1.2275441384702699,1.0 --0.5410821643286585,-0.8353384127802848,1.0 --0.5010020040080168,0.011514648734569227,1.0 --0.460921843687375,1.923991459966882,1.0 --0.4208416833667332,1.7429612882968284,1.0 --0.38076152304609323,4.795723171866381,1.0 --0.34068136272545146,4.54636629856619,1.0 --0.3006012024048097,6.71347057520654,1.0 --0.26052104208416793,10.207380710145657,1.0 --0.22044088176352794,17.05379754332438,1.0 --0.18036072144288617,24.03260721040171,1.0 --0.1402805611222444,30.039954308515547,1.0 --0.10020040080160442,39.65509966135262,1.0 --0.06012024048096265,50.839933964803635,1.0 --0.020040080160320883,59.83356703244535,1.0 -0.020040080160320883,70.77174927475558,1.0 -0.060120240480960874,82.57413247491053,1.0 -0.10020040080160264,94.26731292351295,1.0 -0.1402805611222444,108.7842253668438,1.0 -0.18036072144288617,118.12197503547377,1.0 -0.22044088176352616,128.10754194497878,1.0 -0.26052104208416793,137.83987065241843,1.0 -0.3006012024048097,144.31612130037536,1.0 -0.3406813627254497,149.86355736585713,1.0 -0.38076152304609145,151.67894687994587,1.0 -0.4208416833667332,151.53893510483744,1.0 -0.460921843687375,146.1782477131823,1.0 -0.501002004008015,140.40749059835153,1.0 -0.5410821643286567,134.36534013920146,1.0 -0.5811623246492985,122.86595967078476,1.0 -0.6212424849699403,109.25166123969139,1.0 -0.6613226452905803,95.7871959761425,1.0 -0.701402805611222,81.07845458117394,1.0 -0.7414829659318638,69.20772650990328,1.0 -0.7815631262525038,53.46892018267595,1.0 -0.8216432865731456,40.56062492141462,1.0 -0.8617234468937873,28.57726961959028,1.0 -0.9018036072144291,17.119197677208046,1.0 -0.9418837675350691,8.236255902410566,1.0 -0.9819639278557108,2.1264190967028833,1.0 -1.0220440881763526,1.2337136191665872,1.0 -1.0621242484969944,0.7844324228645757,1.0 -1.1022044088176344,2.8777291819133586,1.0 -1.1422845691382761,7.801303068177803,1.0 -1.182364729458918,14.157189888282193,1.0 -1.222444889779558,23.117420230303594,1.0 -1.2625250501001997,32.803018416777306,1.0 -1.3026052104208414,43.70737133243476,1.0 -1.3426853707414832,55.41358824409428,1.0 -1.3827655310621232,68.70843787249721,1.0 -1.422845691382765,78.06362100440364,1.0 -1.4629258517034067,87.92247280013196,1.0 -1.5030060120240485,96.82221093236596,1.0 -1.5430861723446885,102.50316863279448,1.0 -1.5831663326653302,109.05762241572432,1.0 -1.623246492985972,109.53265198996787,1.0 -1.663326653306612,111.85413080959992,1.0 -1.7034068136272538,110.32786131702922,1.0 -1.7434869739478955,105.61622789205666,1.0 -1.7835671342685373,101.08570383248193,1.0 -1.8236472945891773,95.16283939921662,1.0 -1.863727454909819,87.27683688782662,1.0 -1.9038076152304608,79.87879926613012,1.0 -1.9438877755511026,70.57236933173711,1.0 -1.9839679358717426,62.980553963177215,1.0 -2.0240480961923843,52.91347316691488,1.0 -2.064128256513026,44.27618052912894,1.0 -2.104208416833666,35.18680486772627,1.0 -2.144288577154308,29.54671068027582,1.0 -2.1843687374749496,24.034703689295295,1.0 -2.2244488977955914,19.279105805281407,1.0 -2.2645290581162314,14.195949459649647,1.0 -2.304609218436873,10.033895353910482,1.0 -2.344689378757515,9.417550307544138,1.0 -2.3847695390781567,7.755863673328481,1.0 -2.4248496993987967,4.213866516251714,1.0 -2.4649298597194385,4.08746753464951,1.0 -2.50501002004008,1.1077347863350333,1.0 -2.54509018036072,3.0112938358791324,1.0 -2.585170340681362,2.8744977978628214,1.0 -2.6252505010020037,2.8386226763325055,1.0 -2.6653306613226455,3.984992154024825,1.0 -2.7054108216432855,5.243167899290383,1.0 -2.7454909819639273,5.577536624852963,1.0 -2.785571142284569,5.613324508875632,1.0 -2.825651302605209,5.57495893519239,1.0 -2.865731462925851,8.247319530535911,1.0 -2.9058116232464926,9.969354616831561,1.0 -2.9458917835671343,13.457383523988083,1.0 -2.9859719438877743,15.190491057516896,1.0 -3.026052104208416,16.38368164839577,1.0 -3.066132264529058,19.783530636582498,1.0 -3.1062124248496996,24.46955048006015,1.0 -3.1462925851703396,27.911736482855666,1.0 -3.1863727454909814,25.511917610836743,1.0 -3.226452905811623,27.824304945582647,1.0 -3.266533066132263,29.89380715042387,1.0 -3.306613226452905,26.85484588229658,1.0 -3.3466933867735467,27.60748508589788,1.0 -3.3867735470941884,24.947267411550964,1.0 -3.4268537074148284,23.612912101354485,1.0 -3.46693386773547,20.93903071718411,1.0 -3.507014028056112,16.318423794873134,1.0 -3.5470941883767537,13.141864621950369,1.0 -3.5871743486973937,8.25971927895697,1.0 -3.6272545090180355,7.389318197796944,1.0 -3.6673346693386772,1.0241526263959717,1.0 -3.7074148296593172,-0.35788598946606864,1.0 -3.747494989979959,1.2480050157904934,1.0 -3.7875751503006008,2.360013679909491,1.0 -3.8276553106212425,3.0310411355356672,1.0 -3.8677354709418825,9.485249356932131,1.0 -3.9078156312625243,15.423701616796325,1.0 -3.947895791583166,24.955425481324507,1.0 -3.987975951903808,35.93915927829782,1.0 -4.028056112224448,48.085347926090485,1.0 -4.06813627254509,64.39881362486945,1.0 -4.108216432865731,78.3251483363015,1.0 -4.148296593186371,95.1244224689619,1.0 -4.188376753507013,111.96880040860428,1.0 -4.228456913827655,128.01660735769502,1.0 -4.268537074148297,142.95853961649584,1.0 -4.308617234468937,157.6755971189719,1.0 -4.348697394789578,169.47498097381157,1.0 -4.38877755511022,179.32283781419795,1.0 -4.428857715430862,184.46823422685543,1.0 -4.468937875751502,188.046501014692,1.0 -4.509018036072144,189.8909849494831,1.0 -4.5490981963927855,186.18889567527017,1.0 -4.589178356713425,181.1612629301342,1.0 -4.629258517034067,172.46454186394638,1.0 -4.669338677354709,162.34823082312244,1.0 -4.709418837675351,147.99862064366977,1.0 -4.749498997995991,134.6816529945482,1.0 -4.7895791583166325,118.18837467407036,1.0 -4.829659318637274,103.31597568902603,1.0 -4.869739478957916,83.88787880352622,1.0 -4.909819639278556,68.6648193740868,1.0 -4.949899799599198,55.38163904182336,1.0 -4.98997995991984,40.23732150916877,1.0 -5.0300601202404795,29.060834475139266,1.0 -5.070140280561121,19.903063731216193,1.0 -5.110220440881763,11.70829510254807,1.0 -5.150300601202405,2.8281202830084373,1.0 -5.190380761523045,1.3104548090788992,1.0 -5.230460921843687,-0.6968501214873499,1.0 -5.270541082164328,1.0556589237578704,1.0 -5.31062124248497,1.7560579246387558,1.0 -5.35070140280561,4.319949975308015,1.0 -5.390781563126252,8.52851821398886,1.0 -5.430861723446894,12.273603109897127,1.0 -5.470941883767534,16.899957480452198,1.0 -5.511022044088175,21.897709762524574,1.0 -5.551102204408817,22.96005454880068,1.0 -5.591182364729459,28.153162231539806,1.0 -5.631262525050099,29.23848695261577,1.0 -5.671342685370741,30.451875731780447,1.0 -5.7114228456913825,30.970915399979244,1.0 -5.751503006012024,31.793330237289723,1.0 -5.791583166332664,31.728267717898017,1.0 -5.831663326653306,28.58970477840782,1.0 -5.871743486973948,26.42132952744845,1.0 -5.911823647294588,23.008159650422698,1.0 -5.9519038076152295,21.06591730946243,1.0 -5.991983967935871,18.473186644105603,1.0 -6.032064128256511,14.858307010239562,1.0 -6.072144288577153,14.29952667207823,1.0 -6.112224448897795,10.767584345650821,1.0 -6.152304609218437,7.466390415725671,1.0 -6.192384769539078,6.935486760132253,1.0 -6.23246492985972,5.091419508714355,1.0 -6.272545090180358,3.4738853603328437,1.0 -6.312625250501,4.932009697769395,1.0 -6.352705410821642,3.8398060511189467,1.0 -6.392785571142284,4.364273543777017,1.0 -6.432865731462925,2.903711962778978,1.0 -6.472945891783567,2.966990877279057,1.0 -6.513026052104209,3.1782631012929476,1.0 -6.553106212424851,5.321098556291553,1.0 -6.593186372745489,6.8413073830508715,1.0 -6.633266533066131,8.8688558610967,1.0 -6.673346693386772,9.807385394194743,1.0 -6.713426853707414,10.76678566354017,1.0 -6.753507014028056,18.21510317926816,1.0 -6.793587174348698,22.561325359431965,1.0 -6.8336673346693395,26.82399108865742,1.0 -6.873747494989978,32.930982717746105,1.0 -6.9138276553106195,38.443549595792945,1.0 -6.953907815631261,48.37335608976655,1.0 -6.993987975951903,55.27900922413656,1.0 -7.034068136272545,63.54662051301557,1.0 -7.074148296593187,72.0978341534596,1.0 -7.114228456913828,81.52224016449735,1.0 -7.1543086172344665,88.36806574719823,1.0 -7.194388777555108,96.70588694726365,1.0 -7.23446893787575,99.01297107970831,1.0 -7.274549098196392,105.25260358468473,1.0 -7.314629258517034,105.62678781252097,1.0 -7.354709418837675,105.85423428572197,1.0 -7.394789579158317,105.16745237358982,1.0 -7.434869739478959,103.9372418961632,1.0 -7.474949899799597,95.51075731776592,1.0 -7.515030060120239,86.90218985827241,1.0 -7.555110220440881,78.69464538739662,1.0 -7.595190380761522,69.08960844010696,1.0 -7.635270541082164,58.12102425617019,1.0 -7.675350701402806,47.10437167296776,1.0 -7.715430861723448,34.901032093135626,1.0 -7.755511022044086,25.628051760302803,1.0 -7.795591182364728,16.69312770637949,1.0 -7.8356713426853695,7.766526670971839,1.0 -7.875751503006011,3.983499230404609,1.0 -7.915831663326653,1.6066176147643287,1.0 -7.955911823647295,0.984155941403315,1.0 -7.9959919839679365,2.8539315146344117,1.0 -8.036072144288575,5.917675971274947,1.0 -8.076152304609217,14.231820574106237,1.0 -8.116232464929858,24.09225567344629,1.0 -8.1563126252505,36.488021387943874,1.0 -8.196392785571142,50.40920795215806,1.0 -8.236472945891784,64.27874086062143,1.0 -8.276553106212425,77.50400609399296,1.0 -8.316633266533067,93.48050863476942,1.0 -8.356713426853705,107.53806586707617,1.0 -8.396793587174347,118.22102751310595,1.0 -8.436873747494989,132.3451769355936,1.0 -8.47695390781563,140.35108023320802,1.0 -8.517034068136272,147.94079144612925,1.0 -8.557114228456914,151.46388376879958,1.0 -8.597194388777556,152.39922233504166,1.0 -8.637274549098194,151.93494017059467,1.0 -8.677354709418836,150.94154961311946,1.0 -8.717434869739478,143.97419087499952,1.0 -8.75751503006012,135.26640539921576,1.0 -8.797595190380761,126.92160436831354,1.0 -8.837675350701403,114.6731029128085,1.0 -8.877755511022045,105.14629769442129,1.0 -8.917835671342683,90.68768318338127,1.0 -8.957915831663325,78.95491903455626,1.0 -8.997995991983966,65.5119169710209,1.0 -9.038076152304608,54.45206910647002,1.0 -9.07815631262525,44.82176703834076,1.0 -9.118236472945892,34.8334843456946,1.0 -9.158316633266534,25.769467495885266,1.0 -9.198396793587175,19.248612617369517,1.0 -9.238476953907814,13.552433451994453,1.0 -9.278557114228455,9.619911237066848,1.0 -9.318637274549097,5.8314441278350975,1.0 -9.358717434869739,2.984905849341323,1.0 -9.39879759519038,0.49754965645274574,1.0 -9.438877755511022,-0.43076111357778535,1.0 -9.478957915831664,2.5251562005744113,1.0 -9.519038076152302,-2.037557903437491,1.0 -9.559118236472944,-0.24612576509622608,1.0 -9.599198396793586,-1.9224115381198879,1.0 -9.639278557114228,0.25798535891349494,1.0 -9.67935871743487,-0.13238110640285516,1.0 -9.719438877755511,0.8460598422109116,1.0 -9.759519038076153,-1.0167616135095912,1.0 -9.799599198396791,0.31563882004382454,1.0 -9.839679358717433,1.058044637147122,1.0 -9.879759519038075,1.579418947393283,1.0 -9.919839679358716,1.6784762439614123,1.0 -9.959919839679358,-0.1854111285445491,1.0 -10.0,1.907687127792666,1.0 diff --git a/Day_2/WorkProblems/sinxandcosx3.csv b/Day_2/WorkProblems/sinxandcosx3.csv deleted file mode 100644 index 301536b..0000000 --- a/Day_2/WorkProblems/sinxandcosx3.csv +++ /dev/null @@ -1,501 +0,0 @@ -x,y,errors --10.0,33.06303792806055,1.0 --9.959919839679358,-45.130013207682076,1.0 --9.919839679358718,-41.71446479995397,1.0 --9.879759519038076,176.15075916654132,1.0 --9.839679358717435,81.7509310685625,1.0 --9.799599198396793,217.83673931000823,1.0 --9.759519038076153,510.78840603841553,1.0 --9.719438877755511,566.5746891651339,1.0 --9.67935871743487,840.2579422261795,1.0 --9.639278557114228,808.9758424955211,1.0 --9.599198396793588,1199.7321701540716,1.0 --9.559118236472946,1243.9675231863557,1.0 --9.519038076152304,1496.8821815697183,1.0 --9.478957915831664,1741.1706931408971,1.0 --9.438877755511022,1816.1232611462965,1.0 --9.39879759519038,1853.7008511004265,1.0 --9.358717434869739,1554.8250053695754,1.0 --9.318637274549099,1829.1207093381138,1.0 --9.278557114228457,1595.0119479701825,1.0 --9.238476953907815,1390.7329966857787,1.0 --9.198396793587175,1488.4262943593358,1.0 --9.158316633266534,1157.9922954459553,1.0 --9.118236472945892,1148.7697938211684,1.0 --9.07815631262525,760.9881537696433,1.0 --9.03807615230461,713.5572522229323,1.0 --8.997995991983968,719.6324381336611,1.0 --8.957915831663327,379.4705082998447,1.0 --8.917835671342685,381.88451770308245,1.0 --8.877755511022045,218.00240475602592,1.0 --8.837675350701403,55.24496895357497,1.0 --8.797595190380761,114.62459119243962,1.0 --8.75751503006012,35.60444414153892,1.0 --8.71743486973948,71.97191760701178,1.0 --8.677354709418838,-121.4227440374353,1.0 --8.637274549098196,-66.28189007291355,1.0 --8.597194388777556,-125.32055539738859,1.0 --8.557114228456914,16.279386095567897,1.0 --8.517034068136272,-20.134467839157296,1.0 --8.47695390781563,-98.77131170562987,1.0 --8.43687374749499,-72.53404556193075,1.0 --8.396793587174349,130.43838052894236,1.0 --8.356713426853707,-182.3856542519321,1.0 --8.316633266533067,-51.54336373436576,1.0 --8.276553106212425,105.28182715541578,1.0 --8.236472945891784,54.728842424159815,1.0 --8.196392785571142,-147.83512317870276,1.0 --8.156312625250502,-4.472470563993677,1.0 --8.11623246492986,67.40624884636927,1.0 --8.076152304609218,-12.448836655368083,1.0 --8.036072144288577,-13.28451928730198,1.0 --7.9959919839679365,-3.6511138649466446,1.0 --7.955911823647295,-66.55370654576814,1.0 --7.915831663326653,-27.496026215615522,1.0 --7.875751503006012,39.31043375696379,1.0 --7.835671342685371,107.10052844596318,1.0 --7.7955911823647295,74.54177835426763,1.0 --7.755511022044088,-36.60757994434249,1.0 --7.715430861723447,-22.60566377919224,1.0 --7.675350701402806,40.436794879444754,1.0 --7.635270541082164,-91.21569020220323,1.0 --7.595190380761523,41.39208331203117,1.0 --7.555110220440882,20.67256041634628,1.0 --7.515030060120241,173.10743499822294,1.0 --7.474949899799599,91.36513487629318,1.0 --7.434869739478958,-155.62530260586698,1.0 --7.394789579158317,16.698880822025284,1.0 --7.354709418837675,119.79941549502712,1.0 --7.314629258517034,-13.103241682071404,1.0 --7.274549098196393,-95.24516230725737,1.0 --7.234468937875752,-209.01167212161664,1.0 --7.19438877755511,-149.425991105442,1.0 --7.154308617234469,15.738935355191984,1.0 --7.114228456913828,-69.53845105392568,1.0 --7.074148296593187,-288.97136959598936,1.0 --7.034068136272545,-316.79608146785523,1.0 --6.993987975951904,-584.8023846184167,1.0 --6.953907815631263,-697.4838572144479,1.0 --6.913827655310621,-951.0420027625962,1.0 --6.8737474949899795,-984.0141710896174,1.0 --6.833667334669339,-1393.8309804593598,1.0 --6.793587174348698,-1398.7460042933253,1.0 --6.753507014028056,-1887.4547425377173,1.0 --6.713426853707415,-1938.170369545512,1.0 --6.673346693386774,-1983.1207923790175,1.0 --6.6332665330661325,-2401.260678005646,1.0 --6.593186372745491,-2275.781735542437,1.0 --6.55310621242485,-2389.0336534123053,1.0 --6.513026052104209,-2395.837857951786,1.0 --6.472945891783567,-2312.544229909174,1.0 --6.432865731462925,-2054.775793070969,1.0 --6.3927855711422845,-2244.0436788305237,1.0 --6.352705410821644,-1834.5080572523207,1.0 --6.312625250501002,-1408.0367813842681,1.0 --6.272545090180361,-1182.126810307006,1.0 --6.23246492985972,-1023.8305254559735,1.0 --6.192384769539078,-749.9148074846513,1.0 --6.152304609218437,-633.2706930640054,1.0 --6.112224448897796,-428.94677577898915,1.0 --6.072144288577155,-370.81088770794634,1.0 --6.032064128256513,-164.47989945091646,1.0 --5.991983967935872,-175.76574520159318,1.0 --5.95190380761523,276.7917312609982,1.0 --5.9118236472945895,142.8377255686275,1.0 --5.871743486973948,164.31515825858003,1.0 --5.831663326653307,-34.36749813829701,1.0 --5.791583166332665,63.58513355157062,1.0 --5.751503006012024,-15.686255396515625,1.0 --5.711422845691383,139.41409127056727,1.0 --5.671342685370742,97.81155517364026,1.0 --5.631262525050101,187.5392406096995,1.0 --5.591182364729459,245.9310928585915,1.0 --5.551102204408818,226.76783010107488,1.0 --5.511022044088176,326.36304388785106,1.0 --5.470941883767535,380.4720686977429,1.0 --5.430861723446894,242.05353503554414,1.0 --5.390781563126253,553.8685701419874,1.0 --5.350701402805611,413.61140873741226,1.0 --5.31062124248497,479.7533823200415,1.0 --5.270541082164329,572.5698640489834,1.0 --5.2304609218436875,596.5211851975661,1.0 --5.190380761523047,445.78729405731804,1.0 --5.150300601202405,571.9467094160483,1.0 --5.110220440881764,226.80098634654158,1.0 --5.070140280561122,215.989299169003,1.0 --5.030060120240481,171.48669237489258,1.0 --4.98997995991984,275.6639514968458,1.0 --4.949899799599199,165.2178670699925,1.0 --4.909819639278557,133.82185182388372,1.0 --4.869739478957916,100.22343293125711,1.0 --4.829659318637275,-17.95647689973613,1.0 --4.789579158316633,242.7121695716441,1.0 --4.7494989979959925,-10.911698472900063,1.0 --4.709418837675351,-9.604262595610471,1.0 --4.66933867735471,55.743790616723444,1.0 --4.629258517034068,-62.763409143304195,1.0 --4.589178356713427,94.89422867360967,1.0 --4.5490981963927855,-19.740277944171748,1.0 --4.509018036072145,146.79243976980584,1.0 --4.468937875751503,-198.53521154489275,1.0 --4.428857715430862,28.695264420165707,1.0 --4.388777555110221,162.575306748366,1.0 --4.348697394789579,-23.547497473991655,1.0 --4.308617234468938,-53.31457938019744,1.0 --4.268537074148297,34.56992622809372,1.0 --4.228456913827656,-4.775387160201802,1.0 --4.188376753507014,-61.99107534756537,1.0 --4.148296593186373,-43.50393794394725,1.0 --4.108216432865731,76.80345569875638,1.0 --4.0681362725450905,359.40697496089024,1.0 --4.028056112224449,73.63249190826971,1.0 --3.987975951903808,118.69757179963864,1.0 --3.947895791583167,302.68856446003645,1.0 --3.907815631262525,409.70832618196886,1.0 --3.8677354709418843,408.2222950663798,1.0 --3.8276553106212425,434.24428269837136,1.0 --3.7875751503006017,699.273386657001,1.0 --3.74749498997996,654.2780959496113,1.0 --3.707414829659319,512.9560721607052,1.0 --3.6673346693386772,386.5966808801658,1.0 --3.6272545090180364,583.7009440394337,1.0 --3.5871743486973955,470.60049817468627,1.0 --3.5470941883767537,424.5396634864517,1.0 --3.507014028056113,443.61890689776436,1.0 --3.466933867735471,226.99832946446088,1.0 --3.42685370741483,163.81581417848992,1.0 --3.3867735470941884,192.53148996774092,1.0 --3.3466933867735476,210.9311047873046,1.0 --3.306613226452906,142.69719745659322,1.0 --3.266533066132265,204.03101443254772,1.0 --3.226452905811623,-66.44764982810298,1.0 --3.1863727454909823,9.520960070658903,1.0 --3.1462925851703414,32.80517723768747,1.0 --3.1062124248496996,149.36332722329362,1.0 --3.0661322645290587,66.87895182645985,1.0 --3.026052104208417,90.90681975682278,1.0 --2.985971943887776,-205.50088806897736,1.0 --2.9458917835671343,-244.52466933679113,1.0 --2.9058116232464934,-229.95166362683622,1.0 --2.8657314629258517,-389.8918848462074,1.0 --2.825651302605211,-550.8124429143342,1.0 --2.785571142284569,-897.0784528477208,1.0 --2.745490981963928,-1053.26357417739,1.0 --2.7054108216432873,-1447.4899619233024,1.0 --2.6653306613226455,-1379.8743622425388,1.0 --2.6252505010020046,-1793.2354683156057,1.0 --2.585170340681363,-2038.4147095874494,1.0 --2.545090180360722,-2266.2745557453336,1.0 --2.50501002004008,-2319.663715083221,1.0 --2.4649298597194393,-2394.3222990239087,1.0 --2.4248496993987976,-2310.2838393498646,1.0 --2.3847695390781567,-2220.2453046727514,1.0 --2.344689378757515,-2270.9958240886394,1.0 --2.304609218436874,-2048.5399992392845,1.0 --2.264529058116233,-1815.2757221348816,1.0 --2.2244488977955914,-1746.0653129864088,1.0 --2.1843687374749505,-1252.4740951513313,1.0 --2.1442885771543088,-1127.4641938546447,1.0 --2.104208416833668,-927.5749265869983,1.0 --2.064128256513026,-670.1258964179842,1.0 --2.0240480961923852,-567.300850292545,1.0 --1.9839679358717444,-447.19959223480276,1.0 --1.9438877755511026,-266.0060293841717,1.0 --1.9038076152304608,-241.70736157993463,1.0 --1.8637274549098208,108.00452521552558,1.0 --1.823647294589179,105.7643870890934,1.0 --1.7835671342685373,103.25446696146645,1.0 --1.7434869739478955,-132.08198122690507,1.0 --1.7034068136272555,-26.061849154315112,1.0 --1.6633266533066138,-84.90378661839368,1.0 --1.623246492985972,45.36243872887203,1.0 --1.5831663326653302,50.95208848153028,1.0 --1.5430861723446903,-57.15396964427575,1.0 --1.5030060120240485,155.79184575988253,1.0 --1.4629258517034067,-94.39814451876369,1.0 --1.4228456913827667,139.1507796379711,1.0 --1.382765531062125,165.94366886341334,1.0 --1.3426853707414832,-186.66811481462994,1.0 --1.3026052104208414,-16.449485942664786,1.0 --1.2625250501002014,-100.85087500998168,1.0 --1.2224448897795597,161.85947121019942,1.0 --1.182364729458918,-125.10097536861417,1.0 --1.1422845691382761,51.002453122671184,1.0 --1.1022044088176361,-20.51245725717161,1.0 --1.0621242484969944,92.04452492091603,1.0 --1.0220440881763526,6.751272424807435,1.0 --0.9819639278557126,-112.61828590732367,1.0 --0.9418837675350709,-46.883140221448166,1.0 --0.9018036072144291,117.17293897912568,1.0 --0.8617234468937873,114.36743609380008,1.0 --0.8216432865731473,80.20884210538163,1.0 --0.7815631262525056,-57.792690299580364,1.0 --0.7414829659318638,70.42660909947773,1.0 --0.701402805611222,54.672800670176315,1.0 --0.661322645290582,-183.15160093169473,1.0 --0.6212424849699403,76.84382713946671,1.0 --0.5811623246492985,84.51664315808804,1.0 --0.5410821643286585,-15.809152751040061,1.0 --0.5010020040080168,-38.320153171528794,1.0 --0.460921843687375,-17.00336275588955,1.0 --0.4208416833667332,142.80174009937457,1.0 --0.38076152304609323,252.4044714046765,1.0 --0.34068136272545146,37.38601119377136,1.0 --0.3006012024048097,47.99893978539542,1.0 --0.26052104208416793,-5.247542149497768,1.0 --0.22044088176352794,-16.263408834091436,1.0 --0.18036072144288617,155.07067308168027,1.0 --0.1402805611222444,90.48805315047659,1.0 --0.10020040080160442,408.42571399061467,1.0 --0.06012024048096265,174.64590893179115,1.0 --0.020040080160320883,494.92221117113246,1.0 -0.020040080160320883,766.7079193827934,1.0 -0.060120240480960874,720.7145757307838,1.0 -0.10020040080160264,730.6353958111974,1.0 -0.1402805611222444,1142.662551417452,1.0 -0.18036072144288617,1261.1163674500626,1.0 -0.22044088176352616,1432.94274521813,1.0 -0.26052104208416793,1558.7049775293438,1.0 -0.3006012024048097,1512.4413091602646,1.0 -0.3406813627254497,1768.4176366042616,1.0 -0.38076152304609145,1803.23223611583,1.0 -0.4208416833667332,1857.9323672290893,1.0 -0.460921843687375,1638.7296842102355,1.0 -0.501002004008015,1596.3968322687285,1.0 -0.5410821643286567,1555.6743375921997,1.0 -0.5811623246492985,1356.6462227455954,1.0 -0.6212424849699403,1113.8295223055352,1.0 -0.6613226452905803,905.277077043325,1.0 -0.701402805611222,713.4128828058109,1.0 -0.7414829659318638,493.99132978747264,1.0 -0.7815631262525038,600.0176988426963,1.0 -0.8216432865731456,225.77935608327343,1.0 -0.8617234468937873,326.6653626468951,1.0 -0.9018036072144291,134.37976937122502,1.0 -0.9418837675350691,132.36788718131947,1.0 -0.9819639278557108,-128.69741246419716,1.0 -1.0220440881763526,-87.37598044170201,1.0 -1.0621242484969944,-49.659557777661824,1.0 -1.1022044088176344,-42.19812626795051,1.0 -1.1422845691382761,-133.61697939127095,1.0 -1.182364729458918,-19.74050752779752,1.0 -1.222444889779558,-208.10837272592437,1.0 -1.2625250501001997,-240.08819676019755,1.0 -1.3026052104208414,-289.97268396417445,1.0 -1.3426853707414832,-341.908046085725,1.0 -1.3827655310621232,-656.5643415562238,1.0 -1.422845691382765,-693.9834148085498,1.0 -1.4629258517034067,-948.2141436572651,1.0 -1.5030060120240485,-961.8754485174908,1.0 -1.5430861723446885,-939.7376938923499,1.0 -1.5831663326653302,-1131.4931801747784,1.0 -1.623246492985972,-1025.1929610351353,1.0 -1.663326653306612,-1275.217196802901,1.0 -1.7034068136272538,-1013.4837530780889,1.0 -1.7434869739478955,-1159.9126539261326,1.0 -1.7835671342685373,-1057.0335066903258,1.0 -1.8236472945891773,-818.5488316209876,1.0 -1.863727454909819,-941.50450466623,1.0 -1.9038076152304608,-757.1574761355189,1.0 -1.9438877755511026,-586.2399328459168,1.0 -1.9839679358717426,-692.7066041879409,1.0 -2.0240480961923843,-237.4829069938953,1.0 -2.064128256513026,-451.35417389494097,1.0 -2.104208416833666,-344.3095569032505,1.0 -2.144288577154308,-100.32004029812168,1.0 -2.1843687374749496,-86.03874756929113,1.0 -2.2244488977955914,7.749367061075901,1.0 -2.2645290581162314,-65.67688005825464,1.0 -2.304609218436873,-169.0820424143081,1.0 -2.344689378757515,20.420258846656765,1.0 -2.3847695390781567,36.55826027986451,1.0 -2.4248496993987967,-112.33780216499093,1.0 -2.4649298597194385,-15.577570144953649,1.0 -2.50501002004008,142.41102019911247,1.0 -2.54509018036072,-71.82126205165373,1.0 -2.585170340681362,-122.37625676312977,1.0 -2.6252505010020037,117.58390959424457,1.0 -2.6653306613226455,-170.66983638996982,1.0 -2.7054108216432855,32.61383818839371,1.0 -2.7454909819639273,131.92411435666182,1.0 -2.785571142284569,27.267862344467066,1.0 -2.825651302605209,75.70311597601425,1.0 -2.865731462925851,113.1692455398132,1.0 -2.9058116232464926,-279.96736324512466,1.0 -2.9458917835671343,111.81497456654353,1.0 -2.9859719438877743,-155.60482478481674,1.0 -3.026052104208416,20.932616297494064,1.0 -3.066132264529058,-80.56979350607658,1.0 -3.1062124248496996,-142.8681256067098,1.0 -3.1462925851703396,-87.3787412556828,1.0 -3.1863727454909814,-212.77716450636967,1.0 -3.226452905811623,-129.44930387990408,1.0 -3.266533066132263,41.115808908774625,1.0 -3.306613226452905,-185.30943859081412,1.0 -3.3466933867735467,-94.19173593248405,1.0 -3.3867735470941884,-87.696646592832,1.0 -3.4268537074148284,-196.62037376789175,1.0 -3.46693386773547,-165.9103359404155,1.0 -3.507014028056112,149.4983842893688,1.0 -3.5470941883767537,82.75497397885914,1.0 -3.5871743486973937,-43.45359947125705,1.0 -3.6272545090180355,-55.56840977228109,1.0 -3.6673346693386772,-112.80596308529624,1.0 -3.7074148296593172,53.494363708048056,1.0 -3.747494989979959,125.15403780693875,1.0 -3.7875751503006008,32.13382222014916,1.0 -3.8276553106212425,136.95157978816363,1.0 -3.8677354709418825,117.73362359793725,1.0 -3.9078156312625243,4.287240159994987,1.0 -3.947895791583166,85.15450945048238,1.0 -3.987975951903808,244.47403597018413,1.0 -4.028056112224448,287.68204083335047,1.0 -4.06813627254509,574.0383763542517,1.0 -4.108216432865731,678.0375478599675,1.0 -4.148296593186371,747.6035866939355,1.0 -4.188376753507013,1096.9120383734091,1.0 -4.228456913827655,1491.2325291356565,1.0 -4.268537074148297,1670.156141596374,1.0 -4.308617234468937,2101.889999486108,1.0 -4.348697394789578,2140.789419438206,1.0 -4.38877755511022,2371.8432587710067,1.0 -4.428857715430862,2634.156927840497,1.0 -4.468937875751502,2528.7293889984844,1.0 -4.509018036072144,2634.598087493338,1.0 -4.5490981963927855,2369.641249615989,1.0 -4.589178356713425,2550.0140962211203,1.0 -4.629258517034067,2263.0429749502964,1.0 -4.669338677354709,2096.2852506767117,1.0 -4.709418837675351,1862.678516465068,1.0 -4.749498997995991,1619.7812625449612,1.0 -4.7895791583166325,1321.5747053593325,1.0 -4.829659318637274,1181.8109700602743,1.0 -4.869739478957916,1021.834454561545,1.0 -4.909819639278556,529.8620046479911,1.0 -4.949899799599198,251.3845366891341,1.0 -4.98997995991984,495.2730794427533,1.0 -5.0300601202404795,185.2663779546226,1.0 -5.070140280561121,20.312633494308407,1.0 -5.110220440881763,71.24701020613985,1.0 -5.150300601202405,77.61478183858118,1.0 -5.190380761523045,-90.57665046299982,1.0 -5.230460921843687,50.381634286166026,1.0 -5.270541082164328,59.43405359538049,1.0 -5.31062124248497,-64.05221328400712,1.0 -5.35070140280561,-48.59826872881878,1.0 -5.390781563126252,-136.33565422532646,1.0 -5.430861723446894,-57.19017393544607,1.0 -5.470941883767534,-202.91698358247453,1.0 -5.511022044088175,-58.26408756308233,1.0 -5.551102204408817,-356.4842377410424,1.0 -5.591182364729459,-169.38311226854938,1.0 -5.631262525050099,-238.0025648183269,1.0 -5.671342685370741,-368.60798914954114,1.0 -5.7114228456913825,-191.80969812045097,1.0 -5.751503006012024,-208.65527653669423,1.0 -5.791583166332664,-197.25534833603706,1.0 -5.831663326653306,-58.4602250894626,1.0 -5.871743486973948,-226.19916677933787,1.0 -5.911823647294588,-193.78320927628073,1.0 -5.9519038076152295,-176.16129226257902,1.0 -5.991983967935871,-174.90238270197383,1.0 -6.032064128256511,24.089322137675673,1.0 -6.072144288577153,-13.944349955840863,1.0 -6.112224448897795,-84.05174798860213,1.0 -6.152304609218437,90.85628285069376,1.0 -6.192384769539078,-93.15914075048742,1.0 -6.23246492985972,7.721354252303572,1.0 -6.272545090180358,34.53384645451405,1.0 -6.312625250501,149.28018978006972,1.0 -6.352705410821642,-20.282595767485454,1.0 -6.392785571142284,70.85154707750968,1.0 -6.432865731462925,-23.672153003739606,1.0 -6.472945891783567,-246.27248034083647,1.0 -6.513026052104209,-95.88888017018616,1.0 -6.553106212424851,42.68450680443224,1.0 -6.593186372745489,-39.69243877982126,1.0 -6.633266533066131,-37.81895959328909,1.0 -6.673346693386772,38.30192090135661,1.0 -6.713426853707414,-18.58044805261258,1.0 -6.753507014028056,-11.681741872452399,1.0 -6.793587174348698,53.14439747103751,1.0 -6.8336673346693395,-60.89181876445606,1.0 -6.873747494989978,-119.0726509589646,1.0 -6.9138276553106195,-459.21822084955437,1.0 -6.953907815631261,-353.3708950909421,1.0 -6.993987975951903,-323.4186285288491,1.0 -7.034068136272545,-370.6350261816318,1.0 -7.074148296593187,-531.6064118640506,1.0 -7.114228456913828,-630.9884685149422,1.0 -7.1543086172344665,-803.6656941283452,1.0 -7.194388777555108,-1047.6518008472133,1.0 -7.23446893787575,-1115.3729023142841,1.0 -7.274549098196392,-1037.8133593254645,1.0 -7.314629258517034,-1253.1058213284346,1.0 -7.354709418837675,-1083.06508355643,1.0 -7.394789579158317,-984.8017189645649,1.0 -7.434869739478959,-1058.5882941273649,1.0 -7.474949899799597,-1107.5776088205316,1.0 -7.515030060120239,-796.3352009962803,1.0 -7.555110220440881,-520.7142725978055,1.0 -7.595190380761522,-659.7008886423328,1.0 -7.635270541082164,-459.1349138570584,1.0 -7.675350701402806,-390.6407697599343,1.0 -7.715430861723448,-71.59436651236598,1.0 -7.755511022044086,-198.8443974364605,1.0 -7.795591182364728,4.279797615588166,1.0 -7.8356713426853695,-149.28920668269504,1.0 -7.875751503006011,-78.74844746336518,1.0 -7.915831663326653,44.14304528693796,1.0 -7.955911823647295,139.844235727943,1.0 -7.9959919839679365,-2.0867775873698124,1.0 -8.036072144288575,-30.998994878154733,1.0 -8.076152304609217,-88.60045904487676,1.0 -8.116232464929858,-91.7446211893045,1.0 -8.1563126252505,215.22692767535398,1.0 -8.196392785571142,232.38459455809078,1.0 -8.236472945891784,629.5887277504811,1.0 -8.276553106212425,425.39172218804373,1.0 -8.316633266533067,800.9077289842156,1.0 -8.356713426853705,1238.0983411245063,1.0 -8.396793587174347,1233.1770581972203,1.0 -8.436873747494989,1376.2314013992361,1.0 -8.47695390781563,1705.341353309323,1.0 -8.517034068136272,1787.9238176995248,1.0 -8.557114228456914,1856.954857758288,1.0 -8.597194388777556,1943.3582178189522,1.0 -8.637274549098194,2076.8269260371953,1.0 -8.677354709418836,1709.172190976292,1.0 -8.717434869739478,1613.5647114767085,1.0 -8.75751503006012,1532.3908882978822,1.0 -8.797595190380761,1464.9007740991583,1.0 -8.837675350701403,1265.246225155354,1.0 -8.877755511022045,929.4076635384358,1.0 -8.917835671342683,844.9450044472783,1.0 -8.957915831663325,683.6498292167579,1.0 -8.997995991983966,491.4311140894882,1.0 -9.038076152304608,465.36187058533847,1.0 -9.07815631262525,281.83409456309977,1.0 -9.118236472945892,406.5843155766986,1.0 -9.158316633266534,-34.90413981469584,1.0 -9.198396793587175,61.58721144671156,1.0 -9.238476953907814,20.17832260931102,1.0 -9.278557114228455,36.701926466694644,1.0 -9.318637274549097,129.02003733662687,1.0 -9.358717434869739,-33.011342994882305,1.0 -9.39879759519038,2.123535679996713,1.0 -9.438877755511022,-151.6013247947108,1.0 -9.478957915831664,48.647014741237236,1.0 -9.519038076152302,109.42944705714471,1.0 -9.559118236472944,-35.58102659890346,1.0 -9.599198396793586,-210.95580503978985,1.0 -9.639278557114228,-180.20254357122192,1.0 -9.67935871743487,159.02917105182718,1.0 -9.719438877755511,-74.11984320231029,1.0 -9.759519038076153,29.21772930801316,1.0 -9.799599198396791,13.670807128337708,1.0 -9.839679358717433,-26.12929643497906,1.0 -9.879759519038075,10.394167275034771,1.0 -9.919839679358716,16.994810784688777,1.0 -9.959919839679358,95.79503256985275,1.0 -10.0,-70.72867722313738,1.0 diff --git a/Day_2/WorkProblems/sinxandcosxexp.csv b/Day_2/WorkProblems/sinxandcosxexp.csv deleted file mode 100644 index 832e699..0000000 --- a/Day_2/WorkProblems/sinxandcosxexp.csv +++ /dev/null @@ -1,501 +0,0 @@ -x,y,errors --10.0,0.0815676873776705,1.0 --9.959919839679358,3.41997269039432,1.0 --9.919839679358718,3.343851009045088,1.0 --9.879759519038076,4.323830620324546,1.0 --9.839679358717435,4.571697109075884,1.0 --9.799599198396793,6.200598126865978,1.0 --9.759519038076153,8.002949755133097,1.0 --9.719438877755511,8.56265506312783,1.0 --9.67935871743487,8.404811958137921,1.0 --9.639278557114228,10.130166311112795,1.0 --9.599198396793588,10.76283165412222,1.0 --9.559118236472946,10.28208497256401,1.0 --9.519038076152304,11.89215654774792,1.0 --9.478957915831664,12.386706779124971,1.0 --9.438877755511022,12.24741027112242,1.0 --9.39879759519038,14.020074895879794,1.0 --9.358717434869739,13.761703649530716,1.0 --9.318637274549099,12.348043633026355,1.0 --9.278557114228457,11.157522471021943,1.0 --9.238476953907815,10.25262886250292,1.0 --9.198396793587175,11.487166917705116,1.0 --9.158316633266534,10.574835326449259,1.0 --9.118236472945892,9.128305539697086,1.0 --9.07815631262525,8.899668810900422,1.0 --9.03807615230461,8.296195526476941,1.0 --8.997995991983968,8.844390378066723,1.0 --8.957915831663327,7.581330628299437,1.0 --8.917835671342685,4.901825430324545,1.0 --8.877755511022045,7.923471632881772,1.0 --8.837675350701403,5.166677610952069,1.0 --8.797595190380761,5.4083465434492295,1.0 --8.75751503006012,3.707100081109665,1.0 --8.71743486973948,4.2372843279317065,1.0 --8.677354709418838,1.6594178064076774,1.0 --8.637274549098196,2.4282227518098685,1.0 --8.597194388777556,0.7002008078018782,1.0 --8.557114228456914,1.353394556334035,1.0 --8.517034068136272,1.7789428371699083,1.0 --8.47695390781563,-0.018008421261374363,1.0 --8.43687374749499,0.6770337384609002,1.0 --8.396793587174349,0.743360589739219,1.0 --8.356713426853707,0.058117904488797084,1.0 --8.316633266533067,-0.534179439670818,1.0 --8.276553106212425,0.6396667109843945,1.0 --8.236472945891784,0.36702738199258544,1.0 --8.196392785571142,0.7462136970618706,1.0 --8.156312625250502,1.802217300068643,1.0 --8.11623246492986,0.14415861250575468,1.0 --8.076152304609218,1.0653574534881098,1.0 --8.036072144288577,1.1813527632598801,1.0 --7.9959919839679365,1.0295337492067023,1.0 --7.955911823647295,2.845343997351643,1.0 --7.915831663326653,3.737825332905185,1.0 --7.875751503006012,2.1753895153767315,1.0 --7.835671342685371,1.8585682604833615,1.0 --7.7955911823647295,2.1911614384532396,1.0 --7.755511022044088,2.416226509339419,1.0 --7.715430861723447,0.2873823272014331,1.0 --7.675350701402806,3.3776001782645344,1.0 --7.635270541082164,2.374313349797425,1.0 --7.595190380761523,1.7107576774635387,1.0 --7.555110220440882,1.090175793710843,1.0 --7.515030060120241,2.020748471360556,1.0 --7.474949899799599,0.7378305258462502,1.0 --7.434869739478958,0.6317650580211533,1.0 --7.394789579158317,2.218555199257954,1.0 --7.354709418837675,-0.7870974436265932,1.0 --7.314629258517034,0.2525680934908726,1.0 --7.274549098196393,-1.7208226740033088,1.0 --7.234468937875752,-2.3687748768279864,1.0 --7.19438877755511,-4.36704349859215,1.0 --7.154308617234469,-5.174378408404721,1.0 --7.114228456913828,-2.4816737025563707,1.0 --7.074148296593187,-5.829803727877277,1.0 --7.034068136272545,-6.5100172130390686,1.0 --6.993987975951904,-7.411241224876562,1.0 --6.953907815631263,-8.222888439028953,1.0 --6.913827655310621,-9.289255752287756,1.0 --6.8737474949899795,-10.66777543023659,1.0 --6.833667334669339,-10.101548119402487,1.0 --6.793587174348698,-9.937338891259532,1.0 --6.753507014028056,-10.65749156216567,1.0 --6.713426853707415,-14.248154971346738,1.0 --6.673346693386774,-12.194045502211216,1.0 --6.6332665330661325,-14.85780370840227,1.0 --6.593186372745491,-13.06895947159909,1.0 --6.55310621242485,-14.113470353009767,1.0 --6.513026052104209,-13.174773426212377,1.0 --6.472945891783567,-12.717151093941029,1.0 --6.432865731462925,-13.175110313581168,1.0 --6.3927855711422845,-13.353519492755094,1.0 --6.352705410821644,-12.321117167964042,1.0 --6.312625250501002,-10.46219402313157,1.0 --6.272545090180361,-9.924016912890222,1.0 --6.23246492985972,-9.149962799625357,1.0 --6.192384769539078,-10.00898594128512,1.0 --6.152304609218437,-9.517912308094616,1.0 --6.112224448897796,-7.5894110687164975,1.0 --6.072144288577155,-7.151421788846276,1.0 --6.032064128256513,-6.447093117029306,1.0 --5.991983967935872,-5.967593788426262,1.0 --5.95190380761523,-2.880480344840533,1.0 --5.9118236472945895,-1.3983221881014893,1.0 --5.871743486973948,-1.0566401500055724,1.0 --5.831663326653307,-0.30072135230883873,1.0 --5.791583166332665,1.5971996402124626,1.0 --5.751503006012024,2.515548481618387,1.0 --5.711422845691383,3.8719063356782666,1.0 --5.671342685370742,4.889598066761832,1.0 --5.631262525050101,4.813998099209638,1.0 --5.591182364729459,5.323158405448429,1.0 --5.551102204408818,7.164013981286866,1.0 --5.511022044088176,6.8478729264074225,1.0 --5.470941883767535,8.144279557670497,1.0 --5.430861723446894,8.95287128182026,1.0 --5.390781563126253,9.755386196574467,1.0 --5.350701402805611,8.638266415583663,1.0 --5.31062124248497,8.441801403360598,1.0 --5.270541082164329,8.177281502844929,1.0 --5.2304609218436875,6.456072572807139,1.0 --5.190380761523047,6.411072973981675,1.0 --5.150300601202405,7.1205518674459105,1.0 --5.110220440881764,6.410910119027057,1.0 --5.070140280561122,8.870704673089495,1.0 --5.030060120240481,7.329424053463258,1.0 --4.98997995991984,5.408896266243617,1.0 --4.949899799599199,6.438611501827078,1.0 --4.909819639278557,6.527802030619194,1.0 --4.869739478957916,7.428562261053792,1.0 --4.829659318637275,5.157293647010521,1.0 --4.789579158316633,4.944232750229143,1.0 --4.7494989979959925,4.33455764733163,1.0 --4.709418837675351,2.414680768904415,1.0 --4.66933867735471,2.5717425930335276,1.0 --4.629258517034068,3.1802896137049217,1.0 --4.589178356713427,2.4531142558674186,1.0 --4.5490981963927855,1.9359827959968405,1.0 --4.509018036072145,3.361837882006769,1.0 --4.468937875751503,0.6911500246911029,1.0 --4.428857715430862,1.5072317320443747,1.0 --4.388777555110221,0.41818369825610535,1.0 --4.348697394789579,3.3831207015907356,1.0 --4.308617234468938,2.2709556043169226,1.0 --4.268537074148297,2.3731638284083063,1.0 --4.228456913827656,2.669374929489015,1.0 --4.188376753507014,4.751094184069294,1.0 --4.148296593186373,5.271464520667503,1.0 --4.108216432865731,1.8654510314094637,1.0 --4.0681362725450905,5.673539762134269,1.0 --4.028056112224449,7.567090483983595,1.0 --3.987975951903808,8.032214631477114,1.0 --3.947895791583167,7.2374001442893805,1.0 --3.907815631262525,9.008921962409312,1.0 --3.8677354709418843,8.806274895586366,1.0 --3.8276553106212425,8.617605955108798,1.0 --3.7875751503006017,6.968149995983552,1.0 --3.74749498997996,8.545003813723149,1.0 --3.707414829659319,7.723332656874816,1.0 --3.6673346693386772,8.158373758700154,1.0 --3.6272545090180364,10.444197955815646,1.0 --3.5871743486973955,7.797176528903616,1.0 --3.5470941883767537,9.21536877049858,1.0 --3.507014028056113,9.393874253075209,1.0 --3.466933867735471,6.927632526981376,1.0 --3.42685370741483,5.953970527948124,1.0 --3.3867735470941884,6.752228311944786,1.0 --3.3466933867735476,5.068893423944552,1.0 --3.306613226452906,4.879934052871154,1.0 --3.266533066132265,3.317346709537104,1.0 --3.226452905811623,2.1770130537474452,1.0 --3.1863727454909823,2.728744257296909,1.0 --3.1462925851703414,0.7858690358190628,1.0 --3.1062124248496996,-2.135988509621397,1.0 --3.0661322645290587,-2.6560580066021964,1.0 --3.026052104208417,-3.1260126286665075,1.0 --2.985971943887776,-4.108406387121905,1.0 --2.9458917835671343,-5.211975178613911,1.0 --2.9058116232464934,-4.631793037507222,1.0 --2.8657314629258517,-6.351945582972523,1.0 --2.825651302605211,-6.0835343492843545,1.0 --2.785571142284569,-8.369579175173415,1.0 --2.745490981963928,-10.599049912926134,1.0 --2.7054108216432873,-11.145866439812167,1.0 --2.6653306613226455,-11.412505643043467,1.0 --2.6252505010020046,-11.698933775055206,1.0 --2.585170340681363,-11.86710822268812,1.0 --2.545090180360722,-12.577726076124291,1.0 --2.50501002004008,-13.115164891290288,1.0 --2.4649298597194393,-11.476731964103417,1.0 --2.4248496993987976,-14.764049927239066,1.0 --2.3847695390781567,-12.91153402640195,1.0 --2.344689378757515,-13.299463154913367,1.0 --2.304609218436874,-12.061329061234925,1.0 --2.264529058116233,-10.758124378623087,1.0 --2.2244488977955914,-12.38128586057529,1.0 --2.1843687374749505,-10.243621481222771,1.0 --2.1442885771543088,-12.396613604767643,1.0 --2.104208416833668,-9.708237319617254,1.0 --2.064128256513026,-8.956526590014667,1.0 --2.0240480961923852,-5.982338699815878,1.0 --1.9839679358717444,-7.699742736272512,1.0 --1.9438877755511026,-5.274454887619168,1.0 --1.9038076152304608,-5.26267177984638,1.0 --1.8637274549098208,-5.000993872514754,1.0 --1.823647294589179,-2.554696820546129,1.0 --1.7835671342685373,-3.13404651581476,1.0 --1.7434869739478955,-0.3432361325178004,1.0 --1.7034068136272555,-2.627407345905315,1.0 --1.6633266533066138,0.5225883737666406,1.0 --1.623246492985972,-0.1133142011369987,1.0 --1.5831663326653302,2.0875535852867575,1.0 --1.5430861723446903,1.3441647045006586,1.0 --1.5030060120240485,1.1127258349213351,1.0 --1.4629258517034067,0.697185988012432,1.0 --1.4228456913827667,1.618288083256997,1.0 --1.382765531062125,3.1323569339459443,1.0 --1.3426853707414832,3.3744686634700822,1.0 --1.3026052104208414,1.872254169729311,1.0 --1.2625250501002014,2.7486615045319516,1.0 --1.2224448897795597,2.6609485489740945,1.0 --1.182364729458918,2.9374737872256116,1.0 --1.1422845691382761,1.4453123222151123,1.0 --1.1022044088176361,1.7212916080987917,1.0 --1.0621242484969944,4.106410104915044,1.0 --1.0220440881763526,2.3881114244318216,1.0 --0.9819639278557126,0.07320784067176156,1.0 --0.9418837675350709,2.7595220850605577,1.0 --0.9018036072144291,-0.1462347333830729,1.0 --0.8617234468937873,-0.4616046664563136,1.0 --0.8216432865731473,0.4012714754441187,1.0 --0.7815631262525056,0.15340271237381076,1.0 --0.7414829659318638,0.8687414720519018,1.0 --0.701402805611222,1.9119862496046633,1.0 --0.661322645290582,-0.7521359332065153,1.0 --0.6212424849699403,1.6814699327826983,1.0 --0.5811623246492985,1.3071299759699495,1.0 --0.5410821643286585,2.5529649945799604,1.0 --0.5010020040080168,1.327018391455028,1.0 --0.460921843687375,2.6509638333855388,1.0 --0.4208416833667332,2.6204792958494743,1.0 --0.38076152304609323,2.6395274485902585,1.0 --0.34068136272545146,2.7365800987445135,1.0 --0.3006012024048097,1.7777850143560738,1.0 --0.26052104208416793,4.898902082013975,1.0 --0.22044088176352794,4.461545426627318,1.0 --0.18036072144288617,4.964129856761111,1.0 --0.1402805611222444,6.602140339044653,1.0 --0.10020040080160442,6.602928662737603,1.0 --0.06012024048096265,7.465417807058772,1.0 --0.020040080160320883,9.781114883984042,1.0 -0.020040080160320883,12.345815802533789,1.0 -0.060120240480960874,10.022281243119322,1.0 -0.10020040080160264,12.100760132384496,1.0 -0.1402805611222444,12.14250148777455,1.0 -0.18036072144288617,12.594157994913509,1.0 -0.22044088176352616,12.316094199807086,1.0 -0.26052104208416793,12.574005991533479,1.0 -0.3006012024048097,13.883492540452409,1.0 -0.3406813627254497,13.18029210578265,1.0 -0.38076152304609145,13.236819621024702,1.0 -0.4208416833667332,13.498482116050338,1.0 -0.460921843687375,11.96220721029042,1.0 -0.501002004008015,14.529140356152862,1.0 -0.5410821643286567,12.162521659505199,1.0 -0.5811623246492985,12.773431023082786,1.0 -0.6212424849699403,11.705048706604963,1.0 -0.6613226452905803,11.004438240214823,1.0 -0.701402805611222,11.413510400327459,1.0 -0.7414829659318638,8.91131544610529,1.0 -0.7815631262525038,9.14806971501429,1.0 -0.8216432865731456,9.11701458868731,1.0 -0.8617234468937873,7.558630447543181,1.0 -0.9018036072144291,4.552337523128667,1.0 -0.9418837675350691,5.526953131769259,1.0 -0.9819639278557108,3.6240329914575704,1.0 -1.0220440881763526,1.4742838380408776,1.0 -1.0621242484969944,1.2792815929108305,1.0 -1.1022044088176344,0.1763085937729848,1.0 -1.1422845691382761,0.5071107002663986,1.0 -1.182364729458918,-2.0759821541758967,1.0 -1.222444889779558,-2.7470242893517076,1.0 -1.2625250501001997,-4.319794842276632,1.0 -1.3026052104208414,-5.552158401306495,1.0 -1.3426853707414832,-6.77196274254921,1.0 -1.3827655310621232,-6.684380980419507,1.0 -1.422845691382765,-6.2132464469050905,1.0 -1.4629258517034067,-7.978111893515134,1.0 -1.5030060120240485,-6.073001810237554,1.0 -1.5430861723446885,-7.574898840891429,1.0 -1.5831663326653302,-8.507709246556201,1.0 -1.623246492985972,-8.746957476268227,1.0 -1.663326653306612,-6.220108261937662,1.0 -1.7034068136272538,-6.907597947598624,1.0 -1.7434869739478955,-7.2104113861625105,1.0 -1.7835671342685373,-8.15920241159408,1.0 -1.8236472945891773,-8.374234380938868,1.0 -1.863727454909819,-7.076573821757138,1.0 -1.9038076152304608,-6.555316089177241,1.0 -1.9438877755511026,-5.256607252120139,1.0 -1.9839679358717426,-4.5958502869369156,1.0 -2.0240480961923843,-4.4231847112961615,1.0 -2.064128256513026,-2.891291883722556,1.0 -2.104208416833666,-0.9226346617538779,1.0 -2.144288577154308,-4.7801880836948225,1.0 -2.1843687374749496,-1.7579514329969046,1.0 -2.2244488977955914,-2.3596929925204413,1.0 -2.2645290581162314,-0.7153751974243361,1.0 -2.304609218436873,0.15597436060632336,1.0 -2.344689378757515,-0.7274840695085079,1.0 -2.3847695390781567,0.9859334256162062,1.0 -2.4248496993987967,1.2086456163039563,1.0 -2.4649298597194385,-0.7126152801513315,1.0 -2.50501002004008,1.8511116269332961,1.0 -2.54509018036072,0.375955670970445,1.0 -2.585170340681362,0.5322682556267715,1.0 -2.6252505010020037,1.6198634207284226,1.0 -2.6653306613226455,2.559465463631289,1.0 -2.7054108216432855,3.9651036371550643,1.0 -2.7454909819639273,4.081974525599071,1.0 -2.785571142284569,0.24737165922720994,1.0 -2.825651302605209,1.331239710378306,1.0 -2.865731462925851,0.8023851656675165,1.0 -2.9058116232464926,0.9416506031172316,1.0 -2.9458917835671343,-0.9645693992832423,1.0 -2.9859719438877743,0.7200576213926462,1.0 -3.026052104208416,-0.11815840813448286,1.0 -3.066132264529058,-0.9921964875898008,1.0 -3.1062124248496996,1.5761327931930356,1.0 -3.1462925851703396,-1.2298414073595776,1.0 -3.1863727454909814,-0.7721906662657811,1.0 -3.226452905811623,1.1020344682456955,1.0 -3.266533066132263,-0.16584070656116232,1.0 -3.306613226452905,-1.3866782982190298,1.0 -3.3466933867735467,-0.6404930514417254,1.0 -3.3867735470941884,-1.293860970411662,1.0 -3.4268537074148284,-0.1291944527767095,1.0 -3.46693386773547,0.8910293819568255,1.0 -3.507014028056112,0.9520016491310089,1.0 -3.5470941883767537,3.0756420706233496,1.0 -3.5871743486973937,1.6703646661781522,1.0 -3.6272545090180355,3.3580275820303878,1.0 -3.6673346693386772,5.985889435793331,1.0 -3.7074148296593172,6.295694627597776,1.0 -3.747494989979959,6.201286730891248,1.0 -3.7875751503006008,6.8989384713752555,1.0 -3.8276553106212425,9.33671530209424,1.0 -3.8677354709418825,10.016732123729618,1.0 -3.9078156312625243,10.330965210277187,1.0 -3.947895791583166,11.687709371914572,1.0 -3.987975951903808,13.128209583451303,1.0 -4.028056112224448,15.043652792015052,1.0 -4.06813627254509,14.542492812178391,1.0 -4.108216432865731,15.790874531492967,1.0 -4.148296593186371,19.395162997936414,1.0 -4.188376753507013,19.926462995093722,1.0 -4.228456913827655,18.97202616191815,1.0 -4.268537074148297,18.637949597413105,1.0 -4.308617234468937,19.503148222626596,1.0 -4.348697394789578,23.088405757102098,1.0 -4.38877755511022,21.410851315115945,1.0 -4.428857715430862,22.925010228895786,1.0 -4.468937875751502,23.06645270400189,1.0 -4.509018036072144,22.349315427854638,1.0 -4.5490981963927855,25.0342596647029,1.0 -4.589178356713425,24.222755521252328,1.0 -4.629258517034067,23.429527060986707,1.0 -4.669338677354709,23.629032717701698,1.0 -4.709418837675351,22.08844411524625,1.0 -4.749498997995991,22.84590464015396,1.0 -4.7895791583166325,22.804972192242875,1.0 -4.829659318637274,20.86105653743016,1.0 -4.869739478957916,20.63058986011297,1.0 -4.909819639278556,20.124247056387738,1.0 -4.949899799599198,19.237676101531974,1.0 -4.98997995991984,19.55714226078749,1.0 -5.0300601202404795,18.633389288689056,1.0 -5.070140280561121,18.362538418203453,1.0 -5.110220440881763,16.22425423540707,1.0 -5.150300601202405,15.43565496166492,1.0 -5.190380761523045,15.21251230498158,1.0 -5.230460921843687,14.517061380274239,1.0 -5.270541082164328,12.963475135225496,1.0 -5.31062124248497,13.10936602346605,1.0 -5.35070140280561,13.009194330013752,1.0 -5.390781563126252,11.697237299414198,1.0 -5.430861723446894,10.74643767499065,1.0 -5.470941883767534,11.563299412395931,1.0 -5.511022044088175,11.140949045329853,1.0 -5.551102204408817,11.042601382154128,1.0 -5.591182364729459,10.436917577183072,1.0 -5.631262525050099,13.025271004860809,1.0 -5.671342685370741,10.571518046324957,1.0 -5.7114228456913825,11.968304418572329,1.0 -5.751503006012024,13.464424748103477,1.0 -5.791583166332664,13.837603897447114,1.0 -5.831663326653306,14.135651042556798,1.0 -5.871743486973948,14.395838560379346,1.0 -5.911823647294588,14.067625113598002,1.0 -5.9519038076152295,13.610233199929523,1.0 -5.991983967935871,15.78969174386259,1.0 -6.032064128256511,16.095622179456036,1.0 -6.072144288577153,14.205680253139692,1.0 -6.112224448897795,18.204469928607526,1.0 -6.152304609218437,21.603686543289538,1.0 -6.192384769539078,20.34652094451669,1.0 -6.23246492985972,20.288136494510027,1.0 -6.272545090180358,22.981061009456976,1.0 -6.312625250501,21.597966730162053,1.0 -6.352705410821642,22.456553175540634,1.0 -6.392785571142284,22.916983855230754,1.0 -6.432865731462925,23.32531938122861,1.0 -6.472945891783567,25.155636318285282,1.0 -6.513026052104209,24.507205816109018,1.0 -6.553106212424851,25.86503701162594,1.0 -6.593186372745489,23.827234180553926,1.0 -6.633266533066131,22.182385871516683,1.0 -6.673346693386772,23.968287586443996,1.0 -6.713426853707414,25.89526002720975,1.0 -6.753507014028056,26.732106503165735,1.0 -6.793587174348698,25.697467122925605,1.0 -6.8336673346693395,24.701061952159133,1.0 -6.873747494989978,26.214166440618165,1.0 -6.9138276553106195,24.946084207976558,1.0 -6.953907815631261,25.815079795945262,1.0 -6.993987975951903,25.111119312373347,1.0 -7.034068136272545,25.39096588168042,1.0 -7.074148296593187,26.671883355615037,1.0 -7.114228456913828,26.945190485560953,1.0 -7.1543086172344665,26.728050379545582,1.0 -7.194388777555108,26.15686446876478,1.0 -7.23446893787575,27.07494851889701,1.0 -7.274549098196392,25.85101706901931,1.0 -7.314629258517034,30.94560444581995,1.0 -7.354709418837675,27.782138339414352,1.0 -7.394789579158317,29.911269656072022,1.0 -7.434869739478959,32.203238102880924,1.0 -7.474949899799597,31.394176068690772,1.0 -7.515030060120239,32.546430239145515,1.0 -7.555110220440881,33.13434380024623,1.0 -7.595190380761522,37.47682940669532,1.0 -7.635270541082164,38.51099813871907,1.0 -7.675350701402806,38.58789995834865,1.0 -7.715430861723448,43.42654023533881,1.0 -7.755511022044086,43.37388776748444,1.0 -7.795591182364728,45.268155079335216,1.0 -7.8356713426853695,46.22427073403119,1.0 -7.875751503006011,48.63283654601901,1.0 -7.915831663326653,51.49526956196466,1.0 -7.955911823647295,54.16332038546321,1.0 -7.9959919839679365,54.073402220423546,1.0 -8.036072144288575,58.285547028803585,1.0 -8.076152304609217,59.15473745668916,1.0 -8.116232464929858,61.59686128324424,1.0 -8.1563126252505,65.16886262214112,1.0 -8.196392785571142,66.81559280448157,1.0 -8.236472945891784,68.6560047608272,1.0 -8.276553106212425,71.09679078973704,1.0 -8.316633266533067,75.0707785823781,1.0 -8.356713426853705,76.10670438163886,1.0 -8.396793587174347,78.35160800629691,1.0 -8.436873747494989,80.43903196065814,1.0 -8.47695390781563,82.0598392660228,1.0 -8.517034068136272,82.17934238664654,1.0 -8.557114228456914,84.42526330977088,1.0 -8.597194388777556,88.01122375941539,1.0 -8.637274549098194,86.81721420835359,1.0 -8.677354709418836,89.93763286397146,1.0 -8.717434869739478,90.40223027880393,1.0 -8.75751503006012,91.2464982647566,1.0 -8.797595190380761,91.67835980883888,1.0 -8.837675350701403,94.09367425237988,1.0 -8.877755511022045,95.00875749546022,1.0 -8.917835671342683,96.34468638319572,1.0 -8.957915831663325,96.52142167871155,1.0 -8.997995991983966,96.26760186257533,1.0 -9.038076152304608,99.68909752361552,1.0 -9.07815631262525,100.49633262966864,1.0 -9.118236472945892,101.03467273124801,1.0 -9.158316633266534,103.75895501637822,1.0 -9.198396793587175,103.6925101171074,1.0 -9.238476953907814,104.29680758522953,1.0 -9.278557114228455,107.66646428595797,1.0 -9.318637274549097,110.89806865927088,1.0 -9.358717434869739,109.85391857542498,1.0 -9.39879759519038,111.76962777801994,1.0 -9.438877755511022,112.2521257765494,1.0 -9.478957915831664,114.82446977999658,1.0 -9.519038076152302,117.22009730013853,1.0 -9.559118236472944,117.24705298652609,1.0 -9.599198396793586,121.49284165170774,1.0 -9.639278557114228,123.53183630598599,1.0 -9.67935871743487,125.85399847345037,1.0 -9.719438877755511,129.07248008804868,1.0 -9.759519038076153,131.327726789887,1.0 -9.799599198396791,134.16475042808904,1.0 -9.839679358717433,137.79433973731057,1.0 -9.879759519038075,141.29656160669046,1.0 -9.919839679358716,143.5002378789887,1.0 -9.959919839679358,146.7125583630807,1.0 -10.0,147.96589466419826,1.0 diff --git a/Day_2/WorkProblems/sinxcosx.csv b/Day_2/WorkProblems/sinxcosx.csv deleted file mode 100644 index fc6de76..0000000 --- a/Day_2/WorkProblems/sinxcosx.csv +++ /dev/null @@ -1,501 +0,0 @@ -x,y,errors --10.0,-0.425776851373149,1.0 --9.959919839679358,-1.4396381516927972,1.0 --9.919839679358718,0.6881491598461191,1.0 --9.879759519038076,0.20653795528088714,1.0 --9.839679358717435,-0.2909537651028492,1.0 --9.799599198396793,1.2055319426670597,1.0 --9.759519038076153,1.3206246771402923,1.0 --9.719438877755511,3.726923217788386,1.0 --9.67935871743487,3.7364153132500824,1.0 --9.639278557114228,2.5433428169479795,1.0 --9.599198396793588,3.465681191008987,1.0 --9.559118236472946,4.11042725975779,1.0 --9.519038076152304,5.806028124630713,1.0 --9.478957915831664,3.545849738492476,1.0 --9.438877755511022,3.763390052031965,1.0 --9.39879759519038,3.305108331884175,1.0 --9.358717434869739,4.1947483945582285,1.0 --9.318637274549099,4.627691922255101,1.0 --9.278557114228457,2.9041058081714626,1.0 --9.238476953907815,2.5448494421473997,1.0 --9.198396793587175,3.220614382153631,1.0 --9.158316633266534,2.675322609640157,1.0 --9.118236472945892,1.8594815260161746,1.0 --9.07815631262525,1.4842256876356494,1.0 --9.03807615230461,0.5229276373195768,1.0 --8.997995991983968,-0.850032308250576,1.0 --8.957915831663327,1.3417682880829687,1.0 --8.917835671342685,-2.1610397185260077,1.0 --8.877755511022045,-1.6931733442640717,1.0 --8.837675350701403,-3.536736155930068,1.0 --8.797595190380761,-4.807100808672865,1.0 --8.75751503006012,-5.118114239224083,1.0 --8.71743486973948,-3.947496402306586,1.0 --8.677354709418838,-4.529047060722941,1.0 --8.637274549098196,-5.7197550171340845,1.0 --8.597194388777556,-4.9604545025941364,1.0 --8.557114228456914,-4.507473963353279,1.0 --8.517034068136272,-3.716623499017739,1.0 --8.47695390781563,-4.692363594445897,1.0 --8.43687374749499,-4.181391311927847,1.0 --8.396793587174349,-3.40011440043062,1.0 --8.356713426853707,-2.636530607768073,1.0 --8.316633266533067,-2.912766265161502,1.0 --8.276553106212425,-1.3547665062087877,1.0 --8.236472945891784,-1.8565206886997576,1.0 --8.196392785571142,-2.194863454098596,1.0 --8.156312625250502,-1.7848734705832179,1.0 --8.11623246492986,-1.0788745169149923,1.0 --8.076152304609218,0.14226060520362294,1.0 --8.036072144288577,0.22893794769767764,1.0 --7.9959919839679365,-1.9931128356232437,1.0 --7.955911823647295,-0.07085369958476712,1.0 --7.915831663326653,1.008642433619819,1.0 --7.875751503006012,0.02452144438381114,1.0 --7.835671342685371,0.206196853304224,1.0 --7.7955911823647295,0.23807270525954877,1.0 --7.755511022044088,0.016466471833574303,1.0 --7.715430861723447,-0.4544212666805058,1.0 --7.675350701402806,-1.5323497298766107,1.0 --7.635270541082164,-2.5351489252586026,1.0 --7.595190380761523,-1.3125855683932428,1.0 --7.555110220440882,-3.7679208794255925,1.0 --7.515030060120241,-3.9577128580110377,1.0 --7.474949899799599,-1.3305974503004143,1.0 --7.434869739478958,-1.917032502242029,1.0 --7.394789579158317,-3.455885783498319,1.0 --7.354709418837675,-3.7374111027230055,1.0 --7.314629258517034,-2.2071457308286595,1.0 --7.274549098196393,-1.9548960141216605,1.0 --7.234468937875752,-3.2415100358124334,1.0 --7.19438877755511,-1.663100800540458,1.0 --7.154308617234469,-1.120774824482366,1.0 --7.114228456913828,-0.8999730574906896,1.0 --7.074148296593187,-1.5502178483928084,1.0 --7.034068136272545,-1.2659488802238483,1.0 --6.993987975951904,-0.7539547811848817,1.0 --6.953907815631263,0.018357669571113067,1.0 --6.913827655310621,-0.07247725119664783,1.0 --6.8737474949899795,2.045986156169513,1.0 --6.833667334669339,2.3394790678825332,1.0 --6.793587174348698,1.2869227150161042,1.0 --6.753507014028056,5.637536516859047,1.0 --6.713426853707415,3.8279681685210534,1.0 --6.673346693386774,3.285868198549273,1.0 --6.6332665330661325,4.761807883208727,1.0 --6.593186372745491,5.448841386521833,1.0 --6.55310621242485,3.3177201287503952,1.0 --6.513026052104209,4.076810111657272,1.0 --6.472945891783567,5.155019451515147,1.0 --6.432865731462925,4.938235131818541,1.0 --6.3927855711422845,5.136950006419752,1.0 --6.352705410821644,5.092089442478047,1.0 --6.312625250501002,3.797130147172454,1.0 --6.272545090180361,3.6082821214029246,1.0 --6.23246492985972,5.171966908454643,1.0 --6.192384769539078,0.5534768679772433,1.0 --6.152304609218437,1.466887928702275,1.0 --6.112224448897796,1.3923315086219452,1.0 --6.072144288577155,0.5513451074927821,1.0 --6.032064128256513,1.6809039991594905,1.0 --5.991983967935872,0.8695204157419003,1.0 --5.95190380761523,-0.19326469134888308,1.0 --5.9118236472945895,-1.7392417658404349,1.0 --5.871743486973948,0.15258345338783974,1.0 --5.831663326653307,-0.006072441037880738,1.0 --5.791583166332665,-0.4299287235796269,1.0 --5.751503006012024,-0.06772916579522215,1.0 --5.711422845691383,0.4301628985753896,1.0 --5.671342685370742,-0.5100002047631242,1.0 --5.631262525050101,1.164250498461913,1.0 --5.591182364729459,1.607020940605419,1.0 --5.551102204408818,1.5353081995878113,1.0 --5.511022044088176,0.626207796137499,1.0 --5.470941883767535,1.3458870258496358,1.0 --5.430861723446894,0.8159147687290064,1.0 --5.390781563126253,0.8151795272934453,1.0 --5.350701402805611,2.290030707549174,1.0 --5.31062124248497,0.7587124418257136,1.0 --5.270541082164329,2.707946092383573,1.0 --5.2304609218436875,1.5100461950853972,1.0 --5.190380761523047,2.612387044639097,1.0 --5.150300601202405,1.9499364313647083,1.0 --5.110220440881764,1.2202035587514044,1.0 --5.070140280561122,0.19084308346729162,1.0 --5.030060120240481,0.42281995678698414,1.0 --4.98997995991984,3.0864752406735505,1.0 --4.949899799599199,-0.46462799605564686,1.0 --4.909819639278557,-2.8247346301592904,1.0 --4.869739478957916,-1.190150019401957,1.0 --4.829659318637275,-2.0190091288495675,1.0 --4.789579158316633,-4.214077508807273,1.0 --4.7494989979959925,-3.438287554152086,1.0 --4.709418837675351,-4.020628324077208,1.0 --4.66933867735471,-5.426737934508597,1.0 --4.629258517034068,-4.730770688392318,1.0 --4.589178356713427,-4.680633408666256,1.0 --4.5490981963927855,-7.420080497255739,1.0 --4.509018036072145,-6.391723887354823,1.0 --4.468937875751503,-5.063631301272972,1.0 --4.428857715430862,-6.952867240995252,1.0 --4.388777555110221,-4.748167607753539,1.0 --4.348697394789579,-5.82321670632764,1.0 --4.308617234468938,-4.786718684193782,1.0 --4.268537074148297,-2.8258987946057217,1.0 --4.228456913827656,-2.3333536891752296,1.0 --4.188376753507014,-1.653890159589313,1.0 --4.148296593186373,-2.314096238900896,1.0 --4.108216432865731,-1.1882645315335698,1.0 --4.0681362725450905,-0.8788137932976106,1.0 --4.028056112224449,0.008628933480081091,1.0 --3.987975951903808,0.8942612678623474,1.0 --3.947895791583167,0.9316468799509358,1.0 --3.907815631262525,1.1559695802576975,1.0 --3.8677354709418843,0.5427299500041077,1.0 --3.8276553106212425,0.7184221209671764,1.0 --3.7875751503006017,2.7678178812200835,1.0 --3.74749498997996,1.562769883302627,1.0 --3.707414829659319,2.9941844433908207,1.0 --3.6673346693386772,1.9239424431504886,1.0 --3.6272545090180364,3.3229674667870626,1.0 --3.5871743486973955,0.06947625582481898,1.0 --3.5470941883767537,0.7347723040993652,1.0 --3.507014028056113,1.1026769329361499,1.0 --3.466933867735471,0.6581197810070751,1.0 --3.42685370741483,0.9168521429066592,1.0 --3.3867735470941884,1.0232175298753612,1.0 --3.3466933867735476,-0.3943473768834965,1.0 --3.306613226452906,0.34987934150589134,1.0 --3.266533066132265,-3.2263877199083746,1.0 --3.226452905811623,0.7501803798237153,1.0 --3.1863727454909823,-0.28691650099491467,1.0 --3.1462925851703414,0.8640336126411996,1.0 --3.1062124248496996,-0.8420288837294851,1.0 --3.0661322645290587,-1.414600690095312,1.0 --3.026052104208417,0.8470064793645526,1.0 --2.985971943887776,0.06410234003478904,1.0 --2.9458917835671343,2.3086506743964574,1.0 --2.9058116232464934,1.4857958371965743,1.0 --2.8657314629258517,1.6157301053203446,1.0 --2.825651302605211,3.338410423032256,1.0 --2.785571142284569,1.9398316719022186,1.0 --2.745490981963928,3.080450387445106,1.0 --2.7054108216432873,5.13301362576721,1.0 --2.6653306613226455,3.7578455344151447,1.0 --2.6252505010020046,3.5575952914451707,1.0 --2.585170340681363,4.9989194088868425,1.0 --2.545090180360722,5.089076723338761,1.0 --2.50501002004008,4.825127663633411,1.0 --2.4649298597194393,4.218033364993695,1.0 --2.4248496993987976,4.036490112269771,1.0 --2.3847695390781567,7.003486185979501,1.0 --2.344689378757515,4.942618405618688,1.0 --2.304609218436874,5.466319171845647,1.0 --2.264529058116233,5.792006353652675,1.0 --2.2244488977955914,2.4396713159177748,1.0 --2.1843687374749505,2.6202676324900036,1.0 --2.1442885771543088,2.129943519415195,1.0 --2.104208416833668,1.5938289168717708,1.0 --2.064128256513026,1.4863147351691364,1.0 --2.0240480961923852,0.9480847757095723,1.0 --1.9839679358717444,-0.7133464749195266,1.0 --1.9438877755511026,-0.6359551787933431,1.0 --1.9038076152304608,0.4304906458417388,1.0 --1.8637274549098208,-2.0374671248071383,1.0 --1.823647294589179,-4.241534026807106,1.0 --1.7835671342685373,-2.46816384880366,1.0 --1.7434869739478955,-3.1748834846349827,1.0 --1.7034068136272555,-4.43502608650295,1.0 --1.6633266533066138,-4.723034236927438,1.0 --1.623246492985972,-1.817538468059806,1.0 --1.5831663326653302,-2.90397738260504,1.0 --1.5430861723446903,-3.7406567298298103,1.0 --1.5030060120240485,-3.60350568176683,1.0 --1.4629258517034067,-2.7345271799012174,1.0 --1.4228456913827667,-2.7176809844632146,1.0 --1.382765531062125,-2.0283851998988056,1.0 --1.3426853707414832,-2.4886947172622196,1.0 --1.3026052104208414,-0.9224184260172006,1.0 --1.2625250501002014,0.6086644446466272,1.0 --1.2224448897795597,1.3741206627590028,1.0 --1.182364729458918,-1.078477799408929,1.0 --1.1422845691382761,1.171359548896174,1.0 --1.1022044088176361,0.37295989143413527,1.0 --1.0621242484969944,0.8659335382714298,1.0 --1.0220440881763526,1.2029696035994737,1.0 --0.9819639278557126,0.44015080710065807,1.0 --0.9418837675350709,-0.14974175170091322,1.0 --0.9018036072144291,-0.41890297608769694,1.0 --0.8617234468937873,-1.5803684511550409,1.0 --0.8216432865731473,-1.622061884287977,1.0 --0.7815631262525056,-0.12769722487207025,1.0 --0.7414829659318638,-2.9049755272187476,1.0 --0.701402805611222,-2.1558068036114166,1.0 --0.661322645290582,-2.984978380295995,1.0 --0.6212424849699403,-2.323748298959637,1.0 --0.5811623246492985,-3.408141396495945,1.0 --0.5410821643286585,-4.858959276376871,1.0 --0.5010020040080168,-2.635068586782855,1.0 --0.460921843687375,-4.011928883519314,1.0 --0.4208416833667332,-4.310059443290885,1.0 --0.38076152304609323,-5.411120627445769,1.0 --0.34068136272545146,-5.547260610937407,1.0 --0.3006012024048097,-5.836791342964298,1.0 --0.26052104208416793,-4.001110684154576,1.0 --0.22044088176352794,-4.312658802752942,1.0 --0.18036072144288617,-1.4375185369026764,1.0 --0.1402805611222444,-3.5845386378183797,1.0 --0.10020040080160442,-1.7859624367244327,1.0 --0.06012024048096265,-0.9565234158605268,1.0 --0.020040080160320883,0.031743101834496745,1.0 -0.020040080160320883,-1.1820833339927799,1.0 -0.060120240480960874,-0.22471490352652967,1.0 -0.10020040080160264,0.13553510028454951,1.0 -0.1402805611222444,2.44188231937347,1.0 -0.18036072144288617,2.9181092211260173,1.0 -0.22044088176352616,2.5787222860226873,1.0 -0.26052104208416793,3.0892552418867405,1.0 -0.3006012024048097,4.572492262628463,1.0 -0.3406813627254497,5.221503672362257,1.0 -0.38076152304609145,2.88635729467216,1.0 -0.4208416833667332,3.912267717689319,1.0 -0.460921843687375,4.267408633055452,1.0 -0.501002004008015,3.2629725001317023,1.0 -0.5410821643286567,2.8746582624390227,1.0 -0.5811623246492985,4.969199037329814,1.0 -0.6212424849699403,3.400146097670637,1.0 -0.6613226452905803,2.6737451832988643,1.0 -0.701402805611222,3.804646958933244,1.0 -0.7414829659318638,2.5888529268041527,1.0 -0.7815631262525038,0.4022313973959766,1.0 -0.8216432865731456,1.9190155883836484,1.0 -0.8617234468937873,0.26887578587875693,1.0 -0.9018036072144291,2.4257713331396116,1.0 -0.9418837675350691,-0.3512164615136511,1.0 -0.9819639278557108,0.505675304485768,1.0 -1.0220440881763526,-0.9228640183707901,1.0 -1.0621242484969944,0.4975389198353205,1.0 -1.1022044088176344,0.6198543392068647,1.0 -1.1422845691382761,-0.49993549908336293,1.0 -1.182364729458918,0.3604602297446228,1.0 -1.222444889779558,1.3384088952591686,1.0 -1.2625250501001997,0.7827879543274534,1.0 -1.3026052104208414,1.9299726816384708,1.0 -1.3426853707414832,4.734200265906788,1.0 -1.3827655310621232,1.27881310044318,1.0 -1.422845691382765,2.2610623874138414,1.0 -1.4629258517034067,1.804348081719044,1.0 -1.5030060120240485,3.544503899563343,1.0 -1.5430861723446885,3.4032189493392195,1.0 -1.5831663326653302,4.0111313564341575,1.0 -1.623246492985972,3.8426007335700465,1.0 -1.663326653306612,3.795757459739523,1.0 -1.7034068136272538,2.4927100018700137,1.0 -1.7434869739478955,5.428666438994173,1.0 -1.7835671342685373,1.7448212419053168,1.0 -1.8236472945891773,1.3313927036965278,1.0 -1.863727454909819,2.519870139616806,1.0 -1.9038076152304608,0.18409097050286038,1.0 -1.9438877755511026,1.7025536224993454,1.0 -1.9839679358717426,-0.45384199653379875,1.0 -2.0240480961923843,0.08538415825458717,1.0 -2.064128256513026,-0.49707682237063955,1.0 -2.104208416833666,-1.4220317907362268,1.0 -2.144288577154308,-2.777770693586512,1.0 -2.1843687374749496,-0.4215134367354785,1.0 -2.2244488977955914,-2.5090153245953735,1.0 -2.2645290581162314,-3.6548131220827704,1.0 -2.304609218436873,-7.295356168243606,1.0 -2.344689378757515,-5.123809912837043,1.0 -2.3847695390781567,-6.3662110398386975,1.0 -2.4248496993987967,-4.646668884559351,1.0 -2.4649298597194385,-4.821911593299997,1.0 -2.50501002004008,-6.410782268385596,1.0 -2.54509018036072,-4.262708507906483,1.0 -2.585170340681362,-5.273546895833367,1.0 -2.6252505010020037,-5.893158373824194,1.0 -2.6653306613226455,-7.032951887008716,1.0 -2.7054108216432855,-4.727308357158096,1.0 -2.7454909819639273,-2.099146581896243,1.0 -2.785571142284569,-4.553628997091142,1.0 -2.825651302605209,-1.7818773621726078,1.0 -2.865731462925851,-2.0346184843715003,1.0 -2.9058116232464926,-0.34459681420223987,1.0 -2.9458917835671343,-0.9248026870363075,1.0 -2.9859719438877743,-0.16489996473955065,1.0 -3.026052104208416,-0.256009496456303,1.0 -3.066132264529058,0.38943591286330276,1.0 -3.1062124248496996,1.529028429751906,1.0 -3.1462925851703396,1.0739161816409974,1.0 -3.1863727454909814,2.1667067510726024,1.0 -3.226452905811623,2.5348371671599814,1.0 -3.266533066132263,0.812178259868159,1.0 -3.306613226452905,0.6206780882963903,1.0 -3.3466933867735467,0.3319238097346798,1.0 -3.3867735470941884,-0.8278202388892768,1.0 -3.4268537074148284,-1.2509712571813858,1.0 -3.46693386773547,-4.043930010974777,1.0 -3.507014028056112,-2.208256604970848,1.0 -3.5470941883767537,-2.3400529924237206,1.0 -3.5871743486973937,-1.845279168627025,1.0 -3.6272545090180355,-0.009755196673416622,1.0 -3.6673346693386772,-1.962498625123372,1.0 -3.7074148296593172,-3.304162846686073,1.0 -3.747494989979959,-1.8435793133189613,1.0 -3.7875751503006008,-1.6231872384385864,1.0 -3.8276553106212425,-2.7254568366716545,1.0 -3.8677354709418825,-2.0502884653239204,1.0 -3.9078156312625243,-1.3259890932114116,1.0 -3.947895791583166,-1.147906529462757,1.0 -3.987975951903808,0.8464747544137012,1.0 -4.028056112224448,-0.42405542429926857,1.0 -4.06813627254509,1.5954628617596884,1.0 -4.108216432865731,1.0402026291118915,1.0 -4.148296593186371,4.2363654589562065,1.0 -4.188376753507013,2.9291795769283953,1.0 -4.228456913827655,4.476526211127217,1.0 -4.268537074148297,3.752532174879673,1.0 -4.308617234468937,4.757574088842577,1.0 -4.348697394789578,3.9661364143327633,1.0 -4.38877755511022,5.797100434317724,1.0 -4.428857715430862,6.04719051709667,1.0 -4.468937875751502,4.245243248938507,1.0 -4.509018036072144,6.257344015281635,1.0 -4.5490981963927855,5.291622328590534,1.0 -4.589178356713425,5.786672400008754,1.0 -4.629258517034067,5.5113194503062735,1.0 -4.669338677354709,4.42161500906025,1.0 -4.709418837675351,4.713347340396827,1.0 -4.749498997995991,2.997256494550985,1.0 -4.7895791583166325,3.174810170951396,1.0 -4.829659318637274,2.711736882576277,1.0 -4.869739478957916,-0.002379243313710644,1.0 -4.909819639278556,0.37550784034922347,1.0 -4.949899799599198,1.252664420702897,1.0 -4.98997995991984,0.16238851111250197,1.0 -5.0300601202404795,-0.32491286092475463,1.0 -5.070140280561121,-1.7115895176604523,1.0 -5.110220440881763,0.6480916290436474,1.0 -5.150300601202405,-1.1493137196649017,1.0 -5.190380761523045,-1.2367526520393965,1.0 -5.230460921843687,-2.8408130286460116,1.0 -5.270541082164328,-2.5216255177752527,1.0 -5.31062124248497,-3.059248489717678,1.0 -5.35070140280561,-1.809241996291318,1.0 -5.390781563126252,-1.7359907401394836,1.0 -5.430861723446894,0.0009511228255500992,1.0 -5.470941883767534,-0.8514375657654203,1.0 -5.511022044088175,0.21787453971628867,1.0 -5.551102204408817,-0.24151846297930282,1.0 -5.591182364729459,0.05367690835976047,1.0 -5.631262525050099,-0.17563383181223766,1.0 -5.671342685370741,-2.436188042622763,1.0 -5.7114228456913825,1.1142096619533954,1.0 -5.751503006012024,-0.9688777069003245,1.0 -5.791583166332664,0.6254814811875122,1.0 -5.831663326653306,1.4956668110677,1.0 -5.871743486973948,0.9018072999312947,1.0 -5.911823647294588,0.24025244250519195,1.0 -5.9519038076152295,0.5133865725072555,1.0 -5.991983967935871,0.7335039122373852,1.0 -6.032064128256511,0.14345240491540046,1.0 -6.072144288577153,-0.14607923623530272,1.0 -6.112224448897795,-0.2313113995459639,1.0 -6.152304609218437,-1.7525274128000412,1.0 -6.192384769539078,-2.5451689671648,1.0 -6.23246492985972,-2.5730758314316584,1.0 -6.272545090180358,-3.9278484794599726,1.0 -6.312625250501,-2.205311660775665,1.0 -6.352705410821642,-2.644297056282195,1.0 -6.392785571142284,-5.130010867892212,1.0 -6.432865731462925,-3.223434094493319,1.0 -6.472945891783567,-5.141012023267372,1.0 -6.513026052104209,-5.50372410938494,1.0 -6.553106212424851,-5.7716498240768885,1.0 -6.593186372745489,-5.194809352354915,1.0 -6.633266533066131,-6.7692268315787,1.0 -6.673346693386772,-4.874105388978519,1.0 -6.713426853707414,-4.380417646826108,1.0 -6.753507014028056,-3.283242447803382,1.0 -6.793587174348698,-4.7701349297627695,1.0 -6.8336673346693395,-2.6282807988778996,1.0 -6.873747494989978,-3.233100641080867,1.0 -6.9138276553106195,-1.1618897269199289,1.0 -6.953907815631261,-0.9558851229476705,1.0 -6.993987975951903,-2.1033776717517325,1.0 -7.034068136272545,0.060856252913635,1.0 -7.074148296593187,0.8788543021211715,1.0 -7.114228456913828,2.317110696484227,1.0 -7.1543086172344665,3.1071687885876726,1.0 -7.194388777555108,4.03763794591928,1.0 -7.23446893787575,1.986518227804928,1.0 -7.274549098196392,3.03007564484431,1.0 -7.314629258517034,3.1866063889468976,1.0 -7.354709418837675,4.086501620666799,1.0 -7.394789579158317,1.7719848591647376,1.0 -7.434869739478959,3.710537934876104,1.0 -7.474949899799597,3.197814519409537,1.0 -7.515030060120239,1.595256585406507,1.0 -7.555110220440881,3.19373180673289,1.0 -7.595190380761522,2.4256028064988024,1.0 -7.635270541082164,2.2030886198689243,1.0 -7.675350701402806,1.1420330941716605,1.0 -7.715430861723448,0.4851516149064727,1.0 -7.755511022044086,0.20716002503293035,1.0 -7.795591182364728,0.19580387649446046,1.0 -7.8356713426853695,-0.1467717098359905,1.0 -7.875751503006011,0.27220471509912886,1.0 -7.915831663326653,-0.42473607902301225,1.0 -7.955911823647295,2.712177731164522,1.0 -7.9959919839679365,0.2764252928104951,1.0 -8.036072144288575,-0.353816136027646,1.0 -8.076152304609217,-0.7082522823721131,1.0 -8.116232464929858,1.3242395627905097,1.0 -8.1563126252505,1.0780782495755037,1.0 -8.196392785571142,0.57245890604494,1.0 -8.236472945891784,-0.4886761725660036,1.0 -8.276553106212425,1.9504058527862433,1.0 -8.316633266533067,3.032761416192749,1.0 -8.356713426853705,2.4562561730750967,1.0 -8.396793587174347,4.4095529676972065,1.0 -8.436873747494989,2.658794191087008,1.0 -8.47695390781563,3.904198714229472,1.0 -8.517034068136272,2.4820778673011223,1.0 -8.557114228456914,5.295744676630926,1.0 -8.597194388777556,4.540274492713099,1.0 -8.637274549098194,4.056716118316407,1.0 -8.677354709418836,2.580252850992746,1.0 -8.717434869739478,4.4229753206305675,1.0 -8.75751503006012,4.299854066676796,1.0 -8.797595190380761,5.235608543082918,1.0 -8.837675350701403,1.8195054195405587,1.0 -8.877755511022045,1.5430064754297765,1.0 -8.917835671342683,0.525915329619596,1.0 -8.957915831663325,2.0772523060617005,1.0 -8.997995991983966,-0.48665734070746564,1.0 -9.038076152304608,0.663868157301438,1.0 -9.07815631262525,-1.1216823356645502,1.0 -9.118236472945892,-1.1943688387135163,1.0 -9.158316633266534,-0.9218834894081875,1.0 -9.198396793587175,-1.5130362035760712,1.0 -9.238476953907814,-2.2944802659152788,1.0 -9.278557114228455,-3.2650317642924356,1.0 -9.318637274549097,-4.7246769911659765,1.0 -9.358717434869739,-5.649329406755587,1.0 -9.39879759519038,-6.6041841694082155,1.0 -9.438877755511022,-4.017477318092292,1.0 -9.478957915831664,-3.529920446411498,1.0 -9.519038076152302,-4.193953386930499,1.0 -9.559118236472944,-6.129026083941931,1.0 -9.599198396793586,-4.292813965352905,1.0 -9.639278557114228,-1.9781936552203632,1.0 -9.67935871743487,-2.2542333363110028,1.0 -9.719438877755511,-2.006359098565377,1.0 -9.759519038076153,-2.3553691151533624,1.0 -9.799599198396791,-0.4821931439665318,1.0 -9.839679358717433,-1.4127903866524782,1.0 -9.879759519038075,-1.4089500844438927,1.0 -9.919839679358716,0.9119627172905778,1.0 -9.959919839679358,-1.3877638206109302,1.0 -10.0,-1.3936558611397516,1.0 diff --git a/Day_2/bikes.csv b/Day_2/bikes.csv deleted file mode 100644 index 9ac7990..0000000 --- a/Day_2/bikes.csv +++ /dev/null @@ -1,311 +0,0 @@ -Date;Berri 1;Brébeuf (données non disponibles);Côte-Sainte-Catherine;Maisonneuve 1;Maisonneuve 2;du Parc;Pierre-Dupuy;Rachel1;St-Urbain (données non disponibles) -01/01/2012;35;;0;38;51;26;10;16; -02/01/2012;83;;1;68;153;53;6;43; -03/01/2012;135;;2;104;248;89;3;58; -04/01/2012;144;;1;116;318;111;8;61; -05/01/2012;197;;2;124;330;97;13;95; -06/01/2012;146;;0;98;244;86;4;75; -07/01/2012;98;;2;80;108;53;6;54; -08/01/2012;95;;1;62;98;64;11;63; -09/01/2012;244;;2;165;432;198;12;173; -10/01/2012;397;;3;238;563;275;18;241; -11/01/2012;273;;0;182;443;258;12;194; -12/01/2012;157;;1;134;261;137;9;63; -13/01/2012;75;;0;41;105;64;2;0; -14/01/2012;32;;0;54;56;19;0;1; -15/01/2012;54;;0;33;60;18;0;0; -16/01/2012;168;;2;136;312;137;1;0 -17/01/2012;155;;0;86;256;74;0;0 -18/01/2012;139;;0;66;188;68;3;0 -19/01/2012;191;;1;104;248;79;3;0 -20/01/2012;161;;4;96;217;67;1;1 -21/01/2012;53;;0;47;70;32;1;0 -22/01/2012;71;;0;41;73;35;5;0 -23/01/2012;210;;6;114;357;91;6;0 -24/01/2012;299;;1;189;444;174;4;0 -25/01/2012;334;;1;217;453;180;4;0 -26/01/2012;306;;0;215;495;191;0;1 -27/01/2012;91;;5;79;204;65;0;0 -28/01/2012;80;;1;61;123;33;9;1 -29/01/2012;87;;1;65;132;40;7;0 -30/01/2012;219;;0;146;371;152;2;0 -31/01/2012;186;;1;109;324;122;0;0 -01/02/2012;138;;0;100;271;71;5;0 -02/02/2012;217;;5;134;345;128;2;2 -03/02/2012;174;;1;103;301;111;1;1 -04/02/2012;84;;0;53;119;44;8;0 -05/02/2012;72;;0;46;133;54;7;0 -06/02/2012;248;;1;136;425;167;10;0 -07/02/2012;316;;0;209;516;225;9;0 -08/02/2012;271;;0;202;503;215;4;1 -09/02/2012;342;;0;227;471;231;11;0 -10/02/2012;303;;1;206;478;216;6;2 -11/02/2012;71;;0;63;112;49;3;0 -12/02/2012;78;;0;36;91;53;5;0 -13/02/2012;211;;0;175;408;207;4;0 -14/02/2012;318;;0;186;504;243;9;1 -15/02/2012;307;;0;180;491;232;5;1 -16/02/2012;386;;1;212;569;295;10;0 -17/02/2012;332;;3;237;496;260;11;1 -18/02/2012;220;;1;159;280;134;30;0 -19/02/2012;169;;3;110;205;113;13;2 -20/02/2012;303;;2;224;470;243;14;10 -21/02/2012;441;;5;292;503;286;10;6 -22/02/2012;375;;10;263;501;264;12;3 -23/02/2012;397;;10;293;528;329;22;13 -24/02/2012;243;;4;187;313;137;4;13 -25/02/2012;62;;0;48;52;18;2;5 -26/02/2012;78;;0;76;47;35;4;0 -27/02/2012;119;;4;94;298;134;3;5 -28/02/2012;195;;0;158;350;168;9;3 -29/02/2012;242;;0;164;446;190;7;4 -01/03/2012;92;;0;56;199;63;0;14 -02/03/2012;143;;0;56;283;77;7;1 -03/03/2012;82;;0;65;133;47;2;0 -04/03/2012;107;;2;79;138;60;15;0 -05/03/2012;155;;1;114;363;157;8;2 -06/03/2012;269;;0;192;437;170;10;0 -07/03/2012;438;;3;290;715;266;28;0 -08/03/2012;348;;6;238;530;270;9;9 -09/03/2012;371;;8;279;575;268;8;38 -10/03/2012;182;;13;162;296;165;0;58 -11/03/2012;380;;60;253;540;289;0;285 -12/03/2012;802;;179;618;1265;747;0;548 -13/03/2012;442;;145;321;769;425;0;325 -14/03/2012;469;;146;313;739;451;0;336 -15/03/2012;724;;244;562;1021;631;63;565 -16/03/2012;423;;149;305;695;419;9;422 -17/03/2012;681;;287;422;872;468;334;1008 -18/03/2012;1940;;856;1036;1923;1021;1128;2477 -19/03/2012;1821;;1024;1278;2581;1609;506;2058 -20/03/2012;2481;;1261;1709;3130;1955;762;2609 -21/03/2012;2829;;1558;1893;3510;2225;993;2846 -22/03/2012;2195;;1030;1640;2654;1958;548;2254 -23/03/2012;2115;;1143;1512;2955;1791;663;2325 -24/03/2012;753;;336;517;1001;635;277;1035 -25/03/2012;520;;243;309;691;427;145;723 -26/03/2012;968;;564;729;1493;965;130;1168 -27/03/2012;1049;;517;774;1576;972;163;1207 -28/03/2012;435;;179;329;709;486;28;529 -29/03/2012;878;;406;646;1264;807;78;937 -30/03/2012;1157;;529;910;1596;957;196;1288 -31/03/2012;980;;499;587;1083;706;524;1370 -01/04/2012;662;;341;442;824;471;168;1086 -02/04/2012;1937;;967;1537;2853;1614;394;2122 -03/04/2012;2416;;1078;1791;3556;1880;513;2450 -04/04/2012;2211;;933;1674;2956;1666;274;2242 -05/04/2012;2424;;1036;1823;3273;1699;355;2463 -06/04/2012;1633;;650;1045;1913;975;621;2138 -07/04/2012;1208;;494;739;1445;709;598;1566 -08/04/2012;1164;;560;621;1333;704;792;1533 -09/04/2012;828;;298;560;1048;605;65;1001 -10/04/2012;2183;;909;1588;2932;1736;252;2108 -11/04/2012;2328;;1049;1765;3122;1843;330;2311 -12/04/2012;3064;;1483;2306;4076;2280;590;3213 -13/04/2012;3341;;1505;2565;4465;2358;922;3728 -14/04/2012;2890;;1072;1639;2994;1594;1284;3428 -15/04/2012;2554;;1210;1637;2954;1559;1846;3604 -16/04/2012;3643;;1841;2723;4830;2677;1061;3616 -17/04/2012;3539;;1616;2636;4592;2450;544;3333 -18/04/2012;3570;;1751;2759;4655;2534;706;3542 -19/04/2012;4231;;2010;3235;5311;2877;1206;3929 -20/04/2012;2087;;800;1529;2922;1531;170;2065 -21/04/2012;533;;212;398;710;408;50;755 -22/04/2012;1853;;487;1224;1331;654;198;1779 -23/04/2012;623;;315;544;1076;612;27;846 -24/04/2012;1810;;720;1355;2379;1286;188;1753 -25/04/2012;2966;;1023;2228;3444;1800;445;2454 -26/04/2012;2751;;1069;2196;3546;1789;381;2438 -27/04/2012;1986;;743;1526;2586;1298;139;1899 -28/04/2012;1684;;628;1190;1908;931;523;2323 -29/04/2012;1970;;765;1212;2077;1062;702;2493 -30/04/2012;3610;;1572;2825;4675;2446;851;3541 -01/05/2012;1986;;815;1722;2766;1516;195;1960 -02/05/2012;3724;;1677;2885;4731;2508;876;3501 -03/05/2012;3698;;1618;3001;4943;2577;731;3603 -04/05/2012;2511;;1163;2058;3717;1823;380;2631 -05/05/2012;3492;;1366;2106;3696;1779;1677;4108 -06/05/2012;3411;;1525;1815;3346;1879;2036;4633 -07/05/2012;5552;;2573;3959;6355;3416;1848;5253 -08/05/2012;1241;;625;991;1729;1007;119;1383 -09/05/2012;3297;;1545;2700;4343;2340;737;3129 -10/05/2012;2755;;1227;2130;4056;2075;399;2437 -11/05/2012;4639;;1803;3663;5713;2888;1260;4499 -12/05/2012;3854;;1457;2429;3894;1805;2268;4855 -13/05/2012;2741;;1223;1703;3086;1592;1394;3496 -14/05/2012;6189;;2709;4402;7006;3868;2215;5775 -15/05/2012;3964;;1773;3144;5088;2650;975;3765 -16/05/2012;4947;;2178;3681;5882;3057;1332;4348 -17/05/2012;5351;;2441;4182;6551;3408;1631;4988 -18/05/2012;5980;;2241;4415;6646;3196;1711;5273 -19/05/2012;4732;;1454;2807;4673;1966;2914;5293 -20/05/2012;5255;;1663;2730;4462;2182;4241;5539 -21/05/2012;5129;;1646;2672;4169;2044;3413;5053 -22/05/2012;2315;;938;1847;2599;1610;251;2069 -23/05/2012;5974;;2650;4407;7281;3737;1826;4798 -24/05/2012;6485;;2653;4600;7600;3792;2062;5209 -25/05/2012;5697;;2205;4096;6734;3341;1953;5174 -26/05/2012;4974;;1622;2936;4991;2373;3455;5443 -27/05/2012;4396;;1525;2578;4587;2073;2886;5168 -28/05/2012;4268;;1962;3449;5798;2898;1027;3894 -29/05/2012;3154;;1184;2325;3904;1933;475;2731 -30/05/2012;6459;;2722;4806;7632;3817;2454;5172 -31/05/2012;5104;;2177;3985;6631;3205;1389;4410 -01/06/2012;6097;;2604;4110;7175;3895;1692;6595 -02/06/2012;943;;392;630;1289;628;71;1436 -03/06/2012;2755;;1897;2020;3768;2324;1312;4936 -04/06/2012;2717;;1408;2095;4276;2168;333;3090 -05/06/2012;5842;;2721;3927;7302;3786;1232;5348 -06/06/2012;6037;;2724;4273;7822;3987;1223;5269 -07/06/2012;6246;;2607;4670;8222;3972;1019;5724 -08/06/2012;4169;;1833;3303;5881;3001;623;4474 -09/06/2012;5164;;1672;3418;5557;2451;1551;6026 -10/06/2012;5112;;1812;2905;4798;2328;1833;6170 -11/06/2012;6206;;2577;4333;7015;3757;1264;5721 -12/06/2012;3361;;1556;2362;4230;2381;589;3030 -13/06/2012;6180;;2792;4374;7297;3825;1968;5887 -14/06/2012;6908;;2978;4809;7934;4223;2386;6243 -15/06/2012;7077;;2469;4999;7663;4053;2293;6491 -16/06/2012;5421;;1651;3099;4909;2337;3280;5537 -17/06/2012;4638;;1552;2082;4108;2020;3453;5128 -18/06/2012;5921;;2703;3582;6824;3960;2455;5496 -19/06/2012;5382;;2360;3447;6679;3438;1711;5015 -20/06/2012;5713;;2402;3524;6848;3510;2363;4852 -21/06/2012;5183;;2195;3297;6684;3272;2265;4957 -22/06/2012;5398;;1934;3612;6659;3252;2009;5263 -23/06/2012;3753;;1326;1842;3992;1910;2585;4824 -24/06/2012;3341;;1152;1423;2989;1510;3634;4581 -25/06/2012;2245;;1077;1374;3031;1440;1349;3283 -26/06/2012;3327;;1435;2139;4318;2193;620;3223 -27/06/2012;3141;;1322;2142;4211;2134;536;2897 -28/06/2012;6064;;2381;4411;6864;3523;2415;5175 -29/06/2012;5770;;1935;4004;6656;3090;2109;5332 -30/06/2012;4738;;1359;3007;4226;1822;2870;4527 -01/07/2012;4758;;1343;2911;3935;1777;3732;4522 -02/07/2012;4144;;1397;2658;3998;1883;2783;4464 -03/07/2012;6712;;2634;4398;7416;3896;2606;5462 -04/07/2012;5153;;2038;3591;5814;3070;1246;4493 -05/07/2012;6672;;2603;4830;7764;3816;2746;5153 -06/07/2012;5958;;2095;4137;6942;3387;2248;5610 -07/07/2012;5420;;1289;3802;4978;2132;3156;4939 -08/07/2012;4756;;1497;2407;4495;2089;4386;5535 -09/07/2012;5661;;2330;3524;6769;3577;2599;4987 -10/07/2012;6500;;2625;4064;7436;3749;2822;5952 -11/07/2012;6424;;2548;3921;7374;3781;2779;5474 -12/07/2012;6179;;2371;3772;7200;3687;2433;5140 -13/07/2012;5518;;2005;3525;6492;3088;2219;5185 -14/07/2012;4206;;1244;2441;4284;1562;2900;4252 -15/07/2012;3035;;1060;1798;3437;1449;2034;3545 -16/07/2012;4827;;2092;3141;6049;2965;1703;4379 -17/07/2012;2756;;1046;2001;3802;1700;569;2641 -18/07/2012;6180;;2589;3750;7311;3654;2813;5884 -19/07/2012;6309;;2397;3996;7699;3777;2647;5665 -20/07/2012;5813;;2088;3744;6959;3316;2538;5775 -21/07/2012;5092;;1419;2957;4658;1821;3835;4848 -22/07/2012;4971;;1238;2683;4104;1788;2960;4787 -23/07/2012;3877;;1660;2420;4937;2534;1461;3475 -24/07/2012;5243;;2318;3465;6721;3317;2170;4694 -25/07/2012;6104;;2593;3786;7073;3622;3132;5059 -26/07/2012;5560;;2043;4174;6476;3208;1595;4383 -27/07/2012;5759;;1948;3658;6851;3275;2653;5295 -28/07/2012;4105;;1345;2504;4540;1767;2468;4631 -29/07/2012;4230;;1332;1791;4235;1905;3731;4906 -30/07/2012;5225;;2285;2975;6219;3196;2564;5100 -31/07/2012;5415;;2185;3145;6705;3222;2505;4468 -01/08/2012;4638;;1842;2783;5846;2810;1481;3889 -02/08/2012;5715;;2361;3340;6956;3329;2643;4828 -03/08/2012;5577;;1915;3306;6427;2789;3195;5031 -04/08/2012;4223;;1305;2019;4425;1769;2939;4467 -05/08/2012;1864;;630;1100;2327;970;1066;2268 -06/08/2012;5240;;2277;3193;6375;3187;2274;4604 -07/08/2012;5997;;2410;3622;6840;3480;2780;4903 -08/08/2012;5498;;2313;3365;6862;3403;2226;4771 -09/08/2012;4127;;1802;2601;5377;2656;1471;3636 -10/08/2012;2414;;934;1727;3323;1641;440;2456 -11/08/2012;2453;;815;1589;2628;1043;1340;2608 -12/08/2012;3995;;1338;1937;3427;1719;2988;4178 -13/08/2012;4931;;2298;3124;5936;3151;2090;4842 -14/08/2012;5333;;2322;3571;6332;3260;1456;4362 -15/08/2012;4297;;1882;2936;4948;2686;970;3877 -16/08/2012;6062;;2538;3898;6549;3366;1549;4943 -17/08/2012;4236;;1777;3099;4979;2581;586;4154 -18/08/2012;4427;;1333;2505;3866;1864;1229;4359 -19/08/2012;4644;;1408;2605;3780;1758;1683;4478 -20/08/2012;5335;;2474;3521;6088;3332;1439;5201 -21/08/2012;5792;;2508;4063;6687;3369;2353;5640 -22/08/2012;6529;;2671;4513;7065;3774;2391;5154 -23/08/2012;5437;;2482;3807;6773;3573;1864;5010 -24/08/2012;5690;;2394;3778;6703;3312;2178;5299 -25/08/2012;4242;;1305;2358;4126;1726;2558;4833 -26/08/2012;3964;;1318;2118;3558;1750;2932;4536 -27/08/2012;4314;;2273;3098;5196;2762;1010;3598 -28/08/2012;5817;;2794;3966;6313;3420;1629;4720 -29/08/2012;6327;;3092;4370;7009;3759;2152;5342 -30/08/2012;6003;;2910;4199;6781;3481;1796;5202 -31/08/2012;4106;;1868;2999;5054;2541;875;4188 -01/09/2012;3934;;1225;2371;3917;1887;2801;3845 -02/09/2012;3698;;1232;2093;3827;1788;2959;4247 -03/09/2012;4179;;1532;2233;3718;1620;3783;4709 -04/09/2012;4225;;1934;3057;5275;3006;894;3868 -05/09/2012;5655;;2722;3861;7110;4061;1383;4491 -06/09/2012;5883;;2883;3967;7511;4014;1498;4732 -07/09/2012;6186;;2720;4153;7323;3886;2051;6104 -08/09/2012;2155;;1192;1467;2739;898;801;2626 -09/09/2012;3312;;1810;1828;3458;2668;2118;4355 -10/09/2012;5077;;2792;3718;6644;3849;1271;4984 -11/09/2012;6015;;2913;4081;7148;4314;1484;5451 -12/09/2012;6349;;3124;4209;7292;4510;1981;5697 -13/09/2012;6520;;3076;4369;7514;4494;1986;5742 -14/09/2012;5216;;2257;3480;6104;3574;1524;4918 -15/09/2012;3341;;1100;1900;3099;1593;1268;3426 -16/09/2012;3635;;1231;1614;3040;1852;2348;3696 -17/09/2012;5299;;2658;3670;6582;4076;1704;4788 -18/09/2012;2530;;1277;1695;3254;2275;388;2377 -19/09/2012;4653;;2351;3176;5746;3738;840;4189 -20/09/2012;5260;;2496;3615;6348;3991;1391;5119 -21/09/2012;4022;;1821;2911;4745;2945;889;3886 -22/09/2012;1521;;656;1073;1682;971;365;1974 -23/09/2012;2314;;1093;1496;2572;1470;984;3321 -24/09/2012;4553;;2246;3117;5738;3705;1100;4683 -25/09/2012;5038;;2236;3273;5861;3712;1007;4420 -26/09/2012;3948;;1873;2720;4755;3119;650;3721 -27/09/2012;5119;;2288;3465;5983;3704;1152;4598 -28/09/2012;4652;;2134;3209;5372;3309;1133;4361 -29/09/2012;1896;;755;1206;1963;1062;442;2267 -30/09/2012;876;;359;513;957;520;133;1162 -01/10/2012;3255;;1576;2184;3830;2582;397;2987 -02/10/2012;5139;;2525;3281;5845;3732;1229;4497 -03/10/2012;4685;;2377;3191;5475;3415;858;3941 -04/10/2012;4034;;2025;2705;4850;3066;555;3418 -05/10/2012;4151;;1977;2799;4688;2844;1035;4088 -06/10/2012;1304;;469;933;1589;776;236;1775 -07/10/2012;1580;;660;922;1629;860;695;2052 -08/10/2012;1854;;880;987;1818;1040;1115;2502 -09/10/2012;4787;;2210;3026;5138;3418;927;4078 -10/10/2012;3115;;1537;2081;3681;2608;560;2703 -11/10/2012;3746;;1857;2569;4694;3034;558;3457 -12/10/2012;3169;;1460;2261;4045;2564;448;3224 -13/10/2012;1783;;802;1205;2113;1183;681;2309 -14/10/2012;587;;287;443;852;503;65;952 -15/10/2012;3292;;1678;2165;4197;2754;560;3183 -16/10/2012;3739;;1858;2684;4681;2997;554;3593 -17/10/2012;4098;;1964;2645;4836;3063;728;3834 -18/10/2012;4671;;2292;3129;5542;3477;1108;4245 -19/10/2012;1313;;597;885;1668;1209;111;1486 -20/10/2012;2011;;748;1323;2266;1213;797;2243 -21/10/2012;1277;;609;869;1777;898;242;1648 -22/10/2012;3650;;1819;2495;4800;3023;757;3721 -23/10/2012;4177;;1997;2795;5216;3233;795;3554 -24/10/2012;3744;;1868;2625;4900;3035;649;3622 -25/10/2012;3735;;1815;2528;5010;3017;631;3767 -26/10/2012;4290;;1987;2754;5246;3000;1456;4578 -27/10/2012;1857;;792;1244;2461;1193;618;2471 -28/10/2012;1310;;697;910;1776;955;387;1876 -29/10/2012;2919;;1458;2071;3768;2440;411;2795 -30/10/2012;2887;;1251;2007;3516;2255;338;2790 -31/10/2012;2634;;1294;1835;3453;2220;245;2570 -01/11/2012;2405;;1208;1701;3082;2076;165;2461 -02/11/2012;1582;;737;1109;2277;1392;97;1888 -03/11/2012;844;;380;612;1137;713;105;1302 -04/11/2012;966;;446;710;1277;692;197;1374 -05/11/2012;2247;;1170;1705;3221;2143;179;2430 diff --git a/Day_2/my_pet_info.json b/Day_2/my_pet_info.json deleted file mode 100644 index a629433..0000000 --- a/Day_2/my_pet_info.json +++ /dev/null @@ -1 +0,0 @@ -{"cat": {"color": "black", "weight": 10, "toys": ["string", "box", "squeaky"]}, "dog": {"color": "white", "weight": 40, "toys": ["ball", "frisbee", "squeaky"]}} \ No newline at end of file diff --git a/Day_2/readme.md b/Day_2/readme.md deleted file mode 100644 index a5a62a9..0000000 --- a/Day_2/readme.md +++ /dev/null @@ -1,9 +0,0 @@ -Day 2 -===== - -Required files: - - * Python Core Jupyter Notebook - * bikes.csv - * 511_lab_E_100k.csv.bz2 - diff --git a/Day_2/vehicle.txt b/Day_2/vehicle.txt deleted file mode 100644 index e69de29..0000000 diff --git a/Day_3/ExtraExamples/jla_lcparams.txt b/Day_3/ExtraExamples/jla_lcparams.txt deleted file mode 100644 index e141a0f..0000000 --- a/Day_3/ExtraExamples/jla_lcparams.txt +++ /dev/null @@ -1,741 +0,0 @@ -#name zcmb zhel dz mb dmb x1 dx1 color dcolor 3rdvar d3rdvar tmax dtmax cov_m_s cov_m_c cov_s_c set ra dec biascor -03D1au 0.503084 0.504300 0 23.001698 0.088031 1.273191 0.150058 -0.012353 0.030011 9.517000 0.110500 52909.745220 0.214332 0.000790 0.000440 -0.000030 1 36.043210 -4.037469 0.001697 -03D1aw 0.580724 0.582000 0 23.573937 0.090132 0.974346 0.273823 -0.025076 0.036691 9.169000 0.088000 52902.898002 0.352732 0.002823 0.000415 0.001574 1 36.061634 -4.517158 0.000843 -03D1ax 0.494795 0.496000 0 22.960139 0.088110 -0.728837 0.102069 -0.099683 0.030305 11.580000 0.112500 52915.923670 0.111634 0.000542 0.000475 -0.000024 1 36.097287 -4.720774 0.001692 -03D1bp 0.345928 0.347000 0 22.398137 0.087263 -1.155110 0.112834 -0.040581 0.026679 10.821000 0.123500 52920.249015 0.102828 0.001114 0.000616 0.000295 1 36.657235 -4.838779 -0.000270 -03D1co 0.677662 0.679000 0 24.078115 0.098356 0.618820 0.404295 -0.039380 0.067403 8.647000 0.284000 52954.458342 0.454715 0.011857 0.000780 0.005898 1 36.567748 -4.935050 -0.002855 -03D1dt 0.610712 0.612000 0 23.285241 0.092877 -1.161563 1.641345 -0.094943 0.049652 9.715000 0.092000 52962.253197 0.976562 0.029671 0.000947 0.044357 1 36.629968 -4.052341 0.000026 -03D1ew 0.866494 0.868000 0 24.353678 0.106037 0.376409 0.348004 -0.063269 0.067817 8.530000 0.805000 52991.741625 0.665126 0.003181 -0.001601 0.004093 1 36.058795 -4.665852 -0.017996 -03D1fc 0.330932 0.332000 0 21.861412 0.086437 0.650394 0.118542 -0.017577 0.023794 10.391000 0.036000 53002.763813 0.104433 0.001006 0.000527 0.000540 1 36.431648 -4.144059 -0.000526 -03D1fq 0.798566 0.800000 0 24.510389 0.101777 -1.056666 0.406837 -0.055975 0.064955 10.651000 0.127000 52999.213046 0.655817 0.005897 -0.001115 0.003551 1 36.731935 -4.302217 -0.011577 -03D3aw 0.449562 0.449000 0 22.667126 0.091912 0.810113 0.232169 -0.085962 0.038205 10.695000 0.034000 52767.328075 0.200656 0.005474 0.000792 0.000662 1 215.223049 52.605624 0.001400 -03D3ay 0.371443 0.370900 0 22.273052 0.091326 0.569579 0.197845 -0.054463 0.033075 10.198000 0.067500 52767.625241 0.141892 0.004907 0.000899 0.000913 1 214.493580 52.482613 0.000189 -03D3ba 0.291718 0.291200 0 21.961068 0.093479 0.760688 0.172975 0.116118 0.035364 10.211000 0.093500 52749.605469 0.804557 -0.002911 0.001326 -0.001142 1 214.139297 52.342190 -0.001074 -03D3bl 0.355820 0.355300 0 22.926619 0.087008 0.055758 0.192657 0.204749 0.029878 10.831000 0.092500 52787.142342 0.288522 0.000395 0.000662 -0.000535 1 214.982501 53.097401 -0.000094 -03D3cd 0.461274 0.460700 0 22.575127 0.095948 1.861682 0.565093 -0.043023 0.038208 9.339000 0.152000 52801.729007 0.226325 0.021901 0.001309 0.010876 1 214.666364 52.612079 0.001514 -03D4ag 0.283610 0.285000 0 21.256943 0.086683 0.937399 0.104658 -0.085197 0.023476 10.628000 0.099000 52830.931258 0.095579 0.001009 0.000524 -0.000243 1 333.690959 -17.739655 -0.001153 -03D4at 0.632234 0.634000 0 23.739180 0.093125 0.208542 0.330258 -0.050531 0.066776 8.751000 0.077500 52816.959534 0.560938 0.003702 0.000788 0.001452 1 333.600166 -17.776726 -0.000739 -03D4au 0.466414 0.468000 0 23.789546 0.090350 0.376996 0.332539 0.122416 0.043376 9.464000 0.106500 52816.177200 0.504015 0.003236 0.000840 0.000892 1 334.041366 -18.077590 0.001557 -03D4cj 0.268624 0.270000 0 21.058469 0.086242 1.150557 0.085414 -0.080120 0.023199 6.000000 5.000000 52889.694235 0.093377 0.000600 0.000542 -0.000044 1 334.027611 -17.704656 -0.001259 -03D4cx 0.946890 0.949000 0 24.460375 0.115036 -0.095636 0.673245 0.056711 0.065385 11.008000 0.157000 52882.826564 1.127614 0.018281 -0.002054 0.001302 1 333.640640 -17.587511 -0.026152 -03D4cy 0.924917 0.927000 0 24.705851 0.125384 0.863344 0.639964 -0.057529 0.071520 9.705000 0.235500 52900.101211 0.799756 0.028082 -0.002370 0.006754 1 333.418394 -17.681641 -0.023921 -03D4cz 0.693165 0.695000 0 24.032070 0.100242 -1.763859 0.384538 -0.076797 0.085271 10.389000 0.245000 52894.648688 0.424818 0.010378 0.000565 0.006846 1 334.174418 -17.926271 -0.003731 -03D4dh 0.625233 0.627000 0 23.386939 0.091453 1.127890 0.183953 -0.042907 0.049556 9.414000 0.162500 52906.261322 0.266379 0.001269 0.000652 -0.000286 1 334.379280 -17.629653 -0.000474 -03D4di 0.896944 0.899000 0 24.333328 0.109618 1.454285 0.416048 -0.084188 0.064346 9.879000 0.063500 52901.059111 0.533964 0.009161 -0.001625 0.003864 1 333.542663 -17.506617 -0.021066 -03D4dy 0.608261 0.610000 0 23.245350 0.090819 1.138023 0.183456 -0.098377 0.036013 5.294000 52.870500 52904.254238 0.256673 0.001827 0.000386 0.000832 1 333.710523 -17.956479 0.000104 -03D4fd 0.789054 0.791000 0 24.222261 0.099608 0.784211 0.472461 -0.042547 0.065807 10.005000 0.219500 52938.532304 0.701119 0.004293 -0.000830 0.004876 1 334.060214 -17.395573 -0.010749 -03D4gf 0.578292 0.580000 0 23.324299 0.090072 0.509293 0.312213 -0.036082 0.037889 7.638000 0.290500 52936.584162 0.410489 0.002358 0.000382 0.002465 1 333.595552 -17.734032 0.000897 -03D4gg 0.590280 0.592000 0 23.431058 0.092935 1.001108 0.434531 0.011626 0.039609 10.162000 0.005500 52943.107164 0.554286 0.006612 0.000114 0.000114 1 334.167458 -18.164458 0.000614 -04D1aj 0.719626 0.721000 0 23.898687 0.095665 0.391447 0.356584 -0.033571 0.066572 6.000000 5.000000 52998.962028 0.560532 0.005052 -0.000237 0.001316 1 36.474835 -4.994610 -0.005402 -04D1dc 0.210032 0.211000 0 21.056779 0.085765 -1.236256 0.061648 -0.002674 0.025159 10.635000 0.041500 53230.680282 0.159906 -0.000108 0.000598 -0.000205 1 36.576935 -4.311968 -0.000962 -04D1de 0.766590 0.768000 0 24.132026 0.096641 0.905821 0.243846 -0.118769 0.055806 9.640000 0.166500 53245.997478 0.243700 0.004224 -0.000140 -0.000465 1 36.649621 -4.422623 -0.008877 -04D1ff 0.858513 0.860000 0 24.246115 0.101579 0.728060 0.277044 0.047131 0.057566 8.730000 0.304500 53251.193655 0.334470 0.004969 -0.000690 0.000720 1 36.410924 -4.902631 -0.017206 -04D1hd 0.367904 0.369000 0 22.156621 0.086082 0.788975 0.068129 -0.092073 0.022091 8.199000 0.090500 53268.297565 0.081465 0.000072 0.000438 -0.000149 1 36.536808 -4.109634 0.000125 -04D1hx 0.558746 0.560000 0 23.699983 0.089573 0.285130 0.191809 0.111683 0.035476 9.645000 0.133500 53260.059448 0.267003 0.001349 0.000524 0.000068 1 36.177063 -4.790326 0.001262 -04D1hy 0.848509 0.850000 0 24.295329 0.100520 1.151184 0.275975 -0.054238 0.055401 8.086000 0.825500 53264.640916 0.393570 0.003284 -0.000753 0.000355 1 36.036226 -4.831071 -0.016226 -04D1iv 0.996390 0.998000 0 24.617672 0.112483 1.273360 0.343306 -0.131063 0.051486 9.034000 0.450000 53264.472527 0.474194 0.006260 -0.001508 0.001697 1 36.200212 -4.152559 -0.030989 -04D1jd 0.776594 0.778000 0 24.397768 0.098366 0.135220 0.286534 0.099259 0.070323 10.380000 0.147500 53259.203505 0.451405 0.003488 -0.000644 0.002038 1 36.961818 -4.943790 -0.009695 -04D1jg 0.582932 0.584200 0 23.273246 0.090120 0.265156 0.158945 -0.104618 0.033952 11.338000 0.150500 53273.566497 0.212852 0.001492 0.000369 0.000478 1 36.552319 -4.134800 0.000793 -04D1kj 0.583743 0.585000 0 23.335798 0.089495 0.218145 0.126028 -0.062764 0.029728 9.929000 0.107500 53297.246286 0.166859 0.000804 0.000360 -0.000020 1 36.969417 -4.180252 0.000774 -04D1ks 0.796552 0.798000 0 24.128917 0.098413 0.698641 0.263320 0.094253 0.062032 7.966000 0.719000 53291.390434 0.382333 0.003176 -0.000539 0.001497 1 36.039649 -4.978721 -0.011400 -04D1oh 0.588721 0.590000 0 23.396414 0.091211 -0.066782 0.206670 -0.068922 0.033246 9.170000 0.152500 53308.274600 0.204309 0.003106 0.000397 0.000623 1 36.259840 -4.236187 0.000653 -04D1ow 0.913473 0.915000 0 24.343967 0.104751 -0.044070 0.285862 -0.160738 0.052163 9.397000 1.373500 53311.745728 0.341331 0.005516 -0.000809 0.001067 1 36.677899 -4.306190 -0.022752 -04D1pc 0.768587 0.770000 0 24.546176 0.097274 -0.334164 0.338880 0.085159 0.063616 10.418000 0.179500 53324.791751 0.461441 0.004237 -0.000062 0.003505 1 36.603650 -4.399400 -0.009039 -04D1pd 0.948455 0.950000 0 24.677344 0.112213 0.182611 0.422231 0.041278 0.060796 10.215000 0.114500 53318.504399 0.675128 0.010499 -0.001529 0.002540 1 36.915633 -4.631172 -0.026310 -04D1pg 0.513793 0.515000 0 23.572554 0.090087 0.985964 0.196013 0.102976 0.035785 9.808000 0.161000 53326.887720 0.243145 0.002656 0.000695 0.001284 1 36.767329 -4.175287 0.001678 -04D1pp 0.733609 0.735000 0 23.985380 0.094433 -1.344550 0.222627 -0.116857 0.052643 11.382000 0.089500 53320.365137 0.342305 0.001788 0.000185 0.000375 1 36.301993 -4.701584 -0.006371 -04D1pu 0.637701 0.639000 0 24.012142 0.100758 -1.611068 0.377261 0.106744 0.082963 9.314000 0.239500 53330.125273 0.492420 0.009287 0.001795 0.006549 1 36.868641 -4.744934 -0.000958 -04D1qd 0.765588 0.767000 0 24.244208 0.096330 0.180928 0.273244 0.012060 0.053803 10.496000 0.064500 53329.535944 0.367540 0.003763 0.000191 0.002279 1 36.637813 -4.107177 -0.008797 -04D1rh 0.434861 0.436000 0 22.556493 0.087188 0.828369 0.210173 -0.057039 0.025846 9.888000 0.132000 53350.854462 0.259693 0.000738 0.000490 0.000144 1 36.946448 -4.253765 0.001224 -04D1rx 0.983407 0.985000 0 24.760473 0.114131 0.594869 0.448702 -0.099334 0.059378 9.769000 0.125000 53352.514943 0.842783 0.006063 -0.001797 0.006621 1 36.295958 -4.618349 -0.029759 -04D1sa 0.583743 0.585000 0 23.564276 0.091595 -0.442871 0.288839 -0.066413 0.033662 10.623000 0.064000 53360.231262 0.322116 0.004025 0.000291 0.000418 1 36.983995 -4.176071 0.000774 -04D1si 0.700631 0.702000 0 23.857842 0.094362 0.136556 0.296761 -0.011566 0.054476 10.638000 0.127000 53359.930100 0.367187 0.004167 0.000348 0.001472 1 36.203316 -4.568950 -0.004181 -04D1sk 0.662058 0.663400 0 24.072652 0.093186 0.445170 0.376768 0.084112 0.055869 10.416000 0.150000 53355.272005 0.523580 0.004263 0.000584 0.002546 1 36.094908 -4.353621 -0.002052 -04D2al 0.838126 0.836000 0 24.337904 0.113632 -1.313788 1.141614 -0.090405 0.082993 9.642000 0.219000 53034.721700 1.163701 -0.018419 -0.002960 0.053578 1 150.468646 2.164106 -0.015223 -04D2an 0.621874 0.620000 0 23.581303 0.092366 -0.297309 0.570339 -0.043585 0.048332 7.542000 0.574000 53031.162827 0.689737 0.005765 0.000278 0.007512 1 150.217956 2.041230 -0.000352 -04D2cf 0.370586 0.369000 0 22.462924 0.095747 -0.861569 0.218055 -0.014022 0.034607 10.809000 0.157500 53075.840194 0.923642 -0.005152 0.001364 -0.002585 1 150.483707 1.879478 0.000174 -04D2fp 0.416632 0.415000 0 22.546572 0.087194 0.385152 0.128111 -0.049753 0.027585 10.235000 0.217500 53107.439394 0.161220 0.000646 0.000509 -0.000269 1 149.867166 2.321014 0.000963 -04D2fs 0.358570 0.357000 0 22.425017 0.086485 0.093895 0.101940 0.060514 0.025777 8.386000 0.294000 53107.698108 0.127774 0.000331 0.000522 -0.000332 1 150.092026 1.765398 -0.000044 -04D2gb 0.451681 0.450000 0 22.903257 0.088233 -1.587699 0.173130 0.028125 0.034468 10.246000 0.090000 53108.401364 0.207216 0.001046 0.000577 -0.000303 1 150.594449 1.894161 0.001422 -04D2gc 0.522762 0.521000 0 23.329087 0.089115 1.140206 0.201606 0.018441 0.035678 9.326000 0.202500 53118.117427 0.254157 0.001761 0.000408 -0.000301 1 150.413604 1.883052 0.001640 -04D2gp 0.733996 0.732000 0 24.211821 0.102900 -1.352031 0.412599 -0.140940 0.085790 8.834000 0.230000 53110.449800 0.585038 0.007795 -0.001296 0.004435 1 149.834888 2.508879 -0.006398 -04D2iu 0.701965 0.700000 0 24.226777 0.100028 -1.908278 0.419119 0.040580 0.085172 6.000000 5.000000 53120.735981 0.516810 0.008307 -0.000728 0.002177 1 150.304914 2.415050 -0.004263 -04D2ja 0.742008 0.740000 0 24.094212 0.103483 0.357631 0.400499 -0.201828 0.071427 10.991000 0.094500 53123.200249 0.479119 0.011659 -0.001371 -0.002289 1 149.702091 1.771737 -0.006980 -04D2kr 0.746118 0.744100 0 23.855911 0.095641 0.789091 0.314259 -0.024482 0.052863 6.000000 5.000000 53359.385247 0.421319 0.004924 0.000065 -0.000687 1 150.155592 1.711927 -0.007286 -04D2mc 0.349560 0.348000 0 22.510167 0.089792 -1.796939 0.126086 0.084647 0.030212 10.960000 0.118000 53367.171790 0.131533 0.002033 0.000889 0.000792 1 150.148125 1.784181 -0.000206 -04D2mh 0.591835 0.590000 0 23.386183 0.090444 1.581016 0.174200 0.022525 0.033803 9.097000 0.418000 53376.332260 0.246305 0.001447 0.000514 0.000110 1 149.941201 2.141025 0.000574 -04D2mj 0.514746 0.513000 0 23.776721 0.090306 1.541433 0.225388 0.155788 0.035460 9.548000 0.123000 53374.439927 0.268534 0.002784 0.000699 0.000285 1 150.152215 2.577067 0.001675 -04D3co 0.620632 0.620000 0 23.784306 0.093091 -0.677025 0.195335 -0.027937 0.046320 9.517000 0.243000 53101.471665 0.260652 0.003159 0.000465 0.001031 1 214.458245 52.963611 -0.000308 -04D3cy 0.643646 0.643000 0 23.786478 0.094148 -0.204658 0.205403 -0.027514 0.065250 9.980000 0.212000 53101.095410 0.263867 0.003557 0.000809 0.001339 1 214.551810 52.658332 -0.001207 -04D3dd 1.002795 1.002000 0 25.039369 0.137310 0.671653 0.683790 -0.016458 0.075290 9.480000 0.133500 53113.476527 1.147182 0.028800 -0.003574 0.011905 1 214.451686 52.470678 -0.031582 -04D3df 0.470586 0.470000 0 23.532383 0.087988 -2.069250 0.149804 0.105832 0.030240 6.000000 5.000000 53119.610285 0.189708 0.000550 0.000492 -0.000104 1 214.541671 52.277686 0.001587 -04D3do 0.610643 0.610000 0 23.559958 0.090588 -0.830682 0.148887 -0.118742 0.032813 10.485000 0.071000 53112.989696 0.263410 0.000712 0.000221 -0.000728 1 214.442008 52.267553 0.000029 -04D3ez 0.263487 0.263000 0 21.666480 0.085550 -1.082884 0.084020 0.027124 0.023334 10.290000 0.012000 53115.465586 0.130280 0.000142 0.000511 0.000030 1 214.782644 53.071948 -0.001281 -04D3fk 0.358336 0.357800 0 22.515607 0.086303 -0.386641 0.080806 0.079161 0.023414 10.189000 0.076500 53126.931633 0.083469 0.000310 0.000478 -0.000035 1 214.609102 52.528468 -0.000048 -04D3fq 0.742697 0.742000 0 24.096172 0.097111 -0.429804 0.362643 -0.040737 0.068580 9.299000 0.045500 53120.155695 0.498009 0.003755 -0.000466 0.002506 1 214.241188 52.379472 -0.007031 -04D3gt 0.451556 0.451000 0 23.241924 0.087828 -0.213624 0.112274 0.191395 0.029100 11.340000 0.094500 53137.866656 0.147746 0.000500 0.000531 -0.000192 1 215.635796 52.646921 0.001421 -04D3gx 0.910752 0.910000 0 24.654449 0.112202 -0.449029 0.439178 -0.145757 0.058715 6.000000 5.000000 53125.846461 0.515388 0.009446 -0.001716 0.002166 1 215.056948 52.282777 -0.022474 -04D3hn 0.552605 0.552000 0 23.488130 0.089654 -0.573944 0.124075 0.073514 0.037143 10.790000 0.022000 53137.153215 0.179585 0.000987 0.000538 0.000400 1 215.528731 52.228587 0.001353 -04D3kr 0.337534 0.337000 0 21.966214 0.086047 1.231534 0.086007 -0.018647 0.022822 9.287000 0.066500 53166.004215 0.119512 0.000117 0.000478 -0.000147 1 214.149726 52.478874 -0.000416 -04D3ks 0.750681 0.750000 0 23.808932 0.099406 0.623562 0.308295 -0.030192 0.064184 8.459000 0.226000 53163.232072 0.348431 0.006903 -0.000581 -0.000198 1 215.639780 52.185184 -0.007631 -04D3lp 0.983777 0.983000 0 24.982708 0.141880 -1.385938 0.567714 0.009485 0.079183 8.895000 0.125000 53148.881346 0.859081 0.023079 -0.004558 0.003503 1 214.962120 52.503195 -0.029794 -04D3lu 0.822697 0.822000 0 24.373493 0.100111 -0.556579 0.229796 -0.124503 0.058657 11.111000 0.076500 53169.144934 0.400118 0.001991 -0.000763 0.001743 1 215.283209 52.974792 -0.013766 -04D3mk 0.813696 0.813000 0 24.296809 0.099177 -0.268869 0.283790 -0.147072 0.056741 9.719000 0.349000 53179.533839 0.327778 0.002661 -0.000635 0.002272 1 214.857351 53.163694 -0.012936 -04D3ml 0.950763 0.950000 0 24.595947 0.112327 1.169760 0.445773 -0.137444 0.053817 9.882000 0.208500 53180.277913 0.591101 0.010022 -0.001560 0.001837 1 214.162783 53.093253 -0.026542 -04D3nc 0.817732 0.817000 0 24.272419 0.101608 1.189651 0.449516 -0.082973 0.057216 9.127000 0.076500 53187.937406 0.498744 0.008546 -0.000619 0.003859 1 214.075911 52.273804 -0.013306 -04D3nh 0.340520 0.340000 0 22.137345 0.085900 0.612333 0.087605 -0.003747 0.021528 9.356000 0.020500 53174.383789 0.100258 0.000252 0.000449 0.000103 1 215.611283 52.333448 -0.000364 -04D3nr 0.960751 0.960000 0 24.586694 0.116274 0.200862 0.482331 0.026976 0.059622 6.000000 5.000000 53185.906440 0.589077 0.018011 -0.002114 -0.005166 1 215.660448 52.648687 -0.027541 -04D3ny 0.810720 0.810000 0 24.233543 0.105587 -0.074816 0.490557 -0.014984 0.065642 10.032000 0.262000 53194.181933 0.624883 0.011046 -0.000968 0.006243 1 214.734665 52.187465 -0.012665 -04D3oe 0.756687 0.756000 0 24.054929 0.095935 -1.003254 0.276674 -0.246300 0.060008 7.492000 54.194000 53195.271376 0.343919 0.002535 -0.000255 0.002196 1 214.914099 52.553847 -0.008093 -04D4an 0.611252 0.613000 0 24.015491 0.095195 -0.360563 0.402949 0.020341 0.042269 10.541000 0.154000 53183.689788 0.490409 0.009725 0.000370 0.002030 1 333.987920 -17.695501 0.000009 -04D4bk 0.877971 0.880000 0 24.329232 0.103099 1.742402 0.435908 -0.112510 0.060316 7.844000 1.086000 53192.084465 0.485339 0.007809 -0.000617 0.006922 1 333.782100 -18.060275 -0.019145 -04D4bq 0.548325 0.550000 0 23.336908 0.091450 0.648772 0.262356 0.101208 0.039144 8.706000 0.752500 53193.680951 0.297432 0.004529 0.000649 0.001878 1 333.705933 -17.827595 0.001410 -04D4dm 0.809032 0.811000 0 24.395023 0.101626 -0.265832 0.320488 0.002784 0.062488 8.977000 0.123500 53199.656722 0.580378 0.003685 -0.000776 0.003253 1 333.856058 -17.245159 -0.012512 -04D4dw 1.028800 1.031000 0 24.588830 0.124800 1.103181 0.423865 -0.141513 0.061134 10.785000 0.220000 53200.718702 0.671621 0.009513 -0.002814 0.003824 1 334.186140 -17.834071 -0.033878 -04D4fx 0.627239 0.629000 0 23.512839 0.092195 0.953562 0.194099 -0.033942 0.050867 8.305000 0.655500 53242.512721 0.219309 0.002481 0.000720 0.000162 1 334.158899 -18.066356 -0.000548 -04D4gg 0.422452 0.424000 0 22.726347 0.088070 1.236716 0.138579 0.083147 0.026963 10.057000 0.345500 53246.782961 0.121953 0.001462 0.000612 0.000474 1 334.038579 -17.294411 0.001051 -04D4hf 0.933900 0.936000 0 24.815885 0.126556 0.846145 0.644916 0.039429 0.084021 8.528000 1.276500 53236.711250 1.440780 0.019094 -0.003461 -0.003116 1 334.241251 -17.687025 -0.024836 -04D4hu 0.701158 0.703000 0 23.921414 0.094504 0.014891 0.235769 -0.107691 0.058748 9.475000 0.222000 53256.876643 0.368925 0.003049 0.000195 0.000451 1 333.900866 -17.838900 -0.004213 -04D4ib 0.697164 0.699000 0 23.589462 0.093324 0.976498 0.218386 -0.137669 0.052668 10.994000 0.118000 53267.438664 0.290240 0.002045 0.000295 0.000589 1 334.173804 -18.105089 -0.003970 -04D4ic 0.685179 0.687000 0 24.117729 0.098300 -0.865996 0.390089 -0.011201 0.073401 9.325000 0.155000 53256.304509 0.650148 0.007010 0.000122 0.001596 1 333.591057 -17.943487 -0.003271 -04D4id 0.767075 0.769000 0 24.198308 0.101187 0.680717 0.449001 -0.173571 0.061478 10.324000 0.196000 53261.694965 0.708363 0.007500 -0.000736 0.001812 1 334.089165 -17.229027 -0.008916 -04D4ih 0.931901 0.934000 0 24.398812 0.111047 0.318349 0.345144 -0.213713 0.052888 10.855000 0.128500 53269.568855 0.424262 0.007991 -0.001595 -0.001143 1 334.320937 -17.677382 -0.024632 -04D4ii 0.863978 0.866000 0 24.398482 0.103900 1.338030 0.416917 -0.006560 0.060395 10.931000 0.149000 53273.864714 0.523976 0.007583 -0.000804 0.004942 1 333.981800 -17.657478 -0.017746 -04D4im 0.749100 0.751000 0 23.830175 0.096075 0.936907 0.237321 0.032930 0.060213 11.199000 0.102500 53285.344272 0.308801 0.003328 -0.000230 -0.001498 1 333.753525 -17.396042 -0.007510 -04D4in 0.514353 0.516000 0 22.880762 0.088897 1.531348 0.146971 -0.070064 0.029004 9.101000 0.101500 53278.571558 0.143411 0.001547 0.000503 0.000452 1 333.785725 -17.261028 0.001676 -04D4jr 0.468406 0.470000 0 22.634969 0.087451 1.528124 0.121479 -0.062844 0.026993 8.024000 0.675500 53298.423454 0.153546 0.000563 0.000414 -0.000185 1 333.559666 -17.350217 0.001572 -04D4ju 0.470398 0.472000 0 23.758954 0.088446 0.448213 0.223756 0.153474 0.034825 9.239000 0.177000 53293.122048 0.283319 0.001705 0.000505 -0.000088 1 334.261358 -17.332814 0.001586 -04D4jw 0.958871 0.961000 0 24.773087 0.125337 -0.701676 0.393737 -0.199987 0.064323 11.226000 0.100500 53286.344757 0.607811 0.008819 -0.002887 0.005528 1 334.328720 -17.665495 -0.027354 -04D4jy 0.927909 0.930000 0 24.759723 0.121275 0.086232 0.452249 -0.114189 0.069432 8.357000 1.803000 53289.060160 0.750357 0.010352 -0.002804 0.006606 1 333.464902 -17.404895 -0.024226 -05D1az 0.840522 0.842000 0 24.254281 0.101288 1.861994 0.369253 -0.013003 0.059913 8.631000 0.633500 53603.188214 0.392216 0.008417 -0.000258 0.004794 1 36.302108 -4.602335 -0.015453 -05D1cb 0.630699 0.632000 0 23.700974 0.091576 -0.246796 0.203901 0.001443 0.042662 10.111000 0.079500 53616.781601 0.299918 0.001298 0.000761 0.000326 1 36.737733 -4.117567 -0.000680 -05D1cc 0.562751 0.564000 0 23.506645 0.090329 -0.148276 0.154372 -0.009066 0.031676 9.316000 0.202500 53607.889353 0.228898 0.001987 0.000588 0.001257 1 36.629866 -4.164801 0.001197 -05D1ck 0.615698 0.617000 0 24.070744 0.092238 -0.094687 0.233678 0.108958 0.039039 9.724000 0.172000 53628.253726 0.262531 0.003486 0.000421 0.000648 1 36.103540 -4.712800 -0.000137 -05D1cl 0.828528 0.830000 0 24.361588 0.100853 2.292412 0.344330 -0.067245 0.057616 8.420000 0.474500 53627.265051 0.495457 0.006699 -0.000644 0.000415 1 36.273563 -4.220818 -0.014312 -05D1dn 0.564741 0.566000 0 23.285141 0.089709 1.229575 0.223354 -0.021239 0.035307 10.234000 0.047000 53649.382509 0.328916 0.001941 0.000385 0.000057 1 36.111040 -4.991558 0.001162 -05D1dx 0.578745 0.580000 0 23.278595 0.089343 0.650723 0.122195 -0.045401 0.030716 9.416000 0.113000 53666.525378 0.168757 0.000665 0.000406 -0.000093 1 36.945791 -4.032490 0.000887 -05D1ee 0.557743 0.559000 0 23.522028 0.090291 -0.594306 0.188030 0.000429 0.035409 10.973000 0.087500 53660.634279 0.202348 0.002253 0.000470 0.000277 1 36.194786 -4.000485 0.001278 -05D1em 0.864497 0.866000 0 24.277329 0.102986 0.231091 0.291511 -0.141676 0.059074 11.043000 0.047000 53667.795793 0.394395 0.004205 -0.000974 0.002038 1 36.023010 -4.939906 -0.017798 -05D1eo 0.735608 0.737000 0 24.300208 0.096764 -1.464408 0.301676 -0.051696 0.066321 11.125000 0.177000 53667.103434 0.439056 0.004216 -0.000055 0.001730 1 36.346421 -4.642671 -0.006514 -05D1er 0.858507 0.860000 0 24.622355 0.106173 0.414445 0.446603 0.075253 0.074018 10.379000 0.148000 53667.198123 0.580708 0.008246 -0.001372 0.006513 1 36.422736 -4.004535 -0.017205 -05D1hk 0.261984 0.263000 0 21.189797 0.087390 1.380255 0.120042 -0.010342 0.026024 8.930000 0.147000 53717.917139 0.116296 0.001239 0.000684 0.000089 1 36.163234 -4.634253 -0.001286 -05D1hm 0.585743 0.587000 0 24.087789 0.092677 1.097063 0.289914 0.147914 0.041531 9.854000 0.177000 53696.057435 0.401128 0.005130 0.000692 0.002491 1 36.942572 -4.717286 0.000726 -05D1if 0.761580 0.763000 0 24.040031 0.096723 0.177122 0.251313 -0.056950 0.052854 6.000000 5.000000 53715.517868 0.213355 0.004989 0.000032 0.000149 1 36.123871 -4.570338 -0.008477 -05D1ix 0.488800 0.490000 0 22.868484 0.088390 0.545038 0.136698 -0.068405 0.026726 7.466000 0.709500 53719.435265 0.132872 0.001361 0.000501 0.000322 1 36.083196 -4.670004 0.001678 -05D1iz 0.858516 0.860000 0 24.427101 0.116450 1.158479 0.703275 -0.136633 0.080484 8.911000 0.772000 53694.622356 0.818184 0.028940 -0.000321 0.024497 1 36.553202 -4.773370 -0.017206 -05D1ke 0.688656 0.690000 0 23.604428 0.092298 0.227770 0.256664 -0.107541 0.049745 8.851000 0.332500 53731.345985 0.341612 0.001533 0.000490 0.001262 1 36.692882 -4.862496 -0.003469 -05D1kl 0.558743 0.560000 0 24.148746 0.092898 0.658004 0.296028 0.133007 0.040958 8.425000 0.282500 53742.927446 0.414453 0.004633 0.000591 0.001157 1 36.139745 -4.318960 0.001262 -05D2ab 0.324532 0.323000 0 21.993375 0.086121 -0.146918 0.085037 -0.039628 0.022341 10.098000 0.080000 53381.262279 0.102272 0.000439 0.000500 0.000253 1 150.461859 2.106329 -0.000630 -05D2ac 0.480704 0.479000 0 22.671051 0.087736 1.123507 0.112202 -0.054059 0.024921 11.153000 0.058500 53374.628095 0.152454 0.000532 0.000434 -0.000060 1 149.746812 2.489504 0.001646 -05D2ah 0.185371 0.184000 0 20.758955 0.085522 -0.075113 0.047645 0.001222 0.024163 9.210000 0.014500 53382.699789 0.063313 -0.000036 0.000578 -0.000147 1 150.369700 1.862732 -0.000379 -05D2ay 0.922221 0.920000 0 24.664061 0.117413 -0.433873 0.621851 0.026835 0.094498 10.277000 0.128500 53375.299009 0.872535 0.019311 -0.001771 0.020299 1 150.284664 2.130321 -0.023645 -05D2bt 0.681942 0.680000 0 23.499419 0.094158 0.540880 0.258110 -0.150704 0.049795 6.000000 5.000000 53401.527341 0.246077 0.004768 0.000667 0.001993 1 150.417683 2.566161 -0.003090 -05D2bv 0.475707 0.474000 0 22.715329 0.087947 -0.007188 0.123940 -0.104000 0.025243 8.838000 0.111000 53404.214768 0.140514 0.000953 0.000468 0.000141 1 150.570830 2.240600 0.001620 -05D2bw 0.922210 0.920000 0 24.380224 0.117626 -0.310684 0.475240 -0.138410 0.063435 9.144000 0.540000 53400.216346 0.453569 0.017426 -0.001715 0.004830 1 149.627324 2.536402 -0.023644 -05D2by 0.893183 0.891000 0 24.576000 0.106138 1.324338 0.493157 -0.027739 0.063064 9.413000 0.080000 53413.525069 0.770658 0.005631 -0.001354 0.005490 1 150.116802 2.521539 -0.020683 -05D2cb 0.428646 0.427000 0 23.396803 0.089103 0.900599 0.192193 0.158626 0.029350 9.534000 0.078000 53423.024668 0.196050 0.002559 0.000614 0.000483 1 149.852419 2.328181 0.001140 -05D2ci 0.631882 0.630000 0 23.605938 0.097524 -0.930861 0.247161 0.078491 0.066536 10.977000 0.159000 53427.856115 0.272077 0.006580 0.001055 0.000528 1 150.008506 2.215852 -0.000726 -05D2ck 0.699960 0.698000 0 24.456174 0.097048 -2.641811 0.470924 0.005752 0.069715 10.941000 0.095000 53416.818151 0.566090 0.005422 0.000024 0.004893 1 150.188339 2.572851 -0.004140 -05D2ct 0.736007 0.734000 0 24.383943 0.104933 0.319916 0.466515 0.098840 0.070358 9.761000 0.068500 53425.137648 0.457366 0.017323 0.000175 0.004772 1 150.425759 2.123794 -0.006543 -05D2dt 0.575823 0.574000 0 23.652601 0.091807 0.172239 0.270654 0.033631 0.036182 10.244000 0.096000 53450.659313 0.249627 0.005164 0.000561 0.001756 1 150.349691 1.857741 0.000949 -05D2dw 0.418633 0.417000 0 22.468950 0.087692 1.143228 0.164561 -0.013340 0.024927 8.815000 0.196000 53454.420697 0.129077 0.001889 0.000513 0.000439 1 149.633623 2.032241 0.000994 -05D2dy 0.511746 0.510000 0 22.907226 0.088554 1.237597 0.220903 -0.117279 0.028871 7.738000 0.828500 53441.494592 0.495061 -0.000165 0.000477 -0.000225 1 150.242076 2.183100 0.001684 -05D2eb 0.535771 0.534000 0 22.991731 0.091256 0.880642 0.270825 -0.055537 0.031684 8.676000 0.327500 53451.960943 0.228835 0.005378 0.000600 0.001729 1 150.060683 2.407438 0.001545 -05D2ec 0.641893 0.640000 0 23.677878 0.093291 -0.210581 0.215173 -0.103447 0.053736 9.997000 0.056500 53440.359230 0.419049 0.001845 0.000768 0.000660 1 149.859101 2.013626 -0.001133 -05D2fq 0.734996 0.733000 0 24.000974 0.095665 0.836161 0.314847 -0.075547 0.057981 10.875000 0.134500 53463.298524 0.463028 0.004611 0.000044 0.001465 1 149.785425 2.602000 -0.006470 -05D2hc 0.351560 0.350000 0 22.684908 0.086351 -0.674399 0.164303 0.037449 0.024663 10.977000 0.092000 53471.684222 0.140823 0.000727 0.000513 0.000393 1 150.019137 1.886025 -0.000170 -05D2he 0.609860 0.608000 0 23.927615 0.091831 0.148906 0.374618 0.064417 0.041705 9.418000 0.185000 53467.625838 0.463694 0.004461 0.000444 0.003088 1 150.360462 2.317729 0.000054 -05D2ie 0.349557 0.348000 0 22.228291 0.086821 -0.089736 0.138930 -0.074922 0.026093 9.287000 0.052500 53480.961234 0.128958 0.001147 0.000521 0.000084 1 150.262061 2.658085 -0.000206 -05D2le 0.701969 0.700000 0 23.957522 0.095463 0.509414 0.310934 -0.013323 0.051320 8.033000 0.690000 53694.981702 0.411943 0.006011 0.000584 0.002134 1 150.478650 2.092912 -0.004263 -05D2mp 0.355261 0.353700 0 22.387222 0.088265 1.277236 0.186565 0.013757 0.025069 8.657000 0.182500 53711.643580 0.133842 0.003271 0.000615 0.001054 1 149.785962 2.204004 -0.000104 -05D2my 0.983280 0.981000 0 24.666688 0.114718 1.176787 0.467667 -0.035549 0.059010 10.595000 0.123500 53703.150216 0.936404 0.005424 -0.001626 0.005994 1 149.631042 2.491221 -0.029746 -05D2nn 0.872152 0.870000 0 24.426592 0.119167 -0.742621 0.582209 -0.196303 0.058288 6.000000 5.000000 53713.067425 0.611489 0.028971 -0.001056 0.004337 1 149.707296 2.710264 -0.018561 -05D2nt 0.759030 0.757000 0 24.106956 0.095560 1.512514 0.307273 -0.013177 0.052036 9.054000 0.355000 53727.527982 0.432216 0.004089 0.000292 0.002047 1 150.242620 2.372730 -0.008276 -05D2ob 0.926220 0.924000 0 24.811277 0.111056 1.139028 0.646387 0.056398 0.068017 8.557000 0.374000 53727.943875 0.951501 0.016996 -0.000689 0.018173 1 149.753025 1.848985 -0.024054 -05D3ax 0.643642 0.643000 0 23.615300 0.098759 0.590653 0.420502 -0.062996 0.064740 8.442000 0.390000 53399.920652 0.293806 0.014684 0.000892 0.003944 1 214.823263 52.687519 -0.001207 -05D3cf 0.419569 0.419000 0 22.938058 0.087595 -0.257956 0.174221 0.008142 0.027406 9.609000 0.138000 53418.812826 0.211476 0.001282 0.000531 0.000174 1 214.222462 52.345054 0.001008 -05D3ci 0.515587 0.515000 0 23.621090 0.106833 2.120426 0.582068 0.166871 0.044764 10.011000 0.064500 53432.374568 0.410988 0.032691 0.001650 0.008402 1 215.450443 52.445281 0.001672 -05D3cq 0.890729 0.890000 0 24.223935 0.103551 -0.725251 0.457209 -0.206887 0.051676 8.876000 0.270000 53450.845689 0.835339 0.008303 -0.000589 0.002912 1 214.692345 53.132138 -0.020434 -05D3cx 0.805696 0.805000 0 23.903235 0.097210 0.668773 0.463263 -0.161241 0.049141 9.839000 0.189000 53450.038426 0.831042 0.007340 -0.000065 -0.000091 1 215.277259 52.750509 -0.012212 -05D3dd 0.480568 0.480000 0 22.948758 0.088055 -0.207015 0.126228 -0.029175 0.024904 11.222000 0.097000 53455.294718 0.108811 0.001154 0.000437 0.000153 1 215.626867 52.606794 0.001646 -05D3dh 0.800697 0.800000 0 24.207672 0.098997 0.087341 0.274795 0.120557 0.065251 6.000000 5.000000 53442.396545 0.578531 0.003093 -0.000175 0.001892 1 215.210013 52.662718 -0.011766 -05D3gp 0.580604 0.580000 0 23.516572 0.098350 -0.457982 0.398139 -0.061699 0.047651 9.015000 0.185000 53454.941163 0.355283 0.013406 0.000744 0.005159 1 215.676316 52.724688 0.000846 -05D3gv 0.715652 0.715000 0 23.992197 0.096706 -0.806348 0.294016 -0.064355 0.064403 10.726000 0.113000 53466.612679 0.578924 0.004217 0.000077 0.000651 1 215.246894 53.178877 -0.005137 -05D3ha 0.805695 0.805000 0 24.393736 0.106396 -0.185837 0.394181 0.089062 0.066152 11.164000 0.159000 53484.826903 0.349163 0.015028 0.000169 0.007456 1 215.210132 52.834036 -0.012212 -05D3hh 0.766684 0.766000 0 24.298577 0.103932 0.502459 0.423441 -0.002723 0.065520 10.400000 0.160500 53479.611333 0.423256 0.015208 -0.000057 0.004690 1 214.792304 52.959332 -0.008885 -05D3hs 0.664638 0.664000 0 23.512551 0.094448 0.557724 0.208037 -0.175195 0.050254 9.004000 0.130000 53490.921797 0.272694 0.003636 0.000711 0.001266 1 215.306502 52.903809 -0.002180 -05D3ht 0.901736 0.901000 0 24.408439 0.111342 0.902173 0.388812 -0.154347 0.059355 9.661000 0.136000 53489.256145 0.458136 0.013705 -0.001042 0.003609 1 214.477921 53.167585 -0.021554 -05D3jb 0.745665 0.745000 0 23.919586 0.094967 0.939225 0.232407 -0.063082 0.052368 10.471000 0.046500 53515.305081 0.248851 0.003101 0.000155 -0.000043 1 215.541655 52.878130 -0.007252 -05D3jh 0.718680 0.718000 0 23.723680 0.094949 -0.614033 0.183743 -0.137754 0.051663 10.815000 0.138500 53492.653614 0.362304 0.002357 0.000408 0.000437 1 214.355768 52.618765 -0.005338 -05D3jk 0.736690 0.736000 0 23.691578 0.093659 0.773664 0.180437 -0.131975 0.047852 9.457000 0.310500 53507.774454 0.230956 0.001352 0.000268 -0.000342 1 214.197729 52.592488 -0.006592 -05D3jq 0.579600 0.579000 0 23.295137 0.089310 1.488510 0.207857 0.001938 0.028752 8.832000 0.419500 53501.007745 0.394660 0.000453 0.000435 -0.000168 1 215.439338 53.029918 0.000868 -05D3jr 0.370531 0.370000 0 22.629370 0.086626 -0.892539 0.121406 0.049040 0.023221 8.008000 0.833000 53494.665657 0.267015 -0.000261 0.000489 -0.000197 1 214.869783 52.864848 0.000173 -05D3km 0.960740 0.960000 0 24.718789 0.112338 0.377544 0.314425 -0.159049 0.051350 6.000000 5.000000 53522.914138 0.438558 0.005953 -0.001420 0.002174 1 215.659480 53.066999 -0.027540 -05D3kp 0.850729 0.850000 0 24.140236 0.098855 1.278161 0.234665 -0.144534 0.049809 10.620000 0.126500 53530.045643 0.325975 0.002099 -0.000406 0.000435 1 215.012268 52.270827 -0.016442 -05D3kt 0.648640 0.648000 0 23.974975 0.092708 -0.449789 0.196487 0.056579 0.049995 9.138000 0.174500 53530.939680 0.269615 0.001847 0.000700 0.000438 1 214.973834 52.742986 -0.001425 -05D3kx 0.219462 0.219000 0 20.852122 0.085341 0.698942 0.062624 -0.039907 0.024042 10.630000 0.067500 53534.170662 0.068402 0.000041 0.000531 -0.000223 1 215.458413 53.137119 -0.001103 -05D3la 0.936755 0.936000 0 24.494384 0.108686 -0.191832 0.301264 -0.103022 0.052953 9.599000 0.104000 53535.900318 0.393796 0.006717 -0.001169 0.000188 1 215.355908 52.357973 -0.025126 -05D3lb 0.647639 0.647000 0 23.898505 0.091930 0.310288 0.183447 0.032706 0.050872 8.931000 0.230000 53528.654917 0.252784 0.001308 0.000639 0.000074 1 214.382325 53.167928 -0.001381 -05D3lc 0.461564 0.461000 0 22.975035 0.087413 -0.747912 0.107943 -0.046199 0.024734 10.777000 0.054000 53527.665519 0.137770 0.000239 0.000440 -0.000074 1 215.595501 52.478865 0.001517 -05D3lr 0.600603 0.600000 0 23.845284 0.094433 0.178065 0.345707 0.105799 0.040740 10.873000 0.051000 53541.012444 0.310397 0.008341 0.000443 0.001105 1 215.550590 53.184275 0.000334 -05D3mh 0.670654 0.670000 0 24.064257 0.095777 0.570670 0.473683 0.054159 0.055451 10.078000 0.276500 53558.053888 0.529191 0.008103 0.000604 0.003585 1 214.749304 52.667560 -0.002485 -05D3mn 0.760698 0.760000 0 24.031872 0.094664 -0.119797 0.292142 -0.041340 0.051904 10.432000 0.114500 53559.290942 0.323184 0.002082 0.000038 0.001550 1 214.688507 52.323145 -0.008408 -05D3mq 0.246492 0.246000 0 21.505670 0.086206 -0.683772 0.170341 -0.000463 0.025963 10.723000 0.076500 53575.757963 0.158241 0.001499 0.000614 0.000620 1 214.751783 52.385120 -0.001297 -05D3mx 0.470573 0.470000 0 23.023421 0.088704 -1.668401 0.155811 -0.078211 0.032265 10.788000 0.066000 53569.939971 0.151609 0.001252 0.000530 0.000295 1 215.537919 52.219179 0.001587 -05D3ne 0.169455 0.169000 0 20.293205 0.088415 -1.463743 0.193091 -0.143152 0.030099 11.133000 0.162500 53570.816466 0.096811 0.003303 0.000919 0.001615 1 215.262445 52.495438 0.000182 -05D4af 0.497379 0.499000 0 23.102539 0.088758 0.144823 0.232835 -0.025937 0.031365 8.986000 0.088500 53536.784847 0.315698 0.002149 0.000489 -0.000459 1 334.138087 -18.004923 0.001695 -05D4ag 0.638234 0.640000 0 23.888138 0.097915 0.488591 0.478357 0.096406 0.061210 8.381000 0.392500 53538.621767 0.481692 0.015311 0.001354 0.009242 1 333.447603 -18.165265 -0.000980 -05D4av 0.507371 0.509000 0 23.559122 0.089015 0.884552 0.179518 0.167894 0.030865 10.285000 0.117500 53553.499057 0.261842 0.001276 0.000572 0.000241 1 333.543931 -17.911944 0.001693 -05D4be 0.535327 0.537000 0 22.871803 0.088730 0.907857 0.131158 -0.145357 0.028590 9.440000 0.135500 53551.532227 0.193667 0.000777 0.000448 0.000106 1 334.222449 -17.236099 0.001549 -05D4bf 0.587285 0.589000 0 23.613598 0.091452 0.086435 0.236262 -0.005707 0.041163 10.230000 0.084000 53551.044857 0.356972 0.002980 0.000561 0.002488 1 334.109775 -18.232067 0.000689 -05D4bi 0.773081 0.775000 0 24.049363 0.098805 1.040135 0.389928 -0.140416 0.065557 9.109000 0.218000 53560.317625 0.535716 0.006305 -0.000536 0.001338 1 333.985626 -17.986070 -0.009405 -05D4bj 0.699166 0.701000 0 24.107325 0.094894 0.169319 0.296571 0.059883 0.057492 9.265000 0.096500 53564.034702 0.345551 0.004875 0.000416 0.001393 1 333.748598 -18.133404 -0.004091 -05D4bm 0.370511 0.372000 0 22.212225 0.086476 0.253822 0.070247 -0.065948 0.022602 9.047000 0.077000 53568.061986 0.071325 0.000412 0.000472 0.000100 1 334.269213 -17.677623 0.000172 -05D4cn 0.761089 0.763000 0 24.081977 0.095850 1.042996 0.245294 -0.017217 0.057979 10.461000 0.305500 53583.598852 0.388269 0.002415 -0.000142 0.000522 1 333.381059 -17.288845 -0.008439 -05D4cq 0.699169 0.701000 0 23.733171 0.094016 0.688169 0.268968 -0.122010 0.055575 10.554000 0.159500 53586.374579 0.381107 0.002667 0.000163 0.000945 1 333.540209 -18.226713 -0.004091 -05D4cs 0.788060 0.790000 0 23.975847 0.096033 1.395798 0.212281 -0.109284 0.050060 8.980000 0.632500 53588.622952 0.310170 0.001925 -0.000120 -0.000006 1 334.324243 -17.880150 -0.010663 -05D4cw 0.373513 0.375000 0 22.144613 0.086768 -0.833093 0.104207 -0.129299 0.025202 10.503000 0.072500 53580.414418 0.162182 0.000313 0.000522 -0.000150 1 333.708668 -17.738765 0.000227 -05D4dt 0.405478 0.407000 0 22.801779 0.086962 -0.988887 0.107135 -0.047526 0.024506 10.429000 0.115000 53609.569177 0.172918 0.000412 0.000478 -0.000060 1 333.607695 -17.671111 0.000785 -05D4dw 0.852994 0.855000 0 24.432760 0.101921 0.333205 0.339532 -0.008102 0.062133 9.994000 0.179500 53613.933141 0.576574 0.003600 -0.000796 0.003835 1 334.232825 -18.051461 -0.016663 -05D4dy 0.808048 0.810000 0 24.608413 0.101592 -0.414682 0.390111 -0.126427 0.065808 8.321000 0.170500 53616.381363 0.694348 0.003358 -0.000904 0.004516 1 333.875495 -18.215502 -0.012423 -05D4ef 0.603272 0.605000 0 23.829442 0.092267 -1.558373 0.210171 -0.066074 0.036937 10.437000 0.131000 53625.592943 0.278134 0.002444 0.000229 -0.000274 1 333.497104 -18.212345 0.000256 -05D4ej 0.583290 0.585000 0 23.712643 0.090793 0.226659 0.175164 -0.025602 0.036104 10.133000 0.156000 53627.800948 0.189342 0.002198 0.000457 0.000193 1 333.968823 -18.195719 0.000784 -05D4ek 0.534335 0.536000 0 23.294733 0.090128 0.262525 0.177940 0.069733 0.031743 10.855000 0.107500 53635.630383 0.185443 0.002475 0.000552 0.000778 1 334.114653 -17.736316 0.001558 -05D4ev 0.720140 0.722000 0 24.235443 0.098164 -1.101731 0.288537 -0.027446 0.063891 10.883000 0.061500 53631.273079 0.334088 0.006384 0.000169 0.001662 1 333.376765 -17.700785 -0.005436 -05D4ff 0.400485 0.402000 0 22.587347 0.087548 -0.725286 0.122213 -0.004662 0.025908 10.543000 0.178500 53635.847122 0.127215 0.001152 0.000551 0.000250 1 334.083998 -18.042557 0.000702 -05D4fg 0.837004 0.839000 0 24.189244 0.100539 0.282605 0.271115 -0.157870 0.057629 11.277000 0.091500 53641.780188 0.413317 0.003089 -0.000598 0.003122 1 334.172282 -17.595461 -0.015116 -05D4fo 0.371508 0.373000 0 22.438189 0.086225 -0.700921 0.088176 -0.051433 0.022657 8.381000 0.365500 53658.222390 0.079708 0.000337 0.000427 -0.000192 1 333.837135 -17.268076 0.000190 -05D4gw 0.806041 0.808000 0 24.465540 0.105158 0.028914 0.462087 0.037161 0.068619 8.984000 0.114000 53678.629506 0.695729 0.010116 -0.001032 0.003800 1 333.697318 -17.531901 -0.012243 -05D4hn 0.840005 0.842000 0 24.190851 0.103903 1.229904 0.773653 0.069868 0.073415 8.467000 0.157000 53693.945469 0.831479 0.022589 0.000286 0.028202 1 334.306516 -17.912704 -0.015404 -06D2bk 0.500728 0.499000 0 23.276559 0.089131 0.470647 0.277475 0.005322 0.032227 8.617000 0.085500 53773.746464 0.319041 0.003618 0.000478 0.001062 1 149.678628 2.171984 0.001697 -06D2ca 0.532765 0.531000 0 23.313086 0.089870 1.277665 0.353956 0.048672 0.032195 9.808000 0.289500 53795.693865 0.454444 0.004596 0.000547 0.001446 1 149.789823 2.302517 0.001571 -06D2cc 0.533766 0.532000 0 23.454678 0.094730 -0.552437 0.326856 0.081612 0.050044 10.870000 0.108500 53788.591671 0.445014 0.007480 0.001152 0.004236 1 149.862030 2.451355 0.001563 -06D2cd 0.932229 0.930000 0 24.775178 0.130385 0.258122 0.959484 0.023043 0.093479 7.908000 0.459500 53790.303162 1.369760 0.052919 -0.001771 0.033886 1 150.241504 2.534039 -0.024666 -06D2ce 0.822103 0.820000 0 24.189188 0.104677 2.000361 0.923527 -0.005164 0.076366 11.326000 0.070500 53799.714598 1.275875 0.010754 -0.000991 0.030759 1 150.434247 2.713314 -0.013711 -06D2ck 0.553797 0.552000 0 23.411247 0.091857 0.012383 0.398176 -0.031799 0.038150 11.193000 0.066000 53793.709475 0.419397 0.008153 0.000778 0.005545 1 150.366733 1.863630 0.001336 -06D2fb 0.125298 0.124000 0 19.752286 0.085713 -0.356571 0.055126 -0.019485 0.024631 9.740000 0.124000 53853.757149 0.079571 -0.000072 0.000615 -0.000118 1 149.883946 1.827922 0.002649 -06D2ga 0.842123 0.840000 0 24.283047 0.112024 1.261243 0.690285 -0.059406 0.072925 9.051000 0.288500 53869.081210 0.661137 0.025691 -0.000935 0.014293 1 149.783787 1.938493 -0.015607 -06D2gb 0.443668 0.442000 0 22.994747 0.091594 -1.632468 0.247572 -0.014060 0.038694 10.905000 0.124500 53872.502636 0.328181 0.005695 0.001016 0.003213 1 150.197581 1.985406 0.001333 -06D3bz 0.727675 0.727000 0 23.946563 0.099419 -0.794668 0.406687 -0.106786 0.067278 10.293000 0.062000 53776.526552 0.431772 0.010708 -0.000059 0.006423 1 214.291840 53.024826 -0.005952 -06D3cc 0.683659 0.683000 0 24.054688 0.115261 0.691964 0.632202 -0.000385 0.084790 10.083000 0.127000 53760.478503 0.737077 0.037635 -0.000869 0.002728 1 214.381668 52.912473 -0.003185 -06D3df 0.442548 0.442000 0 22.663818 0.087090 1.153024 0.164206 -0.026193 0.023283 10.460000 0.032000 53850.710066 0.368635 -0.000421 0.000437 -0.000554 1 215.571703 52.957682 0.001320 -06D3do 0.726679 0.726000 0 23.896775 0.094974 0.614699 0.477428 -0.072211 0.052768 8.709000 0.183000 53849.510058 1.293684 -0.002774 0.000327 -0.001666 1 214.247465 52.882543 -0.005883 -06D3dt 0.282510 0.282000 0 22.151638 0.085709 0.087556 0.095000 0.089097 0.022381 9.589000 0.069000 53849.029919 0.227869 0.000010 0.000492 0.000090 1 214.340267 52.452816 -0.001163 -06D3ed 0.404549 0.404000 0 22.602742 0.086483 -0.385357 0.081308 -0.069037 0.022827 9.512000 0.205000 53876.177619 0.135805 0.000170 0.000417 -0.000120 1 214.699174 52.756559 0.000770 -06D3el 0.519611 0.519000 0 22.873755 0.088502 0.770182 0.109068 -0.141498 0.027412 9.554000 0.097500 53894.379818 0.121007 0.000708 0.000449 0.000102 1 214.254528 52.232401 0.001655 -06D3em 0.690652 0.690000 0 24.378431 0.095502 -0.153659 0.359416 0.177029 0.061598 9.459000 0.244500 53881.734365 0.585974 0.004822 0.000504 0.001611 1 214.847581 53.023013 -0.003584 -06D3en 1.060801 1.060000 0 24.707870 0.132013 -0.952679 0.412773 -0.158207 0.061030 7.715000 0.935000 53881.407872 0.949506 0.010444 -0.003050 0.004456 1 215.305567 52.456079 -0.036389 -06D3et 0.576612 0.576000 0 23.456685 0.090075 -1.366428 0.152155 -0.075657 0.034312 10.766000 0.090000 53894.159829 0.191078 0.001135 0.000481 0.000291 1 215.519873 52.338366 0.000933 -06D3fp 0.268489 0.268000 0 21.711856 0.085550 -0.071623 0.054189 0.048305 0.022113 9.550000 0.259500 53905.333652 0.081029 0.000141 0.000491 0.000009 1 215.077523 52.897165 -0.001260 -06D3gh 0.720680 0.720000 0 23.914145 0.098773 0.235317 0.339848 -0.070767 0.065944 9.300000 0.244500 53922.827538 0.373802 0.007399 -0.000216 0.002850 1 214.630912 52.442908 -0.005473 -06D3gn 0.250498 0.250000 0 21.864224 0.086012 -0.526912 0.095929 0.115106 0.025679 10.590000 0.112500 53921.236955 0.090896 0.000548 0.000587 0.000198 1 214.435959 52.361182 -0.001302 -06D3gx 0.760691 0.760000 0 23.906693 0.101218 0.551439 0.692364 -0.129210 0.082143 10.438000 0.146500 53934.639102 0.850936 0.017005 -0.000188 0.025864 1 214.263474 52.936252 -0.008407 -06D4ba 0.698167 0.700000 0 23.730103 0.101565 0.678618 0.413274 -0.097992 0.072346 8.544000 0.424000 53904.778475 0.502098 0.013794 0.000008 0.002377 1 333.898906 -18.229116 -0.004030 -06D4bo 0.550315 0.552000 0 23.219524 0.089299 0.768518 0.177065 -0.042555 0.030271 9.960000 0.222500 53918.738466 0.276025 0.001518 0.000415 -0.000028 1 333.867138 -17.409183 0.001384 -06D4bw 0.730128 0.732000 0 23.905124 0.096543 0.681549 0.261367 0.012517 0.061642 9.276000 0.149500 53909.335689 0.472294 0.004157 0.000108 0.000320 1 333.765511 -17.883454 -0.006124 -06D4ce 0.847985 0.850000 0 24.196452 0.110575 1.752165 0.570617 -0.109364 0.062192 8.861000 0.317000 53926.821806 0.632783 0.022191 -0.000765 0.006876 1 334.321856 -17.235937 -0.016175 -06D4cl 0.997826 1.000000 0 24.529085 0.115855 1.176753 0.387348 -0.086682 0.053789 9.333000 0.461500 53938.076295 0.591048 0.008081 -0.001528 0.004490 1 334.237720 -17.450634 -0.031122 -06D4co 0.435446 0.437000 0 22.524456 0.086944 -0.395993 0.094666 -0.046795 0.023301 10.627000 0.151500 53940.305517 0.159309 0.000118 0.000437 -0.000116 1 333.860492 -17.869185 0.001231 -06D4cq 0.409470 0.411000 0 22.557433 0.086547 0.430851 0.095496 -0.037409 0.022021 9.107000 0.088000 53947.435995 0.106510 0.000336 0.000399 -0.000155 1 334.231344 -17.711980 0.000850 -06D4dh 0.301590 0.303000 0 21.442454 0.086081 0.519102 0.063186 -0.161920 0.021672 9.381000 0.067000 53956.882149 0.067105 0.000441 0.000494 0.000129 1 333.582053 -17.585009 -0.000959 -Aphrodite 1.299106 1.300000 0 25.691231 0.128055 0.664323 0.357559 0.009896 0.036862 9.381000 0.368000 52552.128019 0.628246 0.004579 0.000365 0.006110 4 53.156333 -27.779611 0.000000 -Eagle 1.020807 1.020000 0 24.965297 0.118836 0.305488 0.291649 -0.103800 0.060285 10.004000 0.030000 53295.094876 0.698341 0.003498 0.000915 0.006604 4 189.336460 62.228194 0.000000 -Elvis 0.839734 0.839000 0 24.362709 0.112754 -0.081640 0.330694 -0.019546 0.039680 10.626000 0.099000 52782.896518 0.900230 -0.001794 0.000493 -0.000895 4 189.451420 62.226472 0.000000 -Gabi 1.120850 1.120000 0 25.147113 0.121686 0.620453 0.307485 -0.075491 0.053200 8.335000 0.296000 53488.738514 0.524573 0.004032 0.000388 0.005988 4 189.057630 62.202100 0.000000 -Lancaster 1.230892 1.230000 0 26.046776 0.128558 -0.077374 0.690464 0.087011 0.048271 10.134000 0.194500 53431.841687 1.597837 0.003542 0.000211 0.008241 4 189.236330 62.214814 0.000000 -Ombo 0.974230 0.975000 0 24.886143 0.118033 1.716480 0.295354 -0.004609 0.043876 9.317000 0.051000 53368.835719 0.553316 0.004540 0.000959 0.004381 4 53.105583 -27.750836 0.000000 -Patuxent 0.970782 0.970000 0 25.020075 0.118744 -0.102710 0.629553 -0.218154 0.106505 10.633000 0.035000 53200.957472 1.136538 0.010981 0.001183 0.023467 4 189.537500 62.313122 0.000000 -SDSS10028 0.064229 0.065330 0 18.347945 0.112975 -1.201799 0.187010 0.010060 0.025413 10.531000 0.043500 53693.660337 0.177004 0.001300 0.000548 0.000597 2 17.741856 0.276253 -0.000664 -SDSS1032 0.129030 0.129760 0 20.336945 0.115931 -2.528617 0.199976 0.050506 0.036779 10.456000 0.022000 53626.521013 0.198807 0.002692 0.001133 0.000838 2 46.795696 1.119552 -0.001240 -SDSS10434 0.102890 0.104180 0 19.156988 0.114221 -0.005434 0.280897 -0.087410 0.026896 10.955000 0.128500 53696.470787 0.217773 0.005774 0.000781 0.003225 2 -30.044090 -1.193712 -0.001137 -SDSS10805 0.044130 0.045420 0 17.531728 0.114860 -0.952693 0.402381 0.056678 0.024892 9.998000 0.206000 53698.687684 0.125472 0.006306 0.000594 0.003643 2 -15.072472 -0.013730 -0.000235 -SDSS11067 0.113040 0.114000 0 19.114133 0.112246 1.733698 0.520618 -0.072351 0.026351 9.420000 0.086500 53702.777528 0.402505 0.005632 0.000568 0.006371 2 33.518394 -0.239178 -0.001195 -SDSS1112 0.256110 0.257640 0 21.537919 0.124820 -0.623265 0.675408 -0.026817 0.051918 11.130000 0.217500 53630.086004 0.678610 0.024142 0.001549 0.005258 2 -20.982361 -0.375189 -0.000450 -SDSS11206 0.380260 0.381900 0 22.200281 0.138121 -1.510288 0.872760 -0.205532 0.085154 10.689000 0.160500 53697.222252 1.648688 0.032602 0.001346 0.032911 2 3.289774 1.145541 -0.000372 -SDSS11300 0.145470 0.146800 0 20.283420 0.113737 -1.607355 0.675537 0.075146 0.032765 9.291000 0.108500 53699.544122 0.319765 0.008831 0.000895 0.010478 2 6.750494 -0.586624 -0.001234 -SDSS1241 0.088890 0.090200 0 19.115082 0.112510 -0.547524 0.078724 0.048945 0.022754 10.171000 0.132000 53635.370749 0.078459 0.000578 0.000522 0.000051 2 -22.327475 -0.776589 -0.001014 -SDSS1253 0.260590 0.262000 0 21.218177 0.120435 -0.959241 0.447847 -0.118798 0.048622 11.028000 0.008000 53634.279829 0.418644 0.010964 0.001294 0.002902 2 -36.200954 0.163079 -0.000413 -SDSS12779 0.078910 0.079950 0 19.149702 0.117766 -0.561485 0.260778 0.096243 0.029550 10.590000 0.077000 53971.725984 0.969511 -0.006687 0.001110 -0.003254 2 -50.528126 1.221122 -0.000894 -SDSS12781 0.083040 0.084310 0 19.386270 0.115125 -1.832076 0.188047 0.036206 0.026776 10.955000 0.189000 53979.308963 0.223952 0.004178 0.000693 0.000831 2 5.406564 -1.009973 -0.000947 -SDSS12843 0.165650 0.166950 0 20.412042 0.118455 -1.018684 0.298736 0.037459 0.034757 11.085000 0.017500 53982.099112 0.195338 0.009281 0.001274 0.003463 2 -36.121914 -0.980274 -0.001166 -SDSS12853 0.168170 0.169400 0 20.174020 0.116716 -0.580103 0.345009 -0.061756 0.031989 10.467000 0.220500 53979.293471 0.325165 0.009354 0.001002 0.003283 2 -43.234436 0.723070 -0.001154 -SDSS12855 0.170630 0.172000 0 20.781976 0.114853 -1.918197 0.297270 0.040563 0.034813 5.000000 10.000000 53996.150010 0.327576 0.002792 0.000969 0.000629 2 -29.744469 0.716239 -0.001141 -SDSS12856 0.170280 0.171670 0 20.134725 0.112043 0.860171 0.200447 -0.089735 0.023235 10.118000 0.335500 53996.913924 0.239305 0.001352 0.000465 0.000153 2 -27.134525 0.755978 -0.001143 -SDSS12860 0.120440 0.121700 0 20.209823 0.113463 -0.303464 0.200030 0.140894 0.027658 10.629000 0.209500 53993.777027 0.278716 0.001212 0.000751 0.000619 2 -36.305805 1.175912 -0.001223 -SDSS12869 0.276530 0.278000 0 21.365495 0.126014 0.207633 0.834884 -0.041301 0.050506 10.140000 0.049500 53978.987334 1.213947 0.031787 0.001877 0.012339 2 -32.600876 0.001078 -0.000291 -SDSS12881 0.231600 0.233000 0 21.098958 0.118789 0.772132 0.519462 0.034830 0.038889 9.324000 0.171000 53980.300360 0.562986 0.016145 0.001188 0.004233 2 10.162345 -0.073567 -0.000666 -SDSS12883 0.305690 0.307000 0 21.588074 0.145900 -1.955498 0.844199 -0.057713 0.072093 10.972000 0.148500 53980.131475 0.813259 0.064147 0.003606 0.022383 2 -47.374107 0.398666 -0.000128 -SDSS12898 0.079970 0.081000 0 19.019822 0.112109 -0.069349 0.084661 0.070049 0.021196 10.081000 0.248500 54002.481104 0.080079 0.000438 0.000450 0.000005 2 26.793083 -0.146964 -0.000908 -SDSS12927 0.174150 0.175000 0 20.351422 0.118918 0.026059 0.324204 0.014706 0.032635 10.482000 0.236000 53988.935725 0.807786 -0.002185 0.001246 -0.000626 2 41.786140 0.775128 -0.001121 -SDSS12928 0.138680 0.139700 0 19.897068 0.116765 -0.558040 0.378518 -0.064004 0.032084 10.896000 0.208500 53972.952834 1.104233 -0.006844 0.001083 -0.003817 2 -54.990971 -0.975694 -0.001243 -SDSS12930 0.146070 0.147170 0 20.061964 0.117074 1.887311 0.404985 0.021652 0.032489 10.785000 0.270500 53979.680255 0.385186 0.010853 0.001087 0.003446 2 -50.317223 -0.474529 -0.001233 -SDSS12950 0.081330 0.082660 0 18.915623 0.111814 -0.331500 0.061082 -0.005329 0.020434 9.793000 0.105500 54000.046171 0.068168 0.000156 0.000421 -0.000004 2 -8.332550 -0.840276 -0.000926 -SDSS12971 0.233800 0.235240 0 20.793051 0.115605 1.184623 0.581392 -0.091515 0.036030 11.563000 0.131000 53976.632575 1.192338 0.002014 0.000884 -0.003268 2 6.648502 -0.302072 -0.000647 -SDSS12972 0.256550 0.258000 0 21.306745 0.125870 0.115968 0.663312 -0.080959 0.048539 8.871000 0.292000 53980.992526 0.660242 0.031906 0.001962 0.013525 2 7.958593 -0.382957 -0.000447 -SDSS12977 0.245620 0.247000 0 21.151867 0.115338 0.070492 0.488739 -0.048348 0.038411 9.565000 0.271000 53994.908649 0.813422 0.003127 0.000929 -0.000898 2 13.695609 -0.250863 -0.000541 -SDSS13005 0.125930 0.127000 0 19.890210 0.157613 -0.332265 0.314750 -0.012297 0.043939 10.193000 0.217000 53964.573096 1.241092 0.016150 0.003978 0.003060 2 27.395458 -0.649882 -0.001236 -SDSS13025 0.223510 0.225000 0 21.236545 0.116800 0.742038 0.297371 0.028844 0.035915 10.714000 0.369500 53990.651901 0.441037 0.004694 0.001086 0.001160 2 -18.432674 0.415896 -0.000739 -SDSS13038 0.102640 0.104000 0 19.206141 0.113322 1.419043 0.145956 -0.023293 0.023002 5.000000 10.000000 53988.649261 0.135905 0.002333 0.000582 0.000659 2 -12.173217 0.504606 -0.001135 -SDSS13044 0.119670 0.121000 0 19.578968 0.112171 -0.014339 0.128144 -0.067870 0.022157 9.624000 0.040500 54003.155690 0.091364 0.001205 0.000500 0.000477 2 -27.457249 0.503265 -0.001221 -SDSS13045 0.180550 0.182000 0 20.674320 0.118342 -0.137419 0.468702 0.052129 0.034215 10.414000 0.110000 53993.297561 1.071968 -0.007697 0.001245 -0.002972 2 -14.975061 0.537670 -0.001082 -SDSS13070 0.197090 0.198530 0 20.348239 0.116183 0.949020 0.275205 -0.144736 0.028227 9.999000 0.264000 53986.572092 0.252065 0.007604 0.000840 0.002549 2 -2.214941 -0.746341 -0.000963 -SDSS13072 0.229160 0.230630 0 21.093432 0.122097 0.392184 0.389836 -0.013385 0.043343 10.027000 0.109500 53988.922258 0.475350 0.013545 0.001593 0.003517 2 -25.040529 0.024371 -0.000688 -SDSS13099 0.264510 0.266030 0 21.535410 0.119638 0.347212 0.787464 -0.021876 0.046516 11.053000 0.176500 53995.225429 1.406763 0.000978 0.001303 -0.001618 2 -0.181266 -1.250384 -0.000381 -SDSS13135 0.103370 0.104670 0 19.373517 0.111598 -1.412953 0.069418 -0.056782 0.020707 11.161000 0.121000 54003.618428 0.060304 0.000176 0.000423 -0.000031 2 4.172286 -0.424540 -0.001140 -SDSS13136 0.364410 0.366000 0 22.299562 0.131873 0.659810 0.838318 -0.101528 0.060234 11.250000 0.098000 53993.708711 1.085790 0.034886 0.001675 0.012765 2 6.140682 -0.279061 -0.000215 -SDSS13152 0.205600 0.207000 0 20.662469 0.113718 0.258670 0.243737 0.007696 0.029325 9.363000 0.199000 54001.232270 0.196075 0.003031 0.000761 0.000829 2 7.052111 0.118010 -0.000894 -SDSS13254 0.179810 0.180660 0 21.151747 0.120493 0.479896 0.537821 0.092962 0.039262 9.975000 0.205500 53994.278417 1.149480 -0.007139 0.001508 -0.002912 2 42.058674 -0.347028 -0.001087 -SDSS13305 0.199590 0.201000 0 20.849845 0.115206 1.115009 0.309760 0.017693 0.033020 10.014000 0.104000 54003.846064 0.265389 0.005478 0.000924 0.001398 2 -28.899513 0.691214 -0.000943 -SDSS13354 0.156530 0.157630 0 20.298025 0.113012 1.040064 0.162748 0.087963 0.026774 10.648000 0.225500 54006.605616 0.173794 0.001386 0.000684 0.000469 2 27.564823 -0.887272 -0.001204 -SDSS13411 0.161800 0.163000 0 20.328307 0.120879 1.786215 0.373604 0.019766 0.034148 8.508000 0.256500 53987.459786 0.302829 0.014639 0.001300 0.003839 2 -44.810047 0.191662 -0.001184 -SDSS13425 0.209530 0.211000 0 21.864970 0.131221 -0.977679 0.560278 0.259538 0.057176 10.360000 0.221000 53993.011501 1.131731 0.008805 0.002994 0.002468 2 -21.458321 0.054988 -0.000861 -SDSS13506 0.241790 0.243000 0 21.801057 0.122422 -0.200116 0.625570 0.138533 0.046327 10.402000 0.224000 54000.581012 0.532756 0.021035 0.001645 0.006111 2 25.243341 -0.727846 -0.000575 -SDSS13511 0.236460 0.237370 0 21.461512 0.117789 -1.553624 0.441449 -0.095193 0.040556 11.103000 0.086500 54002.619751 0.323149 0.010449 0.001133 0.003105 2 40.612354 -0.794122 -0.000623 -SDSS13578 0.226680 0.228000 0 21.068080 0.115846 -0.245891 0.365672 -0.040327 0.036470 8.902000 0.837500 53998.759125 0.388279 0.005751 0.000948 0.001080 2 17.394697 0.704158 -0.000711 -SDSS13610 0.296780 0.298260 0 21.381850 0.117785 0.079305 0.535094 -0.095522 0.041829 10.509000 0.121500 53997.009147 0.582206 0.011579 0.000906 0.001050 2 -33.985218 0.726284 -0.000167 -SDSS13641 0.208520 0.210000 0 20.762042 0.114386 1.026478 0.283831 -0.031055 0.032116 5.000000 10.000000 54004.831478 0.290529 0.003820 0.000834 0.000756 2 -14.781268 -0.981143 -0.000870 -SDSS13655 0.251040 0.252000 0 21.936242 0.119872 -1.339461 0.584310 0.082437 0.047361 10.996000 0.017500 53999.849782 0.559823 0.013833 0.001319 0.003153 2 39.020218 -0.994179 -0.000494 -SDSS13689 0.250120 0.251600 0 20.967243 0.116581 1.161118 0.378980 -0.147913 0.037575 10.449000 0.149000 54000.150247 0.474406 0.006689 0.001015 0.001729 2 4.016125 0.807558 -0.000502 -SDSS1371 0.117710 0.119090 0 19.052211 0.111960 0.789563 0.095852 -0.101397 0.021570 10.950000 0.089000 53633.310637 0.106019 0.000651 0.000459 0.000085 2 -10.626181 0.429384 -0.001214 -SDSS13727 0.219710 0.221000 0 20.855502 0.115514 1.253108 0.342124 -0.040277 0.035174 9.620000 0.169500 54004.880110 0.326901 0.006424 0.000909 0.001788 2 -42.411919 0.932620 -0.000773 -SDSS13736 0.149010 0.150400 0 19.972155 0.112283 1.116862 0.164507 -0.026876 0.023147 9.589000 0.274500 54015.786222 0.131890 0.001651 0.000501 0.000394 2 -23.166714 1.030971 -0.001227 -SDSS13757 0.287420 0.289000 0 21.262819 0.118845 1.098936 0.429190 -0.166608 0.042413 5.000000 10.000000 53993.749889 0.633109 0.007656 0.001108 0.001103 2 -9.876911 -1.157847 -0.000219 -SDSS13796 0.143600 0.145000 0 19.837994 0.111766 0.789492 0.115818 -0.018978 0.022019 10.018000 0.344500 54010.412053 0.116713 0.000606 0.000453 0.000159 2 -9.308426 0.532883 -0.001237 -SDSS13830 0.328370 0.330000 0 21.644732 0.134054 1.180448 0.621330 -0.018399 0.053650 11.042000 0.316000 53990.929352 0.740286 0.036511 0.002369 0.011087 2 -8.438282 -0.382281 -0.000083 -SDSS13835 0.246250 0.247700 0 21.075270 0.114566 0.624852 0.246427 -0.078392 0.034754 10.462000 0.314500 54011.462513 0.262924 0.003158 0.000832 0.001062 2 6.059644 -0.248262 -0.000536 -SDSS13894 0.119660 0.121000 0 20.319989 0.113363 0.742978 0.166311 0.166043 0.027230 9.600000 0.078000 54013.687652 0.146341 0.001733 0.000735 0.000765 2 1.690610 -0.036760 -0.001221 -SDSS13934 0.328380 0.330000 0 22.190461 0.136536 -0.707675 0.819169 -0.100206 0.069498 10.510000 0.047500 54007.418002 0.952551 0.041775 0.001478 0.010853 2 -17.888990 -0.435268 -0.000083 -SDSS14019 0.215130 0.216400 0 21.433042 0.120494 -0.316436 0.387780 -0.021846 0.045550 9.670000 0.040000 54012.202325 0.396852 0.010555 0.001580 0.003864 2 -43.357578 -0.647952 -0.000813 -SDSS14024 0.144780 0.146000 0 20.466614 0.114439 -1.922932 0.195002 0.037870 0.031511 8.378000 0.979000 54011.161767 0.204867 0.001906 0.000863 0.000786 2 -41.801437 0.916348 -0.001236 -SDSS14108 0.132430 0.133000 0 20.147341 0.112393 0.248082 0.143225 -0.000855 0.024455 8.938000 0.467000 54010.210638 0.171477 0.000747 0.000564 0.000275 2 53.594646 -1.123210 -0.001243 -SDSS14157 0.210820 0.211500 0 20.480731 0.114470 1.125219 0.266073 -0.058471 0.031877 10.943000 0.227000 54013.951352 0.250926 0.004189 0.000827 0.000926 2 51.136692 1.022365 -0.000850 -SDSS14212 0.203990 0.205400 0 20.912346 0.114245 -0.339599 0.173027 -0.032535 0.031921 10.284000 0.048000 54012.948531 0.182820 0.002056 0.000842 0.000631 2 -29.529888 1.045011 -0.000908 -SDSS14261 0.279520 0.281000 0 21.349608 0.121901 0.694248 0.461519 -0.030169 0.042765 8.814000 0.549000 54018.807905 0.367112 0.017391 0.001403 0.005581 2 -31.759598 0.253794 -0.000270 -SDSS14279 0.044280 0.045390 0 17.546294 0.113705 -0.162447 0.047432 0.090515 0.023033 10.513000 0.224000 54022.045645 0.060993 0.000123 0.000447 -0.000133 2 18.488775 0.371669 -0.000238 -SDSS14284 0.180370 0.181070 0 20.228837 0.112763 -0.308780 0.169404 -0.068514 0.025585 10.514000 0.119500 54014.578081 0.158648 0.001700 0.000579 0.000583 2 49.049294 -0.600944 -0.001083 -SDSS14298 0.260700 0.262000 0 21.261083 0.117389 1.298349 0.380957 -0.080943 0.042088 5.000000 10.000000 54012.467434 0.338976 0.008819 0.000899 0.001041 2 -45.104950 1.223257 -0.000412 -SDSS14318 0.056540 0.057830 0 17.780075 0.122398 0.864354 0.782043 0.028353 0.032176 9.930000 0.083500 54072.073569 0.823627 -0.005013 0.000623 0.010413 2 -19.574804 -0.136905 -0.000516 -SDSS14331 0.212600 0.214000 0 21.246133 0.116607 0.141351 0.293878 0.014187 0.039132 9.741000 0.371000 54013.473532 0.302338 0.005643 0.001073 0.001043 2 7.888675 -0.135814 -0.000835 -SDSS14397 0.369410 0.371000 0 22.210028 0.131466 -1.192797 0.603467 -0.220851 0.051095 10.479000 0.059000 54013.924295 0.746917 0.023081 0.000160 0.005482 2 6.916098 0.649507 -0.000257 -SDSS14421 0.173860 0.174900 0 20.452267 0.112491 -0.745633 0.179967 -0.097808 0.025504 11.260000 0.085000 54010.902243 0.212012 0.001352 0.000456 0.000271 2 31.829882 1.252084 -0.001123 -SDSS14437 0.142650 0.144000 0 19.708683 0.112226 0.594179 0.118823 -0.094830 0.023600 9.770000 0.242000 54009.820871 0.119003 0.000909 0.000527 0.000309 2 -27.919041 -1.196387 -0.001239 -SDSS14451 0.177890 0.179000 0 20.152610 0.112906 0.289199 0.167229 -0.106929 0.026245 9.886000 0.310000 54011.802453 0.176689 0.001832 0.000587 0.000628 2 -51.805046 0.927069 -0.001099 -SDSS14456 0.328370 0.330000 0 21.823069 0.124052 -0.040964 0.547480 -0.029396 0.048258 10.930000 0.236000 54018.663551 0.463304 0.022493 0.001329 0.005937 2 -16.448896 1.050661 -0.000083 -SDSS14481 0.253510 0.255000 0 21.384596 0.123692 -1.039765 0.378598 -0.171954 0.049646 10.917000 0.044500 54020.190417 0.309302 0.015081 0.001583 0.004905 2 2.681734 0.201306 -0.000473 -SDSS14735 0.300010 0.301100 0 21.706687 0.119386 -0.087397 0.420307 0.032811 0.044618 10.469000 0.198000 54013.808429 0.484648 0.011017 0.001098 0.002362 2 35.158035 0.348378 -0.000152 -SDSS14816 0.105890 0.107220 0 19.370566 0.112285 -0.672680 0.092627 0.021313 0.022405 10.383000 0.204000 54021.316287 0.091724 0.000804 0.000507 0.000275 2 -23.283701 0.506056 -0.001157 -SDSS14846 0.223280 0.224700 0 20.941812 0.115872 0.633837 0.287959 -0.044898 0.036059 10.904000 0.069500 54024.973881 0.279668 0.005498 0.000981 0.001245 2 7.663236 0.141581 -0.000741 -SDSS14871 0.118450 0.119000 0 19.436663 0.111824 1.290990 0.111951 -0.046502 0.021557 8.922000 0.187000 54027.354197 0.123605 0.000610 0.000455 0.000095 2 54.277100 0.009360 -0.001217 -SDSS14979 0.176530 0.177100 0 20.178782 0.112560 0.224801 0.139585 -0.117703 0.025137 9.807000 0.210500 54025.602856 0.143253 0.001302 0.000541 0.000246 2 54.946434 0.992816 -0.001107 -SDSS14984 0.182800 0.184000 0 20.867304 0.115225 1.015744 0.332625 0.072977 0.032474 10.083000 0.129500 54024.802172 0.319621 0.005804 0.000907 0.001715 2 -46.166435 -0.092790 -0.001067 -SDSS15057 0.297620 0.299000 0 21.845816 0.131068 -2.484990 0.545017 -0.058957 0.057611 9.519000 0.248000 54017.259550 0.388504 0.025754 0.001793 0.006000 2 17.881262 0.409492 -0.000163 -SDSS15129 0.196990 0.198280 0 20.684448 0.114008 -0.774077 0.222962 -0.070545 0.031567 10.796000 0.281000 54023.823806 0.250777 0.002585 0.000732 0.000355 2 -41.097610 -0.321433 -0.000964 -SDSS15132 0.152950 0.154300 0 19.761456 0.111718 1.067798 0.146912 -0.133296 0.021892 9.218000 0.360500 54026.527016 0.138607 0.000861 0.000427 0.000027 2 -30.299767 0.197686 -0.001216 -SDSS15161 0.248500 0.249540 0 21.203185 0.117491 -0.284969 0.307134 -0.053995 0.041325 10.744000 0.113000 54025.356721 0.359183 0.007082 0.001200 0.003075 2 35.842915 0.819003 -0.000516 -SDSS15171 0.138180 0.139200 0 19.659001 0.111860 0.680037 0.143794 -0.078535 0.022334 8.724000 0.786000 54029.168513 0.136019 0.000743 0.000477 0.000299 2 -55.207462 -1.064489 -0.001243 -SDSS15201 0.207040 0.208500 0 21.801005 0.117479 -1.245704 0.453680 0.094327 0.040301 11.143000 0.140500 54028.286993 0.395634 0.008227 0.001126 0.001288 2 -22.480585 0.003639 -0.000882 -SDSS15203 0.214680 0.216000 0 20.730053 0.114437 1.114217 0.263445 -0.024767 0.032008 10.047000 0.185000 54027.179809 0.268008 0.003543 0.000828 0.000709 2 15.734715 0.183087 -0.000817 -SDSS15213 0.306320 0.307000 0 21.733825 0.127264 -0.084184 0.592276 -0.086541 0.056384 10.901000 0.175000 54026.323110 0.663553 0.027689 0.001851 0.011371 2 53.019138 -0.100142 -0.000126 -SDSS15219 0.246950 0.248000 0 21.056951 0.114812 -0.146222 0.372589 -0.154043 0.035755 10.809000 0.104000 54030.968865 0.334251 0.004943 0.000831 0.001694 2 34.611145 0.226688 -0.000529 -SDSS15222 0.198010 0.199430 0 20.948974 0.115064 -1.339491 0.278284 0.011075 0.034057 11.260000 0.217500 54031.122439 0.250413 0.003905 0.000863 0.001005 2 2.853377 0.702744 -0.000956 -SDSS15229 0.222560 0.224000 0 21.024295 0.115270 0.685016 0.287151 -0.032572 0.037334 8.926000 0.266500 54033.214579 0.299612 0.004428 0.000918 0.001028 2 4.831984 1.090719 -0.000747 -SDSS15234 0.135080 0.136300 0 20.461573 0.113196 0.900230 0.203830 0.127760 0.027496 10.399000 0.278000 54032.802059 0.198382 0.001851 0.000708 0.000808 2 16.958336 0.828198 -0.001244 -SDSS15259 0.210540 0.212000 0 20.791873 0.114240 0.419532 0.264210 -0.014647 0.032773 9.864000 0.359500 54026.334023 0.270027 0.003222 0.000810 0.000330 2 -22.455803 -0.407819 -0.000853 -SDSS15287 0.272580 0.274000 0 21.161073 0.114210 1.423274 0.314104 -0.072993 0.034771 10.405000 0.068500 54030.380702 0.329032 0.003936 0.000771 0.000923 2 -36.040318 -1.057435 -0.000319 -SDSS15301 0.246600 0.248000 0 20.665733 0.114128 -0.338697 0.200401 -0.138562 0.033038 10.280000 0.220000 54035.911583 0.214746 0.002317 0.000768 0.000920 2 -36.420082 0.589106 -0.000533 -SDSS15354 0.220680 0.222100 0 21.520886 0.119891 -2.264612 0.353956 0.085193 0.048488 10.500000 0.040000 54024.622973 0.494399 0.005862 0.001506 0.000780 2 6.773676 -0.125976 -0.000764 -SDSS15356 0.274470 0.276000 0 21.436954 0.118156 -0.707454 0.476230 -0.049484 0.045061 10.276000 0.217000 54029.242189 0.460967 0.010270 0.001140 0.003784 2 -24.946684 0.409944 -0.000305 -SDSS15365 0.176560 0.178000 0 20.504592 0.113446 0.829711 0.230693 -0.036910 0.027501 10.865000 0.012000 54038.074278 0.184407 0.003262 0.000588 0.000979 2 -5.443342 1.249090 -0.001107 -SDSS15383 0.310880 0.312000 0 21.860479 0.122790 0.212271 0.705383 -0.033941 0.049518 10.609000 0.016500 54030.555468 0.709456 0.024801 0.001170 0.005592 2 34.149532 -0.155162 -0.000111 -SDSS15421 0.184350 0.185370 0 20.641715 0.112927 0.174788 0.255855 -0.043682 0.026039 10.149000 0.183000 54035.159684 0.227402 0.002745 0.000563 0.000872 2 33.741600 0.602507 -0.001057 -SDSS15425 0.159350 0.159890 0 19.907795 0.111983 0.971182 0.165340 -0.006110 0.022705 10.398000 0.132000 54038.060608 0.150359 0.001276 0.000477 0.000573 2 55.561081 0.478286 -0.001194 -SDSS15433 0.219760 0.221100 0 20.943822 0.114935 -0.088272 0.258088 -0.067736 0.034762 10.585000 0.097500 54037.383351 0.249863 0.003603 0.000851 0.001033 2 14.879633 -0.256513 -0.000772 -SDSS15440 0.252050 0.253000 0 21.586693 0.118220 -0.623735 0.491254 0.065746 0.042554 10.556000 0.014500 54030.999218 0.416096 0.011144 0.001214 0.004489 2 39.720669 0.090127 -0.000485 -SDSS15443 0.181220 0.181910 0 20.192003 0.112167 1.560538 0.180585 -0.041282 0.023888 10.223000 0.218000 54041.288634 0.167602 0.001600 0.000500 0.000616 2 49.867435 -0.318030 -0.001078 -SDSS15453 0.173730 0.175000 0 20.549280 0.114314 1.150037 0.253016 -0.024265 0.028594 8.651000 0.296000 54038.180564 0.181712 0.004879 0.000730 0.001848 2 -40.331696 -1.024288 -0.001124 -SDSS15456 0.380470 0.382100 0 21.980928 0.125480 -1.212778 0.758937 -0.090551 0.060549 10.799000 0.261000 54030.298116 0.750983 0.025155 0.001001 0.011893 2 -28.132738 -0.903413 -0.000375 -SDSS15461 0.178650 0.180000 0 20.359333 0.113319 -0.162051 0.207846 -0.100206 0.026104 10.186000 0.340000 54036.984243 0.173309 0.002788 0.000606 0.001070 2 -33.152515 -0.494677 -0.001094 -SDSS15466 0.245680 0.247000 0 21.326555 0.120181 -1.061965 0.391983 0.027935 0.046676 10.482000 0.356500 54036.652006 0.353020 0.010340 0.001477 0.003449 2 -42.354958 -0.123220 -0.000541 -SDSS15467 0.209100 0.210410 0 20.592027 0.115699 0.801260 0.260652 -0.067945 0.033460 10.402000 0.250000 54038.902146 0.194597 0.005697 0.000954 0.002154 2 -39.980064 -0.177441 -0.000865 -SDSS15504 0.249470 0.251000 0 21.852696 0.131353 2.337170 0.726209 0.205582 0.057909 10.727000 0.105500 54039.808975 0.570462 0.040892 0.002883 0.017225 2 -14.298554 -0.876422 -0.000507 -SDSS15508 0.133920 0.135000 0 19.607858 0.111798 0.882983 0.104330 -0.059231 0.022270 9.879000 0.261000 54041.562599 0.091954 0.000609 0.000469 0.000273 2 27.168976 -0.576611 -0.001244 -SDSS15583 0.177060 0.178000 0 20.736464 0.113873 -0.296321 0.205496 0.025339 0.030116 9.039000 0.335500 54041.009004 0.189287 0.002797 0.000735 0.001257 2 37.731079 0.946299 -0.001104 -SDSS15584 0.281110 0.282000 0 21.439311 0.117530 0.314981 0.404869 -0.044998 0.044132 10.623000 0.033500 54038.128868 0.391269 0.008833 0.001045 0.003416 2 43.495464 0.986965 -0.000259 -SDSS15648 0.173830 0.175020 0 21.032398 0.122163 -1.453750 0.357753 0.113585 0.043923 11.175000 0.123500 54042.568941 0.290054 0.012903 0.001872 0.006138 2 -46.281616 -0.194823 -0.001123 -SDSS15674 0.196540 0.198000 0 21.017687 0.117701 -0.343601 0.301808 0.004228 0.037156 5.000000 10.000000 54040.283234 0.233265 0.008012 0.001193 0.003642 2 -19.170843 0.262909 -0.000968 -SDSS15704 0.363970 0.365000 0 22.059517 0.127982 -0.048590 0.701920 -0.171360 0.055876 10.679000 0.169000 54043.002778 0.685907 0.027972 0.000890 0.014683 2 40.210735 0.658751 -0.000211 -SDSS15756 0.378350 0.380000 0 22.421908 0.135103 -0.960604 0.712176 -0.250149 0.060550 11.371000 0.094000 54035.760135 0.821046 0.029350 -0.000107 0.008533 2 0.374834 0.275974 -0.000349 -SDSS15776 0.316460 0.317600 0 21.846724 0.121555 -2.281762 0.560543 -0.146321 0.049708 10.899000 0.005000 54033.830189 0.490027 0.016160 0.000877 0.006370 2 32.829441 -0.998258 -0.000096 -SDSS1580 0.183220 0.184000 0 20.272733 0.114206 0.879572 0.228368 -0.038487 0.027067 11.198000 0.162000 53634.026193 0.205026 0.003957 0.000672 0.000584 2 45.323063 -0.644066 -0.001065 -SDSS15868 0.241020 0.242000 0 21.005963 0.116005 0.328735 0.264211 -0.140847 0.037003 10.417000 0.046000 54044.253909 0.220235 0.005325 0.000998 0.001937 2 38.099796 -0.713578 -0.000582 -SDSS15872 0.202030 0.203000 0 20.556408 0.117009 0.781434 0.308151 -0.064131 0.035853 9.304000 0.135000 54047.769325 0.205691 0.008139 0.001205 0.003642 2 36.722496 -0.327810 -0.000924 -SDSS15897 0.168690 0.170000 0 20.889914 0.116161 -2.457456 0.260674 0.015791 0.036647 10.655000 0.048000 54041.523636 0.222584 0.003928 0.001095 0.001464 2 11.681450 -1.032905 -0.001151 -SDSS15901 0.197940 0.199000 0 20.774797 0.114551 0.030200 0.255743 -0.040623 0.029762 9.669000 0.334500 54045.458248 0.203833 0.004620 0.000779 0.001974 2 31.976303 -0.535389 -0.000957 -SDSS16021 0.122760 0.124000 0 19.617670 0.112478 -0.346980 0.126820 -0.038204 0.023745 9.842000 0.114000 54049.119852 0.113526 0.001342 0.000570 0.000751 2 13.843731 -0.388811 -0.001229 -SDSS16032 0.203180 0.204000 0 20.758955 0.114994 -0.819482 0.239931 -0.108570 0.033663 5.000000 10.000000 54046.404693 0.178185 0.004069 0.000914 0.001638 2 44.069218 -0.410771 -0.000914 -SDSS16069 0.127390 0.128770 0 20.104432 0.113191 0.878535 0.217333 0.156630 0.026638 10.980000 0.250500 54050.756670 0.185417 0.002579 0.000714 0.001592 2 -18.753925 -1.006598 -0.001238 -SDSS16072 0.275490 0.277000 0 21.533317 0.127184 0.275684 0.624905 -0.016258 0.053694 10.640000 0.006500 54046.850417 0.490002 0.030689 0.002286 0.013875 2 3.124440 -0.977309 -0.000298 -SDSS16073 0.144680 0.146000 0 20.257201 0.112388 0.723179 0.185303 -0.006892 0.024196 9.779000 0.152000 54051.434569 0.157008 0.001746 0.000569 0.000895 2 8.107763 -1.054001 -0.001236 -SDSS16093 0.334360 0.336000 0 21.862025 0.132618 1.029207 0.735059 -0.063637 0.055995 10.107000 0.415000 54045.023346 0.638371 0.042300 0.001527 0.011413 2 -9.637529 1.132483 -0.000085 -SDSS16099 0.195800 0.196950 0 20.704275 0.113084 2.030525 0.369443 0.023292 0.026103 10.365000 0.077500 54054.439105 0.307148 0.005445 0.000622 0.002735 2 26.420935 -1.054386 -0.000973 -SDSS16100 0.193920 0.195000 0 21.065011 0.117691 -0.393434 0.350479 0.051510 0.037507 9.122000 0.646500 54048.043112 0.255318 0.009565 0.001277 0.004810 2 30.436289 -1.032410 -0.000988 -SDSS16106 0.249730 0.251200 0 21.020950 0.120462 -0.628986 0.364023 -0.150136 0.047564 10.831000 0.096000 54045.424735 0.303232 0.011843 0.001547 0.005396 2 -27.910652 -1.148781 -0.000505 -SDSS16116 0.148600 0.150000 0 19.970319 0.112448 0.508881 0.286742 -0.097611 0.024792 9.512000 0.089500 54053.281814 0.232875 0.003219 0.000562 0.002271 2 -4.697990 -1.105807 -0.001228 -SDSS16165 0.155960 0.157000 0 20.146078 0.112725 0.344199 0.222938 -0.032000 0.024798 5.000000 10.000000 54052.896254 0.192094 0.002637 0.000594 0.001305 2 30.733135 -0.533872 -0.001206 -SDSS16185 0.095820 0.097000 0 19.773444 0.112716 -2.309599 0.224051 0.069933 0.025445 9.507000 0.009500 54053.891092 0.146951 0.001706 0.000589 0.000816 2 16.868069 -0.269365 -0.001081 -SDSS16206 0.150650 0.152000 0 20.403921 0.113842 -2.565430 0.550751 -0.008622 0.031343 10.684000 0.025000 54056.990454 0.248282 0.008228 0.000829 0.007441 2 5.788190 -0.053633 -0.001223 -SDSS16211 0.309330 0.310940 0 22.130926 0.137464 -1.964016 0.625845 0.004445 0.070422 11.226000 0.005000 54041.487206 0.575672 0.033017 0.002280 0.008643 2 -11.835910 0.266932 -0.000115 -SDSS16213 0.248570 0.250000 0 21.127943 0.115731 0.279927 0.429849 -0.096848 0.038360 5.000000 10.000000 54053.580676 0.414496 0.007837 0.000993 0.003899 2 8.971051 0.258405 -0.000515 -SDSS16232 0.365540 0.367000 0 22.136490 0.137024 -0.145799 0.829010 -0.111398 0.068462 10.504000 0.105000 54045.028681 0.842626 0.049056 0.002465 0.026024 2 17.205410 -0.989553 -0.000224 -SDSS16259 0.117710 0.119080 0 20.039870 0.113079 -1.540003 0.224010 0.075020 0.027652 11.013000 0.170000 54052.309148 0.199371 0.001412 0.000697 0.001004 2 -7.970029 0.856355 -0.001214 -SDSS16276 0.191760 0.193000 0 20.740998 0.114367 -0.018646 0.551403 -0.026411 0.031850 8.820000 0.348000 54057.368692 0.366500 0.009763 0.000814 0.006101 2 20.578657 1.010615 -0.001004 -SDSS16281 0.182550 0.184000 0 20.473840 0.114391 -0.873266 0.510227 -0.073170 0.032312 10.141000 0.043500 54055.402926 0.345403 0.008315 0.000855 0.006058 2 -7.057849 -0.668052 -0.001069 -SDSS16350 0.234720 0.236100 0 21.130073 0.116813 -1.345527 0.544367 -0.090072 0.043795 10.525000 0.069000 54054.128124 0.441841 0.010202 0.001209 0.006173 2 -36.360512 -0.885831 -0.000638 -SDSS16352 0.246470 0.248000 0 20.845774 0.115433 0.273411 0.849504 -0.141815 0.039512 9.769000 0.547500 54059.082427 0.673456 0.016410 0.001047 0.014561 2 -13.750642 -0.873365 -0.000534 -SDSS16402 0.288450 0.290000 0 20.859757 0.116661 -0.872064 0.779949 -0.186671 0.042448 10.996000 0.099500 54059.492014 0.554159 0.016799 0.001030 0.012985 2 -23.632280 0.265275 -0.000213 -SDSS16473 0.209600 0.211000 0 20.739132 0.115465 -1.073325 0.867789 -0.148416 0.041364 9.907000 0.239000 54062.360432 0.840318 0.004784 0.000872 0.014738 2 -31.387848 0.588515 -0.000861 -SDSS16618 0.191980 0.193200 0 20.466522 0.114573 -0.792727 0.603726 -0.097090 0.032471 9.907000 0.121500 54067.262264 0.701596 -0.000817 0.000515 0.008545 2 21.279373 -1.218938 -0.001002 -SDSS16619 0.114020 0.115100 0 19.173326 0.113460 -0.191925 0.593399 -0.085850 0.027195 8.332000 0.725500 54069.581329 0.683113 -0.007744 0.000289 0.009223 2 25.938738 -1.111919 -0.001200 -SDSS16641 0.113890 0.115000 0 19.719269 0.113093 1.053347 0.621248 0.039734 0.029796 10.477000 0.166000 54069.197859 0.628262 0.002892 0.000595 0.010691 2 23.561230 -0.403632 -0.001199 -SDSS16737 0.198800 0.200000 0 20.519882 0.124264 -0.443610 0.714551 -0.075727 0.045945 10.070000 0.369000 54068.757201 0.946165 -0.008166 0.000504 0.010785 2 -47.167320 -0.952322 -0.000950 -SDSS16793 0.220580 0.222000 0 20.764062 0.138465 0.524838 1.379829 -0.051257 0.050845 5.000000 10.000000 54070.762749 1.932449 -0.044995 0.000478 0.020375 2 -31.424644 0.447010 -0.000765 -SDSS17168 0.182570 0.184000 0 20.598590 0.115147 0.503374 0.302924 -0.039676 0.032445 9.520000 0.160000 54356.947904 0.286887 0.005314 0.000828 0.001011 2 -20.276196 -1.167083 -0.001069 -SDSS17171 0.158950 0.160270 0 19.937950 0.145393 -0.384727 0.342960 -0.141622 0.041138 10.628000 0.009000 54340.661534 1.432067 -0.002880 0.003042 -0.000371 2 -33.497959 -1.217730 -0.001195 -SDSS17186 0.078490 0.079440 0 19.240437 0.113854 0.642723 0.148902 0.111359 0.024312 10.562000 0.164000 54353.810894 0.186790 0.001927 0.000619 0.000107 2 31.612825 -0.899550 -0.000888 -SDSS17208 0.138640 0.140000 0 19.983006 0.119461 0.836869 0.362615 -0.068099 0.029302 8.200000 0.576000 54342.030357 1.034356 -0.007419 0.001053 -0.001128 2 1.491593 1.103086 -0.001243 -SDSS17215 0.180790 0.181360 0 20.579248 0.117616 0.282892 0.417374 0.015433 0.037024 11.240000 0.101500 54350.994124 0.721577 0.004137 0.001134 0.000941 2 54.926380 1.092299 -0.001081 -SDSS17218 0.177570 0.179000 0 20.428083 0.114576 -0.760469 0.402479 -0.084884 0.031246 10.015000 0.264000 54349.497744 0.543868 0.003841 0.000708 0.000631 2 -3.828185 -0.030055 -0.001101 -SDSS17220 0.170660 0.172000 0 20.557070 0.115012 1.820322 0.283743 0.150278 0.031415 10.466000 0.055500 54356.922823 0.287720 0.004936 0.000877 0.000812 2 9.701524 -0.179249 -0.001141 -SDSS17223 0.233810 0.235000 0 20.863174 0.118911 1.013494 0.666082 -0.070271 0.042764 5.000000 10.000000 54347.004100 1.623021 -0.010717 0.001420 -0.011397 2 26.361374 -0.019418 -0.000647 -SDSS17240 0.071530 0.072750 0 18.172291 0.157968 -1.466919 0.368053 -0.121433 0.057874 10.813000 0.120500 54337.325699 1.137239 0.018415 0.005866 0.005126 2 8.640957 -1.217578 -0.000787 -SDSS17253 0.154630 0.156000 0 19.970376 0.171500 -0.196987 0.420496 -0.122244 0.065854 10.642000 0.259000 54341.617922 1.335320 0.018059 0.007346 0.005678 2 2.994201 0.998150 -0.001211 -SDSS17258 0.090100 0.091000 0 18.818896 0.124187 0.316969 0.210491 0.027279 0.032557 9.647000 0.181000 54342.580001 0.645576 0.002077 0.001602 0.000140 2 35.738617 1.025760 -0.001027 -SDSS17274 0.176580 0.178000 0 20.397033 0.163500 1.957731 0.536123 -0.136685 0.077192 8.963000 0.141000 54352.879758 2.336975 -0.013374 0.008604 -0.011383 2 -0.658497 0.105858 -0.001107 -SDSS17280 0.130450 0.130980 0 19.653248 0.147057 -0.839375 0.287151 -0.198044 0.050477 10.785000 0.304500 54343.803287 0.929324 0.010458 0.004303 0.004365 2 55.791908 0.102471 -0.001242 -SDSS17332 0.182040 0.182860 0 20.680621 0.115898 -0.237165 0.267541 0.036489 0.033755 10.532000 0.103000 54358.077361 0.241959 0.005016 0.000948 0.000746 2 43.773479 -0.147425 -0.001072 -SDSS17340 0.256170 0.257090 0 21.318433 0.119755 -0.075475 0.430044 -0.006613 0.042745 11.229000 0.095000 54356.407457 0.392662 0.012366 0.001173 0.001825 2 41.212090 0.364833 -0.000450 -SDSS17366 0.138110 0.139290 0 19.554935 0.112284 0.567102 0.165926 -0.095614 0.022655 10.756000 0.322000 54351.464017 0.251076 0.001203 0.000493 0.000168 2 -44.212711 -1.029184 -0.001243 -SDSS17389 0.170700 0.172000 0 20.780026 0.115438 1.130390 0.371505 0.058277 0.032568 9.793000 0.194500 54352.993334 0.396311 0.006604 0.000880 0.001056 2 -36.705124 -0.960150 -0.001141 -SDSS17435 0.216740 0.218000 0 20.709521 0.115124 0.901730 0.253993 -0.108869 0.034709 8.005000 0.707500 54359.361579 0.296512 0.003835 0.000861 -0.000006 2 20.344587 -0.014803 -0.000799 -SDSS17464 0.245550 0.247000 0 21.091966 0.116206 0.081440 0.566752 0.052687 0.040525 8.864000 0.445000 54358.584447 1.173957 0.001120 0.001058 -0.003485 2 6.640777 0.041985 -0.000542 -SDSS17497 0.143820 0.144730 0 20.045510 0.111949 0.911414 0.123176 0.037728 0.022481 10.129000 0.221500 54364.735383 0.121128 0.000754 0.000479 0.000114 2 37.136570 -1.042117 -0.001237 -SDSS17552 0.248620 0.250000 0 21.302566 0.117120 1.242872 0.405157 0.000278 0.041987 10.483000 0.108000 54357.267585 0.494621 0.007112 0.001051 0.000398 2 -37.679447 -1.003178 -0.000515 -SDSS17605 0.145330 0.146430 0 20.060753 0.119235 0.715631 0.440708 0.014894 0.037671 10.704000 0.304000 54352.592862 0.919738 -0.000341 0.001501 -0.000994 2 -50.797230 0.098444 -0.001235 -SDSS17629 0.135880 0.136900 0 20.059419 0.113673 -0.347675 0.145355 0.060305 0.026647 11.041000 0.202500 54370.399613 0.111941 0.002142 0.000703 0.000639 2 30.636435 -1.089188 -0.001244 -SDSS17745 0.058740 0.060000 0 17.862299 0.114585 1.071379 0.120922 -0.050084 0.025517 8.744000 0.109500 54372.433810 0.134500 0.002014 0.000642 0.000421 2 2.960264 -0.339205 -0.000560 -SDSS17784 0.036520 0.037070 0 17.320514 0.115396 -0.728272 0.077428 0.060325 0.022264 9.099000 0.175500 54367.036934 0.074744 0.000785 0.000449 0.000043 2 52.461727 0.056828 -0.000035 -SDSS17791 0.277490 0.279000 0 21.739047 0.136665 0.247627 0.692367 -0.186998 0.060528 8.946000 0.303000 54366.713426 0.553463 0.042544 0.001982 0.011952 2 -27.626724 0.737893 -0.000284 -SDSS17801 0.205150 0.206400 0 21.182310 0.131465 -0.677573 0.479734 -0.017143 0.054168 11.251000 0.006500 54365.722455 0.329793 0.026611 0.002937 0.010669 2 -43.906799 -0.898024 -0.000898 -SDSS17809 0.280510 0.282000 0 21.478247 0.122739 1.522800 0.426695 0.030003 0.041139 9.919000 0.288000 54370.574868 0.318607 0.018013 0.001400 0.003972 2 6.364582 -0.839571 -0.000263 -SDSS17811 0.198670 0.200000 0 20.490040 0.114249 1.031546 0.297419 -0.097533 0.026766 5.000000 10.000000 54355.288427 0.273044 0.005667 0.000554 0.000459 2 12.878674 -0.947399 -0.000951 -SDSS17825 0.159990 0.161000 0 20.410888 0.113136 -0.149097 0.157928 0.006164 0.026663 5.000000 10.000000 54367.017701 0.156530 0.001721 0.000620 0.000411 2 32.947124 -0.912429 -0.001191 -SDSS17875 0.210750 0.212000 0 21.022641 0.116152 1.120316 0.320101 -0.030671 0.038450 10.533000 0.228000 54362.455580 0.402220 0.005261 0.000913 -0.000589 2 20.983381 1.254994 -0.000851 -SDSS17880 0.071950 0.072670 0 18.984126 0.112772 -0.923587 0.108898 0.181137 0.025980 10.070000 0.236500 54363.270767 0.157028 0.000753 0.000536 -0.000068 2 44.972374 1.160707 -0.000793 -SDSS17884 0.237820 0.239000 0 20.859375 0.116252 0.664166 0.280440 -0.124117 0.036740 10.134000 0.385500 54369.032977 0.270993 0.006059 0.000897 0.000898 2 27.599838 1.172075 -0.000611 -SDSS1794 0.141390 0.142600 0 20.083504 0.113002 1.153668 0.317399 0.030808 0.025736 9.700000 0.596000 53624.720130 0.552737 0.000467 0.000618 0.000402 2 -42.163181 -0.445333 -0.001240 -SDSS18030 0.155060 0.156410 0 20.385861 0.118308 -0.605778 0.244877 -0.052008 0.033517 9.701000 0.158500 54373.102721 0.201422 0.007663 0.001206 0.002786 2 4.932975 -0.400135 -0.001209 -SDSS18091 0.370130 0.371500 0 21.893176 0.138604 -0.647918 0.649742 -0.195436 0.063142 11.162000 0.228500 54372.177208 0.582745 0.042947 0.001873 0.015513 2 23.367535 0.524681 -0.000264 -SDSS18241 0.093910 0.095000 0 19.176568 0.113740 -1.123374 0.129980 -0.085483 0.025315 10.016000 0.370000 54372.950212 0.157066 0.001639 0.000632 0.000163 2 -47.612259 -0.761723 -0.001064 -SDSS18298 0.118630 0.119820 0 20.541778 0.127194 -2.724252 0.288071 0.255113 0.047959 10.684000 0.170000 54374.325640 0.212193 0.012270 0.002621 0.005152 2 18.266731 -0.540012 -0.001217 -SDSS18323 0.153630 0.155000 0 20.240999 0.115274 0.159760 0.219868 -0.043411 0.028313 9.261000 0.454000 54375.478957 0.162004 0.005078 0.000808 0.001417 2 3.428632 0.652155 -0.001214 -SDSS18325 0.253560 0.255000 0 21.278008 0.120869 0.392895 0.368987 -0.055306 0.044017 11.019000 0.198500 54378.649084 0.426814 0.011958 0.001321 0.003040 2 8.905735 0.370035 -0.000472 -SDSS18375 0.107250 0.108500 0 19.337775 0.111743 0.163568 0.125517 0.066199 0.020874 10.139000 0.098000 54383.896714 0.219497 0.000161 0.000443 0.000092 2 11.516434 -0.010381 -0.001165 -SDSS18415 0.129610 0.130970 0 19.866162 0.113841 -1.623610 0.175057 -0.086675 0.028141 10.832000 0.171500 54381.428755 0.243849 0.001265 0.000735 0.000405 2 -22.521715 1.058609 -0.001241 -SDSS18463 0.266650 0.268000 0 21.322503 0.120817 0.941316 0.405739 -0.043687 0.045963 10.406000 0.099500 54376.516334 0.430768 0.013279 0.001308 0.002764 2 17.565994 0.472038 -0.000365 -SDSS18466 0.212260 0.213000 0 21.093299 0.115112 -0.176160 0.240624 0.031420 0.033372 10.726000 0.225500 54381.410978 0.342213 0.002876 0.000919 0.000535 2 48.418526 0.629998 -0.000838 -SDSS18485 0.278220 0.279000 0 21.278193 0.117336 1.315670 0.410743 -0.088149 0.039231 10.630000 0.121000 54388.937629 0.453899 0.009487 0.000943 0.002316 2 47.959118 -0.692416 -0.000279 -SDSS18486 0.245400 0.246000 0 20.753569 0.114389 1.112899 0.263746 -0.108783 0.034045 5.000000 10.000000 54390.103297 0.283616 0.003544 0.000771 0.000643 2 55.180023 1.003079 -0.000543 -SDSS18602 0.133620 0.135000 0 20.091364 0.113865 0.915935 0.181596 0.062187 0.027935 9.408000 0.211500 54393.768678 0.132757 0.002792 0.000776 0.001358 2 -21.016417 0.609241 -0.001244 -SDSS18604 0.176560 0.178000 0 20.912797 0.113509 -2.079275 0.215555 -0.046418 0.029501 10.812000 0.051500 54387.374357 0.208013 0.001807 0.000695 0.000382 2 -19.079082 0.421380 -0.001107 -SDSS18612 0.113680 0.114930 0 19.674591 0.111953 -0.734703 0.132844 0.060165 0.022187 10.764000 0.161000 54383.704683 0.173415 0.000459 0.000478 0.000088 2 12.287929 0.596957 -0.001198 -SDSS18617 0.320380 0.322000 0 22.008300 0.128160 -0.256202 0.881585 -0.021879 0.056960 9.922000 0.381500 54381.322021 1.259517 0.028865 0.001200 0.005511 2 -14.238505 0.849165 -0.000089 -SDSS18650 0.112710 0.114000 0 19.405852 0.112009 0.885448 0.116191 -0.043361 0.021931 8.825000 0.109500 54387.864611 0.128516 0.000805 0.000478 0.000286 2 -31.552765 0.015055 -0.001194 -SDSS18697 0.106020 0.107260 0 19.224685 0.111682 0.749838 0.119928 -0.002746 0.021180 10.690000 0.250500 54384.431042 0.193364 0.000147 0.000444 0.000003 2 11.223947 -0.997054 -0.001158 -SDSS18721 0.391350 0.393000 0 22.096499 0.148848 0.144953 0.849228 -0.134777 0.070013 11.140000 0.009500 54371.389306 0.737074 0.069409 0.002168 0.019717 2 3.077347 -0.077318 -0.000524 -SDSS18740 0.155750 0.157000 0 20.174029 0.111931 -0.034977 0.125853 0.024848 0.022343 10.454000 0.112000 54391.805751 0.107531 0.000838 0.000467 0.000325 2 16.855324 1.043746 -0.001207 -SDSS18749 0.186670 0.188000 0 21.077159 0.114334 -1.441241 0.373975 0.065591 0.032761 10.876000 0.039000 54385.105524 0.432717 0.003176 0.000834 0.000932 2 12.547336 0.675423 -0.001041 -SDSS18768 0.196710 0.198000 0 20.998833 0.118689 -0.446422 0.294152 -0.030251 0.037405 9.567000 0.239000 54378.986482 0.391012 0.008031 0.001221 0.002946 2 17.216736 1.197879 -0.000966 -SDSS18787 0.191910 0.193000 0 21.201846 0.114568 0.145971 0.430633 0.035637 0.031868 10.829000 0.014500 54387.249523 0.448832 0.006381 0.000812 0.002630 2 29.729753 -1.027071 -0.001003 -SDSS18804 0.190840 0.192000 0 20.425403 0.112617 0.947789 0.197600 -0.045704 0.024894 9.965000 0.033000 54392.808559 0.173571 0.002032 0.000540 0.000490 2 25.265629 -0.448449 -0.001011 -SDSS18807 0.176250 0.177000 0 20.591414 0.113139 0.470167 0.241505 -0.012245 0.026980 10.706000 0.185000 54390.271040 0.236404 0.002799 0.000628 0.001190 2 46.640945 0.793304 -0.001109 -SDSS18809 0.128370 0.129000 0 19.747603 0.112485 -1.172856 0.179369 -0.076940 0.024079 10.933000 0.028000 54383.153867 0.267881 0.000264 0.000543 0.000241 2 50.881317 0.666634 -0.001240 -SDSS18835 0.122620 0.123190 0 19.509318 0.111830 0.757721 0.109313 -0.013286 0.021901 10.535000 0.009000 54392.387209 0.117049 0.000564 0.000466 0.000237 2 53.685146 0.355509 -0.001229 -SDSS18855 0.127150 0.127830 0 19.700776 0.111895 0.247941 0.143765 -0.027883 0.022132 10.246000 0.203500 54385.974864 0.208046 0.000527 0.000479 0.000251 2 48.632347 0.269818 -0.001238 -SDSS18927 0.213230 0.214000 0 21.240608 0.117876 -0.590113 0.377161 0.113055 0.040001 10.489000 0.349500 54386.817775 0.404823 0.007375 0.001266 0.001881 2 46.682472 -0.754019 -0.000830 -SDSS18940 0.218610 0.220000 0 20.966724 0.114762 -0.943963 0.213801 -0.048746 0.034043 10.150000 0.302500 54396.725361 0.190948 0.002941 0.000875 0.000883 2 10.348955 0.411951 -0.000782 -SDSS18945 0.268560 0.270000 0 21.597167 0.118128 0.138984 0.489273 -0.048331 0.042096 5.000000 10.000000 54390.386739 0.436037 0.012404 0.000971 0.002814 2 10.078404 -1.037373 -0.000350 -SDSS18965 0.204660 0.206000 0 20.719102 0.115117 -0.639717 0.226971 -0.111895 0.035023 10.443000 0.008000 54395.342379 0.187653 0.003546 0.000908 0.000767 2 13.509144 1.069021 -0.000902 -SDSS19002 0.267100 0.268000 0 21.206503 0.116111 0.323447 0.313376 -0.097382 0.037311 10.459000 0.176000 54393.131239 0.285150 0.006095 0.000899 0.001389 2 42.615364 -0.551163 -0.000361 -SDSS19008 0.217570 0.219000 0 21.084988 0.119340 1.079449 0.390063 0.085294 0.040022 10.620000 0.233000 54392.648786 0.348655 0.011908 0.001404 0.004563 2 -28.036657 -1.069943 -0.000792 -SDSS19023 0.241850 0.243000 0 20.841931 0.114848 1.302288 0.271810 -0.118980 0.035621 5.000000 10.000000 54389.835746 0.278609 0.004495 0.000833 0.000589 2 -52.883045 0.006237 -0.000575 -SDSS19027 0.293500 0.295000 0 21.355269 0.120303 0.469262 0.540037 -0.006881 0.045438 9.254000 0.205000 54386.552133 0.626798 0.015972 0.001161 0.003692 2 -31.115822 -0.371996 -0.000184 -SDSS19067 0.353460 0.355000 0 21.770897 0.125157 0.552070 0.768177 -0.065751 0.055276 5.000000 10.000000 54386.859606 0.850347 0.030448 0.001246 0.010792 2 -34.371880 0.984606 -0.000144 -SDSS19101 0.185630 0.187000 0 20.773682 0.114490 -0.170077 0.214237 0.045482 0.029797 5.000000 10.000000 54395.197986 0.173785 0.003674 0.000787 0.001427 2 7.972722 0.138298 -0.001048 -SDSS19128 0.285430 0.287000 0 21.594277 0.119195 1.352452 0.599831 -0.064859 0.043817 5.000000 10.000000 54387.047419 0.639086 0.016477 0.001059 0.004310 2 -5.796229 -0.782230 -0.000231 -SDSS19149 0.194940 0.196000 0 20.367563 0.113760 1.179644 0.199894 0.010582 0.028084 9.536000 0.265500 54398.827290 0.165156 0.003166 0.000738 0.001235 2 31.460009 -0.331865 -0.000980 -SDSS19155 0.075920 0.076880 0 18.228158 0.112389 1.204471 0.103122 -0.103885 0.021594 10.732000 0.214000 54399.682689 0.068643 0.000957 0.000470 0.000343 2 31.266495 0.174558 -0.000852 -SDSS19174 0.163570 0.164700 0 20.518238 0.114386 -0.753250 0.213083 0.056574 0.031240 10.709000 0.038000 54394.884731 0.189917 0.002903 0.000814 0.000981 2 25.659727 1.030393 -0.001176 -SDSS19207 0.257210 0.258700 0 21.465617 0.121726 0.432319 0.549482 0.007859 0.050475 10.821000 0.379000 54382.872810 0.760230 0.011495 0.001565 0.001559 2 -26.536062 -0.920283 -0.000441 -SDSS19230 0.219550 0.221000 0 21.280787 0.119543 -1.621070 0.436163 0.079536 0.045823 10.324000 0.060000 54390.410552 0.427567 0.010589 0.001483 0.003625 2 -27.108984 0.764785 -0.000774 -SDSS19282 0.175580 0.177000 0 20.292843 0.113152 1.224646 0.237110 -0.038586 0.024891 5.000000 10.000000 54397.434303 0.162822 0.003539 0.000579 0.000919 2 -0.927778 -0.505872 -0.001113 -SDSS19341 0.226670 0.228000 0 21.438720 0.118938 -1.549530 0.369021 0.033312 0.043363 10.711000 0.004500 54393.203531 0.315061 0.008320 0.001228 0.001441 2 15.860297 0.331394 -0.000711 -SDSS19353 0.153220 0.154030 0 20.209017 0.113617 0.894088 0.205982 0.036376 0.026613 10.731000 0.026000 54398.627535 0.134486 0.003441 0.000703 0.001373 2 43.114372 0.251817 -0.001215 -SDSS19425 0.210250 0.211600 0 21.643220 0.128931 -1.604310 0.465290 0.130620 0.056057 10.390000 0.179000 54392.869287 0.421973 0.019586 0.002822 0.008218 2 -36.491306 -0.740519 -0.000855 -SDSS19543 0.121640 0.123000 0 19.946008 0.112462 -0.835084 0.136685 -0.021891 0.023909 8.828000 0.216000 54401.006018 0.119816 0.001184 0.000562 0.000587 2 -2.091648 0.279826 -0.001226 -SDSS19596 0.291350 0.292000 0 21.541949 0.121220 0.639978 0.498773 -0.044330 0.045916 9.565000 0.232000 54396.521042 0.430263 0.016790 0.001181 0.004372 2 53.884281 0.703362 -0.000196 -SDSS19604 0.294480 0.296000 0 22.400726 0.140875 1.173093 0.944335 0.172962 0.071432 10.190000 0.259500 54396.258554 0.784652 0.063746 0.003160 0.020367 2 5.324447 1.074527 -0.000179 -SDSS19616 0.164550 0.165490 0 20.243770 0.113442 0.151960 0.190744 -0.023369 0.026682 11.215000 0.287000 54403.533325 0.138545 0.002851 0.000708 0.001396 2 37.101109 0.184666 -0.001172 -SDSS19632 0.307020 0.308000 0 21.831913 0.126631 0.153977 0.525367 -0.044536 0.052497 10.803000 0.127000 54396.713587 0.448487 0.024089 0.001624 0.007921 2 40.286488 0.144448 -0.000123 -SDSS19658 0.198630 0.200000 0 20.711318 0.116081 -0.007405 0.254101 -0.084476 0.035302 9.139000 0.532000 54401.812257 0.183558 0.005695 0.001079 0.002395 2 8.903193 -0.232700 -0.000951 -SDSS19702 0.268210 0.269000 0 21.593672 0.121714 -1.095858 0.456154 -0.072910 0.049063 9.986000 0.464000 54397.226220 0.392023 0.014003 0.001332 0.004785 2 47.754841 0.356752 -0.000352 -SDSS19757 0.401280 0.403000 0 21.991848 0.134806 0.174516 0.891196 -0.104570 0.061380 10.274000 0.436500 54400.463763 0.829436 0.053756 0.001884 0.027125 2 -10.518977 1.222840 -0.000692 -SDSS19775 0.136680 0.137900 0 20.268803 0.113428 -0.228704 0.199062 0.093699 0.028261 10.567000 0.224000 54404.425650 0.182811 0.002080 0.000738 0.001031 2 -41.043903 0.651289 -0.001244 -SDSS19794 0.295420 0.296980 0 22.241585 0.134209 -2.427288 0.683233 0.045528 0.070610 11.160000 0.164500 54396.129658 0.593736 0.030568 0.002369 0.007808 2 -0.680949 0.249367 -0.000174 -SDSS19818 0.291920 0.293000 0 21.507747 0.124168 1.181291 0.550491 -0.005108 0.045722 10.190000 0.124000 54402.147430 0.404969 0.024866 0.001607 0.008566 2 35.266689 0.496411 -0.000193 -SDSS19899 0.090070 0.091400 0 18.477718 0.111899 0.284478 0.193639 -0.146258 0.021065 8.529000 0.272000 54418.759693 0.162669 0.001336 0.000451 0.000990 2 -18.507723 -0.648736 -0.001026 -SDSS19913 0.205570 0.207000 0 20.820147 0.114899 0.082692 0.339194 -0.059073 0.034230 10.164000 0.175000 54406.143238 0.349769 0.004965 0.000931 0.002124 2 -26.237507 -0.341257 -0.000895 -SDSS19940 0.145820 0.147000 0 19.800673 0.112189 1.044495 0.206385 -0.004648 0.024568 5.000000 10.000000 54407.779420 0.176588 0.001410 0.000562 0.000988 2 -44.606461 -0.268507 -0.001234 -SDSS19953 0.117670 0.119000 0 19.195923 0.111671 0.743346 0.185080 -0.126086 0.021509 9.460000 0.181000 54415.144207 0.159412 0.001061 0.000448 0.000931 2 -27.069508 0.579076 -0.001214 -SDSS19968 0.054990 0.056030 0 18.010876 0.112893 -1.889117 0.175788 -0.035121 0.023429 10.408000 0.054000 54412.677323 0.102792 0.000815 0.000456 0.000079 2 24.348734 -0.311997 -0.000484 -SDSS19969 0.174200 0.175240 0 20.509960 0.113041 0.046786 0.248222 0.016788 0.026578 10.337000 0.253000 54409.050389 0.235155 0.002317 0.000653 0.001138 2 31.910473 -0.324000 -0.001121 -SDSS19990 0.244950 0.246000 0 21.379199 0.119541 -1.313715 0.332900 -0.086373 0.045524 10.306000 0.044500 54403.329888 0.298159 0.009545 0.001467 0.004573 2 34.806366 -0.384841 -0.000547 -SDSS19992 0.224520 0.226000 0 21.145906 0.122907 0.273495 0.525565 -0.080933 0.045811 9.496000 0.179500 54404.594321 0.455566 0.020739 0.001787 0.009501 2 -2.895839 -1.185004 -0.000730 -SDSS20039 0.245580 0.247000 0 21.379054 0.118174 0.725208 0.490817 0.060682 0.043805 11.334000 0.099500 54408.082701 0.509835 0.012220 0.001386 0.006188 2 9.878607 1.024336 -0.000541 -SDSS20040 0.283510 0.285000 0 21.246461 0.118366 0.704019 0.624506 -0.110345 0.043852 10.045000 0.213000 54410.785895 0.500056 0.016804 0.001018 0.006457 2 -31.120529 0.815071 -0.000243 -SDSS20048 0.180560 0.182000 0 20.906870 0.117002 -0.682083 0.491864 0.021693 0.038640 10.682000 0.005500 54410.164999 0.357633 0.010864 0.001091 0.004911 2 -20.691917 0.736387 -0.001082 -SDSS20064 0.103910 0.105240 0 19.260480 0.112416 0.849269 0.180935 0.055639 0.023801 11.288000 0.178500 54413.139772 0.119874 0.001614 0.000564 0.000761 2 -1.413803 -0.917617 -0.001144 -SDSS20084 0.129610 0.131000 0 20.554137 0.113865 0.129042 0.276748 0.154984 0.029650 8.635000 1.477500 54407.152900 0.252099 0.002812 0.000825 0.001930 2 -12.024707 -0.578091 -0.001241 -SDSS20088 0.243520 0.244900 0 20.934675 0.113403 -0.240940 0.208485 -0.162186 0.031615 10.944000 0.094000 54407.845842 0.225045 0.001691 0.000691 0.000419 2 13.205203 0.631500 -0.000560 -SDSS20106 0.331360 0.333000 0 22.047888 0.135299 -0.983242 0.719073 -0.113775 0.067189 10.137000 0.269000 54404.136568 0.796624 0.037895 0.001631 0.013420 2 -13.445710 0.328961 -0.000083 -SDSS20111 0.243480 0.245000 0 21.412380 0.118064 -0.029582 0.581620 0.009035 0.044236 10.600000 0.087500 54410.055419 0.489712 0.013223 0.001192 0.006192 2 -5.605632 0.248099 -0.000560 -SDSS20142 0.314680 0.316000 0 21.289082 0.118861 0.849074 0.676796 -0.091687 0.040369 8.777000 0.451500 54412.643434 0.485189 0.021162 0.001070 0.007265 2 23.008219 -0.429732 -0.000100 -SDSS20144 0.218650 0.220000 0 20.772595 0.114630 0.204476 0.593762 -0.096868 0.033335 9.762000 0.238500 54415.958979 0.541946 0.008510 0.000845 0.005014 2 -37.568096 0.444623 -0.000782 -SDSS2017 0.260140 0.261600 0 21.301262 0.122802 1.392157 0.544855 -0.105855 0.048195 10.403000 0.293000 53635.992671 0.558427 0.017995 0.001145 -0.000306 2 -31.056664 0.593518 -0.000417 -SDSS20227 0.282430 0.284000 0 21.510915 0.117470 -1.291340 0.514908 -0.149793 0.046241 10.880000 0.102500 54408.484728 0.533645 0.009084 0.000904 0.003745 2 -10.880007 -0.098497 -0.000250 -SDSS20245 0.300410 0.302000 0 21.668758 0.125866 -0.602476 0.897103 0.045499 0.054593 11.404000 0.111000 54411.887512 0.588258 0.039897 0.001952 0.017974 2 -18.294451 0.756290 -0.000150 -SDSS2031 0.151860 0.153000 0 19.681123 0.112035 0.506889 0.140311 -0.129527 0.022797 9.275000 0.192000 53636.918572 0.139273 0.000912 0.000472 0.000010 2 -47.956810 -1.171422 -0.001219 -SDSS20345 0.263570 0.265000 0 21.267444 0.116240 -1.685348 0.524114 -0.110520 0.043312 5.000000 10.000000 54418.208690 0.538249 0.006064 0.000884 0.003875 2 10.701769 0.379731 -0.000389 -SDSS20364 0.213820 0.215000 0 21.110632 0.117610 0.569210 0.818147 0.034751 0.041357 10.213000 0.313000 54419.980824 0.859406 0.017971 0.001242 0.011310 2 25.756645 -0.945469 -0.000824 -SDSS20430 0.162840 0.164000 0 20.750876 0.114429 -1.192480 0.513315 0.023929 0.031743 5.000000 10.000000 54415.231201 0.379840 0.006172 0.000797 0.003437 2 -47.583187 0.468630 -0.001179 -SDSS20470 0.124400 0.125000 0 19.953688 0.113068 0.657385 0.428774 0.044204 0.029080 10.124000 0.258000 54419.227893 0.369973 0.005212 0.000741 0.006570 2 52.604916 1.009437 -0.001233 -SDSS20575 0.194650 0.196000 0 20.622004 0.114454 0.160993 0.493563 0.034235 0.032158 11.031000 0.006000 54416.307984 0.446082 0.007813 0.000831 0.006181 2 -34.734306 0.142561 -0.000982 -SDSS20581 0.205540 0.207000 0 20.612992 0.114345 -0.457773 0.440030 -0.122373 0.033026 9.938000 0.224000 54416.951202 0.397419 0.004994 0.000836 0.003326 2 -22.296984 0.010740 -0.000895 -SDSS20625 0.106670 0.107960 0 19.481026 0.112607 -0.106927 0.203469 -0.004350 0.023623 10.509000 0.282000 54423.595285 0.175131 0.001723 0.000562 0.001330 2 5.683364 -0.479010 -0.001162 -SDSS20687 0.187720 0.189000 0 21.111734 0.118492 -0.570147 0.691649 0.148525 0.045453 10.772000 0.092000 54421.005708 0.726689 0.008208 0.001259 0.009785 2 16.718126 1.154068 -0.001033 -SDSS20764 0.163870 0.165000 0 20.274432 0.113522 -1.407804 0.375679 -0.105729 0.029747 10.761000 0.223500 54421.627499 0.345900 0.003322 0.000632 0.003530 2 26.120781 0.229765 -0.001175 -SDSS21006 0.290210 0.291000 0 21.340912 0.116557 -0.094057 0.757082 -0.124339 0.039348 5.000000 10.000000 54415.151552 0.526666 0.017193 0.000927 0.007210 2 47.941761 0.091911 -0.000202 -SDSS2102 0.094010 0.095100 0 18.639465 0.112725 1.271886 0.195787 -0.110820 0.021591 8.774000 0.141500 53620.190793 0.503312 -0.001607 0.000494 -0.000756 2 -47.779053 0.191135 -0.001065 -SDSS21033 0.227860 0.229000 0 21.000868 0.115650 -0.793564 0.516124 -0.087660 0.040546 5.000000 10.000000 54421.077319 0.556216 0.004045 0.000888 0.004388 2 28.813671 0.643100 -0.000700 -SDSS21034 0.107510 0.108560 0 19.591919 0.112402 0.014210 0.281369 0.032201 0.023906 10.730000 0.301000 54426.030673 0.280165 0.000877 0.000525 0.001585 2 28.141375 1.244067 -0.001167 -SDSS21042 0.310650 0.312000 0 21.379309 0.124489 1.245043 0.754123 -0.095517 0.050719 9.113000 0.580500 54423.402833 0.744704 0.023250 0.001082 0.008100 2 -44.988430 0.827591 -0.000111 -SDSS21062 0.145640 0.147000 0 20.358304 0.114307 0.819513 0.373183 0.013295 0.031111 9.317000 0.364000 54425.645931 0.301899 0.005084 0.000873 0.003815 2 -26.568415 0.396301 -0.001234 -SDSS21502 0.087840 0.089160 0 18.825522 0.112907 -0.319465 0.240419 -0.051292 0.024085 11.214000 0.198500 54427.274472 0.207135 0.002373 0.000557 0.001127 2 -6.400203 -0.890334 -0.001003 -SDSS21510 0.148390 0.149720 0 19.741298 0.113780 -0.845450 0.331799 -0.155197 0.028537 10.821000 0.285000 54429.432196 0.302637 0.002392 0.000599 0.002433 2 7.352812 0.831497 -0.001228 -SDSS2165 0.286620 0.288000 0 21.604938 0.119780 0.477722 0.547855 -0.121457 0.042874 9.203000 0.475000 53634.347411 0.465033 0.016710 0.001112 0.004695 2 17.091730 -0.096247 -0.000224 -SDSS21669 0.122810 0.124070 0 19.555089 0.129766 -1.341157 0.599431 -0.079528 0.030004 11.407000 0.212500 54429.850088 0.902000 -0.033044 -0.000134 0.007803 2 11.614160 -1.062266 -0.001229 -SDSS21861 0.186550 0.188000 0 20.476939 0.121972 0.132373 0.869815 -0.025413 0.052755 9.648000 0.443000 54432.820181 0.920791 0.021423 0.001849 0.025472 2 -10.141306 -0.901638 -0.001042 -SDSS22075 0.128930 0.129960 0 19.928783 0.113756 -1.682693 0.334942 -0.031848 0.029831 10.950000 0.051500 54425.942152 0.328906 -0.000710 0.000594 0.002742 2 29.963102 1.216465 -0.001240 -SDSS2246 0.194220 0.194900 0 20.982429 0.116840 0.350420 0.360628 0.071594 0.035381 10.634000 0.100500 53634.176573 0.328720 0.007884 0.001078 0.001919 2 50.090420 -0.885593 -0.000985 -SDSS2308 0.148000 0.148000 0 19.597186 0.111944 0.675888 0.123132 -0.181409 0.021900 10.336000 0.229000 53630.588096 0.136429 0.000980 0.000442 0.000076 2 34.272865 0.280298 -0.001229 -SDSS2330 0.211790 0.213200 0 21.774529 0.124547 -1.880375 0.548188 0.045110 0.056508 9.752000 0.373000 53635.477139 0.495837 0.015843 0.002059 0.004244 2 6.807123 1.120598 -0.000842 -SDSS2372 0.179620 0.180500 0 20.567619 0.113572 0.279554 0.208947 0.030383 0.027749 10.119000 0.171500 53636.745449 0.221703 0.002232 0.000702 0.000292 2 40.520721 -0.540850 -0.001088 -SDSS2422 0.263490 0.265000 0 21.149878 0.115945 0.989702 0.318116 -0.171197 0.039792 9.007000 0.442000 53639.592847 0.342413 0.005629 0.000850 0.000563 2 1.994533 0.638195 -0.000390 -SDSS2440 0.192150 0.193000 0 20.644093 0.114217 0.395729 0.285398 -0.086867 0.028737 10.194000 0.093000 53634.979725 0.327011 0.004444 0.000670 0.000788 2 42.633732 0.807796 -0.001001 -SDSS2533 0.338800 0.340000 0 21.773378 0.129777 1.761054 0.686843 -0.076670 0.052461 9.382000 0.412500 53640.364826 0.640854 0.035673 0.001418 0.007113 2 31.220692 -0.326468 -0.000092 -SDSS2561 0.117410 0.118130 0 19.784621 0.112518 -0.082681 0.115449 0.039633 0.024351 10.574000 0.086000 53639.254293 0.145747 0.000652 0.000590 0.000178 2 46.343418 0.858359 -0.001213 -SDSS2635 0.143100 0.143700 0 19.815354 0.112072 0.863284 0.133479 -0.049019 0.022843 9.918000 0.213500 53640.272902 0.169984 0.000774 0.000498 0.000110 2 52.704308 -1.238076 -0.001238 -SDSS2689 0.160350 0.161490 0 20.253587 0.114045 1.391688 0.194496 0.080520 0.027945 11.150000 0.098000 53637.053523 0.188838 0.002697 0.000720 0.000233 2 24.900312 -0.758714 -0.001190 -SDSS2789 0.288720 0.290300 0 21.563138 0.124095 -0.761709 0.519355 -0.110724 0.052871 10.949000 0.190500 53636.321441 0.490634 0.018953 0.001541 0.005357 2 -15.798545 0.401109 -0.000211 -SDSS2916 0.123030 0.124200 0 19.970954 0.119632 -0.872609 0.235871 0.056872 0.034899 10.428000 0.401500 53643.892476 0.164405 0.007986 0.001475 0.003521 2 -44.078281 0.569566 -0.001230 -SDSS2943 0.264050 0.265400 0 21.371914 0.117962 0.531823 0.420164 -0.028570 0.046620 8.822000 0.845500 53640.493370 0.522043 0.007346 0.000968 -0.000411 2 17.704947 1.007936 -0.000385 -SDSS2992 0.126080 0.126600 0 20.042918 0.113495 -0.969154 0.139594 0.106470 0.027845 9.881000 0.301500 53641.162158 0.195081 0.000804 0.000747 0.000122 2 55.497040 -0.782655 -0.001236 -SDSS3080 0.173740 0.175000 0 20.242601 0.112831 0.120044 0.188612 -0.049739 0.023767 10.710000 0.029500 53647.652958 0.142855 0.002418 0.000526 0.000513 2 16.932343 -1.039495 -0.001124 -SDSS3087 0.164400 0.165600 0 20.259822 0.113060 0.522789 0.188015 0.005950 0.024492 9.373000 0.088500 53648.886975 0.139594 0.002609 0.000580 0.000674 2 20.406706 -0.977236 -0.001172 -SDSS3199 0.249610 0.251100 0 21.547293 0.118164 1.618942 0.367097 0.005047 0.039292 8.578000 0.261500 53647.330278 0.372340 0.009865 0.001004 0.001636 2 -26.707275 1.050538 -0.000506 -SDSS3317 0.159990 0.161100 0 20.351465 0.113095 -0.522200 0.181355 0.020834 0.025368 9.724000 0.006500 53646.416585 0.168795 0.001860 0.000589 0.000193 2 26.962612 0.640600 -0.001191 -SDSS3377 0.244480 0.245100 0 20.797565 0.115275 1.038449 0.300885 -0.080192 0.035082 8.910000 0.513000 53645.940274 0.322584 0.005078 0.000830 0.000540 2 54.156151 1.079169 -0.000551 -SDSS3451 0.248510 0.250000 0 20.952989 0.116195 0.650834 0.314030 -0.054933 0.037681 10.824000 0.328000 53645.181803 0.306482 0.006079 0.000932 0.000727 2 -25.930761 0.708130 -0.000516 -SDSS3452 0.228930 0.230400 0 20.806588 0.115979 0.906222 0.268322 -0.090738 0.035860 9.397000 0.468000 53648.482961 0.272384 0.005533 0.000949 0.001200 2 -25.328503 0.639188 -0.000690 -SDSS3592 0.085280 0.086420 0 18.765187 0.111932 -0.145167 0.070607 -0.043103 0.020381 10.395000 0.137500 53645.058810 0.061599 0.000438 0.000425 0.000067 2 19.052437 0.791937 -0.000974 -SDSS3901 0.062818 0.062820 0 18.039954 0.112357 1.183262 0.076543 0.041002 0.023476 9.740000 0.051000 53657.000216 0.090698 0.000099 0.000453 -0.000244 2 14.850435 0.002595 -0.000638 -SDSS4000 0.277450 0.278600 0 21.898888 0.128283 -1.042665 0.574214 -0.012101 0.060472 10.689000 0.147000 53643.055091 0.772155 0.020989 0.001721 0.005120 2 31.016014 -0.365828 -0.000284 -SDSS4046 0.275440 0.277000 0 21.521059 0.119548 0.798040 0.508894 0.002380 0.043726 9.604000 0.623500 53645.760684 0.511839 0.014571 0.001067 0.001350 2 -5.501686 0.642106 -0.000298 -SDSS4241 0.330510 0.332000 0 21.962505 0.125680 0.244648 0.636658 -0.056394 0.054148 8.717000 0.806500 53638.471016 0.715237 0.024713 0.001323 0.006148 2 12.237703 -0.905740 -0.000083 -SDSS4577 0.361930 0.363000 0 22.096096 0.132910 0.320107 0.770082 -0.062616 0.053996 10.273000 0.134000 53648.086427 0.716850 0.043281 0.001200 0.007658 2 38.475533 0.280717 -0.000196 -SDSS4679 0.331030 0.332400 0 22.092013 0.136212 0.953409 0.714429 0.090984 0.057670 8.479000 0.473000 53654.327239 0.635768 0.045594 0.002331 0.011634 2 21.528351 0.676858 -0.000083 -SDSS5103 0.160500 0.161900 0 20.359590 0.113217 -0.229365 0.149789 0.016122 0.026871 8.995000 0.168000 53654.605170 0.149708 0.001712 0.000669 0.000698 2 -0.115550 0.737165 -0.001189 -SDSS5391 0.300210 0.300900 0 21.702057 0.128841 0.260674 0.553428 -0.031138 0.055162 8.701000 0.532000 53658.690639 0.568438 0.026112 0.001909 0.006190 2 52.341984 -1.094704 -0.000151 -SDSS5395 0.116350 0.117000 0 19.462745 0.111949 1.193505 0.134314 -0.015778 0.021982 8.604000 0.112500 53663.880486 0.147748 0.000820 0.000481 0.000292 2 49.640949 0.123341 -0.001209 -SDSS5533 0.218290 0.219700 0 21.161023 0.114544 -0.079904 0.271502 0.022297 0.032779 9.440000 0.182500 53667.438750 0.327957 0.003259 0.000885 0.001431 2 -31.330046 0.413430 -0.000785 -SDSS5549 0.119670 0.121000 0 19.648787 0.111976 0.253433 0.097167 0.005345 0.022042 8.749000 0.132500 53666.084652 0.103619 0.000645 0.000493 0.000305 2 3.250535 0.248223 -0.001221 -SDSS5550 0.154730 0.156100 0 19.866725 0.111655 1.893060 0.198242 -0.048817 0.021886 8.939000 0.193500 53671.496675 0.204195 0.001069 0.000440 0.000335 2 3.598291 0.333052 -0.001211 -SDSS5635 0.178100 0.179500 0 20.910170 0.117139 0.040834 0.391185 -0.027909 0.036095 9.548000 0.153500 53662.305183 0.313681 0.009756 0.001134 0.004561 2 -26.817240 -0.034922 -0.001098 -SDSS5717 0.250370 0.251700 0 21.337855 0.115791 1.417706 0.338083 -0.029844 0.035006 9.042000 0.352500 53664.203146 0.316005 0.006834 0.000977 0.002563 2 17.895966 -0.005896 -0.000500 -SDSS5736 0.251740 0.253000 0 21.405599 0.115824 -0.217638 0.302475 -0.008766 0.035540 8.743000 0.315000 53665.747621 0.280192 0.005517 0.001010 0.002160 2 22.862734 -0.631618 -0.000488 -SDSS5751 0.129530 0.130800 0 20.117060 0.112137 0.678679 0.106993 0.146762 0.022919 10.414000 0.026000 53666.075917 0.120211 0.000691 0.000533 0.000318 2 11.634153 0.838215 -0.001241 -SDSS5844 0.309290 0.310800 0 21.575751 0.120483 0.392813 0.509665 -0.142283 0.041928 9.326000 0.478500 53663.635301 0.528474 0.015567 0.001177 0.005160 2 -32.213806 -0.842936 -0.000116 -SDSS5957 0.278520 0.279600 0 21.447891 0.118155 -0.186487 0.424537 -0.121945 0.041095 10.232000 0.147000 53666.666509 0.378943 0.010721 0.001140 0.004664 2 34.760574 -0.272839 -0.000277 -SDSS5994 0.185810 0.187000 0 20.479366 0.114889 0.746501 0.260368 -0.061663 0.028712 11.560000 0.166000 53665.740327 0.265503 0.005127 0.000742 0.001359 2 -47.397449 -0.167889 -0.001047 -SDSS6057 0.066510 0.067070 0 18.639629 0.112612 -0.420885 0.107591 0.108184 0.024844 9.944000 0.075000 53663.607675 0.100872 0.000458 0.000525 -0.000004 2 52.553604 -0.974586 -0.000704 -SDSS6108 0.258000 0.259500 0 21.525113 0.117106 0.454670 0.633542 0.035530 0.043116 9.208000 0.412000 53675.198160 0.614429 0.012704 0.001159 0.007584 2 1.806597 0.348985 -0.000435 -SDSS6137 0.298880 0.300100 0 21.522329 0.124118 0.359245 0.712788 -0.150995 0.051156 5.000000 10.000000 53669.534253 1.077294 0.017098 0.001582 0.005317 2 -52.063877 0.244757 -0.000157 -SDSS6192 0.270440 0.272000 0 21.678524 0.118685 -1.948230 0.770704 -0.061742 0.048034 9.611000 0.567000 53672.820776 0.521548 0.018029 0.001103 0.008641 2 -11.535005 1.256988 -0.000335 -SDSS6196 0.279160 0.280700 0 21.551471 0.119480 -1.519650 0.540099 -0.017038 0.045760 11.251000 0.165500 53669.469041 0.518463 0.013416 0.001380 0.006374 2 -22.368843 -0.502662 -0.000272 -SDSS6249 0.292870 0.294400 0 21.783514 0.119196 0.575567 0.658029 0.031440 0.046484 9.603000 0.443000 53675.380382 0.650547 0.017385 0.001234 0.007323 2 3.265553 -0.620118 -0.000188 -SDSS6304 0.189650 0.190800 0 20.949871 0.113593 -0.537570 0.376780 0.068414 0.028831 10.377000 0.116500 53673.088737 0.270573 0.004936 0.000720 0.002593 2 26.497623 1.195954 -0.001019 -SDSS6315 0.265760 0.267000 0 20.925675 0.114629 -0.091383 0.362529 -0.194032 0.037190 9.785000 0.430000 53676.663645 0.331849 0.004723 0.000761 0.001590 2 -49.517178 1.091998 -0.000372 -SDSS6406 0.123760 0.124490 0 19.608770 0.111795 0.118662 0.130092 -0.003543 0.021630 10.528000 0.124000 53674.772048 0.104774 0.000789 0.000470 0.000513 2 46.088615 -1.062960 -0.001232 -SDSS6422 0.182550 0.184000 0 20.273166 0.112208 0.809261 0.210841 -0.105454 0.023744 9.398000 0.266000 53667.978174 0.258216 0.001223 0.000501 0.000354 2 -10.861195 -0.663224 -0.001069 -SDSS6558 0.056340 0.057420 0 17.716443 0.112867 1.053691 0.089969 -0.029476 0.023607 9.571000 0.074000 53675.029683 0.091419 0.000375 0.000480 -0.000017 2 21.701672 -1.238135 -0.000512 -SDSS6649 0.312880 0.314000 0 21.594837 0.118305 0.720911 0.598824 -0.088234 0.044329 8.394000 0.476000 53677.807923 0.599570 0.015292 0.000898 0.005402 2 34.275860 0.534831 -0.000105 -SDSS6699 0.309150 0.310600 0 21.797035 0.122358 -0.673205 0.833007 -0.139232 0.048378 10.734000 0.478500 53675.414418 0.576672 0.029820 0.000985 0.009638 2 -37.185024 -1.056985 -0.000116 -SDSS6780 0.200610 0.202000 0 20.954698 0.120729 -1.767055 0.372534 -0.030617 0.042850 11.170000 0.221500 53665.563187 0.398885 0.009651 0.001627 0.004040 2 -31.931389 0.267062 -0.000935 -SDSS6924 0.326400 0.328000 0 21.644769 0.119030 0.908728 0.762098 -0.073040 0.042455 10.755000 0.361000 53674.587947 0.652336 0.022352 0.000878 0.007706 2 -1.030669 0.876966 -0.000083 -SDSS6933 0.211630 0.213000 0 20.830920 0.114069 0.076743 0.234763 -0.019791 0.032398 8.975000 0.866000 53679.850667 0.231599 0.002493 0.000800 0.000996 2 11.351705 1.075577 -0.000843 -SDSS6936 0.179690 0.181000 0 20.567027 0.113497 0.027587 0.307512 -0.021299 0.028689 10.365000 0.241500 53680.619370 0.313307 0.002734 0.000665 0.001552 2 -36.766190 -0.699781 -0.001088 -SDSS7143 0.302400 0.304000 0 22.054900 0.118973 -0.477103 0.601368 0.011421 0.044087 10.494000 0.306000 53677.792899 0.577090 0.013830 0.001006 0.005469 2 -14.737640 -0.207367 -0.000141 -SDSS7147 0.109240 0.110600 0 19.474150 0.112096 -1.914343 0.157266 -0.094807 0.023847 10.310000 0.022500 53679.670308 0.136458 0.000390 0.000482 0.000279 2 -9.981533 -0.055446 -0.001176 -SDSS722 0.085220 0.086520 0 18.716573 0.128423 -0.471485 0.186235 -0.087112 0.034741 10.830000 0.082000 53610.851055 0.626358 0.004337 0.002041 0.001662 2 0.705703 0.751234 -0.000973 -SDSS7243 0.202310 0.203700 0 20.782139 0.117808 0.722312 0.380665 -0.020602 0.039008 8.835000 0.390500 53686.287735 0.324176 0.008680 0.001123 0.003099 2 -31.920916 0.471930 -0.000922 -SDSS744 0.126490 0.127800 0 19.808548 0.118731 1.401317 0.317076 0.071402 0.032365 10.155000 0.097000 53612.859169 0.783337 -0.000793 0.001252 0.000581 2 -30.801510 0.317498 -0.001237 -SDSS7473 0.214570 0.216000 0 20.885350 0.114262 -0.182983 0.278254 -0.098014 0.033065 8.474000 0.553500 53679.550123 0.247455 0.003131 0.000812 0.001199 2 4.326408 -0.257275 -0.000818 -SDSS7475 0.320450 0.322000 0 21.537605 0.117246 0.677100 0.578872 -0.169811 0.040338 8.949000 1.337000 53676.477918 0.548064 0.013433 0.000757 0.004801 2 4.753476 -0.281488 -0.000089 -SDSS7512 0.218350 0.219000 0 21.098936 0.116348 0.501492 0.366635 -0.002655 0.037180 9.888000 0.106500 53682.534455 0.353029 0.006692 0.001003 0.002166 2 52.090313 -0.326094 -0.000785 -SDSS762 0.190100 0.191400 0 20.632628 0.114249 1.125919 0.267088 -0.038817 0.030249 10.957000 0.082500 53625.149252 0.282810 0.003804 0.000746 0.000707 2 15.535334 -0.878981 -0.001016 -SDSS774 0.092430 0.093490 0 18.939486 0.122156 0.664571 0.266135 -0.024865 0.031639 10.581000 0.206500 53609.243303 0.997261 -0.005409 0.001499 -0.003235 2 25.463467 -0.876399 -0.001050 -SDSS7779 0.379860 0.381200 0 21.922589 0.118539 1.316854 0.633038 -0.102540 0.042774 11.183000 0.270000 53675.693009 0.599580 0.016609 0.000672 0.006460 2 -49.919785 -0.007178 -0.000367 -SDSS7847 0.211340 0.212400 0 21.203520 0.115658 0.087929 0.382167 0.120136 0.035488 10.314000 0.112500 53682.019206 0.366846 0.005808 0.000993 0.003023 2 32.459949 -0.061799 -0.000846 -SDSS8046 0.258330 0.259300 0 21.619985 0.119891 0.243598 0.475086 0.040716 0.047080 11.196000 0.106500 53683.335557 0.465174 0.011518 0.001177 0.003909 2 39.116806 0.511273 -0.000432 -SDSS8213 0.183270 0.184700 0 21.108509 0.117564 -0.656771 0.327805 0.160219 0.037567 10.339000 0.224500 53683.728664 0.291701 0.006098 0.001149 0.002449 2 -2.479074 -0.921379 -0.001064 -SDSS8495 0.212950 0.214400 0 20.819745 0.121443 0.751948 0.462652 -0.023337 0.041456 10.732000 0.208500 53689.901466 0.345115 0.016881 0.001590 0.006845 2 -24.738964 -0.748159 -0.000832 -SDSS8719 0.116510 0.117800 0 19.419454 0.113022 -0.020801 0.160846 -0.066413 0.023433 9.011000 0.077000 53690.033602 0.105514 0.002550 0.000592 0.001147 2 7.721481 -0.718848 -0.001210 -SDSS8921 0.144010 0.145300 0 19.969519 0.114616 0.872358 0.254959 0.001493 0.028422 9.998000 0.062000 53693.044989 0.197924 0.005251 0.000813 0.002716 2 -34.998005 -0.007925 -0.001237 -SDSS9032 0.252490 0.254000 0 21.349051 0.126796 0.872317 0.593678 0.015949 0.051057 8.580000 0.290500 53688.941038 0.492807 0.025657 0.001987 0.008920 2 -22.115431 -0.493577 -0.000481 -SDSS9457 0.255390 0.256900 0 21.432154 0.130119 -0.361202 0.567166 -0.021164 0.058401 10.864000 0.226500 53688.822633 0.516595 0.025345 0.002448 0.009877 2 -24.185642 0.253036 -0.000457 -SDSS9467 0.216980 0.218400 0 21.069215 0.124622 -1.395764 0.369500 -0.141924 0.055821 11.817000 0.521000 53688.824168 0.335817 0.012891 0.001887 0.005253 2 -31.048607 1.180792 -0.000797 -Torngasek 1.265901 1.265000 0 25.735598 0.128614 0.286816 0.559546 0.021062 0.047718 10.834000 0.391000 52693.622067 1.790259 -0.003817 -0.000169 0.003064 4 189.331960 62.310417 0.000000 -Vilas 0.935774 0.935000 0 24.481915 0.119916 0.055963 0.289916 -0.025416 0.058862 10.906000 0.095000 52726.207785 1.379146 -0.005968 0.000883 -0.003037 4 189.370830 62.191056 0.000000 -sn1990af 0.049519 0.050300 0 17.762489 0.139446 -2.269742 0.112065 -0.048814 0.044286 10.702500 0.280891 48195.572190 0.138372 -0.000220 0.001127 -0.000800 3 323.747521 -62.737091 -0.015152 -sn1990o 0.030325 0.030664 0 16.239518 0.143169 0.475440 0.175158 -0.045540 0.030056 10.797903 0.280891 48076.401017 0.413566 0.000145 0.000882 -0.000211 3 258.900690 16.322030 -0.010330 -sn1992ae 0.074579 0.075000 0 18.465664 0.141947 -0.709402 0.155132 -0.022194 0.053595 10.307791 0.300000 48804.257813 0.546502 -0.001367 0.001693 -0.001312 3 322.074760 -61.550380 -0.025868 -sn1992ag 0.025198 0.025000 0 16.317246 0.148276 0.112905 0.308000 0.125874 0.052069 10.531918 0.280891 48806.376498 0.584576 -0.000739 0.002036 -0.001538 3 201.041058 -23.878802 -0.009539 -sn1992al 0.014411 0.014615 0 14.476032 0.156643 -0.166851 0.084194 -0.108162 0.026736 10.043033 0.280891 48838.335972 0.127386 0.000186 0.000688 -0.000117 3 311.485384 -51.394406 -0.008559 -sn1992bc 0.020792 0.020200 0 15.116795 0.146830 0.735701 0.090316 -0.110033 0.026485 9.915976 0.280891 48912.604999 0.065347 0.000300 0.000679 -0.000111 3 46.316884 -39.559597 -0.009027 -sn1992bg 0.035218 0.035200 0 16.754961 0.144650 -0.463869 0.120276 -0.046527 0.045142 10.712172 0.280891 48916.158799 0.456019 -0.000679 0.001557 -0.000938 3 115.485632 -62.519456 -0.011280 -sn1992bh 0.041657 0.045000 0 17.625150 0.141016 -0.011400 0.152304 0.047159 0.045519 10.542517 0.300000 48920.398091 0.432533 -0.001580 0.001291 -0.002043 3 74.862819 -58.830399 -0.012821 -sn1992bl 0.042400 0.043700 0 17.347518 0.142756 -1.713906 0.088216 -0.045202 0.046268 10.916607 0.280891 48947.275950 0.334726 0.000151 0.001425 -0.000506 3 348.804529 -44.743263 -0.013020 -sn1992bo 0.018902 0.018509 0 15.783958 0.149087 -2.013646 0.066258 -0.042856 0.029151 10.456299 0.280891 48986.148999 0.068970 0.000390 0.000768 0.000029 3 20.492451 -34.211997 -0.008854 -sn1992bp 0.078840 0.079300 0 18.314101 0.138584 -0.955278 0.125006 -0.100756 0.049759 10.875720 0.280891 48980.528700 0.207510 0.000196 0.001197 -0.000608 3 54.156901 -18.354166 -0.028188 -sn1992bs 0.063345 0.063700 0 18.292783 0.141293 -0.175634 0.134453 -0.031870 0.048377 10.918366 0.280891 48985.247971 0.464091 -0.001672 0.001472 -0.001312 3 52.374890 -37.271850 -0.020445 -sn1992p 0.027637 0.025401 0 16.067616 0.144083 0.743997 0.393970 -0.093472 0.043337 10.389000 0.104500 48718.557879 0.558832 -0.002588 0.001247 -0.003043 3 190.703864 10.360068 -0.009889 -sn1993ag 0.049325 0.049000 0 17.841201 0.140167 -0.829836 0.144957 0.059604 0.047162 10.730198 0.280891 49316.726044 0.249152 0.000241 0.001262 -0.001275 3 150.893740 -35.462630 -0.015088 -sn1993b 0.070153 0.069000 0 18.445046 0.146379 -0.438577 0.293983 0.016127 0.056545 10.497219 0.300000 49004.752057 0.806437 -0.005447 0.002351 -0.002154 3 158.717420 -34.441720 -0.023611 -sn1993h 0.023909 0.024207 0 16.695978 0.145547 -2.160571 0.067065 0.110692 0.029151 10.778119 0.280891 49067.465205 0.259132 -0.000276 0.000840 -0.000533 3 208.209220 -30.707860 -0.009373 -sn1993o 0.053126 0.051000 0 17.645061 0.139575 -0.706018 0.091601 -0.080940 0.044527 10.059740 0.280891 49134.235937 0.116608 0.000478 0.001194 -0.000048 3 202.784900 -33.215160 -0.016386 -sn1994m 0.024951 0.023159 0 16.311743 0.145319 -1.525693 0.118051 0.056784 0.030570 11.038000 0.187000 49475.168162 0.279065 0.000062 0.000870 0.000108 3 187.785864 0.605457 -0.009506 -sn1994s 0.015664 0.015177 0 14.780119 0.154751 0.222279 0.188960 -0.064700 0.031411 10.452000 0.085000 49518.395965 0.244110 0.002533 0.000886 0.000608 3 187.841044 29.134436 -0.008625 -sn1995ac 0.048984 0.049902 0 17.075749 0.139079 0.749336 0.121246 -0.038395 0.028157 7.892000 1.013500 49992.918537 0.133365 0.000583 0.000718 0.000091 3 341.392250 -8.751306 -0.014978 -sn1995ak 0.022399 0.022719 0 16.021035 0.149213 -1.090886 0.131289 0.074389 0.035806 10.335397 0.280891 50021.793427 0.388014 0.001485 0.001313 0.001072 3 41.453458 3.230583 -0.009196 -sn1996bl 0.034854 0.036000 0 16.664677 0.141007 -0.027789 0.133676 -0.010328 0.027850 10.429921 0.280891 50376.549769 0.169924 0.000876 0.000746 0.000295 3 9.074875 11.394583 -0.011203 -sn1996bv 0.016966 0.016672 0 15.351431 0.153258 0.823253 0.269504 0.164560 0.028510 9.936639 0.280891 50404.162598 0.614375 -0.003536 0.000834 -0.000338 3 94.054167 57.052472 -0.008707 -sn1996c 0.030721 0.029590 0 16.647115 0.142617 0.659180 0.128330 0.064364 0.029154 10.309000 0.122000 50129.045188 0.340228 -0.000719 0.000806 -0.000581 3 207.746190 49.343600 -0.010400 -sn1997dg 0.030482 0.030800 0 16.849731 0.141700 -0.334473 0.175879 -0.030644 0.026018 6.000000 5.000000 50720.336448 0.286570 -0.000458 0.000632 -0.000176 3 355.059210 26.203280 -0.010357 -sn1997do 0.010550 0.010120 0 14.449314 0.173090 0.791332 0.187870 0.118415 0.030547 9.941915 0.280891 50766.597803 0.115604 0.003521 0.000989 0.001322 3 111.677083 47.093333 -0.008434 -sn1997e 0.014256 0.013539 0 15.128363 0.156400 -1.717814 0.088125 0.026015 0.024684 11.191000 0.073000 50468.057337 0.095269 -0.000004 0.000550 -0.000243 3 101.908750 74.497500 -0.008552 -sn1998ab 0.027688 0.027132 0 16.076604 0.143171 -0.108805 0.127084 0.039115 0.030071 10.789000 0.146500 50914.148915 0.076350 0.001716 0.000780 0.000766 3 192.196833 41.924528 -0.009897 -sn1998dx 0.053860 0.054224 0 17.557966 0.140685 -1.597381 0.283786 -0.118312 0.029980 11.314486 0.280891 51071.926846 0.429550 -0.000774 0.000874 0.000698 3 272.799542 49.861306 -0.016649 -sn1998ef 0.017672 0.017742 0 14.836398 0.150611 -1.170669 0.118180 -0.081454 0.025296 10.429041 0.280891 51113.763372 0.099476 0.001349 0.000613 0.000387 3 15.861958 32.236778 -0.008758 -sn1998v 0.017450 0.017572 0 15.122099 0.150702 -0.177036 0.117393 0.000164 0.025212 11.003000 0.182500 50892.080720 0.292096 -0.000852 0.000622 -0.000426 3 275.655833 15.702139 -0.008741 -sn1999aa 0.015573 0.014443 0 14.726182 0.153276 1.178646 0.043852 -0.083725 0.023186 10.700000 0.146000 51232.695633 0.045242 -0.000059 0.000515 -0.000174 3 126.925125 21.487444 -0.008620 -sn1999ac 0.010060 0.009500 0 14.148421 0.174566 0.202688 0.068440 0.048593 0.025981 9.917000 0.128500 51250.610935 0.078970 0.000244 0.000652 -0.000154 3 241.812542 7.972333 -0.008427 -sn1999ao 0.054931 0.053900 0 17.892389 0.143919 0.257979 0.347778 -0.020237 0.036950 10.363095 0.280891 51243.725544 0.743712 -0.000824 0.001476 0.001923 3 96.860833 -35.840111 -0.017042 -sn1999aw 0.039083 0.038000 0 16.773853 0.139901 2.021537 0.126933 -0.049334 0.027310 7.256355 0.300000 51254.754731 0.194382 0.000002 0.000676 -0.000335 3 165.401542 -6.108778 -0.012166 -sn1999bp 0.078317 0.077000 0 18.420978 0.138307 0.600680 0.224695 -0.054961 0.032081 6.000000 5.000000 51264.287040 0.233029 0.001969 0.000799 0.000353 3 174.942667 -8.860389 -0.027895 -sn1999cc 0.031764 0.031328 0 16.789918 0.140686 -1.609961 0.074743 -0.006560 0.024078 11.039000 0.095000 51315.446370 0.127811 -0.000067 0.000523 -0.000123 3 240.675125 37.359556 -0.010589 -sn1999dk 0.014440 0.014960 0 14.843842 0.157099 -0.014266 0.207970 0.049854 0.038269 10.156000 0.118500 51414.955093 0.342482 0.000966 0.000972 0.000093 3 22.862167 14.284917 -0.008561 -sn1999dq 0.013533 0.014327 0 14.381711 0.158393 0.869173 0.037782 0.026883 0.023491 10.689000 0.138000 51436.128217 0.038129 -0.000050 0.000534 -0.000124 3 38.498667 20.975111 -0.008520 -sn1999gp 0.026677 0.026745 0 16.034919 0.142999 1.779223 0.121179 -0.005868 0.035198 10.819885 0.280891 51550.787799 0.126571 0.000626 0.000753 -0.000519 3 37.913125 39.381194 -0.009746 -sn2000ca 0.023360 0.023616 0 15.581359 0.144317 0.478183 0.082651 -0.097120 0.023886 9.417859 0.280891 51666.340643 0.139785 -0.000007 0.000534 -0.000189 3 203.845750 -34.160278 -0.009306 -sn2000cf 0.036934 0.036425 0 17.077342 0.140956 -0.639110 0.113858 -0.028384 0.026220 10.173168 0.280891 51673.056948 0.322395 -0.000697 0.000707 -0.000099 3 238.234125 65.936861 -0.011659 -sn2000cn 0.023552 0.023493 0 16.554284 0.144309 -2.506817 0.154559 0.087476 0.025623 10.961891 0.280891 51707.305723 0.067676 0.000859 0.000573 0.000314 3 269.418417 27.832806 -0.009329 -sn2000dk 0.017483 0.017442 0 15.364415 0.150122 -2.112751 0.105960 -0.026834 0.025355 10.476629 0.374166 51812.387105 0.097838 0.000278 0.000563 -0.000027 3 16.848000 32.406444 -0.008744 -sn2001az 0.040259 0.040695 0 16.947362 0.142119 1.203835 0.445484 -0.061247 0.024084 10.656000 0.063000 52031.080188 0.915222 -0.007405 0.000589 0.000765 3 248.614480 76.029540 -0.012459 -sn2001ba 0.030381 0.029420 0 16.215141 0.142175 0.143493 0.101153 -0.123778 0.040276 10.975520 0.280891 52034.360512 0.146839 -0.000027 0.001028 -0.000575 3 174.507333 -32.330833 -0.010340 -sn2001bf 0.015504 0.015501 0 14.711780 0.153374 0.549047 0.082741 -0.032776 0.021691 10.205262 0.280891 52046.760285 0.232966 -0.000417 0.000446 -0.000237 3 270.391910 26.250510 -0.008616 -sn2001bt 0.014151 0.014640 0 15.283561 0.157186 -0.865299 0.069400 0.163098 0.026535 10.920564 0.280891 52063.996340 0.071965 0.000165 0.000675 -0.000094 3 288.444792 -59.289667 -0.008547 -sn2001cn 0.015487 0.015500 0 15.285545 0.153742 -0.548801 0.040024 0.129471 0.024043 10.336276 0.280891 52072.932545 0.152222 -0.000306 0.000571 -0.000249 3 281.574333 -65.761611 -0.008615 -sn2001cz 0.015554 0.015380 0 15.047556 0.153417 0.167214 0.068755 0.040909 0.024195 10.696785 0.280891 52103.927004 0.089277 0.000001 0.000552 -0.000120 3 191.875708 -39.580028 -0.008619 -sn2001eh 0.037075 0.037036 0 16.620416 0.139296 1.743056 0.134670 -0.036585 0.022576 11.136430 0.280891 52170.153851 0.310850 -0.000584 0.000433 -0.000678 3 24.550230 41.655260 -0.011691 -sn2001en 0.015482 0.015871 0 15.080250 0.154168 -0.916368 0.069293 0.001671 0.023350 10.662000 0.180000 52193.362613 0.327411 -0.000569 0.000536 -0.000357 3 21.345230 34.025020 -0.008615 -sn2001ep 0.013324 0.013012 0 14.882717 0.158624 -0.925188 0.057481 0.047286 0.021409 10.286000 0.139500 52200.061144 0.083360 0.000095 0.000413 -0.000040 3 74.251450 -4.761120 -0.008512 -sn2001fe 0.014575 0.013539 0 14.661390 0.155333 0.737970 0.094544 -0.066355 0.022359 10.193000 0.126500 52229.379451 0.243985 -0.000216 0.000444 -0.000364 3 144.487590 25.494680 -0.008567 -sn2001v 0.015791 0.015018 0 14.557266 0.152742 0.916702 0.078089 -0.038383 0.021961 10.842000 0.117500 51973.907624 0.052937 0.000571 0.000447 0.000072 3 179.353875 25.202500 -0.008633 -sn2002bf 0.024746 0.024396 0 16.341106 0.147523 -0.791998 0.133954 0.148436 0.025465 10.603000 0.098000 52338.363802 0.371608 0.001509 0.000929 0.000824 3 153.926292 55.668528 -0.009479 -sn2002de 0.027748 0.028116 0 16.657319 0.141846 0.541542 0.319146 0.097210 0.022722 10.806000 0.066500 52434.118333 0.162620 0.002837 0.000470 0.002091 3 244.126390 35.708360 -0.009906 -sn2002dp 0.010888 0.011638 0 14.557203 0.169021 -0.316464 0.184325 0.054095 0.023005 10.470000 0.363500 52451.216137 0.133147 0.001596 0.000487 0.001020 3 352.125430 22.427240 -0.008440 -sn2002ha 0.013652 0.014046 0 14.685508 0.157774 -1.295292 0.070581 -0.092539 0.022754 11.114000 0.153500 52581.355367 0.105603 0.000317 0.000439 0.000010 3 311.827470 0.312600 -0.008525 -sn2002hd 0.035755 0.035001 0 16.778327 0.142258 -0.961981 0.474241 0.000350 0.024307 11.317124 0.280891 52576.318442 0.628833 -0.004562 0.000677 0.000169 3 133.514030 -7.189300 -0.011396 -sn2002he 0.024725 0.024564 0 16.238326 0.145176 -1.737840 0.167114 -0.048509 0.025225 10.957934 0.280891 52586.204774 0.107572 0.003569 0.000612 0.001001 3 124.995020 62.820480 -0.009477 -sn2002hu 0.038086 0.038000 0 16.644355 0.138952 0.514933 0.089255 -0.090391 0.021085 10.349466 0.280891 52592.571975 0.090706 0.000299 0.000384 0.000074 3 34.583450 37.466270 -0.011926 -sn2002jy 0.021541 0.021000 0 15.747200 0.145405 0.977375 0.103933 -0.035146 0.021727 10.587000 0.134000 52634.379179 0.281622 -0.000604 0.000433 -0.000456 3 20.317630 40.498690 -0.009103 -sn2002kf 0.019581 0.019300 0 15.660096 0.147459 -0.973825 0.075906 -0.054622 0.022452 9.380049 0.280891 52639.562699 0.281137 -0.000577 0.000465 -0.000395 3 99.313680 49.853020 -0.008913 -sn2003ch 0.030416 0.030000 0 16.684369 0.140939 -1.232770 0.146937 -0.041780 0.022883 10.946063 0.280891 52726.284171 0.229459 -0.000355 0.000424 -0.000422 3 109.491210 9.693010 -0.010346 -sn2003fa 0.039219 0.039361 0 16.699328 0.138984 1.530311 0.093951 -0.062088 0.021704 10.578520 0.280891 52807.526430 0.066717 0.000603 0.000415 0.000099 3 266.032220 40.880860 -0.012199 -sn2003iv 0.034971 0.034307 0 16.971121 0.140126 -2.039104 0.186512 -0.088580 0.026359 11.028717 0.280891 52934.294883 0.272258 0.000190 0.000522 -0.000086 3 42.530180 12.845910 -0.011228 -sn2003u 0.028561 0.028341 0 16.482973 0.142690 -2.090370 0.294372 -0.032596 0.026589 10.657000 0.093000 52677.456353 0.137762 0.004833 0.000635 0.002632 3 260.690110 62.164070 -0.010034 -sn2003w 0.020997 0.020071 0 15.875946 0.145587 0.072990 0.061371 0.102214 0.021445 10.619000 0.246500 52680.425081 0.058115 0.000137 0.000411 -0.000095 3 146.706230 16.043830 -0.009047 -sn2004as 0.032538 0.031021 0 17.000951 0.140430 0.398948 0.136348 0.073203 0.022522 9.350000 0.090000 53086.187135 0.145578 0.000822 0.000455 0.000135 3 171.413270 22.830290 -0.010736 -sn2004eo 0.015469 0.015701 0 15.064971 0.153515 -1.166690 0.033491 0.041704 0.023271 10.982000 0.253000 53278.520440 0.031951 -0.000048 0.000523 -0.000113 3 308.225792 9.928528 -0.008614 -sn2004ey 0.015678 0.015800 0 14.683230 0.151905 0.097384 0.017738 -0.133126 0.018003 9.950000 0.154000 53304.539956 0.021584 -0.000073 0.000254 -0.000112 3 327.282532 0.444222 -0.008626 -sn2004gs 0.027499 0.026700 0 17.152881 0.140928 -1.806035 0.031362 0.127063 0.019477 11.079000 0.128000 53355.887649 0.056393 -0.000045 0.000276 -0.000132 3 129.596588 17.627722 -0.009868 -sn2004gu 0.046856 0.045900 0 17.452691 0.137271 1.333884 0.058312 0.078323 0.019538 10.225000 0.159000 53362.565547 0.116697 -0.000082 0.000274 -0.000239 3 191.602997 11.948916 -0.014307 -sn2004l 0.033173 0.032309 0 17.370053 0.143111 -0.986549 0.238950 0.172665 0.029109 10.446000 0.182500 53031.303370 0.674840 -0.003941 0.000880 -0.001443 3 156.767190 16.018830 -0.010860 -sn2004s 0.010291 0.009370 0 14.157498 0.173593 -0.117402 0.082511 0.022258 0.024846 9.708903 0.280891 53040.308109 0.290845 -0.000483 0.000665 -0.000348 3 101.431250 -31.231250 -0.008430 -sn2005ag 0.080103 0.079400 0 18.471039 0.136253 0.213846 0.047893 -0.037391 0.023085 10.902000 0.143000 53414.897913 0.085442 -0.000131 0.000369 -0.000202 3 224.181870 9.328472 -0.028904 -sn2005al 0.012317 0.012400 0 14.875843 0.161450 -1.180547 0.028436 -0.091481 0.018963 10.691509 0.280891 53430.552176 0.081061 -0.000102 0.000273 -0.000170 3 207.501373 -30.576166 -0.008477 -sn2005bg 0.024586 0.023100 0 15.865712 0.142334 0.396794 0.056067 -0.032789 0.018761 10.157000 0.164000 53469.934977 0.117931 -0.000254 0.000274 -0.000192 3 184.321579 16.371555 -0.009459 -sn2005bo 0.014467 0.013900 0 15.638553 0.154784 -1.162690 0.108102 0.205443 0.019987 10.357819 0.280891 53478.746788 0.139981 -0.000087 0.000301 -0.000032 3 192.420959 -11.096472 -0.008562 -sn2005el 0.015154 0.014910 0 14.834769 0.153758 -1.282032 0.031880 -0.129815 0.021500 10.842747 0.280891 53646.855746 0.050491 -0.000002 0.000415 -0.000135 3 77.953100 5.194220 -0.008597 -sn2005eq 0.029086 0.028977 0 16.284531 0.141059 1.425425 0.076807 -0.024106 0.021612 10.552000 0.132000 53654.865921 0.089272 0.000174 0.000400 -0.000237 3 47.205650 -7.033400 -0.010120 -sn2005eu 0.034781 0.034897 0 16.450122 0.141596 0.940829 0.095488 -0.086743 0.029831 6.000000 5.000000 53660.755080 0.102348 0.000691 0.000889 0.000139 3 36.930160 28.176860 -0.011187 -sn2005hc 0.044920 0.045890 0 17.322622 0.111373 0.739791 0.025725 -0.028258 0.011882 10.554000 0.046000 53667.801343 0.034019 -0.000007 0.000113 -0.000039 2 29.199791 -0.213720 -0.000254 -sn2005iq 0.034074 0.034044 0 16.764211 0.138969 -1.071301 0.040710 -0.112559 0.019262 10.600063 0.280891 53687.694444 0.040989 -0.000027 0.000272 -0.000127 3 359.635090 -18.709160 -0.011041 -sn2005ir 0.074870 0.076000 0 18.411792 0.110074 0.684284 0.052576 -0.013661 0.014317 10.182000 0.011000 53685.659830 0.048288 0.000137 0.000160 0.000006 2 19.182510 0.794628 -0.000837 -sn2005kc 0.014960 0.015120 0 15.489034 0.153548 -0.697978 0.034823 0.148376 0.019318 10.934000 0.122500 53697.707629 0.031469 -0.000032 0.000289 -0.000090 3 338.530450 5.568340 -0.008587 -sn2005ki 0.020396 0.019607 0 15.541531 0.145393 -1.388082 0.030341 -0.096978 0.019542 11.180000 0.188000 53705.227634 0.049663 -0.000057 0.000282 -0.000156 3 160.117580 9.202280 -0.008988 -sn2005lz 0.040689 0.040998 0 17.617164 0.139014 -1.244258 0.122040 0.069449 0.022872 10.961451 0.280891 53736.254196 0.129180 0.000804 0.000447 -0.000025 3 32.707200 34.982730 -0.012569 -sn2005ms 0.026567 0.025204 0 16.161470 0.142099 0.424016 0.069517 -0.034323 0.021128 10.410000 0.182500 53744.340478 0.055482 0.000284 0.000406 -0.000021 3 132.309670 36.129640 -0.009730 -sn2005na 0.026881 0.026322 0 15.922518 0.141195 -0.449135 0.028448 -0.117667 0.019170 11.025199 0.280891 53740.725937 0.058260 -0.000052 0.000282 -0.000102 3 105.402750 14.132990 -0.009776 -sn2006ac 0.023471 0.023106 0 16.158253 0.143695 -0.858068 0.062060 0.024221 0.021170 10.866000 0.118000 53781.848555 0.081094 0.000092 0.000407 -0.000109 3 190.437060 35.068870 -0.009320 -sn2006al 0.069122 0.067850 0 18.394173 0.145157 -1.408648 0.272816 -0.119809 0.035621 10.249000 0.153500 53787.189353 0.676808 -0.005528 0.001510 -0.002242 3 159.867660 5.183440 -0.023108 -sn2006an 0.065193 0.064001 0 18.095053 0.139912 0.645720 0.208600 -0.044607 0.028883 7.989000 0.910000 53790.529333 0.510293 -0.001908 0.000819 -0.000737 3 183.661450 12.229930 -0.021268 -sn2006ar 0.022980 0.022539 0 16.454585 0.145076 -1.043223 0.216571 0.082429 0.027419 9.847000 0.151500 53813.673595 0.144336 0.000974 0.000705 0.000935 3 159.377570 65.016050 -0.009262 -sn2006ax 0.017387 0.016738 0 14.998450 0.148936 0.153020 0.019140 -0.104973 0.018055 8.933000 1.152000 53827.499359 0.021321 -0.000058 0.000255 -0.000105 3 171.014300 -12.291530 -0.008737 -sn2006az 0.031485 0.030935 0 16.462073 0.140346 -1.343413 0.042176 -0.117484 0.021579 11.314000 0.149000 53826.869273 0.072002 -0.000008 0.000412 -0.000101 3 183.061040 56.179750 -0.010538 -sn2006bh 0.011184 0.010900 0 14.342973 0.166533 -1.648794 0.032216 -0.083155 0.019407 10.915000 0.352000 53833.523740 0.039787 -0.000039 0.000286 -0.000115 3 340.067078 -66.485085 -0.008446 -sn2006bq 0.021956 0.021905 0 16.206993 0.145024 -1.517685 0.066233 0.049212 0.021474 10.843000 0.190500 53848.251720 0.181198 -0.000292 0.000423 -0.000161 3 279.995590 39.982320 -0.009147 -sn2006bt 0.031484 0.032156 0 16.911564 0.140272 0.265652 0.070945 0.062851 0.021271 11.381000 0.236000 53858.338990 0.109348 -0.000066 0.000402 -0.000184 3 239.127190 20.045930 -0.010538 -sn2006cp 0.023321 0.022289 0 16.024534 0.144025 0.353158 0.074526 0.096579 0.022058 10.103000 0.170000 53897.759417 0.067563 0.000295 0.000438 -0.000060 3 184.812040 22.427190 -0.009302 -sn2006ej 0.020390 0.020267 0 15.743711 0.147727 -1.005031 0.077047 -0.031248 0.028040 10.413839 0.335410 53975.485020 0.316092 -0.000822 0.000773 -0.000601 3 9.749220 -9.015950 -0.008988 -sn2006et 0.022466 0.022172 0 15.970889 0.145994 0.936399 0.151537 0.121702 0.029031 10.645467 0.374166 53994.447251 0.221463 0.000721 0.000784 -0.000298 3 10.690750 -23.558280 -0.009203 -sn2006gr 0.033614 0.034597 0 16.968737 0.139809 0.878787 0.067830 0.074036 0.021572 10.781361 0.374166 54013.362166 0.062060 0.000139 0.000412 -0.000092 3 338.094490 30.828830 -0.010948 -sn2006mo 0.036969 0.037002 0 17.409901 0.141722 -2.128216 0.109104 0.007758 0.030362 10.925000 0.119500 54047.627334 0.271270 -0.000624 0.000873 -0.000528 3 11.660330 36.332690 -0.011666 -sn2006mp 0.022629 0.023016 0 15.990616 0.145184 0.941186 0.149069 0.003404 0.027021 9.656586 0.280891 54054.328319 0.107375 0.000393 0.000673 0.000061 3 258.000810 46.555980 -0.009221 -sn2006n 0.014700 0.014277 0 15.058086 0.155008 -1.897040 0.060593 -0.079058 0.022058 10.809000 0.147500 53760.673259 0.151724 -0.000215 0.000443 -0.000188 3 92.130280 64.723560 -0.008573 -sn2006nz 0.036950 0.038090 0 18.130272 0.114021 -2.862959 0.215797 0.228810 0.020260 5.000000 10.000000 54057.617213 0.100140 0.001427 0.000334 0.000841 2 14.121691 -1.226708 -0.000047 -sn2006oa 0.060959 0.062550 0 17.836262 0.110930 0.908837 0.153430 -0.041039 0.016125 8.985000 0.118000 54067.128651 0.125787 0.000635 0.000218 0.000249 2 -39.071068 -0.843469 -0.000603 -sn2006ob 0.059289 0.059160 0 18.195512 0.111086 -2.365543 0.117956 -0.044476 0.017831 11.323000 0.108500 54063.040153 0.090377 0.000434 0.000227 0.000060 2 27.950600 0.263312 -0.000571 -sn2006on 0.069751 0.071900 0 18.412215 0.111409 0.548467 0.212851 0.078950 0.021291 10.327000 0.119500 54064.234436 0.195496 0.000992 0.000363 0.000045 2 -31.006298 -1.070198 -0.000758 -sn2006qo 0.029526 0.029704 0 16.835065 0.142005 0.466840 0.060423 0.165800 0.027182 10.999260 0.280891 54083.095740 0.067149 0.000173 0.000695 -0.000105 3 120.035090 56.368680 -0.010193 -sn2006s 0.033255 0.032102 0 16.841219 0.139885 0.970796 0.066637 0.020955 0.021663 10.450000 0.142000 53770.262659 0.086871 0.000104 0.000412 -0.000136 3 191.412640 35.086710 -0.010876 -sn2006sr 0.024159 0.024140 0 16.150168 0.143380 -1.290353 0.113941 -0.001752 0.022502 10.589000 0.144500 54092.815673 0.118834 0.000216 0.000435 0.000023 3 0.895930 23.196020 -0.009404 -sn2006td 0.015999 0.015878 0 15.719765 0.153171 -1.321070 0.107517 0.090393 0.027021 9.509744 0.280891 54098.980217 0.237676 -0.000293 0.000709 -0.000192 3 29.565670 36.349380 -0.008645 -sn2007ae 0.062961 0.064388 0 17.798492 0.140212 1.849266 0.292740 -0.009638 0.031145 11.320000 0.107000 54153.122732 0.647031 -0.002291 0.000916 -0.000308 3 255.466950 79.031740 -0.020277 -sn2007bc 0.021706 0.020771 0 15.902190 0.146331 -1.223772 0.080538 -0.014061 0.028612 10.814000 0.165000 54200.354760 0.142017 0.000089 0.000766 -0.000160 3 169.810690 20.808960 -0.009120 -sn2007bd 0.031638 0.031018 0 16.580940 0.141210 -1.380962 0.064413 -0.047773 0.026743 10.736000 0.130000 54206.543634 0.056303 -0.000003 0.000664 -0.000165 3 127.889060 -1.199370 -0.010566 -sn2007ci 0.018599 0.018126 0 15.893310 0.149282 -2.724319 0.099408 0.026802 0.028331 11.117000 0.148500 54246.653997 0.064090 0.000495 0.000755 0.000176 3 176.441050 19.770480 -0.008829 -sn2007co 0.027064 0.026962 0 16.504006 0.141685 -0.137806 0.061153 0.105288 0.020382 10.519608 0.280891 54265.212054 0.056635 0.000095 0.000377 0.000007 3 275.765000 29.897050 -0.009803 -sn2007cq 0.025468 0.025918 0 15.797848 0.143429 -0.657941 0.115645 -0.060805 0.025820 9.704507 0.280891 54281.025669 0.070944 0.000392 0.000639 0.000075 3 333.668430 5.080160 -0.009575 -sn2007f 0.023810 0.023590 0 15.895501 0.144315 0.618766 0.041400 -0.055411 0.026006 10.027000 0.118500 54124.058397 0.045234 -0.000055 0.000645 -0.000180 3 195.812750 50.618760 -0.009361 -sn2007qe 0.023867 0.024000 0 16.068268 0.144350 0.760605 0.045650 0.052186 0.026200 6.000000 5.000000 54429.852171 0.037486 0.000101 0.000654 -0.000076 3 358.553990 27.409170 -0.009368 -sn2008bf 0.022068 0.021275 0 15.718540 0.144685 0.430639 0.068523 -0.038367 0.021262 11.212000 0.156500 54555.109466 0.090470 0.000136 0.000409 -0.000104 3 181.011990 20.245080 -0.009159 diff --git a/Day_3/ExtraExamples/slow_cosmology.py b/Day_3/ExtraExamples/slow_cosmology.py deleted file mode 100755 index 0da7c7b..0000000 --- a/Day_3/ExtraExamples/slow_cosmology.py +++ /dev/null @@ -1,237 +0,0 @@ -# adapted from James Schombert's python version -# of Ned Wright's cosmology calculator -# (www.astro.ucla.edu/~wright/CosmoCalc.html) - -helpstring = ''' -Cosmology calculator ala Ned Wright (www.astro.ucla.edu/~wright) -input values = redshift, Ho, Omega_m, Omega_vac -ouput values = age at z, distance in Mpc, kpc/arcsec, apparent to abs mag conversion -''' -from math import * -from scipy import integrate as scint -from numpy import (iterable, array, sqrt, append, abs, - sin, sinh, ndarray, exp, zeros, isfinite, - log10) - -c = 299792.458 # speed of light in km/sec -Tyr = 977.8 # coefficent for converting 1/H into Gyr - -# Omega(radiation) -# includes 3 massless neutrino species, T0 = 2.72528 -Or = lambda h : 4.165E-5/(h*h) -#Or = lambda h : 0 - - -def zfromt( t, H0=70, Om=0.3, unit='Gyr', debug=False ): - """ The redshift z when the universe has an age t. - set unit=None for ages in units of 1/H0 - From Peebles:1993 p.315 - This is crude, and only applicable for a flat - lambdaCDM universe. Errors approach 1% at z=25. - """ - if unit=='Gyr' : tx = t * (H0 / Tyr) - if unit=='Myr' : tx = t * (H0 / Tyr) * 1e3 - elif unit=='yr' : tx = t * (H0 / Tyr) * 1e9 - A = sqrt( Om/(1-Om)) - B = (3*tx/2) * sqrt(1-Om) - z = ( A * sinh( B ) )**(-2/3.) - 1 - return( z ) - - -def agez( z, H0=70, Om=0.3, Ode=0.7, n=1000, unit='Gyr'): - """ The age of the universe at redshift z - set unit=None to get the age in units of 1/H0""" - h = H0/100. - Ok = 1-Om-Ode-Or(h) - - if not iterable(z) : z = array([ z ]) - elif not isinstance( z, ndarray) : z = array( z ) - - # do integral over a=1/(1+z) from az to 1, Quadrature integration - integrand = lambda a : 1./sqrt(Ok+(Om/a)+(Or(h)/(a*a))+(Ode*a*a)) - - zage = [] - for zz in z : - az = 1.0/(1+1.0*zz) - zage.append( scint.quad( integrand, 1e-6, az )[0] ) - zage = array(zage) - if len(zage)==1: zage = zage[0] - zage_Gyr = (Tyr/H0)*zage - - if unit=='Gyr' : return( zage_Gyr ) - elif unit=='yr' : return( zage_Gyr*1e9 ) - elif unit==None : return( zage ) - - -def DC( z, H0=70, Om=0.3, Ode=0.7, n=1000, unit=None ): - """ Comoving radial distance out to redshift z, - which goes into the Hubble law. - Use unit=None for distance in units of c/H0""" - h = H0/100. - Ok = 1-Om-Ode-Or(h) - az = 1.0/(1+1.0*z) - if not iterable( az ) : az = array( [az] ) - - try : - # Faster with SciPy - # do integral over a=1/(1+z) from az to 1, - # using quadrature integration - integrand = lambda a : 1 / ( a * sqrt( - Ok + (Om/a) + (Or(h)/(a*a)) + (Ode*a*a) ) ) - DCMR = [] - for azi in az : - DCMR.append( scint.quad( integrand, azi, 1)[0] ) - except : - # If no SciPy, do the slow way - # do integral over a=1/(1+z) from az to 1 in n steps, - # using the midpoint rule - DCMR = 0.0 - for i in range(n): - a = az+(1-az)*(i+0.5)/n - adot = sqrt(Ok+(Om/a)+(Or(h)/(a*a))+(Ode*a*a)) - DCMR = DCMR + 1./(a*adot) - DCMR = (1.-az)*DCMR/n - if len(DCMR) == 1 : DCMR = DCMR[0] - else : DCMR = array( DCMR ) - DCMR_Gyr = (Tyr/H0)*DCMR - DCMR_Mpc = (c/H0)*DCMR - - if unit=="Mpc" : return( DCMR_Mpc ) - elif unit=="Gpc" : return( DCMR_Gpc ) - elif unit==None : return( DCMR ) - - -def DL( z, H0=70, Om=0.3, Ode=0.7, n=1000, unit=None ): - """ - Luminosity distance out to redshift z, assuming dark energy - is a cosmological constant (does not change with time). - - Units may be Mpc or Gpc, or use unit=None - for distance in units of c/H0""" - h = H0/100. - Ok = 1-Om-Ode-Or(h) - az = 1.0/(1+1.0*z) - - # radial comoving distance - DCMR = DC( z, H0=H0, Om=Om, Ode=Ode, n=n, unit=None ) - - # tangential comoving distance - ratio = 1.00 - x = sqrt(abs(Ok))*DCMR - - if not iterable(x) : x = array([x]) - ratio = [] - for xi in x : - if xi > 0.1: - if Ok > 0: - ratio.append( 0.5*(exp(xi)-exp(-xi))/xi ) - else: - ratio.append( sin(xi)/xi ) - else: - y = xi*xi - if Ok < 0: y = -y - ratio.append( 1. + y/6. + y*y/120. ) - if len(ratio) == 1 : ratio = ratio[0] - else : ratio = array(ratio) - DCMT = ratio*DCMR - DA = az*DCMT - DL = DA/(az*az) - DL_Mpc = (c/H0)*DL - DL_Gyr = (Tyr/H0)*DL - - if unit=="Mpc" : return( DL_Mpc ) - elif unit=="Gpc" : return( DL_Gpc ) - elif unit==None : return( DL ) - - -def DLw0wa( z, Om=0.3, Ode=0.7, w0=-1, wa=0, - H0=70, unit=None, Flat=False, Lambda=False ): - """ luminosity distance calculation allowing w!=-1 - and allowing w to vary with time using a linear parameterization: - w(a) = w0 + wa(1-a) - To get a constant w, just set wa=0 and use w0 as w. - Set Flat==True to enforce a flat universe (Ode=1-Om) - Lambda=True to force a constant w=-1 - """ - if Flat : Ode = 1-Om # Omega_{DarkEnergy} for flat universe - Ok = 1-Om-Ode # Omega_curvature - if Lambda : w0,wa = -1, 0 # DE is Cosmological constant - - if not iterable(z) : z = array([ z ]) - elif not isinstance( z, ndarray) : z = array( z ) - - # the integrand is 1/E(z) where E(z) = H(z)/H0 is - # the dimensionless expansion rate - invE = lambda zz : 1 / sqrt( Om*(1+zz)**3 + Ok*(1+zz)**2 + Ode*(1+zz)**(3*(1+w0+wa))/exp(3*wa*zz/(1+zz)) ) - isorted = z.argsort() - integrated = zeros( len(z) ) - lastz, lastint = 0, 0 - # slightly quicker to handle the redshifts in order - for i in isorted : - zz = z[i] - #if isfinite( invE( zz ) ) : - # integresult=0 - # continue - integresult,err = scint.quad( invE, lastz, zz ) - integrated[ i ] = integresult + lastint - lastz = zz - lastint = integrated[i] - - if Ok<0 : - DL = ( (1+z) / sqrt(abs(Ok)) ) * sin( sqrt(abs(Ok)) * integrated ) - if Ok>0 : - DL = ( (1+z) / sqrt(abs(Ok)) ) * sinh( sqrt(abs(Ok)) * integrated ) - elif Ok==0: - DL = (1+z) * integrated - if len(DL)==1 : DL = DL[0] - - DL_Mpc = (c/H0) * DL - DL_Gyr = (Tyr/H0) * DL - if unit=="Mpc" : return( DL_Mpc ) - elif unit=="Gpc" : return( DL_Gpc ) - elif unit==None : return( DL ) - - -def mu( z, H0=70, Om=0.3, Ode=0.7, w0=-1, wa=0): - """ Distance modulus to redshift z, - for the given cosmology """ - DL_Mpc = DL( z, H0=H0, Om=Om, Ode=Ode, unit='Mpc' ) - return( 5*log10( DL_Mpc) + 25 ) - - -def E( z, Om=0.3, Ode=0.7, w0=-1, wa=0, H0=70, - debug=False ): - """ The dimensionless expansion rate : - E(z) = H(z) / H0 - (squared, this is the LHS of the Friedmann eqn) - """ - Ok = 1-Om-Ode # Omega_curvature - - # the integrand is 1/E(z) where E(z) = H(z)/H0 is - # the dimensionless expansion rate - M = lambda zz : Om*(1+zz)**3 # Matter - K = lambda zz : Ok*(1+zz)**2 # Kurvature - DE = lambda zz : Ode*(1+zz)**(3*(1+w0+wa))/exp(3*wa*zz/(1+zz)) # Dark Energy - return( sqrt( M(z) + K(z) + DE(z) ) ) - -# THIS IS MUCH FASTER TO INTEGRATE AS A LAMBDA FUNCTION -#def invE( z, Om=0.3, Ode=0.7, w0=-1, wa=0, H0=70, -# debug=False ): -# """ The inverse of the dimensionless expansion rate : -# 1/E(z) = H0 / H(z) -# (this is the integrand for computing A, R, Dl, etc) -# """ -# return( 1 / E(z, Om=Om, Ode=Ode, w0=w0, wa=wa, H0=H0) ) - -def DA( z, Om=0.3, Ode=0.7, w0=-1, wa=0, H0=70, - unit=None, debug=False ): - """ angular diameter distance """ - az = 1/(1+1.0*z) # scale factor of the universe - DA = az*az * DL(z,Om=Om,Ode=Ode,w0=w0,wa=wa,H0=H0,unit=None) - DA_Mpc = (c/H0) * DA - DA_Gyr = (Tyr/H0) * DA - if unit=="Mpc" : return( DA_Mpc ) - elif unit=="Gpc" : return( DA_Gpc ) - elif unit==None : return( DA ) - - diff --git a/Day_3/README.md b/Day_3/README.md deleted file mode 100644 index 71cb4f1..0000000 --- a/Day_3/README.md +++ /dev/null @@ -1,17 +0,0 @@ -### Day 3 (Cython Tutorials) - -Some of these tutorials require a C++ and Fortran compiler. On most linux distributions gcc/g++/gfortran (GNU Compiler Collection) should already be installed. To check this run `gcc --version` in a terminal. On MacOS you can check that you have a C compiler with `clang --version`. The clang/clang++ compiler can be installed by running the command `xcode-select --install` in a terminal or though the xcode app. For Fortran code gfortran can be installed though the gcc package on [homebrew](https://brew.sh/), `brew install gcc`. - -For more in depth cython tutorials and templates to start using it with your own project checkout [cython_tutorial](https://github.com/tylern4/cython_tutorial). - -### Morning Session: -- Multiprocessing -- Cython to speed up code -- Cython to integrate C/C++ -- Cython to integrate Fortran - - -### Afternoon Session: -- Complete the tasks for Day 1 and Day 2. -- Use Cython to speed up the code examples. -- Work on your own code you'd like to speedup or integrate C/C++/Fortran with. diff --git a/Day_3/day_3_exercises.ipynb b/Day_3/day_3_exercises.ipynb deleted file mode 100644 index 025d35d..0000000 --- a/Day_3/day_3_exercises.ipynb +++ /dev/null @@ -1,461 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Day 3: Optimizations/Cython/Running C/C++/Fortran code in Python" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "%load_ext Cython\n", - "\n", - "# Inpiration gotten from:\n", - "# http://people.duke.edu/~ccc14/sta-663-2016/18D_Cython.html\n", - "\n", - "import multiprocessing as mp\n", - "import matplotlib.pylab as plt\n", - "import random\n", - "import numpy as np\n", - "import time" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Try it yourself\n", - "Modify the code examples below to speed them up using the methods shown above. First test the example and note the speed on your computer with a comment. Then work on making the code faster by anotating it to see what can use improvement and see if you can speed it up." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Ex.1) List of primes\n", - "This example returns a list of primes between 0 and the max number you put in." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def is_prime(possiblePrime):\n", - " isPrime = True\n", - " for num in range(2, possiblePrime):\n", - " if possiblePrime % num == 0:\n", - " isPrime = False\n", - " return isPrime\n", - "\n", - "\n", - "def getPrimes(max_num):\n", - " primes = []\n", - " # Try to modify the for loop to make have it use multiprocessing\n", - " for possiblePrime in range(max_num):\n", - " if is_prime(possiblePrime):\n", - " primes.append(possiblePrime)\n", - " \n", - " return primes" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "max_prime = 10000\n", - "primes = []\n", - "%time primes = getPrimes(max_prime)\n", - "\n", - "# Between 0 and 10000 there are 1229 primes.\n", - "len(primes)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#%load get_primes_mp.py\n", - "\n", - "def getPrimes_mp(max_num):\n", - " print(\"\\033[1m\\033[91m\\n\\nLoad the py file for the answer\\n\\n\\033[0m\")\n", - " return []" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "max_prime = 10000\n", - "primes = []\n", - "%time primes = getPrimes_mp(max_prime)\n", - "\n", - "# Between 0 and 10000 there are 1229 primes.\n", - "len(primes)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Ex. 1.1) List of primes Cython\n", - "This example returns a list of primes between 0 and the max number you put in." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#%%cython -a\n", - "\n", - "def is_prime(possiblePrime):\n", - " isPrime = True\n", - " for num in range(2, possiblePrime):\n", - " if possiblePrime % num == 0:\n", - " isPrime = False\n", - " return isPrime\n", - "\n", - "\n", - "def getPrimes(max_num):\n", - " primes = []\n", - " # Hint: Do we need to loop over every number??\n", - " # What do we know about primes that could help us\n", - " for possiblePrime in range(max_num):\n", - " if is_prime(possiblePrime):\n", - " primes.append(possiblePrime)\n", - " \n", - " return primes" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "max_prime = 10000\n", - "%time primes = getPrimes(max_prime)\n", - "\n", - "# Between 0 and 10000 there are 1229 primes.\n", - "len(primes)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#%load get_primes.pyx\n", - "# ^ Uncomment to find the answer\n", - "# NOTE: %%cython has to be the first line so remove the load line as well\n", - "\n", - "def getPrimes_cy(max_num):\n", - " print(\"\\033[1m\\033[91m\\n\\nLoad the py file for the answer\\n\\n\\033[0m\")\n", - " return []" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "max_prime = 10000\n", - "\n", - "%time primes = getPrimes_cy(max_prime)\n", - "\n", - "# Between 0 and 10000 there are 1229 primes. \n", - "# Make sure you get the same answer all the time.\n", - "len(primes)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Ex. 2) Compute $\\pi$ using the formula:\n", - "\n", - "\n", - "### \\begin{equation}\n", - "\\pi^{2} = 6 \\sum_{n=1}^{\\infty} \\frac{1}{k^{2}}\n", - "\\end{equation}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%cython -a\n", - "# Hint: \n", - "# import the c functions instead of using python functions\n", - "# from libc.math cimport sqrt\n", - "from math import sqrt\n", - "\n", - "# Hint: \n", - "# Functions that are only called in cython can be defined with cdef\n", - "# This makes a C function instead of a python function\n", - "def recip_square(k):\n", - " den = (k**2)\n", - " return 1/den\n", - "\n", - "def approx_pi(n):\n", - " \"\"\"Compute an approximate value of pi.\"\"\"\n", - " val = 0\n", - " for k in range(1, n+1):\n", - " temp = recip_square(k)\n", - " val += temp\n", - " pi = sqrt(6 * val)\n", - " return pi" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%time approx_pi(10000000)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#%load pi.pyx\n", - "# ^ Uncomment to find the answer\n", - "# NOTE: %%cython has to be the first line so remove the load line as well\n", - "\n", - "def approx_pi_cy(n):\n", - " print(\"\\033[1m\\033[91m\\n\\nLoad the py file for the answer\\n\\n\\033[0m\")\n", - " return 3.0" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%time approx_pi_cy(10000000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Ex.3) Mandel fractal drawing" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%cython -a\n", - "\n", - "cimport cython\n", - "\n", - "## Hints:\n", - "# You can import and use C functions in cython\n", - "#cdef extern from \"complex.h\":\n", - "# double cabs(double complex)\n", - "\n", - "def mandel(x, y, max_iters):\n", - " # Use the c complex type\n", - " # cdef double complex c, z\n", - " # c = x + y*1j\n", - " # z = 0.0j\n", - " c = complex(x, y)\n", - " z = 0.0j\n", - " for i in range(max_iters):\n", - " z = z*z + c\n", - " if z.real*z.real + z.imag*z.imag >= 4:\n", - " # if cabs(z) >= 2:\n", - " return i\n", - " return max_iters\n", - "\n", - "def create_fractal(xmin, xmax, ymin, ymax, image, iters):\n", - " height, width = image.shape\n", - "\n", - " pixel_size_x = (xmax - xmin)/width\n", - " pixel_size_y = (ymax - ymin)/height\n", - "\n", - " for x in range(width):\n", - " real = xmin + x*pixel_size_x\n", - " for y in range(height):\n", - " imag = ymin + y*pixel_size_y\n", - " color = mandel(real, imag, iters)\n", - " image[y, x] = color" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#%load fractal.pyx\n", - "# ^ Uncomment to find the answer\n", - "# NOTE: %%cython has to be the first line so remove the load line as well\n", - "\n", - "def create_fractal_cython(xmin, xmax, ymin, ymax, gimage, iters):\n", - " print(\"\\033[1m\\033[91m\\n\\nLoad the py file for the answer\\n\\n\\033[0m\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "gimage = np.zeros((1080, 1920), dtype=np.uint32)\n", - "xmin, xmax, ymin, ymax = [-2.0, 1.0, -1.0, 1.0]\n", - "iters = 100\n", - "\n", - "%time create_fractal_cython(xmin, xmax, ymin, ymax, gimage, iters)\n", - "\n", - "plt.figure(figsize=(15,15))\n", - "plt.grid(False)\n", - "plt.imshow(gimage, cmap='viridis')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Ex.4) Speed up wave propogration using cython\n", - "\n", - "See if you can beat the C++/Fortran speed" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%cython -a\n", - "\n", - "def wave_propogation_fast(num_steps, scale, damping, initial_P, stop_step):\n", - " from math import pi, sin\n", - " omega = 3 / (2 * pi)\n", - " \n", - " size_x = 2 * scale + 1\n", - " size_y = 2 * scale + 1\n", - "\n", - " # V velocity\n", - " # P presure\n", - " # Initialization\n", - " P = [[0.0 for x in range(size_x)] for y in range(size_y)]\n", - " V = [[[0.0, 0.0, 0.0, 0.0] for x in range(size_x)] for y in range(size_y)]\n", - " P[scale][scale] = initial_P\n", - " for step in range(num_steps):\n", - " if step <= stop_step:\n", - " P[scale][scale] = initial_P * sin(omega * step)\n", - " for i in range(size_y):\n", - " for j in range(size_x):\n", - " V[i][j][0] = V[i][j][0] + P[i][j] - P[i - 1][j] if i > 0 else P[i][j]\n", - " V[i][j][1] = (V[i][j][1] + P[i][j] - P[i][j + 1] if j < size_x - 1 else P[i][j])\n", - " V[i][j][2] = (V[i][j][2] + P[i][j] - P[i + 1][j] if i < size_y - 1 else P[i][j])\n", - " V[i][j][3] = V[i][j][3] + P[i][j] - P[i][j - 1] if j > 0 else P[i][j]\n", - "\n", - " for i in range(size_y):\n", - " for j in range(size_x):\n", - " P[i][j] -= 0.5 * damping * sum(V[i][j])\n", - " return P" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#%load wave_propogation.pyx\n", - "# ^ Uncomment to find the answer\n", - "# NOTE: %%cython has to be the first line so remove the load line as well\n", - "\n", - "def wave_propogation_fast(num_steps,scale,damping,initial_P,stop_step):\n", - " print(\"\\033[1m\\033[91m\\n\\nLoad the py file for the answer\\n\\n\\033[0m\")\n", - " return np.ones((scale*2,scale*2))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "num_steps = 100\n", - "scale = 100\n", - "damping= 0.25\n", - "initial_P = 250\n", - "stop_step = 100\n", - "\n", - "plt.figure(figsize=(10,10))\n", - "start = time.time()\n", - "pressure = wave_propogation_fast(num_steps,scale,damping,initial_P,stop_step)\n", - "stop = time.time()\n", - "print(f\"{stop - start:.2f} Sec, {num_steps / (stop - start):.2f} Hz\")\n", - "plt.imshow(pressure,cmap='viridis_r',interpolation='bilinear')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "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": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Day_3/day_3_lesson.ipynb b/Day_3/day_3_lesson.ipynb deleted file mode 100644 index e78add2..0000000 --- a/Day_3/day_3_lesson.ipynb +++ /dev/null @@ -1,1163 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Day 3: Optimizations/Cython/Running C/C++/Fortran code in Python" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "%load_ext Cython\n", - "\n", - "# Inpiration gotten from:\n", - "# http://people.duke.edu/~ccc14/sta-663-2016/18D_Cython.html\n", - "\n", - "import matplotlib.pylab as plt\n", - "import random\n", - "import numpy as np\n", - "import time\n", - "import pandas as pd\n", - "import multiprocessing as mp\n", - "from urllib.request import urlopen\n", - "from collections import Counter\n", - "from slow_lib import fast_random" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## What about more compilcated Python classes or a Python Library?\n", - "\n", - "These can sometimes be harder to speed-up with cython, but if the tasks are independent we can use multiprocessing.\n", - "\n", - "\n", - "Lets take a libary function that takes a name from a list and returns a hex hash." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from slow_lib import slow_random\n", - "# Some arbitrarily slow function that takes a name and returns a hash\n", - "words = [\"car\", \"question\", \"decision\", \"onerous\", \"surprise\", \"nice\", \"hobbies\", \"jobless\", \"bat\", \"boat\"]\n", - "\n", - "#words = [ \"poetry\",\"abolish\",\"sound\",\"funny\",\"strategic\",\"deposit\",\"enlarge\",\"psychology\",\"snub\",\"pepper\",\"episode\",\n", - "# \"fee\",\"frog\",\"engagement\",\"cheque\",\"determine\",\"era\",\"stroke\",\"premium\",\"pioneer\",\"beard\",\"credit\",\"graphic\",\n", - "# \"tell\",\"scream\",\"continental\",\"absorption\",\"enjoy\",\"block\",\"healthy\",\"eavesdrop\",\"accept\",\"access\",\n", - "# \"outline\",\"temperature\",\"creep\",\"letter\",\"inquiry\",\"interface\",\"accurate\",\"robot\",\"award\",\"difficulty\",\n", - "# \"youth\",\"fuss\",\"escape\",\"fiction\",\"wild\",\"bracket\",\"hero\",\"module\",\"walk\",\"promotion\",\"critic\",\"linen\",\n", - "# \"painter\",\"survivor\",\"glory\",\"reveal\",\"linger\",\"medal\",\"drag\",\"pot\",\"security\",\"mechanical\",\"work out\",\n", - "# \"spread\",\"revolutionary\",\"use\",\"disaster\",\"layout\",\"constitutional\",\"sink\",\"agreement\",\"upset\",\"presence\",\n", - "# \"monster\",\"contemporary\",\"quality\",\"tiger\",\"tolerate\",\"chalk\",\"watch\",\"state\",\"dare\",\"helmet\",\"volume\",\n", - "# \"information\",\"colon\",\"closed\",\"strip\",\"profession\",\"riot\",\"lease\",\"spit\",\"get\",\"double\",\"brother\",\n", - "# \"liberal\",\"formula\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 1.51 ms, sys: 1.52 ms, total: 3.03 ms\n", - "Wall time: 9.06 s\n" - ] - } - ], - "source": [ - "%%time \n", - "random = []\n", - "# Looping over each word and appending it's hash to random\n", - "for word in words:\n", - " random.append(slow_random(word))" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 61 ms, sys: 22.9 ms, total: 83.9 ms\n", - "Wall time: 3.62 s\n" - ] - } - ], - "source": [ - "%%time \n", - "# Using the mp.Pool function\n", - "with mp.Pool() as pool:\n", - " # This maps each value in list words to the function slow_random\n", - " random = pool.map(slow_random, words)\n", - " # This will return a list of objects, in this case strings\n", - " # With more complicated objects you may have to make a reduce function" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 57 µs, sys: 4 µs, total: 61 µs\n", - "Wall time: 66.8 µs\n" - ] - } - ], - "source": [ - "%%time \n", - "random = []\n", - "# Looping over each word and appending it's hash to random\n", - "for word in words:\n", - " random.append(fast_random(word))" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 8.52 ms, sys: 16.4 ms, total: 24.9 ms\n", - "Wall time: 136 ms\n" - ] - } - ], - "source": [ - "%%time \n", - "with mp.Pool() as pool:\n", - " # This maps each value in list words to the function slow_random\n", - " random = pool.map(fast_random, words)\n", - " # This will return a list of objects, in this case strings\n", - " # With more complicated objects you may have to make a reduce function" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### WordCount Example" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "def WordCount(book_url, count_punc=False):\n", - " import string\n", - " response = urlopen(book_url)\n", - " text = response.read()\n", - " # Remove werid characters for encoding\n", - " text = text.replace(b\"\\r\\n\",b\" \")\n", - " text = text.replace(b\"\\xe2\\x80\\x9c\",b\"\")\n", - " text = text.replace(b\"\\xe2\\x80\\x9d\",b\"\")\n", - " \n", - " for p in string.punctuation:\n", - " text = text.replace(p.encode(), (\" \"+p+\" \" if count_punc else \"\").encode())\n", - " \n", - " # Remove header and footer\n", - " header = text.find(b\"CONTENTS\")+8\n", - " footer = text.find(b\"End of the Project Gutenberg EBook\")\n", - " # And split into a list\n", - " text = text[header:footer].split()\n", - " \n", - " dictionary = {}\n", - " # counting number of times each word comes up in list of words\n", - " for word in text: \n", - " dictionary[word] = dictionary.get(word, 0) + 1\n", - " \n", - " return dictionary\n", - "\n", - "def reduceWords(dictionaries):\n", - " ret = Counter({})\n", - " for d in dictionaries:\n", - " ret += Counter(d)\n", - " \n", - " df = pd.DataFrame.from_dict(ret, orient='index', columns=['count'])\n", - " df['word'] = df.index\n", - " df['word'] = df['word'].str.decode(\"utf-8\")\n", - " return df" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "## More complicated example with a map reduce of word count\n", - "# Add more books and see how the speed changes\n", - "books = [\n", - " \"https://www.gutenberg.org/files/98/98-0.txt\",\n", - " \"https://www.gutenberg.org/files/1661/1661-0.txt\",\n", - " \"https://www.gutenberg.org/files/43/43-0.txt\",\n", - " \"https://www.gutenberg.org/ebooks/345.txt.utf-8\",\n", - " \"https://www.gutenberg.org/ebooks/2542.txt.utf-8\",\n", - " \"https://www.gutenberg.org/files/2701/2701-0.txt\",\n", - " \"https://ocw.mit.edu/ans7870/6/6.006/s08/lecturenotes/files/t8.shakespeare.txt\",\n", - " \"https://www.gutenberg.org/files/4300/4300-0.txt\",\n", - " \"http://www.gutenberg.org/ebooks/996.txt.utf-8\",\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 1.3 s, sys: 194 ms, total: 1.49 s\n", - "Wall time: 7.08 s\n" - ] - } - ], - "source": [ - "%%time\n", - "popular_words = []\n", - "for book in books:\n", - " popular_words.append(WordCount(book))\n", - " \n", - "words_df = reduceWords(popular_words)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 224 ms, sys: 68.2 ms, total: 293 ms\n", - "Wall time: 3.02 s\n" - ] - } - ], - "source": [ - "%%time\n", - "with mp.Pool() as pool:\n", - " popular_words = pool.map(WordCount, books) \n", - "words_df = reduceWords(popular_words)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "words_df.sort_values(by=['count'], inplace=True, ascending=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "words_df.head(25).plot.bar(x='word', y='count', rot=0,figsize=(16,10))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
countword
b'fancy'223fancy
b'MESSENGER'223MESSENGER
b'USE'223USE
b'Christian'222Christian
b'horses'222horses
b'Antony'222Antony
b'DISTRIBUTION'221DISTRIBUTION
b'SERVICE'221SERVICE
b'COMMERCIALLY'221COMMERCIALLY
b'INC'221INC
\n", - "
" - ], - "text/plain": [ - " count word\n", - "b'fancy' 223 fancy\n", - "b'MESSENGER' 223 MESSENGER\n", - "b'USE' 223 USE\n", - "b'Christian' 222 Christian\n", - "b'horses' 222 horses\n", - "b'Antony' 222 Antony\n", - "b'DISTRIBUTION' 221 DISTRIBUTION\n", - "b'SERVICE' 221 SERVICE\n", - "b'COMMERCIALLY' 221 COMMERCIALLY\n", - "b'INC' 221 INC" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "words_df.head(1000).tail(10)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Example 1: Matrix Multiplication\n", - "\n", - "#### Matrix Multiplication\n", - "\n", - "Let's write a function to multiply 2 square (NxN) matrices together. \n", - "\n", - "First is out niave wave following the way we would do it by hand.\n", - "\n", - "\\begin{equation}\n", - "A =\\begin{pmatrix}\n", - " a_{11} & a_{12} & \\cdots & a_{1m} \\\\\n", - " a_{21} & a_{22} & \\cdots & a_{2m} \\\\\n", - "\\vdots & \\vdots & \\ddots & \\vdots \\\\\n", - " a_{n1} & a_{n2} & \\cdots & a_{nm} \\\\\n", - "\\end{pmatrix}\n", - "\\end{equation}\n", - "\n", - "\\begin{equation}\n", - "B=\\begin{pmatrix}\n", - " b_{11} & b_{12} & \\cdots & b_{1p} \\\\\n", - " b_{21} & b_{22} & \\cdots & b_{2p} \\\\\n", - "\\vdots & \\vdots & \\ddots & \\vdots \\\\\n", - " b_{m1} & b_{m2} & \\cdots & b_{mp} \\\\\n", - "\\end{pmatrix}\n", - "\\end{equation}\n", - "\n", - "\\begin{equation}\n", - "C = AB\n", - "\\end{equation}\n", - "\n", - "\\begin{equation}\n", - "C=\\begin{pmatrix}\n", - " c_{11} & c_{12} & \\cdots & c_{1p} \\\\\n", - " c_{21} & c_{22} & \\cdots & c_{2p} \\\\\n", - "\\vdots & \\vdots & \\ddots & \\vdots \\\\\n", - " c_{n1} & c_{n2} & \\cdots & c_{np} \\\\\n", - "\\end{pmatrix}\n", - "\\end{equation}\n", - "\n", - "\\begin{equation}\n", - "c_{ij} = a_{i1}b_{1j} + ... + a_{im}b_{mj} = \\sum_{k=1}^m a_{ik}b_{kj}\n", - "\\end{equation}" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "def mat_mul_py(A, B):\n", - " # Make a NxN matrix C, Filled with 0.0\n", - " C = [[0.0 for i in range(len(A))] for j in range(len(A))]\n", - " # Loop over columns of A, and Rows of B\n", - " for i in range(len(A)):\n", - " # Loop over rows of A, and columns of B\n", - " for j in range(len(A)):\n", - " # Do final summation of each element\n", - " for k in range(len(A)):\n", - " C[i][j] += A[i][k] * B[k][j]\n", - " return C" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 19 s, sys: 18.5 ms, total: 19 s\n", - "Wall time: 19 s\n" - ] - } - ], - "source": [ - "size = 256\n", - "# Create two random matrix of size NxN (265x265)\n", - "A = np.random.random((size,size))\n", - "B = np.random.random((size,size))\n", - "%time x = mat_mul_py(A,B)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Now lets just add the cython magic line and see what happens" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "%%cython\n", - "# ^ This is the only thing we added\n", - "\n", - "def mat_mul_cy(A, B):\n", - " # Make a NxN matrix C, Filled with 0.0\n", - " C = [[0.0 for i in range(len(A))] for j in range(len(A))]\n", - " # Loop over columns of A, and Rows of B\n", - " for i in range(len(A)):\n", - " # Loop over rows of A, and columns of B\n", - " for j in range(len(A)):\n", - " # Do final summation of each element\n", - " for k in range(len(A)):\n", - " C[i][j] += A[i][k] * B[k][j]\n", - " return C" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 12.2 s, sys: 43 ms, total: 12.2 s\n", - "Wall time: 12.2 s\n" - ] - } - ], - "source": [ - "A = np.random.random((size,size))\n", - "B = np.random.random((size,size))\n", - "%time x = mat_mul_cy(A,B)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Let's anotate the code to see where we can speed it up. \n", - "\n", - "##### More yellow means more generated C code. The less the C code the faster the code will be." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%cython -a\n", - "\n", - "def mat_mul_cy_fast(A, B):\n", - " C = [[0.0 for i in range(len(A))] for j in range(len(A))]\n", - " for i in range(len(A)):\n", - " for j in range(len(A)):\n", - " for k in range(len(A)):\n", - " C[i][j] += A[i][k] * B[k][j]\n", - " return C" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1) Give types to all of the variables" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%cython -a\n", - "\n", - "# Arrays can be defined by the type and then [:] for 1d arrays\n", - "# 2d [:,:], 3d [:,:,:], etc.\n", - "def mat_mul_cy_fast(double [:,:] A, double [:,:] B):\n", - " # The loop variables and the size of the array are boths ints\n", - " cdef int i,j,k = 0\n", - " # c arrays have a new way to get lengths, shape\n", - " cdef int size = A.shape[0]\n", - " C = [[0.0 for i in range(size)] for j in range(size)]\n", - " for i in range(size):\n", - " for j in range(size):\n", - " for k in range(size):\n", - " C[i][j] += A[i][k] * B[k][j]\n", - " return C" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "A = np.random.random((size,size))\n", - "B = np.random.random((size,size))\n", - "%time x = mat_mul_cy_fast(A, B)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2) Turn off some python features" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%cython -a\n", - "\n", - "cimport cython\n", - "\n", - "# Turn off the bounds checking and wrapping of python arrays\n", - "# NOTE: Now it is up to you to check to make sure you're not going past the length of the arrays\n", - "@cython.boundscheck(False)\n", - "@cython.wraparound(False)\n", - "def mat_mul_cy_fast(double [:,:] A, double [:,:] B):\n", - " cdef int i,j,k = 0\n", - " cdef int size = A.shape[0]\n", - " C = [[0.0 for i in range(size)] for j in range(size)]\n", - " for i in range(size):\n", - " for j in range(size):\n", - " for k in range(size):\n", - " C[i][j] += A[i, k] * B[k, j]\n", - " return C" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "A = np.random.random((size,size))\n", - "B = np.random.random((size,size))\n", - "%time x = mat_mul_cy_fast(A,B)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3) Make the output array a c array as well" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%cython -a\n", - "\n", - "cimport cython\n", - "import numpy as np\n", - "cimport numpy as np\n", - "# A special type to make c style arrays\n", - "from cython.view cimport array\n", - "\n", - "@cython.boundscheck(False)\n", - "@cython.wraparound(False)\n", - "def mat_mul_cy_fast(double [:,:] A, double [:,:] B):\n", - " cdef int i,j,k = 0\n", - " cdef int size = A.shape[0]\n", - " # Define the output array as a c style array\n", - " cdef double [:,:] C = array(shape=(size,size), itemsize=sizeof(double), format=\"d\")\n", - " C[:,:] = 0.0\n", - " for i in range(size):\n", - " for j in range(size):\n", - " for k in range(size):\n", - " C[i, j] += A[i, k] * B[k, j]\n", - " return np.asarray(C)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "A = np.random.random((size,size))\n", - "B = np.random.random((size,size))\n", - "%time x = mat_mul_cy_fast(A,B)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Extra Credit: Why is this one faster? What did I change??" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%cython -a\n", - "\n", - "cimport cython\n", - "import numpy as np\n", - "cimport numpy as np\n", - "\n", - "from cython.view cimport array\n", - "\n", - "@cython.boundscheck(False)\n", - "@cython.wraparound(False)\n", - "def mat_mul_cy_fast(double [:,:] A, double [:,:] B):\n", - " cdef int i,j,k = 0\n", - " cdef int size = A.shape[0]\n", - " cdef double [:,:] C = array(shape=(size,size), itemsize=sizeof(double), format=\"d\")\n", - " C[:,:] = 0.0\n", - " for i in range(size):\n", - " for k in range(size):\n", - " for j in range(size):\n", - " C[i, j] += A[i, k] * B[k, j]\n", - " return np.asarray(C)\n", - "\n", - "# Hint: Look at the loop indexes\n", - "# Further reading: https://courses.engr.illinois.edu/cs232/sp2009/lectures/X18.pdf\n", - "# For anyone interested in optimization for how computers work" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "A = np.random.random((size,size))\n", - "B = np.random.random((size,size))\n", - "%time x = mat_mul_cy_fast(A,B)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### For simple operations though it's not always worth doing" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "A = np.random.random((size,size))\n", - "B = np.random.random((size,size))\n", - "%time x1 = mat_mul_cy_fast(A, B)\n", - "%time x2 = np.matmul(A, B)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Example 2: Wave Propogation\n", - "\n", - "Let's try something more complex that doesn't have a function written for it already.\n", - "\n", - "In this example we'll look at a 2d grid describing a pressure. In the center of the grid there is a forcing function taking the pressure values to $\\,\\pm$initial_P. This pressure propogated away from the center as the wave spreads out in all 4 directions (up,down,left,right). For each step we will calcuate the velocity of the wave in each direction and then the new pressure at each point. \n", - "\n", - "```python\n", - "for each step:\n", - " # Calculate the pressure at the center for this stwp\n", - " presure_at_center = initial_P * sin(omega * step)\n", - " # Loop over rows\n", - " for x in size_x:\n", - " # Loop over columns\n", - " for y in size_y\n", - " # The velocity at each point is the difference between \n", - " # the pressure at the point and it's neighbor in a direction\n", - " velocity_up[x][y] += pressure[x][y] - pressure[x - 1][y]\n", - " velocity_down[x][y] += pressure[x][y] - pressure[x][y + 1]\n", - " velocity_left[x][y] += pressure[x][y] - pressure[x + 1][y]\n", - " velocity_right[x][y] += pressure[x][y] - pressure[x][y - 1]\n", - " # Now we recalculate the pressure for each point \n", - " # based on how pressure was lost to the velocities\n", - " for x in size_x:\n", - " for y in size_y\n", - " pressure[x][y] -= sum(velocities)\n", - "```\n", - "\n", - "\n", - "It's ok if you don't totally understand the algorithm. We can take the same approach as we have before to try to speed up our code." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "num_steps = 100\n", - "scale = 50\n", - "damping= 0.25\n", - "initial_P = 250\n", - "stop_step = 100" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def wave_propogation(num_steps, scale, damping, initial_P, stop_step):\n", - " from math import pi, sin\n", - " omega = 3 / (2 * pi)\n", - " \n", - " size_x = 2 * scale + 1\n", - " size_y = 2 * scale + 1\n", - "\n", - " # V velocity\n", - " # P presure\n", - " # Initialization\n", - " P = [[0.0 for x in range(size_x)] for y in range(size_y)]\n", - " V = [[[0.0, 0.0, 0.0, 0.0] for x in range(size_x)] for y in range(size_y)]\n", - " P[scale][scale] = initial_P\n", - " for step in range(num_steps):\n", - " if step <= stop_step:\n", - " P[scale][scale] = initial_P * sin(omega * step)\n", - " for i in range(size_y):\n", - " for j in range(size_x):\n", - " V[i][j][0] = V[i][j][0] + P[i][j] - P[i - 1][j] if i > 0 else P[i][j]\n", - " V[i][j][1] = (V[i][j][1] + P[i][j] - P[i][j + 1] if j < size_x - 1 else P[i][j])\n", - " V[i][j][2] = (V[i][j][2] + P[i][j] - P[i + 1][j] if i < size_y - 1 else P[i][j])\n", - " V[i][j][3] = V[i][j][3] + P[i][j] - P[i][j - 1] if j > 0 else P[i][j]\n", - "\n", - " for i in range(size_y):\n", - " for j in range(size_x):\n", - " P[i][j] -= 0.5 * damping * sum(V[i][j])\n", - " return P" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.figure(figsize=(10,10))\n", - "start = time.time()\n", - "pressure = wave_propogation(num_steps,scale,damping,initial_P,stop_step)\n", - "stop = time.time()\n", - "print(f\"{stop - start:.2f} Sec, {num_steps / (stop - start):.2f} Hz\")\n", - "plt.imshow(pressure,cmap='viridis_r',interpolation='bilinear')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### What if my code is already in c/c++?\n", - "\n", - "#### Cython can take also take functions written in c/c++ and wrap them for use in python. Here is the same algorithm but now written in c++. \n", - "\n", - "Let's assume someone gave you this code or you found it online and you want to use it in your python program.\n", - "\n", - "\n", - "More details and the build systems for these examples are in the cython_tutorials folder." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### wp.cpp\n", - "\n", - "
#include \"wp.hpp\"\n",
-    "\n",
-    "void wave_propogation_single_core(int num_steps, int scale, float damping,\n",
-    "                                  float initial_P, int stop_step, float *_P) {\n",
-    "\n",
-    "  float omega = 3.0 / (2.0 * M_PI);\n",
-    "  int size_x = 2 * scale + 1;\n",
-    "  int size_y = 2 * scale + 1;\n",
-    "\n",
-    "  int i = 0;\n",
-    "  int j = 0;\n",
-    "  int k = 0;\n",
-    "  int step = 0;\n",
-    "  float P[size_x][size_y];\n",
-    "\n",
-    "  float V[size_x][size_y][4];\n",
-    "\n",
-    "  for (i = 0; i < size_x; i++) {\n",
-    "    for (j = 0; j < size_y; j++) {\n",
-    "      P[i][j] = 0.0;\n",
-    "      for (k = 0; k < 4; k++)\n",
-    "        V[i][j][k] = 0.0;\n",
-    "    }\n",
-    "  }\n",
-    "\n",
-    "  P[scale][scale] = initial_P;\n",
-    "  for (step = 0; step < num_steps; step++) {\n",
-    "    if (step <= stop_step)\n",
-    "      P[scale][scale] = initial_P * sin(omega * step);\n",
-    "    for (i = 0; i < size_x; i++) {\n",
-    "      for (j = 0; j < size_y; j++) {\n",
-    "        V[i][j][0] = (i > 0 ? V[i][j][0] + P[i][j] - P[i - 1][j] : P[i][j]);\n",
-    "        V[i][j][1] = (j < size_x - 1 ? V[i][j][1] + P[i][j] - P[i][j + 1] : P[i][j]);\n",
-    "        V[i][j][2] = (i < size_y - 1 ? V[i][j][2] + P[i][j] - P[i + 1][j] : P[i][j]);\n",
-    "        V[i][j][3] = (j > 0 ? V[i][j][3] + P[i][j] - P[i][j - 1] : P[i][j]);\n",
-    "      }\n",
-    "    }\n",
-    "\n",
-    "    for (i = 0; i < size_x; i++) {\n",
-    "      for (j = 0; j < size_y; j++) {\n",
-    "        P[i][j] -= 0.5 * damping * (V[i][j][0] + V[i][j][1] + V[i][j][2] + V[i][j][3]);\n",
-    "      }\n",
-    "    }\n",
-    "  }\n",
-    "\n",
-    "  // Then we copy from out array P into out output pointer _P\n",
-    "  for (i = 0; i < size_x; i++) {\n",
-    "    for (j = 0; j < size_y; j++) {\n",
-    "      _P[i * size_x + j] = P[i][j];\n",
-    "    }\n",
-    "  }\n",
-    "\n",
-    "}
\n", - "\n", - "\n", - "#### Now we can just write a wrapper in cython.\n", - "\n", - "\n", - "### wave_propogation.pyx\n", - "\n", - "\n", - "
from libc.stdlib cimport malloc, free\n",
-    "from cpython.mem cimport PyMem_Malloc\n",
-    "import numpy as np\n",
-    "import time\n",
-    "cimport cython\n",
-    "from cython.view cimport array as cvarray\n",
-    "\n",
-    "cdef extern from \"wp.hpp\":\n",
-    "  void wave_propogation_single_core(int, int, float, float, int, float*)\n",
-    "\n",
-    "@cython.boundscheck(False)\n",
-    "@cython.cdivision(True)\n",
-    "@cython.wraparound(False)\n",
-    "@cython.infer_types(False)\n",
-    "def wave_propogation(int num_steps, int scale, float damping, float initial_P, int stop_step):\n",
-    "  cdef int size_x = 2 * scale + 1\n",
-    "  cdef int size_y = 2 * scale + 1\n",
-    "  cdef int i = 0\n",
-    "  cdef int j = 0\n",
-    "  cdef float *array = <float *> malloc(sizeof(float) * size_x * size_y)\n",
-    "  wave_propogation_single_core(num_steps, scale, damping, initial_P, stop_step, array)\n",
-    "  P = [[0.0 for x in range(size_x)] for y in range(size_y)]\n",
-    "\n",
-    "  for i in range(size_x):\n",
-    "    for j in range(size_y):\n",
-    "      P[i][j] = array[i*size_x+j] if not np.isnan(array[i*size_x+j]) else 0.0\n",
-    "  return P
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# And then build it\n", - "!cd src/cpp && rm -rf build && mkdir build && cd build && cmake .. && make -j2" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Then import it just like a regular python module\n", - "# The extra .'s are because the module is built in the folder cython_tutorial/chapter_3_cpp/build\n", - "# If you place the wave_propogation.so file in your python path you can import with:\n", - "# import wave_propogation\n", - "import src.cpp.build.wave_propogation as wave_propogation\n", - "\n", - "plt.figure(figsize=(10,10))\n", - "start = time.time()\n", - "pressure = wave_propogation.wave_propogation(num_steps,scale,damping,initial_P,stop_step)\n", - "stop = time.time()\n", - "print(f\"{stop - start:.2f} Sec, {num_steps / (stop - start):.2f} Hz\")\n", - "plt.imshow(pressure,cmap='viridis_r',interpolation='bilinear')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## And similarly with fortran...\n", - "\n", - "\n", - "
SUBROUTINE wave_propogation(num_steps, scale, damping, initial_P, stop_step, P)\n",
-    "  IMPLICIT NONE\n",
-    "  INTEGER size_x,size_Y,i,j,k,step\n",
-    "  REAL PI, omega\n",
-    "  INTEGER, INTENT(in)  :: num_steps, scale, stop_step !input\n",
-    "  REAL, INTENT(in) :: damping, initial_P !input\n",
-    "  REAL, INTENT(out) :: P(2 * scale + 1,2 * scale + 1) ! output\n",
-    "  REAL, DIMENSION(2 * scale + 1,2 * scale + 1,4) :: V\n",
-    "\n",
-    "  size_x = 2 * scale + 1\n",
-    "  size_Y = 2 * scale + 1\n",
-    "\n",
-    "  PI = 3.14159\n",
-    "  omega = 3.0 / (2.0 * PI)\n",
-    "\n",
-    "  DO k=1,4\n",
-    "     DO j=1,size_x\n",
-    "        DO i=1,size_x\n",
-    "           P(i,j) = 0.0\n",
-    "           V(i,j,k) = 0.0\n",
-    "        END DO\n",
-    "     END DO\n",
-    "  END DO\n",
-    "\n",
-    "  P(scale,scale) = initial_P\n",
-    "\n",
-    "  DO step = 1,num_steps\n",
-    "     IF(step <= stop_step) THEN\n",
-    "        P(scale,scale) = initial_P * SIN(omega * step)\n",
-    "     ENDIF\n",
-    "\n",
-    "     DO j=1,size_x\n",
-    "        DO i=1,size_x\n",
-    "           V(i,j,1) = MERGE(V(i,j,1) + P(i,j) - P(i - 1,j), P(i,j), i > 1)\n",
-    "           V(i,j,2) = MERGE(V(i,j,2) + P(i,j) - P(i,j + 1), P(i,j), j < size_x - 1)\n",
-    "           V(i,j,3) = MERGE(V(i,j,3) + P(i,j) - P(i + 1,j), P(i,j), i < size_y - 1)\n",
-    "           V(i,j,4) = MERGE(V(i,j,4) + P(i,j) - P(i,j - 1), P(i,j), j > 1)\n",
-    "        END DO\n",
-    "     END DO\n",
-    "\n",
-    "     DO j=1,size_x\n",
-    "        DO i=1,size_x\n",
-    "           P(i,j) = P(i,j) - 0.5 * damping * (V(i,j,1) + V(i,j,2) + V(i,j,3) + V(i,j,4))\n",
-    "        END DO\n",
-    "     END DO\n",
-    "  END DO\n",
-    "\n",
-    "END SUBROUTINE wave_propogation
\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
from libc.stdlib cimport malloc, free\n",
-    "from cpython.mem cimport PyMem_Malloc\n",
-    "import numpy as np\n",
-    "import time\n",
-    "cimport cython\n",
-    "from cython.view cimport array as cvarray\n",
-    "\n",
-    "cdef extern from \"wp.hpp\":\n",
-    "  void wave_propogation_(int*, int*, float*, float*, int*, float*)\n",
-    "\n",
-    "@cython.boundscheck(False)\n",
-    "@cython.cdivision(True)\n",
-    "@cython.wraparound(False)\n",
-    "@cython.infer_types(False)\n",
-    "def wave_propogation(int num_steps, int scale=100,float damping=0.25, float initial_P=250.0, int stop_step=100):\n",
-    "  cdef int size_x = 2 * scale + 1\n",
-    "  cdef int size_y = 2 * scale + 1\n",
-    "  cdef int *_num_steps=&num_steps\n",
-    "  cdef int *_scale=&scale\n",
-    "  cdef float *_damping=&damping\n",
-    "  cdef float *_initial_P=&initial_P\n",
-    "  cdef int *_stop_step=&stop_step\n",
-    "\n",
-    "  cdef float *array = <float *> malloc(sizeof(float) * size_x * size_y)\n",
-    "  wave_propogation_(_num_steps, _scale, _damping, _initial_P, _stop_step, array)\n",
-    "  P = [[0.0 for x in range(size_x)] for y in range(size_y)]\n",
-    "  for i in range(size_x):\n",
-    "    for j in range(size_y):\n",
-    "      P[i][j] = array[i*size_x+j] if not np.isnan(array[i*size_x+j]) else 0.0\n",
-    "  return P
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# And then build it\n", - "!cd src/fortran && rm -rf build && mkdir build && cd build && cmake .. && make -j2" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Then import it just like a regular python module\n", - "import src.fortran.build.wave_propogation as wave_propogation\n", - "\n", - "plt.figure(figsize=(10,10))\n", - "start = time.time()\n", - "pressure = wave_propogation.wave_propogation(num_steps,scale,damping,initial_P,stop_step)\n", - "stop = time.time()\n", - "print(f\"{stop - start:.2f} Sec, {num_steps / (stop - start):.2f} Hz\")\n", - "plt.imshow(pressure,cmap='viridis_r',interpolation='bilinear')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Further reading on getting C/C++/Fortran working can be found at: https://github.com/tylern4/cython_tutorial\n", - "\n", - "There are instructions and build files in each folder for how to get each sections working individually." - ] - } - ], - "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": "3.7.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/Day_3/fractal.pyx b/Day_3/fractal.pyx deleted file mode 100644 index 45a238d..0000000 --- a/Day_3/fractal.pyx +++ /dev/null @@ -1,42 +0,0 @@ -# ^ remove these two lines %%cython has to be the first line -%%cython -a - -cimport cython - -cdef extern from "complex.h": - double cabs(double complex) - -# color function for point at (x, y) -cdef unsigned int mandel_cython(float x, float y, unsigned int max_iters): - cdef double complex c, z - c = x + y*1j - z = 0.0j - for i in range(max_iters): - z = z*z + c - if cabs(z) >= 2: - return i - return max_iters - -@cython.boundscheck(False) -@cython.wraparound(False) -@cython.cdivision(True) -def create_fractal_cython(float xmin, float xmax, float ymin, float ymax, unsigned int[:, :] image, int iters): - - cdef int x, y - cdef int height, width - cdef double pixel_size_x, pixel_size_y - cdef double real, imag - cdef unsigned int color - - height = image.shape[0] - width = image.shape[1] - - pixel_size_x = (xmax - xmin)/width - pixel_size_y = (ymax - ymin)/height - - for x in range(width): - real = xmin + x*pixel_size_x - for y in range(height): - imag = ymin + y*pixel_size_y - color = mandel_cython(real, imag, iters) - image[y, x] = color diff --git a/Day_3/get_primes.pyx b/Day_3/get_primes.pyx deleted file mode 100644 index 4b7b47a..0000000 --- a/Day_3/get_primes.pyx +++ /dev/null @@ -1,22 +0,0 @@ -# ^ remove these two lines %%cython has to be the first line -%%cython -a - -cimport cython - -@cython.cdivision(True) -def getPrimes_cy(Py_ssize_t max_num): - primes = [2] - cdef int possiblePrime = 0 - cdef int num = 0 - # Hint: Do we need to loop over every number?? - # What do we know about primes that could help us - for possiblePrime in range(3, max_num, 2): - # Assume number is prime until shown it is not. - isPrime = True - for num in range(3, possiblePrime): - if possiblePrime % num == 0: - isPrime = False - if isPrime: - primes.append(possiblePrime) - - return primes diff --git a/Day_3/get_primes_mp.py b/Day_3/get_primes_mp.py deleted file mode 100644 index 0db3840..0000000 --- a/Day_3/get_primes_mp.py +++ /dev/null @@ -1,20 +0,0 @@ -def is_prime(possiblePrime): - isPrime = True - for num in range(2, possiblePrime): - if possiblePrime % num == 0: - isPrime = False - return isPrime - - -def getPrimes_mp(max_num): - primes = [] - # Use mp to make a list of true and false values - with mp.Pool() as pool: - __prime = pool.map(is_prime, range(max_num)) - - # Loop over the numbers to make the list of numbers from the true/false list - for possiblePrime in range(max_num): - if __prime[possiblePrime]: - primes.append(possiblePrime) - - return primes diff --git a/Day_3/mat_mul_cy_fast.pyx b/Day_3/mat_mul_cy_fast.pyx deleted file mode 100644 index 5a7ea00..0000000 --- a/Day_3/mat_mul_cy_fast.pyx +++ /dev/null @@ -1,17 +0,0 @@ -# ^ remove these two lines %%cython has to be the first line -%%cython -a - -cimport cython - -@cython.boundscheck(False) -@cython.wraparound(False) -@cython.cdivision(True) -def mat_mul_cy_fast(float[:,:] A, float[:,:] B, float[:,:] C): - cdef int n = A.shape[0] - cdef int i,j,k - cdef float a - for i in range(n): - for k in range(n): - a = A[i,k] - for j in range(n): - C[i,j] += (a * B[k,j]) diff --git a/Day_3/pi.pyx b/Day_3/pi.pyx deleted file mode 100644 index 92e1d62..0000000 --- a/Day_3/pi.pyx +++ /dev/null @@ -1,24 +0,0 @@ -# ^ remove these two lines %%cython has to be the first line -%%cython -a -cimport cython - -@cython.cdivision(True) -cdef inline double recip_square_cy(Py_ssize_t i): - cdef: - double x, s - x = i*i - s = 1 / x - return s - - -def approx_pi_cy(int n): - """Compute an approximate value of pi.""" - cdef: - int k - double val - val = 0 - for k in range(1, n+1): - x = recip_square_cy(k) - val += x - pi = (6 * val)**.5 - return pi diff --git a/Day_3/slow_lib/__init__.py b/Day_3/slow_lib/__init__.py deleted file mode 100644 index 68bcbce..0000000 --- a/Day_3/slow_lib/__init__.py +++ /dev/null @@ -1,16 +0,0 @@ -import numpy as np -import time -import hashlib - - -def slow_random(val): - val = str(val).encode() - np.random.seed(seed=len(val)) - rand = np.random.normal(loc=0.0, scale=1.0) - time.sleep(np.abs(rand)) - return hashlib.md5(val).hexdigest() - - -def fast_random(val): - val = str(val).encode() - return hashlib.md5(val).hexdigest() diff --git a/Day_3/src/cpp/CMakeLists.txt b/Day_3/src/cpp/CMakeLists.txt deleted file mode 100644 index bab52d0..0000000 --- a/Day_3/src/cpp/CMakeLists.txt +++ /dev/null @@ -1,31 +0,0 @@ -cmake_minimum_required(VERSION 3.5) -project(wave_propogation C CXX) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) -set(VERSION 1.0.0) - -set(CMAKE_CXX_STANDARD 17) -set(CMAKE_MODULE_PATH - ${CMAKE_MODULE_PATH} - ${PROJECT_SOURCE_DIR}/cmake) - -set(CMAKE_C_FLAGS "-Ofast") -set(CMAKE_CXX_FLAGS "-Ofast") - -find_package(Cython REQUIRED) -find_package(PythonInterp REQUIRED) -find_package(PythonLibs REQUIRED) -find_package(PythonExtensions REQUIRED) -find_package(NumPy REQUIRED) - -include_directories(${PROJECT_SOURCE_DIR}) - - -add_library(wave_propogation_static STATIC wp.cpp) - -add_cython_target(wave_propogation wave_propogation.pyx CXX PY3 OUTPUT_VAR _wave_propogation) -include_directories(${PYTHON_INCLUDE_DIRS} ${NumPy_INCLUDE_DIR}) -add_library(wave_propogation MODULE ${_wave_propogation}) -python_extension_module(wave_propogation) -target_link_libraries(wave_propogation wave_propogation_static ${PYTHON_LIBRARIES}) - -install(TARGETS wave_propogation LIBRARY DESTINATION .) diff --git a/Day_3/src/cpp/cmake/FindCython.cmake b/Day_3/src/cpp/cmake/FindCython.cmake deleted file mode 100644 index b8ae305..0000000 --- a/Day_3/src/cpp/cmake/FindCython.cmake +++ /dev/null @@ -1,78 +0,0 @@ -#.rst: -# -# Find ``cython`` executable. -# -# This module will set the following variables in your project: -# -# ``CYTHON_EXECUTABLE`` -# path to the ``cython`` program -# -# ``CYTHON_VERSION`` -# version of ``cython`` -# -# ``CYTHON_FOUND`` -# true if the program was found -# -# For more information on the Cython project, see http://cython.org/. -# -# *Cython is a language that makes writing C extensions for the Python language -# as easy as Python itself.* -# -#============================================================================= -# Copyright 2011 Kitware, Inc. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -#============================================================================= - -# Use the Cython executable that lives next to the Python executable -# if it is a local installation. -find_package(PythonInterp) -if(PYTHONINTERP_FOUND) - get_filename_component(_python_path ${PYTHON_EXECUTABLE} PATH) - find_program(CYTHON_EXECUTABLE - NAMES cython cython.bat cython3 - HINTS ${_python_path} - DOC "path to the cython executable") -else() - find_program(CYTHON_EXECUTABLE - NAMES cython cython.bat cython3 - DOC "path to the cython executable") -endif() - -if(CYTHON_EXECUTABLE) - set(CYTHON_version_command ${CYTHON_EXECUTABLE} --version) - - execute_process(COMMAND ${CYTHON_version_command} - OUTPUT_VARIABLE CYTHON_version_output - ERROR_VARIABLE CYTHON_version_error - RESULT_VARIABLE CYTHON_version_result - OUTPUT_STRIP_TRAILING_WHITESPACE) - - if(NOT ${CYTHON_version_result} EQUAL 0) - set(_error_msg "Command \"${CYTHON_version_command}\" failed with") - set(_error_msg "${_error_msg} output:\n${CYTHON_version_error}") - message(SEND_ERROR "${_error_msg}") - else() - if("${CYTHON_version_output}" MATCHES "^[Cc]ython version ([^,]+)") - set(CYTHON_VERSION "${CMAKE_MATCH_1}") - endif() - endif() -endif() - -include(FindPackageHandleStandardArgs) -FIND_PACKAGE_HANDLE_STANDARD_ARGS(Cython REQUIRED_VARS CYTHON_EXECUTABLE) - -mark_as_advanced(CYTHON_EXECUTABLE) - -include(UseCython) - diff --git a/Day_3/src/cpp/cmake/FindNumPy.cmake b/Day_3/src/cpp/cmake/FindNumPy.cmake deleted file mode 100644 index 3cd23c3..0000000 --- a/Day_3/src/cpp/cmake/FindNumPy.cmake +++ /dev/null @@ -1,63 +0,0 @@ -#.rst: -# -# Find the include directory for numpy/arrayobject.h -# -# This module sets the following variables: -# -# ``NumPy_FOUND`` -# True if NumPy was found. -# ``NumPy_INCLUDE_DIR`` -# The include directories needed to use NumpPy. -# ``NumPy_VERSION`` -# The version of NumPy found. -# -# -# The module will also explicitly define one cache variable: -# -# ``NumPy_INCLUDE_DIR`` -# - -if(NOT NumPy_FOUND) - set(_find_extra_args) - if(NumPy_FIND_REQUIRED) - list(APPEND _find_extra_args REQUIRED) - endif() - if(NumPy_FIND_QUIET) - list(APPEND _find_extra_args QUIET) - endif() - find_package(PythonInterp ${_find_extra_args}) - find_package(PythonLibs ${_find_extra_args}) - - if(PYTHON_EXECUTABLE) - execute_process(COMMAND "${PYTHON_EXECUTABLE}" - -c "import numpy; print(numpy.get_include())" - OUTPUT_VARIABLE _numpy_include_dir - OUTPUT_STRIP_TRAILING_WHITESPACE - ERROR_QUIET - ) - execute_process(COMMAND "${PYTHON_EXECUTABLE}" - -c "import numpy; print(numpy.__version__)" - OUTPUT_VARIABLE NumPy_VERSION - OUTPUT_STRIP_TRAILING_WHITESPACE - ERROR_QUIET - ) - endif() -endif() - -find_path(NumPy_INCLUDE_DIR - numpy/arrayobject.h - PATHS "${_numpy_include_dir}" "${PYTHON_INCLUDE_DIR}" - PATH_SUFFIXES numpy/core/include - ) - -set(NumPy_INCLUDE_DIRS ${NumPy_INCLUDE_DIR}) - -# handle the QUIETLY and REQUIRED arguments and set NumPy_FOUND to TRUE if -# all listed variables are TRUE -include(FindPackageHandleStandardArgs) -find_package_handle_standard_args(NumPy - REQUIRED_VARS NumPy_INCLUDE_DIR - VERSION_VAR NumPy_VERSION - ) - -mark_as_advanced(NumPy_INCLUDE_DIR) diff --git a/Day_3/src/cpp/cmake/FindPythonExtensions.cmake b/Day_3/src/cpp/cmake/FindPythonExtensions.cmake deleted file mode 100644 index b3aa206..0000000 --- a/Day_3/src/cpp/cmake/FindPythonExtensions.cmake +++ /dev/null @@ -1,538 +0,0 @@ -#.rst: -# -# This module defines CMake functions to build Python extension modules and -# stand-alone executables. -# -# The following variables are defined: -# :: -# -# PYTHON_PREFIX - absolute path to the current Python -# distribution's prefix -# PYTHON_SITE_PACKAGES_DIR - absolute path to the current Python -# distribution's site-packages directory -# PYTHON_RELATIVE_SITE_PACKAGES_DIR - path to the current Python -# distribution's site-packages directory -# relative to its prefix -# PYTHON_SEPARATOR - separator string for file path -# components. Equivalent to ``os.sep`` in -# Python. -# PYTHON_PATH_SEPARATOR - separator string for PATH-style -# environment variables. Equivalent to -# ``os.pathsep`` in Python. -# -# -# The following functions are defined: -# -# .. cmake:command:: python_extension_module -# -# For libraries meant to be used as Python extension modules, either dynamically -# loaded or directly linked. Amend the configuration of the library target -# (created using ``add_library``) with additional options needed to build and -# use the referenced library as a Python extension module. -# -# python_extension_module( -# [LINKED_MODULES_VAR ] -# [FORWARD_DECL_MODULES_VAR ] -# [MODULE_SUFFIX ]) -# -# Only extension modules that are configured to be built as MODULE libraries can -# be runtime-loaded through the standard Python import mechanism. All other -# modules can only be included in standalone applications that are written to -# expect their presence. In addition to being linked against the libraries for -# these modules, such applications must forward declare their entry points and -# initialize them prior to use. To generate these forward declarations and -# initializations, see ``python_modules_header``. -# -# If ```` does not refer to a target, then it is assumed to refer to an -# extension module that is not linked at all, but compiled along with other -# source files directly into an executable. Adding these modules does not cause -# any library configuration modifications, and they are not added to the list of -# linked modules. They still must be forward declared and initialized, however, -# and so are added to the forward declared modules list. -# -# Options: -# -# ``LINKED_MODULES_VAR `` -# Name of the variable referencing a list of extension modules whose libraries -# must be linked into the executables of any stand-alone applications that use -# them. By default, the global property ``PY_LINKED_MODULES_LIST`` is used. -# -# ``FORWARD_DECL_MODULES_VAR `` -# Name of the variable referencing a list of extension modules whose entry -# points must be forward declared and called by any stand-alone applications -# that use them. By default, the global property -# ``PY_FORWARD_DECL_MODULES_LIST`` is used. -# -# ``MODULE_SUFFIX `` -# Suffix appended to the python extension module file. -# The default suffix is retrieved using ``sysconfig.get_config_var("SO")"``, -# if not available, the default is then ``.so`` on unix and ``.pyd`` on -# windows. -# Setting the variable ``PYTHON_EXTENSION_MODULE_SUFFIX`` in the caller -# scope defines the value used for all extensions not having a suffix -# explicitly specified using ``MODULE_SUFFIX`` parameter. -# -# -# .. cmake:command:: python_standalone_executable -# -# python_standalone_executable() -# -# For standalone executables that initialize their own Python runtime -# (such as when building source files that include one generated by Cython with -# the --embed option). Amend the configuration of the executable target -# (created using ``add_executable``) with additional options needed to properly -# build the referenced executable. -# -# -# .. cmake:command:: python_modules_header -# -# Generate a header file that contains the forward declarations and -# initialization routines for the given list of Python extension modules. -# ```` is the logical name for the header file (no file extensions). -# ```` is the actual destination filename for the header file -# (e.g.: decl_modules.h). -# -# python_modules_header( [HeaderFilename] -# [FORWARD_DECL_MODULES_LIST ] -# [HEADER_OUTPUT_VAR ] -# [INCLUDE_DIR_OUTPUT_VAR ]) -# -# If only ```` is provided, and it ends in the ".h" extension, then it -# is assumed to be the ````. The filename of the header file -# without the extension is used as the logical name. If only ```` is -# provided, and it does not end in the ".h" extension, then the -# ```` is assumed to ``.h``. -# -# The exact contents of the generated header file depend on the logical -# ````. It should be set to a value that corresponds to the target -# application, or for the case of multiple applications, some identifier that -# conveyes its purpose. It is featured in the generated multiple inclusion -# guard as well as the names of the generated initialization routines. -# -# The generated header file includes forward declarations for all listed -# modules, as well as implementations for the following class of routines: -# -# ``int _(void)`` -# Initializes the python extension module, ````. Returns an integer -# handle to the module. -# -# ``void _LoadAllPythonModules(void)`` -# Initializes all listed python extension modules. -# -# ``void CMakeLoadAllPythonModules(void);`` -# Alias for ``_LoadAllPythonModules`` whose name does not depend on -# ````. This function is excluded during preprocessing if the -# preprocessing macro ``EXCLUDE_LOAD_ALL_FUNCTION`` is defined. -# -# ``void Py_Initialize_Wrapper();`` -# Wrapper arpund ``Py_Initialize()`` that initializes all listed python -# extension modules. This function is excluded during preprocessing if the -# preprocessing macro ``EXCLUDE_PY_INIT_WRAPPER`` is defined. If this -# function is generated, then ``Py_Initialize()`` is redefined to a macro -# that calls this function. -# -# Options: -# -# ``FORWARD_DECL_MODULES_LIST `` -# List of extension modules for which to generate forward declarations of -# their entry points and their initializations. By default, the global -# property ``PY_FORWARD_DECL_MODULES_LIST`` is used. -# -# ``HEADER_OUTPUT_VAR `` -# Name of the variable to set to the path to the generated header file. By -# default, ```` is used. -# -# ``INCLUDE_DIR_OUTPUT_VAR `` -# Name of the variable to set to the path to the directory containing the -# generated header file. By default, ``_INCLUDE_DIRS`` is used. -# -# Defined variables: -# -# ```` -# The path to the generated header file -# -# ```` -# Directory containing the generated header file -# -# -# Example usage -# ^^^^^^^^^^^^^ -# -# .. code-block:: cmake -# -# find_package(PythonInterp) -# find_package(PythonLibs) -# find_package(PythonExtensions) -# find_package(Cython) -# find_package(Boost COMPONENTS python) -# -# # Simple Cython Module -- no executables -# add_cython_target(_module.pyx) -# add_library(_module MODULE ${_module}) -# python_extension_module(_module) -# -# # Mix of Cython-generated code and C++ code using Boost Python -# # Stand-alone executable -- no modules -# include_directories(${Boost_INCLUDE_DIRS}) -# add_cython_target(main.pyx CXX EMBED_MAIN) -# add_executable(main boost_python_module.cxx ${main}) -# target_link_libraries(main ${Boost_LIBRARIES}) -# python_standalone_executable(main) -# -# # stand-alone executable with three extension modules: -# # one statically linked, one dynamically linked, and one loaded at runtime -# # -# # Freely mixes Cython-generated code, code using Boost-Python, and -# # hand-written code using the CPython API. -# -# # module1 -- statically linked -# add_cython_target(module1.pyx) -# add_library(module1 STATIC ${module1}) -# python_extension_module(module1 -# LINKED_MODULES_VAR linked_module_list -# FORWARD_DECL_MODULES_VAR fdecl_module_list) -# -# # module2 -- dynamically linked -# include_directories({Boost_INCLUDE_DIRS}) -# add_library(module2 SHARED boost_module2.cxx) -# target_link_libraries(module2 ${Boost_LIBRARIES}) -# python_extension_module(module2 -# LINKED_MODULES_VAR linked_module_list -# FORWARD_DECL_MODULES_VAR fdecl_module_list) -# -# # module3 -- loaded at runtime -# add_cython_target(module3a.pyx) -# add_library(module1 MODULE ${module3a} module3b.cxx) -# target_link_libraries(module3 ${Boost_LIBRARIES}) -# python_extension_module(module3 -# LINKED_MODULES_VAR linked_module_list -# FORWARD_DECL_MODULES_VAR fdecl_module_list) -# -# # application executable -- generated header file + other source files -# python_modules_header(modules -# FORWARD_DECL_MODULES_LIST ${fdecl_module_list}) -# include_directories(${modules_INCLUDE_DIRS}) -# -# add_cython_target(mainA) -# add_cython_target(mainC) -# add_executable(main ${mainA} mainB.cxx ${mainC} mainD.c) -# -# target_link_libraries(main ${linked_module_list} ${Boost_LIBRARIES}) -# python_standalone_executable(main) -# -#============================================================================= -# Copyright 2011 Kitware, Inc. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -#============================================================================= - -find_package(PythonInterp REQUIRED) -find_package(PythonLibs) -include(targetLinkLibrariesWithDynamicLookup) - -set(_command " -import distutils.sysconfig -import itertools -import os -import os.path -import site -import sys -import sysconfig - -result = None -rel_result = None -candidate_lists = [] - -try: - candidate_lists.append((distutils.sysconfig.get_python_lib(),)) -except AttributeError: pass - -try: - candidate_lists.append(site.getsitepackages()) -except AttributeError: pass - -try: - candidate_lists.append((site.getusersitepackages(),)) -except AttributeError: pass - -candidates = itertools.chain.from_iterable(candidate_lists) - -for candidate in candidates: - rel_candidate = os.path.relpath( - candidate, sys.prefix) - if not rel_candidate.startswith(\"..\"): - result = candidate - rel_result = rel_candidate - break - -sys.stdout.write(\";\".join(( - os.sep, - os.pathsep, - sys.prefix, - result, - rel_result, - sysconfig.get_config_var('EXT_SUFFIX') -))) -") - -execute_process(COMMAND "${PYTHON_EXECUTABLE}" -c "${_command}" - OUTPUT_VARIABLE _list - RESULT_VARIABLE _result) - -list(GET _list 0 _item) -set(PYTHON_SEPARATOR "${_item}") -mark_as_advanced(PYTHON_SEPARATOR) - -list(GET _list 1 _item) -set(PYTHON_PATH_SEPARATOR "${_item}") -mark_as_advanced(PYTHON_PATH_SEPARATOR) - -list(GET _list 2 _item) -set(PYTHON_PREFIX "${_item}") -mark_as_advanced(PYTHON_PREFIX) - -list(GET _list 3 _item) -set(PYTHON_SITE_PACKAGES_DIR "${_item}") -mark_as_advanced(PYTHON_SITE_PACKAGES_DIR) - -list(GET _list 4 _item) -set(PYTHON_RELATIVE_SITE_PACKAGES_DIR "${_item}") -mark_as_advanced(PYTHON_RELATIVE_SITE_PACKAGES_DIR) - -if(NOT DEFINED PYTHON_EXTENSION_MODULE_SUFFIX) - list(GET _list 5 _item) - set(PYTHON_EXTENSION_MODULE_SUFFIX "${_item}") -endif() - -function(python_extension_module _target) - set(one_ops LINKED_MODULES_VAR FORWARD_DECL_MODULES_VAR MODULE_SUFFIX) - cmake_parse_arguments(_args "" "${one_ops}" "" ${ARGN}) - - set(_lib_type "NA") - if(TARGET ${_target}) - get_property(_lib_type TARGET ${_target} PROPERTY TYPE) - endif() - - set(_is_non_lib TRUE) - - set(_is_static_lib FALSE) - if(_lib_type STREQUAL "STATIC_LIBRARY") - set(_is_static_lib TRUE) - set(_is_non_lib FALSE) - endif() - - set(_is_shared_lib FALSE) - if(_lib_type STREQUAL "SHARED_LIBRARY") - set(_is_shared_lib TRUE) - set(_is_non_lib FALSE) - endif() - - set(_is_module_lib FALSE) - if(_lib_type STREQUAL "MODULE_LIBRARY") - set(_is_module_lib TRUE) - set(_is_non_lib FALSE) - endif() - - if(_is_static_lib OR _is_shared_lib OR _is_non_lib) - - if(_is_static_lib OR _is_shared_lib) - if(_args_LINKED_MODULES_VAR) - set(${_args_LINKED_MODULES_VAR} - ${${_args_LINKED_MODULES_VAR}} ${_target} PARENT_SCOPE) - else() - set_property(GLOBAL APPEND PROPERTY PY_LINKED_MODULES_LIST ${_target}) - endif() - endif() - - if(_args_FORWARD_DECL_MODULES_VAR) - set(${_args_FORWARD_DECL_MODULES_VAR} - ${${_args_FORWARD_DECL_MODULES_VAR}} ${_target} PARENT_SCOPE) - else() - set_property(GLOBAL APPEND PROPERTY - PY_FORWARD_DECL_MODULES_LIST ${_target}) - endif() - endif() - - if(NOT _is_non_lib) - include_directories("${PYTHON_INCLUDE_DIRS}") - endif() - - if(_is_module_lib) - set_target_properties(${_target} PROPERTIES - PREFIX "${PYTHON_MODULE_PREFIX}") - endif() - - if(_is_module_lib OR _is_shared_lib) - if(_is_module_lib) - - if(NOT _args_MODULE_SUFFIX) - set(_args_MODULE_SUFFIX "${PYTHON_EXTENSION_MODULE_SUFFIX}") - endif() - - if(_args_MODULE_SUFFIX STREQUAL "" AND WIN32 AND NOT CYGWIN) - set(_args_MODULE_SUFFIX ".pyd") - endif() - - if(NOT _args_MODULE_SUFFIX STREQUAL "") - set_target_properties(${_target} - PROPERTIES SUFFIX ${_args_MODULE_SUFFIX}) - endif() - endif() - - target_link_libraries_with_dynamic_lookup(${_target} ${PYTHON_LIBRARIES}) - endif() -endfunction() - -function(python_standalone_executable _target) - include_directories(${PYTHON_INCLUDE_DIRS}) - target_link_libraries(${_target} ${PYTHON_LIBRARIES}) -endfunction() - -function(python_modules_header _name) - set(one_ops FORWARD_DECL_MODULES_LIST - HEADER_OUTPUT_VAR - INCLUDE_DIR_OUTPUT_VAR) - cmake_parse_arguments(_args "" "${one_ops}" "" ${ARGN}) - - list(GET _args_UNPARSED_ARGUMENTS 0 _arg0) - # if present, use arg0 as the input file path - if(_arg0) - set(_source_file ${_arg0}) - - # otherwise, must determine source file from name, or vice versa - else() - get_filename_component(_name_ext "${_name}" EXT) - - # if extension provided, _name is the source file - if(_name_ext) - set(_source_file ${_name}) - get_filename_component(_name "${_source_file}" NAME_WE) - - # otherwise, assume the source file is ${_name}.h - else() - set(_source_file ${_name}.h) - endif() - endif() - - if(_args_FORWARD_DECL_MODULES_LIST) - set(static_mod_list ${_args_FORWARD_DECL_MODULES_LIST}) - else() - get_property(static_mod_list GLOBAL PROPERTY PY_FORWARD_DECL_MODULES_LIST) - endif() - - string(REPLACE "." "_" _header_name "${_name}") - string(TOUPPER ${_header_name} _header_name_upper) - set(_header_name_upper "_${_header_name_upper}_H") - set(generated_file ${CMAKE_CURRENT_BINARY_DIR}/${_source_file}) - - set(generated_file_tmp "${generated_file}.in") - file(WRITE ${generated_file_tmp} - "/* Created by CMake. DO NOT EDIT; changes will be lost. */\n") - - set(_chunk "") - set(_chunk "${_chunk}#ifndef ${_header_name_upper}\n") - set(_chunk "${_chunk}#define ${_header_name_upper}\n") - set(_chunk "${_chunk}\n") - set(_chunk "${_chunk}#include \n") - set(_chunk "${_chunk}\n") - set(_chunk "${_chunk}#ifdef __cplusplus\n") - set(_chunk "${_chunk}extern \"C\" {\n") - set(_chunk "${_chunk}#endif /* __cplusplus */\n") - set(_chunk "${_chunk}\n") - set(_chunk "${_chunk}#if PY_MAJOR_VERSION < 3\n") - file(APPEND ${generated_file_tmp} "${_chunk}") - - foreach(_module ${static_mod_list}) - file(APPEND ${generated_file_tmp} - "PyMODINIT_FUNC init${PYTHON_MODULE_PREFIX}${_module}(void);\n") - endforeach() - - file(APPEND ${generated_file_tmp} "#else /* PY_MAJOR_VERSION >= 3*/\n") - - foreach(_module ${static_mod_list}) - file(APPEND ${generated_file_tmp} - "PyMODINIT_FUNC PyInit_${PYTHON_MODULE_PREFIX}${_module}(void);\n") - endforeach() - - set(_chunk "") - set(_chunk "${_chunk}#endif /* PY_MAJOR_VERSION >= 3*/\n\n") - set(_chunk "${_chunk}#ifdef __cplusplus\n") - set(_chunk "${_chunk}}\n") - set(_chunk "${_chunk}#endif /* __cplusplus */\n") - set(_chunk "${_chunk}\n") - file(APPEND ${generated_file_tmp} "${_chunk}") - - foreach(_module ${static_mod_list}) - set(_import_function "${_header_name}_${_module}") - set(_prefixed_module "${PYTHON_MODULE_PREFIX}${_module}") - - set(_chunk "") - set(_chunk "${_chunk}int ${_import_function}(void)\n") - set(_chunk "${_chunk}{\n") - set(_chunk "${_chunk} static char name[] = \"${_prefixed_module}\";\n") - set(_chunk "${_chunk} #if PY_MAJOR_VERSION < 3\n") - set(_chunk "${_chunk} return PyImport_AppendInittab(") - set(_chunk "${_chunk}name, init${_prefixed_module});\n") - set(_chunk "${_chunk} #else /* PY_MAJOR_VERSION >= 3 */\n") - set(_chunk "${_chunk} return PyImport_AppendInittab(") - set(_chunk "${_chunk}name, PyInit_${_prefixed_module});\n") - set(_chunk "${_chunk} #endif /* PY_MAJOR_VERSION >= 3 */\n") - set(_chunk "${_chunk}}\n\n") - file(APPEND ${generated_file_tmp} "${_chunk}") - endforeach() - - file(APPEND ${generated_file_tmp} - "void ${_header_name}_LoadAllPythonModules(void)\n{\n") - foreach(_module ${static_mod_list}) - file(APPEND ${generated_file_tmp} " ${_header_name}_${_module}();\n") - endforeach() - file(APPEND ${generated_file_tmp} "}\n\n") - - set(_chunk "") - set(_chunk "${_chunk}#ifndef EXCLUDE_LOAD_ALL_FUNCTION\n") - set(_chunk "${_chunk}void CMakeLoadAllPythonModules(void)\n") - set(_chunk "${_chunk}{\n") - set(_chunk "${_chunk} ${_header_name}_LoadAllPythonModules();\n") - set(_chunk "${_chunk}}\n") - set(_chunk "${_chunk}#endif /* !EXCLUDE_LOAD_ALL_FUNCTION */\n\n") - - set(_chunk "${_chunk}#ifndef EXCLUDE_PY_INIT_WRAPPER\n") - set(_chunk "${_chunk}static void Py_Initialize_Wrapper()\n") - set(_chunk "${_chunk}{\n") - set(_chunk "${_chunk} ${_header_name}_LoadAllPythonModules();\n") - set(_chunk "${_chunk} Py_Initialize();\n") - set(_chunk "${_chunk}}\n") - set(_chunk "${_chunk}#define Py_Initialize Py_Initialize_Wrapper\n") - set(_chunk "${_chunk}#endif /* !EXCLUDE_PY_INIT_WRAPPER */\n\n") - - set(_chunk "${_chunk}#endif /* !${_header_name_upper} */\n") - file(APPEND ${generated_file_tmp} "${_chunk}") - - # with configure_file() cmake complains that you may not use a file created - # using file(WRITE) as input file for configure_file() - execute_process(COMMAND ${CMAKE_COMMAND} -E copy_if_different - "${generated_file_tmp}" "${generated_file}" - OUTPUT_QUIET ERROR_QUIET) - - set(_header_output_var ${_name}) - if(_args_HEADER_OUTPUT_VAR) - set(_header_output_var ${_args_HEADER_OUTPUT_VAR}) - endif() - set(${_header_output_var} ${generated_file} PARENT_SCOPE) - - set(_include_dir_var ${_name}_INCLUDE_DIRS) - if(_args_INCLUDE_DIR_OUTPUT_VAR) - set(_include_dir_var ${_args_INCLUDE_DIR_OUTPUT_VAR}) - endif() - set(${_include_dirs_var} ${CMAKE_CURRENT_BINARY_DIR} PARENT_SCOPE) -endfunction() diff --git a/Day_3/src/cpp/cmake/UseCython.cmake b/Day_3/src/cpp/cmake/UseCython.cmake deleted file mode 100644 index 1c812ea..0000000 --- a/Day_3/src/cpp/cmake/UseCython.cmake +++ /dev/null @@ -1,395 +0,0 @@ -#.rst: -# -# The following functions are defined: -# -# .. cmake:command:: add_cython_target -# -# Create a custom rule to generate the source code for a Python extension module -# using cython. -# -# add_cython_target( [] -# [EMBED_MAIN] -# [C | CXX] -# [PY2 | PY3] -# [OUTPUT_VAR ]) -# -# ```` is the name of the new target, and ```` -# is the path to a cython source file. Note that, despite the name, no new -# targets are created by this function. Instead, see ``OUTPUT_VAR`` for -# retrieving the path to the generated source for subsequent targets. -# -# If only ```` is provided, and it ends in the ".pyx" extension, then it -# is assumed to be the ````. The name of the input without the -# extension is used as the target name. If only ```` is provided, and it -# does not end in the ".pyx" extension, then the ```` is assumed to -# be ``.pyx``. -# -# The Cython include search path is amended with any entries found in the -# ``INCLUDE_DIRECTORIES`` property of the directory containing the -# ```` file. Use ``iunclude_directories`` to add to the Cython -# include search path. -# -# Options: -# -# ``EMBED_MAIN`` -# Embed a main() function in the generated output (for stand-alone -# applications that initialize their own Python runtime). -# -# ``C | CXX`` -# Force the generation of either a C or C++ file. By default, a C file is -# generated, unless the C language is not enabled for the project; in this -# case, a C++ file is generated by default. -# -# ``PY2 | PY3`` -# Force compilation using either Python-2 or Python-3 syntax and code -# semantics. By default, Python-2 syntax and semantics are used if the major -# version of Python found is 2. Otherwise, Python-3 syntax and sematics are -# used. -# -# ``OUTPUT_VAR `` -# Set the variable ```` in the parent scope to the path to the -# generated source file. By default, ```` is used as the output -# variable name. -# -# Defined variables: -# -# ```` -# The path of the generated source file. -# -# Cache variables that effect the behavior include: -# -# ``CYTHON_ANNOTATE`` -# whether to create an annotated .html file when compiling -# -# ``CYTHON_FLAGS`` -# additional flags to pass to the Cython compiler -# -# Example usage -# ^^^^^^^^^^^^^ -# -# .. code-block:: cmake -# -# find_package(Cython) -# -# # Note: In this case, either one of these arguments may be omitted; their -# # value would have been inferred from that of the other. -# add_cython_target(cy_code cy_code.pyx) -# -# add_library(cy_code MODULE ${cy_code}) -# target_link_libraries(cy_code ...) -# -#============================================================================= -# Copyright 2011 Kitware, Inc. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -#============================================================================= - -# Configuration options. -set(CYTHON_ANNOTATE OFF - CACHE BOOL "Create an annotated .html file when compiling *.pyx.") - -set(CYTHON_FLAGS "" CACHE STRING - "Extra flags to the cython compiler.") -mark_as_advanced(CYTHON_ANNOTATE CYTHON_FLAGS) - -find_package(PythonLibs REQUIRED) - -set(CYTHON_CXX_EXTENSION "cxx") -set(CYTHON_C_EXTENSION "c") - -get_property(languages GLOBAL PROPERTY ENABLED_LANGUAGES) - -function(add_cython_target _name) - set(options EMBED_MAIN C CXX PY2 PY3) - set(options1 OUTPUT_VAR) - cmake_parse_arguments(_args "${options}" "${options1}" "" ${ARGN}) - - list(GET _args_UNPARSED_ARGUMENTS 0 _arg0) - - # if provided, use _arg0 as the input file path - if(_arg0) - set(_source_file ${_arg0}) - - # otherwise, must determine source file from name, or vice versa - else() - get_filename_component(_name_ext "${_name}" EXT) - - # if extension provided, _name is the source file - if(_name_ext) - set(_source_file ${_name}) - get_filename_component(_name "${_source_file}" NAME_WE) - - # otherwise, assume the source file is ${_name}.pyx - else() - set(_source_file ${_name}.pyx) - endif() - endif() - - set(_embed_main FALSE) - - if("C" IN_LIST languages) - set(_output_syntax "C") - elseif("CXX" IN_LIST languages) - set(_output_syntax "CXX") - else() - message(FATAL_ERROR "Either C or CXX must be enabled to use Cython") - endif() - - if("${PYTHONLIBS_VERSION_STRING}" MATCHES "^2.") - set(_input_syntax "PY2") - else() - set(_input_syntax "PY3") - endif() - - if(_args_EMBED_MAIN) - set(_embed_main TRUE) - endif() - - if(_args_C) - set(_output_syntax "C") - endif() - - if(_args_CXX) - set(_output_syntax "CXX") - endif() - - if(_args_PY2) - set(_input_syntax "PY2") - endif() - - if(_args_PY3) - set(_input_syntax "PY3") - endif() - - set(embed_arg "") - if(_embed_main) - set(embed_arg "--embed") - endif() - - set(cxx_arg "") - set(extension "c") - if(_output_syntax STREQUAL "CXX") - set(cxx_arg "--cplus") - set(extension "cxx") - endif() - - set(py_version_arg "") - if(_input_syntax STREQUAL "PY2") - set(py_version_arg "-2") - elseif(_input_syntax STREQUAL "PY3") - set(py_version_arg "-3") - endif() - - set(generated_file "${CMAKE_CURRENT_BINARY_DIR}/${_name}.${extension}") - set_source_files_properties(${generated_file} PROPERTIES GENERATED TRUE) - - set(_output_var ${_name}) - if(_args_OUTPUT_VAR) - set(_output_var ${_args_OUTPUT_VAR}) - endif() - set(${_output_var} ${generated_file} PARENT_SCOPE) - - file(RELATIVE_PATH generated_file_relative - ${CMAKE_BINARY_DIR} ${generated_file}) - - set(comment "Generating ${_output_syntax} source ${generated_file_relative}") - set(cython_include_directories "") - set(pxd_dependencies "") - set(c_header_dependencies "") - - # Get the include directories. - get_source_file_property(pyx_location ${_source_file} LOCATION) - get_filename_component(pyx_path ${pyx_location} PATH) - get_directory_property(cmake_include_directories - DIRECTORY ${pyx_path} - INCLUDE_DIRECTORIES) - list(APPEND cython_include_directories ${cmake_include_directories}) - - # Determine dependencies. - # Add the pxd file with the same basename as the given pyx file. - get_filename_component(pyx_file_basename ${_source_file} NAME_WE) - unset(corresponding_pxd_file CACHE) - find_file(corresponding_pxd_file ${pyx_file_basename}.pxd - PATHS "${pyx_path}" ${cmake_include_directories} - NO_DEFAULT_PATH) - if(corresponding_pxd_file) - list(APPEND pxd_dependencies "${corresponding_pxd_file}") - endif() - - # pxd files to check for additional dependencies - set(pxds_to_check "${_source_file}" "${pxd_dependencies}") - set(pxds_checked "") - set(number_pxds_to_check 1) - while(number_pxds_to_check GREATER 0) - foreach(pxd ${pxds_to_check}) - list(APPEND pxds_checked "${pxd}") - list(REMOVE_ITEM pxds_to_check "${pxd}") - - # look for C headers - file(STRINGS "${pxd}" extern_from_statements - REGEX "cdef[ ]+extern[ ]+from.*$") - foreach(statement ${extern_from_statements}) - # Had trouble getting the quote in the regex - string(REGEX REPLACE - "cdef[ ]+extern[ ]+from[ ]+[\"]([^\"]+)[\"].*" "\\1" - header "${statement}") - unset(header_location CACHE) - find_file(header_location ${header} PATHS ${cmake_include_directories}) - if(header_location) - list(FIND c_header_dependencies "${header_location}" header_idx) - if(${header_idx} LESS 0) - list(APPEND c_header_dependencies "${header_location}") - endif() - endif() - endforeach() - - # check for pxd dependencies - # Look for cimport statements. - set(module_dependencies "") - file(STRINGS "${pxd}" cimport_statements REGEX cimport) - foreach(statement ${cimport_statements}) - if(${statement} MATCHES from) - string(REGEX REPLACE - "from[ ]+([^ ]+).*" "\\1" - module "${statement}") - else() - string(REGEX REPLACE - "cimport[ ]+([^ ]+).*" "\\1" - module "${statement}") - endif() - list(APPEND module_dependencies ${module}) - endforeach() - - # check for pxi dependencies - # Look for include statements. - set(include_dependencies "") - file(STRINGS "${pxd}" include_statements REGEX include) - foreach(statement ${include_statements}) - string(REGEX REPLACE - "include[ ]+[\"]([^\"]+)[\"].*" "\\1" - module "${statement}") - list(APPEND include_dependencies ${module}) - endforeach() - - list(REMOVE_DUPLICATES module_dependencies) - list(REMOVE_DUPLICATES include_dependencies) - - # Add modules to the files to check, if appropriate. - foreach(module ${module_dependencies}) - unset(pxd_location CACHE) - find_file(pxd_location ${module}.pxd - PATHS "${pyx_path}" ${cmake_include_directories} - NO_DEFAULT_PATH) - if(pxd_location) - list(FIND pxds_checked ${pxd_location} pxd_idx) - if(${pxd_idx} LESS 0) - list(FIND pxds_to_check ${pxd_location} pxd_idx) - if(${pxd_idx} LESS 0) - list(APPEND pxds_to_check ${pxd_location}) - list(APPEND pxd_dependencies ${pxd_location}) - endif() # if it is not already going to be checked - endif() # if it has not already been checked - endif() # if pxd file can be found - endforeach() # for each module dependency discovered - - # Add includes to the files to check, if appropriate. - foreach(_include ${include_dependencies}) - unset(pxi_location CACHE) - find_file(pxi_location ${_include} - PATHS "${pyx_path}" ${cmake_include_directories} - NO_DEFAULT_PATH) - if(pxi_location) - list(FIND pxds_checked ${pxi_location} pxd_idx) - if(${pxd_idx} LESS 0) - list(FIND pxds_to_check ${pxi_location} pxd_idx) - if(${pxd_idx} LESS 0) - list(APPEND pxds_to_check ${pxi_location}) - list(APPEND pxd_dependencies ${pxi_location}) - endif() # if it is not already going to be checked - endif() # if it has not already been checked - endif() # if include file can be found - endforeach() # for each include dependency discovered - endforeach() # for each include file to check - - list(LENGTH pxds_to_check number_pxds_to_check) - endwhile() - - # Set additional flags. - set(annotate_arg "") - if(CYTHON_ANNOTATE) - set(annotate_arg "--annotate") - endif() - - set(no_docstrings_arg "") - if(CMAKE_BUILD_TYPE STREQUAL "Release" OR - CMAKE_BUILD_TYPE STREQUAL "MinSizeRel") - set(no_docstrings_arg "--no-docstrings") - endif() - - set(cython_debug_arg "") - set(embed_pos_arg "") - set(line_directives_arg "") - if(CMAKE_BUILD_TYPE STREQUAL "Debug" OR - CMAKE_BUILD_TYPE STREQUAL "RelWithDebInfo") - set(cython_debug_arg "--gdb") - set(embed_pos_arg "--embed-positions") - set(line_directives_arg "--line-directives") - endif() - - # Include directory arguments. - list(REMOVE_DUPLICATES cython_include_directories) - set(include_directory_arg "") - foreach(_include_dir ${cython_include_directories}) - set(include_directory_arg - ${include_directory_arg} "--include-dir" "${_include_dir}") - endforeach() - - list(REMOVE_DUPLICATES pxd_dependencies) - list(REMOVE_DUPLICATES c_header_dependencies) - - # Add the command to run the compiler. - add_custom_command(OUTPUT ${generated_file} - COMMAND ${CYTHON_EXECUTABLE} - ARGS ${cxx_arg} ${include_directory_arg} ${py_version_arg} - ${embed_arg} ${annotate_arg} ${no_docstrings_arg} - ${cython_debug_arg} ${embed_pos_arg} - ${line_directives_arg} ${CYTHON_FLAGS} ${pyx_location} - --output-file ${generated_file} - DEPENDS ${_source_file} - ${pxd_dependencies} - IMPLICIT_DEPENDS ${_output_syntax} - ${c_header_dependencies} - COMMENT ${comment}) - - # NOTE(opadron): I thought about making a proper target, but after trying it - # out, I decided that it would be far too convenient to use the same name as - # the target for the extension module (e.g.: for single-file modules): - # - # ... - # add_cython_target(_module.pyx) - # add_library(_module ${_module}) - # ... - # - # The above example would not be possible since the "_module" target name - # would already be taken by the cython target. Since I can't think of a - # reason why someone would need the custom target instead of just using the - # generated file directly, I decided to leave this commented out. - # - # add_custom_target(${_name} DEPENDS ${generated_file}) - - # Remove their visibility to the user. - set(corresponding_pxd_file "" CACHE INTERNAL "") - set(header_location "" CACHE INTERNAL "") - set(pxd_location "" CACHE INTERNAL "") -endfunction() - diff --git a/Day_3/src/cpp/cmake/targetLinkLibrariesWithDynamicLookup.cmake b/Day_3/src/cpp/cmake/targetLinkLibrariesWithDynamicLookup.cmake deleted file mode 100644 index 4986161..0000000 --- a/Day_3/src/cpp/cmake/targetLinkLibrariesWithDynamicLookup.cmake +++ /dev/null @@ -1,580 +0,0 @@ -#.rst: -# -# Public Functions -# ^^^^^^^^^^^^^^^^ -# -# The following functions are defined: -# -# .. cmake:command:: target_link_libraries_with_dynamic_lookup -# -# :: -# -# target_link_libraries_with_dynamic_lookup( []) -# -# -# Useful to "weakly" link a loadable module. For example, it should be used -# when compiling a loadable module when the symbols should be resolve from -# the run-time environment where the module is loaded, and not a specific -# system library. -# -# Like proper linking, except that the given ```` are not necessarily -# linked. Instead, the ```` is produced in a manner that allows for -# symbols unresolved within it to be resolved at runtime, presumably by the -# given ````. If such a target can be produced, the provided -# ```` are not actually linked. -# -# It links a library to a target such that the symbols are resolved at -# run-time not link-time. -# -# The linker is checked to see if it supports undefined -# symbols when linking a shared library. If it does then the library -# is not linked when specified with this function. -# -# On platforms that do not support weak-linking, this function works just -# like ``target_link_libraries``. -# -# .. note:: -# -# For OSX it uses ``undefined dynamic_lookup``. This is similar to using -# ``-shared`` on Linux where undefined symbols are ignored. -# -# For more details, see `blog `_ -# from Tim D. Smith. -# -# -# .. cmake:command:: check_dynamic_lookup -# -# Check if the linker requires a command line flag to allow leaving symbols -# unresolved when producing a target of type ```` that is -# weakly-linked against a dependency of type ````. -# -# ```` -# can be one of "STATIC", "SHARED", "MODULE", or "EXE". -# -# ```` -# can be one of "STATIC", "SHARED", or "MODULE". -# -# Long signature: -# -# :: -# -# check_dynamic_lookup( -# -# -# []) -# -# -# Short signature: -# -# :: -# -# check_dynamic_lookup() # set to "MODULE" -# # set to "SHARED" -# -# -# The result is cached between invocations and recomputed only when the value -# of CMake's linker flag list changes; ``CMAKE_STATIC_LINKER_FLAGS`` if -# ```` is "STATIC", and ``CMAKE_SHARED_LINKER_FLAGS`` otherwise. -# -# -# Defined variables: -# -# ```` -# Whether the current C toolchain supports weak-linking for target binaries of -# type ```` that are weakly-linked against a dependency target of -# type ````. -# -# ```` -# List of flags to add to the linker command to produce a working target -# binary of type ```` that is weakly-linked against a dependency -# target of type ````. -# -# ``HAS_DYNAMIC_LOOKUP__`` -# Cached, global alias for ```` -# -# ``DYNAMIC_LOOKUP_FLAGS__`` -# Cached, global alias for ```` -# -# -# Private Functions -# ^^^^^^^^^^^^^^^^^ -# -# The following private functions are defined: -# -# .. warning:: These functions are not part of the scikit-build API. They -# exist purely as an implementation detail and may change from version -# to version without notice, or even be removed. -# -# We mean it. -# -# -# .. cmake:command:: _get_target_type -# -# :: -# -# _get_target_type( ) -# -# -# Shorthand for querying an abbreviated version of the target type -# of the given ````. -# -# ```` is set to: -# -# - "STATIC" for a STATIC_LIBRARY, -# - "SHARED" for a SHARED_LIBRARY, -# - "MODULE" for a MODULE_LIBRARY, -# - and "EXE" for an EXECUTABLE. -# -# Defined variables: -# -# ```` -# The abbreviated version of the ````'s type. -# -# -# .. cmake:command:: _test_weak_link_project -# -# :: -# -# _test_weak_link_project( -# -# -# ) -# -# -# Attempt to compile and run a test project where a target of type -# ```` is weakly-linked against a dependency of type ````: -# -# - ```` can be one of "STATIC", "SHARED", "MODULE", or "EXE". -# - ```` can be one of "STATIC", "SHARED", or "MODULE". -# -# Defined variables: -# -# ```` -# Whether the current C toolchain can produce a working target binary of type -# ```` that is weakly-linked against a dependency target of type -# ````. -# -# ```` -# List of flags to add to the linker command to produce a working target -# binary of type ```` that is weakly-linked against a dependency -# target of type ````. -# - -function(_get_target_type result_var target) - set(target_type "SHARED_LIBRARY") - if(TARGET ${target}) - get_property(target_type TARGET ${target} PROPERTY TYPE) - endif() - - set(result "STATIC") - - if(target_type STREQUAL "STATIC_LIBRARY") - set(result "STATIC") - endif() - - if(target_type STREQUAL "SHARED_LIBRARY") - set(result "SHARED") - endif() - - if(target_type STREQUAL "MODULE_LIBRARY") - set(result "MODULE") - endif() - - if(target_type STREQUAL "EXECUTABLE") - set(result "EXE") - endif() - - set(${result_var} ${result} PARENT_SCOPE) -endfunction() - - -function(_test_weak_link_project - target_type - lib_type - can_weak_link_var - project_name) - - set(gnu_ld_ignore "-Wl,--unresolved-symbols=ignore-all") - set(osx_dynamic_lookup "-undefined dynamic_lookup") - set(no_flag "") - - foreach(link_flag_spec gnu_ld_ignore osx_dynamic_lookup no_flag) - set(link_flag "${${link_flag_spec}}") - - set(test_project_dir "${PROJECT_BINARY_DIR}/CMakeTmp") - set(test_project_dir "${test_project_dir}/${project_name}") - set(test_project_dir "${test_project_dir}/${link_flag_spec}") - set(test_project_dir "${test_project_dir}/${target_type}") - set(test_project_dir "${test_project_dir}/${lib_type}") - - set(test_project_src_dir "${test_project_dir}/src") - set(test_project_bin_dir "${test_project_dir}/build") - - file(MAKE_DIRECTORY ${test_project_src_dir}) - file(MAKE_DIRECTORY ${test_project_bin_dir}) - - set(mod_type "STATIC") - set(link_mod_lib TRUE) - set(link_exe_lib TRUE) - set(link_exe_mod FALSE) - - if("${target_type}" STREQUAL "EXE") - set(link_exe_lib FALSE) - set(link_exe_mod TRUE) - else() - set(mod_type "${target_type}") - endif() - - if("${mod_type}" STREQUAL "MODULE") - set(link_mod_lib FALSE) - endif() - - - file(WRITE "${test_project_src_dir}/CMakeLists.txt" " - cmake_minimum_required(VERSION ${CMAKE_VERSION}) - project(${project_name} C) - - include_directories(${test_project_src_dir}) - - add_library(number ${lib_type} number.c) - add_library(counter ${mod_type} counter.c) - ") - - if("${mod_type}" STREQUAL "MODULE") - file(APPEND "${test_project_src_dir}/CMakeLists.txt" " - set_target_properties(counter PROPERTIES PREFIX \"\") - ") - endif() - - if(link_mod_lib) - file(APPEND "${test_project_src_dir}/CMakeLists.txt" " - target_link_libraries(counter number) - ") - elseif(NOT link_flag STREQUAL "") - file(APPEND "${test_project_src_dir}/CMakeLists.txt" " - set_target_properties(counter PROPERTIES LINK_FLAGS \"${link_flag}\") - ") - endif() - - file(APPEND "${test_project_src_dir}/CMakeLists.txt" " - add_executable(main main.c) - ") - - if(link_exe_lib) - file(APPEND "${test_project_src_dir}/CMakeLists.txt" " - target_link_libraries(main number) - ") - elseif(NOT link_flag STREQUAL "") - file(APPEND "${test_project_src_dir}/CMakeLists.txt" " - target_link_libraries(main \"${link_flag}\") - ") - endif() - - if(link_exe_mod) - file(APPEND "${test_project_src_dir}/CMakeLists.txt" " - target_link_libraries(main counter) - ") - else() - file(APPEND "${test_project_src_dir}/CMakeLists.txt" " - target_link_libraries(main \"${CMAKE_DL_LIBS}\") - ") - endif() - - file(WRITE "${test_project_src_dir}/number.c" " - #include - - static int _number; - void set_number(int number) { _number = number; } - int get_number() { return _number; } - ") - - file(WRITE "${test_project_src_dir}/number.h" " - #ifndef _NUMBER_H - #define _NUMBER_H - extern void set_number(int); - extern int get_number(void); - #endif - ") - - file(WRITE "${test_project_src_dir}/counter.c" " - #include - int count() { - int result = get_number(); - set_number(result + 1); - return result; - } - ") - - file(WRITE "${test_project_src_dir}/counter.h" " - #ifndef _COUNTER_H - #define _COUNTER_H - extern int count(void); - #endif - ") - - file(WRITE "${test_project_src_dir}/main.c" " - #include - #include - #include - ") - - if(NOT link_exe_mod) - file(APPEND "${test_project_src_dir}/main.c" " - #include - ") - endif() - - file(APPEND "${test_project_src_dir}/main.c" " - int my_count() { - int result = get_number(); - set_number(result + 1); - return result; - } - - int main(int argc, char **argv) { - int result; - ") - - if(NOT link_exe_mod) - file(APPEND "${test_project_src_dir}/main.c" " - void *counter_module; - int (*count)(void); - - counter_module = dlopen(\"./counter.so\", RTLD_LAZY | RTLD_GLOBAL); - if(!counter_module) goto error; - - count = dlsym(counter_module, \"count\"); - if(!count) goto error; - ") - endif() - - file(APPEND "${test_project_src_dir}/main.c" " - result = count() != 0 ? EXIT_FAILURE : - my_count() != 1 ? EXIT_FAILURE : - my_count() != 2 ? EXIT_FAILURE : - count() != 3 ? EXIT_FAILURE : - count() != 4 ? EXIT_FAILURE : - count() != 5 ? EXIT_FAILURE : - my_count() != 6 ? EXIT_FAILURE : EXIT_SUCCESS; - ") - - if(NOT link_exe_mod) - file(APPEND "${test_project_src_dir}/main.c" " - goto done; - error: - fprintf(stderr, \"Error occured:\\n %s\\n\", dlerror()); - result = 1; - - done: - if(counter_module) dlclose(counter_module); - ") - endif() - - file(APPEND "${test_project_src_dir}/main.c" " - return result; - } - ") - - set(_rpath_arg) - if(APPLE AND ${CMAKE_VERSION} VERSION_GREATER 2.8.11) - set(_rpath_arg "-DCMAKE_MACOSX_RPATH='${CMAKE_MACOSX_RPATH}'") - endif() - - try_compile(project_compiles - "${test_project_bin_dir}" - "${test_project_src_dir}" - "${project_name}" - CMAKE_FLAGS - "-DCMAKE_SHARED_LINKER_FLAGS='${CMAKE_SHARED_LINKER_FLAGS}'" - "-DCMAKE_ENABLE_EXPORTS=ON" - ${_rpath_arg} - OUTPUT_VARIABLE compile_output) - - set(project_works 1) - set(run_output) - - if(project_compiles) - execute_process(COMMAND ${CMAKE_CROSSCOMPILING_EMULATOR} - "${test_project_bin_dir}/main" - WORKING_DIRECTORY "${test_project_bin_dir}" - RESULT_VARIABLE project_works - OUTPUT_VARIABLE run_output - ERROR_VARIABLE run_output) - endif() - - set(test_description - "Weak Link ${target_type} -> ${lib_type} (${link_flag_spec})") - - if(project_works EQUAL 0) - set(project_works TRUE) - message(STATUS "Performing Test ${test_description} - Success") - else() - set(project_works FALSE) - message(STATUS "Performing Test ${test_description} - Failed") - file(APPEND ${CMAKE_BINARY_DIR}/${CMAKE_FILES_DIRECTORY}/CMakeError.log - "Performing Test ${test_description} failed with the " - "following output:\n" - "BUILD\n-----\n${compile_output}\nRUN\n---\n${run_output}\n") - endif() - - set(${can_weak_link_var} ${project_works} PARENT_SCOPE) - if(project_works) - set(${project_name} ${link_flag} PARENT_SCOPE) - break() - endif() - endforeach() -endfunction() - -function(check_dynamic_lookup) - # Two signatures are supported: - - if(ARGC EQUAL "1") - # - # check_dynamic_lookup() - # - set(target_type "MODULE") - set(lib_type "SHARED") - set(has_dynamic_lookup_var "${ARGV0}") - set(link_flags_var "unused") - - elseif(ARGC GREATER "2") - # - # check_dynamic_lookup( - # - # - # []) - # - set(target_type "${ARGV0}") - set(lib_type "${ARGV1}") - set(has_dynamic_lookup_var "${ARGV2}") - if(ARGC EQUAL "3") - set(link_flags_var "unused") - else() - set(link_flags_var "${ARGV3}") - endif() - else() - message(FATAL_ERROR "missing arguments") - endif() - - _check_dynamic_lookup( - ${target_type} - ${lib_type} - ${has_dynamic_lookup_var} - ${link_flags_var} - ) - set(${has_dynamic_lookup_var} ${${has_dynamic_lookup_var}} PARENT_SCOPE) - if(NOT "x${link_flags_var}x" STREQUAL "xunusedx") - set(${link_flags_var} ${${link_flags_var}} PARENT_SCOPE) - endif() -endfunction() - -function(_check_dynamic_lookup - target_type - lib_type - has_dynamic_lookup_var - link_flags_var - ) - - # hash the CMAKE_FLAGS passed and check cache to know if we need to rerun - if("${target_type}" STREQUAL "STATIC") - string(MD5 cmake_flags_hash "${CMAKE_STATIC_LINKER_FLAGS}") - else() - string(MD5 cmake_flags_hash "${CMAKE_SHARED_LINKER_FLAGS}") - endif() - - set(cache_var "HAS_DYNAMIC_LOOKUP_${target_type}_${lib_type}") - set(cache_hash_var "HAS_DYNAMIC_LOOKUP_${target_type}_${lib_type}_hash") - set(result_var "DYNAMIC_LOOKUP_FLAGS_${target_type}_${lib_type}") - - if( NOT DEFINED ${cache_hash_var} - OR NOT "${${cache_hash_var}}" STREQUAL "${cmake_flags_hash}") - unset(${cache_var} CACHE) - endif() - - if(NOT DEFINED ${cache_var}) - set(skip_test FALSE) - - if(CMAKE_CROSSCOMPILING AND NOT CMAKE_CROSSCOMPILING_EMULATOR) - set(skip_test TRUE) - endif() - - if(skip_test) - set(has_dynamic_lookup FALSE) - set(link_flags) - else() - _test_weak_link_project(${target_type} - ${lib_type} - has_dynamic_lookup - link_flags) - endif() - - set(caveat " (when linking ${target_type} against ${lib_type})") - - set(${cache_var} "${has_dynamic_lookup}" - CACHE BOOL - "linker supports dynamic lookup for undefined symbols${caveat}") - mark_as_advanced(${cache_var}) - - set(${result_var} "${link_flags}" - CACHE STRING - "linker flags for dynamic lookup${caveat}") - mark_as_advanced(${result_var}) - - set(${cache_hash_var} "${cmake_flags_hash}" - CACHE INTERNAL "hashed flags for ${cache_var} check") - endif() - - set(${has_dynamic_lookup_var} "${${cache_var}}" PARENT_SCOPE) - set(${link_flags_var} "${${result_var}}" PARENT_SCOPE) -endfunction() - -function(target_link_libraries_with_dynamic_lookup target) - _get_target_type(target_type ${target}) - - set(link_props) - set(link_items) - set(link_libs) - - foreach(lib ${ARGN}) - _get_target_type(lib_type ${lib}) - check_dynamic_lookup(${target_type} - ${lib_type} - has_dynamic_lookup - dynamic_lookup_flags) - - if(has_dynamic_lookup) - if(dynamic_lookup_flags) - if("${target_type}" STREQUAL "EXE") - list(APPEND link_items "${dynamic_lookup_flags}") - else() - list(APPEND link_props "${dynamic_lookup_flags}") - endif() - endif() - else() - list(APPEND link_libs "${lib}") - endif() - endforeach() - - if(link_props) - list(REMOVE_DUPLICATES link_props) - endif() - - if(link_items) - list(REMOVE_DUPLICATES link_items) - endif() - - if(link_libs) - list(REMOVE_DUPLICATES link_libs) - endif() - - if(link_props) - set_target_properties(${target} - PROPERTIES LINK_FLAGS "${link_props}") - endif() - - set(links "${link_items}" "${link_libs}") - if(links) - target_link_libraries(${target} "${links}") - endif() -endfunction() - diff --git a/Day_3/src/cpp/wave_propogation.pyx b/Day_3/src/cpp/wave_propogation.pyx deleted file mode 100644 index 1d062e1..0000000 --- a/Day_3/src/cpp/wave_propogation.pyx +++ /dev/null @@ -1,27 +0,0 @@ -from libc.stdlib cimport malloc, free -from cpython.mem cimport PyMem_Malloc -import numpy as np -import time -cimport cython -from cython.view cimport array as cvarray - -cdef extern from "wp.hpp": - void wave_propogation_single_core(int, int, float, float, int, float*) - -@cython.boundscheck(False) -@cython.cdivision(True) -@cython.wraparound(False) -@cython.infer_types(False) -def wave_propogation(int num_steps, int scale, float damping, float initial_P, int stop_step): - cdef int size_x = 2 * scale + 1 - cdef int size_y = 2 * scale + 1 - cdef int i = 0 - cdef int j = 0 - cdef float *array = malloc(sizeof(float) * size_x * size_y) - wave_propogation_single_core(num_steps, scale, damping, initial_P, stop_step, array) - P = [[0.0 for x in range(size_x)] for y in range(size_y)] - - for i in range(size_x): - for j in range(size_y): - P[i][j] = array[i*size_x+j] if not np.isnan(array[i*size_x+j]) else 0.0 - return P diff --git a/Day_3/src/cpp/wp.cpp b/Day_3/src/cpp/wp.cpp deleted file mode 100644 index e7af044..0000000 --- a/Day_3/src/cpp/wp.cpp +++ /dev/null @@ -1,67 +0,0 @@ -#include "wp.hpp" - -void wave_propogation_single_core(int num_steps, int scale, float damping, - float initial_P, int stop_step, float *_P) { - - float omega = 3.0 / (2.0 * M_PI); - int size_x = 2 * scale + 1; - int size_y = 2 * scale + 1; - - int i = 0; - int j = 0; - int k = 0; - int step = 0; - -// V velocity -// P presure -// Initialization -#ifdef __APPLE__ - // Clang/Apple doesn't like weird pointer assignments so we create a new 2d - // array - float P[size_x][size_y]; -#else - float(*P)[size_y] = (float(*)[size_y])_P; -#endif - - float V[size_x][size_y][4]; - - for (i = 0; i < size_x; i++) { - for (j = 0; j < size_y; j++) { - P[i][j] = 0.0; - for (k = 0; k < 4; k++) - V[i][j][k] = 0.0; - } - } - - P[scale][scale] = initial_P; - for (step = 0; step < num_steps; step++) { - if (step <= stop_step) - P[scale][scale] = initial_P * sin(omega * step); - for (i = 0; i < size_x; i++) { - for (j = 0; j < size_y; j++) { - V[i][j][0] = (i > 0 ? V[i][j][0] + P[i][j] - P[i - 1][j] : P[i][j]); - V[i][j][1] = - (j < size_x - 1 ? V[i][j][1] + P[i][j] - P[i][j + 1] : P[i][j]); - V[i][j][2] = - (i < size_y - 1 ? V[i][j][2] + P[i][j] - P[i + 1][j] : P[i][j]); - V[i][j][3] = (j > 0 ? V[i][j][3] + P[i][j] - P[i][j - 1] : P[i][j]); - } - } - - for (i = 0; i < size_x; i++) { - for (j = 0; j < size_y; j++) { - P[i][j] -= - 0.5 * damping * (V[i][j][0] + V[i][j][1] + V[i][j][2] + V[i][j][3]); - } - } - } - -#ifdef __APPLE__ - // Then we copy from out array P into out output pointer _P - for (i = 0; i < size_x; i++) { - for (j = 0; j < size_y; j++) { - _P[i * size_x + j] = P[i][j]; - } - } -#endif -} diff --git a/Day_3/src/cpp/wp.hpp b/Day_3/src/cpp/wp.hpp deleted file mode 100644 index 9f991ca..0000000 --- a/Day_3/src/cpp/wp.hpp +++ /dev/null @@ -1,9 +0,0 @@ -#include -#include -#include -#ifndef __APPLE__ -#include -#endif - -void wave_propogation_single_core(int num_steps, int scale, float damping, - float initial_P, int stop_step, float *_P); diff --git a/Day_3/src/fortran/CMakeLists.txt b/Day_3/src/fortran/CMakeLists.txt deleted file mode 100644 index 25cbe46..0000000 --- a/Day_3/src/fortran/CMakeLists.txt +++ /dev/null @@ -1,32 +0,0 @@ -cmake_minimum_required(VERSION 3.5) -project(wave_propogation C CXX Fortran) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) -set(VERSION 1.0.0) - -set(CMAKE_CXX_STANDARD 17) -set(CMAKE_MODULE_PATH - ${CMAKE_MODULE_PATH} - ${PROJECT_SOURCE_DIR}/cmake) - -set(CMAKE_C_FLAGS "-Ofast") -set(CMAKE_CXX_FLAGS "-Ofast") -set(CMAKE_Fortran_FLAGS "-O3") - -find_package(Cython REQUIRED) -find_package(PythonInterp REQUIRED) -find_package(PythonLibs REQUIRED) -find_package(PythonExtensions REQUIRED) -find_package(NumPy REQUIRED) - -include_directories(${PROJECT_SOURCE_DIR}) - - -add_library(wave_propogation_static STATIC wp.f90) - -add_cython_target(wave_propogation wave_propogation.pyx CXX PY3 OUTPUT_VAR _wave_propogation) -include_directories(${PYTHON_INCLUDE_DIRS} ${NumPy_INCLUDE_DIR}) -add_library(wave_propogation MODULE ${_wave_propogation}) -python_extension_module(wave_propogation) -target_link_libraries(wave_propogation wave_propogation_static ${PYTHON_LIBRARIES}) - -install(TARGETS wave_propogation LIBRARY DESTINATION .) diff --git a/Day_3/src/fortran/cmake/FindCython.cmake b/Day_3/src/fortran/cmake/FindCython.cmake deleted file mode 100644 index b8ae305..0000000 --- a/Day_3/src/fortran/cmake/FindCython.cmake +++ /dev/null @@ -1,78 +0,0 @@ -#.rst: -# -# Find ``cython`` executable. -# -# This module will set the following variables in your project: -# -# ``CYTHON_EXECUTABLE`` -# path to the ``cython`` program -# -# ``CYTHON_VERSION`` -# version of ``cython`` -# -# ``CYTHON_FOUND`` -# true if the program was found -# -# For more information on the Cython project, see http://cython.org/. -# -# *Cython is a language that makes writing C extensions for the Python language -# as easy as Python itself.* -# -#============================================================================= -# Copyright 2011 Kitware, Inc. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -#============================================================================= - -# Use the Cython executable that lives next to the Python executable -# if it is a local installation. -find_package(PythonInterp) -if(PYTHONINTERP_FOUND) - get_filename_component(_python_path ${PYTHON_EXECUTABLE} PATH) - find_program(CYTHON_EXECUTABLE - NAMES cython cython.bat cython3 - HINTS ${_python_path} - DOC "path to the cython executable") -else() - find_program(CYTHON_EXECUTABLE - NAMES cython cython.bat cython3 - DOC "path to the cython executable") -endif() - -if(CYTHON_EXECUTABLE) - set(CYTHON_version_command ${CYTHON_EXECUTABLE} --version) - - execute_process(COMMAND ${CYTHON_version_command} - OUTPUT_VARIABLE CYTHON_version_output - ERROR_VARIABLE CYTHON_version_error - RESULT_VARIABLE CYTHON_version_result - OUTPUT_STRIP_TRAILING_WHITESPACE) - - if(NOT ${CYTHON_version_result} EQUAL 0) - set(_error_msg "Command \"${CYTHON_version_command}\" failed with") - set(_error_msg "${_error_msg} output:\n${CYTHON_version_error}") - message(SEND_ERROR "${_error_msg}") - else() - if("${CYTHON_version_output}" MATCHES "^[Cc]ython version ([^,]+)") - set(CYTHON_VERSION "${CMAKE_MATCH_1}") - endif() - endif() -endif() - -include(FindPackageHandleStandardArgs) -FIND_PACKAGE_HANDLE_STANDARD_ARGS(Cython REQUIRED_VARS CYTHON_EXECUTABLE) - -mark_as_advanced(CYTHON_EXECUTABLE) - -include(UseCython) - diff --git a/Day_3/src/fortran/cmake/FindNumPy.cmake b/Day_3/src/fortran/cmake/FindNumPy.cmake deleted file mode 100644 index 3cd23c3..0000000 --- a/Day_3/src/fortran/cmake/FindNumPy.cmake +++ /dev/null @@ -1,63 +0,0 @@ -#.rst: -# -# Find the include directory for numpy/arrayobject.h -# -# This module sets the following variables: -# -# ``NumPy_FOUND`` -# True if NumPy was found. -# ``NumPy_INCLUDE_DIR`` -# The include directories needed to use NumpPy. -# ``NumPy_VERSION`` -# The version of NumPy found. -# -# -# The module will also explicitly define one cache variable: -# -# ``NumPy_INCLUDE_DIR`` -# - -if(NOT NumPy_FOUND) - set(_find_extra_args) - if(NumPy_FIND_REQUIRED) - list(APPEND _find_extra_args REQUIRED) - endif() - if(NumPy_FIND_QUIET) - list(APPEND _find_extra_args QUIET) - endif() - find_package(PythonInterp ${_find_extra_args}) - find_package(PythonLibs ${_find_extra_args}) - - if(PYTHON_EXECUTABLE) - execute_process(COMMAND "${PYTHON_EXECUTABLE}" - -c "import numpy; print(numpy.get_include())" - OUTPUT_VARIABLE _numpy_include_dir - OUTPUT_STRIP_TRAILING_WHITESPACE - ERROR_QUIET - ) - execute_process(COMMAND "${PYTHON_EXECUTABLE}" - -c "import numpy; print(numpy.__version__)" - OUTPUT_VARIABLE NumPy_VERSION - OUTPUT_STRIP_TRAILING_WHITESPACE - ERROR_QUIET - ) - endif() -endif() - -find_path(NumPy_INCLUDE_DIR - numpy/arrayobject.h - PATHS "${_numpy_include_dir}" "${PYTHON_INCLUDE_DIR}" - PATH_SUFFIXES numpy/core/include - ) - -set(NumPy_INCLUDE_DIRS ${NumPy_INCLUDE_DIR}) - -# handle the QUIETLY and REQUIRED arguments and set NumPy_FOUND to TRUE if -# all listed variables are TRUE -include(FindPackageHandleStandardArgs) -find_package_handle_standard_args(NumPy - REQUIRED_VARS NumPy_INCLUDE_DIR - VERSION_VAR NumPy_VERSION - ) - -mark_as_advanced(NumPy_INCLUDE_DIR) diff --git a/Day_3/src/fortran/cmake/FindPythonExtensions.cmake b/Day_3/src/fortran/cmake/FindPythonExtensions.cmake deleted file mode 100644 index b3aa206..0000000 --- a/Day_3/src/fortran/cmake/FindPythonExtensions.cmake +++ /dev/null @@ -1,538 +0,0 @@ -#.rst: -# -# This module defines CMake functions to build Python extension modules and -# stand-alone executables. -# -# The following variables are defined: -# :: -# -# PYTHON_PREFIX - absolute path to the current Python -# distribution's prefix -# PYTHON_SITE_PACKAGES_DIR - absolute path to the current Python -# distribution's site-packages directory -# PYTHON_RELATIVE_SITE_PACKAGES_DIR - path to the current Python -# distribution's site-packages directory -# relative to its prefix -# PYTHON_SEPARATOR - separator string for file path -# components. Equivalent to ``os.sep`` in -# Python. -# PYTHON_PATH_SEPARATOR - separator string for PATH-style -# environment variables. Equivalent to -# ``os.pathsep`` in Python. -# -# -# The following functions are defined: -# -# .. cmake:command:: python_extension_module -# -# For libraries meant to be used as Python extension modules, either dynamically -# loaded or directly linked. Amend the configuration of the library target -# (created using ``add_library``) with additional options needed to build and -# use the referenced library as a Python extension module. -# -# python_extension_module( -# [LINKED_MODULES_VAR ] -# [FORWARD_DECL_MODULES_VAR ] -# [MODULE_SUFFIX ]) -# -# Only extension modules that are configured to be built as MODULE libraries can -# be runtime-loaded through the standard Python import mechanism. All other -# modules can only be included in standalone applications that are written to -# expect their presence. In addition to being linked against the libraries for -# these modules, such applications must forward declare their entry points and -# initialize them prior to use. To generate these forward declarations and -# initializations, see ``python_modules_header``. -# -# If ```` does not refer to a target, then it is assumed to refer to an -# extension module that is not linked at all, but compiled along with other -# source files directly into an executable. Adding these modules does not cause -# any library configuration modifications, and they are not added to the list of -# linked modules. They still must be forward declared and initialized, however, -# and so are added to the forward declared modules list. -# -# Options: -# -# ``LINKED_MODULES_VAR `` -# Name of the variable referencing a list of extension modules whose libraries -# must be linked into the executables of any stand-alone applications that use -# them. By default, the global property ``PY_LINKED_MODULES_LIST`` is used. -# -# ``FORWARD_DECL_MODULES_VAR `` -# Name of the variable referencing a list of extension modules whose entry -# points must be forward declared and called by any stand-alone applications -# that use them. By default, the global property -# ``PY_FORWARD_DECL_MODULES_LIST`` is used. -# -# ``MODULE_SUFFIX `` -# Suffix appended to the python extension module file. -# The default suffix is retrieved using ``sysconfig.get_config_var("SO")"``, -# if not available, the default is then ``.so`` on unix and ``.pyd`` on -# windows. -# Setting the variable ``PYTHON_EXTENSION_MODULE_SUFFIX`` in the caller -# scope defines the value used for all extensions not having a suffix -# explicitly specified using ``MODULE_SUFFIX`` parameter. -# -# -# .. cmake:command:: python_standalone_executable -# -# python_standalone_executable() -# -# For standalone executables that initialize their own Python runtime -# (such as when building source files that include one generated by Cython with -# the --embed option). Amend the configuration of the executable target -# (created using ``add_executable``) with additional options needed to properly -# build the referenced executable. -# -# -# .. cmake:command:: python_modules_header -# -# Generate a header file that contains the forward declarations and -# initialization routines for the given list of Python extension modules. -# ```` is the logical name for the header file (no file extensions). -# ```` is the actual destination filename for the header file -# (e.g.: decl_modules.h). -# -# python_modules_header( [HeaderFilename] -# [FORWARD_DECL_MODULES_LIST ] -# [HEADER_OUTPUT_VAR ] -# [INCLUDE_DIR_OUTPUT_VAR ]) -# -# If only ```` is provided, and it ends in the ".h" extension, then it -# is assumed to be the ````. The filename of the header file -# without the extension is used as the logical name. If only ```` is -# provided, and it does not end in the ".h" extension, then the -# ```` is assumed to ``.h``. -# -# The exact contents of the generated header file depend on the logical -# ````. It should be set to a value that corresponds to the target -# application, or for the case of multiple applications, some identifier that -# conveyes its purpose. It is featured in the generated multiple inclusion -# guard as well as the names of the generated initialization routines. -# -# The generated header file includes forward declarations for all listed -# modules, as well as implementations for the following class of routines: -# -# ``int _(void)`` -# Initializes the python extension module, ````. Returns an integer -# handle to the module. -# -# ``void _LoadAllPythonModules(void)`` -# Initializes all listed python extension modules. -# -# ``void CMakeLoadAllPythonModules(void);`` -# Alias for ``_LoadAllPythonModules`` whose name does not depend on -# ````. This function is excluded during preprocessing if the -# preprocessing macro ``EXCLUDE_LOAD_ALL_FUNCTION`` is defined. -# -# ``void Py_Initialize_Wrapper();`` -# Wrapper arpund ``Py_Initialize()`` that initializes all listed python -# extension modules. This function is excluded during preprocessing if the -# preprocessing macro ``EXCLUDE_PY_INIT_WRAPPER`` is defined. If this -# function is generated, then ``Py_Initialize()`` is redefined to a macro -# that calls this function. -# -# Options: -# -# ``FORWARD_DECL_MODULES_LIST `` -# List of extension modules for which to generate forward declarations of -# their entry points and their initializations. By default, the global -# property ``PY_FORWARD_DECL_MODULES_LIST`` is used. -# -# ``HEADER_OUTPUT_VAR `` -# Name of the variable to set to the path to the generated header file. By -# default, ```` is used. -# -# ``INCLUDE_DIR_OUTPUT_VAR `` -# Name of the variable to set to the path to the directory containing the -# generated header file. By default, ``_INCLUDE_DIRS`` is used. -# -# Defined variables: -# -# ```` -# The path to the generated header file -# -# ```` -# Directory containing the generated header file -# -# -# Example usage -# ^^^^^^^^^^^^^ -# -# .. code-block:: cmake -# -# find_package(PythonInterp) -# find_package(PythonLibs) -# find_package(PythonExtensions) -# find_package(Cython) -# find_package(Boost COMPONENTS python) -# -# # Simple Cython Module -- no executables -# add_cython_target(_module.pyx) -# add_library(_module MODULE ${_module}) -# python_extension_module(_module) -# -# # Mix of Cython-generated code and C++ code using Boost Python -# # Stand-alone executable -- no modules -# include_directories(${Boost_INCLUDE_DIRS}) -# add_cython_target(main.pyx CXX EMBED_MAIN) -# add_executable(main boost_python_module.cxx ${main}) -# target_link_libraries(main ${Boost_LIBRARIES}) -# python_standalone_executable(main) -# -# # stand-alone executable with three extension modules: -# # one statically linked, one dynamically linked, and one loaded at runtime -# # -# # Freely mixes Cython-generated code, code using Boost-Python, and -# # hand-written code using the CPython API. -# -# # module1 -- statically linked -# add_cython_target(module1.pyx) -# add_library(module1 STATIC ${module1}) -# python_extension_module(module1 -# LINKED_MODULES_VAR linked_module_list -# FORWARD_DECL_MODULES_VAR fdecl_module_list) -# -# # module2 -- dynamically linked -# include_directories({Boost_INCLUDE_DIRS}) -# add_library(module2 SHARED boost_module2.cxx) -# target_link_libraries(module2 ${Boost_LIBRARIES}) -# python_extension_module(module2 -# LINKED_MODULES_VAR linked_module_list -# FORWARD_DECL_MODULES_VAR fdecl_module_list) -# -# # module3 -- loaded at runtime -# add_cython_target(module3a.pyx) -# add_library(module1 MODULE ${module3a} module3b.cxx) -# target_link_libraries(module3 ${Boost_LIBRARIES}) -# python_extension_module(module3 -# LINKED_MODULES_VAR linked_module_list -# FORWARD_DECL_MODULES_VAR fdecl_module_list) -# -# # application executable -- generated header file + other source files -# python_modules_header(modules -# FORWARD_DECL_MODULES_LIST ${fdecl_module_list}) -# include_directories(${modules_INCLUDE_DIRS}) -# -# add_cython_target(mainA) -# add_cython_target(mainC) -# add_executable(main ${mainA} mainB.cxx ${mainC} mainD.c) -# -# target_link_libraries(main ${linked_module_list} ${Boost_LIBRARIES}) -# python_standalone_executable(main) -# -#============================================================================= -# Copyright 2011 Kitware, Inc. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -#============================================================================= - -find_package(PythonInterp REQUIRED) -find_package(PythonLibs) -include(targetLinkLibrariesWithDynamicLookup) - -set(_command " -import distutils.sysconfig -import itertools -import os -import os.path -import site -import sys -import sysconfig - -result = None -rel_result = None -candidate_lists = [] - -try: - candidate_lists.append((distutils.sysconfig.get_python_lib(),)) -except AttributeError: pass - -try: - candidate_lists.append(site.getsitepackages()) -except AttributeError: pass - -try: - candidate_lists.append((site.getusersitepackages(),)) -except AttributeError: pass - -candidates = itertools.chain.from_iterable(candidate_lists) - -for candidate in candidates: - rel_candidate = os.path.relpath( - candidate, sys.prefix) - if not rel_candidate.startswith(\"..\"): - result = candidate - rel_result = rel_candidate - break - -sys.stdout.write(\";\".join(( - os.sep, - os.pathsep, - sys.prefix, - result, - rel_result, - sysconfig.get_config_var('EXT_SUFFIX') -))) -") - -execute_process(COMMAND "${PYTHON_EXECUTABLE}" -c "${_command}" - OUTPUT_VARIABLE _list - RESULT_VARIABLE _result) - -list(GET _list 0 _item) -set(PYTHON_SEPARATOR "${_item}") -mark_as_advanced(PYTHON_SEPARATOR) - -list(GET _list 1 _item) -set(PYTHON_PATH_SEPARATOR "${_item}") -mark_as_advanced(PYTHON_PATH_SEPARATOR) - -list(GET _list 2 _item) -set(PYTHON_PREFIX "${_item}") -mark_as_advanced(PYTHON_PREFIX) - -list(GET _list 3 _item) -set(PYTHON_SITE_PACKAGES_DIR "${_item}") -mark_as_advanced(PYTHON_SITE_PACKAGES_DIR) - -list(GET _list 4 _item) -set(PYTHON_RELATIVE_SITE_PACKAGES_DIR "${_item}") -mark_as_advanced(PYTHON_RELATIVE_SITE_PACKAGES_DIR) - -if(NOT DEFINED PYTHON_EXTENSION_MODULE_SUFFIX) - list(GET _list 5 _item) - set(PYTHON_EXTENSION_MODULE_SUFFIX "${_item}") -endif() - -function(python_extension_module _target) - set(one_ops LINKED_MODULES_VAR FORWARD_DECL_MODULES_VAR MODULE_SUFFIX) - cmake_parse_arguments(_args "" "${one_ops}" "" ${ARGN}) - - set(_lib_type "NA") - if(TARGET ${_target}) - get_property(_lib_type TARGET ${_target} PROPERTY TYPE) - endif() - - set(_is_non_lib TRUE) - - set(_is_static_lib FALSE) - if(_lib_type STREQUAL "STATIC_LIBRARY") - set(_is_static_lib TRUE) - set(_is_non_lib FALSE) - endif() - - set(_is_shared_lib FALSE) - if(_lib_type STREQUAL "SHARED_LIBRARY") - set(_is_shared_lib TRUE) - set(_is_non_lib FALSE) - endif() - - set(_is_module_lib FALSE) - if(_lib_type STREQUAL "MODULE_LIBRARY") - set(_is_module_lib TRUE) - set(_is_non_lib FALSE) - endif() - - if(_is_static_lib OR _is_shared_lib OR _is_non_lib) - - if(_is_static_lib OR _is_shared_lib) - if(_args_LINKED_MODULES_VAR) - set(${_args_LINKED_MODULES_VAR} - ${${_args_LINKED_MODULES_VAR}} ${_target} PARENT_SCOPE) - else() - set_property(GLOBAL APPEND PROPERTY PY_LINKED_MODULES_LIST ${_target}) - endif() - endif() - - if(_args_FORWARD_DECL_MODULES_VAR) - set(${_args_FORWARD_DECL_MODULES_VAR} - ${${_args_FORWARD_DECL_MODULES_VAR}} ${_target} PARENT_SCOPE) - else() - set_property(GLOBAL APPEND PROPERTY - PY_FORWARD_DECL_MODULES_LIST ${_target}) - endif() - endif() - - if(NOT _is_non_lib) - include_directories("${PYTHON_INCLUDE_DIRS}") - endif() - - if(_is_module_lib) - set_target_properties(${_target} PROPERTIES - PREFIX "${PYTHON_MODULE_PREFIX}") - endif() - - if(_is_module_lib OR _is_shared_lib) - if(_is_module_lib) - - if(NOT _args_MODULE_SUFFIX) - set(_args_MODULE_SUFFIX "${PYTHON_EXTENSION_MODULE_SUFFIX}") - endif() - - if(_args_MODULE_SUFFIX STREQUAL "" AND WIN32 AND NOT CYGWIN) - set(_args_MODULE_SUFFIX ".pyd") - endif() - - if(NOT _args_MODULE_SUFFIX STREQUAL "") - set_target_properties(${_target} - PROPERTIES SUFFIX ${_args_MODULE_SUFFIX}) - endif() - endif() - - target_link_libraries_with_dynamic_lookup(${_target} ${PYTHON_LIBRARIES}) - endif() -endfunction() - -function(python_standalone_executable _target) - include_directories(${PYTHON_INCLUDE_DIRS}) - target_link_libraries(${_target} ${PYTHON_LIBRARIES}) -endfunction() - -function(python_modules_header _name) - set(one_ops FORWARD_DECL_MODULES_LIST - HEADER_OUTPUT_VAR - INCLUDE_DIR_OUTPUT_VAR) - cmake_parse_arguments(_args "" "${one_ops}" "" ${ARGN}) - - list(GET _args_UNPARSED_ARGUMENTS 0 _arg0) - # if present, use arg0 as the input file path - if(_arg0) - set(_source_file ${_arg0}) - - # otherwise, must determine source file from name, or vice versa - else() - get_filename_component(_name_ext "${_name}" EXT) - - # if extension provided, _name is the source file - if(_name_ext) - set(_source_file ${_name}) - get_filename_component(_name "${_source_file}" NAME_WE) - - # otherwise, assume the source file is ${_name}.h - else() - set(_source_file ${_name}.h) - endif() - endif() - - if(_args_FORWARD_DECL_MODULES_LIST) - set(static_mod_list ${_args_FORWARD_DECL_MODULES_LIST}) - else() - get_property(static_mod_list GLOBAL PROPERTY PY_FORWARD_DECL_MODULES_LIST) - endif() - - string(REPLACE "." "_" _header_name "${_name}") - string(TOUPPER ${_header_name} _header_name_upper) - set(_header_name_upper "_${_header_name_upper}_H") - set(generated_file ${CMAKE_CURRENT_BINARY_DIR}/${_source_file}) - - set(generated_file_tmp "${generated_file}.in") - file(WRITE ${generated_file_tmp} - "/* Created by CMake. DO NOT EDIT; changes will be lost. */\n") - - set(_chunk "") - set(_chunk "${_chunk}#ifndef ${_header_name_upper}\n") - set(_chunk "${_chunk}#define ${_header_name_upper}\n") - set(_chunk "${_chunk}\n") - set(_chunk "${_chunk}#include \n") - set(_chunk "${_chunk}\n") - set(_chunk "${_chunk}#ifdef __cplusplus\n") - set(_chunk "${_chunk}extern \"C\" {\n") - set(_chunk "${_chunk}#endif /* __cplusplus */\n") - set(_chunk "${_chunk}\n") - set(_chunk "${_chunk}#if PY_MAJOR_VERSION < 3\n") - file(APPEND ${generated_file_tmp} "${_chunk}") - - foreach(_module ${static_mod_list}) - file(APPEND ${generated_file_tmp} - "PyMODINIT_FUNC init${PYTHON_MODULE_PREFIX}${_module}(void);\n") - endforeach() - - file(APPEND ${generated_file_tmp} "#else /* PY_MAJOR_VERSION >= 3*/\n") - - foreach(_module ${static_mod_list}) - file(APPEND ${generated_file_tmp} - "PyMODINIT_FUNC PyInit_${PYTHON_MODULE_PREFIX}${_module}(void);\n") - endforeach() - - set(_chunk "") - set(_chunk "${_chunk}#endif /* PY_MAJOR_VERSION >= 3*/\n\n") - set(_chunk "${_chunk}#ifdef __cplusplus\n") - set(_chunk "${_chunk}}\n") - set(_chunk "${_chunk}#endif /* __cplusplus */\n") - set(_chunk "${_chunk}\n") - file(APPEND ${generated_file_tmp} "${_chunk}") - - foreach(_module ${static_mod_list}) - set(_import_function "${_header_name}_${_module}") - set(_prefixed_module "${PYTHON_MODULE_PREFIX}${_module}") - - set(_chunk "") - set(_chunk "${_chunk}int ${_import_function}(void)\n") - set(_chunk "${_chunk}{\n") - set(_chunk "${_chunk} static char name[] = \"${_prefixed_module}\";\n") - set(_chunk "${_chunk} #if PY_MAJOR_VERSION < 3\n") - set(_chunk "${_chunk} return PyImport_AppendInittab(") - set(_chunk "${_chunk}name, init${_prefixed_module});\n") - set(_chunk "${_chunk} #else /* PY_MAJOR_VERSION >= 3 */\n") - set(_chunk "${_chunk} return PyImport_AppendInittab(") - set(_chunk "${_chunk}name, PyInit_${_prefixed_module});\n") - set(_chunk "${_chunk} #endif /* PY_MAJOR_VERSION >= 3 */\n") - set(_chunk "${_chunk}}\n\n") - file(APPEND ${generated_file_tmp} "${_chunk}") - endforeach() - - file(APPEND ${generated_file_tmp} - "void ${_header_name}_LoadAllPythonModules(void)\n{\n") - foreach(_module ${static_mod_list}) - file(APPEND ${generated_file_tmp} " ${_header_name}_${_module}();\n") - endforeach() - file(APPEND ${generated_file_tmp} "}\n\n") - - set(_chunk "") - set(_chunk "${_chunk}#ifndef EXCLUDE_LOAD_ALL_FUNCTION\n") - set(_chunk "${_chunk}void CMakeLoadAllPythonModules(void)\n") - set(_chunk "${_chunk}{\n") - set(_chunk "${_chunk} ${_header_name}_LoadAllPythonModules();\n") - set(_chunk "${_chunk}}\n") - set(_chunk "${_chunk}#endif /* !EXCLUDE_LOAD_ALL_FUNCTION */\n\n") - - set(_chunk "${_chunk}#ifndef EXCLUDE_PY_INIT_WRAPPER\n") - set(_chunk "${_chunk}static void Py_Initialize_Wrapper()\n") - set(_chunk "${_chunk}{\n") - set(_chunk "${_chunk} ${_header_name}_LoadAllPythonModules();\n") - set(_chunk "${_chunk} Py_Initialize();\n") - set(_chunk "${_chunk}}\n") - set(_chunk "${_chunk}#define Py_Initialize Py_Initialize_Wrapper\n") - set(_chunk "${_chunk}#endif /* !EXCLUDE_PY_INIT_WRAPPER */\n\n") - - set(_chunk "${_chunk}#endif /* !${_header_name_upper} */\n") - file(APPEND ${generated_file_tmp} "${_chunk}") - - # with configure_file() cmake complains that you may not use a file created - # using file(WRITE) as input file for configure_file() - execute_process(COMMAND ${CMAKE_COMMAND} -E copy_if_different - "${generated_file_tmp}" "${generated_file}" - OUTPUT_QUIET ERROR_QUIET) - - set(_header_output_var ${_name}) - if(_args_HEADER_OUTPUT_VAR) - set(_header_output_var ${_args_HEADER_OUTPUT_VAR}) - endif() - set(${_header_output_var} ${generated_file} PARENT_SCOPE) - - set(_include_dir_var ${_name}_INCLUDE_DIRS) - if(_args_INCLUDE_DIR_OUTPUT_VAR) - set(_include_dir_var ${_args_INCLUDE_DIR_OUTPUT_VAR}) - endif() - set(${_include_dirs_var} ${CMAKE_CURRENT_BINARY_DIR} PARENT_SCOPE) -endfunction() diff --git a/Day_3/src/fortran/cmake/UseCython.cmake b/Day_3/src/fortran/cmake/UseCython.cmake deleted file mode 100644 index 1c812ea..0000000 --- a/Day_3/src/fortran/cmake/UseCython.cmake +++ /dev/null @@ -1,395 +0,0 @@ -#.rst: -# -# The following functions are defined: -# -# .. cmake:command:: add_cython_target -# -# Create a custom rule to generate the source code for a Python extension module -# using cython. -# -# add_cython_target( [] -# [EMBED_MAIN] -# [C | CXX] -# [PY2 | PY3] -# [OUTPUT_VAR ]) -# -# ```` is the name of the new target, and ```` -# is the path to a cython source file. Note that, despite the name, no new -# targets are created by this function. Instead, see ``OUTPUT_VAR`` for -# retrieving the path to the generated source for subsequent targets. -# -# If only ```` is provided, and it ends in the ".pyx" extension, then it -# is assumed to be the ````. The name of the input without the -# extension is used as the target name. If only ```` is provided, and it -# does not end in the ".pyx" extension, then the ```` is assumed to -# be ``.pyx``. -# -# The Cython include search path is amended with any entries found in the -# ``INCLUDE_DIRECTORIES`` property of the directory containing the -# ```` file. Use ``iunclude_directories`` to add to the Cython -# include search path. -# -# Options: -# -# ``EMBED_MAIN`` -# Embed a main() function in the generated output (for stand-alone -# applications that initialize their own Python runtime). -# -# ``C | CXX`` -# Force the generation of either a C or C++ file. By default, a C file is -# generated, unless the C language is not enabled for the project; in this -# case, a C++ file is generated by default. -# -# ``PY2 | PY3`` -# Force compilation using either Python-2 or Python-3 syntax and code -# semantics. By default, Python-2 syntax and semantics are used if the major -# version of Python found is 2. Otherwise, Python-3 syntax and sematics are -# used. -# -# ``OUTPUT_VAR `` -# Set the variable ```` in the parent scope to the path to the -# generated source file. By default, ```` is used as the output -# variable name. -# -# Defined variables: -# -# ```` -# The path of the generated source file. -# -# Cache variables that effect the behavior include: -# -# ``CYTHON_ANNOTATE`` -# whether to create an annotated .html file when compiling -# -# ``CYTHON_FLAGS`` -# additional flags to pass to the Cython compiler -# -# Example usage -# ^^^^^^^^^^^^^ -# -# .. code-block:: cmake -# -# find_package(Cython) -# -# # Note: In this case, either one of these arguments may be omitted; their -# # value would have been inferred from that of the other. -# add_cython_target(cy_code cy_code.pyx) -# -# add_library(cy_code MODULE ${cy_code}) -# target_link_libraries(cy_code ...) -# -#============================================================================= -# Copyright 2011 Kitware, Inc. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -#============================================================================= - -# Configuration options. -set(CYTHON_ANNOTATE OFF - CACHE BOOL "Create an annotated .html file when compiling *.pyx.") - -set(CYTHON_FLAGS "" CACHE STRING - "Extra flags to the cython compiler.") -mark_as_advanced(CYTHON_ANNOTATE CYTHON_FLAGS) - -find_package(PythonLibs REQUIRED) - -set(CYTHON_CXX_EXTENSION "cxx") -set(CYTHON_C_EXTENSION "c") - -get_property(languages GLOBAL PROPERTY ENABLED_LANGUAGES) - -function(add_cython_target _name) - set(options EMBED_MAIN C CXX PY2 PY3) - set(options1 OUTPUT_VAR) - cmake_parse_arguments(_args "${options}" "${options1}" "" ${ARGN}) - - list(GET _args_UNPARSED_ARGUMENTS 0 _arg0) - - # if provided, use _arg0 as the input file path - if(_arg0) - set(_source_file ${_arg0}) - - # otherwise, must determine source file from name, or vice versa - else() - get_filename_component(_name_ext "${_name}" EXT) - - # if extension provided, _name is the source file - if(_name_ext) - set(_source_file ${_name}) - get_filename_component(_name "${_source_file}" NAME_WE) - - # otherwise, assume the source file is ${_name}.pyx - else() - set(_source_file ${_name}.pyx) - endif() - endif() - - set(_embed_main FALSE) - - if("C" IN_LIST languages) - set(_output_syntax "C") - elseif("CXX" IN_LIST languages) - set(_output_syntax "CXX") - else() - message(FATAL_ERROR "Either C or CXX must be enabled to use Cython") - endif() - - if("${PYTHONLIBS_VERSION_STRING}" MATCHES "^2.") - set(_input_syntax "PY2") - else() - set(_input_syntax "PY3") - endif() - - if(_args_EMBED_MAIN) - set(_embed_main TRUE) - endif() - - if(_args_C) - set(_output_syntax "C") - endif() - - if(_args_CXX) - set(_output_syntax "CXX") - endif() - - if(_args_PY2) - set(_input_syntax "PY2") - endif() - - if(_args_PY3) - set(_input_syntax "PY3") - endif() - - set(embed_arg "") - if(_embed_main) - set(embed_arg "--embed") - endif() - - set(cxx_arg "") - set(extension "c") - if(_output_syntax STREQUAL "CXX") - set(cxx_arg "--cplus") - set(extension "cxx") - endif() - - set(py_version_arg "") - if(_input_syntax STREQUAL "PY2") - set(py_version_arg "-2") - elseif(_input_syntax STREQUAL "PY3") - set(py_version_arg "-3") - endif() - - set(generated_file "${CMAKE_CURRENT_BINARY_DIR}/${_name}.${extension}") - set_source_files_properties(${generated_file} PROPERTIES GENERATED TRUE) - - set(_output_var ${_name}) - if(_args_OUTPUT_VAR) - set(_output_var ${_args_OUTPUT_VAR}) - endif() - set(${_output_var} ${generated_file} PARENT_SCOPE) - - file(RELATIVE_PATH generated_file_relative - ${CMAKE_BINARY_DIR} ${generated_file}) - - set(comment "Generating ${_output_syntax} source ${generated_file_relative}") - set(cython_include_directories "") - set(pxd_dependencies "") - set(c_header_dependencies "") - - # Get the include directories. - get_source_file_property(pyx_location ${_source_file} LOCATION) - get_filename_component(pyx_path ${pyx_location} PATH) - get_directory_property(cmake_include_directories - DIRECTORY ${pyx_path} - INCLUDE_DIRECTORIES) - list(APPEND cython_include_directories ${cmake_include_directories}) - - # Determine dependencies. - # Add the pxd file with the same basename as the given pyx file. - get_filename_component(pyx_file_basename ${_source_file} NAME_WE) - unset(corresponding_pxd_file CACHE) - find_file(corresponding_pxd_file ${pyx_file_basename}.pxd - PATHS "${pyx_path}" ${cmake_include_directories} - NO_DEFAULT_PATH) - if(corresponding_pxd_file) - list(APPEND pxd_dependencies "${corresponding_pxd_file}") - endif() - - # pxd files to check for additional dependencies - set(pxds_to_check "${_source_file}" "${pxd_dependencies}") - set(pxds_checked "") - set(number_pxds_to_check 1) - while(number_pxds_to_check GREATER 0) - foreach(pxd ${pxds_to_check}) - list(APPEND pxds_checked "${pxd}") - list(REMOVE_ITEM pxds_to_check "${pxd}") - - # look for C headers - file(STRINGS "${pxd}" extern_from_statements - REGEX "cdef[ ]+extern[ ]+from.*$") - foreach(statement ${extern_from_statements}) - # Had trouble getting the quote in the regex - string(REGEX REPLACE - "cdef[ ]+extern[ ]+from[ ]+[\"]([^\"]+)[\"].*" "\\1" - header "${statement}") - unset(header_location CACHE) - find_file(header_location ${header} PATHS ${cmake_include_directories}) - if(header_location) - list(FIND c_header_dependencies "${header_location}" header_idx) - if(${header_idx} LESS 0) - list(APPEND c_header_dependencies "${header_location}") - endif() - endif() - endforeach() - - # check for pxd dependencies - # Look for cimport statements. - set(module_dependencies "") - file(STRINGS "${pxd}" cimport_statements REGEX cimport) - foreach(statement ${cimport_statements}) - if(${statement} MATCHES from) - string(REGEX REPLACE - "from[ ]+([^ ]+).*" "\\1" - module "${statement}") - else() - string(REGEX REPLACE - "cimport[ ]+([^ ]+).*" "\\1" - module "${statement}") - endif() - list(APPEND module_dependencies ${module}) - endforeach() - - # check for pxi dependencies - # Look for include statements. - set(include_dependencies "") - file(STRINGS "${pxd}" include_statements REGEX include) - foreach(statement ${include_statements}) - string(REGEX REPLACE - "include[ ]+[\"]([^\"]+)[\"].*" "\\1" - module "${statement}") - list(APPEND include_dependencies ${module}) - endforeach() - - list(REMOVE_DUPLICATES module_dependencies) - list(REMOVE_DUPLICATES include_dependencies) - - # Add modules to the files to check, if appropriate. - foreach(module ${module_dependencies}) - unset(pxd_location CACHE) - find_file(pxd_location ${module}.pxd - PATHS "${pyx_path}" ${cmake_include_directories} - NO_DEFAULT_PATH) - if(pxd_location) - list(FIND pxds_checked ${pxd_location} pxd_idx) - if(${pxd_idx} LESS 0) - list(FIND pxds_to_check ${pxd_location} pxd_idx) - if(${pxd_idx} LESS 0) - list(APPEND pxds_to_check ${pxd_location}) - list(APPEND pxd_dependencies ${pxd_location}) - endif() # if it is not already going to be checked - endif() # if it has not already been checked - endif() # if pxd file can be found - endforeach() # for each module dependency discovered - - # Add includes to the files to check, if appropriate. - foreach(_include ${include_dependencies}) - unset(pxi_location CACHE) - find_file(pxi_location ${_include} - PATHS "${pyx_path}" ${cmake_include_directories} - NO_DEFAULT_PATH) - if(pxi_location) - list(FIND pxds_checked ${pxi_location} pxd_idx) - if(${pxd_idx} LESS 0) - list(FIND pxds_to_check ${pxi_location} pxd_idx) - if(${pxd_idx} LESS 0) - list(APPEND pxds_to_check ${pxi_location}) - list(APPEND pxd_dependencies ${pxi_location}) - endif() # if it is not already going to be checked - endif() # if it has not already been checked - endif() # if include file can be found - endforeach() # for each include dependency discovered - endforeach() # for each include file to check - - list(LENGTH pxds_to_check number_pxds_to_check) - endwhile() - - # Set additional flags. - set(annotate_arg "") - if(CYTHON_ANNOTATE) - set(annotate_arg "--annotate") - endif() - - set(no_docstrings_arg "") - if(CMAKE_BUILD_TYPE STREQUAL "Release" OR - CMAKE_BUILD_TYPE STREQUAL "MinSizeRel") - set(no_docstrings_arg "--no-docstrings") - endif() - - set(cython_debug_arg "") - set(embed_pos_arg "") - set(line_directives_arg "") - if(CMAKE_BUILD_TYPE STREQUAL "Debug" OR - CMAKE_BUILD_TYPE STREQUAL "RelWithDebInfo") - set(cython_debug_arg "--gdb") - set(embed_pos_arg "--embed-positions") - set(line_directives_arg "--line-directives") - endif() - - # Include directory arguments. - list(REMOVE_DUPLICATES cython_include_directories) - set(include_directory_arg "") - foreach(_include_dir ${cython_include_directories}) - set(include_directory_arg - ${include_directory_arg} "--include-dir" "${_include_dir}") - endforeach() - - list(REMOVE_DUPLICATES pxd_dependencies) - list(REMOVE_DUPLICATES c_header_dependencies) - - # Add the command to run the compiler. - add_custom_command(OUTPUT ${generated_file} - COMMAND ${CYTHON_EXECUTABLE} - ARGS ${cxx_arg} ${include_directory_arg} ${py_version_arg} - ${embed_arg} ${annotate_arg} ${no_docstrings_arg} - ${cython_debug_arg} ${embed_pos_arg} - ${line_directives_arg} ${CYTHON_FLAGS} ${pyx_location} - --output-file ${generated_file} - DEPENDS ${_source_file} - ${pxd_dependencies} - IMPLICIT_DEPENDS ${_output_syntax} - ${c_header_dependencies} - COMMENT ${comment}) - - # NOTE(opadron): I thought about making a proper target, but after trying it - # out, I decided that it would be far too convenient to use the same name as - # the target for the extension module (e.g.: for single-file modules): - # - # ... - # add_cython_target(_module.pyx) - # add_library(_module ${_module}) - # ... - # - # The above example would not be possible since the "_module" target name - # would already be taken by the cython target. Since I can't think of a - # reason why someone would need the custom target instead of just using the - # generated file directly, I decided to leave this commented out. - # - # add_custom_target(${_name} DEPENDS ${generated_file}) - - # Remove their visibility to the user. - set(corresponding_pxd_file "" CACHE INTERNAL "") - set(header_location "" CACHE INTERNAL "") - set(pxd_location "" CACHE INTERNAL "") -endfunction() - diff --git a/Day_3/src/fortran/cmake/targetLinkLibrariesWithDynamicLookup.cmake b/Day_3/src/fortran/cmake/targetLinkLibrariesWithDynamicLookup.cmake deleted file mode 100644 index 4986161..0000000 --- a/Day_3/src/fortran/cmake/targetLinkLibrariesWithDynamicLookup.cmake +++ /dev/null @@ -1,580 +0,0 @@ -#.rst: -# -# Public Functions -# ^^^^^^^^^^^^^^^^ -# -# The following functions are defined: -# -# .. cmake:command:: target_link_libraries_with_dynamic_lookup -# -# :: -# -# target_link_libraries_with_dynamic_lookup( []) -# -# -# Useful to "weakly" link a loadable module. For example, it should be used -# when compiling a loadable module when the symbols should be resolve from -# the run-time environment where the module is loaded, and not a specific -# system library. -# -# Like proper linking, except that the given ```` are not necessarily -# linked. Instead, the ```` is produced in a manner that allows for -# symbols unresolved within it to be resolved at runtime, presumably by the -# given ````. If such a target can be produced, the provided -# ```` are not actually linked. -# -# It links a library to a target such that the symbols are resolved at -# run-time not link-time. -# -# The linker is checked to see if it supports undefined -# symbols when linking a shared library. If it does then the library -# is not linked when specified with this function. -# -# On platforms that do not support weak-linking, this function works just -# like ``target_link_libraries``. -# -# .. note:: -# -# For OSX it uses ``undefined dynamic_lookup``. This is similar to using -# ``-shared`` on Linux where undefined symbols are ignored. -# -# For more details, see `blog `_ -# from Tim D. Smith. -# -# -# .. cmake:command:: check_dynamic_lookup -# -# Check if the linker requires a command line flag to allow leaving symbols -# unresolved when producing a target of type ```` that is -# weakly-linked against a dependency of type ````. -# -# ```` -# can be one of "STATIC", "SHARED", "MODULE", or "EXE". -# -# ```` -# can be one of "STATIC", "SHARED", or "MODULE". -# -# Long signature: -# -# :: -# -# check_dynamic_lookup( -# -# -# []) -# -# -# Short signature: -# -# :: -# -# check_dynamic_lookup() # set to "MODULE" -# # set to "SHARED" -# -# -# The result is cached between invocations and recomputed only when the value -# of CMake's linker flag list changes; ``CMAKE_STATIC_LINKER_FLAGS`` if -# ```` is "STATIC", and ``CMAKE_SHARED_LINKER_FLAGS`` otherwise. -# -# -# Defined variables: -# -# ```` -# Whether the current C toolchain supports weak-linking for target binaries of -# type ```` that are weakly-linked against a dependency target of -# type ````. -# -# ```` -# List of flags to add to the linker command to produce a working target -# binary of type ```` that is weakly-linked against a dependency -# target of type ````. -# -# ``HAS_DYNAMIC_LOOKUP__`` -# Cached, global alias for ```` -# -# ``DYNAMIC_LOOKUP_FLAGS__`` -# Cached, global alias for ```` -# -# -# Private Functions -# ^^^^^^^^^^^^^^^^^ -# -# The following private functions are defined: -# -# .. warning:: These functions are not part of the scikit-build API. They -# exist purely as an implementation detail and may change from version -# to version without notice, or even be removed. -# -# We mean it. -# -# -# .. cmake:command:: _get_target_type -# -# :: -# -# _get_target_type( ) -# -# -# Shorthand for querying an abbreviated version of the target type -# of the given ````. -# -# ```` is set to: -# -# - "STATIC" for a STATIC_LIBRARY, -# - "SHARED" for a SHARED_LIBRARY, -# - "MODULE" for a MODULE_LIBRARY, -# - and "EXE" for an EXECUTABLE. -# -# Defined variables: -# -# ```` -# The abbreviated version of the ````'s type. -# -# -# .. cmake:command:: _test_weak_link_project -# -# :: -# -# _test_weak_link_project( -# -# -# ) -# -# -# Attempt to compile and run a test project where a target of type -# ```` is weakly-linked against a dependency of type ````: -# -# - ```` can be one of "STATIC", "SHARED", "MODULE", or "EXE". -# - ```` can be one of "STATIC", "SHARED", or "MODULE". -# -# Defined variables: -# -# ```` -# Whether the current C toolchain can produce a working target binary of type -# ```` that is weakly-linked against a dependency target of type -# ````. -# -# ```` -# List of flags to add to the linker command to produce a working target -# binary of type ```` that is weakly-linked against a dependency -# target of type ````. -# - -function(_get_target_type result_var target) - set(target_type "SHARED_LIBRARY") - if(TARGET ${target}) - get_property(target_type TARGET ${target} PROPERTY TYPE) - endif() - - set(result "STATIC") - - if(target_type STREQUAL "STATIC_LIBRARY") - set(result "STATIC") - endif() - - if(target_type STREQUAL "SHARED_LIBRARY") - set(result "SHARED") - endif() - - if(target_type STREQUAL "MODULE_LIBRARY") - set(result "MODULE") - endif() - - if(target_type STREQUAL "EXECUTABLE") - set(result "EXE") - endif() - - set(${result_var} ${result} PARENT_SCOPE) -endfunction() - - -function(_test_weak_link_project - target_type - lib_type - can_weak_link_var - project_name) - - set(gnu_ld_ignore "-Wl,--unresolved-symbols=ignore-all") - set(osx_dynamic_lookup "-undefined dynamic_lookup") - set(no_flag "") - - foreach(link_flag_spec gnu_ld_ignore osx_dynamic_lookup no_flag) - set(link_flag "${${link_flag_spec}}") - - set(test_project_dir "${PROJECT_BINARY_DIR}/CMakeTmp") - set(test_project_dir "${test_project_dir}/${project_name}") - set(test_project_dir "${test_project_dir}/${link_flag_spec}") - set(test_project_dir "${test_project_dir}/${target_type}") - set(test_project_dir "${test_project_dir}/${lib_type}") - - set(test_project_src_dir "${test_project_dir}/src") - set(test_project_bin_dir "${test_project_dir}/build") - - file(MAKE_DIRECTORY ${test_project_src_dir}) - file(MAKE_DIRECTORY ${test_project_bin_dir}) - - set(mod_type "STATIC") - set(link_mod_lib TRUE) - set(link_exe_lib TRUE) - set(link_exe_mod FALSE) - - if("${target_type}" STREQUAL "EXE") - set(link_exe_lib FALSE) - set(link_exe_mod TRUE) - else() - set(mod_type "${target_type}") - endif() - - if("${mod_type}" STREQUAL "MODULE") - set(link_mod_lib FALSE) - endif() - - - file(WRITE "${test_project_src_dir}/CMakeLists.txt" " - cmake_minimum_required(VERSION ${CMAKE_VERSION}) - project(${project_name} C) - - include_directories(${test_project_src_dir}) - - add_library(number ${lib_type} number.c) - add_library(counter ${mod_type} counter.c) - ") - - if("${mod_type}" STREQUAL "MODULE") - file(APPEND "${test_project_src_dir}/CMakeLists.txt" " - set_target_properties(counter PROPERTIES PREFIX \"\") - ") - endif() - - if(link_mod_lib) - file(APPEND "${test_project_src_dir}/CMakeLists.txt" " - target_link_libraries(counter number) - ") - elseif(NOT link_flag STREQUAL "") - file(APPEND "${test_project_src_dir}/CMakeLists.txt" " - set_target_properties(counter PROPERTIES LINK_FLAGS \"${link_flag}\") - ") - endif() - - file(APPEND "${test_project_src_dir}/CMakeLists.txt" " - add_executable(main main.c) - ") - - if(link_exe_lib) - file(APPEND "${test_project_src_dir}/CMakeLists.txt" " - target_link_libraries(main number) - ") - elseif(NOT link_flag STREQUAL "") - file(APPEND "${test_project_src_dir}/CMakeLists.txt" " - target_link_libraries(main \"${link_flag}\") - ") - endif() - - if(link_exe_mod) - file(APPEND "${test_project_src_dir}/CMakeLists.txt" " - target_link_libraries(main counter) - ") - else() - file(APPEND "${test_project_src_dir}/CMakeLists.txt" " - target_link_libraries(main \"${CMAKE_DL_LIBS}\") - ") - endif() - - file(WRITE "${test_project_src_dir}/number.c" " - #include - - static int _number; - void set_number(int number) { _number = number; } - int get_number() { return _number; } - ") - - file(WRITE "${test_project_src_dir}/number.h" " - #ifndef _NUMBER_H - #define _NUMBER_H - extern void set_number(int); - extern int get_number(void); - #endif - ") - - file(WRITE "${test_project_src_dir}/counter.c" " - #include - int count() { - int result = get_number(); - set_number(result + 1); - return result; - } - ") - - file(WRITE "${test_project_src_dir}/counter.h" " - #ifndef _COUNTER_H - #define _COUNTER_H - extern int count(void); - #endif - ") - - file(WRITE "${test_project_src_dir}/main.c" " - #include - #include - #include - ") - - if(NOT link_exe_mod) - file(APPEND "${test_project_src_dir}/main.c" " - #include - ") - endif() - - file(APPEND "${test_project_src_dir}/main.c" " - int my_count() { - int result = get_number(); - set_number(result + 1); - return result; - } - - int main(int argc, char **argv) { - int result; - ") - - if(NOT link_exe_mod) - file(APPEND "${test_project_src_dir}/main.c" " - void *counter_module; - int (*count)(void); - - counter_module = dlopen(\"./counter.so\", RTLD_LAZY | RTLD_GLOBAL); - if(!counter_module) goto error; - - count = dlsym(counter_module, \"count\"); - if(!count) goto error; - ") - endif() - - file(APPEND "${test_project_src_dir}/main.c" " - result = count() != 0 ? EXIT_FAILURE : - my_count() != 1 ? EXIT_FAILURE : - my_count() != 2 ? EXIT_FAILURE : - count() != 3 ? EXIT_FAILURE : - count() != 4 ? EXIT_FAILURE : - count() != 5 ? EXIT_FAILURE : - my_count() != 6 ? EXIT_FAILURE : EXIT_SUCCESS; - ") - - if(NOT link_exe_mod) - file(APPEND "${test_project_src_dir}/main.c" " - goto done; - error: - fprintf(stderr, \"Error occured:\\n %s\\n\", dlerror()); - result = 1; - - done: - if(counter_module) dlclose(counter_module); - ") - endif() - - file(APPEND "${test_project_src_dir}/main.c" " - return result; - } - ") - - set(_rpath_arg) - if(APPLE AND ${CMAKE_VERSION} VERSION_GREATER 2.8.11) - set(_rpath_arg "-DCMAKE_MACOSX_RPATH='${CMAKE_MACOSX_RPATH}'") - endif() - - try_compile(project_compiles - "${test_project_bin_dir}" - "${test_project_src_dir}" - "${project_name}" - CMAKE_FLAGS - "-DCMAKE_SHARED_LINKER_FLAGS='${CMAKE_SHARED_LINKER_FLAGS}'" - "-DCMAKE_ENABLE_EXPORTS=ON" - ${_rpath_arg} - OUTPUT_VARIABLE compile_output) - - set(project_works 1) - set(run_output) - - if(project_compiles) - execute_process(COMMAND ${CMAKE_CROSSCOMPILING_EMULATOR} - "${test_project_bin_dir}/main" - WORKING_DIRECTORY "${test_project_bin_dir}" - RESULT_VARIABLE project_works - OUTPUT_VARIABLE run_output - ERROR_VARIABLE run_output) - endif() - - set(test_description - "Weak Link ${target_type} -> ${lib_type} (${link_flag_spec})") - - if(project_works EQUAL 0) - set(project_works TRUE) - message(STATUS "Performing Test ${test_description} - Success") - else() - set(project_works FALSE) - message(STATUS "Performing Test ${test_description} - Failed") - file(APPEND ${CMAKE_BINARY_DIR}/${CMAKE_FILES_DIRECTORY}/CMakeError.log - "Performing Test ${test_description} failed with the " - "following output:\n" - "BUILD\n-----\n${compile_output}\nRUN\n---\n${run_output}\n") - endif() - - set(${can_weak_link_var} ${project_works} PARENT_SCOPE) - if(project_works) - set(${project_name} ${link_flag} PARENT_SCOPE) - break() - endif() - endforeach() -endfunction() - -function(check_dynamic_lookup) - # Two signatures are supported: - - if(ARGC EQUAL "1") - # - # check_dynamic_lookup() - # - set(target_type "MODULE") - set(lib_type "SHARED") - set(has_dynamic_lookup_var "${ARGV0}") - set(link_flags_var "unused") - - elseif(ARGC GREATER "2") - # - # check_dynamic_lookup( - # - # - # []) - # - set(target_type "${ARGV0}") - set(lib_type "${ARGV1}") - set(has_dynamic_lookup_var "${ARGV2}") - if(ARGC EQUAL "3") - set(link_flags_var "unused") - else() - set(link_flags_var "${ARGV3}") - endif() - else() - message(FATAL_ERROR "missing arguments") - endif() - - _check_dynamic_lookup( - ${target_type} - ${lib_type} - ${has_dynamic_lookup_var} - ${link_flags_var} - ) - set(${has_dynamic_lookup_var} ${${has_dynamic_lookup_var}} PARENT_SCOPE) - if(NOT "x${link_flags_var}x" STREQUAL "xunusedx") - set(${link_flags_var} ${${link_flags_var}} PARENT_SCOPE) - endif() -endfunction() - -function(_check_dynamic_lookup - target_type - lib_type - has_dynamic_lookup_var - link_flags_var - ) - - # hash the CMAKE_FLAGS passed and check cache to know if we need to rerun - if("${target_type}" STREQUAL "STATIC") - string(MD5 cmake_flags_hash "${CMAKE_STATIC_LINKER_FLAGS}") - else() - string(MD5 cmake_flags_hash "${CMAKE_SHARED_LINKER_FLAGS}") - endif() - - set(cache_var "HAS_DYNAMIC_LOOKUP_${target_type}_${lib_type}") - set(cache_hash_var "HAS_DYNAMIC_LOOKUP_${target_type}_${lib_type}_hash") - set(result_var "DYNAMIC_LOOKUP_FLAGS_${target_type}_${lib_type}") - - if( NOT DEFINED ${cache_hash_var} - OR NOT "${${cache_hash_var}}" STREQUAL "${cmake_flags_hash}") - unset(${cache_var} CACHE) - endif() - - if(NOT DEFINED ${cache_var}) - set(skip_test FALSE) - - if(CMAKE_CROSSCOMPILING AND NOT CMAKE_CROSSCOMPILING_EMULATOR) - set(skip_test TRUE) - endif() - - if(skip_test) - set(has_dynamic_lookup FALSE) - set(link_flags) - else() - _test_weak_link_project(${target_type} - ${lib_type} - has_dynamic_lookup - link_flags) - endif() - - set(caveat " (when linking ${target_type} against ${lib_type})") - - set(${cache_var} "${has_dynamic_lookup}" - CACHE BOOL - "linker supports dynamic lookup for undefined symbols${caveat}") - mark_as_advanced(${cache_var}) - - set(${result_var} "${link_flags}" - CACHE STRING - "linker flags for dynamic lookup${caveat}") - mark_as_advanced(${result_var}) - - set(${cache_hash_var} "${cmake_flags_hash}" - CACHE INTERNAL "hashed flags for ${cache_var} check") - endif() - - set(${has_dynamic_lookup_var} "${${cache_var}}" PARENT_SCOPE) - set(${link_flags_var} "${${result_var}}" PARENT_SCOPE) -endfunction() - -function(target_link_libraries_with_dynamic_lookup target) - _get_target_type(target_type ${target}) - - set(link_props) - set(link_items) - set(link_libs) - - foreach(lib ${ARGN}) - _get_target_type(lib_type ${lib}) - check_dynamic_lookup(${target_type} - ${lib_type} - has_dynamic_lookup - dynamic_lookup_flags) - - if(has_dynamic_lookup) - if(dynamic_lookup_flags) - if("${target_type}" STREQUAL "EXE") - list(APPEND link_items "${dynamic_lookup_flags}") - else() - list(APPEND link_props "${dynamic_lookup_flags}") - endif() - endif() - else() - list(APPEND link_libs "${lib}") - endif() - endforeach() - - if(link_props) - list(REMOVE_DUPLICATES link_props) - endif() - - if(link_items) - list(REMOVE_DUPLICATES link_items) - endif() - - if(link_libs) - list(REMOVE_DUPLICATES link_libs) - endif() - - if(link_props) - set_target_properties(${target} - PROPERTIES LINK_FLAGS "${link_props}") - endif() - - set(links "${link_items}" "${link_libs}") - if(links) - target_link_libraries(${target} "${links}") - endif() -endfunction() - diff --git a/Day_3/src/fortran/wave_propogation.pyx b/Day_3/src/fortran/wave_propogation.pyx deleted file mode 100644 index a6572d6..0000000 --- a/Day_3/src/fortran/wave_propogation.pyx +++ /dev/null @@ -1,30 +0,0 @@ -from libc.stdlib cimport malloc, free -from cpython.mem cimport PyMem_Malloc -import numpy as np -import time -cimport cython -from cython.view cimport array as cvarray - -cdef extern from "wp.hpp": - void wave_propogation_(int*, int*, float*, float*, int*, float*) - -@cython.boundscheck(False) -@cython.cdivision(True) -@cython.wraparound(False) -@cython.infer_types(False) -def wave_propogation(int num_steps, int scale=100,float damping=0.25, float initial_P=250.0, int stop_step=100): - cdef int size_x = 2 * scale + 1 - cdef int size_y = 2 * scale + 1 - cdef int *_num_steps=&num_steps - cdef int *_scale=&scale - cdef float *_damping=&damping - cdef float *_initial_P=&initial_P - cdef int *_stop_step=&stop_step - - cdef float *array = malloc(sizeof(float) * size_x * size_y) - wave_propogation_(_num_steps, _scale, _damping, _initial_P, _stop_step, array) - P = [[0.0 for x in range(size_x)] for y in range(size_y)] - for i in range(size_x): - for j in range(size_y): - P[i][j] = array[i*size_x+j] if not np.isnan(array[i*size_x+j]) else 0.0 - return P diff --git a/Day_3/src/fortran/wp.f90 b/Day_3/src/fortran/wp.f90 deleted file mode 100644 index 5341955..0000000 --- a/Day_3/src/fortran/wp.f90 +++ /dev/null @@ -1,50 +0,0 @@ - - -SUBROUTINE wave_propogation(num_steps, scale, damping, initial_P, stop_step, P) - IMPLICIT NONE - INTEGER size_x,size_Y,i,j,k,step - REAL PI, omega - INTEGER, INTENT(in) :: num_steps, scale, stop_step !input - REAL, INTENT(in) :: damping, initial_P !input - REAL, INTENT(out) :: P(2 * scale + 1,2 * scale + 1) ! output - REAL, DIMENSION(2 * scale + 1,2 * scale + 1,4) :: V - - size_x = 2 * scale + 1 - size_Y = 2 * scale + 1 - - PI = 3.14159 - omega = 3.0 / (2.0 * PI) - - DO k=1,4 - DO j=1,size_x - DO i=1,size_x - P(i,j) = 0.0 - V(i,j,k) = 0.0 - END DO - END DO - END DO - - P(scale,scale) = initial_P - - DO step = 1,num_steps - IF(step <= stop_step) THEN - P(scale,scale) = initial_P * SIN(omega * step) - ENDIF - - DO j=1,size_x - DO i=1,size_x - V(i,j,1) = MERGE(V(i,j,1) + P(i,j) - P(i - 1,j), P(i,j), i > 1) - V(i,j,2) = MERGE(V(i,j,2) + P(i,j) - P(i,j + 1), P(i,j), j < size_x - 1) - V(i,j,3) = MERGE(V(i,j,3) + P(i,j) - P(i + 1,j), P(i,j), i < size_y - 1) - V(i,j,4) = MERGE(V(i,j,4) + P(i,j) - P(i,j - 1), P(i,j), j > 1) - END DO - END DO - - DO j=1,size_x - DO i=1,size_x - P(i,j) = P(i,j) - 0.5 * damping * (V(i,j,1) + V(i,j,2) + V(i,j,3) + V(i,j,4)) - END DO - END DO - END DO - -END SUBROUTINE wave_propogation diff --git a/Day_3/src/fortran/wp.hpp b/Day_3/src/fortran/wp.hpp deleted file mode 100644 index 8952b6e..0000000 --- a/Day_3/src/fortran/wp.hpp +++ /dev/null @@ -1,5 +0,0 @@ - -extern "C" { -void wave_propogation_(int *num_steps, int *scale, float *damping, - float *initial_P, int *stop_step, float *_P); -} diff --git a/Day_3/wave_propogation.pyx b/Day_3/wave_propogation.pyx deleted file mode 100644 index 0080291..0000000 --- a/Day_3/wave_propogation.pyx +++ /dev/null @@ -1,53 +0,0 @@ -# ^ remove these two lines %%cython has to be the first line -%%cython -a -import time -# Import the c versions of pi and sin to speed up computations -from libc.math cimport M_PI as pi -from libc.math cimport sin as sin -cimport cython - -cimport numpy as np -import numpy as np - -# Using array to make C style array -from cython.view cimport array - -# These turn off some python features for accessing arrays as python arrays -@cython.boundscheck(False) -@cython.wraparound(False) -# This turns off checks for divide by 0 and will seg fault instead of throw a warning -@cython.cdivision(True) -# Gave all the input variables types -def wave_propogation_fast(int num_steps, int scale,float damping,float initial_P,int stop_step): - # Give types to variables we use to calculate with - cdef float omega = 3.0 / (2.0 * pi) - cdef int size_x = 2 * scale + 1 - cdef int size_y = 2 * scale + 1 - - # Give types to loop iterator variables to make loops C loops - cdef int i = 0 - cdef int j = 0 - cdef int step = 0 - - # Setup - cdef float [:,:] P = array(shape=(size_x, size_y), itemsize=sizeof(float), format="f") - P[:,:] = 0.0 - cdef float [:,:,:] V = array(shape=(size_x, size_y, 4), itemsize=sizeof(float), format="f") - V[:,:,:] = 0.0 - - P[scale][scale] = initial_P - - for step in range(num_steps): - if(step <= stop_step): - P[scale][scale] = initial_P * sin(omega * step) - for i in range(size_y): - for j in range(size_x): - V[i][j][0] = V[i][j][0] + P[i][j] - P[i - 1][j] if i > 0 else P[i][j] - V[i][j][1] = V[i][j][1] + P[i][j] - P[i][j + 1] if j < size_x - 1 else P[i][j] - V[i][j][2] = V[i][j][2] + P[i][j] - P[i + 1][j] if i < size_y - 1 else P[i][j] - V[i][j][3] = V[i][j][3] + P[i][j] - P[i][j - 1] if j > 0 else P[i][j] - - for i in range(size_y): - for j in range(size_x): - P[i][j] -= 0.5 * damping * (V[i][j][0]+V[i][j][1]+V[i][j][2]+V[i][j][3]) - return np.asarray(P) diff --git a/Day_4/1D_Generated_Data.dat b/Day_4/1D_Generated_Data.dat deleted file mode 100644 index 0bedebc..0000000 --- a/Day_4/1D_Generated_Data.dat +++ /dev/null @@ -1,21 +0,0 @@ -time height error -0.1 22.59152345967664 10 -0.6210526315789474 33.16424244378304 10 -1.142105263157895 60.50106128150062 10 -1.6631578947368424 92.01296523301181 10 -2.18421052631579 104.50930566327254 10 -2.7052631578947373 89.62998020400596 10 -3.2263157894736847 119.81201713924483 10 -3.747368421052632 117.0452755624524 10 -4.268421052631579 123.11371452915583 10 -4.7894736842105265 131.17828418835876 10 -5.310526315789474 128.77847172268912 10 -5.831578947368421 139.48584864303695 10 -6.352631578947369 127.49790910742558 10 -6.873684210526316 113.38804102365405 10 -7.394736842105264 106.2330229091717 10 -7.915789473684211 92.09357429867337 10 -8.436842105263159 87.99940291994166 10 -8.957894736842105 52.64815143436417 10 -9.478947368421053 36.810873692409174 10 -10.0 1.4590426069826954 10 diff --git a/Day_4/1D_Generated_Data_ChiProblem.dat b/Day_4/1D_Generated_Data_ChiProblem.dat deleted file mode 100644 index cc01450..0000000 --- a/Day_4/1D_Generated_Data_ChiProblem.dat +++ /dev/null @@ -1,21 +0,0 @@ -time height error -0.1 -21.029898158340785 10 -0.6210526315789474 18.303498973794188 10 -1.142105263157895 0.5294035398415815 10 -1.6631578947368424 -62.56846460849658 10 -2.18421052631579 -106.63070860776942 10 -2.7052631578947373 -245.20293652438113 10 -3.2263157894736847 -358.6823040236615 10 -3.747368421052632 -516.6419216027515 10 -4.268421052631579 -682.2220693522604 10 -4.7894736842105265 -892.7856366777286 10 -5.310526315789474 -1143.008697488183 10 -5.831578947368421 -1405.0050784050866 10 -6.352631578947369 -1709.0426774208997 10 -6.873684210526316 -2038.5004854578629 10 -7.394736842105264 -2367.8489275874445 10 -7.915789473684211 -2735.633186209238 10 -8.436842105263159 -3124.870223386629 10 -8.957894736842105 -3552.275370487225 10 -9.478947368421053 -4022.448060417848 10 -10.0 -4503.023027505753 10 diff --git a/Day_4/1D_intro_examples.dat b/Day_4/1D_intro_examples.dat deleted file mode 100644 index c43a054..0000000 --- a/Day_4/1D_intro_examples.dat +++ /dev/null @@ -1,51 +0,0 @@ -x y error -0.5859208811172378 13.648840419356967 10.32168148751835 -0.6641524630269491 7.321433237905272 12.30735255409887 -0.6662988484015897 8.691449835441968 11.459852851677873 -0.7171262850591409 -7.759707810062682 14.261307332834518 -0.8285515554596379 8.019604202283139 11.597092065136776 -1.0621652623586642 13.098026796159242 13.452339950812945 -1.0721273060321301 -3.6614341277807263 11.57025625855428 -1.406866841277693 7.464632698548301 14.01522904058345 -1.4244829687392868 10.007290853470874 10.113370387406505 -1.5895731486920284 18.55525289108428 11.02845424504546 -1.8047250872468568 -0.1916298684129103 13.177106552805757 -2.0319637280455947 27.61329296586468 13.531301152049904 -2.0727832299572357 4.59211181441602 11.541943572369366 -2.329111817911491 15.157979444752566 10.702901827722597 -2.3667679706120848 14.034872219552526 13.009399378342417 -2.6232429431266824 38.54057734395387 12.233189422319823 -2.623601550355612 37.94742554052121 11.199117670099087 -2.646354234772149 24.063379455236394 11.30725256090075 -3.394019423667464 22.759751518498085 14.132311019359658 -3.5104925524344766 52.225507569372795 13.093561530248445 -4.567410840523199 59.2055383550765 10.067738770596504 -4.602598140662353 61.43966384313868 10.471604687964808 -4.7714246504276625 43.599977465414 10.033226667483113 -5.055274082123587 78.64957709032825 10.872683776290852 -5.129588022595625 51.54774529091725 11.803311093457973 -5.3145508402791 64.65258320934662 10.430116582041688 -5.353929300879021 67.97358575803115 11.4125504953471 -5.436735449223653 58.07045369868454 12.944111590595796 -5.483698917778704 86.50376325824786 13.740628772161323 -5.910903450777859 81.46799694538338 14.187524224170593 -5.954774279919228 119.59141340421888 14.824789151283689 -6.055013809536769 113.75952958684815 14.179072695597423 -6.17578617929948 80.24431278152299 13.44749397702417 -6.237057117039342 85.04362574401212 11.053275785815211 -6.4693500646986655 91.84005742543829 10.153827228828048 -6.779555482085757 88.13254745844098 12.903026210813444 -7.028910623765771 123.64119332784561 11.330522453586608 -7.102534684712332 90.66751426971902 13.955285977158244 -7.187329457415746 119.60406575186974 12.44544588767899 -7.514984405811353 107.47123960437843 13.433714900932312 -7.8653461426416795 142.43989448201518 12.137418052516406 -8.167216084882174 152.02083703505593 10.396303553519921 -8.544225458926606 161.66966789207473 14.002536474019632 -9.053083673472717 187.29009919632816 14.55946022826626 -9.118727482880745 210.96012025220134 14.76417927818167 -9.508648660726735 189.55812132953506 10.938586370778085 -9.525434777307835 212.44097439786165 13.515639863104036 -9.733024329107279 212.77581632167127 13.255120650974233 -9.84575599078041 202.59836279095998 11.486879814836014 -9.945062052393304 217.88730023994438 14.06789793179362 diff --git a/Day_4/1D_intro_examples_bad.dat b/Day_4/1D_intro_examples_bad.dat deleted file mode 100644 index 61923db..0000000 --- a/Day_4/1D_intro_examples_bad.dat +++ /dev/null @@ -1,51 +0,0 @@ -x y error -0.12729204677920825 32.14274160581497 13.735818932658008 -0.5550161924796848 20.14113269931139 10.244920869680278 -1.1186519505858006 21.951164212039807 11.61672023907167 -1.3076752542224468 29.603861294291463 11.680615872746191 -1.3809164450196654 19.557805050247353 12.768343101486305 -1.3972570160805309 10.999243620209821 12.960012846579977 -1.413954046341741 -11.605628281379929 11.094627394707198 -1.6236386143712356 36.322282695444635 11.869499621144376 -1.662236835926184 15.889556006873017 12.051116247933724 -1.6872613757892685 20.98985158817731 13.166763437006825 -1.7789397030326515 39.685329284117074 12.93193987454723 -2.0688840290968824 41.210291252348696 13.006919973195323 -2.124665420559423 48.61401044338433 14.377681066476562 -2.2945069682282258 42.469125786342005 11.283896024758562 -2.3583115166101765 29.496249820820772 10.345532157920223 -2.558162549596028 20.782045847354617 13.47807035637342 -2.6027861435212896 31.44865614708557 13.988219706412552 -2.8646892119891487 47.34759883096335 10.595756165702838 -3.040674616170911 62.03870745130569 14.824850338164786 -3.350058236574822 65.36746174295676 11.002659673414371 -3.84436484533155 73.8686446911166 12.041322518392292 -3.9113595495423725 103.99806004690386 11.233901876026662 -4.168221056929687 74.24862613330833 13.534126506740499 -4.286273406071098 99.56920590580516 10.658265717860521 -4.531915181325813 93.1882393470634 13.26397912938294 -4.6126022253491294 93.84328835728432 10.196129411004183 -4.673489925729059 111.69438119789028 11.487681104481863 -4.929775734093874 120.51202467400279 11.577435427046618 -4.944084070664935 103.9296048877608 13.899934757006234 -4.9824076169325116 113.88804419043022 14.057417479530594 -5.113462043748701 108.58835196319343 10.822635024726672 -5.255796124098976 132.94844725957165 14.88131073916542 -5.735971991270211 147.16653344947238 14.758154172666256 -5.947145496251308 143.75552199711368 13.806985831513483 -6.133535814681885 147.6884217161624 11.626528908521982 -6.381693406430697 157.10037741465752 11.627487931744877 -6.567250070326162 164.78303959992706 12.592663382216823 -6.890040978299467 177.7742183148264 12.984101474507497 -7.123494379920507 187.54448129826673 12.190477468609032 -7.354335162449455 189.74430886311234 14.746215406868991 -7.966108877152157 222.3384937929381 13.506915809976437 -8.041596934404044 234.1016226701862 11.759678730777294 -8.12542984405072 201.66412258162515 14.73782838970741 -8.180178123382207 237.15921340490831 11.170219539600389 -8.266399226413064 242.3264047192325 13.98074581452846 -8.873909262047928 266.1519008318777 14.563978585694647 -8.912555421394229 272.5684881992492 10.909037858517282 -9.071581551592486 256.5069633746739 13.5798684969533 -9.482704473766768 299.04967718557293 10.260991205681568 -9.834834162349402 316.54193970818915 10.50843692765677 diff --git a/Day_4/1D_mcmc_example.dat b/Day_4/1D_mcmc_example.dat deleted file mode 100644 index a98e0f2..0000000 --- a/Day_4/1D_mcmc_example.dat +++ /dev/null @@ -1,51 +0,0 @@ -x y error -0.5859208811172378 8.486964103319439 1.32168148751835 -0.6641524630269491 6.811867675080206 3.307352554098869 -0.6662988484015897 7.446175070868994 2.4598528516778737 -0.7171262850591409 -4.461307455515341 5.261307332834518 -0.8285515554596379 7.15889717507032 2.597092065136776 -1.0621652623586642 10.122959059543826 4.452339950812945 -1.0721273060321301 1.9204914588929434 2.5702562585542816 -1.406866841277693 5.526852455468356 5.01522904058345 -1.4244829687392868 8.244525110853147 1.1133703874065044 -1.5895731486920284 11.076570628105141 2.0284542450454603 -1.8047250872468568 0.10617328425221118 4.177106552805758 -2.0319637280455947 17.468806809543537 4.531301152049904 -2.0727832299572357 4.821254029549717 2.541943572369366 -2.329111817911491 9.695978462365394 1.7029018277225962 -2.3667679706120848 7.4680440323786375 4.009399378342417 -2.6232429431266824 20.285959634748643 3.233189422319823 -2.623601550355612 17.96469395276489 2.1991176700990867 -2.646354234772149 12.493228201767787 2.3072525609007495 -3.394019423667464 7.254701460740437 5.132311019359658 -3.5104925524344766 26.13202240290152 4.093561530248445 -4.567410840523199 21.458375206206078 1.0677387705965042 -4.602598140662353 22.44926669286725 1.4716046879648084 -4.7714246504276625 18.398013350619685 1.0332266674831128 -5.055274082123587 27.94963164310265 1.8726837762908528 -5.129588022595625 16.664094982398115 2.803311093457973 -5.3145508402791 22.905418840272347 1.4301165820416868 -5.353929300879021 23.446904412315835 2.4125504953471 -5.436735449223653 16.290145050769542 3.9441115905957957 -5.483698917778704 34.28820799594406 4.7406287721613225 -5.910903450777859 26.128579147235538 5.187524224170593 -5.954774279919228 55.37475044243095 5.824789151283688 -6.055013809536769 47.94068690073212 5.179072695597422 -6.17578617929948 22.627134637954896 4.44749397702417 -6.237057117039342 27.11753085872862 2.053275785815212 -6.4693500646986655 29.725017789873622 1.1538272288280473 -6.779555482085757 21.942372692167293 3.903026210813443 -7.028910623765771 38.88700358772799 2.3305224535866076 -7.102534684712332 17.332441533814897 4.955285977158244 -7.187329457415746 36.52991604538673 3.44544588767899 -7.514984405811353 24.33378667539887 4.433714900932313 -7.8653461426416795 42.25646670492912 3.137418052516405 -8.167216084882174 43.83507091952513 1.3963035535199206 -8.544225458926606 45.27624151878357 5.0025364740196325 -9.053083673472717 54.83239742449949 5.5594602282662615 -9.118727482880745 72.0437959176934 5.76417927818167 -9.508648660726735 51.112976829753904 1.938586370778085 -9.525434777307835 63.089449286452 4.515639863104036 -9.733024329107279 59.53710500333536 4.2551206509742325 -9.84575599078041 53.21091734741761 2.4868798148360134 -9.945062052393304 59.31899407832193 5.0678979317936195 diff --git a/Day_4/2D_Generated_Data.dat b/Day_4/2D_Generated_Data.dat deleted file mode 100644 index 725f85f..0000000 --- a/Day_4/2D_Generated_Data.dat +++ /dev/null @@ -1,21 +0,0 @@ -time height -1.3980085754175695 28.420907905286384 -0.7141110083488954 67.40810002859229 -1.6535450372279454 62.12447740162554 -2.04174712632486 97.67672997395152 -3.2472840080144145 121.9827090438956 -3.055861303799251 119.46628617254191 -4.090273021081623 121.73839047140994 -3.561224725439395 119.19553774390326 -4.085446954181424 122.65971733921022 -5.339553570532041 141.27667862163995 -5.881835428421122 142.39676317920947 -7.134338632159125 143.13599755076663 -6.43720957610325 115.90808015485779 -7.626408633410243 148.6939283958427 -7.911095041028195 122.1187283634225 -7.419961124421717 72.42666897181516 -8.34255295646648 47.52021331763183 -9.042469963657448 45.67855631062443 -10.272417844929338 53.1789438209982 -10.159149435402547 16.979553496630572 diff --git a/Day_4/Library_DatasetGetMaximumDatapoint.py b/Day_4/Library_DatasetGetMaximumDatapoint.py deleted file mode 100644 index bd4bfa2..0000000 --- a/Day_4/Library_DatasetGetMaximumDatapoint.py +++ /dev/null @@ -1,28 +0,0 @@ -""" -DESCRIPTION: - Gets the maximum theoretical point of a domain box - This is the same as taking a list of each maximum observed cartesian coordinate - -ARGS: - Dataset: - Type: Type_NumpyTwoDimensionalDataset - [ - [X1a, X2a, ... XNa] - [X1b, X2b, ... XNb] - [X1c, X2c, ... XNc] - ] - -RETURNS: - MaxPoint - Type: `Type_NumpyTwoDimensionalDataPoint` - Description: [ Max(X1's) , Max(X2's), ..., Max(XN's) ] -""" - -import numpy -def Main(Dataset = None): - DatasetTranspose = Dataset.T - DatasetDimensionMinimums = [] - for DimensionValues in DatasetTranspose: - DatasetDimensionMinimums.append(max(DimensionValues)) - Result = numpy.array(DatasetDimensionMinimums) - return Result diff --git a/Day_4/Library_DatasetGetMinimumDatapoint.py b/Day_4/Library_DatasetGetMinimumDatapoint.py deleted file mode 100644 index a25c54e..0000000 --- a/Day_4/Library_DatasetGetMinimumDatapoint.py +++ /dev/null @@ -1,29 +0,0 @@ -""" -DESCRIPTION: - Gets the minimum theoretical point of a domain box - This is the same as taking a list of each maximum observed cartesian coordinate - -ARGS: - Dataset: - Type: Type_NumpyTwoDimensionalDataset - [ - [X1a, X2a, ... XNa] - [X1b, X2b, ... XNb] - [X1c, X2c, ... XNc] - ] - -RETURNS: - MinPoint - Type: `Type_NumpyTwoDimensionalDataPoint` - Description: [ Min(X1's) , Min(X2's), ..., Min(XN's) ] -""" - -import numpy - -def Main(Dataset): - DatasetTranspose = Dataset.T - DatasetDimensionMinimums = [] - for DimensionValues in DatasetTranspose: - DatasetDimensionMinimums.append(min(DimensionValues)) - Result = numpy.array(DatasetDimensionMinimums) - return Result diff --git a/Day_4/Library_GraphTwoDimensionDensityColorMap.py b/Day_4/Library_GraphTwoDimensionDensityColorMap.py deleted file mode 100644 index e8e58bc..0000000 --- a/Day_4/Library_GraphTwoDimensionDensityColorMap.py +++ /dev/null @@ -1,372 +0,0 @@ -""" -DESCRIPTION: - Graph an arbitrary function of 2 variables - 3d graph tends to be difficult to view because of angle - 2d heat map is likely to be a better choice - - - Matplotlib Compatability: - This function does not play nice with other graphs: - Does not allow for graphing additional things on the same plot: - fig = plt.figure() - Included in this method - plt.draw() - Included in this method - Can still be called amist other programs without a delay, because plt.show() is not invoked - - -ARGS (9 Count): - Function: - python function which has ARGS (1 count): - Point: - numpy array of two values - point looks like [x_coord, y_coord] - - GraphTwoDimensionalDensityColorMap is designed to graph on 3 dimmensional coordinates - Point == [x,y] - F( Point ) => returns z - - - DomainMinimumPoint: - used for plot boundaries - numpy array of two values: - [x_min, y_min] - - DomainMaximumPoint: - used for plot boundaries - numpy array of two values: - [x_max, y_max] - - ObservedDataset: - Type_NumpyTwoDimensionalDataset - [point, point, ..., point] - Used for scatter presentation - - PlotThreeDimensional: - The plot generated will be a heat map regardless of choice with color correpsonding to density - This can be two values: - True - Show visual third dimension and look at the surface from a fixed point - Cannot plot contours - False - 2D plot - Will show contours - - Xlabel: ... - Ylabel: ... - Zlabel: ... - - -RETURNS: - None - -""" -import matplotlib.pyplot -import matplotlib -import matplotlib.pyplot as plt -import numpy -from matplotlib.colors import LightSource -from mpl_toolkits.mplot3d import Axes3D -from matplotlib import cm - -#------------------------------------------------------------------------------ - -import Type_NumpyTwoDimensionalDataset -import Library_DatasetGetMinimumDatapoint -import Library_DatasetGetMaximumDatapoint - - -def Main( - Function = None, - DomainMinimumPoint = None, - DomainMaximumPoint = None, - ObservedDataset = None, - PlotThreeDimensional = False, - ShowContours = False, - PluginPointCount = None, - Xlabel = "X", - Ylabel = "Y", - Zlabel = "Z", - LogX = False, - LogY = False, - CheckArguments = True, - SaveFigureFilePath = None, - PrintExtra = False, - ): - - if PluginPointCount is None: - PluginPointCount = 10000 - PluginPointCountX = int(numpy.sqrt(PluginPointCount)) - PluginPointCountY = int(numpy.sqrt(PluginPointCount)) - - if (CheckArguments): - ArgumentErrorMessage = "" - #TODO: Match dataset and function dimensions - - if (Type_NumpyTwoDimensionalDataset.Main(ObservedDataset) != True ): - if (DomainMaximumPoint is None or DomainMinimumPoint is None ): - ArgumentErrorMessage += "(Type_NumpyTwoDimensionalDataset.Main(ObservedDataset) != True)\n" - if (ObservedDataset is None): - ArgumentErrorMessage += "(ObservedDataset is None)\n" - if (DomainMinimumPoint is None): - ArgumentErrorMessage += "(DomainMinimumPoint is None)\n" - if (DomainMaximumPoint is None): - ArgumentErrorMessage += "(DomainMaximumPoint is None)\n" - - if (len(ArgumentErrorMessage) > 0 ): - if (PrintExtra): - print("ArgumentErrorMessage:\n", ArgumentErrorMessage) - raise Exception(ArgumentErrorMessage) - - #Arg Fixing / Inferring (very pythonic - i know) - #Extract all the observations in X, Y coordinates: - if (ObservedDataset is not None): - ObservedX = ObservedDataset.T[0] - ObservedY = ObservedDataset.T[1] - else: - ObservedX = numpy.array([]) - ObservedY = numpy.array([]) - - #If a dataset was passed, and min/max plot domain not passed: - # infer the plot domain minimums and maximums from the dataset - if (DomainMinimumPoint is None): - DomainMinimumPoint = numpy.nanmin(ObservedDataset, axis = 0) - if (DomainMaximumPoint is None): - DomainMaximumPoint = numpy.nanmax(ObservedDataset, axis = 0) - - print('DomainMinimumPoint', DomainMinimumPoint) - print('DomainMaximumPoint', DomainMaximumPoint) - - xrng = DomainMaximumPoint [0] - DomainMinimumPoint [0] - xmin = DomainMinimumPoint [0] #- xrng*0.2 - xmax = DomainMaximumPoint [0] #+ xrng*0.2 - - yrng = DomainMaximumPoint [1] - DomainMinimumPoint [1] - ymin = DomainMinimumPoint [1] #- yrng*0.2 - ymax = DomainMaximumPoint [1] #+ yrng*0.2 - - #Using the minimum and maximum points, we create a meshgrid of points - # This grid of points will be plugged into the function - X, Y = numpy.mgrid[xmin:xmax:complex(PluginPointCountX), ymin:ymax:complex(PluginPointCountY)] #1000 x 1000 -> 1 million points - PointsToPlugIn = numpy.vstack([X.ravel(), Y.ravel()]) - PointsToPlugInDataset = PointsToPlugIn.T - PlugInPointsCount = len(PointsToPlugInDataset) - - print('PlugInPointsCount', PlugInPointsCount) - print('PointsToPlugInDataset.shape', PointsToPlugInDataset.shape) - print('PointsToPlugInDataset[0]', PointsToPlugInDataset[0]) - - #Plug in each point into the function which is contained in the meshgrid - # Keep track of the values, and store them as "Z" coordinates - FunctionResultValuesForGrid = [] - MaxObservedValue = None - MinObservedValue = None - for PointToPlugIn in PointsToPlugInDataset: - FunctionValueForPointToPlugIn = None - try: - FunctionValueForPointToPlugIn = Function(PointToPlugIn) - if numpy.isinf( FunctionValueForPointToPlugIn ) : - if FunctionValueForPointToPlugIn < 0: - FunctionValueForPointToPlugIn = 'MinMarker' - elif FunctionValueForPointToPlugIn > 0: - FunctionValueForPointToPlugIn = 'MaxMarker' - elif not numpy.isnan( FunctionValueForPointToPlugIn ): - if MaxObservedValue is None or MaxObservedValue < FunctionValueForPointToPlugIn: - MaxObservedValue = FunctionValueForPointToPlugIn - if MinObservedValue is None or MinObservedValue > FunctionValueForPointToPlugIn: - MinObservedValue = FunctionValueForPointToPlugIn - - except: - print('Function(' , PointToPlugIn, ')', ' failed... skipping') - FunctionResultValuesForGrid.append( FunctionValueForPointToPlugIn ) - - print('MaxObservedValue', MaxObservedValue) - print('MinObservedValue', MinObservedValue) - FunctionResultValuesForGrid = [MaxObservedValue if item=='MaxMarker' else item for item in FunctionResultValuesForGrid ] - FunctionResultValuesForGrid = [MinObservedValue if item=='MinMarker' else item for item in FunctionResultValuesForGrid ] - #print 'FunctionResultValuesForGrid', FunctionResultValuesForGrid - FunctionResultValuesForGrid = numpy.array(FunctionResultValuesForGrid) - - Z = numpy.reshape(FunctionResultValuesForGrid, X.shape) - - print('Z.shape', Z.shape) - - #print 'Z', Z - - #Plug in the observed points to get thier Z-values: - if (ObservedDataset is not None): - k = 0 - ObservedPointsCount = len(ObservedDataset) - ObservedZ = numpy.zeros((ObservedPointsCount)) - while (k < ObservedPointsCount ): - PointToPlugIn = ObservedDataset[k] - - FunctionValueForPointToPlugIn = None - try: - FunctionValueForPointToPlugIn = Function(PointToPlugIn) - print('FunctionValueForPointToPlugIn', FunctionValueForPointToPlugIn) - #NumberGood = numpy.isfinite( FunctionValueForPointToPlugIn ) and not numpy.isnan( FunctionValueForPointToPlugIn ) - except: - print('Function(' , PointToPlugIn, ')', ' failed... skipping') - FunctionValueForPointToPlugIn = 0.0 - - ObservedZ[k] = FunctionValueForPointToPlugIn - k = k + 1 - - - zmin = numpy.min(Z) # This should always be 1 dimension - zmax = numpy.max(Z) # This should always be 1 dimension - zrange = zmax - zmin - - - #size the graphs - # Default to common monitor size: - # 1920pixels by 1080 pixels - Inch_in_Pixels = 80.0 - MonitorSize = (1920.0/Inch_in_Pixels,1080.0/Inch_in_Pixels) - - fig = matplotlib.pyplot.figure(figsize=MonitorSize) #figsize=MonitorSize - - #matplotlib.pyplot.subplots_adjust(bottom=0.14) - - #Design the plot with the coordinate values (this could be done in it's own function) - #fig = plt.figure() - if (PlotThreeDimensional == True): - subplot = fig.add_subplot(111, projection='3d') - - #Draw the mesh: - surface = subplot.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=plt.cm.gist_earth_r) #cmap=cm.YlGnBu_r) - fig.colorbar( surface ) - - #Draw a contour( This blows ): - #contour = subplot.contour(X,Y,Z,extend3d=True ) - - #Draw observations - veritical lines at each point (somehow this blows too): - #k = 0 - #while k < ObservedPointsCount: - # subplot.plot( [ObservedX[k],ObservedX[k]], [ObservedY[k], ObservedY[k]], [ObservedZ[k] , ObservedZ[k] + zrange/20], 'w-', lw = 5 ) - # k = k + 1 - - #Draw observations - points in 3d (This blows ): - #subplot.scatter(ObservedX, ObservedY, ObservedZ, marker= 'o', s=100, c = 'white') - - #ax.set_zlim3d(0, 1) - subplot.set_xlabel(Xlabel) - subplot.set_ylabel(Ylabel) - subplot.set_zlabel(Zlabel) - - else: - - #From http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.gaussian_kde.html - - subplot = matplotlib.pyplot.subplot(111) - #matplotlib.pyplot.subplot(2,1,2) - - - #aspectratio = xrng / yrng - aspectratio = 'auto' - - heatmap = subplot.imshow( - #heatmap = matplotlib.pyplot.imshow( - #heatmap = matplotlib.pyplot.imshow( - numpy.rot90(Z), - #cmap=plt.cm.gist_earth_r, - extent=[xmin, xmax, ymin, ymax] , - aspect = aspectratio , - interpolation = None, - #norm=LogNorm(vmin=0.01, vmax=1) - ) - - - #Add color bar (showing Z-axis sense of scale) - matplotlib.pyplot.colorbar( heatmap, label = Zlabel ) - - #matplotlib.pyplot.tight_layout() - - #Add the observations: - if (ObservedDataset is not None): - matplotlib.pyplot.plot(ObservedX, ObservedY, 'k.', markersize=2) - - #Set image boundaries: - #subplot.set_xlim([xmin, xmax]) - #subplot.set_ylim([ymin, ymax]) - subplot.set_xlabel(Xlabel) - subplot.set_ylabel(Ylabel) - #plt.axis([xmin, xmax, ymin, ymax]) - #matplotlib.pyplot.autoscale(axis='y') - #heatmap.set_aspect(1) - - - #subplot.set_xscale('log') - - - #Plot Countours: ( From http://matplotlib.org/examples/pylab_examples/contour_demo.html ) - if( ShowContours ): - CS = plt.contour(X, Y, Z, 6, - colors='k', # negative contours will be dashed by default - ) - - plt.clabel(CS, fontsize=9, inline=1) - - plt.title("Density Map + Contours") - else: - plt.title("Density Map") - - - - if (SaveFigureFilePath is not None): - plt.savefig( SaveFigureFilePath ) - - plt.draw() - - - - - - """ - #Extract boundary regions (minimum and maximum points) - if (DomainMinimumPoint is None): - - DomainMinimumPoint = Library_DatasetGetMinimumDatapoint.Main(ObservedDataset) - else: - #Better: We have to spotcheck infer where the Z axis ranges with monte-carlo - raise Exception("DomainMinimumPoint not defined") - if (DomainMaximumPoint is None): - if (ObservedDataset is not None): - DomainMaximumPoint = Library_DatasetGetMaximumDatapoint.Main(ObservedDataset) - else: - raise Exception("DomainMaximumPoint not defined") - """ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/Day_4/Library_PrintFullTestSuccess.py b/Day_4/Library_PrintFullTestSuccess.py deleted file mode 100644 index 9f6e6c8..0000000 --- a/Day_4/Library_PrintFullTestSuccess.py +++ /dev/null @@ -1,11 +0,0 @@ - -""" -Just contains a simple print statement which is intended for use at the end of test files: -""" -def Main(FullTestSuccess): - print("\n\n\n") - if ( FullTestSuccess ): - print("Full Test Success") - else: - print("Full Test Failure") - print("\n\n\n") diff --git a/Day_4/Library_PrintNumpyArrayInfo.py b/Day_4/Library_PrintNumpyArrayInfo.py deleted file mode 100644 index 5274e20..0000000 --- a/Day_4/Library_PrintNumpyArrayInfo.py +++ /dev/null @@ -1,21 +0,0 @@ -""" -DESCRIPTION: - Prints some useful informatoin about a numpy array - includes things like: - shape - type - the values - ... - -ARGS (1 Count): - Dataset -> any numpy.array - -RETURNS (0 count): - None - -TESTS: -""" -def Main(Dataset = None): - print(" Dataset: \n" , Dataset) - print(" Dataset.shape: \n", Dataset.shape) - print(" type(dataset): \n", type(Dataset)) diff --git a/Day_4/Test_NumpyTwoDimensionalDataset.py b/Day_4/Test_NumpyTwoDimensionalDataset.py deleted file mode 100644 index cdae4b8..0000000 --- a/Day_4/Test_NumpyTwoDimensionalDataset.py +++ /dev/null @@ -1,68 +0,0 @@ -import numpy -#------------------------------------------------------------------------------ -import Type_NumpyTwoDimensionalDataset -import Library_PrintFullTestSuccess -import Library_PrintNumpyArrayInfo - -FullTestSuccess = True #Assume inocent until proven guilty - -############################################################################### -#GOOD FORMS: - -GoodData1 = numpy.array( [[1,2]] ) #1 point dataset (2dim) -GoodData2 = numpy.array( [[1,2], [3,4]] ) #2 point dataset (2dim) -GoodData3 = numpy.array( [[1,2,3], [4,5,6]] ) #2 point dataset (3dim) -GoodData4 = numpy.array( [[1,2], [3,4], [5,6]] ) #3 point dataset (2dim) - -GoodDatasetList = [GoodData1, GoodData2, GoodData3, GoodData4] - -############################################################################### -#BAD FORMS: - -# Datapoints which are different number of dimensions -# Omited coordinate could be mathematically treated as every possible value -# This would be arguous task to make work, so we will mark data of this form as BAD -BadData1 = numpy.array( [[1,2,3], [4,5]] ) - -# Tensor which is not projected onto a 2 dimensional code dataset -# (this is 3 Dim code data -> 2x2x2, and this is not handled by any code. -# these same coordinates could be projected into a 2 dimensional code tensor, -# of the form: [ point1 == [x,y,z] , [point2], ... , [point8] ] -# But that is out of the scope of the work that is logical -BadData2 = numpy.array( [[[1,2], [3,4]],[[7,8], [10,11]]] ) -BadData3 = numpy.array( [[[[1,2], [3,4]],[[5,6], [7,8]]],[[[11,12], [13,14]],[[15,16], [17,18]]]] ) -BadDatasetList = [BadData1, BadData2, BadData3] - - - -for Dataset in GoodDatasetList: - if ( Type_NumpyTwoDimensionalDataset.Main(Dataset = Dataset) ): - print("Dataset Check Sucess") - else: - print(" Dataset Check Failure. \n") - Library_PrintNumpyArrayInfo.Main(Dataset) - FullTestSuccess = False #Mark that at least one test has failed - -for Dataset in BadDatasetList: - if ( Type_NumpyTwoDimensionalDataset.Main(Dataset = Dataset) == False): - print("Dataset Check Sucess") - else: - print(" Dataset Check Failure. \n") - Library_PrintNumpyArrayInfo.Main(Dataset) - FullTestSuccess = False #Mark that at least one test has failed - - - -Library_PrintFullTestSuccess.Main(FullTestSuccess) - - - - - - - - - - - - diff --git a/Day_4/Type_NumpyTwoDimensionalDataset.py b/Day_4/Type_NumpyTwoDimensionalDataset.py deleted file mode 100644 index 3e76ffb..0000000 --- a/Day_4/Type_NumpyTwoDimensionalDataset.py +++ /dev/null @@ -1,76 +0,0 @@ - -""" -Type_NumpyTwoDimensionalDataset - -Desciption: - - Checks to see if the only arg `Dataset` is of the following form: - Dataset - == - [point, point, ... , point] - == - [ - [point1_x1,point1_x2,..,point1_xN], - [point2_x1,point2_x2,..,point2_xN], - . - . - ., - [pointK_x1,pointK_x2,..,pointK_xN], - ] - -ARGS: - Dataset: (any) - This is the thing we are checking - - PrintExtra: (boolean) - -RETURNS: - True -> when dataset meets criterion for `Type_NumpyTwoDimensionalDataset` - - OR - - False -> when dataset does NOT meet criterion for `Type_NumpyTwoDimensionalDataset` - - -""" - -import numpy -#------------------------------------------------------------------------------ - -def Main( - Dataset = None, - PrintExtra = False - ): - - #Must not be null - if Dataset is None: - if (PrintExtra): - print("Emtpy Dataset: Must not be null") - return False - - #Type Numpy Array - if ( str(type(Dataset)) != "" ): - if (PrintExtra): - print("DatasetType: must be Type Numpy Array") - return False - - #Must be Two Code Dimensions (even for N-Dimensional Datasets) - if (len(Dataset.shape) != 2): - if (PrintExtra): - print("len(Dataset.shape) != 2: Must be Two Code Dimensions (even for N-Dimensional Datasets)") - return False - - #For N-Dimensional datasets - they must be at least 1 dimension - if (Dataset.shape[0] < 1): - if (PrintExtra): - print("DatasetShape[0] < 1: For N-Dimensional datasets - they must be at least 1 dimension") - return False - - #For N-Dimensional datasets - they must have at least 1 observation - if (Dataset.shape[1] < 1): - if (PrintExtra): - print("DatasetShape[1] < 1 For N-Dimensional datasets - they must have at least 1 observation") - return False - - return True - diff --git a/Day_4/day_4_student_completed_workbook_introduction.ipynb b/Day_4/day_4_student_completed_workbook_introduction.ipynb deleted file mode 100644 index fc5c0f0..0000000 --- a/Day_4/day_4_student_completed_workbook_introduction.ipynb +++ /dev/null @@ -1,650 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# import all packages\n", - "import scipy,corner,emcee,nestle,warnings\n", - "import pandas\n", - "import scipy.optimize\n", - "import scipy.stats\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "warnings.simplefilter('ignore')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# read in some data we created for this example (.dat is a generic filename, it's just a text file)\n", - "example_data_1D = pandas.read_csv('1D_intro_examples.dat',sep=' ',header=0)#this file is separated by spaces and its first line contains the names of the columns (header) \n", - "print(example_data_1D.head())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Let's plot the data, with error bars, that we read from file (See Day 2)\n", - "plt.errorbar(example_data_1D['x'], #x,y,and error are the column names\n", - " example_data_1D['y'], \n", - " yerr=example_data_1D['error'],#yerr denotes an error in the y-direction for plotting\n", - " fmt='.') #fmt is \"format\", saying that I want data marked by \"points\"\n", - "plt.xlabel('x') #set the x-axis label \n", - "plt.ylabel('y') #set the y-axis label\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#The data were generated with a simple quadratic equation:\n", - "#ax^2+bx+c. The true model values are:\n", - "a_true=2\n", - "b_true=1.3\n", - "c_true=6\n", - "def my_model(x,a,b,c): #We define the model described above\n", - " return(a*x**2+b*x+c)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Data Fitting: Chi-Square Minimization**\n", - "===============================" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Let's try and fit the data with a chi-square minimization\n", - "def chisq_likelihood(theta, args):\n", - " #This function accepts an argument \"theta\", which is \n", - " #a list of model parameters a, b and c. It then calculates\n", - " #a chi-square statistic that it returns, which compares\n", - " #the observations, errors, and model provided in args.\n", - " \n", - " x, y, yerr,mod = args #args is a list, so this is the same as x=args[0],y=args[1],yerr=args[2]. x,y, and yerr are numpy arrays, mod is a function.\n", - " a,b,c = theta #theta is also a list, so it follows the same as args above\n", - " model_observations = mod(x,a,b,c) #mod (a model) is the 4th element of args, and it accepts x values, and the three model parameters a,b,c. Now model_observations contains the model values at every point in x (and is a numpy array)\n", - " inv_sigma2 = 1./yerr**2 #The chi-square statistic contains an inverse-square error, which we calculate here\n", - " chisquare = np.sum((y-model_observations)**2*inv_sigma2 )#calculate the chi-square statistic. \n", - " return chisquare\n", - "\n", - "#Use scipy.optimize.minimize to minimize the chi-square, which\n", - "#is the same as maximizing the likelihood.\n", - "result = scipy.optimize.minimize(chisq_likelihood, #the first argument is the function to minimize, which must accept a list of parameters as its first argument, and any other necessary agruments as its second.\n", - " x0=[1,1,1], #x0 is a first guess for each of your parameters (depends on your model/data)\n", - " bounds=[(-100,100),(-100,100),(-100,100)], #optional bounds for each of your parameters\n", - " args=[example_data_1D['x'],#the args passed to chisq_likelihood above\n", - " example_data_1D['y'],\n", - " example_data_1D['error'],\n", - " my_model])#my_model is defined in a previous cell\n", - "\n", - "print(result)#scipy.optimize.minimize returns a dictionary which I've called result. It has other information, the \"x\" element has the best fit values\n", - "a_ml,b_ml,c_ml = result[\"x\"] #get our best fit values from result\n", - "\n", - "#set up plotting the model over the data\n", - "plt.errorbar(example_data_1D['x'],\n", - " example_data_1D['y'],\n", - " yerr=example_data_1D['error'],\n", - " fmt='.',\n", - " label='Data')\n", - "\n", - "plt.plot(example_data_1D['x'],\n", - " my_model(example_data_1D['x'],a_ml,b_ml,c_ml),\n", - " 'g--',#make the line green and dashed\n", - " label='$\\chi^2$ Fit')\n", - "\n", - "plt.xlabel('x')\n", - "plt.ylabel('y')\n", - "plt.legend()\n", - "print('Chi-square fit:',a_ml, b_ml,c_ml)\n", - "print('True:',a_true,b_true,c_true)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Now let's look at a drawback of the chi-square minimization.\n", - "#This fitting technique depends on finding a minimum in the chi-square. \n", - "#What if there is some sort of local minimum in the chi-square?\n", - "\n", - "#my_bad_model was used to create new data, read in below.\n", - "#This model has one of the parameters squared, \n", - "#so either -b or b will give the same result\n", - "def my_bad_model(x,a,b,c):\n", - " return(a*x**2+b**2*x+c)#b is now squared \n", - "\n", - "#These are the true values used to make the data\n", - "a_true_bad=2\n", - "b_true_bad=-3.3 #note b_true_bad is negative here\n", - "c_true_bad=6\n", - "\n", - "#Read in some data generated with this model. \n", - "my_bad_data_1D=pandas.read_csv(\"1D_intro_examples_bad.dat\",header=0,sep=' ')\n", - "\n", - "#Plot the data\n", - "plt.errorbar(my_bad_data_1D['x'],my_bad_data_1D['y'],yerr=my_bad_data_1D['error'],fmt='.')\n", - "plt.xlabel('x')\n", - "plt.ylabel('y')\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Try and fit the data with this model\n", - "result_bad = scipy.optimize.minimize(chisq_likelihood, \n", - " [1,1,1],\n", - " bounds=[(-100,100),(-100,100),(-100,100)], \n", - " args=[my_bad_data_1D['x'],\n", - " my_bad_data_1D['y'],\n", - " my_bad_data_1D['error'],\n", - " my_bad_model])\n", - "\n", - "a_ml, b_ml,c_ml = result_bad[\"x\"] #get our best fit values\n", - "\n", - "#Plot the bad data\n", - "plt.errorbar(my_bad_data_1D['x'],\n", - " my_bad_data_1D['y'],\n", - " yerr=my_bad_data_1D['error'],\n", - " fmt='.',\n", - " label='Data')\n", - "#Plot our fitting result\n", - "plt.plot(my_bad_data_1D['x'],\n", - " my_bad_model(my_bad_data_1D['x'],a_ml,b_ml,c_ml),\n", - " 'g--',\n", - " label='$\\chi^2$ Fit')#note we can use latex directly in labels for matplotlib\n", - "plt.xlabel('x')\n", - "plt.ylabel('y')\n", - "plt.legend()\n", - "print('Chi-square fit:',a_ml, b_ml,c_ml)\n", - "print('True:',a_true_bad,b_true_bad,c_true_bad)\n", - "plt.show()\n", - "#However, in this (very contrived) case, the true value of b is -3.3" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Data Fitting: Markov Chain Monte Carlo (MCMC)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# We can combine a likelihood function with a prior function.\n", - "\n", - "# Create a log-prior function. \n", - "def lnprior(theta):#accepts the model parameters (theta)\n", - " a,b,c = theta #set a,b,c (see above)\n", - " \n", - " #give the following bounds: a=(0,5),b=(-5,5),c=(0,10)\n", - " if 0 < a < 5. and -5 < b < 5 and 0 < c < 10: #we are assuming a \"uniform prior\" on all parameters, which is the same as just giving each parameter bounds.\n", - " return 0.0 #if you try parameters inside the bounds, return a probability of 1 (log(1)=0)\n", - " return -np.inf #if you try parameters outside the bounds, return 0 (log(0)=-inf)\n", - "\n", - "# This is a log-likelihood function, which is commonly used.\n", - "def lnprob(theta, x, y, yerr,mod): #accepts theta (the model parameters), and the same x,y,yerr, and mod from above\n", - " lp = lnprior(theta) #get the probability from the prior function\n", - " if not np.isfinite(lp): \n", - " return -np.inf #return a probability of negative infinity if the prior is negative infinity\n", - "\n", - " #the chisq_likelihood function returns a chi-square, \n", - " #which you want to be as small as possible. We are \n", - " #maximizing the likelihood here, so we take\n", - " #the negative of the chi-square function.\n", - " return lp - chisq_likelihood(theta, [x, y, yerr,mod]) #the total likelihood is the product of the prior and the likelihood (or the sum of the log-prior and log-likelihood)\n", - "\n", - "\n", - "#Set up the MCMC to sample the full parameter space\n", - "ndim, nwalkers = 3, 100 #number of parameters to fit (3); number of individual \"walkers\" that randomly sample the space. Choose any number, the higher the slower.\n", - "starting_positions = [result[\"x\"] + 0.1*np.random.randn(ndim) for i in range(nwalkers)] #Start the walkers in a random (small) gaussian near the result from the chi-square fit\n", - "\n", - "#Let's just look at where walkers are starting in the a-b parameter space\n", - "#(Doesn't need to be included in your own code)\n", - "#Notice we get a random sampling near our previous result to start. \n", - "plt.scatter([x[0] for x in starting_positions],#get the \"a\" parameter locations\n", - " [x[1] for x in starting_positions])#get the \"b\" parameter locations\n", - "plt.scatter(result['x'][0],result['x'][1],color='r')#the chi-square result\n", - "plt.xlabel('a')\n", - "plt.ylabel('b')\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#set up the MCMC sampler\n", - "sampler = emcee.EnsembleSampler(nwalkers, \n", - " ndim, \n", - " lnprob, #the likelihood function to maximize \n", - " args=(example_data_1D['x'], #the arguments passed to the likelihood functions (other than the model parameters)\n", - " example_data_1D['y'], \n", - " example_data_1D['error'],\n", - " my_model))\n", - "\n", - "output = sampler.run_mcmc(starting_positions, 500) #run the MCMC with the starting positions we defined and 500 sampling points per walker\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#sampler.chain contains all of the samples from the MCMC.\n", - "print(sampler.chain.shape)\n", - "#It currently holds the samples separately for each walker.\n", - "#We don't care about what each walker does, so let's flatten it:\n", - "samples = sampler.chain.reshape((-1, ndim)) #The -1 here means we don't care how many rows it takes, give us the same number of columns as we have parameters\n", - "print(samples.shape)\n", - "#So we tried 50000 total model realizations for 3 parameters" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#So what did we actually get from this? Let's use another\n", - "#python package to see the output of the MCMC sampling\n", - "fig = corner.corner(samples, #samples is defined above\n", - " labels=[\"$a$\", \"$b$\",\"$c$\"],#parameter labels\n", - " quantiles=(0.16,0.50,0.84),#plot some quantiles for reference\n", - " truths=[a_true, b_true,c_true])#show the true values for comparison\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#But how close did we get to the true values?\n", - "#We can take the 50th percentile of the distributions you see above\n", - "#As our result. Incidentally we could calculate the uncertainty as \n", - "#well, perhaps as the differences between the 84th and 50th for\n", - "#the upper uncertainty, and 50th and 16th for the lower uncertainty\n", - "a_mcmc, b_mcmc,c_mcmc = np.percentile(samples, 50,axis=0)#axis=0 means we want to calculate percentiles along columns, not rows\n", - "print('Chi:','a=',a_ml,'b=', b_ml,'c=',c_ml)\n", - "print('MCMC:','a=',a_mcmc,'b=',b_mcmc,'c=',c_mcmc)\n", - "print('True:','a=',a_true,'b=', b_true,'c=',c_true)\n", - "#Definitely closer" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Now let's see how the MCMC deals with the \"bad\" data/model above\n", - "#The set up is exactly the same as before.\n", - "\n", - "ndim, nwalkers = 3, 100\n", - "#I've increased the range around the chi-square result\n", - "#That I want my walkers to start at because I know \n", - "#We're far from the right answer. You could always make\n", - "#The walkers start over a wide range to start with,\n", - "#look at the posterior, and hone in on the correct area\n", - "#of parameter space. \n", - "pos = [result_bad[\"x\"] - 2*np.abs(np.random.randn(ndim)) for i in range(nwalkers)]\n", - "\n", - "sampler_bad = emcee.EnsembleSampler(nwalkers, \n", - " ndim, \n", - " lnprob, \n", - " args=(my_bad_data_1D['x'], \n", - " my_bad_data_1D['y'], \n", - " my_bad_data_1D['error'],\n", - " my_bad_model))\n", - "sampler_bad.run_mcmc(pos, 500)\n", - "samples_bad = sampler_bad.chain.reshape((-1, ndim))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Let's look at the result\n", - "a_mcmc_bad, b_mcmc_bad,c_mcmc_bad = np.percentile(samples_bad, 50,axis=0)\n", - "print('MCMC:','a=',a_mcmc_bad,'b=',b_mcmc_bad,'c=',c_mcmc_bad)\n", - "print('True:','a=',a_true_bad,'b=', b_true_bad,'c=',c_true_bad)\n", - "fig = corner.corner(samples_bad, labels=[\"$a$\", \"$b$\",\"$c$\"],\n", - " truths=[a_true_bad, b_true_bad,c_true_bad])\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Bayesian Nested Sampling" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#An alternative to MCMC is Bayesian Nested Sampling. It is\n", - "#extremely similar but randomly samples the full parameter\n", - "#space instead of sending out individual walkers. MCMC\n", - "#is often better for a many-dimensional model.\n", - "\n", - "# Define a likelihood function. Let's use a chi-square again.\n", - "# The package doing the fitting needs a likelihood function\n", - "# that only accepts the parameters, so we can create this \n", - "# small wrapper function that calls our original chisq_likelihood\n", - "# function with the necessary arguments.\n", - "def loglike(theta):\n", - " args=(example_data_1D['x'],\n", - " example_data_1D['y'],\n", - " example_data_1D['error'],\n", - " my_model)\n", - " chisq = chisq_likelihood(theta,args)\n", - " return -1.*chisq #again we need to take the negative of the chi-square because we need a function to maximize\n", - "\n", - "#This can be a bit difficult to understand. We are using the same bounds\n", - "#on our parameters as before here [a=(0,5),b=(-5,5),c=(0,10)].\n", - "#However, nested sampling always reduces the problem to the \n", - "#range (0,1) for every parameter. Then we always need to provide\n", - "#a function prior_transform, which applies our prior to the model\n", - "#and simultaneously tranforms from this unit space, to our actual\n", - "#parameter space. This essentially means rescaling the parameter \n", - "#from the range (0,1) to the range (-5,5).\n", - "#\n", - "#For example, we want a uniform prior on b, meaning bounds from -5 to 5.\n", - "#The algorithm will only try values for b from 0 to 1. Suppose it tries\n", - "#b=0.5. That's halfway between 0 and 1, which means it is halfway between\n", - "#our bounds (-5,5), which is 0, not 0.5. So we need to find a way\n", - "#to change 0.5-->0. We take the second parameter (b) and \n", - "#multiply it by 10, giving us 5. Now we subtract 5,\n", - "#so that a guess of b=0.5 transforms to b=0, as desired. \n", - "#Test some other values of a,b,c to make sure you understand this step. \n", - "#Would this same equation work for different bounds?...Nope.\n", - "def prior_transform(parameters):\n", - " return np.array([5., 10., 10.]) * parameters + np.array([0., -5., 0.])\n", - "\n", - "# Run nested sampling.\n", - "result_nest = nestle.sample(loglike, prior_transform, ndim=3,npoints=1000)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Let's see how we did\n", - "a_nest, b_nest,c_nest = np.percentile(result_nest.samples, 50,axis=0)\n", - "print('Chi:','a=',a_ml,'b=', b_ml,'c=',c_ml)\n", - "print('MCMC:','a=',a_mcmc,'b=',b_mcmc,'c=',c_mcmc)\n", - "print('Nested:','a=',a_nest,'b=',b_nest,'c=',c_nest)\n", - "print('True:','a=',a_true,'b=', b_true,'c=',c_true)\n", - "fig = corner.corner(result_nest.samples, \n", - " labels=[\"$a$\", \"$b$\",\"$c$\"],\n", - " quantiles=(0.16,0.50, 0.84),\n", - " weights=result_nest.weights,#nested sampling provides weights for each sample based on bayesian evidence\n", - " truths=[a_true, b_true,c_true])\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Let's see how Nested Sampling does for the \"bad\" case\n", - "def loglike(theta):\n", - " args=(my_bad_data_1D['x'],my_bad_data_1D['y'],my_bad_data_1D['error'],my_bad_model)\n", - " chisq = chisq_likelihood(theta,args)\n", - " return -.5*chisq\n", - "\n", - "\n", - "def prior_transform(x):\n", - " return np.array([5., 10., 10.]) * x + np.array([0., -5., 0.])\n", - "\n", - "# Run nested sampling.\n", - "result_nest_bad = nestle.sample(loglike, prior_transform, 3,npoints=1000,maxcall=10000) #I know it'll have a difficult time converging, so I'm setting the maxcall to a finite number\n", - "\n", - "a_nest_bad, b_nest_bad,c_nest_bad = np.percentile(result_nest_bad.samples, 50,axis=0)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Let's see how we did\n", - "print('Nested:','a=',a_nest_bad,'b=',b_nest_bad,'c=',c_nest_bad)\n", - "print('True:','a=',a_true_bad,'b=', b_true_bad,'c=',c_true_bad)\n", - "fig = corner.corner(result_nest_bad.samples, \n", - " labels=[\"$a$\", \"$b$\",\"$c$\"],\n", - " weights=result_nest_bad.weights,\n", - " truths=[a_true_bad, b_true_bad,c_true_bad])\n", - "plt.show()\n", - "#The nested sampling perhaps did slightly better at mapping\n", - "#The full parameter space, but we could always add more \n", - "#walkers (or samples per walker) for the MCMC" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Likelihood with Assumed PDF " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Let's consider one other problem. Here instead of defining\n", - "#A likelihood function and maximizing it, we want to \n", - "#See how a theorist's model of a distribution does when\n", - "#we compare it to experiment.\n", - "#\n", - "#Suppose we have a distribution of energies that we are\n", - "#measuring. It has the following properties\n", - "true_energy_mean = 10\n", - "true_energy_std = 1\n", - "N_energies = 100" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "np.random.seed(67)#Set the seed so everyone always gets the same data\n", - "\n", - "#This will pull N_energies (100) points from a normal distribution\n", - "#with mean (loc) true_energy_mean, and standard deviation (scale)\n", - "#true_energy_std.\n", - "observed_energies=np.random.normal(loc=true_energy_mean,\n", - " scale=true_energy_std,\n", - " size=N_energies)\n", - "plt.hist(observed_energies)\n", - "plt.xlabel('Energy (GeV)')\n", - "plt.ylabel('Number')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Our theorist tells us that if we measure these energies,\n", - "#we're going to see a perfect Gaussian. We set that up here\n", - "def my_energy_model(energy_mean,energy_std,possible_energy):\n", - " return(scipy.stats.norm.pdf(possible_energy,\n", - " energy_mean,\n", - " energy_std))\n", - "\n", - "#Just see how this would actually compare before fitting\n", - "possible_energies=np.arange(0,20,.1)\n", - "model_energies=my_energy_model(10,1,possible_energies)\n", - "plt.hist(observed_energies,density=True)\n", - "plt.plot(possible_energies,model_energies)\n", - "plt.xlabel('Energy (GeV)')\n", - "plt.ylabel('Number')\n", - "plt.show()\n", - "#Not terrible." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## What is the all data likelihood function for all the data?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def all_data_likelihood_function(model_parameters):\n", - " model_mean,model_std = model_parameters\n", - " #Since our model IS a PDF, a single point on it\n", - " #would already be a likelihood. Therfore we can\n", - " #just take the value of the PDF at all of the \n", - " #observed energies, with the fitter trying a\n", - " #range of means and deviations, until we find\n", - " #the most likely distribution. This is the same\n", - " #as the commented code below, if it makes more\n", - " #sense to you one point at a time.\n", - " likelihoods = my_energy_model(model_mean,\n", - " model_std,\n", - " observed_energies)\n", - " #likelihoods = 1.\n", - " #for data_point in observed_energies:\n", - " # single_datapoint_likelihood = my_energy_model(model_mean,\n", - " # model_std,\n", - " # data_point)\n", - " # likelihoods *= single_datapoint_likelihood\n", - " return(np.sum(np.log(likelihoods)))#we take the sum of the log instead of the product of the original likelihoods" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Does it work for a single point? Does the likelihood go down\n", - "#If we move away from the true answer as it should? Yes.\n", - "all_data_likelihood = all_data_likelihood_function( ( 10, 1) )\n", - "print(all_data_likelihood)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#See above for an explanation of the prior transform. This\n", - "#is the same as uniform bounds on our parameters of\n", - "#mean=(0,20), std=(0,5)\n", - "def prior_transform(x): \n", - " return np.array([20., 5.]) * x + np.array([0., 0.]) \n", - "\n", - "# Run nested sampling.\n", - "energy_result_nest = nestle.sample(all_data_likelihood_function, \n", - " prior_transform, ndim=2,npoints=1000)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#How was the theorist's prediction of the distribution? We\n", - "#Could also compare this to other distributions. For sanity\n", - "#We see that we recover the actual mean and standard deviations.\n", - "mean_nest, std_nest = np.percentile(energy_result_nest.samples, 50,axis=0)\n", - "print('Nested:','a=',mean_nest,'b=',std_nest)\n", - "print('True:','a=',true_energy_mean,'b=', true_energy_std)\n", - "fig = corner.corner(energy_result_nest.samples, \n", - " labels=[\"$\\mu$\", \"$\\sigma$\"],\n", - " weights=energy_result_nest.weights,\n", - " truths=[true_energy_mean, true_energy_std])\n", - "plt.show()" - ] - }, - { - "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": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Day_4/day_4_student_completed_workbook_likelihood_analysis.ipynb b/Day_4/day_4_student_completed_workbook_likelihood_analysis.ipynb deleted file mode 100644 index 90c857a..0000000 --- a/Day_4/day_4_student_completed_workbook_likelihood_analysis.ipynb +++ /dev/null @@ -1,1725 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Imports" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: corner in c:\\users\\doug\\anaconda3\\lib\\site-packages (2.0.1)\n", - "Requirement already satisfied: matplotlib in c:\\users\\doug\\anaconda3\\lib\\site-packages (from corner) (3.0.3)\n", - "Requirement already satisfied: numpy in c:\\users\\doug\\anaconda3\\lib\\site-packages (from corner) (1.16.2)\n", - "Requirement already satisfied: cycler>=0.10 in c:\\users\\doug\\anaconda3\\lib\\site-packages (from matplotlib->corner) (0.10.0)\n", - "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\doug\\anaconda3\\lib\\site-packages (from matplotlib->corner) (1.0.1)\n", - "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in c:\\users\\doug\\anaconda3\\lib\\site-packages (from matplotlib->corner) (2.3.1)\n", - "Requirement already satisfied: python-dateutil>=2.1 in c:\\users\\doug\\anaconda3\\lib\\site-packages (from matplotlib->corner) (2.8.0)\n", - "Requirement already satisfied: six in c:\\users\\doug\\anaconda3\\lib\\site-packages (from cycler>=0.10->matplotlib->corner) (1.12.0)\n", - "Requirement already satisfied: setuptools in c:\\users\\doug\\anaconda3\\lib\\site-packages (from kiwisolver>=1.0.1->matplotlib->corner) (40.8.0)\n" - ] - } - ], - "source": [ - "!pip install corner" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: nestle in c:\\users\\doug\\anaconda3\\lib\\site-packages (0.2.0)\n" - ] - } - ], - "source": [ - "!pip install nestle" - ] - }, - { - "cell_type": "code", - "execution_count": 104, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting emcee\n", - " Downloading https://files.pythonhosted.org/packages/3f/d3/7635106605dedccd08705beac53be4c43a8da1caad6be667adbf93ed0965/emcee-2.2.1.tar.gz\n", - "Requirement already satisfied: numpy in c:\\users\\doug\\anaconda3\\lib\\site-packages (from emcee) (1.16.2)\n", - "Building wheels for collected packages: emcee\n", - " Building wheel for emcee (setup.py): started\n", - " Building wheel for emcee (setup.py): finished with status 'done'\n", - " Stored in directory: C:\\Users\\doug\\AppData\\Local\\pip\\Cache\\wheels\\2f\\5d\\a5\\78f84e23329ad7d9b1787c9d24371100cae74cdefe25eba50d\n", - "Successfully built emcee\n", - "Installing collected packages: emcee\n", - "Successfully installed emcee-2.2.1\n" - ] - } - ], - "source": [ - "!pip install emcee" - ] - }, - { - "cell_type": "code", - "execution_count": 111, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "done importing\n" - ] - } - ], - "source": [ - "import numpy\n", - "import scipy\n", - "import corner\n", - "import nestle\n", - "import emcee\n", - "import pandas\n", - "import warnings\n", - "import scipy\n", - "import scipy.stats\n", - "import matplotlib.pyplot as plt\n", - "import astropy\n", - "import astropy.io\n", - "import astropy.io.ascii\n", - "#---------------------------------------------------------------------\n", - "import Library_GraphTwoDimensionDensityColorMap\n", - "#---------------------------------------------------------------------\n", - "warnings.simplefilter('ignore')\n", - "print (\"done importing\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# What does the theorist give you?" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [], - "source": [ - "# Both data set cases:\n", - "def my_model( v, g, t):\n", - " return v*t -.5*g*t**2\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Case 1--Height Error Only" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## What does the experimentalist give you?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### With a perfect stopwatch, but not so perfect meter stick?" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [], - "source": [ - "#(YOU NEED THIS CODE)\n", - "#The experimentalist has a perfect stopwatch,\n", - "# and knows his height measurement error\n", - "\n", - "# Read in a file using astropy called \"1D_Generated_Data.astropydat\"\n", - "\n", - "data_with_height_error_only = pandas.read_csv( \"1D_Generated_Data.dat\" ,sep=' ',header=0)\n", - "height_standard_deviation = 10" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [], - "source": [ - "#These should run without error\n", - "assert data_with_height_error_only['time'][0] == .1" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Probability Density Result of Measuring Zero')" - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEJCAYAAACUk1DVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzt3Xl8VPW5+PHPk50ESCAJiwEMkCAEVNSALIoLomit2FYrtnWrrV2k997aRW2v/Vnbe2+97a29ba23tlqtXURtrVGpVESlLLKo7GFJWJOwJIGEQMg6z++Pc4LjOEkmYZIzM3ner9ckc875zpnnO3PmPOd8v2cRVcUYY4yJ8zoAY4wxkcESgjHGGMASgjHGGJclBGOMMYAlBGOMMS5LCMYYYwBLCN0iIioiN5zmPB4Ukc1dKdPZcKzqC/XsyTqKyHgRWSUiDSKypyfeI9KE4zfaF8VsQhCRp9yFQkWkWUR2ichPRCTN69i64CfAJaFOd+v8yum+qYhc6vfZ+UTkmIhsFJH/FZHRpzv/buiNeqqIVIvIUhGZebrzDkNsYamj64dAPTAemNLO+z3ofgZLgkz7qjstmpLycODlnnwDEbk9YPkJfPyuJ9+/J8RsQnAtwVkwxgD/DnwVZ+USlIgk9lJcIVHV46pa3d3pYTAROAM4H/i++3+TiHSUpMKul+o5HLgUqAReFZEhPfh+vS0PWK6qe1S1soNyB4GLRSQ3YPzngX09FFtYiUgSgKoeVNXGHn67hTjLTeDjB0AT8JvuzritHr1OVWPyATwFvBIw7jfAAff5pYAC1wBrcL7Aa91pXwJK3HElwBcD5qPAAuBVnC2vvcDnAsr8CNgOnAT2AP8NpPhNfxDYDHwB58d2EvgbkBVYJpRh97kGPC4FlgK/DIhtoBv3J9v57No+m6yA8fHAP4FSIN5v/MeBd4EGYDfwH0CS3/Q9OAn518AxoAz4VsC8vwTscOdRCSwGEnq7nsDZ7riPB5S9A9jqxrcD+DoQF2L8T/HRZbHL32U7dYgDHgD2A43AJmBewLLq/3iwnfk8iLM8/hX4vt/4c4DjwP/4xxvi9/45YC1QBxwGngdy/KYnAj8HKtzY9wM/Clhuvhnwnm/5f89umQeBJ4Ea4Hm/et/gPs91hz8FvO4uE1uBOQHz/hjOb7YBWAbMd1+X24X1ziVAM/CFgPEFOOuLts/iz8CwwPUVcC/O7+OwO34Q8DRwFGcdsQSYGI51ZND4e2rGXj/a+RH+HKhyn1/qftmbgCtx9iKygU+4X+gCYBzwNXf4437zUaAaZyUwDvgu4AMK/co8AMx0F8ZrcFb6Pwj4AR53F/Dz3LJbgKLAH2kow0B/nC2W14Fh7iMJuBk4AiT7ve5L7kKZ2M5n1/bZZAWZ9kl3WqE7fBXOSv4OYCxwmfuj+knAj7ba/Uzz3M9Ugenu9EKgBfgscCZwLs4KN1hC6LF6AqnAT91xc/3KfRE4ANwAjMZZER4EFoQY/1N0LSEErWM7dfi6+/l/BmdZfAhoBSa704cB23D2jIcB/duZz4M4CeFj7vcV547/X5yVbWC8oXzvn8dZ9scAU4E3gWV+07+BkwRmAaOAGcAdActNKAnhGPBtnGUr3+83GpgQtrnfXT7OSra67fNw37/R/f7Pcr/rfXQhIbjf/WE+umEyHKgCHgYm4CTZl3E2RNs+56dwksUfgUnA2e74l9y4Z+FsrBS5n1m/Hllv9sRMI+FBwI/QXSCrgIXu8KXul/2pgNetAJ4MMq/lfsMK/CagzBLgDx3E82WgJOAH2AqM8ht3kTvvfL8yISWEYHV2xyW79Z7vN241fj/cILG2fTbBEsJ4d9qn3eFlwAMBZa7HSXbiDu8B/hxQZifw7+7zTwK1wIB24unpeh53Hz53eC1+SQRnxXBLwGv/DdgaYvzB4u3yd9nOvMuB7wWMe8t/WcRZ0T/YyXwedMvFu/Oc4/eZXhQkvk6/9w6WnRHu8M+BNzoov4fQEsLLQV4bLCF8yW96jjvuInf4v4Bi/1iA7xBiQsDZmHgPJ+klBEx7CHgjYNwgd95T/b7vSj68QZPvlpnlNy7dXda+0FlM3XnEeh/CXBE5LiINwCqchfhrAWXWBQxPwEkK/pbj7PL5WxVk+FQZEblBRJaLyEEROQ48grMV4q9cVf3bZlfjrJQmdFCnLlGnHfUZnK01RKQAJzk+2c1ZStus3f8XAN91P+fjbl3/BKThbJG22RgwnwqgrZ3+dZxmt90i8kcRuU1EBnQlqNOs52U4/SM34zR93Kaqze58soGRwK8D6vgjnC3jsMTfHSIyEKePJ5TlNSSq2oqz9fx5nBV8taouD1K00+9dRM4XkZdEZK+I1PHBb63td/AUMBnYISKPisjHRKQ766TA33B7/JfBCvd/2zI4Hlir7lrXtboLMTyBs5K/UVVbAqZdAMwK+Kz2u9PG+pXbrB/u95iAsz44ta5R1VqcVo1ufb+dSeiJmUaQZcBdOE0+FW0/8gAngozTEMcFJSLTgGdxOmK/jtO2eR0ddGj3sN8CG0VkFHAnsEpVt3ZzXm0L4i73fxxOPZ8PUta/AzPws1f3tahqnYicj7NbPAe4H/hPEZmiqhWErrv13K2qVTgrphTgryJyrvvjbFtBfRlYGezFIcTv44NE2iacBzCc1vIaxJM4K89c2k+oHX7v7tF8i3H2nG/BaUrJwumDauv4fc/twJ4LXI6TiDaIyBxV9RH65xbsNxzMqWVQVVVE2uqB+z7d+sxE5F6c3/cMdzkKFIfTf/DNINMO+T0PrEdg3f2dzvfbrljfQ6hX1RJV3dtOMgimGGcX2d9FOJ1Q/qYFGS52n8/E2fr/gaquVdWdOO2LgXJEZKTf8FSc76Q4SNlQNOHs8n+Iqm7B2dr5Ik5HX7f2DkQkHqeppBRY745+Dxjvfs6Bj8AtpXapaouqLlXV+3HaWNOAa9sp3pP1fAZnpXO3O89DOE0oY4PVMcT4K3Hakf1N7iSOoHX0p6rHcLZ0Q1leQ+bWay1O38jv2ynW2fc+HicBfEdVl6nqNj7YGvd/rzpVfV5Vv4LTf3E5Tl8ABHxubrIe3916daKYjx6SO7WzF4nIXJzO9NtVdUM7xd7DOZJtb5DPqq6D2W/FWR9M93u/gTh9Cd3+fjsS63sI3fFj4HkReRf4B87Wy2dx2on9fVJE1uK0ad4AzAYudKftwFnZfxZnd+8qnOaIQCeBp0XkHqAf8H/Aq24C6Y49wNUichZOh1mtXyL8jTv/ZpwOy1AMEZEEnE7Oc3D2ds4DrnGbFsBpH31FRPYCz+F0rk7CaRv9dihvIiLX4uw6L8PpGL4MGED7iTHc9TxFVX0i8jPgARH5taqewGk//4WI1ACLcBLG+ThHzPxXCPEvBb4tIp93y3wSZ6OhrINQOqqjvx8DD4nITpwjfj4HXIzTTHE6rsZpzz7azvTOvvd9OJ20C0TkUZzmjx/4z8Bd7g/gbFw043SMtx2FBs7n9nkRKcJJDt8lvHtW/v4PuEdEfoKzDE3EOSgB2tkaF5F8nKOFfgv8U0SGBRRpVecw30dxNlIWisjDOHUZA3wa+EZ7SUFVd4rISzjNlXfhtDT8B85n9Kdu17QjPdExEQkPOumUo+OO0y/jHG7aTMeHnb6Gs1Lfh9Pu7F/mv3C++OM4h/J9xfm4P9KJdxdOe+JJnCMKsgPLdGE4GyeJ1RFwqCJOp1cdAR3mnXw2bY86N9afA2OClL8SpymgHmdhXYd7BI47fQ8ddA7ibNG+ibPiO+m+1x29WM/Aw2vTcFbs3/EbdzPOll4DziGAy3E7sDuL368OB3A6BH8F/Gd3v8uA+fofdtqE0758fUCZkDuVuzI9hO/9Jpy9yQacI2qu8q8LzkryPbeOx4C3cZpd2l4/EGeFW4uzl/ZVgncqfzNIvME6lQvbK+MOX8sHhw7/E+cIKgWGtvOZ/D8+eliv/2OPX9l84AU+OHx0O/AL3KPHaGd9RS8fdtp2FIiJcSJyBk7iukRVAzshY0ZfqafpeSLyrzh7QoPU6dOIedZkFOPcs6+H4+xqvh+rK8m+Uk/Tc0Tkbpy+k0qcPsEHgKf6SjIASwh9wUyc5oydOG2Wsaqv1NP0nDyccw8ycfox/g9nD6HPsCYjY4wxQOwfdmqMMSZEUdVklJWVpbm5uV6HYYwxUeXdd9+tUtXszspFVULIzc1l3bpQz1I3xhgD4J4v0ilrMjLGGANYQjDGGOOyhGCMMQawhGCMMcYVUkIQkbkisl1ESkTkviDTk0VkoTt9deA9WUVklHsd8G+GOk9jjDG9q9OE4F7y+FGcqx8WADe7Nx/xdydwVFXzcG4E83DA9EeAv3dxnsYYY3pRKHsIU3Fu/bhLVZtwbvwyL6DMPJwr8oFzRb/Z4t59QkSux7mZypYuztMYY0wvCuU8hBw+uN0bONf4uLC9MqraIiK1QKaInATuxbmL1DeDle9gngC41wG/C2DUqMA7UBrjndqTzTy7Zh8nGp37ACXEx3H95BxGZaZ6HJkx3RNKQgh2G7fACyC1V+b7wCOqetzdYejKPJ2Rqo8DjwMUFhbahZdMRFi85SAP/G0zh+saaVu0VeGxt0r55lVncfuMXOLjOroDojGRJ5SEUIZzk/E2I/jgBtWBZcrcO2yl49xg5ELgBhH5byAD8Lk3vH83hHkaE3FONLZw71828srGA0wYPpAnb5/CpJx0AA7UnuS7L27mB69s5ZWNFfx8/nmMHGx7CyZ6hNKHsBbIF5HRIpIEzAeKAsoUAbe5z28AlqrjYlXNVdVc4GfAf6rqL0OcpzERRVX59l82smjTAb4xZxxFC2aeSgYAw9P78cRthfzspsmUHD7OF3+/jpNNrR3M0ZjI0mlCUOeG2QuAxTj3iH1OVbeIyEMicp1b7AmcPoMS4B6gw8NI25tn96thTM97YvluXt14gG9dNZ6vzc4nMf6jPx8R4frzcvjlZ85n+6E6vvPiJuwS8yZaRNX9EAoLC9Uubme8sGb3EW7+zTvMHj+EX99yAQF9YkH975KdPLJkBz+YN5Fbpuf2fJDGtENE3lXVws7K2ZnKxnSisq6Ru//0HiMH9eMnnz43pGQA8LXL87j0rGweemUr6/fX9HCUxpw+SwjGdOKnr++gpr6Jxz53AQNTEkN+XVyc8LObJpOZlsz3XtpsTUcm4llCMKYDuyqP89y6/Xxm6igmDB/Y5ddnpCZxz5Xj2FhWy2ubD/ZAhMaEjyUEYzrwP6/vIDkhjgWX53d7Hp88L4e8If358T+209LqC2N0xoSXJQRj2rGprJZXNx7gCxeNJntAcrfnkxAfx7euOotdlSd44d2yMEZoTHhZQjCmHf+9eBuDUhP5wqwxpz2vKwuGMnlkBj9bspOGZjs3wUQmSwjGBLGqtJp/7qzi7svyutSR3B4R4d654zl4rIFnVoV0e1tjep0lBGOCeHLFbjLTkvjctDPDNs/pYzOZNmYwT63cQ6vPjjgykccSgjEBymtO8kbxIW6aMpKUxPiwzvvW6bmU15zkzW2HwzpfY8LBEoIxAf68eh8KfObC8F9ufU7BUIYMSOYPq63ZyEQeSwjG+Glq8fHs2n3MHj+EEYPCf6XSxPg4bp46ird3VLK3+kTY52/M6bCEYIyf17YcpOp4U1j7DgLdPHUUcSL8afW+HnsPY7rDEoIxfv7wzl5GDU5lVn52j73HsPQUriwYysJ1++0QVBNRLCEY49p+sI41u4/wuWmjiOvhu53dMu1MauqbeXXjgR59H2O6whKCMa6Fa/eTFB/HjReM7LzwaZo+NpMx2WksXLu/88LG9BJLCMYArT7llY0VXDY+m0FpST3+fiLCJybnsGbPEQ7Unuzx9zMmFCElBBGZKyLbRaRERD5yNzQRSRaRhe701SKS646fKiLr3ccGEfmE32v2iMgmd5rd9cZ4avXuag7XNfLxc8/otfdse69XNlizkYkMnSYEEYkHHgWuBgqAm0WkIKDYncBRVc0DHgEedsdvBgpVdTIwF/i1iCT4ve4yVZ0cyp18jOlJL2+oIDUpntnjh/bae+ZmpXHOiHSKNlT02nsa05FQ9hCmAiWquktVm4BngXkBZeYBT7vPXwBmi4ioar17/2SAFMDO1zcRp6nFx983H2ROwVD6JYX3zOTOXHfuGWwqr2V3lZ2TYLwXSkLIAfx7vsrccUHLuAmgFsgEEJELRWQLsAn4sl+CUOAfIvKuiNzV3puLyF0isk5E1lVWVoZSJ2O6ZHlJJTX1zVzXi81FbT52znBEnD0UY7wWSkIIdvxd4JZ+u2VUdbWqTgSmAPeLSIo7faaqno/TFHW3iMwK9uaq+riqFqpqYXZ2zx0bbvquovUVpPdL5OIePPegPcPT+zEldzBFGyrsFpvGc6EkhDLA/zi8EUDg5sypMm4fQTpwxL+AqhYDJ4BJ7nCF+/8w8CJO05QxvepkUyuvbz3E1ZOGkZTgzUF3Hz/3DEoOH2fbwTpP3t+YNqH8AtYC+SIyWkSSgPlAUUCZIuA29/kNwFJVVfc1CQAiciZwFrBHRNJEZIA7Pg24EqcD2phetXTbYU40tXrSXNTmmknDiI8T61w2nus0Ibht/guAxUAx8JyqbhGRh0TkOrfYE0CmiJQA9wBth6ZeBGwQkfU4ewFfVdUqYCiwXEQ2AGuAV1X1tXBWzJhQLNp8gKz+yVw4JtOzGDL7JzMzL4tFmw5Ys5HxVELnRUBVFwGLAsZ9z+95A3BjkNc9AzwTZPwu4NyuBmtMODW1+Hh7eyXXnjOc+B6+VEVn5hQM5YG/baa08jh5QwZ4Govpu+xMZdNnrd5dzfHGFuYU9N65B+25YsIQAF7fajfOMd6xhGD6rCVbD5GSGMfMvCyvQ2F4ej/OzklnSfEhr0MxfZglBNMnqSpLig9zcX522G+T2V1XTBjKe/uOUnW80etQTB9lCcH0ScUH6iivOcmcCd43F7W5omAIqrC02JqNjDcsIZg+aUnxIUTgsvFDvA7llILhAzkjPYXXrdnIeMQSgumTlhQf4ryRGWQPSPY6lFNEhCsKhvLPnZV2JzXjCUsIps85WNvAxrJaroiAo4sCXTFhKA3NPlaUVHkdiumDLCGYPueNbU6TTCT1H7S5cMxg+icn2NFGxhOWEEyfs7T4MKMGp5I3pL/XoXxEckI8l4zL5o3iw3bWsul1lhBMn9LY0srK0mouPSsbEW/PTm7PJeOyOVzXaBe7M73OEoLpU97dc5STza1cMi5yL6U+y41t2Q67/4fpXZYQTJ/y9o5KEuOFaR5ezK4zw9JTOGvoAN62hGB6mSUE06e8vaOSKbmDSUsO6bqOnrnkrGzW7jnCicaWzgsbEyaWEEyfcehYA9sO1p1qkolks/KzaW5V3tlV7XUopg+xhGD6jLYmmEjuP2hTmDuIfonx1mxkepUlBNNnLNtRyZAByYwfFvn3G0hJjGf62EzrWDa9KqSEICJzRWS7iJSIyH1BpieLyEJ3+moRyXXHTxWR9e5jg4h8ItR5GhNOrT7lnzurmDUucg83DTQrP4s91fXsrT7hdSimj+g0IYhIPPAocDVQANwsIgUBxe4EjqpqHvAI8LA7fjNQqKqTgbnAr0UkIcR5GhM2G8tqqD3ZHBXNRW0uOcu58J7tJZjeEsoewlSgRFV3qWoT8CwwL6DMPOBp9/kLwGwREVWtd+/JDJACtJ16Gco8jQmbt3dUIgIXRcDNcEKVm5nKyMH9rB/B9JpQEkIOsN9vuMwdF7SMmwBqgUwAEblQRLYAm4Avu9NDmSfu6+8SkXUisq6y0n4Ypnv+ubOKc0ZkMCgtyetQQiYizMrPZmVpNU0tPq/DMX1AKAkhWINr4EVW2i2jqqtVdSIwBbhfRFJCnCfu6x9X1UJVLczOjp7dfRM56hqaWb+/houjaO+gzcX5WdQ3tbKhrMbrUEwfEEpCKANG+g2PACraKyMiCUA6cMS/gKoWAyeASSHO05iwWLP7CK0+ZUZe5J6d3J5pYzIRwS6HbXpFKAlhLZAvIqNFJAmYDxQFlCkCbnOf3wAsVVV1X5MAICJnAmcBe0KcpzFhsbykipTEOM4fNcjrULosIzWJs3PSLSGYXtFpQnDb/BcAi4Fi4DlV3SIiD4nIdW6xJ4BMESkB7gHaDiO9CNggIuuBF4GvqmpVe/MMZ8WMabOipIopuYNJSYz3OpRumZmXxfv7auwyFqbHhXRBF1VdBCwKGPc9v+cNwI1BXvcM8Eyo8zQm3A7XNbDj0HE+ef4Ir0Pptpljs3jsrVLW7D4SUfeANrHHzlQ2MW1liXMtoJljo69DuU1h7iCSEuKs2cj0OEsIJqYtL6kiIzWRgjMGeh1Kt6UkxjMldxDLLSGYHmYJwcQsVWVlSRXTx2QSHxcdl6toz4yxWWw7WEfV8UavQzExzBKCiVm7q05QUdvAzCg8/yBQ2xnWK0vtctim51hCMDFrhbvyjIWEMCknnYEpCazYac1GpudYQjAxa8XOKnIy+pGbmep1KKctPk6YPjbT+hFMj7KEYGKSz6es2lXN9LGZUXO5687MzMuivOYk+4/Uex2KiVGWEExM2nrgGLUnm5kZhZeraM/0MU5dVpbaXoLpGZYQTExquxfx9DHR33/QJm9If7L6J7PKOpZND7GEYGLSytJqxmSlMSw9xetQwkbE6UdYWVqNatCLAxtzWiwhmJjT0upjze4jTBsbO81FbWaMzeRwXSOllXZbTRN+lhBMzNlUXsvxxhZmxGBCaOtHWLXLmo1M+FlCMDGnbWU5bUzsJYQzM1M5Iz2FVdaxbHqAJQQTc1aVVnPW0AFk9U/2OpSwExGmjc3knV1H8PmsH8GElyUEE1OaWnys3XOE6THYXNRmxtgsjpxoYvuhOq9DMTHGEoKJKev319DQ7IvphNBWN7uukQm3kBKCiMwVke0iUiIi9wWZniwiC93pq0Uk1x0/R0TeFZFN7v/L/V7zljvP9e7D7vxhTtuq0mpEYNro2E0IORn9GDU41c5HMGHXaUIQkXjgUeBqoAC4WUQKAordCRxV1TzgEeBhd3wV8HFVPRvnnsuBd0/7rKpOdh+HT6MexgDOWbwTzxhIemqi16H0qBljM1m9q5pW60cwYRTKHsJUoERVd6lqE/AsMC+gzDzgaff5C8BsERFVfV9VK9zxW4AUEYm9nj4TERqaW3l/X82pQzNj2fSxmdQ1trClotbrUEwMCSUh5AD7/YbL3HFBy6hqC1ALBP4qPwW8r6r+d/j4ndtc9IC0cwUyEblLRNaJyLrKysoQwjV91Xt7j9LUGtv9B21OnY9gzUYmjEJJCMFW1IH7qR2WEZGJOM1IX/Kb/lm3Keli93FLsDdX1cdVtVBVC7Ozs0MI1/RVq3ZVEx8nTMkd7HUoPW7IwBTGZKfZCWomrEJJCGXASL/hEUBFe2VEJAFIB464wyOAF4FbVbW07QWqWu7+rwP+hNM0ZUy3rSqtZlJOOgNSYrv/oM30MZms3X2E5laf16GYGBFKQlgL5IvIaBFJAuYDRQFlinA6jQFuAJaqqopIBvAqcL+qrmgrLCIJIpLlPk8ErgU2n15VTF9W39TChrK+0X/QZvrYTE40tbKp3PoRTHh0mhDcPoEFwGKgGHhOVbeIyEMicp1b7AkgU0RKgHuAtkNTFwB5wAMBh5cmA4tFZCOwHigHfhPOipm+Zd2eozS3ap/oP2gzzfoRTJglhFJIVRcBiwLGfc/veQNwY5DX/RD4YTuzvSD0MI3p2Kpd1STECYVnDvI6lF6T1T+ZcUP7886uau6+LM/rcEwMsDOVTUxYVVrNuSMzSEsOaRsnZkwfk8m6PUdparF+BHP6LCGYqHe8sYVN5bV9qv+gzfSxmZxsbmVDWY3XoZgYYAnBRL21u4/Q6utb/QdtLhydiYj1I5jwsIRgot6qXdUkxcdxQR/qP2gzKC2J8cMGWkIwYWEJwUS9VaXVTB6VQUpivNeheGL6mEze3XeUhuZWr0MxUc4SgolqtfXNbKnom/0HbaaPzaSpxcf7+6wfwZweSwgmqq3eXY1Picn7J4dq6ujBxIndZ9mcPksIJqqt2lVNSmIck0dleB2KZ9L7JXJ2TrrdZ9mcNksIJqqtKq1mSu5gkhP6Zv9Bm+ljs1i/v4b6phavQzFRzBKCiVpVxxvZdrDu1CUc+rLpYzNpblXW7TnqdSgmillCMFHrHbfNvC/3H7SZkjuIhDix+yyb02IJwUStVaXV9E9O4OycdK9D8VxqUgLnjcqwfgRzWiwhmKi1qrSaC0cPJiHeFmNw+hE2lddyrKHZ61BMlLJfkolKB2sb2FV1ok9erqI9M8Zm4lNYs+uI16GYKGUJwUSlVbucphFLCB84b1QGyQlx1o9gus0SgolKK0uqyUhNZMKwgV6HEjGSE+IpzB1kJ6iZbgspIYjIXBHZLiIlInJfkOnJIrLQnb5aRHLd8XNE5F0R2eT+v9zvNRe440tE5OciIuGqlIltqsrK0mqmj8kkLs4WG38zxmZRfOAYR040eR2KiUKdJgQRiQceBa4GCoCbRaQgoNidwFFVzQMeAR52x1cBH1fVs3HuufyM32seA+4C8t3H3NOoh+lD9h2pp7zmpB1uGkRbE5pd/dR0Ryh7CFOBElXdpapNwLPAvIAy84Cn3ecvALNFRFT1fVWtcMdvAVLcvYnhwEBVXaWqCvweuP60a2P6hOUlTv/BzLwsjyOJPOfkpDMgOeHUZ2RMV4SSEHKA/X7DZe64oGVUtQWoBQI33z4FvK+qjW75sk7mCYCI3CUi60RkXWVlZQjhmli3sqSa4ekpjM5K8zqUiJMQH8eFYzJZaecjmG4IJSEEa6TVrpQRkYk4zUhf6sI8nZGqj6tqoaoWZmdnhxCuiWU+n7KitIqZeVlYt1NwF+Vlsre6nv1H6r0OxUSZUBJCGTDSb3gEUNFeGRFJANKBI+7wCOBF4FZVLfUrP6KTeRrzEVsPHKOmvpmLrLmoXRflO5/NCms2Ml0USkJYC+SLyGgRSQLmA0UBZYpwOo0BbgCWqqqKSAbwKnC/qq5oK6yqB4A6EZnmHl10K/DSadbF9AFtbeMz8qxDuT1js/szdGCy9SOYLus0Ibh9AguAxUAx8JyqbhGRh0SQQ811AAAZv0lEQVTkOrfYE0CmiJQA9wBth6YuAPKAB0RkvfsY4k77CvBboAQoBf4erkqZ2LWipIpxQ/szZECK16FELBFh5tgsVpZW4/MFbYk1JqiEUAqp6iJgUcC47/k9bwBuDPK6HwI/bGee64BJXQnW9G0Nza2s3XOEm6eO8jqUiDczL4u/vl/OtoN1FJxhJ++Z0NiZyiZqvLfvKA3NPus/CEHbIbnWj2C6whKCiRorSqqIjxOmjh7sdSgRb1h6CmOz06wfwXSJJQQTNZaXVDN5ZAYDUhK9DiUqXJSXxZrdR2hq8XkdiokSlhBMVKg92cymshpm2uUqQjYzL4uTza28t89uq2lCYwnBRIUVJVX4FC4eZycnhmra2Ezi44RlO+wMfxMaSwgmKizbUcmA5AQmj8zwOpSoMTAlkfNHZbBspyUEExpLCCbiqSrLdlQyIy+TRLtdZpfMys9mc/kxqo43eh2KiQL26zIRr7TyOBW1Dcyy5qIua/vMlu+0o41M5ywhmIj39g5nZTYr3xJCV03KSWdQaqL1I5iQWEIwEW/ZjkrGZKUxcnCq16FEnfg44aL8bJbtrLLLWJhOWUIwEa2huZXVu6utueg0zMrPoup4I8UHj3kdiolwlhBMRFu75wgNzT5mjbPLVXRXWzJdtsP6EUzHLCGYiLZsRyVJ8XFMG2MnpHXX0IEpjB82wPoRTKcsIZiItmxHFVNGDyI1KaQL85p2XDIum3V7j3CiscXrUEwEs4RgItbB2ga2H6qzo4vCYNa4bJpblVWl1V6HYiJYSAlBROaKyHYRKRGR+4JMTxaRhe701SKS647PFJE3ReS4iPwy4DVvufMMvHGOMQC8uf0wAJeeZYvG6SrMHURqUvypz9SYYDrdDxeReOBRYA7OvZDXikiRqm71K3YncFRV80RkPvAwcBPQADyAcyOcYDfD+ax7oxxjPuKN4sPkZPRj3ND+XocS9ZIT4rk4P4ul2w6jqjh3rjXmw0LZQ5gKlKjqLlVtAp4F5gWUmQc87T5/AZgtIqKqJ1R1OU5iMCZkDc2trCipYvaEIbbyCpPZ44dyoLaBbQfrvA7FRKhQEkIOsN9vuMwdF7SMew/mWiCUw0J+5zYXPSDt/OpF5C4RWSci6yor7SiJvuKdXdWcbG7lsvHWXBQul453+mKWbrNmIxNcKAkh2Io68JTHUMoE+qyqng1c7D5uCVZIVR9X1UJVLczOts7FvmLptsP0S4xnuh1uGjZDBqRwzoh03ig+5HUoJkKFkhDKgJF+wyOAivbKiEgCkA4c6Wimqlru/q8D/oTTNGUMqsrSbYeZmZdFSmK81+HElMvHD+H9/TUcOdHkdSgmAoWSENYC+SIyWkSSgPlAUUCZIuA29/kNwFJVbXcPQUQSRCTLfZ4IXAts7mrwJjbtPHycsqMnudyai8Lu8vFDUIW37GgjE0SnCcHtE1gALAaKgedUdYuIPCQi17nFngAyRaQEuAc4dWiqiOwBfgrcLiJlIlIAJAOLRWQjsB4oB34TvmqZaPZGsbOysoQQfpPOSCd7QDJvWD+CCSKk0z9VdRGwKGDc9/yeNwA3tvPa3HZme0FoIZq+Zum2Q0w8YyDD0lO8DiXmxMUJl581hEWbDtDc6rMbDpkPsaXBRJSa+ibe3XuU2bZ30GMunzCEusYW1u7psJvP9EGWEExEWbrtMD6FyycM9TqUmHVRXhZJCXEs2WrNRubDLCGYiPLa5oMMT0/hnJx0r0OJWWnJCczKz2LxloN0cOyH6YMsIZiIUd/Uwts7KrmyYChxcXZ2ck+6auIwymtOsrncbppjPmAJwUSMt7dX0tji46pJw7wOJeZdMWEo8XHCa1sOeB2KiSCWEEzEeG3LQQalJjI1d7DXocS8QWlJTBszmNc2H/Q6FBNBLCGYiNDY0srS4sPMKRhKgh0K2SvmThxGaeUJSg7bxe6Mw355JiKsLK2mrrGFudZc1GuunOh81raXYNpYQjARYfHmg/RPTmDG2CyvQ+kzhg5M4bxRGby2xRKCcVhCMJ5r9Smvbz3EZeOH2MXsetncicPYXH6M/UfqvQ7FRABLCMZza/ccofpEE1dNtJPRettVbrPRYttLMFhCMBGgaEMF/RLjuczundzrcrPSmJQzkJc3BF7R3vRFlhCMp5pafCzadIA5BUNJSw7pWosmzOadm8OGslp2V53wOhTjMUsIxlP/3FlJTX0z8yaf4XUofda15w5HBIrW215CX2cJwXjqpfUVZKQmcnG+3R7VK8PT+3Hh6MG8tKHcrm3Ux1lCMJ6pb2rh9a2HuObs4SQl2KLopXmTc9hVeYItFXZto74spF+hiMwVke0iUiIi9wWZniwiC93pq0Uk1x2fKSJvishxEfllwGsuEJFN7mt+LiJ2NbM+5vWthzjZ3Mq8c625yGtXTxpGYrxQZJ3LfVqnCUFE4oFHgauBAuBm9zaY/u4EjqpqHvAI8LA7vgF4APhmkFk/BtwF5LuPud2pgIleResrGJ6ewhS7dpHnMlKTuGRcNkXrK/D5rNmorwplD2EqUKKqu1S1CXgWmBdQZh7wtPv8BWC2iIiqnlDV5TiJ4RQRGQ4MVNVV6jRa/h64/nQqYqLL0RNNvL2jkuvOPcMudR0hrpucw8FjDayxO6n1WaEkhBxgv99wmTsuaBlVbQFqgcxO5lnWyTwBEJG7RGSdiKyrrKwMIVwTDV7ZWEGLT7nOji6KGFdMGEJqUjwvvlfudSjGI6EkhGCbb4H7lKGU6VZ5VX1cVQtVtTA7245EiRUL1+2nYPhACoYP9DoU40pNSuDac4bz8sYKjje2eB2O8UAoCaEMGOk3PAII7Hk6VUZEEoB0oKP9zjJ3Ph3N08SozeW1bC4/xvypI7FjCSLLTVNGUd/Uyqsb7efYF4WSENYC+SIyWkSSgPlAUUCZIuA29/kNwFLt4IBmVT0A1InINPfooluBl7ocvYlKz67dR3JCHPPODdpKaDx0/qgM8of0589r9nde2MScThOC2yewAFgMFAPPqeoWEXlIRK5ziz0BZIpICXAPcOrQVBHZA/wUuF1EyvyOUPoK8FugBCgF/h6eKplIdrKplZfer+Cas4eTnprodTgmgIgwf+oo1u+vYdtBOyehrwnp4jGqughYFDDue37PG4Ab23ltbjvj1wGTQg3UxIZFmw5Q19jCTVNGdl7YeOIT5+Xw8N+3sXDtfv7fxyd6HY7pRXZ6qOlVC9fuJzczlQtH27kHkWpwWhJXThzKi++X09Dc6nU4phdZQjC9prTyOGv2HOGmKaOsMznCzZ8yipr6Zv6x9ZDXoZheZAnB9Jo/vrOPhDjhUxdYZ3KkmzE2k5GD+/GHVXu9DsX0IksIplfUNTTz3Lr9fOyc4QwZkOJ1OKYTcXHCbdNzWbPnCJvLa70Ox/QSSwimVyxcu5/jjS3cedFor0MxIfr0lJGkJcXzxPLdXodieoklBNPjWn3KUyv3MCV3EOeMyPA6HBOigSmJfHrKSF7eUMGhYw2dv8BEPUsIpsf9Y8tByo6etL2DKHTHjNG0qvL7VXu8DsX0AksIpsc9sXw3Iwf3Y07BMK9DMV00KjOVKwuG8sfV+zjZZIegxjpLCKZHbdhfw7q9R7ljxmji7TLXUenOi8ZQU9/MX98v67ywiWqWEEyPeuytUgYkJ/BpOzM5ajl9P+k8vmwXLa0+r8MxPcgSgukxWypqeW3LQT5/0Wj6J4d0lRQTgUSEr12ez97qev76vt0rIZZZQjA95mdLdjIgJYHPW2dy1LtiwhDOzknnF0t30mx7CTHLEoLpEZvKanl96yG+ePEY0vvZVU2jnYjw9Tn57D9ykr+8a30JscoSgukRP1uyg/R+idwxM9frUEyYXHbWEM4dmcEvlpbQ1GJ7CbHIEoIJuw37a3hj22G+ePFoBqTY3kGsEBH+7Yp8ymtO8vy7dgOdWGQJwYSVqvLwa9sYlJrI7TOt7yDWXDoum/NGZfDzN3Zywu67HHNCSggiMldEtotIiYjcF2R6sogsdKevFpFcv2n3u+O3i8hVfuP3iMgmEVkvIuvCURnjvdc2H2RlaTX3zBlnRxbFIBHh3z82gUPHGnn0zRKvwzFh1mlCEJF44FHgaqAAuNnvNpht7gSOqmoe8AjwsPvaApx7ME8E5gK/cufX5jJVnayqhaddE+O5k02t/PDVYsYPG8DNU0d5HY7pIRecOZhPnpfDb/+5m73VJ7wOx4RRKHsIU4ESVd2lqk3As8C8gDLzgKfd5y8As8W5A8o84FlVbVTV3Tj3T54antBNpPn1slLKa07y4HUTSYi31shYdu/V40mMF37wSrHXoZgwCuVXmwP49yCVueOCllHVFqAWyOzktQr8Q0TeFZG72ntzEblLRNaJyLrKysoQwjVeKDtaz2NvlXLtOcOZNibT63BMDxs6MIWvzc5nSfEh3tp+2OtwTJiEkhCCXYBGQyzT0Wtnqur5OE1Rd4vIrGBvrqqPq2qhqhZmZ2eHEK7pbarKg0VbEYHvXDPB63BML7ljZi6js9L4/stb7cJ3MSKUhFAG+F+IZgRQ0V4ZEUkA0oEjHb1WVdv+HwZexJqSotZf3itnSfEhvjHnLM7I6Od1OKaXJCfE8x/XT2J31Qkefm2b1+GYMAglIawF8kVktIgk4XQSFwWUKQJuc5/fACxVVXXHz3ePQhoN5ANrRCRNRAYAiEgacCWw+fSrY3pb2dF6vl+0hamjB9slKvqgGXlZ3D4jl6dW7mFFSZXX4ZjT1GlCcPsEFgCLgWLgOVXdIiIPich1brEngEwRKQHuAe5zX7sFeA7YCrwG3K2qrcBQYLmIbADWAK+q6mvhrZrpaT6f8q3nN+JT5X9uPNcub91H3Tt3PGOy0/jm8xuoPdnsdTjmNIizIR8dCgsLdd06O2UhUjyxfDc/eGUrD3/qbG6aYoeZ9mXr99fwqcdWct25Z/DTT5+Lc5ChiRQi8m4oh/fbsYGmW97ZVc1/LSrmiglD+XSh3eugr5s8MoOvXZ7Hi++X88w7e70Ox3STJQTTZfuP1PPVP77HqMxUfnqTbQ0ax79cns/s8UP4/stbWVlq/QnRyBKC6ZL6pha++Pt1NLf6+O2thQy0i9cZV1yc8LP5kxmdlcbdf3yP/UfqvQ7JdJElBBOyllYfX1+4nh2H6vjFzecxJru/1yGZCDMgJZHf3FpIq0/5wtPrqKlv8jok0wWWEExIWn3KPc9tYPGWQzxwbQGXnjXE65BMhBqdlcZjn7uA3VUnuOWJNXbkURSxhGA61epTvvX8Boo2VHDf1eO5wy5rbToxMy+L/7vlfLYdPMatT67hWIMlhWhgCcF0qLnVx7de2MBf3y/nm1eO48uXjPU6JBMlLh8/lF999gK2lNdy+5NrOHrCmo8inSUE066jJ5q45YnV/PW9cu6ZM44Fl+d7HZKJMnMKhvLLz5zP5vJjfOJXKyg5fNzrkEwHLCGYoEoOH+f6X63gvb01/PTT5/Ivsy0ZmO6ZO2kYf77rQo43tvCJX61g2Q67anGksoRgPkRVWbh2H9c/uoITjS38+a5pfPL8EV6HZaLcBWcO5m93zyQnox+3/24NP168jcYWu0JqpLGEYE45dKyBzz+1lnv/solJOQN5acFFXHDmIK/DMjFixKBUXvjKDD51/ggefbOUeb9cwdaKY16HZfzYtYwMDc2tPLVyD4++WUJzq497547ntum5xNnF6kwPeaP4EPf9dRNHTzTxuWln8rXL88jsn+x1WDEr1GsZWULow5pafPxtfTmPvL6DA7UNXHZWNg9cW2AnnJlecfREE/+9eBsL1+4nNSmBL18yhltn5NrZ7z3AEoJpV9XxRv60eh9/eGcvh+saOXdEOvddPYHpY+3Wl6b3lRyu4+HXtvP61kOkJcVzwwUjuG1Grm2YhJElBPMhdQ3NLCk+xCsbDrBsZyXNrcol47K5fWYul+RnW/OQ8dymslp+t2I3L2+soLlVKTxzEB87ZzjXnD2coQNTvA4vqllC6OMaW1rZXH6MlSVVrCit4r29NTS1+jgjPYVrzh7O/KmjyBtiW2Am8hyua+D5dWW8vKGCbQfrEIFJZ6QzIy+TmWOzOG9UBgOsWalLwpoQRGQu8L9APPBbVf1RwPRk4PfABUA1cJOq7nGn3Q/cCbQC/6Kqi0OZZzCWED6qudXHgZoGSquOs6vyBDsP1bGpvJYdh+pobnW+24LhA7koP4urJg7jvJEZtjdgokbJ4ToWbTrI8p1VvL//KM2tigiMzkzj7BHpnDVsAGOy+jM2O42Rg1NJSYz3OuSIFLaEICLxwA5gDlCGc4/lm1V1q1+ZrwLnqOqXRWQ+8AlVvUlECoA/A1OBM4AlwDj3ZR3OM5hYSwg+n9Ls89HSqjS2+GhsaaWx2cfJ5lbqm1qob2rleEMLdQ0tHGtopqa+meoTjVQdb6KyrpEDtSeprGvE5/cVZqQmcnZOOpNy0jknJ50Lx2QyOC3Ju0oaEyb1TS2s3XOUDftr2FRey6ayWg4ea/hQmcFpSQxPT2HowBQy05IY3D+JwalJDOyXyICUBAakJJKaFE+/xHhSk+JJTownJSGOpIQ4EuPjSIqPi8kNplATQkII85oKlKjqLnfGzwLzcO6T3GYe8KD7/AXgl+LcNWUe8KyqNgK73XsuT3XLdTbPsPnC02vZU935tdnbS44aMNA2rKoooAqKOv/1g/E+VVp9znCrKq2+Dx4t7v+uiI8TBqUmkdU/iaz+yczKz2Z4Rj9GZPRjdHYaY7LSGJyWZDesMTEpNSmBS8Zlc8m47FPj6hqa2V11gl2VJyg7Wk9FbQMHak5y6FgDxQeOUX28iaZWX5feJ04gIT6OhDghXoS4OCE+TogTIU449V9EEMF54D6HU78/OfUHAp5+RCi/2Vf/5SKSE3p2DyiUhJAD7PcbLgMubK+MqraISC2Q6Y5/J+C1Oe7zzuYJgIjcBdwFMGpU9+7be2ZmWugfZDvfi/9oETk17L8QnPovnFpwxP3ftkAlxAnx8c6ClhjvbJkkxAnJCXEkJ8aTnBBHv8R4+iXFk5acQGpSPOn9EhnYL5H+SQkxufViTHcNSEnknBEZnDMiI+h0VeVEUyt1Dc0cO9nC8cZm6ptaOdHYysnmFppafM7eebOPplZnb72ptZVWH7T6fLT4FJ+vbYMOQPH5oFU1YEPwg41Dp9SHNzA73PQLcbtQOkwp4RFKQggWRWAV2ivT3vhgZ0gH/VhU9XHgcXCajNoPs30PXFvQnZcZY6KciNA/OYH+yQkMT/c6msgXyqUrygD/u6iPACraKyMiCUA6cKSD14YyT2OMMb0olISwFsgXkdEikgTMB4oCyhQBt7nPbwCWqrO/VATMF5FkERkN5ANrQpynMcaYXtRpk5HbJ7AAWIxziOiTqrpFRB4C1qlqEfAE8IzbaXwEZwWPW+45nM7iFuBuVW0FCDbP8FfPGGNMqOzENGOMiXGhHnZql782xhgDWEIwxhjjsoRgjDEGsIRgjDHGFVWdyiJSCez1Oo4uygKqvA6il1md+warc/Q4U1WzOysUVQkhGonIulB692OJ1blvsDrHHmsyMsYYA1hCMMYY47KE0PMe9zoAD1id+warc4yxPgRjjDGA7SEYY4xxWUIwxhgDWELocSLyTRFREclyh0VEfi4iJSKyUUTO9zrGcBGRH4vINrdeL4pIht+0+906bxeRq7yMM9xEZK5brxIRuc/reMJNREaKyJsiUiwiW0TkX93xg0XkdRHZ6f4f5HWs4SYi8SLyvoi84g6PFpHVbp0XupfvjxmWEHqQiIwE5gD7/EZfjXNfiHycW4M+5kFoPeV1YJKqngPsAO4HEJECnEuiTwTmAr8SkZ69OWwvcevxKM73WgDc7NY3lrQA31DVCcA04G63jvcBb6hqPvCGOxxr/hUo9ht+GHjErfNR4E5PouohlhB61iPAt/nw7UHnAb9XxztAhogM9yS6MFPVf6hqizv4Ds6d8MCp87Oq2qiqu4ESYKoXMfaAqUCJqu5S1SbgWZz6xgxVPaCq77nP63BWkDk49XzaLfY0cL03EfYMERkBfAz4rTsswOXAC26RmKuzJYQeIiLXAeWquiFgUg6w32+4zB0Xaz4P/N19Hst1juW6fYSI5ALnAauBoap6AJykAQzxLrIe8TOcDTqfO5wJ1Pht9MTcd93pHdNM+0RkCTAsyKTvAt8Brgz2siDjoubY347qrKovuWW+i9PM8Me2lwUpHzV17kQs1+1DRKQ/8Bfg31T1mLPBHJtE5FrgsKq+KyKXto0OUjSmvmtLCKdBVa8INl5EzgZGAxvcH80I4D0RmYqzVTHSr/gIoKKHQw2b9urcRkRuA64FZusHJ7lEdZ07Ect1O0VEEnGSwR9V9a/u6EMiMlxVD7jNnoe9izDsZgLXicg1QAowEGePIUNEEty9hJj7rq3JqAeo6iZVHaKquaqai7PSOF9VDwJFwK3u0UbTgNq23e5oJyJzgXuB61S13m9SETBfRJJFZDROh/oaL2LsAWuBfPfokySczvMij2MKK7ft/AmgWFV/6jepCLjNfX4b8FJvx9ZTVPV+VR3h/n7nA0tV9bPAm8ANbrGYqjPYHoIXFgHX4HSs1gN3eBtOWP0SSAZed/eM3lHVL6vqFhF5DtiK05R0t6q2ehhn2Khqi4gsABYD8cCTqrrF47DCbSZwC7BJRNa7474D/Ah4TkTuxDmS7kaP4utN9wLPisgPgfdxEmXMsEtXGGOMAazJyBhjjMsSgjHGGMASgjHGGJclBGOMMYAlBGOMMS5LCMYYYwBLCMYYY1z/H4g66t+e0tliAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#plot the instrument error function of height\n", - "Points = numpy.linspace(-50, 50, 100)\n", - "Values = scipy.stats.norm.pdf(Points, 0, 10)\n", - "plt.plot(Points, Values, )\n", - "plt.title(\"Probability Density Result of Measuring Zero\", fontsize=14)" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#Plot the 1D-error data\n", - "times_1d = data_with_height_error_only['time']\n", - "heights_1d = data_with_height_error_only['height']\n", - "#plt.errorbar( times_1d, heights_1d, yerr = [height_standard_deviation]*len(times_1d) , fmt = \".\")\n", - "plt.scatter(times_1d, heights_1d)\n", - "plt.ylabel('Height (Meters)',fontsize=14)\n", - "plt.xlabel('Time (Seconds)',fontsize=14)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\\begin{tabular}{lrrr}\n", - "\\toprule\n", - "{} & time & height & error \\\\\n", - "\\midrule\n", - "0 & 0.100000 & 22.591523 & 10 \\\\\n", - "1 & 0.621053 & 33.164242 & 10 \\\\\n", - "2 & 1.142105 & 60.501061 & 10 \\\\\n", - "3 & 1.663158 & 92.012965 & 10 \\\\\n", - "4 & 2.184211 & 104.509306 & 10 \\\\\n", - "5 & 2.705263 & 89.629980 & 10 \\\\\n", - "6 & 3.226316 & 119.812017 & 10 \\\\\n", - "7 & 3.747368 & 117.045276 & 10 \\\\\n", - "8 & 4.268421 & 123.113715 & 10 \\\\\n", - "9 & 4.789474 & 131.178284 & 10 \\\\\n", - "10 & 5.310526 & 128.778472 & 10 \\\\\n", - "11 & 5.831579 & 139.485849 & 10 \\\\\n", - "12 & 6.352632 & 127.497909 & 10 \\\\\n", - "13 & 6.873684 & 113.388041 & 10 \\\\\n", - "14 & 7.394737 & 106.233023 & 10 \\\\\n", - "15 & 7.915789 & 92.093574 & 10 \\\\\n", - "16 & 8.436842 & 87.999403 & 10 \\\\\n", - "17 & 8.957895 & 52.648151 & 10 \\\\\n", - "18 & 9.478947 & 36.810874 & 10 \\\\\n", - "19 & 10.000000 & 1.459043 & 10 \\\\\n", - "\\bottomrule\n", - "\\end{tabular}\n", - "\n" - ] - } - ], - "source": [ - "#Make the data pastable to latex \n", - "print ( data_with_height_error_only.to_latex() )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## What is the correct single datapoint likelihood function?\n", - "### \"Likelihood function\" fixes the observation values and allows the parameters to vary:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Lets focus on the first datapoint first \n", - "### and plot against a single choice of v & g" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#Plot the 1D-error data\n", - "times_1d = [ data_with_height_error_only['time'][0] ]\n", - "heights_1d = [ data_with_height_error_only['height'][0] ]\n", - "plt.errorbar( [times_1d[0]], [heights_1d[0]], yerr = [height_standard_deviation]*len(times_1d) , fmt = \".\")\n", - "plt.ylabel('Height (Meters)',fontsize=14)\n", - "plt.xlabel('Time (Seconds)',fontsize=14)\n", - "\n", - "PointsToTry = numpy.linspace(0, 10, 100)\n", - "ValuesWeGet = my_model( v = 40, g = 12, t = PointsToTry)\n", - "plt.plot(PointsToTry, ValuesWeGet)\n", - "\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "``` If we think about what it means to believe the model, \n", - " then we have to shift our believe about the measurment differently.\n", - " What is the probability we measure a point if the model is correct? ```" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#Plot the 1D-error data\n", - "times_1d = [ data_with_height_error_only['time'][0] ]\n", - "heights_1d = [ data_with_height_error_only['height'][0] ]\n", - "plt.scatter(times_1d, heights_1d)\n", - "\n", - "plt.ylabel('Height (Meters)',fontsize=14)\n", - "plt.xlabel('Time (Seconds)',fontsize=14)\n", - "\n", - "PointsToTry = numpy.linspace(0, 10, 100)\n", - "ValuesWeGet = my_model( v = 40, g = 12, t = PointsToTry)\n", - "\n", - "\n", - "ModelFirstPointHeight = my_model( v = 40, g = 12, t = times_1d[0])\n", - "\n", - "plt.errorbar( \n", - " times_1d[0] , \n", - " ModelFirstPointHeight , \n", - " yerr = [height_standard_deviation]*len(times_1d) , \n", - " fmt = \".\")\n", - "plt.plot(PointsToTry, ValuesWeGet)\n", - "\n", - "\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Then the single datapoint likelihood function asks what the probability of that observation is given the correct model" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [], - "source": [ - "def probability_of_single_measurement_1D(\n", - " observed_time, \n", - " observed_height,\n", - " v,\n", - " g,\n", - " ):\n", - "\n", - " ##########################################################\n", - " ##########################################################\n", - " #If we assume a velocity and gravitational constant\n", - " # What height should we observe? \n", - " # (students do this)\n", - " assume_model_is_correct_height = my_model(v, g, t = observed_time )\n", - " \n", - " # What is the probability of a measuring an observed height and observed time?\n", - " # (students do this)\n", - " def gaussian_centered_on_model( possible_height_measurement ):\n", - " #Create a 1D Gaussian with a mean=assume_model_is_correct_height,\n", - " #sigma=height_standard_deviation, which returns a probability for a \n", - " #given possible_height_measurement\n", - " result = scipy.stats.norm.pdf(\n", - " possible_height_measurement, \n", - " loc = assume_model_is_correct_height, \n", - " scale = height_standard_deviation)\n", - " return result\n", - " \n", - " \n", - " #Return the associated probability by plugging in the observed height:\n", - " likelihood_value = gaussian_centered_on_model( \n", - " possible_height_measurement = observed_height\n", - " )\n", - " #\n", - " ##########################################################\n", - " ##########################################################\n", - " \n", - " \n", - " return(likelihood_value)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.01782956107129033\n" - ] - } - ], - "source": [ - "#Test for the correct number in the 1D-height-error-only case:\n", - "print( probability_of_single_measurement_1D (\n", - " times_1d[0],\n", - " heights_1d[0],\n", - " v = 100,\n", - " g = 20\n", - " ) )\n", - "#Expected result: 0.017829561071290335" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": {}, - "outputs": [], - "source": [ - "#(provided by instructors below):\n", - "def generate_single_data_point_likelihood_function(\n", - " observed_time,\n", - " observed_height,\n", - " probability_of_single_measurement_function,\n", - " ):\n", - " def single_data_point_likelihood_function(\n", - " vg_vector, \n", - " ):\n", - " v = vg_vector[0]\n", - " g = vg_vector[1]\n", - " likelihood_value = probability_of_single_measurement_function(\n", - " observed_time = observed_time,\n", - " observed_height= observed_height,\n", - " v = v,\n", - " g = g,\n", - " )\n", - " return likelihood_value\n", - " \n", - " return single_data_point_likelihood_function\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.01782956107129033\n" - ] - } - ], - "source": [ - "#Test for the height-only error case:\n", - "likelihood_function_for_first_datapoint1d = generate_single_data_point_likelihood_function(\n", - " observed_time = times_1d[0],\n", - " observed_height = heights_1d[0],\n", - " probability_of_single_measurement_function = probability_of_single_measurement_1D,\n", - " )\n", - "print ( likelihood_function_for_first_datapoint1d( [100, 20]) )\n", - "#Expected result: likelihood_fn(100, 20) == 0.017829561071290335" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## What is the all-data log-likelihood function?" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "metadata": {}, - "outputs": [], - "source": [ - "#(provided below by instructors)\n", - "#numpyize the data:\n", - "# Each row of the datapoints is a datapoint\n", - "datapoints1d = numpy.vstack([times_1d, heights_1d]).T\n", - "\n", - "# Looks like:\n", - "# [\n", - "# [time0, height0 ],\n", - "# [time1, height1 ],\n", - "# ...\n", - "# [timeN, heightN ],\n", - "# ]\n", - "#\n", - "\n", - "def generate_log_likelihood_function_fixing_all_observations(\n", - " datapoints = None, \n", - " probability_of_single_measurement_function = None,\n", - " ): \n", - " #Make a list where each element is a single-datapoint likelihood function\n", - " single_datapoint_likelihood_function_list = []\n", - " for datapoint in datapoints:\n", - " single_datapoint_likelihood_function = generate_single_data_point_likelihood_function(\n", - " observed_time = datapoint[0],\n", - " observed_height = datapoint[1],\n", - " probability_of_single_measurement_function = probability_of_single_measurement_function,\n", - " )\n", - " single_datapoint_likelihood_function_list.append( single_datapoint_likelihood_function )\n", - "\n", - " #Define a log-likelihood funciton for all the data using the list defined above:\n", - " def log_likelihood_function_fixing_all_observations(parameters):\n", - " result = 0\n", - " for single_datapoint_likelihood_function in single_datapoint_likelihood_function_list:\n", - " result += numpy.log( single_datapoint_likelihood_function(parameters) )\n", - " return result\n", - " \n", - " return log_likelihood_function_fixing_all_observations\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "-4.959055610687092" - ] - }, - "execution_count": 76, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "loglikelihood_alldata_1d = generate_log_likelihood_function_fixing_all_observations(\n", - " datapoints = datapoints1d,\n", - " probability_of_single_measurement_function = probability_of_single_measurement_1D,\n", - " )\n", - "loglikelihood_alldata_1d( numpy.array([40, 10]) )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## What is the posterior using MCMC?" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "metadata": {}, - "outputs": [], - "source": [ - "#Define the required functions for the mcmc tools:\n", - "def bounds_to_ppf(bounds):\n", - " ppfs={}\n", - " for key in bounds.keys():\n", - " ppfs[key] = scipy.interpolate.interp1d([0.,1.],bounds[key])\n", - " return ppfs\n", - "\n", - "def prior_transform(nested_parameters):\n", - " actual_parameters = numpy.empty(n_varied_parameter_dim, dtype=numpy.float)\n", - " for i in range(n_varied_parameter_dim):\n", - " actual_parameters[i] = ppfs[varied_param_names[i]](nested_parameters[i])\n", - " return actual_parameters\n", - "\n", - "varied_param_names = ['v','g']\n", - "parameter_bounds={'v':(0,100),'g':(0,20)}\n", - "n_varied_parameter_dim = len(varied_param_names) \n", - "ppfs=bounds_to_ppf(parameter_bounds)" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "sample_result1d = nestle.sample(\n", - " loglikelihood_alldata_1d, \n", - " prior_transform, \n", - " n_varied_parameter_dim,\n", - " npoints=10000, \n", - " maxiter=None,\n", - " maxcall=None\n", - " )\n" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Best fit: ['v', 'g'] \n", - " [63.47756871 10.00870017]\n", - "Covariance: ['v', 'g'] \n", - " [[705.53045182 0.92338866]\n", - " [ 0.92338866 33.28926524]]\n" - ] - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAFwCAYAAACo8oBFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzsvXuMpOd15ve89dX9q/u9uqq6e+68azQmZIiIDAZy1iBNQ17QK6wMeeNdwHQsCAFhO4vIsQMENqyFEwdCAghr+o9VYsW2BNiBI1u+QEoIGRK5ligNRQ6p4cz0varrfr/f3vzRPMfdo+nhcDgzdenzAwbkTHVXff1V9fne73mf8xyltYYgCIKw+FhmfQCCIAjCvUEKuiAIwpIgBV0QBGFJkIIuCIKwJEhBFwRBWBKkoAuCICwJUtAFQRCWBCnogiAIS4IUdEEQhCXBOusDmBWRSESvr6/P+jDumhs3bgAAzpw5M+MjWXzu5ly+9tprZa119H4dkyDcDSe2oK+vr+N73/verA9DWFCUUtuzPgZBuBmRXARBEJYEKegLyuc+9zl87nOfm/VhLAVyLoVl4cRKLovOK6+8MutDWBrkXArLgqzQBUEQlgQp6IIgCEuCFHRBEIQlQTT0BSWdTs/6EJYGOZfCsqBO6gi6J598UosPXbhblFKvaa2fnPVxCMJhTpTkopR6QSn1PaXU90ql0qwPRxAE4Z5yogq61volrfWTWusno9HF7tp+8cUX8eKLL876MJYCOZfCsiAa+oJy+fLlWR/C0iDnUlgWTtQKXRDeD+vr61BK3fKPIMwjUtCFpeB2xfduUzW3t7ehtb7lH0GYR0RyEZYCKr63QlbUwklBCvqCcv78+VkfwtIg51JYFsSHLiwFSqnbrtDv5nP+Hs8pPnRh7hANXTjR3E57X1tbm/XhCcL7QiSXBeWFF14AALz00kszPpLFZnt7G7/yK78CQM6lsPhIQV9Q3nnnnVkfwsKwtrZ27Mbo2tqanEthaRDJRVh6tra2jrUfbm1tzfrwBOGeIQVdEARhSZCCLgiCsCSIhr6gXLx4cdaHsDTIuRSWBfGhC0vB3XrNP8DriQ9dmDtEchEWgtv5xcUzLggHiOSyoHz6058GAHz5y1+e8ZE8GG6X1fJBOWnnUlhepKAvKHt7e7M+hKVBzqWwLIjkIgiCsCRIQRcEQVgSpKALgiAsCaKhLygf/ehHZ30IS4OcS2FZEB+6sBA8aJ/5eyE+dGEeEclFEARhSZCCvqA8//zzeP7552d9GEuBnEthWRANfUGpVCqzPoSlQc6lsCycqBW6UuoFpdT3lFLfK5VKsz4cQRCEe8qJKuha65e01k9qrZ+MRqOzPhxBEIR7yokq6IIgCMuMaOgLysc//vFZH8LSIOdSWBbEhy4sBOJDF4T3RiQXQRCEJUEK+oLyzDPP4Jlnnpn1YSwFci6FZUE09AWl1+vN+hCWBjmXwrIgK3RBEIQlQQq6IAjCkiAFXZgbbjcIWoZAC8J7Ixr6gvLcc8/N+hDuOfdzEPTtWMZzKZxMxIcuzA3z5jW/HeJDF+YRkVwEQRCWBCnoC8rTTz+Np59+etaHsRTIuRSWBSnogiAIS4IUdEEQhCVBCrogCMKSIAVdEARhSRAf+oLyyU9+ctaHsDTIuRSWBfGhC3OD+NAF4YMhksuC0u120e12Z30YS4GcS2FZkIK+oDz77LN49tlnZ30Y75t5zGtZ1HMpCDcjGrrwQJlVXosgnARkhS4IgrAkSEEXBEFYEqSgC4IgLAknSkNXSr0A4AUAWF1dnfHRfDB++Zd/edaHsDTIuRSWBfGhCw+URfKa3w7xoQvziEguC0q5XEa5XJ71YSwFci6FZeFESS7LxC/8wi8AAF5++eXZHsgSIOdSWBZkhS4IgrAkSEEXBEFYEqSgC4IgLAlS0AVBEJYE2RRdUH7t135t1oewNMi5FJYF8aEL95z19XVsb2/f8rG1tTVsbW092AO6D4gPXZhHZIW+oOzu7gIAMpnMjI/kx1m0RMV5PpeC8H6Qgr6g/NIv/RIA8U7fC+RcCsuCbIoKgiAsCVLQBUEQlgQp6IIgCEuCFHRBEIQlQTZFF5Tf+I3fmOnrv5c1cZGY9bkUhHuF+NCFu2JZcs3vFvGhC/OISC4LytWrV3H16tVZH8ZSIOdSWBZEcllQfvVXfxWAeKfvBXIuhWVBVuiCIAhLghR0QRCEJUEKuiAIwpIgBV0QBGFJkE3RBeW3f/u37+vz385nDiye1/x23O9zKQgPCvGhC7fkpPvM3wvxoQvziEguC8rly5dx+fLlWR/GUiDnUlgWTtQKXSn1AoAXAGB1dfUnbicpzDtPP/00gPvnnT5JK/S7OZeyQhfmkRO1Qtdav6S1flJr/WQ0Gp314QiCINxTTlRBFwRBWGakoC856+vrUEq97z/L5GIRhJOC2BaXnEUb2CwIwt0jBX1B+f3f//1ZH8LSIOdSWBakoC8oTz311KwPYWmQcyksC6KhLyjf+c538J3vfGfWh7EUyLkUlgVZoS8ov/VbvwVAMrzvBXIuhWVBVuiCIAhLghR0QRCEJUEK+hJwO6+5+MkF4eQgGvoSIF5zQRAAKegLyxe+8IVZH8LSIOdSWBakoC8Itxs4IbLKB+PixYuzPgRBuCdIQV8QbpZVvvGNbwAAfvqnf3pWh7Q0yLkUlgUp6AvK7/3e7wGQInQvkHMpLAvichEEQVgSpKALgiAsCVLQHyB3m00ufnJBEO4E0dAfIOIXFwThfiIF/R7zoOyFf/RHf3TPnuukI+dSWBakoB/D7Qrz7VhbW3sgq/ALFy7c99c4Kci5FJYFKejHMO/yyNe+9jUAwM/93M/N+EgWHzmXwrIgBX1B+cM//EMAUoTuBXIuhWXhxBb0N954A0qpYx8XV4kgCIvGiSroSqkXALxAf59nSUUQBOH9cqIKutb6JQAvAYBSSqq5IAhLhTQWCYIgLAnqpMoOSim9yD/77u4uACCTycz4SBafuzmXSqnXtNZP3q9jEoS7QQq6INwFUtCFeUQklwXlK1/5Cr7yla/M+jCWAjmXwrIgK/QF5emnnwYAvPzyyzM9jmXgbs6lrNCFeURW6IIgCEuCFHRBEIQlQQq6IAjCkiAFXRAEYUmQTdEFpVwuAwAikciMj2TxuZtzKZuiwjwiBV0Q7gIp6MI8IpLLgvKlL30JX/rSl2Z9GEuBnEthWZAV+oIiPvR7h/jQhWVBVuiCIAhLghR0QRCEJUEKuiAIwpIgBV0QBGFJkE3RBaXb7QIA3G73jI9k8bmbcymbosI8cqJG0C0TUsjvHXIuhWVBJJcF5Ytf/CK++MUvzvowlgI5l8KyIJLLgiI+9HuH+NCFZUFW6IIgCEuCFHRBEIQlQQq6IAjCkiAFXRAEYUk4UbZFpdQLAF54969tpdTVB3wIEQDle/mESqkH/pp3wEK+5h2cy8Nc+CCvJQj3gxPrcpkFSqnvPWhnhLzmcryeINwJIrkIgiAsCVLQBUEQlgQp6A+Wl+Q1l+Y1Z/EzCsJtWVgNXSmVAVAAYNNad5RSFq31dNbHJQiCMCsWcoWulPpZAH8L4H8H8J+UUhe01lOl1EL+PIIgCPeChSqA6oAMgP8A4LMA/kcA/xnA/6eUelSKuiAIJ5mF8qFrrbVSKgfgFQDXABS11n+olBoB+Ael1H+ptX7nuO8/7EM3TfMnzp49y49Np9Mj/7XZbFBKwWI5en2YTCYYjUaYTCZwOBwwDIMfG41GAACLxYLxeEyvidFoBKvVCovFAqUUrFYrvw59/3Q6xXg8xmg0wng8ht1uh9aav5b+TCYT2Gw2OBwOPhZ67sFgAKUUbDYbf+9kMkG328VkMoHT6eTnsdvtmEwmUEqx/1prjel0yj8zPW+324XW+si5cDgc0Fr/2PcOh8Mj53QymcBisWA6nUIpxd9DUp9Sio+JXsNisfDzTSYT/hk6nQ4cDgdcLtePvSc3/xvR7XZht9uP+0hwFvqtUEod+7zVarWutQ4e972RSESvr68f+9x3y40bNwAAZ86cuefPLdwZD+I9eO2118pa6+j7/b6F0dCVUmcBBAFsAPgigNe01n9w6PF/D+ARAP8NgMF7RSlevHhR//mf/zl6vR4CgQCsVitM08TOzg4SiQTcbjfa7TZ8Ph9M0+Tv6/V6eP3119Fut/HQQw8hmUwCAEqlEt5++20AQCaTQbVaRbvdRrfbRTabxenTp3Hq1Cm4XC5YLBbs7++j2WzC5/NhbW0NnU4HnU4HvV4P9Xodfr8f9XodiUSCv77dbmM0GmF9fR2xWAw2mw3Xrl2Dy+VCPB5Hv9/nC0mj0cDZs2exvb2N7373u2g2mzh16hScTicAYHV1FfV6HVarFR6PB91uFzs7O2i1WlhZWYHX60U0GsX+/j5++MMfwmq14oknnkC73Uav10MsFkO73eYC7fV64Xa7sbGxgb29PTidTn7uaDSKZrOJfr+PYrEIh8OB6XSKXq+H4XCIXq/HhTwUCsEwDJRKJb541Wo1vPrqq/inf/onrK+v44knnjjyXtbrdTz66KO3fJ9/+MMfYnV19djPwWuvvXbsYzabDY888sgtH/uzP/uzy1rrDx/+t8MLhtXV1Z/Y3t4+9rkF4XbcbZrnQqzQlVLPAfh9ADUAbwD4vwD8b0opQ2v9+Xe/7KsAPqe17t/Jc1osFrhcLlQqFXS7XQQCAVy9ehWFQoG/Jp1O8wptOp2i0+mgUCig0+mgWq0CADY3NxEMBuHz+ZBIJNDv95FIJBAMBlGpVDAcDjEYDOB2uxEMBnn13e/3sbOzA6016vU6PB4P8vk8Ll68CJ/PB7fbjU6ng6tXryKTycButyOZTMIwDEynU1y/fh2pVAperxe1Wg1erxeJRAKdTgdbW1vodDrY2NiAx+PBhz/8YXS7XV59R6NReL1e5PN5+Hw+TCYThEIhOBwOZLNZdDodjMdj+P1+rK2tYTwewzAMdDod5PN52Gw2NBoNaK3RarXQ6/X4dejOotFowDAMeL1eaK0xGo2OrPL7/T56vR4cDgcSiQQMw0C5XEa9Xsd4PEan04Fpmkin03C73e+3i/NB8GPLfq31S3jX/fLkk08uxkpJWCrmvqArpZ4C8L8A+JTW+gdKqZcAfATAUwBeVUoZAP4cwH8B4CeUUkGtde0Onpcn1YzHYzQaDbTbbXQ6HeRyOdTrdQwGA3z4wx/G/v4+Op0OywZWqxVutxs3btxAu92G2+1GJpPh4t1sNvn5I5EIAoEAr0gnkwlarRYuXLiAVCqFTqcDl8uFjY0NVCoVZLNZpNNplnNKpRJCoRB8Ph/6/T6vZP1+P4LBIEzThMPhgMViwWQygdYafr+fZSFaqbdaLRSLRbRaLTgcDgyHQ0wmE/R6PVitVpYzAODatWuIx+NIpVLo9Xo4d+4cstksSxsOhwM2mw39fh+DwQD1eh3T6RSVSgU+nw+rq6soFAr83NVqFbVa7Uihb7fbGA6HCIVC0FrDMAwMBgNMJpMjss/huwDDMPgOZA44Xse5j3zuc58DAHz+859/j68U7hfz/B7MfUF/l/+gtf7Bu///PwD4ktY6p5R6GsBvA/h1HBT5f3snxZywWCxwOp282l5fX0c0GkUymcTm5iYikQh2d3dx/fp19Ho9XLhwAV6vF6lUCvl8Hm63G1tbW5hOpxiNRggGg3C5XBgOh+h0OphMJggGgxgMBmi1Wmg2myw3eL1ellr29/d5hR+LxeB2u+F2u5FMJlEoFBAOh2Gz2dDpdNBoNBCLxeByufi1PB4PqtUqWq0WhsMhgsEgIpEIyuUybDYbdnZ2YLVaufju7+8jHo9DKcV3DFarlQv7ZDLBcDhEo9HAYDDA1tYWgAPdfGVlBXa7HS6XC0optFotKKXQ7//zjRGt6E3ThN/v5+cjHdxqtcJms8HtdsNqtWI8HqPZbGI4HMLr9cLr9fJdTLFY5P0G0zTR6XQ+6GfpXjGT351XXnllFi8rHGKe34NFKOj/GcCbAPDuatwBYEUpldRabyil/icAWQCm1rpxp0+qtcZ4POaVc7/fh2mavLomucVutyMSiUBrDZfLhXK5jMFggF6vh9FoxPq63++Hy+VCrVZDqVRCs9mE1+vF5uYmTNNEsViE1WpFIpFgiQM4KJKmaXJBpw1AAIhEIlhfX0epVMJ0OoXT6UQgEOANTbpzMAwDdrsdjUbjyCZkJBJBrVbjYkk/5+Hi22w2YbFY4PF4MBgMYLPZsLKywrJKtVrFaDSCz+dDOByGx+PBZDJBIBBgeapWq8HpdCKbzWI6nWIwGMDpdGJ1dZU3Va1WK8sudPdCd0h0J2EYBm/2ulwuuN1ulMtltFotjEYjWCyWeSrotlkfgCDczNwXdK31BEDz3b8qAHUAVa31vlLq0wA+BuDF91PMgYMisr29zatzr9cLALDb7SyVDAYDVKtVmKYJu93OBYV09EAggEgkwm4Q2sizWCyIx+O86m232zBNEy6XC6ZpotvtYn9/n34+9Pt9BAIBBAIBbG9vQ2uNXC6HQCCAfr+PRqMBq9WKlZUVBINBTKdTtFoteL1eTCYTGIaBRqOBcDgMpRTC4TBarRam0ykXZXLThEIhXtlbLBaMRiNUq1V0u12+s6ANzWaziUgkAr/fj3A4jF6vxxetbreLZrMJt9uN1dVVTCYT9Pt9jEYjTKdTmKaJfD6Pfr/P5zQQCKBYLLK8QhejTqeDwWAAu92OWq12xFkDHGxOTqdTGIaBbreL3d3dI44bwzDw1ltv3fJ97na7fIdxK+r1+rGPBYNBXL58+biH5/53Rzh5LNSHUms9xkHs7a5S6vMA/gWAX9Za997vc5ElzeVywe/3wzAMlgl8Ph8GgwHr42RDtFgsyGQyGI/HeOutt1Aul1GpVPi5fD4f4vE4otEoptMpisUiOp0Okskkdnd3eXNzOByiWCyiVqvxStRms6Hb7aLX66FYLPLruVwuBAIBjMdjmKaJ4XDIG5kkTdBGptVqxdmzZzEYDAAALpcL0+kUVquV7X4ulws2mw29Xo8LfiAQQCwWQ71eRzqd5n0EcuGk02kMh0N20ZDk0mq1YJomrFYrIpEIKpUKms0m3+lYrVbW9Mnd43a74ff7j9zFuFwu/nkNw4DP5+M7J6UU4vE4wuEwut0uCoUCzp49i2Dwnx2Dly9f5juemxkMBjhsT70Zn8937GP/8A//cMSWKgjzzkIVdHVgdbDhYFVuA/BxrfW1u3ku2kRstVoYDAYYj8cYj8dIp9MwDAMulwvdbpfteIVCAeVyGalUChaLBadOnUK5XIbD4UCz2eRNytXVVYxGIzSbTZRKJVgsFmxvb/PfU6kUnE4nUqkUO0tGoxGcTid8Ph9KpRJM08R0OuXCppRCo9HAa6+9Bq/Xi0gkAp/Ph1qthvF4zF710WjE8gxdkAzDQDqdZk2ddOharcYXrJWVFQBApVKB1WqF3+9HpVLBaDRCo9HA5uYm2u02ptMpWznJS97tdjEYDLhAT6dT9Pt93jy2WCzo9/vIZrNsDfV6vRiPx9BaY2Vlhb8mn8/zBYc2WUejEW/CejweAAeWzMMFfUbMpIEtnU7P4mWFQ8zze7BQBf1db/lQKfW7AL57t8UcAK9cnU4nYrEYCoUC9vf3sbW1hdXVVZimicFgwLbFfr/PhbDX62E8HsPhcCASiaDdbsPj8SCVSnGR9Xg8OHPmDDfbeDwexONxdLtdVCoVhMNhhMNhXLt2Df1+H5VKBQ8//DAeeugh1qbdbje2t7e5+cflcmE0GsEwDC62VqsVdrsd0WgU1WoVb731FjqdDrTWOHXqFHw+H7xeL0qlErtS/H4/r/R9Ph9SqRSAg4Lu9XpRr9cxHA7ZKhgIBHhvATjQ3ZvNJtsR2+02n59YLAaPx4NWq8VSis1mg8vlYgcLfS1ddHw+H5xOJ0tahmHwhYXcN0opLujNZvPmt3MWTGbxol/+8pdn8bLCIeb5PViogn6I/+O9GofeC7L/ORwOhMNhpFIpLoadTgc7Ozuw2+3o9XrodDoYDoeIx+Pwer3odru8GZrP55HP53nDki4U5NYgu2MoFMJwOORVNHVpXr9+HcVikb3YjzzyCPx+P9soybsdCoXYL09SCDlEaLOx0+lgNBphNBqxbu1yuVj6odX+5uYmN/WYpolyuQy/3882RdK919bWEIlEWAahOwc6tkQiAb/fD601S1VWqxXhcBhWq5UbkMiTX6lUuJkJONjHKBaLR1b9VquVN3BJDguHwzAMg/3oc7Ix+r5lPkG43yxkQf+gxRw4aNN//fXX4Xa7WQem1TitZgOBAIADL3Sr1cL58+e5EEciEQyHQ7YJJpNJlgZcLhdr1O12GxaLBY1Ggwsl+cHJX052R6UUCoUCotEo2xmLxSKAAxdNq9VCuVxGoVBALBZDMpmEy+ViyWhlZYWlF4oEoOJOdyL7+/uo1+u84icbJW1g0s87GAwQiUR40zWTybBPvNlscuxAo9FgHZ6kkm63i263i3a7Db/fz04ZABxR4HA4WCqijWSn08ne+36/D4/Hc0SeoXgAcsfMmJn87rz44osAgC984QuzeHkB8/0eLGRBvxeQrvvwww8jnU6j1ztYcIXDYe6AJO07Go3C7/djOp2iWq1iMjm427bb7ZhOp+zy6Ha7vOHXarUQj8fh8/m4kadSqfBrDAYDFAoF3gw8deoUSqUSr+7D4TAXMrL9WSyWI7q5aZqoVCrsWnE6ndyt2ul0UK/Xj0g0BHVhmqYJwzCwt7cH0zQRCAQwGAz466vVKtxuN8shzWaTV/61Wg2FQoEboOgiMZlM+DmSySQSiQRyuRx30NpsNr4okG2UVvTk1yeni81mg2EYsFgsGA6HLLUcjmKYITPZLb2N60Z4QMzze3BiC7rT6cQjjzyCD33oQ9xqr5TibknTNPH666+jVCohHA4jEAig0+lgc3OTPdvlchntdptXwJVKhaUNCtmqVCpIJpOsvU8mE9bfHQ4Hr9ybzSZnyjidTpY+0uk09vf3kUgk2Ivt9XqRTqeRy+XQarVYpnA4HLh27RrK5TI8Hg9LE3SMlUoF6+vr7JIZjUbc5k/fr7VGMplkuahUKsHpdMLj8fAFxmKxoNfrsf88HA5zwfd6vbBarej3+9Baw+l0sjuHvPskrTidTrjdbsRiMTSbTbRaLW5U8ng83IBEFzEK/pqTgj4TDV24P6yvr+O47J21tbXbWl/niRNb0O12Oy5cuIBoNHokjY+81ZlMBt1uF43Ggb2dVq39fv9IOz/Z8XK5HEsffr8f6XQa1WoVOzs7KBQKvGIPBoMolUoYDAYIhUJ8IYhGD4LVhsMhrl27xpuGpLtfvXoV8XgchmFgdXUVzWYTwWAQ7XYboVAIpVIJuVwO4/EYuVwO0WgUjz76KBqNBlqtFl8IaM8gl8uhXC5zGz4V5H6/j3q9zvJSoVBgmyV9nd1uRzqd5jRKn8+HdrvNdwTU0aq1Zi95pVLhPQmbzYZ0Oo1oNMoyFUktdNEwDANaa9jtdg45I8/4ZDJBrfbPDcFKKQ4duxmr1Xpb6+Gbb7557GMrKyu8YXwz3/nOd0a3fEBYSKj/41bMYY7QsZzYgk75LeQfd7vdsNvt6HQ6rAWTn7pWq2EwGPCmIgDOJ0kkEigUCmw7BA4uCtvb23A4HKyTV6tVBAIB+Hw+bpyh2AFa/UajUeRyOWxubqJQKMDlcrHrhLzl1D1ps9mwt7d3xEVSrVa5Q5S83SsrK6jVaojH43C73dzy73a7UalU0Gq1YLPZuBmKLj69Xg/r6+u8Qbu1tYVTp06xnZAuSIfdMB6Phz31drud7ygKhQIajQa8Xi8Mw8BwOITdbofNZuONUpJyKBqYXDEAuPOVOmiPi7R9wMgKXZg7TmxBp85Lp9PJ2my73cb+/j6q1SpOnz4Nl8uFtbU19Pt9VKtVdDoduN1uRKNRaK25AYgcLlRIr127hnw+j1wuh7W1NW6DV0phf38f/X6fOz4BcIGnrlSSMABgd3cXfr8ffr8fDz30EIrFIuvqjz/+OHZ2dvhiUiwWEY1GEY1GUavVkM1mMRqNkEwmeTVdLpfR7/d5M5gKJcX0+nw+rK+vs2RD5ySbzcLv9yMQCKBaraLX63EcAABuJKLAsOl0inq9jl6vh0gkAuCgiccwDJZehsMhxuMxAoEAwuEwNjc34XK5OMs9FArxBi1wsO9Bev4cMJNwrvPnz8/iZYVDzPN7cGILulIKiUSCbYa0+Ueujb29Pdjtdl7lUkMO6bskP9Bm3enTp9kKGAqF4Pf7UavVkE6n4ff7eaW9vb2NRqPBzUUWiwWJRALD4ZADvGq1GksXLpcLHo+HXSZ2ux2tVguFQgGZTAbr6+vodDro9/vc+p/JZAAcWCoLhQInGno8Hjz66KNHfuZ8Pg/goEW+Vquh3+/zXUWv1+MWfGqmuHLlCux2OzweD2w2G0s8tEqvVCqw2WzsGhqPxxgMBuxuOaylU7RBKBSCaZpwOp3cREV3JZPJBB6PBxaLhY95TpjJ785LL8ls6lkzz+/BiS3oBAVTEV6vF8ViEfl8njdDyTtN+nG5XOaVJblJisUi1tfXj0wrAv5Zw6WCRpo9JRKSd5tcNpFIBKZpol6vc9YKdW5ms1loreH1epHNZnkIBXWc7u7uYjgcYmNjgwugw+GA0+nkDc9KpYLxeMyhYf1+H+12G06nk+8E+v0+Wq0WgsEgarUapyyWy2XulqVpSPV6nRuryOFCHZ+hUIiTIOkuaDAYcDBav9/njlXTNDkilwK66DGShGq1Gne1zgFzcZsgCIc58QX9MN1ulzflSqUSZ6VQ8iBp14Zh8JAIyhSv1+tHukgp/zsWi3EnJPm+g8EgNxO1Wi00Gg3uvCS3DTXTkG5Mm5p0IXC73Txart/vsxuFNgh7vR7W1tbQarWwvr4Oq9XKEhFllVN2ysrKCm9AUoQu2R1TqRT29va4qNJKnLzuiUSCv69er8MwDMTjcTSbTfR6PVQqFfT7fZ4+RHLMcDhkrZ02mCmAyzAMtjt2u90j2evHZbbMgPYsXvSFF14A8OOrxNu5NIC7d2osi/vjTs7PnXLcezAPnOiCTvox3cYPh0OUy2XPuei1AAAgAElEQVSWMMgSSLG30+mU42tdLhc6nQ7i8Tjr8B6Ph3fKKbHQ7XZjNBohm83yc9ImZqlUYk3b4/HA4/HwCplWuPl8nlf4DocDp06d4magYDDIdwS0kibfezwe5+k/lFfTbrdZ//Z6vej3+xxIRtZHGpgRCoUQCARQq9VQq9W4WDudTs4yp1V2LBZjP7xpmtwoRHk5JE+ZpolIJMJZM1prllTIv0/vCw3ruNmDPgcZLnfEG2+8caw74oMUwnfeufXI3Nu5NIC7d2osi/vjvc7P++G492AeOLEFnfLEyTNOKYeVSgUWiwXJZJKlD9M04Xa7kU6nuYOUwqkoGqBWq6HVanHBoo27ZrOJvb099nmTo+Ww7dHhcMBut8Nut6Pb7aJYLHIGOc3DJC92o9HgfBb6gNbrdV7R04YppURS1C7dAVDBj8ViPGFpOp2yp5yicTudDv93Op0euTOhQk5FudFoHElv7PV6R4K7aHVPMQTj8ZjPHw2/qFQq2NnZOeJsoWMjayWdz5tb/4fD4bEFcjQa3XZl1mq1jn2MIoCP4dY+yUPHtAyFcN6YxR3D2traLd8zpdTc3aWc2II+Ho9Z+6UscJfLhVQqhXq9zpuR0Wj0SGFstVpc2GjMG8kGtHKmDtDTp0+zjt3tdnHmzBmUSiUuwBQtQPoxuUxoJUzdoPQ6ZCccDoeoVqtwOBxHVufBYJA7MGleJ202ejweLroul4u97Ha7nVcvgUAAFosF3/3ud3mm6XQ65alDg8EASim+CFJXK20AkxuIYnbJpkl3KpT3TkWU7hACgQCv0G02G5xOJ1tIKTGy0+nA7/fjwoULP/Ze7u7uHhuRm8/njx30DOC2uTDFYvFYzX5jY+O+dIq+lzRA3Fxg3o9kMO+8V9F+0BfKmwv2008/DQB4+eWXb/uas7j4nNiCThne/X4fw+GQizsV9mAwyCmGAHheZrlcBnBgcaSWfrvdzo1Hb7/9NnZ2duB2uzGZTOBwONhnTRIOZbpQwYrH47wxa7VaEY/HUSqV0G63udmHhk/Q9wLg7lLybZO1kjZUKeccOJCABoMBPB4PKpUKCoUC7xHQz0q6uNvthtfrhc/nw+bmJnw+H5LJJJrNJndu0iYr7QEcnv5E2e1UnFOpFEqlEvL5PAd7USMQafmkswPgn4+cMhSrcFyTzzLxXtLA4WLyfjhulXkn33c/uNuivUjMQq46sQX9sI2OChUVTZq1ebiFnfzY+/v7aLfbyGQycLvdaLVaqNfriMVi2NzcxOXLl2GaJkKhEKrVKgqFAjweD5LJJM/3nE6nnB3e7XZ5xUpFncKpqMuTNixpziZp1bS6pKAw+uUbjUa8gdvr9Xg0HXW60ui6RCKBM2fOcGdnu91mOydF1gJAoVBgCYnuECjKl/YAyJdO55amEpEbh+QbSoekmAWK4KXXIu2d7lKAgyauOdsQBWb0u3Px4sW7+r4HKUXQY7d7zXupaT9oDr8H73UOHjQLX9CVUupu0hdp4208HvMGHAVzUfdjrVZDNBrlxhayMJJVEPhn2yM5Qyis69KlSygUCjyxhwKtrFYrotEoUqkUu1NyuRym0ynOnz/PsgUNnwBwxPMdj8fhcDg4SIsam6gAGoaBfr+PeDyORqPBx0XavWEYiMVinFy4sbGBdruNyWTCG8BKKd74pI3ZWq3G/nLqqqUNWaUU7zOMRiP2rlP2O+XBUyJltVpliYkGWJAFlIZJAwd3FaPRCN/61rdgs9nmXlZQSr0A4IX7+RrzlPB3u4K9vr5+21Xo/XgvH1RxPfwe3O2F8m7vmN6LhS3oSqmE1jp/t1G6VIhJb15ZWYHb7eZVMyUJUlGPx+OcIgiAV4yZTAa7u7toNBpwu9147LHHcOnSJY67pSJHmeM07JliBwCwZEFyDg2Utlqt6Ha7CAaDXPhoCARtbtbrdWSzWQDgCFu32412u82uFtK5aW5qIBDAcDhkiSMajfIm5eEYXWrDp1RJumBprbG9vc2rdEpupLuJaDTKE5+o8NOFghxC5Kjp9XocB3A4Jpeaj775zW+iUCjg0qVL89RUBACDm/9Ba/0SgJcAQCl17Ody3lZ194NZbBTO0+bke/Fex3q3xX4hC7pS6hkA/1Yp9Vta6+t3+zw02Hlzc5NtgK1WC+12G+PxGIlEgq2BNIWHNujcbje3w9MUHurWJFdHMpk8InUEg0EeSpHP53n82tmzZ1GtVnnS0RNPPIF+v49CocAFOZFIcJohNer0ej3s7e2hVCrxRKTD0bdkVzx9+jTfaQwGA5imyTo4NU1RNg3NIa3X6zwP1W6380XHarWyb97tdiMcDsPv9wMA9vb2joyQm06nyGQy3PVKDh7gwAVyWFahQn44nGtzcxNXr17FM888wxefOeKuf3c+SOH59Kc/DWC+p+YsO/P8HixcQVdK/SSAP8LBcOjrNz1m0VpPb/O9fEucyWTYjjcajbC1tYX9/X1Eo9EjOdzUtg4AoVCINWQasUbSCGWpkF7scDhYqmk2m7zCBg4amCj6FgC32ddqNfj9frYVBoNBPPzww/D5fGg0GqhWqzAMA36/H06nE1prrK+vIxaLscZOAy0ikQgGgwGazSZbBZvNJssy1MR02OdNm6ZerxeVSoV94bR5Sncp/X6fR8S1222OCl5ZWeFhIOROoTAyOs/tdptX65TISLo5XXC01qjX6/jWt76FlZUVhMNhXL16le9EbsY0TVy7dutphNPpFN/4xjeO/TxRh+6tGI/H2N3dPfbxWbC3tzfrQzjxzPN7sHAFHcA5AH+itf5/lVIrAD4MIKy1/j+11tPbFfXDt8SXLl3SvV4PqVQKlUqFc0va7TYuXLjAwypoxQgcFGKSHMiKaBgG7HY7EokEKpUKTNPE7u4uD26gzUCSa8bjMbLZLBcS6rAkOWMwGHAOuGmaiMfjaLfbnApJXvROp4NgMAibzcb2x8FggGq1ys03Sik0m012zVC0r91uZz/3Yf85SRokyYzHY3i9XjidTnbskN5O1kLgoCj2ej0kEgkopdijTrNBKV6XbI/hcBij0YizaQzD4I1iGoDx/e9/H263Gz/zMz8Dl8uFN954g0fX3Uwmk8GZM2du+dhXv/rV296+/vqv//qxj/3pn/7p7bLXf0xyEYRZs4gFfQ/AR5VSGQB/BeAfAPy0UupZrfW/vt0K/TDk+aZc8XA4zMFSw+EQbrcbzWbziF2QXCfBYBBOpxOZTIZXuMPhEBcvXkSr1cKVK1cwGAx4g9DtdiOZTHIRp6YhKtAej4dlDqUU/H4/gsEgr8qp5Z4GYTQaDdb8B4MBGo0GVldX2S2SzWYRiUQ4c9ztdvNEoFAohGvXrrHfnfRyj8fDF6DBYMDSUSgUYk3/cPcmFe1arQav18tt/GRfpCTJXC4H0zR589Tj8bDUQ/8GHEgupONns1k0m00899xz8xKVeyvm9sCEk8tCBAwppQ7nVdYAZAD8GwBf1lr/91rrJwGsKaX+2/fzvBRV6/f7kclkcOnSJZw7dw5ra2ucSGi327G3t4d8Ps8DkCkjnPzfNG2HhjmnUilEo1GOgE2lUpwHQzq11Wrl7s1IJAKfz8exsXRxIU86xe7mcjlYLBaeQUqt9bTxSK3/5BUPBAKcNzOZTOB2u1EsFrlQRyIRrKyswOPxwDAM1s8PTzHK5XIoFAqo1+uIRCJ47LHHkEgkEAqFuGOUzlOhUGDfPK3O7XY7hsPhkRAuauRyOp3wer1cyMlhdPXqVaysrHBq5Jxim/UBCMLNzP0KXSn1HICvKqX+Smv9Ka3160qprwP49wD+Rinl11o3APzfAI7v474FNKyh1WpxZ2M8HkexWMTe3h76/T5WVla4weXcuXPo9/vY3d3lAcmUlULOjmKxyB2c1WoVw+GQm3z29/dZR+/1ekeGLpPu7ff7sbu7i0qlgnA4jGQyyc1Efr8fLpcL7XYb+Xyehy0fdrz4/X7U63WEQiGMRiPs7+/zXYLdbufsFAC8OienCc0B1VrDZjuoVxRxm0wm0el0kM/neQh0r9eDaZp8gcnn82w7JN2cOlPJCUPTnWhYCDVnvftes7uHLip0HHPI8S2m95GPfvSjs3hZ4RDz/B7MdUFXSpkAPgvgRQBPKaX+VGv9i1rr/6iUmgD4BRy4XYIAPgngX76f5y+VStwqX6vVYLfbOWUxEomgVqshl8txazolG7ZaLXi9XgSDQY6jvbnhh2yDw+GQ5Q3ydNtsNng8HjidTu4epfhaSjmMxWLQWmNvb49jckejETY3NzmBcDKZsLZOI9reeustvjhZrVZMp1N+Lmr20Vqzr5xW7v1+HzabDV6vF71ej1039HrlcpkTJwOBAEzTxOnTpzEajeD1ejnmt9VqsVuFZqjSfgJFBLhcLu6kbTQavClK3vRLly7h5Zdfxte//nV8/OMfPxJvPEfckbR3r/n85z8/i5cVDjHP78FcSy5a6w6AfwfgTwH8JgC7UurP3n3sjwH8LoAbOJjA/vNa6x/d6XNThCu5NaxWK4dYNRoNhEIhnmLv8Xg4dItWypFIBK1WC91uFx6PB/V6HXt7ewiHw4jFYjwYOZ/Pc6MQcOAYofF24XAY8XgcdrudG242Nzd5dW6323lD1uFwoN/vY3t7m2MLVlZWYLPZOADr8IXJarVid3eXbYrxeBwulwt+vx+hUAjJZJJX5aSbkztlY2ODLzzhcJgHWpNUA4DdNFSYm80mNjc3YbVakclksLa2xpuzNMmIsuRpEDU5eijagLR3r9eLRx99FIVCAV/5yldw48aNe/OBEoQlZ65X6ACgtc69+79tpdSvAnhJKfVnWutPAWgC2NRaf+39Pi91QwLgaFen08m+bPKcp1IpeL1elMtlLmCrq6uw2+3Y2NhArVZDOByG0+nkVbnf74fNZuNGGo/Hw1KFxWKB0+nEcDjE9evXOYtlNBqxF5sez2QyqFar7F2n8LBwOIxIJALDMHil73A4OMclFovxbNTJZIKdnR0YhsEbl9Vq9UjUAQC2GdKMT5fLhX6/D9M0eWB2MBiEYRic9U7OFGowosEU5F4hRw353mngM90pGIaByWQCpRQ3Oo3HY/bwe71eXL16FX//93/P1tFbOVa01nj99deP+/zc9nNw9erVYx873LV6C2ayQn/++ecBAH/xF38xi5cXMN/vwdwX9MNorSvvFvX/WSl1FQcr86fv5rmUUgiFQjzlh3JIyN/9zjvvIJfL4bHHHsP29jYmkwkSiQS3qE8mEw4RWl9fR71eR6VS4QsFSSehUAjxeJxXr5STcvnyZVy9ehXhcBinT5+Gx+OBz+eDy+ViTZ8cIpPJBKZpwuVy4YknnuDVPDlMKBlybW0NnU4HqVQK5XIZDocDW1tb2Nvbg9vtRiqVQiAQQLFYZBcNxRyQbk2WSZJBqtUqnXv0ej0e7EGrd3K2KKXg9XoRi8XQ7XYRCARgGAay2SwsFgv29/c59IwcMPR9tLG6trbGCZNk53ziiSfwyiuv4M0334TD4cAnPvEJjhQmvvCFLxxraXzyySc50OpWkH5/K/76r//6dpbHmWTgViqVWbyscIh5fg8WqqADgNa6rJT6IYBnAPxXWuu7cvnTVKJCocB54ORsCQaDHMplt9uRTqc5enZ7e5vliHPnziEQCKDVarG9jnJMtre3eZOy0+kgFAqxrJDP59Fut9HtdnllHwwG4Xa7eQh1LpdDvV5HtVrlhh7gwF5IHu5ms4nd3V14vV6cO3eOV7Z05+Hz+eDxeHjT0ul0crTtjRs3uMCePn0alUqFB3SQndFutyOfz6NYLGI4HPLQDLJZ0jkql8vY2dlhWWY8HrOlcjQa8d6Cz+dDLpdDr9fj56hUKpx5TrEF4XCYm536/T4+9KEPIRAI4I033sCf/Mmf4KmnnsJP/dRPcQzDjFi43x1h+Vm4D+W7G6DPAvgXWus37vZ5ptMpisUiz6kcjUa4fv06IpEIkskkPvKRj3Aho2K0vb3N8bDUDENSATWgFItFDAYDLpA0EYiy1Le2tri79LHHHkOr1WIdmdIOaaAzrbKHwyEXOZvNhlwux550Soy0WCxYWVmB1+tFq9VCtVpl+SYej6Pf73OSpGEYHB0AHGTJAGCZhFbOFKpVr9dZSqEmoVKphEgkwh2olEhZqVRYjqFsmXa7zYmM5GenlThF8NLmqcPhOOIAogEg8Xgc4XAYly9fxre//W3s7OzgF3/xF2fpglnMqEBhqVm4gq61rimlfk5r3f8gz2OxWBCLxWCxWJDJZJDNZmGaJgKBACKRCLfRv/3227h+/TpOnz4Nm82Ger2Oc+fOsTY7GAx4QLTT6cTe3h7H1dKYtt3dXd74pNFvsVgMhmGgWCyi2Wxy+7/b7cZwOOSLDUXMlkolrK+vc7clFWryj1PUL3nJSeqhwk2t/h6PhwvsdDrF7u4uWw9pU5I6SynXnbJm6GIzHo856ZE2YIPBIA+kBg48/hTEdePGDY4JvjkdMhwOH2mq6vV6nKVDNkv643K58JGPfATBYBA/+MEP8Pbbb+OJJ5744B+qu2OmtweCcCsWrqADwAct5sCBhn54sMSZM2e4nb3b7fKcT9M0uW2ebIg0V7NcLsPlcmE8HuPKlSs8sYg81p1OBy6XC7FYjCcj0fNPJhMYhsGrZJJfKD+FPOdWqxXXrl1DsViE1hoPPfQQb1bm83kupD/60Y94mhBJMMViEdeuXeOvp6HR1WqVZ5i6XC6cOXMGoVCIO2Mp8+Vw0BhdHEgqUUrh1KlT7Dcvl8twOp0ciUtt/KFQCOl0+sgADdqQpdgEutuhcXTkg6eLFJ0rigxYW1vDxsYGrly5MsuCPpNN0Y9//OOzeFnhEPP8HixkQb+XNJtNZLNZnD9/Hl6vFxsbG5hOp/B4PLBYLLDb7ajX6+wkAcAdl1prrKysYDAYoF6vo9frIRgMIpFIwOVyYTAYcMGnwqi1RjweRywW4/wYGhFHm5C0qRoKheB0OpFMJvkik8sdmH7q9TqAgwvT5uYmisUiMpkMwuEwyxu5XA7D4RD9fh+j0Qi5XI4L6Pr6OuLxOHeWUqGn4RwUQTAajVAoFBCJRBAKhVg3J63eNE3kcjmO9qVN2kajgf39fTQaDbYnkqOFJCZqqKLnAQ4C0Ei+oeJ/uLuU9jVSqRTefvvt246Qu8/YZ/Giv/M7vzOLlxUOMc/vwYkt6OTayGaz2Nvbg8PhQCQSQTAYxGAw4E1KGuwcDAZ50DP5ukm2cDgcHIXr8/kwHA4RCoW4YcftdsNms3H41+HURWqPp4sHPQ9F7trtdoTDYSQSCU4qpNRFSiiMRCJQSiGdTnO+ilIKPp8PqVSKN1CpOB4egWez2VAqlfiYtNbY2triodQkFQHgtETSyIGDC8t0OuVzl8vlWIN3uVxspYxGo0caqWj2KgV/2Ww2bsiivQng4IKllGKvPHCw/5FMJvHWW2/h29/+9pGvv5npdIof/OAHx34OaP/guM/IbZBJz8LccWILOskTNEWH/NrxeJyte1arlbX2UCiEXC6HyWSCer2OYDAIpRS7NKjBh1r/y+Uye6dPnTqFyWTC3ZCkQdPFYDKZoFwuY319HYFAAC6XCzs7O9jf38dkMkE4HObuShr5Rk1F1I1JeecUbGUYBne0ut1uxGIxnDt3DoPBgFe1brebvewU90sboNSyD4BdMjT8g8LKbDYbzp8/j263i0gkwjkxFA1AEg7tDZw6dQo+nw/9fh+vvvoqarUa4vE4UqkULBYLdnd3Warq9/twOBwsx5D8RbKN3+/HysoKSqUSHn/8cW5iuhm6KzkOuuO5FT/5kz+Jj33sY7d87A/+4A+qd/RBu8c888wzAIC//du/ncXLC5jv9+DEFvTJZIKtrS24XC6cPXuWi1UkEkGlUsFgMEChUGAZpVqtYjQaYTgcolAo8PdS632v10OpVGKfdj6fR6vVgs1mQywWg9frZQml1+shFApxgbp+/Tomkwmq1SoHflFTEAV2UcRvNptlD/3hYRaTyQRer5cblyhnxe/3s6xitVqxubnJwzAoObLT6XAeezabxXA4hNfrZW96NpvFqVOneFOVLJhkNVxZWcHu7i5nkicSCZRKJezv77PTptvtYmtri107dExkySQ5iqIYbDYbNywd7pilLHeLxYJEIoHvf//73EPwgJnJCv12+e3Cg2Ge34MTW9Bpdez1euHxeFgzBg6GLm9vb2NnZwftdpsTA+nraVQdBWP5/X7cuHGDB1ZQEaeIW+Dg1p7+nUK1SMIAgFQqxfkne3t7qNVqqFarPESZhkjY7XZ204xGI5w7dw7RaJQLrM/nYxmEMmJohVoul1Gr1Y5YKWlVTjZF0uqpoYZCuBwOBw/FXl1dxerqKur1One/0uxS0zR5P2Bra4t/bpJLSBunGIV6vY7hcMhRw/1+HxaLBS6X60gnKc0o1VrD5/PBNE1cunQJ3//+91GpVJBMJu/r5+XKlSt4++23D//Tif3dEeaXE/uh1FojGAwiFosBABdjKu6kgdOmqWEYyGQynAa4vr7O0sRoNEImk4HD4eDYgGAwiHg8DovFglarxW32rVYLkUiEV/p0YaBJPwB4YAWt4ofDIee70Abh7u4unE4n6/07OzvcXUkyEPnryR5J3aK1Wg21Wo3DxmhSkt/v5wYhcqWEw2GOOygUCsjn83wnYrFYOJxsMpkglUrxnY7dbsfjjz+OcrnMzpjpdIpWq8XuoMPBYK1WiztWabVOFyJyA1FkMDlwSqUSO2HuN3/zN39z8z+d2N8dYX45sR9Kq9XKxTyfz3PW+NmzZ+F2u+H1evHII49w671hGAgEAiiVSqjVajBNE2fPnkW9XmddmGyBTqeTPd6pVIoDwH70ox+xtBEMBkETkyjydjQaIRAI8KrUZrMhnU6jWCzyQOtMJoNcLgen08kDOnZ2drgrNR6Ps+RCcbjVahXXrl2D0+lENBrFhQsXoJRCsVhktwlp2JT3QvIN3WW43W4kEgnk83n0ej10Oh3OuNna2kKr1WI3C+Xg0Ep+PB6jUqlwc5PD4eCfk4o5XSjp3BHkTQcOvO10wbh27Rr+6q/+Ch6PB+l0+r5/Xj7zmc8gn8/z3//yL/9ybnN9hZPLiS3otGGZz+ext7eHcrnMK3LaDHS73VhbW4PL5UKv1+OvoYKzt7fH7fE0sGI8HgMA2/s6nQ6GwyGHVB0OAltdXeVuy06ng0ceeQQ+nw+dTgfRaJSzXWgq0mAwgNPpxMrKCv8M9LyDwYCLcCKRQDabZYtiMBjEysoKqtUq6/U0vJlCx+r1Oss/gUAAPp8Pa2trGA6HfDdA1kLykdPQCuCgy/TMmTNc7EnamUwmqNVqPKaORtLRcRaLRezv77PEc/i/FNRFFzin0wmn04mtrS18/etfx3g8xosvvohvfvOb9/3z4vF4cPbs2cP/NJOp1c8999wsXlY4xDy/Bye2oAMH+nCj0UC9XucxbbRBOJ1OuWBTkSELXyQSwZUrVzgTnQY0nz59GhsbG1zQvF4vvw5ZE2luaLFY5FREcrQAQDQaZQlEKYVCoYBWq8UrdJJuaNPU4/EgGo3yUOjDDVCdTgeFQgHBYBDJZBKFQoEza+x2O+vVAHiVm0gkuCuTHDAU+Vuv12G32xGLxTgXhl6XUhptNhuP1lNK8Vg8v9/PXbA0vIIsoJFIBKZp4p133mEZiyJ1/X4/HyM1dH3rW99Cs9nEZz/72fuund+G8c3/cHgI+f3iN3/zN+/n0wt3wDy/Bye6oNNqmhwd0WgUgUCAXRnU+Ui55yQJbG1tYTweYzwes2+dbH+UUUK54TQijrR18mWTz5w2GmlD9Pr167wBqpSCaZocepVOp9FsNtkxQwOjaXwdAJ5D2uv1uMGIYgk6nQ7H9Y7HY9bkadM3Go0iFArh1KlTKBaLfMGj3HhqHKpUKpwESZG6NOWJ5CmSYGj+KGnmNFuV/r1QKPBdDEUVj0YjlEoljiL2er145ZVXsLW1hVwuh+3tbTz55JO4ceMGbty4wWFgtyISieD06dPHfgZu12n6ta99Da+++upxD//Y787hIeRKKcl6ER44J7agT6dTlEol9Pt9dLtdtspFIhGWRSKRCOeKAAe6OzXtJBIJHhJdqVRQLBZZu6a0xr29PV6x7u/vs74eCATQbrd5yARllbRaLZ6tSbp2NBplv3m32+UW+Xg8jmvXrmFjYwPFYhGpVIo3GGl2Z6fTYZcLNRVRzjldtKgphxqiSHaiiF4atReJRBCNRlke6vV63PBDk47IQ06bpJVK5Ujols1mw97eHm84t9ttuFwuvgsimyblu1itVs6O0VqjWq1ie3sbmUwGqVRqxp+g2bT+UxTwyy+/PIuXFzDf78GJLehaa57Ek8lkOLuFVpJUGH/4wx9yVjkNaAYOJAqlFJrNJmezkK0ul8uh2WxyJyStqr1eL86ePYvBYMAXk1AoxDY/yi2ZTqecyEgSDXVrUqdmvV7nodOTyYTnipbLZQDg5wkEArzCjsVi7O0mDzntDYxGI8TjcQAHo/lGoxEXZzpftLcQCAQ4cZJa8mlMn9aazwt9v91uRzweRy6X46lQ5MAhLz7NY3W73cjn89ysNR6P+SJGYWGPP/74Pf0skGtoOBzi7Nmz/HO+B9IpKswdJ7ag0xBnCsCiDchGo4G9vT14PB40m02MRiOe6xmJRHgSfbFYhGma2N7eRi6Xw9mzZ3nANOWdBwIB7gglWyBJCpStQlo6XShqtRqHWwUCAV6Zk6UxGAyyrDGZTDhvpd/vc8Hz+/089o4uKOS0Oezr9vl8vDFKnZnxeBymaXIDD93B7O3t8eYrNViZponV1VXuPCUpyel0IpPJwDAMlEol7k6l9ESr1cpdpVarlS2JtFcxnU7ZukjhX8ViEb1eDx/72MfueWTuH//xH/NkoieeeAI/+7M/eyffNrinByEI94ATW9CtVivS6RG4bKgAACAASURBVDT29vZYRvB6vdjc3ESpVILP50MkEkEikeDcceAgLnd7exvD4RCPPfYYLBYLDMPgFTWtUqmbkjYuw+Ewa83ZbJY7OA/7q2u1Gvr9PgzDgNfr5ZFs9Xqdhz2srq5yZngikWDv+dbWFidBxuNxRCIRxONxaK2xubnJkb40kYm0fmqWSiaT3NxDVkcA6HQ6LBd5vV5OYCR7I2120oWH7Ik0rCIWiyGXy8Fms8EwDFitVr6TUUphMBjwHQvFGNAGcrvdZt85OYuOa/H/IDz//PP4x3/8R6TTaTz00EN3+m2ikQtzx1IUdKWU0u81PPImDMNgrffwEGWyAZIefPbsWS7M/X6fhyzTxKFAIIDRaIRarYZisYhkMskDpfP5PMrlMkzTRKPRYCdILpdDOp1GPB5nTZ4cMclkkr+eLhSxWAx2u50lC9qstVqtCIVCPAeVimu1WkU4HGZ3SjqdxubmJtsnfT4fF8larYZkMgmn04l33nkHjUYDHo+HR9PRGD2r1coNQrRKbzQaSCaTnNdCxdxqtXKOO8k7w+GQN5ApC502MmlfgbpfKZALOLjwBgIBHjFHF4J7STqdxqc+9an3+23iQxfmjoUs6EqpSwDcAIZa6396v8UcAA+BAMCraeDAmnj+/HlUKhUOh6IZmpVKBY1GA3a7nTsfnU4nHn/8ceTzeVSrVXi9Xm6eKZfLKBQKrG8XCgXs7u7C4/FgMplgc3MThUIB/X4fyWQSFouFpSAAPGrO7/fj7NmzPGDZ7XYfmQcajUZZi6bVME0LonFviUSCV9bT6ZSjdavVKnvS6W6hUChwS36j0YDVauWvMQyDPeH9fh+VSuXIfgFlpdNFkbLSqZuT7kY8Hg9vwFJQGnXakgzWarUwGo3YYUPvG/3/jLkjof1e88lPfnIWLyscYp7fg4Ur6Eqp5wD8LoA3ADiVUt/UWv/RHX4v+4RpkDJtxnW7XR52QYWPuh6BgyaebDaLfr+PQCCAaDSK6XQKn8/HK06r1coSwmg0gtfrhWEYiMViPFuUhmDQipWieePxOKcSXr9+nb3r1LhDc0knkwnnhAeDQbTbbR5112w2eSbndDpFtVpFo9HgNnqyVtJzUyYKNSqtrq7CarViZ2eHOzLJ+05ThqjJiSQbKrA+n48vdCS/2O12lMtldq/QuaMNU5KAer0eWzppBU+vQyFqJLlks9kf09AHgwE2NjZu+Z67XC5cuXLl2M/E7YZE9/t9FIvF4x6eicvlM5/5zCxeVjjEPL8HC1XQlVIfBvD7AH5Ja/26UupfAXjqTr//sE/4Qx/6kG42m9jc3ES320U4HEY0GuWiTKtrGs1GlsVms4lIJAKbzcZpgufPn+eGI601dnZ2ePIPDXXu9XpIJpOcZ0KRsDSQYjqdYjweo1gsclQtxcRSFjjZDMkKmclkOHOF/OAUNkbFleJ4SRbZ2dnhIRsWiwUOhwPNZpM3WMnOWK1W0el0YLFYuOkqHA7Dbrej1+shFovxXgEVaKUUqtUq8vk8zpw5w3cQdA4SiQRCoRCuXr3KF7RwOIxiscgZ8JPJQQMmNRcdHjINAI8++ihfeIlvf/vbx2rrlUqFIx5uxZtvvnnsY6Zp4tFHH73lYz/60Y9mMlmDNm+P890L9595fg8WqqDj4Db3i1rr19/9+w8A/HdKqQyAvfcjvdBtfKfT4aHMtAHX7/c5cbBSqfAbSEMbSCemzVLKOjFNk2dvkjxSLpdRLpexurqKeDyOixcv4saNG9jY2OCNQgCc8UJTjnw+H8rlMm+eVqtVLqZ2u507NJvNJg+vGAwGbGkkd4rdbkcgEIBhGGxVHA6HaLVaqFQq/DNR3jh5yimkLBaLYX9/H6Zp8oSh0WjEK2jaID686UrRvofPNV0U2+02yyy0+qdmLir8lKlOko3VauXno4I/B8zkt/nZZ58FMJ8e6JPCPL8HC1HQlVLntdbvaK2/o5S69u6/GQByAAoAGlprrZQ6p7W+difPSbU/Eomwnlur1Ti1kCQGrTUmkwm3z9dqNTQaDTgcDtbgacOO5AaSE8gKWS6XWWqhKfZkJzycROjxeFCr1eByuRCJROD3+xEIBI4MaKaYAVrNUpAWvT7FC9AxNRoNvPnmmzAMA6dOnUIqleIuTK01Z724XK4fK/KUXU53La1Wiyf8xGIxOBwOdLtdnm7kcrng9/thtVoxmUywt7fHLqBQKITRaMQ56BaLhQPE3G43Sy10PofDIXvYAfBjzWaTY45njGPWByAINzP3Bf1dzfyrSqn/R2v9r7XWJaWURWs9UUr18e70daXULwH4V0qp/1prXXuv59VaIxqNIh6PH+kaJScIDZqgJh/gQMfudDqoVCowTROZTAbtdhvZbJbb6EmOKRQKPEWIVpilUgmlUoknBymlcP36dQyHQ16dUiAVNfmUy2XWp51OJwBwsbt+/TpPWPL5fBgMBmyTJJmCJgz1ej2+m6BGnUgkAq/Xi/F4jHw+j3feeQd+vx8PP/wwAGB/f58dMBSPQBe5QCAAt9vNE47sdjvS6TSm0ynHDVN8QSQSwWQy4SwZulWlDBiSvEh2Gg6HrJ9T5AI1YL388sv4+Z//eb6IzZDjxXdBmBFzXdCVUiaAzwJ4EcBTSqkva60/rbWevrtCVwA6AP5XABcB/Js7KebAwa07uTo6nQ5vcI7HY27Vp0LjdDrRaDTgcrkQj8cRDAZZmmk0GhzmVSqV2Kdtt9uxu7vLq0/a4KT5naSnk88cAE6fPs0ZKxaLBeVyGaVS6Ug3KbXzU3cqTTBKpVKw2+3IZrNoNBo4d+4cxuMxLly4wHnsNpuN9XLS4klrp/F6pKtTXothGDh9+jRHCFDhNQwDhUKBkyptNhsP0s7n89xENRgM0Ov1kE6njziEDMPg3HWLxcL+/eFwCIvFwn+UUtxcdOnSJbz66qv4u7/7O3ziE5+47Wi5B8Adfc4E4UEy1wVda91RSv07AE0AfwngPx4q6hMAUErZcLAx+i+11lfv9LlJ5y6Xy+zrzmazbJ2jzk4apkxxtuFwGIPBAJubmzwnM5/Po9vt4vr16+wYoQ1ByjMpFovc6OPz+bgZJ5FIsLRhmiba7TanKQYCAc5KKRaLUEohEAjwRCJaAZOjhWyIVAQpWoB83hQhsLKygk6nA7/fz4mP4XCYx8s1Gg1u2+90OtztSVKJzWZDo9H4/9l70xi50vNc7Plqrzq170tvnO5mDzkUZ0aLPR5ZRmTIhpVAV5KtRJARJ7pGcnVtJ4gD3T9WnASBDedGgW4MB7CuFMSWIRiIZcPChW15kSx5kXWl8UhDcYZLk2z2Uvtep/b95Ef386qaIjnkDJvdnK4XGHC6q7vOqTrV7/m+530WOBwOLCwsyOt79dVXsbq6KtCQz+eTGwjZOJTV7+3twWQyCWOIqlUKuOito2ma6AUsFgsuXLiAV155Bd/4xjfw0z/904+ckz6veT3JdaIbOgAYhsEU37ZS6hMAPs+mrpRaB3AVwKceppkD+w09EAjAMAzx6mYAA1ebZrNZYtZI0WPT1HVdjLnIxgAgEI3X64XFYhEYhX7iDMzgANNutyMSiUjIBl0euVKlL0qpVJImTpYMjbtcLhfy+byoWyl4YrQcH6OSlUEXjJwrl8toNpuIxWKIRCKyY1lYWIDP5xMrgHa7LewTesRbrVZsbGyIYyPplC6XC51OR3Y2+XwenU4HlUpFKIy00OXgk37yHOBSSTocDjGdTmEYBnw+H5566ilsbW3h5ZdfRjKZxHA4PBQ+cWdtb2/f8zEOvO9Wdrsdr7zyyr0ePpbtwcc//vHjOOy8ZuokX4MT39BnyzCM6kFT/z+VUjcOvv0ewzCKD/tcbM7kQdNadjKZwGq1ShxaOByGy+VCqVRCo9GQVSzj6CqVikTGMex4aWlJ1KTD4RBOpxPRaBRutxsOh0NCJWhZy/MxmUy4efMmhsMh9vb2kEqlhHJHUy3eMBKJhAxUuXplElC5XEar1RIHRb62ZDKJcrmM3d1diYtjrihFQgsLC0KP7Pf7iMVictNgWpHb7Uar1cLOzg58Ph+effZZPP300yiVSiiXy8Lpr1QqEsrh9XrFUXIwGKDdbotFABs7o/+SyaTg54SZxuOx8PsvXryIfr+PW7du4UMf+hDq9TrC4fBdr/P169fl+tyt7ufb8ulPf/p+O4Bjoduc5GZyWuokX4MnqqEDgGEYFaXUZQDvB/BTb6SZA5DwBboJknpIhSRVm36/Xxpno9GQsOKzZ8+iVCqh2WwiHo8jGo2KpJ4ME0a4jcdj9Pt9FAoFuN1udDodMfeiuCcYDEoUG82uAAgDhm6NjUYDfr8fwWAQ0+kUxWIRPp8PkUhEhqYMnx6NRlheXobb7RYefS6XQ6PRgMPhgNPpFGuBWq2GRqOBa9euYTgcStoSfc8Zjk3TMnre8JiERTggpViLoRTE2ZlAxOHsrICIj3MoyudmEMdsWtTb3/52/Pmf/zm+8pWvHKJIvpl6SFuBY8F66KZ5rxvYvI6+TvI1eOIaulIqAOA/BfDThmG8+iaeRyAWl8sFr9crgzkGT/T7fVQqFUSjUcTjcRlw0hyLNETiwpSkM580Go0iFouhXC6jUCiI13kkEjm0MmfDJ/xBpgr9xulX3m63ZZCbyWSEA+50OvGOd7xDoIt4PC5NnUPaTCYj8nu/3w+/3y8rZL4HDPnodDrwer0IBAKwWCyo1WrIZDJwuVxYXV0VPJwOio1GQwbGHDZTgel2uwWicjqd6Ha7ooj1+XywWq0oFApiSjbro65pmrgxAjg0OHW73Th37hy+/e1v4+LFi2/6j2trawtf+tKX8HM/93M4e/bsg/zKsfDQP/KRjwA4mRzo01In+Ro8cQ3dMIy6UuoDhmG8qaj38XiMer0u4h4aR+m6jn6/D6/Xi2g0im63C7vdjmw2i0ajgXg8DrfbLSv16XSKarWKTqeDbDaLQCAgfiq8SbjdbrjdbnS7XZRKJXE31HUdt2/fFq45fddtNps0ZTouAhAPmHa7jUuXLmEwGMDr9crNwTCMQ+ZXlNJ7vV4sLCyg1WpJU58Na6apl8lkQrfbFVaNx+OBYRhiyNVoNHD79m0opbCysoKFhQVhsuRyObjdbtkZ8CZJ9gxnFLVaTaiZFBzxhuZ0OiVaj54zbObk15Mlo5TCuXPncPXq1UM7mjdapKZWKpUHbejH4uUyr3ndr564hg4Ab7aZAxBIhQrL7e1tmEwmGSoGAoFDIchKKQyHQ+Fhkw0zGo1w+fJlbGxsCOWPboVsWJqmIR6Pi5zeZrOh1WpJXih3BVR1Uo5PjjcdCF0uF7rdLgqFAhwOBxKJBFwul8AEd0bnMfWo0+nIzefg/ZPVudvtRjKZRDgcxs7OjuDVHArXajVZSdN3HQBCoRDa7bbsChh7R4tcCpWoIAUgrByz2Sxcfz5GmGp2GDz7NQCJqiOFk99/FGZdFy9exMWLFx8GcjkxktV5zYv1ug39QJn5KoDvA7gE4PuGYewc8Xk9lmo2m+j1emIFa7PZsLS0hEgkglu3bsFsNmM0GiEajaLZbCKdTmM8HouylFxuAOLHQrihXq8jnU7DbreLq2A6nZafpbrS4/EgmUwK22Jzc1NuGhaLRVbJbKZczUajUaRSKSilxJp3VmKv6zqazSauXLkCr9crAh2LxSLiHa7mW62WDCRppEXWSa1Wg91ul3zQYrEoJl+EOfr9vpiVcUVtGIbsfsg7p2/LeDwWjxbSG3mzYYgGz5O0TA5PgR/4vPDm8iioi2/gOeYNfV4nrh5khf45AE8BqGJ/EPmHSqltAF8G8BuGYYyO8PyOrEwmE4LBILrdruC5NKDqdrsCG8TjcQCQpsohZbVahcvlwtraGnw+H5xOJy5fvozpdIpwOCwDPUrj0+m0yNZnLQCq1aoEXtTrdVSrVVitVgSDQQQCAbEYMJvNh+Agp9OJwWAgcM4sdbLdbiMWi4lKdDAYwO/3SwKQxWJBtVpFLBYTr3JaHGiahmw2ewjuoeKUnjdKKTQaDWnM9HDhbqff78uAkfxyrtypNqUXOweapCU2Gg1RmbpcLtmhkPFCVg+ToID9G8q9HBV5re5Vly9fvudjnHncox7NJHZe83qE9SAN/b80DOM5fqGU+vcA/iX2xT7/DsB/f0TndqRFj28yT+LxuDSu8XgsgztCFk6nU5wLGUhBZSSpgsTVU6kUzGYzCoUCrly5Ij4uoVAIXq9XGjgj2cgXDwaDSKVScmNxOBwolUpIJpMwm81yTvSJUUohFAqhWCzKypeQEQCcO3cO165dEyOy0Wgkx2KzIuwxHo+l0e/t7WF1dVXsA/h+zEIcVJWSM89YPQq2UqkUPB4PnE4ncrmchEAnk0kkEgk0m02x9qVVL3dLXNnTCnhnZwfRaBQejwfBYFC8aFj0jLlbXbhwQUJ971bF4r1JUjz/u1WhUPgh+9xZe+ajql/6pV86yqef1wPUSb4GD9LQdaXURcMwLgOAYRiXlFIvGIbxPyilvnfE53ek5Xa7MR6PMR6PhYoEQAyy6PjndDphGIZAL/T/nqUn2u12nDlzBpPJBPV6XWAUwiSLi4vQNA3ValXS7jkEpTrS6/XihRdekF1DpVKBruu4dOkSPB6P2MwuLy/D5XIhGAyi0+mInwwzT71erxiHxeNxFAoFTCYTweXNZjOWl5flplGr1WAymcQbncwZ8tTZzGkRPLsC9/l8KJVKomSlgIrD5EajgUajIUlQxOJnoRSyijjspKMlXS05CKWSlkKjYw66+KE5zqw9s1LqSCLqPvrRjx7F087rIeokX4MHaeifwD7Mcgn7GPoGfmDuf6xmGm+22u22ZHJ2u13hZHP1ypUst/cc5BGv5moU2G/qkUhE2DO0uaWcnd7iXMlqmiae4ADkplGv18XdMJVKYXd3V0y9OARlPunOzo5g+IlEAvF4HLFYTOiOzWYTu7u70oy5mmcSE8/l6tWrCIfDwqXvdDqwWCzIZrMipCoUCqjX6+Izw4ExB6yERrxerxiNZTIZdDodBAIBGfbu7u6iUCjAZrPBarUKC6jVagn8QiGTzWaT+QNvXGTAmM3m+0Ipj6GOxW2RcxiGlc/r8ddJvgav29ANw7iulPoRAD8L4CKAWwD+1wPjrP/viM/vyGoymaBSqWA4HAqvnKyV6XQq1rbEnHVdFyw6FAohHA5LiEW73RZmCLM36aXSaDQEEnnqqacwGAwwHo9x/fp1uFwurK+vi5Ph1taWrIYXFxcxmUzw4osv4pVXXsF4PBbVKGmS29vbCAaDCAaDWF1dRSqVkufPZDJoNpuClYfDYezu7soqOhwOSw5oOp3GcDjEysqKhHjQM52pTZVKBa1WC/F4XCxxDcNAtVrFeDzGdDoVyb/T6ZRdj1IKsVgMdvt+/zOZTDAMQzj9xKg5jAV+IPChmpaCJV3X5Tgmkwm5XO7uF/fx1LEkFv3CL/wCgJPJgT4tdZKvwQPRFg+MsP744L/Z+s1HfkaPsTgMZaxZPp8X35PpdCq+51SU1ut1ofnZ7XZZXVcqFXS7XYTDYcRiMREfcUXe6/Ukqo6Zo1RWEl7pdDrQNE1W9s1mU0KeL168KDeNq1evilcMo+uYWNTr9bC7uysDTg4PdV1HIBBAPB4XuiOVoB6PB2fPnoXf70elUpH8VLI+6vW6hIBwzjAej7G8vCxyfN4QOG/QNA0mk+lQ2hOhHtIvmW40Go0ERzebzdA0TXYrzDblMJc3gfF4DMMwcOXKFTz99NO4efOBLPAfdc1dweZ14uqJ5KE/ijKbzUgkEigWi4Ihx+NxcVSkPH8ymWA4HCKVSiESiQiTZDQaCZzAJhQOh0XUwwbMm8asaMjv9yOfz6NcLgtThCveZDKJTCYjNxEqPwnfLC8vY3NzU3JEWcThC4WCBENQ/QlAVKD0QyH/u9/vw+PxyAq6VCqJV/uZM2cEnyeLZdbS1uPxyEyBHuftdhuDwQCJREKsfjudjjRzi8UiMBYHzIZhyGtlcEaz2RRIqNvtiv+5w+GAYRjY2tpCuVzGxz72seNq6HPa4rxOXJ3ahg7sr9BTqZTYvRL/ZYYnV5E2m01CkUulkgh1aA9A33RCFf1+X9wF6T1OXNnhcAhLhoEUgUBAlKK6rqNcLsPj8Qg9sdfrSfMnz3symSCVSsnzk/Vy4cIFtFotNBoNCakmRLK5uYlCoQCTySRDWe4SRqMRYrEYHA6HDIopAIrFYuIzw2i+3d1dsdS12WyIRqMIBoO4dOmSPM4bBxOMRqOReLMDEItgDkjL5TIymYzYKhDu4ntXLBblRvStb30LmqbJgPZ+6YPf+c537vnY/TzVp9PpI1Ghzmtej6tOdUOnLwgHlPV6HZqmodVqSdo78d5qtSrYOkMo6AtOLDibzQL4gaGW3+8XbjYAsXl1OBxwOBwIhUISDG2324VCyBsEfVOoriSVj+fkcDhQLpdFhON0OuXner3eIZUm/dKVUiIUIlOmVquhVCqhWCzKYJQmWQygmH3OSCQiWDstd8mKofCJBlvD4VAgFu6KOKhdXl4WeKXdbkPXdVGX0laY843XXntNdjP9fh/ZbBYvvPACLBYLnn/+edEL3Fnf/e537xtZ97M/+7P3fGxvb++eyUiXL19+ogkB83pr1qlu6Fz5UnnYarVgs9nEayQajYoHeLVaRSAQQCAQkBAKGnzRP52WtNlsFpqmYWVlBb1eD3t7eyLQCYfDIukH9jFqrtKTyaQYX8XjcZRKJdhsNsGQHQ4HptOp7Aw40B2NRhLUkclkpBnS7IrDS8rlm80mNE0TXJ0DUgp6yAfnqpchzgypNplMWFlZEc8aGmvlcjlh/XCwSW91+rCXy2XhmjebTWxtbSEUCmE4HKLX62E8Hsug1+fzCXVx9j24efMmzGazROU9qnpIKuSxYOif/OQnj+Ow85qpk3wNTnVDpyyfrAw2FNoAUK7u8XjE+Y9Ml9FoJIPAwWAg/i92u10aN1d3VqsVdrtdBqMWiwWlUgnD4RC6rqNer2M4HCKfz0sGZ7lcxtLSkqgrlVLCHKEbI5s+GSCTyQQejwfD4VDi7NicGX1HdSmhFYZ7MOzi+eefR7vdFhaJ3W4XG2G73S5qVaYP5fN5uaFxbsCBMGcI9JYpFArCinG73chms4LBUwXKpkoFK/F32hFsbW0hk8ng+eefv6fo541UrVbD5z//eXz4wx/GxsbGg/zK+JEd/CHqAx/4wHEcdl4zdZKvwalt6IZhiPMfMVufzyepOwCEjeJyuRCJRFAqlQRrttvt2NvbAwAJk3A4HBICsbi4KI2Kwphmswm/3y80wFmBDaXvwWAQVqsV6XQanU5H2CRLS0tyEyCrhZ7j/J1er4ebN2/KboDWtXxuDiZpeUs2DpOJhsMhGo2GeLLXajUMBgMZmmqahgsXLmBnZweBQECYNH6/H9FoVHjnxMu5wqf3+qzXC1Wv5KgzyYiD0k6ng2azCaUUrFYrDMNAq9XCtWvXsLGxgRdeeOGRfh4ymQwMw8D3v//9B23og0d6Ag9Ym5v7wVwPeI7zOoI6ydfgiW3oSqkXAKwDuAnge4ZhPFQKOwUxpVJJIAyGOXu9XlitVmSzWfF2IQ7LCLlcLod0Oo3NzU0ZetrtdlQqFblJUOBDEy02uUajgX6/j/X1dTSbzUNNmVRIMkUIhcw2fPqp0DCLYp/NzU25eXDVTuyfzoo05crn83C73RJQvbi4KBJ8m80msnxN0wTSmbUQKBQKchPTNE0Go5ubm+h2u6Iw5eqcjo3hcFgEQfTPGY1GwjQCIDshwzDEE4erdGBfRPWos0QvXLiAjY2N+w5X76hjgVw+8YlPADiZHOjTUif5GjyRDV0p9S+wz4F/BcAHAfwa9hv7wzyHNMlyuSypOqPRCM8884wk+JC22Gq1kM/nJY6OAcYrKyswmUw4f/68cMOBfeXn5uYmGo0GvF6vSPJrtRp2d3eF+cHGzEDmSqUCTdMQi8VgsVjQarUAAOVyGbquC6uEFMLxeIxisYhmsyl+M61WC9VqFaFQCI1GA9lsFn6/X5SumqZBKQWlFMrlMkqlEiKRCPx+P6rVKqrVKux2OxKJBMbjMcrlMvr9Pnq9ngRdV6tVGZoSsiHrhelLZOBwBmE2m+F2u2Gz2eDxeMRXfjQaIZlMSvi13W6Hw+GQps6bC3dOR1FU0z5EuY/qXOY1rzdaT1xDV0qFAPwKgJ83DOM1pdTvAXhOKaUDaD6MVzoHnFxBhsNhUXWSXmg2m1EqlZBKpQDs+78wuPjMmTNCZyRuTCUm6YfcBRSLRbz97W8X5SRx9U6ng1AoJM2WjJVmswmHwyFCH7vdDsMwsLe3h3w+j6eeegqRSESyNQnPOJ1OFAoFCZFm0DIA4XMzXYixeM1mE61WC8lkUpwggf0h4bVr19BoNLC2tiZyfOaFErePRCJiwUsYh/ms5Jlz5sBsVAZ1ECunCyOFRwwdoYUBz2f232OuJ9JldF5v7XriGjr2h1FOAE8rpfYA/CcAIgA+DOC2Uup/NwzjruThWTe8hYUF7O3tIR6PCw7MVB3CDslkUkKR6eeiaZo0PcbDkaM9Ho9x48YNRKNRwbqXlpZEQk93xvX1dZHLO51ORCIRWXEHAgEZFrIB67oOh8OBQCCAnZ0dcS6kPwzZL16vVwa4hJAikYhg3WazWbjtk8kE29vbEkzBcAwAIj6y2WxQSsHtdgsThb7vDocDuVxODMy42yFEw5g9WipwqEqsmoZdNCsjVDQajWRwGg6HRbTEGcDdqlQqCTZ/Z9VqtUPOjHfWX/zFX9zzMZfLhTNnztz1scuXLx8Lhj6ved2vnriGbhiGrpT6HezDLP8GwO8bhvEbSqmfBPBfAVjDfhjH3X5X3PAuXrxotNttNJtNLCwsoFqtCk7s9XoRi8Vw+/ZtcUtks6LohhYA0+kUe3t7SKfTwnYxDAOLi4sIYiejJgAAIABJREFUBAKSBUqXQXq+kCoZjUZF3DIej5HNZsW3hSwZNlqKfbrdLm7evIlYLAa/3y/0RMbb2Ww21Ot1sQb2eDwol8uoVCpYXFzE9vY2crkcRqMRLBYLksmkxMjZ7XbhjwcCASwuLooitlwuS1CG3++HrutyQ6Fsn7YD3W5XjLTC4TBarZYcj/MFQjJcnXNwWqvVZBiaSCRkvkCq50Pg3EdZJ+Ik5jWv2XriGjoAGIbxJ0qpvwXwKezj6DAM4+tKqV8BsIx7NPTZoqTeMAy8+uqrcDgc6PV6CAaD4vG9trYmTodOpxOJRAI+nw9msxnJZFJYLJTme71ebG1tyUCS7AyukDVNQ6PRQKfTwd7eHsLhsBhNzfLAKXYCIE2MsMv6+jr29vZE4ETBT7lclsbLFW2xWES/38czzzwj4in6o/B8+HpdLpfsNshdp4AJgKg0SdNkFJzL5YLL5YLH45FBKIeq5XJZMHMOi5kyxEFtr9eTGyH94SeTySGvHN4IqTA9IQ39WKT/v/7rv34ch53XTJ3ka/BENnRAwqK/DuC/UEoNATgAnAFw7wiaw7+PZDKJW7duiUyesAgA4YFHIhFJJ6J3OOmOtHb1+/1YWlpCr9cTi9hkMolmswmz2Yx2u41KpSJMEDoXTqdTGSYGg0FEo1ExtgIgPjNu9/78rdVqodVqIRAIiAMkG+9sTicVpYxtU0ohkUjIboPnRV8UeqFrmiY8d03TxAa43+/D7/eL4pODWmalGoYh3jSzyUls9GfOnIHf78dwOEQ2m0WtVgMAuTGMx2N0u11h4pjNZklsGgwGYtN7zP7nd9axJBa9733vO47DzmumTvI1eGIb+kH9R+xTF/8n7AcO/MsHzTudTCbCBOl2u1hcXEQ+n8etW7cQCASEcQJA8FkGOpM1srGxgWQyiWAwKP4opPB1Oh1Z7S4sLKDVasHpdKLdbmNpaUnSi9LpNLrdrljjMmCZxla04SVsksvl4HK5YLfbUSgUxOHx7NmzMiykZ8z58+fFf6XZbMpgd2NjA4PBQOiTpAAyso5qVABiMRwMBuH3+5HL5QRyIT5fq9WQyWTk3AmjhMNhEVhR9ckBLHceFEKRDWO32wWOIZ+dlEVd1wFA3qtjrmMRFl26dAkA8Nxzz73OT87rqOokX4MnuqEbhtEA8DtKqd8HoAzDaD7M71cqFWnWbEKVSgWDwQCBQECw5KWlJRkaulwuJJNJAPsrVFrclkolWcWGw2HY7XbUajVZ3a+vrwubwzAMPP300wLZEP5hiEapVBIFJb1LyKTx+XwYDAYS0pxIJOBwOKQ5NxoNxGIxMZZi041Go4Lvm81mRKNRwfGtVivq9ToASMAFAASDQVkhk/NOoZTFYsHt27eF0cMUJFrqMgSk2+1iOp0KK4biofF4jHa7DZfLJdx+2inQoZEJTWQQ0SjthDT0o+NQ3qd+9Vd/FcDJ5ECfljrJ1+CJbugswzBaD/s7hCjYZL1eL+x2u8TGEa4go4P2t06nE1arFa1WC+VyGcB+4ESxWJTm6ff70e/3xYExkUjA6XSiVquJWKZSqSAajYpCkwyS4XAoTBBa8/JG0263hQ7IhtlsNsUPhg2bK3tCHAzcoIdKMpkUamCv10M+n4ff70cqlYLP5xO3Rtr8chcyGAygaRrMZrOEZ8wGQzcaDTgcDvj9fmns9HghE4UwCxkrs6t6/g6H0L1eT2YHwP4NeGFh4aRAL2+Jv515vbXq1H4oufLL5/MYj8fS0JPJpFD2er0e6vU6dnZ2EIvFEI1GZdtPj5RSqSRRdjSqIlxgsViwvr4uwz/CGxxm2u12WK1WwdJpYzscDpFIJCTzlDmaZIMwCYhQCaX+9H6h3ayu6ygWi2i324JH9/t9VKtVOR9y34F9b/R6vQ5d16GUQqVSQblc/iHFJ2mYxPbpD9NqteQ1krnT6/VQKBRkkEkYi+9Dr9eTVTyZLBQRkb0zHA7lffb7/fjWt7516FpSaXqvIqZ/t+I84G5FWOsedSLI8POa12yd2oaulMLS0hImkwkajYaEQpC6GAqF4PP5oJRCPp8XcQ5ZKkzYMZvNkv8ZDAbFAOvq1avCVCHMkUwmYTabxe98Op0in8/LMXlD4A0lEAhIYHWj0UAqlZKdAuGKYrEoBlaapiEUCklEncvlQrfbhclkkoEqm67f7xfYhhz7K1euiDDIZrMJNDKb30kFKx0bc7mcRNNRKMWEJloS7+3tIRKJIBwOIxgMirMirQy8Xi8ymYzQHOmF43A4kM/nMZ1Ocfny/qybCt7Z8vl8WFhYuOt19vl8WFpauufn4Ed/9Efv+di1a9ewurp618e+853vHAuGPq953a9ObUMH9rf7q6ur2N3dlXBmACKeAfaNpSKRiPiT+P1+mEwmXLt2DV6vF5FIRPxVuBKkMIeiHEIENMuq1WriukiMPZFIoNVq4caNG8IJJxZNWiIl/9FoVAzCGGtHf/Zut4tMJoNutyty/9lACbfbLcrOYrEIn88Hj8cjwbfhcBiapmEwGKDZbArTh+fQ7XZRLpdRLBaxsbGBRCIhux2GgyilxEOGMn9N00T5CkCGu7QgpqKU7xmZL81mEy6XC9PpVJKSjqJ6vR5eeuklABDGT7lcht/vl/nFHf4xxxISPa953a9OdUOn+18sFkOn05HBJPHxarUqWDgDHS5cuIDhcIh0Og273Y5gMChiHnKsHQ6HQAX5fF4k+ZTz032QvG/+fCgUQiwWkzDkSqUi+DiTh8rlsjRGqknD4TAsFouwU9bX11GtViVBaZZGqGma7AQAyIp4PB5LRqnT6UQ6nRZ+vKZpknREzxgqZrlLmR1qApB0Ip/Ph+XlZRkQ002Rr5nYOb1tqJ7lDY9zBEJYR1Vf+MIXZBcyW//wD/9wr1/5Ie/eWSXyUdVv/dZvHeXTz+sB6iRfg1Pb0A3DEJMsQgOtVkuGoVylM6YOgODJ9Eq32Wyo1WpoNpvCM+dQdHV1FT6fD5PJBMFgEI1GA7lcDo1GQ/BrCpKA/SxOp9MJp9OJwWAAi8WCWCwmFD5mcNIrvN1uS8gyWSEmkwmJRAIAkEwm4XA40Gq1UCgUUK/XkUwmxUuFPPpkMolWqyVOjAx65m7AarXiypUrMjugpzs9YzhLGI1GoqRllB5X+bRFoK0CBVp8nOIkmm9x8Dwej4Wx86jdFe+s4XCIxcVFXLx4UXJMO50Orly5IjucO+qHTmhWiayUOhL104svvngUTzuvh6iTfA1ObUMfj8eo1+sIBAIIBoO4ffu2sCgSiQRGo5HI2geDgcjjybhYWVkRmbyu6+j1egiFQlBKQdM0+Hw+wdpnDauYMsQEoMFgIJQ+Dkmj0SjC4TC63a5I6AFgd3cXsVgMGxsbgkHTRuD73/8+nE4nSqWSGIYFg0Fhs7TbbUlAarVaGAwGcLlcyGQycn6dTgf1eh2hUEi44rVaTYKu19bWZOdgNpvR7/fFPyYUCokRGYVXdrtdXn+z2ZThL3nshmEIxk/zsdmBKIe0vJHxfTiKYjoV3SyHwyFu3LiB4XCId7zjHXjPe95zKH/005/+tH5kJ3Of4kD4JDeVt3qd5Gtwahu6xWJBKpVCKBQSP/PJZIJ4PI6FhQXk83n0+31sb2/DYrHA5/OJTN/lcmF5eVmw6kgkgng8jn6/L1xtWspSMNNoNFAqlWT1Sc9yeqTU63U4HA6Ew2E4HA6hTpKn3mw2kc1mhVtOVgzxbq/XK7sDNp7pdIpgMCiwhqZpEqmXTCbR7/dx+/btQ5DLnSZb5M0vLy/L+QD7w2EGUJCCSE8Xp9MpDZ5QFIVFDLJoNpuSRUpLAb4nVqtVBEeVSuUQxk6c/VFXNBrF9va2OEzevHkTmqbhYx/7GBYXF+/2K/d2/DrC+tSnPgXgZHKgT0ud5Gtwahu6UkrENdlsVoIilpeX0el00G63YbfbEY/HhRtNjxKLxSKDzOFwKHg2myKbOTNKgX26Xq1Wg9Vqxfnz52EymWC1WqGUgsfjQT6fF8iDaUJ+vx8ej0caPbA/tCR2zTDn8XgMj8cjjY6CJ/Lb2ZjZ1LlDmM3/bLfbMgBmopFhGMJy2dnZgcVigcfjkccYZdfv9wWTJ/d81meG/Hpi4HRTrNfrh3xsgH21aqFQELYQKZKz2Pydg1FaD9ytKNS6V12/fh0ARHPw13/91xgMBlhbW0MqlUKhULgXdfFEkOHnNa/ZOrUNHYAwLLg69Hg8wtXudDrwer0ypMtms8hms+j3+wgEAkilUhLPduPGDRmq+nw+VCoVdDoddDodaJqGRCKBCxcuiB8JhTXZbFYwZXrIOJ1OCW1mnie9VcgFDwaDGI1GmE6nwlEPBoOizOQw1GKxiPhplirIG9LCwoJknf7TP/0Tzp07h7W1NRiGgZs3b8psgM/lcDjEcGw4HIptLlfp9JNnDipTinZ2duS94OCTs4hOp4NGowG73S4+9JPJBA6HQ7j0TqcT73nPe/D1r38dL7zwAt71rncduo5//Md/LJz4O8vn8+Gd73znPT8DvOG63W788z//M5xOJz70oQ9hYWEBX/nKV8Qg7C7leLhP27zmdfR1qhs6AGkcVDwC+8EXlUpFMGxa0FIE43A4sLOzg+l0ilQqJTCJx+MRNsdkMkG/30cymRTDreeee04abjqdRq/XQ6lUEjk84Q3S5pghyoAIu90uTo7cWZAWGQwGZbC6t7eHxcVFNBoNgXiI9VssFlHEcjg6mUxw7tw5hEIhoVYqpbCwsCB8de4ueLNot9vo9/vyul0uF/L5vBh9NZtNuYHwRscVOV8jby4cMtOKoNPpwGq1wul0CsRitVrh9XpRLBaP5HMQCoXw8z//8wiHww+ajDQXFs3rxNWpb+jdbhe6rqPT6SCRSMDlcskKmwIbNnHS+sg02dvbE9yaZlZkjzDn0+12y4qU8AKAQwNCDgKZAUqmRaFQgNPpFH92SuTpHcPVMwDU63VomoZOp4N+v49isSiCImLw9DKnnL7X60HXdcRiMclRzWQy0HVdwqVdLhdarRbS6TRsNhscDgfG47HcgPx+P5LJJLa2tqDruvC2x+MxRqORrL5tNpsMQUlxZNOezUwli2bW54bYudPpPLKGDkAYQg9Yc2HRvE5cnfqGziKE0e/3ZZBosViQy+XEjAqADDQzmQyy2Sym0yni8bg8j9vtFvm9xWJBrVbD5uYmYrEYJpOJiJMcDgei0agMNAuFgphctdttYcgEg0GEw2FhfLjdbuTzeVnFu1wu4Xc7HA4kEgm89tprWFtbk7SgVquF9fV1wbun06lg76TnEY/v9/tot9vo9Xqi+Oz1esIAmQ2mSCaTGI/HMiuw2+3C/KEdLumL0WhUbiS6rgtm7/F4ZNVOKMfv98v14OocwCMRFZHT/6TWb//2bx/3KZz6OsnX4FQ3dHqSx+NxocZx+KlpGnZ3d1EsFmXlzhCGZrMp2KvX6xUnQsIgjUYD1WoVGxsbKBaLqFarcDgcSKVS4hEznU5FqET3RK/XKxDEzs4ODMMQrjmHiFSSnjt3DqlUCna7HSsrK+IQmcvloGkaWq2WDEe5a+CgslQqiY9Lt9tFNpuV0GZN03D27FmBQ+gZw2EqBVIejwder1f80+kEySEkjbrI6aa9AZk/vEG2222BvNhoZ71XyO2nD/u9JP4PUjs7O/jLv/xLfPSjH0UwGHwTnxwAxxQSfRItW09bneRrcGon9fRR2dnZkfBjyuFrtRrK5bJQG5PJpGDphCy4kqTAZzAYCIRBhWO/30csFkMqlRL2Cxs/K5fLSa4oZfFutxtPP/205HBS/j+dTsWnhStbOkG6XC6Uy2W5AbTbbWSzWWGu9Ho9XL9+HbVaTewLgH0/8qWlJfGYyWQyKBaLMvSkgRZhJvrFxGIxAPsN1+Vywev1ot/vQ9d18bspFArCeuGglFCK2+2G2+2WQGrgB86LhJEI2xiGITuH2d3Qw9aNGzcwHo9F4v8my/b6P/Lo62tf+xq+9rWvHceh53VQJ/kanNoVOtPle72ewBvAvo9LJBIBAJHUj8dj6LouQRakGrIBEyMfDoew2Wx46qmn4HA4YLFYUK/XhZXS6/Uk6u3MmTNot9vi803WC7A/oAMgkvhAICCD2Xa7DU3TBOOm3W+tVhN/d5PJhGg0KuHW9XpdAq7JnCHEsrS0hHa7jUKhIANX5n7quo7JZCLnQUYNIZFOpyN4+mQygdvtFvk/qZFkrdBwjBRHSv35/0xu4jE5tOVAmG6KnU5HjLpYdGO8W7lcLoHGbt++DZPJhJs3b2J5eRler1d84O9Wg8HgnnRIAHcNIj/q+s3f/E0AJzs1561eJ/kanNqGPmu+Rc9zpgxlMhm0Wi10u11ZaQ+HQ1SrVcngpKtgtVqF3++XEGg2cmC/0XQ6HVFDsnEGAgGMx2OkUilks1lRUhaLRRmi0qrW5/OhUCjA5/MhFovBarWi3W6jXC5DKSXNkA2e/HL6kr/00kvyGNWYXMUvLy+j2+1ic3MTrVYLS0tLgqNzlUy/mn6/L7uEcDgMv98vdgkcuNKsrNfryezh3Llz2NvbQ6VSgdVqRSwWkzkCh6xU2pJv3+12YTabxSbBbDbjpZdeglIKsVjshzDw+1jcYn19He9973tx69YtjEYjvPvd78bLL7+MWq2GD37wg/jTP/3Te/6uYRj3DNPY3t6es1zmdeLq1Db00Wgkw8Q7V2kMO/Z4PBL/1ul00Gw2ZYXsdDoFYvD7/bBYLIL9ttttWcU7nU5RkzIgglAGBTYUGZFrXq/XBfJpNpvSTM1mM/x+vwwce70e0uk0yuUywuGwYP83b95Er9fDtWvXkM/nD+WN0rOFIRbA/qA3EAggHo+jXq/L8YbDodgJ6LouQ1feVPgaySU3DAPT6RRer1cYNZlMBp1OB8FgUFg4nEO4XC4ZiBKvZwCGyWSC1+uVwG7aDLzRgearr74Ki8WCs2fPot1u45VXXsHP/MzPvJmPkPZmfnle8zqKOrUNfTYkmYZS4XAY2WxWcjM7nY54rRCqYCTc0tKSCGsajQYikQjMZjMymQym0ykikYhQH6fTKXZ2dqQJc/jJZk8OeTAYFBYNYRfCAgymIMskFApJw9Q0TQI6DMMQqiGHiD6fDwAE9vH5fCJkqlarGAwGiEQiMAxDGiodFXkDASA8+H6/j8FgIKEVFENRiGWz2SRH9fr16+j1eohGo4hEIuLSSLim0WigVqvJAJVmX5qmIRwOi7Co1WrdS4L/ujWdTvHqq69ibW0NAPDOd74T169fx1/91V/Je/MG6mjdwuY1rzdQb4mGrpRSBrvLA5bZbBbPcvqXRyIRofdR6cmwZofDIZ7j9GCh/ziFNYZhwG63Cye9XC7D5XKJI2MoFILf70exWJStPL2+d3Z2AEDcEKPR6KGIuUwmA6WUxNlFo1E0Gg2EQiFxK6QwKhQKSfj0wsLCIZHOzs4OxuOxBFv4/X5ZqVOhSWMsNtl+vy+DV5/PJz7mu7u78j0mDtEXJhgMCtecq/BcLiceOZwJ0FmSnvOU4DNQmp4yHNrOGqQ9SBmGgT/8wz9EuVzG2972NoTDYUwmE6ysrCCXy72Zht59o784r3kdVT2xDV0p9eMAzhiG8UXDMIyHbeqTyQS9Xk+aGD1HvF6vRM0xa7RWqwlH3OVywW63o1KpIJ/Pw+v1SrAyPWFYwWBQ4BE2McazsYkSetF1HalUCmfPnkWpVILdbpfQDbfbLcIaskpu3LiBUqkkXHX6hdNdkf/y3EitXFxclJi8ZrMpHO/RaCQ7EVIrs9msQER2ux39fh/lclkUpGSp8KbVaDTQ7XZldkDYZDqdyuqbN5dAIAClFHRdF4EVZw8cVDMc2+VyYXV1FTdu3MC3v/1tvPvd735gb/StrS2Uy2U8//zzOHv2LIB9SG17e/u+aUUPUMfCEPvc5z53HIed10yd5GvwxDV0pZQJgAvA5/a/VJphGP/+oKmbDMN4oGGV2WwWU6pAICAe3blcTlanZJdYrVbU63Vhw0ynUxSLRWFumEwmVKtVdLtdRKNRkbU3Gg3oug63243FxUW43W4sLS1B13UZtubzeezu7sJsNgv0YrFYoOu6QB2GYaBarSIWi8HlckmaUbFYRCqVglJKMG1CNoFAQF7jcDjE7du3pfk2m02USiVUq1XUajWEQiEEAgEkk0kxwep0OqhUKqhWq1hdXYXX65XAD4qT/H4/IpGIDHBJd7TZbFBKyY4mnU7LsJbceM4w6vW6GG6Ri04KKGEZ+sAsLy/j9u3bePnll/EjP/IjDyQ0KpfLePbZZ3Hx4kUxK6Olwvnz53Hjxo03+lE8lr+djY2N4zjsvGbqJF+DJ66hHzTstlLqDwBMALyolHIahvF/PWgzByA0u16vB7fbjWg0ilwuh1dffRUej0fYLDTj4pZ/NvWHHOpcLgellHi60CmQakfK5E0mEwaDgYha6LtNcRJl8kwAIr+bQ0LaFJCrTd90qlDNZrP4rNCBEYBwwx0OB86ePSsrfb/fj8lkIipTwkZOpxO6rqPVasncgKtsXddhs9kOQSO0vXW5XOj3+xJsPRgMRBhks9lgs9kkA5U0RWB/KEsLYb5HVI6S1sj80clkgt3dXfz93/+95L7erxYWFrCxsSHReJ1OB9evX5f3ilTRu9V4PEYul7vXw8fCcvmzP/szAMAHPvCB4zj8vHCyr8ET19BnagxgCcAfAPhvlFL/DsAAwKcAqLs199mIMKos/X4/AKBYLKJQKMg2fzgcYnd3FzabDWfOnJEtvs/ng9/vl+CHWCwmykyXyyVcbppcDQYDCZsmdMLQBxpcsbHW63VJEarVauJG6Pf7sbS0BMMwkEqlJC+0XC4jFArJDYMrfyo7HQ6HQC6NRgMejwd2u12oiVzRcxZA6qDVakUqlRJxEW9AwWAQ9XpdVv5s9LOmW4zZ4+vlMegG6Xa7YTabxRKAq3Py0mkeRvSMAqpnnnlGfF++973v4fbt29je3gawf0NYW1vD2toa1tfX8c1vfhMvvfQSXnzxRcTjcblB0jOmUqlgeXlZjNXuVdevX5eb4l3qWCCXz3zmMwBOZjM5LXWSr8GT3ND/A4D/3DCMv1VKPQfgNwD8/gGOflcsfTYi7PnnnzdSqRRqtZpAL2azGYlEAsFgUGh7k8kE5XJZYtZcLpdAJ+FwWJKJtra20Ol0RJRTqVQEJ+909jUoHIDShZBSeop/YrEY1tbW4HK54HA4kM1mZSextraGQqEggh7CKrquC+uEz03GDRsrKZFWq1Vw936/L/mj3KnQpjcajeLq1asYjUYIBAIolUoYDAaIx+NIpVJyQxiPx8ISIiuIu4mVlRWBWwCgVqvJnIKeL9QAUJjEVT9vetztABBLBKUULl68iPPnz2MymSCbzWJvbw/1el1WTgDw4z/+43juueeQzWZlJ2QymcQA7e1vf/ub/fwdXXzSvOb1ButJbug9ABtKqf8WwL8G8G8BvEsp9QnDMF53akGWC+1ZI5GIDPmIfTcaDRnskXM9GAykwQH7GK3P55NACbJNyG2Px+OIx+OoVCrCfmFj4sCUWHMikRA73NnjUHlqtVrRaDTE/MpsNiOdTsPv9x9KVCqVSuLjzfOPRqPi/07/8tFoBK/Xi42NDWQyGaFc0jqYdEQyYSjbJ/zCvFB6t8+GZHMVTntgvreapglnn0Ha7XZbbA40TRP7YeL//H8OlxkTZxgGVlZWEI/HJdGI7zOtjAnbEBrKZDIAgNXV1Tf7+btr0Oi85nWc9cQ2dMMwckqpNID/GcCvGIbxZ0qp9wK49YC/L8HFFotFGgX/dTgcSCaTACDNkRxw4udkvxSLxUMWsVy9UqxDWh8NtgAgEolIgDNXs/RqCQQCcqPRdR17e3sAICt/whKtVktW2uFwGPl8XjxURqMRPB6PrHSj0Sj8fr/gyBQqBYNBKKVkNZzL5WR3srS0JCEVFFLRDz2fzwt/nStfruKXl5fl9YZCIdkBKaXkxkBPdKZB0fOFEBAxd3q8m0wmNJtNESE5nU6Mx2Pxi+duJJlMynWkRTBvNteuXcPOzg7C4bDw/N9EPbF/O/N669aT/qH8fwD8B8Mwvnvw9d8/6GB0MpngpZdewsLCgnh800OEfOjV1VVpBq1WC5VKBcFgUGxjGXpBLjtX4oPBQIaMXq9XAi44ZCSuztXn9va2YOEMeCA3m8KkbreLer2ORqOBlZUVBINBDAYDbG1twWQyodPpoFarYXFxUeYDbLwUTtHDhY1/fX0doVAI1WoVrVZL6IYAZOBYKBRkyElBUKfTEQFTMpkUSwMA4qRYKpVQLpeRSCQOYeWDwUAEWbQXAH5gk9DpdATv5o6Ctgq0LwAgSlReA849OIidfX+vXLmCzc1NtNttnD9/Hj/1Uz/1KHJJ55DLvE5cPdEN3TCMNIA0OegPw3IZjUYol8viKz6ZTCS/U9M0mM1mSS7a3d0VX3Gr1YpLly4hFouh2WyKkyDpisSVm82mrGLX1tZQq9XQarWkkTAUmlJ8RrLV63URJ3U6HcGsx+MxyuWyRL1Fo1F885vflKZOSbzX60UikUCz2cTu7q5QCqnIdLlc0PUfBNY3Gg2x5XW73bDZbAiFQtA0TdSp4XBYrHWZPBQOh2EymeDxeFAul2Gz2aRxb25uwjAM6LouLowUYzmdToGnuIPodrtotVoA9uEoUkNppdDtdgVbD4VC4isPQBhFzE6lc+Z4PEY6nca3vvUt1Ot1pFIpfOQjH3lT9rt31A/dEWaH7kdVX/ziF4/y6ef1AHWSr8ET3dBZD6sSBfYbwfPPPw8AuHbtmvizkF9tsVgkX3Q4HEo822AwwO7uLnRdx8rKCkajkbgwkqdN1kmj0ZCQjHK5LGIbqi0pHuJqlDa5xKKZYcqg6na7LS6E9JCx2WzweDzY3d2VFf5oNJLVPIVDpDySI7+wsACLxSL8eQZPM7WnXq84KteHAAAfRklEQVSLqZimabJCp1EXeeqMqwMgrpKkeDJAg42f1sPk13O1TnWpYRjCtDm4rjCbzWKRQPiHYjAGZNTrdYFeeFN85ZVX8Nprr0HTNHz4wx/G+fPnH8Wq/NBH6M5vzA7dlVIP/Zl8kHqj9gfzenR1kq/BW6Khv5EymUw4d+4cstksEokEksmkOAdSWORyuRCPxzGZTOD1eqXRa5qGpaUliWgDgFKpJE2Zsnc6D9K2ttFoyFCTTbvX60lY8+3bt4XS6PF4oGma8MFpoEVxkNPphNPpxNLSElwulzTuW7duod1uH4J6TCYTAoEAAoEAWq0W2u02bDabQEh+v1+YM9lsVoau5LpzBWyxWFAsFrGzs4NqtSq8efLXzWazQD0MsuDwk4EeFy9ehNfrRalUEjolXRbdbrdYJDAlSSmFaDSKy5cvo1AoCAREHN1sNmN3dxeGYaBcLqNSqcic4V3vehcuX76Mr371q/jqV79618/B+9///nt+RlKpFF544YW7PvbJT37yWCLo/uiP/ggA8NGPfvQ4Dj8vnOxrcGobOrDPUAGAtbU1cfWLxWIIBAIiPWcGKAefJpNJAi/olAhAfLwnkwmuXr2KcDgsXuGGYcDj8YhdLNWlHGbSMpeWt41GQ3YKzWZT4ukItRB+YXZoMpmE1+vF7u4ums2msG6i0Sjq9Tp0XReGDtOPWq2WYOerq6uw2WxyrsT5h8MhGo2GNOpGo4GdnR1UKhU0m03Bwk0mk/jiUGBEfJureMJR0+kUbrcbDocD1WoVoVBIIKB+vw+z2SxiIWLuhGaIiQP7NMZsNotqtYqdnR2BYDRNwzPPPINnn30WTqcTr7766pv6jBiGgWvXruFv/uZvDkFVuAc19qjrs5/9LICT2UxOS53ka3BqG/poNJJVJ+PZBoMBFhcX0ev1ZHDIdJ9AICCeL8PhEPV6HfV6HYFAQNKG/H4/tre3JdGI8n6fz4derycRdrOeMcR7XS4Xzpw5I7md2WwWhmEgEAjIgI8mXAxfpk0A8WVy0BOJBEKhkOw2Go2GQCH1el1YPPRo/+53vwuv14szZ84glUqJdznhIzookolCRsloNJIdS6vVgtlslgi/VColc4pZv3l6qBNmokkXQz5oxmUymeD3+wWWmV3937p1Czs7OzKQ9vl8eNe73oXFxUURVj2KqtVq+OxnP4utrS2Ew2EkEgnx0W82m6PX+fV5zeux16lt6KS/ARAYhBg0+eF+v1++jkajYnbldrtFGUq8mtDG6uqqqCJJAWS4MxvfdDoVaTzhBQ5XKQJi80omkwgEAjh37hyazaZ4uLTbbRmoMpaODJN8Pi+rb66iGbjs8XiETmkymcTBkMwQerxMJhNEIhExMePKnmpPr9cLAOLMSFjF5/MhkUhIkhLdDbmToSGY2WwWB0eTySRpS7xJzCYsUV1qtVoxHo9x69YtOJ1O/ORP/iRWVlZw48YNLC8vP9LPx3A4xNe//nU4nU68+OKLWFhYOHSzuXnz5qn925nXya1T+6FkY6KIiAwXcsCdTicKhQLS6TTMZjPW1tYwmUzQ6XQQDoexuLiIdDotwc/APkRAVeJoNEI4HEatVhOYwGazwev1iiCHQRYLCwvodrsS8cZUII/HIxx2ipV0XcfS0pJ4sgwGAxQKBZHO67qOSqUCYN+LhCwQ8udpSTsej6WxVqtVAPvNORQKCb7P8yTfezbQYjZ6jipV0gw7nY68bxQ8zRpwEd5hg6aClq+RDouGYciNg4ImpRT8fj+cTieefvrpI/t88IaulMJzzz0nilYOp3FMXi7zmtf96tQ2dJPJJGk8TPQJBAJIp9MiyuEKnKIWrpDNZjPa7TYymQw8Hg9WV1cF16Y032q1YnFxEdFoVFgkZJiwaddqNeTzebTbbUSjUeTzeUn2IeuGoRPFYlH8x3u9HprNpuDtoVAItVpNVv38XUI+29vbsgNhU6LPezQahcPhQLFYPNSAy+WyyOQJr5DNM5lMhMHDpCE6RZIzPhwOxQCLQ+JGoyEDZnL5K5WK5IHSwIwhILONnjsAYB8nZ07ogzguvpFSSuGZZ57BN7/5TWxubuK5556T9308HgOn+G9nXie3TvWHkhFw5C9nMhlx12Ne58rKimDQTLqnUIf0RHKe9/b2ZDgXj8fhcrkQCAQEy/Z4PEin0ygWiwiFQgiFQrKav3r1KrrdLlwuF/x+P27duiWrQsI+ZN2MRiPcuHEDu7u7CIVCwohpt9ti7UljMXLSDcOAyWQSe9pcLidmW8FgEJFIRGAiNl232w1d19nA0Gg0BCKhCpMDXTY6sm3o98KGTFUuI+pos8sm3mq1ZOjcbrfR6/XQaDQwHo9lmErsPhqNYmdnB+VyWa7JUVQ8Hsfi4iJeeeUVLC8vy87igB9/LNL/P/mTPzmOw85rpk7yNTi1DZ2sCZPJJDJwrlCTyaSsYhOJxCGWhcPhkLxPMjSsVqsMB7vdrjBTaHq1tbUlnuX5fF4yPYln09GPz8kMT8a1kR8eCAQQi8UwGAzQaDQE6ohEIoJNB4NB2O12uQHU63X4fD7BpQHIToKe7RySdjodlEolTCYT8ZVhGDTwg1AQYH+VzPeRNgV8j3K5nCg7AYgKl8PdSqWCdrstq30Ogvv9vrg4EgozmUxiV0AoiDDMa6+9JnYE3/ve9+56nQ3DEMjrbpXP5+/5mMfjwdmzZ/G3f/u3uHz5MtbX1+UGA8DxIJ+zR133c4ec1+Opk3wNTm1DJ95NgQypdGwybrdbDKpCoRB2d3eRz+exuLgoDA02XErVz58/j2w2i52dHWl+9BmxWCwijOl2u8jlctjb24Pdbsf6+jqA/UFcOp2WBur3+zGdTtFqtQRyoPnUaDSSHFTSIJm9GYvFcOHCBZTLZTSbTQSDQQSDQfF+4QA3Go2KGIdMHfrJcAjKTFJaBuu6jlqtJja+hmGgXq/LLsRutyOZTMrrX1xcRKVSEQvba9euyc2MNxSu1KvVqqhiqRhlWLfX6xVfen4P2M8H/cd//EcRI91Z73znO/ETP/ET9/wc3K/Zb25u4ty5c7hy5Qpu3LiBxcXFWQXrsWDoX/jCFwAAH//4x4/j8PPCyb4Gp7ahA/sGWdPpVNKBwuEw0um0eJdQNDTLgLFYLBJ8EQqFhD5IX5KVlRVEIhFJ47lx4wYikQiefvppjEYjMZWKxWJi1mW1WmEYBlqtllD9ksmkHIciGg4UdV2Hw+EQ3JpzAF3XBVqhNXC9XofX64XD4ZBwCg47B4MBAoEAut0ustksvF4vFhcX4fF4xOGQbBUAkkM6Go2QTqeFlUM+/iymnkqlZN7AnYTP5zsUwsFVPI26qtWqKECJ43PVzoAPi8WCbrcLr9crpmUPWoZhoFarAcChcyXscze6o1IKP/ZjP4Yvf/nL+MY3vvHA0XdHVSe5mZyWOsnX4FQ3dPK2uSqs1Woixyfv2+v1yhB0dXVVVKT0MrHb7ZhMJkITNAwDsVhMzLA6nQ5cLhdarRZKpZJQ8Rj7RnfEXq8nmDkFRN1uF6VSCTabDW63W/D4UCgEpRSUUvK4zWaD1WqV0GWeJ18bm3gwGMTGxgYKhYKs9sfjsTQzpjBVq1VkMhnZYdhsNgSDQSwtLQncw9U9M0j5/jBIIhQKYTQaiWUBbXSZQFSpVOB0OqGUEpyfr7XT6cDtdqPdbkvgBfF4snEuX75MxskD1be//W383d/93X1/5hd/8Rd/aEt95swZPPvss8IGOqhjERbNa173q1Pb0CeTCfL5PGKxGEKhkBhMsXnl83kRExWLRbRaLcm5ZKMHIFzqarUq2LTdbpe80aWlJYRCIeTzeeRyOREK1Wo1uN1uTCYTFItFadiBQEDweO4caDtLmTx3DjyvQCCAcDiMfr8vJmFMYqIBGBkxAA4N93Z2dsRVktAQz93hcAg1sdfriScLFah8H3Vdh67riMfjCIfDYm5Grj5vGpqmyaCTYiVN0wSaIj2SPvAUKjWbTYE6yGzhDYhD2Aep14urCwaDcLvdP/R9pRTe9773HfreZz7zmUdqDDOveT2KOrUNHYCIcXq9HkqlEpxOp4Qdt9ttgSgcDodYAlSrVYldy+VySKVS0DQNgUBA1JVUmQL7TcTr3U++pyc5/dKZ3JNIJGC1WgU+CQQCwnmnJwtNrLjy9nq9Io5iTulkMkGhUMDOzo7g64QwAMhKent7WzxWarUanE4nEomE8OJpiEXzMfqd8+dJK9Q0DbVaDR6PRxgswL7xGQM8isWipCuRgkkIy2w2C/ZvNptRq9VgsViE1jmZTOT1UlU6GAxkKOz1esVO90Hq/PnzQpd0u91iTTyveb1V6lQ3dLvdLrmcbAyUrTcajUOQCBkoZrMZ3W4XmUwGhUJBeN/T6RSLi4siWMrlcqK+BPZ9SlZXV2XVSvUjJf/Eg5l4z9V5KBSS8GXCIQAkyJp0QDpCUvlJzjwbfyaTQalUEm8Wj8cjg0UyRXw+n2DVhHMMw0A4HEa320U+n0cymZSQaZPJJDxzmpfV63XB5nd3d6UBu91uUYMylJqPccDMWQKZQ8TXafzlcDgk5Yjiq4ct7lweQc390Od14urUNnSllHDGZ31disWiDL44DCS7gxgvsD/8TKVSwqmm9wizMTkMZFao2+0Wsy/SCMlh39raEooe1aHMOLXZbBL3NhwOsby8LB4nFPfQ0pYZpHa7HYVCAdVqFT6fT1g6zWYTNpsN0WgUS0tLciMoFArw+/2Ix+NCORwMBvJ6OPykj42u62LERTsAq9WKarUqA+J6vS6Sf5p1ceXODNRZo61ZT/NZSums/S8pg+PxGMViESsrK7hy5YrAMner8XiMy5cv3/NzcD8M3mQy4dq1aw/5yTra+spXvnLcp3Dq6yRfg1Pb0MfjsWC3wL5bIsU+3W5XeOjBYFCGmvRHJ/yRSqWg6zosFgu8Xi9Go5EEQoRCIezt7YnCFACSySRMJhOy2ayYgpHFwWEq/cMBiFqSMnjDMMTOlsNHBnFMJhNh0VDoc+vWLQmM5mqflroUIjFv0+/3IxKJwDAM5HI52UFUKhV0Oh1RrZJ1w50JYR8qX7kCD4fDgq3XajUZ1kYikUMr9VkrgEAgINx5ctd5syJ0Qz/04XCIxcVFLCwsSFbp3YpGaPcqJi3drRh2fY/qv+6H7AiK85N5HV+d5Gtwahs6VZMMH26324jFYgIFEM7I5XLwer3iwEi4xG63I5vNYjQayfC03W7LAC8ajQoeT6EMB4A066LakmpN7gRo9sUQjMFgILxxi8WCcrmMRqOBZDIpePwsl54eKMwE7Xa76PV6SCaTCIfD6HQ62NnZEVjG4/FA13XZRTBQgt4vDG+eTCYCo7RaLSQSCdhsNhSLRXQ6HbEjCAaDyGazYhlMrryu6xgOh3C73VBKwev1wmQyoVgsSmMlq4bDz0AgIGInRvuRevgIckHf1EfoOA76u7/7uwCAX/7lXz6Ow88LJ/sanOqGzixOAEKJy+fzElzBbT/zLkejkagxb968KatyYtH82VKpBIfDIRRDqiTZpNxutyQG9Xo9gXhWVlawvb19SOhDXLxWqwnc43K5JPXH6XRK6hD9WbjyJ0uEcM50OkUul4PdbpdVMwenFDzRXItiH+5gqD5l7iepjmSjUCU7Ho9x+/ZtYazQzrdarYpvjc/nk9X5LJOFFFCn04l4PI5eryfMIbot8j0D9lkpx1jO1/+RR19f+tKXAJzMZnJa6iRfg1Pb0Nlw2u22cMK56na5XKIKHI/H0vAY2nDlyhVks1lhkuTzecF6mR9K2AaAyPspYmKDJZWPkn673Y5IJAJgnx0zHo/FhGo8HsvglH4qVGYy3zSbzQoUQnx6bW0NNpsNLpcL7XYbe3t7iEaj4olCc65OpyMDynq9LuIg0hbNZjOWl5cxGAxQLpdRrVYFOuJrDAQCkjLEZt1sNmX3wpthMBgUqwKHw4HFxUWZFXg8HtjtdlitVtTrdVQqFWG8UMBEZsoxb33Nx3nwec3rbnVqG7rFYoHNZhP72FAoJPQ8wi7D4RDj8Vg8Vxi08La3vU3451w9UmwzqyQMBoNIJBLynBxCElIBIL7mNKiikMlut6PdbiOdTsvNZjQaSUMmPEQ8mYZamqYhGo3i0qVL6Pf7KJfLcDqdh3jmuVwOTqdTfF9mxT88R03TEIlEEAwGUSwW5YaSSCRkmMzhqM1mQ61Wg9VqFQYMsXCv14tWqyVqV3qf04udGDxZLVarFZ1OR+x6OVDlTYaK1BNQ84Y+rxNXJ+Iv4zhqOp1KHiaZINPpFGfOnJEUoWazKTFo5KuTqeFyuXD9+nVJFeJgstfr4ezZswKxZLNZCS6eTCbw+/3SjDnEJDxz69YtwaB1XYfP50M4HIbL5ZIbAp0POcwk5EGediwWk0FpqVQSewEyXpaWllAoFIS9woGnz+eT1+1wOETtSeiJrJ1ZG91ZhSvVqQwBASBWwi6XC4PBAFarFfl8Xhwui8UigH0qITnhhFj8fr+8r0opZDIZkeqfEO74sQxF5zWv+9WpbegAJNOTDAmyWuj3zUEkh4RsrmS9aJoGh8OB5eVlhMNhZDIZwab7/T4KhQLa7TaSyaSk+BiGIWlB5JUTPqG7o9lsFmw/EonA7/fLToGQDM83nU6LiRiHlxQy+f1+wdR1Xcd0OkU8HkcymRQYg02cNynePNisCa8EAgFhtTSbTXi9Xng8HpRKpUOOiEopJJNJWK1WtFotBINBERDRv4U30nq9LuwWio4YYkHK5iwv3mazwePxPJQ69AhrcNwnMK953VmKQ6/TVkqpMoA0DrvmmQ6+nmUwTO94bLYsB9+b/b4N+6ITPocBgAnxYQCVO45jPvj5KQ4fw4IfbOuZXzn7s7jj56Z3ecx0cMzSzM/emVZ/52u+87knAOwH52AcHMt08P3xHcefAlAA/Aevk+/FFID14DEX9pvh5ODx4cHjlpnnMx18f/Z46uDf/sH3HQf/z2sQANA6+D3HwX+jg+MMD/7fdPCfC0D34Jxw8DOOg+cZHZzX9OD5+f4YB+fAx84YhnHIJ0Ap9a8A/KuDLzcAbOLxFT9bj7Pmxzy62jAMw/Owv3RqG/pxlFLqZcMw3jk/5pN/zON4jfer0/Cez4/5+nUsXNp5zWte85rXo695Q5/XvOY1r7dIzRv6463Pz4/5ljnmcbzG+9VpeM/nx3ydmmPo85rXvOb1Fqn5Cn1e85rXvN4iNW/ob9FSB5E+/PetWqfldb5enfbXP6/9mjf0x1RKqUWllE0ppR18fdTvffTgX8tjOt5xvEbgGF7nSSml1I8rpX4BAAzDMB5HU1dKvaCU+oWDf21HfbyTUo/rhvlm/4ZOzYf/OEsp9Z8B+EsA/zeA31dKbRiGMT2q5nNwvC8rpT4P4H9TSq0c5fFmjvnYXuPMMR/r67zj+Oo4dghKKZNSyg3gcwB+TSn1rwFp6kf5fv8L7A/r3gfg3wBYPqpjPcC5PK4GGwf239vHcKw3/Tc0b+hHWAd/74sA/i2A/w7A/wLgOwC+oZR65iiaj1JqFfsfiF8D8EUAHQB/pJRaP6pmp5RKAfg/8Jhe48Ex17D/Oj+Fx/Q67zj+BwH8HoDfU0q953H8wbMMw5gahtEG8AcA/l8ALyql/kc+dhTHVEqFAPwKgJ83DOO/BtAE8JxSKqqUchzFMe84/tsPdiQ/Ajy2Bvt+AL9z8Fk7yuM8sj5xqr1cjroOVkw5AP8RwE0AJcMwPqOUGgH4G6XUew3DuPGID1sB8A3D+P/bu/dYuaoqjuPfny1QIlAqj0CFiiK1ai1qA5WLBAihRB4iioWGFinIIxhBS0DkFaUKRAgqiVChhjc0KBKqlEdsERGKApZCCwgFEQiKBJGXVmzz84+1b51coDXcfWbgdH2SSeacmd51zjzWWXvtM6e+rVQxvyV+zn6ZpANt/7lmMEnrlpi3A4/QnX2kxLzR9q+7sZ+dJG1LHMCmA6OACySdAcwpibZblpf4lwJflnQucVmFk4gz2Gom9+XENeDHSHoS2AXYBNgPeFzSmbZfrRhvJUl7AzOAB4BhkubZ/nETsTpiTiBGQIfYXjrgsXfVfG1r5oms0Bsi6YOStiOuazIcOKi/qrB9HvBD4CRJw2oMH0v1MoWonEZLOtEFcA4wF5gqaUit4WqpUs8BRgLvAaY1uY8l5qdK33gHojKd3vR+voHNgIdtz7U9k2g/TAX2LtvYre/V9cBfbc8D7gGOAjYoL0fVSt32i8B5xMjvFuBi2/sAs4AtgEaqWEmfAM4gEuvBwE+BMU3EGmAb4HLb8yWNlLSXpIMhRkG13uPaeSITegNKRfFzIsF8G7gSOFrSNzuedg3wb9vLBjN8HNBPPRXYF5gEHCTpWFg5DP89MNL2ihrDVUk7E1XqHNt/Ao4HjpA0veNpVfaxI2Z/D3cPYC/gW8Chko6GZvbzTdwHvCRpQqnWbiKS3WmS+ppqe7yBfwEfknQ4kczPAkZJOrKJYLZ/RvTPbwcWlnXzgfVprp++LnC+7UVleSGwo2LysMk++tPAhqUV8ktgJ+AYSbOhTmuriTyRLZfKJPURb9Bk2wsVE3bbA33AXZKGALOBTwPjJY2w/cJbjVc+WK9IupS4auAk4sqDuwF3SFpu+0fA5sSXf33glQrJbjwwy/bNkkYB6wGnAOdLWgbMI6roQe8jvK6Hu1jSZURCOwa4pHy3L6D+fvbHn0BckfFV2/dIegI4AHhW0lO2byy91v2BO2vEXB3bz0h6ijiQf8X2LyTtCixdzT8dTMwXJM0HJknqv7Ll+4H7a8aRNNr2I7bvlPRoWTcEeAZ4FnixtCq2sf1ozZhl8QVgS+Bg4Arb55bnLJB0TKmeBxOrkTyRCb0ZZ9leWO6fDFxSvny7EElvOvHmTRtsouvQ30/9CXA4MQx+ADigTCRtD0yy/XLFeP2nrc0mvmiPlZgTicvH9lFvHzt7uE8RFdNGwEPAXcBhwDhgZ+ru58rJMeBWYDNJS21PlzSTOKBcR1StJi413E0XAdfbvrcs39aFEcICoiVxMnGJ4Wm2n6j1x0vleo2kObYPtP1cGQmtKMXCkPK8qcAXJX2pQsHQH/N625NtL5I0FzgBuEHS8NJ2uo64THMN9fOE7bxVvBEftg067m9BDBM3L+veRxxIh1eOuzVwYrl/HDFBeFpZXhvYuHK8scT1vmeXDxzAaOBMYN+yPKJyzP2Be4kE3r9vE4HvEZXMMGCTBt7P2cDUsrwBkdAuLMunAlcANwFLgG179LlTD2Ku3/9Zr/g3311eyyOAS4jquPO9GEr00WcR8wYfaSDmVR2PHQ7cDHyNaIs8BIyp9Lmqnieyh16Zo3f7UlkU8A/g77b/UiYtTwLWchztaxrYT/0OMEHSUbZfs131Av22FxOTgROIITeO4eqmxOQOxL7XjNnZw/1DWXcLUS1u5OgzPlc55gpKv7gsv2R7B2CspLNtzwC+CnwX2MP/6/V2lUsW6HLMlzs+67X+5qvAocBVxOdrmKQrymMrbC8n/mOSPmIC8cEGYq4t6ery2EXEGTaPEYn3c7YfrhCzmTzR7aP6mngjjvpnEtXlxxqMczrwJLBPWd4V2LLBeEOJHuPjRMvjMKJq2rrh1/MzwMVEdf5ZIrlvVTnG6I77U4DFwKiOdRsTE1qDrhDztsr3YSPgWkqlThy8z2jyde+IeXVZHge8twv7Oug8kVdbbFCZhV+LGKatBezmShM4bxJvS2BTl35q7fNlVxH3k0Q7ZB2iD/hAw/E2JA4kXyB6uCe4YmXc308lzuA5sKybARwC7Gj7ybJuNvB927+rFTu9nqSNgbOBHcuqnWw/26WYfURlvovtpxuKVS1PZELvAkmHAHfbXtKlePIa8MaWM1nkisN+xTU0riWq7z5gHduTy2MziBHB+USFPgXY03HaZmqQ4pew3wB2b7pg6FXMGnkiE3oXrCkJti0kjSR+oDUMmAn8pyOp70f8sGg88APHXEJqkKQRxIjpONtVT498m8UcdJ7IhJ7SKpTz3y8EXrM9WdJHifPbG7u0QHo9ScNsL2t7zMHKs1xSWgXbzwNHAssk/ZH4uf2K3m7VmqcXifWdlswhE3pKq+U45fN+4nTM/ZqaHEtpsDKhp7QapZ+6JzCxWxNyKb0V2UNP6f/wTuynpjVPJvSUUmqJbLmklFJLZEJPKaWWyISeUkotkQk9pZRaIhN6Sim1RCb0lFJqiUzoKaXUEpnQU0qpJTKhp5RSS2RCTymllsiEnlJKLZEJPaWUWiITekoptUQm9JRSaolM6Cml1BKZ0FNKqSUyoaeUUktkQk8rSRov6daO5bGSFvRym1K7SPqwpN9Iul/S8ZKW9nqb2iQTeur0EDC6Y/l04NQebUtqGUlDgSuBY22PAz4ALO7tVrXL0F5vQHr7sP1PScskbUh82UbY/lWvtyu1xueBRbYXluUHgb/1cHtaJyv0NNCDwBhgBnBKj7cltcs44L6O5bEDltMgZUJPAy0BpgGyfUevNya1yvOUlp6kjwNTgEU93aKWyZZLGmgJcCmwXa83JLXO5cANku4GFgBP2H68x9vUKrLd621IKa0BJK1n+5Vy/3hguO1s61WULZeUUrd8XdISSfcBWxHzNKmirNBTSqklskJPKaWWyISeUkotkQk9pZRaIhN6Sim1RCb0lFJqiUzoKaXUEpnQU0qpJTKhp5RSS/wXhFQeMv9XLCMAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "vparameters1d, cov1d = nestle.mean_and_cov(sample_result1d.samples, sample_result1d.weights)\n", - "print('Best fit:', varied_param_names, '\\n',vparameters1d)\n", - "print('Covariance:',varied_param_names, '\\n', cov1d)\n", - "fig = corner.corner(\n", - " sample_result1d.samples, \n", - " labels=[\"$v$\", \"$g$\"],\n", - " quantiles=(0.16, 0.84),\n", - " levels=(1-numpy.exp(-0.5),),\n", - " #truths=[v, g]\n", - " )\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "metadata": {}, - "outputs": [], - "source": [ - "#plot the best fit values against the data:" - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#Plot the 1D-error data\n", - "times_1d = data_with_height_error_only['time']\n", - "heights_1d = data_with_height_error_only['height']\n", - "#plt.errorbar( times_1d, heights_1d, yerr = [height_standard_deviation]*len(times_1d) , fmt = \".\")\n", - "plt.scatter(times_1d, heights_1d)\n", - "plt.ylabel('Height (Meters)',fontsize=14)\n", - "plt.xlabel('Time (Seconds)',fontsize=14)\n", - "\n", - "PointsToTry = numpy.linspace(0, 10, 100)\n", - "\n", - "BestFit_g = vparameters1d[1]\n", - "BestFit_v = vparameters1d[0]\n", - "\n", - "ValuesWeGet = my_model( v = BestFit_v, g =BestFit_g , t = PointsToTry)\n", - "plt.plot(PointsToTry, ValuesWeGet)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# CASE2: JOINT HEIGHT AND TIME ERROR\n" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "#(YOU NEED THIS CODE)\n", - "#The experimentalist knows \n", - "# his joint time, and height measurement error\n", - "\n", - "#DATA:\n", - "data_with_heights_and_times_error = astropy.io.ascii.read( \"2D_Generated_Data.dat\" )\n", - "times_2d = data_with_heights_and_times_error['time']\n", - "heights_2d = data_with_heights_and_times_error['height']\n", - "\n", - "#ERROR:\n", - "covariance_matrix = numpy.array(\n", - " [\n", - " [.58, 13.35],\n", - " [13.35, 501.77]\n", - " ])\n", - "covariance_matrix_list = [covariance_matrix]*len(times_2d)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 85, - "metadata": {}, - "outputs": [], - "source": [ - "def generate_experiment_error_function(mu, cov):\n", - " def experiment_error_function(Point):\n", - " x = Point\n", - " return scipy.stats.multivariate_normal.pdf(x, mu, cov)\n", - " return experiment_error_function" - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "DomainMinimumPoint [ -2 -40]\n", - "DomainMaximumPoint [ 2 40]\n", - "PlugInPointsCount 10000\n", - "PointsToPlugInDataset.shape (10000, 2)\n", - "PointsToPlugInDataset[0] [ -2. -40.]\n", - "MaxObservedValue 0.014979618176543238\n", - "MinObservedValue 2.5933311664554566e-12\n", - "Z.shape (100, 100)\n" - ] - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAABR4AAANOCAYAAAB3ARCKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzs3Xd4XFeZ+PHvq1GxZFtyd2TL3U6vJCQhpDdIgbBJgEAIBFh63R+woW+WXnfZpW4ICwRSaAsJKSSkOIlTnEqqk7j3XuUi2dK8vz/OHevO1ZR7pJm5I+n9PM881ozPPffcfu97TxFVxRhjjDHGGGOMMcYYY0qpJukCGGOMMcYYY4wxxhhjBh8LPBpjjDHGGGOMMcYYY0rOAo/GGGOMMcYYY4wxxpiSs8CjMcYYY4wxxhhjjDGm5CzwaIwxxhhjjDHGGGOMKTkLPBpjjDHGGGOMMcYYY0rOAo9mQBKRK0VEQ5/Tky5TX1RqOURkbmgey4qk/VW4TEXSXh0p//QSFtsYbz77rzHlIiKrQvvh3UmXJ0kiMlZEPici94rIOhHpjFw3Lky6jMaIyNci+2Vb0mUaSETk2tC6+07S5UmKiBwlIpeJyKdE5BMicqmIzE66XPmIyD9H9vuTky5TpYnIMBE5R0TeIyKfFZH3icjrRWREH/I6L7QuXxKRunKU2ZiBqDbpAhh/QXBnaYykO4FtwBLgUeAvqvpI+UpmzNAR8zjsAjqB7cD6IP0LuOPxQVVtL2MRzRATvLi4r0gyxV0btgOLgSeBW1T1/vKWzgxFInIucBMwuozzmAe8Nsd/naWq9/YhvynAMnq/nF+vqgf4l9CYwU1EjgPeHXzdBnwrweIkQkQ+AHwCOCTP/z8KfFNVb6lowUxeIjIO+BpwGdCSI8luEfkT8AVVXRknT1W9Q0TuB04DDgI+Dny/REU2ZkCzGo+D2wigDTgV+FfgYRF5TESOTLZYZigaLLVUPdUCw4FJwDHAxcCXgNuADSLyJxE5LcHyDWlDtMauACNx14bTgP8HzBWR50XkNYmWbIgSkXmhfXBR0uUplaCWz58oY9CxiHf2cborsPtjY3z8gJ5j5j9UdUu+hCJyduS6+47KFLE8RGS0iNwG/Iw8QcfAicDNIvJTEbGKPwkLnkGeBT5A7qAjQBPuevCMiLzRI/svhf7+soiM7VMhjRlk7MZq6Hk18JiIvCHpghgzxA3DBSLnishdIjIz6QKZIe0wYJ6IvD3pgphB47O4F6AZfwbOBQ4EZoQ+95Rp/peISFMfprui5CUxZpASkfPoqXG8E/hxgsWpqCCA+Efg/Mh/LQL+AtwOrI383weB/y5/6Uw+QQWcvwKtoZ+7gIeB3wMPAntD/zca+EPcZuiq+iCQaWHYDHymv2U2ZjCwNy6Dw2og18mwGXeDfynwZnoCzQ3AjSJyvKq+WJkiGjPo5ToOBXccjgIm4AL/JwGvIfvFzznAUyLyZlX9ewXKaoaG+bgmRFHNwBzgDcDbgUwfRDXAL0XkeVV9tjJFNIPYP4X+fhy4RFUr0e/qPtw+PQL3cue3cScUkeOBg3PkZYzJ7Suhv/+3UG3HQeibwJmh71uBK8PNqYPg5D/jgo2Zc8mHRORRVb2uYiU1AAQvo24h+6XYPcA/q+qyULpJwI/ouY7VA/8nIoeq6qYYs/oersY/wEdF5PuqurG/5TdmILMaj4NDl6ouy/F5VlX/qKqXAa8DdoWmGQ58PZnimkpT1dNVVYLP9BLme3UoXwlftIegXMfhUlV9RlXvV9U/qOq/qurJuH5ffgx0h6ZvAW4RkVMTKf0goapXhvfJpMuTsI4C14Y/qeqVuCB4+EGxHtfnkTF9JiIzgDGhn26uUNAR4I7Q377NrcPpnwQ29L84ZiBQ1S9G7mdWJV2mahc0Vz0u9NP/JFSUigv6gv146Ke9wNnRPhxVtUtVfwZcHsni6yLSUOZimt4+BkwLfX8IuCD6/KKqa3CVdv4a+nk8ruuyOG4B1gV/D8fVdDVmSLPA4xChqneTfYEEeJOItOZKb4wpH1VdpKofxdV0DD/YDgNusv5gTKWo6pP0viG+QERGJVEeM2hMiHxfU8F5h2sRnRXUXCkqGH00XEPYaiMZU9gnQn8/MsRaUX0e96Iu43uq+lS+xKr6B1x3ExltuJqQpkKC2o7hZs/dwPtUtTNXelXtxt0f7Qz9/BERGV9sXqraBfw69NOHRKQ+X3pjhgILPA4tv8Y1Bw07M1dCY0z5qep9uNrI4ZueVuDqRApkhqo/4kZdz6ghd/cdxsQ1IvJ9XwXnfSc9L3Rq6F3TKJ8LgcxLny7gxhKXy5hBQ0Qm4rrryBgyx4uIpIC3hH7qIl6/jf8R+R733GRK43X0nOMB7lDVBYUmCGo+/i70UxPZ3YgUclPo71bcNcaYIcv6eBxCVLVbRO7H9emVcZBvPiIyAvdQOhlXq2E37uT9SpHpGoFTgKnAOGAP7uHgKVV92bccReY1FtfZdRtuBNe1wNOq+lw/850JHIqrpt+Mu9nYAiwFHlXV3f3JP8f8yrIcpnqo6j9E5CrcqJAZ7xORr/j2ByMiY3D7Syvu5monrqnHw6oafenQLyJyHK4vtMm4Y3k1MFdVN/cxv3rgSNwgJ2NxTVP2ANuB5cACVV1ZgqKbCFVVEXmS7A7yp/Q1v+A8eSzu+tACbAZWAQ+oans/8p2BGx1+Mu58mMZ1IbIKWAy8ENRQMH0gIgfSs90agU3ACmBeH65tSXZzkAkaZmpjvRP4bozpws2s/6aqG0X6vxgiMhJ3Xp6Ma6q3Bxfof1xVF/cz73z3JEtw9yR7+pn/wcBRwAG4Y24f0A6sBBYCL1ewCX2fBYGik4DpuOuj4tbPg2WaX+ZaPBW33jYC/8Dd7/Z5fQXXyVOBWbgBLzbgro8PqureQtOWwduAVOj7Xyo8/yS9luyuJO5W1fX5Emeo6jwRWU5PU98TRGSCqlZtlw4iMhy3z03FLfNm4EXc8dOVZNn6IDqwatz+f68H3hv6/kbgmmITBff3S4DM4JHvAP4v5jyNGXxU1T4D7IO7cdLQZ5nHtN+OTPuTHGmujKQ5Pfh9Mu4kvSvy/wp8ssA8D8SNErY7x3SZz2Lgo0BtzOXIV8bZuDdMHXnm8wLwBo/1VQdcQE9t0XzlV1z/Ln8CXuWRf6WWY27c/QX4VXheRdJeHSnX9BxpCq2zfJ9fBdO2RPa3+X04XupwD3mZPBYBkuRxmCOvBtwDRKxjKsf0FwDzcM1G8q3TJ4ELPfLstT2C36/A3XTmmkc37oF/isd8xuNqCmyOsV+sBH4GHNTX/TfHdov7uTqY/sDI77/rw/Yeh6vlmsnjnv7uj0G+p0fKNtdj2usj037ec971wCeBlwusw724h9NDPfKtwTVHey7GNtqJG0X00gL5/TaUvstzGVeFpr27P2mDZerLfnhyKfaVUDlqgQ/jzov55rkH+EOh4y7Huo37eUcJlmFeJM9hwKsivx1TJI8xkWPyzTm24zrPcp0C3BXs9/mWfwHuYTTWNQl3PbsQ1wx8TZF1uzfYbkf34Vj+DO6+rNj224qrMX1WzO2zyHPfDM/r2gJpZ0fSfjH4vRH4Fu4FXLTsf4zk8bXI/7cVmF/OtMAk3Lk0373b8r7s88FyfCNY37ny3YS7vx/muyz9OO4eCuX/XIz0q/KUvdCn4Hk2qQ/w1Ug5Y18v6X2efFvCyxK9Fp0c/N6C64t8R55tsxH3cqcm6e3hsawrI8swKeZ0jWSfx3cDqZjT/jA0XQcwPOn1YB/7JPWxptZG4yQSkbOBZ3DNApriZi4inwCex3XQ21gg6UzcyflpEZkaN//IvM4Cngbeigvk5HIobgCPn0i8qgxfAm7F1YQo1k9UHW4EzcdE5FPxSt1bmZZjQFLV7WQ3cTheRI7wzOYNZPc39gtVjbXfV4q6/mV+Efn5/Fxpw0RktIjcidtHX0vh7jNeBfxVRG7sSz8zIlIvItfhHngPyZOsBtdH2nwROTRGnicAL+E6+x5TJDm4mr8fAM6LVegyUFez+77QT2/qQ5+c7yS7b6hr+12w/hsZ+d4Rd0IRORq3Hf8TF5jNpw64CHhWRD4WI9/huMDNz4HDYxRlOG7f+GyMtEOeiLQBT+EeLmcVSDoMuBR4XkT+XyXK1l/q+lp7PvRTsUFm3kbPMbkNNzBAn4jIMBG5AXgA149voVGxDwZ+A9wV1Iws5t9xgx1cgau5V0gdbrs9ISKfjJF3pvns48B36KmlU8go4BLgQ3Hyr6SghvRjwFXAxArM7wxcrca3k//ebSrwGxH5nke+U4J8P4db37mMxQ168ZCITI5d6D4K+gA+IfTT/eWeZ5U5LPL9MY9pHy2SV+JE5EjcteHD9L43yBiHa6lzQzByd1UTkWbcPWTGanXNqItSV3P82dBPjcCMmLMOHxsNWBdnZgir+hOFKbnoDUmcZpFzgO/hmvFkpnkC15xnPK7pWy8i8kXcW8GwbtxN7XJcH1BHR8p0OO7G6RT1GyH5YNyNcqZfqc3BfLbhAoYnkv2g/yHc26tiN+PRQE477mFmA652TRPuTfuh9DQ5SQHfE5Fd6kay81Gu5RjIrgHeHfr+XvyWN9x5dxeuRlw1upfsgMmJIiL5gqTBgAl30zsIuBEXuN6E24+OInsEv8uAFhG5UFXTHuX7Me5hF1xzu8dxNRjqgCNwx0FGK/AHETlG8zT/EpEJuNFnR0f+ayGu1tx23L4+Cndc9LnpbxlcA5wR/F2PWy8/yJ+8l3CTnS1UR9OboyPfl8WZKHjYvpneDyeLcLW52nEPxSfQ89CcAv5bRBpUtdAD+C+AsyK/bcG9BNuAu56MxNViPYjsc6MpIHjBN4/ex9Ua3PljJy5Icjw917Za4Psi0qyqV1eoqP3xG1wtMIC3ichnNH/TwHeF/v695hlsoJjg4fZvuNHiw7bhap1vxAVyDyG7q5uzgXuDe59CQf/oPckOXAuIzD3JcFwQOXpP8p8islNV877kCF5i/hnX5UXYOlyN40yt9BZcUHIO2U1tq0kjrnZ15oXFHlxwaC3uXrboizFPh+Nql2bu3VbizlPtuGbqr8Ft94xPicgTqnoTBYjIONyLruiLgfW4wND2IP8T6anp+zvcsV1OZ5K97R8q8/yqTbSbqiUe0y4tklfSJuH2oUxli424Z76tuGDjSWT34ftWXGD8WxUsY1/0Z5uB227HRvJbFGO66LF4DtkjZRszdCRd5dI+/h/62MQTd5MQrWbeq8kHvZv/tgf/rsF1ppyKpK8HWiO/nYHrfyuczw050gnwJno3GZoXnU+RMmaan+wA3g/URdKPAX4amUaB1xVZZ1/DXZy+iLsZz9kcCnfj9y1cQCaT9x5gapH8K7Ucc+PuL5S+qfX04PPpSNrLQv8X/YyL5PFMaLpNQEPMfb6N7ObHf0n6OCyQXwu9j5kZBY7l+yNpnwDOzpP+dFwQKJz+qiLlCafdFPzbjWvuNSpH+gvo3QzsQwXy/24k7R3AwQXST8QFoOcBn+jr/osLnmT2sx9EynBygX1yVCiPerKbxhdtZhaa9qTIPH9Qwn3y9Ejec2NOd05kui7ggJjH16bItDeQo0lusN7fR3azrX3AiXnyPjqS71pch+45m3XhahKci6s9+mCBMldLU+sRoX3r8VDaZQX2wenEPPcVKVsN2deEzPq9OLp+cde2aDP8NDnONbiH0kw5L49M88k8y9PvZmfkaGod/D6J7PP/+XmmPygy/WvzbMeiTa1xD+zhvF7Jt9/iHmLnR9L/sEj+38I97H4B98In3z1JK+4c2xXKexcwuUDeF0XK8jJwRoH0w3H3b78Dboy5fSrV1Dpz39qBe6HXlGOaaZHv/WlqnTkP/gM4LUf6sbjAZHiaFbn2i8h0N0Sm2YC7d4oepyOAr4f29+h5uaRNrYP9MJz/YTGmacPv3DAdmJgnr3zpS/WZVmRZwk3p00C9x7o7KrL8T5dy2/RhW0abWmf2naW4c4JE0g8HfhSZZjfQUmQ+4WteuT5jC8z/skiZr/dcT/8ZmT7vfWiOacP3i95dRtnHPoPlk3gB7NOHjdb3wOO7ItMpOfq3oHcwTHGBwZxBkBzT1+ButsPT/1eRaWbTuw+e9xVIn6uMe4BTiszn6sg0iyhw44e7UYrdfwmuyVE4/28XSV+p5Zgbd3+hxIHHAst6usd6/XBk2stiTvflyHSx+ziMkXefjsMiea6N5HlqnnSfiaT7I0VufHG1zcL95O2i8E1adL9MA28pMo9zI9M8ViBtOBC6gEiQvch8hlV6/80x7Xci0+YMoOWY7n8j0x1ewn3y9Ejec2NMMxUX7ApP9+eY87szMt3HY0zzKrL7bX0oT7ovRPJ+TYn2j6oIPEbS9ikw04/95MrIul0PzC4yTTRIv4TCLwfPjqTvd1+OMdefhrc/rql+5veb8kz/jXzrH4/AI665drgcDwAjiu2ruBpt4fPsgQXS+96TvCVSpq8XSPvzULpOirw4jS5Hqfdv+hd4VFzQteBL2Uge/Qk8Kq7WX97tjXth+EhkmnMLpH9NJO1WigT4cLXpo+UquCx9+USOq334Xb/7dW7IsV+U45P32oB7yRVOu82z/JMi0y8p5bbpw7bM1d/wy+QJ+oam+31kmvf3YT6l/hQ6R3wgktbrpS/w+cj0X/aY9t7QdB3EHM/APvYZbB/r43GIEJHTcW+owm7VmP1bAB9V1aUx056Ha4KT8TxQsM9DVV0EfCTy8ydypS3gW1p8hMJ/x9UuyZgFvL5AuVapR3NUVf0T2c0m3xp32pCSL8cg8FvcG9WM9+ZLmBE0Gws30V6Nq1VXzbZFvo+LJhCRBiDcz9pi3I17wREtVXUbPTUNwHUT8H6Psl2jqr8vMo+7cA9WGccG/fTlEu7L9VZV3Re3IFq4KWKlXEPPuoR4++RIXCAgY76qPp8vfbmIyEgReZWI/Buuhs600H9vxwW2i+VxLC7QnPFrVf3vYtOp63vvi6GfThKRV+dIGt4/1qnqIznS5JtHNewf1ezjke8fDq7BhXwa9+IiYwa9RwitRteF/r5IRFrC/xlcJy4P/fSbfszrc6G/twD/pKo7C00Q7KuX09OnqlDg3qcP9yS/J7u/ykL3JOFj7ilVXeExn2o85v5bVe+s0Lw6cC9E825vVe2md/dDpxXIM9pv5lWq+kKhQqjqL6jM6NLhe/w1PtfvQWBE5Lvvvh8daT6aXzV4hxYfpfvqyPdC+3I1SHK7LQ/93UB1dR1kTMVY4HFwqBWR6Tk+h4vIJUEn53eTfZLcg6tREscSXL8/cV0e+f5Vzd+v0n5B0O4foZ8OCwYtiGMP8B8x5qG4N9Vh0fL2182hv6cFnbXHVU3LUTVUdQdulO+Ms0RkepHJzsbVSsz4VXDjX82igcdcAzJdjGv+mPH1uA99qvosrvZrxgUeZftOzHS3h/6uwTUHLGa8RzmqQhCouS/002UiUuxG9DJcM6WMcg8qc5qIaPSDa+78JO7BIdzH5iZcreA4/RZ9OPS3Av/mUa7/Ifumv9h+OEpECg3QYWIKBn0K98v8XHDtLSi4hv975Od3lLJsZfJ/uL4PwdUufHPk/88gO+DWp8CjiJxC9rnuB6oapw9tghfAfwj95HNejiN8TzIr6DewmAF3To5I45pGVsqNqroyRrq7cTUEM47KlUhEUrhrfcZGXG35OL5dPEnfiUgN2YN0xK3AMFhEX6b6BrCi6fO9nE3Kfar6eLFEqvoi2QG1nPtyFUlyu62OfJ+WM5Uxg5wFHgeHybi+OKKf53BNMN9GdifQ+4ArgiBEHLcGga64Tgr9vRu/0SFvKJBXIXeoanvctLgH74xoJ/BFiUiNiLSISFs04IvrYyfsYI+sK7ocA8w1ob8FeE+R9OFBZZTeo0ZXozjn5PCIeIr/wCQPhP4+LqhBWcxCVY3bEfdLke/5HmBfDv39ZhE5Pmb+1SS8T44guzZjLuF9cifZwfQkbQG+DxyiqnEHJQjvh0+o6vK8KSNUdTcu8Jnx2hzJwvvHMOArcfM3BUWvqTd6TPtXXN95+fKqOsG+Fg6sRke3flfo73ke57mo6EilRYO5EeHz8rRgxPHYRCTlcU+SbzCL8DE3S0Q+4FOGKvOPmIHAUvlbnERBy4TwPpbv+ng42YGNW+K8wA/m8Si9Ax2lNJrswUm3lnFevahql6pKmT8+g6/6PB/lSi+e05dbrH05EL7fK/iyQlWvrcB2++dCZYgWySNtrvQ+261oayZjhgIb1XroeQZ4r6o+WTRlj38UT+KIyFiy3+Q849kE59HI92Nzpuqt6Nu5DFXdJyLPAKcEP80QkdGqmvfmKQjOXIDrw/EY4EDij+YYHbW3kLIux0CmqvOD5c28VX23iFydq9lZsB++KfTTPR5dBSSpJfI92rQDsoM0a4HRIuKzj4VrWzTg+hsqtm4WeOS/PfK9OU+6m+ipedUEzAtqZ98A3K99HFW2wv6Mq4mSueH+Z/LUShGRw3EjBGfcVKwZZgUNB/YS8wFSRFrJrk28LEYN5KjwS5OZOf7/T7gBDDI1HT8bjKB9LfDXGM3ATG7Ra+r8uBOq6l4ReRo4NfipVURaVXVtyUpXHr+hJ8B4sohMV9VlQTcQF0fS9VX4vNwFdHoeE9Hr2Axc/5I5BfckF9JzT+IzwnS+68VNwMdC338mIpcAv8S9FI0+PFez2PetJdLXa2S+62N0ZPGn/IrD07hKCeXQFPlejc3sy2lX5HuulimFRNNXy31ARqn35WqR5HaL3stXWy1XYyrCAo+D227cRWEJ7uHiZlV9oPAkOW30SBt947XQc14vR77Hbe6z2HM+i+gJ2AFMIM9Dt4hcgOsfc7rnPDJ8LsZlW45B4n+AnwR/t+H6tbw9R7p34kYezih3k9ZSGRX5nuvYC9eEiRM0LGZMjDyiwcRCon095Wsi+0NcDcFjQ+neFXw6RGQ+rrP++3E1kXbnzCVBQSDml8C/Bj+9RkQODZogRb0v8r0S++R8XPPusBbcy6GzcYHSRlwA+nO4lxdvj1HDPdo/0Zvp3YTVx5joD6q6POiD8huhn08IPojIAtz+8QCuaVjeII3JUopr9Kmh7+NxL0Cq2X3AStx+K8AVuL72LqanC5pO3GAJfRU+L9firs390euYyBCRN+LOn1PzpSki5z2Jqj4sIteQ3ffvOcEnLSLP4gaKeQA3cJXPvWGlVbpsfb1G5rs+jo189629Wc7antFnx1g1MQeRaABrmOf00fTVFngs9b5cLZLcbtFjpNrXlTFlYU2tB4fleaqcD1fVSap6sqp+qo9BR/A7uUYDJztypsovesGLW5Orv/OJlhsAEXkPrnnZdM/8w3yOs7IsxyByPdk3D/kG9Aj/vgm/PkoTEdRanBD5eWUkTS0wssSzjtNBduzBDOJS1T245onX07sJyzBcR+Wfx42cvFFEbhSR40pdjhLIjASb0WufDGonhfvDe05VY9c064cOVV0W+Tyjqreo6sdxtaTCQdLLgKti5Js3KNJHOfdBVf0mbiTKXDWtDsEFTq8DVorIoyLy7uAYMfkldY1OTFAr/vrQT1cE/4abXd/Szxp9FTkmROR9uMFD+hp0hML3JB8CvkzvGjo1wNHAR3EB2nUico+IXBwM0FNtKh3MKfU1Mtr6IW4XPBm+x7WP6EtA3wDOgBa04goP5tcsIvX50ucQffnjE+irhJLf71WJ6Hr2be7cn+0WrS1ZdS/SjakEu0E35ebbh0al5lP0RllE5gA/jaR9AfcAMx/XqfIGoFNDIwoHI4iHB53wUfLlGExUdYeI3ERPcOcNIjJBVTdk0ojIa4DDQpP9RouM+Fwljid7e+4gu+NuKM9b0sT2IXWDBr1DRL6FG4H8fHL3idqEC4pdJiI/BT6hVTKKpqouEpF7gbOCn94pIp+L7HMXkx2YqIoauKq6WkTegGuWl6kF9e8icrOqFmpuVer9MO8+qKrXiMjvcYHbS3B92ebqlzRTG/KTInKpqvrW5Buqhso15zrgs8Hfc0TkUrL7Zbyu9yReyn5MiMjBwI8j//ccrnuK+cAKct+TnA38Pc5MgyDtV0Xk57ja5/+Eq5UefV6owa2/M4G5InKZdX9QUtF7Ft/9K07fzX0VDYJWvNloH7r28KVF+i1ejHv5Be54zPS1H0e0xYBvS6cBKRh8r9x9G7Zr/kG9ouvZd2Tp/my3aPcEvi8SjBkULPBoSi1aYyD61raYaPq4zYZ95xNtapSrpsNVZDfX/R7wrzGaIfanRlo5lmOwuYaewGOmee53Q/8f7Vy6KoI8MUQHJ3g0uq+p6h4R2UPP29P7VfX0ShSunFT1eeBTwKeCUeBPwjXnPBvXyX7Yh3APvR+saCELu4aewOM44CKyR6kN75OdwG8rVK6iVHWJiHwZ+EHwUz3wH8B5BSbbEvn+JVX9WjnKBxDURPsR8CMRGYYLhJyCG5H4NLIfso8E7haRY1Q1Ws7+GgytRHJdo31qR0WvOQOiaw9VXSAiTwCZWtM/p2d7bsBvQIVcttDzcmGxqs7uZ365fJbsANS3gc+V455EVdcF+X87CBicgDvmzsSdn8P9SZ4O3CEiJ5T4hdBgON76Knpc+dYsLltNZFXdLSLh/f2Acs0rl6BWe7n77O6m8DPyS/QEHsH1Uxy3TDNy5DUUXIY775bTL+j9DJARXc+5+pYupD/brTXyvZIDXxlTNYbyRd2UR7RfHd+b7wOL5JfPLM/5RMu1IUeaC0J/vwJcFeMGH/p3E1aO5RhUVPUxXA2tjP1NW4MHpPDIwo/k6W+vqgRNcaOjdN+WJ3l4G88pT4mSo6rrVfXPqvovqnoErgbkLyPJ3i8ihyZQvHz+TPZ2Ce+TM3ABsoz/K0NArL9+gqstlfF6ETm5QPpozaaK7Yeq2qGqD6nqt1T1dbjuCT5O9oP6VFwgO5dwX0s1ns1EB0NXFkldo6tBuFZjeFveqDFHDC4gfExME5Fy1E4P35O8SLygI/QzMKSqO1X1HlW9WlVPxT1Ef5nsQUWOAa7Mk0V43fpUeBgMx1tfrYh8Pyxnqvx80/taFvq7XIPYVLMXIt9P8Jg2mrbq71EHg6CFTbgv6DYRmRRn2uCFZ3jApz34Bb/hIUmXAAAgAElEQVSj8ylUm9aYQcsCj6akgiru4RPqUUFQJa4TI9/jjr796rgzCB4Ijgr9tDQ6ErSINJF9s/73XKMn5xFdBh8lXY4qU8pm99eE/j4oFCS5jOy+scr9drVUPkR2E5ROXPO5XMIjv08Kmt8NWqr6sqq+h+yaq4Ib0bXf2ZcgD4JaPuHg6DkikumD7b1kN42sun0yKP83Iz//W4FJFpMdcIrW1q0YVd2hqj8ke3RigDfkmSRcu0+IWcs8aNrnOwpmXJXqkgR6X1NjPzAH/ZgdE/pp7QAY0TrsRnoPfgX9b2YN2eflWlxN3JIRkRayrxF/jxl0hP7dk/SiqhtV9av0rnUe55jzCSZW08ulSnss8j32/iQio+g9KnapPRf6u0VEov1TF1LJ81253B35fkrOVLmF06Zz5GXKp6/b7USya5vfq6rdHvM9KPT3qip/VjOmbCzwaMrh4dDfw/ELELw98v2RmNOdJyJxmxOdR3ZzsVzz6FMH/EHA8k0xy5FLqZejmnRGvvt0xh2Vb5CZcBOLdvo3SmlFiMgxwLciP/9cVTflmSR643RlyQtVnX4d+T69BHmWcp8MDzJTA7xbRFJkb5/FwNx+zKOcfkn26MRni0jOgEUQ8Lg39FNb0I9cYlR1LtkvvabnSRqtoRc3cP86zyL5CO+H/dkH43g48v2tHtNeyMC65mQJzql3RH5+QVWfKkH25T4vRwPkce9JhuO6fiiHG8nui3B6nnThY65FROLWwCznMVfVgn31+dBPp4pI3BYxb6f855EnIt+jXaIU0q/rrqp25RlQs5SfYjVz55Hd5cjZQTcxBQUvyaeFfpof7qN8MFPVayuw3fI1s864JfL9HTlT9RZNF80nr+CFXTjwGD12jBkyLPBoyiFaU+sLIlJ0XxORN5Fdm+IFVX06X/qIRuD/xZiHAF+M/Hx9jqTRfrCizcvy+TT9G92y1MtRTaIjwPW5+ZeqtuMeejLeIiInkV1750ZV3UUVCwYi+hvZfdStBa4uMNnvyW5W+rFgIKTBLvqgXYoBg0q5Ty4G7gn99G5c08hwM7RfeNRSqihV7QT+M/LzlwpM8rPI9+8GzZGSFN5H8u0fz0S+v75YpkGt/X/pa6FiCO+HE+JcL/sq6HriH6Gfjg4GGCooCKJ/OfJz1fRV6uEjuFoumU9/XhSG/Z3swQbeFgx0Vip9vSe5Cv++o2NRN4BNuLl1KY+5McD7+li0wSJay/8H+RLuTyQylsK11Uvlwcj343Kmyq1k192kBLXdwi+2a3FdfhQTvb+v9vv2weZOIDz4zHkicki+xABBc+zwC7o9uO514jqK7C4mHvCY1phBxQKPphxuJ/sGPFeNriwiMhM3gnTYDz3n+9ki/ZKBuyELN2deQo5O5VV1d/B/GRcWC+6IyIUUflCPq2TLUWVejnw/I2eq+P4n9HcTcFPk/6uuSWuGiMwSkR/iasmEmyh1AG/V/KPyZfqp+X7opybgtqAvQZ8yHC0iPg8LJSMi40TkvZ7dMFwe+R7dn/qinPvkNNyAKBldwK/6mX+5/ZTsoPb5IvKqXAmDGoZzQz8dDVwf1PqORZw3ikivkS5F5N0iEnvUSRE5kuxaN/n2j4fIbm77keBhPW8ZcdvxoHxpSiBc1gbcyN3lFL22/izGKLHfIbtrj2V41PqoFqq6SlXnhT6LSpRvN/DV0E81wJ9F5Kg8k+QkInNE5NQc+e8gu0bvRcVqwInIRcDnPeb94VzHYoH0byC7Bmy+Y+6+yPerCp37g//7DZD3uBwifgWsC32/UET+KxhcpRcRGY+7//Zp9twnqvoM2TXke+2zBSzCDd6S0d/rblK+SXaw/dNBC5acRORS3CjxGauIMfihiMwTEQ194tbSMxHBs114MMoU8POgVmIvwUvAn5LdhdOPVdWnb+NoNwl3ekxrzKBigUdTckFfiB8gux+Xz4jIr6NNEYIHz4twb0/Dbz0fwW804m3AMOB2EXlftGN3ERktIj+h95vgjxTou/GPob8bgLtyBQRFpEVEvop7A1YL5GsiG0c5lqMqqOoSsjt2fqeIfFdEThGR2SIyPfQp+vCjqk+QPchMOEjxTPD/lVQbWYbM54hgGS8VkW+LyIO4wYo+SvbIoNuBi1Q1WpMgl28D94e+zwGeFpGrgpoiOYnIVBH5iIjcj1t3iQQecTdx1wLLReQHInJydF/PCI6vr+NqE2d0AH8qQTkex729zrhKRL4gIicGweHwdozTN9nNZA8yEd4nb6v2/vBUdSfZwVLoXbM67F1kN6O8GHhSRC4rdCMfBL2vxgUqbiZ3v2/vBZaIyJ9F5G359usgvzfimtCG+9LMWRsvCOrfHPppHHCniPSqQRb89ldcFw6dZO8rpRStAfFbEXmPiBwlIjMi+6FPsD6fX+GaCmZMAuaJyEVBoHU/EZkoIteRXVNHgQ969nE16Knqr4HfhX6aCDwqIl+TAoMYBOv4PSJyO26k1HPzJA2f84bh7klOypHfKBH5Bu4eJkX8e5LPAytE5DfBvpCz2xcRqRORK+l9jOU75p4ju3nhwcDNudaJuJdh9wHn07uW55CiqttxNXTDPo47x35YRI4TkQNF5NTgHvQl4HjcueqvFSjiraG/817Do1R1D9n7w9kico2InBkE3sPnu6LNl5OiqiuA/w79VA/cI5Ea5CJSKyIfpHdrsC8GLQ1MZf2I7Jc4r8U9c4WbwCMircAfgDeGft6Iu//2Ee4De4kOgAEvjSkbVbXPAPvg+tHR0GdZifO/MpL/6X3M5+pIPoqr9fMQrpnsLcDKHGlWAzM9y/ghXJ9+me+bcG9+b8DVyunMMZ8fFJnH+CCf6HQLcBejm3AB072h/9uIexgPp78yyeUI5jM37v6Ceyjdn7/nNp5eJP1VOcqf6/OrmPvYB/JM/9EEjsP+fO4CZnnOfzyuyWQ0r25c07abcQ+C/xds/w050n6wQP7e2yOY7vRi+3+eddeBexi5BVfb5ffAfLKPr8znE6XYf4P0P425ja6OufzfzDP9hRXYJ6Prfm4f8hgL7AzlkQaOKJD+tbi+rqLLuwd3rv9TsB/eEmzPXTnSzs6R77wc6RbjagvciGuidhfunBtN9wiQKlDmA4PyRY+bR4O8/4QbPCHrWMG9OMl8v7vIevRJW4MLGsTZD08u0b4yPVLGzGclLmhxY7ANunKk+VqM/M+OTPOOMu730X1lWAnzDq+jdTHSN+ECZ7m23UvBuv0tLih4D+5+J9b6xb2c3Zwj/Yu48+VNwboInzPX47p9KLotcuwP3bj7nduD4+2GYNm25yjDH4qsl9Nw55LwNHtxQffrgb/gasJl/q8L1zdlOP21BfKfHUn7xT5s669F8mgrRdoi++uiGOk/n2d/yvf5YI7yTS7DcXdaZB7neEx7ecxlKXjuTPqDq2xwb45yv4K797qV3Mf4T/u4v5T8XIp7sdanawzuXLb/mE16e3iU+yiy73MU1xJiHu48ej/unjR6vjrFcz4jI/l8Jellt499kvwU6zzXmD5T1atFpB3XzDqzr6WAk4JPLi8CF6jqMs/ZLQAuwV3oh+Mens8rkP5nFOmzS1U3isg/4W4cws2JDib3gATrcX26xR0cJpeSL0eV+R5wLPDmEuV3Q5BnuBlEBwOj77EO3APdD9U1W/US7J8n4Zr3hpve1OBGtCw2qqXSu6+lJDXg9o1jC6TpAj6vqv9Vwvl+Bjd6qk9TsUJ+jguwh2uOrab3oBZVSVU3i8jPgU8GPwnwBdyI8bnSPyQir8a9jAk3MxtG/vN82B56DzaQz8zgU8g9wKVaoDaeqr4iIu/CnScytXRqcH3ERkd5VuCzqvozESlU+7PPVDUtIm8BbgPayjGPHPNcFpw/bgcOC/1XW4EydOOOv++Uu3wDlaruFpFzcM35Pk52y6KDiNdkP2dNP1VdJyKX4IL44fuMQ4JP1FpczcHYzacjash/vxN2A/CeQglU9X4R+TTZ3YTUkXtU2b1BfrfFL+rgparfEJENuHudQv117gTeq6q/F5Hv5/i/UnsAV3NsWvD9Elxfp0Wp6vUicgLwsTKUq2JUtSs4Jq8n+159TvDJ5Rr8ljvad/KQrglcCqr6TFAz9QZ6WtvV4l6k5rIVeLfGa5EUdgE9fbgr7qW6MUOWNbU2ZaWq3weOwL3d7yiQdCnuQffoPgQdM/O6Cxe0+BPZfXiFLQDeqKofUlWNkeeDuOaot+IuGrlsAX6MqxX0pHfBe8+z5MtRLVS1W1XfApwD/C+uZt5W8i9nsfyig8wA/FFVq+XGLI0LrKzD1U78M/B13M3IBFW9pC9BxwxV3a2qV+D2lz9Q/OEiU6vr33A1LKPrrlJW4poT/gi3Lxfbh3fhbuyPUtXvFknrRV3z4jNwzYRvwL382I4LcvYlvyX0HuH2l4UCYVXoe2T3XfVmEckbfFA3sM6xwKW4Wo7F1t0uXMDrA0Crqq7MkeY9wGdxNQ/iNHF+BHi7qp4d5/hX1d/jaojOL5DsAeCMSgTaVPVZXADwo7jrzTJ6amSUa54rcMHij5Pdp3FUJ+5l2BEWdCxO3ai7/4J7ofErsvtNzTkJ7vrwLeAwVf1egbzn4vp3vr1Afptx/Xgeqar/KJAu6kLgK7hjotg1uRt3nnu9ql6uMZqMqup/BPNYUCDPW4HjVdUG3QhR1WtxQevP4kamX487R6/CnXM/jWsplBnwJNx9heJa0pS6TIq79814q3gMMKaqH8f1Z/sTXGuHzZRm0LiKUtWtuHu6D+FqNeczH9edzgdUNdb9hYg04p6hMhYyQF5iVjtVvQ/3gv4aeg9emLEb94LyKFW9OU+aQt4d+vtOVV3YhzyMGTRkAMUszAAnbtCBU4CpuDfwe3DNP59W1Xw3on2d1zjcm6s2XM2AdcF8oqMr+uQ5CVf+NtybsXXACuAhdaM7llw5lmOwEZHvkt3/3+mqen++9IOZuE7njwdm4WrLDscFeTbj+tN7MQi0VZWg/77DcLXZxuGaK+7GBfUX4PrsLFf/eiUnIn/ABeHAPfTNUtWlCRapooK+4U7Cjeg9Dne+bMfVwHoJeFlVY79sCPoOOwzXnHISroZzGhcgXgY8parr82ZQPP9ZuPPsxCDflcCTQUB1yAiCy6/CDU7RiOvqYyXwgLpO+U0fiBug4BhczcGxuBYUmfPbQuCFvrwsE5HJuHuSyfTckyzH3ZP06WVeKO9G3GBNs3A1gkbggpHbcE2inwoCLn3N/3BcAHUC7qX0KuARVV3Tn3IbR0QeoqfG+QZVLUtfiUHfx6tw9xoAV6jqQGhxUjbiBpg5GHdcduFaPPyjL0EnETkTV4s/40p1fcmaEgrOd5nn0/G4e+ZVwINBBYe+5DkVV6kmU8nrPFWt9kFAjSkrCzwaYwasINC2kp6mEgtVtdcgEcZUShBEXYvraB5cH1XnJFgkY4wxQ4SIDMf1fdsY/HSnqr6+jPP7NvCvwdfHVfX4cs1rqAkGYfu34OsS4KC4tSVNskTkO7iufACeUNVXJ1keY6rBkG1qLSIpEXlaRG4Nvs8QkfkislBEfpdvRE5jTFV5A9mjofuMhG5MObyLnqAjuD4fjTHGmEq4nJ6gI8BjZZ7fd+hpyv1qETmjzPMbSk4L/f0NCzoODCLSjOtKJuPLSZXFmGoyZAOPwCfI7mfm28B/quocXH88702kVMYYH+GBdTqBXyZVEGOCGrjhTuPX4/r1NMYYY8pKRCbgRrQOu6Gc81TVzbjgY0ZZBuEaaoIKMCcGX5cD1yVYHOPnY/QMSjpXVa1fTmMYooFHEWnDdQR8bfBdgDNxA6AA/Bp4UzKlM8bEISKXkj0i5k2qujGp8hiDGyBrRuj7z/rb15oxxpihS0R+LSJnxUg3B5iL66Mu4x5VLTTgSal8D9efHcCZInJuBeY52J1Az4jW37R7iYEh6G4n08S6Gzd4mzEG1xn1UPQDXH8kI4PvY4FtoSrsq3CdAhtjqkAwUmKmSfV44PW40R0zuuj9lt+YshGREbiBUwRoBS4h+wZzO+5aY4wxxvTVCcA7ReQVXAWJ+biBDXcCLbjRrs8DLiP7uW43bqTlslPVDhG5Asj0ZzyqUHpTnKo+iLu/MAPLLHru/Rap6nNJFsaYajLkBpcRkQuB81X1wyJyOm403HfjRtObHaSZAtyuqkfkmP79wPsBUqSObdpfk9oYUy5d7GMP+QdjrqeBBpoqWCIz1O2jkw7yD/TbQBP1NFSwRCYxZXk0LH2mXjlKmZ53Y2frMX+/BUsuT/Bbr0mvqzLsA1qq+auyr3sP+/btpju9F4CG+pHU143MPw2UL4xTjnUV/Ltr13o07du1nzBs2Ghq6xqLJ60wrzWV9DNqrNkrO3dvQBCaGschRRsTxl8mKcfi+6zTcqx/r/l7ZVyGpAOprPHtSG/epKrji6cc/F53xnDdvKU76WLs9+SznWUdDCxpQ7HG42uBN4rI+bgq7M24NxOjRKQ2qPXYBqzJNbGqXgNcA9AsY/SE4q0fjDH9tEU38BQP5Py/sRzAUZxEjQzJniNMQtboMl7kiZz/N5kZHCLHVrhEA0C5glmx51+ec4TUlGG5fMoac/7is/5rPOafSsVOGrsMHnmSKn1ZpUzLT61H2phlUJ88PdJq3HVQG39dqU/aHPv1rj2bWLX+MdZueoau7g6GNYxi2vjX0jrxVQxriFHJzuNYzTX/vGlTpQ/+anCsPPXEz9ixfXns7Ec2tzHnoDfS3DwlfpkqSDwCT5KOmTbtMf+4eQLETLt9yxKefPk66mubOObAd1BTU+A4S8cvrHR5LFh3vLJKt0eQpSt+WumOWVaf+cfNE6ArfnBe4+ab9imrR1qPfVDj5utxXN2167r4J5RBbvOWbh67c2rSxdgv1bpwXNJlKKchF3hU1c8BnwPI1HhU1ctF5A/ApcBNuFFJb06skMaYgmqpYwQttDKNSUz3e6A2pgzqqGcko5nMDCZKW9LFMcaYQUE1zaZtC1m5fj5bti9GJMWEMYcyefyrGN08HZEa1Cf4PMAcc+wH2LF9BVu3LmLH9pV07NlC59520t17Eamhtq6JhoZmWkZNZ+zYgxg9ZnbSRR5yRjdP59Dpb+SFpX/mpeW3ccj0N9h9qTHGRAy5wGMBVwE3icjXgKeBXyRcHmNMYIxM4GwuTboYxuw3SaYzielJF8MYYwalfV27WbPxaVatf5w9nVtpqBvJzLYzmTzhWBrqRiRdvIoREVpGTaNl1LSki2IKmDT+aHZ1bGLZ2gcZ3jiOaQeclHSRjDFFKJD2qTJt+mVIBx5VdS5uBDhUdQlwfJLlMcYYY4wxZqhq372OlesfY+2W50in9zFq5DRmTzmH8aMPLtyE1ZiEzW47k90dm3hlxV00NYxl/OiDki6SMcZUjSEdeDTGGGOMMcYkJ63dbNz6EivWz2fbzhXU1NTSOvYo2iYez8jhByRdPGNiEanh8JkX8/iCX/Lc4j/y6kPfy8gm23+NMQYs8GiMMcYYY4ypsK7uTtZsfJrl6x+hY+92htWPYk7bOUwefwy19cOTLp4x3lKpeo4+8G089sLPefqV6znh0PfTUF9ktHVjTEKUbrWm1pVigUdjjDHGmJCBMlI1eIwUnfRI1T75lmGkavAYrbrW4/bYo6xeI1CXYV15jSodd/4eeaaDtHs6trFq3aOsXv8E3d2dtIycxoEzL2Dc6IOQ4DjxGlU6Zlqt9cjTY79Wn7Ft4pY14bFJxGPwZ59Ren0WLO42EK/Cxhd3BO7oqOYNjS0cdcjlPPH8tbyw7C8cffAV+8+T4rNhvQaoKcM6sAFyjDElZIFHY4wxxhhjTFltb1/BijUPs3HzAgDGjz2UqZNOomXklIRLZkxpNQ9vZc60c3l56W2s2fAUkycem3SRjDEmURZ4NMYYY4wxxpRcWrvZsGUBK9Y9wvZdq6hNDWPKpJOY0noCwxpGJV08Y8qmbeKr2bD5RV5Z/jfGjJpFo+3vxlQVN6p1eWpMm94s8GiMMcYYY4wpma7uDlZteJKV6+fTsXc7jQ1jOHDGBbROOIbaVEPSxTOm7ERqOHTWm3j0mR+zYPFfOOaQd2GNl40xQ5UFHo0xxhhjjDH9tnffblauf5QV6+fT1d3B6JHTOWja+YwfdSBaZ48dZmhpHDaa2dPO5eWlt7J+8/O0jjos6SIZY0wi7A7AGGOMMcYY02ede9tZvu5hVm14gu70XsaPPpgZrafSMmLy/jTWoM0MRW0Tj2PNhidZuPxOJoycQypVn3SRjDGBNDaqdaVY4NEYY4wxxhjjbU/nNpatnceajU+T1m4OGHs4M1pPYUTTxKSLZkxVEKnhoOnn88QLv2Dp2geZ3XZW0kUyxpiKs8CjMcYYY4wxJradezawbP3DrNv6HCC0jjuKGa0n0zRsbNJFM6bqjGqexgHjjmTZuoeZPP5YG2jGGDPkWODRGGOMMX0jCXeVLzXJzt+nDDXx15X4rNeamPP3yNNr/qmUR9qYZfXIU+IuP0BtzNveuOUEtNZn+T3SxsxXa33K6pE2zzrY1r6cZWvmsWnbK9TU1DH5gOOZNvlkhjW0ABRstJYvz9xp4++DcdN65elxvKrPaXCAjC6iHu3ixWddeWQs3XHTepzbPOavcTdszCxnTTuH9ZufZ/n6Rzh42vnFs/U5Z8c9tNIDZAccxHyOAVNeitJt26NiLPBojDHGGGOMyWvrjmUsWT2XrTuWUlfbxMzJZ9A28XjqGkckXTRjBoRhDaM4YOyRrN74FDMnnUZ93fCki2SMMRVjgUdjjDHGGGNML+GAY33dCA6c+nomTzh2/wAZVlfEmPimt57M2k3/YMX6+cxuOzPp4hhjTMVY4NEYY4wxxhiz39Ydy1my+r7sgOPE40jV1CVdNGMGrBGN4xk/+mBWrn+M6a2vpTbVkHSRjBnS0vb6rGIs8GiMMcYYY4xha/tylqy5ny3tLuA4Z+rraJtw3P4ajsaY/pnRegobt77Eqg1PML31tUkXxxhjKsICj8YYY4wxxgxhW9tXsGTNXBdwrB1uAUdjyqRlRBujR85gxbpHmDrxBGpq7HHcGDP42ZnOGGOMMcaYIWj7ztUsWn0vW9qXUF87nAPbzqVt/HHU1FsTUGPKZcakU3jq5etYs+kZ2iYcm3RxjBmSFOi2ptYVY4FHY4wxxvQQSboEIDVlyNJjuXzmHzNf8VmvNR7zj5mvpFLx8/RK61HWmPmKz/LXetzK1sabv/osU138+avPeq2NVwaNmS6atn3XOhavvIdNW1+mrraJOdNeR9vEV/cMGhNzHaTrPObvcQxqrUfamPmqx2b1KSseSX3KkCRJeyROezy4p8txfYk/f9X484+9DsRj/sHsR7fMZETTAaze+CSTJ+YOPFbBlThZagGh2NI+B6wxybDAozHGGGOMMUPArt0bWbLqPtZvfp7a1DBmTTmLKa0n2iAXxlSQiNA67kgWrriL3R2baRo2NukiGTMk2eAylWOBR2OMMcYYYwax3R1bWLLuAdZufIZUTR0zJp/G1EknUVfbmHTRjBmSJo49goUr/s7aTc8yq+2MpItjjDFlZYFHY4wxxhhjBqGOzu0sWfsAazY9jVDDtNaTmDb5ZOrrhiddNGOGtGH1zYxuns76zc8xc/Lpft1xGGPMAGOBR2OMMcYYYwaRzn3tLF0zj1UbnwCUtvHHMX3KaTTUj0y6aMaYwAFjj2DB0lvYsWsNLSMmJ10cY4YUBbqtL9GKscCjMcYYY4wxg0BXdyfL1z3C8nUPkU53MWncMcyYdCqNDaO8BqIxxpTfhDGH8tKy21i3+VkLPBpjBjULPBpjjDHGGDOApbWbNZv+weLV97F3304mjD6E2W1nM9wGrTCmatXVNjJu1IGs3/w8B059HSL2csAYMzhZ4NEYY4wZCpLuP6oMD1RS47FMZXqgi90vV43H/D22laRS8RLGTQeQ8iirR74Sdx3Uetye1safv8ZN65Onx/Jrncc2iFk7sTslbNqygEUr7mb3no20jJzCEQe9jVHNUwFIh+fvUeNRa+Ptg+lU/H3Va/4eq0pjnge88vQ5X3ok1aRPwzFbFarP6SpdPM3+tN3x08bO06OlpPqUtSZmxun+bdQDxh7Bxq0L2Nq+nDHNM3r+w+eS1c8yDClxd4K0x47lk9aHNQMuO49TguknCzwaY4wxxhgzwGxtX87CVX9ne/tKmhrHccRBlzF+zKE2SIUxA8jYllkINWzdviQ78GiMMYOIBR6NMcYYY4wZINp3r2fRyrvZtP0VGupHcvCsi2idcAw14lGlzxhTFWprhzFy+CS27FjGrKQLY4wxZWKBR2OMMcYYY6rcns5tLF51L2s3P0ttqoHZbWfTNvk1pFL1SRfNGNMPY5qns3zdw3R1d1Kbaki6OMYMCYrSjTVnrxQLPBpjjDHGGFOluro7WbZ2HsvXPgzAtANOYsakk6mrbSLt0x+nMaYqjW6ZybK189jWvoJxo+YkXRxjjCk5CzwaY4wxxhhTZVTTrN30LAtX3c3efe0cMOYIZk85m8aGUUkXzRhTQqNGTEEkxdYdSy3waIwZlCzwaIwxxhhjTBXZ2r6cV1b8jR271tA8fDJHzX4ro0ZOSbpYxpgySKXqaRnRxtYdS5MuijFDh0K3tbSuGAs8GmOMMcYYUwX2dG5l4ep7WL/tBRrqmjl85iUcMPZwRKxJtTGD2ZjmGSxZfT/7uvZQV9uYdHGMMaakLPBojDHGDFQiCc+/PMEQqUl4uXzmXxNzHXhsK0l5jE4ct6w+y+Qxf4m7/AC1MW87a+PPXz3Sxs1X6zzm77GutC7/utrXtYelqx9g5fr5iNQwY8oZTJt8MqlUPQp5u78vlGeUT3+Q6bp4+4um4u9XPmnTtR75xlysuOkAr+NVkz4Ne9TYUY2XWNJeJfBIG7+wcZdLfTZATfz5azpmvh4boNC1raV5KqxW2vesY0zLTOiOnQR+2PEAACAASURBVO2QF3e/rgppr4PLmEHDAo/GGGOMMcYkIJ3uZtWGx1m6ei77ujqYNP5oZradSUOT9eNozFAycvgBAOzcHQQejTFlpYCFgSvHAo/GGGOMMcZU2Obti3l52e3s7tjEmOaZzJn6uv3BhwFUf8cYUwL1dSOorxtB+671SRfFGGNKzgKPxhhjjDHGVMiejq28suJONm5dQGPDGI468O2MG3UgknTXCcaYRI1omsjO3euSLoYxxpScBR6NMcYYY4wps+7uvSxbO4/lax4CEWa1ncW01pOoqbHbcWOMCzyuWvcYqmmvnjONMX0hdNuRVjF2p2OMMcYYY0yZqCobti3gldV30bF3OxPHHsGcKecwrKEl6aIZY6rIiKYJpLWLPR1bGV5r/bwaYwYPCzwaY4wxxhhTBjv3bOTlVXewpX0pI5omcuysixndPD3pYhljqtCIpokA7Ny9nuHNFng0ppwUSFuHyhVjgUdjjDHGlJ3UlKE5i9TET+sxf6++9uKmrSlPWUmlYuYZMx0gPmWt9biVrI1XBo2ZzidPAK2LOf+Y6QA0lXtd7evazZJVc1m1/nFSqQYOmnY+k9pOoEZSsQaOSdfF2wbp2vj7itbG365x81WPzZ9OeZTVJ23MxVKPwypuntVAPB6cJR1zJficrn0K4JFx7G2g8efvdR3yWq7SGN44HoBdezZCy8EVn78xprqJyOuB/wJSwLWq+q3I/zcA1wHHApuBt6rqMhEZC/wReDXwK1X9aI68bwFmqurh5Si7BR6NMcYYY4wpAdU0qzc+xeKVd7Ovq4O2Cccxs+0M6uuGo+IRUDXGDDmpVD11tU107t2RdFGMMVVGRFLAj4FzgFXA4yJyi6q+GEr2XmCrqs4WkcuAbwNvBTqALwGHB59o3hcDO8tZfgs8GmOMMcYY0087dq3hpaW3sWPXKkaNnMbB0y/Y33TSGGPiaKhvpsMCj8ZUxAAbXOZ4YJGqLgEQkZuAi4Bw4PEi4Org7z8CPxIRUdVdwDwRmR3NVERGAP8PeD/w+3IV3gKPxhhjjDHG9NG+rj0sXnUvq9Y/Tn1tE4fNupgDxh7p12TfGGOAYfXNVuPRGJPLZGBl6Psq4IR8aVS1S0S2A2OBTQXy/SrwfWB36YramwUejTHGGGOM8aSqrN30LAtX3Mnefbtom/hqZrWdSV1tY9JFM8YMUA0NzezYtTrpYhhjKm+ciDwR+n6Nql4T+p7rbWa0M9o4aXoSixwNzFbVfxGR6XEL2hcWeDTGGGOMMcbDzj0beWnF7WxtX0bz8MkcfdDlNA+flHSxjDEDXEN9M3v37SKd7qKmxh7VjSkXpeqaWm9S1eMK/P8qYEroexuwJk+aVSJSC7QAWwrk+RrgWBFZhosNThCRuap6umfZi7KzmTHGGGOMMTF0d+9lydoHWL7+EVI19Rw8/UImTzgW8Rlh3Rhj8hhW3wJA5752GhtGJ1waY0wVeRyYIyIzgNXAZcDbI2luAd4FPAJcCtyrqnlrPKrqT4GfAgQ1Hm8tR9ARLPBojDHGVJek+4WrhgBK3DLUxF9XXv3tpTxGH66JV1ZJeaxXr/nHSyu1Hnn6zN8jX42b1ifPutKnzZVOVdm4ZQEvL7uDzr3baR1/DHOmnUtt40iUAu2YsvKNvw+kY6ZN18bfr9N18dNqKl5an/mrxyGQ9tgFNe55wOMUoD6nYZ+0cXYUT5L2SCvxCuBzuqzxWrHxV4DG3Ad8lt9nu2rMleBzbYmTZ31DMwAde3cMnMBj3O2a9jgA0h4b1kfcsmr8+ReI6/RPzHVQtvmbqhL02fhR4E4gBfyvqr4gIl8BnlDVW4BfAL8RkUW4mo6XZaYPajU2A/Ui8ibg3MiI2GVlgUdjjDHGGGPy2NO5jZeX3Mqmba8womkiR8y5lFHN0wAo06OxMWaIqqttAtygVcaY8kp7vWVKnqreDtwe+e3Lob87gDfnmXZ6kbyXAYf3u5B5WODRGGOMMcaYiLR2s3LtoyxeeS+CMGfa65nSegI14lEdzxhjPNSmGgDoTu9NuCTGGFM6Fng0xhhjjDEmZHv7KhYsuYWdu9cxbvSBHDTjQhobRiVdLGPMIJdK1QPQ1d2ZcEmMMaZ0LPBojDHGGGMM7mF/0ar7WLnuMRrqRnDkgZcxfswhfn2EGmNMH2UCj93dVuPRmHKqwlGtBzULPBpjjDHGmCFNVdm47SVeWnEHnft20nbA8cyecha1tcOSLpoxZghJ1dQDQlfaajwaYwYPCzwaY4wxxpghq2Pvdl5acQcbt73MiMaJHHnQZbSMnJJ0sYwxQ5CIkErVW41HY8ygYoFHY4wxxhgz5KimWbnhMRatvhdFmdN2NlMnnIg01CddNGPMEJaqqbc+Ho0pM0XopibpYgwZFng0xhhjKiHpPuKk9DdXUuOxTD7zj5mvV797NT7zj59WUjHTpjxGQq6Jn7Ys86+Nn1Y90sbNV+s85t/HtDt3refFJX9hx87VjB01h4NnXEjjsNEApGvjb/90ffz5p+vi769aGy+tT57pmHn6pE17bH5NeSy/x+EaN636nILLdbrWmLOPmQ781lVNd8wF8yiAepQ17vIDaDpeWX22q9dl2GcjlFgqVUc63ZXY/AHPDZuw9AAq60Bar8aUkAUejTHGGGPMkJBOd7F01f0sW/MgtalhHD7nUiaOPcIGjzHGGGOMKRMLPBpjjDHGmEFv247lvLjkZnbv2UTr+KOZM+111NcNT7pYpsqpKumuvXR37qarYxfdezsAkJoaRFJITQ3U1CBSs/+3zPea2lpSDcMtsG38WK04Yyoi7VUV3vSHBR6NMcYYY8yg1dXdycJVd7Nq4xMMaxjFMYdcwdhRc5IulklYumsvnTs207l9I53tm+nas5Ouzl1BgHG3+7fT/avp7j7PR1J11I8cTf2IMTSMGE39yDHU7/93DLWNIywwaXqxfcIYM5hY4NEYY4wxxgxKG7e9woLlt9G5bwdTWl/DrClnUptqSLpYpkLSXfvobHfBxY72TXRu30Tnjk107tjIvl3bs9JKqo7ahiZSw5qobWhi2OiJpBqaSDU0UtswfP//peobATc4Eek0mk6j2o2m06CZ7+7/urv2sm/nVvbu3Epn+xZ2b1xBd+fuyHxrqR8xmmGjJjCidQ4jJ8+hsWWiBZ6MMcYMGhZ4NMYYY4wxg8refbt4acUdrN/6AiMaJ3DUrDfTPHpa0sUyZdS9r5M9m1eza+MKdm9cya5NK9nbvoXwiCa1w4bT0DyOEa2zGdYynobmcTS0jKOhedz+gCJQ1sFluvd2sDcIRu7duYW97e7f3ZtXs335C66cjSMZOWkOzZPmMHLSHOpHjC5TgUw1sobWxpSfAt1lO9mbKAs8GmOMMcaYQUFVWbv5WV5ZeSdd6b3MmnQ60w84mZqalD3MDyLp7i72bF3L7k0r2bVpBbs2raRj2/r9fePVDR/F8PFTGTv7WBqCAGN9yzhqG5oSLjmk6ofROKaVxjGtvf5vb/sW2tcspH31QtpXv8LWxU8B0NAynpFBbciRrbOrYjmMMcaYuCzwaIwxxvRV0k3hpKYMWXosk8/8PfKN3cSwxmP+qVTp5w/x10GNx/xTHstVG/NWrjb+/NUjrVe+dfHSxk0HkK7vWf6Ozh28vOQWNm19mZaRUzlk1kUMb5rg0gFaG2+9puvir/90vUfauvj7Vdy06VqPPD3u+uPmm/bYVdRjt9ZIvl0du9m5djE7Vy9k1/rldGxZu7/fxdSwJprGT6Vl5hE0TZhK0/gp1DWN7J2nuP0gFp9Tu0dEW4qkrRs9hjGjT2DMoSegqnRsWUf76oXsXP0KWxY/yaaXHgYRmqcczISjz2RE60wk9kLFX/4ajxWgNR4rwGcgh2Ira3+6+FkOHJ6vSZIejCbu/H3KmfQype1V1eAndPtcmEy/WODRGGOMMcYMWKrK2g1Ps3DZHaS1mznTz2NK64lIGQLzpjK693Wya+1SF3Rbs4g9m1YDSk1tPU0TpjL+iFNoHNdG04Sp1DePGZT9IYoIjWNbaRzbyoQjT0W7u9m1cQXtKxawecF8Ft3yY4a3zuSAo89m5OQDB+U6GKq60/tI1dQnXQxjjCkZCzwaY4wxxpgBqaNzOy8tvpnN2xYyqnk6h8x6E02NY5MulvGU7trHrg3L2bl2Ee1rFrJr4wpIp5GaFE0Tp3PAcecy4v+zd9/BcZ3pfue/p3OjGzkTgSBAMGdROVI5a+SRRprRzGjmTrq1vi7v2lu1rtraXdeW/7CrXOu1617bd3y9nrlz70gaSaM8ylSmJIo5kwBJgEQkcgY6nHf/AMVRoMhzRAAH6P59qlQCyQfv+/Q5p093P/2GRUvJKavF5//Kx5csqbdZfj/xiiXEK5ZQvvFW+o58ypm973D81V+TU1JD+cZbyF+8WgX3DJBOJ/D7VXgUkcyhwqOIiIiILCjGGNp7d3Gs7U2MsVm25B6qK65Q0WUBSY4PM3TqIEOtBxnpaMKkU2BZ5JTUULZuC7lVS4mV1+ELqgDzVb5giNK111O86moGju6ge+9WTr75GyKFFZRvuJnC+g1YLpZ3kPnDGBtbIx5FZp0BbPSeYa6o8CgiIiIiC8bE1CCHWl+if/gEhXlLWLn0O0QjRV6nJRdhjGFyoOtcsXG85xQAodxiSlZcTW7VMuIVS/CHol9b41HOz+cPULLiKoqXXc7Aib1073mb1nd+T+fO1ylffzNFjZu/PkJU5rV0OgFAQCMeRSSD6JVIREREROY9YwxtPTtpansTgBW197CoSqMc5zNjpxntPsnQqYMMnjpIYqQPgJzSWio330V+7WoihRVan/ASWT4/RUs3UdiwgaHWg3TvfpvTHzxN1643qNx8N0WNl+kYLxCps4VHjXgUkUyiwqOIiIiIzGsTUwMcbHmRgZEWinLrWVV3H9FwAbaKjvOOsdMMtx9j4MRuhtoOk54ax/IHyF3USPm6LeTXriIYy/c6zYxkWT4K6taSv3gNI+3H6Nz5Oqfee4Kh1gPUXPcQoVDc6xTlItL2FIDWeBSZA+lsWSR4HlDhUUREJAtYvgX05srnsJjkYgSPq9E+fhfzPP3OcrUcxgEQcPH2LOAsV+MwDoCg8/5N0Hm7TmPt0J/7N8bQcWYnTSdfBctiRcMDLCqbHr1lAybg/LjaIWexaYdx0206v67sgIvYoLNY28Wl4qp/h+1+PiXaGJvx7lYGmncz2LyX1OQo/nCUvMWryK9bQ27Ncnzh8J/bv1i7Lu5XZgHVnq2LPfBzccZFo+f/y9zFy4nXNNKz7z06t7/KkWf/PXVbHiO3qvGiTRoX3bs5/k4fP4BxeAm46d9pm146N9XaF3J3IpxwdWJnuO/ZZFxcWG6eW47bdNG/C8bpOZiNxyQyw1R4FBEREZF5ZyoxzOHm5+kbbKIwv55VSx8kEi7wOi05yxjDeG87g817GGjeQ3J0AMsfIG/xKooaLyO3dsWX1hfUR+O5Z/l8lG3YQm7NClrf+h3HX/tv1Fz/PYqXbfY6NfkGydQEAAF/xONMRERmjgqPIiIiIjJvGGPo7t3H0ZOvYNsp7Vg9z0wOdjNwfA8Dx3czNdQDPh951cupvOJO8uvW4A+pYDLfRIsrWfqdf0bL67/h1HtPkBjtp2LjbVr3cR5KJEcBCAU1LV5kNhljkV5IQ+YXOBUeRURERGReSCTHOHTiT/T0HyI/t4ZVS79LTrTY67SyXmpynIHmnfQd+4yJvnbAIl5ZT+n6GylYspZAVEWS+S4QjtJw5y+mN53Z+TqJkX5qrntIu17PM4nE2cJjIOZxJiIiM0evNCIiIiLiuTMDRzjc+jLJ9CQNtbexuOo6jXL0kDE2o50n6DvyCYMt+zHpFNGSaqqueoDC+vUEY/nn1niUhcHnD1B746OEcovo2vUGybFBltz6OP5Q1OvU5KxEchSfL0DAH754sIjIAqHCo4iIiIh4Jpma5Ojp1+js20tutIKNq39CPFbhdVpZKzE+RO+JHfQd/ZTEcB/+UITi5VdSvPwKckqqvU5PLpFlWVRedgeh3EJOvf80x178axru/DmheKHXqQnThcdQMK5p8CJzwNau1nNGhUcRERER8UTf0HEOtrxAIjnKksobqK+8ASIa6TPXjJ1mqO0Ivcc+ZajtMBibeGUDlZvuoGDJOnyBoNcpygwrXnYFwVgBJ9/8Lcde+E/U3/lzcoqrvE4r6yUSo1rfUUQyjgqPIiIiIjKn0naSprY3OX3mM2KREtYvfYT82HTRw/Y4t2ySGBuk58jH9DVtJzkxTCCaS8WamyhceQWR/FKv05NZlle1jGX3/xXHX/tvNL30N9Tf8TNyKxu8TiurJZKjRMIFXqchIjKjVHgUERH5Iq+nN3m9pp2b/n3Oj5WraWMOYy2/iwXmXMW6OAZO252l/k3AYbtB52/5TNB5rm5i7dB0DsOjHRxseprxiV5qKq+mofY2/P7guYKjCTh//HbIeWzaYawdcn6t2kHnsWkXsU7btV28k/881hjDWOcJeg5+yNDJA2AMebUrqF75XfJqVmL5/dgBi7SDNt2s8ehq81AXscbrW7ZxHmscVtUt28W91cmJOstnvp5spKSSZQ/+c5pf/ltOvvkblj/8vxIJ5ztu07h4HTBuDlaWzoCcSoySl1vjdRrunOe6uqQ4pu9TGcnWV2vzhQHSbl5s5JKo8CgiIiIis84Ym9b2DzhxeiuhYJyNq35CUYFGV82VdHKKgaZd9B78kMn+LvzhKGXrbqB41TWE87RzeDYLxvJZcsdPOPbsf+DU1t/TeMevsHz6QD7XUukpkqkxohrxKCIZRoVHEREREZlVE1MD7D/6PEMjpygrXsOK+vsIBnO8TisrTA710Ht4G71N27ETk0SLF1Fz4/cobNiILxjyOj2ZJyIFZVRd+x1Ov/cHuve/Q8X6W7xOKetMTg4CEIloox8RySwqPIqIiIjIrDDG0NG3h6OnXsOyfKxufIjyknXasXWWGdtmuO0IPYc/YrjtCFg+ChrWU7r6WnLK63T85byKll/ByOmjdOx8jdzKpcTKFnudUlaZmOoHIBou8jgTkWxgkXa1BohcChUeRURERGTGJZLjHG59mTODhymML2bV8oe0acIss1NJ+pp3cGb/u0wN9xLMyaNy0x2ULL8Kf16e1+nJPGdZFtU3PMx49ylOvvsPrPzOv8QfinidVtaYODviMRopnF6ATkQkQ6jwKCIiIiIzqneomYMtL5BMjdNYfSuLy6/GhDWtd7akpiboPbKNMwc/IDUxQk5JDXVbfkhh3Tos3/QOMNrSQJwIhKPUbXmMY6/8Dae2PcuSmx7zOqWsMTHVj98XIhjIgaSLXYNExDUD2NpcZs6o8CgiIiIiMyJtJ2lqe5PTZz4jFillU+Nj5OZUABrAMxsSY0OcOfg+vUc+xk5OkVe1nPJ1W4hXLtV0avnW4uVLqNxwG5273yCvajnFjZu9TikrTE4OEI0U6rkrIhkn6wqPlmVFgPeBMNOP/xljzP9lWdYS4EmgCNgF/MgYk/AuUxERmTHz4U28NfPfqlo+F4/Laf8u2nT14cjvdx7rdDdVN4/fTayLXK2Aw7dSAReP382xchhrXLRpgs5j7dCfH//IWBcHjz3N2MQZaiqvpmHxbfh9wXMj7UzI2XlNB50/V2yHbU7HOrsG0kHn10raYZsAtot27YtcVhP9nZzZ/x4DzbswxqZg6QbKNm4hWlI1/fvnbdN5/8bhJeA0DsDVUlounq5mFm7vlosquXERazkdxObm1u4mV4fnwPigfNOtDHc0cfrjPxKvaiAUP/+GJ66O/2yc13nw8u6UdZGLZXJqkEi4YDrOzRBlp7G284vFchHrOTdPQuPsYBlXbS6gYyXikawrPAJTwM3GmFHLsoLAh5ZlvQr8C+A/GGOetCzrvwI/A/6Ll4mKiIiIzHfGGNq6PqW55XUCgQgbVj1OccFSr9PKOMYYRtqPcWbfu4y0H8MXCFG06ipKN9xIOK/Y6/Qkw1g+P4tv+j6Hn/53dO1+i9rrH/Y6pYxmjM34ZD9F+fVepyKSNdKz8c2VnFfWFR7N9NcXo2f/GDz7nwFuBn5w9u9/C/xrVHgUERER+UaJxCiHmp+jb/AYJYXLWbn0QULBmNdpZRRjbIZaDtK1+00m+toJRHOp3HwXJSuvwZeb43V6ksHCecUUr7iK3sMfU75uC+H8Eq9TyliTiWFsO0lOtNTrVEREZlzWFR4BLMvyAzuBpcDfAMeBQWNM6mxIG1DlUXoiIiIi897nG8ikUpMsW3Iv1RVXaG2yGWSMzeDJ/XTtfpPJ/k7CeSXU3vA9Cpdehs8//RZeG8bIbKvYeCt9R7fTuet16rZoo5nZMjbeA0AsquKuiGSerCw8GmPSwAbLsgqA54CV5ws73+9alvVL4JcAEfQts4iIiGQX207R1P42p7o/IRYtY+Oqx4nHKrxOK2MY26a/eQ/du99icrCbcEEZi7f8gML6Ded2qBaZK8GcPErXXMeZve9Svv5mokWVXqeUkcYmPi88asSjyFwwWKS1q/WcycrC4+eMMYOWZb0LXAUUWJYVODvqsRro+Ibf+TXwa4A8q0gryYqIiEjWGJvoZf+JZxmZ6KKm9HIa6u/C7w96nVZGMHaa/hO76dz7FlPDPUQKK6i7+YcULFmP5XTDJZFZUL7uZnoPfUznztepv+0nXqeTkcYnegkGcrRUhYhkpKwrPFqWVQokzxYdo8CtwL8D3gEeYnpn68eBF7zLUkRERGT+MMbQ3ruLo6dfw+8LsmHpo5QWLMf2Z91byRln7DR9zTvo3Pc2iZE+ooWVLLn1cfLr1mA53Y1eZBYFIjmUrbuRrp2vM9ZzilhprdcpZZyxiR5yNM1aRDJUNr5brAR+e3adRx/wB2PMy5ZlHQKetCzr3wC7gf/uZZIiIrIAzEJRwPK5WCPPTf8O23W1Rp+bUVguYi2/w1i/i2mnLqaouhpd5jTWRa4m6PztmdNYE3bT/5djk8lxDp18kZ7+QxTl17N66T8hHMrDACbg/Filg85i7ZCLNsPOY+2gs2s7HXLcJGmHbU73/+U/GztN/7EddO96i8RIP9GSapZc81PyFq/ChPyO1m+0XbyTN25iHV4utpuZ3y5uLV5vNGq5WDzTcjP/yenjSjtv0nbRv892mMB5HlTZ2hvoOfABnTteZendv/pCrPP+vT6v89nYRC+lRcu/1e9axuFF4DTObazTi9B28cRyc2G7MVvtzoaFlOsCZRt9uTdXsq7waIzZB2w8z9+fAK6Y+4xERERE5qfB4VYOND3DVHKEpbW3s3jRNRqFd4mMsRk8voeuHa8zNdRLTmkN1df9E3JrVpwr/Ovjpsw3/lCE8vVb6Nj+ChP9nVrrcQYlkqMkU2Na31FEMlbWFR5FRERE5MKMsWlp/4ATp98hEs5n85pfkB+v8jqtBc0Yw1DLAbp2vMZkfxeRokqW3PFT8hav1m7gsiAULb+Cjs9epe/oZ1Rffb/X6WSMkbEuAHJjKuaKSGZS4VFEREREzplKjHCw+Vn6h05QXryWlfX3EQhEvE5rwTLGMNx+lI7drzHRc5pwfimLb/khBQ3rNXpUFpRgNE7+4tUMNO+k6sp7tMv6DBkZ6wQgN6fC40xEsocB7Wo9h1R4FBEREREAeoeaOXDyedJ2gpX1D7CobJNG412Ckc7jdOx6lbHuk4Ryi6i56RGKGi9TwUYWrOLllzPUsp/htqPk167yOp2MMDLWRSSUTzCY43UqIiKzQoVHERERkSxn22mOd2ylpWsbsWgZa5f9lHhOmddpLVhjZ1rp2PUqIx1NBHPyqLnmuxSuugKfdgGXBS63ejn+UITBE3tVeJwhI2OdmmYtIhlN735EREREstjE1AD7TzzL0Fg7VaWXsWzJ3fj9wYv/onzN5HAvHTteYbBlH4FInKor7qd0xTX4AkF3O0CLzFM+f4D8xWsYajmInU7h08fJS5JOJxif7KOiZK3XqYhkFYNF2mhGx1zRK4WIiIhIluruP8ih1pcAWFf/EOVFqzF+VcjcSk2O0bnnTXqPbMPy+anceAdla27EHwx7nZrIjCuoX09/0w5G2psorFzpdToL2sh4F2A04lFEMpoKjyIisnAtoLXnLJ+3uTpep8/nYqFtF8ff1TqBTgtfLtbJswIuimkBF2+PHLZrXPRvgs77N0GH/X8lLp1Ocqz1Ndq7PyMvXs3axoeJRgoxgO2wTYB02Pn1YoecxbppMx1yfl05jbVdDPZM+pL07v+AM7u2kk5OUrTySiouv4NgTt50n19q13muTnMwLi5VNyMujcNY42Zdfje3wNm4XRoXoS5irfTFY87FOr0Nu+nfzTlwGHux8xqvXYYvFGGwZT8Fi2ap8Oj4NcPFwXLDabNuLhb767Ejo2c3lomWf+nfLTftOjUbbbpp103/xnYee57jeslsF/27YGblvM5OriIzSYVHERERkSwyNtHL/mN/YHS8i8WLrqWh5lZ82uzEFWPb9DfvoGPXayTHhshdvJLKK+8hWqxRS5L5fP4A8cp6xjpPeJ3Kgjc82kYoGCccyvc6FZGsY2tX6zmjwqOIiIhIlujq3c/h4y/g8wXYsOKHlBQu8zqlBcUYw/Dpw7TveIXJgS5yymqpvfUx4osavE5NZE7FKpYw3HqI5MQowWjc63QWrKHRNvLj1e5mBYiILDAqPIqIiIhkuOmp1a/S3r2D/Nwa1jZ+j0hYI2zcGOs5Rfv2lxntOk44r4QlN/+Y3GXrVDCQrBSrqANgrKeFgto13iazQCWS44xP9rGodKPXqYiIzCoVHkVEREQy2NhkH/tanjk7tfo6Gmpu0dRqFxJjQ3TseIX+5p0EInFqrn6QkhVXY/n8pFVzlCyVU1qD5fMz1q3C47c1PNoGQH68xuNMRLKPMZB2tVCxXAoVHkVEREQyVFf/AQ6degmfz6+p1S7ZqSRntvnUMgAAIABJREFUDrxH1963MXaa8vW3ULHuZvyhiNepiXjOFwgSLa1mtPuk16ksWEOjp7HwkRdb5HUqIiKzSoVHERERkQyTtlMca3uNtt6d5MdqWLtcU6udMsYw1HqAtu0vkhjpJ3/xGqqvuJ9wXrHXqYnMK7GKOnoPfISdTuHz62OlW0OjbcRzyvH7Q16nIpKFLGw0bWGu6BVCRETmF6/XS7M8nnbhpn+fi2Plc9iui+Nv+V1M13UT6zAHy+/iWLnpP+A81gQdvpVyGgeYoIv+w1+PHZ/oY9+xp740tZpwCNthm3bI+XF1E5sOO4tNh5xfg+5iLx4z0d9J2/YXGG1rIlJUQf19vyK3ZnqUaPoS+7eDjkMdxxoXl7W7WOMszs3t0sXtyriItZylCk7jAMvjOfRujqurWIcPy2lcTkUdZu97jPedJl6+5OK/kIGf8R1ff1+JNcZmaLSdypJ152/DuGjYTexstOkw1sxGngDG6avbLOVgO+9fJFup8CgiIiKSIbr7DnLo+PP4LB8bVjxGSeFyAMdFx2yVnBihc+dr9B39FH8oQtX1D1K8enodRxE5v5yy6bUJJ/o7nRUe5ZzR8W7S9hQFWt9RRLKACo8iIiIiC5xtp2hqfYPTXZ+QH69h7bKHiYQLvE5r3rPTKXoOvE/XnrexUwlKV11H2ZW3E4jkeJ2ayLwXjOVj+QNMjfR5ncqC0z/SAkBhbp2neYhkK4M2l5lLKjyKiIiILGCTU0PsP/YUQ6Nt1FReTWPt7dq12oGh04dp+/g5EsN95NWspOrK+4gUlGNruTURRyzLRyhexNSwCo9uDQy3EA0Xae1dEckKKjyKiIiILFB9g80caHoG26RZu+wRyotXe53SvJcY6aftkxcYaj1AOL+Uhjt/QV71Cq/TElmQwnklJEZ6vU5jQTHGZmCklbKilV6nIiIyJ1R4FBEREVlgjLE50fU+JzrfJ55TxtpljxCLlnid1rxmp1Oc2f8uXbvfAqBy892Urb1Ru/GKXIJwXjGjnc0YY7C83hxugRgd7yaVnqBI06xFPJVGU63nit5piYiIiCwgidQ4B07+kb6R41SWbmDFknvx+zU/+EKGO47R+tkfmRrqIX/xWqqvfoBQvNDrtEQWvHBuMXYqQWpylGA01+t0FgSt7ygi2UaFRxERmX1ej4KwZucbTcvn4nE5zcFFm65GlziN9bk4Vn4X6wj6nbdrBRy+PXEaBxBwnqtxEeu0XRN00X/wm4/V0Mhp9jX/gWRynJVL7qOi6gosy7rortV2yPnxT4dnK9bZNZgOOb+u0xeptyZGB2n/5EUGT+4llFdM3b2/IG/x9PTG9Df8jps1Hl3Furhc7aDTNo3jNo2bZT8dnlbj5tY+Sy8DxuEhsJwfKrfBM96sm/0OvD4H4bzpkdZTw70zW3h0emJdnCo3p9Vy3L+LRs/GDgyfJBouJBLK++bf/xbtzlic29jZkO39i2QYFR5FRERE5jljDG3d2zl26nUioTw2r/4ZebFF2F4X9ecpO52iZ//7dO1+E2NsKi+7k+LNW/AFHFb0RMSRcG4RML12KuVLPM5m/rNNenp9x0Kt7yjiJYOF7eqbG7kUKjyKiIiIzGOp9BSHT75Ed99+SgqWs7rhQYKBqNdpzVvDbUdp2/bc2WnVq6m66gHCecWkA/qAITLT/OEcAFKJCY8zWRiGxzpIpScpzmvwOhURkTmjwqOIiIjIPDU20cu+pqcYm+ihofoW6hZdhzVLSwcsdMmJEdo/foGB47sJ55VQf+fPya/RqCKR2eQPTX8Jkk5MepzJwtA31AxYFOXXe52KiMicUeFRREREZB4603+Egyf+iM/ys3HFjyjO1wiZ8zHGpv/oZ7Rvfwk7maBi022Ur79F06pF5oDPH8DyB0hrxKMjfUPN5McWEQrkeJ2KSNbTrtZzR4VHERERkXnEGJvjbVtp6fiAvNgi1jU+QiRc4HVa89LkQDenPnyGsa4TxCrqqb3uISKF5V6nJZJV/KEotkY8XlQyNc7QWAf1i27wOhURkTmlwqOIiIjIPJFIjbPv+HP0Dx9nUekmltfdjd+nkXtfZaeTdO5/m679W/EFQ9Re/z2Kll+uaegiHvCHIppq7UDf8AnAUJy/1OtURETmlAqPIiIiIvPA0HgHe088zVRqlJVL7qeq7DKvU5qXhruaaf34GaaGeyhs2EjVVQ8QzMn1Oi05yxiDPTZKcmAAk0hgEknsZAKTSv7552Ry+r9EAjuZBGPwx2L443H88Ti+WAx/bPpnfyyGFdBHlvnMH4yQTqrweDF9Q80E/BHyYou8TkUk6xnANvqycq7oVVxERBauWRjdZPlc7Hzrpn+H7VqWi/59bvp3Fmv5Z/4xAeD3z3ysi1xNwEX/Qedvj0zQWbsXi2vv2cWRllcIBWNctv4X5OVWYzto1w476z8ddn6s3MU6vwbSIWex6fD5/z41OUr7Jy/Tf+wzQrnFNNz9S2L1K6Z/5xLa/So75CwOwHbxTtoOmRlv17i4rI3fef8X+yxmUilSAwOk+vtJ9vWR6usn1ddHqrePZG8fZmrq4p1YFlYohBWcHtFrj4+Dff6r3opEpouQuXGCpSVEGhuJLFtKID8fy8kTBTC282vV1SuLcXFcHebg5mUAF7HGaazLDeCnRzw6WOPR+aFyzOn5n+7fRQJOQx3GGWPoGzpOcV49PnwXz8XN47KdJWE5jHPT5nSsw2Rnrf9ZyNUF4+a6cpOrcZarcdOmiEdUeBQRERHxiG2nONL6Ku09OyjKq2dtw0P443lepzWvGGPob9pJ+8cvkE5MUr7hZio23YYvEHJUcJRvzxhDemiIqVOnmWptJXG6jWRvL+nBoS8XTgIBgkVFBEqKCDfUEywuJlBchBUO4ztbXLRCQazg9M++UBD8/i990WJsG3tiEnt0lPTYGOmR0T//PDqKPTJKenSE8YOHGN2+A4BgeRnRxsbpQuTSBvzR6FwfIgEsXwBja3OZCxkZ62AqOUJJfqPXqYiIzDkVHkVEREQ8MJkYZl/TUwyNtVFXeS0N1bfgs/wqpn1BYnSAUx88w8jpI8TK66i5/iGiRZVep5Wx7MlJpk6dZrLtFFOtp0icOk16eHj6H/1+QosqidTXEyguJlhcRKCkmEBxMf68XCw3I7DPw/L58Mdy8MdyuNCqpsa2SXR0MnmsiYljTYxs387whx+BZRGuqSbS2Eh0WSPhujp8Qa2POjcMrodJZpme/iOARUn+Mq9TEREALNK6b80ZFR5FRERE5tjgyCn2Nj9FOp1gbcPDVBSv8TqlecUYm95DH9Ox/RUwhuprHqRk9TXaPGaGJfv6mDjWxFRLC1OnT5E803NuJGOgtJRI41LCtbWEF9cQqqr6+lqLHnxms3w+wtVVhKuryL/5JkikmGxtnS5ENjUz9M67DL29FSsQIGftWgpuv5VQuXY6n1XGuJwfnn16+w9TEK8lFMzxOhURkTmnwqOIiIjIHDHG0HbmM46eepVIqIDLlv+YeI6KIl80OdjDqff/wFjXCXKrllFzw8OEc4u8Tisj2FNTTDY3M3H0GONHj5Lq7QXAF48Rrq0ltnET4doaQrU1+GMLo0BiBQJEGxqINjRQeNf0qM3J4yeYOHqUke2fMbZnD7GNGyi49XZCZWVep5uRDMbd+sRZZmJygNHxbpZV3+51KiJyljaXmVsqPIqIiIjMAdtOcbj1FTp6dlGc38jahu8SDGhNus8ZO03Xoffo2PM6Pn+Q2hsfoWjZ5SpoXILpackdTDQdZfzYUSZbWiCdxgoFiTQ0kHfdtUSXLSdYVvrl9RYX8GcxXyRCzupV5KxeRcHttzH0zrsMf/QRY7v3ENu4icLbbiNYWup1mpnFaKr1hUxPs4bSguUeZyIi4g0VHkVERERm2RfXc1xSeT0N1Tdr2vAXjPd30LLtKcb72sivW0PNdd8lmKNNdr4NOzHF+KFDjB8+xETTMdKjowCEFi0i/4briS5bTqR+ydenTWcgfzxO0X33kn/TjQxtfY/hbR8xtnsX8U2XUXDbbQRLSrxOMTNoqvUF9fYfJhYtIyeikdsikp0y/x2HiIjMngXyQcPyLYw8AXCzQYPf7zjU8agxF23ic9G/m8cVcNauCbp4G+Mi1gSc52pCF2/3S+s5rvg+ZSWrMUxP8zkfO+T8uKZDznJNh50/pnTI+fPFVWz4639np1N0736Lrt1vE4jkUHfrj8ldsR7LshxtsnO+Nr85V2dxtov9SOzQN53F88S6uFxNwFm75mybdiLJxOEjjO3ey8TBg5hEEl88RnT5cqIrlhFZsQx/Qe6ff4/p6bHfyOnlMlu3Vtt5qLEvnoRVmEvhd+4l75YbGdr6DiMffczo7l3EN2+i4M47CBQWfrlN4+KBubhlGoc7R7kacermHMzS+TK4eI1xyHL+1JqVNi3jLPhiccnkOIPDrSyuus7Vde20f+DLO8zPRNxsxRoXB8Br9gLKVb41bS4zd1R4FBEREZklbWd2cKT1T0TCBWxc81PiMa3n+LmxM6c49d6TTA50U7j0MqqveYBAJEZ6gXyh4TU7kWD8wBHG9uxj4uAhTCKBLx4jfsXlxDauJ1y/5EsF/wsWGrOEPzeXogfuJ2/LTQxvfZeRbdsY27OPwnvvJvfaay55Z+5sZew0PhdfRGWTnoGjGGxKC1d4nYqIiGdUeBQRERGZYbad5uipV2k78xnF+UtZvfIRred4lp1O0bXzDbr3biWYk0f9nT8nv3al12ktCPbUFBNHjjC2dy/jhw+fG9kY27yJ2IZ1RJY2YLkZtZylAnl5FH3nfvJuuI7ep5+l/4/PM3n8BKU//EFWTEGfaenEBIE8rZt5Pmf6DxEJ5ZMXr4KERtGJSHbSK6uIiIjIDEokR9nb/AcGR1pZXHEtjTW3YgIu5u9msPHeNlrfeYLJgS6Kll9B9dX34w+pIHshxraZbG5mZPtnjB84gEkm8cXjxC/bTM6mdUQa6lVs/JYCRUWU//LnDL/7HgMvvsyZRILSnzyOz+dwXr4AZwuPYT2PvyqVnqJ/8DhV5Zu1SZbIPGOMpV2t55AKjyIiIiIzZHisk71NT5BIjrGm/rtUlqwDvnk9x2xh22k6d7xF1+63CETj1N/5M/JrV3md1ryW7O9n9LPPGP1sB6mBAXzRKPHLNxNbv55IfT2Wz3dujUf59izLIn/LTfgiEfqefpbuX/83Kn/yM3yRiNepLRjpqQl9gXAefYPN2CZFaZFGdItIdtPbFREREZEZ0NV3gIMnnycYiHL5qp+RF1vkdUrzwvhAJy0fPsF4f/v0Wo7XfodAOMfrtOYlO5lk/OABhnd8ymRzMwCRxkYK77mbnDVr8AU1cna25F59FVY4TO8/PkHnr/+Wip//An+OrtOLMXYaO5VQ4fE8evqPEAzkUJBX63UqIiKeUuFRRERE5BIYY3O8/R1OdrxPfryG9UsfIRzKvfgvZjhjp+k68A4de9/AH4qy5PafUFC31uu05qWp9jZGdmxndPcu7IkJAoWFFNx2G/HLNxMsKvI6vawR37QRXzDImd/+js7/+p+p+MWvCOTquXwh6cQEAH5Ntf4S207TO3CU0qKV+CwthSAyH6U11XrOqPAoIiJf5vU6RJbHbwLc9O9zfqwcr+/k4vi7WjPK6RpwLnYmtQIuPky52bDBaa4u1rUzQRexIee5JqwEB5ueoXfgKIvKL2N5/b34fAG+uoWAHXJ+XbmKDTu7BtIh59dK2mGb07Hn//vJgW5a332S8Z5TFNSvp+a6fwIFuaQuoc3zsV0sxec0Nh1yPjHedjEA0QS+3K6dSDK2cxcjH20j0dYOgQCxdWuJX3k54ZUN53ZYti8wUf+rbV6Qm1u7NQuLAxg39yvnoVbaYfcOD0B0wxoqgj+j+3/8Dzr/y99Q8Ze/IlBY8M2/YDt/XMbn8Li6uLe7OayzITV5tvAYjF50TQlXl5XTQ2W7aNRV7KW1OTB0klR6ktKCFediLOOi/9mInaX+zaz073wjHsf9u81BRGaMCo8iIiIi38L4ZD97mp9gfKKPZfX3Ul1xRdZvIGBsmzP736Nzx2v4AiHqbvkRhQ0bABwVHbNBamCQkY+2MfLxp9hjYwQXLaLouw8S27QRf2x6aq/jApXMiujyZVT88pd0/d1/p/M//xcW/S//XNOuv8HnIx4Dmmr9JT39h/H5ghQXNHidioiI51R4FBEREXGpf7iFfcefwgAbVj9OUUG91yl5bmqkn9Z3nmCs6wT5dWuoue4hgjmapgrTI3Imjx9n+P2PGN9/AIwhZ81qcm+4jsjShqwvWM9HkfolVPzy53T+9X+m96k/UPaTx3WeziM5PgSg5/oX2CbNmf5DlBQ04vdpXVaR+cgAtqupAHIpVHgUERERcaGtZydHWl8hJ1zEulU/JCda7HVKnjLG0N+0g7aPngOg9qbvU9R4mYo0TG8WM7ZrF8MffUiiowNfTg55N91A3nXXENDajfNepK6Oonvupv+llxnZto28a6/1OqV5JzE2CEAwdoHp6FlmcLiVRGqM8qI1XqciIhnEsqw7gf/I9IIkf2eM+bdf+fcw8PfAZUAf8IgxpsWyrGLgGeBy4DfGmL86G58DPA00AGngJWPMv5qN3FV4FBEREXHANmmaTr/Bqe5PKc5fytr6h/BHY16n5anU5CinP3iWwZP7iFXUs3jL9wnnqqCWGhhgeNtHjHz6Kfb4OKHKSoofeZjYZRvxhVwsTCmey7vxBiaamul/4SXCdXWEq6q8TmleSYwOYvkDBCJxr1OZN7r7DuD3hSgpaPQ6FRHJEJZl+YG/AW4D2oDPLMt60Rhz6AthPwMGjDFLLct6FPh3wCPAJPB/AGvO/vdF/94Y845lWSHgbcuy7jLGvDrT+avwKCIiInIRydQk+48/Td/wcWrLr6Kx5jZ8lt/x/gOZaKj9CCc/epL01DiLrryXsrU3ntsQJVslOjsZ3Po2Y3v3np1OvZa8664jUl8PmnG5IFk+H6U/eJT2f///0PP3/8Cif/E/4wu72AEpwyXHBgnFCjTC+SzbTnNm4BAlhcvx+/Ulg8j8ZS20Xa2vAJqNMScALMt6EngA+GLh8QHgX5/9+Rngry3LsowxY8CHlmUt/WKDxphx4J2zPycsy9oFVM9G8io8ioiIiFzA+GQfe5qeYHyqn5V191FdepnXKXkqnUrQtuMleo5uI1JYQcPdvySneJHXaXlqsrWVoa1vM37wIFYoRN7115N/3fUECgvPxRin2/TKvOOPxyl97Ad0/de/pe/ZP1L6g+97ndK8kRgb0DTrL+gfPkEyNUFFsaZZi8iMqgJOf+HPbcCV3xRjjElZljUEFAO9F2vcsqwC4D6mp3LPOBUeRURERL5B//AJ9jb/Acuy2LTsxxTl1XmdkqfGek9x4oPfMzXcQ/mqG6m46i58gewcymeMYeJYE4Nb32ayuRlfTg4Ft99O3nXXawfkDBRtXErBbbcy+Mab5KxZQ2zdWq9TmhcSY4PkVmpK8ee6+w4Q8Ecozl968WARkT8rsSxrxxf+/GtjzK+/8OfzDSv/6jeaTmK+xrKsAPAE8J8+H1E501R4FBGR2WfNzlQGy+dwapeb/p22Ce6mlvn9Dvt3kavTNgH8ztq1HMa57j/gPNY4jDVB529jTNB5//bZ2Lauzzh28mVyIsWsX/lDopGir02ttkPO2k2HnfefDju/rtIhZ+fLVZvnmUVq7DTde7bSufMNgjl5LL3nL8mtaiQVsRxPNz9fu+dju5id6LRNADvsbMShHbxwnLFtxg8cZPDtrSROn8afl0fhg/cSv/aqc1Nw7fMcFXORdr/E7zDW56JNN7dhaxZGZ9ou7pcuYp1m6uYhmW/oP/+OWxnbv5/+l14iumYlViCAqxnGC2U2ssNjZew0yfFhQk5HPLo4B47P12y0CVi2s+AvtmnbKXoGjlBauAK/Ffh6bsbNReg81ko7vAu76d92sZCIw2PlOM517MwvemJcHStXNxcXoRolP5sMYJt5dVPuNcZsvsC/twE1X/hzNdDxDTFtZ4uJ+UC/g75/DTQZY/5fF/m6osKjiIiIyBcYY9PU8hqnOz+muGAZa5Y9TCAQ8Totz0wN99Gy9R8ZP9NK4dKNVF/7XQLhqNdpzTmTSjG6YydD77xLsqeHQHExRY9+l/gVm7FcFMFl4bL8fgrvv5czf/t3jO3aQ/yKC31GzHyJkQEwhpA2lAKgd7CJVHqSCu1mLSIz7zOg0bKsJUA78Cjwg6/EvAg8DnwMPARsNRepoluW9W+YLlD+fMYz/gK9SxIRERE5K5WeYv/x5+gdOEpN5dU01t2JNUsjdheC/qadnP7wWSzLou7mH1K4dKPXKc05O5lk5JNPGXp7K+nhYULVVZT++IfE1q7FRLL32shW0RXLCZaVMfzhR1lfeJwaOANAJL/M40zmh46+PYSCcYry671ORUQyzNk1G/8KeB3wA/+fMeagZVn/N7DDGPMi8N+B31mW1cz0SMdHP/99y7JagDwgZFnWd4DbgWHgfweOALvOzuT6a2PM3810/io8ioiIiACTiSF2Nz3B2EQPy+vvo7riCq9T8kw6McnpD//IQPNOYhVLqNvyg6wb1WRSKUa2b2fwrbdJDw4Rqa+n9AePEmlsPLfMgjaMyT6WZZF73TX0//F5pk6dJlpR63VKnpkc7AYgUlDucSbeSyTH6B1sorb8SnyWi2VIRMQzaVdrkHjPGPMn4E9f+bv/8ws/TwIPf8Pv1n1Ds3My31yFRxEREcl6Q2Pt7Gl+EttOsn7lDykuzN7NEsa6W2nZ+g8kRgeouOwOKjbeguXLng/SJp1m9LMdDL75FqmBAcJ1dZR+/1EiS5e6W9dVMlb88ssYePlPjHy4jehD2Vt4nBo4QyASJxDWZkpd/QcwJk1lyXqvUxERmXdUeBQREZGs1t1/iAMtzxEKxLlsxY/IyV/kdUqeMLZN14GttO95nVAsn8b7/inxiiVepzVnjG0zunsX/W+/Qaqvj1BNDeUPP0R0+TIVHOVLfNEosc2bGPtsB+m77sMfi3mdkicmB7o12vGszt69xHMqyM2p8DoVEXHAYM23zWUymgqPIiIikpWMMbR0fUhz+1byY9VsWPoooWDM8S7NmSQxNsjJD3/PSNdxCho2UHPdQ1mzgYyxbUb37WXgrddJ9vQQqqqi/Gd/QXTVShUc5RvlXXcto9s+YWTHdgpu3OJ1OnPOGMPUwBkK6zd4nYrnRid6GB5rZ1ntHV6nIiIyL6nwKCKSDRbQh2fL522urgoNPhdrwziMtfxu2nSRq9/hVFmncQAB57HGRSwOdwg2QRf9fyXWtlMcPvEinT17KC9ey6ql38HvC2IAE3J+DtJhZ7F20Pm5ctomQCrsrN10+Jv/bfDkfk69/xQmnab2pkfJW3s5WBapS2z328baYefrJqZDzvv/arvGGMb3HWDw1ddIdnUTrKyg9C8eJ3rZaizLmr4WLrKGowm4KFMHnD8uy2G7ls/FGpOzcWt10b1xMbLEpGZ+3a0L7+v5FemL5xqsriTcsIThTz4m78YbsZzc4x0eglkbhOPwGFgO4lLjI6QTE0Tzyx3FO233HKexbp4CtqsL1lmcbdPZswcLi4rCNWBf4LmbdtF/2sW9xWmus9EmgFlAX9dd6PyIyKxR4VFERESySiI5zr6jTzA40kp99RaWVN+UlSPb7FSCto9fpO/wx+SU1rD45seI5JeSyoJjMXniJAMvvsxUSyvBsjJKH/8hOevXYfl8GFfVEclmuddfS+9v/oHJ48eJNmbXurCTA2c3lsnyHa1tY9PZt5eivAbCwbjX6YiIC/YC21xmIVPhUURERLLG2EQve478A1NTw6xZ+hAVpeu8TskTE/2dtLz1OyYHuylbv4XKzXfi82f+28JEVzcDL/+JiQMH8efnUfzIw8SvvNzZaDWRr4iuXgk+HxNNx7Ku8DjR0wZATnGVx5l4q2+omankCMtr7/I6FRGReSvz32GKiIiIAP1DJ9l39Eksy8em1T+hIDf7dqM1xtB35FPatj2HPxSl4e5fkVe9zOu0Zl1qeJj+ra8z+smnWKEQBffcRd6N1+MLuZirLfIVvnCY8OLFTDQ1eZ3KnBvvaSMYLyQQye5Rfu29uwgFYpTmZ/59VETk21LhUURERDJex5ldHD7xEjmRIjas+CHRSKHXKc25dGKSUx88zeDxPeRWNbJ4y2MEc3K9TmtW2VOTDL73LkPvv4ex0+Refy0Ft9+KP57dxRKZOdGljQy+9SbpiQn80ezYkAlgvOc0OWU1XqfhqcmpYXoGj1FXcQ0+n4t1jEXEc8ZAWrtazxkVHkVERCRjGWNo7thKS/eHFOU3sHbZ9wgGsqc48Lnx3jZa3vodUyN9VF5+F+UbbsayMnd6sUmlGN7+CQNvvYk9Nkps3QYK7r+TYEmJ16lJhok2NjL45htMHm8mtmat1+nMidTUOInhPopXXOl1Kp7qPLMLMFSVbPI6FRGReU2FRxEREclIaTvJgZbnOTN4iKryzSyvuyfrRqUYY+g5+hGnd7xIIBqn8d7/iXhlvddpzRpjDOMH99P3p1dI9fUSqW+g6O6fEampdbVbtohT4dparGCIyaamrCk8nlvfsbTa40y8Y4xNR/dOinKXkBMp8jodEZF5TYVHEZGFaj7sPOv1iCmn/ftcHCs3m0y4OAeOd032uyiMuSiiOd48I+C8TeMi1l27znI1wW+Om0qMsLfpCYbHOmisvYPqxdeDZWE7aDd9gXa/ynYYm4o4v1bSLpYdTIcv0OfUOKc++ANDLfvJrVtFza2PEojGSV9Cm5cUG3FW+LNd9f/nNhOdXQw8+zyTx5oJVpZT9qufEVm1AsuySGMwISdnHwg6jAMsF7G+gPPCp+VwZ23L5yLXWXjJMLbzRm3bxfPK0TP1bA7GYbtuDoCLnc2toJ9I/RImmpsc/J5Bo0S8AAAgAElEQVSzHNxsrD4rsReJGz9beIyWVGM5P1UXbfeLLNvhc8Bh3Ez33z/YzOTUII2LbsXp5WoZN7nOQqyLNs2s9O/meT1Lx8opV9eVmyfBLPC6/wXM1lTrOaPCo4iIiGSU0fFu9hz9RxKpcdYve5TSwhXY86FQP4fGultp2fo7EmNDVF15P0WX3+i8+L3ApMfGGXrtDUY+2IYvEqbooQeJX3sVlpsivsgliDQuZeDlV0gNDRHIz/c6nVk30dNGKLeIQCQGk15n44227p0EAzHK8ld4nYqIyLynwqOIiIhkjL6h4+xregq/L8TmVX9BXmyR1ynNKWNszux/j47tfyIUK2DZ/f+MWFkt6QwsOpp0mpEPPmbwldexJyaIX3MlBffchT8e8zo1yTKRxXUAJDo7M77waIxhrOsksQxesuFiJqeG6O0/Qu2iq7Nu+Q4RkW9DhUcRERHJCB09uzl88kVikVI2LH+MSDizCwBflZocp/Xd3zN8+jAFS9ZRc/33CIQzcyOdiaZj9L74AsmuLsJLGyj67gOEqrKryCzzR6CkGIBUb6/Hmcy+xHAfyfFh4osavE7FM+1ndmIwVJdf4XUqIvItGSxsp0t1yCVT4VFEREQWNGMMJ9q2crLjfYry6lnX+AiBQMTrtObUWM8pWt76e5Ljw1Rf8yAlq67NyKnVyd5e+l5+ifGDBwgUFlH6s8eJrluTkY9VFg5/bi5WKESyt8/rVGbdaOcJAGIV2Tni0RibjjO7KcqvJxophKmLrZorIiIqPIqIiMiCZdspDp14ga6+fSwq3cSKunuzauqbMYbeQx/R/smLBHLyaLzvr4iV1Xqd1oyzJycZ2PoWQ++/j+X3U3jX3eRffwMmrrey4j3LsggWF5PMghGPY90n8YdziBSWeZ2KJ/oGm5lKDLGs7k6vUxERWTD0bk1EREQWpGRqnL3HnmRwpJWG6luoW3R9Vo18Sycnadn2DIPH95BXs5LFN31/erOHDGKMYWz/fvpefJ700BDxyzZTdNfd59bRS7vZplZkFgVKSkh2d3udxqwb6zpJrKIOy8rOKYrtZ3YRDMQoLVzudSoiconSZM97Rq+p8CgiIiILzvjUALtOPsnE1ABrGh6iomSt1ynNqYnBLprf+y2TI71UXn435eu3ZFwhINnfR+9zzzFx5DChykWU/+hxIosXe52WyHkFS4oZP3QIY9tYvsx6Ln4uNTHK1GAPRcuzc23DqcQovQNHqK24Gp9PH6NFRJzSHVNERL5sFooXls/FN4pu+nfYrqtRcC5iLb+LKb1OY11ME7YCLvoPOHzJn43HBJigi9jQhXMdHD3NnqYnMNhsXPtTCvLrsC/Sph1yfl3ZYeex6Yiz6yUdcn5dpcIXju1v2snpj57BFwxT/52/JF7deNHHn3Kx5GU67CI26nzEoR1yFpcKJBje+j5Dr70FlkXhg/eRe+O1WH4/6a88UhNyMeIxdLGjNM0Xcr5mm9/vrE0Af8B5rM/nLNbdrc35sTLG4XWddnG/TrsZner8fpF22q6Lx+/qwJ4VKCmBdJrU4CDBoiLXvz8nLvFQjXWdBCBevuTPMW4Oq+082HL4dHF1Wt1cg+fJtfPMboyxWVS68dy/W7bz5zVuYo2LXJ3Gusr10o7VpbfpIlcXjJvjOhv9uzkGIhlEhUcRERFZMM4MHGF/89OEQ7msW/NjYjmlXqc0Z+xUkrZPXqDvyMfEK+qpu/lH+Ioza+fuieYmep7/I6nuM+SsX0Phdx8gUFjgdVoiFxUsnC42pgYG5m/h8RKNdp7A8gfIKa3xOpU5Z4xN+5mdFOQuJhbNntcdkUxlANvhl2xy6VR4FBERkQWh7cxnHG55hbzYIjYu+wH+nMwqul3I1EgfJ9/6eyb62ihbt4VFm+/C8vnJlP1UU8PD9L/8IqO7dxMoLqL0V39BzpqVXqcl4pgVmR4qbKamPM5k9oy0HSNesQSfP/s+QvYNHWdiqp+G6pu9TkVEZMHJvlcNERERWVCMMRxv38rJjvcpyW9k3dLv4feHMqbodjFDpw7R+u7vAUP9bT8lf/Ear1OaMSadZvjjbfS//hommaTg1tvIvWsLvlDQ69REXPGFptcSMMmkx5nMjuT4MJMDXRQ1XuZ1Kp5o695OKBinrEhfiIhkBgvbZOZ6vPORCo8iIiIyb9l2msMtL9LRu4eq0k2sqLsXn+ViHcoFzBibrl1v0rX7DaLFi1hyy08I5xV7ndaMmTx9it5nnibR0UF02XJKvvMgwdJS0g7XYhSZT6yzhUd7KuFxJrNjpO0YALnVyzzOZO5NTA7QO9jEkqobtKmMiMi3oDuniIiIzEup9BT7mv9A31Az9VU3Ub/oJncbBS1gqalxWt/9PcOnD1PUuJmaax/CF8iMUYB2IsHA668x9MH7+PPyKPvR48TWrs2acyuZ6dyIx0RmFh6H248RiMSIFi/yOpU513bmMywsqso2e52KiMiCpMKjiIiIzDtTiRF2H/tHRse7WbXkfqpKs2d633hfByff+g3JsUGqr/kuJSuvzpii3ERzEz1PP02qv4/cq66m+O578EWjXqclcsnOjXjMwMKjMYaR9ibiixqxrOyampi2k3T07KK0cAWRUJ7X6YjIDLLJjPdWC4EKjyIi880CKjBYPoe5uvmg4rRNcF6M8bnpf3Zyxe+sXcth3HSbLqYcB5zFGodxACbo/G2ECTpvdyTZy57DvyOZHGfdqscoKVzG+Sbf2iHnbabDzo+rq9iQs2sgHXYW19e8k1MfPY0/HKX+wX9KrLLuvI/9z+06atZxHEA6apzHOu1/YoL+515kdNt2AqUllP/zvyTS2IAB0l95hMbFVGsr7DzWF3K2Kmgg4Hz10GDQeWzA5yJXF7GzwbYd3q8s59cKuLi32M7btXzOYl1tXvotXoat4PSIZJOcgcKjm8PqItbx6frK5Tc50E1qfJi8qmVf+zfLzblycVk7bddKu+nffWx3z36SqQlqyi4//+8bFyfATf9pFwfLaaybXM3M34OMm/7dsDN0eY5ZOAciXlHhUUREROaNwZFT7D7+JD7Lz6a1f0FevMrrlOaEsdO0ffoiPYc+JLaogdo7f0wwJ9frtGbE2P599D33HOnRUfJu20L+Xbdp8xjJOJbPhxUMZuRU65H2s+s7VmXX+o7GGE6f2U4sWkphbp3X6YiILFgqPIqIiMi80DN4lH3HnyEczmfj6seJRgq9TmlOJMeHObH17xnrPknZ6hsov+E+LDejWeep1NAQfc89x/iB/YQWLaL0L39KuLba67REZo8xC2rWglNDpw4RKSgnFM+Oe/LnBkdbGRnvZOXiezNmuQsRmWYMpF0NhZdLocKjiIiIeK69dzeHW14iN6eS9Wt+TCgY8zqlOTHa3cLJrb8lNTVB3U2PUdSwiZR/Yb8RNrbNyKef0v/Ky5BKUXjPPeTfcCMmtrAfl8iFGNvGpFJYwZDXqcyodGKC0c7jlK290etU5lxr18cEA1EqS9Z7nYqIyIKWdYVHy7JqgL8HKphepeTXxpj/aFlWEfAUUAe0AN8zxgx4laeIiEg2MMbQ0vUhze1bKc5rYF3D9/AFc7xOa9YZY+g98jFtnzxPMFbA8vt/QU7Rwt8tNtnTQ88f/sDUyRNEli6l5KGHCZaUAGAuuFqlyMJmkkkArAxbRmC47SgYm/zaVV6nMqfGJnvpGTzKkkU34Pdl1jkVEZlrWVd4BFLAvzTG7LIsKxfYaVnWm8BPgLeNMf/Wsqx/Bfwr4H/zME8REZGMZozh2OnXOXXmUyqK1rC67jv4fP6ML0/Z6RSntz1L37Ht5FWvoO6mxwiEF3ax1dg2w9s+YuCVV7ACAUq+9wjxyy/X9MQZYowh1T9Ksm8EeyqJnUhhT6UwiRR2Ivnnn8/9WxJ/NESoooBQRQHhikKCxbnuNq8SV0wyBYAvmFlFqqFTh/CHc4iV1Xmdypw61fUJPstPTdkVXqciIrPENnpNnCtZV3g0xnQCnWd/HrEs6zBQBTwA3HQ27LfAu6jwKCIiMitsO83Blufp6j9AbdmVLKu5IyuKVImxIU6+/VvGelopX38LizbdieVmJ/V5KNnfT+9TTzF5vJnoihWUPPw9Avn5Xqe1INnJFInOARLtPUy19zF5uo+ptj4m2/qwJ5xtWmIF/FjhwHT8F3bRtQI+QmUFXypGTv+/gEhtCZaLHe3l6z7fzTqTplobO83w6cPk1axc8PcpNxLJMTr69lBRvI5wMO51OiIiC17WFR6/yLKsOmAj8ClQfrYoiTGm07Kssm/4nV8CvwSIsLBHJ4jIHPK6oGJl6AcGpx+EXGzU4WpEkJsNQJzGumnTRaHAOI0NOn9rYILO+7dDf243lZ5if/OT9A8201B7G4urrsdYFuZcrLN27ZDzc5UOO49NhZ0/X53GpiIWY10ttL76G+zEJLV3PE7B0vWkzxObDjvu3nFsOmIuHuSyTWMMwzs/ZuCPL4MFRY89TPzq6VGO9nnGrZqQs7GsVuR8R+X8AqGU49hg0Fm7oYDz/oMuYv2+Lz/+1MgEw/tOM3qoncm2PiZO9zPVNfilYmGoNJdoTTF5t68hWlNMuDQXXySILxTAFw6c/f/ZP0cC+IKBc/cwk7aZ6hlmqnOIyc5BJjoHmeocZLJriMFjHaRHJ8/1E8jPoeiGFRRvWUl8ZRW2z+Fz2/nhx7hYuzSddvGa6fT1bZZfhu3E2anWFxrx6PRp6PzpiuVmiPj/z959x8dxnoe+/73bF4veQYAgGjvFKlFU793qVLWsYrmnnZzk5iY5cdo5J4lP7omT3DjnWrIsy5ZkySq2ehcpierspNhBFKL3vtgy894/AEoQRQIzBMDZBZ7v58MPgd1n33lmdnax8+xbLMaq0e0PttZhRIbILF36+W2T2f605GraOFgWYxubPsE048zLWwfG+I9RE9z/5e3bOADaRrsWY/U0tDkSOw3jFGxs39Z+WT1fbOyTtnMOCjFLzdrCo1IqFXgG+C9a6z6rvSy01g8ADwCkq2x5lxFCCCFsiEYH2L73VwwMtrC48kbmFKx2OqVTomvvJzRufApvagbl1/0hwZzkns8x3tNDx1O/Ibx/P4GFVeR8/VY8ObNrxVu7jMEI/Xsa6NtRT9/2OoaqW0GD8roJFGcTqswn58LFBObmEJqbRbAkG3fw5HvPKbeLQGEmgcJMMlbN+8rqnfH+YSItPQw3dNH90UE6Xt9J2wtb8RVkkH3hUvKuWYUvN32yuz1rzMQ5HnvqP0O53KQXL3Q6lVPGNOM0tHxMTnoVqcHj9kMRQswAGoUpq1qfMrOy8KiU8jJSdHxMa/3s6M2tSqmi0d6ORUCbcxkKIYQQM094uJtte35BJNrP8kV3kps98y9mtWnQ8MkLtO95j9SSBZRe8Q08geRdsVtrzcCnn9L1/HNowyT7thtJPXfdrBqGaZUxFGHgswb6d9XTv7OOoUMtYGqUx0Xq4mKK7zqX9BXzCC0swnVM72H3ibqXTSFPWgBPWiGh+YXkXLQEYyhC9wcH6dy4h5anPqT1mY/IvngZhevXESjJmfZ8kp05HAbA5bfRZTmBaa3prdtNamElbl/A6XROmeb27URjA8ybd5bTqQghxIwx6wqPaqRr40PAXq31v4y563ngHuCfRv9/zoH0hBBCiBmpf7CF7XsewdQGq5feS0ZaqdMpTbv48CA1G35Jf/MhcldcQNHZX0NZHcKagOK9vXQ89RThfXsJVFSSe9ttuEukl+NYw41ddG/aR+9HBxk82Px5oTG0cA5zbltH2vJSUhcX4w4kXq84d4qf3EuXkXvpMoaa+mh99mM6Xt9J5xs7yTx7IYW3nEVoQZHTaSYso68PAHf6zJjfNNzVRKSvg4LTLnQ6lVPG1AZ1je+RFiomO63c6XSEEGLGmHWFR+Ac4BvALqXU9tHb/pKRguNvlFL3A/XALQ7lJ4QQQswo3f11bD/8BG63jzVL7yM1ZeYPXwt3NVP95s+JDfUy77zbyVievCujaq0Z2LKFrt/9Fm0YZN9wA+lnn4NyuY47l+NsM3ykk+5N++jetI9wzciAmZQFRRTeehZpy+eRuqgYV8D7lTkeE5m/MJPSH1xB0Z3n0vbcZtpf3ELP+/tJW1lG6Q8ulx6QxxHv7QXAkzEzhqd31+wA5SJj3mlOp3LKtHV8Rni4i+UL75gVi50JMduZ0z35r/jcrCs8aq03ceLppS85lbkIIYQQM11bzz521TxNIJDFqiX3EPBnOp3StOup3UXtu4/j9gZYcPXvEcqfZ2cNjoRiDA7S8fTTDO3aib+8nLzbbsebm+t0Wo4brm+n9/299H6wh+G6DgBCS0oo+c4lZJ2zCF/ezCg+eTNDFN9zAYW3rKP95W20PP0R+/7rL6n4ixtIXyU9wsYy+vpQfj+uQPIPS9Za01Ozg7SiSrzB2bGqs9YmtY3vEArmkZu9CCLJ80WBEEIkullXeBRCCCHEqdHYsY099S+QkTKH5Uu/gc+bvHMbWqG1Scv2N2ne9hopuaVUXHovvpTkHXYZPrCf9ieewBgcJOuaa8i44MJZPZfjcF0bPZv20PvBPiJHOkBB6tK5zP3eZWSevRBfbprTKU4bd4qfwvXryDpvEYf+9mkO/vBJSr9/OZlXnOF0agnD6O3Fkz4zCs7hrmYifR3kz6Jh1p09hxgcamNJ1c0o5cLystpCCCEmJIVHIYQQQkwprTW1re9zqOktctIrWVF+K8qb4nRa08qIRah77wl6aneSXbmG0nNuweVJvHn8rDBjMbpffpm+997Fm19Awf334y8ucTotR5jDUXre20Pna1sJH2gClyK0tJSca04nY91CUvJn9nl9LH9BJov+9zeo+dFz1P/kNQbru5jzzctQ7tlbkD4q3tuHe4YMs+6p2QFKkTlvmdOpnDJHmj7A70ujIHf27LMQs5kGWdX6FJLCoxBCJCs1PRd6ymXjj7DVHGy0aWteJYux9tq0cVxtLBRiuaeYx3qb2kas1Xa118b2vV/dJ61NDtS/zpGWjyjMOY0lFTfgcnkwfdaPq+G3Fmv4rT+vhm+aYv0QHeim9pWHCXc0UXTWteStuACtFMaYONPGQreGjdh40Nrqx4bF0Z/RpibanniMWFMLaRecQ+YNV+Py+U44l6P2W+8V5ApYG3Du8RkTB43y+2PWYz3W2vV74gwebqP15R10vPUZxlCUYGkOZd+9mJyLFuPL+qLnrtcdtbx9j8NzPMZN66/BmDHO+0Cah8V/dyN1P9tI87OfEmvspPzPb8AdGv8kM2xc4Cll473V4grg07ZO+Oh+Gb19+MvLP/99MknYWdRc2TitlDlxw1prumt2kFZYic+fNmHHv6nevt1YW20ax092YKiVrt5qKksuwa1dYJgobbFd08YBsNqm3VirOdg4VtMSa+dY2YkVQiQ8KTwKIYQQYkqYpsGemudo6djB3MJ1LCi9YnTI2sw12FpH7SsPY8ajlF99P+nzFjud0knRpknfu+/S9crLuFKC5P/gfoJLFzmd1illDEXofmcPXW9sZ2B/M8rrJuf8hRRcvZK0pcWy2MQYyu2i7LsX4y3Jp/4nr3Lwr55gwf+6C5d3dl5aaNMk3tdLaAYMtR7ubCbS107B0vOdTuWUqWv5EJfLS3H+6U6nIoQQM9Ls/HQghBBCiCllmDF2HXyKjp79VJZcTNmc82d8oabr8FZq338Sb0o6ldd9j0B2odMpnZR4Tzftv36C4epDpCxdSvY31uNOmy0LSmgG9zbQ8doOet7bixmJEZyXS9l3Lyb30qV404NOp5jQcq9YiTs1QM0/PEvDA29S+ntXOp2SI+Ld3WAYePOSf+Gl7gObQbnIKlvudCqnxHCkl5aOnRTnr8E3w6cEEUJ8maln9pfjiUQKj0IIIYSYlFg8zI4Dv6anv55FZddQUrDW6ZSmldYmzdtfp3nHG4SKKii74h48Sbry68C2rXQ++yzaMMi95VZS165Fp0zboNSEYUbjdG3YTdtznzBc14Er6CPrwqXkXr6CrKX5M75oPpWyzlnE4M3raHvmI0KLS8i5ePbNkRdrbQXAm1/gcCaTo02T7oPbyChZjCeQnO9pdtW3fARaM6/wbKdTEUKIGUsKj0IIIYQ4aZFoP9v2P8pguJ1lVespzJnZRQczHqV20xN01+4gp+oM5ly8Hpc7+T5OmcPDdDzzNIPbtuGfN4+8O+7EmzvSW0tP32x4jov3DdH+8lbaX9hMvGeIYHk+pX94NVnnL8Ed9AGglLW5KMUXiu+5kKH9jdT/x8ukVOQTLMt3OqVTKtY2WngsSO7C40DjQeJDfeScucbpVE6JWDxMY9tmCnKWEgxkOZ2OEOJU0koWlzmFku+TshBCCCESwtBwF9v2/ZJobJCVC+4kJ7PK6ZSmVXSol+q3H2aoo4Hi079GwdILMdzJ96E1Ul9P22OPEu/uJvPyK8i85BKU28ZCRUko0txF6wsf0vnmTnQkTvqaCvJvPJO0lWXSu3EKKLeL8v/7Rvb+wUMc/odnWfSv9+FOsbFKUpKLtrXhSk3FnZLcQ3W7D2zB5QuQUbLE6VROiYbWTzDMKPOKznU6FSGEmNGk8CiEEEII2/qHWthy+HG0Nli9+B4yUkucTmlaDXU2cOitn2NEw1RefC+ZpcnXs3NkAZl36Hr5ZdzpGRR9/wcEysudTmvaaK0J72ug43cf0v/xfpTbTfZFS8m/Ye2s65F3KnizUyn/8xs4+JePU/evL1H+FzfOmqJurLUVX5IPszZiEXoP7yJzwWpcHq/T6Uw7w4xR3/IxORlVpIWSc35eIYRIFlJ4FEKIk5VEF1TKZSNXO6sQW2zX1sWny872Lcba2X+39e0rG7FY7VFmZ/9t9FLTXmuxVuK6++vYfuhx3G4fq5Z/k1BKPuYEjzG81vfLtBhr+Kw/r4bfRmzgy7/31Oyi/q3HcQdSmH/DHxDMnYPxeay1duOBiWM+337Q+lBn02Knslisj45fPcHwnv0EVywj5+u34E5JwTzOM6f9Ez2bX3AFrQ9L9vmtxQZ8McttHi9WGybdHxyg+ZlPGNzXjDs1wJzbzqL0phX4skOjUZETtul3W98nj8v6sfIo67FWxW1MjO9i6nu1GscMU8taWcLc+y7gyEMb6XzhYwpvPOPz+6Jx69tXyuHh/nY2b2pibW2EVqxkorSVafF9yNb2rYeOdwr2Ve/CjEfJrlqDMk4c99U2rSdrp11X3GK7ho3tj8m1uW0bsfggZYXnHH8fTIsH1tb+23iy7MRqizloO21aj9WWtz9Nr2sbz4GtYzAdnN6++JwGTJLnWi7ZSeFRCCGEEJa1d+9n56HfEPBnsvK0ewkEMp1OadporWnbvoHmj18iJb+U8ivvw5uS7nRatg0dOED7k49jDIXJvu1GUs89a0b2RNOGSec7e2l67AOGG7vwF2Uy7weXkXv5MtwBHz6PzN14KhSuX0v/nkaO/Gwj6avKSCnLczqlaWX092OGw/iSfH7H7oNb8KVlEyosh2Gns5lepjaoa/mAjFAxWWllTqcjhBAznhQehRBCCGFJS+dudh9+hrSUQlYtuAt3IPmKcFZpw+DIe0/Tte8TMitXUnrR7Uk3/FAbBt2vvUrPxg14C/LI/71v4Sue43RaU04bJl3v7qPx8fcZPtJFsCyPqr+6gayz5tvrlSymhFKK8j++iu13/SdtL26j7PcvdzqlaRVtbgLAV1jkcCYnL9rfRX/DQQpWXzojv5Q4VkvnbsKRbhbMvXxW7K8QQjhNCo9CCCGEmFBj+xb21LxAZlopqxbciccdwMaouaQSjwxR+/ojDDQeomD1pRSecQXKzhQECSDW1UnbY48Rqa8jbe2ZZN52HS6/z+m0ppQ2NZ3v7KXxsfcZru8kWJZL1X+7nqxzFtqbXkJMOW96kKyz59O5cS+l37kYl2/mXnJE6o+AUvhLknee264DmwFN9qK1Tqcy7UxtUNP8LqnBAvIyFzmdjhDCQbKq9akzcz8FCCGEEGJK1Ld8yP76V8nJqGJF1W243TOrgDVWpL+Tg2//jGhfJ6UX3U72wjMmflCCGdixnfannwIg/+t3kbpyFYaNeRsTnTY1vR/spfWJ94jUtxMszaHyL68j+9xFUnBMILmXLqNr4156Pqkm+9yFTqczbSJH6vHm5+MK2JjINYFobdK1/1NS51ThT8t2Op1p19q5m6HhTpZX3iq9HYUQ4hSRwqMQQgghjktrTU3Tu1Q3vk1+1mJOq1yPyzVzPzoMtNVyaMPDaG1Qcc13SCuucjolW8xYjM7nfkf/xx/hLy0l/+t34c3OcTqtKaNNTd9H+2j99XsM17XhL8mh8s+vJfu8RTKkOgFlrCrDmxWi463PZmzhUWtN5Eg9wUWLnU7lpA00Hyba30Xh6Vc6ncq009rkcPO7pAbzyc+S3o5CCHGqzNyrByGEEEKcNK01B4+8Tl3LBxTlLGdJxQ241NSvipsoumq2UbPpCXyhDMqv+RaBzHynU7Il1tFB668eIdrURMaFF5F95VUoG6ueJzKtNf2bD9Hy6EaGa1rxF+cw909uIPPcJQSDM3XAf/JTbhc5Fy+l9XebifUOQWDm9ZQ2unswBgbwz53rdConrWv/p7h8ATLLT3M6lWnX0jW2t6N8WSHEbKaRodankhQehRBCCPElWpvsqX2BpvatzM1fy8J5V83YizStNc273qJp2yuk5pdTedG9qMxUp9OyZXDXTtp+8yRKKQruu5/QkiVOpzRlwodbaH74TQZ21OIrymLuH19P5vlLx/RwlMJjIsu9dBktz3xC58a9pF95ltPpTLlI3REAAnNLHc7k5BjRYXoP7ySrajUu78wrDI+ltUlNk/R2FEIIJ0jhUQghjuX0nD8zscDjsrFPNnppWR5eaafn13TFeqzFaq/1P832Yq1tP+42+ezA07R1fkZZyQVUlF6CVgp9nFjTZ33/TcnvyT4AACAASURBVJ/1c8DwW3sNWo0bif3qbaYR58h7T9F1YDNZVaspveA2XG4PcTvtWpzWzTzO9k/YpsVYHY/T8fKL9G94D9+8ueR98y48OdkYfHU+Rx2wPsejKxi3HOsPxCzHBnzWYlN8MSId/Rz5xXu0v7kbT1qQsh9cQsE1K3F53MAX+YW8Ueu5eqztl89lff89LhvH9bivouMzsXYOxk3rryuXsrH9uLXtT9RmakUuKZX5dL61m8yr1lne/rT8Gba++5Zjo7X14HbjKyjiOC+7r1AWTxercZON7T20AzMeJWfBGV+6T5nWD5bLRu3fTrtWY5VhLa61YxeDwx0sL1+PMhUTPclW28W08QRoGyehjVhtNdbO9m08V7aOgUWW92maaDv77zSHj5UQVkjhUQghhBAAGGaMnfueorP7IPPLrqS0+BynU5o28cgQNa//goHmagrXXE7h6suTaqGBeE83rY/+amTV6gvOIevGr6E8yf+xzhiKUP/4+zQ/+yna1MxZv5bi29fhSU3OhTsE5F2ylLoHNhBt68GXn+l0OlMqUluPf05x0r72Ovd/jD8jj5T8eU6nMq1MbVBzZAOhQB75mTOnR7gQYnJkqPWpk5x/JYUQQggxpQwjyvZDT9LVX8OiyuspLjzd6ZSmTaSvk+pXR1aunnfRnWTPX+N0SrYM7dtL2xOPow2D3G/eRWj1CqdTmjQzZtD56lZan9iE0TdE7kWLmXvveQQKZ1ahajZKXVoMQLi6ZUYVHs1olEhtPRnnnud0Kicl3NnEYFsdxWdel1RfupyMlrYdDIU7WFEhK1kLIYQTpPAohBBCzHJxI8K2g4/TM3CEJfNvpCh/ldMpTZuh9iNUv/oztGFQefV3SZtT6XRKlmnDoPuN1+l5+018RUXk33UP7nnJvWq11pre9/fR/MsNRJu7SV0+j/LvXEDq/EKnUxNTJKUsD1yK8OEWMs6aOXPrRQ7XgmEQrKhyOpWT0rHvQ5TbQ/aCM5xOZVqZZpyaI2+TnlpMXsbMOf+EECKZSOFRCCGEmMVi8WG2HXyMvsFGTqu4ifz8lU6nNG166z6j9q1H8QRTqfzatwhkFTidkmXx/j7aHnuU4cPVpJ2xlpwbbsLl9R53PsdkMbivkaafvc7Q/iYC8/Io/5vbSFtTSchvfY5FkfjcAS/BkmzCh1udTmVKDR+oBpeLQHm506nYZsQidB3cQlbFSjyBFKfTmVaNrZ8yHO1lcdWN0ttRCPE5jZKh1qeQFB6FEEKIWSoaH2LrgUcZCLeyvPIW8rMWJ3EZa3ztn22i4YPfkZJbQsUV38Sbku50SpaFD1fT9tivMIeHybv1dtJOT+4eSvHeQZof2UDXGzvwZKdS8ofXkH3xcuuLRYmkE6oqoHfHEafTmFLDBw/hnzcXlz/55h/trt6GGYuQu3jmrTQ+lmFEqWl4h6z0crIyKiBiYyUcIYQQU0YKj0IIIcQsFI0NsuXArxga7mBF5e3kZc53OqVpobVJw9aXaNnzDumlSyi75C7cXhtLTDtIa03ve+/S9fKLeLNzKPrWd/EVFTmd1knThknnq1tp+dU7GMNR8m5cR8Ht5+JOSY7nQ5y8lMp8Ot7eQ7x3EE9GyOl0Js0cHiZa30D6pRc6nYptWms69nxAILtoxi8q09i6mVhskIqFl0hvRyGEcJAUHoUQ4lRQU9+TR7lsfIi2s30b7Vr+IG/jA7+tiwOr++Vy22jSxrHyWG9XW4210SYe67lq7xftRqL9bD3wCOFIDysW3UVOZiV69D7TZ337ps/69g2/nVhr50B8gjgzHqPunV/TU7ODnNPOYc75N4LLxUR9XgwbHZgMizWzeEBPHHS0zaCJGYnS+fhTDG3dTnD5UnLvuh1XMPCVodU6YK2PqitoffiyPxCzHBv0Ry3FDe5toPqnrzJ4qI2MlaVU/v7FhObljt4b+VJsitdamwApHuuxAbe1Y+B3WT9WLmX9ebXD6vCviLL+Ud7OkDK3svY+YLXF1KqRaQ2GD7eQtrrCch5TysZTpczx9yxyqBZMk2BVFXZOAWWxS7my0THPbuxQ+xHCnY3MPetmXCfYT5edNk3rB0AZNmIttquM4x9Uw4xR17hppLdjaC4YJsq00affYuyJtn+CpKZ8+yPtWnzCbDxX08LOPiUTPUP3axYwLf8VE5MlhUchhBBiFglHeti65xdEowOsWvwNstLLnE5pWsSHBzn8xs8ZbK1lztpryVl7YdL0eIm1d9D+s0eINbeSee1VpF96ob2CeAKJdQ/Q/MhGut7ciS83lUV/9TVyz1+YNM+FmBopFfkAhGtanSs8TqHhg9XgduMvL2PCbzISTMe+D3B5fGRXrnY6lWnV1LaVaKyfZZU3O52KEELMelNWeFRKpQKrgfnAPCAXCAFeYAjoB44AdcBOrfXBqdq2EEIIISY2FO5ky55fYBgRVi+5h4y0uU6nNC0i/V1Uv/oA0YFuyi6+m6yKFRhJUuga2ruH1icfQykX+d+/n+DihU6ndFJ03KD9xS20PP4eOhoj/+Z1VN2zFnfQ53RqwgHe9CCuFD+xrn6nU5kS4T17CVSU4fL5IOx0NtbFwgN0Hd5GzvwzcPuSb25KqwwzRm3Te2Smlc7YL9eEEJOk7Y0EEJNz0oVHpZQbuAS4CrgMWIT1ERcopfqATcBrwIta69qTzUUIIYQQ4xsYamPrnl+gtcnqpfeRHkreuQLHM9TRQPVrP0Mbcaqu+i6phcnRu0qbJj1vvUH3m6/jKykm71t348nJdjqtk9K/vYaGn75O5EgnaWsqKf7OpQSKc3BbHJYtZiZ3ig9jMDJxYIKLdXYSa2kldd2ZTqdiW8f+j9BGnLwl5zmdyrRqbN1MJNbPsqr10rtaCCESgO3Co1JqBfBt4FYg5+jNx4SNN4HE0dgM4OrRf/+mlPoQeAR4VGudRN8dCiGEEImtb6iZLdUjvejWLP0mqSn5Tqc0Lfoa9lPz1iO4fUGqrv4ewaxCp1OyxAiHaX/iMYb27SV19elkff0mXD6v02nZFu8dpPGBN+l+5zN8RVmU//UtpJ9RJRf+AgB3SgBzKPkLj+HP9gKQsmyJw5nYYxpxOva+T1rxAoKZBU6nM20MI0pt03tkp1dIb0chhEgQlguPSqmvAX8CnH/0ptH/j1dktPsJUwFnjf77kVLqQeBftNatNtsRQgghxBg9gw1srX4MtzvAmiX3khLMmfhBSajr4Gbq3n2SQFYBlVd8G18ow+mULIk2N9Pyq4eJd3eTc8NNpK87G9Pn8AIANmmt6X57N40PvYk5FKHgjnMpuOVsXD6ZSlx8wRXyY8yAwuPQZ3vw5OfhzcudODiB9FbvIBbuo3TJrU6nMq2OtH5MND5IRcnFTqcihEhgGhlqfSpN+IlQKXU58N+B04/exFeLjZ8BO4HdjMzh2Aj0MDLrSRwIAilAETAHWAicxsickJnHtJsJ/Cnwe0qp/wT+SWvddXK7J4QQQsxeXf21bDv8BD5PiNXL7iPoz5z4QUlGa03bzg00ffoSqUVVVFx2L25f0Om0LBnYvo32p3+DKxBgznd/QKCsfPSe5Ck8Rlq6afjJq/RvqyG0uIS5f3AVgdI8p9MSCcgd9BHvS+5BTebwMMMHq0k//1ynU7GtY8d7+NPzSC9JznljrYjHh6lrfp+cjPlkztA5jIUQIhmdsPColJoP/Ctw5dGbxtzdDjwHvAS8p7XuPpmNq5GxN8uBi4EbgHNGt6MYKVT+CXC/Uuqvgf+jtaxVL4Q4SUk01E+5nM3V1rBIqyvt2lmR187+u621qyzGAeCx0UvL7Z7yWO213qbpO3FsZ88hdlT/mqA/i9WL78abmo2VP6Kmz/qxMvzWnys7sXGLsXGvpmnT7+jctYnM+asoueQOcHuOu8hs3MY6CobfRmzAWpHQCH4Rp02Tnhdepu+tjfgrysi97248GekYowVHHbD+cccVjFuK8wdiltsMBSaei1EbJm2/+4Tmx95FuV1U/cElFF27YvT96/i92tJ81nq7pXqs94oLeazPG+l3WTtWHpfzyxRHTWvvQ6aNQUYel/X3Fpeydl5bjQNwhwJEWnosxWobtXdtWjwGdnq2nOAlGN53EAyD4JLFn8coG1cnyuKpZecUVObEB2uwpY5wWz2lZ96I25z4PV4Z1p8AZe1lBYArbqdda7Fj9/9Iy0fE4mEqiy88/nGxsV8YFp9YWyer9Vht2jixrLZr41JaT8N+2WrTwnn9RcM29stOu0KIKTPep5rdo/cf/SsdAX4H/Ax4W9t65zi+0TZ2jP77sVIqH7gHuI8vFqvJAv4dSAP+abLbFEIIIWa6tq597Dr0G0LBPFYvuhufN2Sp6JhMzHiM+nd+TW/1DnJXXkjR2V9DKRsFZoeYQ2Haf/kYw3v2kXruWWTfdD3KTrE7AQxVt1D3by8Trm4h+6wK5v/hpfjz0pxOSyQ4V4o/6ed4DO/ZiwoECJSXTxycQDp2vYvLFyCn8gynU5k2sXiYupYPyMtcSEao2Ol0hBBJQIZanzrjfdI9Oqt5F/CfwL9rrTumMxmtdRvwz8A/jw7x/jNGekMC+KZz20IIIcRM0da9h7SUQlYt+gZeT3IMO7bDiIapfvNhBlqqKTrnevJWXuB0SpbEWttoe/Bh4p1dZN+2nrRz1jmdki3GcJTmR9+j7blP8KSnUP7nN1JySbksHiMscfk9mFEb3eMSjDZNhj7bQ3DhApSd3u4Oi/Z30VO9k7zl5+H22ujOnWRqm98nbkSoLL7I6VSEEEIcY7zCYx8jRcB/1VoPnqJ8Pqe1fh14XSl1JvCPp3r7QgghRLJaUnE9phnH4555F5mxoT4Ovf4g4e4W5l72dbIWrHE6JUvCe/bS/shjKLebgt//HoHK5Oox1bu5miM/eZVoWy85V6yk+L6L8KQFUSq5e7CJU8eMxHAFkm+19qOGqw9jDgwQWrnC6VRsad/xDkopcpefP3FwkhqO9FLf+hFFOctJSyl0Oh0hhBDHGK/wWJEIi7porT8GLlZKZTudixBCCJEMXMqNK4l65FgV6evg4GsPEA/3U3nZ/YTmL3Y6JUv6tnxK+9NP4p1TRP6378OTneV0SpYZg8M0PPgmnW/sJDA3hwU/uovUZaVOpyWSkBmO4fInb+FxaMcOlM9LcPEip1OxLB4eoHPvx2TOX40vNROGnM5oehxqfBvQVBbLStZCCGs0SoZan0InLDwmQtFxrETLRwghhHCC1hrDjOFxz64ZSIY6Gzn0+oNo02D+ld8jlD+PZBm0GSwrJ3XdWrJuug6XP3l6ofZtq6Hu314i1tlPwa1nU3Tnubi8yTUfpUgc5nAUVzA537e0aTK4YxfBxYuT6jXcsft9dDxG/soLnU5l2vQPtdDcuYN5hWcT9Gc6nY4QQojjkE+PQgghRJIwtcHuuufQaNKDhcwrTY65DServ/kQ1W8+jNsXYMFV3yeQWeB0SrZ4c3LJueMWp9OwzAhHafz523S8vBV/STYL//luQotksQYxOeZwFHcgOQuPyTjM2oxF6di9ifR5Swhkz9zhxwePvIHHHaC86DynUxFCCHECUngUQgghkoCpDXbWPktaMJ+SnNPZXf8cueF2QsE8p1ObVj21u6h551H8aTlUXfEdfKHE6tESPlyNjsdIWbAIbZooV+KvrD2eoT111Pzkt0Rbe8i/cS1zvnFBUg+PFYnDHI7hTg04ncZJGdo+Osx6SXJM7wDQfWALxvAQeTO4t2Nn9yE6+6pZMPfyGbmQmhBiemkZan3KSOFRCCFOlnK4wGBn+y4bf1jtFE4srmar3DbatDM3odVYW21az1V7rLerLQ5R1d7jtzkw1E5qqJCK4gsBcHt8NHZsw+MJUF468Sqeps/afhkW40ZirZ9XtmJHRzJ27vmIhnefJiW/lPKrv4U7kIIxNs5GDcOwMTrSCGhLcYN7P6PtxWdRbg9F//UPcaekAMd/rPablrfvClofRO4PxCzFhQLRce83IzGafvkObc99QqAwg5X/ciuZy0sAY/Tf8aV5rS8uk+4LW4pL9Yyf61hBl/VYv8vacXUpa8+/XTE99fOuGjYumiLK+sf+6TgGRjiKNzfd0oWerYtBq7HWX4KoMbHaMBjcuYuUxUtwe3xfaUed+OXxVRYPq502XceJ1dqkfcc7BHNLSMuv+Lw9ZVhLQNk5VhbbHGnXxnk1QazWJodqXyXgy2BuzhlgIQ9l2tgxq7F22jRsPLFTeKxOrs1pOFbCHj09f4uEcMIpLzwqpbKAS4AyIArsB97UWtv5sy2EEELMGlqbpKUUfr5aZ0P7FoajvZTNu5hd+54glFJAfu4Sh7OcOlpr2ra9TcvHL5NWuoh5l9+N25tY86p1v/MWQ4cPkX//fQzvP0CspRV3RTlaa5TFgnyiGNzXSO2PXyDS0EXuNatZ9P2z8STpXHwicZnhKK4kHGr9+TDrFcudTsWyvvp9RHrbmXfRnUn3fmRVS/sOBgZbWFZ2Iy6X9KURQohENql3aaVUIbB2zE2vaq1P+NWzUurPgB8CKcfc1aKU+p7W+oXJ5COEEELMJKZpUN34NnEjQkH2EoL+bIL+TObkrKQ4dxU64KOk6EwMw3qvs0Sntab5wxdo3/EOmfNXUXrRHagEXKHbnZZO4e3fQOcEGfj4U8x9+wlUlCfVRb4ZjdP86Lu0/vZjvDlpVP2PO0hfVY4nMOx0amKG0YZJrHsAT3aq06nYNrBlCyoQILgkeb7cad3xNt5QJlmVK51OZVoYRpTq+jdICxVTmHWa0+kIIZKUSfJ8Zkt2kx0n+KfAb0f//d0ERccfAv8IhOBLz7ACioDfKqVun2Q+QgghxIzxWc3vUC432enldPXV0Ny5g/6hZlwuN0q5aGrdQnvnHjIzypxOdUpo06Dug9/QvuMdcpadQ+kldyZU0VHH40SamwBIX30G7uDI96gZF1/A8MFDRJuanUzPlqGaNvb90c9pfeYjci5bwZL//Dbpq8qdTkvMUJGOfjBMfAWJNUfrRMxIhMFduwitWIHLmxxznQ60HGawpYb8FReiXInz/jmV6hrfIxLtZ0H5VUn1ZY8QQsxWk+2Xfh0jhUMNPHCiIKXUfOCvR3/Vo48Z+1dCM1IEfVAp9Z7WunGSeQkhhBBJLxTMZU7uSgK+DFICOXT319LdV0fQn00k2k9L2w4Wz7+RYCDL6VQnzTTiHH73UXrqd1Gw5jIKzrgioS4oY12dtP32KVzBIJ5QKqGlpxGsqAIU7rQ0ggsXYPT3A0UJvciMNjXtz39K48MbcKcFqPy728g4vdLptMQMF2npBcCXn1yFx6Hdn6EjEdJOX+N0Kpa1bn8bTyBE7qIznU5lWkSi/dQ1vU9+zjIy0+fBsPU5cYUQQjjjpD8VK6XygKoxN704Tvj/BRz9yk0BrwPfAK4HnuKL4mUK8Jcnm5MQQggxk2itOVD/GgBpKYVkhEroGagnFh8iFMxl+eKvkxLMcTjLyTNiEQ6+9TN66ncx94zrKVx7ZUIVHQEG9+8lZf4Ciu68B9+cYobrahj8bBcAyuMBl4veDe+M/J6gRcdY9wDVf/skDQ++Sdrqchb/5FtSdBSnxHBrH0DS9Xgc2LwZT3Y2/rIyp1OxJNzZRF/9XvKWnYfLk3zzaVpRc2QjWhtUll7qdCpCiCSmNZhaJcy/mW4yPR6Xjfm5RWt95HhBSikvsJ4v1nF7HrhR68+XaXpBKdULfGv099uUUn8oi80IIYSYbbQ22Vf3CkF/BkF/FpXFF7Kn9nl2HPoNK6puJSO1BL8vnb7BJoL+LDyexFpw5WTEI0McfPNBBjuPUHbObeRWrcXaOs2nmGmiPCNDLdNOW8nggX1E29vw9PTiycwg49KL8Wzf4XCSJ9b7ySHq/vVFjHCUud+/gtxrVidccVfMXJGWHlDgzctwOhXL4r29hA8eJPPSSxP2y4Rjte54G5fXT+6Sc5xOZVoMhTtpatvMnILTZ8SXbkIIMVtMpvBYNvq/BvaME7cOyBwT+/djio5H/RD4JiM9MLOA04Dtk8hNCDEbJNFFs3LZyFVZvMCx0aatAoONWMvz71ndJwAbc1JZvhj0WG9T24i11a53/FitNXvqX8QXzMAXzKR7oIGuwXoWzb+RvdXPsbPmGULBPHoGjzC35DxMrxvTZ/24Gn5rsYbf+vNvL/art8UGezn02gNEetspv+weMstOw7DR7vHaPGFs4NiPHhPHDmzZijszA19+PsHVy2j56YN4SgsJVlXhW1jGwN4deIe6cBWlAZByzgpMzOO2qYLWhwP6AtZjQ4ETTq8NgBmJ0fjzt2l/cQuhilyW/Lf1hMpygRMvSJThC1vefobP+kI0GV5r7aa4xt+nL8W6rcd61dR/p21o66/BmJ76+faipvWP8i5l/TVgldVeGuHmPjzZ6eDxYFpIQ5vW31u0YS1W2WhTGYrBzdtAa9JWnY4aZxvj3Xcsl8VT0M6pejQ20tdJd/V28peej9ebAsdpw+r2XYb1c0VZeUKPxsZtxBpffS89XP8WSrmpKDr/8/uVefz33OM6Tpsn3L7V/bLRJl+5BB4v1k671mK/egk+brD1WKtsnCu29n86OL19IWaYyRQex04o1TVO3IVjfj6std56bIDWulUptRM4uvTaMqTwKIQQYhZRSuFx+cnPWUZaqJCcrAXUHNnA4fq3WFx5PZ3dB9HapDBvBQF/utPpTlqkr5NDL/1/xIcHqbzq26QVz3c6pS/pev4FYp1dePNyGdyyldS1a8m9/TY6nniSgvu/iTcvF092NvGOTvyV85xO97jCtW3U/K/nGK5rp/imVVR8+zzcvslO7y2EfZGW3qQaZq21pn/rZvzzyvDm5jqdjiWtuzeilIv8ZRc4ncq06B9sprVzF2VzzsPvS3M6HSHEDKBnwRDnRDGZT5/BMT+P93X30b7+GnhtnLhqvig8JsdfeCGEEGIKHO2F4Pel0zfQQFqoEI/bT+mcs6lr3ER4uJucrMQqzE1GuKuFQy//FG3Eqbrme4TyS51O6Ut0PE68v5/c9TfhTksjXF1Nz2uvkbP+ZjIvv4yuF17EFQwQ7+kl7bqLnE73K7SpaX9hdAGZ0MgCMnPPLXY6LTFLaa0JN3SSesZCp1OxLHLkCLGWFnJvWu90KpZEB3vpPPAJ2VWn4wslz3B2q7TWHKx/Ha8nyLyimTmMXAghZrLJFB7HFhuP2/VCKeUGzhpz07vjtDd2/E3KJPISQgghksrRofA5aZUcbHkbj9tPbtYi/L50TDPO0HDnjFi5GmCwrZ7qVx5EuT3Mv/b3CGYXOp3Sl2jTRJsaT1YWsfb2kRWrKysx+wfoe/c9cm68gUBFBebQEL45RRgpiTUcK9Y1QN2PX6Rv62HS11Yx74+uwZsZYvzviIWYPtH2fuK9YYIVifVaH0//xx+ifD5SV65yOhVLWne+jTZNClde4nQq06Kz5yBdfYdZMO8qvJ7gxA8QQogJzY5FXRLFZAqP3WN+PtGSiGcBY/vCvz9Oe2PjTjzxkBBCCDEDaK3pG2pCa5PM1LkApKUUUD73Io40f8BQuANTGwyFOwgFZ8ZAgP6mag6/9hCeQIiqa76HPz3xFgdQLhcunxdPRgb9n2zGW1CAOxQiUFnBcF0dOh7Hk5kBmYnXq6hvew21//w8RjjC3B9cQe7VsoCMcN7goVYAgpVFDmdijRkeZmD7dlJXrcIVCDidzoSig7107P+InPmn409LvPfUyTJNgwP1r5ESyKEk/wyn0xFCCHESJrNE297R/xWwRCk15zgxt475uVZr3ThOewVjfh5vzkghhBAiqWmt2XroUZo6t1PdvIEDDa9/Ptw6I62EytJLycwox+sJsbjqBgL+5Jkb7UR66/dQ/coD+FIzmX/d7ydU0XHgs50MHtjH8JG6z29LP+dsXD4fvRs2MlxbR7yrm2hDI/G+PnsT9J8C2jBpeuxdDv3Vr/GkB1n04/vIu2aNFB1FQhg81AouRaC8YOLgBDC4eRs6FiXtzHVOp2JJ684NI70dV1zqdCrTorFtM0PDHcwvvRyXjcXnhBBiplFKXamU2q+UOqSU+vPj3O9XSj05ev/HSqmy0dtzlFIblFIDSqn/OOYxa5RSu0Yf8+9qmj48TqbwuB0YZGTuRgX8z7F3KqXKgXtH79fAqydqSCnlApaPualmEnkJIYQQCa1vqJmgP5PFpdewqvLrDEd7Odj45uf3u91+stLLKJ1zFsFAtoOZTo3O2u0cfu1hgtlFzL/2Bwk1B1nvpx/S/d4G4n09tD7za8K1hz+/L/v6a/FkZjK0Zw9dL79CxsUX4c3OTqiCXqxrgEM//DUtj28i++LTWPjjewnOy3M6LSE+N3ioleDcHFx+r9OpTEhrTf+mj/DNmYO/ZK7T6UwoNtBLx/4PR3o7JtCXOVMlFg9zuHEjWenl5GYmzxyhQojkoLVKmH8TGZ3G8CfAVcAS4A6l1JJjwu4HurXWVcCPgR+N3j4M/BD40+M0/X+A7wDzR/9deRKHckInPdRaax1WSj0D3M1IYfFupVQx8FsgD/gekHo0HHhknOZW8OV5HfecbF5CiCSXCBf0ytp3MsrlbK62ih8uG98z2Yq1mIPbepvKRiwei3/G3DZ6SdiI1V47sV/slzeYSl+4he7hBjLTSlk6/2Z2HHyCA81vUlpxEc1dOyiesxa32zdum4bP+rEyfdaeK8NinNXYjn0fUf/+04TmlFN27bdQvgDGRO1aHN1oNe5EsdowiHS3kXPLzfiLS1CZITo3vkbWnOvxFhUAitBlI1NVGwODuFNDGHx5TkcVmGhvRngDccu5pgSszTjTv7OW/f/Pc8QHIyz40ysovHIZIx+5vvr4DF/4K7edSJaN2EzvkOXYdI+1eSZT3NZn3Ako68fVpb547qLDBu21YVqqB4lHTbx+Fx6/C2/Ajdfnwhtw4fW7Pr/d53fjDbrwBb78mo9pVQTixgAAIABJREFU6+8BQ4bfcqxXWTuvXDjb+9bK1gcOtZKxcp6tubRszbtlWow1Jo6L1jcQa2gi94abcWllaQctPlUjsRZPV2VYe17bt4zM7Thn2aW4LLRttV0bLytccevnoDJtxBomtQ3vEosPsaDkMlzm0b4sx8TFbcyza05DrI0e8LZ6y9uJtXpcp2P/sblf00DbOK8cl2AjJkRSWQsc0lofBlBKPQFcz5drZ9cDfzv689PAfyillNZ6ENiklKoa26BSqghI11p/OPr7L4EbgFemOvnJzPEI8DfALUCAkV6Pl4z+Y/T3o6+sV7XWn4zTzs1jfq7RWrdNMi8hhBAiYQX9mZQWrqOz9xAul4f00ByWz7+NA3WvApo5RWdMWHRMBq27NtD4yYuklyym9Np7cHkSa5+U240nM5P+Dz/Ce911hJadhtHTS9fjz5L/X7/H8O69uFJS8FeW4U4NOZ3u57Rh0vqb92n59SaCJVmc9qP1hMpnxjygUy3cH6f18CCthwdpOzxIS/UgrYeH6DwSPqnrv6L5IZZdlMOyi3IpW3HctRXFGNGuAWKdA4SqkmOY9cAHH6F83qRYVCY22Efnng/JqVwzI+d2DEe6qW/7mDk5K0lPSY75QYUQYhoVA0fG/N4AnHmiGK11XCnVC+QAHeO02XBMm8VTku0xJlV41FrXKaXuAH4DHHs1cXQIdi3wzRO1MTrM+k6+KFK+PZmchBBCiGSQkzGfeHyY5o4dxOJhTDNGeLgbl8uL2534QxLHo7WmedvrtGx7nazylcy74A5MT2LuU2j5cvo//oTB7dtIW3sm6eeeS6SzmcjBw7hzsvHNSayVeGPdA9T97+cZ2F5L1oVLWfInF+MOJlZB1ylG3KR2ey+fvdNJ/a5e2g4P0dcR/fx+t1eRX5bC3CVpnH5tAYWVIQorU/CnuIlFTGLDJrGoSWzYIB4xiY7eFo+YxCIm4f44Bz7u5q2HjvDGA/WULE7lwvvLWH55Pm7PZGYvmrkGD7QAEJqf+IVHMxxmcPM2UlatxB1I/JWT27a9hTZNik6bmXM7HjjyBkq5qCy+yOlUhBAzkMZm7/rpl6uU2jzm9we01g+M+f14yR77FaqVmMnEn7TJ9nhEa/28UmoV8PeMjDc/OmS6k5GC5N9orU9UYQW4CSgb8/sLk81JCCGESHQ+bwpFeSvp6a+jsXUzbo+fBfOumBFFx6ZPX6J11wZy5p9B6bm3olwubAzwOqXcGZkEyssJHzpIz1tvkrZuHbGWVtxpqaPDrRNH/85a6v75OYzBCHP/4GqyL1+BOxid+IEzWLg/zr5NnXy2sYM973Yw1BvH5VHMXZLGovNyKKgIkV+WQmFliNy5vkkXCK/8QRlDfTG2v9rO2784wqN/upvs4gAX3DuPM26cgz9FFr8Yq2/XEZTXTeqCQgYS9U1g1MBHn6KjUdLOP8fpVCYU7eui87MPyV60lkD6zOvt3Nl9kLaevVTOuYiAT3oWCyFmhQ6t9enj3N8AjJ18uARoOkFMg1LKA2Qw/sLNDaPtjNfmlJh04RFAa70XuGV0BZyc0dvGKzaOtRMY+1XWh1ORkxBCCJHoPG4/uZkLyEovB8Dt8iZsgc4KrU0aPnqO9j2byF10NnPPvhFlcc5UpyilCFRV4cnNpee11+h64UVSVi9PqKLj2KHV/jnZVP73OwiW5TudlmO6GobYu7GdvRvaqNncjRHXpGR4WHJ+LksvzGXRuTkE0776EXfsHI+TkZLu5exb57BufRE73u7i7Yfq+O3/3M9rPznMuXeWcM6dc0nNll6oAH076kldPGdkYRnrU4eecto06X93E/6KcvxzS8D61KWOaNn8GihFwemXOZ3KlDPNOAcOv0TQn01Z4dlOpyOEEIniU2D+6CLOjcDtjIwcHut54B5Gamrrgbf1OJOwaq2blVL9Sql1wMeMrN/y/05H8lNSeDxqdKesFhyPPuYAcGAq8xBCCCGSiduV3L0cYeTCvf6DZ+jc/xH5yy6geO21CbX683iUy4U3O5vc225DR6PonMR5PuJ9Q9T983P0b6sh66JllPzgylk5tLppXx87X2lh78Z2Wg8NAJBfEeKCe0pZdmEuZSszcLlP7fnmcimWXZLPskvyqdnaw4aHann9P2vY8PM61t40h6v/uIpAaEo/aieV+MAwg9VtlHw98YtH4d17iHd2kXndNU6nMqHhrha6D2whb/n5+FIzE75Iald94/sMhTtYNf9OXK7Z+/oRQkwznVxr/YzO2fj7wGuAG/i51vozpdTfA5u11s8DDwG/UkodYqSn4+1HH6+UqgXSAZ9S6gbgcq31HuD7wC+AICOLykz5wjIwicLj6PDqb4y56X9rrRsnn5IQQggx83T11hDwZ5ASyHY6lSmnTYO6d5+kq3oLBSsuYc6aqxK26KhNEx2N4gp8dZlr5XKhAgGMCdfdPjWGqluo/YdniHUOUPL7V5FzxcqEPa7TITIYZ/tLzXz6TAMNu/tweRRlq7O45s8WsvjCPHLnhWytaj2dyldnUr56Ja3Vg2x8uI4Pn2yk+cAA3/7pKnzB2Tn8un93A5ia9OVzJw52WP+7m3BnZZKyfJnTqUyo5ZNXcXl85K+62OlUptxwpIeaIxvJzV5MbsZ8p9MRQoiEorV+GXj5mNv+eszPw4ws/ny8x5ad4PbNwLT/8ZvM10gXAv+Fkcknm4A/mYqEhBBCiJmms7+G7YefIDujgpULv+50OlPKNOLUbnyMntqdFK25iqKVibvQgRmL0f74Yxj9/RR9/wcod+IWhLre2sWRn7yCJy1I1Y/uIrRwWhYZTDhaa+p39vDJ0w3sfLmFaNigYH4q1/7FIlZ+rYhQZmL39iyoDHHb/1jC/LOyefzPdvPwH+zgmz9ZgdefuOfadOndUY/yeUhdNMfpVMYVbWpm+MAhMq+9OqHfEwDCHY301uyi4PTL8QRTnU5nyh2seRXQLKi42ulUhBBCTKHJFB7HfvLbNd7YcSGEcJzT88zZ2b7LYo8ml402bfSSstWjyupFmp2LOVux1o6B9lhvU3ut/2nU3onb7eqrYVvNEwSDOSxeePOEjzG81p9X02c91rAYa/itP/9RT5y6Nx+hv24PRedcT97KCzhR/7P4VzsYjpOD1TjrHz1i5gDtP/sFkeoasm6+Dh1S6BPNqOm3PhegJxCzFJcSsLYIjBkzaHvwFZqe207G8hIW//BafFkpQOS48Vl+a+MscyzGAWR6rcdmeazHprmHT3jfYE+MzS+08NHTjTQfHMSf4uL0a/I5Z30h5SvSRt+XDI6dKNA3DT0eDW39deU+wTl09tdyULGFPPqX+3n0j3fw7X9bitfG+5BbW3sfmqp5K48VN60dg/FWBO3bUU/akmLwejE1mKb19xZt2Pg7ZDHWdYJTZeCd91EeD+lrz/w8xs5ppWx0kLYae6JcWz95HbcvQP6S87/I1bD+PuiyuH07bdqLPfH52tVbTVvHbipKLiLFm4EatvgkmNZfA+Nt/yusxho2TgA7sab142r5Mtzpy3WdALNYJ0IOImGYx13UWUyHyRQeW8b83D3ZRIQQQoiZpru/jm0HHyfoz2L1svvweUNOpzRlzHiU2jceZqDhAMUXrCdnWeLO4xbv6aH1Fw8Qa+8g976vE1qz0umUjivW1U/dj55haG8DxevXUPHt81EWi+vJyDQ1hz7u5uNnm9jxRjvxqMncZWnc+XfzOf2aPIKpyT2/21k3FhIbNnny7w/y2A8PcNM/JuZ5Nx3i/WGGDrdRfNe5TqcyLiMcZmDLFkKrV+EOJfb781BHA711uylcfTkef9DpdKaUqQ3217xC0J/FvOLEPmeEEELYN5lPdEfG/Jw32USEEEKImaR3sJFtBx4j6MtgzcJ78PpmzrA4Ixah+s2fM9BSTckld5C96AynUzqhaGsrzQ89gBkJk//9bxFcWOV0Ssc1uKeeuh89izEUYdF/u4b8ixY5ndK0MQ3N9tfbeOOntTQfGCCY7mHdzUWsW19MyeI0/C5rvUiTwfl3zKG3NcKrP63n9Nt7KF2R6XRKp0TPJ4dBQ8aacqdTGVf/Bx+iozHSz0v8YlfTp6/g9gfJW3a+06lMuabWLQyG2zhtwW0zYrE1IYQQXzaZwuMmoAfIBNYqpTxa68SY4VsIIYRw0MBQK9v2P4rXk8Lqhd/A70090aDepGPEIhx68yEGWg8z99I7yVq4xumUTmi4vo6Wh3+Gcrkp/KPv45ubePMkaq3pfGkzTQ+9gS8/g4q/v5P8xWlOpzUtjJjJ5hdbePPBOtprh8ivSOHOf1zCqivzZ/QciJd9u5T3n27mtX87yLd/nrhF+qnU/cEBvDmphBYUOZ3KCel4nL73NhFYMB9/ceK9N4w10FxNf8M+5qz92ozr7RiLh6k+8jZZ6WXkZy9xOh0hxCyhAT3OdCFiap104VFrHVVKPQF8D0gD7gMenKrEhBBCiGQ0NNzJlgO/QrncrFl4NwFfhtMpTRkjNsyhNx5ioK2G8vPvJC2Bi45D+/bS+ugvcaelUfSt7+Cam3iriZuRGA0/eYmejbtJWzuf0v9yPe7UAMfOY5jsYhGDbb9rYNPPD9HdNEzxolTu/fEyll+Wj8vqnLZJLBByc8V3Snn6H6s59FEnVetynE5pWhnDMXo3Hyb3stNQCfz8DmzdhtHXR+4dtzmdyri01jR9+jLelHTyliZ+z0y7Dh/ZQCweZkHZ1fbmmBZCCJE0Jjt5zl8AVwOlwP9SSn2qtd4++bSEEEKI5DMc7WXL/l+itcHpi+4jJZB4xa6TZUSHOfjGgwy211N+wV1kl68kUQfE9m/dQvtTT+ArLKTwm9/Bk5aGkWB9TqOtPdT+w1MM17ZScOcF5N96bkIXaU5GZCjO5qfqef+Rw/S3R5i3Ip31P1zIkvNzZl2B4dzb5vDGw4289m8HqTwze0bvf9/WGsxInKxzFjidyglprenduBFfURHBBYmbJ0Bf/R4GW2uZe+56XJ6ZNQx5cKidhtZPKM5fQ1qo0Ol0hBBCTJNJFR611r1KqeuB54G5wLtKqR8CD2itZ9bX9UIIIcQ4orEBtuz/JbF4mNMX3UtqMN/plKaMEQ1z8PUHGew4QsWFd5FVtsLplE6o59136HrpeQKVVRTefR+ugI3ltE+Rgd111P3TM2jDoOyHt5N+emLOO3mywn0xPv51LR8+WsNQT4yKM3NY/48rWX5WaEYX3Mbj9bu45PuVPPs3n7F3QztLLp457w/H6vrgIO7UAGmnzXU6lRMK791HrKWV3DvvSOhzUmuTps2v4E/PJWfhWqfTmXIH6l7B5fJSWXqx06kIIWYdhSlDrU+ZSRUelVJ3j/7478DfAKnAvwB/r5R6G9gOtAMDdtrVWv9yMnkJIRJQAn+wP5blXkfKxmqzNnoyWb4IsnFMldvG/Gl2Yl3WYpXLxrHyWN++thprp02v9VhzNDYWG2LrgUcZjvaxcsndpKbP/Ur/OtNn7RhYjQMwfNbPgbjfWqzhO+ZxkTD/P3v3HV9lmef//3Wfk1PSe4H0hAAi1UKz0AQErNgrKo7OulP3N7M7uzPjjDPulJ1Zp+x+Z8c6dgULigqoIKgoCtI7pJDeezvtvq/fH0FJIIH7Tk6474Tr+XjwSEiuXPf7tPuc8zlXKfzwCTrqy8iafzdR2RNQj/9Oc+k+PKqRtm6hq53WrZ0QgsZ31tCyYSNhkyeScOdt4HCg0dVGuPWPeLSH6l+yOtStb9xnpNsLQNXaPRT9z4e4UqIZ96ulhKbFAd4ebaOdHt3Hj3d16GoX52zX3WdciP620fYTx/d7NTa9WM77jxfT2aoyflYcCx/MIGdK13IDYTZ9Wd0GNpexo+++AqCi7zHgFwZeHhsYSDv9umQ+eaaID//nKBPmxAVlmrkm9J8vAgba6l33KqD2PF+KgErTlnyip+ehKU409cTvVNXA8Q20VVR9Wbu3a974MfboaCInTO717206++xqq7spNp2nFuV4n40FO/E0VJI1+05swg69HEtvn1396nu82Pz679hKoH9ta5uOUN+Uz+j0BbiUMDi5H739qgYehEL/+QJNX7/CSJ+G2hq4XDqzGmHocmkG2uo9/iD0OWiMXFeSdI4a6FTrZ6HHKz4BKHSt+XjN8X/9IQuPkiRJ0pAQCHjYeeB52jtrmTT2DmKjssyOFDQBbweF7z1BZ30F2fOXEZ013uxIvRKaRv3KN2jb8iWRl8wg7sbrjRW7zwKhahQ9vpHKt3YQc1EWY/7jakIirDcasz+EEOxYV8vbjxVRX+Zh/Kw4rvpeFunjhucmOf1ld9hY+J0cXvrxPnatreaCJcNvamnr3hLUdg8xM8aYHaVP3rJSPAX5xC25CiVkoG+FBo+mBqjYvo7QuJHEZlt3lHl/aJrKkdL3CXPHk540/EZySpI0NMia8dkTrGdbhRMFyN5uPj0fG35dtJQ3vyRJkjQkBFQvuw6+QFtHFRPH3EZ8bJ7ZkYIm4Omg4L3Hu0bbzF9GdNb5ZkfqlQgEqH3+JTp27yV6wRXELF5ouamTalsnB/77dZp2FDNy6YVkfWs2it1ahdH+yt/ezKr/KuDYnlZSx4bz3WcmMnZGrNmxLGvyomQ+/L9CvnitfFgWHps2H8TmchB1QbbZUfrUtHEjittN1LTpZkc5rbpDW/C1NZA7/34UIzMshoCS6i/o8NQzOe82bDbrFn8lSZKk4AjGmV456etA+5EkSZIky1O1AHsOvUJzaykTxtxCQpx1R/gY1VV0/DuehiqyF95LVMZ5Zkfqleb1UvPMc3gOHSH2+muInn252ZFO4S2ro/g/V+CvaWLUv1xJ8pUTzI4UFLVFbbz/pyMc+Kia6CQnd/7nGKZdm4zNLl/OnY7NpjBqWhzbV1eiaWJY7eqt+QM0fnqImJljsLmsuQmKr7qa9r17iJkzF1toqNlx+hTwdlC56wMiR+QRlTbW7DhB1eltoqBiE4kxY0iMGT7Pm5IkSVLfBlp4nBOUFJIkSZI0hGhCZU/hazS2FDJu1A0kxVtzNGB/BDztFLz7dzxNNWQvvI+oDGu+6VU7O6h+4Wm8x4qJv+1mIqdbb7pe644CSv/4BkpICOP/cAtR56eZHWnA2uq9bPhbPtteL8XhtnH197OYuywNZ6iBtWHPcWnjIvn8lTLqSzpIzAo3O07QtGwrQG33EDfHmksyADRt/AglxEH0Zdb7kKK7qt3rUb2dpE69xnIjuAfqcMlaAMZkLDI5iSRJ5zq96xlLAzfQXa0/DlYQSZIkSRoKhNDYV7SKupYjjMm5mhFJk82OFDR+Txv5Gx7H21TTNdIx3ZpFx0BrK5XPPI6vtobEe+8mfJK1RhEKIahf/SVVz67HnZlExn/cTFTm0F7P0depsvn5Ij55upCAV+Pim9KZ90+jSE0ysLOGBEDauCgASve3DqvCY8PGfYTEhBM5OcvsKL3y19fTtnMH0Zdcij0iwuw4ffI211N7YDPxeRcTFj/S7DhBVdN4iNqmw+SlXUGoK8bsOJIkSdJZIhfVkCRJkiSdhBAcKH6H6qb95KXOJy3FeqPs+svvaePIh3/H21LbNdIx3ZpT4PyNDVQ+/TiBlhaSH1hO6NjRZkfqQfMHqPi/NTRt2E3UjLGkfv9a7KFOQP9O1VYihGD/+mre/d0BWqq9jJuXzMIfjCYx++vCjb6dqqUTUkaFE+K0Uba/Zdis8xho7aR5az6JSy6w7PqlTZs2gqIQPWu22VFOq2rLuyg2OyMuvNLsKEGlqj4Ol6wlPDSRjOQZZseRJEmSziJZeJQkaegaZoutf0PvbrxGdu01so6YgTeNut9g2g1MwzRyuXT2Kxz6jy8cvR9fCMHh4rVUNOwiO3U2mWmXojn1Z1V1tlVdBvp06b9dVWffv/N3tpF/vOiYefVywjPHoGccm+rSfXhUt/694zRX72191dVUPf4Ewucj5dsP4BifgYamq09baED38d1uv+62EW7vN9/7mzo48qtVtO4vJ/WOmaTdeQmKTQBeYtyduvuMd7Xrbhvn1Nc2wdGqu89oe1fW+nIPK36dz95NDaSOCef+/x5L3kXRx1t1HTfKpv9yhdm8Z24EOJXBGUWp6lxO3KPpv6/6FeNTzO0OGyPGRFB+UP9t0hfNwBLpmoEpZT5N3+UKqF3nq/pPDyMCKlGzJn7zs1OO38fPeyMC+rPqubsEGpto3baVqIum4oiI5kwnOEX/6cJQW1vg9Pet9qoimvN3M3LSAtzOaNDRt03Vf3+1+fW1NdKnorPtsWMf4fE1c9Hoe7EJG5zh7xRN37kdve0Gq62B88WgtdXdp4HLP5SIYXq5pEElhJxqfTbJwqMkSZIk6VBQtoGy6q1kpMwkJ3W22XGCxt/ZSv6av+NtqSNnwXLCMq050tFbWkbVE0+i2GyM+Od/wjlyJKrOouPZ0Flaz6Gfv4Gvvo28/7iG+FnWnKauh+rX+Oj5ct7932IAlv44m7l3p2LvoygvGZc2LpKda6oRQgyLNfyaNu3DlRZPaK41R3C2fPQxCEHMrLlmR+mTEILyz1fjCIsiedxss+MEVVt7NSWVnzMyfjKxERlmx5EkSZLOMll4lCRJkqQzOFbxKccqPiU16SLyMhYMi0IBHB/puObveFvqyV2wnMjU0XoG2Jx1nsJCqp58GntYWNdIx8REsyP10LKnhMOPvIUtxMa4P9xK5Nihuy5b8c5GVv96P+WH25kwJ45bfjaK+NShvT6lFaWNi2LLinIayjqJTw8zO86A+KqbaN9fQvKdsy15blRbWmn77EsiJ1+AIy7O7Dh9airYRUdNCemzb8HuMDCc3OKE0DhUuJoQu5u81Plmx5EkSZJMEPTCo6Io04AlwCVADhAHRABCCHHK8RRFSTjeBsAjhCgJdiZJkiRJ6q/ymq/IL11PSvwExmYtseQb6/4IeNrJX9s10jF34f1EjswzO1KvOg4dpuYfzxISG0vKtx8gJMZaGxLUrt9P4Z/W4h4Zy5hf34A7xVr59Gpv8rHusSNse6Oc2BQnD/x1HJPnxw+b+7vVhEV3vST2dgz9zXka1u8CBWLnWGuTp681f7gREQgQM+cKs6P0SfV7qdjyDqEJqcSNvmioLgnbq/Lqr2huLeG8UdfjDBnaRXZJkoYXI0uQSAMTtMKjoigzgMeA7ivt67klpwGrj3/fqSjKSCFES7BySZIkSVJ/1TQc4GDRu8RH5zEu53qUYbKuaMDbQf7aJ/A215Iz/z7LFh3b9+6j5vkXcKYkk/LgA5baiVYIQd3Kj6l79WOiJmUw+ufXERI59EYGappg+1vlrP3vI3haA1x+bxbXfycVd7jxtQsl/cTx5duGemFXqBqN63cTeUEuzsToM//BWRZoaqZ18xbCp16I02Ijpbur2bURf3szmVfchWJknWOL83hbyC/+gNjoHEYkTgHv0C+0S5IkScYFpfCoKMq/Ao8CdrqKjYITRcfu359CCPGeoihHgdFAKHAr8EQwckmSJElSf9U1HWVv/utER6QxMe9mbLbhUYhRfZ0UrHsCT2Ml2fPvJSrNmms6tu3YSe3Lr+BKTyf5gfuxh4aaHekbwq9S+bd3aN60m4T548n5/kJsBjYwsoqqI62seuQAxbuayLoghut+Po6U0ZG47fo3jJH66et9I4Z23ZHW7fn461sZ+cBCs6P0qvmDDaBpRF9p3Sm+vrYmanZvImbUZCJGZJsdJ6iOHluDECpjc68d8kV2SZIkqf8GXHhUFOX7wO+O//frl1GdwDagFbhKRzcvA788/v01yMKjJEmSZKLGlmPsOfIq4aGJTB5zB3b7abaEHkJUXyf5a5+gs76C7HnLiE4/z+xIvWr9cit1K1/DnZND8vJ7sbmtM5JQbeuk7Pcr6dh3jITbZpO77OIh94ZaDWh8/HQRG/5WgDsyhBsfHc8F147EZhtal2Mo+2bEo7kxBqz+g52ExEYQdbH1Rk0HGhpp+3wrETOm4kiI+3ojdsup3LoWhGDE1CVmRwmqhqYCaur3k5M+lzC3ddfWlCTp3CUGYfN4qXcDKjwqinI+8EdOFBw7gH8HnhRCeBVFyURf4XEVXYVHBbhcURS7EGLQxuIrivLM8Vw1Qojxx38WB6wAsoBjwM1CiMbByiBJkiRZU3NbGbsOv0SoK5YLxt6NI8Q6I+0GQvV5yF/3JB11ZWRfsYzozPPNjtSrpi2fUvfeW4SOHUPSPcuwOa1T9PVVNVL66Ev4q5sY+YPriZ41EUUZWqMDawrbWPnveynb18LERSlc+9PzCI+1znV8rhAM/cqjt7aV1q/ySVw6AyXEeiN+m9etBwWiF84zO0qfOmrLaDzyFUmT5+CKGj7FOU0LcLjoXULdcWSkXmp2HEmSJMlkAx3x+Bu6plcDNAOzhBB7+tHPPro+hww//m8McGCA2U7nWeB/gee7/ewnwAYhxO8URfnJ8f//2yBmkKShbzBG+QzSGnqKkZE8ejMY6NPQiCidbQ31aTfwptDIbaC3XwNvSoWRtg59T2MiRN9lau2oYufhF3A6w5k84V4crihO92GoprNfAM2pr63m0H+7qk59bVW/h/wPnqSjrpTMK+8mMmcCfX26pxrYTNVIW01H26aNH9Gw5j1CJ48n4b7bEY4QVLQ+2yuh+vfgdrn9uttGuL2n/KztYDnFj7yBUDXG/OYWIidkAF6iXfp2gYh36R9uFefU3zbB0aqrXYzSxkcvVLDqsWKcbhvfemwMFy9OBPzH/3Vra+/Qffxw26nXVV/cir7bwKb0fZufTBP6H4M+oe/c4jdwfNtp7p+ndYY1HlWdl0s1sDC+T9X/sl/Vznz8yrX7QRNEX3EhAR3tVdXAc0tAf1slcOp14K+rp+3Lr4i8ZCaOyFgIgKL/dIHNSFv9p5YeGYQQVHy+mhB3OCkT5/X4nS2gfxiOkbaKqq+tYuj4pz4GSio/p6Ozjsmj78Ch2UHTjh9uTWmbAAAgAElEQVTfwLgSVd9jS9HZzkifAELT2VYzcJmEgeMbGYqls62hPjUjx9d3uYSRPs0mh8JJUlD1u/CoKEokcCUnRjv+sJ9FR4QQQlGU/ZzYmOY8BrHwKIT4RFGUrJN+fC0w+/j3zwGbkIVHSZKkc0a7p47tR17AbncyZfx9uF1RZkcKCtXvJf/Dp+ioKSFz4V1E50w0O9IphBA0fvA+Tes/JGLKFGKX34xipFg+yBo2H6LwD+/ijIsg79c3EZoWb3YkQxrKOnjmZ3s5sq2FibNjuevXeUQnylGOZhJDfI1HoWpUr9tL+KRsnCmxZsc5RfP6DWCzET1vjtlR+tRSsp+2ygLSZi7F7hweI+sBPL5mCss/JjFmDIkxo82OI0mS1Cchd7U+awYy4vEywHH8+wa6inUDUd3t+5QB9tUfyUKISgAhRKWiKEkmZJAkSZJM4PG1suPIiwBMGX8voW7rvZHuDy3gp2DDM7TVHCNj/l1E504yO9IphBA0rHmP5k0biZw6lYQbbkK1W2ekQfXq7ZT8/UPCx6aS9/ANOGLCzI6km6YJtq4sZc0fj2CzwbL/zGPm0qQhtyblcORt6xre5gy1ToHdiMathXhrWkhctsDsKKfw19TStq1rtGNItPV22gbQ1ABlX67GHZNMwtjpZscJqsPFaxFCY0zmIrOjSJIkSRYxkMJj2vGvAtgqDI3d7lVLt+8jB9jXoFEU5QHgAQA3Q+fNhyRJktQ7f8DDzqMv4g90ctGYZYSHJZodKSg0NUDBxudorSwg6/LbiMqbbHakUwghqF/9Ni2bPyVqxkzir7sexWaDPieCn91s5c99QuWKLcTMyCP3367B5nKc+Q8toqG8kzd+vo+CLxvImxnPfY9mEz/SOpv0nOvqSjsJcdqITjawXoGFVLy1HWdCJJHTxpgd5RSNa9ehhIQQM9+6azvW7PsYX0s9uVc+gGIbmsXn3tQ0HqSm8SCj0q4g1DU8PsCTJEmSBm4ghcfu84zqBxoE6D7HwMBqKUFTrSjKiOOjHUcANb01EkI8wfFdt6OUOOsMyZAkSZIMU7UAu/Jfpd1Tx5RRtxMVPrK/K7ZZitBUij5+iZayg2TMuJH43AtNeWI9HaFp1L+1ipYtnxN12eXEX32NZUbiaQGV4r+uo+7DvSQunkzmQwtQ7IOz/mywCSHY+loZ7/3hMADX/2IcU29KIz5E/7qN0uCrL+kgPi10SO4k3nGsjuadJWTed7nlNpXxlpbRsWs30QuuwB5pzXEMvvZmqneuJzpzPFFp1ivc9ldA9XDo2BoiQpPJTJlpdhxJkqTTEihyqvVZNJDCY3O374PxzD6i2/fBKGQatRpYBvzu+Ne3TcggSZIknSVCaOwrfJOmtmLGZy8lPjrX7EhBIYTGsc0raCreQ9rUa0gcO8PsSKcQmkbdG6/TuvVLomfPIW7xEusUHT0+8n/3Js3bChh556WMvP0Sy2Q7k7Z6L6/9dB+HP60jd1ocNz46ntiRw2ftuOGkrqST+MyhedtUvLUdmzOE5MUTaTM7zEka31uDLTyM6NmzzI7Sp4qt7yCERur0a8yOElT5ZR/h9bcyKe8WbMNoFKckSZI0cAMpPHYfEThuICEURXECU7r9qHwg/ek43it0bSSToChKGfALugqOKxVFWQ6UADcNZgZJkiTJPEIIDpWsoabpIKPTFzIifoLZkYJCCEHJljdpKNjOyClXkny+9d58C02jduUK2rZ/Rcy8K4hdeKVlCnuBlnbKf/MynoIKMr+7kKTFU878RxaR/0U9K36yl85mP9f8x1im35YxJEfTnQuEENSXdpA3I87sKIb5Wzqp3XCAxHnjcESFgoUG0nYezcdz+Aix11yFLdSaRd22qkIaC3aSMmU+rsihtUnV6TS3lVFavZX0pIuJjkg78x9IkiRJ55SBFB6/Ov5VAXIVRRkjhDjcz75uAL5eeCgAfD6AXGckhLitj19ZdzEYSTpbLFIA0EMx+U21oWKJzcA0Tb1tjez6ayCroSmlOrMKI9PxDLXVd3zN2fPprrB0I2W128kceSnp6Zf2mF6tOfVfftVloK1T320QcOm/rdRuy8MJIajYspq6w1tImjKXxKlXoHbrStO5lJxqYMk51a1/xRHVrSFUlfoXV9C+fSfRixcQvWg+GoKu5aK7cemb8O5wB3QfP9TlO+3vfTXNHHv4FXzVzUz85RKSLh0FeM7Yb5xLX+UlztGuqx1AgkP/OLJo0ca7fythzf+VkpQVyg+enEj62AhOrgjFh+jvM9LWqbutW9E/id+hBH/tTo3gT4H3iMFZcEETXVlbar34OjXi08O++dnJ/ELfedCn6X8pH+jjWL32q/Z+/Mo1+9C8ARKuvgifaicQ0H++FgH9x1dU/edBm19BCEHTu2uwR0cTPe1SbP5T/763n/V5fP2nFmwBfedBoWmUfb4KR3gMI86fi+00x9DbJwxOVkXV/xjQAn4OFK3G5YhgVOpc0Pr+W0U1sErVafrpVzsA1cA5SNOZVW87w20NXC4jbYeKQToPS1J3ct28s6ffr9aEEMeA7oXGh/vTj6IoYXSNOISu236LEEL/q3NJkiRJMqCsahtFpR8xInEKuZnW25G1v6q2raN2zyckjL+MEVMXW2YU4deEqlL3/Cu0f7WTmKuuJGbRfLMjfaOzqIYjP3qOQGMHuY/efrzoaH3NVZ08ds9e3vtbKdOvTeKnr08+XnSUrKyupKsoHJ8+tDYpFKpGzbs7iJyUQVh2ktlxeujYvw9vSQmx8xdic1hzE6j6A1/Q2VBB2kVXYwtxmh0naEqqv6Cts5qxGYsJsQ/NzZIkSZKkwTWQEY8A/wf8+fj3tyqKslUI8Re9f6woigt4ERjd7cd/7qO5JEmSJA1Idd1eDhe+Q3zMaMbmXmu54lx/Ve/YQPWO9cSNnUbqJdbZpOVrQlWpe+5lOnbuIebaJURfMdvsSN9o21tM4aOvY3c7yf39nYRmJWGp+aN9OLixmtd/thfNr3Lv70cz41prFYKkvtUWdn2+Hp8xtAqPjZ8dxlfTQsaDV5gdpQehqjSsW4sjMZHICy8yO06vAp1tVH25loiUXGKzJpkdJ2g6OuspqNxEYsxYkmLHmh1HkiRJP4HcXOYsGuj8lP8DCo9/rwCPKYrynKIoWWf6Q0VRFgBfANfCN/Ostgsh3hpgJkmSJEk6RX1TPvuPvkFMVAYTxgyfxe9r93xC5dY1xOZdQPrlN6Io1tp9Wagq1SteomPnHmKvu8pSRcemzw9T8PCrOOIiyPvD3ceLjtbm96qs/s0BXvjuDmJHhvLT1yfLouMQU7y7ibAYB/Hp1lyHsDdCCCpWbMGdFkfMNGuNCG7dthV/TTVxi5agGFmC5Cyq2PIuqt9DxrSllvtgqL+E0DiQvwqbEsLYjMVmx5EkSZIsbEAjHoUQfkVRlgKf0LWztQLcCdypKMpOoKh7e0VRHgbygDl07WKt0FVwVOjayVpu6CJJkiQFXXNrKXsOvUx4aCITx96B3T48prnVH/yC8s/fJjp7AhlzbkUxspboWSBUlZoVL9O+Zzex111F1DzrbHZTv24npX9bR1jeSHJ+cRMhUdYffVZT0MarP95F1ZFWLrk7i4U/GE1KmPVHZ0o9FW1vImtKzJAqQDVvK6SzsIbsf1lsbB3gQaZ1emj8YB3u7BzCzh9vdpxetVUU0HhoG0lT5hIam2J2nKAprfyS5pZizs+6Drcz0uw4kiRJkoUNdKo1Qog9iqJcA7wGJB7/sULXLtXdt4NUOLGW49evtL4uOlYD1wkhigeaR5IkSZK6a+usYVf+C7ickUwetwxHyNAZZXQ69UU7KP30dSLTx5J5xZ0oFhvBKVSVmpUv07ZnF/GLryLCQkXH6te3UPnsRqIuyiXrJ0uxua25JtzXhBB89UYZ7/7uAI5QO8v+diFjLpejHIeillovdSUdTLt56Oz8K4SgcsXnOJOiiJtzvtlxemj+cBNqWxvJ9y63ZCFXUwOUffwGzsg4ki+aD/r3YrK0js56Coo/JD52NCPiJ5odR5IkqX/k7jJnzYALjwBCiE8URZkMPAUsOvnXvf0JXQVHBVgP3CuEKA9GFkmSJEn6Wqe3ie0FL2FTQpgy7h5czuGx8UZjyV6KNr9C+Igcshcsw2YPytN50HQVHV+hbfcu4hYtIebyOQQwf4dKIQSVz22i5vUtxMwaR+YPr0Yxsou6CXydKm89so9d71aQOz2em347kahEt9mxpH46tqMRgJwLY0xOol/r3lLaDpST8dB8bBZ6vAQam2jd8DERk6fgTs8wO06vand/jLexmuzFy7E5nMOi8HhiirWd83KvRcF6BV9JkiTJWoL2TkUIUQksURRlIvAQMBfoaxGYKmAD8HchxGfByiBJkoWZve6ckePbdL6INjKt1cBIDN3T2PTmBDCy7lWIgacGvW9CDVxXwqE/6+naev1tfFXwIqoW4ILJ38IdHq/rg03VqT+r5tR/G6g626pnmAXeUnaIwk9eICwhjczrliOcLlQd/QZ01qpUl/6Pf3trK1SVupWv0r57J7FXLSZq3hxUBMKtv/BoDw3oaud26X8XH+n0kP/XDdS8u5sRV01i1HfnotgDwKnHinZ16u43ztmuq128Q187gNiQrrZ1ZZ089d0DlB9q5+rvZXLltzOw2VTgRF/xIW26+oyx6Z+SHWbz6m7rVvTc+7rYdQ4tUA0UMvwGhiv4CH7RTDOwXLpf2Mnf3ozDbSNxbCx+0fffBjR9/XpV/edrI219gRPXVfkrWwiJCSd67pQePwdQAwbO7QEDz4M62ja9vQ4hIG7BYvTcDW36TitBa+ttbaB624dEZ00gJm0cBMAW0Hd/tel/WOnus6utvvOwcpp2ZdVbaW4pZlzOdbjtESheA1dWQP8FU1Sdzxl62wEIA8ObhM5+9baj68MvA42D3682CJcfEEb6NZuR20CSpKAJ+hAJIcQe4NsAiqIkAmlAHOAAGoBqOaVakiRJGkz+gIedh1/A62/lwjHLiAhPNjtSULRVFlD44bO4Y5PJvfJb4LTWyDehadS9/CrtO3YSs2Qx0fPmmh0JABFQOfTbNdRuPET6rVPJWn6pJadldndgcwPP/H+H0AQ89PfxjJ8VZ3YkKQiKdzSSPjGGEId11kk8nY4jFbTtKmLEPXOxuayzJIG3tIz2bTuImjcbR6w1HxvlW95GURTSZl5rdpSg8fpayC9dT1xUDiMSJpsdR5IkaUDkrtZnz6DOzRJC1AK1g3kMSZIkSepO1fzsOvoybZ21TM67nZjIdF0jAq2uvbaEgg+exhUZR+6VDxLiCutlrJ55ehYdFxFzhTWKjprXT+kf3qB121Gyll9Kxm3TzI50WkII3n+ihLf/fIyRo8J58H/HkZgxPNYlPdd52vxUHm5hzgPW2hX6dKpf+wx7uJv4RReYHeUbQgga33oHW1gY0fPnYoFVHE7RUnqI5mP7GDl1Cc6IWLPjBM2R4nUITWVs9lWW//BGkiRJsg5rLQolSZIkSQMghMb+wlU0tRYzIfdGEmKGzhv80+lsrKJg3ZOEuMPJXfQgjlBrrVUpNI36FStp376DmMVXEnPFPLMjAaB2eCn5zxW07y9m1PevYOTVk8yOdFqeNj9v/mw3BzdUc9HiRO58dDSuMOusqScNTP6WeoQGOVOtOULvZJ2FVbR8cYTkWy/FHuYyO843Ovfsw3u0gLgbr8cWGtp95QFL0AJ+Sj97E1d0IokTLjc7TtDUNh6iumE/OWlzCXPHmx1HkiRJGkIGVHhUFOXhbv/9sxCipZ/9RAPf//r/QohfDSSXJEmSdG46Wvoh1Q37yUtfQEr8BLPjBIWvtYGCtU+g2OyMWvQgzvBosyP1IDSN+tfeoG3rV8QsXEDM/CvMjgRAoLWT4kdeprOgkrQfXsfIxblmRzqtmoJWXv7BdhpLO7jpP3KZc9dIOaJomDn4UTWh0Q4yLxgaI+CqXv4UW7iLxGutM0pY8/lpWLUax8gRRFwy3ew4varetQFfSz2jljxouY2/+isQ8HCo6F0iQpPJGnGJ2XEkSZKCQi75efYM9Nnwl5zYtfpZoF+FRyDmpL5k4VGSJEkypKTqC4qrPic9eRqZKTPNjhMU/s5W8tc+jhbwkbfkIVxRCWZH6kEIQcObq2j74kui588jeuF8syMB4G9s49gvXsJXXk/GT24iatoYQP/mKmfbvvcrWfXz3TjDQrj3qWlcNOMMOwxJQ07Ar3Ho41rGzk7CHmL99R078itp+fIIybdfjj3COmvJtmzYiNrQSMJ3v41iZNO0s8TTVEv1ro+IHTWFyNTRZscJmqOlH+D1tzFx9G3YbMOjmCpJkiSdPcF45lDAwJaCZ68vSZIk6RxR3XCAwyXrSIo9jzEZVw6LkWIBbycFa5/A197MqEUPEho/0uxIPQghaHhrNa2fbSFqzmxiFlnjevfVNnPs4Rfx17eS+fNbiZicY3akPqkBjQ//fIjPnisifVIMtz52IVFJbqDV7GhSkOVvb6Wzxc95c5LMjqJL9SufYg93k3jNxWZH+UagsYmW9RsJmzwRd571ltEQQlD22RvYQhykTr/G7DhB09BSRHnNdjJHzCQ6ItXsOJIkSdIQJD+ykiRJkoa0hpZC9ha8TnREGuNzlqIo1h9NdCaq30vh+0/haaomZ8F9RKRkmx2pByEE9e+/S+vmT4m6/DJir15iiaKjt6KeYz9/EbXTS/YjdxB2XrrZkfrU3uBlxY92ULStgWm3ZnLlv44bMjsdS8bt3NBAiNNG3iXWGrXcm5ZDVbRsPUrKnbOwh1tntGPTO2sQQhB77VVmR+lVU8EuWsuPknbJ9TjCosyOExSq6uNg4WpCXXHkpM4xO44kSVLQCOSu1meTVQqP3edKWGmTTkkaHixQENBLsRnIaqTAZKBf3QUUA9eroSlhei+XTX+fis3AdWXX31aE6Mzg0P90Ixz6L1eTt5JdR14hLDSeSePuRHG4e93gVHPqv0yageKPaqBf1anv/uIPUTn2/rO01xaTufBuwnPP63NXbtXAfg+aztm72hn6FELQuG4tTZs3ETFrJtG3XoVQBOIMExYUt/69xd1uv652kW7vN993FNdx7KcrEKpg/B9uJTw3CTjx+1iX/qnWCS79u1XEO/S1jQtp++b7yvx2nvynfbTU+rj7d2OZdm0y3aeCx9vbeumhdzE2fZcr0ubR3WeYov+lmFMJ/pbCPmHg8Urwn9809B/fJ858bhNCsGN9AzkzErCFuvDrmNvj1Rz6jq/pP196A/rOwwXPfok9MpToxdPxq6fvX1UNPLcE9Le1nXQK8BQX0/7VDqLnzcMZFQf+7m313QdsBt5hnHz8M7VVfZ2UbXmbsIR0kkbNROnj7/VmsOm5k3zTVv9jUAno61dRu/osLN1Ip7eBC0ffTQghoJ56LCVg4BygDUJbA4u0iV7y90lvW83AZL3BuPxDiRiGl0mSJF2s8tF6YrfvLbY3nSRJkmRF7Z56dh14AYcjlMnjluFwhJkdacCEplLywQu0lR4hbc4tROdabxfmxg8/oOmjDUROm07sLddYYqRje2EN+3/8KgDn//E2wnOTTU7UtwObG/jjbTvxeVR+8Pyk40VHaTgrP9xOQ4WXsXNSzI5yRq0HK2jcWkj8tTMss5N117IOb2OPjCRmrjVH3ZXvWEegs42MGTcY+6DPwprbKyiu3kJqwgXERVlr1L0kSZI0tFhlxOO8418FUG5mEEmSJMn6vP42dh55EYRg8rhluF1Df1qbEILiLa/TUriXkZdeR9x5U82OdIqmjzbQ9OEHRFx8MQlLb0C1mb8sc3t+NQd+sgKby8G4/7qF0NQ4syP1adNL5bz+m3xG5oXz7b+NJ26kdaaxSoNnx/t1KDYYM8v6ReaS5zYTEhVK7BLrrO3Ytn073pISEm65BZvbeo+Z9tpiag99RuLYmYQnWHd5ByNULcD+ordwOsLJS7PGpmGSJElBJQA51fqsOWPhUVGUDJ19pRoc9eAEUoA5wL91+/kuI51IkiRJ5xZV9bHr6Ct4A21cMP4+wsMSz/xHQ0DFznXUH91K0kXzSZh0udlxTtG8+VMa1q4hYsoUEm+8+fioHv3TpwdD25FKDvz7SkLCXYz7r1txp8SYmqcvWkBjxW+P8skrFYyfHce9fzwPd7hVPvuVBpOmCbaurmbsjFgiEqwxgrAvTTuLadpRTNaDc7CHWiOr1tlJ47vv4crIIOKiC82OcwpNDXBs80ocoVGkXrjY7DhBU1D+Ee2eWqbk3Y4jxHrFXkmSJGlo0fOq9xhn3mlaATYPIEf3iuXrA+hHkiRJGsaE0Nhb9CYtHRVMyr2F6MjhMbqk5uCnVO1ZT8Lo6SRPvdLsOKdo2fol9W+/Rdj540m85TZLTCXsPFJG6a9WEhLpZtzvb8WdEm12pF55Wvy8/uOvKNxSy7z70rjuX3Kw2eUn7OeKgu3N1Jd7ueYHWWZHOS0hBMVPf4IzMZIRV0+m2SIrrje+/wFqWxvJy5db4rxzstrtG/A0VTHqiuXYncOjQNfYXERx9RbSEi8kITrP7DiSJEnSMGDk4/YzvUru76tocfyfAnwihHi7n/1IkiRJw5gQgsMl66htOsyYjEUkxY7tdSOZoaahaCelX75NTMZ4MqYvJWCBNRO7a9u1k7rXXyN09BiS77zL2EZJg6TjUCmlv3oRR0wY5//XrbiSrDnVvqGkjVe/u5WG0nbu+PVoZt44wuxI0ln2xds1uMLsTJqXQLPZYU6j/tMjtB2pYtSPFmFzhlhiq0dfZSUtn31G5PTpuNLTzI5zCk99JTVfrScu5wKi08eZHScoAgEPB468Qagrlry0BWbHkSRJGlQG9qaSBkhv4XEw3wUpQCfwHPDjQTyOJEmSNIQVV2+htHYbmckzyEiy3vqH/dFScYRjn75CRHI22ZffiWJgp/KzoX3fPmpeeRl3djbJy+5BCTF/enDHgWJKf/0yIbERnP/HW3AlRJodqVfHvqrjtR9uA+DOJ2YwbYa+XYql4cPnUdmxtpYLFibgCrP32InZSoSqUfzsp4RmxpM0zxoFNCEEdW++ic3tJnbxIrPjnEJoGqUfrcDmdJM+7Vqz4wTNkaI1eLzNXDz2XkLsTrPjSJIkScOEnncQj5zmd784/lUAfwWadB5XAF6gGTgMbBNCtOn8W0mSrEKx3rSnoNA7ncvItC+bgc9v7Pr6VXS26+rTQEHLSNsQfW1FiP6sopfLVV2/n6NlH5IUN45RmQsQx+97mlPf8TWH/uNrTv23lWrgfZl60pJpHbVlFGx8FldMIlmL7kW4HKiAZmBpNUPHd+v7WFd1d40j7Tx0hJqXnseZnkriP92LcIegnjzG1KV/zKnTrb/qEuby9frz1j3FlP7qNZxJUYz+7e0kpiqAR1efsc5O3cePc7TrbpvgaD3lZ1veqGTlI4dJSA/lgb9NIDHTRVxIi+4+4+36XxLF2PRdrjCb/iFsbkX/EAAjzwJ67y3qGVf46cZAU03oS6vqbAfgF32fg3asr8PTrnLhNSPwCzseTX/xuVPV19ajsx2AT+09a+3avXjKGhn18FL8OEAFv1/nudWv/7pS/PrPrR1f7cRbWETi0ptwOMNPOwLTpvPUorddV9vT37Fq9nxCZ3UJmfPuwBkSDgF9d0SbznaKznYAimqkbd+PwpqGg1RW7yAr9XJi3akQ0PmI1fQ/D5zu+KfQ21Y1sNawMHB8nW2FkSFTBtoa6lfT2dbA5Rd6+7QCOWxN6i951zlrzlh4FEL0WXhUFOUXnLi5/iSEKAlWMEmSJEkCaGotYX/Bm0RHZHB+7lKUYVDw9jbXUbj2SeyuMHIWP0CIK8zsSD148gupfeJZHElJJP3T/ZbYSbZlZxH5v3odV3IMo397O47YcEB/gfBs0DTB6v8u4KNnShkzM5Z7/3Q+YVFypOO5atvbVcSOcDFqqjU3PQJQPX7KX9pMxLhUYqZbYz0/rdND/Xvv4EpLJ/Ji641u97bUU7ltLVEZ44jJndI1b2uI8/nbOVS4moiwFHLSZoNXvhuXJEmSgicY796stRiVJEmSNGx0eOrZfeRl3K5oJo2+Dbtt6Bdx/B2tFKx5AiE0chd/C2e4tTZF8ZaUUfP4M9hjY0j+zgPYw80virbsKCT/kddxj4hl9O/vOF50tBa/T+P5Hx3go2dKufS2VL79+ERZdDyHNdd4OfhZAxddnYzNyIj3s6xm9Vf469tIu2cWikXWl21a07WhTMJ1Sy23oYwQgtKPV6IoNtIuu8Ey19lAHTm2Fn/Aw/hRN2Czmb+khiRJkjS8DPSZJbvb9+UD7EuSJEmSvuHzd7Dr8EuAwuQxd+J0mF8AGyjV56Fw7ZMEOlrJverbuGOTzY7Ug6+mmprHn8QWFtZVdIyMMDvSiaJjejyj//M2QqKtdz/obAvw9Hf3ceSLRq79US5z70sfNgUJqX8+W1EBAqYvte6GQv6GNipe3ULMtFFETsgwOw4A3tIyWjdtJuriabjTrZGpu4ZDX9JWkU/aZTfijLDuSFYjahoOUlW3h+y02USEW+s5SZIkafAoCCFfq50tAyo8CiGKgxVEkiRJkr6mqj52H3kZj7eZC85bRpg7zuxIA6YF/BS9/w86GyrJWXgf4cmZZkfqwd/YQMXTj4PNRvJ3HiAk1vw31S27jpH/qze6io6/uY2QKOsVHVvqfPz9gd1UHGnnzt+ex9TrUsyOJJnM79P4bEU542bFk5hpvfvs18pe+BThD5D+rblmRwG6NmxpePkNbBHhxC1aYnacU/jbmyn/4h0iRo4i/rzpZscJCr+/g0OF7xARlkJ26uVmx5EkSZKGKWvNX5AkSZLOeZpQ2Zv/Os1tZYwfdQMxkdYb9WKU0DSKP3qJtop8MmbdSlTGeWZH6iHQ2krl048jvF6SH/oWjsQEsyPRureE/EdewzUitutP9PYAACAASURBVGukowWLjg0lbfzp9u3UHOvggb9NkEVHCYDt71bTWu9n9l1pZkfpU0dBNXXv7ybpmgtxp1rjg53WTz7HV1pG3A3XYg+z3uO9bPMqhBog/fKbhs2I5sPH1uIPdHD+qOvlFGtJkiRp0AzKM4yiKDnAJUAOEAdEAAghlg/G8SRJkqThQQjB4WNrqGs6zJjMxSTFjTM70oAJISje+ibNRXsZOeMa4kZfaHakHtTOTiqfeYJAczMjlj+II22k2ZFo219G/i9W4kqKZvRvrTm9uupgEyse+hxF0/jOPyaTNclaa3VK5hBCsPEfpYwcE87oGbFmx+mVEIKSx9cTEhnKyNsuMTsOAIHGJpreWYd73BjCLpwEHWYn6qmpcA/Nx/YyYtoSXNHmfzATDLUNh6iq20122mwiw627JIAkSdKgkftonTVBKzwqiuIA7gN+AIw++dd03aynFB4VRVkK/Oj4fxuEEFcFK5MkDXtD6BN3Re/i+kZ2LDawYL+h0Qk62xrr08Dl0tvWbtffZ4j+tsKuP6vQmUE49LUrrPiY8prtZKZfTmrGTLQztNdC9N0Gqkv/baU6g9u2Ysf71B79gsQL5pJw0ewzXibVpfvwqG79r5hU16ltNZ+P6uefxldTTdLye3GOyUJzq7r7tLsDutuGuvy62rUfKqPwlytwJ0Qw5bGluOLtgKfXtnEu/dWJOKf+HbDjQk7f9siWel7+3i7CYhz8xzNjGJljB9pO+zfx9tP/vrsYm/5tciNt+q5XAw8BnIP03OIT+u6v9kF6J6Dq3A/RL/SfL71azw2EDn1aR2V+O7f+djw+4ezxpsar6X/Z3anq25ioM6B/AyOvv+v4zZ8fonVvKanfXoTqikDt5S4UCOi8Dnz6ny8Uf9/Xf+PKt0HTiF+6FFvAhs2nu1tsOk9DOh8qp/QZ8HZStvlNQuNTSRk3CyXQd9szsfv03bdt/jM9U5ygBIy39Qc6OFi4mojQZHKSLz21D9XAYzCg/zkDTX9WvW2FaqBPI201ndfBIFymIUcM08slSVLQBGWqtaIoY4Avgb8BY+i50/WZXuVtAMYD04FFiqJYY6EZSZIk6awqr91JQflGUpImk5M53+w4QVF74DOqdn5IfN7FpMy01pplIhCg9tnn8R47RuIdtxF23lizI9FxtIKiX76CMyaUKf99I6548ze3OdmuNZU88+0dxKaF8s8vTWVkTqjZkSQL+fjZY0Qnu5h0pTWn3Wv+AJX/WI87M5G4hVPMjgNAx779dOzdR8zC+Tji482Oc4ryre8Q8LSTeenNKDYDH/hZ2OGSdfgDHYzPuU5OsZYkSZIG3YALj4qijAM+AyZxasHx65GOfRJCNAOvdfvRLQPNJEmSJA0tdU1HOXhsNfFRuYzNu35YrJ/VWLSb0i2riM4YR8al1loTTGgada+soPPgIeJvvIHwKZPNjkRnQRVFD79MSGQoUx67CVei9YqOm18s5qUf7yVzcgwPPXcx0UlusyNJFlJ+sIX8Lxq45I4MQpzWXEa9bvVWfNVNjLhvPoqB0e2DRfN6qX9zFY4RKUTNnmV2nFO0VuRTf+RLksfPIizBumt2GlHbdJjK+j1kj7iMyDA5xVqSpHOUACEUy/wb7gb0ikNRlHBgLV3rOH7tK2AZkA2cx5lHPAK82e374THMRZIkSdKltaOKPQWvERGWzMRRN2MbBiNK2qoKObbpJcKTM8mec5elRskIIWhY9RbtO3YSs2QxkTPN353VU1xD4cMvYQtzkvPonbiTIs2O1IMQgjV/Osrbvz3M+CuSuP+JCwiN0j/FVTo3fPRkEa5wO9NvsmaByl/fSs3KzUROzSNySo7ZcQBoWvc+amMT8TfegGJk+ZCzQPV7Kd68AldUAiMuWGB2nKDw+ts4UNQ1xTp7xGVmx5EkSZLOEQP9qPNfgXROjGp8VAgxVQjxghCimL4WZTrVRkCjq0iZqSiK+SvbS5IkSYPO629j19FXCLG7mJx3OyF2AwscWpSnqZqCD5/BGRlH7vzl2EKcZkfqofmDD2nd/DlRc2YRPW+O2XHwlNVT+LOXUBwh5Dx6J87kGLMj9aAGNF77+X42PlXE9JvTuOuxSThc1iqQSOarym9j7wfVXHpHhmWL0pXPbkD4VUYut8Zn/N6SUlo+/pSIGdNx52SbHecU5VvfxdfaSOZlt1juPN4fQggOFL1NQPUwIWepnGItSZIknTX9LjwqXXPGvs2JouOzQoiH+9OXEKIDyO/2o6G/jakkSZJ0WqrmZ/fRV/EF2pmcdxtuZ5TZkQbM39lK/gdPodjsjFrwLUJc1tqNueWzz2la9wERUy8i9uqrTJ/+7a1qpOjnLwKQ8+gduEbEneEvzi7Vr/Hyv+5l26oK5j+Uw9KHz8NmH/7TYSTj1v+9EGeoncuWZZodpVdNe8po+ngfiUtnWOJxJlSV+pWvYY+MJPZqa61/C9BSfoS6Q5+TNP5yIlKsMTp0oMoqvqCu+Sh56QuICEs2O44kSZL5hIX+DXMDGfF4EZBI1yhFFfj3AWYp6va99T72lCRJkoKma+TFaprbyxifvZSo8KE/0F31eyn44Gn8Ha3kLliOK8pamyS079xFwxurCD1/HPE3m7/mpK+2mcKfvYjmC5D96ztwpyWYmudkfq/K8z/YzZ73q7nqx6NZ8M+jTL/OJGuqzm9jz7oqLrkjg/AY642M01SNo//zEY7EKJJuusTsOAC0bPoEX3kFcTdcjz3UWhs0qd5Oij9dgSs6iZEXLjI7TlC0tVdRUPQ+CdF5pCdNNTuOJEmSdI4ZSOHxvONfBfCVEKJmgFmaun0/9Ie9SJIkSX0qqvyEqoa9jEqdS3Lc0B/kLjSVY5teoqO+jOw5dxKemGF2pB468g9T+9IruLKySLz7LtPXUvM3tFL4sxdR2z1k/+p2QrOSTM1zMl+nyrPf2cWBTbVc/7OxzLony+xIkoWtf7wQh9vO5RYd7Vj5zh7aC+sYcd98bC7zp4H7a+toev99wiaMJ3ziBLPjnKJi89v4O5rJuvxWbCHmX18Dpap+9h96jZAQN+dnXys/QJEkSZLOuoEs7tH9XULxQIPQtcbj14b+s7wk9ZfZLwgV83e51MvQi2ebgcult63NwPEN7B6qe6fRwbhMACEGilIh+vrVHCf6rK7bS0H5RlISJ5GRMRvtpNtROPRn1XTuHKs59N9WqtNI267Rm+WfrKK5ZD+pl11PZN541N7a6ly+UjUwYErT0dZTUkLly8/iSEki8Tv3QJgdrcdTbk+Ku7f0vXO7/brbhrt8APibOzj6i1cJNLUx7re3EHlePODr0Tba2amrzxiHvnYAsSEdutp52wM8/72tHN3Wyn2/yeGyG2KAlr4z2PX1G2PTnzXSpv96DdN5d3UbOLfbDJxbNaF/fpCqcy6Rqmtfwi4+A5+h+4W+l70eof9laGm+l91rq7j03lzs0eF4+n5o0Wngwe1RdWYNnL6dv6mDwn98RtTkTEKnno8voO+61fz6rlclYOB+5VO6Nrda+QbYQki4eik2X+95bH799wGb78xtuvo88/2vufgAjQe2kjJ+HpGxmaDjoain368pAX1tbTrbdbU9zZ0OOFq0jvaOaiaPvQuXEgZnaA+gqPqfBxT1zP19w1BbnRmEgT4NtBV6z20GzoG6+wTQDLTVebmEkT7NZuS6kqR+kx/EnC0DqTB0P8MFY+hE9zlpjUHoT5IkSbKY5tZSDuS/SXRkBuflXjcsRl7UbF9P/YEtJE2ZS8L4S82O04OvppqqZ57EHhFB0neXYwszd0pjoN3LwZ++hqeyibGP3EDkedaaYt/Z4ue5B77k6PZWHvjjKC67wVojMSXr2fTEUULcdi5ZZs1Vgkr/8Qlap5+sh+ZZ4nzbtn0bnflHiV+0hJDoaLPj9BDwdFD68Wu440YwctLw2MW6rvEIpVVfkp4ynYTYPLPjSJIkSeeogRQeu0+tTh1oEGBit+/rgtCfJEmSZCEdngZ2H3wRlzOKiWNuGxY7ajYc2krVtnXEjr6IlGmLzY7TQ6C5maqnnkSx2Rlx/4PYo81dxUT1+jn0izfoKKxlzM+vJXqStaajtzf6ePb+L6g40MxDfxnN9KusteakZD2VBR3sXVvBtFszCY/TOaT5LGo9VEHN+3tIue5CwjLNvz8HWluof+8dXFlZRE6bbnacU5RtfpOAp42Mubdisw/95yefv50DBauICEtmVKY1djKXJEmSzk0DKTwWHP+qABcoitLvrTsVRZkMpHT70fYB5JIkSZIsxu/vYPfBFxAIJp93N05nhNmRBqyl8gilH79GRFoe6bNvtsRooq9pnZ1UPfMkakcHKcvvx5FgbtFBBFSOPPo2rfvKGPVvVxE7NdfUPCdrrfPwzH1bqCls4/a/XsSF883f9VeyvlV/KcXhtnPpPdbb9VioGkV/+QBnfASpd8w0Ow5CCOpWvYHw+Ui84WYUI8t/nAWNR3fQlL+TlAsXEJaQZnacARNCcLDwHfwBD+ePugG7Ta5iJUmSdAqzd7KWu1rrspUTG8I4gfsH0Ne/dfv+mBDi2AD6kiRJkixE01T2HH6FTk8jE8feTliotXZ77o/OxkoKNj6HOyaJrAXLTN+spTsRCFD1/LP4qqtJvnsZrlRz30QLTVDx17do2lpIzvcWkDBrrKl5TtZc1cnT93xBU3knd/2/ixl9mZxeLZ3ZsX1tbH+/npl351hytGPlqq/oKKwh66ErCAk3P1/HV7vo2L+P2AVX4kxKNjtOD762Zso+fZOw5EySp8w1O05QVNXtobbhALnpc4kMTznzH0iSJEnSIOp34VEIoQKrjv9XAX6tKIrhxUMURbkLuIUTtd7n+ptJkiRJshYhBPtL3qGp5RjjRl1PbFSW2ZEGzNfRwtENT2MLcZK9+H7sLnPXTexOaBo1K1/FU5BP4k23EDZ6jLl5hKD6yTW0fLqPjPtmkbx4sql5TtZQ2sFT92yhrd7L3Y9PJWea+dNRpaFh1Z9KCI8JseTajp6qZsqe/4zY6bnEzjR/XT+1pZWGlW/hSs8g+rJZZsfpQQhB6ccrEZpK5tzbUWzW+RCpvzzeZg4XvUd0ZDqZIy8xO44kSZIkDWjEI8AjgJeugmEk8ImiKLo+KlQUJURRlH8Hnjn+9wpd20b+ZYCZJEmSJIsorPqEysY95KTPIyVxktlxBkz1e8nf8DQBbzuj5i3HGRlrdqQeGta8R/uuncQtWkzkhReZHYfalzfSuO4r4q+fSeot08yO00NtURtP37MFb1uAe5+aRuYUOb1a0ufw1mb2bW5i8QOpuCOsNYVVCMGx//chKJD1z/NNXwJCCEH9K2+ieX0k3nSr5aZYNxzaSmvpIUZMX4Ireuh/8CCE4EDBW2hC5fzcpSgGdrOXJEk655g9vfocmmo9oJWThRAliqL8FPgjXVdXMvChoiifAG8Atd3bK4qSDowG5gG3ARmc2MNcAA8JIZoHkkmSpF4M0gtPxWbgDY3eDEb6NMLAmy/db9SMTK8djLYh+vsURto6gtO2sm43BVUfMyJhMplZcxA6r1fVqf/+qjn19qn/9ledvf9caBqFG1+ko7GcnAX34R6Rht818H5Pprn0v/pQ3Sfatmz6lOZPNhF52UwirpyDqpzUj0vT1afDFdB9/DCXr8/fVb/5JfWvf0rCwklk3H85Ua4O3f3GOjt1tYt3tunuM8HR+s33VYUdPLt8D0IT/MvzE0gbYwdO/D7Grj9rjE1f20ibX3efYQZOg2E6R2jZGZxzqzYIr5ZVoT+rX+g/X3mEviJhu9b31GQhBK89Vkp0kpNpt2VSq+qfxtyu9yQAdAT0tfX4e76Ub9p8kKathYy8/wpEbByebnc7v0//y37h03e92nynv606duymc/c+Yq9egjs+GXSeXhT9pyH0PrRObudtbaD887eJGDGKpNEzUbr93u7Xf7+2BQy0VfW1VQL6ztcnty2r2UpDcwFjM5cQ7oiFk/vR26+q//hog9NW6M1gKKuB85XerEYu/1AihunlkiTJFAPesk0I8ZiiKBnA9zgxcvHy4/+6U4BjJ/2fbn/zWyHEKwPNI0mSJJmvseUY+4veJjYyi3FZV+suOlqVEIKyLW/RUnKAtEuWEp0xzuxIPbTv2EXjqtWEThxP7A3XmT7Kqe6D3ZQ99RGxl44l4ztXmp6nu5pjnfx5WVfR8YfPTWTEqHCzI0lDyP5N9RTtbOGWX47G6bZ3zfuxCLXdQ9njHxCam0Li1eaPeFZbW6l/402cGelEzb4cPGYnOkEIQcnmlQBkXnbLsBgZ2O6p50jpB8RH5ZKWaP7tL0mSZGkCMPBBpzQwQXmWFUL8AHiQU19SKPQcQKrQs+AI4AceEEL8LBhZJEmSJHO1e+rZdfRVQl2xTMq7FZttwJ9xma523yfUHfiMpImzSRxnrTWzPEfzqXvhFVzZWSTcfYfpUxkbPztM8V/XEnVBNlk/vhrFbp039LUlnfxp2W7UgOD7/5BFR8kYTRW8+5dCEjJCmb50hNlxTlHx3CYCzR2kf3eR6Y87IQT1r7+J5vGScPstltqAC6Du0BZaK46SOvVqXJFDf5kFTajsL1yFTQlhXPY1lvqwR5IkSZKC9qpECPEkMJauNRrbOFFgVOhZcPz6Z37gaWCsEOKpYOWQJEmSzOMPeNh15GUURWHK6DtwhFhn45X+ai7eT/kX7xCTPZGRU5eYHacHX2UVNU89iyMhnsRv3YvNae56c627iyn6/duEjx5Bzs+WYnNYp+hcX+bhz/fswe/R+P6zE0gdLYuOkjFfrqqk4nA7S76Xjd1hnYI6QNu+EurX7CDhqosIG2V+UbR9x046du8lZtFCnCnW2lXZ01RD2ZeriRw5moQx082OExSF5R/T3F7G2MzFuJ3RZseRJEmSpB6C+o5ACFEK/FBRlH8FLgYuAdKAOPj/2bvv+Cqq/PH/r7k1vYc0AglJ6E2ayioqWBAVCyCIKLa1rbtu+7jl62f3o6677upnd/19trjrWijSFQVBmlhBRaX3hFRCerlpt8/5/RHAEIHMEMJM4nk+Hnk84OZ9z7xn7p3J3HPPeR/sQC1QAXwOfCKE0F5ESZIkSTI1IVT2HlmB21vLqAFzCQvp/qNIWmqOUbh5IWEJvel75R2mmo4XaGyg8l+vYLHb6fXIA1jDwwzNp+VIOXlPr8CZGkv2U7djDdFez66rucrd/PO+3bibgvzk9eH0HhBhdEpSN+NuDPDuX/PJvCiKUVN6GZ3OKVRfgJL/W4sjKZqUu4xfNTrgclH75kqcGX2Jnnil0emcQqhBCj9ahMVmp++EWT1iZGBNQz4FZR+TmjCSlPjhRqcjSZLUbYjvwKIuZtElQxGEEH5g6/EfSZIk6Tsg7+j7VLtyGdj3BuKiMoxOp9P8zS7y17+C1RlKv2vvxWIzT0ea6vVSNu8V1KYmkn70A2xxxnbyesvryf3NMqwRIeT8bia2SPOMdG2ocLPwgS246/38+LXhpA+WnY6Sfu/9rYCmWj8PvTTcdJ1V5Ys+wVtaS9Yzd2ANNfY6JYSgZukKhD9AwmzzrWJdtn0DLdUlZE6aiyO8+48M9Pma2Jv/FuEhCQzsM8XodCRJkiTptMx1NyBJkiR1S2XVuyks+5S0xDGkJ40zOp1OC/q9HFn/CkGfm37X3Y/dRB9QRTBI+ZIFeMtKSbjnLpx9ehuaj9/VQu6TSxD+IDnPzMSREGVoPm01VXt44/tbaa728MP/DKPvsEijU5K6oWOHm/j4jVLG355Kn6HmeX8DtOSVU/nW58RdM4LIizKNToembV/i3n+A2BunYO+VaHQ6p2gqz6d89/vE54wlNqP7jwwUQmX/4TcJBNwMy5qO1WqeL8ckSZIkqS3zFF+SpJ7MZKMjzkaxGJurrpEkekZS6InVWgRfR666Rn3YtG1f6Fk8QGObre1qj1XtFlyNJewveIeYqL70z74B9TT7quqoh6YnNujQFht0aH+tAjaVwg0LcdceI2PK/ThTehM8Q6yq43Oe6tQWF3Seed6HEIKat1bScugA8dNuI3T0QASqpnatIWfai1OFOP2a4gDC1CZ2/s8y/NUNjHh+OtE5kZxpmd8Yh1tzuzF2bZVYYm3NZ/xdU62Pxd//msYKN9//9yhGjRbAmeNP2b5VeyWYaIu2ZY0jFe3zecIs2s/BEOX838oFdc090vb+A854HrXn1/G9uEdor2varPEkbGlzYgshWPpMHiERNib+aMApv2sf2+H2A9pjW/wdx6qBIEUvrsUaHU7C3dfh9Z/9WAT8OhZ48Wu7Zlp838QF6uqoXbmKkH5ZRF98GYpPOWNsR6w+zaFY/B2/X4NeN4UfLcYZHkefMbdg6eAyZwloPwe0bP+bWG3niyXQcVxh6afU1uUyqPcUohy9QMNzlKC2s1AJaD1bgaD2awAatw+A0Niu1jhaz2cdwee/TVXP9nXsl552jSTntUpmI9+SF4wc8ShJkiSdM7enjl0H3sDpjGLYwDu6/QrWQgiOffo2jUX7SZswjaiMwUandArXhx/SuPUzoq+6kqjx4w3NRQSC7HvmXRpzKxn85A1ED00zNJ+2mut9vHT/dmpL3dz/z5H0Gx1jdEpSN7VrTRkFX9Vx3Y9zCI8x14iyY8u/xFtQTvKDU7BGGFveQAhB1YqloKokzphpuinWpZ++ja+5jszLZ2O1hxidTqe5Gks4UrKJXnGD6Z0w2uh0JEmSJOmszHVXIEmSJHUb/oCHnfvnI9QgIwbNwWHv/qsEV+7/mJo9W0gYeSXxQ43t2GuvacdO6t5dQ/jIkcROMbaWlxCC0r+vofaLAvo/PomE72Ubmk9bLS4//7p/B1UFLdz3txFkj+v+ixxJxvA0BVjzwmF6D4li7DRjSxq011JcQ/HCrUReOpioSwYZnQ6Nn3+GOzeX+Btuwh4fb3Q6p6jP20nd4a9IGXY1EYkZRqfTaf6Amz25y3E6ohiUdbPpao5KkiRJUntnHZqiKIohQz2EEPuN2K4kSZKkjSpU9hSswO2pZeTguYSHmauW17moL9nP0W2ric4aTsr4G41O5xSegkKqlyzBmZlJwizjRxNVLvqQ+s27yJh7Kak3mqdWmqcpwMsP7qA8r4n7/jaC/uPN1QEidS+b/p5HU7WXu/+/kVis5uncEUGVvD+vwxpiJ/nB641OB39VFTVrVhOa05/ISy41Op1T+BrrOPrRCsJ69SFl+DVGp9NpQggO5K/C62tgzJD7sdtCIRAwOi1JkqTuSZjnb3tP19GcuL1c+JnvAll7UpIkydRySzdQ03iEgVk3ExfTz+h0Os1dV0bBhwsJi08jfdJsFMU8EwL8NTVUvP4a1pgYku69B4tde027rlC7fjtVyz4l9pqL6HvXJYbm0pbfG+TVx3Zx9EAjc/8yjIGXJxidktSNlR5oYMvCIsbdnk76cHNN1S998ysa9x8j5xc3YIsxdpV2EQxSuWQRitXaOsXaRKPvhBqkaNNChFDpc/WdWHTUTjWrsqqdVNbsI7vPNURHphudjiRJkiRpouWTlWLAjyRJkmRSpdU7KK78gvTEcaQljzE6nU4LeJrI2/QqFruDrEn3YbGbp45b0O2m4pVXIKiS/MD9WMONnc7e+FUux15aS8TobFIfmWKaTgY1KHjjib0c2VbHHb8fwtBJvYxOSerG1KBg5W/3ER7rYPKPc4xO5xQtRdUUz/uUuO/lkDjR+CnWde9vwltSTMK0GdhizNVBW/HVRlrKC+k9YTrO6O7/RUSLp5ZDBWuIjcqgb+r3jE5HkiRJkjTTMrKw7YhHc3zCkCRJkgxR31TMgZJ3iYvsR//e1xmdTqepwQBHPpiP393AgOsfxREejfY1nbuWCAapnDcff3UNyQ9+H3uisdPZ3bnHKP7Tm4RkJpP+X9NQ9Kyq3oWEELz59EH2bKzi5l/1Z9SNyUanJHVzny8p5ui+Bmb9aTihUcaOMG5LBFVyn38Pa5iDrB9dY3jHvze/iPrNm4gYPYaI4SMMzaW9prJ8KrZvInbAGGL7jzI6nU5T1SB7c1egKBaGZE8z1ah8SZKk7kqRq1pfMFqnNCuAD9gIVHddOpIkSZJZuX0uduUvI8QRw/DM6VgUC6rRSXWCEIKSz1fSVH6EjAmzCU/sa3RKJwkhqF7xJp7cXBJmzSQ029jFW3zldRQ+swRbTDgZv5mFNdQ8o0LX/y2fz5eXMun7GUy4q4/R6UjdXF2Fj/Uv5pIzPp4RU8zViX10yRc0HS5nwJNTccQaO/pZ9XioeX0JtugYEm6+1dBc2gv6PJS8vxhHZBxpl5krt3NVUPoRDU1HGZozgxBntNHpSJIkSZIuWjseBeAArgXeA+YDq4UQZhkYIknfTUZ/461n+xaNIzP0LJqhY7SHrtFZWnO16qgXpSdW6zHQ0aaw64n99vaDQR8785cQFAFGD7gXW2g44gyxp6M6tB9/1aH9dQ1qHIwUPE0/WdXeLVQf/pxeIycSM2g0wRPbd2re/GnbPWNsiLavVVWnwLVhM03bthE9eRLhl41BPVO5ZWfw9I+fht2hbQGCiBDvKf/3u1rIe/oNFDXI4GdnEZpsB1pjIu3e07RwenGOFs2xsTZtsdsX5bHxn4VcPj2RO3+WhKI0nTE2xqJ9+zEWj+bYcIu27vcQHdfLEEV7qWuLpqo5+gTR/r4K6igF7tcY6hHar1ceoX1EYrOGk3ves0cI+AXX/HokzWpoh/GN/hDN22/ya7+4uP2n7ldLfgUlb2wldsIgwi8divv4HbjPp+Pa7tP+XrF0EFu79F0C1bWkPfAoNmsIWoaKW3R8arD4dMS2a7fk45X4murof+Nj2JVvcrNqfQMCFj2xAe2xil/b9UIJfBNX31hMwdGPSIkfQUrMEAi0a6P9/88mqDFW1dGmjlihdfugI1cdQ5a6aL+6DdED90mSvkMURZkMvAhYgf8IIZ5r93snfYi5MwAAIABJREFUrX11o4EaYKYQovD4734F3A8EgR8JIdYff/wnwAO09vntAe4VQmi/EdaoozuQQ3xTd/HEoi83AcuBckVR/qEoinkqy0uSJEnnnRCCffkraWqpYFj2dMJDu/8K1o1HD1O69R2i+g4mZZzxq8K21bx9F/Wr3iNs9EiibzB2Orvq9XPof1birWpkwFPTCE03zyrRe9YdY9GzhYy6Opa5T/UzfNqp1P3t/biW7euquezB/sT1MXbRlrZUf5CiP6/GGhlK+iPGl7ho3rWHps+3EX31REIzzLW4WF3+LmpzvyJ55NWEJ2UYnU6nBYIe9uS/RagzmoF9zfW3SpIkqVsTJvvpgKIoVuDvwPXAYOAORVEGtwu7H6gTQmQDfwH+ePy5g4FZwBBgMvAPRVGsiqKkAT8CxgghhtLaoTmr42z0O2vHoxBiEHAx8A+gllMXgIkFHgK2KIpyWFGUJxVFyeiKJCVJkiTjFBz7mMra/WSnX01CTH+j0+k0r6uKwo3zCYntRd+Jd5qqVpanuIiaBUtw9utLwpzbDe1ME6og7/m1NB0oJfuJG4gckmZYLu0d+ayaN3+1k5zRkTz0vzlYbbLTUeocb0uQJU/nkdwvlEvvMba0QXvliz/BXVBJnx9OwRYVZmguAZeLmqXLcaT3JmbyNYbm0p6vuZ6SLcsJS+xD8kXmyu1cCCE4WLQWj8/F0MzbWkeWSpIkSd9V44A8IUS+EMIHLAFubhdzMzDv+L9XAJOU1g8TNwNLhBBeIUQBkHe8PWgdXBiqKIoNCAOOdUXyHX7aEkJ8KYR4DEgBbgXeprXeI3zTCZkFPAUcURTlI0VR7lMUJbIrEpYkSZIunOr6w+Qf/YDk+OH0Ten+q2gGvG7y170KFoXM6+7D6jDPB7lAfR3l81/DGhVF4vfvQbEbu6hFyeufUPvJIfo8cCXxlw8wNJe2SvfVs+jxr0jIjODxfw7AEWKejmOp+1r9YiE1Rz3MfioHm0NHaYwu1rS/hPLlnxF/zXBiLjZ2hW2hqlQvWIzw+0m4azaKTXtJgK4m1CCFH7yBUFX6XjkbxWKe1/BclVZvp6xmN/1SryAmUtavlSRJ+o5LA0ra/P/o8cdOGyOECAAuIP5MzxVClAIvAMVAGeASQmzoiuQ1360LIQJCiHeEELfR2gn5Q2Db8V+3HQl5GfAyrVOxlyiKMkUx03ASSZIkSZMWTw17894kIiyJQZk3dfuprEINUrRpPt6GajKvuQdnlHmmDas+L2XzXkP1+Uh8+F6skcZO86zcsIdjSz+n15QRpEwba2gubVUXNjH/kS8Ji3Mw91/jCIsyT8eH1H3l72xg8/xSJtyRQs7YGKPTOSnY4qXwhdU4ekXT+0HjR/A1bP4QT24ecbfdgiOpl9HpnKJ8x0aay/NJHz+NkOgeUA6kpYxDRWuJi+pHv9QJRqcjSZLUAykgTPQDCYqifNXm58FvJ/wt7SdpnynmtI8rihJL62jITCAVCFcUZY6+46jNOXUICiHqhBB/F0JcAgwEnqO1lxS+6YAMBWYAq4FSRVH+V1GUkechZ0mSJKmLBQIedh1egqIojOg/C6vVPKsYn6vSz1bRePQw6ZdPJyI1y+h0ThKqSuWyJfjKjpF0xxwcKcaupNuwu5iCF9cTfVFfMn5wtWk6nBsqPcx7eBuKAvf862IiE80zWlXqvvw+lYVPHiYm2cktP8s0Op1TlPxrI74qFxk/m4o1TMeqV13AW1xC3Zp1hI0cTsQl4zp+wgXUeCyP8p2biMsZQ1zOaKPT6bRAwMOuvGXYbWEM63ebqcqBSJIkSV2mWggxps3Pv9v9/iiQ3ub/vfn2tOiTMcenTkfTWjLxTM+9GigQQlQdXzj6LWD8+dqhtjr9l0wIcVgI8WshRAYwidZVdE4sK3miEzIJ+DHwtaIouxVF+ZmiKCmd3bYkSZJ0/gmhsufIClo81QzLvp1QZ6zRKXVa9f6tVO/9lMRhE4gfeLHR6Zyi7v2NNO/ZTfz1NxA+qH2N6AvLd6yaQ0+9TUhqLDlP3ozFZo7piu4GP/Mf3kZLnY+7/jGW+L7hRqck9RDrXiqmLK+F2U/lEBphnhG0dZ8eoHbTbpJvH0/E4N6G5qJ6vVTNfwNrVCTxM6eb5ssIgIC7iaIP38AZlUDv8bcZnU6nCSE4cPgtPN56hmVNx2E3zyJHkiRJPY7RC8roWFwG+BLIURQlU1EUB62LwKxqF7MKmHv839OBzUIIcfzxWYqiOBVFyQRyaJ29XAxcoihK2PFakJOAA5qy0em83mEJIT4APlAU5RFad/QuYCKtHZwn7lKGAn8C/qAoyiVCiO3nMwdJuqBMdPN9Noqli/LU0a7mDyo6jqli1dEpomfEgMbaUIpFR5tW7bFCa2ePjk4hYdO+/dzSTdTU5zIgayqxCTln/VuoamxXtWt/XYN6Yh0dxzYey+XopyuJ7DuI5AlTUTt43wZ1DO5UndruFFrb/XZs846d1G3aQMS4MURcfQVBRSCcquY2bc6g5thQp++svw80uil87g0sVoWLfj+VsFgL35R0Pr1YZ4vm7cfYtcfG2ZpO/tvnCfL6j/ZQXdjEoy8NY+AIGye+34yx6ti+jthwJaA5NkzjNcupaL/lsqH93LZqvLb5hfb3ior296BfaD8HPEJbrh6hvb5ps6p9JGBT8NRRskf3N7Du3yWMmppCxvdSaTp+iJoC2tts1BHb4td2cfFWN1H8t3WEZKcSO/1KPP4zvx8CPh238j7tfwcsvm/e1zUrVhGoriHloUew28JPuSy0jeuI9eyXk1O37+/4fSWE4OiGxQQ8zQya+AAO4TzrJcsS0P5e1bL9b2K1ny+WwNlji8u2UlWzn/6pVxMXmg4dxAMoQe3nthLQGBvUvk/o2D5CR7saY4WOaxA6YjW3q+rZvvb9F3raNZqe10CSpHMihAgoivIYsJ7W1adfFULsUxTlaeArIcQq4BVggaIoebSOdJx1/Ln7FEVZBuwHAsAPhBBB4AtFUVYA248/vgNoP9LyvOiSr3aFEG5gAa07nUprB+RdtC77fWKOuRWQ86QkSZJMpLR6B8WlW+idcgm9U8w1ne5ceFxV5G+ehzM2kT6T79LXWdzFvMUlVC9egjMzg/gZxo4iUv1BCn+/An+li9F/nkZYarRhubSlqoJ5vzhI/nYX974wiIHju//oW8kcAj6VJb/aQ0Scg5t/OdDodE4SquDw8++h+gKk/eRWFINHHTft3k3jti+ImTiJ0CzzlKgAqN79CY1FB0gfewthce3r63c/9Y3F5BZtIDF2IH17XWp0OpIkSZLJCCHWAmvbPfabNv/20Fru8HTPfRZ49jSP/xb47fnN9Nu6/BOYEOIYsJjW5b7ru3p7kiRJ0rmpayziQNG7xMVkkdPveqPT6bSA182RTa+AopBx4wPmWsHa5aLy1dewRETS6965hq4OK4Tg6D/W0ry3mPTHbyRmaKphubT39v/ms3NDNbc+0Y/RU8y1mIXUvW34Wx4Vec3MeHowYdHGriDf1rF3dlC/vYike6/BmWbsAliB+nqqVyzDmZ5O7LXXGZpLey1VJZR9/i5RGUPoNfAyo9PpNJ+/mT2HlxHiiGZw9q2mms4uSZLUYxk9vVrfVOturcs+6SiKEkFrb+vdwOWcfiUdSZIkyQQ8vgZ2H1lOqCOWoQNnYVHMUdvvXAlVpfCjhfgaa8me/BDOaBOtYO33U/naPFS3h5THH8MaGWloPlUrP6du026SZl1G7JVDgWZD8znhk6XHeP/Vo0yYncrEucbWuJN6lvyv6/jw1ULGTUtj4OXmWQG5Ob+Kgpc/Iu7ifsReZ+wiKSIYpHLRG4hgkF533KmvtEkXC3rdFG1YgC00gvSrZqIEu/dHDCFU9uW9hc/fzNih38duC4WA9nIPkiRJkmR257Xj8XhByuto7Wy8mW+mUre/I/iK1kVodp7P7UuSJEn6qWqA3UeWEVT9jB4wt/VDTzdXtmM9DUcPkn7pNCKTszDLRzghBDXL38RXVEzivXNxpBo7utC17TBlr28m+rJBJM2eYGgubR3cWseyZ3IZfHkc03+VLUf/SOeNu9HPkl/uIa53KFN/McDodE4KevwceHY1tsgQcn4+mUaD3/N1mzbiKcgncdZs7Inm6ZwVQlD8wRJ8TXVk3/wDbCHhZvmu5JwVl33WWlc58waiIswz4lySJEmSzpfz0vGoKMoIWms4zqZ1BWv4dmdjCfAGMF8IcfB8bFeSJEnqvIMl63A1lzI863YiQhN1LCthTvVFeyjftYn4nHEkDDRXnayGjz6m+cuviJl8LeHDhxmai7uwkuIX3iE0K5k+j99kms696vwGFv54H8n9wrnvz4Ow2syRl9QzvP3sQVwVXh5dMBZnuHlWsc5/6QPcJbUMfW4GjpgwQzvTPAdzqX9/ExFjxxI52tiRl+1V7/6YhoK9pI6fSnhyhtHpdFpD0zHyijeRGDuI3kndv66yJElSt/IdmOJsFud8x6UoSjJwJ62jG4eeeLhdWBPwFq2jGz8QupYdkyRJkrpaadV2Squ+JiP5eyTFDjI6nU7z1FdQ+PFiwhLSSb/0NtN0pgG0HDhI3ap3CRsxnOhrrjY0l4CrmcJnlmEJdZD55AwsIeaocddS52X5Dz/H5rDw8D+HEhphno4hqfvbubaM7avLuOYHWfQdEWN0OidVfXyI8jW76X37WGJH9TU0l2BDI9WvLcae2IuEW241NJf2WipLKPt8DVGZQ0kYbp4R2ucqEPCw5/AyHPYIBmfdbKq/V5IkSZJ0Pum6o1cUJQS4ldbOxqv5ZnGatn8pVeB9Wle1fksI0XIe8pSk7w7F4FV3u2r7WlcT1rPqsEXHTbqOWMWqMQc9Na/0xGo8BsKm/VgJ+7e339BUysHi94iL7kdWxjWI46+9atfebtChLTbo0H78VR19YKqjzTZ8Ho5sfg2L1U7G5LkQZj85ejPoOO3TO2yzI1rb9VVVUrVgIfa0ZOLvmQlOBXGGr1kVZ1Dz9h1Ov+bYMEdrrOoPsv+5Ffjrmxn6wh1EpIQC37QT6fBobjPa7tYeazv77UDAp7L4p1/SVOnmyYUDyOqjAmd/TpRFe66Rio5jpePSEqKxHqpT0X7LZTX470BQxxAA7UcVWoS2Y9CiOjW32RjUVhqi5piHFU8foPeIWC6+fxCNwTMf44aA9nITTX7tuTb7vn3B8FW6yP3zRsL6p5AweyLNvtb3k9er7VgJn/b3iqWDWKGq1Ly6FOH2kHzPw9hwgq+DNnW8ASwdtNWWtV1s0OehaMMC7KFRZHxvJjb/Nyep1a/t/WrRGAdg8Wkf+6/4dcQGWmOFEBzMX43bW8eYgffgUEIg0KadgI65BwHtfzNQNbarNQ4QQR256olVNb5eOnLVFdudiB66X5Ik9Ria7moURbmS1s7GaUDEiYfbhe2ltbNxoRCi7HwlKEmSJJ1/Pn8zuw4twWEPZ2jODBSjO7w7SQiV4s2L8bpqyL7pYRwRsUandFLQ7abi9VdRrFZ6PXQvFqeOns3zTAhBwd820rj3KDm/vJGIASmG5dKWEIK3fruPwu313PHCcHJGau/MkaSOqEHBa7/MRQQFt/x+FBYdX9p0JRFUKXj+HYSqkvnELVhO8wXRheR6/wM8hw4TP3M6ziRzXBvgeF3HLcvxNdXR/4ZHsTnDjE6p08pqdlFeu4estKuIjTR2lKskSdJ3kgCEHGl+oZy141FRlN8Bc4D0Ew+1C6kEFtNat3HH+U9PkiRJOt9UEWRv7nL8/mbGDH0Ahz3c6JQ6rWL7+7gK95I2/mYiUrOMTuckoapULlqIv7aGpB8/hC3e2A7R8lXbqVy3m7RZl5Bw1WBDc2nrw5cL2L7qGFf/IIsR16cAtUanJPUgG18v5fA2F1OfHklcunmud2WLPqF5/1Eyfj4VZ4qx1wbPkQLq164nfNRIIi692FQLttTkbqMufyepo68nIinT6HQ6rdldzYGiNcRGZpCZcrnR6UiSJElSl+toxOOvae0Lbtvh6AFW0Tq6cZ0QQsf4fkmSJMloR4rfp9aVz+CsW3vECpr1BXso/3Idsf3HkDDMXB/iatetwX34IAm3zSAku5+hubh2FFH40mZiL80mfa55jtOeDeWsfzGXkTemMOkR83QaSz1D0b4m3v5rEaOujWfEzekdP+ECadxVSPnSLcRNGkbcVUM7fkIXCjY1UzV/Iba4WOJnTjdVrUF3XTklW1cSmZpD0vCJRqfTacGgj91HlmO12Bja77ZuP9tAkiRJkrTQU+PRB7wHLANcxx+7rituToQQa897o5IkSRKVtQcoOvYpab3GkNrrIqPT6TRPXQXFmxcTlphO+gRzfWBu2r0L10cfEnXJeKIuvoSggeuF+yrrKf79KkJ7x5HzxA0oeuqjdqHS/Q0s+9Ue+oyMYdrTQ0z1+kndn6c5yH9+dojIODtznsqm3iTvL39dEwXPv4MzLY70R64zNBehqlQtWESwqZmUHz+GJSTE0HzaCvo85L//OlZHCBlXzEbRUwPahIQQHCx6lyZ3BRfl3EmII8rolCRJkr7TFLn08QWjp+PRDkw9/tOVBJ1YbVuSJEk6vaaWSvblvUVkeCr9M683Op1OC/jcFKx7DYvNTsZ192CxmWNlZgBfeRlVy5fg7JtB/E03G5qL6vVT+seliKDKgP+5FWuYOeonNlR5mffYdsJjHdz14kjsTmPr20k9z5Jnj1BZ5Oan84YREWunXsfiJl1FBFUKn3+HYLOXnN/dgTXUuJqvAK6Nm/EcPNRa1zG9t6G5tCWEoOjTZXgbqsm5/mHsYd2/k6607AvKanbTL/VKEmJyjE5HkiRJki4YrV8dtu0LVi7AjyRJknQe+QNudh1ahNViY8SAO7BazNNJdy6EUMn/dBHexhoyrp2LIyLG6JROCrrdVCx4HYvTSdKcu1Fsxn2XJoSg/B+r8BaWk/OrmwhNizMsl7b8niDzH9uOpyHA3X+7iMgEc3SGSj3HtjVVbH2rkimPpDNgXLTR6ZxUvnQLjbuKSH/4WkIzehmaizs3j/r31hM++qLWuo4mUnVgC/UFu0gdfT2RKdlGp9Np9a4icvPXkhDdn36pE4xOR5IkSZIuKC0dj7JTUJIkqRsTQmVv7nI8XhfD+88ixGmeD+Hn6tjujbhKD7QuJpNibO3EtoSqUrV0Ef7aWpLuvBtblLHHunbVZzR8speEOyYSO9Ycx0kIwcqn9nN0bwMz/ziM1IHdfySTZC7VRz0s/G0eWRdFcuOjfYxO56SGnQWULfqEuIlDib92hKG5BBsaqZ7/BrbEBNPVdWwpL6L0i1VEpQ8mafhVRqfTaV5fI3sPLiHEGcvQfrfKuo6SJElmIUz008N1NAzjqQuShSSZiYluvjuiuU6bnptcHbXfdH1Q0Rirr009+6VjGqfWHKzaty90xGLTlquwa4vLLdlMTX0eA7NvJjo2s8O/bapN+2sgNMaqdu1tBh1nj60v3MOx3RuJzxlL7EWXEdTweqk6ZjMGndr/+qvtYuvXbaTlwH7ipt2KY2AmapujLRzaajzaHdrXbAtz+s/4u4YdBVQt2ETM9waQPvtiopzalqmNtns0bz/a5tYea22N/XBhKdtXHeOGx/oy/too4NttRFq1tRtp0Z5ruEV7jc0wHdcLp6Jt9LC1izobgkLbfvl1rAXo1dgmQLOqfb+ahbYTsUHVXmfQFQw75f8Bn8pLP90DisLtfxxJkxIKx3e9IaCtXZdf+/Zb/Nr2yVfTRMGfVuFISyDx+zfh6eB5AZ/GkdI+7cff4m29VgpVpXreItQWN2n3P4SNEPC2i/Vpu2ZbdUxft/g7vrYGPM0Ur52HPSyafuPvwBroeP+0tAtg8Wm/tlsCemLPfL6oapB9+xcTCHgYNfRuHDjhLPEnKEHt56sS1FE/WGusju2j43qhJ1YIja+B1jg9bQKoWrevY5+0tmkGeo6VJElSB856VyOEkB2PkiRJ3VhZzR4Ky7eQljyOtOSxRqfTae76Cgo/XkxYYh/Sx0/T1Ol4obTs20/9+o2Ejx1N5GXjDc3FW15PwR/fJiQ9nr4/uck0o5lyv6xn+XP5DLsqjikmGokm9Rxr/3yY4t0u7vrLCOLSQo1OB2it63jwD2tQ3V76PHU3lhBj6zrWv78Rd14uCdNvx5GSYmgubQmhUrxpEYGWRgZO/iE2Z1jHTzK53KL11DcWMzRnBhHhSeAJGJ2SJEmSJF1wcqy/JElSD9XQXMb+gneIiehD/35TjE6n04I+N/mbXsNitdNv0lxTLSbjr6qiasEiHGmpxM8wdtqi6vGT/7sVoAr6PTnd8MUrTqgr9/Lyjw+Q0DuEe/80EItJVtaWeo49Gyv4ZEExl83pw/Brk4xO56SiBVtx7Soh+aEbCOlrdF3Hw9Rt2kjEqNFEjh1naC7tVW5/n8aSg6RedgvhCelGp9Np5dV7KCn/nPSUS0lOGGZ0OpIkSZJkGNnx2I6iKJMVRTmkKEqeoii/NDofSZKkc+EPuNl9ZBl2Wxgjsm/HYjFugZPzQQhB0SdL8TbU0G/SXBzh5llMRvV6qXx1HorVQuK9c7E4jOsQFUJQ/Lf3cBdWkvHEzYSkmmMxmYAvyL8f34/PrfLw34cQGtm934+S+VQVNrP0/+0lfWgUN/ysv9HpnFS7LZ+SRZ+TdO0QYiaONDSXQH0dFYvewJ7Yi4TbpplmJDRAQ/FByretJyZnFPGDLzU6nU5rdldz4Mg7REemk9PnWqPTkSRJkiRDyY7HNhRFsQJ/B64HBgN3KIoy2NisJEmS9BFCsL/gHTw+F8OzpuOwRxidUqdV7fuE+sI9pI29kYhkcyySAq3HumbZCvzlFSTePQd7vLEdfdVrtlP7wV5S7pxA9JgsQ3Npa+Pzeync1cjdv+9PSlb3nz4pmYuvJcC8x3ditVu46y8jsDnMcXvrKXdx6Lm1hGcmkvXDqw3NRfgDVCycj/D7Sbp7LhaHeVaS9zbUULxpISHxyaRfMcNUHaLnIhj0sefwUiwWG8Nybseip8a0JEmSJPVA5rgzM49xQJ4QIl8I4QOWADcbnJMkSZIuxRWfU1l/kJzeVxMT2f3r6DVVFHB022qi+w6j19AJRqdzisZPttD89Q5ipkwmdICxo6yaD5Zy9OWNRI3NJnnm9wzNpa3dq0v4emkhV9/Xm1GTE41OR+phhBAs/+1+KvObufOF4cSmmqOuo+oLcODpVQhVMOg3U7GGGFsaom7FarzFxSTOnIWjl3mmoasBP0Xr54GAjMn3YrGbozREZxwqWEtTSyVDsqcR4ow2Oh1JkiTpDBRhnh8zUxTlurP8boaWNmTH46nSgJI2/z96/DFJkqRuob6phNyjG0mMGUCfpO4/Xc3vbqJg8wKcEbH0vXymqUbCuIsKqH17FaFDhxA96SpDc/HXN5P/+7ewx0eR8bObtK9438UqDrlY+8wu+o5N4JafZhqdjtQDbXmjmJ1ry5n8o2z6XxpvdDonHfnHBzTlVjDgiesJTYs1NJemz7+m6ePPiL7iSiKGDTc0l/ZKt7yNu7qU9El34Iwyz+t3ro5V7uBY1XYy0yaQEJtjdDqSJEmSdD6sVRTlA0VRTtc39istDZyxyJKiKMOFELvPObXzSFEUB5AhhDjc1Zs6zWOn9D8rivIg8CBACHK6mKSR0kP7+C069ktrrJ4OE6v27Ss6YrFprD9n1TF9yqY9Vti1xQrbqfvk87ew+8gKQhzRDM65DWzWkxcw1a59//XEBh3aXq+gXfvrGjw+4EWoKgUfv0HA20zObT9CiQwl2C5W1Tg4JqhjEI2qYWBSoLGB8iXzscXHEn/v7YhQEKhnfY7ibJ/96Tmc2lc9DXf4EEGVPc+vJNjoZsSLs4mItwK+b8VG2L2a2oyxt2jefrTVfcbfuRv8vPXTLwiLsjH3hSHEO5s0txtp8WiKC1O0H6sQHZ3WdrSfr5bT3jpcOCravib3f+vsOTOP0P7Ve4vQPpKvUQ3RFFcfDNcUd2S7i9XPH6b/lcmMumcwruDZX4t6v7b7tia/9mnIzb5vX1xqNu2mfM0ukmZcSsiYwTQfPx19Xu21TYVX23vQ4j379dp7tJS6RW8Skp1FwqQpKH5t27d++xJy+u1rjDtdmzW5X1G7/3OShk8kLnXIKZctq1/7e9Dq0xZr8Z/9Gn2usUqgNbappYKDBauJjcwgK+WKk4+fEuvXeB4GtJ+vBHXEBrRdM0VQ+/6jJ1bVMaxH1diu1rjuRvTQ/ZIkMxHm+KK+G9gNLAI+VxTlp0KI5W1+p+kgnu1uZbuiKPMVRTGsmJaiKBZFUeYCh4FZF2CTR4G2y+j1Bo61DRBC/FsIMUYIMcaOeerjSJL03SaEyr68N/H5mxjW/3bsNnNMN+yM8q/W03T0MGmX3UpognkGn4tgkMqFC1DdbhIevBtLmLHHumjeFlw7i8n64SQiso1dMfcEVRUs+dVe6ss83PWXEUQmyL+X0vnlqvLx8uMHiE4NY+rvRplmlG/LkXKK/76OyBF9Sb3rCkNzCba0UPXafCxhYSTOnYOi58uyLuauLaN4ywoikrNIHT3Z6HQ6LRD0suvIMmwWJ8OypqH01C+cJUmSpO8iIYR4GZgEPKEoymuKopz4NlfTN0pn+6toAe4EDiqKskhRlNGdy1U7RVHCFEV5DMgDXuXUzsCu9CWQoyhK5vFRlrOAVRdo25IkSees6Nin1NTn0j/jeqIizNNJd65cBXup/HoTcQPHETfoYqPTOUXte2vwFOSTMG0Gjt4phuZS89kRShZ/QfKU4SRPHmZoLm198HIB+z+s4qYnBpAx0jwrkEs9Q9Cv8spPDtDSGGDGn8cREmVs/cQTAo1u8p99C1tUKBlP3KJvpP15JlSV6oWLCdTXk3jP3VgjIw3Lpb2gz0P+5nlYHSFkXjUHpZsvviKE4EDhalo8tQzLmo7Tbp7PcyGBAAAgAElEQVRjLUmSJEnny/EZyJcCFcAORVE0f0g725yPSqDX8ZiZwExFUXYALwMrhBA1557y6SmKMh645/j2TizDeuIr7Irzvb32hBCB4x2e6wEr8KoQYl9Xb1eSJKkz6hoKySt+n6T4ofROGmt0Op3mra+iePNiQhPTSbv8NlPVdWzesxvXxx8RNf57RI4aTUDH9NXzzVdRR9Gf1hKRk0TWDyYalkd7uZ/XsP7/8rjoxhTGz75Q3xtK3yUrXygg9ysX9z4/gF79o4xOB2idmlr4/Dv4axro/8e7sMdomy7eVVwbNuHef4C4abcSkplhaC5tCSEo+mQp3oZqcq5/GHuYOV6/ziip3EZ57V6y0iYSFyVr2UqSJHULAo1j9STaTKcWQgSAXyqKsg5YDGhaOfJsHY85wFPAY23iLgL+AfyfoiifAu8BHwFfCyF0f/pSFCUZmABMBG4Ckk/8qk3YPuBxIcRmve2fCyHEWmDthdiWJElSZ3m8Dew5vIywkDgG9Ztqqk66c6EG/BRumIeiWMi47m4sNnOMZALw11RTuXwpzvQ+xN841dBchD9I6QsrEAIG/fdNWBzaa8d1JVelh0VP7KFXv3Cm/3ZQt38/SuazbXUF788r5aq7Uhl3UxKlOmoMdqVjCz6i4et8+jx2PeEDjR113rJvP/XrNhA+djSRl403NJf2KnZvpr5wN2ljbyQyJdvodDqttj6fw8XrSIjpT2bKZUanI0mSJEld4an2DwghPjw+K/ohLQ2c8ZOKEKIR+KmiKP84vqGZtE6/Fsefd8XxHwCvoigHaO0kLKK1LmId4AECQAgQTmvHYhrQHxgKtJ2j1v7TSSHwDDD/XDo1JUmSejpVDbDn8BKCQR+jBt+DzaZt4QYzK972Fp6acjJveABHZJzR6ZwkAgEqFi5AURR63XkXitYFiLpIxbwNePKOMei3NxOSYo6pzMGAyqL/2oO3JcDDr47BEWaOzlCp5yjZ38TC/84lZ0w0054wrAT5t9R9coCK5Z+RMHkkCddfZGgu/qoqqhYswtE7jfgZ003V+e86epBjX71HbL+R9Bp2pdHpdJrbU8feg0sIC4lnWL/bZF1HSZIkqUcSQrx9hsfrgOe0tNHhpwIhRB5wp6IozwA/B2bT2pGo0NoJqRz//0XASE2Zt2p7J9R2kOsu4K/AG8eHcUqSJEmncbhwHa6mowzrfzsRYeZYVKQzqnO/oDpvG0mjryGqz0Cj0zlFzbur8JUeJWnuvdjjjO0QbfjsAHVrthF308UkXJZjaC5tvf/vAvK/qmPm74eSlB3R8RMkSYemOj8vPbaPiBgbD/x1EFa7OTp53IWVFP3lXcIHptH74WsNzUX1eql85XUUq4Ve983F4jDPiHFvfRWFHywkNC6ZPpfdbqoO0XMRDPrYvf8NhFAZkXMHNmv3/+JPkiTpO0dOtb5gNA9HEEIcBB5QFOUXwF20LjzTdsGZti/bme4mzvTSuoAVtI5u/FRrTpKkWTe6wdW1MqfWb9d1tKnrw4COWM3t6hkxoCdWz2qeGhcEEDoWDhA6ti9sHbd7rHonRyu20SftMhKThqN2EK/atL9WqkNHrF1brNrB59+WmmMUbXuLyNQcEi+9DlXjezbo0BSG6tB+Z6E6T41t3rGThq1biLpyAqGjhqC2/VPm6OjIf8Pm0PZdWqjDf8bfecvqKP/7O4T1T6XP/VcSZndr3n603aMpLsqmLQ4g2toCQO7XLjb98wgXT01k0m0xQMu327Vobzdc0TZ/Ntyi/fg7Fe3noF1HrLULRjkFhfb98oozv1/aalG1Tx5pVrXvU6OqvcOlMahtBXhXu7igX+WVx/fiqvLx8PxxiNhoXMd3xxXQvqq8y68t1wavtjh/g5u8p9/EEuYk5ecz8aih4D1zfMCn/X2l+LS9BhZv67VSCEHNomX4KypJvv9BHOHx38rF6tV+bbdonMJu9XV8bQ36vRSvfg1QyJlwDw7hgA6eZ/Frv2YrAW2xFo1xAErwzOegEIL9uW/S1FLByIFziLDGgF/j+RXQFne27X+Lnlih8RjouF6g43oltG6/Nfj8t6nq2b62/RJ62jSanmMlSZJ0HumeB3V8UZm/An9VFCUDmAxcA4yjdRr12Zy44/HQOrLxE2Ad8IkQGu+cJUmSvuMamss4UPguMdGZ9Mu8xuh0Oi3oc1OweR42ZxgZV94JFnOMZALwV1VTvXg5zoy+xE69wdBcVH+AgudWAgqZv7wFi90cK8E2uwL85+eHiU8LYfZvsoxOR+phhBC88+wB8rfVcvsfhpE+3BylBURQJfe51fhrXPR95h5sccauZOz6+EOad+8i7vobCMvpb2gubQkhKP5gCZ76CvpP/D7OyHijU+q0wmOfUFmzj+w+15IQmwNu+RFGkiRJks6mUwWYhBCFwEvHf1AUJZHWRWn60rq6TdjxbbQAjUAJrbUbc2XdRkmSJP18gRZ25S3Fbgtj6MCZWHSMzDIjIQSFHy/F21hL/xsexR4aiVk+wqk+P1WvzUexWkicOwdFz6jZLlD6n/dpySun35PTcSaZpPNFCBb8dy6uKh+/WDSc0AhZ11E6v7a+Ucy25Ue58oFMRk1NNTqdk4pf+xjX14UkP3IjoQOMXb3dnZdL7XtrCB82nOgrrjI0l/Yqd27Glb+b1EtuJDrVPB2i56q6Lpcjxe+TFD+MvqnfMzodSZIkqRMUOQj4gjmvnxCEEFVAFbD1fLYrSZIkgRAqe/PfwutvZOzAe3E4un8dvcp9H+Mq2kPauKlEJGUanc4pale+g6/0GL2+fx+2uFhDc6nbcpCqd7+m163jiLnUPB/eP11RwfYNNdz28wwyhxs74kvqeQ5/Ws27fzzI4Em9uPZx89Qzrf7wAMeWbyNpyghirhnd8RO6kL+ulopFC7An9iJxxkxT1U5sKD5I2RfvEZM9ksQRV56uAkO34vbWsy9vBRFhvRicdbOpjrUkSZIkmZl55rNJkiRJZ1VUvpUaVx4D+kwmOqK30el0WnNVMce+XEN0nyH0GjrB6HRO0fT1Dpq2fk7UpKsIGzrY0Fy8FfUUv7iGsP6ppM41z2immsImlv4+n4GXRnPtfR1VWpEkfSrymnjjZ7tIzolk5h+GYdFT/7gLNeWWc+TP7xE5OI2MR682NBfV66Ni3msQDJJ0971YnOZZ4MRTX0XhpgWExKeQfkVPWEzGz+5Di1GFyvD+s7BaNRYYliRJkiRJdjxKkiR1B66mo+SVbqZX7GB6J44xOp1OC/o8FHywEFtoJH0nzDLVh1J/ZRU1S1fgzMwg9obJhuYigiqFf3oHISDzF+ap6xj0q6z89XZsDgv3/qG/aTqFpJ6hqcbL649uxxFiYe7fL8IZbo4p/L7aJg79z0psUaH0/42x56NQVWrmL8FXXkav2XfhSEw0LJf2gj4PBetfQ7FYybzuXqx2p9EpdYoQgoMF79LYXMbQ7GmEhXb/OpWSJEkSrUsfm+Wnh5Mdj5IkSSbnD3jYk78Cpz2SwRk3maqT7lwIISjeshxfUx2ZV83B5gwzOqWTRCBA1byFrXUd777T8LqOZYs+oflgKX1+eD3OZHPUdQT4+F+HOba3nrueyiY2uXt3Kkjm4vOqzP/RTppqvNz9t1HEpGhftborqb4Ah55+m0Cjh4FP3YYjNtzQfFzr3qdlxx7iptxI2ICBhubS1onFZLz1VWRcczfOqDijU+q00sqvKKvaQWbaFSTGmedYS5IkSVJ3ITseJUmSTEwIwYGi1Xi8LoZlTcduM8eH8M6oyd1GXf5OUkZdZ7q6jtXrVuM7WkrC7FmG13Vs3F1E+dItxF8zgrgJxk73bqt4ew2f/ucwI25OZ/TkBKPTkXoQIQQv/7qA4p313P77YaQPizY6JaA1ryN/XU/TgWNk/9cUwrOSDM2nZcceXO9uIPzi0URffoWhubRXuWMzroI9pF5yI5Fp2Uan02muxhIOFawlPiaHfunmKXUhSZIkSd2JOeauSFJ3pfTAvnuLjn3SFatxlJ5Ve5uKjtgu2S+bjtFwNu3bF21iSyu+pqJ2H1l9riY6pu8pI/GFXXubqkNHrF37iMqgxtjg8XJYntpySj5bSURaNoljJhI8TVqqjtJZql1jnIY2m/fuwfXZp0ROvIyQ0YNQUTt8juLoOOYEpzOgKS7c4cPvaqHohXcI7R3HgB9egdXhO21shN2reftRNremuEir54y/8zQFeOf/fU1sWijT/18OkVaX5u2HWbTnGqZoO1YaX34ArBg7UjgotL9XvELb/gN4RFBTXIuOaTyNQvuRbVC11xWsD559dPPavxeydXUtV/xoMH0m9cOl4TC4/Nq/jGn0acu1xXfq/leu2Er1+/tImj2B0IuH0tLmdPR5tR8r4dX+N8PiPf371Vd6jOp5i3H07UP8tOnYzhB32jZPfxk5LavG2LZtuo4epGzbe8RmjiRp4ASUdm1Y/Hq2r/0Na/VpO7csfu3noOIP4vU3sfvQEkIcUQzLuBVLQADfPt+UgPZ2UTXGBrWd13pjRVDj9lUdFwxdsV1wrPTQcR3uEkZvX5KkU30HpjibRQ/sNZEkSeoZmloqOVS4lrjofmSkXmZ0Op0W9HspXD8Pqz2EPpPuRNHTGdzF/HW1VC1birN3OjG3TjE0FyEEh/93Pf4GNwN/fQPWUPMsYvD2sweoL/Nwxx+GEWKSuntSz/DVmkrW/K2Ii29J4tL7zLNyu+uLw5TN/4CYyweTNMvY63CwqYmKV17DEhpKr3vvwWLX0/XetbwNNRR+uJDQ2GT6XNb9F5NR1SB7jqzAH3QzIuv2HjHbQJIkSZKMYp5PfZIkSdJJwaCfvbnLsVocDMmehtLNR9cKITj60Qq8rir6XjMHe3iU0SmdJIJBKhcuRAhBrzvnoNiM7VArW72T2s+OkHn/5URkGzuls61d68r5elUZkx7qR8ZF5qk3KXV/BTsbWPDrg2SNjuaOp/qbptPKXVBB8QtvE5qVQvrjNxqalwgGqXx9PsHGRnrddw+2aPNcQ4N+L/nvvwZAv0ndfzEZgNzSTdQ1FTG4701EhiUbnY4kSZIkdWvd+5OsJElSD3WocA1NLRUMyb4VpyPS6HQ6rfbgNupzt5M89joiTFb3q27DerzFRSROn4E9wdiahZ7iSgr+9RGxYzNIvXW0obm05ar08OZT+0kfFsXVD/UzOh2pB6kucfPSD/YSk+zkwf8bgl1HWYiu5K9rouCZZVjCnGQ+OQOL07jRhUIIat96G++RfBJmzsDZp49hubQnhErRJ0tw15WTeeUcnFHdf8XnsortFFd8TnqvcaTEDzc6HUmSJKkLKMJcPz2dOe7uJEmSpJOOVe7gWOV2MtImkBBrnimH58pdX07ppyuJ6J1Dr1GTjE7nFO4jedR/sJnIceOIGDHS0FyEP8ixv7yNNcxO//+6HkVrXdQuJoTgzf/ZT8Cncsdzw7DqqC0qSWfTXO/nHw/tQQQFj740jIhYc0wdVr1+Cn+3nGCDm8z/vh17vLFf/jRu2Urj1s+ImngVEWPM84UEQNmODdQX7iZt3E1E9e7+Kz7XNxRxIO8d4iIz6d/7WqPTkSRJkqQeQRZokiRJMpFGdwUHC94lNiqjR6ygqQb8HPl4AVa7kz6TZptqyniwpYXKxYuwx8cTP/UWo9OhcvEHeArKGfzULThiw41O56QvVx7jwEfVTP3lABIzzJOX1L353EH++cheao56+OGrw0nKPPvCMxeKUAXFf1lNS+4xMn41nbDsFEPzcR86TO3KdwgdMpjYG643NJf26g5vp3znRuJzxtFryASj0+k0t6eO3QcWEeKMYXjWDCwWHQvISZIkSd2PMMeX/N8F5vkEKEmS9B0XCHrZVbACm9XJ0JwZWJTu/6Gn5Kt38NSXkz7xDuxh5qlJJoSg+s0VrfXSZs/B4jS2Jlnz3kJqVm4l5ppRxI83z1T0+nIPq/94iH5jY/neneaZ3il1b0G/yis/2U/hrgbueWEQ2WPMUzP0yGtbcW05QMo9k4i+dIChufgrq6iatwB7UhKJc2abakGulvIijm5aQkRyP9LHTzNNXc5zFQh42b1/IUINMmLwHLmYjCRJkiSdR3LEo9R9daObXF1TJvWMCNPYrq4PBDpi9bWrcb/07L9VR8ecVXu7wqatXaFj+8J29u0LIdhXtIYWby0XDbsXe1g0agdtqjqmvKp27a+VatMeGzzLgst1+buoOvwZvYZfRUTWoA73R0ub7akObUVR2sc1fr6N5t27iL1pCvas3qi0+b1Da6ZgcwQ0x4bYTx8baPJQ9uLbOFNi6fvQRMLsPs1tRtq8mmMjNMZGWD1A63ty/jP7CAZU7nk2myj7t58fbtG+/RDFryNW22tg13ENsnTRd61+EdQUp2o+A8ArtL+vWoS2c6BZaL/lqw9qH32oJ7Y2EIEQgpVP7mbvR7VM/e+h9J3Yl9p2u1vv195mnVd7bJP/7BeXqo17KXzjS6ImjSJiyuW4vR2/vwJe7X8HFK+O92C9m8qXXwWLlZS778NuCYXTXBosGnI8war90oLFd+b3la+pjsI1r2IPiyL7srnYg1YIdvw+tPq1F7Gy6IkNaDu3FP/pz1UhVPYdXkZzSxUjB84hwh6H4tV+DhLQdg0AULTGBrVfL4SqPRZV4/aFju0Hte8/Gq9XraEaY9WuKY4muqjdLqHjuEqSJBnBPF+dSpIkfYcdrfqSitq9ZKdNJDam+y/e4W2soeTTZYQl9iF1jLmmB/orq6h9821CcrKImnil0elQ8s/1+GoayfjZVKyhOnpdu9hXa6rY82EtUx/PILGPHP0jnR8b/nKQHauOMvHR/oy9va/R6ZzUsKeE/L+uI2pkH5K+b/wK1hWLFuCvqyX5rrnY4+IMy6W9oN9LwbpXUf0+Miffjz2k+5dfyC/9iOr6Q/TvO5n46Cyj05EkSZIuFGGinx5OdjxKkiQZzNVcyqHi9SRE55CR8j2j0+k0oQYp3LwQISBj4hwUE9XJEoEAVfPfQLFZSZhzh+FTF2s/2kfdh/tIueMywgemGZpLW421PpY9e4SM4ZFMvNs8eUnd25Z5+Xz6Wj7jZvblyofNU1Ig0ODm8FMrcSbH0P/JW1A0jnrvKnVvrcade5jEW6cTmmmeL6KEUCn+YDHu2jIyrr6L0Lhko1PqtKq6QxSUfkhKwkh6J40zOh1JkiRJ6pHkVGtJkiQD+QMeductx2mPYEi/W021+Mq5Kvt6HS1VxWRMvBtnZLzR6Zyibs06fCVHSbxvLrYYY+vK+aobKPn7esIGpJI801wdziv+kI+7KcCc3/XHYu0+ZS0k89r6dhXrXjjCkGuSueFXQ0xVE9AWFUrfhycROTgVW2QINBqXS+Mnn9H40RaiL7+CqLHm6ggr/2oDroI9pF46lag+g4xOp9Oa3dXsPfImkeGpDMw0dpSrJEmSJPVknep4VBTl7jb/XSGEaDnHdsKBaSf+L4SY35m8JEmSuouDRWvw+lyMGXQfDps5VnXtjKayI1Ts+oD4/uOI7TfC6HRO4c7No+GDj4gYfwnhI4YZmosQguIX1yICQTJ+NhVFRw3SrnZgSx3bVlcy5dE+pPXv/tMoJePt+qiOV359hMxx8Ux/bqQpO7MTrx5idAq49x+idvnbhA4ZRPyUG41O5xR1R3ZSsX0jcQPGkjis+69g7Q+42XV4ERbFxoicmVgtdqNTkiRJki4w5TswxdksOjvi8XW+mZH+IVB8ju0ktGtLdjxKktTjldXsobx2D/1SryQmIt3odDot6HNT+NFiHFFxpF16i9HpnCLY0kL1wsXYEhOIu+Umo9OhZt1OGrbnk/7ItYSkmad+m98TZPFTufTqG8rkh+Qq1lLn5e1o5B8/yiV9YDizXxyNzWGe0gtm4isto+rVBdhTk0m4706UgHm+jGipKqH4wyWEJ2fQ+/Lp3X5koCqC7Mlbjttbz+hBcwlxmmdVdUmSJEnqic7HXc35vPvo3ncykiRJGrm99RwsepfoiHQyUy83Op3zomTrSvzNLjKuvBOr3Wl0OqeoXb6SYEMjiXfNxuI0NjdveT1HX3mfyBEZJEwZbWgu7W35z2Gqij3M+m02dqd5Oj6k7qk0t4W/PnSQmCQHP315ICERxo8qM+NKtQFXA5UvvYrF6aTXQ/cafo1qy9/cQMH617CFRJBx7T1YrN2/SlNe8UZqXUcYmHEDMZHmWeBIkiRJknqq7n/3IEnnWw+osfctehbQ0BWr47sCjVNJdU05/f/Zu+/4KO478f+v2aZV7x2EGqJ3DDYYVzDYGBts3O3YcS8p3+Qud0ku7ZLc5XKX/HK+JDbuJS7YBndMscE2tmmmd4F676uubTOf3x8StgAJzSKhGYnP8/HQA7H73s+8Z3c1u/OeTzlX+6U3B5v+NkW3WCE0Dha+gwAmjL4RxW4/aTEzYdfXrmbT//xrdv2xagCxWlcdwXV8D668XSTOvIrg1FFop8YFsFizFkBtQk9s657dtO3eQ+R1C7Fnp6Kdlt3JFMeZ7+/O4VB1xwbbfQhNkPf4BygKjPnJQoKC/KfFhdk9utsMt7t1x4ZZzxxbk9/K1uePM+e6WGZeHAL0nYdT8enevlPR/1zpfQtaz9H1yr7eI2fDLU5/rXuP1b/9Nk3f8aJRc+pus1HTP+1DgxrW4+2uCjd/vS8Xi93K/c/MwB8VjMujr90Gr/7tt/j6LtK15tfib/PgjnNii+i7bZ83gK/HHv09OC2e09+vmsdD7YoX0NraSX7sMRwh0eABq1vfe9vq1b15LPoPLVi9oPl9FK57AdXTwZjF38dpDYdTtmfVfwjA4tVf9LV49f8NKD59sYpPo7xuDyVVW0mLn82I6GnQy2MVn/7jFWoAsZrO/QqkzYC2r+81EGoAx0ARQDFf7/6D7lwJ4Hh5Thi9fUmSzp75rkUOW2apsHTPQx69JUka1ooqvqSxpZgxGYsJcZpnmO3Z8rY0UL55NSEJaSTOmG90OifxuRqoe2c1QVnpRCy83Oh0qHx/D837S8l46HKCEiKMTucbQgje+d1hHCFWbv+Z7AEk9U+ry8vTD+7G067y4DPTiB0RbFgu9dsLOfKHtdRvzaf49ytp2ZWH2h5ABe4cEppG7Wuv4y0rJ+HOOwgaYZ4V5IUQFH/1Ju11JaRfcjshMSlGp9Rvja0lHCn9kJjwTEaPuMrodCRJkiTpvGGWHo/R3X5vNSwLSZKkc6y5tZyC0k0kxk4kOc5ci6+cDaGpFH/yKkJopC24E8VinvnbhKZR+8ZKhBDE3nMrSiC9Xs+BjnIXxc99TvQFGSQsMnZxm1PtereCwq9d3PCb8UTEGj8cVhq6PG1+nntkLw1lbh58dhopY8INzac1v5bs711O9NSRKBvLaPriEJrbS+Tc8YbmBeBa8xHtBw8Sc/31hEwwfnGb7qoPfEpDwW5Spl9N9ChzHa/OhtvTyL6CN3E6opicsRzLcBzdIkmSJEkmZZZP3bld/wqg2shEJEmSzhW/6uHg8VU47GGMzVwy5CfoB6j6egPtVUWMuHQ5QRGxRqdzkqbNn+EuyCfu+mXY4oztWSpUjeN/Wotit5L1o4Wmeu3bXF4++lMuo6ZFMfPGEUanIw1hPrfK89/bR9mhFu7680SyZkb3/aBzTGgaFR/sQwhBxOwxRC+YSv3722ndW2BoXs3bttH02WeEz5lDxLyLDc3lVE2FBynf9RHRGVNJmnyl0en0m6p62X/kNTTNz9TMW7HbjOuBK0mSJJmE6FzV2iw/w91AFh4DeroURbEpijJCUZS7gF93u2v/AOYkSZJkGrmFa2h3NzAh+8ZhceLTUl1Aze6NRI+9gOjR041O5yTeykoa1q8jdNJkwmbMNDodXB/toOVQOZmPXEFQnLE9wE617i/HcLf4Wfbr8VgCmbdVkrrxeVSe//4+8ne4uO0PE5h4ZYLRKQGQfueFBMWHU/jcV6gdXkInjCLuhjm4i2sMy6n9aC71q98meMwYYpdeb6oLEe01pZRseIWQuBGkX3yrqXI7G0JoHDz2Fi1tVUzMuJGw4HijU5IkSZKk806fhUdFUdTefrqHAUVniu3hsR6gGHgR6N4V5f2B3EFJkiQzqKjdS2XtXjJHXEZMZIbR6fSb39tB4Zev4YiIIfXiZUancxKhqtS88TrW4GDibrjR8BNnb7WLmlc3EX1BBvHzzTWcsnR/IzvfLmfOnWkkjTZXQVQaOvxejZd+uJ9jXzVw8+/HM/3aJMNyUb1+jv5pA+1lLqBzrsLkayah2C2UP/4envJ6XBv3gkHHBU95OTUvv4wjKYmE79yFYjXP9BTeFhdFa57FFhxK9pX3YrEN/WkX8orWU9dwlJyMq4mPHG10OpIkSZJ0XtLT41E5w4/euL5+TvSWPAi8cdZ7I0mSZEJt7jpyCz4kKiKdjBGXGp3OgCjd8Q7e9ibSrrwDq73vVWUHk2vjJ3gryom7YTnW0J5X3B0sQgiqnvwQRVHI+uFVhhdBu9NUwXu/P0JYrIP5j2YbnY40RKk+jZd/fIAjm+tZ/puxzFpm7CIkuX/aQOvxag7+6n2aDlWgKAohqVGk3XIBIeNG4tqwh+CsZOKumz3oufldjVQ/+xwWp5PE++/D4tS/0vi5pno6KPrwGTS/j4zFD2APMc/iV2errHIHJRVbGJE8m5EpFxmdjiRJkmQ2wkQ/w5zexWUEpxcaB5ICrAPuE0L4zuF2JEmSBpWq+dlftBqLxcbE7OUow2BC+4bCPdQX7CJlylWEJplrBWRPWSmNmz4hbPoMQicavyBC06a9tO0rIOmha0y1ijXA16vLKDvUzC1/nIQzzCxrzUlDieoXvPKTgxzaVMuyX4zhopuNnSPU19xB3JwsEn5+DXVb8jn6x3VkPXQJcXOzsVotxF1/oWG5aR0dVD/3HJrXS8r3HsMWGWlYLqcSqkrx+pdwN9aQce2DOGOToH1onywvhwYAACAASURBVAU1NBZwrGANsdE5jM642uh0JEmSJOm8pudM46Uz3Hd3178CeBv9K1ILOodaNwG5wGYhRL7Ox0rDnYl6BPVF0TsfWiDFpgDmWNPdeyqA5zSgHlmB7Jfe2ECGnVn1b1/Y9LcrdOYgbH1v/1jxx7R0VDN5wl04QqPQdLSr2fXtl2bX/1pptgBizzC6ztvqonj7akLi00icPh+/zhxUh+7Nozn0n/B2jxV+PzVvvo41PIzo5def3I5df5s2u9p3UJcge+/XynwNrdS8sIHQCSNJXDyFEHu7rjZDbV7d2w+xnF1sm8vH+v89RtbMSOYsiUVRvr0v1OLR3aZT0X+t0K7r3d/pXAw+1QLYvhZAzUXVeZm6XdP/vmoR+v9emzR9PY4b1RDdbTaqoX3GaKrghZ8cZf+GWhb8ZCITbs7C5e+jXZ++HBq9+ufAbfF07r8QAiUoiOCLomjxQNCM8Yx4LITjf19Ph6/zoOYJjsOZndpnm6pb/ztQ8fR9vBaqSt2KV/BVV5NyzwMEx6R0fgs+A71/hgEcArB6T3+vCiEo/XI1raXHSLvkFqISR4NX9Bjbc5v6/64svsGJ7fC4OHh0JSHOWCZn3ojNrwAaiqr/bxC//lhF1Z+r3naFFkCbIpADls79CmT7AcSKQHI9B0QgB3ejGfxcSZIkDaQ+C49CiO/2dp+iKHfzbcfQfxJClAxUYpIkSUNddcNhSmu+Ji3xIuJixhidTr8JoVG0eSVCU0m/7A4Ui3nmJgNwrV2Pr6qahAfvwxqiv8hyLgghKHtiHZrPz8gfXKP/IsUg+fDxAtytKjf9MsdUw7+loUFTBS//PJedH9Vy5Y/GM/uuLEPzqVq9HXdpHc7UGJypsUTNyQEgYmo6Y/54BwfufZLgUXEk/er+Qc9NCEH9yrdw5x4n4cZbCMnOGfQczqTmwGfUH91G4tQriR0zy+h0+s2veth77HUEgik5t2Gzmmc4uyRJkmQysr4/aAZizJ88Y5EkSTpFh6eRw0XvExGawugRVxqdzoCoOfg5rZV5jLhoKc6IOKPTOYm7qJjmTZ8RduEsQsaPMzodmr46StO2YyTdPg9naqzR6ZykPLeVLW9VMO/2VFJyjJ0DUxp6NE3w6q+Osf29Gq77f+lc9F1jF+xwfZVLy74iEpbMJCglhobNh6late2b+/2tbkLHpJD1y+VYggLoej1AmtZ9TNv2nUQuWkDEDHMV9hqLDlKxfQ1RGVNInrnI6HT6TQiNQwXv0NpRw+Tsmwh1muvYK0mSJEnnq/4WHi/v+rkCqOp/OpIkSUOfEBoHC1YDgkmZy7FYhv78eR0NlVTsXEvkqEnEjjbXybPm9VH36kqsUZHELL3O6HTwt3ZQtmIDwVlJJCwb/AUs+vLuf+cRHG5j0aPpRqciDTGaJlj573lsWV3NNY+mcfXDaUanhDU0iJCMREKyEomeO4aUO+fhLq2n/tODAGjtHjJ+sgRH3OCv2t66bQdNazcQOmsmkVdfNejbP5P22lKKP32VkPgRjLrs1mEx/3B++afUuI6Qk3YVsZFywSxJkiRJMot+nQ0LIT4fqEQkSZKGi+KqLTS2ljIhYxkhzhij0+k3oakUbX4dqyOYtLnLTTc0t/Gjtfhra0l89CFTrBJb+eKn+Jvbyfz3W1ACmId0MBzb5iJ3i4tl/5pNaNQZJvOUpFMIIXjj93l88UYlCx8YybXfN8fCUvaYMNR2Dx1FtQSnx+McEUv0xWPoKK4DIHRs33M6ngsdh45Qv3IVzjE5xN5qruOmt9VF/vrnsDpDybzqXiy2we8JOtDKa/dQWLGZlPjppCXKFawlSZKkvilyqPWgMdcZkSRJ0hDX0l5NXvmnJESPIzl2stHpDIiqvZ/QUV9O2tzl2IPNNTTXXVhE8+dfED73IoJzjB3yCdB6uJT6dXuJv34WIVlJRqdzEiEEH/ylgKikIC6+LcXodKQhRAjBW/+Zz+bXKllw3wiu/3G6aQppwWlxONNiqfjHZtpyKxBC4IiLoOVACf6WDkMWs/AUl1D7wss4UpKJv+87KDbz9HpXvR3kr3sWze8ja+H92EMijE6p3xqaCzhS9D4xEZmMG3Wtad6bkiRJkiR1Ms83IUmSpCFO0/wcKnwHu9U5bE5+2uvKqNz7CdFZ04hKn2R0OifRfD7qXnsDa1QU0UsWG50Omk+l7G9rscdHkHT7PKPTOc3BT+sp3t/Mrb8dgz3IXAsDSeZ1Ynj1F29UcuXdqSz75wxDj23exg5Ub+fy2cEJncOnE5bMxOJ0ULd+H9YvjtB2vIq4qyZjC9e/OvZA8dXWUbPiOazh4SQ8fJ8pemGfIDSVwk9ewt1YQ/bVDxAcY66LI2ejrb2GfcffIMQZx+TsW7CYbNEzSZIkSZLOUeFRUZQEIB6IAAIeyyWE2DzgSUnnt2Ewd1GPLDr3S28cQCCr7wYwjFT3kNOAcg0gNpAhrzZ9seKUuPySzbS0VzF5zG3Yg8NPWijt1Ngz0az6XgPNpv+1Uu36Y7Wuo7am+in6YiW24FBS593wze09xfZFBPBpo+mMrV+/Dn9tLQk/eBAlIghxhqXpFIeqe/t2h193rNP+bWz5qq24S+oY8+83EBphAU5uJ8Tm1dVmqFVfHECY1aMrTlMFrzyeT2K6kytujMFq6f1xTsWne/t2Rf/zajV4PIsawNKFWgC95NxC0xXXHsDut+j9wwIaNX0ruDeq+ld6r/N1FvRUv8a7v97P3vcrmXdfFpf+cAz1/pOPJfVe/b2gXV59hcBmT8/FOl9jG4d//h5R09Nxbc8n/aErYPx4AEIumYltTBYIcM5oIXh8Gu2nvM19Hp0HF4/+47XV/e3zoba2UPPEMyAESfc+iMMRCe5vYy0e/cdhvYcBq0ffG0sIQdnnq2gpP0763FuIis+BM2zD6tXXrkVnXGeb+v5WABR/37Eebyv7DryERbEyPfNWHMIOvt6PScoZ7jt9+/pjCSRW1RmrNy7AWN09gM9VT2EtgHZ1HltFIG0GQuf2JUmSpL4NWOFRUZSFwHeBS4GEfjQlkD0xJUkaYppaSikq/4Lk+GkkxBi/qvJAqN71Me6GSjIW3YvNqb9oMRjcxUU0ffE5YRdfSPBY44dYuytclL22lZi5OURfaL5FDQ6sraD8WDsP/SUHawAFa+n8pfo03vrpXg5tqOTK7+Vw6YPZhvfibthynPgrJ5By4wVEThtF+cpthF3uI/KyKZ0BAhxJ0TiSogc9N83joer551Cbm0l+8BEc8fGDnsOZ1OzeSF3eDpKnLCAu21wLhJ0NVfWx/+ireH1tXJD9HYIdUUanJEmSJElSL/pd4FMUJQl4CZh/4qb+tilJkjSUqKqXQ3lv43REMCb9aqPTGRBtVYVU79lIdM5MItMnGJ3OSTSfj9q3VmKLjCL6BuOHWAshKPzbx1hsFtIfvdLodE7j92ls/PsxRo4LZeaiWKPTkYYAn0fljX/eTe5nNSz653HMvTvT6JQAsNhtuLblk3j1ZKJmZGCPDCH3T+sRmsCZkYS3ot6QoqNQVapfeRlPeRmJd38X5yhzLLxzguvYLqp2rCU2cwYpUxYanU6/CaFx6PgqmlvLmTzmViJDjFlASJIkSRri5OIyg6Zf408VRYkGNtJZdDxRcJQvnyRJ55W8ko9pd9czPnsZNpt55vM6W6rPQ/Gm13GERTPi4mVGp3Ma18fr8NXWErf8ZlPMn1b/+VGadhUx8p5LcMSFG53OaXa/U4qrrJ0bfpSGJZCpFKTzkqdD5dXv7yT3sxqW/GKiaYqOAPELJhI6OpHqtfvxutoIzU4k5fvX46trwpESS8Tcwb9IIoSg7u1VdOQeJW7ZjYSON9eFmtaKfEo/fYPQlCxGzbnZ8F6rAyGv+GNqGw4zOn0R8bHjjU5HkiRJkqQ+9LfH4++AcXQWGwWdxcdyYB1wFGgC9E8WJUmSNMTUuY5TWrWdkUkXEhNpnhP0/qjY+gHe5gayr38Uq8P4wl53nrJSmjZ/TvgFswkZnYOKsXMwqe0eip/aROjoRBKvnWpoLj3xe1U+fzqPtKnRTLpEDkWUzqyjVeXxh49SsKuFZb+bzPSlI41O6TQxc3Nwbc+n4q0dJF4zhdZdBfhqm7AEBTyl+IBwrfuIlq93EDV/AREXXmRIDr3pqK+kcO3zOCLjSF90DxZ16M9kVFa1g5KKL0lNmsXIZHM935IkSZIk9eysv4EoihIGPMC3BUcv8H3gWaF75mJJkqShy+tr5XD+O4SFJJI9aoHR6QyIpopc6g9vJX7KpYQlm6uQKlSV2tVvYQ0LI2bxEqPTAaDsta34GtrI+fUy/QsoDaLd75bRXO1m2W8nD4ueTtK5097s58/3H6XoYCs3/XEakxalGJ1Sj0IzE7CFB9O4I5+SFzajhYSReK8xw4ebP9lM46ebCL/wIqIXmGsIs7fVReGaZ7DYHWQufgBbUAi0G51V/9Q35nGsYA1x0WPIybhGHtMkSZKksyfA4HUPzyv9ufR5Kd+uWC2Ah4QQL/U/JUmSJPMTQnAo7x38fjfTx9+N1WJMb5uB5Pd2ULTlDYKiEki+wHxzVTZ9uRlvRTkJd96NNVjf6rjnkqesjqp3dhK/cBLhY81XpPH7NDY/k8fIKVFkXRQHNBqdkmRSLQ0+/nTfESqOd/DY/+WQcIn53s/dBcWHk7h4KnFXjKfZF4Zisw56Dq3bdtG4+kNCJ00mbukNpiqCqZ4OCj98FtXnIXvp93CED/68lwOtrb2GA7krCQ2JZ0LOTViUwX/NJUmSJEk6O/0pPKZ3+71cFh2lMzLRF/K+KIHMgabo7OEUQJsBnbzojA2szQB6bQUSa9V5khBArzERUKz+kxRh67vdkurt1DceZ3TmYkIikvsc8KsFsJKw0Bmr2fW3qemoi5ZufQ9fRzPZi38AwQ5dg5g1h97t67+kqDlOj/XV1eP6eD3BEycQPH0iWtclSmHTN9Taatc/JNthU/uMEUJQ9vxHWILsjH5gLg6bv8/HOK19xwAEW7264gCclt5nM9n6XilNVW5u+vfxBFv9OBV9M5/YFX15Auh7l3SyBjAFdN+vQCdfINNKBzAYwxtArEdnaIueP8IujZr+wnqjqm/F+Tp/RI+3N9d6ePr+A9SXurn7r9NImxdHpVf/KvaNPv25Nnn1xbZ5dB5YLA48fifofMsKt77PAYv7zJ8B7YcO0/CPt3COHk3SjXeg+PV9FgXwp43VozPulDY11U/R+hfxNNWSvfABwsKTO8ckAVav/ve1xacv1uLVfwxQfPpjLb5vjwJeXxv7jryCVbExNfs2HJoNtG5HCb/Odv16jyyAFsA0HgHEClVnrN44AC2A46DeXAPZp6E00E0YOz1LIJ9DkiRJw0l/xoWFdf0rgJ0DkIskSdKQ0NpezfHSDcRG5zAi5UKj0xkQTSWHqT/+NYmTryAkyVwrsgohqH9rFVgsxN5ojp5Fzdtyad1bSMZ35+CI1l+kGSx+r8ampwtJmxxJzly5krXUs8ZKN0/e/TWucjf3PTmdsfPijE7pJEIIylesw/X5QaNT+Ya7oJDal17GkZpCwr13o9jMM2+iEIKSL96ktTKPtHm3EJ4y2uiU+k3T/OzLewOPt5kpo28lOEjOVStJkiQNEGGin2GuP4XHym6/d/Q3EUmSpKFA1XwcKFiNzepkXI45imD95fe0U/zlmzijk0medpXR6Zymbecu3MeOE33tNdiiIo1OB83jo+K5j3GOSiDluilGp9OjXe9V0FjpZsGjmcPiPSoNvJrCNp74zg5a67088MwMsmfHGJ3SSYQQVD77MfUf7cRTUmt0OgB4KyqofuY5rFHRJD54PxanuRbfqty9job8XSRPX0Rs9gyj0+k3IQRHij+ksbWE8RlLiQoz32JHkiRJkiT1rT+Fx7xuvyf1NxFJkqSh4HjpJ7R21DAhYykOR1jfDxgCSre+jd/dRvqlt2Gxmqf3DoDa0kLDu+8TlJ5O+BxzrGBas3oLvpomUh5aiMWEC8qoPo2NzxQwclIEYy42Vw82yRxK9jfy9zt34PNoPPTCTNKnmasXmRCCqpc/pe6DHcRdN4vEOy8zOiV89fVUP/UslqAgkh5+AGuYuY7/dbnbqNr7CbE5s0iaOt/odAZEcdVXVNTtJTPlUpJjJxmdjiRJkiRJZ6k/Z0xbgRI6V7SerShK0MCkJEmSZE71zQWU1mxnZMIs4qKG/hA2gMbig7jy95A8dT4hsalGp3Oahvc/RPN4iL1lOYrF+CKft6aR2tVbiJw3nrCJ5hqSfsKeNZW4yt0seCRL9naUTnP0i1pW3LsTZ7iNx16ZxYjxPc/9aKTq1zdTu3oLMYumk3zfAsPfx/7mZqpXPI3w+0h86AFsMebqHdpYcoiSr1YTnppD2tzlhj9fA6Gq/iDHyz4hMXoCmSmXGp2OJEmSNBwZPbxaDrXum+icSfjxrv8GA48OSEaSJEkm5Fc9HC58jxBnLKNHLjA6nQGhejso3bIaZ3QyiVOuNDqd03Tk5dG2cxeRV1yGI8kcHesrX9wEikLyPeZ7vqCzp9jnLxWTNDqMsZfI3o7SyXa+V8ELj+0lIT2Ux/4xi7g0881PWviP7dSs/ILo+VNIffhqw4toans71U89g9rcQsID9+NINsex6ITW6kIKN71MSGwqmVfcjWIZ+qs9uxoLOFj4DlFhI5mQuRQlkIXsJEmSJEkynX59kgsh/gJsoLPX4+8VRTHHODhJkqQBdrz0Y9zeJiakX4/Von91WjMr/3oNvo4WRs272XRDrIXfT/2bq7HFxhI53xxFvrbDJTR9eZj4Gy7CEW/8XJM9ObalnqpjrVxy9yjDCzaSeQgh+Oz5Qt74+UEyL4jm4RcvICLefANVil//msIXthB1+SRGPLYYxWLse1jzeKh55nl81TUk3HsPznRz9XJ211eSv+E5HKFRZF11P1aHueacPButbVUcOPwaIUHRTB1927D5vJUkSZKk89lAXEK8GVhPZ6/HjYqi/ERRlNABaFeSJMkU6psLKKvdSVriRUSFpxmdzoBorSqg7uhWEsbPIzTefPvU9Mkm/LW1xC6/AYvDYXQ6CE1Q8ezH2OPCSbhxjtHp9OrzF4uJiA9i2uJko1ORTELTBK/+oZQ1fz7OlEWJ3PfkdJxh5rrQAFDy1i7yn/mSxCvGMPIHS1AMnj9V+P3UvPgynuJi4r9zJ8FjcgzN51Te5gYK3nsKi81O9qKHsAeba87Js+H2NLLv4MtYrHam5dyJ3Wa+HrmSJEnS8KAAijDPz3DX6zdPRVF+FUA7XwMzgVjgv4BfKoqyBTgCNAFaIEkJIX4bSLx0nhquQ28CmcdOb2wgvUYCONkL6MRQd64BtGkLYEiZVf9zIGzf5uBXPRwuep8QZyxZo65EdNvn7nF90ez6Y1WHvly1AGoH3WM1v4/iL9/EER5D4uxFp7WjBdDBRG8OWgC1Q29DNY0bNxIycxrOSTmIM0184tD38WKzqbq3H2T3n3Zb3ccH6MirJPMnSwgOU4DOmGCbT3e7wVZ9sUGW07ffe+y3bZbntnJ8Sz1LfpxJqFMFTt5nu6LvObCeo4lmVPT/DWo6c/AK/bkG8kXELfTn2q7zj6BRC9bdZr2qv4h0pli/V+PVfzvKrg+rmXFbJgv+ZTLNFuXE27dXDV79148bPPqLQ03unnvk1by/k9IVm4m+eCypP7qeFndQnzmeoLr1HwgVt77jsKVDUP3667iP5hJ/481EjJkMnp5jrW797xVrL230mIO39/e2v6OVwvefQvN5GXfVo4QERcMZ4r/Zvo6Yb2I9+mItfv1tWry9H4N8/g72HX4Jv+ph5vh7CbGEg0/fMUtR9cYFcBTw6//MQOf2ARA6c9AbR2eP5gCCB75NLZDtB7BfgbRrpECeK0mSpPPUmb6t/YbAp7kUdBaPw4AFXT9nQxYeJUkyhbziDbg9TcyccC9Wq/E97wZC9e5P8DTVknnNg1jt5hpuKYSgfuXbWOwOYm5YYnQ6AKgdXspe/JzQMSnEXDbe6HR69dlLpTiCLcy9OcXoVCQTcLf5ee4HB8nd4uLaH2Uy8e7Jphx+X7tmN6UrPibqohwy/uU643s6CkHtu2/TdmAfsYuvI+KC2YbmcyrV56Fg7bN4W1xkLX6IkMih//euaj72HXuddncD08bcRXhIEnj0X4CRJEmSJMnczsW3u/6sy2O+b8SSJJ23GpoKKKv+mrTkC4mKMNfcXmero76C6n2biM6ZScTIMUanc5qW3TvxHM8naulirBHhRqcDQOVb2/A1tJL20JWGzznXm5Z6Lzs/rGbW0mRCIuWcaOe7lnovf717L8e3N3L7f4zlqgfNOedn7bq9lPx9PZGzssn46VKUQHqxnyON76+lZcc2oi6fT9Q8c62mrKl+ija8SHtdOenz7yIsOdPolPpNCI3D+e/S2FLMhKxlxERmGJ2SJEmSdL4weiXr82hV677Gp5jvW6okSdIg8KseDue/2znEeqQ5FjfpL6GplHz2BjZHCKkXXWd0OqdR29qo//B9gjLTCbvoAqPTAcBT00TV6h3EXDaesHGpRqfTqy1vVaD6BJfead4cpcFRV9rBk/fvo7HGw/1/ncjEy825unntR3so+ds6ImZmkvlvy7DYjS86Nm3YRPMnnxJx4RxirlpkdDonEUKj9LOVtJQdY+SlNxOZPtHolAZEftkmqhsOkj1yAUmxk4xOR5IkSZKkc+BMhUd5yVGSpPNWfsknuD2NzJxw37AZYl2z/3M66spIX/AdbE7zrQHWsO4jNLebmFtvRAlkrs9zqOKVL0EIRnzXXD2fuhNCsP2dKkbPjiIx03yvqzR48nc18uz3DyI0wWPPTyVzmjlXX6/5YBelT24g8oIsMn9xAxa78YvdNH/+FY0frCVk5jTirltmqh6iQggqtn6AK28PybOuIXasuYZ/n62ymp0UVXxBavwMRiXPNTodSZIk6XxynizqYha9ftMTQhQPZiKSJElm0dCUT2nVdkYmzR42Q6zdjbVU7VxPZMYkojKnGJ3OadxlpTR/vZ3IufNwpCQZnQ4A7UW11H1ygMRlswhKMGcBB6BwbzN1JR0sfGR4vFels7Pj3Spe/9VRYlOdPPjEZBIyzLkicPW7Oyh7eiORF44m82dLTVF0bN32Na5V7xI8eQJxd96iexGawVKz71NqD2wmbuI8EqZeYXQ6A6Ku8Ri5hWuIjcxmTMZiUxV6JUmSJEkaWMZ/25MkSTIRn9/Nobx3CHHGkZ12tutjmYsQGqWb38RitTNi7g1Gp3MaoWnUvfs21rAwYhYsDGj14XOp/KXPsQYHkXLLRUanckZfv1eFI9jClAXxRqciGUDTBB/+bwGfPFNCzoVR3Pu/E007z2f129spe3YTUXPGkPGv15tieHXb7n3Uv/YWzrE5xN9zJ4rV+Jy6a8j9msrta4jKmkrqnOuGRYGuua2CA8ffIiwkkUmjb8aimOs5lyRJkiRpYMnCo3T2htCX34AWhFAC6Omgs92AThQCiNXdbiD7FEhsICdoOlcqFYGsaBrAcFxh0xd7tGQ9Xm8rM6c8iMXhPONcv8Kq/7USNv2xms5YvXG1R7fTVlnAiCtuxRoV2WdhTwugZqE3VrP1/ky2bNuBp7SE+Ntvg7AghF1/6dGiM9ZuV3W36bCqNB8qp3FbHmn3XExItAPo+fFOq093u8E6Y50W/W3i9bF7bQ1TF8QRHqbQW54AVkXfc2XRGRcoVej/G/Cdg1m2vUL/8aJd6P961KI5dcXVq2G622zw64v1tKs8/S9HObqxihk3jWLRTyfRbrfQ3sNbqMGnfxi+yxusO7bZo2//y9/YRtnzmwmfO4Gk/7cMt2YFT8+xXncAByGP/tfVekpPxrbDh6h76TWc6Rkk3fVdLKoDVLC69b9XrV7doVh62d8e2/VAU+lhSj5/k/Dk0WTMuQ2L9/R9DWj7Xv1/VxafzuOFV/+xVVE1OjyN7M19FbstmKnZt2HHDurp21L8ARyH/DpzUPXnGkis6CH/3tvVGasFcAzUAth+ILFDhRiG+yRJ0uCQQ60HjSw8SpIkdaluOExlw34yRl5ORPgIo9MZEN5WF2U7PyRsZA7R48yxYEt3ans7DWvWEJSZQej06UanA3TOp1b83GbsMaGkLJthdDpndODTejqa/Vx4faLRqUiDzFXl4alHD1KW28rCf5nIrDsyTNsbruy1LZS+9CUR8yaS8sNlKIFcYDpH2o8do/rllwhKHUHSvfdhcZhrLt/WmmLyP3uZkJgUsq64B4t16H9l9/k72HP8VTTNx4yxd+F0hBudkiRJkiRJg2Dof4uRJEkaAB5vC0dKPiQiJIX0kZcZnc6AEEJQsnU1CEHqZTeZsijhWrsWze0mbpl5FnNwfV1I88EyMr8/H2uwuYoRp9r2XjWRCQ7GXBhldCrSICo51MKKRw7iaVe59a+zGT3PnIVnIQRlr3xF2StbiLtyAnGPmKPo6C4soPrFF3AkJJB0/wNYnPp6bQ4Wd0M1+RufxRESQfb8+7HazZXf2dA0P/vy36Td08D00XcSFpxgdEqSJEmSJA0S47/9SZIkGUwIweHiD1BVHxMzlmGxDI/5plwFe2gqO0LK9KsJiow1Op3TeMrKaNm6jYi5c3CkpBidDgBCExQ/vxlnShSJV082Op0zaq33cOgLF7OvS8ASwLB/aegLCrESEe/gn16bZuqiY+lLX1L2yhbir5pE9j9dbY6iY2kJlc89iy0qkuQHH8IaYq5FeLytjRR+8BSKYmX0ggexBw/9XoFCaBzJewdXSxET0q8nJiLD6JQkSZIkqXOotVl+dFAUZZGiKLmKouQpivLTHu4PUhTlja77tyuKkt7tvp913Z6rKMrCbrdHKYqySlGUo4qiHFEU5ZxMbt+vHo+Kojw/UInQ+XS3Ak1ANbAH2CuEaB/AbUiSJJ2msn4/dU3H4my24QAAIABJREFUGTNyIaHBcaZZ3KQ//J52Sne8R0hcGgnjLsZvdEKnEEJQ/867WEJDiVq4sO8HDJLmrw7RXlBLzs+uxWIzdwH6yMYqNL/ggiWy59D5JjEjhH9dNR1FUagOYErQwSKEoPjZz6hc9TUJiyaT+cOFgc21fI54ysuoeuZprKGhJD/0CNYwcxX1/B2tFL6/AtXTwZhFjxIUEWd0Sv0mhCCvaD1VtfvJSrmc5FhzX9CRJEmSJDNSFMUK/B1YAJQBXyuK8r4Q4nC3sPsAlxAiW1GUW4E/ArcoijIeuBWYAKQAnyiKkiOEUIHHgXVCiOWKojiAc3JFtr9Dre/h3E7J6VMU5T3gCSHE5+dwO5Iknac8vlZyS9cRFTaSkQmzjU5nwJTv+gi/p43RVz2IEsAiPIOlbe8+PEVFxN10E9Zg/QtZnEtC1ah9YzMho+KIu3Ss0en06eimKuLTnKTm6F80RDK/ikI3zRY7salnHl5rlqkJTiU0QdGKjVS9t5vEJdPIeHS+KYqO3rJKap5+CkuQk+SHHsEWGWl0SqfxtrhQPW7Sr72fkMjhMc9wSfmXlFRsYUTybDKS5xmdjiRJkiQNVbOAPCFEAYCiKCuB64Huhcfrgd90/b4K+JvS+YXxemClEMIDFCqKkgfMUhTlEHAJnXU9hBBeIIBl6/QbyDkeT/1W2VdBsq94BXAAy4HliqK8APxQCNF29ilKkiSdLLdkHarmY/yo60x7Ih+o1ppi6nK3kTBhHiGxqUancxrN56NhzYc4UlIIm2WeBW+avzyEt6yOjF9cZ4pCyZl42vwU7qjnsjuSh837VoJVf61k/5ZmPH4r825NYfb1iQghhsxrLFSN/MfXU7v+AMk3zGTUg5ebIndfRTU1jz+NYreT/NDD2GNijE6pRyEJIxl718+x2BzQNvSX2qyo3kVe8QYS4yaRk3ENinc4jCeQJEmShgtlaH3UpgKl3f5fBpzaa+abGCGEX1GUJiC26/Ztpzw2FegAaoEXFEWZAuziHNXcBqLw2P0bpeh2W1/fNLu/zL3Fd2/vu0CaoihXd3UJlYYrxXy9s/otkB5nAcXqPKELYF6tgObgCuSEUu9+BbD/whZAbA/7VdNwhGrXIbJGXEFIWMI3Bxxh07dfml3/9jW7/udK03lk1uyn3yY0lZJtq7CHRpB0wcJvYnqK7e/2O9vV94ndffuNmz5HdTUSd+dtiCDL6VepbPpPTq1WfbF265k/NoSqUffm5zjTE0i5LBPFom9wusOi/+MoyKJvTKxd6bvNo19Vofo0ZsyPwqHoy9VyDiYRUPv8qP+WL4BppTWhr91Atu8W+ofOt2tBumMbNX0jUurVsDPev/qPBTTXq/zwlRnk5lv4/Ok8Mq7KwGa3nLH4WO/T1+O1zqO/Z6zLo3+UTYu787nSfCplf3mH5q+OEH/zPGJuv5RWz8k5u936DkTCrf+1snSc+X3lra6h+u9PoyhWUr/7CI7wOPD00WYA1/qtfbR1cmzfx0srdlAFVq++Y6s1gGKexRdArM52e2uz1pXLkbz3iI3IYmL6Uix+UHwBfH336Z8gRPHrbFfVv/9CC+B4qQWwX0Jfu0INpE39Z85Cb6wWwNm4zn3qDB1CZ/kBPK+SJEkDIE5RlJ3d/v+0EOLpbv8/U72sr5jebrcB04HvCyG2K4ryOPBT4Jf609anXxUeIYRFCGEBrgPq6dwhDVgD3AtMpbPCagfCgJHAIuA/6azEnngC3gIi6RxPngJcAfwKKOqKOfFkXQn8tj85S5IkAfj8HRwtWkNYSCKjki82Op0BU3voKzrqK0i96HqsDvOthOpvbKL5402ETJmEc3SW0el8w/X5ITzlDSTeNs/0vR0BjmyqISTaTva0CKNTkQbIgvtH8N3/7hziX7SznuYaN5+tOI63QzVFr8Ez0Tw+Sv7wJs1fHSHx7itJvOMyU+Tsq62l+okVIASJjz6MIy7e0Hyaig/h62gxNIfB0NRazv78twgPSWby6FuwWAZygJUkSZIkDUt1QoiZ3X6ePuX+MjrraSeMACp6i1EUxUZnja3hDI8tA8qEENu7bl9FZyFywPW7a5miKD8A3gNigL3ADCHEEiHEi0KI/UIIlxBCFUK0CyHKhRAbhBC/ALKBf6OzULkc2AkECyGqhBCfCSF+D4ymswAJ3xYff6AoytCfbVuSJEPllXyM19fK+Mylw2YVa29bE5U71xExcixRGeacwN/13ocITSNq6bVGp/INoWpUr/wCZ0YCkReOMTqdPqk+jaNf1DD20gSsOnvnSua0Z0PdN7+HRXX2Bmys9rBvTQUX35NJXVEba/94yKj0dFHbPRT99nVad+eR8sg1xN8wx+iUAPDV1VP1xAqEqpL06MM4Eo1dhKnm609oKjmELWh4z8na7m5g77FXCbKHMW3MHdis+nsPS5IkSdKgMnol68BWtf4aGK0oSkbXIjC3Au+fEvM+cHfX78uBTaKzq/v7wK1dq15n0Fln2yGEqAJKFUU5cQJ0JSfPGTlg+ruq9SXAn+ksCO4HLhNCNOt5rBDCD/xBUZRy4EU6C5GvAVd3i9GA3yuK4gX+q+vmEOAWOlf0kSRJClhDUwHltbsYlTyXiNAUo9MZMOVb30NoKiPm3mCK3kancucX0r5rD5EL52OPjTU6nW+4PjuIt9JF+s+XD4nejoW7GnA3+xl/eaLRqUhnyefVeOlfc9mzoZZLbkvhll9kY7EqaJogKjGI7zx5ATaHlaiUYHauKsHvVbE5zHeBxNfcQdGv3qUjv5IRP15G1CUTjU4JAH9DA1VPPInw+jqLjslJhuZTu/sz2quKyb7iXhryduFrbyYoPNa0F4jOltfXzp5jryCExrScOwmyn3l6AUmSJEmS9Omas/F7wHrACjwvhDikKMpvgZ1CiPeB54B/dC0e00BncZKuuDfpLCr6gce6TV/4feDVrmJmAZ1THA64/vZ4/B2dOy2Ah/QWHbsTQrwMrKOzeHmVoiiX9xD2P8DRbv/vKUaSJKlPqubjSOEHBAfFkJl6mdHpDJjmslwaC/aRNG0+QRHmKeqdIDQN1+p3sUZFEjHfPIdwoWpUv/ElwZmJRFyYY3Q6uhz9rAZbkIXsi8z3Okv6uCo9TJkfy98PXULhvhZ2fFADfDtVms1h5fDGKt799QEScyJMWXT01Ley78dv4i6qJu2nN5mn6OhyUfX3FQi3h8SHH8SRaoKLS5qKr6mB8u3v01yWiz0kkup9m6g7stXozAaMqnrZe/w13J4mpubcTmiwHJwkSZIkSQNJCPGRECJHCJElhPiPrtt+1VV0RAjhFkLcJITIFkLMOrECdtd9/9H1uDFCiLXdbt/bNbR7shBiqRDCdS5yP+vCo6Io2cA8OouOx7uNCz8bL3f7/bQKa1f30H90u2lSP7YlSdJ5rLB8Mx2eBsZlLMFqdRidzoDQVD9lX71DUEQcCVPMU9Trrm3nbrxl5UQtWYwlyDxD7xq/OIy30kXirRebspdoTwp3NpA2NRpHiJw3bagp2NOM36uRMCqYmdd0zjd4088yWfO3YqoK2rHaFFSfRn1xGztWFrPwx2OZdt0Ig7M+XUdlE/t+9AbuqiZG/fI2ImabY4oCv6uRqr+vQG1vJ/HhBwgaaexz5+9oBSB+5pXETJ6Dp6mO9MtuJyZ7OqMuv4OG4zvxu9sNzXEgaJqffXlv0NRaxsSsG4kKTzM6JUmSJEk6M6OHVgc+1HpI689Zy9Ruv/d3HHj3x/c2meWWrn8VOheskSRJCkhrew3FlV+RHDeFmMhMo9MZMLUHNuNpqiVz0f1YrOYrRmleL40frsWRNoLQGVP7fsAgEZqgZtUWgkbGmaZw0hd3i4+q3BYufzjb6FSkAPi8Gq/8+hiuSg8po0OJSnRw5T2dRbGs6ZFcekcKK3+Xx0P/N57Sw61ETkzhricuwGrv91TcA66tuJ4D/7IKzetn8v/chD8tw+iUAPA3NlL19ydR29pIevhBgtKMLX41HttDS8EhsFiIGjOD6PEXkJj17fyXVntQ1wJgQ/tsQwiNgwXvUN+Ux/iM60iMGW90SpIkSZIkmUx/vtF2v4zs7WceJx6vnNJudzXdfpfLeEqSFBAhNA4XvIfN6mB02kKj0xkwnpYGKndtIDJ9IpFp44xOp0dNX21GbWwieukSFIt5CinNO47hLq4l8aY5Q2JuR4CSfY0IAekzoo1ORQrA8X3teNpVvv/sJGYvTaCmpIP1T5d8c/8V30nF16Hx88u2U1/hxuawmrLo2JJbxb4frQQhmPLnm4kYl2x0SkAPRcdRxhYdPY211O7cROJFVxM2YjQd1aXU7fkCb0tD5/3N9RRt+geR6ZOwOYfuYjNCCI4Wf0R1w0FGj1xAavwMo1OSJEmSJMmE+tM1pvs34vR+5jGql3a7U7v93t9Cp3QmQ2S4YUCFAiWAE7gA2tU9NDOA5zSg4Z569yuQ/Q9k+1b97Qq9sTb9c5kJm/7tF1dvo7mtjAk5N2EPDj9jHxNNZ7taACsKawFM0aa3XdUqKN3yNoqikDJvKdoZjuhnuq8/saKP/fK3tOD6bBPBUybgHJuJnt49ik1/DyCrTdMV57CpJ/1fCEHtqq8ISooi4YqxKNZv7w+y+nVvP5BYm0VfrnZF7fW+kt31WKwKmZPDsSsqFvS1GQhN6P+78qH/ja0y8J8tvr7egN24hV13bIsWrDu23t/3AhpuO4Qma1S3OAnNCuWCO8NY95dcEnZ4SJ8eQ/EeF9ZQB3f831iyZsdS49VfjGrQGdvk0b9Pze7Tp0NoPVBMwW/fwhYRTNbvbkNNiaHZDW6P/udV7dB3cFHcAXy21DRT/fQK1NZWUu57CGfSKPD0HGv16HsP2ty6N4/V8+3xSgiBoig4lBDCk7MJdsYQnBVDW3UxbZUFdFSV4nRE464sIXrkZBIzL+z1G63Vq+84aNEZ19mm/uOF4u87tqBkI2U1X5OeMIeMuIvA1/uxC0Dp4/6TaAEc2/w62/XrP16jBpKr/tdAqDr3SwTQEzaQ50pvrmLgP1sCZoYcJEka1pShPehgSOnP5fSKrn8VYIaiKP2ZvXtpD+2e6sQs1QJo7Me2JEk6z7R21FJQspH4mHEkxg2fKWKbCw/SUnyEpFmLcISbswec6+P1CL+P6KWLjU7lJM17i2nLrSTppgtRAiigG61wl4vUceEEhZpvSL3Uu/BYO/Wl7VQcaQIgekQIWbNjcZV3ABAa4+D2v0wja7Y5Z5Jp3JpL/q9WYo8LJ/uP3yEoJcbolADwNzZR8fQT+FtbOouOaaP6ftA5JLTOYpXVHoS3pYHyrR8AEJo4iqDoRBoK96CpPmIyppE4bp6RqfZbScVWiso+IzV2GqNTrjQ6HUmSJEmSTKw/Z1sn5lwUXe385WwaURRlCnAv306ruaWX0Cndfi8+m21JknT+EULjcNF7WKx2xmQtGTILiPRF9bkp/+IdnLEpxE2+2Oh0euStrqJl+zYiLpqDPTHe6HROUvnGFuyxYcTNN8dKvHr4vRqlB5rJkMOsTa34UCsbni9j59rab26LSQ5i/OUJbHm1mMrcZiwWBb9Xo6PJB0DcqFCCTLpYUP26PRT94W2CMxIZ/V934YgLNzoloLPoWP1/KzqLjvc+aHjRsb2mlNLP3qRm3+e0lB8nfcFdeFzVlH3xNgCRozrnPvS1Df1r55W1ezle9BHxMeMYN3LxsPlclSRJkiTp3DjrwqMQogTY2vVfBViuKMrTiqLoXq5UUZTZwDo6h3yf+Nbyai/hV3T7/UCA6UqSdJ4qrt5GU1s5YzIXE+QwxwnzQKjYuwFfWzMjLl2OYglgHPcgaljzIUpQENHzFxidyklaj5TTsq+EpBtnYXGYs9jTk9KDTfi9GunTo4xORepFxfE2XvttPiGRNnatr+eN/yz45r6p16aSPj2avR9WsOoX+znyaQ2p4yMNzPbMhBBUrfyS0r+tJXxaJln/eTu2yBCj0wLA39RZdFSbW0i59wGco9INzcfb2kT51g+ITJ+AIyyKpsID1Oz9jMxr7sfvbqN08yry1zyNMzwOZ2SCobn2V11DLkeOv0N0ZCYTcm7CEshULpIkSZJkJkavZC1Xtdbtx8BXXb8rwH3AfEVR/g94u6s4eZKuwuRc4LvAbXQWP0883WuFEBt7eEwMcC3fviRf9DNvSZLOA+0eF/kVnxIfNYbEuMlGpzNgOhqrqDn8BTHjZxOaZGwvn950FOTTfvQIMVcvxhoahnoO5iI8W1Vv78Aa5iR+kXlW2NajMrcFgLRJ5i1Wne9qSt2MmhDGxTcmceGSBJ743hG+XFXFxcuTAJhzRzruVh+FXzcwcnIUYbG6r9UOKqFqlD+9gbo1u4m+fCJpP1yMEsD8u+eS39VI9V+fQm1uIeHR+3AmGr+qtsVqJSgyjohR47HY7ISmZFK1Yx11h7eSvuAu3I21CL+P8JD+zEpkPFdTEQdyVxIWmszksbdjtdgBn9FpSZIkSZJkcv26TCmE2A78lM6io+j6Nx34M1CoKEqtoih7FEXZrCjKVkVRjgItwMfA7XxbdFSAPOD+Xjb1z0BQV5wf+Kg/eUuSNPx1rra5BgULY9OuGTZDwYQQlG5/F6s9iOTZVxudTo+EEDSs/QhrRAQRc801DNxT04RryzHiF03BGuwwOp2A1JV0YHdaiEgwZ7FKgszJ4biqPRTub8HmsLDsR6P49NVK6srceNr85H5RizPMzrjLE01bdFS9for++A51a3aTcOOFpP14iXmKjg0uqh9/ErWllcTH7seZaXzRUQgNxWLFGhRMa0UeAPbgcOImXoynqQ5N9eOMiic4bmgXHVvbqth/9FWcziimjr8Lm9Wc719JkiRJ0ksR5vkZ7vo9xkwI8SdFUdzA/9BZHDxRSASI7fo58VR2P/MX3WK/BpYKIap62cxmYFfX781CCFd/85YkaXirajhAfXM+Y0YuwumIMFF/u/5pLDlIS+VxRs5ehi2479V0jdB+5DCe4iLibliOxWGu4l7NB7sBSLh2usGZBK6+pJ3YtJBhU0QfjkIibEyYG83hr1zEJAcxclwYs5fEs3tDHUmzgoiIN3exxtfqZt8vPqBpfzkpD8wn4fpZRqf0DV9dA9V/XYHocJP42AMEpacZnRIAimLBGhRMeEo2tQe+RFEshKZkERybjKexBm9THc6YJKPT7JcOdyN7j7yM1Wpn6ri7cdj1r7ouSZIkSZI0IJNbCSH+pijKJ3T2dFzItwXGU2u33YuSClAJ/Al4XAjRa11ACLFuIPJUFOUm4DfAOGCWEGJnt/t+RudQcRX4gRBi/UBsU+oyXOcAsujcL71xAJYAigp6YwNp0xpAz5aA9ktfrLAGsv89t+nzt5NbuoGI0FRGJs0GxYIIYOVizaYvBxHAEVRvm52xvdzu91H29fs4o5OInXARagBtBpSrXf9lt1NjhabRsP4jbHFxhM65AM3aeb+w6m/TYtNfJrbZVF1xDquK6vZRt34fsXNGE54SRufhvoc2Ff3bd1j8umPtir5cLb1sv76knYTMkF7v74umc5CDV5yb3m2WAMr/mtCXq1vYdbfZpukv+jWq+ucybFC7FWGsMGKulX1rq1j1t0qu+/lYCnK9TJifiCOrs8dbnY6RqQ0+/YWdBo++XJs8zjPe761r4dgvV+Muqyf5RzcSfvEkOjx9t+tzB3Bw8eh7Xa3uk+N8dXVUr3gC4fWS/OBDBCWNBPeJWP3HQauO/QGw9BGnej342hoRfj9BoZ2va3TSOCw+QcPRXbSXFeJpqcPuCCM0NAm62rN59B8HrV59sVaP/r8rizeQ2M5jm9ffzt6jL6KqXmaO+S6hljDwdjvu+QM4Hvn1HQMBlABi0fTlINQAchUBdD9RBz5X3XF0jjIwktCGUFcdg58rSZKk89WAzaovhDgKLFYUZRRwI3AhMJnOHo+RgBdwASXADuBTYI0QIoBP6347CNwAPNX9RkVRxgO3AhOAFOATRVFyBjk3SZIGyLGSDfj9HYwf+x2UYVT0rt7/Kd7WBrKvecS0C8q07d6Dr7KK+O/ciRJIEXsQ1G48jL/FTfLSodfbUVMFdaXtjLsszuhUpD7EjAhhzu1pbPhrHm//5jCOYCsTrkigxmt0Zr3rKK3n2C/ewN/iZvRvb0YZM9bolL7hramm8qkVCFUl+aFHCEpNNTQfze+l8P0nCUnOpLXsOPHZs0gY1zmlROTI8QRFJqD5PHQ0VhGbNcPQXPtLVb3sPf4abk8j03PuIjwk0eiUJEmSJGngyGsRg2bAl/MUQhQD/99AtzsQhBBHgJ6GqV0PrBRCeOicmzIPmMW3q3ZLkjRENDQXUlG3h/TkuYSHDO3hbd15Wxqo3reRqIwphKdkG51Oj4TfT+PadThSUwmZYq7FfIQQVL67m9CsBCImjTA6nYA1VXtQfYK4tGCjU5F0CAq1seSnY3G3+HCG6++VaYTWI+Uc+/VbKDYrY//7dkKzk2huNzqrTt6qSiqfWgFAysOP4EhKNjgjaC3PJzQli+Q5S/A01VP56So0v4+kSZcDYLHacEbEERJrbIG0vzRNZX/BKpraKpiSdTPR4eZcyEySJEmSJPMbPl2B+icVKO32/7Ku2yRJGkJUzceRog8IDoomM+Uyo9MZUOU7PgQUUmcvMTqVXrVs3Ya/wUXU4qtRAhmGPwia9pbQXlxH8rIZQ3KOxLqSzkpQXJr+IcDSueX3aax5soSiXb1PO232omPj9jxyf/Y6tvBgxv35LkKzzXOxxlNRQcWKJ8FiIfmRR01RdASwOpy0lufjaawlKDKW9Itvpan0EFUHNuFtddFUetjwoa/9JYTgcPEH1DUdZ9yoxSREm6cHrCRJkiRJQ4+5zgwHgKIonyiKcrCHn+vP9LAebuvxW6OiKA8qirJTUZSdPnROFiRJ0qAoqvyKdnc9Y0ddi9VqrkVN+qO1Mp/Gwn0kTrkCR1i00en0SPN6afx4I0FZmQSPHWN0OqepfG83tshg4i8bmifQrorOCe1iUs88T580OCry2vnjbft57/ESjnxWY3Q6Z6Xmw90c/+1qnGlxjPvznTiTo4xO6RvukhIqVzyBYrOR8vCjOBLMM8Q3NDmD6JzpNObtxdNYiz0kgozL7sLX0Yo1KJi4nAuH5MWNE4QQ5BWuo7J+H5kplzIifmgPF5ckSZKkHgmT/QxzAz7U2mhCiPln8bAyYGS3/48AKnpp/2ngaYAIJeY8eItI0tDQ4XFRVPEFiTETiIv6/9m77yg56ivh+9/q7pnuyTnnoFEWSiCyEEJkmRxNxhin9bP77nk3Pd7z7HrfDd4cnt21CTLBBGOWaBCghCICSSjnyUmTc+fu+r1/jDBC0miqpBlVd+t+ztFBaG7/6lbomq7bvxCZQ5HPhlI6rZ+/R1xSOnlzllidzpiGt2xFHx4m49GHI+6hO9g3TN/WOoruWogtPjp/7YWPL+DgiI+57wujih5WrHmpnbf/pRFnop3v/us0KpdF1xBUpStanl9L51vbSLukiqo/uQ17QuR8UeM72kD3MyuwJyVR8PT3iMvMsjqlU6RWzGKwbg+9+zajlczD23+MwEgfNnt8xPX2NquheS3N7Zspyb2EyoLFVqcjhBBCiBgQnU9gE+894FVN0/6Z0cVlpjC6AI4QIkocblqJptmoKb3B6lQmVH/tl3h7Wilb/CA2R2QO29QDAQbXrsM1pRpXVaXV6ZxiYM0u0BV5N0XWvJOmfPU1V4QVdS8k3S0+XvjTIxzdPsRF12by8E+rSc2Opy+KlqEL+4LU/8P7DGw5Qu7y+ZQ+fR2aPXIKZd6DR+j57xdxpKdT8N3v4UiPnF6YJ4pPzSRj2kJGWo/SsXcd9ngXJZfcFvVFx6bWjTS0rKMgbz5Ti2+MuC+RhBBCCBGdLqjCo6ZpdwD/AeQAH2iatkspdYNSar+maW8AB4AQ8MOYW9E6ij48ajaDuZpZrdhom5x28aEzBU9Cm2b2y9iqwaYehkw8hCozD6wGY5WJlZB1x2ibPf1H6B44TFXpMuITM9BP167D+DlQdmOxuok2zcSq43dmPRigffuHJOSUkD5tHuqkJnQTdUjdxN1emYpVDH26GX14mLTvPIxyjNER3HG6s3J6drvxWIftzLFKVwyu3knaRSUkl6bDaa+Ob4q3hwxv364Z7/hu14zv18m+mjLu5FuJbmLGlLAyFhvUTLwHDbYJmBpGElDGcvDoTsNtDunGF+YZDH8dq+uKL95o4cN/PIJm17jnb2Yx/1uFhDSNvjD0BFMMtdkXSDK8/V6/8dhBv7Hh9wMdQVr+5nV8tW3kPXEDmcsvxR0EgqfG+n3Gby7Ka/yGYfeOfb149u+n+4WXiMvNpejRp3EkpGBkRhu7iVlvjMba/eNfrHZbEs7SueRkTEfTbNjsDgic+XW2oPE3gS1g7H5hCxm/r2jBsT/atnR+QW3jx+RlzmJG2fLj2x8/Xy1o/H6phUx8tDYTGzYYq0zcg422Cebm9DQYa6pN3cz2jR0DZaZNM8ycA8NtygA0IYR5Gqefb09MjjE/LWqadvJvXKXUNx9HTxMzEU7ZzgQ2/Dbw9hg/+2vgrydju0KIyRPWgxxp+IDEhGxKCy+3Op0J1bVnA0H3IGVLv41mpiB9Huk+H0Or1+GaPhVXZYXV6ZxiZHcDgc4BKp+I7mvjq4fQKPoOKSa0HRzinZ8eoGXPIFMuz+Kun84kvSD6Vhb3NPXQ+JO3CA2MUPzH95GyKLLmOh3ZuYvuV14lvqiI/O8+hUMzXni1mt0ROcPUz1Z79y4ON35AdvpUZlbdefz3zSQUiIQQQghxQTpTgc/I4408AgkhLNXUugGvr4+5Mx7FZoudTtxBzxBdu9aSVjGb5MIqq9MZ09Cnm9DdHtJvicwh7n2f7MSekkD2VVOsTuWcKBlqfV75RkKs+o+jbHm1maSMeO77u9nMvbUgKoeeDu6y1VDeAAAgAElEQVRq4shP34E4B2V//TgJ1YVWp/QNw19so+fXb+AsLyf/qSexuVzgsTqrb1J6GDRbVJ7/8XT1HeJA/TtkplYye8o92AyOpBBCCCGEMGq8p3TF+MVFIzFCCDHhRtydNLZtJD/nIrJiaEEZgGOff4gKhyhcdIvVqYwp7PUyvHYDCbNn4CwrGf8F51lowM3Q54fJujl6F5X5nTGGWouJpZRix8pu3vjbBkZ6/Cy6r4TrfzyFxLTInF91PN2r9lH/Lx/hKs6k6M++TVxuZM2ZOLRpM71vvY2rZgp5jz+GzWl86Pz5oodD1K/6JQmZBRRdcqvV6Uyo/qFG9tX+htSkQubU3I/dFp3XuRBCCHFWZKaG8+ZMT2IvGni9kRghhJhwSikO1r2Lw+5kSvlNVqczodydzfQd3kbu3CU403OsTmdMgxvXo3u9pN8cmb0d+9ftQYV0Mq+fZ3Uq5+x3831J4XHSdDV5ef0vj3JwywBFM1N55D/mUTI7zeq0zopSitaXNtH26mekzStjyk9uw22LrKLjwJq19H/wIYkzZ5DzyMPY4iKv6KWHQzSsfpHh1kOkl8+2Op0JNeRuZ9fhV0lwZjB36kM47JFX9BVCCCFEbBiz8KiUeny8FxuJEUKIydDWs4OhkRZmVN9FfFz0zAc2HqUU7VvexZGYQt7866xOZ0xhn5fBLRtJvGgW8cWRNXTzK/1r95JYU4SrNAfwWp3OOUnNHi0K9Lf7SM6I/jnlIkkwoLPquRZW/rwZR7yN+35SxUX3VmEzuMhUpNEDIer+eSW96w6Sc8NsKn58PTaHPWKGLyul6P/tBwyu+5Sk+fPIeeB+NBOLip0vo0XHFxhqOUjx5XeRPe1Sq1OaMCOeLnYeepk4RwLzpj1MfFyi1SkJIYQQIoZF+dgzIcSFyB8c4Wj7GjJSK8jPucjqdCZUf/Me3J2NlCy+B3u8sdVqrTD02WZ0n4+0GyKzOOpt6MTX1EXh0zdancqEKJ45unJy6/4hSmamWpxN7DjyxQCv/cVROuq9LLgph7v/pJL0PCeD4egsOgb63Rz56TuMHGij5PGrKbxvUUTNS6h0nd4332J461ZSrricrDtuR7NF3sJZeihIw5oXGWo5SMkVd5E9PboXpzqRx9fLl4deRNPszJ/+KC5ndPbqFUIIIc6VJkOtzxspPAohos7RtlWE9QBTK5dH1EP1udLDIdp2fIArs4DMqZdYnc6Y9ICfgY3rSZw6nfiSIqvTOa2BT/eB3Ub6VTOsTmVCZBYnkJDqoHX/sNWpxIS+dh/v/msjX7zXRVaxix89O4uZV2VandY5cdd2cvgv3iI05GXKn32LrMWRtXK1CoXo/tUbuHftIu26pWTcdGNE3r/1UJCm1S8w1HqIkivvJnvaZVanNGF8/gG+PPgiSuksmP44ia7ovuaFEEIIER2k8CjOnhZ5vRQmxGT0vrCZeLiajFgz+zRJscpo7DjDG/uGGjjWt4eKgqtITMk1PCewbmLYpB5nLNZUmwbutp2HNuMf6aXiW0+j4u3j7psycQc3FTvOqMfBbVvRPR7Sr7sODI6Q1BzGv1K02XXDsY7TxKqwzsCGfaQtqMKV4QJ04m1h49s38fWn7TzNSq1pGkXTU2g9MPSNfw8r4+9BnzI2h15YN35dj3+VntCuiQkqfbqxXD268XnpBsOJ+IaDfPpcPVtebgRg8XcqWfJ0NfEJdvpDX8f2hYxP39AXMBbb6zfe5oAvwXDsiM/J4JaDtP7ru9iTE6j428dwVhUw4vtmnM9nfA5F3Wv8hmHzjX8N6oEAPStewnvwEFk33Ur61UsgMHa83WfiGvQbDsXuO/P1qoeCNH/8S4ZbD1N22T3kVF56xjxHt2/8PWD3G7+32QPGYjWDcf7AMDsOvkgo7GPBlEdJicuG4Niv1YIG75lh4/dWdOP7b6ZdFTbYrtE4AN3Evd3MfpmJjRYqBvdJCCHEhJLCoxAiauh6iENNH5DgzKCi8Gqr05lQIZ+bjl2rSC2aRkrpVKvTGZMeDDK4/lNcVdW4yssJE3kPHCP7mgn2DpP51FKrU5lQxTNT2fhyM6GAjiM+Rr/4mSShgM6W1xpZ+/NaPANB5t5ayLIf15BRaLzAF4mUUnS9voGu19aTMLWI0j+9l7iMZKvT+gbd66XzmRX4GxrJufMeUi+OzLkS9VCQho9XMNx6lLLL7iVnyiKrU5owwaCHnQdeIBAcZn71w6QmFlidkhBCCGE9GWp93kxK4VHTNBdwMVAJZALJgKaU+ulkbE8IcWFo6tiC29fDvCnfxm6Lw0Q/i4h3bNcqwkEfRZcstzqVMxrZvo3w0BC5DzxodSpj6lu3D1tCPGmXTLE6lQlVPDOFcFDRUTtC8QyZ59EIpRQ7P+nhvX9uoLvZS+WiLG76w6kUzYj+ee3CviBH/3ElvRsOk75kDoU/uAVbfGR9nxweHqbz588RONZBzqMPkTp1rtUpndaJRceSxfeQUxY7RcdQyMfOAy/i9fYxr/pB0pNLrE5JCCGEEBeYCf2EqmnaDcDvA0uA043pOaXwqGnaTcDDx/+3Tyn1o4nMSQgRG7z+furbN5CbMZ3s9NgqKPkGu+k+uJnsmkUkZOQTGv8lllDhMAPr1uIsLcNVVW11Oqel+4P0bzpExpXTsDmNDy2NBsXTR4uNrQeGpfBoQP3OQd76+3oadg1RUJ3Io/+1gJqrciJyXkGz/F1DHPyLt3HXdZH/2HVk3X5pxO1XqH+Ajv/6BeH+AXKfepzE6dMiZmXtE+nBAPUfr2CkrZaSa+4la+ol4LU6q4kRDgfYdfBXjHg6mDP1QTITK6xOSQghhIgc0uPxvJmQwqOmaTnAL4Gbvvqn04SNdVp3Am9zvFCpadpLSqkvJiIvIUTsONz8EZqmMbU0NlYpPlH7jpXY7HEUzI/sfRvZtYtQfz9Zt98ZcUWOrwxur0P3BshYPNPqVCZcVmkCyVnx7P64k0vvjsxFfSJBT6uXd/+pgS8/6iYtJ54Hf1rDpXfkM6IZn2Mxkg0daOPQX76D7g8x/ad3Yp8Tedd6oLOLzv/6BbrfT973v4urKjILXuGgn4aPVjDSXkfpNfeROfViq1OaMOFwkN2HXmFwuJlZNfeQnTkVfJH6tZYQQgghYtk5Fx41TSsENgOljBYcTy4wKk5fiBz9oVIdmqa9C9xzPPZBQAqPQojf6R2qp3vgMNVFS3HFR/8QyRN5eloYaNxN/txlxCWkWJ3OmJRSDK5fR1xeHonTp1udzpgGthzGnpJAypwyq1OZcDabxjWPl/HbfzxK3fZ+qhZmWJ1SROnv8LNmRQsbX2/H5tC4+YdlXPdECc7E4ysgxcDcDF2f7KP23z7BmZPCrL+/j8SybAYjrHeev6mZzl88BzY7Bb/3A+KLCq1O6bTCQT/1K5/H3VFP6ZIHyKxZYHVKE0bXQ+w9/Br9gw3MqL6TvOzZVqckhBBCiAvYORUeNU1zAB8CZXxdcGwHngE2HG9/lYGm3mS08Ahw/bnkJISILboKc6T5IxKc6ZTmR+aiBOeibceH2J2J5M26xupUzsh79AiBY8fIufe+iO3tqAfDDG2rJe2yqWj22Fx85coHi9nwYhMr/7WOH74cO4WSc9Hd7GXVcy1sfacDpeDS2/K45ffKSc8zvuJ1pFNhncbn19P+5nbS5pYy9SffIi418hbG8R46TNfzL2JPSSbv+98lLifb6pROKxzwjRYdOxsou/ZBMqrnW53ShNFVmH1HfkPvwFGmVd1GQW5kzqsphBBCWEqBJkOtz5tz7fH4Q2AOXxcdXwKeVkr5ATRNM9rl5JPj/9WAqZqmZSules4xt9gXoQ//p6PZJiFXE21OWqHEZrC4oZkogpjI1fB+Gc3T5PYxU9xxGItVJ8W1dmxnxNvFnJr7scU7v9Gl+uTYM9HjjMcqu7FjoJu4gyr7qf823F7LcNsRihZ9C1uC63f7ZrRd/TRtjh1r/DerOk3swPpPsaemkrRw3jd+frrY07HZja9+bTcR67B/3Y1tcFcDYbefrCuqv/HvAJqJTxYOzfj2bSZijQqe7mL5itPOtd+r4p2/Osj+jf0sXGyil6zBVIOaiQvLhIAy/obx6+PPz9lRO8LKZ2rZ82EbdoeNBXeWctUTlaQXJqIDfSeNKu0PJRre/kDQeGyv31jsYMBluM1h39dF09CQh+Z/eoeRnfVk3byQwu8sw+ew4/ON/tzrMzaXadhr/PhrPuP3S5tv9H45svNLut54jfi8fPKfeApHSir4vhlr9xm7t9r9hjePzUSsPXC86LjqWdzdzVQsfojM0rkQ+Gacw2/8fmEPGI+1BUzcWwzG2oJf3+uU0jnU8C7dfQeoKbmBksx5cMLPtaCJbr8hY7GawTgzbQIo3cS9VTfYbtjE9k3Eokz8fjUaq5t4GlbGj5Uy067VTBxXIYQQke1cC49/yNdFxw+UUo+dTSNKqUFN0xqB8uP/NBNYf465CSGiXDDkpb5lLRmp5eRkRu7w3rOhlKJ92wfEJaWRPeNyq9M5I39bO74jR8i45WY0R2Stmnui/i1HsLniSJtfbnUqk2rhncVs+GUjH//bUeZfNQ/bZHyxE8FaDwyz6pkG9qzqJs5l5/KHK7ji0UpScowX9qKFp+4YTX/7JqG+EYp+eDNZN0Rmz7zBTRvoff9dXJVV5D/yOLaEyOuNCRAOeDn6ybO4e1qovOYhMsovsjqlCaOU4nDzRxzr3U1l4TWU5V9mdUpCCCGEEMA5FB41TZsNFB//Xx34X+eYy1G+LjxWIYVHIS549S3rCIa81JTfHLHDe8/WYNN+PF3NlF51LzZHZK++PPjpp2hOJymXR+6DrNIV/Z/VkrawMuZWsz6ZI97G0h9U8Zs/28euVT3MvyHH6pTOi4adA3zy80YObuzFleJg2ffKWfDgFBLT461ObVL0rdlN23+vxJGaQNXfPUJiTeQtKKSUom/lBwx8upbEWbPJvf/b2OIi8/0X9nup++QZ3D2tVC55hIyy2Jr3sK5tLS1dX1CWdxmVhYutTkcIIYSIfNKx+rw5l64rc47/VwG7lVIN55jLwAl/Tz/HtoQQUc7t6aa18wuK8haQkpRvdToTSuk6x7Z9iDMth8yahVanc0ah/gHcO3eReuUV2CO0FxPAyKF2gn0jZFxRY3Uq58W8WwtZ/1wDv/33RuZel43N4PQA0SYU0Nm/voeNr7RQ+8UASRlx3PL7VVz5QDEJKQ6Gw7FXdNSDYdp+vpLeD3eQNLuMsv/3ThzpkbcitwqH6X39f3B/to2USy4l+4670MxM63Eehf1e6t/7Ob7edqqWPEp62SyrU5pQDcc20XBsI0U585lScn3MfVEnhBBCiOh2LoXHvBP+XneuiQDBE/4ee+OlhBCmHGn6CLstnqqSpVanMuH6677EN9BJ+dJH0GyTM5/eRBnatAmA1KuvsjiTMxv47Ciaw0b6JVVWp3Je2Oway36vmlf+YDernm/hhu+WWp3ShOqsd7P1zXa2vXeMkb4gaXlObv/jKVx2T9HXq1THIH/PCAf/6n2G9reTc8dl5D+yJCIXStIDAXpWvIJ33wHSly4jY9kNEVvsCvncNLz3C3y9x6hc8ijppTOtTmlCNXd+Tm3ravIzZzG97NaIPQ9CCCGEuHCdS+HxxE/+JmZgHlPGCX8fGDNKCBHzevqP0DtwlCllNxAfF3k9fc6F0sN0fLmKhKxC0ivmjP8CC+mBAMNbPydp9iziMjOtTueMBnc1kTy9CEdS7KxkPJ5Zy/KYf2MO7/1LAwnJdq5+MPKG4poR8Ib58uMeNr95jNrtQ9gcGrOWZHPpnYVMvSITu4nFpKLR4N5WDvz0fcLeIKV/dCfpV86wOqXTCo+46fr5CgJNLWTeewfpC660OqUxBT3DNLz73/gHeyi76XHSc2NrruDWY59zuHklOenTmFlxB5qZheyEEEKIC5ysan3+nEvhseuEvxecayLAiZ+wZUVrIS5QYT3I4YYPSUzIpiR/kdXpTLi+2h34h3qoWPZ4xPdMcX+5E93rJfWqyC0sAIRGfHjqOil8ILIX6Zlomqbx2N9PIxjQ+fVf1eJw2rj8ron4dXx+tR4aYdMbHXzxfife4TA5ZS6W/2E1l9yWT0p27BeSlVK0v7OT+p+vx5Wfxpx/uIdgbonVaZ1WqLePzv96jlBvPzlPPkzi3Nngtjqr0wuMDFD/7n8THBmk/JbvkFJSA57YecJoPfYFh+veJyd9KnOq7sYW4b3nhRBCCHHhOpfCY9Px/2rAAk3THEqp0Nk0pGlaNVB2wj/tOYe8xLmy+htzy7dvvBhkuHBkZtVZu4mHB6OxJobqKTPD+kzM56UMzkHXdGwzXn8f82Y9hhYXf8Y5f422aTZWN3hYTbXpGJ0TrWPnahKyi0ipmok+xsuV4e0b3jzKxN1ed4wWQwY3bSKusIC4KRVj5ord2IO8ZjAOwGYzEaspRvY1g65In1uCbYyvLh023XCbZujK+HsgbDA2qBs/WUGHk4f++SJW/Gg3r/75EZQjjgXfOn3xMYzBXE3UZszsv++Ei3Ck18/+NV1se7OV1v1DOOJtzFqWx8V3F1N5cQZe3UUQ6BvnU8VQyPjMLAPBRMOx/QHj85n2+421O+Q7tYiq+4K0/N8P6f90P6mLplD2/ywnnOTC4zE+d2XIZ3AxF7/xc2XznRrrb2+n87lnUcEgBd99GldlJfjA7jd+H7T7jcU5fIabxOE79YL1D/dSv/LnhHxuptz4XZJzKsCnsPuNXdz2gPH7hc1ErD1oPFYLjj2YKBwO0Ny6kez0Gi4qvRNbWIPw+IOPtKDxj+layOBgprCJe6uBHCczVikTNzczsbqJY6AbbFdNzu8sw6zevhBCiJhyLoXHLYAHSDz+50HgpbNs6w9O+HuHUurQOeQlhIhSXv8Aja0byM2aSWZ6tdXpTLi+Q18QGOql4qYnI763o/9oHcH2Y2Q+cE/E5zq0pwUtzk7ytEKrU7GEI97GY/82h+e+v4tX/2Q/n73RxpIny5i+OBubmS89JpFSio4jwxz8tJuDn3bRsmcQpSBvSjLL/3Qa824tiNnVqcfib++j4W/fwtfYRf5DV5N37xVoEXK+TuY9eoTOF1/E5nRS8MMfEZ8fuQt++Qa7Obryv9GDAabc9D2ScmJr/lMAuz2eBTOeIN6RiC04frwQQgghTiN2BkJEvLMuPCqlApqmrQTuYrTX4880TVuplOo2046madcBT/P1aX/9bHMSQkS3Iy0fAzCl8iaLM5l4eihIx45PSMwvJ6Us8ucZG1q/EVtSIkkL5ludyriGdjeTMqMIW/y5fJcW3eIT7Dz187lsfbON9S80s+KHu8mrTOKaJ8qYf2s+jvjz35M8FNCp3dbP/nU97F/fQ1/baDe24pmpLP1BFdOX5FI4LSXiC9uTYWDTQZr/7QM0u43K/3MfqQsjd1Gk4R3b6fn1r4nLzSX/O9/BkZ4x/oss4u3v4OjKn4NSTLnlByRmxu6XEa741ON/O6vBRkIIIYQQ5825PqX9JXAHo4XHPGC9pmm3K6WOGHmxpmkPAM8ef70G+IB/OMechBBRqGewlq6BQ1SVLcPlTLc6nQnXs3cTIfcQZdc9FPGFlmBPL959B0hddi22eIPDOC0SGvLiqe+i+OHInofyfIhz2bnqoVIuv7+Y3R93se75Rn79kwOs/LdarnqklKvvKyAhZXKLs8O9AQ5u6mX/uh4Obe7F7w4T57JRc1km1zxVybTFOaTmGh8eHWv0QIj251fT88GXJE4tpPyP7yA+N83qtE5LKcXgmjX0f7QSV3U1eY8+hi3B+BD0883T20btR79As9mpvvn7JKTnWZ3SWVNKEQr7cdhjf45TIYQQQsS+c3oCUUrt0zTtX4A/ZLTH4jRgr6ZprwBvAf0nxmujT9uVwFLgMWARowVHjr/+J0qpjnPJSQgRfXQ9xOHmlSQ6sygtusLqdCZcKOCla+daUkqmkVwYuT2bvjK8YTNoGilXRv5iLUP7WkFB6kWxN5zybNkdNubfks+8m/M48lkf655v4oN/qmX1zxu47K4CLlqWQ/nc1LNeJVrXFf3HfHTUeuio89BZ76GzwU1Xgwd3/+i4z7RcJ/NvzmPWkhymLMogPsH+jTkeL0T+9j4af/Y23rpOcu5YRMEj12CLi8wFQVQ4TO/bbzG8dStJ8+eTc+99aI7IPX/u7mZqP3oGe5yL6pu/hys12+qUzpquhzl46E0IhUlJKqC88CqrUxJCCCFikqxqff5MxKfIPwIqgDsZLR7GAY8e/8Pxf/uquOg7aZvaCT//lVLqnycgHyFElGnu+gKPv495U76NzRa5D7dnq+vgJsJ+D/mLIn8IuR4M4v58G4kXzcaRHpk9sU40fKBtdH7Hmsidc84qmqYx9fIspl6eReuBIdavaGTDq218+lIriWkOSmamEJ9gJ85p+/qP6+v/d5zw70M9ATrrPXTUuulq9BDwfr3wQHJmHLkVScy5LofciiSqL06neMaFOYR6LN3rD3P4Hz9Bs9uo+PN7SFs0xeqUxqT7fHSveBXv4UOkXbuUjJtuiuhzOdxeS93qFThcyUy56Xs4UzKtTums6XqY/Qd+TXJyHiWZC9hf9zY5GdNISsixOjUhhBBCiLN2zk/4Simladq9wF8Bf8zXRcYTezJ+VUs+ccyeOiHub4A/P9dchBDRJxD00HBsA1mp1WSnVRNr6yiGAl46D6wntXwmiTnFVqczLvfe3eheLylXXGZ1Koa4j3aSWJ59Qc/vaETxjFQe+6eZeIdDHNrcx751PXQ1ehjuDRD06YQCOgGfTtCvE/SFT7ugaUaBk7yq0cJiXlUS+VWJ5FamkJQe2cPxraQHQtT/Yj3t7+6K+KHVAKGBQbr/85cE2zvIuvseUi+91OqUzmioYT9Nn7yIMyWb6hu/S3xS5B5bI9yeLpKT86goX4rNG8Jmi6OtawcORwKVRYutTk8IIYQQ4qxMyJOaUkoH/remae8BfwIs59QC5Mk0YA3wF0qpzRORhxAi+tQfW084HKCm5HqrU5kUXQc3EQ54yVsYHfs39MVWHDnZOKdE/pBwpRTu2k6yrqqxOpWokZDiYN6Nucy7MfeMceGgTjCgE/SN/klMd+BKOvUjg67O/6I10cLbPsDB/++3jBzppOjuBWR9+7qIHVoNEGg7Rvd/rkD3eMl74kkSp02zOqUz6j+8g5ZVr5GYVUT1DU/hcCVZndI5UUonJbmAlOQCANq6duAPDFFZdA17a98gOSGX3MzIX5hMCCGEiAondo8Tk25Cu4gopT4H7tA0rRC4BrgCKAYyGe3t2Ad0AluB1UqpuoncfkyI4OFMJ9Ns1uZqauiXzcTDsalYgzmYyNXUfhmNnaT9VybmiFP2U2Pd3m5au7ZTlLuQpOS80fu/ietKdxiPNTO9nLIba1cfp83w8d6OaaUzScgvMfy7zWiuykQNw0h9KNDZga+xgfTbb0FzgKHfxjZje2UzMYmK0dhA1wDhER/JU/LH/IZrsukmthw0eMKMxgF4wsYXn7BrJvoT2wAXaC6IZ3Td3JHwqWHmco03Hqsbix0MGl/sZDBgfFGbwYDxdkf8p56D/s2HaPrXD9A0jcqf3E36ZTWMeJ1wmmN4OkGf8RuW8hk7B3bv2DcB7+EjdL7wIjaXi4If/YjEjKLRyXGMtGswzkys3X/me0DPgS20bHqL5IJKahY/jt3mgsD49w27gRgAm8G40TaNv6+008Tqepj65tWEwgFys2eS6MrE5UynMH0ORekXoWk2irMXEg740IKnvl4LGryoAMKTEBsyvqq2Cpu4B5mJ1Q2eL91Mm8ZjlbL2yVUZ3f/R4MlLxND25SlfCCEuRJMyNk0p1Q68evyPEEKc1tHmVdhscVQWX2N1KpOia/9ob8eCecusTsWQoc8/B7ud5EsXWp2KId76YwAkVUfv6rUituiBEG2/XEv3e9tJrCmk4k9ux5mXbnVaZzT8+Rf0/OZN4vPyyHvqSRzp6eCxOquxde5aS/sXH5BaOoOK6x7BHozuaRYO1r6Fy5VJRnIR/YP1DA41k505lTT76LyObb076Ro4xKyy2y3OVAghhBDi7ET3pzUhRNTqG6ynZ+Aw1SXXER+XbHU6Ey4c8NG1bz1ppTNIzC7BeJ8Qa+jBICNfbidp5izsKdFxPnx1HWDTSKyQhReE9bxN3TT+w7t4G7rIue1iih6/NqKHViulGPjoYwZWrcZVU0PeY49gcxnvFXq+KaU4tm0lnbvWkF41l/IlD6LZ7BCM7h5UiQnZFOTOx+VMI9GdxcBQI/2DjSSlpeEPDtHRt5eZZd8iwRnZBWwhhBAi6kT3R4ioIoVHIcR5p5TOkeaPccWnU5If2YsXnK2uA1/1doyOuR09+/aiezykLoqe8+Gt7yCxTBaWEdZSStH9/nbaVqzFnuSk6v/cS9ol1VandUZ6IEjP67/GvWsXyYsuIfvuu9DskVwk1Wnd/DY9B7aQNe1SSq68C83MFCIRTKE42riS2VPvJyWpAF0P0dy+mbykapJc2VxUeR8Ou/EpFYQQQgghIo08rQkhzrtjPXsY8XQwq/pu7LbYWxE3HPTTtW89qSXTScwusTodQ4Y+34ojMwtXVfSsLO6r7yBjYbnVaYgLWKDPTd3P3mVoex2pC6so+/1biMuI7B7D4eFhOle8gL+piYxbbyFtyTXm5hY+z5QepunTX9Nfu4PcOddQuOjWiM53PErpHGlcSYIznQRnBpUl13Kw7l32Hn6d2VPvJy2lBGd8KkOedhKcGVJ0FEIIISaBBpiYgl6cIyk8CiHOK10PUd+6lpSkQvIyZ1mdzqToObyVsN9DwdzomNsx2N+Hr76OjBtuPN6LKPJLj7o/SKh/BFdhhtWpiAtU7+f1HPr7Twh5AhR/73pybl0Q8QWxQPsxOp5/Hn3ETe5jj5I0Z7bVKZ2RHgrSsLxT11cAACAASURBVPolhpoPULDwJvLmLY34Y3wmSikO1r2LMz4FZ3wqA8PN9A81Mr3qNg7Wvcu+I2+QlJDD4HAz5VnR0/tcCCGEEOJMzlh41DSt9HwlciKlVLMV2xVCTL7Wrm34AoNMr7wtqh8gx6KHQ3TtXU9yQRVJuWVWp2PIyJdfApA8b4HFmRgX6BoAwJmfZnEm4kITDoSof2YjbW/vJKkii+q/fpCE8lyr0xqX58ABul5+BZvTScGPfoizpNjqlM4oHPBS//EKRo41UHzlXeTMuNzqlM6Zpmk47E5ys2aRkpRPVkYNDS3rqG9ew/Sq2+jtP4pSOvk5F+Ei1ep0hRBCCCEmxHg9Hhs5/1NuKqQn5sTTLJ4Lycz2bZNQjDJR4DJVDDO6X2b238z27cbaVWaOqcE2R9s1EWu3EQr5aGjbQEZaJZmZU057c1EO47ma2S/dbibW4PZPE9d3dAdBzyCli+/9xs9PF3vu2zd+ex4rVinFyJfbcVVW4sjJQKFQNuPtagZz0Ey0aTMw7iF0vPCYkJ9qKN4MXRm/VsImYv36xP9qi9PCE96mmf03s09ePd5w7EjIWOxQIMFwm8NB40NWh3ynX2TF29hFwz+8i6+xm5xvLaTo8Wvx6om4feO3GfAZP1bKazzW5j3zfVgpxdD6jfS/+z7OgiIKHn4CR1oanCFnu8/4NWD3Gw7F7jf2XtUHR6hd9Sze/nYqrnqQrMr5MMZ2jLZpJtbuN97r2xYwFquUjhYI4bInMzzYTFp8DvE4KMtZRFPHFvwjPeQkV/4uXvMZXJIsZPweoJmINdqu0k30kNdNbF8Zb1eFDbarTPzONBGLbiLW4H4pM21azcyxEkKISCK3r/PGyCfb2OuSJISwRHP7FoIhD9Wl0TEE2Syl63TuXkdCVhEpxVOtTscQf3Mzwe5u0pZcY3UqpnzV49ElPR7FeaCUoueDHbQ+vxZ7YjxVf3kfaQurRn9ooOhoFRUO0/s/bzOyZStJM2aTd+8D2OIje87AwHA/DSt/gd/dT9W1T5BePN3qlCaEptnQNBtZaVXUtq7BYXeSkz4VV3wqYT2Ix9dLglOmjhBCCCFE7DFSeDyfdWApcgoRo/yBYZrbN5OTOYPUlMge4ne2Bhv34R/spnzpw1EzjHxk+w40h4OkOXOsTsWUQOcAWpyduIwkq1MRMS7YN0LTv3/I0LbaqFlABiDs8dD9wsv4jhwlbem1ZC+5KeJXgvb1d1H/21+g+33ULHualLzK8V8UwZRSDI20oZROeuro7EUpiflUFi6mqXMrHl8vuh7C4+sl0ZVtcbZCCCGEEJNjvMJjM+YKjw6g6Pjf1fHXCyEEdc2r0VWY6rLrrU5lUiil6Ny9FmdqNukV0VHEU6EQ7l27SJw1C1uC8SGrkSDYOUB8bjraZEzNIASj7+m+dftp/cUn6IEQxU8vI2f5wqj4UiHY3UPns88T6u0j64H7SFl0MZonsvP2dLfS8NtnAZh6w/dJzIruL6iUUuza/wIJCdl4PN2kJBdSUzC6OE5acjHVjmvxB4cZdh9jRvm3SHCmW52yEEIIcUHRZKqI8+aMhUelVLmZxjRNKwfqT3h9xdkkJYSILUOeYxzr2klp4eUkJmRZnc6kGGk/iqe7hZKr7on4XkVf8Rw8hO7xkLwwehaV+Uqga5D4PHlQF5Mj2DdC839+xODWIyRNL6bsD27BVRQd9y5fbR1dK14EIP8H38VVVWVxRuMbaa+jYeUK7PEJVC1/msT4HKtTOmfDI+24XBlMq1qOrofYf+RNjrauoqZk9Ms3h91JoiuTjJToWIRMCCGEEOJsTfTTsZSMhRDfoJTiSOsnxDkSqCi+xup0Jk3XnvU4ElLIrFlodSqGuXftwpaUREJNjdWpmBYadONIl2HWYuJ1rT/CgR8+y9COOoqeuJaanz0UNUXHoc1b6PivX2BPTqbgD34cFUXHwYa91P/2GeISU6m+40c406O/6AgQF5fI8Eg7A0NN2GwOZtbcjdvbzdGWVQRDXtp7dxMOB6xOUwghhBBi0snq0UKISdU7VEv/SCM1FbfgcJx+pdho5+vvZKjlEAULb8Rmj47bqh4I4Dmwn+QFC9DsJpbcjhC6L4jNZXyVZCHGExz0cuTf19L96WESawoo+4PlJJRGx7x7Khym7613GN78GQkzppHz8LejYvqE3oOf07r+NyTmlFBxy3dwuGLny4QEVwYlhZfT238Umy2O1ORC5lTfy5Hmj1EoinMWYLfLPUwIIYSwhEK6zZ1H0fGEHAuiYE6oqGNmOKupWBPnymismTbNFIGM7peJ/VdmjpX9zPullM7R9tUkODMoKrjY0HFQJo6VGmf732zXcKjhdvXjp6pz/wY0u4PMWZf97t9ObdP49o0us2WmzZP333P4ECoQJHHeRaceGzN94TVjv7E1g3FGY3VvAFtCnOE2dWX8WgmZuFgCuvFfo3YTx8Ao/yT8Gg+NdRGfbvsm9t8dMl5kGQkZW3l5OGB8heYR/9ixA1uP0PwfKwmPeCl4eDEpy69Gt9txG1ix2u8zdh3qXuPHSvObeBP2uOl85SV8DXWkL15C5g03o2m2U1bbtvuMvwfsfuObt5tY1furdpVSdO5ZQ/uOlaQWTaXi2kexa044/nN7wPh7xVysbijOZjAOQAuGx/xZdnIVnX4PHR1fEkofQfl9eH19OMJ27DYH6GO/VguGjG0/NHYbpwgb3y/CBts1GmcyVpnJ1egcXbqJNnUT92tlot3JYPX2hRBCiHFI4VEIMWnae3Yz4u1iTtU92GyxebsJeobpP7ydjKkLcSRE/kq3X3Hv2o0tORlXZfStGqtCYVQojD1BeguJcxMa8dH6zCr61uwloSKX6r+6n8TKPNy+6OgFHGhrp/uZFwgPD5F734OkzIv8+VqV0mn9/D26D2wko3IeZVfdHzU9xc2Kj0ukIGcuA8NNtHVux6HFUVO4bLToKIQQQghxgZBPPkKISRHWg9S1rSU1qYjcjBkx25O9Z+9GVDhM7kXXWJ2KYXoggPfAAZIXLozaYdYANqfxHo9CnGxoRz1N//4Bwb4R8u+/gvz7r8QWFz3vB8/uvfS8/Bo2l4vCp3+Iq6TU6pTGpYdDNG18nf76neTOvJqiS5aP9s6MYQ67k+z0GjJSK7D5w1J0FEIIISLEJAxGEmOQTz9CiEnR3LkVf3CY2VV3oWlaTBYewwEfvfu2kFY5O6oWRPAeODg6zHruRVanclbCvtEFGWzS41GcheCAm9ZnV9P/6X5cJVlU/tOjJNUUWp2WYUrXGfx4NYMrPyG+rJSCbz+OIzXV6rTGFQ74aVz9AkNthylccDN5c65Fu4CmobHb4rCZmXZFCCGEECJGSOFRCDHhAkE3jcc2kZM+lYyUcqvTmTS9B7YSDnjJnbfE6lRMce/eMzrMuir6hlnDCT0eZXEZYYJSit5PdtO2Yi26L0j+g1eSf+/l2OKi56OQ7vfT+6vX8ezeS9IlC8i6724cwch/H4S8IzT89jm83S2UXnkv2TWLrE5pUvQPNZKWXILNFj09Z4UQQgghJlv0fNoWQkSNhmObCIUDVBcvtTqVSaPrYXr2bCCpsIrE3Mgf4vgVPRjEe+AgyQsXoJlZSCiCqOOLLmgOebgXxnhb+zn6T6sZ2dtM8qwSSn90E66S6Fix+ivB3l66n32B4LEOMm5fTsqSq0d7DAatzuzMAkO91L//DMHhfiqvfZT0stlWpzQp2rt3cqD+XSqLl1BZtNjqdIQQQggxnlgckhehpPAohJhQvsAQrV3bKMiaQ3JCrtXpTJqBpj0E3YMUXX2X1amY4q+rRwUCJMycYXUqZ+2rno768SHXQoxFD4VpfWM7TS9/hi3eQenv3UTW9XPRomzIq+9ILd2/fAml6+Q+/SQJM6ZZnZIh3u5WGn77LCocovJb3yMto8LqlCZFS+cXHG78gMy0KsryL7M6HSGEEEKIiCKFRyHEhGo8tgmFTmVhbPf46Dy4kfjULFLLpludiimeAwfR4hy4qqutTuWs2ROdAITdfoszEZFs+FAHR/75E9z13WRfNYWC795EXGb0rDwPo8PDh9etp//dD3Dk5pD71OPE5UbHfLLDLYdpWvkCdmcClbd9H1dmPnhjr2tB07HNHG3+hOz0qcyZci82WTxGCCGEEOIb5NOROIXhniBmVqI00bvE8GTzJialNzWBvZn9MtiuqSGtpnI1GGs3sX278e2rk86rzz9Aa/cOCrPnkZCY9Y3e68phrF3dYNxom4ZDUSb2Sx9nBK+7uxl3dxOFV94OdruhXvrjtXkiZTBWmRkpbRstYngPHMBVMwWb6wwrQptY4k2zGYudyDUk7EmjhUfd60dXBq8rg3EAId34gQ2aOLE2g+M5zLRphtFj4NeNv7F8YeOxnpDxuQg9BuctHPafGhf2Buj41Xq6399OXHoSFT+5m7RLa/D64wn4jG3f7zO+YrruNXYMNJ/x68ru1dD9fnre/DXuPbtJnDWb3Hvvx+Z0gfekWJ+x82o3Uad3GDxOo+2eel33Hd1B04bXcaXnUXXjU8QnpYFf4ThNrNE2JyLWFtCNxQXD48YopWho/ZT61nXkp89kVvnt2EIaMPZrNQPt/k7YYKzROIBQyHCoChs7VhiNA9BNFJ51M+0ai1XK+sK3MnMMrBQBx0oIISabrGp9/kjhUQgxYRraNgBQUXS1xZlMrq79G7DFuciYfonVqZgS7Owk1NtH2tLoWgznZJrDjhbvkB6P4hSD22pp/e+PCXYNknXzfAofvQZ7ksvqtEwL9nTT8dILBLs6ybzpFtIWL4mKFaCVUnTtWUf7tg9ILqiictnj2OMTrE5rwimlqG1eRVP7JgozL2Jm6XI0M19aCiGEEEJcQKTwKISYEB5fH+09OynKXYjLmW51OpMm4B6kv2E3uTOuxB4fXQUN7/6DACTMjK7h4adjT3QS9pjoliVimr+tj7ZnVzG0vQ5nSRbVP3uY5JklVqd1Vjx7D9Dz4mtodhv5TzxFYs1Uq1MyROk6rVvfoefAZtIr51K2+AFs9tj7mKmUzuHGlbR2fE5x3iVML7ghKorCQgghhDiJ9Hg8b874iVDTtPpzae8sXg+glFJVZ/E6IYSFGtrWo2GjovAqq1OZVD2HtoBS5Ey/0upUTPPsP0B8cRGO9OgvDNuTnNLjURD2Buj89Wa63/kCLc5O4RNLyV6+EFtc9K14rnSdwZWrGPxoNfFFxeQ99ChxmZlWp2WIHgrSuO4VBpv2kjt7MYWX3BqTPQCV0jlY9x7t3V9SWnA5U8puQPObGOoshBBCCHEBGu+r6HJG68Bn81Wudvz1ZkndWYgo4/b2cKxnN6X5l+KMT7U6nUmjh4L0HP6MtNIZOFOzMD5blvXCbg/++kbSrl9qdSoTwpYohccLmVKK7k8PU/+L9QR7h8lYOpvCx5YQlxFdi8d8Jezx0PvS63j3HyRp0UJylt+NLc74PJNWCvnc1K9agbuziaJLbyN3VmxOtaHrYfbXvkVn714qiq+hsjg6hr8LIYQQQljN6BiY81UMlE9wQkShxvaN2GwOygqjrxegGQNNewj53FHZ29FXWwdKkTA1OoZtjseZn4H7cJvVaQgLuBt7qPvPNQzuaiGhKp/yP7mDpOnFVqd11gJt7XQ/9yKh/kEy772D5Csvw+aNjh6b/sEeGt97hoB7gPJrHyaj8iKrU5oUuh5i79Hf0N13kOrSZZQXxXbPfiGEECLmKVlc5nwyUniUYqAQYkweXx8dPXsozr8EZ1x09jYyqvvQZzhTs0kprLY6FdN8R2vR4uNwlkXnvHcnS6jMY2DjAYJDXuJSY2/xCnGqkCdA88tbaH/7S+yJ8VT9+DqSly5Es0fvkF739p30vvobbAkJ5P34e7gqy61OyTB3RyON7z8PSlF90/dIzq+wOqVJEdaD7Dn8Or0DR6kpv5nSgkutTkkIIYQQIqqMV3iMzU+RsSIG508CwGbxftlM1NrtBnulmNknEw/Rymisie0rM7F2G43HNqFpNsqKrzpjPsrgcVV248ffaJsAuokOROo0sd6+Y7g7GyhctBwcNtQYcWbaHDPW4ClQJi5VX20tzsoKiLejxuvEbvHXTcrAjrkq8wFw13eRNrds3Piw0YMKhEycLF/Y+FelIYM52EwMMtBNnKyQbmz7Ad34giD+kPFYdzDecKwn8PUwY6UUAxsO0L5iNaH+ETKXzaXgkSU40hLxBeIwOueB32d86LLuNb5fms/YcbV7v45ToRC9v32PoS2bcVVUkvvQwzhSUsE7+nOHz/h5tRucccBuYi0mu//M1+BAw14a1/6KuKQ0pi75Dq7UHBjnNUba/TpONxQHYAuYiTU2H6MWHI0Lh4Psrn2dvuF6ppfdSnH2Agie1EbI+PYJGZ8PUjMaa6JNpZvIVTfYrjLeplImupaYiDXcrm5m+yb2y0y7k7B9IYQQItKd8ZO1UqrpfCUihIg+Hm8vx7p2UpR/cUzP7QjQc/AzNLuDrJqFVqdiWmh4mOCxTpIWzrc6lQmTUDFaePTUGSs8iujkPtBC+y/X4DnURkJ1ARX/+24Sa4qsTuucBPv66HrlJfwtLaRddTWZN9+KZvRLrAjQtXcDbZ+9R2JuKZU3PIGLJKtTmhShsJ+dR19jYKSZmeW3UZg91+qUhBBCCDGRZKj1eWP8K30hhDhJfcsaNJud8uLFVqcyqcJBP31Hd5BecREOV/QNJ/fV1wLgqom+IeJjcaQn4chMwV3XaXUqYhL4Wno49tI6hrYewZGZTPHv3ULm0jlRPawawH1gP92/fg2UIu+Rx0iaNdvqlAxTuk7b1vfo3reRtPLZlF/7bWyOOPDG3qf2YMjHzqOvMORuY3blneRnzrI6JSGEEEKIqCWFRyHEWRn2dNDZs5eyoqtxxqdYnc6kGqjbhR70kT39MqtTOSveujo0l5P4kujuKXayhMo83LVdVqchJlCgb4TWX22hc+VubM448h9aTPZtl2B3GR+mHYlUOEzvhx8y+Ok64guLyHv4EeKysq1OyzA9FKBx7SsMNu4jZ/bVFC1ajmb1tCiTJBj0sPPIS4x4O5lTdQ+5GdOtTkkIIYQQIqpJ4VEIcVZq29fhsLsoK4q+FZ7N6jn4Ga6MfJLyyq1O5az46mpxVVVG1XBOIxIq8+n6sg7dH8TmND5/n4g8YV+A9je30f6bL1DBMNk3LyDv/itxpEX/MN7QwCA9v3wFf10DKZdeRtby27DFRc/1GvQMU//x83i6Wym6/HZyZ8Xuis6BwAg797+Ax9vDRVX3kZNeY3VKQgghhJgEGrKq9fkkhUchhGmD7jZ6ho5QVXodcY7YXlHY09uGp6eF4stuR9MsXnXlLIRHhgn2dJN0xcVWpzLhEqcWga4Y3NVMxqIqq9MRZ0GFdbpX76P5hY0E+9xkXjWV0sevRuXkWp3ahPAePELPi6+iAkFyH/g2yfOia55Vb18H9R89R8jnpuL6x0gvj90hxz7/ADv3vYAvMMTc6gfISpN7ihBCCCHERJDCoxDCtPpj64mzJ1BccKnVqUy6vsPb0Gx2MqoXWJ3KWfG3tADgLI+9BViS51biSE2gZ81+KTxGoYEdjTQ9uw5PQzfJ0wuZ+ue3kzJjdDoAT8Di5M6R0nUGV65m8KPVxOXnkvPkw7hSC6xOy5Sh1sM0rHoJW1w8U5b/gMScEqtTmjRuTzc7979IOOxn3sxHyXTG1rQUQgghhBBWksLjuYqSHlCazdo8J6WnmJn5pczsv4lcDe+Xmf03E2vwGCi7iTbHiR3t7XiU6qJrsce7DC8GpgyeA6NxALqJkcNm2lXHD6seDtFXt4O08lnYExNP2Vdl4hKclFgDcb7WZtA04kuLRscUGDEJb1dlYiiDrgwm4HCQdc0MulbuIjDsx5HsGjM0pBs/Af6Q8V+Nuon3lkPXDccaFTJxYRk9Bv6w8f33mThWvsDo8OKRfU10vrYB994m4vPSKf2jO0m7YjqapuH2fzN2PAG/iXPlNR6r+YwfV7v3m7HhkWG6X30F79GjJC9YSPadd2KLd2L3GW4Sm994rNF27X7jb8K+PZ/R9PlbJKTnMWXpk8QnZcAYOZlp12isLWD8vWILhI3HBk9td8h9jC8Pv4wGLJz2GCkJBWj+oOE2tWBoUmIJGdyvsPH9n4xYFTZxXzOz/Um4X0YEZfF+mfllLIQQsU7uieeNFB6FEKbUt6/HYXdRkneJ1alMuqGmA4R9HjKnRu8wZX9LC3F5edicTqtTmRRZ182k870d9G08RO5Nc61OR5yB+0Azna9uYGRPI46MZAqfup7MG+dji4udjyLeulq6Xn0F3eMh+557Sbn4kqiaokHpOu2f/5bu3etJK5pO5eKHsMeNXdCPdgPDzew88goOu5P5Ux8hKSF6FvwRQgghhIgWsfNpXwgx6Ybc7fQMHKGqaAkOuwsTfReiUt/hL4hLTCWleKrVqZwVpRT+lmaSZsbuvGxJU/JxlWbRs3q/FB4j1PCBVtpe3sTQziYc6UkUPLmMrBvnx9SCQCocpn/VJwysXUNcdjb5Tz6Fs7DQ6rRMCQf9NK95lcHGfeROu4KSi29Ds8XWglQn6h2sY9fR13DFp7Jg6qO4nGlWpySEEEIIEZOk8CiEMOx3vR1zF1mdyqQLuocYajlE7kVL0MwM648gob4+dI8HZ0mp1alMGk3TyL5uFq0r1uNr78dVmGF1SuK4kYNttL68iaEvG3GkJ1LwxHVk3bQgpgqOAKH+fjpf/RX+xkaSF15M9u13RF0P46B7kPqPVuDtaaPoitspqI7dlasBuvoPsaf2DZJc2cyf9gjOuGSrUxJCCCHEeSarWp8/UngUQhgy5D5G98BhqoqWEOeI3aF3X+k7uh2UInNq9A4p97c0A+Asjd1FIQCyl8yg9Zfr6Vmzn+KHr7Q6nQveyOF22l7exOD2BhxpCZQ8eQ25y+cRtCVZndqE8+zaS+8rv0HpOrkPRt+q1QDennbqVz5H2O+l4sYnSCubAV6rs5o8x3r3sr/uLVKSCpg/9SHiHIlWpySEEEIIEdOk8CiEMKTh2IYLprejUoq+w9tIyi/HlZ5jdTpnzd/SguZwEJ9fQIxO0w9AfE4qqXPL6PlkDwV3X4I9Id7qlC44SilG9rfS/sbnDH5RhyM1geInFpO3fP7vzkcwyleqPpEeCNL/9vuMbPwMZ3EJud9+iLjs6JsfcLDpAE2rf4U93kX17T8iMTu2V3Nu6drGocYPyEgpY27Ngzjs0dUzVQghhBAiGknhUQgxLre3m67+g1QUXH1B9Hb0drfgH+gid/G9VqdyTgKdncTl5qLZ7RDTpUcofPAKDv3xazT++8dU/tGtUbWgRzRTYZ2+zYfpeHMb7iPHRguOj15F3m0LsCfGZlEn2NFJ94pfEWzvIPXaq8ladguaI7o+Timl6N6zgfat75OQVUjFjU8Snxy7cxwqpWhs+ZSGpjVkp9cwp/pe7LbYGvIvhBBCCBPU8T9RRNO0G4F/A+zAc0qpvzvp507gJWAB0Avcp5RqPP6zPwWeBMLAj5VSH5/wOjuwHWhTSt06GblH1ydlIYQlGju2YLM5KM2L/d6OAH1Hd6DZHaRXzLE6lXMS7OrEWVZudRrnRersEoofvpLWFzeSPKOIvOXRN+Q1moQ8ATpW7qX1f74k0DWEsyiD8t+7nqyls7C7YrOgo5Ri5LNt9P/mHTRnPLnff5KEmdPQvNE1B6weDtG26W16D24lrWI2pdc+gD0uNovEAErpHK3/kNb2rRRkXcSMituwxfCiOUIIIYSIPceLg/8JLANagW2apr2nlDpwQtiTQL9SqlrTtPuBnwH3aZo2A7gfmAkUAqs1TatRSn21Vuz/Ag4CqZOVvxQeI40WAQ8wRnOwTVKPIoM9lUz1aDJzXE3FGszBbrxNZSLW8DkwsTiKOqlNX2CIY717KMpdQJwz+ZtfDNmNnwPdYSxWmXgePDnXiWhX6WEGaneRWj4TW0LCmb8IM/N2NXG5Kpuxr9/UGdrU/X5C/f0kX7JoNM7iDoDqTMmeHGui3VD46xObc/eVDB04RvMv1uCqKiJp6terCgcnafZo3cSB9U/C9kO68YswGDb2JvCHxv5oEOgZovu97fR8tJOw20/ijBLyvnMjKRfXoNk0fDDmjvr9xguSoYCxXHWf8Y8xmt/4sbL5vhmre730/s+buHftwjVlCjkPPIgjNRV8YDN4Yu1e49eKw2c4FLvP2LVt90HI56b+0xcZ6agjf85SCufdiBa2jX73fWKs3/j7xR4wHmsLGut5bTcYB2ALhMf8ma6HOFj/Dh29eynNv5yp+UvRwkB47NcAaMEz//wbxmnrpIQmvF0VNtOmiVjd4Hk1s08mYpUycc82mqsysX2jbUYCM8dKCCHE72jRNSDsEqBWKVUPoGna68BtwImFx9uAvzj+9zeB/6uNFk1uA15XSvmBBk3Tao+39/+zd9/RcV13gue/91UGCqGQEwECBJjFKFJZJGUFKrcVrGDJSW212+6e2dme3Vlv7872mTk+feb0tre3t2e3xz2dZFtWsiTLSlQOVKbEnEGCyDkXUPnd/QOQRFEM75EAHqrq9zlHRyDqh/t+9epVAfWre+/vA6VUDXAz8DPgf5yt5KXwKIQ4q/aeD0Br6ioudzqVOTHaeZhkNExo8XqnU7kgif5+ALxlZQ5nMneUoVj4Z7dy6N/+Ey1/+TRL//YHuPOlccRMmDzWS98zHzH0zgHQmsIrllL+zUtwL8rsxkUA0ZYW+n/zKMmREUI33kTBlvTsdB8d6aX59X8iPjHMwqvup3hRer/GnUsqFWfP0ccZHG2mccG11FVeibJR0BRCCCGEmEeqgfaT/t0BnLoc8YsYrXVSKTUKFE9//8NTfvbzjb3/BvifgbxZyPkLUngUQpxRIhmho3cH5cUrCPhDTqczJ4aad+Dy55K/YKnTqVyQRw4VqwAAIABJREFURF8vAJ7ycoczmVvuvAD1P72DI//+EU781XMs+otvoezMIhZfMONJRt4/zMC2XYT3tGL4PZTesp6y2zbgqygEIJZ0OMlZpFMphl95hdE3XscdClH54x/jX1jvdFrnZbz9MK3bHkG53Cze+scEy9LzfliVSE6y6/CjjIY7WFZ/G9VlmV1kFUIIIUTaK1FK7Tjp37/QWv/ipH+fbunMqVPezxRz2u8rpW4B+rTWnyqlNtvK1iYpPAohzqij9xNSZpy6yiudTmVOJGMRRtr3U7TskumGLOkr3tsLhpGWnXYvVG5TJTU/uo72v3uZnsffo/L+q5xOKa1MHu9lcNsuht7aTyocxVteSNX3t1CydS3uYOY3lwKI9/XS/+ijxDs6CG7cSPFtt2P40/O+D+x7j87tz+IvKKPx2ofwBYucTmlWxeJjfHbol0xGB1nVdA9lRcucTkkIIYQQ89H82qliQGt98Vlu7wBOXmpUA3SdIaZDKeUGCoChs/zsbcBtSqmbAD+Qr5T6ldb6gQu6J6chhUchxGmlzATtPR9SXNBIXm6F0+nMiZHWPehUkqLFZ3vNTw+J3j48xcVp1213ppRsXcvEwU66H30Xf10pFZsanU5pXktNxhh46yC9L+5m4mgvyuOi8PIllNywhuBFdajZ2tN3ntFaE373A4affh7l8VD23e+Re9FFTqd1XrSZovO93zG47z3y65bTcOW3cXnSs3hq1WR0iJ2HHiGemGDtkgcoKmhwOiUhhBBCiJnwCdCklKoHOplqFnP/KTHPAd8FPgDuAt7QWmul1HPAo0qpnzPVXKYJ+Fhr/QHwU4DpGY//fjaKjiCFRyHEGfQM7CGenMia2Y4AQy078eWVEChL/33rkkODuLNwtuPnlFLU/ngr0fZBWv7yacz+zVTeucFeU6osED7aQ++Luxl48wBmJEFOfSk1f3QdRVtW4s4LOJ3enEqNjjHwqyeJHjhEYMlSSu65Z6qBTBpKxiZpfeURwh1HKV29icpLb8Flo7lOOhqb6GbXoV+i0axb9j0KgtXn/iEhhBBCiDQwvWfjnwDbABfwT1rr/Uqp/wTs0Fo/B/wj8Mvp5jFDTBUnmY57gqlGNEngJyd1tJ4TUngUQnyN1pq2ng8J5lQQyl/odDpzIhENM97dTMVF12REcSo5NoavttbpNBxl+D0s/stvc+Lnz9H6D28x2TZIw59ej+FJ72X0Fyo+FGbovaP0vbKXiSM9GD43xZuWUX7TaoJLK4mnrHefzhSTe/Yz+Osn0bEYRd/6A/I3XJm2rwPR4T5aXvpHEuPDLNhyD0VLNzqd0qwbGm1h95Hf4HH7Wbv0QXIDpU6nJIQQQoh5Ts2vpdbnpLV+EXjxlO/9x5O+jgJ3n+Fnf8ZU5+ozjf0W8NZM5Hk6UngUc8NOB1CrsXaW/s1arMVc7byBtRNr8fjaxpjaZTA40sxEpI/ljXeA23XG7S+0jXNlNdbemJZDzxk70rZ3qltvw2obudo4vp0ahtXYM/y21MkkZjiMKz9/1n+jaot3TNtIwzStn9hk6hwDe3ws+J/uwr/gTXoee59I5wiL/vwO3AVn7nadsvFguWbh/No5fsriuYoPjDO4/Sij7x1k4kAbaPDXlVH18A2ENl+Ea3rvxsk4xJPWC7OJhPXYZNz6nxw6am1cFbd+rRiRr8easRhDv3+O8Q8/xFtVRen938ZbUYEranlY3BFrj5crZn1Mw0bsyeOOth+k5a1fYbjcNG39EcGKBpi+3RW3fq3aio3ZibXWVVrFrXef7uvbx94TT5PjK2Jd4wP43flwhu7VKmHxw/2k9UkAykasnXG1afEcmDaOr62fV231RdvGi7vlMQFMG7EW75e2M6YdNs6rEEIIkY2k8CiE+Jq2zu14PUHKS9Jzb7PzMdyyG19+CYGiKuZ03vksSI2PA+AqSM9lojNNGYrq72zCv6CY1r95kYP/7l9p/Iu7CdRm9lL0eP8Yw+8dYvjdQ0wc7ASmio3l911NweXL8Ndm96ywWFsbfY/+muTgIAVbthC6YWva7omqtaZ375t07XiRQFElDdd+P+ObyAB09HzC4ZbnKMitYe2i+/G4s2t7ACGEEEKIdJCef2ELIWbN6HgHQ6PHaKy7AcPIjpeIZDTMePcxyldtSdvllSdLjo4C4C4ocDiT+aV4y0p8FYUc+8+/5eCf/CNF11xExV2X4K8pdjq1GRPrHWF4+2FGth9i4vBUo7tAQzlV37ma4KUrMuq+ni+dTDL8yiuMvvkGroICKn70xwQWLXI6rfNmJuO0bn+C4eM7KaxfTd2V9+Dy+JxOa1ZprTnR8RbH29+gJL+JVQ134zKyb4sAIYQQQpwnjb0lWeKCZEdVQQhhWWvnO7jdAaorNjidypwZObEPtElo4SqnU5kRqenCoytNG2PMpuCyGpb97Q/oeeJ9Bl7Zw+Cruym8YikVd19KblOl0+nZlhieYHxvG+G9rYzvaSPaPghATmMF1d/bTOEVS/BXT818S9hYQp2pYu3t9D/2GIneHoIbN1J8620YgfSdJRcfH6b1hX8mMthJ1fqbKF+VGXvUno3WJkdaXqSj5yMqStewouYWDCXXthBCCCHEfJVVhUel1F8BtwJx4Bjwfa31yPRtPwUeAlLAv9Fab3MsUSEcEo700z90kPqazbhdmT1j5mTDLbvx5RUTKM6MLqjJsTFAZjyeibckj9of30DlfVfS99wO+p//lJHth8hbu5CKOy+lcF0tys5er3MoMTLB2J42xve0M7anjWjbVKHRCHgJrqih+PrVhC5fjK8y5HCm84tOJBl66fWpWY7BIOUP/SE5y5Y5ndYFmehuofWlf8FMxmm49vsU1q5wOqVZZ5pJ9h/9LX2D+6ituoLGuusxLO4bKYQQQgghnJFVhUfgVeCn063I/wvwU+A/KKWWM9VqfAVQBbymlFo81y3GhXDaid73MAwPCyovczqVOZOMTTLe3Uz5RZszZqZQanwcDAMj58wNVAR4QrlUf3cTFXddQv+LO+l99hOO/m+P4SkJUrxpOcVblpOzqMyx60Kbmnj/GJPH+xjb08bYzlYiJ/qBqUJj3soair+xirxVteQ0VqBcNrodZZF4eycDjzxGoquH4MUXU3Tb7bjS/LkxuP8Dut55Gk9eiKYb/5hAYbnTKc26ZDLKnsO/YXj0OI11N1BXfeX0LVJ4FEIIIYR96dbVOp1lVeFRa/3KSf/8ELhr+uvbgce01jGgRSnVDGwEPpjjFIVwTDQ+Ts/wXqrLN+LxpPebcjvGOg6BNimsW+l0KjNGx+Morxdlp5t8FnPl+qm4+zLKbt/AyPuHGX57P73P7qDntx/jry2mYO1CfJWF+CoL8VcU4qsowPDN3H5yqViCaMcwkfZBIh1DTLYNEe0YJNoxhBlLAqC8bvJWVFO8+Wry19SR2zRVaLTa1Tob6VSK0W1vMPrSa7iCuZR//wfkrEjvWYFmKknXu88ytP998mqXsuC6B/Dr9F0qblUsHmb3wUcIT/ayvPEOKsvWOp2SEEIIIYSwKKsKj6f4AfD49NfVTBUiP9cx/b2vUUo9DDwM4MfZ4oytpYDKxptTi+PamgU0GzOG7NwnG8eflftlZ0wbxSLtsjiuhbj2wY/R2mRB9WWWrwFt5xq0eLe0jYfVVuwZUh1p24/bHyRQtuDLmFnIFTtPgQt8uuhEAsNzAYUxO5/+WYzVNgpkqZT1BJTVjyqTFmIMF3lXrqLg6pUkxyYZee8Qw28foH/bHsxo4iuhnqIg3opCvBUhfBWFU1+XF6LcLnQ8gRlPYsaS6HgSM5748uvY9G2ROLGeYWIdg8T7Rr88jwq8ZYX4aoopWlmPr7oY/4ISAk1VGN4vf2XHUkAKkjbOayJhbR+8pI29IM249Vgds56riluLNaKnj4v39ND/2G+Id3SQu3YtxX/wTbzuXIhZO74rav1J6LI6psU4AHf069d1IjJOy+v/ykRvC+WrtlC1/iYUBu6YteeA1TgAl43ly0bcTqy1hSRG/Msn7GR0iM+O/opYIsyaxnspKWiCk25XCRuLUxJWXghAJW2MaSc2NQuxNsbUKRuzQ62Oa2bojFPt8P2ShgdCCCEySMYVHpVSrwEVp7npz7XWv5uO+XOm3ob++vMfO038aX/ja61/AfwCIF8VyV8FIiOkUnE6+nZQFlpGTiB7ut5qM8V4xyEKFl6EslPInufMeAJ1IYVHgTs/h5Ib11Fy4zq01iRHJ4n3jBDvGSHWM/zF/8N7TjD85ritYq1yuzB8HrwVheQsrabo2lX4aorx15TgqwpBhncknk3aNBl96y2Gt72M4fdT9p3vkrtqummUjcLffDPR387x1/+ZZHSShZsfoGhRdsz4G5vsZueRX6MxuXjxdygI1jidkhBCCCEyhVRz5kzGFR611tee7Xal1HeBW4BvaP3Fx4kdwIKTwmqArtnJUIj5p2tgF8lUlLry7NnbESDc00IqHqUgw5oy6EQC5ZXC40xRSuEpzMVTmEvu0q9PhjfjSeJ9o8R7RtCmieHzYHjdqOn/G143hs+N8k5//xx7MWbqBKLZFu/qYuDJJ4m1t5Fz0UWU3HEnrrw8p9O6YANHPqL9/afxBPJYcuufkpMhTbDOZWishV3Nj+Fx+1nX9D1yAyVOpySEEEIIIc5DxhUez0YptRX4D8AmrfXkSTc9BzyqlPo5U81lmoCPHUhRiDmntUlr7wcU5NZQmFebVdv0j7btRxku8qoXO53KjNKJuMx4nEOG142/phh/TfbMFp5PzESCkddeZfTNNzECOZR++9vkrlmb9s2izFSSjg+fZeDQB+RVNVG/5UHc/lyn05oTvUMH2NvyNDm+ItYtfgC/N9/plIQQQgghxHnKqsIj8HeAD3h1+g3Jh1rrH2mt9yulngAOMLUE+yfS0Vpki/6Rw0RiwzTVnHWycMbRWjPatp9gVROuDFvaOrXHo9fpNISYdZHmZgaeepLkwMBUx+pbb8OVm/7FufjE6NR+jv2tX+7nmCXNojq6P+bw8d9TGFzAmsb78Lgzv3mOEEIIIeaWQrpaz6WsKjxqrRvPctvPgJ/NYTpCzAutPR/g9xZSGlrqdCpzKjbaR3xskLKVm5xOZcaZ8QSunOzpTC6yjzkZYfjZFwi//xHuoiIqHn6YwOIlTqc1I8Kdx2h7+RHMRIz6a75DqH610ynNCa01x9ve4ETHW5QULGZVw124XDJzWwghhBAi3WVV4VEI8VXjk92MhNtYvOB6DGW9O20mGOs4DED+gswruCrDQEtHTJGhJvfsY+ixZ0iNj5O/aROh62/A8KX/rGWtNQO736H7vd/jyy+m6cYfEQidrlde5jHNFIeO/Y7uvp1Ulq1j+YKbMTKo4ZcQQgghRDaTwqMQWay99xMMw01VSXZ0SD3ZeOcRvPnF+PIyb18+5fWi43Gn0xBiRqXGxhl66ndMfrYbT3UlpX/0PQLldU6nNSPMRIyON55g5OhO8utXUn/lvbi82bHEOJmMsvfwYwyNHKN+wRbqF2zBiMtuN0IIIYSYRVpP/SfmhBQe50omfnI/S/tNzUpDADvn387xz9Gd9nPaYhwAho3jW8xVn2bMRDJC99BeKopX4fbmoM8Se8ZxXTZiLY6rbZwqO7ly0rhmKkm4+xhFTeu/8v0vxrU6rJ1LdQ77XBgeD8mJia9+087vVRux2rT4uFo+qZBK2TlZ1mbq2rlWlWn9IrRzv6z+bWPaOH4qaSM2ZS3WjNuY/Ry3fnwVt36uXNEvx9VaE/70UwafexYzHie09UYKN29BuVwYMeupuiI2jm9nXIuxrujpL4DoaD8tr/0L0eFeKi++kfLV1+CJKYhZu2BccYtxMeutw+zE2ikSGomvjhuNj7HzyK+ZmOxjef3tVJeug6RGJayPqRJJ67FJi+NajQNIWY/VKRvt26zGmjZesE0bx7cYa2t2vZ1ctfVctZ1xnSZvcoUQQmQhKTwKkaW6+ndimgkWlG90OpU5N9nXhpmMk1eTWd2sP6e8XkyZ8SgyQLyvl4Fnniba3Ixv4UJK7/4W3rJyp9OaMaOt+2l9+1FQBou2/pD8ms/3qcz84kQ40s/Ow78kkYywZvH9lBQ2OZ2SEEIIIbKINJeZO1J4FCILaW3S0fsJBcEF5OVWOp3OnBvvPAJKEazKzDe6stRapDszHmfk9dcYefstDK+Xkm/eSd6ll2ZMZ2dtmnR/to3eXa8RKK6h/trv4ssrcjqtOTM83squI7/BMFxcvOz75OdWOZ2SEEIIIYSYJVJ4FCILDY4eIxIbYlHNFqdTccRYxxFyShfg9mXmHmqGTwqPIn1N7t3P8JO/Izk8THD9xRTfcguuYJ7Tac2YxOQ4J976FeGuZooWb2TB5XdguLOne3Pv0AH2Hfstfl8B65Y8SMAXcjolIYQQQggxi6TwKEQW6uj9GK87l7Ki5U6nMueSsQiT/W2Ur/mG06nMGllqLdJRcnCIoaeeJbLvIJ6KCir/+CcEGhqcTmtGhXuOc+KNX5KMTVJ79T0UL86urS7aej/icOtLFARrWNN0H15PrtMpCSGEECJbyVLrOSOFRyGyTDQ2wsDIURZWXYVhZN9LwETPcdCavOrMXGYNYPgDYJqY0SiG3+90OkKclU4mGXvjHUZffg2UIvQHt1B46dUol40mN/Oc1prePW/R9ckL+PKKWHzDD8kpzp7lxVqbNLdso73zPUoLl3DRortwubxOpyWEEEIIIeZA9lUdhMhyXf07AaguW+dwJs4I97SgDBe5ZXVOpzJrPMVTe8UlBgfxVVc7nI0QZxZtPs7Q40+T6OklZ/VKQnfejjtUiIpmxl6OAKlYhPbXfsPY8X0ULlxF7dXfwuXNzG0eTieVinPg8FP0Dx5gQfklLKndilKZ8/gKIYQQQoizk8LjPKMM5XQKKDULOdhpCGD1HNg5V7ZibeRq9VzZOac2jq+txk7ff61Nuvp3UlTQQCBw+kYG2mU9V23jbmmLqWo7j5Wd40/HTvS0ECitQXk8Z55d7/zT8IK4S0oASJ5ceLSzlMDOA2taizVTdk6qjZlu2rR2fIt52qVtnFdtWnsSaBvn30zaeL4mLD4JrcYBKm491hX7Mtfk+DhDL75AeMcnuEMhyr//ELnLp7d+iIIRszhm1Pr9d1kcE8AdtR7rip75Ipgc6KDltUeIh4dZcPFtlC+9CqUVxM5+4bji1i+ssx3/ZEbM2nMFwIjbiU2d8bZ4Isyuw48yNtHF4tqtLCzaAAkTOPv4KnHmMb8mNQuxyaTlIXXSxvHNmc9V27n/Nl6wtNVY09aLoI3QWVj/ZuP4QgghMp90tZ47UngUIosMjhwjGh+laeENTqfiCDOZYLK/ndKLrnI6lVnlKS4GIDEw4HAmQnyVTiYZ3f4uw6+9ik4kKNxyDYXXXofhzaxlt1prBg9/RMf7z+D259J0648pLFjodFpzaiLSz67DvyaWCLOq6R7KipZBzHpBTwghhBBCZAYpPAqRRbr6PsXjzqE0tNTpVBwx2d+ONlPkVtQ7ncqsMvx+XHl5JAYGnU5FCGCqEDdx4ACDv3+O5MAAOcuWUXTLbXjLypxObcalEjE63nuaoaM7yKtezMJrvo3bHwSLMxMzwfDYCXYfeQxDGaxf9j0KgjVOpySEEEIIIRwihUchskQsHqZ/+BALKi7NyqYyMLXMGiC3fKGzicwBd3ExSZnxKOaBeHcPw08/R/TwETxlZVQ89ENylmbmhx/R4V5aXn+E6HAvFetvoGLNtSg723dkgJ6BPew//iwBX4i1Sx4g4A85nZIQQgghxFdp7G0XIi5IdlYfhMhC3f070dqkuny906k4Jtx9HH+oAncg6HQqs85TUkyk+ZjTaYgslpqYZPSlbYxv/wDD56P49j8g/7LLM6pb9ckGj3xCx3tPY7i9LLrxh+TXLHE6pTmlteZE1zsc63iDwryFrF58Lx539jTREUIIIYQQpyeFRyGygNYmHb2fEMqvJzdQ6nQ6jtCmyURPC6Gm7Ojm7S4pIbXjU8xYDMPnczodkUW0aRJ+70NGXngZMxIheMWlFN60Fa8nMwv+Jy+tDlY2svCab+PJyXc6rTllmkkOnnie7v6dVBSvYnnD7Vk7s14IIYQQQnyV/FUoRBYYHGkmGhuhqe56p1NxTGy0HzMRI7eszulU5oSvthaAaMsJcpZm18wr4Zxo8zGGfvs7Ep1d+BoXUXTn7Xirq6ZutNFVOl1E+jtpe+kRYmMDVKy7noq112Xd0upEcpI9Rx5nePwE9dWbaKjeglKz00VeCCGEEGLGyErrOSOFRyGyQGfvp3jcuVnbVAamGssABEoXOJzJ3PDX14PLRfToUSk8ilmX6O1j+LkXiOzdjytUSMn3HyRnzaqMLUBprRna9wFd7z6Ly5dD400/Iq+q0em05txkZIA9+x8hEhthxaI7qCxZ7XRKQgghhBBinpHC44VSDs5ssHNsYxbe/Nl4Q2nrzafV+zVrx5+FWBtjajvHt3CqYvFxBoYPU1t9OcrtOfcHOzYOr21cV5ZjbR3feuxkfweG24s/VHbOY2iLOViNsxs7E4MaHh/+uoVEjjZPxdn5RE/bCDat3TGdtP5gmZiWY7Vp9fXC8pD22DhVehbOFUkbr4MWx1Vx62Oa/eMMv7qN8R0fozweQjfcSMGVV2N4vRD9aqwrbnlYjJi1HFw2ZlHaiz3zA5uKR2h/50lGju8mr2YJDZfdhyeQd84ZnXbuv9tGB2wjYe354rIYNzVm6pwxw2Mn2HP4N6BhfdN3CAVr4RzHUEmLOSTPffwvx7Qea3VcbVo/V5g2jp+yHqutvg7ber22c7+sHt/GmLPF6RzsPAZCCCFEFpLCoxAZrmtwFxqTqvKLnU7FUZH+dgIl1Vm1DNLf1MTIK9tITUygvDlOpyMyiBmNMvrGW4y9+Q46lST/0ssJfeM6XMHM3Mfxc5P97bS89kvi4WGqNt5M2erNuGPZ85ryua6+nRw8/txU5+pF95HjK3I6JSGEEEIIW5R8bjRnpPAoRAbTWtPZ/xmhgnpyAsVOp+MYbaaIDHZRtOwSp1OZU4GmRka2vUy0uZnAhlVOpyMygE6lGH//Q0a2vYIZniB31RqKtt6Ip7jE6dRmldaa/n3v0vXR87gDeTTd+mOCFfVOpzXntDY51v4GJzrfIZRfz6ol9+JNeZ1OSwghhBBCzGNSeBQigw2NHScSH6Fh4XVOp+Ko6GgfZjJOTpbs7/g534JalM9HpPmoFB7FBdFaM7l7D8PPv0RyYAB/4yJCt91MoDzzmzUlo2Ha3n6C0db9FNStoHbTvbj92TeDOJVKsP/Y0/QN7qeqbB1L62/FMFy2lg8LIYQQQojsI4VHITJYx8BneNwByoqXO52KoyYGphrL5JTWOJzJ3FIuF/6GBiJHjzqdikhj0WPHGHruBeKtbXgqyil7+CECy5ZO7Z2bgZ2qTzbeeYTWN39DMjpB9WW3U7ryqoxtmHM2sXiY3YcfZSzcSWPt9dRVXZGV50EIIYQQGUT26J0zUngUIkPFk5P0jxxiQekGDCO7n+qR4W6U24OvoNTpVOZczrLlDB78LbHWNnx1tU6nI9JI9EQrIy9tI3r4CK6CAorv+xbBDRdnxT6pZipJ946X6dv9Fr7CUhZvfYickuz64OJz4Yledh36FfHkJKsW35P1H2QJIYQQQgh7srsaIUQG6x3aj9YmVSVrnE7FcdGRPvyFZVlRMDlVcO1ahp7/PePvvi+FR2FJrL2DkZdeJnLgEEZuLqHbbyHvisunOlVngdhIPx0v/YrJ/naKl15K9WW34fL4nE7LEQPDR9h79ElchpeLVzxEfrDK6ZSEEEIIIWaENJeZO1J4FF+XLsUZZSNPO0vCXLMwrp0xXTZyPctj1T24h2CgnLxgFaaN+68NG8e3s9LOYqy2MabV2OhoLzkVC62PPQsrCG39YrMYq8xzx7h8AYLr1jO+4xOKbr8VV27uuX8oZeN6mYVzpbX154s2ZuEvBjt3ysJj8GWsxXFtnH+VsB5rnCM21tnFyLZtTO7bj5GTQ9HWmyi4/EoMn2/qmjzNsmojZv34LhvLso34zI/pjp79WtFaM3R0Bx3bn0YZLhZt/i6hulVTj/FZjuOOWbsGXTHrF4uRsH5dWx1Xxa0fXyVStPV+xOH2beQFylnTdB9+bz4kvr6fozrN984okbR2/KSNMe3EWt2P0s6+lab1x0qnbLxgmBZjrcYxdY07Sds4V46T5XdCCCHEjJHCoxAZaCIywOhEB00Lrnc6FcelEjHi4WGKQtnV0fpkBZdfwfiHHxD+6GMKrtnidDpinon39DC87RUmd+/B8PsJXbeVwiuvwvD7nU5tziRjEdq3P8XIsV0EKxfRcMV9eHNDTqflCFOnONL6Iu39n1BauISV9XfgdmXHbFchhBBCCDHzpPAoRAbqHtwNKCqLpZNxbLQfAF+ozOFMnOOtqMS3qIHx9z4gf/OmrFxyLr4u3tfHyLZXmdi1C+X1UnjdteRvuhqPx8Ks2AwS7mmh9Y1fE58YpXLDjZSvvgZ3IjufI8lklH1HnmBw5Ch15ZfRVHMtys7qAiGEEEKIdKCxvMpMXDgpPAqRYbQ26R7YTXHBInzePKfTcVx0tA8Af6jc4UyclX/l5fT/66+IHDxEzgppDpHN4l1djLzxFhM7d6I8bgq2bKFgy6Yvl+EnnM1vrmgzRc/O1+n57BW8wRCLb/sTcsvrnE7LMZHoMLsP/orJ6ADL6m6hpnS90ykJIYQQQogMIIVHITLM8Hgr0fgojQuuczqVeSE60gvKwFtQ4nQqjsq5aCWu/DzGt78vhccspLUm0nyM0TfeJHLoMMrrJX/T1RRu2YwrL/s+oIiNDdL65qNM9J4g1LiOBVfeicubPUvLTzU63s7uQ79GmynWLPsOJTnZW4AVQgghhBAzSwqPQmSY7oHduAwfZYVBkr9VAAAgAElEQVRLnE5lXoiO9uPLK8JwZffLnXK7CV52KaOvvEa8swtvtXSnzQbaNInsP8ToK68TP9GGEQwSuulG8i6/DFdOjtPpzTmtNUOHP6bjg98Birot91PUlN0z+3r6d3Ow+Vl83jxWr3iQ3JxSiFtrAiOEEEIIkY4UoKSR2JzJ7nfiQmQY00zSN3yQstBSXNIMAIB4eBhvsMjpNOaF/KuvYvzd9xh65neU//iPZK/HDKZTKSZ37mH0lTdIdPfgKgpRfOcdBDdswPB6nE7PEYlImPZ3n2T0xD6ClYuo23wv3rzsfW3Q2uR42+uc6HyHwvyFXLTkXrxZtr+nEEIIIYSYfVJ4nCPKUFYDrQ9qdUxAKeux1o8/O7lajrU1po1cbZwr7bI4rp3zb+f4p4QOjB0jmYpSXrLyK7dpG+fq1DHPykas5XFn+FJNTIwQCC2d8XFnldUP32x8SKdMcAdyCN18E4NPPsX4W+9SsHnT6YdN2XltsRZn6/NEG8dHzcInlXaeBHYeg6S1ca3GnS5WJ5OM7/iE0TffJDk4iKe8gtL77yd39Rpcpmsq39jZx3TFrR/fiFsOtRXrOkeOn3NHrT0Ao20HaHvrCVLxSWouvpXy5VdPNU05w3FcMesPrCtuWouzM2YsZTnWiFuLNRJfxiVTMfYff4a+kUNUlaxlWd3NGLhhOkYlrB9fJazPjlRJi+NajQNIWY/VKWuPFVbjbB4fc+bH1XZma5g2YrW1XLWdMe2weHwhhBBCzH9SeBQig/QO7sPtClCU3+B0KvOCmUqSiIzjDRY6ncq8kXfpJUQOHWLohRfxN9Tjq611OiUxA1ITYcY/+oix7dtJjY3hW1BL0a23kbN8+ZczW7PwfXwqEaPrw98zcPADAqFKFl/3MDlF2b3NQCQ2wq6jvyEc6WPxghuoLb90dj6cFEIIIYSYz7Lwb2OnSOFRiAyRMhP0Dx+mvHgFhiFPbYDE5Cig8eRK4fFzSilK7vkWnX/9c/oe+RXVf/bvMAIBp9MS5ynW2cHY9u1M7NyJTibxNzVRet99+Bubsr6YNNHXSuubjxIbHaRs1WZqVm3FcGXnMvPPjYy3sbv5cUydZO3i+ykpaHI6JSGEEEIIkeGkOiFEhhgcOUrKjFFevNLpVOaN+MQIAF4pPH6FKyeHsgceoPu//r8MPPkUpQ8+kPVFqnSiUykm9+5j7J3txI63oDxeghs2kH/FlXgrKpxOz3HaTNHz2Wv07HwNT04+jbf8iLyqRgyLy7czVdfALg6c+D1+bz7rm75HMFDqdEpCCCGEECILSOFRiAzRO7gPjzuXUP5Cp1OZN+Lh6cJjMORwJvOPv34hoRu3MvzCi/ibmsi/7FKnUxLnkAqHGf/gI8a3v09qdBR3cTFFt95GcMOGrOxQfTrRkX5a33yUyf42Qo3rqbnim7h92T2jV2uTY62v0tq5nVDeQlY1fguvW64XIYQQQmQ36Wo9d6TwKEQGSKXi9I8coapkDYZyOZ3OvJE4acaj/Fr5uoItm4k2NzP0zLN4KyvxL6xzOiVxGvGubsbeeofwZzshmcS/ZDHFd99BYPkyDFOe7zBVXOvft52uj1/AcHtY+I0HCS1a43Rajksmo+w/8hQDw4epKb2YJbU3YhhyzQghhBBCiLkjhUchMsDgaDOmmaCsaLnTqcwryWgYw+3DcHux0Xc0ayjDoPT+++j627+j5+//G+UP/YBAU6PTaQnAjEaZ2LWb8IcfEzvRivJ6yLtkA3lXXYm3ovykQOdynC9iYwO0vvU4Ez3Hya9dRu1Vd+PJLXA6LcdNRgbZc/DXTEYGWdxwM7VFF8uWCkIIIYQQYs5J4VGIDNA3dBCPO0BhvsxYO1kyHsGV5cssz8WVl0fln/6Enr//b/T+w3+n7Hvfwb9KCthO0FoTO36Cifc+YWLXbnQ8jqe8jNBttxC8ZCOuXFkeezKtTfr3v0/XR8+jDIPazfdS1CTFNYChkWPsPfw4AGtWfJeiwgZUXD5+EUIIIYQAQE//J+aEFB7F11l802brzZ0yHD6+w7E2xtR2jm8oTDPFwMgRSouWYrjO8JS2cfoxbORqIxaLodpOzeAcsal4BJc3YPnYdik7v6xsxCrT4nPAzr4kqTOP6cktoOpHP6b7v/8Dvf/0L5Q++G1y16y2NKy2eHKVnZl5dq5XyxeWjSFt5Gr1sQJQydN/Pzk2RnjHDsIff0Kivx/l8xFcs468izfiq6398rXuNM1RjDOMeTpG3FquRsL6mK64jVgbzV1csbM/YPHxIVrfeYJw51Hyqxaz8PJ7pppInSWfc435ldi49VjD4rhG3PqFZSs28WWs1pq23g850raNYKCU1U33keMvgoSJilu/WFTCRpEyNQuxSeu56qSN45sWY23cJ23nddhGrOVxTTvHd3iKtOPHl3eYQgghhBOk8ChEmhseO0EyFaW0aJnTqcw7qVgEt1dmPFrhyg1S9fCP6Pnnf6T/kV9hxmLkXbLR6bQylk6lmDxwkPGPPyJy6DCYJv6GBgq+cQ3BFasxvD6nU5yXtNYMHv6Izg+eA6DusrspabpEZjkCKTPBwRPP0z2wi9LQUlY23IHbJdeREEIIIYRwlhQehUhz/UMHMQwPxQWLnE5l3knFI9LR2gYjEKDiD39Iz7/+C4OPPYGOx8m/6kqn08oYOpUieuw4E3v2MLF3D2Z4Ald+PgVbNpO3YQOe0lIAVFKKaKcTD4/Q9u6TjLcfIljVSO2me8jxFjmd1rwQjY+x++hjjE100lC9mYaqTSg7Kw2EEEIIIbKKlpnwc0gKj0KkMa1N+ocPUVzQiMvldTqdeScVi+AqqnI6jbRieH2U//AH9P3rLxl6+llS42EKb7gO5ZJOuOdDp1JEjx5jYtduJvfsw5yYQHm95CxfTnD9OgJLlsi5PQetNUNHP6Xz/WfQpknN5d+kZMXlU4U1G8u3M9VIuJ3dRx8nlYqxuvFeymT2uxBCCCGEmEek8ChEGhsLdxGLj1G64BtOpzIvSXOZ86Pcbsq+9x0Gn3iS0VdfI3LgAMV334mvTpoXWaFTKaJHmqeKjXv3YU5Monw+cpYvJ3fVKgJLl2B45YMCK+LhYdq3/5axtoPkVtRTt+lefAUlTqc1b3T1fsqh5ufwe/NZv+RBgjnl5/4hIYQQQggh5pAUHoVIY31DB1DKoLRoqdOpzEtmIobLI3ucnQ/lclFy370EVqxg6Kmn6f6b/4fA8mUUbr0B34Iap9Obd3QySfR4C5O79jCxa8/UzEafj5yVK8hZM1VsdBkep9NMG1qbDB78kM6Pngetqb7sdkpXXIkyZPkwgGmmONryEh3dH1KU38BFi+7G65Gu50IIIYQQVtlqFCouiBQehUhj/UMHCeUvxOOWWX2nmuoIqu11VBdfk7vqIgKLmxh79z3G3nqL7p//DYGVKyi84Xp8NdVOp+cYrTXJ/gGiB44QOXSY6NFj6Hgc5fUQWLGC3HVrCCxbguE5qdhoowN1NouN9NP52hOEu48RrG6i9qq78eUXO53WvBGLh9l36DeMjLVSW3UFTdXfwFCyXF8IIYQQQsxPUnjMFuk0S8RqochOF1OHY7WdMS3e/YnIAJPRQWoqLz3n+HaOr2epr8VsjXuWIwKcX7dbq59+2fiUTJkzH6tS1sfEzmWd+mqwyxMgdM21FFx2BaPbtzP2ztt0//X/Rc7KiwhtvQ5v1bn30dSGww1TZuCxMiMRIs3NRA4fJnL4MMmhYQDcRcXkrb+YwOIlBJqavuxIbfKVPQitPl6GjQKlEbd+Xq2Oa8StH99lY49FV/zsD4I2Tfr2vk33jpcxDDd1l3+LksaNU8/hMxznXGOed2zM+hPWFbcWa8StP2HPFDsa7mR382MkkhFWNtxJZckqVDyJlaq2Stp4EUpYvwiVjViS1s6BNm3katp4IUxZPL6dzeYtjgmAnfvlMG2m0TQQaQ4ghBDifMjvjzkjhUch0lTfyCEAWWZ9Jl/8IpEOwTPFCAQIXXcd+Vdeydi77zD6zjtM/p97yVl1EaHrr8dbVel0ijPKTCSId3RMFxuPEGttBdNE+XwEGhsp2LyZnKaleIplz8ELFRnqpu3tx5nsb6egbgV1G+/Em1PgdFrzSlf/Tg6eeB6vJ8iGZQ+Rn5tZzzchhBBCCJGZpPAoRJrqHzlMXm4lfl+h06nMS5/PWDmvGY/irFyBAKHrbyD/yqsYe/dtRt95l8k9e/E11BNoaiKwuAlfbW1adWvWpkmit49YWxvx1vap/3d3fzFDyVtTQ8GWzQSWLMFfV4dyT/36PHV2qLDHTCXp3fU6vTtfx+UNsPAbD1DYsAa3jZmcmc40kxxp20Z738cU5ddP7+eY63RaQgghhBBCWCKFRyHSUCwRZnSig/oF1zidyvz1+YxHKTzOGldODqEbt5J/9dWMbd/O5P4DjLzyKiPbXkH5fPgXNUwVIpsacVdXzqsicHJklFhbG7HWNuJtbcTaO9CxqfW8yu/Ht2ABBVs246utxV9Xhysvz+GMM89EXxttbz9OdLiHUONaai7/A9z+oNNpzSux+Dh7mp9gJNxGXcXlNC64VvZzFEIIIYS4UNreVljiwkjhUYg01D9yBJBl1tbMn2JXpnLl5hC64XpCN1xPamKSaHMzkaNHiR5tZujAQQCMYJBAUyP+xU34FzfiLiqa1UKkTqVIDg+THBwiOTBIYnBw6uvp/5uRyHTyLrxVlQQvXo+vdgHeujq8xSXSPXkWpeJRune8TP/+7Xhy8mm44SEK6pY7nda8MzLezu7mx0mmoly06G4qilc6nZIQQgghhBC2SeFRiDTUP3IYv7eAYE6F06nMW1pPf4Qldcc55crNIXf1KnJXrwIgOTxC5OhRIs1HiR5pZmLnrulAF67cXIxgEFcwF1cwiDH9/5O/NnICkDIx43F0IjH1XzyOGZ/+OhFHT39tTkamCowDA1ONX07eMNrlwl1UhKe4CF9dHZ6yUry1tXirq77aeRr59HM2jZzYS8d7z5CYGKNk+WVUbbwJlzfgdFrzitaa9r5PONz6En5vAeuWPEheTrnTaQkhhBBCCHFepPAoRJoxzSRD4y1UFa+eV0tX5xvDNVVMMpMJhzPJbu5QIXkbNxC8dANaaxK9vUSbj5McHsYMT5AKhzHDE8QG20iFw18sd7bN5cLw+3AXF+OrqyN33VrcJcV4iotxFxfjys+XWYwOio8P0/3W04y27sdfVEn9td8lt7zO6bTmnVQqweHm5+jp20lJQRMrF92Jxy2FWSGEEEKIGSddreeMFB4vhAJlWCz8KItveK2Oxyw2zbD65txGrrMSa6eIYOP4elZytT7kufYkHJ5oxzQTlBQ2WZ/NZ+dSsXFd6dmo48zQZa0MA8Pjw4xHp75h4/eKshpr53eVneNbnHFnq7GJ5Ttl73LVprUcpi4rhau4En/xmbvxmokE5kSYVHiC1EQYMxJBudworwfl8WB4vF98rTxeDPf01+doZKOSNu6UjRmPKmU91khaO1eGjVq5YeN+GfGZjQNwxc9+XWnTpP/Adrp3vAymSfX6mylfvgnDcMFZasznGveLuJj169oVs/7A2ok1YtYuAiN+9rhIdJjdRx8jPNnLoopNNFRcjdIKEmf/OXWO27+QsH6xqKSNCztl4wmTsjiu1TgA0/o1oK3matq4TzZitZ03OFbvl7ZxfBvnysagMz+mEEIIITKKFB6FSDODo80oZRDKW+h0KvOey+snGY84nYawwfB4MApDuAtD1n5APqictyYHOmh/9ykmB9rJq1nKwo134MsrdjqteWlg5Cj7mn8LaNYsuZ+ynEVOpySEEEIIIcSMkMKjEGlmcPQYhcFa3C6fnYlZWcnlCZD6fMajEGJOpBIxuj/dRv++d3D7gyy85gEKG9bgTsjWEKfS2qSl6x2Od7xFMKecVU33kOMvgnPMjhRCCCGEEBdIJjDMGSk8CpFGovExwpFemmqudTqVtODy+jFlxqMQc2a07QDt7z1NIjxM8dJLqdp4M25fjtNpzUuJZIT9x55mYOQIFcWrWFZ/Ky6X1+m0hBBCCCGEmFFSeBQijQyNHQOguKDR4UzSg8vrJxkNO52GEBkvNjZIxwfPMtZ2AH9hOQtv/QnBigan05q3xie62XP0caLxUZbU3URN+UZpFiaEEEIIITKSFB6FSCMDo814PUGCgXKnU0kLLq+f2NiA02kIkbHMZILe3W/Qu/sNlDKo2ngzpSuvxnDJnxdn0tW/k0Mtz+Nx57B+2Q8ozFvgdEpCCCGEEFlHSVfrOSPvDIRIE1prhsZaKClokpkxFrn9QRKR8elOonLOhJhJYyf20/XOM8THhyhctIbqS27Dm1vgdFrzVspMcPjEC3T17ySUX89FjXfh9QSdTksIIYQQQohZJYVHIdLERLSfRHKSovyFTqeSNnx5xZiJGMnoBIZP3uALMRPiY4N0vfssYyf24y8sp/HmPyavSrZ/OJvJyCD7DvyG8GQP9VVX01CzBaUMp9MSQgghhBBi1knhMVs4PUMunY4/G7EzMObw+AkAQvn1X8ZYHFfbOL6281DZip37a8CbXwxAfHwQf4GNwqPFWffKxux8ZaMFubYYq2w1vrVx/m0sO7D6sNq5ruycV8uPlY3zr1LWk7XzGFiNNRLWx7QT64pbO1lG/PTfN1NJ+va8Sc+u16aWVW+4mYrF08uqYzN3fDuxrpj1B9ZOrBG3EZs8e2zf4AH2H3sGA4O1i+6npKAJkgBn/jmVsHFhJa3FKotxdsacik1aDtVWY1M2nrApG7maFse18Rqo7SzTMm3EWvxFoO2MaYfVX0SzRZa/CSGEmG3yu2bOSOFRiDQxNHYCv7eQgC/kdCppw5c3VXiMjQ3gp87hbIRIX2Mdh+l4/2liYwMU1q+aWlYdDJ2xSCnANFM0t71KW/f75OdWs2rhXQR8hU6nJYQQQgghxJySwqMQaUBrzfD4CUoKFzudSlr5ovA4PuhwJkKkp9jYIJ0f/57RE3vx5ZewaOvD5NcscTqteS8WH2PvkScYGW+jpnwjixduxZWQfWaFEEIIIUT2kcKjEGlgIjK9v2PeQqdTSSuG24Mnp4DYmBQehbAjFY/Ss+s1+ve9gzJcVK6/kbJVm6VbtQVDo8fZd/RJkqk4KxvvoqJ01fQttvZGEEIIIYQQs0Vztl1vxAyTdxBCpIHh8RYAQtJYxjZffgmx8QGn0xAiLWjTZODQR3TveJlkNExR0waqLr4Rj3SrPietTVo63+F4+5vkBIpZt/z7BHPKnE5LCCGEEEIIR0nhUYg0MDzeit9bIPs7ngdffjGjbQfQWqOcbnIkxDwW7mymc/uzRAe7ya1oYNGlf0hOyQKn00oL8USYfUd/y9DoMSpKVrG04VbcLp/TaQkhhBBCiNNQaJQ0l5kzUngUIg2MhNsJBaU5yvnIKVnA4JGPiY8P4Zvuci2E+FJ8fIiu93/P6LE9ePJCLLzmOxTWr5JCvUXDYyfYd+RJEskISxtuo7psvZw7IYQQQgghpknhMZ0ZxqwMa/kNk3L6+Dbe2M3Wm8BZyFWfEhqNjRKLj5GfV/O12079d0aw88GThdjcsoUATHa34M+zVnhUFnNQNvYFma3Y2aBMO8+tWUhghq8BsHn+bWzFZ9iIVUmLYyZsHD9h/WS5TulAnUrE6N3zJr173wQUleu2Un7RZtzaAxZzcMXtHN9GbMzaA2bErT+wtmIT535gtTY50fkux9rfIMdXxNol95OXUwFnOI6yMOYXEhYvFkBZjU3aOH7KeqxO2XhyWZ1ZYOv4Nu6X1eObNu6TaeMFSzv84u748WVmiRBCCJGNpPAoxDw3Gu4AoCBY43Am6SlQWIHh8TPRc4KixRc7nY4QjtPaZPjYTjp3vEBiYpRQw1qqN9yMNzi9lYONwme2+srS6tBKltXeIkurhRBCCCHSiXwgNmek8CjEPDca7sBQ7qmZNMI2ZRgEy+qY6DnhdCpCOG6s8zCdn7xAZLCTQHE19ZsfIFjR4HRaaeXkpdXLGm6jumCNLK0WQgghhBDiDKTwKMQ8NxpuJy+3EsOQp+v5yi1byNjOV0jGIrh9AafTEWLOTfZ30PP+C4x3HcEbDFG36X6KFq1FzdKWGZnoK0ur/UWsWfYgebkVqJiNpb5CCCGEEEJkGalkCDGPmWaS8Yluaso3OJ1KWsstXwhoJvtayV+w1Ol0hJgz8bEhuj9+iZGjn+Hy5VB9yW2ULrsCwyW//u04eWl1efFFLFt0myytFkIIIYRIZ7LUes7IOw8h5rHxyR5MnaQguMDpVNJabmkdKMVEd4sUHkVWSEYn6fvsNQb2bgelKFt7DRUrrpEZv+dhaLSF/Uefkq7VQgghhBBCnAcpPAoxj41PdAGQH6x2OJP05vL4yCmrZaz1AJUbb3Q6HSFmjZlKMrjvPXo/fZVULEpo6cVUbNiKN1j4ta7W4uxMnaKl421aOt7+ytJqIYQQQgghhHVSeLwgCmZ4f6xZm0VhzMI+XoaNXO2cJ6vnYLbO1SyMq+2MedJ5HZ/swe0K4PcXnj4vy+fK+uFnjeWp7NaTVTZmx4fqV9P54XPEhwfwFZScPdi0OKjVOEDZiDUsbhlnZ3WAtnF87fC2f3YeVyzGWj2nAMpObNJ6rGEx1khYPwHGdAdqrU2Gj++i67OXiY8Pkl+9hKoNt5BTVDUVEAOXxXHtFCjt5OqKWb8Ijbi1WFtjJqw/sLGJYfYe/y0j4TaqitewpPZG3C4vnGYMZXFclbB+saikjYvQamzS+vG1neObNmJT1mK1rRc3G7GmtevF1vFniTadz8GSeXCuhBBCCNs0tt7LiQsju8oLMY+NT/RMNS+QZX0XrLBhFQDDLbsdzkSImaO1ZuTEXg4++9ecePvXGG4vjdf/kMYbHv6y6Chs6R86xIf7/57xyW5W1n+TFfW3TxUdhRBCCCGEcIhSaqtS6rBSqlkp9b+c5nafUurx6ds/UkotPOm2n05//7BS6obp7y1QSr2plDqolNqvlPq3s5W7zHgUYp4ydYrwZC81FRudTiUjeIMhcspqGTm+h4o133A6HSEuiNaa8fbD9Hz8EpODHfjyS1m4+QFC9aulU/V5Ms0kR1tfob3nQ/JyKrio4S5y/cVOpyWEEEIIIbKcUsoF/FfgOqAD+EQp9ZzW+sBJYQ8Bw1rrRqXUvcB/Ae5RSi0H7gVWAFXAa0qpxUAS+DOt9WdKqTzgU6XUq6eMOSOk8CjEPDUZGcDUSfJyK51OJWOEGtbQ+eFzRId78YfKnU5HiPMy3tVMzycvMdFzAm+wiLqr7qFo0XqU4XI6tbQ1GRlk79EnGJ/oZkHFpSyu+gaGIX8iCSGEEEJkKpVe24VsBJq11scBlFKPAbcDJxcJbwf+Yvrrp4C/U1NLJ28HHtNax4AWpVQzsFFr/QHQDaC1HldKHQSqTxlzRshf1ULMU+MTPQBSeJxBoca1dH70PINHPqH6klucTkcIWyZ6W+n+5CXCnUfx5ORTc9WdlDRsxHDJr/IL0d2/m0PHf49huFi95H5Ki5Za3rdRCCGEEEKIOVANtJ/07w7gkjPFaK2TSqlRoHj6+x+e8rNf6V47vSx7LfDRTCb9OXm3IsQ8NT7RjaHc5ARkqd9M8eTkU1C7jKEjO6jacKPMEBNpITLYRffHLzHWdgC3P0jVZbdRsvxyDLfni+Yywr5kKsbhlhfo7t9FYV4dK5vuwu8rcDotIYQQQgiRfUqUUjtO+vcvtNa/OOnfp2v6cOqUzTPFnPVnlVJB4LfA/6C1HrOYry1SeBRingpP9hDMKcNQUhybSUVLNjLaup/h47spalzndDpCnFFksIueT19ltGUPLm+Ayo03UbLySlwen9Oppb2xcBf7jj7JZHSI+prN1NdsktdaIYQQQohsMr+WWg9orS8+y+0dwIKT/l0DdJ0hpkMp5QYKgKGz/axSysNU0fHXWuunL+genEVWFR6VUv+ZqfXtJtAHfE9r3TW97v3/Bm4CJqe//5lzmQoBE5EBigoanE4j4xTULcdfVEnPp68Qalgtsx7FvDPZ307PZ68ydmI/htdP+bprKV21Gbcv4HRqaU9rk7bu92luex2vJ5d1y79HUUG902kJIYQQQghxNp8ATUqpeqCTqWYx958S8xzwXeAD4C7gDa21Vko9BzyqlPo5U81lmoCPp+tg/wgc1Fr/fDaTz6rCI/BXWuv/HUAp9W+A/wj8CLiRqZPfxNQ6+f+Pr6+XvzDG6Wa3zlOzkaudMQ2LHVntjKlmJ1bbGdeGZDJKLD5GbqBkVsafz5SdD57OI1ZhULl+Ky2v/jNDR3ZQvOTrT3VlWhvYSFl//E07l4rV+2WjZmrnUtWm9VjLx5+lx1VZzFXZ2LLPTqyRtJ7suZZFT/S20rPzVcbaD07NcFx7A2XLrvqy4Bg/3ZjWj++KW4t12RjTsDjmVKz1C8uVsBarktbHjE+MsO/EswyNHaescBnLF96Kxx2A0+znqGLWLwKVSM5oHAApGxehae0caItxU2PaOL6NXHXKYg62crUeq63ObLD4O2BqUBvHtzPuLBxfCCGEEOlpes/GPwG2MfUu8J+01vuVUv8J2KG1fo6pIuIvp5vHDDFVnGQ67gmmmsYkgZ9orVNKqSuBB4G9Sqld04f6X7XWL850/llVeDxlvXouX769vR14RE/9RfqhUqpQKVWpte6e8ySFACaigwDkBEodziQzFdStIKe0lu7PXiXUuF6acwhHhXuO0/PZq4x3HsHly6Fq/U2ULb0Cl9fvdGoZo3/oEAebnyFlxllWdwvVJetQs/TBkRBCCCGEmO/0fFtqfU7TBcEXT/nefzzp6yhw9xl+9mfAz0753nZOv//jjMu6d9tKqZ8B3wFGgS3T3z5dh6BqpluLCzHXJiL9AORK4XFWKKWovHgrx176BYOHPqR0xZVOpySyjNaacPcxej57lXB381TTmJwn8VcAACAASURBVI23ULL8cjxK9nCcKalUgubWbXT0fEReoIKLGu7MypnkQgghhBBCOCXjCo9KqdeAitPc9Oda699prf8c+HOl1E+BPwH+D6x1CPp8/IeBhwH85MxM0kKcYjLSj1IGAV/I6VQyVl71YoKVi+jZ+RrFSzZiuL1OpySygNYmY20H6d39JhO9LbgDeVRfehslyy778hq0sSJXnFl4ood9R55kItJHbeXlNFVuwTAy7s8eIYQQQggh5rWM+wtca32txdBHgReYKjxa6RD0+fi/AH4BkG8U///t3Xl8XHd97//XZ7Raiy3JkiXv+5bE2VcSEpIAWRrCEqBhp0C55dK9XGjhltJefr3QFrjthbIU+msLFCiEEAIJ2RdC9j1xHMe7LS+yZHmRrXVmPvePM45H45F0jj2aM5Lez8fDD8858z3f85kz3zmSPvNdJlbfXJkwjvR1Ma26iYQWPhk3Qa/Ha9hw61fpXPsQrWdcEXdIMomlk0N0r3uSvS88wMDBTipqG5j3mrcyc+UFJMor4g5vUnF32vc8xsatd1BeXs2Zq9/PzMblJAaV0RURERERgm5mE2yo9UQ26RKPozGz5e6+IbN5PfBy5vHPgd83sx8SLCpzUPM7SpyO9HVSW61h1uOtrm0x0+evouO5+2hacR4V0+rjDkkmmWT/Ebpe+A1dLzxEqv8I05rnsfDy92hF9XEyOHiYdZt+Rtf+9cxsWMEpy95KZWVd3GGJiIiIiExZUyrxCHzBzFYCaWAbwYrWEEzQeS2wEegFfiee8ESCoZj9/QdoaVgZdyhTwpwL3sT6m7/C9gd+yJKrPoxZyFXVRUYx2LOfzuceoPulx0gnB6lfsJrW0y+nrm2JFjUZJ13d63lp082kkgOsWHwt89ou1LUWERERkfzScQcwdUypxKO73zDCfgc+XuRwTl6EP6gi/fEVNvEygf6g80SEWCOVLXA5YCDZS9qTVFU34qV+jSP0TrewZcejTsBG+MFSM6ONeedfz45HfkrXCw8x67RLRyybyyP8sLJU+LJhr0GU1+9RbgHhi4avM8pIhijXNWTZKNc/kQwfbCJn9G5f9246XriP7k3PANC09Cxa11zOtKbZQdmhEHUOhT9/WYSyYetNDEY4/2D4NysRoawlw5VNDCZJpQZZ334nOzufom7aLE5b/j7qa1phaPibbkPhG4GlIjSYsGWj1JkMX9aTIYeQRzl/OsIHNlLZkG0gQqweZZhU2Fgj3Nw9yuuPIsoPmHE5v4afiYiISGFMqcSjyETQ198NwLRqLSxTLM2rX8OhnevZ+cQvqGtbQnXbvLhDkgnEPc2hna/QufYhDrWvI1FeyaxTLmHWaZdSWafP8Xg6eHgnL275Kb0D3SxsvYilc6+gTAvIiIiIiIiUDP12LlJi+gb2A2hF6yIyMxa+9p2su/lLbLn/e6y44U8oq6iKOywpccmBPg6se4LOdb9h4FAX5dPqmX321bSccjHlVTVxhzeppT3F1vYH2brjPqoq6zlnxftpmr447rBEREREZIIw9e4vGiUeRUpMf3+QeKyubog5kqmlvLqORZe9mw23f5OdD9/CgsveGXdIUqL69u2ma+1v2L/hKdLJQWpnLWL22VfRsOh0EmX6sTreevv2sXbDTRw6vIO2pjWsWnAtFeXVcYclIiIiIiJ56C8kkRLT17+fqsp6yhIVcYcy5dTPWU7r6ZfT8fy9TJ+/koYlZ8QdkpQIT6U4uO1FOl/8DUd2b8LKymlcdjazVl1MTbOG5heDu7Nr71Ns2HI7ZglOXfEO5kw/Je6wRERERERkFEo8ipSYvoH9VFc1xR3GlDXnnKvp2bORHQ/8mJqW+VTW672Yyob6eti37lH2vfQIQ0cOUlnfxJwLrqNp1fmUV9cet7iMjI/BwcOs23QLXftfpnHGEk5Z9jaqq2bAoN4AERERETkBGmpdNEo8ipSY/oGDNNTPjzuMKcsSZSy84j2s/+lX2PTLb7Hk2t+lavrMuMOSIvJ0mp6dr7Dv5cc4tHUtnk5RP28F8y65gekLVmOJCMvUy0nr6l7PS5tuJpnsZ/miq5k/+yLM9B6IiIiIiEwESjyWmkn6B62ZRSlc2HITiLszOHSYysr6uEMpvJBfKFmEL54sHb6wpcO3l+r6ZpZe9WE23/mvbPjZP7Hkqg9T27LguHKJVPjzR/lCzRPhYvUIt4u4Py2WjlI2yvsaslxq7DKDh/ezb/3jdK9/gsEj+ymrqqFl9SW0rLiA6obWoFBOB7tEMnysYXtHJoai1Bmh7GC4smWD4d+sRISyloxSb5KhZD+v7LiDXfuepW5aK+csfx/1Na0wlAaCumwoxBt79PwRyjIUvielJUPWm4rwIUhFiDVs2XE6v0eJNeSN0KPcMCPcL/AI12A8xH5+9ewQERGR4lPiUaSEpNKDpNNDVFbUxR3KlFfXtoQV1/8Bm27/Nhtu/WcWXfFeGhadFndYUmDpVJJD21+i6+XH6GlfD8D0OcuZd96bmLHgVC0WE5N9hzbz0taf0z94iMWzX8uS2ZeRSJTFHZaIiIiITAZOtC8v5aToLyqREjI4dASAyoramCMRgOqGVla8+Q/ZfMd32HLXvzHvojfTctpr4w5LCqD/QEfQu/GVJ0n2H6aidgZtZ7+ephXnM22a5vWMSyo1yMZtd9K+5zFqqmZy/qoPMaNOi/eIiIiIiExUSjyKlJDBocMA6vFYQipq6ll23cfYdt/3aX/kZwz0dDP3wjdpjrkJaOjIQfZvfo79m5+hd+92sAQzFp7KzFUXMH3uymNzN2q9klgcOLSdlzbeRF9/NwtmXcCyuVdSVlYRd1giIiIiInISlHgUKSEDyUzisVKJx1JSVlHF4td/kJ2P3kLniw8yeHg/iy5/D6akSMlL9h3mwJYXOLDhGQ7v3gw402bOZc7519G0/FwqaibhfKoTTDqdZPOOe9m28yGqq2Zw9qkfYuY0LbAlIiIiIuPFNfdxESnxKFJCjg21VuKx1FgiwdyL3kJlfRM7H72VDb/8Okuu+RDl0/RelZrUYD8Ht7zI/o3P0LPzFUinqZrRQtvZb6Bx6ZnHFoqR2PUc3sXajTdxpHcvc1rPZfmiqykvq4JBdTsVEREREZkMlHgUKSFB4tGoqKiJOxTJw8yYteYyKusa2Xrf91l/01dYcPm7qJ+7LO7Qprx0KklP+yvsf+VJDm5di6eSVNQ1Muv0y2hYdhY1DXMwi3ttbzkqnU6xbeev2dJ+HxXltZyx+n00N66IOywRERERESkwJR5FSkgy1U9ZWSUJ0+qtpaxh8eksr2tg233fZ9Ot36Dl9EuZff41JMo19LqYkv299Gxdx6Gtazm042XSQwOUVdcyc/UFNC47i5rWRa8mGy0Vc7Dyqp4je1i38WZ6juyitXkNKxdfpy9bRERERKS4NNS6aJR4PFmJcD1oxqWnTWKcFrcI+ZqYSItrRLn+4/FehawzlR6iLFFZ+PNHYBHuv5Yeh3rH6/xRyoZIUtU1LWDlW/+UXY/9gs7nH+DA5udpPf11zFx5Qf4EZKTXFa6wR2mqUcpGiXU83tdRrv9ATzcHt73IwW1rObxnM3ia8mn1NC4+i4aFpzF97goskUncDx07LhEh8ZhIhg82EWFEcNh6E0MRzh+l7GC4D0FiKPyHxZIRPlj9A2zZ8xBbdj9IeVk1py95B61NpwRtI2dotQ2Fe8PClgNgKPybZckI9YYtmwx/fk9FuK5hy6bCvyaP8ot4lLLpCK8rRh7yHiwiIiIiY1PiUaSEJNMDlJfFm3iU8Moqqph/yQ00LF7D7qfuoP3hm9nzzN3MWnMpzatfQ1llddwhTnjuTl9XOwe2vcjB7Wvp794NQHVDK62nX07D/FOpaZmvVcZLXM+R3bz0yk0c7uugrek0Vs6/hkr1chQRERERmfSUeBQpIanUIGVlVXGHIRHVz11B3ZzlHN69iY5n72bX47+k47l7aTn1tbSc+lrKq5VgiWLwyAF6dm3k8K4N9Ox8haHeQ2BGXeti5pz/JhoWnkbVjGYgWk9WKb50OsmW9gfYtvNBKsqmccbS32ZW46q4wxIRERGRqU5DrYtGiUeREpJMD1KmHo8TkplRP2cZ9XOWcWTvdjqevZs9T9/J3hceoHn1RbSccRkVNdPjDrMkJfsOB4nGnUGyceBgFwBlVTXUz1nG9PmnMGPBasqrtYL4RHLo8E7WbbyZw70dtLWcwaq5V1FRPi3usEREREREpIiUeBQpIanUIJVV9XGHISepdtYClrzxQ/R176bj2XvY+8IDdK59iJmrLmDWGa+jsr4p7hBjlRro4/DuzRzetZGenRteHT6dqKiibvZSmle9hro5y5nW1KYh1BNQOp1k84772L7zISorazlj1XtpblpJYjDChJgiIiIiIuPFAc3pXDRKPIqUkFRqgPKy5rjDkAKZ1jSbRVe8l7ZzrqLj+XvZt+5RutY9wvR5K6mft4K6uSuobmwdn8WnSkRqaIC+rp307t1Bb+cO+jrbMz0aHSsrp7ZtMbPPv5a6OcuoaZmHJcq0AvUEdrCnnXUbf8qRvk5mzzqb5YuuVi9HEREREZEpTIlHkRKS9hQJ08dysqme0cKCy36btnPeSOcLv+bg1hc5tH0dAOU19dTPWU7dvOXUz11OZV1jzNGeOE+l6OveTW/nDnr3bqe3cwf93XtenT+lonYGNS3zaVxxDnVtS6hpXUiiTO19MkilBtm8416273qYqsp6zlz9fmY2Lo87LBERERERiZn+4psqEjH3qIrSoyts2VLoJVYKMcQpSu/0kGUtSpf3CNc/Sr0JwtUbZV0TS0N1dSPzz7ue+eddz0BPNz27NtCz6xV62l9h/8anAaia0UL9nOVBMnL2slEXphm35jfGpUr29zJwqJOBg11Z/3fRt383ngqG05ZV1VDbsoCGM06jpmU+dU3zj5/jMpX5lyPKgjGWCve+JiL0okwkI7SVKGWHwpW1cagTIDEY7iIkkuHfABtKse/gJtZt+wV9gweY23IOy+e+noryasgZWm1D4d+E0GWTEeqMUDZKvSTDDSH3VISGnY5wfg9Xr0eZQD0V4fzp8K8rdAxRfg6EfP1B0XEYUhXh/ONCE+OLiIicAI//Z/gUosSjSEnRHxBTRVV9E1UrL6B55QW4p+nbvydIRO7cQPeGJ+la9zAA5dPqqaxrpLK+icq6Rqrqm6isa6KyPtiXKK8oaFzpVJJUfy/JgV5SA70MHj4wLLk4cKiT1EDfsQPMqKxtpGp6My2rL6amZT61zQuorG8aNoRcq09PPkNDvbyy5XZ273uOmqqZnLvygzTWL4w7LBERERERKSFKPIqUmqnei3IKMktQ0zSHmqY5tJ52Gal0kt7O7Rzes5nBQ/sYOLyf3q4dHNz6Ap7TE+poYrKidkYwbNkSWCKBZf4Ptste3UciAen0q4nF5EDvsERjOjmYL0Iq6hqomt5M4+IzqZrRQtX0ZqpmNFNVN1PDpacYd6dj34u8svU2hpJ9LG67hMVzLqMsoXYgIiIiIiLD6a8EEZESkygrp65tCXVtS4bt93Saob4eBnu6GTzczeDh/ZnH+xk4sBdPp3BP4+k07mlIp3FPBdvpNHg6SFxagvLqGsqqaiivqqGiroFpM+dQVl1LeVUNZZU1rz5fUTOdqvqZI/asNHXSnVL6Bw7y8pZf0LV/PfW1czh7+Xupr2mLOywRERERkWg0XUnRKPEoUkJ075PRWCJBZe0MKmtnAIsJORXlcdx99JW01Q4lh3uanR1PsmH7XbinWb7wKubPvpCycNMbioiIiIjIFKXEo4jIFDNq0lEkx5G+TtZtuoUDPdtpmrGEVUuup6a6KfNshEVIRERERERkylHiUaSEmKFujyJSEtLpJFt2PsjWnb+mLFHJKUvfyuyWM5W4FhEREZGJzYG0/u4uFiUeS81E+oMuMYFinSDXtSxRSSqdb3GPkxTlnhoh8Rllfr+wZS0d4b2KUjRKx6yQ1yDh4QPwRPjTh641SrMeh/cqSr0W4Qd7IsJ7FXa17EQyQrtORYh1KELZkDEkhsIvAW4RyiaS4craUIruQ1tZt+0X9A7so61pDSvmv5GqijrIqcOGwr9ZUcqSDFfWQpaLUicAqfBlPRXyPUjHff4IS8tHKOtRviwLex/wCOcfjz8aIpx/3OhLSBEREZkklHgUKSFlZZWkUuOQeBQRCWFwqJeNW37Frn3PMq2ygbOWv4fmGcviDktERERERCYoJR5FSkh5oopkaiDuMERkinF3dnc9x4atvyKZ6mdR28UsmX0ZZWX5VzMXEREREZnQNLqgaJR4FCkhZWWVDA4eiDsMEZlCevv28fKWW+k+uJkZdfNZveC3qK9pjTssERERERGZBJR4FCkhZYlKkhpqLSJFkE4n2brrIba2P0giUc6qxW9ibus5kebDFBERERERGY0SjyIlpLysipSGWovIONt/aCsvb76VI32dtM48jRWLrqGqsj7zbJSVmEREREREJiANtS4aJR5FSogWlxGR8TQw2MOGbXeyp+s5qqsaOHPVe2luXBF3WCIiIiIiMkkp8ShSQirLa0l7kmSyn/Ly6rjDEZFJwj3Njt2PsWnHPaTSQyyaeymL515KWVll3KGJiIiIiMgkpsTjSTDAzMIVTiQKf/6w5wawCOePUm9YUV5/IuT5xyPOmFVVTgdgYOAgFWVVY5b3kN3DLUI3cvPw1zVK73QLOXoz0ttq4QNIUPjXFfb6A3g6dNHYWYRYw74FkepMRXhfQ5a1ZPjzR5nj0JLhX1jYehND4eu0obE/WAcPt/Pyll/Q07ubpvolrFp4LbXVMyENpI8/PkydJ1KWZIR6w5aNUCep8GU9Sr15ruFJnz8V4QOTDlk2yvmj3NzTEcrGfSOM/fwa0iUiIlIaXD+Xi0iJR5ESUn008Th4iLqaWTFHIyIT2VCyl4077mbn3qepqqhjzZK309p4SrQvrURERERERE6CEo8iJeRo4rF/8FDMkYjIROWeZlfns2zccRfJZD8L2i5kybzLqaAi7tBERERERGSKUeJRpIRUVQSryg4o8SgiJ6DnyB5e3voLDh7ewYy6BaxefB11Na3Bk1GG74qIiIiITFZO+Olq5KQp8ShSQhKJciorahkYUOJRRMIbSvayuf1+2jueoLy8mlOWvIXZzWdgUeb3FRERERERKTAlHkVKTFXldPoHD8YdhohMAGlPsXPvU2xuv5ehZD/zZp3L0vlXUFFeE3doIiIiIiKlS4vLFI0SjyIlprqygSP9nXGHISIlbv/BLbyy6Zcc7t1DY/0iViy6hvqatrjDEhEREREReZUSjyLFEOHblNqaFrr2ryedTpJIFOgjGuXLnHT4wpaOsDquhavXIk21EeH8Ub7RClk0yuv3mBcSDnn5AxGuVdj3K8r7mkhGOH8qZLsKWQ4gMRShbIR5E20oXFlLjl6uf/AQG7bfSce+F6iumM7pi9/OrIbMatWjnMNSqZBxhisHQDJ8WYtQNnS9yWToKj3KPD7pCLGGvK5R7q2R5hwK+Xn1KPfAKLF64edH8ijnFxEREZGSpsSjSImpq2nFSXOkr4v6WvVeEpFAOp1k+55H2LLzQdzTLJ57GYtbLqYsodWqRUREREQi0VDrolHiUaTE1NXMAuBI714lHkUEgK4Dr/DKtl/R27+PlsZVLF9wFTXVTaF7UYqIiIiIiMRBiUeRElNT3YxZGT29e2jj9LjDEZEYHenrYsP2O+g68Ao11c2ctfJ9zGxYFndYIiIiIiIioSjxKFJiEokyaqc1c7i3I+5QRCQmg0NH2LzzfnbufZKElbN8wRuZ33pB4eZ9FRERERGZsjzanNZyUvQXjEgJqqtpY/+hLXGHISJFlkoP0b7rEbbsepBUapC5s85hybzLqaqoizs0ERERERGRyJR4FClBdTWt7Ol6jsGhI1RW1MYdjoiMM3dn7761bNx6B/0DB5jZsJzl89/46pyvIiIiIiIiE5ESj6UmkYg7gtDMLO4QJqe001A3H4ADB7cyq+mUEYta2N7hEVbssnSE9zVC93QP2V4sFf70EKV7fPjXFfa6eug3IMrZw4tw+kiXyqK0l5Dvl0VoK1HagCXDLa6SSEU4fzJK2fCLuyRGKHvoyC7Wb/8VB3q2UTetlbOXvY+Z05cET46xeIwNhb9YlgpZNhmhzghlo9RLMhmqmKcjLK4T9vVDtKE3Ict6lPNHWWUx7DUogeFEPh4xeMwLLGlFTBERkYnHweP+HWIKUeJRpARNr51DWaKS7kNbRk08isjENTDYw8b2e9jV9SwV5TWsXvQm5racHTH5LiIiIiIiUrqUeBQpQYlEOQ31C9h/UPM8ikw2qdQg2zoeYeuuh0h7ioVtr2HxnEupKK/OlNC3ryIiIiIiMjko8ShSohqnL2bjjrsYGOyhqrI+7nBE5CSlPcWuzmfYvPN+BoZ6mNW4muXz30BN9cy4QxMRERERmVpKYBqaqUKJR5ES1TR9MQD7D22lrXlNzNGIyIlydzq7X2LTtrvp7e9iRt181ix7B431C+MOTUREREREZFwp8ShSouprZ1NeVk33oc1KPIpMUN0HN7Np210cOtxO7bQWzlj+LloaVmpxLhERERERmRKUeBQpUWYJGuoXsv+Q5nkUmWh6juxh07Y72XdgA1WV01m99K3MbTods0TcoYmIiIiIiGuodbEo8ShSwmY2LKPrwHoO93ZQV9MadzgiMob+gUNs3nEPu/c+Q3lZFcsWXsW8tgsoK6vAklo0RkREREREphYlHotlPIbVJSLUGaWseuSEF/Zbkij5hrJjD1ubTuGVrbezp+t5li14w0mcP/z7b4nw3/x4hNc1Hh8Bi/AllUdo1mHLxj1Y1qK0qwjXyiJMtBw2hkh1JqOcP1zZxFD4i2WpCGUzycShZB/b9jzMto5HcHcWtl6YWal6WvD5T6fCJx4jJCgtlQpdlqFkyDojNKxkhPMnw50fwNMhY4jy+qO8rgj1etiyUb5VD/v6CeYQLbgIN3cfj4nZo/xwGQ/qASEiIiJSMEo8ipSwyoo6mmYsZc++F1g6/0oN0xQpMcnUADs6HmPrnodJpvppazqNZfOuZFpVY9yhiYiIiIhIPu6RvuiVk6PEo0iJa2tew9pNP+Xg4R00aBVckZKQSg3R3vE4W3f+mqFkLy0NK1k693Lqa9riDk1ERERERKRkKPEoUuJaGleRSFSwu+t5JR5FYpZOJ9m592m2tD/A4FAPM6cvZency5lRNy/u0EREREREJCxNrVI0SjyKlLjysipaGlext3stKxdeQyKhj61IsaU9xe7O59jSfj/9AwdoqF/ImhXvoKlmQdyhiYiIiIiIlCxlMEQmgLaZa+jY9wL7Dm6ipXFl3OGITBnuaTq6XmRz+3309u9jeu1cVi+5nqYZSzGzSAvBiIiIiIiITDVKPIpMADNnLKOivIZdnU8r8ShSBO5p9navY/OO+zjSt5e6mlbOWPlumhtXBglHERERERGZsFyLyxSNEo8nwwwSWmU4FP2hHprlmWqizMqYN+tctux6kCO9ndRNawEiTEsRYf4Ki3D/jZaAGYc5NDzC+dMRrsFEaa4RLmm+djVi2SjXKhWubNhy0cuGa7AWsmeie5q9XS+xefcDHO7bS211M2uW3EBr46l5eziGrTdS2WQqdJ2kwpe1sPWGvKYAJJOhi0b65S7s64oSa4Rr5RHKhr6/Rnj9HmXOobCfV49w/gj3gEgixDA+59dcTiIiIiLFpsSjyASxoPVCtu15hG27f8OpS94Sdzgik4p7mr37X2ZL+/0c7uugtrqZ0xa/jbamUzHTF0wiIiIiIiInQolHkQmisqKWuS1n0773CZbOvZzqqhlxhyQy4eUmHGuUcBQRERERmeRcIyGKSIlHkQlkYdtraN/7BNv2PMzKhdfEHY7IhJU34bj0BlpnnkZC072IiIiIiIgUhBKPIhPItKoG2prW0N75FIvnXEp5eX3cIYlMKMGiMS+xZdeDHO7toKZ6JqcuvYG2madl9XBU5lFERERERKQQlHgUmWAWzbmE3fueY0fH4yxedGXc4YhMCKn0ELs7n2Xb7ofpG+geIeEoIiIiIiKTnhNp8VE5OUo8ikwwddNm0dK4iu0djzF/3sWUl1fHHZJIyUom+2nf8zg7dj7MYPII02vnsmzBbzOrcZUSjiIiIiIiIuNMiccSY2ZxhyBRJpkNOSLTEuHr9BDnXzL7Uh7b/y227LiPFYuuHrvSCCNHPUIbNIvyLVG4ei3C9fcI57dE+NflMX8MQ7+sCN/SWYQ2EOU9sGS4shblG8UIZRPJ/C9scKiX7R2PsqPjMZKpfmZOX8qi1ktorFsY3GeTMNIHw1Kp0Oe3Ec6fVypk2UjnD1829PmTydBVejrK6x+HWCPUGebemlU4fNmQ1yDS+aN8XjzmqQFiP796K4iIiIiUMiUeRSag6bVzmNt8Njv2PMrcWedQW9MSd0giJWFgsIdtex6mfe+TpNKDzGpczeI5lzK9qi3u0EREREREpFTE/eXpFKLEo8gEtWzeFXTsf4n1W2/jrNXvV29ZmdJ6+7vZtudhdnU+Q9pTtM1cw+LZr6WuZlZQIErPRBERERERESkIJR5FJqjKijqWzL+CV7bext59a2ltPi3ukESKyt3Z37ONbXsepnP/eswSzGk+k0WzL6Gmuinu8ERERERERKY8JR5FJrB5beexp/M5Xt7yCxqmL6Kqsi7ukETGXTqdZO++tWzf9TA9R3ZRUTaNxbMvYV7r+VRXTo87PBERERERKWEOuFa1LholHkUmsISVccqyt/L489/g5S23cvqKGzXkWiatoaFednY8QfuexxgY7KFmWjOrF13H7JlnUFZWGXd4IiIiIiIikkOJR5EJrq5mFkvmX8HG7Xeyp+t5ZrecEXdIIgXV27eP7bseZnfnM6TTQzTNWMqqJ6gMIQAAHzRJREFUpW9hZsMyyiIslCwiIiIiIiLFpcSjyCSwcM5r6Oxex8ubb6W+po262ta4QxI5Ke7OgZ5t7Nj1CJ3d6zBL0NZyBgtmX0RdbfYK1Vo0RkREREREInDXqtZFpMSjTA0e8/wNUc4foaylgrKGcfqyd/L42m/x7Mvf4/xTP0pVxbH5Hj3C8Gsbp7kuLOTr8kSEoeJRhpVHeF1xD1YPe62I8FYdbSuhykZpAyHLWircD/ZUapA9Xc+zo+NxDvd1UF5WzeLZlzC/9YJjbXroWDdHC7tadYRVrS0d4ZeQZPgulxa27Didn1S4sh7l/CHrDMoWvl6Pcm+NEmuEaxA6hiifq3H4RVjzGImIiIhIHJR4FJkkqiunc+aKd/PEun/luVd+wDmrP0hZoiLusERC6e3vpr3jCXZ1Pk0y1U/dtFZWL3oTs5vWaP5GERERERGRCUqJR5FJZHrtHNYsuYHnNv6QtZt/xpqlb9diM1Ky3NN0H9zMjo7H6DqwAcOY1bSa+bPOp6FugdquiIiIiIiMC40GKZ4pmXg0s08Afw+0uHuXBX/d/iNwLdALfNDdn44zRpETNatpNcvmvZ6N7XdTW93M0nmXxx2SyDDJZD+7up6lveNxevv3UVley+K5lzJ31rlUV04ft+H+IiIiIiIiUlxTLvFoZvOBNwDbs3ZfAyzP/LsA+Hrmf5EJadHsS+jt38fmXfdTUz2T1rYz4w5Jpjh35+DhHezsfJq9+9aSSg8yo24epy69gdamU0gkptyPIxERERERiYsWlymaqfiX3leATwK3ZO17M/AfHswQ/6iZNZjZbHffHUuEIifJzFi96Dp6B7p5acstVNc2MaN+QdxhyRQ0MNjDns5n2dXxNL39XZQlKmmdeRrzZp3L9Lq5cYcnIiIiIiIi42hKJR7N7Hpgp7s/lzN32FxgR9Z2e2afEo8yYSUS5Zyx7EYef+lf6Oh6UYlHicXzL/8nhw6301C/gEVz3sKsplMoL6uKOywREREREREpAgs6+U0eZnY30Jbnqc8Anwbe6O4HzWwrcG5mjsdfAv/b3R/K1HEP8El3fypP/R8FPprZPA14cRxehkxszUBX3EFIyVG7kHzULiQftQvJR+1C8lG7kHzULiSfle5eH3cQpcDMfkXwOSkVXe5+ddxBjJdJl3gciZmtAe4hWDwGYB6wCzgf+Gvgfnf/QabseuB1Yw21NrMn3f3c8YtaJiK1C8lH7ULyUbuQfNQuJB+1C8lH7ULyUbuQfNQuJC6JuAMoFnd/wd1nufsid19EMJz6bHffA/wceL8FLgQOan5HERERERERERGREzel5ngcxW3AtcBGgh6RvxNvOCIiIiIiIiIiIhPblE08Zno9Hn3swMdPoJpvFSwgmUzULiQftQvJR+1C8lG7kHzULiQftQvJR+1C8lG7kFhMmTkeRUREREREREREpHimzByPIiIiIiIiIiIiUjxKPEZgZn9vZi+b2fNmdrOZNYxQ7mozW29mG83sz4sdpxSXmb3DzNaaWdrMRlwlzMy2mtkLZvasmT1ZzBil+CK0C90vphAzazKzu8xsQ+b/xhHKpTL3imfN7OfFjlOKY6zPv5lVmdmPMs8/ZmaLih+lFFOINvFBM+vMuj98JI44pbjM7F/NbK+ZvTjC82Zm/5RpN8+b2dnFjlGKL0S7eJ2ZHcy6X3y22DFKcZnZfDO7z8zWZf4O+aM8ZXS/kKJT4jGau4DT3P104BXgL3ILmFkZ8DXgGuAU4F1mdkpRo5RiexF4G/BgiLKXu/uZ7j5iIkomjTHbhe4XU9KfA/e4+3Lgnsx2Pn2Ze8WZ7n598cKTYgn5+f8wsN/dlwFfAb5Y3CilmCL8TPhR1v3h20UNUuLyb8DVozx/DbA88++jwNeLEJPE798YvV0A/DrrfvE3RYhJ4pUE/szdVwMXAh/P83NE9wspOiUeI3D3O909mdl8FJiXp9j5wEZ33+zug8APgTcXK0YpPndf5+7r445DSkvIdqH7xdTzZuDfM4//HXhLjLFIvMJ8/rPby0+AK83MihijFJd+Jkhe7v4g0D1KkTcD/+GBR4EGM5tdnOgkLiHahUwx7r7b3Z/OPO4B1gFzc4rpfiFFp8TjifsQcHue/XOBHVnb7Rz/YZepyYE7zewpM/to3MFISdD9YuppdffdEPxyCMwaoVy1mT1pZo+amZKTk1OYz/+rZTJffB4EZhYlOolD2J8JN2SGx/3EzOYXJzQpcfp9QkZykZk9Z2a3m9mpcQcjxZOZnuUs4LGcp3S/kKIrjzuAUmNmdwNteZ76jLvfkinzGYJuzN/PV0WefVo6fIIL0y5CuNjdd5nZLOAuM3s5802lTFAFaBe6X0xCo7WLCNUsyNwvlgD3mtkL7r6pMBFKiQjz+dc9YmoJ837fCvzA3QfM7PcIesReMe6RSanTvULyeRpY6O6Hzexa4GcEw2tlkjOzOuAm4I/d/VDu03kO0f1CxpUSjznc/fWjPW9mHwCuA65093wf0HYg+9vnecCuwkUocRirXYSsY1fm/71mdjPBkColHiewArQL3S8modHahZl1mNlsd9+dGdayd4Q6jt4vNpvZ/QTfWCvxOLmE+fwfLdNuZuXADDSsbjIbs024+76szX9B835KQL9PyHGyE07ufpuZ/bOZNbt7V5xxyfgyswqCpOP33f2neYrofiFFp6HWEZjZ1cCngOvdvXeEYk8Ay81ssZlVAjcCWpF0ijOzWjOrP/oYeCPB4iMytel+MfX8HPhA5vEHgON6xppZo5lVZR43AxcDLxUtQimWMJ//7PbyduDeEb70lMlhzDaRMw/X9QTzd4n8HHh/ZrXaC4GDR6f1kKnLzNqOzgtsZucT/O2/b/SjZCLLvN/fAda5+5dHKKb7hRSdejxG81WgimCYLMCj7v57ZjYH+La7X+vuSTP7feAOoAz4V3dfG1/IMt7M7K3A/wVagF+a2bPuflV2uwBagZsz7aYc+E93/1VsQcu4C9MudL+Ykr4A/JeZfRjYDrwDwMzOBX7P3T8CrAa+aWZpgj8SvuDuSjxOMiN9/s3sb4An3f3nBH88fNfMNhL0dLwxvohlvIVsE39oZtcTTPnTDXwwtoClaMzsB8DrgGYzawf+CqgAcPdvALcB1wIbgV7gd+KJVIopRLt4O/AxM0sCfcCN+vJq0rsYeB/wgpk9m9n3aWAB6H4h8THde0RERERERERERKTQNNRaRERERERERERECk6JRxERERERERERESk4JR5FRERERERERESk4JR4FBERERERERERkYJT4lFEREREREREREQKTolHERERERERERERKbjyuAMQERGR4cysHZib2bzH3V8fZzyTnZm9F/huZvMJdz8/4vEzgfOABUADwe9XR4B9wFZgvbt3FixgKVlm9j3gPZnNlLsX7XdtM2sBNgN1wCBwqrtvLNb5RURERPJR4lFERGQMZrYI2DLOp/lrd//cOJ9DcphZHfDFrF2fCnlcOfB+4GPAuSHKbwMeBm4Hbnf3rujRiozM3TvN7MvAZ4FK4CvAm+KNSkRERKY6DbUWERGRqewvgDmZx/e6+31jHWBmpwCPAd8hRNIxYyHwLuA/gM+fQJwiYfwDcCDz+DozU29pERERiZV6PIqIiMiUlBma+odZu74Q4phTgQeAmTlPDQJrCYZW9wLTgTZgDVBdgHBFxuTuPWb2DeDPM7v+F3B3jCGJiIjIFKfEo4iIyNjagcUhy/4QuCBr+13AoyGOOzB2ESmwTxLMhwfwnLvfNVphM6sEbmZ40rET+Cvg++5+KM8xFQS9Im8A3gnML0DcIqP5R+DPgArgQjO71t1vizkmERERmaKUeBQRERmDuycJerKNycz6c3btcfdQx2adb16U8hKdmdUDH83a9a0Qh30UWJ61vR14rbtvH+kAdx8CHgEeMbNPESQgc3tLihSMu+8xs58TtDUIkpBKPIqIiEgsNMejiIiITEUfIhgODdAHfD/EMTfmbP/xaEnHXO6ecvf/cvevhz1G5AR9O+vxFWa2JrZIREREZEpT4lFERESmot/Neny7ux8crbCZlQEXZu3qA34xHoGJFMBdwL6s7d8dqaCIiIjIeNJQaxERkUnMzM4DVgJzgSHgJeB+d88dEp573OnAecAsgiTbVuAed+8pQEynEiy6MguoJZgncSvw0FhxFYKZnQmcmrXr5hCHNQNlWdudmWHURWNmcwiSn61AI8G8oLuAX7v7vtGOjXCOlcAZQAvQQLBQzm7gRWCtu/sJ1DkLuJhgsZ1GYD+wh+D97ixE3FnnmkMwx+p8gkV99gKPu/tLJ1lvNfA6grleZxBck03Aw+6ePpm6M/XXAGcCqwmuUTXB524/sIXg2u8NW5+7pzLDrX8ns+tGM/vTzLQRIiIiIkWjxKOIiEiJMbN2gkQhBMm+10cpa2ZG0MPpz4AVeQ7rNrO/dPd/zlPf9cDfMjwxd9SAmf0D8L/cfSD8K3p1TsVPECRCRlpgpdfMfgL8T3ffEaX+iN6R9diBX4U4piJnu8HM7EQScVGYWQJ4H/AnBAnBfNJm9gDwGXd/5ATO0UjQVj4AjDa/aKeZ3Qr8s7s/FaLeqwgW3rkQsDxF3MweBT7n7neGjPV7wHsymyl3L8/sXw18EbiW4Qnio8e9BPyZu4d5r7OPqwH+huDzND1PkXYz+xrwpRNJRJvZEuBzBPMx1oxRdgPw88y5doeo/pccSzy2ECROtcK1iIiIFJWGWouIiEwimVWU/wv4JvmTjgBNwNfM7P/mHPsPwC3kTzoCVAGfAW7KnCdsTK8n6B32WUZf1bkGeD+w3szeFrb+E3B11uOX3L0rxDFdBEnKo6YDlxY0qhxmthB4Cvg3Rk46QvD73OXAw2b2xUziOew53k7Qo+4zjJ50hCB59SGCtjVanVVm9gOChO5F5E86ktl/EXCHmf0gs2p4ZGb228ATwJvIk3TMOAW4zcz+MEK9C4HnCZKy+ZKOEFyz/w3ca2YjlRmp/rcCawkSy6MmHTOWZ2I5J+QpHszZviZ8dCIiIiKFoR6PIiIik8tXgbdnHg8AjxIMC60DXkOQdDzq983sUXf/vpl9jiCpAZAiSORsAyoJhq7OyTrut4BPAZ8fKxgzexfw7wzvMegECZdNBEN5Wwl6xR1NvkwDfmxm73b3H439ksMzs5nAWVm7fhPmOHfvN7PnGZ4A/JaZXRV11fIwMouB3EkwPDnbDuAFgmHWDcC5BEPWj/okwfD13w9xjj8D/p7jE4O7M+foInhP5hMMjR8zMWhm5QRzX+b20j1M0Ba7CIatX0jQJo+6EWg2s2uiDAc2syuB73Hsd9oNwDqCdrUAOD/rOQO+YmZPuvvDY9TbCtwLLMl5qh14FjgCLCKYjiABXAL8gGBodJi41wA/YuTPRQ/B56CJIGnaGqbebO7eaWbrCaZaAHhD1DpERERETpYSjyIiIpPHOcCVBAmMvwP+1t0PHX0y06Ps88D/yDrmbzPDtT+b2f4O8Ons+eQyw33/CPgSx5JUnzazr7r7gZGCycyl+P9zLLniwNeAL7p7e07ZaZlz/E2mfIIgsfe4u28JfwnGdB7DE23PRzj2hwxPPK4A1prZf2See7gQ8z5mhqX/lOFJxzsJhlI/mVPWgLcRJJyPlv+4md3n7jeNco5rOT7peD/Be3/ccG0zqyLoKfpBjg3tz+evGJ50HMzs+0d378uqrwb448xzRxOar89s/+Uo9WdLEPTuLQceIFhl/NmcuBcA3+VY79QEweu+eIy6v8bwpOMe4L8DP8seXm9mc4F/JBgqfS0hE48ca+dH/TvB+7szX2EzW0TQozPqIjHPcyzxeKqZ1br7kYh1iIiIiJwwG+epiURERKYUM7sfuCxr1+Xufn/EOk50jsejftfdvz3KMTcRJKuOOtp77q/d/XOjHPd/CJKDR33U3f9lhLIJggVJVmd2pYB3uvtPR6o/c9y1wK0cmw7mP939PaMcEomZfRr4/7J2hX5/MgnB5wl6uuXTBzxJ0LPvMYJFX0IvCJJ1nm8CH83a9WXgE6PNJ5kZFvwYx3rGbQeWuHsqT9k6guHVzVm7/4kgcTfmL4Zm1uruHXn2LwNe5thw5xTwdnf/2Sh13UCQPExkHbPa3TeMUD57jsejbgJuHKmnpJnVErTFRVm7V7r7KyOUv5LhcyF2Aa8ZKabMMd8hGIae7dU5KHPKlhH0AK3O7LrD3a/OLTfKuarDLsJkZp8F/jpr12vd/aGw5xIRERE5WZrjUUREZHK5ebSkY8aXc7YbgMcJemGN5ksMn+fwdaOUfSvHko4Anx8r6Qjg7rcR9N476h2ZlYoLZXnO9rawB2ZW9H4TkLdXGsHQ2NcS9Cj9CdBhZi+a2WfNbLS5LV9lZrMJehUedT9jJB0zsW0DPpa1awHDk8vZ/hvDk453EzLpmDnXcUnHjI8zfI7Fr4+WdMzUdRPwraxdZZl6wtoNfGi04dmZHn5/n7P7snxlM3KHqf/xaEnHjI8TrMwexiyOJR0h3Krqr4q48ntu+85t/yIiIiLjSolHERGRyeVLIco8AuQmL/6Pu6dHOyiz0vTGrF2jLXjy37Me9xAM/Q7rH7MeVwBvjHDsWBbmbO+KcrC7v0gwpP17wKjXK+NUgh5nm8zsS5nehqP5CMPnUvxshJWzf0bQ0/Go3xqhXO5w3dBJxzG8O+txkhBzgGZ8LlP+qPdGOOc3sqcTGMVtOdt5225mgZjrsnZtAf5zrMozycDc5GZYLSd4XBi5SfLc9i8iIiIyrpR4FBERmTx6CJKKo8okGHPnTbwr5DmyE495EyaZ+QBfk7XrV+7eG7J+3H0zwxMmY83HF0V2zH3uPhC1AnfvcPf3EfTo/ALBgiZjqQD+FHgoMy/gSK7IetwBhB4Wm0ke/jpr13HXzczaODbnH8Aj7r427DlGkhlmnb3Izb2j9IwcJlPu3qxdM81spBXZc/0qZLltDE+2j5TsO4/hc6D/OEJS9r8Y3iN4JB0E0xsc9fHMHI7jIXcO1ua8pURERETGiRaXERERmTw2jNVrMUt2L7Fud+86geOmj1DmHIYPJW0/gcRKN8fmrsxdWfhk1GQ9jjJk9TiZOQL/AviLTDLxEoLE1bkEqylPy3PYGcDNZnaJuw9mP5GZ+++CrF2bgYXB+jGhZSd4F5uZ5STOLswp/2sK45yc7cciHv8ow3u2ngPknYMxx7owlbu7m9khjrXLkdru2TnbT4SpP3OOLjPbCiweo1zazH7MsZ6nbcDzZvZvBCtdP5pvbs4T1JezXVugekVERERCUeJRRERk8jgYoWz20NYTPa5ihDLzcrb/JPPvRDWdxLG5sn/3GXFewKgyqxH/KPPv6AribyCYdzF3yPN5wO8RLOiSbSbDk5UXcXzP1CjKCBJs2e9vW06ZUIm7EHJ7EIbpBZpt/Rj15ZPOzLsZVvaK4yO13dac7U0R6oegR/CoiceMvyRYJfzo3J/1wB9k/vWY2cPAbwjm+HwsN0kdQW4bH+l1i4iIiIwLDbUWERGZPML2dizUcSMpZKIQYKx5EaPI7hFYPWKpk+Tug+7+S3e/DngLx/eu/KM8hxX6usHx125mznbuUNwT1ZCzHWbexWy5ye/GEMcUYl7KXIV+HXllhpdfRP4pDuqBqwgWe3qQYJGib5nZiSwMk9vrNvSUByIiIiKFoMSjiIiIFFqhe1VFGms8huwecjUWcRzziXD3WwhWus62JM/w8/HojTbW6xuP5N2J1Dvu78MJGrfX4e473f2NBHNxfouRV1hvIBiW/ZKZfSZiPDU521F6iIqIiIicNCUeRUREpNC6c7bf5+52Ev+WFTC2HVmPyyjeYhvf5vj59nJ7sOVet++c5HUzd28f4xy5PfxOVG7PyRkRj8+dc3H/ScRyMgr9Osbk7g+7+39z90UEq06/G/gGxw+zLwc+b2afiFD97JztHXlLiYiIiIwTJR5FRESk0HJXMz6RIaLjZWvO9mgrTBeMu/dz/GIpucOJOxk+7H08rtuenO3VBaq3M2c7arI4dxXr3PqKJbftLo14/Eklyd19u7v/wN0/5u5LCBYp+mVOsc+ZWdiE8Zyc7ZF6VYqIiIiMCyUeRUREpNCeALJX5b0yrkDyeCFnOzfhNZ5y59IcNn9gZgGRZ7J2XWBmhV6F+JGc7dcWqN6ncrYvyFtqZLmrbefWVyxP52yfF/ZAM2sGFhUyGHd/ArgeuCdrdy1wRcgqVuZsP1+IuERERETCUuJRRERECsrdDwJPZu26yMxyEyBxeTJn+7RinNTMyoElObt35yl6d9bjKuBdhYzD3fcAL2fteo2ZnXSvR3ffyPBeildkEnFjMrNZDE9O73P33N6hxfIEw1eCfkeEeUDfyTjMVenuaeB7ObsXhTx8Tdbj/e4edZVuERERkZOixKOIiIiMh29kPU4AXy7GQi4hvATsy9o+N8xBFlgzdskR3cDw+QL3cXzvSwjmgszuLfpXZpa7EvXJ+lbO9lcKVO9/Zj2uAP4i5HGfJZi/8KjvFyieyNz9EPCLrF2LCeZcHJWZVXP8AkKFlLu69uBYB5hZGXBm1q5fFzQiERERkRCUeBQREZHx8D1gQ9b2tcA/ZZIhoZhZhZm918wK9vtKpvfYXVm7Lg5ZfxnwnJn9wMxOiXLOTPl/ytn9k0wsufFtBL6btWsecEvU5KOZvc7MRppv8F8Y3jvxKjP7UtjEsJm1jvDU1xieNP1DM7t2jLreAnwsa1c6U0+ccs//FTMba77NrxKyF6KZrTazGyK269zk5/oQx5zF8MVu7ohwPhEREZGCUOJRRERECs7dk8CNDF/J+feB35jZtSMlIM2s3MwuMrO/I1jV97sU/veVW7MeTydkr0eCYbQ3AmvN7GEz+6iZ5Q6fPlbYrNXMPg08BszKeqoH+Nwo5/ljhi9EczFB0vOjo835aGYrzeyTZvYMcB8jJMLc/TDwAcCzdv8pcJeZ5Z2b0cyqzOx6M/spw69fdr0bgL/L2lUO3GRmn8j0CMyub5qZfQr4EcPf3y/EOMwaAHe/G7gpa1cL8ICZvSU3OWtmc83sx8CHM7tyV8XOZy7wE2CDmX3ezM4aKQlpZrPN7DsEPWaP2kXw/o4lex5IZ3hPThEREZGiKB+7iIiIiEh07v60mb2XYOjs0cTTBQSr9PaY2dPAXmCIYBjyHOAUgrkNx9MtwBGCRToA3gw8HrGOizL/MLM9BL07u4BegmTmMoKFPXITSgPAb2fmWszL3Q+a2fUEPTPnZ3bPBb4JfNXMngPagcNAPUFi7FSGD+UelbvfbmafJEgUHk2mXQlcaWa7CBYh6QamZWI4HajMlBtt4Ze/Ilgo5vLMdjXw98BnzexRgmvUnClTn3PsfYyekC2mjwNnEwy1BpgN3Ay0ZxK7vcACgvZ89D3+FcEQ+veEPMcS4DOZf0fM7AWCVbV7CK77UoLrnt2GHPi9TGJ/LG/JevyQu28PGZeIiIhIwSjxKCIiIuPG3X9qZpcA/8XwxVXqgctCVHGQ4T3zChHTETO7CXh/ZtfbCJI/o0kDGwkSirnaMv/GsgH4iLs/GCLG9WZ2DsG8ia/PeqqCoIfmWL00kwSJydHO8Q9mtpMgoZmdBJyT+ReZuw9lhld/F3h71lP1wBtGOfTHwPvcfehEzlto7t5hZlcQLPazNOupeZl/uR4h6A17osPEazl+Ze9cRwjaT94ep9nMbE5Off9xgnGJiIiInBQNtRYREZFx5e5PAauAjwDPMHYicT/BUNf3ArPdPTVG+RPx1azHq8xs1KSPu6fdfTlwBsFiKPcS9HobSxp4CPhdYE2YpGPWOTvd/Q0ECbs7CHpLjmYgE9efAvPc/dEQ5/gBQUL4ywS9T0ezh2DRoA+NUWe/u78D+C3gUUZ+vz3z/G+5+zvdfazXV1TuvpXg/f4Sxy/uctQugvbwusxq7mE8BLwV+Fdga4jy3cDXgZXu/sOQ5/gAx3qydjN84R8RERGRojH3gnYiEBERERmVmTUTDFNuA44umtJDMHx4HbAx38Ir4xDHQwTzJwJ8x90/EvH4coLecCsJegjOIBhN0kPQU3MD8Jy7HylQvNMIerEtJLhu1ZlzdQIvA+vcvf8k6jeCVZBPIRi+XUfQa7IdWAu87Cfwi2NmMZqLCYYrNxDMg7gb+I27d5xovMWUmaPyCoJ5M6cTJGE3E7yGk0qMm1kbwTVfAjQSTDVwhGBY+ovAi1F7gprZBo71zv2iu//5ycQoIiIicqKUeBQREZEpycyuAW7LbPYBC9y9K8aQRE5aZqj7LzObvcCSiZLgFRERkclHQ61FRERkSnL32wnm5oNgMY8/iDEckUL5ZNbjryrpKCIiInFSj0cRERGZsszstcDReRe7CXqHhZ2rT6SkmNnFBHNIQtCel7t7d4whiYiIyBSnHo8iIiIyZbn7r4GjC3Y0AZ+KMRyRk/WFrMd/qaSjiIiIxK087gBEREREYvYJYH3mcUEWghEpNjNrAe7J/BsAvhlvRCIiIiIaai0iIiIiIiIiIiLjQEOtRUREREREREREpOCUeBQREREREREREZGCU+JRRERERERERERECk6JRxERERERERERESk4JR5FRERERERERESk4JR4FBERERERERERkYJT4lFEREREREREREQKTolHERERERERERERKbj/B/Rhl3F1lVSvAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#PLOTS (YOU DONT NEED THIS CODE)\n", - "# (You dont need this code but its here for illustration):\n", - "#Make a quick plot of the experimentalist lab testing of his camera:\n", - "experiment_error_function = generate_experiment_error_function(\n", - " numpy.array([0,0]), \n", - " covariance_matrix)\n", - "Library_GraphTwoDimensionDensityColorMap.Main(\n", - " Function = experiment_error_function,\n", - " DomainMinimumPoint = numpy.array([-2, -40]),\n", - " DomainMaximumPoint = numpy.array([2, 40]),\n", - " ShowContours = True,\n", - " PrintExtra = False,\n", - " )\n", - "plt.title(\"Probability Density Result of Measuring (t=0, h=0)\", fontsize=40)\n", - "plt.ylabel('Height (Meters)',fontsize=40)\n", - "plt.xlabel('Time (Seconds)',fontsize=40)\n", - "plt.draw()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 87, - "metadata": {}, - "outputs": [], - "source": [ - "def PlotContour( \n", - " Function = None, \n", - " DomainMinimumPoint = None, \n", - " DomainMaximumPoint = None, \n", - " ):\n", - " \n", - " if DomainMinimumPoint is None or DomainMaximumPoint is None:\n", - " #Figure out a domain box upon which to sample points\n", - " raise Exception( 'No domain box defined, and we have not yet built an algorithm to generate one' )\n", - " else:\n", - " xmin = DomainMinimumPoint[0]\n", - " ymin = DomainMinimumPoint[1]\n", - " \n", - " xmax = DomainMaximumPoint[0]\n", - " ymax = DomainMaximumPoint[1]\n", - " \n", - " X, Y = numpy.mgrid[xmin:xmax:50j, ymin:ymax:50j] #1000 x 1000 -> 1 million points\n", - " PointsToPlugIn = numpy.vstack([X.ravel(), Y.ravel()])\n", - " PointsToPlugInDataset = PointsToPlugIn.T\n", - " PlugInPointsCount = len(PointsToPlugInDataset)\n", - " FunctionResultValuesForGrid = numpy.zeros((PlugInPointsCount))\n", - " k = 0\n", - " while (k < PlugInPointsCount):\n", - " PointToPlugIn = PointsToPlugInDataset[k]\n", - " FunctionValueForPointToPlugIn = Function(PointToPlugIn)\n", - " FunctionResultValuesForGrid[k] = FunctionValueForPointToPlugIn\n", - " k = k + 1\n", - " Z = numpy.reshape(FunctionResultValuesForGrid, X.shape)\n", - " CS = plt.contour(X, Y, Z, 2, colors='k', )\n", - " return\n" - ] - }, - { - "cell_type": "code", - "execution_count": 88, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\\begin{tabular}{lrr}\n", - "\\toprule\n", - "{} & time & height \\\\\n", - "\\midrule\n", - "0 & 1.398009 & 28.420908 \\\\\n", - "1 & 0.714111 & 67.408100 \\\\\n", - "2 & 1.653545 & 62.124477 \\\\\n", - "3 & 2.041747 & 97.676730 \\\\\n", - "4 & 3.247284 & 121.982709 \\\\\n", - "5 & 3.055861 & 119.466286 \\\\\n", - "6 & 4.090273 & 121.738390 \\\\\n", - "7 & 3.561225 & 119.195538 \\\\\n", - "8 & 4.085447 & 122.659717 \\\\\n", - "9 & 5.339554 & 141.276679 \\\\\n", - "10 & 5.881835 & 142.396763 \\\\\n", - "11 & 7.134339 & 143.135998 \\\\\n", - "12 & 6.437210 & 115.908080 \\\\\n", - "13 & 7.626409 & 148.693928 \\\\\n", - "14 & 7.911095 & 122.118728 \\\\\n", - "15 & 7.419961 & 72.426669 \\\\\n", - "16 & 8.342553 & 47.520213 \\\\\n", - "17 & 9.042470 & 45.678556 \\\\\n", - "18 & 10.272418 & 53.178944 \\\\\n", - "19 & 10.159149 & 16.979553 \\\\\n", - "\\bottomrule\n", - "\\end{tabular}\n", - "\n" - ] - } - ], - "source": [ - "#Make the data pastable to latex \n", - "#print (data_with_heights_and_times_error)\n", - "\n", - "#pandas_data_with_heights_and_times_error = pandas.DataFrame()\n", - "#print (pandas_data_with_heights_and_times_error)\n", - "#print ( .to_latex() )\n", - "\n", - "\n", - "pandas_data_with_heights_and_times_error = pandas.read_csv( \n", - " \"2D_Generated_Data.dat\" ,sep=' ',header=0)\n", - "\n", - "print (pandas_data_with_heights_and_times_error.to_latex())" - ] - }, - { - "cell_type": "code", - "execution_count": 89, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 89, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.scatter(times_2d,heights_2d)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#PLOTS (YOU DONT NEED THIS CODE)\n", - "#Plot the data:\n", - "plt.scatter(times_2d,heights_2d, marker = '.')\n", - "for i in range(len(times_2d)):\n", - " mu = numpy.array([times_2d[i],heights_2d[i]])\n", - " Function = generate_experiment_error_function(mu, covariance_matrix)\n", - " PlotContour( \n", - " Function = Function, \n", - " DomainMinimumPoint = mu + numpy.array( [ 3, 40] ),\n", - " DomainMaximumPoint = mu - numpy.array( [ 3, 40] ),\n", - " )\n", - " \n", - "plt.xlabel('Time (Seconds)',fontsize=14)\n", - "plt.ylabel('Height (Meters)',fontsize=14)\n", - "plt.draw()\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## What is the correct single datapoint likelihood function?\n", - "### \"Likelihood function\" fixes the observation values and allows the parameters to vary:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Again lets focus on the first data point first:" - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "metadata": {}, - "outputs": [], - "source": [ - "#Choose a (v, g) for illustration only --> we need to explain whats going on in the kernel\n", - "\n", - "v_illustration = 40\n", - "g_illustration = 12" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### IF we used the closest tangent point on the curve then we would need the next code block:" - ] - }, - { - "cell_type": "code", - "execution_count": 92, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.80923875]\n", - "ClosestPointOnModel [ 0.80923875 28.44034601]\n" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#PLOTS (YOU DONT NEED THIS CODE)\n", - "#Plot only the first observation point, and the nearest point on the line's error function\n", - "plt.scatter(times_2d[0],heights_2d[0])\n", - "\n", - "#Plot a single attempt at the model, choosing a v and g ( our choice is for illustration only)\n", - "PointsToTry = numpy.linspace(0, 5, 100)\n", - "ValuesWeGet = my_model( v = v_illustration, g = g_illustration, t = PointsToTry)\n", - "plt.plot(PointsToTry, ValuesWeGet)\n", - "\n", - "\n", - "#Find the point on the model closest to the true point:\n", - "def DistancePointToModelAsFunctionOfTime( Time ):\n", - " TimeDistance = Time - times_2d[0]\n", - " HeightDistance = my_model( v = v_illustration, g = g_illustration, t = Time) - heights_2d[0]\n", - " return numpy.sqrt(TimeDistance**2 + HeightDistance**2)\n", - "\n", - "ClosestPointOnModelTime = scipy.optimize.minimize( DistancePointToModelAsFunctionOfTime, 0, method = 'BFGS' ).x\n", - "print (ClosestPointOnModelTime)\n", - "\n", - "ClosestPointOnModel = numpy.array( [ClosestPointOnModelTime, my_model( v = v_illustration, g = g_illustration, t = ClosestPointOnModelTime)] ).flatten()\n", - "print ('ClosestPointOnModel', ClosestPointOnModel)\n", - "\n", - "plt.scatter(ClosestPointOnModel[0],ClosestPointOnModel[1])\n", - "\n", - "NearestTangentContourFunction = generate_experiment_error_function( mu= ClosestPointOnModel , cov =covariance_matrix)\n", - "\n", - "PlotContour( \n", - " Function = NearestTangentContourFunction, \n", - " DomainMinimumPoint = ClosestPointOnModel + numpy.array( [ 3,40] ),\n", - " DomainMaximumPoint = ClosestPointOnModel - numpy.array( [ 3,40] ),\n", - " )\n", - " \n", - "plt.xlabel('Time (Seconds)',fontsize=14)\n", - "plt.ylabel('Height (Meters)',fontsize=14)\n", - "\n", - "plt.draw()\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### If we thought about the correct analytical answer we would do the following math:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "``` If we think about what it means to believe the model, \n", - " then we have to shift our believe about the measurment differently.\n", - " What is the probability we measure a point if the model is correct? \n", - " HOWEVER\n", - " Now we struggle with the fact that we don't know where to recenter our distribution \n", - " if the model is 'correct' ```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "``` The point could have come from ANYWHERE on the model line, even if the model line was absolutely correct ```\n", - "\n", - "``` The trick is to treat the point as though it was equally likely from anywhere on the line```\n", - "\n", - "``` Which ammounts to averaging the value of the points probabililty along the entire line. ```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "$ \\int_{curve} Gaussian( \\mu = CurvePoint, \\sigma = \\sigma) / ( \\text{Curve Length} ) $ " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### IF instead we approximate the correct analytical math using kernel density approximation then we will use the next code block" - ] - }, - { - "cell_type": "code", - "execution_count": 93, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#PLOTS (YOU DONT NEED THIS CODE)\n", - "\n", - "\n", - "#Plot the model which makes a curve\n", - "PointsToTry = numpy.linspace(0, 10, 100)\n", - "ValuesWeGet = my_model( v = v_illustration, g = g_illustration, t = PointsToTry)\n", - "plt.plot(PointsToTry, ValuesWeGet, linewidth = 4, zorder = 2)\n", - "\n", - "\n", - "#Plot the contours along the curve model which will make up a kernel density estimation for observation probability\n", - "TimesAlongModel = numpy.linspace(0, 10, 20)\n", - "HeightsAlongModel = my_model( v = v_illustration, g = g_illustration, t = TimesAlongModel)\n", - "for Time, Height in zip(TimesAlongModel, HeightsAlongModel ):\n", - " #print ( Time, Height )\n", - " mu = numpy.array([Time,Height])\n", - " \n", - " ErrorFunctionAlongModel = generate_experiment_error_function(mu, covariance_matrix)\n", - " PlotContour( \n", - " Function = ErrorFunctionAlongModel, \n", - " DomainMinimumPoint = mu + numpy.array( [ 3, 40] ),\n", - " DomainMaximumPoint = mu - numpy.array( [ 3, 40] ),\n", - " )\n", - " \n", - " \n", - "#Plot the single data point:\n", - "plt.scatter(times_2d[0],heights_2d[0], marker = '.', color = 'red', s=200, zorder = 3)\n", - "\n", - "\n", - "\n", - "#Add our desired labels, etc to the plot:\n", - "plt.xlabel('Time (Seconds)',fontsize=14)\n", - "plt.ylabel('Height (Meters)',fontsize=14)\n", - "\n", - "#Finally show the plot:\n", - "plt.draw()\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 94, - "metadata": {}, - "outputs": [], - "source": [ - "def probability_of_single_measurement_2D(\n", - " observed_time, \n", - " observed_height,\n", - " v,\n", - " g,\n", - " ):\n", - "\n", - "\n", - " ##########################################################\n", - " ##########################################################\n", - " #If we assume a velocity and gravitational constant\n", - " # What height should we observe? \n", - " # (students do this)\n", - " observed_timeheight_vector = numpy.array([observed_time, observed_height]) \n", - "\n", - " # What is the probability of a measuring an observed height and observed time?\n", - " # (students do this)\n", - " def kernel_density_estimation_centered_on_model2d( possible_timeheight_vector ):\n", - " result = 0\n", - " kernel_count = 20\n", - " for t in numpy.linspace(0, 10, kernel_count):\n", - " \n", - " assume_model_is_correct_time_choice = t\n", - " assume_model_is_correct_height = my_model(v, g, t = assume_model_is_correct_time_choice )\n", - " assume_model_is_correct_mu = numpy.array(\n", - " [\n", - " assume_model_is_correct_time_choice, \n", - " assume_model_is_correct_height\n", - " ])\n", - "\n", - " #print ('observed_timeheight_vector', observed_timeheight_vector)\n", - " #print ('assume_model_is_correct_mu', assume_model_is_correct_mu)\n", - " #print ('covariance_matrix', covariance_matrix)\n", - " \n", - " result += scipy.stats.multivariate_normal.pdf(\n", - " possible_timeheight_vector,\n", - " assume_model_is_correct_mu,\n", - " covariance_matrix, \n", - " )\n", - " #print ('probability', result)\n", - " \n", - " return result/ kernel_count\n", - "\n", - " #Return the associated probability by plugging in the observed height:\n", - " likelihood_value = kernel_density_estimation_centered_on_model2d( \n", - " possible_timeheight_vector = observed_timeheight_vector\n", - " )\n", - " #\n", - " ##########################################################\n", - " ##########################################################\n", - " \n", - " \n", - " return(likelihood_value)\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 95, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.0005510152162280714\n" - ] - } - ], - "source": [ - "#Test for the joint-time-height-error-case\n", - "likelihood_function_for_first_datapoint2d = generate_single_data_point_likelihood_function(\n", - " observed_time = times_1d[0],\n", - " observed_height = heights_1d[0],\n", - " probability_of_single_measurement_function = probability_of_single_measurement_2D,\n", - " )\n", - "print ( likelihood_function_for_first_datapoint2d( [100, 20] ) )\n", - "#Expected result: likelihood_fn(100, 20) == 0.0005510152162280714" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## What is the all-data log-likelihood function?" - ] - }, - { - "cell_type": "code", - "execution_count": 96, - "metadata": {}, - "outputs": [], - "source": [ - "#(provided below by instructors)\n", - "#numpyize the data:\n", - "# Each row of the datapoints is a datapoint\n", - "datapoints2d = numpy.vstack([times_2d, heights_2d]).T\n", - "# Looks like:\n", - "# [\n", - "# [time0, height0 ],\n", - "# [time1, height1 ],\n", - "# ...\n", - "# [timeN, heightN ],\n", - "# ]\n", - "#\n", - "\n", - "def generate_log_likelihood_function_fixing_all_observations(\n", - " datapoints = None, \n", - " probability_of_single_measurement_function = None,\n", - " ): \n", - " #Make a list where each element is a single-datapoint likelihood function\n", - " single_datapoint_likelihood_function_list = []\n", - " for datapoint in datapoints:\n", - " single_datapoint_likelihood_function = generate_single_data_point_likelihood_function(\n", - " observed_time = datapoint[0],\n", - " observed_height = datapoint[1],\n", - " probability_of_single_measurement_function = probability_of_single_measurement_function,\n", - " )\n", - " single_datapoint_likelihood_function_list.append( single_datapoint_likelihood_function )\n", - "\n", - " #Define a log-likelihood funciton for all the data using the list defined above:\n", - " def log_likelihood_function_fixing_all_observations(parameters):\n", - " result = 0\n", - " for single_datapoint_likelihood_function in single_datapoint_likelihood_function_list:\n", - " result += numpy.log( single_datapoint_likelihood_function(parameters) )\n", - " return result\n", - " \n", - " return log_likelihood_function_fixing_all_observations\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 113, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "-188.9535845568174" - ] - }, - "execution_count": 113, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "loglikelihood_alldata_2d = generate_log_likelihood_function_fixing_all_observations(\n", - " datapoints = datapoints2d,\n", - " probability_of_single_measurement_function = probability_of_single_measurement_2D,\n", - " )\n", - "\n", - "loglikelihood_alldata_2d( numpy.array([40, 10]) )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## What is the posterior using MCMC?" - ] - }, - { - "cell_type": "code", - "execution_count": 114, - "metadata": {}, - "outputs": [], - "source": [ - "#Define the required functions for the mcmc tools:\n", - "def bounds_to_ppf(bounds):\n", - " ppfs={}\n", - " for key in bounds.keys():\n", - " ppfs[key] = scipy.interpolate.interp1d([0.,1.],bounds[key])\n", - " return ppfs\n", - "\n", - "def prior_transform(nested_parameters):\n", - " actual_parameters = numpy.empty(n_varied_parameter_dim, dtype=numpy.float)\n", - " for i in range(n_varied_parameter_dim):\n", - " actual_parameters[i] = ppfs[varied_param_names[i]](nested_parameters[i])\n", - " return actual_parameters\n", - "\n", - "varied_param_names = ['v','g']\n", - "parameter_bounds={'v':(0,100),'g':(0,40)}\n", - "n_varied_parameter_dim = len(varied_param_names) \n", - "ppfs=bounds_to_ppf(parameter_bounds)" - ] - }, - { - "cell_type": "code", - "execution_count": 115, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "sample_result2d = nestle.sample(\n", - " loglikelihood_alldata_2d, \n", - " prior_transform, \n", - " n_varied_parameter_dim,\n", - " npoints=100, \n", - " maxiter=None,\n", - " maxcall=None\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 116, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Best fit: ['v', 'g'] \n", - " [53.6199624 10.44355233]\n", - "Covariance: ['v', 'g'] \n", - " [[7.71155935 2.0031923 ]\n", - " [2.0031923 0.60235745]]\n" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "vparameters2d, cov2d = nestle.mean_and_cov(sample_result2d.samples, sample_result2d.weights)\n", - "print('Best fit:', varied_param_names, '\\n',vparameters2d)\n", - "print('Covariance:', varied_param_names, '\\n', cov2d)\n", - "fig = corner.corner(\n", - " sample_result2d.samples, \n", - " labels=[\"$v$\", \"$g$\"],\n", - " quantiles=(0.16, 0.84),\n", - " levels=(1-numpy.exp(-0.5),),\n", - " #truths=[v, g]\n", - " )\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Lets make the plot even nicer looking for the sake of illustration" - ] - }, - { - "cell_type": "code", - "execution_count": 121, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "DomainMinimumPoint [40 5]\n", - "DomainMaximumPoint [65 15]\n", - "PlugInPointsCount 10000\n", - "PointsToPlugInDataset.shape (10000, 2)\n", - "PointsToPlugInDataset[0] [40. 5.]\n", - "MaxObservedValue [0.00870706]\n", - "MinObservedValue [2.95467431e-05]\n", - "Z.shape (100, 100)\n" - ] - }, - { - "data": { - "text/plain": [ - "Text(192.25, 0.5, 'G (m/(s^2)')" - ] - }, - "execution_count": 121, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "values = sample_result2d.samples\n", - "kernel2d = scipy.stats.gaussian_kde( values.T ) #takes in points sideways....\n", - "Library_GraphTwoDimensionDensityColorMap.Main(\n", - " Function = kernel2d.pdf,\n", - " DomainMinimumPoint = numpy.array([40, 5]),\n", - " DomainMaximumPoint = numpy.array([65, 15]),\n", - " ShowContours = True,\n", - " PrintExtra = False,\n", - " )\n", - "\n", - "#Add our desired labels, etc to the plot:\n", - "plt.xlabel('V (m/s)',fontsize=40)\n", - "plt.ylabel('G (m/(s^2)',fontsize=40)\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 102, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "kernel2d_v_marginal = scipy.stats.gaussian_kde( values.T[0] ) \n", - "Points = numpy.linspace(30, 90, 100)\n", - "Values = kernel2d_v_marginal.pdf(Points)\n", - "plt.plot(Points, Values, )\n", - "plt.ylabel( \"probability\",fontsize = 14 )\n", - "plt.xlabel( \"$v$\",fontsize = 14 )\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 103, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZYAAAEPCAYAAABhkeIdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzt3Xl8VfWd//HXh4SEfQ+yBQKEHRUEcV8RXFrFuuJ0qr9qf3ZqnU5r2xmt1bbW37R2GW2rY7WtU7WLW9Vii+KCK6AlgLIvSdgChCyEEMiefH5/3INzjUm4F+6S5f18PO4j557zPed+7s3yyXc95u6IiIjESpdkByAiIh2LEouIiMSUEouIiMSUEouIiMSUEouIiMSUEouIiMSUEouIiMSUEouIiMSUEouIiMRUarIDSIZBgwZ5VlZWssMQEWlXVqxYUeLuGUcq1ykTS1ZWFjk5OckOQ0SkXTGz7ZGUU1OYiIjElBKLiIjElBKLiIjElBKLiIjElBKLiIjElBKLiIjElBKLiIjEVKecxyLS1tQ1NFJYXk1BWRV7yqs4WFNPZW0DVbUNdE0xuqel0r1rChm90xnerzvD+3enb/euyQ5bpFlKLCIJ5u5sLTnE0rxS1hSUs3pXOVv2VlDf6FFdZ3DvdI4f3pepw/syM6s/J2cNoFvXlDhFLRI5JRaRBKhraGRJbgmL1hXyzuYSdu2vAqB/j65MHd6Xc8aPYfSgHgzv14Nh/brRt3tXuqel0C01hfpGp6qugcraeooO1LBrfxUFZZVs3FPBml3lLN5UhDukp3bhlDEDuWDSYC6eOpSM3ulJftfSWZl7dP8ldQQzZ850Leki8eburNyxn+dW7OSVtYWUVdbROz2V07MHcta4DM7MHsSogT0ws2N6nUM19fxj6z7e3lzMO5uLyS85RBeDM7IH8bnpw7nk+KGqyUhMmNkKd595xHJKLCKxdbCmnhdWFvDHD3awsbCCHmkpzJl8HJ89YRhnjx9Eemp8/8hvKqxgwUe7WPDRbnbuq6J/j65cMzOTz58yipEDe8T1taVja5OJxcwuAn4BpAC/dfcfNzmeDjwBzABKgWvdfZuZzQF+DKQBtcC33X1xcE4a8CBwLtAI3Onuf2ktDiUWiYeiA9X8z9Jt/OH97VRU1zN1eB8+f8ooLjtxGD3TE9/q7O4syyvlyfe38+r6vbg7nzlhGF85ZyyTh/VJeDzS/kWaWBL2025mKcBDwBygAFhuZgvcfX1YsZuAMnfPNrP5wH3AtUAJcKm77zazqcAiYHhwzp1AkbuPN7MuwIAEvSURAHbtr+KhN3N5LqeA+sZGLp46lC+dNZrpI/snNS4z4/TsQZyePYjC8mr+Z8lW/vD+dl76aDezJw7mG3PGM3V436TGKB1TwmosZnYa8H13vzB4fgeAu/8orMyioMwyM0sFCoEMDwvSQg3SJcAwd68xs53ARHc/FGksqrFILOwpr+LBxbk8k7MTw7h65ghuPnsMowb2THZoLSqvrOOJZdv47XtbKa+q4zMnDOWbc8YzJqNXskOTdqDN1VgI1TB2hj0vAE5pqYy715tZOTCQUCI57EpgVZBU+gX7fmhm5wJ5wK3uvjcO8YsAsL+yloffyuP3S7fR6M61J2dyy7nZDOvXPdmhHVHfHl3519njuP70LH7zTj6PLdnKK2sL+cKpo/i32ePo3zMt2SFKB5DIxNLc0Jem1aVWy5jZFELNY3ODXanACGCJu99mZrcBPwO+8KkXN7sZuBlg5MiRUQcvUlPfwO+XbOOhN3OpqKnniukj+MaccYzo3/46xPt278q3LpzADadn8cDrm3li2TZeWLWLr18wjn8+dRRdU7Qohxy9dtMUZmYjgMXAF919SVDegINAb3dvNLNM4BV3n9JaLGoKk2i4OwvXFPLjVzawc18V507I4PaLJzJxSMfpAN9YeIB7/7aB93JLmDikN/fMm8qs0equlE+KtCkskf+WLAfGmdnoYCTXfGBBkzILgBuC7auAxUFS6Qf8HbjjcFIBCPpeXiI0IgxgNhA+GEDkmKzdVc41jyzjq39aSc+0VJ68aRa//+KsDpVUACYO6cOTN83ikS/MoKK6nmseWcZtz3xI6cGaZIcm7VCihxtfAjxAaLjxY+7+/8zsHiDH3ReYWTfgSWA6sA+Y7+75ZvZd4A5gS9jl5rp7kZmNCs7pBxQTqtHsaC0O1VjkSEoO1vCzRZt4Omcn/Xuk8a25E7j25ExSuhzbZMb2oLK2nofezOXRd/LpmZ7KnZdM4qoZI455Iqe0f21yHktbocQiLalraOTJZdu5//XNVNU2cMPpWXxt9rhOueDjlr0V3PH8GnK2l3HamIH86IrjyRrUdke8SfwpsbRCiUWasyS3hO8vWMeWooOcNW4Q37t0CtmDO/cw3MZG58/Ld/DjhRupbWjkm3PHc+MZo0lV536npMTSCiUWCbdzXyX3/n09i9btZeSAHtz12clcMGmwmn7CFJZX890X1/D6hiJOHNGXn1x1IhOG9E52WJJgSiytUGIRCC3e+Ou383jknXxSzLj1/GxuOnO0Fmxsgbvz0uo9fH/BOiqq6/ja+eP4l3PHamhyJ9IWJ0iKtAmNjc7zq3bxk1c2UlRRw2UnDuOOSyYytG/bn+CYTGbGZScO44yxA/negnX8/LXNvLKukJ9edaLWHpNPUI1FOpX380v5z4UbWF1QzomZ/bj7s5OYMUrzNY7GK2sL+e6La9lfWcut52dzy7nZpKWq9tKRqcYiEia3qIIfv7yR1zcUMbRvN+6/9kTmnTicLp1g+HC8XDR1CKeMHsAPXlrHA69v4ZW1hfzs6hO1sKWoxiIdW0FZJb98YwvPrSigZ1oqXzlvLDeeoX6UWHt1XSF3vriWfYdq+co5Y/nX2dlxv++MJJ5qLNKpFR2o5r/fyuNPH+wAgy+eMZqvnpfNAC2yGBdzpwxh1ugB3PO39Tz4Zi6L1hXy06tPZFpmvyOfLB2OaizSoezeX8Ujb+fx5+U7aWh0rpk5gq/NHqeO+QR6c2MRdzy/hqKKam48YzS3zR1PjzT9D9sRaLhxK5RYOp4teyt49J18XvxwF+5w9cwRfOWcbN2KN0kOVNdx38sb+eMHO8gc0J0ffe4Ezhw3KNlhyTFSYmmFEkvH4O4syS3lsSVbWbyxiG5du3DNzExuPntMu1zKviN6P7+UO55fw9aSQ1wxfTh3fmYSA3ulJzssOUpKLK1QYmnfyqvqeHHVLh5fto384kMM7JnG9adl8YXTRqkPpQ2qrmvgwcW5/PrtPHp3S+U7WtSy3VJiaYUSS/vT2Oi8v7WUZ3MKWLhmDzX1jUzL7Mf1p43ikuOHapRXO7B5bwXfCRa1PDmrP/fMm8qkoZpY2Z4osbRCiaX92Fh4gBdX7ealj3aza38VvbulMm/aMK6dOZLjR2i+RHvT2Og8t6KAH728gQPV9dxwWhb/dkHnXD26PVJiaYUSS9vl7mwsrODlNXt4eW0hW4oOktLFOGvcIC6fNpyLpg5R7aQDKDtUy08WbeKp5Ts63f1u2jMlllYosbQt9Q2N5Gwv47X1e3l9w162l1bSxWDW6AFccvxQPnP8UHX4dlBrd5Vzz0vr+ce2fUwa2ofvXDKRs8ZlJDssaYESSyuUWJKvvKqOtzcX88aGvby1qZjyqjrSUrpwevZA5k4ewtwpxzFIyaRTcHf+tnoP972ykYKyKs4aN4g7Lp6khS3bICWWViixJMeO0kpe27CX19fv5R/b9tHQ6AzomcZ5EwZzwaTBnD0+g57pmkjXWdXUN/Dksu38anEu5VV1fPaEoXz9gvGd/mZrbYkSSyuUWBJn894KXl5TyCvrCtmw5wAA44/rxQWTjmP2pOOYltlP7eryCeWVdfzm3Xz+Z8lWquoauHzacG45byzZg3VjsWNxoLqO3KKDnDSy/1FfQ4mlFUos8bVrfxULPtzNXz/cxcbCCsxg5qj+XDhlCHMnD9FseIlI6cEaHnknnyeWbaOmvpELJw/hlvPGcsIIrT92JFW1DazdXc6qHWV8VFDOul3lbCutxAzWfv/Co24ZaJOJxcwuAn4BpAC/dfcfNzmeDjwBzABKgWvdfZuZzQF+DKQBtcC33X1xk3MXAGPcfeqR4lBiib36hkbe3FTMHz/Yztubi3GHk0b24/LpoZFcg3t3S3aI0k6VHqzh90u38ful26iormdW1gBuPDOLOZOHqLYbKDlYw/Kt+8jZXkbOtn2s232A+sbQ3/YR/btz/PC+TA0ep44ZcNQrT7e5xGJmKcBmYA5QACwHrnP39WFlbgFOcPd/MbP5wOfc/Vozmw7sdffdZjYVWOTuw8POuwK4KjhXiSWBKmvreeofO/ntu/nsLq9mcO905p+cyVUzMlUzkZiqqK7jqX/s5PdLt7FrfxXD+3XnulmZXD0zk+P6dK5/XIoOVPP+1n28n1/KB/ml5BUfAiA9tQvTMvsxY1R/ThrZn2kj+8V0EExbTCynAd939wuD53cAuPuPwsosCsosM7NUoBDI8LAgLbQORAkwzN1rzKwX8ApwM/CMEktiHKqp53+WbOWxJdvYd6g2+C9yNLMnDdY90CWu6hsaeX3DXh5fup1l+aWkdDHOnziYK6YP57yJgzvkPKeiimo+yA8lkvfDEkmv9FRmZvXnlNEDmTV6AMcP7xvXu3i2xfuxDAd2hj0vAE5pqYy715tZOTCQUCI57EpglbvXBM9/CPwcqGztxc3sZkLJh5EjRx7lW5DGRue5lQX8bNEmiipqOG9CBrecl83JWbq9ryRGakoXLpo6lIumDmVrySGeWr6Dv6zYxWvr99K7WyoXTx3CxccP5fSxA9vtzcb2lFfxj637+GDrvk/USHqlp3JyVn+uPTmTU8cMZPLQPqS2wX/kEplYmmsMbVpdarWMmU0B7gPmBs+nAdnu/g0zy2rtxd39UeBRCNVYIo5aPra6YD93PL+GdbsPMC2zHw//8wxmjDr6ESYix2r0oJ7ccfEkvj13AsvyS3lx1W4WrinkmZwCeqalcO6EwZwzPoMzxw1iWL+2eU+euoZGNhVWsGrnflZs28fybWXs2l8FQO+gRnL1zExOGzOQKcPaZiJpKpGJpQDIDHs+AtjdQpmCoCmsL7APwMxGAC8A17t7XlD+NGCGmW0j9F4Gm9lb7n5uvN5EZ1Rb38ivFm/hv9/KY1CvNH4xfxqXnThMq9NKm5Ga0oWzxmVw1rgM/rN+KkvzSnl1XWglh7+v2QPAmIyenDJ6ANNH9mfGqP6MHtiTLgnu/K+pbyC/+BDrdh9g7a7y0GN3OdV1jQBk9E7n5Kz+3HjmaE4ZPYBJQ/u0ywEKiexjSSXUeT8b2EWo8/6f3H1dWJmvAseHdd5f4e7XmFk/4G3gHnf/SwvXzwL+pj6W2MotquDWP61iY2EFV540grs/O5m+PbRgoLQP7s6mvRW8t6WE93JLWLm9jAPV9QD0TEthwpDeTBrah7EZvcga1IORA3oyon/3Y+qnqa5roLC8mj3l1ezYd4htpZVsKznE5r0VbCutpCEYrdW9awqThvbmxMx+TMvsx/TM/mQO6N6m/2Frc30sQZ/JrcAiQsONH3P3dWZ2D5Dj7guA3wFPmlkuoZrK/OD0W4Fs4C4zuyvYN9fdixIVf2f06rpCbnvmI9JTu/Cb62cyZ/JxyQ5JJCpmxsQhfZg4pA9fOmsMjY1OXvFBVu4oY/3uA2zYU8GCj3ZTESSbw3qnp5LRO51+PbrSq1tXeqWn0C01hS5djC4G7lDb0EhtfSOVtQ3sr6rjQFUd+w7VUl5V94lrpXYxMgf0IHtwLy6aOoTxx/VmyrA+jB7Uq13WRiKhCZLyKY2NzgNvbOGXb2zhxBF9efifZ7TZ9mmRY+XulB6qZXtpJTv2HWJPeTXFFTUUVdRQXllHRU09h2rqqa5rwJ2PaxzdunYhLbUL3bum0Kd7V/r1SKN/j64c16cbx/XpxtC+3Rg5oAdD+3ZrF/0ikWhzNRZpH+oaGvn2sx/x4oe7uWrGCO69fGqHHL4pcpiZMahXOoN6pWswSowoscjHauobuPVPq3ht/V6+feEEbjl3bJtu7xWRtkmJRYDQDPovP7mCd7eU8IPLpnDD6VnJDklE2iklFqG2vpEvP7mCJbkl/PSqE7h6ZuaRTxIRaYESSyfX2Oj8x19W8+6WEn5ypZKKiBy7jjFUQY7afYs28sKqXXxr7niuOVlJRUSOnRJLJ/bEsm088nY+Xzh1FF89LzvZ4YhIB6HE0kmt2L6Pe15azwWTBvP9y6Zo9JeIxIwSSydUcrCGW/64kuH9u/Pza6Z12Nm/IpIc6rzvZBoana/9eRX7K+t4/paT6dtd636JSGwpsXQyv3h9M0vzSvnpVScwZVjfZIcjIh2QmsI6kY927ufBN3O58qQRGlYsInGjxNJJVNc18M1nP+K4Pt24+9LJyQ5HRDowNYV1Eve/vpncooM8fuMs9auISFypxtIJrNhexm/eyee6WZmcMz4j2eGISAenxNLB1TU0csfzqxnatzvfuWRSssMRkU5AiaWDe2LZdjbvPcj3Lp1M725qAhOR+FNi6cCKK2p44LXNnD0+Q7cVFpGEUWLpwH7yykaq6xv43qWTtWSLiCRMQhOLmV1kZpvMLNfMbm/meLqZPR0c/8DMsoL9c8xshZmtCb6eH+zvYWZ/N7ONZrbOzH6cyPfTlq3aUcazKwq48czRjM3olexwRKQTSVhiMbMU4CHgYmAycJ2ZNZ1QcRNQ5u7ZwP3AfcH+EuBSdz8euAF4Muycn7n7RGA6cIaZXRzHt9EuuDs/eGk9g3un86/nj0t2OCLSySSyxjILyHX3fHevBZ4C5jUpMw94PNh+DphtZubuq9x9d7B/HdDNzNLdvdLd3wQIrrkSGBH3d9LGvbZ+Lx/u3M83546nV7qmKolIYiUysQwHdoY9Lwj2NVvG3euBcmBgkzJXAqvcvSZ8p5n1Ay4F3ohhzO1OQ6Pz81c3M2ZQT648qdPnWBFJgogTi5m9aGafNbOjTUbN9R57NGXMbAqh5rEvN4ktFfgz8Et3z2/2xc1uNrMcM8spLi6OKvD25KWPdrNpbwW3zR1PaorGZohI4kXzl+cQ8DRQYGb/aWbRNt4XAOErH44AdrdUJkgWfYF9wfMRwAvA9e6e1+S8R4Et7v5ASy/u7o+6+0x3n5mR0TFnn9c1NPJfr21m8tA+XDJ1aLLDEZFOKuLE4u6fB4YCPwQuADaZ2Ttmdr2ZdY/gEsuBcWY22szSgPnAgiZlFhDqnAe4Cljs7h40c/0duMPdl4SfYGb3EkpAX4/0vXRUz+TsZMe+Sr594QS66OZdIpIkUbWVuPsBd3/Y3WcBxwMrgEeAQjN7xMxaXDMk6DO5FVgEbACecfd1ZnaPmV0WFPsdMNDMcoHbgMNDkm8FsoG7zOzD4DE4qMXcSWiU2cpg/5eieU8dRU19A796I5cZo/pz7oSOWSMTkfbhqIYMmdkwQiO4PgvUExrBlQmsNrM73P1nzZ3n7guBhU323R22XQ1c3cx59wL3thTO0byHjuavq3ZTeKCa+646QZMhRSSpoum872pmV5nZQmA7cDnwE2Cou9/k7pcAnwe+G59QpSUNjc6v38ljyrA+nD1uULLDEZFOLpoayx5CtYM/Abe7++pmyrwGlMUiMIncq+sKyS8+xIP/NF21FRFJumgSyzeAZ4Pmqma5exkw+pijkoi5Ow+/nceogT24WCPBRKQNiKbz/jzgU+uum1lPM3ssdiFJNJbmlbK6oJwvnz2WFI0EE5E2IJrEcgPQ3LDi7sD1sQlHovXwW3kM7p3OlTOaLmIgIpIcR2wKM7MBhPpWDOhvZvVhh1OAzwB74xOetGZj4QHeyy3h3y+aQHpqSrLDEREBIutjKSG0rIoD65s57sD3YhmURObxpdtJT+3CdSePTHYoIiIfiySxnEeotrKY0AKQ+8KO1QLbw1YelgQpr6zjxVW7mDdtGP17piU7HBGRjx0xsbj72wBmNhrY4e5NF46UJHh2xU6q6hq44fSsZIciIvIJrSYWMzsJ+NDdGwktXz+wpXkS7r4y9uFJcxoanSeWbefkrP5MGdY32eGIiHzCkWosOcAQoCjYdlpe2l69xwny1qYiduyr5N8vmpDsUEREPuVIiWU0UBy2LW3A75duY0ifblw4ZUiyQxER+ZRWE4u7b29uW5Jne+kh3t1Swm1zxtNVN/ISkTYokj6WiKiPJTGeydlJF4NrZmYeubCISBJE0sfSUr9KOPWxJEB9QyPPrSjgvAmDGdK3W7LDERFpViR9LNJGvL25mL0HarhnnmorItJ2RdzHIsn39PKdDOqVzvkTByc7FBGRFkU8j+VI/S3qY4mvoopq3thYxJfOGq1OexFp0zSPpZ14fuUuGhqda9VpLyJtnOaxtAPuzjPLdzIrawBjMnolOxwRkVa12qbi7tsPrw0WbLf4iOTFzOwiM9tkZrlmdnszx9PN7Ong+AdmlhXsn2NmK8xsTfD1/LBzZgT7c83sl9YB7827csd+8ksOcfXMEckORUTkiKJqrDezoWZ2j5k9Fzx+aGbDIjw3BXgIuBiYDFxnZpObFLsJKHP3bOB+4L5gfwlwqbsfT+iGY0+GnfMwcDMwLnhcFM17ag9eXLWLbl27cPHxuvWwiLR9EScWM5sD5AHXApXB42og18zmRnCJWUCuu+e7ey3wFDCvSZl5wOPB9nPAbDMzd18VtjT/OqBbULsZCvRx92VBzeoJ4PJI31N7UFvfyN9W72bO5CH0So/kLgciIskVzV+qXwK/Bf4tfOl8M/sF8Atg0hHOHw7sDHteAJzSUhl3rzezckKrKpeElbkSWOXuNWY2PLhO+DWbvUevmd1MqGbDyJHt58ZY72wupqyyjs9Nj6hiKCKSdNE0hWUBDzZzP5aHgFERnN/SaLKIy5jZFELNY1+O4pqhne6PuvtMd5+ZkZERQbhtwwsf7mJAzzTOGtd+YhaRzi2axJIDHN/M/uOBVRGcXwCEj5UdATS98+THZcwsFehLcMdKMxsBvABc7+55YeXDe7Sbu2a7daC6jtfX7+XSE4Zq7oqItBvRLEL538D9ZjYOeD/YdyrwFeBTI7yasRwYF9yJchcwH/inJmUWEOqcXwZcBSx2dzezfsDfgTvcfcnhwu6+x8wqzOxU4APgeuBXEcTSLryytpCa+kYun95s656ISJt0NItQ/mcz5f4A/Lm1CwV9JrcCiwhNpnzM3deZ2T1AjrsvAH4HPGlmuYRqKvOD028FsoG7zOyuYN9cdy8ilNh+D3QHXg4eHcJfP9zFqIE9mJbZL9mhiIhELKGLULr7QmBhk313h21XExpp1vS8e4F7W7hmDjA1lnG2BYXl1SzNK+Vr54+jA07NEZEOTItQtlEL1+zBHS49UaPBRKR9iWpiRNChPgsYCaSFH3P3J2IYV6e3cM0eJg7pTfZgLeEiIu1LxInFzCYCLxFqHjOgITi/DqghNDlRYqCwvJqc7WV8c874ZIciIhK1aMawPgCsIDQEuJLQhMiZwIeEJi1KjCxcsweAS07QEi4i0v5E0xR2MnCOux8ys0Yg1d1Xmtm/Exrie0JcIuyEDjeDjdVKxiLSDkVTYzFCNRUILaV/eHJFAaGhwBIDe8qryNlexmdVWxGRdiqaGsta4EQgH/gH8B9m1gD8XyA3DrF1Si+vKQTgEq1kLCLtVDSJ5f8BPYPt7wJ/A94ktEDkNTGOq9P6+5o9TBraRzf0EpF2K+LE4u6LwrbzgclmNoDQ/VOaXfhRorOnvIoV28v41lyNBhOR9ivqG3yYWXdgbPA0T0kldl5dtxdAN/QSkXYtmht9pZvZA4TW8PoIWA3sM7NfmFm3eAXYmby6vpDswb00GkxE2rVoRoU9TGjF4S8RugVwdrD9OUIrH8sxKK+s4/38fcydfFyyQxEROSbRNIVdDVzh7q+F7cs3syLgL8CNMY2sk3lj414aGp25U4YkOxQRkWMSTY3lEKH7qDS1C6iKTTid16vr9jKkTzdOGN432aGIiByTaBLLr4DvBZ33wMcd+XfRgW6ulQzVdQ28vbmYOZOPo0sXLZEvIu3bke4guaDJrnOBXWa2Onh+fHCNnshRe29LCVV1Dcydov4VEWn/jtTHUtrk+V+aPN8aw1g6rUXrCundLZVTRg9MdigiIsfsSDf6+mKiAums6hsaeX3DXmZPHExaajQtkyIibdPRTJAcA0wGHNgQzMKXo7RiexlllXUaDSYiHUY0EyT7mNmzhBacfBH4K7DFzJ4xs94RXuMiM9tkZrlmdnszx9PN7Ong+AdmlhXsH2hmb5rZQTN7sMk515nZGjNbbWavmNmgSN9TW/DGxiK6phhnj89IdigiIjERTdvLLwjdc+U8oHvwmB3se+BIJ5tZCvAQcDGhGs91Zja5SbGbCK09lg3cD9wX7K8mNPrsW02umRrEdZ67n0BoNYBbo3hPSffGhr2cOmYgvdKjrjyKiLRJ0SSWy4Avufvb7l4XPN4CbgYuj+D8WUCuu+e7ey3wFDCvSZl5wOPB9nPAbDMzdz/k7u8RSjDhLHj0NDMD+gC7o3hPSbWt5BB5xYc4f+LgZIciIhIz0SSW7nx6lBiE1g6LZK2w4cDOsOcF/O/Nwj5Vxt3rgXKgxaFS7l4HfAVYQyihTAZ+F0EsbcLijUUASiwi0qFEk1iWAD80sx6Hd5hZT+AHwNIIzm9u5l/TlZEjKfO/hc26Ekos04FhhJrC7mih7M1mlmNmOcXFxRGEG3+LNxaRPbgXowZqGpCIdBzRJJbbgFMITZB828zeIlTrOAX4egTnFwCZYc9H8Olmq4/LBP0nfQnViFoyDcDdDy/f/wxwenMF3f1Rd5/p7jMzMpLfUV5RXccHW0uZrdqKiHQwEScWd19DaFXjfwdygJXAt4Fx7r4ugkssB8aZ2WgzSwPmA01n9i8Abgi2rwIWH+F+L7sI3XDscKaYA2yI5P0k23tbSqhrcDWDiUiHE9FQpKDJ6Q/Ad9z9N0fzQu5eb2a3AouAFOAxd19nZvcAOe6+gFD/yJNmlkuopjI/LIZthDrn08zscmCuu683sx8A75hZHbAd+D9HE1+ivbGxiD6ilSSCAAAQo0lEQVTdUpkxqn+yQxERiamIEou715nZXFrov4iUuy8EFjbZd3fYdjWh5fmbOzerhf2/Bn59LHElWmOj8+bGIs6dMJjUFM22F5GOJZq/as8DV8QrkM7ko4L9lB6qZfYkNYOJSMcTzay8HcB3zewsQn0sh8IPuvt/xTKwjuzNjUV0MThHs+1FpAOKJrH8H6CM0Ez7E5occ0CJJUJvby5mWmY/+vVIS3YoIiIxF3FicffRh7fNrFew72A8gurISg/WsHpXOd+4YHyyQxERiYuoeo7N7OtmtoPQjPhyM9tpZt8IllORCLyzpRh3OHeCmsFEpGOKuMZiZj8htC7YT4Flwe7TgLuBoYTmt8gRvL2pmIE905g6TPe2F5GOKZo+li8RWoTyubB9i81sE/AISixH1NjovLOlhHPGZ+je9iLSYUU7iWJ1C/s0GSMCq3eVs+9QrZrBRKRDiyYhPAF8tZn9XwGejE04Hdvbm4oxg7PGKbGISMcVTVNYOvBPZnYh8H6w7xRCqwr/0cx+ebigu38tdiF2HG9tLuKEEf0Y0FPDjEWk44omsUwktPAkwKjga2HwmBRWrrVFIzutskO1fLhzP187f1yyQxERiato5rGcF89AOrp3c0twh3PUvyIiHZw63RPknc3F9OvRlRNH9Et2KCIicaXEkgDuzrtbijlj7CBSNMxYRDo4JZYE2FJ0kL0Hajhr3KBkhyIiEndKLAnw7pYSAM5UYhGRTkCJJQHe3VLMmIyejOjfI9mhiIjEnRJLnNXUN/B+filna1KkiHQSSixxtmJbGdV1jepfEZFOQ4klzt7ZUkLXFOPUMQOTHYqISEIkNLGY2UVmtsnMcs3s9maOp5vZ08HxD8wsK9g/0MzeNLODZvZgk3PSzOxRM9tsZhvN7MrEvJvIvLulmOkj+9MzPZpFDkRE2q+EJRYzSwEeAi4GJgPXmdnkJsVuAsrcPRu4H7gv2F8N3AV8q5lL3wkUufv44LpvxyH8o1JysIZ1uw9wtprBRKQTSWSNZRaQ6+757l4LPAXMa1JmHvB4sP0cMNvMzN0Puft7hBJMUzcCPwJw90Z3L4lP+NFbkhsKRasZi0hnksjEMhzYGfa8INjXbBl3ryd0C+QWOyfM7PD6KD80s5Vm9qyZHRe7kI/Nu1tK6Nu9K1OH626RItJ5JDKxNLeWSdOVkCMpEy4VGAEscfeTCN0y+WfNvrjZzWaWY2Y5xcXFkcR7TNydJbklnJE9UMu4iEinksjEUgBkhj0fAexuqYyZpQJ9gX2tXLMUqAReCJ4/C5zUXEF3f9TdZ7r7zIyM+DdNbS05xJ7yas7IVv+KiHQuiUwsy4FxZjbazNKA+cCCJmUWADcE21cBi929xRpLcOwl4Nxg12xgfSyDPlqH+1fOVGIRkU4mYWNg3b3ezG4FFgEpwGPuvs7M7gFy3H0B8DvgSTPLJVRTmX/4fDPbBvQB0szscmCuu68H/iM45wGgGPhiot5Ta97LLWF4v+6MHKBlXESkc0no5Ap3XwgsbLLv7rDtauDqFs7NamH/duDs2EV57BoanaV5pVwydShm6l8Rkc5FM+/jYM2uciqq6zlD81dEpBNSYomDw/0rp4/VMi4i0vkoscTBe1tKmDS0D4N6pSc7FBGRhFNiibGq2gZWbC/jzGzVVkSkc1JiibHl2/ZR29Co+Ssi0mkpscTYktzQMvmzRg9IdigiIkmhxBJjS/NKmZ7Znx5pWiZfRDonJZYY2l9Zy9rd5Zyu/hUR6cSUWGLo/fx9uMPpY9W/IiKdlxJLDC3LK6F71xSmZfY7cmERkQ5KiSWGluSVcvLoAaSl6mMVkc5LfwFjpOhANblFBzXbXkQ6PSWWGFmWXwrAGepfEZFOToklRpbkltCnWyqTh/VJdigiIkmlxBIjS/NKOXWMbkMsIqLEEgM791VSUFalZVxERFBiiYmleVomX0TkMCWWGFiSW0pG73SyB/dKdigiIkmnxHKM3J1l+aWcNmagbkMsIoISyzHLKz5IcUWNmsFERAIJTSxmdpGZbTKzXDO7vZnj6Wb2dHD8AzPLCvYPNLM3zeygmT3YwrUXmNna+L6DT1uWF5q/cpoSi4gIkMDEYmYpwEPAxcBk4Dozm9yk2E1AmbtnA/cD9wX7q4G7gG+1cO0rgIPxiPtIluWXMqxvN0YO6JGMlxcRaXMSWWOZBeS6e7671wJPAfOalJkHPB5sPwfMNjNz90Pu/h6hBPMJZtYLuA24N36hN6+x0VmWV8ppYwepf0VEJJDIxDIc2Bn2vCDY12wZd68HyoEjtTH9EPg5UNlaITO72cxyzCynuLg4mrhbtGlvBWWVdWoGExEJk8jE0ty/9H4UZf63sNk0INvdXzjSi7v7o+4+091nZmRkHKl4RNS/IiLyaYlMLAVAZtjzEcDulsqYWSrQF9jXyjVPA2aY2TbgPWC8mb0Vo3iPaGleKaMG9mB4v+6JekkRkTYvkYllOTDOzEabWRowH1jQpMwC4IZg+ypgsbu3WGNx94fdfZi7ZwFnApvd/dyYR96Mhkbng62h+SsiIvK/UhP1Qu5eb2a3AouAFOAxd19nZvcAOe6+APgd8KSZ5RKqqcw/fH5QK+kDpJnZ5cBcd1+fqPibWr/7ABXV9WoGExFpImGJBcDdFwILm+y7O2y7Gri6hXOzjnDtbcDUYw4yQofXB1ONRUTkkzTz/igtyy9lbEZPBvfpluxQRETaFCWWo1DX0MjyrfvUDCYi0gwllqOwZlc5h2obOG2M7r8iItKUEstRODx/5dQxA5IciYhI26PEchTezy9lwnG9GdgrPdmhiIi0OUosUaqtbyRnW5n6V0REWqDEEqXVBfupqmvgVA0zFhFplhJLlJbllWKm/hURkZYosURpWX4pE4f0oV+PtGSHIiLSJimxRKGmvoEV28s0215EpBVKLFFYtWM/NfWN6rgXEWmFEksUluWV0sVg1mj1r4iItESJJQrL8kuZMqwvfbt3TXYoIiJtVkJXN27vpg7ry7B+WnRSRKQ1SixRuPvSyckOQUSkzVNTmIiIxJQSi4iIxJQSi4iIxJQSi4iIxFRCE4uZXWRmm8ws18xub+Z4upk9HRz/wMyygv0DzexNMztoZg+Gle9hZn83s41mts7Mfpy4dyMiIs1JWGIxsxTgIeBiYDJwnZk1HWZ1E1Dm7tnA/cB9wf5q4C7gW81c+mfuPhGYDpxhZhfHI34REYlMImsss4Bcd89391rgKWBekzLzgMeD7eeA2WZm7n7I3d8jlGA+5u6V7v5msF0LrARGxPNNiIhI6xKZWIYDO8OeFwT7mi3j7vVAORDRwlxm1g+4FHjjmCMVEZGjlsgJktbMPj+KMp++sFkq8Gfgl+6e30KZm4Gbg6cHzWzTka7bgkFAyVGeG0+KKzqKKzqKKzodNa5RkRRKZGIpADLDno8AdrdQpiBIFn2BfRFc+1Fgi7s/0FIBd380KHdMzCzH3Wce63ViTXFFR3FFR3FFp7PHlcimsOXAODMbbWZpwHxgQZMyC4Abgu2rgMXu3mqNxczuJZSAvh7jeEVE5CgkrMbi7vVmdiuwCEgBHnP3dWZ2D5Dj7guA3wFPmlkuoZrK/MPnm9k2oA+QZmaXA3OBA8CdwEZgpZkBPOjuv03U+xIRkU9K6CKU7r4QWNhk391h29XA1S2cm9XCZZvrl4mnY25OixPFFR3FFR3FFZ1OHZcdoaVJREQkKlrSRUREYkqJpQVHu/xMnGPKDJa22RAsYfNvzZQ518zKzezD4HF3c9eKQ2zbzGxN8Jo5zRw3M/tl8HmtNrOTEhDThLDP4UMzO2BmX29SJiGfl5k9ZmZFZrY2bN8AM3vNzLYEX/u3cO4NQZktZnZDc2ViHNdPg2WSVpvZC8EcsebObfV7Hoe4vm9mu8K+V5e0cG6rv7txiOvpsJi2mdmHLZwbz8+r2b8NSfsZc3c9mjwIDS7IA8YAacBHwOQmZW4Bfh1szweeTkBcQ4GTgu3ewOZm4joX+FsSPrNtwKBWjl8CvEyoT+xU4IMkfE8LgVHJ+LyAs4GTgLVh+34C3B5s3w7c18x5A4D84Gv/YLt/nOOaC6QG2/c1F1ck3/M4xPV94FsRfJ9b/d2NdVxNjv8cuDsJn1ezfxuS9TOmGkvzjnr5mXgG5e573H1lsF0BbODTqxe0VfOAJzzkfaCfmQ1N4OvPBvLcfXsCX/Nj7v4On56TFf4z9DhweTOnXgi85u773L0MeA24KJ5xufurHlr5AuB9krBMUgufVyQi+d2NS1zB7/81hCZrJ1QrfxuS8jOmxNK8uC4/EwtB09t04INmDp9mZh+Z2ctmNiVBITnwqpmtsNAqB01F8pnG03xa/oVPxucFcJy774HQHwZgcDNlkv253UioptmcI33P4+HWoInusRaadZL5eZ0F7HX3LS0cT8jn1eRvQ1J+xpRYmhe35Wdiwcx6AX8Bvu7uB5ocXkmouedE4FfAi4mICTjD3U8itHr1V83s7CbHk/l5pQGXAc82czhZn1ekkvm53QnUA39sociRvuex9jAwFpgG7CHU7NRU0j4v4Dpar63E/fM6wt+GFk9rZt8xfWZKLM2LZvmZw2uVRbr8zDExs66EfnD+6O7PNz3u7gfc/WCwvRDoamaD4h2Xu+8OvhYBLxBqkggXyWcaLxcDK919b9MDyfq8AnsPNwcGX4uaKZOUzy3owP0s8HkPGuKbiuB7HlPuvtfdG9y9EfhNC6+XrM8rFbgCeLqlMvH+vFr425CUnzEllubFZfmZYxW04f4O2ODu/9VCmSGH+3rMbBah73FpnOPqaWa9D28T6vxd26TYAuB6CzkVKD9cRU+AFv+TTMbnFSb8Z+gG4K/NlFkEzDWz/kHTz9xgX9yY2UXAfwCXuXtlC2Ui+Z7HOq7wPrnPtfB6kfzuxsMFwEZ3L2juYLw/r1b+NiTnZyweIxQ6woPQKKbNhEaY3Bnsu4fQLxtAN0JNK7nAP4AxCYjpTEJV1NXAh8HjEuBfgH8JytwKrCM0GuZ94PQExDUmeL2Pgtc+/HmFx2WEbvSWB6wBZibo+9iDUKLoG7Yv4Z8XocS2B6gj9B/iTYT65N4AtgRfBwRlZwK/DTv3xuDnLBf4YgLiyiXU5n74Z+zw6MdhwMLWvudxjuvJ4GdnNaE/mEObxhU8/9TvbjzjCvb//vDPVFjZRH5eLf1tSMrPmGbei4hITKkpTEREYkqJRUREYkqJRUREYkqJRUREYkqJRUREYkqJRUREYkqJRUREYkqJRaQNMLOJwf00qoJ7dpxuZnVmdk6yYxOJlhKLSJKZ2URCqzcsJ3SvjzsIrTmVSmgmtUi7opn3IklmZq8CFe5+Zdi+J4Cz3T0raYGJHKXUZAcg0pmZWSYwh9BS8OFqCK0rJdLuqClMJLlOInTPk6ZNXpMILSQo0u4osYgkVwOh+7T3OLzDzGYAZ6Aai7RTSiwiyZVDqNnrp2Y2NrgXyh+CY6qxSLukznuRJDOza4CfABnAm8C7wDfdvbn7k4u0eUosIm1IcCfAV4A8d78l2fGIHA2NChNJIjM7ExgCrCR0t79vEBoh9sVkxiVyLJRYRJJrCHAfMBwoBt4CZrj77mQGJXIs1BQmIiIxpVFhIiISU0osIiISU0osIiISU0osIiISU0osIiISU0osIiISU0osIiISU0osIiISU/8f5I0liq7ABCEAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "kernel2d_g_marginal = scipy.stats.gaussian_kde( values.T[1] ) \n", - "Points = numpy.linspace(0, 20, 100)\n", - "Values = kernel2d_g_marginal.pdf(Points)\n", - "plt.plot(Points, Values, )\n", - "plt.ylabel( \"probability\",fontsize = 14 )\n", - "plt.xlabel( \"$g$\",fontsize = 14 )\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## What is the posterior using a different MCMC algorithm ? (MCMC Hammer)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# We can combine a likelihood function with a prior function.\n", - "\n", - "# Create a log-prior function. \n", - "def lnprior(theta):#accepts the model parameters (theta)\n", - " v, g = theta #set a,b to be the parms\n", - " \n", - " \n", - " if 0 < v < 100. and 0 < g < 100 : #we are assuming a \"uniform prior\" on all parameters, which is the same as just giving each parameter bounds.\n", - " return 0.0 #if you try parameters inside the bounds, return a probability of 1 (log(1)=0)\n", - " return -np.inf #if you try parameters outside the bounds, return 0 (log(0)=-inf)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 117, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "starting_positions [[23.15946624 69.64553637]\n", - " [92.71758871 84.65478322]\n", - " [23.3531457 23.80077855]\n", - " [ 0.24234001 74.30811351]\n", - " [30.15350612 72.4989255 ]\n", - " [19.57333016 87.9187752 ]\n", - " [ 3.18685793 90.83499393]\n", - " [97.68780287 29.10192468]\n", - " [ 3.80535856 11.74760144]\n", - " [69.24943206 21.61243051]]\n" - ] - } - ], - "source": [ - "#Set up the MCMC to sample the full parameter space\n", - "\n", - "#number of parameters to fit (3); number of individual \"walkers\" that randomly sample the space. Choose any number, the higher the slower.\n", - "ndim, nwalkers = 3, 100 \n", - "\n", - "#Start the walkers randomly in the domain box\n", - "v_starts = numpy.random.uniform( 0, 100, nwalkers)\n", - "g_starts = numpy.random.uniform( 0, 100, nwalkers)\n", - "\n", - "starting_positions = numpy.vstack([ v_starts, g_starts]).T\n", - "print ('starting_positions', starting_positions[:10])\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 118, - "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "could not broadcast input array from shape (100,2) into shape (100,3)", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 9\u001b[0m )\n\u001b[0;32m 10\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 11\u001b[1;33m \u001b[0moutput\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msampler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun_mcmc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstarting_positions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m500\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m#run the MCMC with the starting positions we defined and 500 sampling points per walker\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\emcee\\sampler.py\u001b[0m in \u001b[0;36mrun_mcmc\u001b[1;34m(self, pos0, N, rstate0, lnprob0, **kwargs)\u001b[0m\n\u001b[0;32m 170\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 171\u001b[0m for results in self.sample(pos0, lnprob0, rstate0, iterations=N,\n\u001b[1;32m--> 172\u001b[1;33m **kwargs):\n\u001b[0m\u001b[0;32m 173\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 174\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\emcee\\ensemble.py\u001b[0m in \u001b[0;36msample\u001b[1;34m(self, p0, lnprob0, rstate0, blobs0, iterations, thin, storechain, mh_proposal)\u001b[0m\n\u001b[0;32m 277\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mstorechain\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mi\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mthin\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 278\u001b[0m \u001b[0mind\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mi0\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mthin\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 279\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_chain\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mind\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mp\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 280\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lnprob\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mind\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlnprob\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 281\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mblobs\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mValueError\u001b[0m: could not broadcast input array from shape (100,2) into shape (100,3)" - ] - } - ], - "source": [ - "#set up the MCMC sampler\n", - "sampler = emcee.EnsembleSampler(nwalkers, \n", - " ndim, \n", - " loglikelihood_alldata_2d, #the likelihood function to maximize \n", - " #args=(example_data_1D['x'], #the arguments passed to the likelihood functions (other than the model parameters)\n", - " # example_data_1D['y'], \n", - " # example_data_1D['error'],\n", - " # my_model)\n", - " )\n", - "\n", - "output = sampler.run_mcmc(starting_positions, 500) #run the MCMC with the starting positions we defined and 500 sampling points per walker\n" - ] - }, - { - "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": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Day_4/day_4_student_completed_workbook_probability_theory.ipynb b/Day_4/day_4_student_completed_workbook_probability_theory.ipynb deleted file mode 100644 index 63dd64b..0000000 --- a/Day_4/day_4_student_completed_workbook_probability_theory.ipynb +++ /dev/null @@ -1,708 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!pip install nestle corner emcee" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Imports" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import warnings\n", - "import numpy\n", - "import scipy\n", - "import scipy.stats\n", - "import pandas\n", - "import matplotlib.pyplot as plt\n", - "import Library_GraphTwoDimensionDensityColorMap\n", - "warnings.simplefilter('ignore')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Read in the data:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data = pandas.read_csv( \"RandomVariable_Generated_Data.dat\",sep=' ',header=0)\n", - "print ( data )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Some useful helper functions:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def plot_1D_function( \n", - " Functions = None,\n", - " minp = None,\n", - " maxp = None,\n", - " nump = None,\n", - " Labels = None,\n", - " ):\n", - " \n", - " \n", - " if None in [Functions, minp, maxp, nump]: \n", - " raise Exception(\"arg missing...\" + str([Function, minp, maxp, nump]))\n", - " \n", - " if Labels is None:\n", - " Labels = [None]*len(Functions)\n", - " \n", - " for Label,Function in zip(Labels,Functions):\n", - "\n", - " TrialPoints = numpy.linspace(-20, 20, 100)\n", - " Values = []\n", - " for TrailPoint in TrialPoints:\n", - " Values.append(Function ( TrailPoint) )\n", - "\n", - " plt.plot(TrialPoints, Values,label=Label)\n", - " if Label is not None:\n", - " plt.legend()\n", - " \n", - " \n", - " \n", - " return" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Marginal Distributions:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Make a histogram of the data from column A" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Adata = data['A']\n", - "plt.hist(Adata, density=True,bins=50,label='A')\n", - "plt.xlabel('A')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Make a histogram of the data from column B" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Bdata = data['B']\n", - "plt.hist(Bdata, density=True,bins=50,label='B')\n", - "plt.xlabel('B')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Approximate the data from column A as a univariate gaussian: (MARGINAL)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "A_standard_deviation = numpy.sqrt( numpy.var( Adata ) )\n", - "A_mean = numpy.mean(Adata)\n", - "\n", - "ATrialPoints = numpy.linspace(-20, 20, 100)\n", - "AValuePoints = scipy.stats.norm.pdf(ATrialPoints, loc = A_mean, scale = A_standard_deviation )\n", - "\n", - "plt.hist(Adata, density=True, bins=50,label='Binned A')\n", - "plt.plot(ATrialPoints, AValuePoints,label='Gaussian A')\n", - "plt.xlabel('A')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Approximate the data from column B as a univariate gaussian: (MARGINAL)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# STUDENTS WILL WORK ON THIS CELL\n", - "B_standard_deviation = numpy.sqrt( numpy.var( Bdata ) )\n", - "B_mean = numpy.mean(Bdata)\n", - "\n", - "BTrialPoints = numpy.linspace(-20, 20, 100)\n", - "BValuePoints = scipy.stats.norm.pdf(BTrialPoints, loc = B_mean, scale = B_standard_deviation )\n", - "\n", - "plt.hist(Bdata, density=True, bins=50,label='Binned B')\n", - "plt.plot(BTrialPoints, BValuePoints,label='Gaussian B')\n", - "plt.xlabel('B')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Conditional distributions" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### (1) Approximate the joint probability density function of A and B with a multivariate gaussian\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "ABdata = numpy.vstack( ( data['A'], data['B'] ) ).T\n", - "print(ABdata)\n", - "\n", - "ABMean = numpy.mean( ABdata, axis = 0 )\n", - "print (ABMean)\n", - "\n", - "ABCovarianceMatrix = numpy.cov( ABdata, rowvar = False )\n", - "print (ABCovarianceMatrix)\n", - "\n", - "def jointGaussian(ABpoint):\n", - " return scipy.stats.multivariate_normal.pdf( ABpoint, ABMean, ABCovarianceMatrix )\n", - "print ( jointGaussian( [0,0]) )\n", - "\n", - "Library_GraphTwoDimensionDensityColorMap.Main(\n", - " Function = jointGaussian,\n", - " DomainMinimumPoint = numpy.array([-5, -10]),\n", - " DomainMaximumPoint = numpy.array([10, 15]),\n", - " ShowContours = True,\n", - " PrintExtra = False,\n", - " #PlotThreeDimensional = True\n", - " )\n", - "plt.title(\"Probability Density of A & B\", fontsize=40)\n", - "plt.ylabel('B',fontsize=40)\n", - "plt.xlabel('A',fontsize=40)\n", - "plt.draw()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### (1) Fixing A = 0, plot the unnormalized conditional probability density of B:\n", - "P(B|A)\n", - "=====\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def BdensityConditionalOnA0_unnormalized(Bpoint):\n", - " return jointGaussian( [0, Bpoint] )\n", - "\n", - "\n", - "plot_1D_function( \n", - " Functions = [BdensityConditionalOnA0_unnormalized],\n", - " minp = -20,\n", - " maxp = 20,\n", - " nump = 100,\n", - " Labels = ['P(B|A=0)']\n", - " )\n", - "plt.ylabel('P(B|A)')\n", - "plt.xlabel('B')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### (1) Fixing A = 0, plot the normalized probability density function of B:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "FullIntegrationResult = scipy.integrate.quad( \n", - " BdensityConditionalOnA0_unnormalized, \n", - " -100, \n", - " 100, \n", - " full_output = False\n", - " )[0]\n", - "print (FullIntegrationResult )\n", - "\n", - "def BdensityConditionalOnA0_normalized(Bpoint):\n", - " return BdensityConditionalOnA0_unnormalized(Bpoint) / FullIntegrationResult\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plot_1D_function( \n", - " Functions = [BdensityConditionalOnA0_unnormalized, BdensityConditionalOnA0_normalized],\n", - " minp = -20,\n", - " maxp = 20,\n", - " nump = 100,\n", - " Labels = ['UnNormed','Normed']\n", - " )\n", - "plt.ylabel('P(B|A)')\n", - "plt.xlabel('B')\n", - "plt.legend()\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### (1) Fixing A = 7, plot the probability density of B:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# STUDENTS WILL WORK ON THIS CELL\n", - "\n", - "A_fixed_values=[1,3,5,7]\n", - "\n", - "def generateBdensityConditionalOnA_unormalized(Afixed):\n", - " def BdensityConditionalOnA_unormalized(Bpoint):\n", - " return jointGaussian( [Afixed, Bpoint] )\n", - " return BdensityConditionalOnA_unormalized\n", - "\n", - "function_list=[generateBdensityConditionalOnA_unormalized(A) for A in A_fixed_values]\n", - "\n", - "plot_1D_function( \n", - " Functions = function_list,\n", - " minp = -20,\n", - " maxp = 20,\n", - " nump = 100,\n", - " Labels = ['P(B|A=%i)'%i for i in A_fixed_values]\n", - " )\n", - "plt.ylabel('P(B|A)')\n", - "plt.xlabel('B')\n", - "plt.legend()\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### (1) Fixing B = 0 plot the unnormalized probability density of A:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def AdensityConditionalOnB0_unnormalized(Apoint):\n", - " return jointGaussian( [Apoint, 0] )\n", - "\n", - "\n", - "plot_1D_function( \n", - " Functions = [AdensityConditionalOnB0_unnormalized],\n", - " minp = -20,\n", - " maxp = 20,\n", - " nump = 100,\n", - " Labels = ['P(A|B=0)']\n", - " )\n", - "plt.ylabel('P(A|B)')\n", - "plt.xlabel('A')\n", - "plt.legend()\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### (1) Fixing B = 7, plot the unnormalized probability density of A:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def AdensityConditionalOnB7_unnormalized(Apoint):\n", - " return jointGaussian( [Apoint, 7] )\n", - "\n", - "\n", - "plot_1D_function( \n", - " Functions = [AdensityConditionalOnB7_unnormalized],\n", - " minp = -20,\n", - " maxp = 20,\n", - " nump = 100,\n", - " Labels = ['P(A|B=7)']\n", - " )\n", - "plt.ylabel('P(A|B)')\n", - "plt.xlabel('A')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### (2) Approximate the joint probability density function of A and B with a kernel density estimation\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "kernel_object = scipy.stats.gaussian_kde( ABdata.T ) #takes in points sideways...\n", - "\n", - "kernel_pdf = kernel_object.pdf\n", - "\n", - "Library_GraphTwoDimensionDensityColorMap.Main(\n", - " Function = kernel_pdf,\n", - " DomainMinimumPoint = numpy.array([-10, -10]),\n", - " DomainMaximumPoint = numpy.array([20, 20]),\n", - " ShowContours = True,\n", - " PluginPointCount = 10000,\n", - " PrintExtra = False,\n", - " )\n", - "plt.ylabel(\"B\",fontsize=14)\n", - "plt.xlabel(\"A\",fontsize=14)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### (2) Approximate the conditional probability density function of B, fixing A to 5:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# STUDENTS WILL WORK ON THIS CELL\n", - "def AdensityConditionalOnB5_unnormalized_kde(Apoint):\n", - " return (kernel_pdf( [ Apoint, 5]) )\n", - "\n", - "\n", - "plot_1D_function( \n", - " Functions = [AdensityConditionalOnB5_unnormalized_kde],\n", - " minp = -20,\n", - " maxp = 20,\n", - " nump = 100,\n", - " Labels = ['P(B|A=5)']\n", - " )\n", - "plt.ylabel('P(B|A)')\n", - "plt.xlabel('B')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### (2) Approximate the conditional probability density function of A, fixing B to 5:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def AdensityConditionalOnB5_unnormalized_kde(Bpoint):\n", - " return (kernel_object.pdf( [ 5, Bpoint]) )\n", - "\n", - "\n", - "plot_1D_function( \n", - " Functions = [AdensityConditionalOnB5_unnormalized_kde],\n", - " minp = -20,\n", - " maxp = 20,\n", - " nump = 100,\n", - " Labels = ['P(A|B=5)']\n", - " )\n", - "plt.ylabel('P(A|B)')\n", - "plt.xlabel('A')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Note how the double peaked nature of the gaussian could be missed assuming gaussianity\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Final Excercise: Code up your own 1D kernel density estimation function against the data in Column A:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def Triangle_Kernel( Point, Mean, StandardDeviation):\n", - " if Point < Mean-StandardDeviation or Point>Mean+StandardDeviation:\n", - " return 0\n", - " elif Point >= Mean-StandardDeviation and Point <= Mean:\n", - " return((Point-Mean)/StandardDeviation**2+1/StandardDeviation)\n", - " elif Point >=Mean and Point <= Mean+StandardDeviation:\n", - " return(-(Point-Mean)/StandardDeviation**2+1/StandardDeviation)\n", - " else: \n", - " print('What happened?')\n", - " return \n", - "\n", - "plt.plot(numpy.arange(0,10,.01),[Triangle_Kernel(x,5,1) for x in numpy.arange(0,10,.01)],label='Triangle')\n", - "plt.plot(numpy.arange(0,10,.01),scipy.stats.norm.pdf( numpy.arange(0,10,.01), 5, .5 ),label=\"Gaussian\")\n", - "plt.legend()\n", - "plt.xlabel('A')\n", - "plt.ylabel('Probability Density')\n", - "plt.show()\n", - "\n", - "def GenerateKernelDensityEstimationFunction1D_Triangle( Data ):\n", - " def KDE( Value ):\n", - " Result = 0\n", - " Bandwidth = numpy.sqrt( numpy.var(Data) ) / 6 #Bandwidth calculation\n", - " for Datapoint in Data:\n", - " Result += Triangle_Kernel( Value, Datapoint, Bandwidth ) #\n", - " Probability = Result / len(Data)\n", - " return Probability\n", - " \n", - " return KDE" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Personal_KDE_Function_triangle = GenerateKernelDensityEstimationFunction1D_Triangle( Adata )\n", - "\n", - "scipy_KDE_Function = scipy.stats.gaussian_kde( Adata ).pdf\n", - "\n", - "plot_1D_function( \n", - " Functions = [Personal_KDE_Function_triangle, scipy_KDE_Function],\n", - " minp = -20,\n", - " maxp = 20,\n", - " nump = 100,\n", - " Labels = ['Personal_Triangle','Scipy']\n", - " )\n", - "plt.ylabel('P(A)')\n", - "plt.xlabel('A')\n", - "plt.legend()\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# STUDENTS WILL WORK ON THIS CELL\n", - "def UnivariateGaussian( Point, Mean, StandardDeviation):\n", - " return scipy.stats.norm.pdf( Point, Mean, StandardDeviation )\n", - "\n", - "\n", - "def GenerateKernelDensityEstimationFunction1D( Data ):\n", - " \n", - " def KDE( Value ):\n", - " Result = 0\n", - " Bandwidth = numpy.sqrt( numpy.var(Data) ) / 6 \n", - " for Datapoint in Data:\n", - " Result += UnivariateGaussian( Value, Datapoint, Bandwidth ) \n", - " Probability = Result / len(Data)\n", - " return Probability\n", - " \n", - " return KDE\n", - " \n", - " \n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Personal_KDE_Function = GenerateKernelDensityEstimationFunction1D( Adata )\n", - "\n", - "scipy_KDE_Function = scipy.stats.gaussian_kde( Adata ).pdf\n", - "\n", - "plot_1D_function( \n", - " Functions = [Personal_KDE_Function, scipy_KDE_Function],\n", - " minp = -20,\n", - " maxp = 20,\n", - " nump = 100,\n", - " Labels = ['Personal','Scipy']\n", - " )\n", - "plt.ylabel('P(A)')\n", - "plt.xlabel('A')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Discuss choice of bandwidth, delta functions, and understand the overfitting problem:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# STUDENTS WILL WORK ON THIS CELL\n", - "def GenerateKernelDensityEstimationFunction1D_bad( Data ):\n", - " \n", - " def KDE( Value ):\n", - " Result = 0\n", - " Bandwidth = numpy.sqrt( numpy.var(Data) ) / 100\n", - " for Datapoint in Data:\n", - " Result += UnivariateGaussian( Value, Datapoint, Bandwidth )\n", - " Probability = Result / len(Data)\n", - " return Probability\n", - " \n", - " return KDE\n", - "\n", - "Personal_KDE_Function_bad = GenerateKernelDensityEstimationFunction1D_bad( Adata )\n", - "\n", - "scipy_KDE_Function = scipy.stats.gaussian_kde( Adata ).pdf\n", - "\n", - "plot_1D_function( \n", - " Functions = [Personal_KDE_Function_bad, scipy_KDE_Function],\n", - " minp = -20,\n", - " maxp = 20,\n", - " nump = 100,\n", - " Labels = ['Personal_Bad','Scipy']\n", - " )\n", - "plt.xlabel('P(A)')\n", - "plt.ylabel('A')\n", - "plt.legend()\n", - "plt.show()\n" - ] - }, - { - "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": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Day_4/day_4_student_empty_workbook_likelihood_analysis.ipynb b/Day_4/day_4_student_empty_workbook_likelihood_analysis.ipynb deleted file mode 100644 index 94c7e81..0000000 --- a/Day_4/day_4_student_empty_workbook_likelihood_analysis.ipynb +++ /dev/null @@ -1,829 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Imports" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy\n", - "import scipy\n", - "import corner\n", - "import nestle\n", - "import pandas\n", - "import warnings\n", - "import scipy\n", - "import scipy.stats\n", - "import matplotlib.pyplot as plt\n", - "\n", - "#---------------------------------------------------------------------\n", - "import Library_GraphTwoDimensionDensityColorMap\n", - "#---------------------------------------------------------------------\n", - "warnings.simplefilter('ignore')\n", - "print (\"done importing\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# What does the theorist give you?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Both data set cases:\n", - "def my_model( v, g, t):\n", - " return v*t -.5*g*t**2\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Case 1--Height Error Only" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## What does the experimentalist give you?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### With a perfect stopwatch, but not so perfect meter stick?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#(YOU NEED THIS CODE)\n", - "#The experimentalist has a perfect stopwatch,\n", - "# and knows his height measurement error\n", - "\n", - "# Read in a file using pandas called \"1D_Generated_Data.dat\".\n", - "# It is separated by spaces, and has a header in line 0\n", - "\n", - "data_with_height_error_only = \n", - "height_standard_deviation = 10\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#These should run without error\n", - "assert data_with_height_error_only['time'][0] == .1\n", - "assert data_with_height_error_only['height'][6] == 119.81201713924483" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#plot the instrument error function of height\n", - "Points = numpy.linspace(-50, 50, 100)\n", - "Values = scipy.stats.norm.pdf(Points, 0, 10)\n", - "plt.plot(Points, Values, )\n", - "plt.title(\"Probability Density Result of Measuring Zero\", fontsize=14)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Plot the 1D-error data\n", - "times_1d = data_with_height_error_only['time']\n", - "heights_1d = data_with_height_error_only['height']\n", - "plt.errorbar( times_1d, heights_1d, yerr = [height_standard_deviation]*len(times_1d) , fmt = \".\")\n", - "plt.ylabel('Height (Meters)',fontsize=14)\n", - "plt.xlabel('Time (Seconds)',fontsize=14)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## What is the correct single datapoint likelihood function?\n", - "### \"Likelihood function\" fixes the observation values and allows the parameters to vary:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def probability_of_single_measurement_1D(\n", - " observed_time, \n", - " observed_height,\n", - " v,\n", - " g,\n", - " ):\n", - "\n", - " ##########################################################\n", - " ##########################################################\n", - " #If we assume a velocity and gravitational constant\n", - " # What height should we observe? \n", - "\n", - " assume_model_is_correct_height = #Fill in this\n", - " \n", - " # What is the probability of a measuring an observed height and observed time?\n", - " # (students do this)\n", - " def gaussian_centered_on_model( possible_height_measurement ):\n", - " #Create a 1D Gaussian with a mean=assume_model_is_correct_height,\n", - " #sigma=height_standard_deviation, which returns a probability for a \n", - " #given possible_height_measurement\n", - " \n", - " result = #Fill in this\n", - " return result\n", - " \n", - " \n", - " #Return the associated probability by plugging in the observed height:\n", - " likelihood_value = gaussian_centered_on_model( \n", - " possible_height_measurement = observed_height\n", - " )\n", - " #\n", - " ##########################################################\n", - " ##########################################################\n", - " \n", - " \n", - " return(likelihood_value)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Test for the correct number in the 1D-height-error-only case:\n", - "print( probability_of_single_measurement_1D (\n", - " times_1d[0],\n", - " heights_1d[0],\n", - " v = 100,\n", - " g = 20\n", - " ) )\n", - "#Expected result: 0.017829561071290335" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#(provided by instructors below):\n", - "def generate_single_data_point_likelihood_function(\n", - " observed_time,\n", - " observed_height,\n", - " probability_of_single_measurement_function,\n", - " ):\n", - " def single_data_point_likelihood_function(\n", - " vg_vector, \n", - " ):\n", - " v = vg_vector[0]\n", - " g = vg_vector[1]\n", - " likelihood_value = probability_of_single_measurement_function(\n", - " observed_time = observed_time,\n", - " observed_height= observed_height,\n", - " v = v,\n", - " g = g,\n", - " )\n", - " return likelihood_value\n", - " \n", - " return single_data_point_likelihood_function\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Test for the height-only error case:\n", - "likelihood_function_for_first_datapoint1d = generate_single_data_point_likelihood_function(\n", - " observed_time = times_1d[0],\n", - " observed_height = heights_1d[0],\n", - " probability_of_single_measurement_function = probability_of_single_measurement_1D,\n", - " )\n", - "print ( likelihood_function_for_first_datapoint1d( [100, 20]) )\n", - "#Expected result: likelihood_fn(100, 20) == 0.017829561071290335" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## What is the all-data log-likelihood function?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#(provided below by instructors)\n", - "#numpyize the data:\n", - "# Each row of the datapoints is a datapoint\n", - "datapoints1d = numpy.vstack([times_1d, heights_1d]).T\n", - "\n", - "# Looks like:\n", - "# [\n", - "# [time0, height0 ],\n", - "# [time1, height1 ],\n", - "# ...\n", - "# [timeN, heightN ],\n", - "# ]\n", - "#\n", - "\n", - "def generate_log_likelihood_function_fixing_all_observations(\n", - " datapoints = None, \n", - " probability_of_single_measurement_function = None,\n", - " ): \n", - " #Make a list where each element is a single-datapoint likelihood function\n", - " single_datapoint_likelihood_function_list = []\n", - " for datapoint in datapoints:\n", - " single_datapoint_likelihood_function = generate_single_data_point_likelihood_function(\n", - " observed_time = datapoint[0],\n", - " observed_height = datapoint[1],\n", - " probability_of_single_measurement_function = probability_of_single_measurement_function,\n", - " )\n", - " single_datapoint_likelihood_function_list.append( single_datapoint_likelihood_function )\n", - "\n", - " #Define a log-likelihood funciton for all the data using the list defined above:\n", - " def log_likelihood_function_fixing_all_observations(parameters):\n", - " result = 0\n", - " for single_datapoint_likelihood_function in single_datapoint_likelihood_function_list:\n", - " result += numpy.log( single_datapoint_likelihood_function(parameters) )\n", - " return result\n", - " \n", - " return log_likelihood_function_fixing_all_observations\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "loglikelihood_alldata_1d = generate_log_likelihood_function_fixing_all_observations(\n", - " datapoints = datapoints1d,\n", - " probability_of_single_measurement_function = probability_of_single_measurement_1D,\n", - " )\n", - "loglikelihood_alldata_1d( numpy.array([40, 10]) )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## What is the posterior using MCMC?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Define the required functions for the mcmc tools:\n", - "def bounds_to_ppf(bounds):\n", - " ppfs={}\n", - " for key in bounds.keys():\n", - " ppfs[key] = scipy.interpolate.interp1d([0.,1.],bounds[key])\n", - " return ppfs\n", - "\n", - "def prior_transform(nested_parameters):\n", - " actual_parameters = numpy.empty(n_varied_parameter_dim, dtype=numpy.float)\n", - " for i in range(n_varied_parameter_dim):\n", - " actual_parameters[i] = ppfs[varied_param_names[i]](nested_parameters[i])\n", - " return actual_parameters\n", - "\n", - "varied_param_names = ['v','g']\n", - "parameter_bounds={'v':(0,100),'g':(0,20)}\n", - "n_varied_parameter_dim = len(varied_param_names) \n", - "ppfs=bounds_to_ppf(parameter_bounds)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "sample_result1d = nestle.sample(\n", - " loglikelihood_alldata_1d, \n", - " prior_transform, \n", - " n_varied_parameter_dim,\n", - " npoints=100, \n", - " maxiter=None,\n", - " maxcall=None\n", - " )\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vparameters1d, cov1d = nestle.mean_and_cov(sample_result1d.samples, sample_result1d.weights)\n", - "print('Best fit:', varied_param_names, '\\n',vparameters1d)\n", - "print('Covariance:',varied_param_names, '\\n', cov1d)\n", - "fig = corner.corner(\n", - " sample_result1d.samples, \n", - " labels=[\"$v$\", \"$g$\"],\n", - " quantiles=(0.16, 0.84),\n", - " levels=(1-numpy.exp(-0.5),),\n", - " #truths=[v, g]\n", - " )\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# CASE2: JOINT HEIGHT AND TIME ERROR\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#(YOU NEED THIS CODE)\n", - "#The experimentalist knows \n", - "# his joint time, and height measurement error\n", - "\n", - "#DATA:\n", - "data_with_heights_and_times_error = pandas.read_csv( \"2D_Generated_Data.dat\",sep=' ',header=0 )\n", - "times_2d = data_with_heights_and_times_error['time']\n", - "heights_2d = data_with_heights_and_times_error['height']\n", - "\n", - "#ERROR:\n", - "covariance_matrix = numpy.array(\n", - " [\n", - " [.58, 13.35],\n", - " [13.35, 501.77]\n", - " ])\n", - "covariance_matrix_list = [covariance_matrix]*len(times_2d)\n", - "\n", - "def generate_experiment_error_function(mu, cov):\n", - " def experiment_error_function(Point):\n", - " x = Point\n", - " return scipy.stats.multivariate_normal.pdf(x, mu, cov)\n", - " return experiment_error_function" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#PLOTS (YOU DONT NEED THIS CODE)\n", - "# (You dont need this code but its here for illustration):\n", - "#Make a quick plot of the experimentalist lab testing of his camera:\n", - "experiment_error_function = generate_experiment_error_function(\n", - " numpy.array([0,0]), \n", - " covariance_matrix)\n", - "Library_GraphTwoDimensionDensityColorMap.Main(\n", - " Function = experiment_error_function,\n", - " DomainMinimumPoint = numpy.array([-2, -40]),\n", - " DomainMaximumPoint = numpy.array([2, 40]),\n", - " ShowContours = True,\n", - " PrintExtra = False,\n", - " )\n", - "plt.title(\"Probability Density Result of Measuring (t=0, h=0)\", fontsize=40)\n", - "plt.ylabel('Height (Meters)',fontsize=40)\n", - "plt.xlabel('Time (Seconds)',fontsize=40)\n", - "plt.draw()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#PLOTS (YOU DONT NEED THIS CODE)\n", - "#Plot the data:\n", - "plt.scatter(times_2d,heights_2d)\n", - "for i in range(len(times_2d)):\n", - " np=numpy\n", - " mu = np.array([times_2d[i],heights_2d[i]])\n", - " cov = covariance_matrix\n", - " Function = generate_experiment_error_function(mu, cov)\n", - " xmin=-3*np.sqrt(covariance_matrix[0][0])+times_2d[i]\n", - " xmax=3*np.sqrt(covariance_matrix[0][0])+times_2d[i]\n", - " ymin=-3*np.sqrt(covariance_matrix[1][1])+heights_2d[i]\n", - " ymax=3*np.sqrt(covariance_matrix[1][1])+heights_2d[i]\n", - " X, Y = numpy.mgrid[xmin:xmax:50j, ymin:ymax:50j] #1000 x 1000 -> 1 million points\n", - " PointsToPlugIn = numpy.vstack([X.ravel(), Y.ravel()])\n", - " PointsToPlugInDataset = PointsToPlugIn.T\n", - " PlugInPointsCount = len(PointsToPlugInDataset)\n", - " FunctionResultValuesForGrid = numpy.zeros((PlugInPointsCount))\n", - " k = 0\n", - " while (k < PlugInPointsCount):\n", - " PointToPlugIn = PointsToPlugInDataset[k]\n", - " FunctionValueForPointToPlugIn = Function(PointToPlugIn)\n", - " FunctionResultValuesForGrid[k] = FunctionValueForPointToPlugIn\n", - " k = k + 1\n", - " Z = numpy.reshape(FunctionResultValuesForGrid, X.shape)\n", - " CS = plt.contour(X, Y, Z, 2,\n", - " colors='k', # negative contours will be dashed by default\n", - " )\n", - "plt.xlabel('Time (Seconds)',fontsize=14)\n", - "plt.ylabel('Height (Meters)',fontsize=14)\n", - "plt.draw()\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## What is the correct single datapoint likelihood function?\n", - "### \"Likelihood function\" fixes the observation values and allows the parameters to vary:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def probability_of_single_measurement_2D(\n", - " observed_time, \n", - " observed_height,\n", - " v,\n", - " g,\n", - " ):\n", - "\n", - "\n", - " ##########################################################\n", - " ##########################################################\n", - " #If we assume a velocity and gravitational constant\n", - " # What height should we observe? \n", - " # (students do this)\n", - " observed_timeheight_vector = numpy.array([observed_time, observed_height]) \n", - "\n", - " # What is the probability of a measuring an observed height and observed time?\n", - " # (students do this)\n", - " def kernel_density_estimation_centered_on_model2d( possible_timeheight_vector ):\n", - " result = 0\n", - " kernel_count = 20\n", - " for t in numpy.linspace(0, 10, kernel_count):\n", - " \n", - " assume_model_is_correct_time_choice = t\n", - " assume_model_is_correct_height = my_model(v, g, t = assume_model_is_correct_time_choice )\n", - " assume_model_is_correct_mu = numpy.array(\n", - " [\n", - " assume_model_is_correct_time_choice, \n", - " assume_model_is_correct_height\n", - " ])\n", - "\n", - " #print ('observed_timeheight_vector', observed_timeheight_vector)\n", - " #print ('assume_model_is_correct_mu', assume_model_is_correct_mu)\n", - " #print ('covariance_matrix', covariance_matrix)\n", - " \n", - " result += scipy.stats.multivariate_normal.pdf(\n", - " possible_timeheight_vector,\n", - " assume_model_is_correct_mu,\n", - " covariance_matrix, \n", - " )\n", - " #print ('probability', result)\n", - " \n", - " return result/ kernel_count\n", - "\n", - " #Return the associated probability by plugging in the observed height:\n", - " likelihood_value = kernel_density_estimation_centered_on_model2d( \n", - " possible_timeheight_vector = observed_timeheight_vector\n", - " )\n", - " #\n", - " ##########################################################\n", - " ##########################################################\n", - " \n", - " \n", - " return(likelihood_value)\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Test for the joint-time-height-error-case\n", - "likelihood_function_for_first_datapoint2d = generate_single_data_point_likelihood_function(\n", - " observed_time = times_1d[0],\n", - " observed_height = heights_1d[0],\n", - " probability_of_single_measurement_function = probability_of_single_measurement_2D,\n", - " )\n", - "print ( likelihood_function_for_first_datapoint2d( [100, 20] ) )\n", - "#Expected result: likelihood_fn(100, 20) == 0.0005510152162280714" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## What is the all-data log-likelihood function?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#(provided below by instructors)\n", - "#numpyize the data:\n", - "# Each row of the datapoints is a datapoint\n", - "datapoints2d = numpy.vstack([times_2d, heights_2d]).T\n", - "# Looks like:\n", - "# [\n", - "# [time0, height0 ],\n", - "# [time1, height1 ],\n", - "# ...\n", - "# [timeN, heightN ],\n", - "# ]\n", - "#\n", - "\n", - "def generate_log_likelihood_function_fixing_all_observations(\n", - " datapoints = None, \n", - " probability_of_single_measurement_function = None,\n", - " ): \n", - " #Make a list where each element is a single-datapoint likelihood function\n", - " single_datapoint_likelihood_function_list = []\n", - " for datapoint in datapoints:\n", - " single_datapoint_likelihood_function = generate_single_data_point_likelihood_function(\n", - " observed_time = datapoint[0],\n", - " observed_height = datapoint[1],\n", - " probability_of_single_measurement_function = probability_of_single_measurement_function,\n", - " )\n", - " single_datapoint_likelihood_function_list.append( single_datapoint_likelihood_function )\n", - "\n", - " #Define a log-likelihood funciton for all the data using the list defined above:\n", - " def log_likelihood_function_fixing_all_observations(parameters):\n", - " result = 0\n", - " for single_datapoint_likelihood_function in single_datapoint_likelihood_function_list:\n", - " result += numpy.log( single_datapoint_likelihood_function(parameters) )\n", - " return result\n", - " \n", - " return log_likelihood_function_fixing_all_observations\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "loglikelihood_alldata_2d = generate_log_likelihood_function_fixing_all_observations(\n", - " datapoints = datapoints1d,\n", - " probability_of_single_measurement_function = probability_of_single_measurement_2D,\n", - " )\n", - "\n", - "loglikelihood_alldata_2d( numpy.array([40, 10]) )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## What is the posterior using MCMC?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Define the required functions for the mcmc tools:\n", - "def bounds_to_ppf(bounds):\n", - " ppfs={}\n", - " for key in bounds.keys():\n", - " ppfs[key] = scipy.interpolate.interp1d([0.,1.],bounds[key])\n", - " return ppfs\n", - "\n", - "def prior_transform(nested_parameters):\n", - " actual_parameters = np.empty(n_varied_parameter_dim, dtype=np.float)\n", - " for i in range(n_varied_parameter_dim):\n", - " actual_parameters[i] = ppfs[varied_param_names[i]](nested_parameters[i])\n", - " return actual_parameters\n", - "\n", - "varied_param_names = ['v','g']\n", - "parameter_bounds={'v':(0,100),'g':(0,20)}\n", - "n_varied_parameter_dim = len(varied_param_names) \n", - "ppfs=bounds_to_ppf(parameter_bounds)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### With an instrument which measures time and space at the same time with error?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### JOINT HEIGHT AND TIME ERROR\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "sample_result2d = nestle.sample(\n", - " loglikelihood_alldata_2d, \n", - " prior_transform, \n", - " n_varied_parameter_dim,\n", - " npoints=100, \n", - " maxiter=None,\n", - " maxcall=None\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vparameters2d, cov2d = nestle.mean_and_cov(sample_result2d.samples, sample_result2d.weights)\n", - "print('Best fit:', varied_param_names, '\\n',vparameters2d)\n", - "print('Covariance:', varied_param_names, '\\n', cov2d)\n", - "fig = corner.corner(\n", - " sample_result2d.samples, \n", - " labels=[\"$v$\", \"$g$\"],\n", - " quantiles=(0.16, 0.84),\n", - " levels=(1-np.exp(-0.5),),\n", - " #truths=[v, g]\n", - " )\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Lets make the plot even nicer looking for the sake of illustration" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "values = sample_result2d.samples\n", - "kernel2d = scipy.stats.gaussian_kde( values.T ) #takes in points sideways....\n", - "Library_GraphTwoDimensionDensityColorMap.Main(\n", - " Function = kernel2d.pdf,\n", - " DomainMinimumPoint = numpy.array([40, 5]),\n", - " DomainMaximumPoint = numpy.array([65, 15]),\n", - " ShowContours = True,\n", - " PrintExtra = False,\n", - " )\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "kernel2d_v_marginal = scipy.stats.gaussian_kde( values.T[0] ) \n", - "Points = numpy.linspace(30, 90, 100)\n", - "Values = kernel2d_v_marginal.pdf(Points)\n", - "plt.plot(Points, Values, )\n", - "plt.ylabel( \"probability\",fontsize = 14 )\n", - "plt.xlabel( \"$v$\",fontsize = 14 )\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "kernel2d_g_marginal = scipy.stats.gaussian_kde( values.T[1] ) \n", - "Points = numpy.linspace(0, 20, 100)\n", - "Values = kernel2d_g_marginal.pdf(Points)\n", - "plt.plot(Points, Values, )\n", - "plt.ylabel( \"probability\",fontsize = 14 )\n", - "plt.xlabel( \"$g$\",fontsize = 14 )\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "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": "3.7.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Day_4/day_4_student_empty_workbook_probability_theory.ipynb b/Day_4/day_4_student_empty_workbook_probability_theory.ipynb deleted file mode 100644 index 5dd77ea..0000000 --- a/Day_4/day_4_student_empty_workbook_probability_theory.ipynb +++ /dev/null @@ -1,784 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\\begin{equation}\n", - " \\begin{aligned}\n", - " &\\underbrace{ \\boxed{ \\overrightarrow{X}_{all} \\& \\overrightarrow{\\Theta} }_{pdf} (\\overrightarrow{v_{x}}_{all}, \\overrightarrow{v_{\\theta}}) }\n", - " &= \n", - " &\\underbrace{ \\boxed{ \\overrightarrow{ \\Theta } | \\overrightarrow{X}_{all} }_{ pdf } ( \\overrightarrow{v_{\\Theta}} | \\overrightarrow{v_{x}}_{all} ) }\n", - " &\\times\n", - " &\\underbrace{ \\boxed{ \\overrightarrow{X}_{all} }_{ pdf } ( \\overrightarrow{v_{x}}_{all} ) }\n", - " &=\n", - " &\\underbrace{ \\boxed{ \\overrightarrow{X}_{all} | \\overrightarrow{ \\Theta } }_{ pdf } ( \\overrightarrow{v_{x}}_{all}, \\overrightarrow{v_{\\Theta}}) }\n", - " &\\times\n", - " &\\underbrace{ \\boxed{ \\overrightarrow{ \\Theta } }_{ pdf } ( \\overrightarrow{v_{\\Theta}}) } \n", - " \\\\\n", - " &\\underbrace{Joint}\n", - " &=\n", - " &\\underbrace{Posterior }\n", - " &\\times \n", - " &\\underbrace{Evidence }\n", - " &= \n", - " &\\underbrace{Likelihood} \n", - " &\\times \n", - " &\\underbrace{Prior }\n", - " \\\\\n", - " & \\text{Omited In Textbooks}\n", - " &\n", - " & \\text{ (all data) }\n", - " &\n", - " & \\text{ (all data) }\n", - " & \n", - " & \\text{ (all data) }\n", - " &\n", - " & \\text{ (data marginal) }\n", - " \\\\\n", - " \\end{aligned} \n", - " \\end{equation}\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "!pip install nestle corner emcee" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Imports" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import warnings\n", - "import numpy\n", - "import scipy\n", - "import scipy.stats\n", - "import pandas\n", - "import matplotlib.pyplot as plt\n", - "import Library_GraphTwoDimensionDensityColorMap" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "warnings.simplefilter('ignore') #This ignores warnings that don't actually crash the program" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Read in the data:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Read in some random variable data\n", - "data = pandas.read_csv( \"RandomVariable_Generated_Data.dat\",\n", - " sep=' ',\n", - " header=0 )\n", - "print ( data )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Some useful helper functions:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Don't worry about this function, maybe come back to take a look\n", - "#at how it works. For now, just know that we use it to make some\n", - "#plots throughout this process.\n", - "def plot_1D_function( \n", - " Functions = None,\n", - " minp = None,\n", - " maxp = None,\n", - " nump = None,\n", - " Labels = None,\n", - " ):\n", - " \n", - " \n", - " if None in [Functions, minp, maxp, nump]: \n", - " raise Exception(\"arg missing...\" + str([Function, minp, maxp, nump]))\n", - " \n", - " if Labels is None:\n", - " Labels = [None]*len(Functions)\n", - " \n", - " for Label,Function in zip(Labels,Functions):\n", - "\n", - " TrialPoints = numpy.linspace(-20, 20, 100)\n", - " Values = []\n", - " for TrailPoint in TrialPoints:\n", - " Values.append(Function ( TrailPoint) )\n", - "\n", - " plt.plot(TrialPoints, Values,label=Label)\n", - " if Label is not None:\n", - " plt.legend()\n", - " \n", - " \n", - " \n", - " return" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Marginal Distributions:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Make a histogram of the data from column A" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Adata = data['A']\n", - "plt.hist(Adata, density=True,bins=50,label='A')#You can define bins as a total number of bins, or as an array of bin intervals. Density=True normalizes the histogram\n", - "plt.xlabel('A')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Make a histogram of the data from column B" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Bdata = data['B']\n", - "plt.hist(Bdata, density=True,bins=50,label='B')\n", - "plt.xlabel('B')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Approximate the data from column A as a univariate gaussian: (MARGINAL)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "A_standard_deviation = numpy.std( Adata ) \n", - "A_mean = numpy.mean(Adata)\n", - "\n", - "ATrialPoints = numpy.linspace(-20, 20, 100)\n", - "AValuePoints = scipy.stats.norm.pdf(ATrialPoints, \n", - " loc = A_mean, #mean\n", - " scale = A_standard_deviation )#standard deviation\n", - "\n", - "plt.hist(Adata, density=True, bins=50,label='Binned A')\n", - "plt.plot(ATrialPoints, AValuePoints,label='Gaussian A')\n", - "plt.xlabel('A')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Approximate the data from column B as a univariate gaussian: (MARGINAL)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# STUDENTS WILL WORK ON THIS CELL\n", - "\n", - "BTrialPoints = numpy.linspace(-20, 20, 100)\n", - "\n", - "# Find the Gaussian values of each of the BTrialPoints \n", - "#BValuePoints = \n", - "\n", - "plt.hist(Bdata, density=True, bins=50,label='Binned B')\n", - "#plt.plot(BTrialPoints, BValuePoints,label='Gaussian B')\n", - "plt.xlabel('B')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Conditional distributions" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### (1) Approximate the joint probability density function of A and B with a multivariate gaussian\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "ABdata = numpy.vstack( ( data['A'], data['B'] ) ).T#vstack vertically combines the two columns, the .T transposes the data\n", - "print(ABdata)\n", - "\n", - "ABMean = numpy.mean( ABdata, axis = 0 )#axis=0 means we want the mean along columns\n", - "print (ABMean)\n", - "\n", - "ABCovarianceMatrix = numpy.cov( ABdata, rowvar = False ) #numpy calculates a covariance matrix,rowvar=False tells it we're interested in columns\n", - "print (ABCovarianceMatrix)\n", - "\n", - "def jointGaussian(ABpoint): #A Gaussian in more than one dimension, so we give it an (x,y) point and covariance matrix\n", - " return scipy.stats.multivariate_normal.pdf( ABpoint, ABMean, ABCovarianceMatrix )\n", - "print ( jointGaussian( [0,0]) )\n", - "\n", - "Library_GraphTwoDimensionDensityColorMap.Main(#this is an imported function, not defined here\n", - " Function = jointGaussian,\n", - " DomainMinimumPoint = numpy.array([-5, -10]),\n", - " DomainMaximumPoint = numpy.array([10, 15]),\n", - " ShowContours = True,\n", - " PrintExtra = False,\n", - " #PlotThreeDimensional = True\n", - " )\n", - "plt.title(\"Probability Density of A & B\", fontsize=40)\n", - "plt.ylabel('B',fontsize=40)\n", - "plt.xlabel('A',fontsize=40)\n", - "plt.draw()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### (1) Fixing A = 0, plot the unnormalized conditional probability density of B:\n", - "P(B|A)\n", - "=====\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#conditional probability for B, given a value of 0 for A\n", - "def BdensityConditionalOnA0_unnormalized(Bpoint): \n", - " return jointGaussian( [0, Bpoint] )\n", - "\n", - "\n", - "plot_1D_function( \n", - " Functions = [BdensityConditionalOnA0_unnormalized],\n", - " minp = -20,\n", - " maxp = 20,\n", - " nump = 100,\n", - " Labels = ['P(B|A=0)']\n", - " )\n", - "plt.ylabel('P(B|A)')\n", - "plt.xlabel('B')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### (1) Fixing A = 0, plot the normalized probability density function of B:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Numerical integration of the conditional probability\n", - "FullIntegrationResult = scipy.integrate.quad( \n", - " BdensityConditionalOnA0_unnormalized, \n", - " -100, \n", - " 100, \n", - " full_output = False\n", - " )[0]\n", - "print (FullIntegrationResult )\n", - "\n", - "def BdensityConditionalOnA0_normalized(Bpoint):\n", - " return BdensityConditionalOnA0_unnormalized(Bpoint) / FullIntegrationResult\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plot_1D_function( \n", - " Functions = [BdensityConditionalOnA0_unnormalized, BdensityConditionalOnA0_normalized],\n", - " minp = -20,\n", - " maxp = 20,\n", - " nump = 100,\n", - " Labels = ['UnNormed','Normed']\n", - " )\n", - "plt.ylabel('P(B|A)')\n", - "plt.xlabel('B')\n", - "plt.legend()\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### (1) Fixing A = 7, plot the probability density of B:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# STUDENTS WILL WORK ON THIS CELL\n", - "\n", - "A_fixed_values=[1,3,5]\n", - "\n", - "# Fill in functions to calculate B probabilities given A values in A_fixed_values here\n", - "\n", - "function_list=[]\n", - "\n", - "# function_list[0](3) should be the probability of B=3 given A=1\n", - "\n", - "plot_1D_function( \n", - " Functions = function_list,\n", - " minp = -20,\n", - " maxp = 20,\n", - " nump = 100,\n", - " Labels = ['P(B|A=%i)'%i for i in A_fixed_values]\n", - " )\n", - "plt.ylabel('P(B|A)')\n", - "plt.xlabel('B')\n", - "plt.legend()\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### (1) Fixing B = 0 plot the unnormalized probability density of A:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Same as before, but now we fix the value of B and\n", - "#calculate the conditional probability for A\n", - "def AdensityConditionalOnB0_unnormalized(Apoint):\n", - " return jointGaussian( [Apoint, 0] )\n", - "\n", - "\n", - "plot_1D_function( \n", - " Functions = [AdensityConditionalOnB0_unnormalized],\n", - " minp = -20,\n", - " maxp = 20,\n", - " nump = 100,\n", - " Labels = ['P(A|B=0)']\n", - " )\n", - "plt.ylabel('P(A|B)')\n", - "plt.xlabel('A')\n", - "plt.legend()\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### (1) Fixing B = 7, plot the unnormalized probability density of A:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def AdensityConditionalOnB7_unnormalized(Apoint):\n", - " return jointGaussian( [Apoint, 7] )\n", - "\n", - "\n", - "plot_1D_function( \n", - " Functions = [AdensityConditionalOnB7_unnormalized],\n", - " minp = -20,\n", - " maxp = 20,\n", - " nump = 100,\n", - " Labels = ['P(A|B=7)']\n", - " )\n", - "plt.ylabel('P(A|B)')\n", - "plt.xlabel('A')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### (2) Approximate the joint probability density function of A and B with a kernel density estimation\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "kernel_object = scipy.stats.gaussian_kde( ABdata.T ) #takes in points sideways...\n", - "\n", - "kernel_pdf = kernel_object.pdf #get the pdf from the kernel_object, just a scipy structure thing\n", - "\n", - "Library_GraphTwoDimensionDensityColorMap.Main(\n", - " Function = kernel_pdf,\n", - " DomainMinimumPoint = numpy.array([-10, -10]),\n", - " DomainMaximumPoint = numpy.array([20, 20]),\n", - " ShowContours = True,\n", - " PluginPointCount = 10000,\n", - " PrintExtra = False,\n", - " )\n", - "plt.ylabel(\"B\",fontsize=14)\n", - "plt.xlabel(\"A\",fontsize=14)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### (2) Approximate the conditional probability density function of B, fixing A to 5:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# STUDENTS WILL WORK ON THIS CELL\n", - "def AdensityConditionalOnB5_unnormalized_kde(Apoint):\n", - " #Fill in function here\n", - " return \n", - "\n", - "\n", - "plot_1D_function( \n", - " Functions = [AdensityConditionalOnB5_unnormalized_kde],\n", - " minp = -20,\n", - " maxp = 20,\n", - " nump = 100,\n", - " Labels = ['P(B|A=5)']\n", - " )\n", - "plt.ylabel('P(B|A)')\n", - "plt.xlabel('B')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### (2) Approximate the conditional probability density function of A, fixing B to 5:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#same as what we did before, but notice the differences\n", - "#between our previous answer and here. Can you \n", - "#make sense of the difference on the 2D plot?\n", - "def AdensityConditionalOnB5_unnormalized_kde(Bpoint):\n", - " return (kernel_object.pdf( [ 5, Bpoint]) )\n", - "\n", - "\n", - "plot_1D_function( \n", - " Functions = [AdensityConditionalOnB5_unnormalized_kde],\n", - " minp = -20,\n", - " maxp = 20,\n", - " nump = 100,\n", - " Labels = ['P(A|B=5)']\n", - " )\n", - "plt.ylabel('P(A|B)')\n", - "plt.xlabel('A')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Note how the double peaked nature of the gaussian could be missed assuming gaussianity\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Final Excercise: Code up your own 1D kernel density estimation function against the data in Column A:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#We're going to approximate using a Triangle shaped kernel (see plot)\n", - "#The if else statement here just creates the triangle.\n", - "def Triangle_Kernel( Point, Mean, StandardDeviation):\n", - " if Point < Mean-StandardDeviation or Point>Mean+StandardDeviation:\n", - " return 0\n", - " elif Point >= Mean-StandardDeviation and Point <= Mean:\n", - " return((Point-Mean)/StandardDeviation**2+1/StandardDeviation)\n", - " elif Point >=Mean and Point <= Mean+StandardDeviation:\n", - " return(-(Point-Mean)/StandardDeviation**2+1/StandardDeviation)\n", - " else: \n", - " print('What happened?')\n", - " return \n", - "\n", - "plt.plot(numpy.arange(0,10,.01),[Triangle_Kernel(x,5,1) for x in numpy.arange(0,10,.01)],label='Triangle')\n", - "plt.plot(numpy.arange(0,10,.01),scipy.stats.norm.pdf( numpy.arange(0,10,.01), 5, .5 ),label=\"Gaussian\")\n", - "plt.legend()\n", - "plt.xlabel('A')\n", - "plt.ylabel('Probability Density')\n", - "plt.show()\n", - "\n", - "#The outer function here accepts a dataset, and the inner\n", - "#function creates the KDE by passing one point at a time\n", - "#to our Triangle_Kernel function, which only knows\n", - "#how to deal with one point at time. \n", - "def GenerateKernelDensityEstimationFunction1D_Triangle( Data ):\n", - " def KDE( Value ):\n", - " Result = 0\n", - " Bandwidth = numpy.sqrt( numpy.var(Data) ) / 6 #Bandwidth calculation\n", - " for Datapoint in Data:\n", - " Result += Triangle_Kernel( Value, Datapoint, Bandwidth ) #The total probability will be the sum of each point\n", - " Probability = Result / len(Data)\n", - " return Probability\n", - " \n", - " return KDE" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Get our KDE pdf from above\n", - "Personal_KDE_Function_triangle = GenerateKernelDensityEstimationFunction1D_Triangle( Adata )\n", - "\n", - "#Compare it to a scipy gaussian KDE\n", - "scipy_KDE_Function = scipy.stats.gaussian_kde( Adata ).pdf\n", - "\n", - "#Plot them using our defined function at the top again\n", - "plot_1D_function( \n", - " Functions = [Personal_KDE_Function_triangle, scipy_KDE_Function],\n", - " minp = -20,\n", - " maxp = 20,\n", - " nump = 100,\n", - " Labels = ['Personal_Triangle','Scipy']\n", - " )\n", - "plt.ylabel('P(A)')\n", - "plt.xlabel('A')\n", - "plt.legend()\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# STUDENTS WILL WORK ON THIS CELL\n", - "def UnivariateGaussian( Point, Mean, StandardDeviation):\n", - " #Fill in kernel function here\n", - " return \n", - "\n", - "\n", - "def GenerateKernelDensityEstimationFunction1D( Data ):\n", - " \n", - " def KDE( Value ):\n", - " Result = 0\n", - " Bandwidth = #?\n", - " for Datapoint in Data:\n", - " Result += UnivariateGaussian( Value, Datapoint, Bandwidth ) \n", - " Probability = Result / len(Data)#normalize it\n", - " return Probability\n", - " \n", - " return KDE\n", - " \n", - " \n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# STUDENTS WILL WORK ON THIS CELL\n", - "Personal_KDE_Function = GenerateKernelDensityEstimationFunction1D( Adata )\n", - "\n", - "scipy_KDE_Function = scipy.stats.gaussian_kde( Adata ).pdf\n", - "\n", - "plot_1D_function( \n", - " Functions = [Personal_KDE_Function, scipy_KDE_Function],\n", - " minp = -20,\n", - " maxp = 20,\n", - " nump = 100,\n", - " Labels = ['Personal','Scipy']\n", - " )\n", - "plt.ylabel('P(A)')\n", - "plt.xlabel('A')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Discuss choice of bandwidth, delta functions, and understand the overfitting problem:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# STUDENTS WILL WORK ON THIS CELL\n", - "def UnivariateGaussian2( Point, Mean, StandardDeviation):#Another Gaussian from scipy\n", - " return scipy.stats.norm.pdf( Point, Mean, StandardDeviation )\n", - "\n", - "def GenerateKernelDensityEstimationFunction1D_bad( Data ):\n", - " \n", - " def KDE( Value ):\n", - " Result = 0\n", - " Bandwidth = numpy.sqrt( numpy.var(Data) ) / 100 #Change this Bandwidth and see what happens\n", - " for Datapoint in Data:\n", - " Result += UnivariateGaussian2( Value, Datapoint, Bandwidth )#The total probability will be the sum of each point\n", - " Probability = Result / len(Data)#normalize it\n", - " return Probability\n", - " \n", - " return KDE\n", - "\n", - "Personal_KDE_Function_bad = GenerateKernelDensityEstimationFunction1D_bad( Adata )\n", - "\n", - "scipy_KDE_Function = scipy.stats.gaussian_kde( Adata ).pdf\n", - "\n", - "plot_1D_function( \n", - " Functions = [Personal_KDE_Function_bad, scipy_KDE_Function],\n", - " minp = -20,\n", - " maxp = 20,\n", - " nump = 100,\n", - " Labels = ['Personal_Bad','Scipy']\n", - " )\n", - "plt.xlabel('P(A)')\n", - "plt.ylabel('A')\n", - "plt.legend()\n", - "plt.show()\n", - "#Oh my." - ] - }, - { - "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": "3.7.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Day_5/Class_Vehicles.ipynb b/Day_5/Class_Vehicles.ipynb deleted file mode 100644 index d3a6d74..0000000 --- a/Day_5/Class_Vehicles.ipynb +++ /dev/null @@ -1,172 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "class Vehicle:\n", - " def __init__(\n", - " self, \n", - " name = None,\n", - " width = None,\n", - " length = None,\n", - " ):\n", - " self.name = name\n", - " self.width = width\n", - " self.length = length\n", - "\n", - " \n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "classname boat\n", - "name santa maria\n", - "width 30\n", - "length 70\n", - "doorcount 0\n" - ] - } - ], - "source": [ - "class Boat(Vehicle):\n", - " def __init__(\n", - " self, \n", - " name = None,\n", - " width = None,\n", - " length = None,\n", - " doorcount = 0,\n", - " ):\n", - " Vehicle.__init__( \n", - " self, \n", - " name = name, \n", - " width = width, \n", - " length = length)\n", - "\n", - " self.classname = 'boat'\n", - " self.doorcount = doorcount\n", - "\n", - " def printself(self):\n", - " print( 'classname', self.classname )\n", - " print( 'name', self.name )\n", - " print( 'width', self.width )\n", - " print( 'length', self.length )\n", - " print( 'doorcount', self.doorcount )\n", - "\n", - "boat1 = Boat(name = 'santa maria', width = 30, length = 70)\n", - "\n", - "\n", - "boat1.printself()\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "classname Automobile\n", - "name dadcar\n", - "width 6\n", - "length 12\n", - "doorcount None\n", - "Automobilebrand toyota\n", - "Automobilemodel camry\n", - "year 2019\n" - ] - } - ], - "source": [ - "class Automobile(Vehicle):\n", - " def __init__(\n", - " self, \n", - " name = None,\n", - " width = None,\n", - " length = None,\n", - "\n", - " doorcount = None,\n", - " Automobilebrand = None,\n", - " Automobilemodel = None,\n", - " year = None,\n", - " ):\n", - " Vehicle.__init__( \n", - " self, \n", - " name = name, \n", - " width = width, \n", - " length = length)\n", - "\n", - " self.classname = 'Automobile'\n", - " self.doorcount = doorcount\n", - " self.Automobilebrand = Automobilebrand\n", - " self.Automobilemodel = Automobilemodel\n", - " self.year = year\n", - "\n", - "\n", - "\n", - " def printself(self):\n", - " print( 'classname', self.classname )\n", - " print( 'name', self.name )\n", - " print( 'width', self.width )\n", - " print( 'length', self.length )\n", - " print( 'doorcount', self.doorcount )\n", - " print( 'Automobilebrand', self.Automobilebrand )\n", - " print( 'Automobilemodel', self.Automobilemodel )\n", - " print( 'year', self.year )\n", - "\n", - "Automobile1 = Automobile(\n", - " name = 'dadcar', \n", - " Automobilebrand = 'toyota', \n", - " Automobilemodel = 'camry', \n", - " year = '2019', \n", - " width = 6, \n", - " length = 12)\n", - "\n", - "\n", - "Automobile1.printself()\n", - "\n" - ] - }, - { - "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": "3.7.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Day_5/Classless_Vehicles.ipynb b/Day_5/Classless_Vehicles.ipynb deleted file mode 100644 index 4b43263..0000000 --- a/Day_5/Classless_Vehicles.ipynb +++ /dev/null @@ -1,102 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "True\n", - "False\n", - "False\n", - "True\n" - ] - } - ], - "source": [ - "Boat1 = {\n", - " 'classname': 'boat',\n", - " 'name': 'santa maria',\n", - " 'width': 30,\n", - " 'length': 70,\n", - " 'doorcount': 0,\n", - " }\n", - "\n", - "Boat2 = {\n", - " 'classname': 'boat',\n", - " 'name': 'pinta',\n", - " 'width': 30,\n", - " 'length': 70,\n", - " 'doorcount': 0,\n", - " }\n", - "\n", - "Automobile1 = {\n", - " 'classname': 'Automobile',\n", - " 'name': 'dadcar',\n", - " 'width': 6,\n", - " 'length': 12,\n", - " 'doorcount': 4,\n", - " 'Automobilebrand': 'toyota',\n", - " 'Automobilemodel': 'camry',\n", - " 'year': 2019,\n", - " }\n", - "\n", - "\n", - "#Does it quack like a duck? Does it look like a duck? --> then its ducktyped as a duck\n", - "def IsBoatCheck( BoatCandidate ):\n", - " Result = True\n", - " BoatKeys = [ 'classname', 'name', 'width', 'length','doorcount',]\n", - " ExtraKeys = set(BoatCandidate.keys() ) - set(BoatKeys)\n", - " MissingKeys = set(BoatKeys) - set(BoatCandidate.keys() ) \n", - " if len(ExtraKeys) != 0: \n", - " Result = False\n", - " if len(MissingKeys) != 0:\n", - " Result = False\n", - " if BoatCandidate['classname'] != 'boat':\n", - " return False\n", - " return Result\n", - "\n", - "\n", - "def IsAutomobileCheck( AutomobileCandidate ):\n", - " Result = True\n", - " AutomobileKeys = [ 'classname', 'name', 'width', 'length','doorcount','Automobilebrand','Automobilemodel', 'year']\n", - " ExtraKeys = set(AutomobileCandidate.keys() ) - set(AutomobileKeys)\n", - " MissingKeys = set(AutomobileKeys) - set(AutomobileCandidate.keys() ) \n", - " if len(ExtraKeys) != 0: \n", - " Result = False\n", - " if len(MissingKeys) != 0:\n", - " Result = False\n", - " if AutomobileCandidate['classname'] != 'Automobile':\n", - " return False\n", - " return Result\n", - "\n", - "\n", - "print ( IsBoatCheck( Boat1 ) )\n", - "print ( IsBoatCheck( Automobile1 ) )\n", - "\n", - "\n", - "print ( IsAutomobileCheck( Boat1 ) )\n", - "print ( IsAutomobileCheck( Automobile1 ) )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Day_5/Example_WorkingFizzBuzz.py b/Day_5/Example_WorkingFizzBuzz.py deleted file mode 100644 index de557e0..0000000 --- a/Day_5/Example_WorkingFizzBuzz.py +++ /dev/null @@ -1,20 +0,0 @@ - -def FizzBuzzStringConstruct( MaximumNumber = None ) : - - for num in range(1,MaximumNumber): - string = "" - if num % 3 == 0: - string = string + "Fizz" - if num % 5 == 0: - string = string + "Buzz" - if num % 5 != 0 and num % 3 != 0: - string = string + str(num) - print(string) - - return string - - -ExampleMaximumNumber = 21 -FizzBuzzStringConstruct ( - MaximumNumber = ExampleMaximumNumber, - ) diff --git a/Day_5/Example_WorkingRobotMove.py b/Day_5/Example_WorkingRobotMove.py deleted file mode 100644 index 22192b8..0000000 --- a/Day_5/Example_WorkingRobotMove.py +++ /dev/null @@ -1,40 +0,0 @@ - -import numpy - -#w -> up -#s -> down -#a -> left -#d -> right - -def MoveRobotBasedOnMovementString( - StartPosition = None, - MovementString = None, - ): - - - Position = numpy.array( StartPosition ) - for Char in ExampleMovementString: - if Char is 'w': Position = Position + numpy.array([0,1]) - elif Char is 's': Position = Position + numpy.array([0,-1]) - elif Char is 'a': Position = Position + numpy.array([1,0]) - elif Char is 'd': Position = Position + numpy.array([-1,0]) - else: - raise Exception( 'unrecognized character' ) - - FinalPosition = Position - return FinalPosition - - -ExampleMovementString = 'wasdwsdwadwasdssssddddwwwwwsswwdasdwadaaadsdwdwdwdwdwdwd' -ExampleStartPosition = [1,1] - - - -FinalPosition = MoveRobotBasedOnMovementString( - StartPosition = ExampleStartPosition , - MovementString = ExampleMovementString, - - ) - - -print ('FinalPosition', FinalPosition) diff --git a/Day_5/README.md b/Day_5/README.md deleted file mode 100644 index fbbf891..0000000 --- a/Day_5/README.md +++ /dev/null @@ -1,51 +0,0 @@ -# Coding Practices - -This day will contain 3 examples, designed to get the students familiar with problems encounted when writing code in a collaborative fashion with many poeple. - - -## (Example 1) Code Golf - And The Unreadability Problem - -For this example we will have students write small code which incentivises them to accomplish a task in as few lines as possible, and then be forced to modify that code to change behavior. The goal is for students to learn the value of code readability. - -### Class is split into 2 groups -#### Group 1 -will be coding the ```fizzbuzz``` example - -#### Group 2 - will be coding the ```robotmove``` example - -Useful syntax to help in the code golf excercise: -``` -a%b #modulus -str.split() #split a string by delimeter into an array of strings -int(string_instance) #cast a string to a number such that math can be done upon it -``` - -All code made in the first example will be forced to be commited through git, and be tested through a test driven development mindset. TODO -> decide if we want the class to write any of their own tests for this example, or purely rely upon the provided tests when coding. - -## (Example 2) Class Inheritance Trap -### Class is split into 2 groups - -#### Group 1 -students will inherit vehicle instance code which does an example from day 1 perfectly. - -#### Group 2 -will be given already working equivalent code which they have not seen before, which uses functional coding paradigm, without class objects, and without class inheritance. - - -Both groups in the class will race to accomplish the creation of a ```vehiclemerge``` function routine given their own paradigm. After both groups have the code working, they will be given 10 minutes to refactor the code such that they feel it is the most easily added onto afterwards. Then the groups will be given an unknown task they cannot prepare for, and race to see who can add functionality fastest with the different paradigm. It should become obvious that in this particular example, using class inheritance and class instances is both slower, and harder to maintain than using objects with static functions. - - -# Machine Learning Random Forest - -Students will be introduced to a random forest machine learning tool, and use it to predict weather in seatle. - - - - - - - - - - diff --git a/Day_5/random_forest_explained/Improving Random Forest Part 1.ipynb b/Day_5/random_forest_explained/Improving Random Forest Part 1.ipynb deleted file mode 100644 index 30045a0..0000000 --- a/Day_5/random_forest_explained/Improving Random Forest Part 1.ipynb +++ /dev/null @@ -1,1552 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Improving the Random Forest Model Part 1\n", - "\n", - "## Using More Data and Feature Reduction\n", - "\n", - "This is the first of three parts that will examine improving the simple random forest model created in a [previous blog post](https://medium.com/@williamkoehrsen/random-forest-in-python-24d0893d51c0). The first post will focus on achieving better performance through additional data and choosing the most important features." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Three Approaches to Making a Better ML Model\n", - "\n", - "1. More high-quality data\n", - "2. Hyperparameter tuning of algorithm\n", - "3. Try different algorithm\n", - "\n", - "We will look at approach 1. here with the other two methods to be explored later!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Recap of Previous Work\n", - "\n", - "We are working on a supervised, regression machine learning task where the goal is to predict the maximum temperature tomorrow (in Seattle, WA) from past historical data. The first attempt used one year of data for training. \n", - "\n", - "The code below recreates the simple random forest model written up in [my article](https://medium.com/@williamkoehrsen/random-forest-in-python-24d0893d51c0) on Medium. We use the default parameters except for increasing the number of decision trees (n_estimators) to 1000. The original model achieves a mean average error of 3.83 degrees and an accuracy of 94%." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average baseline error: 5.06 degrees.\n", - "Average model error: 3.83 degrees.\n", - "Improvement over baseline: 24.27 %.\n", - "Accuracy: 93.99 %.\n" - ] - } - ], - "source": [ - "# Pandas is used for data manipulation\n", - "import pandas as pd\n", - "\n", - "# Read in data as pandas dataframe and display first 5 rows\n", - "original_features = pd.read_csv('data/temps.csv')\n", - "original_features = pd.get_dummies(original_features)\n", - "\n", - "# Use numpy to convert to arrays\n", - "import numpy as np\n", - "\n", - "# Labels are the values we want to predict\n", - "original_labels = np.array(original_features['actual'])\n", - "\n", - "# Remove the labels from the features\n", - "# axis 1 refers to the columns\n", - "original_features= original_features.drop('actual', axis = 1)\n", - "\n", - "# Saving feature names for later use\n", - "original_feature_list = list(original_features.columns)\n", - "\n", - "# Convert to numpy array\n", - "original_features = np.array(original_features)\n", - "\n", - "# Using Skicit-learn to split data into training and testing sets\n", - "from sklearn.model_selection import train_test_split\n", - "\n", - "# Split the data into training and testing sets\n", - "original_train_features, original_test_features, original_train_labels, original_test_labels = train_test_split(original_features, original_labels, test_size = 0.25, random_state = 42)\n", - "\n", - "# The baseline predictions are the historical averages\n", - "baseline_preds = original_test_features[:, original_feature_list.index('average')]\n", - "\n", - "# Baseline errors, and display average baseline error\n", - "baseline_errors = abs(baseline_preds - original_test_labels)\n", - "print('Average baseline error: ', round(np.mean(baseline_errors), 2), 'degrees.')\n", - "\n", - "# Import the model we are using\n", - "from sklearn.ensemble import RandomForestRegressor\n", - "\n", - "# Instantiate model \n", - "rf = RandomForestRegressor(n_estimators= 1000, random_state=42)\n", - "\n", - "# Train the model on training data\n", - "rf.fit(original_train_features, original_train_labels);\n", - "\n", - "# Use the forest's predict method on the test data\n", - "predictions = rf.predict(original_test_features)\n", - "\n", - "# Calculate the absolute errors\n", - "errors = abs(predictions - original_test_labels)\n", - "\n", - "# Print out the mean absolute error (mae)\n", - "print('Average model error:', round(np.mean(errors), 2), 'degrees.')\n", - "\n", - "# Compare to baseline\n", - "improvement_baseline = 100 * abs(np.mean(errors) - np.mean(baseline_errors)) / np.mean(baseline_errors)\n", - "print('Improvement over baseline:', round(improvement_baseline, 2), '%.')\n", - "\n", - "# Calculate mean absolute percentage error (MAPE)\n", - "mape = 100 * (errors / original_test_labels)\n", - "\n", - "# Calculate and display accuracy\n", - "accuracy = 100 - np.mean(mape)\n", - "print('Accuracy:', round(accuracy, 2), '%.')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Collect More Data\n", - "\n", - "All data is obtained from the [NOAA climate data online](https://www.ncdc.noaa.gov/cdo-web/) tool. Feel free to check for your city! \n", - "\n", - "This time we will use 6 years of historical data and include some additional variables to augment the temperature features used in the simple model. " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
yearmonthdayweekdayws_1prcp_1snwd_1temp_2temp_1averageactualfriend
0201111Sat4.920.000363745.64040
1201112Sun5.370.000374045.73950
2201113Mon6.260.000403945.84242
3201114Tues5.590.000394245.93859
4201115Wed3.800.030423846.04539
\n", - "
" - ], - "text/plain": [ - " year month day weekday ws_1 prcp_1 snwd_1 temp_2 temp_1 average \\\n", - "0 2011 1 1 Sat 4.92 0.00 0 36 37 45.6 \n", - "1 2011 1 2 Sun 5.37 0.00 0 37 40 45.7 \n", - "2 2011 1 3 Mon 6.26 0.00 0 40 39 45.8 \n", - "3 2011 1 4 Tues 5.59 0.00 0 39 42 45.9 \n", - "4 2011 1 5 Wed 3.80 0.03 0 42 38 46.0 \n", - "\n", - " actual friend \n", - "0 40 40 \n", - "1 39 50 \n", - "2 42 42 \n", - "3 38 59 \n", - "4 45 39 " - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Pandas is used for data manipulation\n", - "import pandas as pd\n", - "\n", - "# Read in data as a dataframe\n", - "features = pd.read_csv('data/temps_extended.csv')\n", - "features.head(5)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "We have 2191 days of data with 12 variables.\n" - ] - } - ], - "source": [ - "print('We have {} days of data with {} variables.'.format(*features.shape))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Numerical and Visual Inspection of Data" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
yearmonthdayws_1prcp_1snwd_1temp_2temp_1averageactualfriend
count2191.002191.002191.002191.002191.002191.002191.002191.002191.002191.002191.00
mean2013.506.5215.717.370.120.0161.1761.1860.2961.1860.31
std1.713.458.803.150.250.1513.0913.0810.7313.0815.87
min2011.001.001.000.890.000.0029.0029.0045.1029.0025.00
25%2012.004.008.005.140.000.0051.0051.0050.1051.0049.00
50%2014.007.0016.006.710.000.0060.0060.0058.8060.0060.00
75%2015.0010.0023.009.170.120.0071.0071.0070.2071.0071.00
max2017.0012.0031.0021.252.203.0096.0096.0077.4096.0097.00
\n", - "
" - ], - "text/plain": [ - " year month day ws_1 prcp_1 snwd_1 temp_2 temp_1 \\\n", - "count 2191.00 2191.00 2191.00 2191.00 2191.00 2191.00 2191.00 2191.00 \n", - "mean 2013.50 6.52 15.71 7.37 0.12 0.01 61.17 61.18 \n", - "std 1.71 3.45 8.80 3.15 0.25 0.15 13.09 13.08 \n", - "min 2011.00 1.00 1.00 0.89 0.00 0.00 29.00 29.00 \n", - "25% 2012.00 4.00 8.00 5.14 0.00 0.00 51.00 51.00 \n", - "50% 2014.00 7.00 16.00 6.71 0.00 0.00 60.00 60.00 \n", - "75% 2015.00 10.00 23.00 9.17 0.12 0.00 71.00 71.00 \n", - "max 2017.00 12.00 31.00 21.25 2.20 3.00 96.00 96.00 \n", - "\n", - " average actual friend \n", - "count 2191.00 2191.00 2191.00 \n", - "mean 60.29 61.18 60.31 \n", - "std 10.73 13.08 15.87 \n", - "min 45.10 29.00 25.00 \n", - "25% 50.10 51.00 49.00 \n", - "50% 58.80 60.00 60.00 \n", - "75% 70.20 71.00 71.00 \n", - "max 77.40 96.00 97.00 " - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "round(features.describe(), 2)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Use datetime for dealing with dates\n", - "import datetime\n", - "\n", - "# Get years, months, and days\n", - "years = features['year']\n", - "months = features['month']\n", - "days = features['day']\n", - "\n", - "# List and then convert to datetime object\n", - "dates = [str(int(year)) + '-' + str(int(month)) + '-' + str(int(day)) for year, month, day in zip(years, months, days)]\n", - "dates = [datetime.datetime.strptime(date, '%Y-%m-%d') for date in dates]\n", - "\n", - "# Import matplotlib for plotting and use magic command for Jupyter Notebooks\n", - "import matplotlib.pyplot as plt\n", - "\n", - "%matplotlib inline\n", - "\n", - "# Set the style\n", - "plt.style.use('fivethirtyeight')" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAABA4AAAKmCAYAAADNQdz5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRERERERERF6xcEBEREREREREXv1/NncWE6tISSAAAAAASUVORK5CYII=\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Set up the plotting layout\n", - "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2, figsize = (15,10))\n", - "fig.autofmt_xdate(rotation = 45)\n", - "\n", - "# Actual max temperature measurement\n", - "ax1.plot(dates, features['actual'])\n", - "ax1.set_xlabel(''); ax1.set_ylabel('Temperature (F)'); ax1.set_title('Max Temp')\n", - "\n", - "# Temperature from 1 day ago\n", - "ax2.plot(dates, features['temp_1'])\n", - "ax2.set_xlabel(''); ax2.set_ylabel('Temperature (F)'); ax2.set_title('Prior Max Temp')\n", - "\n", - "# Temperature from 2 days ago\n", - "ax3.plot(dates, features['temp_2'])\n", - "ax3.set_xlabel('Date'); ax3.set_ylabel('Temperature (F)'); ax3.set_title('Two Days Prior Max Temp')\n", - "\n", - "# Friend Estimate\n", - "ax4.plot(dates, features['friend'])\n", - "ax4.set_xlabel('Date'); ax4.set_ylabel('Temperature (F)'); ax4.set_title('Friend Estimate')\n", - "\n", - "plt.tight_layout(pad=2)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAABA4AAAKmCAYAAADNQdz5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXd4FOX2x7+zu+kk2RBCEiAhlIgEKYI0gQCKCIKhaaQI\niF5QREXvBQHbtYAI5KIiCPwQ6R1DR0AkElqCIr0ZAqEmIW3T6+78/gi72ZnZTbYluzN7Ps+TB6bs\nzJmz77z7nvOec15GpVKxIAiCIAiCIAiCIAiCMIDM3gIQBEEQBEEQBEEQBOG4kOOAIAiCIAiCIAiC\nIAijkOOAIAiCIAiCIAiCIAijkOOAIAiCIAiCIAiCIAijkOOAIAiCIAiCIAiCIAijkOOAIAiCIAiC\nIAiCIAijkOOAEBVt27ZF27Zt7S2GyRw7dgxKpRJz586ts3tu2LABSqUSGzZsqLN7EgRBEARhHXPn\nzoVSqcSxY8fsLYrNUCqVGDRoUK3eg8Y9hpFieyLsCzkOiDpHqVRCqVRWe86gQYNqpbNTKpWicjzY\ng169ekGpVKJr1672FsUokyZN0rWjefPmGT1v48aNuvMGDBhQhxIaRl9uU/6GDBlib5EJgiAII/D7\nbD8/PzRt2hQDBgzAqlWroFar7S2iyajVajRt2hT+/v7Izc0VHFepVPD394dSqcSSJUsMXmPixIlQ\nKpXYtm1bbYtrNenp6fj444/RrVs3NGrUCIGBgWjTpg0GDBiAzz77DFeuXLG3iAThcCjsLQBBmMPu\n3bvtLYJZdOrUCadPn4a/v7+9RTGJM2fO4OLFi2AYBtevX8fJkyfx9NNP21ssoygUCqxfvx7Tp0+H\nTCb0g65duxYKhQIVFRV2kE7I4MGD0axZM86+8+fP48CBA2jXrh0GDhzIORYWFlaH0hEEQRCWMGPG\nDACVxvetW7ewd+9eJCQk4I8//sCaNWtMvs6kSZMwYsQINGnSpLZENYpcLkfPnj2xb98+HD9+XBAl\ncPz4cajVajAMg/j4eEyZMkVwjfj4eABA7969dftOnz4NDw+P2hXeTK5evYrBgwcjKysLERERGDly\nJPz8/JCVlYW///4bP/zwA/z8/BAREWFvUQnCoSDHASEq+EaXo+Pp6YnHHnvM3mKYzKpVqwAAH3zw\nARYuXIjVq1c7tOPg+eefx759+3DkyBH069ePc+z69etISEjA4MGDsXfvXjtJyCUqKgpRUVGcfWvX\nrsWBAwfQvn17zJo1y06SEQRBEJbC77svX76Mfv36YdeuXWY54P39/e060dC7d2/s27cPR48eFTgO\n4uPjIZfLMWjQIMTFxaGiogIKRZUZcfXqVaSnpyMiIgINGzbU7XfEMdBHH32ErKwszJw5EzNnzhQc\nv3fvHnJycuwgGUE4NpSqQIgKQzUOysrKsHz5cvTu3RvNmjVDUFAQnnjiCbz00ku6CAVtrQEAuHv3\nLie0cPLkyZzrHTt2DNHR0WjWrBkaNmyI9u3bY+bMmcjMzBTIM3nyZF1KxebNm9G3b180atQIPXv2\n5NzXUI0DlUqF2bNn4+mnn0ajRo0QEhKC7t2745NPPoFKpdKdd+7cOcyYMQM9evRAWFgYAgMD0bFj\nR8yaNcumP2x5eXnYsWMHQkJC8PHHHyMkJAS7d+/myKKlY8eOCAgIQFZWlsFr/fjjj1AqlViwYAFn\n/2+//Yb+/fsjODgYYWFhGDNmDG7cuKEL4b9//75ZMkdHR8PDw8PgjI5237hx4wx+VqVS4fvvv8fg\nwYPRunVrBAQEoGXLlhg1ahROnz4tOH/69OlQKpUGjftt27bp0iFqM7phy5YtGDRoEEJDQxEYGIjO\nnTtj7ty5KCoqEpwbGhqK0NBQlJSU4IsvvsATTzyBoKAgdO/eXRdGqtFosHjxYnTp0gWBgYF44okn\n8N133wmudeHCBSiVSowePRopKSkYP348mjdvjuDgYPTr1w+//vprrT0zQRCE2GjTpo1uHHDmzBnd\n/rZt20KpVKK0tBRz587V/ZZqjdfqctJtOTYxhjZSQBs5wL9/+/btMWjQIOTn5+Pvv//mHNd+JjIy\nkrPfUI0D7XNu2LAB8fHxGDRoEJo0aYKQkBBER0fj+vXrBuW7efMmxo8fj6ZNm6JRo0bo378/Dh48\nWO0zGSIxMREA8NZbbxk83qRJE8FYU1+n69evR8+ePREUFITw8HC8++67ePjwocFr5ebmYs6cOeje\nvTuCg4PRpEkTDBgwADt37jQqX3x8PEaOHIkWLVogICAATzzxBP7zn/8gPT3d4Pnnzp3TRaqEhIRg\nyJAhBscxBGEt5DggRM/bb7+NGTNmoLS0FC+//DImT56Mnj174s6dO7qZ5tDQUF0ooY+PD2bMmKH7\n0/9BW7t2LaKionD8+HEMGDAAU6ZMQePGjbFs2TL06dPHqGG7ePFivP/++wgLC8OkSZMEP5x8UlJS\n0KtXL8TExEChUOC1117D2LFjERoaip9++gl37tzRnbtmzRrExsYiPDwcY8aMweuvv47AwEAsXboU\n/fv3R35+vrUqBABs3boVhYWFGDlyJORyOUaNGoWSkhJs2rRJcO6oUaNQXl5uNI9x06ZNkMlkGDly\nJOf60dHRuHjxIoYOHYoJEyYgOzsbzz33HO7evWuRzL6+vhgyZAgOHDjA+dEuLS3F5s2b0aNHD7Rs\n2dLgZ69evYrZs2dDoVDovuvevXvj6NGjeOGFF/D7779zzp89ezbatWuHpUuXYv/+/br9ycnJ+OCD\nD+Dn54effvqJMwNjK1iWxaRJk/Dmm2/i3r17GDp0KN544w14e3tj3rx5iIqKQklJicHPjRo1Crt2\n7cLAgQPx8ssvIyUlBRMnTsS+ffswZcoULFmyBN26dcPYsWNRVFSEzz//XBd5wic9PR39+/fH/fv3\nMX78eAwfPhxXr17F6NGjsX79eps/N0EQhFhhWdbosXHjxmHNmjXo3r07Jk+ejPDw8GqvVVdjk1at\nWiEoKAjXrl1DWlqabv/Dhw9x9epV9OrVC7169QIAHD16lPNZ7bZ+mkJNHDx4EMOHD4e3tzcmTJiA\n7t2749ChQxg0aJBgYiI5OVkXxdG5c2e89dZbaNy4McaMGYM9e/aYfE8A8PPz013TXJYsWYLp06ej\nXbt2mDx5Mpo3b45169ahf//+yM7O5pz74MEDPPPMM1iwYAGUSiVee+01jBgxArdv38Zrr71mcFLp\nu+++Q1RUFBITE9GvXz9MnjwZrVq1wsqVK9G3b1/Bd52YmIiBAwciLi4Ozz77LCZOnAh3d3cMHjyY\n47QiCFtAqQqE3ahupQF9w7k6cnNz8csvv6BDhw44fPiwwGjT/vA0bdoUs2bNwrx58+Dr62tw1vje\nvXuYNm0aPD09cfjwYbRu3Vp3bPbs2YiJicG///1vbNmyRfDZY8eO4dChQ2jXrp1Jck+aNAl3797F\nRx99hA8//JBzTKVScZ7jgw8+QExMDORyOee8tWvX4r333sNPP/2EDz74wKT7Vsfq1avBMAzGjBkD\nABg9ejQWLFiAtWvXCqIyRo0ahblz52LTpk0Cj/2lS5dw8eJF9O7dGyEhIQAqv6dp06bBxcUFhw4d\n4njyP/nkEyxevNhiucePH4/Nmzdj48aNeP/99wEAe/fuRXZ2NsaPH2/0c61bt8b169dRv359zv47\nd+6gX79++OSTT/Dss8/q9ru5uWH16tXo3bs3pkyZgvj4eDRs2BCvvfYaCgoKsGnTplrLS12xYgW2\nbt2KkSNHYtGiRXB1dQVQOTD99NNPsXjxYixatEjQlrROpRMnTuhyTF988UVER0frBp4nT57UDaJe\nf/119OjRA99++y0mTJggkOPMmTMYP348vv/+e92+KVOm4JlnnsGMGTMwcOBA0dTzIAiCqC0uXbqE\n48ePAwCeeuopwfG7d+/ixIkTJvWXdTk2ASojBrZu3Yr4+HhER0cD4EYTNGrUCC1atMDRo0cxffp0\nAJW1HU6cOAG5XI4ePXqYfK99+/YhNjaW42z44osv8O2332L9+vWYOnWqbv+0adOQnZ2Nr776Cu++\n+65u/4EDBziTFKYwbNgwLF68GCNHjsTrr7+OXr164YknnqixcDcA/P777zh8+DDat2+v2zd9+nSs\nWLECX375JSdqb/Lkybh58yZ++uknvPTSS7r9eXl5GDx4MObPn4/BgwfrxkQnTpzAF198gc6dO+si\nGbVs3rwZb731FmbOnIl169YBqBwDvPPOOyguLsaaNWs4BZVXrFih+34IwlZQxAFhN+bNm2f0z9QZ\naIZhwLIsXF1dBYY1ALOMmM2bN6OsrAxvvPEG54cZqPzBCg4OxsGDB5Gamir47Pjx403+YT537hxO\nnz6NiIgITJs2TXBcqVSiXr16uu3Q0FCDzzZ27Fj4+PjgyJEjJt23Ov766y9cunQJ3bt31xXkCwsL\nQ48ePXD16lUkJCRwzm/SpAkiIyNx/vx5XL58mXNs48aNACodD1r27t2LvLw8vPzyy4Lwvw8//BA+\nPj4Wy969e3c89thjWLt2rW6GZ82aNVAqlYJ6AvoolUqB0wCo1PeLL76Iq1evCr7r5s2b4/vvv0dO\nTg7eeOMNzJw5ExcvXsSUKVMEhQ1tyY8//ggvLy98++23OqcBUNn+P/30U7i7uxscNAKVgzD9wlT9\n+/eHn58fVCoVZs2apXMaAEBERATatm2LO3fuGKyq7e7ujk8++YSzLyIiAmPGjEFhYSFiY2OtfVSC\nIAjRMXfuXMydOxezZ8/Gv/71Lzz77LMoKSnBkCFD0L17d8H5H3/8scnjk7oam2jRGvH6EQXx8fFw\ncXHRPUuvXr3w559/ori4GEBlkd/c3Fx07NjRrN/zESNGCCIUtA5//dny+/fvIy4uDk2aNBFMZAwY\nMEAXBWEqn376KcaPH4+cnBzMmzcPgwcPRlhYGJ588kl88MEH1a6o8Morr3CcBkBlzQQvLy9s3boV\n5eXlACrrXGhrReg7DYDKyNeZM2eCZVlO5OayZcvAsiy+/fZbgRNj5MiRaNeuHfbv36+bFEhMTERS\nUhK6du0qWIXpjTfeQPPmzc3SC0HUBEUcEHbDUO68lkGDBuHEiRM1XsPHxwcDBgzAgQMH0KNHDwwe\nPBjdu3dH586dOca3KZw/fx6AMD8PqDSYunXrhh07duDChQsIDg7mHO/UqZPJ9/nzzz8BAM8884zB\nlQD4lJeXY9WqVYiNjcW1a9eQl5cHjUajO25osGAuq1evBgBdtIGWMWPG4Pjx41i9ejW6devGOTZ6\n9Gj88ccf2LRpE2bPng0AqKiowLZt2+Dj44MXX3xRd+6FCxcAQHANoPI7bNOmDU6dOmWx/OPGjcMn\nn3yC+Ph4hISE4NixY5g0aRLc3d2r/dzJkyexfPly/PXXX8jIyEBZWRnneGpqquC7Hj58OI4dO4ZV\nq1bh9OnT6NSpEz7//HOLZa+J9PR0pKSkoFGjRgbrDwCAh4cHkpOTBcWqFAqFwarQQUFByMnJMTig\nDA4OxoULF/DgwQP4+vpyjj322GMICAgQfKZHjx5YuXKl7nsmCIJwJrTLAjMMA29vbzz55JOIjo42\nGvVmzpihrsYmWvr06QNA6Dh46qmn4OnpCaDScbB69WokJCSgb9++FqUpAECHDh0E+7SRe/pjRP0x\nhKF0wB49epi1fLebmxu+//57fPzxxzh8+DDOnDmD8+fP4+zZs1i1ahXWrVuHhQsXGqyRZCiiQrsC\nw59//omkpCRERETo6ijk5+cbjLDVRsTq13NITEyEQqHAnj17DKZflJWVQa1WIzk5GR06dNC1DUMy\nyWQydOvWDTdv3jRRKwRRM+Q4IETPqlWrsGjRImzfvh3z588HALi4uGDAgAGYPXs2mjZtatJ18vLy\nAIBTDVifwMBAADA4E2vsM4bQfp7/A2+MCRMmYO/evQgLC8MLL7yAwMBA3azz0qVLUVpaavK9jckT\nGxsLLy8vgcd6yJAh+PDDD7Fr1y588803HA/4iy++CB8fH2zbtg1ffPEF5HI5Dh8+jIyMDIwdO1Y3\nwABq1q0hY9QcRo0aha+++gpr165FSEgIWJatNk0BAHbu3InXX38dHh4e6NOnD8LCwuDp6QmZTIb4\n+HicOnXKqG6HDh2qqwPwr3/9Cy4uLlbJXx3anMkHDx7oBqfGKCws5Bj7np6eBgdZ2ggWQzND2mPa\nWRN9avr+tN8zQRCEM1HdRIghtOMJU6irsYmWxo0bo2XLlrhx4wZu3rwJhUKBlJQUXdoCAE6dg759\n+xpchtEU+M5pALrfLLVardun1YGxsYIlz6n93OjRo3URkjk5Ofjvf/+LtWvXYvr06RgwYIDg2qb+\nDmp/u48ePSqoB6FPYWGh7v/Z2dmoqKio8be+oKCAcy9b64UgjEGOA0L0eHh46Aodpqam4tSpU9i2\nbRv27NmDa9eu4eTJkyYZdlojylhlXG01W0PGFsMwJsur/aE0JVLg7Nmz2Lt3L/r06YPt27dzjECN\nRoNFixaZfF9jbN26VVeVv7oc/S1btuDNN9/UbXt4eGDYsGFYs2YNDh8+jOeff95gmgIAeHt7AzCu\n24yMDKuewd/fX7fsore3N7p06VLj+stz5syBu7s7/vjjD0Fhqvv37xuNgMjOzsbbb78NNzc3uLi4\n4NNPP0Xfvn0RFBRk1TMYQ9veunfvbvfVC2r6/qxJOSEIgnAWzBkz1NXYRJ/evXvjxo0bOHr0qG7c\noZ8O0LBhQzz++OM4evQoSktLkZCQAA8PD3Tp0sWi+9WE9tmMjRWM6cZc/Pz88N133+HIkSO4d+8e\nEhISBCmPpv4Oav+dPXs23nnnHZPu7+Pjg/LycpPTdetKLwShhWocEJIiODgYw4cPx6ZNm9ClSxck\nJSXh2rVruuMymYwT5q+PNmfNULhbaWmpLuyMn9tmLp07dwYAHDlyxKgsWrQhZgMHDhTMHJ85c0aX\nX2gN2jSFoUOHYuzYsYK/l19+GQAMLnmodRBs2rQJOTk5OHDgAJo3by7I6dSGxPNrJQCVHnN+nQRL\nGDduHEpLS5GZmWl0CUZ9bt26hdatWwucBmq1Wvdd82FZFpMnT8b9+/cxZ84cLFy4EJmZmZg4cWKN\n36WlNG7cGE2aNMHFixd1swz24p9//jE4QNGmFZmbS0sQBEFUT12NTfTRpkUcPXoUx44dM+gU6NWr\nF86fP49Dhw6huLgY3bp1g5ubm81k0Ef725KYmGhwyWNTUltNRSaTwcvLC4DhlTEM3UulUuHKlSvw\n9PTUjSm0+jInDbNz587Iz8/HxYsXTTpf+50bkkmj0RgccxGENZDjgBA1mZmZuHTpkmB/aWmpLmxP\nP2S+fv36yMzMNGhwR0dHw9XVFStXrsQ///zDObZw4UI8ePAA/fv3NznFwBgdOnRA165dceXKFcTE\nxAiO5+bm6gzE0NBQANBVZ9aSkZFhsLCiufz555+4fPkymjdvjlWrVuGHH34Q/K1YsQIRERG4cuWK\nYF3grl27omXLlvj111+xcuVKlJWVYdSoUYL7DB48GN7e3ti2bZvgB3H+/Pk2CXGPjIzExo0bsX79\neowYMaLG80NCQpCUlMRZF5llWXz99ddISkoy+JnFixfj4MGDiIqKwr/+9S9ER0djzJgxOHbsWI2h\nhdbw9ttvo6CgAFOnTjWoK5VKhbNnz9ba/bWUlJTo6llouXLlCjZs2ABPT08MGzas1mUgCIJwJupq\nbKJPZGQkZDIZjh07hvj4eHTp0kXgFOjZsyc0Go0uf9/cNAVzaNy4Mfr27Yu7d+9i6dKlnGMHDhww\nq74BAHzzzTe4ffu2wWO7du3CP//8A4VCYTCCYsuWLbraAlrmzJmDwsJCvPzyy7oI1w4dOqBHjx7Y\nv38/1qxZY9AJcePGDU50wZQpUwAA77//vsElNktKSjiOiK5duyI8PByJiYnYtWsX59yVK1dSfQPC\n5lCqAiFqHjx4gMjISERERKBNmzZo3LgxCgsLceTIESQnJyMqKgotWrTQnd+3b19s27YNI0aMwNNP\nPw03Nzc88cQTGDhwIEJDQzFv3jz8+9//Rt++fTF06FAEBgYiMTERJ06cQOPGjfG///3PJnIvX74c\ngwcPxtdff419+/bpQgBv3bqFI0eO4ODBg2jXrh06duyIbt26Yc+ePejfvz+6deuGhw8f4vDhwwgP\nD7d6oKCNNhg3bly1IY1jx47FrFmzsHr1asEP6ejRo/Hll19i3rx5kMlkBpdFUiqVmD9/Pt5++208\n//zzGDp0KIKCgpCQkICrV6+ie/fuOHXqlEnFIo3BMAxeeOEFk89/++23MX36dPTq1QtRUVGQy+U4\ndeoUkpOT8fzzz+PgwYOc8//66y98+eWXCA0N5aSILFiwAH/99RcWLFiAnj17ml3d2RQmT56Mixcv\nYtOmTfjjjz/wzDPPoHHjxsjJycHt27dx8uRJjBgxQjCgsjWdOnXC/v37cfnyZfTq1QsZGRnYsWMH\nSkpK8MMPP6BBgwa1en+CIAhnoy7HJlr8/PzQtm1bnYE8ceJEwTk9e/YEwzC6FQhq03EAADExMXju\nuefw6aef4ujRo2jXrh1u3bqFPXv26Ipkm8qPP/6Ib775Bu3atcOTTz6JBg0aIC8vD+fPn9dNkHz5\n5ZcGx1jPPvssBgwYgGHDhiEwMBAnT55EYmIiwsLC8Nlnn3HO/emnnzBkyBBMnToVy5cvR+fOneHn\n54cHDx7g2rVruHDhAtavX69bujoyMhJfffUV/vvf/6JTp0547rnnEBYWhpKSEty9excnT55EaGio\nbjKJYRj88MMPGDZsGCZMmIAXX3wRLVq0wOXLlxEXF4d+/frh8OHDlqqcIARQxAEhakJDQ/HRRx/B\n398fJ06cwI8//oidO3eiQYMGWLRoEX7++WfO+XPnzsUrr7yC5ORkLFy4EHPmzMHu3bt1xydMmICd\nO3eie/fu2LdvH3744QfcvXsXkyZNQlxcnK5zt5awsDDEx8fjgw8+QFFREVasWIG1a9fi1q1bmDhx\noi7SQC6XY9OmTXjjjTeQmpqK5cuXIyEhAePGjcMvv/xisPCdqeTm5mLHjh1wcXER1CTgM3LkSLi5\nuWHnzp2CAkwjR46ETCZDeXk5evXqZVRHo0aNwqZNm9CmTRvs2LEDP//8M5RKJX777TddVIi2FkJd\nMHHiRPzwww8ICAjAhg0bsG3bNoSGhuLw4cOCJSNVKhVef/11ANDJrcXT0xOrVq2Cm5sbJk6caHW9\nBkMwDIOlS5di3bp16NixI+Li4rBkyRL8+uuvyM3NxTvvvIP33nvP5vflExgYiN9++w2NGjXC6tWr\nsX37drRu3RobN27E2LFja/3+BEEQzkhdjU300XcEGFrRwd/fX1dLSKlU2jRVwhAtWrTA4cOHERUV\nhdOnT2PZsmW4d+8eNmzYwFnFyRS2bNmCadOmwcvLC7///jsWL16MdevWISMjA6+88goOHTqEt99+\n2+Bnp0yZgvnz5+P8+fNYunQpkpOT8eqrr+LQoUOCJTaDg4MRFxeHzz//HG5ubvjll1+wdOlSnDp1\nCv7+/pg3bx569uzJ+cy7776LgwcPYtCgQThz5gyWLVuG7du3486dO4iOjhZEN3br1g2//vor+vbt\ni99//x3/93//h5KSEuzdu9eiVTUIojoYlUoljJ0hCIKoIyoqKtCuXTswDGOTWgeE7blw4QIiIyPx\nwgsv6ApgEgRBEISzMHnyZGzatAl79uyplchCghADFHFAEESdoFKpBLUlWJbFN998gwcPHmDQoEF2\nkowgCIIgCIIgiOqgGgcEQdQJf/31F15//XX07dsXTZs2RUFBAU6fPo1Lly4hJCQEM2bMsLeIBEEQ\nBEEQBEEYgBwHBEHUCS1btsTzzz+PhIQEHDp0CGq1Go0bN8Zbb72F//znP4LcQIIgCIIgCIIgHAOq\ncUAQBEEQBEEQBEEQhFGoxgFBEARBEARBEARBEEYhxwFBEARBEARBEARBEEYhxwFBEARBEARBEARB\nEEYhx4EZJCUl2VsEh4N0woX0wYX0wYX0wYX0IYR0Ii3o+xRCOuFC+uBC+uBC+uBC+hBSlzohxwFB\nEARBEARBEARBEEYhxwFBEARBEARBEARBEEYhxwFBEARBEARBEARBEEYhxwFBEARBEARBEARBEEYh\nxwFBEARBEARBEARBEEYhxwFBEARBEARBEARBEEYhx4FESMmvQEp+hb3FcBjyyzU4l1kGtYa1tygO\nQamaxd8ZZShVkz4AQK1hcS6zDPnlGnuL4hCwLIsrOeXIKFbbWxSH4WGxGldzysGy9M4QBGFDcnMh\nu3kT0NDvD0EQ4kJhbwEI6yjXsGi3LQ2pRZU/QK18FUgY1hAMw9hZMvux6GI+PvsrT7d96eVANKnn\nvE398L0SvPRblm5738AG6BHkZkeJ7MuN3HI8FftQt72wuxKvP+5lR4nsi6pUg7CNqbrtCa088e3T\nfnaUyL6wLIsxR7Kx/06Jbl/6uEZwkztvn0oQhG2QnTsHr5degiwzE+XPPYeiLVvsLRJBEITJUMSB\nyHn9j2yd0wAArudWYHxcth0lsi9nMso4TgMAeGJbutPOGqYWqTlOAwAY9GsmSiqcUx/FFSzHaQAA\n/z6lctpoHZZl0WJTKmffqutF2J1SbCeJ7M9P1wo5TgMACFz7wE7SEAQhJTxmzoQsMxMA4PLbb1Ac\nOWJniQiCIEyHHAcipqhCgz23SwT7d98uQVGFc4bAPbcvw+D+7y4W1LEkjsHEo4adSM/vN6wnqbPo\nUr7B/R22p9exJI7BifQyGMpeGefEzsfpCbkG919TldexJARBSA1FQgJn22X3bjtJQhAEYT7kOBAx\nK68WGj228ILzGcoZxWoYK2nwxZk8wwckjIZlcTytzOCx81nlTln/YWtykdFjD50wv3/5FeP9xKn0\n0jqUxDE4mWb8maedUtWhJARBEARBEI4FOQ5EzKmHho1CAIg5b3hmVcrEp1Zv6NzKc65w9EvZ1c+Q\nxt5yrnD0gnINbucbdw7MPet8zqXzWcbbyNQTzmco/3ZPGMGl5XhaGcqouChBEARBEE4KOQ5EioZl\nq50dA+BhZD6WAAAgAElEQVR0edvHa9DHDifL2zYWbaDl8H3jRpIUSXxYhupKO1RnREuR2/kVuFNg\n3JGSVqx2uqiUmvqQPx44XxQGUXcwmZnwHD0a9Tp2hOuyZfYWhyAIgiA4kONApFzMLoeqrGpQr3QV\nVvyuaRAsNY6lcg1ldzn3+PEaIhKkxjHe84Z5cxVyIq3MqYpG8vXRsYELZ/tcVjnyypynNgi/f+jg\nz9VHXhmLizVErUiJ/HIN/s6s/nmdrU8l6hbXH3+Ey/79kN+8CY+ZM8Hcvm1vkQiCIAhCBzkORMpp\nXppCjyA3TH2iHmcf31CSMtklatzQS0WQM8CBFwI45yQ8dK5Q4z8zuG3kp971Odv3CtVIqSZ0X2ok\n8t6ZKW3qwUtR5XDTsMCp9OqjNKQEvw95PsQdTwe6cvYdcyJD+e+Mck6hyHBfBVZEcpeldCZ9EHWP\n+8KFnG23RYvsJAlBEARBCCHHgUj5J5ebhtCxgSv6h7hz9h1PdZ4ZZb4+WikVaO/vgiCPqiZeVMHi\n70znMAxzSjXILKmaPXeTA0/6u6BXkPMahkkG3pmRLT05+5xpRtlQHzIkzIOzz5midJJyudEGTzZw\nQZ9Gbpx957PKketEUSmEfWEqnCvdkCAIgnBsyHEgUm7wBv0tfRV4KsCVE55/v8h5ZpRv8Aofhvsq\nwDAMegZzB/415f1LBb4R1MJbAbmMQS++PpzEMFTxHCmuMiC0nhy9grj6cKYoHX4fEu6rQE+ePk6l\nl6HCSeocCPoQHwUCPOR4XKnQ7auMSnGeNkIQBEEQBKGFHAcihT97Gu6rgJucQZeGPEPISWZQBUaQ\nT2W+trMahvz20cK30vjhG4bH0kqdIiqFr4/mPpWOlB68CIwL2eVQlUp/RllVqkGGniPF5ZEjpbWf\nAv5uVT8LeeUsLjhJ0UihI8VYH+IczkeCIAiCIAh9yHEgQrJK1LhXWBVJIGOA5t5aw5BrCP2d4RyD\n3HM846alEUP5bKZzpG/wVwgIf6SPTgGu8JBX5fWnFmmQVix9Q/l8Fvc9aOlTqQ9DM8rOsLoC/xmb\neyugkDGQMUJnijOk97AsK9CJrg/hRek4gz4IgiAIgiD4kONAhFxVcWfGIvxc4P6oyFt7f+6g/5rK\nOXIkr+ZwB/3aivnNfeTwdqkylPPKWTwokr6hzNfHkw0q24WbnMHjfgrOsWs50jeU+e9Mx4Cq96Qd\nbzWBaypn0Icwn1+LM/YhmSXcCAwPOaNzKLU30D6cwflY1yxcuBB9+/ZFSEgIWrRogVdeeQVXrlzh\nnMOyLObOnYvHH38cQUFBGDRoEK5evVrjtY8fP47evXsjMDAQ7du3x88//1xbj0EQBEEQkoUcByLk\nJi8Xt5VvlSGoP3sKAJeyyyWfo1xQrkG63qy5gqkMRQcAhmHQiqcT/uyzFLnFq22h30ZaK7mGkDPM\nsPPfmcf09BFB+kArPR3w+xBneF/4+mjuI4dCVulwDK0nh6fe6hs5pSzuFjpH7Zi65Pjx43jjjTdw\n8OBB7N69GwqFAkOHDkVOTo7unO+//x5LlizBvHnzcOTIEQQEBGDYsGHIz883et2UlBRER0ejS5cu\niI+Px7///W98+OGH2LVrV108FkEQBEFIBnIciJCUfO4gt5l31UA/tJ4cDdyrvtaCClZQ9Etq8AtA\nhtSrGvQDVbPtWs7WsFa72CmuYDmpLAyApnptRH92GRCmeUgRoWGorw9u+zjnBKHo1fUh+tEYQGXd\nB7XEnY98R5t++5AxjCDqQOp9iD2IjY3Fq6++ioiICLRp0wbLly9HZmYmEhISAFRGGyxduhTvv/8+\nhgwZgoiICCxduhQFBQXYvn270euuWrUKQUFBWLBgAVq1aoXx48dj1KhRWLx4cV09GkEQBEFIAody\nHLRt2xZKpVLwFx0dDQCYPHmy4Fi/fv3sLHXdczOPO8ht5lO1lALDMGhbnxdamyNtx0F1RiEAtOPr\nQ+Kh6LcLuPpoUk8ON726Bs6mjzK1cIY4zLvqneGnKiTlVaBc4oZydX1IsKccAXrOx1I1cCtf4n0I\n7/maezt3H+IIFBQUQKPRQKlUAgBu376N9PR0PPPMM7pzPDw88PTTTyMxMdHodU6fPs35DAA8++yz\nOHv2LMrL6XskCIIgCFNR1HxK3REXFwe1umpAm5aWhj59+mDo0KG6fX369MHy5ct1266u3NkxZ6Am\nQ/lxpQJxD6pWDziXVYahzbjrs0sJvlHDH/S39nOuGXZ++2jG08fjPH0k5VYgv1wDbxeH8iPajDsF\nFdD3AwR7yuCpqHpWPzcZAj1kunSXcg1wJadckOsvFSo0rMC5JGgjSgUy9JYuPZdVjpa+3HYjJW7x\n3xmfGvoQijiodWbOnIm2bduiS5cuAID09HQAQEBAAOe8gIAApKamGr3Ow4cP0adPH8FnKioqkJWV\nhaCgIIOfS0pKskJ6y671FG87NzcXt20ohyNhS/2KCUPfMeC8+jAG6YML6YML6UOIrXQSHh5e7XGH\nchw0aNCAs71u3Tp4e3tj2LBhun1ubm4IDAysa9EcBpZlazSUOzZwBVCo276YLe1BrsBQ5g36n/Bz\ngYus0iAEgLsFaqhKNVC6SdNQFjiW9GbXAcDXVYYWPnIkP5p1VrOVUSmdG0rTUBbMrnsLu70nG7ji\nwN0S3fbFbOk6Du4VqnXvAgAEuMvg48p9Fzo2cMUxPcfBxaxyvNS8riSse2pytvHTe6Tep9qbjz76\nCAkJCThw4ADkcnnNH6gFaho8mUpSUpLF1/L19bWZHI6ENTqRGr6+vgBs196kALUPLqQPLqQPIXWp\nE4e1nFiWxbp16/DKK6/Aw6NqtvzUqVNo2bIlOnXqhPfeew8ZGRl2lLLuySrVIL+8avrUS8GgoQf3\na+TPKPNn06QGPz+5Gc9QdlcwAkOAn+MtJfg1H/gRKQDwOK8goJRD0QWONgP6aM0rCJiSJ93id9XV\nN9Ai6EMk3D4AQzUOuH1IK18XMHrb9wvVKFVLO53FXsyaNQu//PILdu/ejbCwMN1+7YQB/zc/IyMD\nDRs2NHq9hg0bGvyMQqGAv7+/7QQnCMI0ysrg/sknqNezJ9xmzwYquL8vrvfuwXP4cHj17QvFkSN2\nEpIgCEM4rOMgLi4Ot2/fxrhx43T7+vXrh2XLlmHXrl2YPXs2zpw5g6ioKJSWllZzJWnxgJer3cRL\nDoZhOPv4hvOdArWkc7ZTi/jFEYWGED8KgT/DKCUeCPQhnLHjG89S1oewfQj1IWgfEjaU+X2IwfbB\n60Nu5kvXkVJSwSK7tCoEQ8YAjTyFzsfGXlX7WAC3JdxG7MWMGTN0ToPHHnuMc6xp06YIDAxEXFyc\nbl9JSQlOnTqFrl27Gr1mly5dOJ8BKscXTz75JFxcHDz9hpb9JCSIy969cFu8GPJLl+AeEwPFH39w\njod8/z1cjhyB4uxZePzrXwDVIiEIh8GhUhX0WbNmDTp27Ii2bdvq9o0YMUL3/zZt2qBDhw5o27Yt\nDh48iKioKKPXskeuYm3xd7YMgLtu2welBmXyd/FAVnmlQ6GCBY5fSkYTj9oZhNhbJw8KPAC9+cDi\ntBQkZXHPqa92AVA1SPwzJR1t1bUz8Le3PlKy3QBUGTkVWalIqtBwzqlXrABQFYp//n4Okuql14o8\n9tbHP2mu0O/qZPmZSEriPqtrHve9uppRgCR+I7IR9tbHpbvc796tNE/4rGUA4KnbTFaV4Z9/ksDz\nUdoEe+vjfgkDoCqqzd9Fg5vJNwTnBSnccE/vvTp+/Q6Y+hrBebagrnIVHYlp06Zhy5YtWL9+PZRK\npa6mgZeXF+rVqweGYTB58mQsXLgQ4eHhaNmyJWJiYuDl5YWXXnpJd50333wTAHS1kCZMmIAVK1Zg\n5syZmDBhAhITE7Fx40b89NNPdf+QBEHA8/XXOdse77yD/GvXdNt+eo4EWXY25ImJUPfsWVfiEQRR\nDQ7pOMjIyMD+/fsRExNT7XnBwcFo1KgRbt68We15jpCraCtO/VMIQKXbbt6gHsLDQwXnPZaUgVPp\nVTnKGv8mCG/sLjjPWuytk4JyDQqPVxXGcpEBT7VuKYjC6FhRgE0PcnXbuS5KhIf72Vwee+sDAHL/\nTgNQNUPcuVVThPHC0bt5lQDJVcZiJjwQHt7U5rI4gj6KbmYCqIpKah/WCOEh3HfBq1ANXEzTbd8v\nU6BlyxBBO7IWR9BHRZYK+jVQWjf2R3i4N+cclmXhfTZVlxZVrGHg26Q5Aj1tm2/uCPrITi8FkKnb\nbuLjhvDwEMF5bR7m4K/cIt12Sb1AhIfXs7k8jqATe6A15IcMGcLZP2PGDMyaNQsAMHXqVBQXF2P6\n9OlQqVTo1KkTYmNj4e1d1X7v3bvH+XxYWBi2bt2Kjz76CD///DOCgoIwb948wX0IgrAPTFFR9Seo\npRvxRhBiwyEdBxs3boSbmxsnwsAQmZmZSE1Ndapiifyw6yAPwwP5MG8Fx3FwK68CaFyrotmFNJ4+\nAj2EqRuAMI9bqjnbag2LtGKhTvjwHQn8AoJSIpUXmh/oKczQCvKUwUPOoPhR3npeGYucUg3qu9un\nMFttYkofwjAMwrwVnCKAt/IrbO44cARSi7hRA4beF8B5+hB7oVKpajyHYRjMmjVL50gwxL59+wT7\nevbsifj4eKvkIwjCPjCa2onsIgjCfByuxgHLsli7di2GDx+OevWqZnMKCgrwySef4PTp07h9+zaO\nHTuGUaNGISAgAIMHD7ajxHXLvQJejQMD+cmAMEc5KVeag9x7JuRrA8Kc/hu5FWAlmD+aVqyBfs02\nfzcZPBRCR0oTLzn0V1/MKtUgp1R6P84sywrbiJewjcgYRlAbxFneGaN9iI9z6ONuIfe5DLUPQFgH\n44ZE9UEQBOFQSHCsRhBixeEcB8eOHUNycjLGjx/P2S+Xy3HlyhWMHj0aTz31FCZPnoyWLVvi0KFD\nnDBFqSMY9BsZ5LbiVc0/lyXN4jJ3+Y4UI/oIrSeHh7zKgM4o0eB+ofRm2e8VcI0ZY0ahXMYg3Jdr\nCJ3NLDN4rphRlbEorKgadHgqGPgZWYaT/86cleg7I3A+OnkfYqoz9nGl9N8XgiDqmNooHCM1yHFA\nEA6Dw6UqREZGGgxZ9PDwQGxsrB0kciz4joPGRgb97fy5g36pVgA3VR8KGYPWfgr8nVll/NwuUKOJ\ngRUYxIyp+gCAdvVdcCWnql3clmDl/Ls8R0pjA6uQaGnr74IdKcW6bSm+MyUVLDJKuCsIBBtJP2hb\nn/oQfcJ9FXCTA6WPTleVscgt08DX1eH87wRBENKBHAcE4TDQiEdEsCwrmCU3tPQgULmcmL55lFas\nkeS643x9GJs9NXSMbzBIAbP0wWs79wqlZxhS++DCX6oz2EMOhcywI4Ufsi9FfQCmtxEZw6Axz8nC\nj1YgCIIgbAw5DgjCYSDHgYjIKdWgSC/s2kvBQOlqeNDvKmcQxCsCxy+KJgVMzdcGhE4WKQ7675qQ\nz68llKcr/melgKmpPYCwPoYUDWVBak+174vQSJZiXRDz2gjf2Sa9NkIQRB0iwT6VIAjpQo4DEcE3\n7JpUE3atPa7PHQkaysJ8beOpB3x98MPYpYCp+dqAIX04Qftwdn3wokqqM5Lru8k4dUEKKlioyqQ1\nyC2uYJGpl7ohZ4CgalaO4LcfKfYhBEEQDgU5VwjCYSDHgYgwxwgCDM2wS2uQW1kx33RDyBlmlIWz\np6Y7UpxDH8bbR7CnHHp2MjJLNCiukNaAxRx9MAwjeGekZijf5/UfwZ7GUzcA50nfIAiCcBhoOUaC\ncBjIcSAizBn0GzoutUFuVqkGJXqP5O3CwNdI6gbgpDPK1c2w8449KFRDrZG6oWzckaKQMYJCgXzD\nUuyY63yUeh9i6nKuWoQRB9LSB+Fg0EwrQdB7QBAOBDkORAS/iFd1FfMB6RvKfCOouor5gOGIAynl\nbBeWa5BTWvU8CgZo6G78FfdUyOCvtzShmpVeHQxziiMCwjYitXfG7D5E4vowZxUSgCIOiFqipARu\ns2fbWwrCHtByjDUjoXEaQYgdchyICH5F9BoHuRIPzTdXH/XdZPBUVP1IF1awyCmVTggc3+gP9pJD\nXk3YNSA0DKXURtQaVqCTRmYahlIrGGl2HyJxQ/kB33FQTX0DQJj+JbXUDcI+uMXEwD0mxt5iEIRj\nQo4DgnAYyHEgIgSGYQ2DXOFye9Ia9KcVcY3+6oqaAZU524IoDAnpJJWnj2CP6vUBSNswzCzRQH8F\nUqUrAw+F8zpSABv0IRKLODC3D2nEO55apEG5xNJ7iLqHnAYEUQ3kOCAIh4EcByIijTfor2mQKzAK\nJbacmsAIMsVQlnDodXoxv33U/HpLORQ9rdg8IxkAQrz4M8rS0UdRhQa5eqsiyBmgQTWpLICB90Vi\nNR/MdaS4KxgEelTpjIUw/YMgCBMoLITn2LHwadwYnuPHA0VF9paIcFQkNG4lCLFDjgORwLKsYHas\npkGurysDb5eqGdZiNYssCYfmm2QoS3iGPbXQPMcSIHF98NpHoCn64EccSCgUXTC77iGHrIb8WkH7\nkJAjBaA+hCDshUtsLFz27AFTWAiXXbvgsmOHvUUiHBVaVYEgHAZyHIiEnFINCvWWhvOQM1BWs4IA\nYDg0X0oDf/6Avab8dUDahhA/7aKm/HVA2kt28r9bfpi5IaRsFPKjJxp51dz9N/KUQ7+XSSvWoFQt\nndkfi/oQgXNJOm2EIOoKz3ff5Wx7aLdVKrhs3gz5n3/aQSoHpbgYLlu3Qh4fb29JCIJwcshxIBLu\n8AanofWqX0FAC98QklJYLb8wWWg940vtaeHnbPOLxYkZvmEYWsPScoCB9lEkHc++RfrgL1FZJJ30\nHn6agSnvi6ucEczC81OmxEpRhQaZJVXtXc6Y6lySbh9CEHaDZYHyctTr2xeeb70Fr/79odi1y95S\n2R+WhdeLL8Jz0iTUi4qC68qV9pao7pHIbzBBSAFyHIgEvuOgpvXGtTTkDYQzSqRhGGpYVmAYmqIT\n/fxkAHhYLJ1B/x2eI4UfTWCIhjx9ZEhKH+a3D28X7sobpWogr1wagxaL+xAPafYhwggMORQ1rEIC\nSLsPIQh74vLLL5DfugUAYFgWXuPH21ki+yNPTITir7902x7/+Y8dpbEPDDkOCMJhIMeBSOCHoZs8\n6HeX5iD3YbEGZXr2i48rA1/XmptzAN8IKpaGEQQYaCMmhF0HuHPPySzRQCORH2n+DLspjhQACHCX\npjPFEkcbIN0+RKAPE94XQNp9CEHYE/n58xZ9jklNhefYsfAaMACKuDgbS2VfZDdu2FsE+yORMQlR\nPUxaGjzHjYPX889DceSIvcUhjECOA5FwJ9/8MGNAuoNc/uy6qfoQGEEl0jCCVKUa5OlVzHeXC6MJ\nDOGuYOCjVytDzQLZEimgaSi9xxT4envo5O+MVPsQS1JZAEPtQxp9CEHYHRPSLw3h/vnncNmzB4qE\nhMoVGkpLbSwYYVfIceAUuH/+OVx274YiMZHeYweGHAciweKIA94gl79kn1ixdLbQ313GKfaWU8pK\nYh12fvto4qUwqQYGADTkRR1IwVAuqWA5zyFjTCsWCQgNZSnoA7Ai4kCifYglqT2AgYgUiaRuEA6I\nExlM1oSju27ZUnWdvDwoDh2yhUiOgRTaQH4+kJtr+ef5OlCpgMJC62QiHA7XzZt1/2fy86E4eNCO\n0hDGIMeBSLB0dowfii6VQa6l+lDIGPgLQtHFrxNhoUjT9AEAARKsc3CPl6bQyFMOFxPy1wFphuZX\naFg8sCCVBZBwxEGhpREH0nQsOQInTpzAyJEj0bp1ayiVSmzYsIFzXKlUGvybNm2a0WseO3bM4Gf+\n+eef2n4cwk4w5eX2FoF4hMuOHfB57DH4NG8O1+XLLbuI3nKMbnPmwDcsDD6tW0suLYXgwlRIZ5Uv\nKWHaFAthdyydHZNqWK2lhd6ASsNQv5r6w2K1ScuwOTJW6UOCofnW6EMQcSABZ1tqkRp6q7nC300G\nLxfT/MZSTe+x1PnY4FHUklad2aUalGtYkx1ThHEKCwsRERGBUaNG4a233hIcv379Omf77NmzGDly\nJIYOHVrjtRMSEuDn56fbbtCggfUCE46JFGbpJYLnhAm6/3vMmIGy114z/yKPvk/m4UO4L1hQ+f+8\nPHhMmYL8K1dsISZBECZCjgMRkFumQa5e/rqrTFjZ2xjCqvniN4IA4Qy7qY4U4JFhqKr6vBSiMIRh\n6KbrQ5CqIEV9mOEYkuJKE5amKQDSdCwBljtjFTIG9d1kyNKrBZJZokGwCUs5EtXTv39/9O/fHwDw\n9ttvC44HBgZytvfv34+WLVuiZ8+eNV47ICAA/v7+thGUIAiLYDIyzP/QI8eB/MwZzm7Zgwe2EIkg\nCDOgVAURwB/0N/GSQ2Zi/rqfmwxyvVPzylmUVIjfG8+fUW5qlSEkfsNQWPjOuVMVLC0ECEgzFN3S\nQpGANFMVStUs0oqqnoOB6TUwAGn2IWKjoKAAsbGxGG/ikn19+vRBq1atEBUVhfj4+FqWjrAIC4sj\nCqCIA2lB3ydBOAwUcSACrJldlzEMAtxlSNMb7D8sUZtlSDkaLMtaXCwSMGQoi98Qsm5GWXqGsjX6\nEBa/E79RaE0fIjCSJaCP+4Vq6A9FgzxlcJObbrQEeMhxVT9qSQLvjLmwLIvLly/j+vXryMrKAsMw\n8Pf3x2OPPYY2bdqYXJzVUrZv346ysjKMGjWq2vOCgoKwcOFCdOzYEWVlZdiyZQuGDBmCffv24emn\nnzb6uaSkJJvJWtO1njKyPzcvD7dtKIejk5OTgyDePlO+B77+0lJTke2geuPLmvuoaKCx5/R/+BDN\nePts2TZrG/7zpty6hXa8fWqNhvNM/M+kp6UhKykJvg8eIJx3TEy6sAZneE7+956amoocI8/tDPow\nF1vpJDyc/5ZxEa/16ERYmourJcBDznUcFGsQWs8motmF7FINivSiJrwUlaHDphLIM5SlUCVe2Eas\nMAyloA8LC98BhlYREL9RaI0+6j+KWlI/euXyyiqjltwV4s3pFxYTNe+nkJ8qJoU+xFTi4+OxYcMG\n/PrrrygoKADLmw1kGAb16tXDgAEDMGbMGPTu3btW5FizZg1eeOGFGmsVhIeHcwZCXbp0wZ07d7Bo\n0aJqHQc1DZ5MJSkpyeJr+fr42EwOR8LYAFe/BoUWS54/KDAQ/iLRm6+vLwDjz+nSsKFgn5jbRFhY\nmGCfXCar9pkCGzZE/fBwKG7cEBwTqy7kJ07A/csvwfr4oGTePGiaNzd6rjV9iJgJDg5GAwPP7az6\nqI661Ak5DkSANYXeACDIQ4aLettpReIe5BrShzmzW0LHgbgNw8JyDSffWsFUfuemEsTTR5rI9QEA\nd/Itf2cCebnq6UVqsCxb6zOotYk1fYiMYRDoIcMDvdD+tGI1wrzF+/NhbZ8qtT7EFA4fPow5c+bg\n3LlzaN26NcaOHYsOHTogLCwMSqUSLMtCpVLh9u3bOHfuHOLi4jB06FC0b98en376KZ599lmbyXLh\nwgWcPXsWn332mUWf79SpE2JjY20mD2EjKFVB+ljyHUvt+1Sr4TlhAmQPH1ZuMwyKtm61r0wEYSLi\nHfk5EXcLLQ8zBoAgniEkOceBmSsiBHlyjepUkeuDP5vc2EsOuRkV3vmGstjbR5maRWoxvy6I6e+M\nt4sMXgoGhY+iWso0QE6pBvXdxVv8zppUBaCyjXAcB0UScxyY2YcESqwPMYVx48Zh7NixWLZsGVq1\namX0vC5duuDll18GULkKwsqVKzFu3Djcv3/fZrKsWbMGTZs2RZ8+fSz6/MWLFwWFFmsbJisLspQU\nqCMiAA+POr230yE1Q9MQZWWQX7oETZMmYA1EJTgslnw3Gmk5ZmU3blQ5DQC4HDpkR2lEAstCdvky\n4OVlb0mcHvGO/JwIawqbAQYcByKfHRMUvjPTgOHrI13kg37+7Lq57aOhB3d5ucwScS8v96BIDY3e\n2CTQQ2Z2WH2QpwzJeVV6TSsWr+NAw7JWrTIBaKNSqtZGF/sMu7WpCvwVFMTeh5jCxYsXzV6VoFWr\nVpg/fz5mzJhh0vkFBQW4efMmAECj0eDevXu4cOEC/Pz8EBISAgAoKirCtm3b8N577xmMAnrzzTcB\nAMsfrRn/448/IjQ0FK1bt0ZZWRm2bt2Kffv2Ye3atWY9izXIrl+H1+DBkGVkQN26NQp++w2oJ+J8\nwdrCCSMOWEueubwcXgMHQnHmDFgfHxTu3Al1x462F46oFVg3N+FOtRqQi3OMURe4f/gh3FasACuT\nwf/jj4H//MfeIjkttKqCCLCm0BsgnGEX+yDXNkZQFaI3gqyMSHGRMWjgLp0l96wNQwcMhKKL+J15\nWKxBmd7X6ePKQGlGTRBA2IeIPSrFmuKqgHOmKlizlKGpnz179iwiIyMRGRmJ4uJizJ07F5GRkfj6\n669158TGxqKwsBBjxowxeI179+7h3r17uu3y8nJ89tln6NGjBwYOHIiEhARs3boVUVFRFj+Pubh/\n8QVkj5ahk1+9CtdVq+rs3oRjw1jg5HCJjYXi0dKETF4e3MVkRFmTqiDidEEOBhwETF6eHQQRB0xG\nBtxWrKj8v0aDZl99ZWeJnBuHijho27Yt7t69K9jfv39/bN26FSzL4ptvvsGaNWugUqnQqVMnxMTE\noHXr1naQtm4oqtAgs6RqUCpngEZmrhfu78Y9P7tU3INca4tF+roynGJvhRUsStWsWVXVHQlr9QEA\n/u4yZOi1s+xSjVnL0zkS1s4mA5X60EfM74w1hTO1SK0PsTaKS0rtw5Ho1asXVCpVtee8+uqrePXV\nV40e37dvH2d76tSpmDp1qk3ksxSX/fs5265btqDs3Xdr/qCIZs4dConrjd+eFGfP2kkSC6jpuzF0\nXGrfp6HnUYvbGV8rsCxcNm+G28KF9paE0MOhHAdxcXFQ6708aWlp6NOnD4YOHQoA+P7777FkyRIs\nWU/l6+IAACAASURBVLIE4eHhmD9/PoYNG4Y///wT3t7e9hK7VuEP+ht5yaEwM4S8vsQGufxUBXNn\n2BmmchUGvqHMDz8WC7aYYeevSpFdIt42Ym3+OgD48/Uh4ndG8L5YoA8p9SEVGhYPeBEHTcx1HEjo\nfbEUlmWxevVqrFu3DikpKQYNfoZhkJWVZQfpHByJ5WzXKrm5wKOVB0xGaoamM2Hgu2Oc4X2hNitA\nceQIXDdssLcYBA+Hchzwl1Vat24dvL29MWzYMLAsi6VLl+L999/HkCFDAABLly5FeHg4tm/fjgkT\nJthD5FrH2rB8QFpGIWB9mDEAgeMgq0S8jgNrC98BBtpIqXi939am9gBCQzlLxO+MTfQhoT4ktUit\nizYCgAbuMngqzEvd8OPpI6dMAw3LQiaVUFoT+Oyzz7BkyRK0bdsW0dHRUCqV9hZJPNDsosm4rViB\n0mnTzPuQxI0wi+oiiAVD353UHAfOEFVhA8hp4JhY7Di4f/8+9u/fj8TERFy7dg3Z2dmVM7n166NV\nq1bo2rUrBgwYoCtoZC4sy2LdunV45ZVX4OHhgZSUFKSnp+OZZ57RnePh4YGnn34aiYmJknUcWBtS\nC0grrFZVqkFeWVUH6yavLO5nLvXdZUBu1baYdUJthIugeKZNHCli1ge1D31soQ9XOQMfFwZ55ZV9\nkYYFcstY+LlJeEDPY9OmTYiKisLq1avtLYr4qKio+RxnxIBBzOhVnyecAGcI4ycnASFizLa4/vjj\nDwwfPhzt27fHhx9+iISEBPj6+uLJJ59E+/bt4e3tjYSEBHz44Yfo0KEDhg4diri4OLMFi4uLw+3b\ntzFu3DgAQHp6OgAgICCAc15AQAAeSviHpXZmkzVgRdpx8aMNmnjJLZrl4+skR6SGUEkFyynMJmNg\nUW0CKc0o18YMu1jbB1B7fYhYsUX7AIRRB2J+ZyyhpKTE4uUQnR6pGUK1iSVjFZGObwwipWcxheoi\nDpwt0oIgHBCzRpCDBg3C6dOn0a9fPyxduhR9+/YVpBdoyczMxJEjR7Bz50688sor6NKlC/bu3Wvy\nvdasWYOOHTuibdu25ohokKSkJKuvURvXMoUraa7Q/5rcCzORlJRu9nU8ZB4o1lR2umoWOHvtBmy1\nDHtd6iQxSw6gaimbBrIyi+6vKOXq9eqdNESU22YWqC71caeYAVC1JniAqwYpyTfMvo46XwHAVbed\nnJ5tUTszRF3qQ80C9wo9AFQNMMrSU5CUad51SrJlANx123ezC5CUZJtc7bruQ27kuEPfR8zk3EdS\nuXmDlDxeO0svKLXZc9S1Ps7e4bZ17/J8JCVlm30dT7gBqHI6nLtxGxof2zgPbKWT8PBwm1zHEJGR\nkfj777/x2muv1do9pArjjI4DloXi998rjcCwMLAMY9GKAqbeqy6QXbgAeXIyyp99FvDxsegaLMPA\n6/JlKK5cQUX//oCHR80fkjLVOQ4kYlwbbPcSeTZC+phlOrZp0wbLli0zKf2gQYMGiI6ORnR0NO7e\nvYsffvjB5PtkZGRg//79iImJ0e0LDAzUHdO/f0ZGBho2bFjt9Ww1eEpKSqrVgZghcq5nACjTbT/V\nohHCG7kb/4ARGpxL48y0KRs3Q3Mf6z0Hda2T38sLoJ9j0KqhN8LDQ82+TrPcXCC9QLct9/FHeLhl\nP/z61LU+7t0vAVBl0DZXuiM83Pz0oFYoBFKqiptpPHwQHl7favnqXB8FFVCzVQ6P+m4ytH/c/Pur\nHpYBVzJ02yVyd4vaGZ+61gfLskhPSAVQNSjp0boZ/N3Nm2VvWKYBzqTqtvPVcps8hz361KL0HABF\nuu12TRogPLye2ddpfCsTVwtKddteDRshPMT6Qb89dGIJ//vf/zBixAjExMRg3LhxNf4OE3pILWfb\nBNxnzYLbsmUAgNARIwyfZGhG2UF1pThwAJ5jxoBRq6Fu0QIFCQmAi4vZ13HduhWtH6X7VHTogMK4\nOGnPrNeEM9Q4IAgRY1aqwvz58y2qWRASEoL58+ebfP7GjRvh5uaGEXo/Lk2bNkVgYCAn7aGkpASn\nTp1C165dzZZJLNwttD7MGJBOqLEtikUCEtIHv1CkjfSRI9Kwa1sUzgSk0z6ySzUoqqgaiHkqGMGz\nmYKPCwOF3li2sIJFSYU4Z0hqrY2I9J0xlaCgIAQHB+v+OnbsiBs3buDrr7/G448/jsDAQM7x4OBg\nNGrUyN5iOyZOGHGgdRoAQMNffjE462qzon91MHvrOXGiLnJEnpwMl61bLboOU1TlxFScOweFBam9\nkqK62XipOFQo4oAQMQ61qgJQOUO2du1aDB8+HPXqVc0CMQyDyZMnY+HChQgPD0fLli0RExMDLy8v\nvPTSS3aUuPYoVbNILaoajDKwLH8dkM4gV1D4zsJ8C0F+skgNwzv5/EJvtnEsZYlVHzYofAcYKAYo\n2vdFqA/GgsEXwzDwM7CEaSOF+FYiuZNvffFMQNiHiPWdMZVhw4ZZ1HYIA1BxRNNx0BoHTH4+Z1v+\n558oHzPG6uvKLl8G9IqAG765eN9D+Y0aUimdIeKAnAQ6ZP/8A/dPP7W3GIQZmD1iatmyJRYuXIio\nqCgAQFlZGbZs2YL+/fvr0gms4dixY0hOTsb//d//CY5NnToVxcXFmD59OlQqFTp16oTY2Fh4e3tb\nfV9H5D5vZizIUwY3uWU/GFKpim6riAO+PsRa/O6OICLFRoaySPVhq8J3Pq4MZExltXwAKKhgUaZm\n4Wrh+2cv+I4DS98XoLKNCBwHVlzPHmhYFvdsFHEglT7EVJYuXWpvEaSDE0YcWIwFRlat1U6o7p62\nMm6lZiTz8Jg8ufoTDHx3NtOto0ARBzo83nwTirNn7S0GYQZmOw6ysrJQWlqV11lQUICpU6dix44d\nNnEcREZGQqVSGTzGMAxmzZqFWbNmWX0fMcCvhm7pzBggnRl2vmFo6YyyVELRa00fIp1ht9U7I2Mq\nQ/ozeYZykKe4DGWBPqyoiCqFPuRhsQZlemIrXRn4uJqfugEYiNIR6TtD1D0mF0fUGhOlpZVRCl5e\ntSeUoyIWg8pWxq1YntcQBQWAm1u1tR5kaWnVX8MZlmM0hJi/d0vRaMhpoEVEfbxlIyYeYl3az9E5\nn1XO2W7qbbnRIgXD8F5BBScc2FUGBFtoyElh0K9hWVwUtBHLDENfVxn059LzylmUa8T3XvPfmTAb\nvjNibCMX+Pqw0LEESKMPOZdVxtkOs8KRIhXnozWoVCrMmTMHkZGRCAsLQ1hYGCIjIzFnzhyjEwAE\nzDIy5cePw7t1a/g0aQK3BQtqUSgHwEAIvkXRA/YYk9rIcWDK8zrcqhwsC4933oFvkybwfuopyK5d\ns+paApwh4sAZoZQtAID85El4R0RU9vFz59pbnBqxieOAqB2uqbgv1ZP+rkbOrBl/CQxyr+dy9dHe\n3wVymfOmbtwvVKNAr0Cd0pVBMwsNZbmMEf269CzL4p9cG74zEmgj/D6kQwPn1sd1fp/awPwq6Fr4\n+hCjY8kabt68iZ49eyImJgYVFRXo1asXevXqhYqKCsTExKBHjx5ITk62t5iOiRkDZo/334csOxsM\ny8J9zhwwGRk1f0hKWGJk2cPQrMOIA8XBg7a5l42Q//UXXNevBwDIbt+G+5dfWn4xZ4g4oFSFSqT2\nvVqIx7RpkGVlVfbx8+aBSbfNUui1BTkOHJibedzBRbivFbNjEhj0J/OMwpa+lg/6lbwZ9twyFhUi\nm2Hnt4+WvgqripeJfQY1tYi7goCPC4OGHpZ3cWIPzWdZ1rZ9iMj1AQDJgnfG8j6E3z6kXuOAz/Tp\n05GXl4ddu3bh5MmTWLduHdatW4eTJ09i586dyM/Px4wZM+wtpmNihpHALyYnT0y0tTQOjevatWBS\nUuwtRs3UoeOA0UsXdgRcNm3ibu/fb/nFnMGAJsdBJfaKONBo4LJxI1wXLwby8uwjgx7yK1c424oT\nJ+wkiWlYNIrcunUrzj7KSyktLQXDMPj5559x0IAXlGEYzJkzxzopnZSbvOrfLXyce9BvS33IZQx8\nXRmoyqo6a1WZBg3MXN/eniTncb21za3QByD+NsI3Cpv72NaRIjbDMKNEw4lIqadgEGiFI0XYPsQ3\nW8B3pLTwsWHqhsjah7WcOnUK77zzDiIjIwXHevfujTfffBNLliyxg2SEw2GlUeTToQNyU1IApbJO\n7mcRtrqnGMPybbnKgzMY0M7wjKZgp4gDty+/hPt33wEAXGJjUXjkiF3kMIqD9wEWWRqHDx/G4cOH\nOft2795t8FxyHFhGXpkGD4urGo+Csbz6N1A5w65PbpljN0xD2HLQDwBKNxlUZVUdV24piwbuVl2y\nTuEbytY4UgBA6cb98RdbGxG2Dyv1IfJ3ht8+mlnpSFG68fUhvsGPLduIUB/iah/W4uvrC2U1hpxS\nqYSvr28dSkRIGbfly1FqagSLiGscOL1R6azP74TPba9aHVqnAQAo/v4bsitXoImIsIssBnHwtmD2\nqCmtpoqohE3gD3DDvBVQWJjPD1QWv9NHJcLZsRu8VIVmVhQ2A7Q6qeq4VCIb+N/gz7DbRB9ViK2N\nCPRhpePA15X7volOH7m2daSIvX0UlmvwoKhKZhkDNLVipRovBQM5A6gf/caXqIGSChbuCnEt2Wkp\nY8eOxfr16zF27FjBksi5ublYv349xo0bZyfppIHs5k17i2AbTB0IV+PYlF++bPv72RJndhzYMOLA\n4NKLWp0Yuk9paeVKDmJCjN9xbVAXqQoVFXCfMQNuK1cCAAq3bROcwmRm1r4c5iC1iAM3sb2gIuXv\nTG41dGtykwHxzyarSjW4mV9l5DMAWlirE5HPKJ/L5FaIt7qNCPQhrh+3szx9PGb1OyN2fdi4D3EV\ndx9yzsAKE65yywe8DMNA6SrjrPSSW6aBu0I86U7WEB4eDoZh8NRTT2HUqFFo3rw5ACA5ORmbN29G\nQEAAwsPDsWPHDs7nhg0bZg9xRYkiIUG4U8pGhy1D3usYgwavJYjx+7Xl91adHg3oxmXLFpSLzUFJ\nNQ4qqQPHgWLvXp3TAAC8Xn651u9pNVJzHBB1wzUVd5DbKcDyaugABGuV55ax0LAsZCL5ob7O00dr\npQL/z96ZxzdRrf//M5ms3QstLfsaUBZZFVxQRAX1AoICfpH79YogmwuoKIt6EUERQRQUWb4IXhDx\nhwuoiF5FUaFCEQUqixha2lLaQvd9STLz+6M0TWaSNslsZ9J5v16+ZJLJmScn55ye5znPEmkQltuT\na0xR0wlqUQ2Dy26hLEYd0LtF8IneACCaoyirzQODW0FA6JzhnbCrrj+4a4jA8cHrD3VtdMReU4G6\nNaTALU9ZcS2DhCBLxKqN6dOnu/69Zs0a3vtXrlzB9OnTPco1UxSlGQ6aI2IqRQwD3V9/gY2LA9uq\nlfTP8xexNvuEKw2SE6D7ethTT6FEMxwQDVVUBCo7G0yPHoDeTe2UIVQh/JFHmr6JtL4PYA1QInFs\nQIaD+++/H88++yxuvvnmgB5y6NAhrF69Gp9//nlAn2vOcN2MhZ4WGnQUIvSUK1kaC6DMzvLcsUnF\nxs0OHyPc5sVVhNR0omwr8VSCukYJC2UB+K75ajpRLq5hkF/taUjpKCAnCKB+jxRu6EZ3ARUEAPXH\n9NtEXlMBfriT2vpECF999ZXobSYlJeGdd97ByZMnkZOTg3Xr1mHy5Mmu92fNmoWdnAzugwYN4uVc\n4nLo0CG88MIL+Ouvv5CYmIg5c+bg0UcfFV1+DYlhWYRNngzDN9+AjYhAxUcfweklOWeol2MMaQIN\nVVAjzeg31qWkIHzcOOgKCuAYPBgVe/cChrq9iFI5DojHz7XE+PbbsLz8MliKQsLcucDixRILVkdA\nO6d27dph7Nix6Ny5M8aNG4dhw4ahb9++CAsL87ivsrISJ0+exIEDB7Bnzx6kp6fjoYceElXwUIer\nKHcTGJ8M1G38yx1uMf01DE95JhWuIUWU/lDxiTJXCeomghLE6w8VeWB4y29ACzSkqNkjpYSTXNWg\nAzqIbUhRUX8A4htjAW9zpvlsCG+55RbR26yoqEDPnj0xadIkzJw50+s9w4YNw8aNG13XRmPjniPp\n6emYOHEiJk+ejE2bNuHIkSN49tln0bJlS9x3332iyu83QhQHNSodYsjMsqCTk2H45hsAAFVejrAn\nnkBZSorwtsWgORsOxFToNWUypLAsXAhdQQEAQJ+cDMOePbDXhwtov7V3/FkDGAaWl18GAFAsi/Zv\nvYUSEg0Ha9euxZw5c7Bu3Tps2LABK1euhE6nQ1xcHGJiYsCyLIqLi1FQUACGYRAVFYUJEybg448/\ndsU/ajRNcQ2DrHLPeH6hid4AIMpIARVuz6ll0FFwq/JwutDzhF1I/fV61Jzs7XSRuPHrgLpd0bnj\nQ2giQEDdoQrc/ugsMLkqAEQaPD9famfhZFjBBho5YFmWN2ckWUNUNEZIZMSIERgxYgQAYPbs2V7v\nMZlMSEhI8LvNrVu3IjExEStXrgQA9OjRA8eOHcO7776rnOEgBKEPHkT4mDGA0YiKL76Ac8iQwBtp\nQgHV//STx7UuM9P7jWoux9jcaQbKpLdkfBTLgmUYGDdvhu7sWdgffhjO/v0VkE5c9ElJHtcehgM5\nkiOqEX+MkHZ70/dIRMC7665du2L16tV47bXXcPDgQRw9ehQ2mw2FhYUAgJ49e6J79+4YMmQIbrrp\nJi2ZYhCkFNrh/ifo2hg9LCJk6lZz8jtuYrN+LYVv+tWcMPIkpz/6thQjXlu9rugnCjwTI4oyPrT5\n4gGtoxBlpFDq1g+ldhaxJvINB3nVDHLcKiqYaaCHGB4HKl5DxODgwYP48MMPkZGRgeLiYo98BkBd\nToMj3hL8CeDw4cPo1q0boqOjcfPNN+Oll15CfHy8z/uPHj2K4cOHe7x2xx13YOfOnbDb7TAYhM+N\n5g6Vno6I0aPrLmpqEHH33Sg9exZs69Z1r4nkcSDJvWIh1jPVaIAQ6nHg/nk1fv8ACffhgW18/31Y\nnn++7t87dqDs3DmwsbFyiiYvmuHAK6IlWpWIoHdOZrMZd911F+666y4x5dEAkMpxqe0lMOldPWo9\nYS+uYZBX7el2LYmbsYoUQ67bda9YMfqDowSpZHwA/NANMeaMt/nCsiwoFcRZppZKs4bEGHUorfUM\nd4o1kR/uxB0f3aMNgioq1KPm8B6hvPPOO1i8eDHMZjO6deuGuLg4yZ955513YvTo0ejYsSMyMzOx\nbNkyjBkzBj/99JPPQ4orV65g2LBhHq/Fx8fD4XCgoKAAiYmJXj9ns9nEFt8FyzAe7Q8K4LM5OTko\nllC2YOj84ou816pffBGZCxYAACiHAwP9aKewsBBtfLxXXl6OysJCtOW8brPZeP2Xn5eHyxL3EfeZ\nlWVlfo2Zpn7rovx8XHJrJ+7yZXTyQx4px2tTtC8pAdcHqF4ef8a20+l03W/KzEQfzvuFhYXIttkQ\nnZ0Nq5fPK/ndA4UuL0f/ykre6+kXLqDPc8+5rqnaWpQvXYrsWbMAqOs7usP9/csrKpB69btY0tPR\nK8h2/e0Pf8bfpUuXUKZg/3JlvJKbi7wm5KFqa3lrqlhjxGr1Nssa0KoqEIitlOtSK87PxD1RVotb\nLVcJEsPtGlCvm3FprWdFBT0FdIxs3q753DEihmHJrKdgpoHqq3qygwUqHCwiDOQbDng5MEQI3QD4\nyQDVMka440OsNVXNCVaF8s4772Dw4MH4+OOPER0dLcszH3jgAde/e/XqhX79+qFPnz7473//izFj\nxoj6rKY2T/7ibTOnczjQ/3/+BzAaUbV2bUDttU5MRHwAstFHjsAydy7gcKBq1So4OUYUMQj34nod\nW1YGU72ctbW8973RokULn+9FhIfD4uV9b79TXFwcokT6/fwlzGIRZczExsYizK0dg59hOWKN12Aw\nx8TwXgtEHlqnc93vzQzdokULxBw+jLC5c71+XsnvHijewhQAoM/99/NeiysvR7jVCpvNBmuXLjAv\nXgzD55/Def31qFy7FoiOBp2UBMszzwAMg6rVq+EcOlTqryCIiIiIht+6oqKJu30j5m/etm1bOAka\nQ63i4hDTlDzV1byX5JoH5B8VNUNsxdJs+tVah/1vCRIBAup1zed6G3SK1MMggiGF3x/qUIJKaz3d\n0GkK6Bgh1pxRZ59wq26INmfUuoZw19RmbowVg+rqakycOFE2o4E3WrdujTZt2iAtLc3nPa1atUJe\nXp7Ha3l5edDr9WjZsqXUIvqETk0FffYszFdP5aXC8txzoP/6C/T583UGBLlcwVXgmSUqIrkXU2p0\n1RczVMFLjgOquBiWZ58V9gw14hbHTh85AtO770KXnQ3DF1/A+PHHAADLvHmgz50DbbPVGRBIHz/N\nbV0IBsJDFTTDAWGwLIvf8z03/dfESBOqUKKSDOB/5HmeVlwjQilGgK8EqcXN+JhE/RFpoOBuf6hw\nsLAz5I+R4/me/dEtSi+KGzqgzvCe7AqnhyHFqKvz0hEDnqKskjXkd84YuUY0Q4r6xodYDB06FKdO\nnVJUhvz8fOTk5DSaLPGGG27AgQMHPF47cOAA+vfvT0R+A/3vv0vaPv3nnw3/Tk8HysokfV49lBd3\nbEEEohApsPEWLS5ZRMWPPnwY+h9/JF4R8fjOXgwHhs8+A6VgMjh/0F24AP2+fUBxsWhtUm45ACwc\nA6Nl/nwAAH32rOs12mZrNLkknZwM/Q8/EDMeVGkkkwNCfh9faIYDwiioYVDotvm00BSuFSF+HVDv\nCfs5zgn7gDjhiQAB9boZc93QB8aL0x86ikKUQX0nyuc4p8n948RTBtQ4Z/7meBv0aSFOPD/gbc6Q\n3x8Af4wMEGnORKvUA0MMVq5ciaSkJLz11lu8E/1gKS8vR0pKClJSUsAwDLKyspCSkoKLFy+ivLwc\nL774Io4ePYqMjAwcPHgQkyZNQnx8PEaNGuVqY8aMGZgxY4bresqUKcjJycGCBQtw7tw5bNu2DR99\n9BGeeOIJUWRWHVKc+HlRAPQHDzYkP/NXQVDzaSRhhgPTqlWIuOcehN9/PyyPPy5Kmz4R83fz1o+E\nK5j0778j4uabEf7QQ4i85RagtFScht2NJf72i4/fwrhmDSJGjkT4Aw/A4rY+yk4zS4QZFP6sJd76\nTqb+1AwHhMFVCrtE0dCJtCjzkwGqY5PLTRYpRvw64F0J4mYFJxGbRPHagLcTZfLHCLc/rCKU2auH\n55WigjnDy28g5vhQ4RpSWO30MMaaaKB9OC1K2/xQBfLXD7Fo06YNJk+ejGXLlqFHjx5ISEhA69at\nPf5r08ZXqjvvHD9+HLfeeituvfVWVFVVYfny5bj11lvx2muvgaZpnDlzBg899BAGDRqEWbNmoVu3\nbvjuu+8QGRnpaiMrKwtZWVmu606dOmHXrl349ddfMXToUKxatQorVqxovqUYZfwbZ9izR7ZneaDE\n33GxDAcitWNetsz1b+POnaCKikRp1ysShyqQjmXuXJeHjS4rC6ZNm8RpuKmqAwEoj5bFi13/Nn7y\nCagrV4RIJg4q2G8rgj/9oqDhQEuOSBjcTb+YShD3dEwNSmGFncGlyoY/JDQlntu1kaYQpqdQ6aib\nbE4WKLOziDKSferBzXEgliEF8Jb8jvyFndsfYirKagxV0NYQT85zDEtdI/WgRcgJAqjXA0MMli5d\nirfeegtt2rRBv379EBUVJbjNoUOHorgRV9/PP/+8yTa+/vpr3mu33HILfvnlF0GyaQQOnZwM+/jx\nomxoDfv2wbBvn1/3enOBpvLyYP73v0EVFqJ64UIw/foJlskDqTwO/Ow7w4cfwrB7N5w33IAaL/kA\nqIIC6Ur7iehx4O2304no/i8F7uFAAKD/4QfUzJsnvGGHA1RhITouXQr69Gn++wKUR+ryZbCtWgkU\nMAgk9jjQnT8P85IlYGka1S+/LHr7suBrLXE6YXr7bdBJSaBqauSVyQ1RdtgXL15EUlIS8vLyMG7c\nOLRr1w4OhwNFRUWIjY2FXq/ZJ/zlz0JpkpoB6kx+x+2PjhG0aG7XQN2Jcr3hAKjb+EcZyXXEKah2\nIquiQbHXiWhIAbwlAyRbEWJZljdGRDWkhMCcEbM/1LiGpBRIuKaq0CNFLD744AOMHDkSO3bsgE5H\n7pqpwYHkUz6xFFAv39G8cCGMn34KAKBPnEDZmTMALY7nkagE+fuEXQ29MfzwA5j27cWUqGnEDFVQ\noceBVFB2O8z//jeivvzS+w1CTp1JCAuSYC0KmzoV9MmTAADd5cuity8LPvrFsGcPzEuXBvw5sRH0\n155lWTz//PPo378/Zs2ahcWLFyM1NRUAUFlZiQEDBmDjxo2iCNpcOMXZ9PdvKeZpofrcjHn9IVJ+\ng3r4rtcEb6oAnCr0PD3tGWuASUxDikldJ8qXqxjkVzfIGK6n0F1KjwPC5wzLsjjNmTP9RFxD1Biq\nIOUawjUyltaycKogoagY2O12jBgxQjMayA3Jij+XeuVEbplZtu4/u9317HqjAVCnUNBHj4r7TIJC\nFcJmz+a/SPK4aSI5YrOFYWD88EPf7wsxHJAwHsSWobzcZTQAAP3hw8rIIRBfiVbDpk6VWRLvCPqL\nv2bNGmzevBlPP/00vvrqK4/48KioKIwaNQp79+4VLGRzIo3jVnttbPPe9HPrr4tVQaAe7oky6Yoy\nb3yI3R8qGyPc8dEtWg+9SG7ogPoqb+RXMyi1N6zD4XoKHSLEO1VTY+iGlGuIXkchkpNQtMxO1iZE\nKu6++24kJSUpLYZGoEixSSbh9NINqrgY4ffei+j4eIRNnAh4q/BQW8t/TQiEJUdUGtPrrwf3QcIz\nyhOFgnHuoiCyrJTYc5ogDI0ZkOpRg8fBtm3bMGnSJLzwwgvo2bMn7/1evXrh/PnzQh7RrCizM7hc\n1bBo6imIuunnniaT7oYO8BXlrlHSKsqk9wlXCeoicn/wQxXI/iPE7Q+xx4faqipw+6NzlB6U/XRS\n3wAAIABJREFUiJt6da4hnidYUq8hpBvbxGLevHk4d+4c5syZg2PHjiE3Nxd5eXm8/zSaAb42rAp5\nHBg/+MB12mj4/nsY3LwNJKM5Gw68/I0xv/46qEuXAv+85nHgPyoMVWClzHFAeMnOoLHbXaFIjaKG\n5IiXLl3CDTfc4PP98PBwlMlUMzgU4CrJHSJoUU9PLTQFgw6wX/37VuMEqhwsLHqyTgvcSeVs+sVX\nlNUVoyy3okz6iTJ3zog9PvhKIdmbOp5hKVLcGF61eS1VOjyTq+oooKOIOUGAujnjnnekuIYBIhv5\nQIgwePBgAMCpU6ewfft2n/cVFhbKJZKG2hFJmaE4+07zkiWitNsoTRgO6KNHpS+LSBiG777z70Z3\nhScI5ce4cSPsEydKl/xRKZqaD2r0OJDSYBGiHgcUYbkaBO2g4uPjcakRi+LJkyfRtm1bIY9oVkh9\nuk5RFGKMOuS5xYQX1zKw6AlMEATAwbBIL5NZMVSZoiz+6almSHFHbcki5fbQKa4he5NygWN4bBdO\ni5oTBODPGdLHiFg8//zzonqzaPiHt6zzxKO0zDKMU19xyQCgO3sWESNG+NeQVH2l9G/gL0HIaZk/\nH8b/+z+UHz0KhFLOFSkNBySs3WKHKjRVvjLUUYPHwb333outW7di8uTJHnWUAeDnn3/GRx99hCef\nfFKQgM0JbjZ0sZVkoG7j7244KKll0DqMTMPBX8UOuBU8QLxZJ3rFAzVlia+wM5qizIGbMb9rlLhj\nmacUEm5YknoN8Ra6wbIssQoktz/Eni+A+hKsisXChQuVFkEjCCiWhWwjlNB1QRIaMRyET5jgfztq\nUfDd8SVzMEpskN+fPn8e+v374fDXQBMKqN3jQGxZg/Q4UKUx1htqyHGwaNEixMXFYejQoXj88cdB\nURTWrVuHf/zjHxg3bhx69OiBZ555JqA2c3NzMXPmTHTt2hUJCQkYPHgwDh065Hp/1qxZiImJ8fjv\nzjvvFPI1iOFMkadSOCBe3AoCgLqy5p8t4mZDFy9RZD1qcr22lXgaUjpF0jxFTij8UAVyF9RKB4OM\n8oYTZZoC+rQQueoGtz8IHh+AlzVE5CokJpqCxe3E3sECFQ5yx4gsa4jK8mBoaBCLVIYGOQwYjXkc\nZGUF345YykCoKEeNED5xIqj8fKXFEI9gPA7UhJpyHJSVwTJ9OiIGDoRpxQr1970ABGkdMTEx2L9/\nP2bMmIHU1FRQFIUffvgB2dnZePrpp/Htt98iPDzc7/aKi4sxcuRIsCyLXbt2ITk5GW+88Qbi4+M9\n7hs2bBjOnTvn+u+TTz4R8jWIgeuWb5XhdIzkE/YL3P6IFn/TryY34/QyT7drMcsO1qOmZJHc/mgf\nQYuer0NN86XGyeKSW6w9BWlO2Plzhtw+UWINIdkYK4QPP/wQjiBcQZ1OJz70JyO0RujSHDbZzVnB\nJ0hm47p1SosgHlqoQmBIGKpg3L4dxl27QKemwrx8OejjxyV7VtCoIVQBqEuA+MILL+CFF14AAEFu\nq2vXrkViYiI2btzoeq1Tp068+0wmExISEoJ6BqkwLD+ev7NEoQrukHyCeoGjGHYWOdEb4OW0kOBN\nP1cJ6iRykjdAXckiL5RK3x8RBgo6CmCurscVDhZ2hoVBxKSlYpFZ7vBwQW4TRsMsQeLTGJMOuW7V\nX4prGLQNJzPcSZY1REVrqhCWLl2K119/Hf/7v/+LsWPHokePHo3ef+7cOezevdtlcPjnP/8pk6Qh\nTiDZ+6VwZa6ogC43F0z79oDR2HibJCgngFc5dHl5YDIzwXboIM4zfPwuVG5uQM2o0m1aTJkFtmV+\n6y3ULF4skjAKI6XhgIRxpqIcB5ZFizyuzS+/jIovv5TseSQT9E67srIS3bp1w3PPPYenn37a9bqQ\nWNevv/4ad9xxB6ZMmYKDBw8iMTERDz/8MB577DGPdg8fPoxu3bohOjoaN998M1566SWeV4LayK1k\nUO22x40yUjwlTgzUlDWfa0iRQjFUU9Z8OQwHakoWye0PKZRCHUUh2kihyC1ko7iGQbyFPEWZmwiw\nowT9AajH+Mh6McbKMWdI9sAQwvHjx7F+/Xps2LABK1asQGJiIvr164dOnTohJiYGLMuiuLgYGRkZ\nOHHiBHJzcxEXF4dZs2Zh5syZSosfMoRNnYqS++8PXikXsFmnsrIQPno06AsX4LzuOpR/9RUQHS3e\nM2UMVQibNg0AUDNnDqrFqLrgxXBg2LkTlkDzfJGg0IlFEN9FlYYTpdByHHgi53cnsfQj6R4HYWFh\nCA8P5yVFFEJ6ejref/99zJ49G3PnzsWff/6J+fPnAwCmT58OALjzzjsxevRodOzYEZmZmVi2bBnG\njBmDn376CSaTSTRZ5IbnbRApbv31erjGCLJd0fl9IjZqSgbIdc2XwwOj1M6CYVnoSDk5ciOD1x/i\njw+gbowU1TQ8q6SWTMOBHB5LgHrWkMIaBmX2hj+kYXoKCRbxM26ryRgrhLCwMDz77LOYM2cOvvnm\nG+zbtw9Hjx7Ft99+C/bqhoWiKHTt2hXDhw/Hvffei5EjR4KmyZsraodOSoLzlluavlFkxcL05pug\nL1yokyElBcatW1E7d67vDxD4d4OLac0a1Dz1FNiWLYU15MVwEDZrVuDtqLGqAkEeByGFFqoQGE5n\n0/eIBYnVO0g3HADAmDFj8MUXX2Dq1KmiKLkMw6B///5YfNXNqG/fvkhLS8PmzZtdhoMHHnjAdX+v\nXr3Qr18/9OnTB//9738xZswYr+3abDbBsknRljtHLtMAGgwf8VSVJM+yl+oBNCRMS79cCJtNWI1Q\nKeSsdgI5lWGuax1Y1ORegO2KuM8pqqEAWFzX+ZW1gr+PVGPEVmiGe1oSXdEl2CRIXmjRWVDF1M1n\nhgVO/nUeEQJWCqn649RlE4AGpcRUngebLTC3UH8ws57P+TM1E2xk8MqhVP1x/KIBQEMMf1RtMWw2\n8RNF6WqMcP/TcS4zB9aa4P9gSzY+ynQAzK7r1kYnzp8/L/pzKgs9n5NdXA6brUBQm2L1idVqFaUd\nd/R6PUaPHo3Ro0cDqMthUFRUBABo0aIFdCRuqEIMw7ff+mc48IYQw8HWrZ7XGzY0bjgQ4Zmi0MT+\nVPf333DeeKOwZwQSQiJHOyQQwO9OJyXB8NVXoCorJRRIHvSHD9d9d4F6kS41tfEblJ5XwSClwULO\nudOMDeKCDAcTJkzA008/jVGjRmHKlCno1KkTzGYz777evXv71V5CQgIvbrJ79+7IaiQjbevWrdGm\nTRukpaX5vEeszZPNZpNkIwYAlWWlAMpc131ax8Bq9cMFMEC6sRVAenHDC2HRsFpjg25Pqj6py4be\nYCVoF6FHzx7iP6e1nQF+y3Fdlzt1gr6PVP1R62RxOSnb47Vbe3UVPRkgAMT+kYOqyoYFuEW7zugY\n5Gm+lHPmSkougAaF9cbu7WBtKX4lkoS0fJwtr3FdR7ZqC2s7/jrnD1L2R1FGAYBq1/WgTgmwdgnz\n/YEgaVdQDORVuK5Nsa1gtUYE1ZaU/XEitRJAkeu6R1wYrNb2oj+n4HINcKbBQGPXm2G1Bh83LWWf\nSAFN04iLi1NajOaFv8YZuVyZm1AG9D//LEo7RCOW0qJGjwOBUCUlCB89GlQIGU2MW7ei9tFHBbUR\nUDWOq/hdblWpuSZlqEKw4ycIOVgSDeRq8Di45557XP8+fPiwz/sKCwv9am/IkCG8E6Hz58+jfXvf\nm738/Hzk5OSoPlmiXG7Gasmaz49fl6Y/wvUU9BRcZQ6rnUC1g5UkqZwQMssdrgR9ANAmTCeJ0QCo\nc83PdjMckDhGnAyLzHJOqIJkc0YdrvlyhPYA6gnvkWsNUVMJUw0FkGAzJ2jTKsXmsonkiGGzZ4v/\nzECQQUkSTeklWMGXklAyGgCA5ZlnBBsOmsJrPggVjR/R81nIOYZINBzIhKCd1JtvvilqHP7s2bMx\nYsQIrFq1Cvfffz9SUlKwadMmvPTSSwCA8vJyvP766xgzZgwSEhKQmZmJV155BfHx8Rg1apRocigB\nN0N8RyG+4Y2glqz53GzonSRK9EZRFGJMOuRXeyrKZj1Zbkjc/gjWA8AformKEIHJ3rIqnLC7Dd04\nsw6RBmkWcjVkzfdWlUWqOaOWmH651hC1GFI0FEKKzWwjbrL6H3+E+YUXwIaFoerNN/k3KKBYUGVl\nTd8UJBH9+/shQBCx4oFCeqiCEgolIUqsx5x4+20wffooLZI4EGg4oLKyEOWnl7mqkyNKHargdML0\n6qsw7N4Ntl07/z6jBo+DR0W2pg0YMAA7duzAK6+8gpUrV6Jdu3ZYtGgRpl3NfkvTNM6cOYOPP/4Y\nJSUlSEhIwNChQ7F161ZRkzQqQQbv9FSejOikZgDPkOm0EKg7Uc5v8PBGSS2DhDCyDAfy9gf5ihB3\nvkilFALeFEPy5syVKn5VlliTNIYUtXhgyDVnvFWZEFKWWCO0kCRLvK+xxTCwzJoF3eW6vEWWhQv5\n9xCizIlFfbLGkCGUQhVIGGsMA8vs2dBdLYtpWbgQFXv3KiyUSBBoOAifNMn/m4XI6iWHhKxeKxJ7\nHNBHjsC8enXdhb9rnBoMB1IwcuRIjBw50ut7FosFn3/+ucwSSU+Nk/U48dZRdTXYpUAtp4U5lZ6K\nYbsIqRXDhueReKIsb39wvFIIHCO8/giX0QNDFf1BS6a48j0wCNgcekGuOWPRUzDRQH1+SDsDVDlZ\nhBEW7qShEFJk+vaxadX9/bfLaABcTdLGRc4M+IGuQTKWY/RAhD6hcnOh/+EHOPv1E16hQQo4SpXu\n9GlQ+fl1STZDONGb7vx5l9EAAPSHDikojcj4YTigrlwBfeqUTAIB9J9/Nn4DiTkOgkFiw4FlwQJJ\n2xeCoN32s88+2+Q9FEVh1apVQh4T8lyu8txYtDLroNfJs+kn9bQwl7Ppby2hBwDvxJDAGOWcSs/f\nSSrDEuD9BJU0+ONDukVcDaEKXCVZzvFB4hrCsixyOXNG6jXkSlXD84prWIQRZ5Ynn6SkJLzzzjs4\nefIkcnJysG7dOkyePBkAYLfbsWzZMnz//fdIT09HZGQkhg4disWLFzeaB+ngwYOuChDuHD16FN27\nd5fsu9QjiceBr02rP8+S8USSTk6W7VlKQzkcCH/gATCJiSj/8UewbdoE15AMHgeGjz6C5fHHQbEs\n7Pfcg8qdOyV/pmKQljtBTONYE4YDKj0dEXfcAV2Blyo/JHjEiexxIGdyRMmNbQ5H0/dwUYPHwVdf\nfcU71XI6nSi4OkijoqJgsVg0w0ETcJUgKd3kIw2ev1eZnYWDYSUzVAQLV1FOtEh4wm4iXxHijREJ\n6tHXw+8PAv74c+AqyomSKoXku+ZzlWQp1xDe+CDQA6OklkWVs2HcWmgKUQbp1rgYruGglkGb8NA9\nxZOKiooK9OzZE5MmTcLMmTM93qusrMTJkycxb9489OnTB6WlpXjxxRcxfvx4JCUlQa9vfDtz5MgR\nxMY2VBCSrRKEjB4HpKH/7TelRahDRiVJl5sL09tvo/qNN4JrQKrNv5tS5Z6s0vDNN9CdPQvm2muD\nb9tX/5JgOCBBBndk9Pgxv/KKd6NBKODtu4eQx0FQa5YaDAd///2319crKyvx/vvvY9u2bdi9e7eQ\nRzQLeEqyhJt+Wkchykih1E0ZLK1l0MJMziaXYVmeF0aCdqLscS3l6SnPK4VAxVBOw4EasuZnc8eH\nlIY2FSRY5Y8PnaQ5B9TiySWEFStWBPwZiqLw/PPP+33/iBEjMGLECAB1yZLdiY6Oxp49ezxee+ut\ntzBkyBCcO3cOvXr1arTt+Ph4tFTAhVyKuFvza6+BPnECNTNnwnnrrYHJU1gI06pV0BUWonrePGGJ\n4uo3qmJtWEk4BfWFwwHT22+D/uMP1D74YKO3GnbtEsVwoEtNRdjcucG1EwD0X38JMxyojLCHH0b1\nM8+A6ddPaVGE0YTHgZH00G4VhyqwIRze0xSSOFOGhYXhySefxPnz5/Hcc89hp1RuUCHCxXJPl5T2\nEp9UxRh1KK11j+ln0SK4svSScLmK8ciYH2uiECFRxnyAf6JMWgw7y7K4WC5fjgNefxCoBGVx+0PC\nOaMGw1IWZw2RdnyQn+Mgq0K+8QGQv4aIweuvv857rd4Yw3LjainKlSAyEMNBoJRdzdYfExPT5L3D\nhg1DbW0tevTogXnz5uHWABXuoJFoM2vYtw/6H35A6blzgB/fvx7LggXQJyUBqAslKDtzRjUeDJLh\nhwJj2LED5mXL6v69b1/jNzfh/dIobuMl/O67g2+HgyLlDkk47fcig+HLL0EnJaHs3Dlhv5VI8oja\nlr/tK2WkEyvHQah7HASDTPNN0m/er18/HDx4UMpHhATyb3LJPh3jK4XSLuyku+aX1LIodzTIZKaB\nlhJlzAfUEbpxUcY5o4ZQBTnXkEgDBdqtSyodLGqdZM0Z3hoiUXnbekhfQ8SgqKjI47/Tp0+jZ8+e\nmDhxIg4cOIDMzExkZmbixx9/xMSJE9G7d2+cPn1aMnlqa2vx4osv4u6770bbtm193peYmIjVq1dj\n+/bt2L59O6xWK+677z78+uuvksnmjpQKG1VTA9OWLQF9pt5oANS51dNHjgQvAAmKoUyEzZnj/81C\nlNGrfUpdvgxdXl7w7fhoV6MOXUEB9AcOKC2GMAisqtAkWnJE/yDY+0rS3dTBgwdhNhN0lE0ocp4m\nA+Rnzed6YEhtSCH9RJmvJOtldbsmzTW/2sF6xJPrKEgaT66GSiRyriEURSHaqEOhWz+U1DKIlzA8\nIlAuVmhriNTMmzcPVqsVGzZs8Hi9f//+2LBhAx555BHMmzcPO3bsEP3ZDocD06dPR0lJSZMejVar\nFVar1XV9ww03IDMzE2vXrsVNN93k83M2m00UWfVS5Dhwo+Tvv3HxqqzmjAz4WUHdRXZaGkrj4/26\ndxDn2ul0wmaz4dqaGp+bSZvNxvucL/ILC+FnxfKAsDudMDXy/qWsLJQ18Xv7+x0AwM6ysP39d0Cf\nqae8rAxpNhsMubnoG8TnfXExIwMVUVEA+N8lJzsbRUGOd31hIfqtW+f1vbwrV9AhqFaF4T53LRkZ\n8BXElJOWhmIR5rmv39nbGqIvLIRYARIX0tJ4Y+RCWhrs5eWNygUAGRkZqJZA+W1qzJeWlSH9ar9E\nX7oEaxP3++K8zQaWY6CLyc5GtyDaunTpEko5v1Xk77+jy6JFMBQWev1MaUWF63sIhdtn+fn5aFFb\ni7AA20lLTYXz6hwXgvvfS28IMhysWbPG6+slJSX49ddfkZycjCeeeELII5oF3NNCqUMV+B4HZCmG\nvP6Q2JBCvgcGJ5SlmffHpQp+PL9BwuSe3P4otbNgWBY6QizCTobl5TiQwzW/sKbhmjTDAdfjQO45\nQ6JxSWwOHjyIl19+2ef7Q4cObfT9YHE4HJg6dSrOnDmDvXv3okWLFgG3MXDgwCZLOze1efKXdDFP\njb0QExYG81VZdUFk4m7bti0SgvyutF4Pq9UKUyMHRN0tFr/bi5MoB4XBYGj0/bZt28Ip0u8NAAaz\nGdauXYP6bGR4OKxWK6iwQNWGxmnfrp3P79g6MRFxQX5/06uv+nwv3k+DlNi4z11dba3P+1q3bo14\nEX/3xuSoh7pyRbT2O3fq5PU1thEPrHo6duwIRsLv7ouoyEhXv+gFKN7dunYFjEaP1/RBerh5WwMj\nHnoItA+jAVCX/F+svxFc4uLiYDA1Zur0TtcuXcC6JQCWCkGGA1+bAovFgk6dOmH58uV47LHHhDyi\nWaC0Wy1pp2PcE3bJcz6YyPbAkDuUhfTxkcU9TZZYKTToKITrKVRcDRdh2LpqJNwQBqW4Ui1vThDg\n6hgp88yTQhJyryHRJvLDWcTGZDLh6NGjmDp1qtf3k5OTYQpi89MYdrsdjz76KM6ePYu9e/ciISEh\nqHb+/PPPoD8bMBJ7HOjy82HYtg1s69Zg2gV+Xs8KMYD6kxvgiy+Cb18sZDbysnq98NJwYssskRu3\neeVK32+S7jbvJ7qTJ6H/4w/Yb78drBdl3V+oggIYt24VTzA1hiq4I7KsQYeFeZGDbsqooYUqBEdu\nbi7vNYqiYORYgTR8U2FnUOCmqOopIFHCUnuAN1d0sja58odukJ3sTfFQFsKUIJ5SKHF/AHVjpMLh\npijXMLxTZqXgh/ZIn+xJW0M8IX0NkYIJEyZg48aNiIqKwmOPPYYuXboAANLS0vB///d/+PTTTzFj\nxoyA2iwvL0daWhoAgGEYZGVlISUlBbGxsWjdujX+9a9/4fjx49i5cycoisLly5cBNJR+BuB65saN\nGwEA7733Hjp06IBrr70WtbW12LVrF77++mts27ZNlH5oCkrijbzhyy9h+PJLAEDNI49I+iyfNPYd\nA/n+EhtZfCL2b6TXB99mvfIjtuKghEJJghLbmAx+9DF99CjC770XlMMBc1QUypKTwbZuHbgctbWI\nuO026LKyAv+sL9RoOAiV5IhSE6rlGE+dOoUuXbp41EZ2p7i4GKmpqRg4cKCQx4Q0XLfrNuE0aAnd\nrgHyk73xQxWkVYRId82XO5QlTE9BTwH1+RhrnHV5Bcx6MiygPKVQ4v4A6ubMpcqGa5LGiNxu+QDZ\nc8bBsLxyjFIbU0juD6lYsmQJCgoKsHnzZrz//vseFRZYlsX48eOxZMmSgNo8fvw4Ro8e7bpevnw5\nli9fjkmTJmHBggXYdzWb/bBhwzw+t27dOkyePBkAkMXZmNvtdvz73/9GdnY2zGYzrr32WuzatctV\n9lFyZFSGTR98INuzAIi+UdX/97+itqcYNC3c40BsQkmpkhHL3LmgroYAUaWlMK1cierVqwNux/DZ\nZ+IaDQBhhgMSDAxqTo4oNaHqcXDXXXdh48aNmDBhgtf3f/zxR0ybNg2FjcSJNHcUOT0l3BVd9uSI\nausPiQ0pFEUhxqRDfnVDPxTXMkjUkxHDLncODACI5o0RAv7oXkXOChP1kOyVklPphHuRhzizDhaJ\njV5qSKApNkajEZs2bcJTTz2F7777zqWwt2/fHnfeeSf69OkTcJtDhw5FcXGxz/cbe6+er7/+2uN6\nzpw5mBNIRnyRUaQMXiAQtEHV//67JO02GY5BkseBVKEKLAs6KQnmf//b9zNDEOpqydZgoc+c8bjW\nHzoUXDtSVJjRPA48kXOtDXJ+6i5cgPmZZ0AVF6PmpZfgGD5cPJnU4HHArd3MxW63Q0dirUuCkLMe\nfT0kZ80vrWU8kjUadUAriUM3ogyeC0BpLQsnw0ru+eEvcnscAHUn7PnVDdfFtQwSw8gwHPA9Dpq3\naz7P40CW8UHuGiJ3ThCA78VFkiFFanr37o3evQPN5d+MIN1w0BxQIMeBLjMzuA9LFargdCJs9mzo\nLl3ivyeVwkGAEttoTgE5xwUtwd+h5uxxoLThIEjZzS++CMPVMqC6adNQdu6c9+ZDKVShsrISFRUV\nruuysjLkeckaXFxcjN27dyMxMVGYhCEOP4mX9EoQyW613NCNtuG05NnraR2FKAOFUnvDpCuzs7yk\niUpQ62SRW9nw+1CQtvRgPXWKcsNvUUKUoiyvRwpAdngP32tJBkOKidw1RInQDa5hqZQgjxSp+emn\nn3Dw4EHk5eXhiSeeQPfu3VFeXo6TJ0+iV69eiImJUVpERSHe40AIJCgf/iC3V4VOB5OPEoVKQael\neTcahDjGXbuUFgEAeKUDiaCyElReHtj27YUn+6uuBuVPBRmx5iLDgMrKAtuiBVBfgYS0tZZl62SM\niQEiIwEABjePOF1hIejkZKWkC5qAR/LatWvxxhtvAKhzaZ43bx7mzZvn9V6WZbFo0SJhEoY4XDd0\neUIVyD0dUyJ+HahzRS+1u2eJZ3jKkRJkVzrhvjVLsOhgoqXfBPHDN8jYIDIsi0uVWniPO/xQFgW8\nlkjqDyVCN5phqEJVVRX++c9/4sDV0xMAeOCBB9C9e3cYjUb861//wmOPPYb58+crKCUBkLaZ1ZAe\nigJqapq+zxtShSoEUaoz5FG5x4GQxKu6tDSEP/ggdJmZcNx0Eyr27OGVN/RbjqwshI8dC/r8+cA+\nKED+8HHjoD96FEz79qjYswdM165khSqwLMIefhiGr74C06oVKj75BJSXUDvjRx8F176PZ8pBwIaD\nW2+9FXq9HizL4rXXXsN9993Hc1OkKAphYWHo378/hgwZIpqwoQjPrVaBxGYkbXL5/SGPlTbaqMNF\neGbNR6Qsj24UubPD18MbI4QohnlVDGrcuiTKSCFKhuoGPC8dzTXf45qoNUTm8rYAEGmgQAEuI1+5\ng4WdYWEgJNxJCpYuXYpDhw5h06ZNuPHGGz32AUajEWPHjsW3337b7A0HzcHjQOrKEaqDZYM/wVUi\nbED7/aRHCo8DL78bxbLw59c0bdjgCqfR//orDP/v/8H+v/8blBimt94K3GgAYeuG/uhRAIDu4kWY\nli1D1datwvOKiAh96BAMX30FANBduQLLokXQXbzIu69ZGA5uuukm3HTTTQDqwhYeeOABLb5RADle\nXPOlJpbQ02Sg7oTdHTn6AwBiCY1R5maHl60/CD1B5WXLlynvAn/OkNEfFXbPnCAGGXKCAOpaQ+Qw\npOgoCjEmCkVuBqWSWgZxZjLygkjBnj17MG3aNIwfP95rAmSr1YrPPvtMAcnIIhQMB/r9+0EHmRSO\nCGQOVdAfO4ba7t2D+/DV8UKfOiWiRFDE84WqrZX9mQBgnjcPzn79YL9aacUnKvc48KYompYsQfXL\nL4Pt1KnRj+oPHvT83HvvBW84eP99v+817tiBqjVrhCUQ5ba5ezeYnj1h/M9/gvq87q+/YEpKAtOr\nF+zjx4syLgx79nhc65OSAmuAoKS1XASZwBYvXiyWHM0SlmWRW+W5mCdalHEzZlhW8lwC/pDL2fS3\nUUgxLKwmY7PH7Y/WcvUHZ4wUEWo4aK2QIYWU/rjMWT8SLNLnBAG8zBdC+gPwNmfkCTlBxgyVAAAg\nAElEQVSKNepQ5OYOU1gd2oaDgoIC9OjRw+f7FEWhurra5/vNBtINB02sF3RSEsLHjw/qs8Qgd1UF\nNHKS2BQsC+ryZYTff7+4AingcWBeulSSdpvCtHkzAKAq2HARCZAkx4GX3824Zw/0v/yCsrNnA2qK\nKioSS6omMS9diuolS0Qdd+ZXXw36s5aXXnL9u9Juh/2hh4QLJHTdDyWPA2+cOHECJ0+eRGlpKRhO\nZ1EUhaeeekqMx4QcpXYWlY6GH9pCUzwXYCkw0hQiDRTKriYDZNi6ZF4kJAPkbvoT5dr0cxVDQk6U\neYqyXIYDM5mKoXuiSACyVXpoQaiizB8f8swXkmP6+WuIjMalsoZnk2Jckop27drhnI+M0ABw5MgR\ndOnSRUaJyETtHgeWxkpZqsXFXS0GDgBgWZjWrJGk3aDeUzHGd95RWoQGpKgw5+N30xUWwhBgUkiq\nvFwMifzCtGZNneGAQMJmz0aJGIYDoXOK4DVLkOGgtLQUkydPRlJSEliWBUVRrhKN9f/WDAe+4W76\nE8J0oGQaLDEmHcrckgEW1pCTDNAdOTwwAC+KISEeBzmV/BNlOeCFbhCiBOVUcceHQoYlUvqDu4Yo\nNV8I6Q8Hw+JKtTJzhtQ+kYoJEybg3XffxahRo1yeB/V/v95//33s2bMHr7zyipIiEoGOoFPPYPAn\ndjmo0mEaPtHZbKK3aWkkUXnYzJkoHTKkSfd2tUGnp/t9r3HrVhg3bYLzmmtQ/eabddn6RUSXnS1q\newAaVU4DrqBxtS39F1/A/MYbYNq0QdXbb4Nt21aIhD6JuPlm0KdPS9K2LDSx5gkxGJuD/bspkwFQ\n0K57yZIlOHr0KNauXYvk5GSwLIuPP/4YSUlJePDBB3HdddfhtJoHhsRwE9/JFb8O8De5pChC3MRm\nSsX0k+JxkFXhmQlZrv5oYSZzfPDnjDzJM0mdL0qtIZEGCu7FPSodLKodyp9aXapwgnETI94sTxUS\ngFzjklQ888wzuPHGGzFq1Cjcc889oCgKCxYswDXXXIN58+Zh5MiRmD17ttJiKk7s/v1KiyAdIXpS\nzcOtBLksSHE63QRmQk+AJeWq8kdlZsLy9NOgz56FcfduGN97T/RHmdavF71N0edfWRnCpk8Hffo0\nDN9/D/Py5eK274aqjQb+oMTaqAbDwTfffIOHH34Y//znPxEXFwcAMJvN6NmzJ9avX49WrVphqUIx\nTmqAu+nvIFMFAYDMGOXiGgal9oaBb6blSfQG8F2vSfE4yOSNEWVyHJAwPgBlypcC5CqFvDUkUp41\nhKIoIo1t3FKMcs0XwMsaQsgYkQqj0YhPPvkEGzZsQLdu3dC9e3c4HA707dsX69evx0cffQSdAgoQ\nabT69FOlRWgcLydnVHY26KQkoKrKvyZINyA0dTrYhPyGffvElKZppEik1wTG3btlfyYpmNau9bg2\nr1qlkCQB0ti4DdQLiKJg2L0blJuHlPHDD4MULPShf/0VVE6O7xtIXxMFIGiXWVBQgD59+gAADAYD\ngLpKC/WMHDkSyyW0WKkdpZQgwEuCRAI2ufz663r5Qjd4CSOVn/RVDhZX3JLf6SigjUwnyrwYdgKU\nQsCbsU2e/ogyeI7DUjsLJ8OCVrjcHm8NkdFrKcaoQ76bga24hpEtB4cvuOOjvYzGWP6cUX4NkYMJ\nEyZgwoQJSotBJirMb0D/8QfC77sPVFkZnD17Ki2OPDS1yXc4Gn9fbDSDmzzUexyoNYmryMopJfc4\nJxU/+pVOS0Pk4MEo//JLMP36BdWG6KjB4yA+Ph4FBQUAgMjISEREROC8WzxcaWkp7Ha7MAlDGO5p\nsrybfvLKD2aWKWhI4Wz6SwjoD26YQpswWra68HzDkvJKkJNhcYlnXJJnjNA6ClGcOUPCGFHKIwUA\nL5kqEWsIQcbYEgKMsXJQWlqKAwcOYNeuXbhy5YrS4hCF/ttvlRYhYMzPPAOqrAwAQJ850/jNajlV\nE3oAIWcOB5ZVxONAQ4WI6XGglrksB34afKnSUljmzxfUhqiooarCwIEDceTIEdf18OHD8e6776JD\nhw5gGAbr16/HoEGDBAsZqpAUqkCix4GcShCR/cE7PVXOkFJcy7iSnSpFTqUT7mH0cWYdwg3ynczE\nGnUorW34TYprWbQwy/Z4HizLKjtnSPRaUtCQwltDCDCkSM2bb76J1atXo7KyEhRFYffu3WjVqhUK\nCgrQu3dvvPrqq3j00UeVFlMxuLXSiYSzputPnFBIEAlRoByjEFjNcCAP9eOCsN/fbxqTOxjFVa39\nIDYB9IM+OVlwG2pD0K572rRpaN26tatW8yuvvAKLxYJHHnkEjz76KMLCwrRQhUbgno4pGY9Lglst\n/7RQRjdjEj0wFDQcmGgKYfqGPnGyQLnCye+U7A+AvBKEBTWMRznXcD0/74CUkLmGKGhsI7QSiVRs\n2bIFy5Ytw/jx47F161ZXRSUAaNmyJe69917s2bNHQQkJQA2bRzXIqDRyG8w1w4GGPzQyd407dwbW\nllYZpQExvAVCOFRBkGY2dOhQDB061HXdsWNHHDt2DCdOnABN0+jZsydMJpNgIUORGieLXLf4dQry\nVlXgutWSkOxNydNCrhJERn9wDUvyGVKAOkXIXTEtqmEQKeMJPxclT9cBL3NGYeOSt/kip0cImWuI\ncnOGt4YQYHyUko0bN2Ls2LFYs2YNCgsLee9fd911WC9FJnE1oSnlZPSBmkIVAC3HgVyoXVlurBxj\nRoZobTU71Go4kImgV6eqqio8++yz2Lt3r8frer0egwYNQv/+/TWjQSNwyw62DtPBKFPZMIDM5HdK\nnhZG6D3Ly1U7oXh5OSUNKQB5CTR5OTBkKsVYDy+mX+n+UNgDI5qwNYRhWWRVKOlxQF5eEClJT0/H\nbbfd5vP9mJgYFBUVySiRRlCIoTwRvkmmz54N6H7diROIGDAAUR06wLh1q0RSNYLmcSAvAYxf+u+/\nAx7vhg8+CEwef5Fh3lHp6QgfNgxRbdvCtGyZ5M8jgahOnYQ3EsIeB0EbDiwWC3bu3On1pEEIubm5\nmDlzJrp27YqEhAQMHjwYhw4dcr3PsiyWL1+Oa665BomJifjHP/6BswH+USCBixVKnyaTpRQCyuZ8\n8FZeTmlFSMnEd4A3xVBhQ4qCSiHgJaZf8fGh7BpCWl6Q3EoGdjcRYk2UrB4ypK0fUhMTE4O8vDyf\n7589exYJCQkySkQghCvUQiG+DKO/cL6H5YUXQKel1SU/e/ppUBUV8sqjGQ7kIUijGe0rrt0bZWUI\nmzs3qOc0idgJ+LzMZ/Nbb0F/4gSoigqYV62C7u+/xX0mgYhSZSOEkyMK2lX17dsXp0+fFksWFBcX\nY+TIkWBZFrt27UJycjLeeOMNxMfHu+5Zs2YN1q1bhxUrVuDHH39EfHw8xo0bh7KrWYDVgtKnhaRl\nRC+3Mx51z/UUkGiR112PX5KRLFd02U/YCTMuKW1I4ec4aOY5HwjLC8LLkULAfGFDRbHywogRI/Cf\n//zHq1fBqVOnsG3bNtx7770KSKYhF1RJCXRpaSFnINEnJXlc04cPy/ZsimUVTY6ou3ABhh07oEtN\n5b1H//orDDt3AirbbzdJgOPXtHq13/fqf/01UGn8xrJ4sWRt12P8z388rzdvlvyZIUGIrYnuCNpZ\nLV++HBMmTECvXr0wefJk0AIXu7Vr1yIxMREbN250vdbJzWWEZVmsX78ec+fOxX333QcAWL9+PaxW\nKz799FNMmTJF0PPlRHEliFc6jCy3/HYRNGiZSg/WQ5Ireq2TRXYlv0/khLQTVG78upzJMwHyYvpJ\nC2VRuvyg0v1h1lMw03VhTgDgYIEKB4sIg8rjaH3w4osv4sCBA7jxxhsxYsQIUBSFHTt24D//+Q++\n/vprtGnTBs8//7zSYiqLGjaPAkMVIoYOBdRe/52030mhHAe6tDRE3HorqPJysGFhKD9wAEyPHgAA\nw86dCJs1CwDgXLMG5b/+qv5cDMGO/UA+J2EeBf3PP0vWtk9ImyukooUqeOepp56CwWDA3Llz0aFD\nB9xwww249dZbPf5rLAaSy9dff42BAwdiypQp6NatG2655RZs2rTJdWqTkZGBy5cvY/jw4a7PWCwW\n3HTTTUgOxHWIAJSsIACQl8iLd3oqY6LIekhSDLMrnXBfAhItOphkzIEBkGVIYVhW0fKUAHl5QYhb\nQ5q5BwZA1hoiNQkJCfjpp58wcuRIfPXVV2BZFp988gn279+PCRMm4Pvvv0eLFi0CajMpKQn/8z//\ng2uvvRYxMTHYsWOHx/vBhioeOnQIt912GxISEtC3b19s2bIlILk0fENVVICqqVFajNBCIY8D07Jl\noMrLAQBUZSXML7zgeq/eaAAA9F9/Qb9vn+zySYamDNeh9YN4hHBfCtppGo1GtG3bFm3bthVFmPT0\ndLz//vuYPXs25s6diz///BPz588HAEyfPh2XL18GAI/QhfrrnJwcn+3abDZR5BOzLVueCUDDHwdd\nSS5sNvk2mQwL6GABgzrlsNLB4sw5G4IJCRajT37P0QMwuq6jmUpRfzd/oGuNcJ8S5zJz0K3G6fsD\nPhBD7t+KdQDMrut4vV32/nCWef4mabkFsJlzA25HDLnza4FaJsx1HUmzuJKRiiuCW/afqkIaQEPC\n16zCUths+QG3I9bvmFFqAdBg3LFfyYCtWJSm/aK0ggJgcV1fKa8O6ruJ1R+nsg0ADK5rc1URbDbf\nMfhSYKHMcLfHp9jSUR0R+AZCrD6xWq2itOOLuLg4rFmzBmvWrEF+fj4YhkFcXBx0QZ5EVlRUoGfP\nnpg0aRJmzpzJe78+VHHdunWwWq144403MG7cOPz222+IjIz02mZ6ejomTpyIyZMnY9OmTThy5Aie\nffZZtGzZ0uW5qKFBCvqff1bmJBmAgWMMMOzf7/Ne+tQpOEaNklokSQkfP973e8OHo/bRR4U/RE2V\nG/xRdpWI3VcRdHIyzEuWSBqi4hM1lGPc38iiEgwMw6B///5YfDVup2/fvkhLS8PmzZsxffr0oNsV\na/Nks9lEayvveC6ABqX0xh4dYI02+P6ABMQcy/HIKxDXoQtaWQKzdIvVJzXFJQDKXde928TCao0S\n3G4gdMgvBvIakiCZYlvBao0IqA2x+iPZVgGgQQvsHhcBq7WD4HYDoau9HMgscV3rwmNgtcYE1IZY\n/VF8pRZAgxLYMdoIq7Wd4HYDITu8GvirwHXtMITBau0YUBui9UcNg4pDDcZSMw0M6dlN1nKM4RVO\n4HiDIakS+oC/m5hraumFfAANJ58DOyfC2tHi+wMSkGjLw4XKWtd1VGJ7WFsHVl1IzD6RE6OxzsgY\nrNEAqMubMGLECADA7NmzPd4LNlRx69atSExMxMqVKwEAPXr0wLFjx/Duu+9qhgNAXYqNhrQE4umg\n9vCUJtD/8Qf0f/whvKFQm18hfJIuGIZB2JQp0GVnKy2JpBAVoJSQkIAeV+Op6unevTuysrJc7wPg\nZXLOy8tDq1at5BFSBOyMl/h1mRN5AV6SmynoVkuCmzFJ5eWUdssHyHLN51cQUGB8EOSGzu2PduF6\nWY0GgPdQFiWTARKxhhCWYFVq0tPTMWPGDHTu3BmdOnVCp06d0LlzZ8yYMQNpaWmiPivYUMWjR496\nfAYA7rjjDhw/fhx2u11UGXlom2x10NTvFGrKny8CMBxQUs8dDTLR1jSf6P76K+SNBoBAjwMAKCkp\nwaZNm3Dw4EHk5+dj7dq1GDRoEIqKirBt2zaMGjUKXbt29autIUOG4Pz58x6vnT9/Hu3btwcAdOzY\nEQkJCThw4AAGDBgAAKiursbhw4fxyiuvCP0qspFd4QTjNvdaWXSw6OX/wxRj0gFlDZttRRVlhctT\nAmQbUpTpD3KqKvArTChvSClRsDyl0okAAcBCUzDqgPplo5YBqpwswhRYy1iW5SXP7NjM1xCpOX78\nOO677z5UVVVh+PDh6NatG4C6v9mff/45vvnmG3zxxRfo37+/KM8LNlTxypUrGDZsGO8zDocDBQUF\nSExM9Po5McJFOhQVgfQjjaysLJS7fddBIrefn5+P9iK3KTbZly6hpJE+KC0vR5y8IimCE3yloH4e\ncPukKC8PWTab6ONFDVRUVOC8H3Om6vHHUXrDDeguj1iCYBgGeVeuoCm/1tLiYmRw1sbmOAbcqZ8j\nlrQ09FJQjowLF1AjgkGvKY9HQTurrKws3HPPPbh8+TKsViv++usvVFytdxsbG4vt27cjOzsbK1as\n8Ku92bNnY8SIEVi1ahXuv/9+pKSkYNOmTXjppZcAABRFYdasWVi9ejWsViu6deuGVatWITw8HOMb\niVUiDRISAQLeEnk179NCkhJG8isIyN8f3KoKSvYHz5AS2bwNKSTMF4qiEGPS4UpVQz8U1bAIk/+n\nQV4146pmAACRBgrRRoWMsW6EsuFgwYIFiIiIwIEDB3iHA+fPn8fo0aOxcOFCfPvttwpJKAwxwkXM\nMYGFdilBu/bt4ZQwNCYujnyVu02bNmjVSB9E+cifEWroDPxwWV/zIDYiAhYVhlSJQXh4uF/rQ+KO\nHYi6/noZJBKOTqfjGWW9ER0Z6fHdKS/leJsb9f2hq65WVI5OHTuC6dJF8ucIClVYvHgxqqurkZSU\nhC+//JLnpvqPf/wDPweQ5GXAgAHYsWMHdu/ejRtvvBFLly7FokWLMG3aNNc9c+bMwaxZs/Dcc8/h\n9ttvR25uLj7//HOfiZFIhKsUKnGaDJBTbq/KwXooHzoKaKuAMSWWoPJySpfrBMiqqsAzpCgwPqKM\nFNwrhJY7WNgZZYxtJHjoAPw5o9QY8RbaI3foBkDOmioHKSkpmDZtmlePwm7dumHatGk4efKkaM8L\nNlSxVatWXj+j1+vRsmVL0eTzigrcevX//W/dPxyO5lujvaYGxvfeg3HtWqCioun7Q5VAcxyoYHwr\njT4pSWkRRIXK9UyQrfvzT4UkIQuqsBDmVauUFkMWBO02f/zxRzz++OOwWq0oLCzkvd+pUydcunQp\noDZHjhyJkSNH+nyfoigsXLgQCxcuDFheUiDhtBAg53Qsi6MEtQmjYdARcFqokCu6g2GRXcHNgaF8\naTllcxwob0jRUXWn2O6eOcU1DOIDTCgqBpllhK4hCo0RpUtT1sOfM6G7sW7VqlWjiRBpmnYp+2IQ\nbKjiDTfcgL1793q8duDAAfTv3x8GLyeszQ3z22/Def310P/8M0ybNiktjiKEPfkkqJK6RMBeKxpo\nOQ54UHY7jB98IJ0saqEp44majCt+yGr47jvA6VSsZCiRsCzCR40CfeaM4nLIgSCPg+rq6kYt9uXl\n5T7fa85crFBeCQK8xOMqtOknIREgQI4rek6lEw63+R9n1iE8mDqZAvEW088o8EeQZVmC5gwZijI5\n/UGGVwoJOR8AcoyxcvD4449j8+bNruTF7mRmZmLz5s14/PHHA2qzvLwcKSkpSElJAcMwyMrKQkpK\nCi5evOgKVVyzZg2+/PJLnDlzBrNnz+aFKs6YMQMzZsxwXU+ZMgU5OTlYsGABzp07h23btuGjjz7C\nE088EfyX9xeVKA1hU6c2W6MBAJfRAAAMP/ygoCQKE6DHgeXpp6WTRSXQv/yitAiyo//6a6VFIAr6\n+HHljQYyIuhYpkePHjh8+LDPMkj79u1D7969hTwiJMksI/R0TKFNLrEeGM3ckGLQUQjXU6i4asVg\nWKDMzsoeO15Qw6DSzZISrqd4LuFywUsoqlBeEFJO2EmpRMLzSCEkb0wohypUVVUhOjoa119/Pe65\n5x50uRpbmZqaim+//RZdu3ZFZWUl1qxZ4/oMRVF46qmnfLZ5/PhxjB492nW9fPlyLF++HJMmTcL6\n9esxZ84cVFVV4bnnnkNxcTEGDhzIC1XkGjI6deqEXbt2YdGiRdiyZQsSExOxYsUKeUoxqsRwQCkc\nm0s6uosXlRZBHgSUUm2u6K4mbW1O6Nw9yZuLN04j6FJSlBahDpn+3gjabU6fPh1PPPEE+vTpgzFj\nxrhez8jIwIoVK5CcnIwPNFcmHtzTQqUUQ+6mX6nycrycDwqUpgT4p6dFV8vLyR0rzRsfCilBQF3M\ndoWjQZ6iGoZXck5qvJ0mKxG/DpChGJbZGY9wCT0FJFoUMqQQUqKSn0yUnDUkVHn55Zdd/969ezfv\n/dOnT+P06dMerzVlOBg6dCiKi4t9vu9PqOLXXk7DbrnlFvzSDE8GNcRBf+iQ0iLIAqu5nzdvVGLo\nJA3Te+8pLUIdajAcPPTQQ8jIyMDLL7+MJUuWAADGjx8Pp9MJiqKwaNEiD4OCBuBkWGQRcqLMT+Sl\n1OkpN2O+Mv0RpiejvBzXI0WpxHcAEG2kkOWWK6q4hgFkzkNKikcK4KXShAKKIdeQ0i6CBq1AThCA\n4DWElDU1hA0HYiY+1NDQIIBADAfN+KSZPn0a9LFjcF53ndKiaBAA/fffSosgK4I1koULF+LBBx/E\nF198gdTUVDAMg86dO2Ps2LGilDMKNXKrGI/49VgThUgF4tfrnk3GJpfnmq/QCTtF1bnAX1a4vBwp\nHikAGVniuW75ShpSSDQcKO2R4o4SawjLssSE95BUwlRqOnRoquK3BqWd4GmoiQAMB/pvvpFQELLR\nZWUh4s474ejXD7WPPdb4zWoysGjrlbpRg8dBPV26dMHTWpIUvyBJCYoze25y86vJyIiuZJ+0NHsa\nDvKrnbKXhiTl9BQA4syez1ZijJDkcdCSgDnDmy+RzXsNKaphUO5mjbXQFE8uuYg16UABqJemqIaF\ng2GhV8gjRE4KCwvx/fffIzc3F1arFXfffXejVReaBdpGXENNBGA40BUVSSiIOtCfOAH2s8+UFkND\nQ1ZE2XFmZWVh//79yMzMBFBXNmn48OFo3769GM2HFCSdFsZzNtd51U4fd0pHrZNFTqWnsiG3ou5O\nvJkG0KCYKaEIkRKvDXgZI1XN+4Sd2x/5CswZkvtDiTXEm2FJqRwYeh2FFiYdCtw8LwqqGSSEhUbs\n8Keffopt27Zhy5YtiIuLc71+7NgxPPjggygqKnLlhbn++uuxe/duhIWFKSixhoaG36jpdJwQDD/+\nqLQI4uGvoVMziJKJGjwOGIbBSy+9hI0bN8Lp9Ny80TSN6dOnY9myZdqpgxu8RG8KxfMDdadjOqou\nWz5QV26v1snCSMv3xyO70gn3oZ5o0cEsc04Bd7gnlXIrygxBbtcAEGchQVEm94SdBEOKkh4pLbke\nKUr0ByGlKeuJM3saDvJCyHDw+eefw263exgNAGDmzJkoLi7GvHnzMHDgQHz33XfYsmUL3nnnHcyf\nP18haQlA22BrqAktOaKGhnqR6e+NII1++fLleO+99zBmzBh88803sNlssNls2LdvH0aPHo0NGzZg\n+fLlYskaEvDKqClUQQAAdBSFliZlXY0zyshRkgEviqHMivKVKgbuYdHRRkr2Kgbu8PtDeUVZyRN2\nMkI3CPZIaeahLAAZxjap+PPPP3HTTTd5vPb7778jNTUVjzzyCBYtWoSRI0fizTffxIgRI/Dll18q\nJKmGhkagaFUVNDQ0mkKQRrJ9+3aMGjUKW7ZswZAhQxAXF4e4uDjceOON2Lp1K+655x5s375dLFlD\nAlsJd9Ov7EKttKvx+VK7x7WSShAAxFuUPUHljw9l+4OrKMt9wn650olSe4MV1UQD8QqVHgT4z5Z7\nvjAsi/Ol5Kwh3JwPhTUMHIy8p6znSwhbQxSeM1KSn5+Pjh07erz2ww8/gKIoPPDAAx6v33777UhP\nT5dROg0NDUF4MRxEdu6M8HvvVUAYDdnx88Ta9N57iOzaFeHDh0OnrfHkoIZQhbKyMgwbNszn+8OH\nD8fPP/8s5BEhBcuySCnw3OT2bmFQSJo64iw0UKxcTP9JwvpD6RPUEwW1Hte9YpVWgpQ9PeWOj16x\nBugUjMNUenxkljtR4lbyMMpIKeqBUR/TX6hgTD9vDYlt3muIlERFRaGsrMzjteTkZNA0jX79+nm8\nHhERAba5u+o39++voS68GA50RUXQ/fqrAsJokIouK6vu/wUFoLQkmeSghlCFwYMH49ixYz7f/+23\n3zB48GAhjwgprlR5Zv+O0FPoSJrHgcynY6mc09OeCivK/Czx8irKaZz+UFwJ4p2wKz0+lO2P+rwg\n9ZTWsqhxyqcccPvj2hgDaIUz9iupKLMsS94aEsKhCj169MDevXtd10VFRThy5AgGDBjAS4J48eJF\ntGrVSm4RyUIzHGh9oCa0UAXxYULHcOwN+sIFpUXQuApVWSnLcwQZDlavXo3k5GQsWrTIwyUxPT0d\nCxcuxG+//YY333xTqIwhA3eD2zlKr1j273qUjunnKspdo5QOVVBaUfbs/y5K94fCye9IGx86il/q\nT04vnVROKIvS4wNQVlEurGE8PDDMNNBGQQ8MILRDFZ588kkcOXIE9913H1577TWMHTsWVVVVmDp1\nKu/e/fv347rrrlNASg0NjaDQDAeiY9TKNWrIBFVYKMtzBO06b7vtNjidTmzYsAEbNmyAXl/XnMNR\nt7m1WCy8UAaKonChmVqouIYDpZUgQNmY/go741GKUUcBHZt5fDJPUY5Wtj+ijRT0FFDvKFPuYFHp\nYBCmlyfPAHfOEKEom3W44jYu8qqcspUQJXINUXDO8MZHpF7RUBaAjISiUnHXXXdh6dKlWLlyJX75\n5RdYLBbMnz8fEydO9LgvOTkZf/zxB6ZMmaKQpISgnbZrqAgtOWIzR1uvVA1VXCzLcwTtOu+44w7F\nT8zVxIUy7qZf+UVaSTfjC2X8MmpyloL0hrfT0/q65FJT5WCR5VZajgLQSWFDCkVRiLfoPAw8+dUM\nOkQoYzggR1FWJi+ItoZ4kkaYhw7A91oKpVAFAHjiiScwa9YsFBQUID4+3uvaeN111yE1NRXR0dEK\nSKihoREMlMPR9E0aIQlVUQHLSy8pLYaGEJzy7DUE7bK2bNkilhzNgtOFnkm8SMbwQyQAACAASURB\nVNjkKhnTf7rIsz9IUAoj9BTMNFDfDdXOulP2SIP0hoNzxZ790S6ChlmvvGEuzkx7Gg6qGHSIkP65\npbUMLlZwFUMCFGUFw1lOFxLogaGgokziGqJ03hg5oGm60fwFFosFFotFRokIRTvB01AR+kOHlBZB\nQ0MjWGQyHChX16wZksIxHPRtaVRIkgaUVIK4FSb6tlQ28R1Qd8LOLUEoV/gGNzt8X4UrTNSj1Iny\nqUI73Cv7dY/WyxYi0Rg8Y1uVPIt1QbUTlyobnmWigWtilB8jSoYqkLiG8NaPEApV0AgQzXCgoaGh\noSEHMiXiFOV4Jjk5Genp6SguLvZafmnmzJliPEbVlHHi+fUU0COGhNMx5Tb93PrrvRTOmF9PvEXn\nETKQV+1EZxlOMm2cxHc9CTEccE+U5UqgeZ4TpkDM+FBIMeSOj25RepgUDu0BlI3pP8/pk14EzJlo\nIwWDDrBf7YYKB4sKO4Nwg/JGLw0NDQ0NDY0QRA2Gg1OnTmHq1Kmw2Ww+6zVTFKUZDsDPht4pUg+D\nwmXUAGVj+nmKkMKJAOtRytXYxlGUrQS4XQPKVVbgKoXEjA+FvHS488UarbySDCgX019hZzw8MHQU\n0DlS+TFCURTizTpkc/KCaIYDDQ0NDQ0NDSnQZWfL8hxBu6ynnnoKOTk5WL58OQYNGoSoqCix5Ao5\n/i4hK1t+PUrF9Fc7WGSUeyoYJMQnA8q5Gts4OQ6shIwRpUIVuHOGlP5QKi+IN48DElDM0Mbpj44E\nJFetJ85M8wwHHSMVFEhDGbRQBQ0NDQ0NGTC/8QZqFi2S/DmCdp5nzpzBggULMGPGDLHkCVmO5dV6\nXF9LQJgC0BDT7+6an1/FIFLi07GTBbVwuu2p2kfQiCDkRE4JRbmohkFamefpKSkn7EqFKvzOmTMk\nhPYAXsJ7ZDKkcNeQa2LJ6A+lDG1/5Hsa2kjI91AP3ysltCoraPiJZjjQ0NDQ0AghBGlqnTt3Bq3V\nffUL7ulp/zjlEyPWw49Rln6Te47THwPiyNn08/pDhuR3f3O8DXpE6wkypMgfqlBUw3go5EYd0JuQ\nHAf88aGMB8YAQtaQ+pj+eupj+qWGW4WEpDWkZTOorKAEffr0QUxMDO+/iRMner0/IyPD6/379++X\nWXINDQ0NDQ31I+jIav78+Vi8eDEmTJiAxMREsWQKSbjx2t0JOU0GlHE15uZ8ICVeGwDiLPKfoPLy\nG5A8PuTojxJ+mT2agJwggDJ5QYprGI9xaNTVueaTQJ3Xks6zZKcMMf3c5JndSfI4CMHKCtddd13A\nY5yiKJw4cUI0GQ4cOACnW8mp3NxcDBs2DGPHjm30c5999hl69+7tuo6NjRVNJo3Gsfz730qLoKGh\noaEhEoK0k7Fjx6K2thaDBg3C7bffjjZt2vA8ECiKwquvvipISLVT6WA8QgF0FBn11+shQVEmJV4b\nUEZR5hqWSDIceFOUpYbUxJmAMnlBuEpyF4IMKUBduALXcCB1TD93jJCSIwXwtoaoP1Th5ptv5hkO\nTpw4gbNnz+Laa69F165dAQCpqamu1/r16yeqDHFxcR7X27dvR2RkJMaNG9fo51q0aIGEhARRZdHQ\n0NDQ0GhuCNppHT58GM899xwqKiqwd+9er/f8f/bOOz6qKv3/nzszmcmkkAmkAwklQ+8kNAsCigUF\nUVyVlUUsu1LsuOouFsSVr2uvrMjPn7oL/lZWXBsW/Iq7IEVFUASFoYUSEpJASCaTqff+/piUuWX6\nnZl7Z57365XXK7ede+bMOWfO85ynkOLAm4/el9IsrSLSqLUjXOTGQ3EgzL+uKEE5AcHvhO1RriQL\nDIn+Eesd9t2nlds/EhEXRNQ/FCQkA/GfQ844WBzzCa7KQFmKA7GyTf0WBytWrOAdf/zxx1i/fj0+\n/PBDnHfeebxr//nPfzB37lz8+c9/jll9OI7D3//+d1x77bUwGo0B750zZw7sdjv69u2LBQsWYMaM\nGTGrl6CS8XkPQRAEQcSBqFZa9913H4xGI/72t79RVoUA/ChY9I/spgzf5HbivTt22u7hCV16DTBY\nIf7rAJCfAAsMYR9Rkr92hk6DLB0Dq9u7CHaxwFknB5MhdooD0ZhRiD9/O/lGDa8P19k96B1DwfXH\nBn5gRKW1R7wDaP7U4IKvSDYwVwejTknK2MSkMI0nTzzxBH7/+9+LlAYAMHHiRNx66634y1/+gmnT\npsXk/Rs3bkRVVRV+97vf+b0nKysLy5Ytw7hx46DT6bB+/XrMmzcPK1aswLXXXhuwfIvFEnUdezc1\noVvUpRAEQRBEcOT43TKbzQGvR7XSPXjwIB5++GFceuml0RTTwfLly/Hkk0/yzhUUFGD//v0AgPnz\n5+Odd97hXa+oqFB8oKNfG/kmtUO7KUcoBOLvqvBLo9gsP11Bi36pHXaW46CJ0Q57vd2DBkdnm6dr\nlbejnGfUwOqT9aHe7oHJEJsddo7j8Ksg8N3QrsoaM/GOCyKaQxTXHvEVlFXXP5LA4kDIoUOHYDKZ\n/F43mUw4fPhwzN7/1ltvYdSoURg6dKjfe7p164bbb7+943jkyJE4c+YMXnjhhaCKg2CLp1AwZlMO\nToIgCCI+yPG7FYyoVv79+vWD1WqVqy4AvB963759HX9btmzhXb/gggt419euXSvr+2PBznpBGjWF\npJVrJ95CkLA9BirI2gAADFoGXfSdSgKW85pGx4of6oRm+WmK8l8H4isIVVk9OOPo3E/O0DEoVUgg\nwHbimYLQxXIi1w2lpGJsJ96CsmgOUVBgRCAxcUHiTe/evbF69WrJNUBzczNWr16NXr16xeTddXV1\nWL9+PebOnRv2s6NGjcKhQ4diUCsJyFWBIAiCSCKiWn0uXboU8+fPx0UXXSRbECSdThcwiJHBYFBV\nkCOW47D3DH/RP1ppZsZxdlUQxnxQSlo5X/LTNWhy+pqis+iWHhvh9WdB/6jIV5YQBLQLyp31jKVy\nSSgkj8xLi5m1R6TEU1A+3OSGzd0pgOSna9AzU2GKlDi7Kvx8RpCaMl9Zc4hUys5YxwWJN3/+858x\nd+5cVFZW4rrrrkPv3r0BeC0R/vnPf6Kurg5vvvlmTN69Zs0aGAwGXH311WE/u3v3blWtIQiCIAhC\nKUSlOFi1ahWys7MxefJkDB48GD169JDMqvD3v/895DKPHDmCAQMGQK/Xo6KiAg8//DBv12Lr1q0o\nLy9HTk4OzjnnHDz00EPIz8+P5mPElGNWD3zX0LkGBoUZylr0x9unXxghfqDCLDAAr+n1wSYfxUEr\niwH+rXKjQhgdfoDCdk8Br0+/L7HsI8JUnUrbTQYkBOXW2AnK4v6hU5wAGk9XBZbjJPqIsuYQYVwQ\nNxf7uCDx5vLLL8fatWvxyCOP4Pnnn+ddGzp0KF5++WVMmTJF9vdyHIe3334bV111FbKysnjXli5d\nih07duDDDz8E4FUwpKWlYdiwYdBoNPjss8+watUqPProo7LXy09l4/MegiAIgogDUa22vv32WzAM\ng/z8fJw6dQqnTp0S3RPOAreiogKvvvoqzGYz6uvr8dRTT2Hq1KnYtm0bunbtigsvvBBXXHEFysrK\ncPToUTz++OOYPn06vv76axgMBr/lyhEsItKytp7RAEjvOO6hd8taHzlwsgCQ0XFc3+rBvv0WhGot\nH87n4Thg32kjvHHQvehOH4fFpqwFVrpHD9/hsfvwCRRZQxMOw/1+f641AOgUvDKstbBYToZVRqzR\n2tIAdArwv544BYvW7f8BH8Jtjx3H+G2f4zwDi6UurDJiDXtWC6BzzjlSfzbkOobbHtuO6wB07qgX\noAUWS2NYZcSa1mb+PHf8bGvInzPc9qixM2j1dEbRz9ZyOHPsEBoVJpPn6NJhdXcqmL779RB6ZYQ2\nz8n1GxFrf8fJkydj8uTJqK2txbFjxwAAPXv2jOmO/qZNm3Dw4EGsXLlSdK2mpkYUV+Hpp5/GsWPH\noNVq0bdvX7z88stB4xvIBikOCIIgiCQiKsVBe9BCubjooot4x5WVlRg+fDjWrFmDRYsW8cwSBw8e\njBEjRmDo0KH4/PPPMX36dL/lyrV4slgsYZf1xR4rgLMdx0MLs2E2l8pSHznp8n01mpzeRQ4LBnml\nfUIyzQ+3TWpsHjR7ajqOjVoG5wzpqzhT9N51Z/B1g63jWGcqgNmcFeAJL+G2B8txOPrtScAnRvzE\nQWUozVLWDmo/pxU43tmPuYxcmM3BTTAiGTMn952Cr1vE+L7FMPdI9/9AAhiSYQf2N3Qc23WZMJvL\ngj4XSXvU15wB0NkXR5fmhdQX44nB6gZ+rO04buZ0IX3OSNqj6rgdQGfb9++qR79+PcIqIx6U7DuF\nE/bOfpxZ2BPmIv8K7nYiaZNEU1hYGDfz//PPPx+NjdKKM2HKyNmzZ2P27NnxqBZBEARBJD3Kkk4E\nZGZmYsCAAX4DGRUXF6OkpCR+gY4iYEcdP4jXYIVF/24nXj79W2sdvOOBuTrFKQ0AcfC7WPmwH2xy\n46yzU2nQRc+gh8L81wGpOBixaQ+nhxOlYhyksOCZQHzjgojmEDW0Rwx9+oVziBL7ByARFyQJMyt4\nPB787//+L44cOYLGxkZwgh12hmHwxz/+MUG1IwiCIIjUwPbCC3F5T9SKA7fbjffeew+bNm1CXV0d\nHnroIQwZMgSNjY3YuHEjxo8fj6KioojKttvtsFgsknmiAaC+vh4nT55UdKCjX88IAwEqc5EbL5/+\nXfX89jgnhB24RCAMfherqOh7BUHeRnTTK1KRIoxxECtB+WCTGy4f+ao4Q4MSBSpSRD79MRIKHR4O\nBwUxQUYqcA6Jp0//rgaVzCFJnllh586dmDNnDqqrq0UKg3ZSXnFArgoEQRBEHHBFECw4EqJKx9jY\n2IiLLroIt912Gz766CNs2LABDQ1eE9Ls7GwsWbJE0g/RH0uWLMHmzZtx5MgRfP/995g7dy5sNhuu\nv/56WK1WLFmyBN9++y2qqqqwadMmXH/99cjPz8fll18ezceIGXY3B4soEKDyFv2AeMcwVotcYXso\ncfcUkBCUYxTsbY9AsaS0IG/tiNIPxq09lNk/xOOFBRsDIWFfowsen2J7ZGrRRR/VtB0z4pVZQRgs\nUrFzSJzT3Mabe++9F3a7HatXr8bhw4dx5swZ0d/p06cTXc2EwpDigCAIgogxTaNHA1nxcWGNagX6\n6KOPwmKx4IMPPsAPP/zA23XQarW44oor8MUXX4RcXnV1NW655RZUVlZizpw50Ov12LBhA0pLS6HV\narF3717Mnj0bFRUVmD9/PsrLy/HFF18gOzs7mo8RM34+4+LtnvbI1MJkUOaiP16C8gHBot+coxJB\nOUY7yj8IzNCHdlOJEBSr9qgXtIdCXXv0WgY5+s7ddJYDzjjkb5MfBBY6QxTaHkB8BOVWN4djPkFK\nGQB9u6T2HJIo9uzZgzvvvBOXXnopTKYYpZwhCIIgCCIgWqs1bu+KasW1fv16/OEPf8D5558vubNQ\nXl6ONWvWhFzeG2+84fea0WjEunXrIqpnotjfyF/0j1CoUAhAFM8gFoKhi+VwuJmvOChXrOIgMbun\nI7opKx99O90E7XHawcLNctCFmnojRCyN/PYYruAxk5euwdkYxwXZf1Ztc0hsffoPNrnhu4fbI0sL\no055rj1AfONgJIKSkpJEV0H5kMUBQRAEEWPiad0W1fZ3U1MTSkv9ZwhwuVzweJJrsRQOB5rUsbsO\nxMenf3eDS+S/nqNQs2uxT7/8QpDdzeGoSnZPdRoGXQXWMg0xaBOhK4tSFUuAOM5BLHbY1WKhA8Rn\nDhFapAxQcnvEyYorUdx9991466230NTUlOiqEARBEETqEkfFQVSrrt69e+Onn37ye/3rr79Gv379\nonmFqhHuJitbCIr9InefoD1G5Slzdx0AcvUaaBivCToANDk5ODwcDFr5djcPNatn9xTw9pHTPub4\ndXYWhRny7bA7PHxFCqBcRQoQn7ggqppD4iAo7xNYpIzKV+4ckuyuCmfOnEFGRgZGjRqFGTNmoHv3\n7tBq+Z+ZYRjccccdCaph4kn78MNEV4EgCIKQgdbHH4dxyZJEVyPhRLUK/e1vf4tly5bh/PPP78h8\nwDAMXC4XnnrqKWzYsAHPPfecLBVVIzsF/sn9cpRrZpxnjP0i96CKdk+1GgbdDBqepUG9nUV3GSP8\nC3dPzQoWkgFv8Lt9ZzuPvYKyfH36pwZXh6IG8MYEyUxTpkUKEHtBudHB4nAz3yKlXMF9JB6Cspqt\nuJLNVeHRRx/t+N+fm2EqKw6YY8cSXQWCIAhCBtiSEngqKxNdDUUQ1apr4cKF2LNnD2688UZ07doV\nAHDbbbehoaEBTqcTc+bMwdy5c2WpqNpocrI43tK5UNQxwDAF+yfHY5FraeIrUpS8ewp428RXcVDX\n6pFVcfCLIBXjaAXvngKxN83/RRATpELh7SEUlOV2ZxG2x0CTTtmKlDjMIQcEMR+UrEgRxgU54+Dg\nYjmkyRwXJFH8+OOPia6Coklbvz7RVSAIgiBkgDMoM+1zB2pxVWAYBitWrMB1112HDz74AAcOHADL\nspgyZQquuuoqTJo0Sa56qg5h7vVe2TpZzdzlRrh7eioGZsY/Nahn0Q+0WWH4mEbL3SbC3dP+Ck3F\n2I5QMKxtlVcwFJrl91NZe5yKeXsoV/EIxH4OaXLyLTAAoK+ClY/tcUF47j2tLEpkVD4mkkDxjQgA\nGuUq+QiCIIgwUHqgWyUrDt555x1MmDABZWVlHecmTpyIiRMnyloxtaMm32QA6GrQQK8BnG1r3GYX\nh2YXi2yZdjibXfxFv5ZRbqq9dooz+J+9RmbBUE27pwBQJIhnUGOTVzAUjRmFt0exQAA8aZO7f6hr\nDinOiG177D3Dd2Xpl6OTbX6KFcUZfMVBjc2TNIoDIgiMcjcKCIIgiNCJZ9aCSIhn/cJeiS5cuBCv\nvfYaT3FAiPnlDF8oVLIvLgBoGAZFGVpecLqTLR5km+RZmAvjG/TKVrb/OgCUCAShEy3yCUItLhZH\nBLunShcMhQJPtcyCodrGjLB/VMusSFFbewgVB9UtHnAcB0YmAUqoWBqYq+z2ALx9ZI+PS9IJmwej\nElifaLj88suh0Wiwbt066HQ6XHHFFUGfYRgGH6ZqgEBSHBAEQSQPSlYeKDkdI6fkhlMQOwVm+Urf\nXQcg8t+XUzAUmuWXKzhQZDsiQVlGxcHu0y54fIZS72wtshSvSOHXT872OG33oMpHaZWmUb7rRiz7\nB6C+OaSLXoPstE5hyckCDQ75lClC9y9zF2W3BxD7PhJPOI4Dy3Z+nyzLguO4gH++96cc5KpAEASR\nHJDs24GyV+YqRmhmrOTAiO2IdlBlXOQK06gpPYMAIG4POU2vhbunqugfcVQsmbvokKFT9sK7IF0D\nLYMOBdBpB4tWNydLSs1GB8vLSqDXAP0VbnEAeMeMb9rV6haPKIhkpAjnECXHN2hHOGbkdt+IJ598\n8knAY4IPR4oDgiCI5EAhigO2oACaU6dE5xlP/NYWEf2yyWV6mqzY3PyMChoG6J2t/EWuyNRYRtPr\nHXX81IOqMDOO4W6hGndPpXzYWZkmU7XFBAG8KTuLjLERDIWKlD5ddNCqIBq/MO6DnMqlHYL0pQMV\nbpECSLtvECkCrZMIgiCSA4UoDlpffFH6gpJjHADeOAe33357SPcyDIPq6upIXqNahNYGpVlaRWdU\naKcoI3ZR8/cL2mRknrJT7QHiYIC1MkaJF7aHGnZPs9K8pujNLu8E5WK9O+NdZdhRVlsgwHaKMjQ4\n4SMc17Z60EcGa5r9jeoKnNlOUYwyKzQ6WF5ZaRpgiMJdNwCx4kDOOSQRDB48GOPHj8fYsWMxfvx4\nDBkyJNFVUi6kOCAIgiBkgmMYuC+5RPpiHN0CI1qNjh49Gr169ZK5KsnDD/X8Rb8aTIwBoFCweypX\nejmhBQYDoI8KLDDy0zVgALTr8RocrGx52HcKdk/V0kcKjBo0uzq/y5pWeRQHOwRjpp8KYmAAQIFR\nC6Cz7nIJyjuFc4gKdtcB8RxSK5PFgdBCp0+2DjoVWGAUiBQp6rY46N69Oz766CO89957YBgG2dnZ\nGDNmDMaPH49x48Zh9OjRMCg933W8UMgOlZrhMjPBtLQkuhoEQRAJn9MDZU5QdFYFAJg3bx6uueYa\nueuSNPwq2C0cla/83XWgXQjqRC4h6GATf7FcmqVFugx+4LFGp2GQl65BnV3ePOyNDhYnbfzd06Eq\niHEAeAVD3+/zVKsHg3Kjr/s+wZgZna+W9hBY6cgkKIvmEBVY6ABAYUZs5hA1urIAELmyqN3i4Isv\nvoDD4cCOHTuwfft2bNu2Ddu3b8eXX34JhmGg1+sxYsQIjBs3ruPPZDIlutqJIZUDQ8qE4/bbAZaF\n4ZVXSIFAhI3zmmugX7s20dUgkgGlK4LjGONAHasvlSE0u+6nkkVuocBVoUYmIejHBv7uutLTyvlS\nYOQrDmpbo8/DLtw97Z2tk8WKIR6IdpRlEISanCyvHB0D9FKBRQoAFIhM0WMT46CfaiwOBHOITO2h\n1jkkL10DDQOwPgE0nR4OehW4rvnDYDBgwoQJmDBhQse5PXv2dCgStm3bhhdffBEvvvgiNBoN6uvr\nE1jbxBHPYFVJi8EAx913wzN6NDKvvTbRtUkqbG+8gYybbkp0NWJK6+uvQ7dpEzQ1NYmuCqF2OE7R\nyoN4WhxQ2N8YoMbUg4B4d0yu3cI9p/m7p2qIb9COWFCOfjGo1t1TQGx6LccOu1DR1ruLehQpsdhR\nbnbxLVK0alKkxGgO+Vmlc4i2zWrJF7W7K0gxePBgzJs3D4sWLcLChQsxZsyYmKRjXL58OUwmE++v\nX79+AZ/Zs2cPLrvsMhQVFWHgwIF48skn45NWurU19u9IFShDhfwoWAgiCMWhIAsyx7x5CX2/Olaj\nKuKsk8VRK39h2CdbnnRksSZHz0Cv8eZfB4AWN4cmJ4su+uh+tIWCoRqiobcjNL2ukSHTxJ4zfCFI\nDakp2xEGjJQji8DPZ9QZCBAQK1LksNLZKxCSe2Vr1aNIEVgtxSrLhKrmEKOWp0CpaWXRIyuBFZIJ\nu92O7777rsPK4LvvvoPVakVubi4qKyvxyCOPYOzYsbK/12w24+OPP+441mr9/742NTVh5syZmDBh\nAr766itYLBYsXLgQGRkZIQd4jhTGbo9p+SkFKQ7kR8WKA/sf/4j0v/41tJsj+JzOuXOhf+utsJ8j\niHacV10F/bp1MSnbdcMNMPzf/xuTskMh7NXXmTNnYlGPpOHn064Os1TAG/QuM00dP3oMw6A4Q4sq\nH8XHSZsnesWByAJDPYv+EoEgJEd6uR8b+ILhiDx1WKQAUikZo1ek/CRoj+EqifcAACWiFKbR949d\nwv7RTR2764BYsVTTlrJTE0WEeauKLTAA7xyy+3TncXWLB8hPXH2i4eOPP+5QFPz444/weDwwm80Y\nM2YMnnjiCYwdOxZmszmmddDpdCgsLAzp3rVr16K1tRUrVqyA0WjEoEGDsH//frz66qtYtGhRbFNL\nh6k4YHv0gOb48RhVRuWQ4kB+VKw4cF9wAbiXXwZjswW9t/Xpp5E5Z0545VdUkOKA4BPueInl+JIq\nm1wV1IvQf32wClKG+SL03z8RZd7xFhfLU0QAQF8V7SjL3R4AcOAsXzAcLENwwXghbg+3nztDR+i6\noaYxE5P+oeI5JDtNgy76TmHMyQL19uiUS/sa+e3RK1urqhgBsegjiWLOnDlYuXIl+vbti9WrV+Pg\nwYPYvn07XnrpJdxwww0xVxoAwJEjRzBgwAAMGzYMN910E44cOeL33m+//Rbjx4+H0WjsODdlyhSc\nPHkSVVVVMa0nE66rAgnHfuGobeRHxYoDpKej9YUXwPmMa3+4p06F+9xz41ApIqlR0nhJcF3UI8Gp\nBDX7rwNAD8Ei93iUi9xvTzl5Fhh9srWqscAAgO4yL/qtLhbVKt49FfYPeRQp/DGjlsB3gNdVIU0D\nuNq+0rNODlYXi6wo+riwPdTkugEAPTK02Ovs/AwnWjyi2AfhsKXGwTtWk6INALpn8r8/NSsOxo0b\nh127duHdd9/Fli1bOtIwjh07FoMGDYrtDj6AiooKvPrqqzCbzaivr8dTTz2FqVOnYtu2bejatavo\n/lOnTqGkpIR3Lj8/v+NaoLTSFoslqrqaSktRHsb9To8H6VG9Mfmor69HjcWC7Opq9E90ZZKM404n\nAkcHUS5Hjx+HbcQIaL74AqPOO8/vfR1j+LnnUPTmm+jxyishlX/C40HsVaDKp2HqVGhcLuRu3Jjo\nqiQcj9uNI2fPYkiI9582GlEUg3pYLBZkVlVhoJ9rchBsA0BdK1IVsF8oBKls0S8UlKujXORuEiz6\nzy1WV45v4aI/2vYQCoVlWSrbPRWaoreycLMcdBH64FtdLE74mPdrGG+WCbWgaXPv8Y1rUt3iQT9T\n5IoDofJRTYoUwDuH7G3kKw5G5kVenmgOKVLbHCK/O0ui+PTTT+FyubBz505s374dW7duxRNPPIGG\nhgZkZ2ejsrKyQ5FQUVGBjIwMWd9/0UUX8Y4rKysxfPhwrFmzBosWLZL1XVFbT5jNwP33h3x7mkFd\n/Toe5OXlIdtshra2NtFVSToKZ88GZB4z8aJnaSnYIOPTdfnlvDGszw/dP6xgzhywjz4KTWNjxHVM\nBvTLlyP9kUcSXQ1FoNVo0PPSS+EZPBjaPXsC3svm5yNr7lxgzRrZ62E2m6FtaBBf4Li4WPwB5Kog\nKxzHYUcdP21YfxUF8QLk32HfWstvj/PUtugXxDg40eKJKiL396L+oa7d03Qdg26GzjZhuegCAv5Q\nz3fb6JOtg0FFihRA3jFTY/PwrHzSNEAflSkf5TTN5zgO204J5hCVKR+FyjY1WxwAQFpaGsaMGYPb\nb78da9aswYEDB7Bt2zY8/vjjKCwsxDvvvIMrr7wSZWVlmDx5ckzrkpmZ1U0FSAAAIABJREFUiQED\nBuDQoUOS1wsKClBXV8c7135cUFAQ07qFDZnj+4faRn7U3KYhWDa5hXNPONZQWi2s33wD17RpYVYs\nuWD79lVUNgEl0PLhh0Hvsf7v/wJpMVzbU4yD5KHOzvL8eY1aRoVmtfIu+vcKIuZXFqgn0BsA5Bo0\nMPoIsi1uDmedkQ/QXwT+2mprD0DePiLsH6Pz1TVeAHnde4TtMbRrWkorUk60eNDkM966pDEYoDJl\nbCzce5RG//79ccMNN+DWW2/FzTffjMrKSrjdbuzatSum77Xb7bBYLH6DJY4ZMwZbt26F3SdQ4caN\nG1FcXIyysrKY1i1sFCLIKTGeADtQyjCXSFlCUQII7wlRceAZPBgAwHXvDtvq1Wj+73/DrV1S4Zw9\nO9FViCnuiorQbmwTzLlu3eC85hq/tznmzwdXWipH1YLWJVEo7xdCxQgX/f1MOmhVkkatHTmjxFfb\nWJ6QnaljUJqljtSU7TAMg5JMsdVBpAj7iNqEIEC8oxxNZgVhewxSmaINkHfMCFN1DlRje8homr/3\nDF/R1t+ki7kfvdyIx4s304TaaWlpwddff43ly5djxowZKC0txZQpU7BkyRL8/PPPOO+887B48WJZ\n37lkyRJs3rwZR44cwffff4+5c+fCZrPh+uuvBwAsXboU06dP77h/1qxZMBqNWLBgAfbu3YsPP/wQ\nzz//PBYsWKC8fqQUgV1B7cLpvL+PXG4u7Pfdl+DaEIohkj4awjNsbi5an32Wf1Ip41JmXJMmhXSf\n+5JL4JoyJca1SSAaDex33RX8Pp/fbMeDD0re4unfH4477pCrZiHVJRGoT2pRMEKzfLVZGwDidHvR\nmKFvq+X7Jg/K1UWVli1RFGVocbCpsx1qWz0YjPC/W7ubww/16u8j4jgH0fQR9beHMAVhbRSKlGRo\nDznnkK216g6MCABGHQOTnkFjmxLVw3kzTUQTMDJRfPDBB9i6dSu2bduGPXv2wOPxum7l5+dj0qRJ\nGDduHMaPH4/hw4dDq5X/81VXV+OWW25BQ0MD8vLyUFFRgQ0bNqC0bYenpqYGhw8f7rg/JycH77//\nPhYvXoxJkybBZDJh4cKFssdDkAWlCChK+o32Mfd1/PnPcN5yC7r0pzCJKU8M+qht5Uq4LrkE6NIl\n5u9SArb33gNTXY0uQ4KE+9PpYFu7FjkSwWeTAoYB26dP8Pt84vWwffrgbHU1NMePg0tPB1dcDKa2\nFlxeHpAehxC3pDhIHjad5C9yzylSnxl6XroGWsa7uAWARieHVjcHoy78yVPcHuryTW5HKAidjFAQ\n+rbOCYfPoz0ytaqzwACAIkHch0gFwxqbhxdMVMsAYwvVN2bk6h8sx+GbGvXPIUVGoeIgckWKMDCi\nmueQRp9MEydt0WWaSBQ33ngjAKBv37649tprOxQFffv2jcv733jjjYDXV6xYITo3ePBgfPrpp7Gq\nknwoRUBRSj0AkZ8w58clhUgxYtBH2aIisdIAUI5CT240GnA9esAxfz4MEvOm6F69HozTGfg+BeMe\nPRq6HTvEFzQauGbOBPfHP4Jpc2lz/u53cJ9/PjJuuaXjttYXX+Q/l5EBtl9nXhKuR4+Y1FsSCcUB\nQzEO1IfDw4kC36kt+jcAaDUMCo38blEb4Y7yFmFgRJUFNWtHLkFImFbuvGKD8sxlQ0C4wx6poCzc\nTR6Zl4ZsFaXqbEekSIlwvOw94+7YlQYAk57B0K7q22Evlkmx1OJisVMQPFO1c4jICkOdAafefvtt\nWCwWfP/993j55Zdxww03xE1pkPRICCju8ePhifcOu4J+k9pdFWKNa8aMuLyHkIkQ+igXbowDf4KX\ngsZDLHDce29oNwZRoLQuWyZDbWKH/Ykn/LtcZGfDtnIlPMOGwXXppbA/+CBc06fDsWABPEOGwL54\nMdwTJ8a3woGgGAedLF++HCaTiffXz1ejw3FYvnw5BgwYgKKiIkybNg2//PJLAmvcieWsG06ftWD3\nDC3KVJRWzhfhIjeSFIStbk6UVm6MCgMBAmLBMFJB+efTfCFovAp31wGpHfbIhCBhe4wrSA6h8GSE\nMTCE7TG2QK9K155cgwYGnyaxujk0OcPvI/sa3R2WTwDQK1sramu1IJeyLdFcccUVyMuLIrcm4R+J\nhXnLp5/CM2xYfOuhpDknDooD+113wXXllTF/j5Lg1J7606ePcllZYT8TFslqcdAGF+J87hakwhXi\naoszo1TYXr1ge+89v9fd06fD+t//wvbOO+CKiwG9HvYnnoB182Y4liwBwnS9Y2PwO9mhvKCsCnzM\nZjP27dvX8bdly5aOay+88AJeeeUVPPnkk/jqq6+Qn5+PmTNnorm5OYE19vJrozDImzqVBoDYhz2S\nRe7+sy74duPSLK0qd5MB+fKw/yrIqKDGQICAWHEQiWIJEGeYUOuYEbZHbSsLFxv+JC6eQ9TZPxiG\nkcV945ckaQ8AKBEoH6MJGEkkJ36zGcgsyHMxiD0RM2KZ0qwN56JFgMsV/MYkovXllxNdhejwGRO2\nVati+64kVxyEin3pUnA+fv6+uC64IGQFRMKI8/fIlZbCJXM64tYnnvB/MY4KX8WNCJ1Oh8LCwo6/\n9t0NjuOwYsUK3HXXXZgxYwYGDRqEFStWwGq14l//+leCaw18cKSVd6zGaOjtiKKiRyAYfnlcGBhR\nxe0hg6Bcb/fgQFOnoMzAGyFejQgVKSdt3gBp4cBxHNYftfPOqbWPGLQM8tM7p1IOQG0EguHf99t4\nx6qeQ2QYM/8+zJ9T1do/AHnmVCLJidPCtuXLL9H63HNo+vnnuLwvKuKgOOC6dQPc7uA3JhHuCRMC\nXrd+8EGcahIhPkKS+5JLwn5GkiR1VXCPGSNLOWyfPt4d+eefR/PWrbB++ilckyejddky2N59V5Z3\nxJQEKIBs77zj95ojzAC9HrMZbFuqUIZNrKuj4hQHR44cwYABAzBs2DDcdNNNOHLkCACgqqoKtbW1\nmOyjwTEajZgwYQK2b9+eoNp6cbEcPqriC0FqNUMHxIv+ExEIQct+aOIdT1BxexTLYHHwzI98q5hh\n3dTpzw94fe+N2s4f0xY3x0u7GQqfCJQG2WkMhqjQn78dkRVGmH3kuNWNBgf/x0DVc0hmdHOIw8Nh\nwwm+8lHNc4gcihQiuXH99re8Y+fVV3v/kXnB6xk5Es558/wH81KQRYKUX3Hrc8/xjx97LOLyuS5d\nvIJhCikOOL0+aJBJj5L8uaUIQZh3X3ppWM/4DS6ncosD16xZQe+xS6TNddx8s+gcW14O1403gh04\nEJ7x42Fbtw7O228H9Cr4bU7E92gwgC0rk7zknDNHHIcjAKzZ3PG/u6Ii6qpFg6K2PCsqKvDqq6/C\nbDajvr4eTz31FKZOnYpt27ahtrYWAJCfn897Jj8/HydPngxYrsVika2OUmVtb9QA6EzBwYBDYctx\nyPjauKJp1gLo9IHbX3sWFkud3/uFbVLjYAAYeedKXadgsdTIWc244WIBBkZw8A7yulYP9u6zwJ/c\nL9VHth43AOhckA022GTtl/EmLy0dxzydDbD1l0Moz5T+4ZX6nGstevhOP0Oz3Dh88IDs9YwXOeB/\nnh0HTyD3rLRwKNUe/ziuA9D541tiYGE/eRhq7SFGRxrgk7J099FTsEB6cS7VHt+c5s+pWnDo1nxM\ntXOq28qfEw83tgYc/3LNDWafxQahbDx9+8J+770wPP882L594XjgAe+FeJvY6vVgbLbgN8YBTiIF\nnPOaa6D77DPovvwS7gsvhGvmTBgffjj8snNyYHvlFQAAI6E4cF12GdLWrw+/0gqn9eWX4xI7IqaE\nEhwx3AwcfhQH4Qh3iiQEa1DnLbdA95//QPfddwAAz+DBcNx5Z9ivYktLoTl6NOzn4kGivke/Lmg6\nHVpXrEDGbbeFWJDP95idLb4cx8+nqNnjIkHwjcrKSgwfPhxr1qxBZWVlxOXKtXiyWCySZf2/HWcB\nWDuOOTAYNVC9C7aaLAewv77j2KrJgNksrTWTapMdB2wAzvDOzRjZR5UZBNrJ33kSp1q9O8IcGGR3\n742eWeLhI9UeNjeLPVv4yq15I0tgVmmwSAAoPVCHYzWdWTP0+T1g7i7OX+tvzPz0Yw2ATsH64r65\nMJvFk6FaMNc1YtPpls4TXQpgNouDNvlrj1+P1APo3GEfmGeE2dwzFlWNC4OcVuDE2Y5jlzEXZrNJ\ndJ+/9nj7O/6cqtMyGD5AvXNqTqsH2NWpOD3t1vr9XfLXJkTy43joITgeeoh/Ms6/m1xZGdDYGNd3\nhkVWFmz//GfncYR1baqq6vify8wUXbetWcM71r/2Goz33x/Ru+Si9fHHYVyyJOLnW959F+6pU2Ws\nUYIIMibYoiLROS4nJ3CZSeqqIBU8khOkneSKitCyYUPU73JddhkMf/tb1OXEhARZjnAlJcDhw+Lz\nOh1c110HG8Mg4w9/CF5QMPeEVI5x4EtmZiYGDBiAQ4cOobBNe1hXx9/5rqurQ0FBQSKq18EzP1l5\nxyvOy01QTeRBlE4tzPRyT+zkuyncPiRL1UoDQJySsbY1dB+j9UftcPncnqljUKlipQEgNs0PJ+Xe\nkWY3Djfz779lgHjhpiaEmTfCSWHqZjl8LogJ8lhFkEWOwhH1jzDnkJd+Fsyp56p7Ts1L10DnMwWe\ndXJodSc2pRIRe5y/+13oN/tzEYjzb6dz7ty4vi9qIhAI7HffzTt2TZ/OUx44FiwQP6QEd4ZohZ8Q\n+pJj3rzo3hEPIhgTrpkzI3uXil0VOKMRrquuEp23vfZaTN7nuPNO5VpoJOh7lApo6OnXD1xpKQBv\nKlhOwoJARDDLEVIceLHb7bBYLCgsLERZWRkKCwuxceNG3vWtW7di7NixCavj/kZxNN6JKs013k6h\nhFAYavC7JieLo1a+kHBhd3W3ByBWpoQTJf7F3Xwh6Oo+Rj93qgehIiWcvPTCeA/jCvTIVGm8h3ai\nSbe35gDfLLibQYOBKs0w0Y5QkRKOYkmYlhIAzi9R9xyiYRgUipSPFOcg2WldtgynZs2C+/zzg9/s\nLxBgnBe8ckcCF5V/wQXyFhhG+7imTYNj0SI47ruPf8FoRMv778N1+eVwzJ8P+5/+JH7YI994dftY\n0LL+Yk1IEW1QtFBiAwRJu6cIIgl0aDTCGmhXPQktDhyLFnk/9xdfdJyzP/xw6AElw4QrLobtn/+E\np3fvmJQfFQlSHLDDh8O2cmXHmHfOnOm1ZmrvV+npaHn//eAFxTHdYjAUtTpdsmQJLrnkEvTo0aMj\nxoHNZsP1118PhmEwf/58PPvsszCbzSgvL8fTTz+NzMxMzAoh+EeseHN/C++40KgRBQZTG9lpGmSn\nMWh2eTuqkwVOO1h0Sw/+uT4/ZhedU/vuOiAhGIYY3MzNcvhJIAidW6RuIQgQC4bhCMp/t/AF5Yp8\n9fcPcfrB0Bd4q37hzyEj8tKgUfFiBYhOkfLGr/z26JmlRV4Ic4/SKcrQ8IJEVrd40CtbUT/BhNzk\n5ODo/ffDkJ+PnF69At/rT3EQb1eFbt1i+wKjzIpzQ+i/p7bVq/1e84wZA9s//uH3OiOj4kBoFp5j\nErtxSZEWrTl5KMKTGn57BHV0TZuGtE8+6Tz2IxN4Kitxts21RdTmyRgcsW2secaM6fjcscY9dSps\n3bsj+5xzZCuT7dEDmuPH/V53zpoFfbDsegn8Hl2/+Q1cv/mN3+ueigq4pk9H2ocf+i9EQYoDRY2I\n6upq3HLLLaisrMScOXOg1+uxYcMGlLaZdNx5552YP38+7rvvPkyaNAk1NTVYt24dskMx84gBHMfh\n1T38RW4yCIWAOOXeMWtoP5oPf3+Wd1xg1CBDp6huFhGi9ghRcfDOAXGQqVlJYHHQI5Mv8ByzhmbG\n+auEhc6tA9XtpgCIo+aH2h7NLlakWLq+XDpXspooztDCd2lXY2Ph8AT/4WM5Dm/s48+pF6jcgqud\nSOcQQv34jdjuA+fHVUHK9FcqCrocsMXFkoG3FE1aGi+1WVg7+OGgAFcFJkgg8OAFqEApEALCMWFf\nuhRsrtedjS0piSiwX7SKAy4MBVbcSHDavnjhuPtusCUlAAA2N1d6foxDateYoiBXBUVtd7zxxhsB\nrzMMgwcffBAPPvhgnGoUmANN4h+SP45Q2Y+uH8qytPi1sfPzHWn2YERe4Gfsbk600/r0uNA06Uqn\nTLAzeKQ5tEXEx4K0g9PL0lW/mwwAZdn8Re6R5tCEoH8dbOUdDzDpRG2rRoTtcczqgYfloNUE/q6/\nEFjoGLTA9DL1K5YMWgYlGdqOHXYOXmVKeU7gH++9Z8Tj6p5hSTKnRjiHEClCGBYHjiVLkP70036L\ncl16KdI+/TTsKghTHUrR8t574HJzwTQ2IlPCfzooMfj9sy9bBtcVVwBaLTT79iFj4ULZ36GGnWfr\nxx8j6/LL/V73G+Gdd5NydjZDhS0vh3XrVmh/+QWeESPA5UYQEycKVwUuLQ3WrVuRPWpU+O+NJYlS\nHMg9xoOUxxUUwPrNN9Du3AnPwIHQffWV+CYFpZmVggum2FCQEkj5M6GCefT7JtG5/iaVa7XaKBUs\ncqtC2EF9S+C2AQCXlYoj7auRsiz+pFMVgqDs8HAi142LeiRnexy1Bo+DwXEcnv6JH99gaNfkGC9Z\naRrkpXdOp24OPLN0f8zfxM8+YtJroNeqX7EEAKUCZUpVCFZLD313VnSudxf1K5aAyOYQIkkIRRjz\nlyLPj7DX8tZbfosKKyhjKHVow/b663BPmQLPqFFwRxoLIRYCOMPAM3YsPBUVfhUw0Qb9c86ZE9Xz\n7bT+9a+ylCNJmMKRe+TIGFUk/nBFRXBPmhSZ0iAQIfRX9+TJYPv0kfe9cpAgYZPt3x+sT9D6kGK8\nBIJhOiwK/MHl5sI9eTK44mLAJbZs9f+gMhRlorgrAhhSHCQH+8/yheklo7r4uVN9RLLI/c9JfmT4\nWX2M0AXZcVULwt3CUBQpH1e1is5dlwRm6ACQa/DGwWin1cN1pKv0x88Su8l/TuExY3WxEFrvv3SO\nurMH+BLJHHJQYMW1rDKJ+kcEcwiRQoQZ48B96aVw3HorPBICCxeiz7zouSCKA9cVV0RULo8YW9xJ\nmYy7Jk+GI0r3Dk6m7F3OAL7OwSsRRMgJJjAJTfyffTbyuiSSWPShaFwVlGqNImNcjrDQatG6YgU8\ngwbBXVmJ1uXLoyuPYeC4446A13mH4SgOFAI7YABaly6Fp08fuGbMEN+gEAUHQIqDiKmxeWARKA6u\n6q1+E+N2hEG7gi1yWY7DeoFZ/iU9k2N3HfAGvfSNz3bWyaHREVhQFrZHzywt0pJEkcIwTNiC0JfH\nxYEzhcKlmgm3PbbUOEWKg4kqzx7gi2gOCWKaX9XsFmVkmdkrmeZUsjhIVYKaocJ/jAO2LcaTCL0e\n9qeegvWHH6KpGr8OARQO7nPOAdID/6a7pkwJ/pJYu+pJBF+0rVsHrnv3qIv2DBsWdRnIEG8e+Pvu\nRQTZdWSCxWEQCLieJLI4iBa/fT+U/qpU99MECpvuKVNg3bIFLRs2gB08OOryuPx8/xeF7R+G4oA1\nmyOskfw477wT1h9+gE3KmizI2I9nGkxSHETIX3fxTa6zdAz6JIlJLRC+P+77h8W768mkSNEwDEqz\nQm8TN8vhPUGb/M+YnJjULVEIhf5gcQ6W7uC79vxxRDYYpf7gRoBQMAzWHvds5Uc5vriHAYYkcVMA\nJOaQIIqUv+zk94/8dA16ZCXPnNpTEFD0RIsHzhACRhJJQHa2V/AOhB/lgnPePHCZnQFkW598MqRX\n2lasCHqP46abOv739O8Pdvhw/zeHYiobiqCSAIsDuWgNEFciZCQ+fzBLj84b5bU4CPQOxy23hFan\nRCBDH3LcdlvH/57eveHxk9Y9pLgQSrU4kFBSqZGgQnEYioOWN9/sLFerhf3++6OoWRyh4Ijq58cG\nJ+/4liSIDO+LUCg8ZvWA5Ti/gf02CHaTLytNjiCAvpRlaXnuKVVW/wEjN1bz3Ta0jPpz0QsRBgQM\ntKMsde3yJIl/0U6ZQMg9GqA9XCyHMwKLlWv6JsePfDvhuioIAyPOG5Bcc2q6jkFxhqYjgCwH4HiL\nJ6kUzoR/WlavDpyS0d/CMDsb1i+/hP7tt+Hp3x+uuXNDep/ruuuA+fMD3mNfvhxcz55gzpyBY8GC\nwItPmXxspYQAx/z5MISg6AiJIFYR0eAZMyYm5YYinDpuvBG6//wn8E1yKA7asP/lL2B79EDahg3Q\nffON//vuvhvIyoJ+1Spoos36EEfsjz0GtqQEmvp6rxIhmvWqQhUHXJckcfVjmMCCcxiuCu4ZM2Bb\ntQraHTvguvpqcLHKwiI3weZfsjhQNk1OFrsa+B1zRhKZ1AJAF70GuYbOjuhk/eem5zgOm2v4ipTz\nkiQtpS/CHdRAguEmQbyHNA2QnZZcw00oKAcKfrepxiE6lyyBEdsRKVICtMfOeida3PwfwkuTyLUH\nCM9147Tdg58FaSmTyU2hHdGYocwKqUOQuAOBgrqxAwfCvnw5XDfeGLrpNMPAddFFge8zGOC4+27Y\nH3sMXFFRx2n36NHie0NJRxiCxYFr1izROfvy5bC9/jrvnPPKK4O/TwK2Xz9+lfT6iMqJGRLfX00I\nwSw9558P5403Br5nxIjAhUgIH5wg/aan3erEYIDzrrvQ8sknfotzTZ4MxyOPwHHvvfFNdyeHkKTX\nw3nHHd6+HyjwXihZFRSqOIg6KKGMuM89N/KHGQbuigq/l4XKSI/AcornYsQwcM2aBfvy5d5gqmpB\nQRYHyuztCmdbrdg3eWSewn6cZEDoo+zPNL/K6sFxQU7yef2Ta7cQkEhBGEAw3CwQlJ+bkDxB79oJ\ntX8AwGaBIuW2QZlJ5aYAiIXCgO0hULSNL9QjM8kUS8UZGuh9PtIZB4ezTmnl4ze1/PZI1wIDc5NL\nsQREnsaU8PLss89i0qRJ6NmzJ/r27Ytrr70We/fuDfhMVVUVTCaT6O/LL7+MU62DY3/gAUnf/KiJ\ncI61P/WU+KQMFgfuiRPhvvRSHF28GJxGA06j6XC9cE2bBndlpfdVPXrA8cgjEb2D88njzun1aH31\n1ajrHSr2hx4KfpPEd3IqSMBE94gRcE2dKql04drM0R233hp891QiWJ5t5UpwbX3P8fvfh7wDy5pM\ncDzwgM+JOEZ9j+faQaUxDhw33ihSoiUSx733+r82bx5ckyYFfJ7r1QuOm2+WvihQ3LjPOw/uceO8\nz2m1sAfJWKAKFBQckWwkI0C4e3pzkpnUtlOWpcPO+s5dwKpmN86RsCT4r0AoPK9Ij3Sd8ibSaAl1\nt/CshEXKlO7JaIER2g67lEXK5WXJt5vcI0sLDQOwbfN7bSuLVjcHo8RYEFqk/KZPcrkpAJ1xQQ74\nZEqoanZjWDexklXYHsmoeAQos0K0bN68GTfffDNGjRoFjuPwxBNP4Morr8T27duRGyQN23vvvYch\nQ4Z0HAe7Px54+vdHy7/+Ba5nT3kLbhdkIhRoPFL56GXYUW55/31Ao8Gpa6+FqS2+Qsdub0YGWtav\nh+boUbCFhUBWVsTvcSxZ4k1JmZ4uWzaEQHBduqB51y5oDh8Gli0L+3mPH5Py5p07geZmb3A5rRac\nhNVH808/AVYruEBuMG1ImXC7L70Uzbt3h1wG4A2U2bJ6Nd+KJlER/GNNKGNIgRYH9uefT3QVeLgn\nTYJr+nSkffgh73zTrl3efsey0Bw+DDYvDzllZfyH274D+zPPgB00CEahEkIYXDQ93TuX7N0LrrgY\nXLduMn+aBEDpGNWNcDc5Gc3yAQkfZT+Coag9ipO0PUIUlLfVOjuERwAYYNKhwJg82QPaKRX0jxMt\nHrhYsVZUaJGSrgUqktBCJ03DoHsmv02OSgiGTg+H7acErj3FydceQOhjJmXmkAhSVBKdrFu3Djfc\ncAMGDRqEwYMH47XXXkN9fT22bdsW9NmuXbuisLCw40+vABN2tlcvWZQGrqlTO8vs2lVa8A+TVoHV\ngf3xx4M/xHHSbg4AWtau5QlYXEmJ2EQ8LQ1s375RKQ06yi8tjYvSAADc48eD69pV8prHJ6K8Z+BA\nsZATAM5kAjtsWOczEgIql5cXssDvz90krDIA73ckdL2JgWDjuPNO2cuMCW2CreP3vw96qzuFM1lI\nZa7o6Hcajbdf5UgEEfdR3rAS6Wclx5RGA3bIkORQGgCKsjggxUGYnHWy+FGwm3xOUeIXILFAtDsm\nscPOcWIz9HOTVpEiiHFgdYOVGMzC3dNkbY8MnQYFxs4phOW8ygMhQouUyvzktEgBQhMMf6h3wuYT\n36DIqEHfJA2QF4qVTr3dwwuMyACYUJicY4YsDuTFarWCZVmYgsQPAIA5c+agvLwcF198MT744IM4\n1C5+2P/yF7jHj4dn0CC0/u1vslgHOK+/Hs7rr4envBz2++8PyR+Y4Ti0PvOM5DV3EFNkVRNgt731\nxRfhHjkS7hEj0PrCC2EVKwokGYbSQZJY5rePgcUBm+cn+rTSXBXaFDqOe+6B6+KL4QmQ4q/1uefk\nqpn6iPR783lOMp5EtONCDSgoHWNyrlZjyNZaB283eaBJh/wk3E0GJPKOS+wWHrczqPYJmmjUMhid\nn5yKFJNBA5OeQaOzLV2RB6ixsSgR7DKnyu4pAPTK0uFUa+fu+ZFmtyj2QSq1R1m2jueWIRXnQBRI\ntNiQdPEe2hHNIRKKlG8E7TGsWxpMhuTUafcKM4UpEZgHHngAQ4cOxZgA0e6zsrKwbNkyjBs3Djqd\nDuvXr8e8efOwYsUKXHvttX6fs1gsstWzvSyh6N1iteKAXO958UXfFwIAyltaIFSphPW57rmn8/+D\nB0WXhZ/HZrNhf2am6PzpCy/EocOHI6+HgpBSn9SVlqLaYkHGsWMYJLj2a5cuwMqVnSfC+NyHDh2C\nxyd4IeN2Q2jPIWzHYQUF0J86JVlezfHjOBNBuws/89mzZ1ElKGek5/ScAAAgAElEQVSEyyX7TmTD\nqVOQirhw+PBhuGw2md8mDeNyidpcSJPViiPt7dFmmVN+990wbd4sunefxPgIxO7338fQmTPDeMKL\nEsdXfkEBBE4IkvUUto/T6ey4L+vkSQwQlnHokCLdRaJBOI4bzGYc82krUR9iGNm+c3MAxRdAioOw\n2SJY5CbrbjIQ2m7hzrP8wTqmQJ9UueiFlGXr0OhjcVJldfMUB1Y38NPp1LBIAbym6N/WdR5LCYap\nNWaCK9u21KSGRQoQ2g57KrVHcYYWaRrA1aZrPe1g0exiky7jSjz405/+hG3btuGzzz6DNsCOU7du\n3XD77bd3HI8cORJnzpzBCy+8EFBxEGzxFCoWi8VvWZmZmbK9R4oMCZP/mL4vI0Oy/GzB+UBtonRs\nzz+PjLvu6jjmsrKQ9dBDMOfmQtvUJLo/lM/pb8Hfp08fkUuA8+qroX/vPe//110nKt/5+utImzkT\njMQOZXFeHvJkaPecnBzRe7VBsm6whYXQ1NaG/A7HwoXI8+P+0bt3b3Ddu4dcVrQ4Z86E/v33vf/P\nng39mjW8610k2iPTz5wUTr+3L16Mst69w6xt+O+JG/fcA/att6BpE4htq1aFVE+9wdBxn7auTnTd\n3L+/vPVUAM5Vq5B25ZVgWBZcdjaMjz0Gc2FhwGfi9Z2T4iBMhCnDxhUmr1DYI0sLBt5844A3HaPD\nw/EUAxYbf8GbzO0BeAVDX1eVqmYPxvuM5cM2Dc8ixZyjQ156clqkAFIpGfmLh2Y3ePENdAyS1iIF\nEGeakFK2/XwmdeaQUFw39qRQe2g1DHpmanHIpx2qmj0Y0pUUB+Hw4IMPYt26dfjoo4/QKwzf7HZG\njRqFf/zjH/JXLFwU5LcqC/4+j4ICe0WL68Yb0VJcjLR33gHbrx+cN9wQMJVmVEhYorX+7W9wX3AB\noNHAJaH48kyciJZPP4V2504YfbMeALF1VbDbA152XXUVXBdfjKwQ02y6Zs2CbuNG6YtxttBrfe01\nr6uNVgvXtdeKFAeS9XGIU1AHwzV5MtwTJwIcB66sDK4rrwRTVRVhrRWI0Qjrf/+LtH//G2z//qG7\nLyWpRWYgPOef3zGO3RdfDC6I0iCebUSKgzD5pZE/8Q5KwpRh7Ri03mBv7YIfB+CY1Y3ynM7PfLCF\nv+BN5vYAJARDgaB80MYfvANNyT3ERMHvBIKhsH+Yc3TJbZESxOKg0QWcau1cROs1SNr4BoD0eOE4\nrsM1g+OAXxv5Y2iQKfnnEL7iwI0hXZP7M8vJ/fffj/fffx8fffQR+kWYbmz37t0oDLYQiwe62I59\nx/z5SPvss87jG2+Ut/zf/x4GHzN8xx13AACcc+dC/9Zbnef/8AdZ35to3BdfDPfFF4vOewbwjahZ\nYXT4ALDdukHT0MA/KSUMpKXBNWdOwLI8Y8fCM3Ys0v79b+h8Aoe6p0wJuT6+OH/zG+jffbfzeO5c\n0T1MsBgHDAPPBReE/E6OYZSTqUGvh+t3v/N/XeJ7cixeDJ0gfSbn43YihXvaNDiFKQcjEAids2eH\n/Uy84IqK4LzttvAeSkHFAdA5jpUGbXOEQbWdwUlb6iz6AXHkfF+fXDfLYY9VqDhI7vYIlof9pyb+\n9WRXpAhN0YU+/T81p5ZiSSqgKOezC/ejoH/0N6VBp0neH0WTQYMu+s7PZ/d401S2c8zOoN7Oj5Ei\njIuQbIjmED+ZJggxixcvxpo1a/D666/DZDKhtrYWtbW1sFqtHfcsXboU06dP7zhes2YN1q5di337\n9sFiseCll17CqlWr8PsQIqDHGqko43LiOe88OK+/3vv/oEFw+MYskAHHnXfCM3QoAMB5zTUdO4j2\ne++FZ5DX2995/fXwnHOOrO9VLJmZaH3mGXBpaWBzc2HzjTsRBMmgklEKTPbly8GWlIBjGNgffDD4\nrqUfHA88AE+bks5x002yZO0ICsOox1JFwr/efcEFcAuEPusXX4RfdgR9wHHffeG/R8mkqOIgLMji\nQJn8IPDnr8jXQ5/Eu6eAVxDaUtvpo+67w76rwQWbp/Pz56drUJ7kipRgcR92CPrI+CSNDt9OsB32\nHWf518cnsRk6ABQaNUjXegVkAGhycWh0csg1eMeJcA5J9vYAvGNm92lf9x43ijK8/eJ7QXuMLdRD\nm8SKFCC02DGENKtWrQIAzJgxg3f+/vvvx4MPPggAqKmpwWFBIL6nn34ax44dg1arRd++ffHyyy8H\njG8QL9hY+2lrNGhdsQKtL73kFW5kDiDGde8O63/+4xXwtNqOxStXWgrr5s3e8zG2qlAazptvhrPd\nsiOMaO/uyZNlr4tn5Eg0797tNe2K4ntg+/SBddu2gN8n16ULGIkYDxETSHGgNEFSqj46HVo++8zr\nHuJ2A+npQccfl5kZWtlBYCOMi6BUOH/ZNYgOXLFyl5IgtWb0KBHurp+TxEG82gnko/xDHT/o3TlF\nyRsdvh3hbuFRH0G53u5BtaOzj+g13mCRyUz3TC20DOBp21Svt7OwulhkpWnAcRz2NKfWmGEYBmVZ\nOuw72ykMVjW7kWvw9oNUnUN4igOrB2PbNr72NvPHUzIHRmxH5N5DFgch09jYGPSeFStW8I5nz56N\n2Qox3bUvXoz0p58GAHB6PZzz58fnxbEU3v0pJGKgqFANkaSHk3pGjvWUXKnqgnyftpdeQqaEC0MH\n4X4WFVkcSKYIBLyfQa/3/gUrIysLLoFCNOT36/VgnN71uOPOOyMqQ0nYVqxAhs/c2NqWqYLoxLZy\nJTJ8rOaO3XMP4hUulBQHYXBI4K89oltym10DgX36hb7Jw1OgPXpm6ngBI0+0eOD0cNBrGVF79DOl\nwahLbkWKTsOgZ5aW57Jx1OrBoFwN6uwszro7P79Ry6BfTvJPOWXZWr7iwOrBiDyA4ziaQ8DfYT8k\niAmSCnOIsD2OksVByuC4+24wzc3QWCxw3nYbuG7dEl0lQilICZ8q2ohxX3457I88Au3mzYBGg7QN\nG/g3RKI4UEvwUBm+p5Z//hMwGiMqu+Xdd2F45RWwvXrBvnhx1HVJNK5rroH96FFov/0WrpkzwQ4b\nlugqKQ7XVVfBXlUF7bZtcM2YAdvAgXF7d/Kv4mXCw3KwCBb9A5PcXxsI7NP/YwPf4mBAkgcCBIB0\nHYPiDA2q22JdeANGetA3R8fLtgAkf2DEdsqydLx+caTZjUG5aaL26GfSJb0ZOtBuit4ZUbk97kOV\n1YNmH9eeTJ1X6ZLs+PPpd7EcDgjm1FSYQ6Tce3wDRhJJTGYm7E8+mehaEEpELsuARKHVwnH33cDd\ndwMAcqKN38Ew8IweLXkpWJDBuBOmZQ1bWgrN0aMdx63PPOM/DkgIvwueCy6ALYzAk4pHp4Pj/vul\nr6lFmRRrdDp+LAs/KV1jQYrakYXP7tMu3qI/15D8QbwA//64zS4WuwSC4ci85DbLb8dfbvrNJ/np\nd1KnPaTdWYTtMSov+RVtAFDqrz1q+O0xIi8NmhQQFv3NIbvqXWhlOz9/gVGDHpnJP6fmGjTITuv8\n3DY3hzq7OkxyCYKIESq3OJAdhoH74ovhGT6cd9o5dy6QlZWgSvkhTMWB7dVXwRm8bnmePn3g/O1v\n/d8s0QdYnyCX4QTgJAg5SP7tHZkQLvrPKTSkxKK/KEMDgxZwtG0oNzo5nHWy2F7r7PBrB4B+ObqO\ngGfJTmmWFltrO4+rmj3wsBy+qeX3kfOKk99fG5AQDNsUKZsEY+a8FPBfB/y3h1CRkjLt4cenX9g/\nzk2BGCmANw5GaZYWe874xsHwoMCYGvMnQRASpLDioGXNGmRKxSHRaGD9/HPovv4amupqePr0gWfi\nxPhXMBhhfk+ec8+FdfNmaA4cgPvcc72BE8Mou+Xdd6E5dQpcXh48I0eGW1t1kyJjQsmQ4iBENtXw\nzfLPTRGhUMMwKM3SweLjs32k2S1SpKSKkAyIfZSPNLux+7QLTc5OTUqugcHgJE9N2Y7Q8uZIswdN\nTrFFSqqMGXF7eFMybk7ROaRUoEg50eKBi+VSVpECeOcQX8XBkWY3KpM8kCpBEAFI9kCSAQQ+92WX\nSZxsmx/T0+G+5JIYVUomIsl8YDaDNZsjK9tggPuii8J+ZzLgqaiAOzsbuuZmAIBrypQE1yj1SPKZ\nSh7cLIetKbp7CgC9BYLQjw0uid3C1Fn09hYoDn48LW6PVLFIAcTtsbvBha21TrA+Fin9c3Qps6Mq\nVCwdbvZg92kXjrd0xoFI1wIVKeLKYtQxKMno/KlhOWBnvRPbTgkVKanRHoDEHCJQshEEQaglq0BI\n+FkPscXF0re7VRQ0NpZKHz+pHlMWvR5HliyBp1cvuEeMgH3p0kTXKOUgxUEI7D7tQpOrUwrqatBg\nYIrsJgPAKIGAs7XWKVropkIatXZG5/N99X8+7RLtJqeSBcbgrmnQ+8wkJ2wefFTVyrsnldqji17D\nyx7BcsDrv7Tw7qnM1yM9yTNu+CKcQ97eb4PN3TmnFhk1KO+SOnOqaA45Q4oDgiAEpMDmg8dfxHyX\neuZEtqQkdoVLKSXUHkgzShonT4Z11y60fP012CFDEl2dlIMUByHwa6MbvsHgzy3Sp8xuMgAMEmSP\neOeAjbebPMCkQ36K7CYDQJ9sHQw+H7fezuLzY3bePamkSDFoGZQL0iz+w2LjHadSewDiMfN3QXuk\nkiIFAAZ15beHqH8Up0Z8g3aE/WNHnRNnnUm0u0gQRNi4pk7t+N89YQKQkZHA2sgLpw/TokzBioPW\nZcs6/ucyMuCcNy9m7+IkAkFyye7WQiia1NniiYLryzMwrTQd7+08jINMXsr5ogazrkgltw0A0GoY\n9MtJw+7T0j9s3VLMIgXwCkJ7z/g3LUwlM3TAO2b+fcT/9VRTpARLTZpqc0ifLjroNUC7rqDZxaFs\n9UkM65qG4cY03GhyYnR+ao0Zgkh1bCtXwvD882Dcbm9qw2TCEOYcr2BXBeeCBYBOB43F4lUaxDLL\ng9EoOsU4HKCkhESiUKza6tlnn4XJZMJ9Pnkq58+fD5PJxPu78MIL41KfLnoNzu3K4vExOZjRSzyQ\nk5nyLjrkpfvvKqkS5M2XcYX+F/XnFqeWRQoAjAugTBtk0iEvPXUsUgBgXIH/MWHUMiknFI4rDDxH\npJoFRppGug/8dNqFv59Iw1cn7BJPEQSR1JhMcDz6KOyPPw6uW7dE10ZWuLbMAa5Jk3jnXVdfLXk/\n279/zOsUMVotnPPnw/7ss2CHDo37633TMRJEvFGk4uC7777Dm2++icGDB4uuXXDBBdi3b1/H39q1\naxNQw9SCYZiAO6TnpFBgxHYC7ZCm2m4yEFh5dE6KCYUAMKZAz4v7ILxm0KaWYqk4Q+s3hkFJhkYU\ngDUVCKQsSUVlLEEQSUybxYF92TKweXkAAHdlJVwzZwIAWt58s8Od4dTVV4PzEzQxFTn86KPg2gIi\nOhYtAnJyElwjIpVRnD312bNnceutt+Lll1/Gk08+KbpuMBhQSNq2uDOluwH/PtIqeS3VdpMB4OKe\n/vPuzuydWhYpgDdrgj+uKEu99jDqGGgZBpAwKBwfwFolmenbRYsDTWLz0+6Z2pSKb9DOVb2N+Ouu\nZslrY1PMHY4giOSm3eKAHTIE1u++A1Nd7bUqaBOI3VdeieYxY8C0tOAogBASFaYMDdOmodusWWDs\ndrDl5YmuDpHiKM7i4K677sKMGTNw/vnnS17funUrysvLMXr0aNxxxx2oq6uLcw1Tkzn9MiXPPz3Q\nIXk+2TFoGZwnYWmRo+NSUpHCMAyWVXaRvHZ+iu6efnJpnuT5+0dkx7kmyuD/XNBV8vz/uzC5THJD\nZYApTfL8rGJXyrk6EQSRXDivuabjf85ggGvGjM7j3FywgweL0gpyJSVgzaQykILr0YOUBoQiUJTF\nwVtvvYVDhw5h5cqVktcvvPBCXHHFFSgrK8PRo0fx+OOPY/r06fj6669hCBB4xWKxyFZHOctSG/8a\nzWDxXgNO2BnoGGB+mQsTu3lStk2e7gs87NHj6wavomBEFxbPDHKkbHtcYgCO9EzDP054p5UyI4cX\nBttTtj2yATxk1uLZQ3o4WaDAwOHJAQ4cOHAg0VVLGK8M0eDhfQY0uYEsHfCw2YHTxw7hdKIrliC+\nGgfcuceA/S1eHf6MQjcW93HJNmbMtAgnCCIBOP70J2iOH4emuhr2Bx4g83qCSBIUoziwWCx47LHH\n8NlnnyEtTXon5mqfICqDBw/GiBEjMHToUHz++eeYPn2637LlWjxZLJaUXoiZAewSpNxN9Tb5Zz/+\ncaq3xzNm4Bmf41Rvj3vNwL3ndh6nenuYAfy2svM41dsDADYN5B9TmxAEoXbY3r3R8umnia4GQRAy\noxjFwbfffouGhgaMGzeu45zH48GWLVvwxhtvoLq6WmRVUFxcjJKSEhw6dCje1SUIgiAIgiAIgiCI\nlEAxioNp06Zh5MiRvHMLFy5E3759cc8990CvF/uT19fX4+TJkxQskSAIgiAIgiAIgiBihGIUByaT\nCSaTiXcuIyMDubm5GDRoEKxWK/7nf/4H06dPR2FhIY4ePYrHHnsM+fn5uPzyyxNUa4IgCIIgCIIg\nCIJIbhSXVcEfWq0We/fuxezZs1FRUYH58+ejvLwcX3zxBbKzUzNKOUEQBEGkGqtWrcKwYcNQWFiI\niRMnYsuWLQHv37NnDy677DIUFRVh4MCBePLJJ8Fx4lSpBEEQBEH4RzEWB1J88sknHf8bjUasW7cu\ngbUhCIIgCCKRrFu3Dg888ACeeeYZjBs3DqtWrcI111yDbdu2oWfPnqL7m5qaMHPmTEyYMAFfffUV\nLBYLFi5ciIyMDNx+++0J+AQEQRAEoU5UY3FAEARBEERq88orr2D27NmYO3cu+vfvj6eeegqFhYV4\n4403JO9fu3YtWltbsWLFCgwaNAgzZszAnXfeiVdffZWsDgiCIAgiDEhxQBAEQRCE4nE6ndi1axcm\nT57MOz958mRs375d8plvv/0W48ePh9Fo7Dg3ZcoUnDx5ElVVVTGtL0EQBEEkE4p2VSAIgiAIggCA\nhoYGeDwe5Ofn887n5+fj1KlTks+cOnUKJSUlovvbr/Xq1UvyOYvFEn2FY1BWskBtwofagw+1Bx9q\nDz7UHmLkahOz2RzwOikOwiBYY6Yi1CZ8qD34UHvwofbgQ+0hhtpEGdD3QBAEQRB8yFWBIAiCIAjF\n061bN2i1WtTV1fHO19XVoaCgQPKZgoICyfvbrxEEQRAEERqkOCAIgiAIQvHo9XqMGDECGzdu5J3f\nuHEjxo4dK/nMmDFjsHXrVtjtdt79xcXFKCsri2l9CYIgCCKZIMUBQRAEQRCqYOHChVizZg3efvtt\n7Nu3D/fffz9qamowb948AMDSpUsxffr0jvtnzZoFo9GIBQsWYO/evfjwww/x/PPPY8GCBWAYJlEf\ngyAIgiBUB8U4IAiCIAhCFVx11VU4ffo0nnrqKdTW1mLgwIF49913UVpaCgCoqanB4cOHO+7PycnB\n+++/j8WLF2PSpEkwmUxYuHAhFi1alKiPQBAEQRCqhGlsbKRExgRBEARBEARBEARBSEKuCgRBEARB\nEARBEARB+IUUBwRBEARBEARBEARB+IUUBwRBEARBEARBEARB+IUUBwRBEARBEARBEARB+IUUBwRB\nEARBEARBEARB+IUUBwRBEARBEARBEARB+IUUBwRBEARBEARBEARB+IUUBwRBEARBEARBEARB+IUU\nBwRBEARBEARBEARB+IUUBwRBEARBEARBEARB+IUUBwRBEARBEARBEARB+IUUBwRBEARBEARBEARB\n+IUUBwRBEARBEARBEARB+IUUBwRBEARBEARBEARB+IUUBwRBEARBEARBEARB+IUUBwRBEARBEARB\nEARB+IUUBwRBEARBEARBEARB+IUUBwRBEARBEARBEARB+IUUBwRBEARBEARBEARB+IUUBwRBEARB\nEARBEARB+IUUBwRBEARBEARBEARB+IUUBwRBEARBEARBEARB+IUUBwRBEARBEARBEARB+IUUBwRB\nEARBEARBEARB+IUUBwRBEARBEARBEARB+IUUBwRBEARBEARBEARB+IUUBwRBEARBEARBEARB+IUU\nBwRBEARBEARBEARB+IUUBwRBEARBEARBEARB+IUUBwRBEARBEARBEARB+IUUBwRBEARBEARBEARB\n+IUUBwQhI8uXL4fJZMKmTZsSXRXVMG3aNJhMpqjLWb16NUwmE1avXi1DrSJHrs9DEARBpA60fiAi\nxWQyYejQoYmuBpECkOKASGlMJhPvLzc3F2VlZbjkkkv+P3t3HhdV1f8B/DMz7CAMmuCGoIQ+7vu+\nxaNZbqm5JG5lmuKW/RRDLBW0RHJJw0RLn0pzKVITN6wUUVHUSMn0SSEW0VxIBYQAkZnfH8Y8DDMD\nMzAzd5bP+/XyJXf/3nPvwDnfOfdcfPHFFygtLRU6RJ2VNVzL/2vYsCF69uyJFStWICcnR+gQjUrf\nf1DLyjczM1Nv+yQiIvNiifUHAIiPj8ekSZPQokUL1K1bF40bN0bHjh0xYcIEREZGori4WOgQ9api\nnal27dqKc540aRK2bdsmeL2JiQEyFaKcnBy50EEQCaXsm+Hg4GAAQGlpKdLT03Ho0CEUFxdj+PDh\n+Oqrr7Te34MHD/DgwQM0atQITk5OBom5KkOGDEFCQgICAgLQuHFjyOVy3Lt3D0ePHsX9+/fh6+uL\n48ePm8y34llZWSgsLESzZs1qtJ/c3Fzcu3cPnp6ecHNzU8yXSqXw8vLClStXahoqgP+Vb3JyMry9\nvVWW6+t8iIjIdFli/WH9+vUIDQ2FjY0N+vfvD19fX9jZ2SEjIwPnzp3DvXv3cP36dXh6egoSnyGo\nqzPl5+cjKysLiYmJyM7OhpubGyIiIjBu3DhBYqyqHqPveg6RJjZCB0BkCkJCQpSmr169igEDBuDA\ngQM4e/YsevbsqdV+6tSpgzp16hgiRJ2NHz8effr0UUx/8MEHGDBgAK5fv47PPvsM7777roDR/Y+X\nl5de9uPm5qaUMBCKvs6HiIhMn6XUH7KysrBixQq4urri6NGjaNWqldJyuVyOM2fOwMXFRaAIDati\nnQkASkpK8NVXX+G9997DzJkzYWdnh1dffVWgCImEx0cViNRo1aoVevfuDQBISkpSzG/Tpg2kUimK\ni4sRHh6Ojh07om7duli0aBGAyp9RPH36NMaOHYsmTZrAw8MD7dq1w6JFi/DXX3+prDtz5kzFfvbs\n2QN/f380aNBAEVN11KpVC+PHj1c5J22PderUKYwbNw6+vr6oW7cuWrdujQULFuDevXtqj5eTk4MP\nPvgAPXv2RIMGDeDl5YUePXrg/fffV+r2p25MgNOnT0MqlWLmzJn4/fffMW7cOPj4+KBBgwYYNGgQ\nTp48qXK8imMclO0DeFYhKt8VcebMmYrtDh06hOnTp6NTp05o0KABGjRogL59+2LTpk0qXU2lUikS\nEhIAAO3atVPsr3wXQk1jHMjlcmzfvh0DBgxAo0aNUL9+ffTp0weRkZEoKSlRWb/sXnv69CnWrl2L\njh07wsPDA61atcKyZcvw5MkTteVORETCMdf6Q1JSEkpLS9G7d2+VpAEAiEQi9OnTB87Ozkrzy/4G\nFhQUYMmSJWjdujU8PDzQoUMHrF+/HnK5+o7NMTExGDp0KBo3bgxPT0907doVH374IfLz85XWmzp1\nKqRSKa5fv640f8GCBZBKpejXr5/SfJlMBm9vb3Tq1KnS89WGra0tpk2bhtWrV0Mul2Px4sUoLCxU\nWU+X+lFZHSEjIwORkZHo3LkzPD090apVK7z33nt4/PixYl1t6zFldL0GRLpijwMiDSr7RTt58mQk\nJyejf//+GDp0qNou6+Vt374d8+bNg6OjI4YPH4569erh/Pnz2Lx5Mw4dOoRjx46hYcOGKttt3LgR\n8fHxGDRoEPr161fjxmJl51TZscq6L7q7u2PgwIHw9PTE1atXsW3bNhw9ehQ//vijUvwZGRkYNmwY\nsrKy0LZtW7zxxhsAgD/++ANbt27F2LFjtXpUIjMzEwMHDkTr1q0xZcoU/Pnnn/j+++/x6quv4osv\nvsDw4cM1btu4cWMEBwcjIiICrq6uSn9kyzf0w8LCIBaLFYmDvLw8nDp1CosXL8Yvv/yCrVu3KtYN\nDg7Grl27kJWVhcDAQEUPB216OgQGBuKbb75BgwYNMH78eNja2iI2NhZLlixBXFwcvv32W9jYqP5K\nnjZtGs6dO4cBAwagVq1a+PHHH7FhwwZkZ2dj06ZNVR6XiIiMyxzrD+7u7gCe/f0uLS2FRCLR4kyf\nefr0KUaNGoU7d+5gwIABsLGxweHDhxEaGoqioiJFcqTMhx9+iNWrV8Pd3R2vvvoq3NzcEBcXh9Wr\nV+Po0aM4evQoatWqBQDo168f9u7di5MnT6J58+aKfcTHxwMArly5gocPH6J27doAgMuXLyM3Nxej\nRo3SOv6qTJw4ER999BFu3bqF06dPY+DAgYplutaPyixatAiJiYkYOXIkXF1d8eOPP+LTTz9FYmIi\njhw5Ant7e63rMYDu14CoOpg4IFLjt99+w5kzZwAAnTt3VlmelZWFhIQErboV3rp1C0FBQXBycsJP\nP/2EFi1aKJZ98MEHWLNmDebPn49vvvlGZdvTp0/jhx9+QNu2bWtwNs88fvwYu3btAqD+nDQdKyEh\nAWFhYejSpQuio6OVGvx79uxBYGAgFi1ahB07dijmT58+HVlZWVi8eLHKIxE5OTlqG8jqnD17FnPn\nzsWKFSsU89566y289NJLeOedd9C/f3+N3Sa9vb0REhKCiIgIuLm5qXQnLfPtt9+iSZMmSvNkMhlm\nzZqFPXv2YMaMGejSpQuAZ11Sz5w5g6ysLMycObPKCl+Zffv24ZtvvkGrVq1w9OhRuLq6AgCWLVuG\n0aNH48SJE4iKisLcuXNVtk1PT0diYqKiUrdkyRL07t0be4teSDkAACAASURBVPbswbJlyyzqWVMi\nInNnrvWHzp07w8vLC9euXcPQoUMREBCATp06oXnz5lX+zb5z5w5at26N/fv3w9HREcCzRHunTp2w\nadMmLFiwALa2tgCAixcvYvXq1WjQoAGOHz+O+vXrAwBCQ0Mxc+ZM7NmzB8uXL8fq1asBAH379gXw\nLFEwY8YMAMDt27eRmpoKf39/xMXF4fTp04ovEsoSChV7ItSEWCxGz5498e233+Lnn39WJA6qUz8q\nc+HCBZw+fVrxeOPSpUsxadIkHDlyBJ9++inmz5+vdT0G0O0aEFUXH1UgwrMuguHh4fjggw8wbdo0\n9O/fH0VFRRg+fDh69Oihsv57772n9bOIe/bswZMnTzB16lSlP/oAEBQUhPr16+PYsWO4c+eOyrav\nv/56tZMGu3btQnh4OFauXIl58+ahc+fOuHHjBpo2bYq33npL62Nt3rwZcrkcH3/8sUovgXHjxqFt\n27Y4cuSIonvd5cuXceHCBbRs2RJBQUEq+5NKpVo/I+nq6qqSeOjcuTNGjhyJR48e4ciRI1rtpzIV\nkwbAs0pCYGAgAODEiRM1Psb27dsBPEsUlCUNAMDOzg4rV64EAI2DaIWFhSmSBgDg7OyMMWPGQCaT\n4dKlSzWOjYiIqs9S6g/Ozs7YvXs32rRpg3PnzuHtt99Gr1690LBhQwwcOBCRkZEqjxGUFxERoWiw\nAkDdunUxePBg5OXlISUlRTG/rBE9f/58RdIAePYoxPLly+Ho6Ihdu3YpHuHz8fGBt7c3zpw5o3h8\nsCw5sHDhQjg6Oio9vhgfH694rEKfGjRoAODZIJZldK0flRcYGKg0JpJEIkFYWBhEIhG+/vrrasWo\n7TUgqi72OCDCs1+2wLM/XLVq1UKHDh0wduxYvP7662rX1+XZueTkZAD/y5qX5+DggO7du2P//v34\n9ddflf6I6nqcinbv3q342cnJCT4+PpgwYQLefvtttY8JaDrW+fPnYWNjg4MHD+LgwYMqy588eYLS\n0lL88ccfaN++PS5evAgA+Pe//w2xuGa5yXbt2im6K5bXq1cvfPfdd/j1118xduzYGh3j4cOH+OST\nT/DDDz8gMzMTBQUFSsvVVch0VXYPqKvItG7dGnXr1kVqairy8/NVkirt27dX2aZRo0YAIPgrooiI\nrJ0l1R9at26N06dP49KlSzh9+jSSk5Nx4cIFxb+tW7fi4MGDaNy4sdJ2rq6uaNq0qcr+1P2tquyc\nPDw80LJlSyQlJSE1NVWRLOnXrx+2b9+OX375BV26dEF8fDzc3NzQvXt3dOvWTZE4KC4uxvnz59Gm\nTRvFowv6Uvb4iUgkUszTtX5UXq9evVTW9/Pzg4eHB9LS0vD48WO19R9NdLkGRNXFxAERdP+Fqkv3\n8Ly8PADP/iBWtq/c3FyVZZq20cbBgwd1yrhrOtbDhw/x9OlTReVIk7JvIsrOo2Ilpjo0xVS3bl0A\n/yvb6srJyYG/vz8yMzPRqVMnjBs3Du7u7pBIJMjNzcXmzZv18s7qvLw8uLq6Kn0TUJ6npyeys7OR\nl5enkjhQl+Qpe/bUXN8TTkRkKSyx/tChQwd06NBBMZ2UlISZM2fixo0bCAkJUQxCXEbTOD/q/lZV\n55zKEgfx8fHo0qULTp06hT59+kAsFuOFF15AaGgobt68iYyMDBQWFur1MYUyZV8iPPfcc4p5utaP\nyqusfnPv3j2dEwe6XAOi6mLigKgaymecq1LWNf3+/ftql5eNulu+C3t1jlNTmo7l6uqKkpISZGVl\nabWfsj9e+vimXlOZZWdnK2KriR07diAzMxPBwcEqzw5euHABmzdvrtH+y7i6uuLRo0coLCxUmzyo\n7B4gIiLLYY71h06dOmH16tUYPnw4Tp06VaN9lT8ndYlxdefUt29fiEQinDx5EsOGDcOdO3cUyYGy\n/0+ePInMzEylefoik8lw9uxZAMrjVuhaPyrv/v378PPzU5lfVr/RJWlAZCwc44DIwNq1awcAal+x\nVNatrvx6pqZLly54/Pgxrly5ovX6wLOxAWQyWY2OnZycrPbZwLJXImrz/KZYLNYYR1paGgDglVde\n0XiMisqy97qcW9m1LRswq7xr164hOzsbzz//vMW+H5uIiHRnSvWHsr9PNX21X2XnlJ2djf/+979w\ndnZWalTXrVsXLVq0wMWLFxEbGwsAeOGFFxT7k0qlOHXqFOLj42Fra6t2bIma+Prrr3Hr1i2V11rq\nWj8qT10dIyUlBffv30fTpk2VEgeV1WOIjImJAyIDGzt2LOzs7LBt2zbcuHFDadm6devw559/YuDA\ngXrp2m8Is2fPBgC88847uH37tsryoqIinDt3TjHdvn17dOvWDdeuXcOaNWtU1s/Nza10gKXy8vLy\n8NFHHynN+/nnn7F//35IpVIMHjy4yn3Url0bf/31l9p3L5c9p1mxQZ+cnIyPP/5Y4/4A6PQNw6RJ\nkwAAy5cvVzr3kpISvPfeewCevaKLiIiojDHrD0lJSdi5c6fav5UlJSVYv349AKBnz541Os7EiRMB\nPIu/rHcB8CwhsWzZMvz9998ICAhQeQNAv379UFxcjI0bN6Jhw4aKxIJYLEafPn1w/PhxXLp0CZ07\nd4azs3ONYixTUlKCbdu2YeHChRCJRAgPD4eDg4Niua71o/I2b96sVI8oLS3FsmXLIJfLMWHCBKV1\nK6vHEBkTH1UgMrDGjRsjIiIC8+fPh7+/P0aMGAFPT0+cP38eCQkJaNiwIdauXSt0mBr17dsXK1as\nwLJly9CpUye8+OKL8PHxQVFREbKysnD27Fk0btxYqfG9ZcsWDB06FCtXrsThw4cVYy2kp6fjxIkT\nOHbsmFa9BXr06IGvvvoKSUlJ6N69O/7880/s378fcrkcGzZs0Ooben9/f0RHR2PUqFHo2bMn7O3t\n0bp1awwaNAjjxo3DJ598gpCQEJw+fRq+vr74448/cOzYMQwbNgz79u1Tu7/vv/8e8+bNwyuvvAIX\nFxe4ublh+vTpGmMYNWoUYmNjER0dje7du2PIkCGwtbVFbGwsUlNT0a9fP8yaNavKcyEiIuthzPrD\nnTt3MHv2bLz77rvo3r07mjVrBkdHR9y9exfHjx/HvXv34OHhgQ8//LBGx+natSvmz5+PdevWoUeP\nHhgxYgRcXV0RFxeH5ORktGzZEkuXLlXZrl+/foiKikJ2djYCAgKUlr3wwguKwQmr+5jCrl27FPWY\ngoIC3Lx5E+fOnUN2djakUik2btyoeOVjmerUj8qXQ58+fTBy5Ei4urrixx9/xLVr19CxY0fMmTNH\nad3K6jFExsTEAZERTJkyBU2bNkVkZCQOHz6MgoIC1K9fH9OnT0dQUFCNBjEyhrlz56J79+7YvHkz\nzp07h9jYWLi4uKB+/foYO3YsRo4cqbS+j48PTp06hcjISBw6dAiff/457O3t0ahRI7z11lsqIzJr\n4uPjg48//hihoaHYtm0bnjx5go4dOyI4OFjRTbEq4eHhEIvFOHnyJBITEyGTyRAQEIBBgwahfv36\nOHr0KEJDQ5GYmIgTJ07Az88Pa9euRb9+/dQmDiZNmoRbt27hu+++w6ZNm1BSUgIvL69KEwfAs2RK\nz549sWPHDuzYsQMymQy+vr5Yvnw5AgMDq3xPNhERWR9j1R/69euHbdu2IS4uDpcuXUJycjIePXoE\nZ2dn+Pr6YvLkyQgMDNT6VZKVWbp0Kdq2bYvPPvsM0dHRKC4uhre3N4KCgjBv3jyNb1OysbHB06dP\nVZID5afVva1BG2VvohKLxXBxccFzzz2Hbt264d///jdeffVVteMxALrXj8qsWrUKMTEx2L59O27e\nvInnnnsOs2bNQkhICOzt7ZXWraweQ2RMopycnJo9rEREpGenT5/GsGHDEBAQgKioKKHDISIiIqqx\nIUOGICEhAcnJyfD29hY6HCKdcIwDIiIiIiIiItKIiQMiIiIiIiIi0oiJAyIiIiIiIiLSiGMcEBER\nEREREZFG7HFARERERERERBoxcUBEREREREREGjFxoIOUlBShQzA5LBNlLA9lLA9lLA9lLA9VLBPL\nwuupimWijOWhjOWhjOWhjOWhyphlwsQBEREREREREWnExAERERERERERacTEARERERERERFpxMQB\nEREREREREWnExAERERERERERacTEAREREZm8zz//HD179oSXlxe8vLzw4osv4tixY5Vuc/XqVQwe\nPBj16tVDixYtEBERAblcbqSIiYiILAcTB0RERGTyGjRogLCwMMTHxyMuLg59+/bFhAkT8Ntvv6ld\nPy8vDyNHjoSHhwdOnDiBVatWITIyEhs3bjRy5JWzjY5GrTZt4Orujlpt2sA2OlrokAzOYcECuNap\nA1epFK516sBhwQKhQyITZ42fEyJTYyN0AEREpJ44NRUOISEQFRejcPlyyNq3FzokIsEMGTJEaXrJ\nkiXYtm0bLl68iNatW6usHx0djcLCQkRFRcHR0REtW7bEjRs3sGnTJsyZMwcikchYoWtkGx0Nx7ff\nhqiwEAAgysqC49tvAwBKxowRMjSD8Vq1CnZ790JR+qWlsNu2DQBQtHatYHGR6bLGzwmRKWKPAyIi\nE+U4bx5sf/wRNqdOwWnaNIBdrIkAAKWlpdi7dy8KCgrQtWtXtetcuHABPXr0gKOjo2Je//79cefO\nHWRmZhor1Eo5LF+uaAyVERUWwmH5coEiMjyP/ftRMWUjAmD35ZcCREPmwBo/J0SmiD0OiIhMlE1C\nguJnSWoqRPfvQ+7pKWBERMK6evUqBg4ciKKiIjg7O+Prr79Gq1at1K57//59NGjQQGle3bp1Fct8\nfHw0HiclJUVvMVe2r063bqmdL7p1S68xmJJOMpn6BaWlFnvOVbHW89akYnlY4+ekPGs4R12wPFTp\nq0z8/PwqXc7EARGRuWCPA7Jyfn5+OH36NPLy8nDgwAHMnDkThw4dQsuWLfV+HH1ISUmpdF/yRo0g\nyspSO19fMZgcsRhQlzyQSCz3nCtR1T1ibdSVh1V+Tv7B+0MZy0OVMcuEjyoQERGRWbCzs0PTpk3R\nvn17LFu2DG3atMGmTZvUruvh4YHs7GyleWXTHh4eBo9VG0VLl0Je7lEKAJA7OqJo6VKBIjK8+yNH\nomIKVA7gyRtvCBANmQNr/JwQmSImDoiIiMgsyWQyPHnyRO2yrl274ty5cygqKlLMi4uLQ/369eHt\n7W2sECtVMmYMCj/5BDIvL8hFIsi8vFD4yScWPeBb1qJFeDJ1KuQSCeQA5BIJnkydyoERSSNr/JwQ\nmSI+qkBEZC5MYBR4IqGEhoZi4MCBaNiwIfLz8/Hdd9/hzJkz+PbbbwEAYWFhSEpKQkxMDABg9OjR\niIiIwKxZsxAUFITU1FSsX78e7777rkm8UaFMyZgxVtcAKlq7lokC0ok1fk6ITA0TB0RaEt26BVFJ\nCWRNmggdClkrMx7jQHT7NkTFxZA1bSp0KGSm7t27h+nTp+P+/ftwdXVFq1at8N1336F///4AgLt3\n7yI9PV2xvpubG/bv34+goCD4+/tDKpVi9uzZmDNnjlCnQEREZLaYOCDSgu2OHXB85x2ISktRFBSE\n4vffFzokIrNhu2cPHOfMgejpUxS98w6KQ0OFDonMUFRUlM7LW7VqhaNHjxoqJCIiIqvBMQ6ItOA0\ndy5EpaUAAIc1a4DHjwWOiMh8OAUGQvT0KQDAYf16ICdH4IiIiIiISBdMHBBVg/j+faFDIGtkQs9l\n14RYzWu1iIiIiMh0MXFARGQuzHiMAyIiIiIyX0wcEBEREREREZFGTBwQEZkLC3lUgYiIiIjMCxMH\nRERERERERKQREwdERGRc7DlBREREZFaYOCAiIuPiII9EREREZoWJAyIiIiIiIiLSiIkDIiIyLj6q\nQERERGRWmDggIiIiIiIiIo0ESxysW7cO/v7+8PLygq+vL1577TVcu3atyu2uXr2KwYMHo169emjR\nogUiIiIg5/OyRGQN+E09EREREQlAsMTBmTNnMHXqVBw7dgwxMTGwsbHBiBEj8OjRI43b5OXlYeTI\nkfDw8MCJEyewatUqREZGYuPGjUaMnIhIIEySEhEREZEAbIQ68L59+5Smt2zZgsaNGyMxMRGDBg1S\nu010dDQKCwsRFRUFR0dHtGzZEjdu3MCmTZswZ84ciPhtHBGR6ePvaiIiIiKzYjJjHOTn50Mmk0Eq\nlWpc58KFC+jRowccHR0V8/r37487d+4gMzPTGGESEQmHDW4iIiIiEoBgPQ4qWrRoEdq0aYOuXbtq\nXOf+/fto0KCB0ry6desqlvn4+KjdLiUlRW9x6nNflsIayqRzhemMjAwUy2Rq17WG8tAFy0OZLuVR\n8b5LS0vD09xc/QZkBBXP4+bNmyi0swPA+0MdfZWJn5+fXvZDREREZBKJg8WLFyMxMRGxsbGQSCR6\n37++Kk8pKSmsiFVgrWXi4+MDma+vynxrLQ9NWB7KaloeTZs2hdzDQ48RCaNx48aQ+fnx/lCDZUJE\nRESmSPDEQUhICPbt24eDBw9q7DFQxsPDA9nZ2UrzyqY9LKAyTURERERERGRqBB3jIDg4GHv37kVM\nTAyaNWtW5fpdu3bFuXPnUFRUpJgXFxeH+vXrw9vb25ChEhEREREREVklwRIHQUFB2LVrFz7//HNI\npVLcu3cP9+7dQ35+vmKdsLAwvPLKK4rp0aNHw9HREbNmzcK1a9cQExOD9evXY9asWXyjAhERERER\nEZEBCPaowtatWwEAw4cPV5ofHByMkJAQAMDdu3eRnp6uWObm5ob9+/cjKCgI/v7+kEqlmD17NubM\nmWO8wImIiIiIiIisiGCJg5ycnCrXiYqKUpnXqlUrHD161BAhERGZNkvpWWUp50FERERkJQQd44DI\nbLHhQ0KQy4WOgIiIiIisEBMHRNXBBhwREREREVkJJg6IiIiIiIiISCMmDoiIzAUfkSEiIiIiATBx\nQFQdbMAREREREZGVYOKAqDo4xgEREREREVkJJg6IiEh/5HLYRUbCpU8fOLzzDlBQoLoOe+wQERER\nmRUmDoiISG/EyclwXLIEkitXYP/ll7DbuVPokMhCrFu3Dv7+/vDy8oKvry9ee+01XLt2rdJtMjMz\nIZVKVf799NNPRoqaiIjIMtgIHQCRWeI3pnol+usv2EdEAKWlKF64EPL69YUOiarJcelS5el33xUo\nErI0Z86cwdSpU9GxY0fI5XKsXLkSI0aMwPnz5+Hu7l7ptnv37kXr1q0V01WtT0RERMqYOCAiwTlO\nnw7bEycAAJLffkPBDz8IHBFV299/Cx0BWah9+/YpTW/ZsgWNGzdGYmIiBg0aVOm2tWvXhqenpyHD\nIyIismh8VIGoOjg4ol6VJQ0AwObCBfXPxRMRlZOfnw+ZTAapVFrlupMmTcLzzz+Pl156CQcOHDBC\ndERERJaFPQ6IyPTIZEJHQEQmbtGiRWjTpg26du2qcR0XFxesWLEC3bt3h42NDY4cOYIpU6YgKioK\nr732msbtUlJS9BanPvdlKVgmylgeylgeylgeylgeqvRVJn5+fpUuZ+KAqDo4xgERkWAWL16MxMRE\nxMbGQiKRaFyvTp06mDt3rmK6Q4cOePToETZs2FBp4qCqypO2UlJS9LYvS8EyUcbyUMbyUMbyUMby\nUGXMMuGjCkRkepiYUc9SysVSzoMEERISgr179yImJgY+Pj46b9+xY0ekpaXpPzAiIiILxh4HRNXB\nMQ4Mi+Vr2Xh9qZqCg4Oxf/9+HDx4EM2aNavWPq5cucKBEomIiHTExAERkblgg5usWFBQEL755ht8\n/fXXkEqluHfvHgDA2dkZLi4uAICwsDAkJSUhJiYGALBr1y7Y2tqibdu2EIvFiI2NxdatWxEaGirU\naRAREZklJg6IqoNdrQ2L5WvZSQJeX6qGrVu3AgCGDx+uND84OBghISEAgLt37yI9PV1p+Zo1a5CV\nlQWJRAJfX19s3Lix0vENiIiISBUTB6SWKCsLTm++Ccn16yieORPF/1TKiIiIhJCTk1PlOlFRUUrT\n48ePx/jx4w0VEhERkdXg4Iiklv369bC5eBGivDw4RERAnJoqdEhERCZNkpgIl3794OzvD0lSktDh\nEBEREekNEweklv22bUrTdlu2CBQJkZUy10cVzDXumpLL4Th3LiTJybC5dAmO8+cLHRERERGR3jBx\nQERkLqy1UW4OCgshSUlRTEqSkwUMhoiIiEi/mDggIiLj4uCIRERERGaFiQMiUiK5cAH24eGQJCQI\nHQqR+WAyhIiIiCwY36pAVB0W2mVcfPUqnAcNgqi0FPKPPkLBjz+itHNnocOyThZ6j1ksJg6IiIjI\ngmmVOMjPz8fx48dx/vx5XL9+HQ8ePIBIJEKdOnXQrFkzdOvWDf/+979Rq1YtQ8dLRAbkGBICUWkp\nAEAkl8Px//4P+adPGz8QNsLUYzLBdPGeJSIiIgtWaeLg6tWriIyMxKFDh1BQUABHR0c0bNgQUqkU\ncrkcGRkZSEhIQFRUFJycnDB06FDMnTsXrVu3Nlb8RMKw0EaC5OeflaevXBEoEiILIJdb7O8KIiIi\nsi4aEwdTpkzBgQMH0KFDByxatAj+/v7417/+BYlEorReaWkpfv/9d5w4cQIHDhxAv379MGLECGyr\n8Do/IiLSAXsXmD8rTxw8ePCg0p6KderUETpEIiIi0pLGxIFcLkdcXBzatWtX6Q4kEglatWqFVq1a\nYe7cubh8+TLWr1+v90CJyAisuJFDpHdWmPx58uQJvv32W+zcuRPnz5+HXEMZiEQidO3aFRMmTMDY\nsWNhb29v5EiJiIhIFxrfqvDll19WmTRQp3379vjyyy9rEhMRCcUKGzokAEtMUKn77Mhkxo9DQP/5\nz3/Qvn17LFiwAG5ubggPD0dsbCx+//133L17F3fu3MF///tfxMbGYuXKlZBKpQgKCkKHDh3wxRdf\nCB0+ERERVYJvVSAiYTFZoT1zKCtziNFYrKws1q5di9mzZ2PixIlwc3NTu069evVQr149dOvWDYGB\ngcjNzcWOHTuwdu1aTJkyxcgRExERkbZ0Thz8/vvvyMjIQE5OjtouiAEBAXoJjIgEYCrfBFtZg0st\nloH5s7JrePnyZdja2uq0jZubG+bMmYMZM2YYKCoiIiLSB60TB+np6Zg+fTqSkpIqfWaRiQMi0omV\nNa7IiljZva1r0kBf2xIREZHhaZ04eOedd3Dt2jWEh4ejR48ekEqlhoyLiIjIfKhLElhZ4qAy+fn5\nGnsqenl5CRARERER6ULrxMH58+cxf/58dickIjIGNjrNn5Vfw6KiIkRERGDHjh14+PChxvUqW0ZE\nRESmQevEQZ06deDq6mrIWIjIGvGbWu2xXMyLlV+vBQsWYPfu3RgyZAh7KhIREZk5rRMHb775Jr79\n9lu89dZbkEgkhoyJiIgsmakMwmloVp44OHjwICZPnoz169cLHQoRERHVkNaJAx8fHzx9+hS9evVC\nQEAAGjZsqDaBMHLkSL0GSERklSy50WmJ56bunGQy48dhQkQiEdq1ayd0GERERKQHWicOpk2bpvg5\nNDRU7ToikYiJAyJzJsQ3wZbYiCQCrP7eHjx4ME6ePIkpU6YIHQoRERHVkNaJg4MHDxoyDjJ1Vl4B\nJiPj/UaWwMrv4wULFuDNN9/E22+/jcmTJ6NRo0ZqeyrWrVtXgOiIiIhIF1onDnr37m3IOIjIWll5\n40on5lBW5hAjGUWXLl0AAFeuXMHXX3+tcT2+VYGIiMj0aZ04ICIjKS199j8HIbVultwAt8RzU3NO\nIrkcFnimWnv33XchspaBMImIiCycxsTB7NmzIRKJsGHDBkgkEsyePbvKnYlEImzcuFGvARJZE5tD\nh+A0cybw9CkKN2xAydixQodERNVliQkSHYSEhAgdAhEREemJxsTBqVOnIBaLIZPJIJFIcOrUqSq/\nOeA3C0Q14xQYCFF+/rOfp09H7qhRxu15YCqDI1p5g4ssBO9jIiIishAaEwdXrlypdFofEhISEBkZ\nieTkZNy5cweffvopJkyYoHH9zMxMta92+u677zBgwAC9x0dkbGVJA4X8fMDNTZhgSFiW3Oi05HMr\nz1rO8x+7d+8GAIwbNw4ikUgxXZWAgABDhkVERER6IOgYBwUFBWjZsiUCAgIQGBio9XZ79+5F69at\nFdPu7u6GCI9IN3I56u7dC+dTp/C0a1cUL14M2NkJHRVZEitriJoVdddGJjN+HAKaNWsWRCIRRo0a\nBTs7O8yaNavKbUQiERMHREREZkBj4kAmk0EsFldrp9puO3DgQAwcOBAAtKpglKlduzY8PT2rFRuR\nXqjp0i9OTob3qlUAAJuzZyFr2hQlkycbOzLzw8YwWSoru7eTk5MBAHb/JEzLpomIiMj8aWzdd+rU\nCV9++SUKCwu13llhYSG++OILdOrUSS/BaTJp0iQ8//zzeOmll3DgwAGDHotILTUNAsclS5Smnd5+\n21jRWB4ra3CRhbKy+7hx48Zo3LixynRV/4iIiMj0aexx8Oabb2LFihV4//338eKLL8Lf3x/t27eH\nj48PXF1dAQC5ubnIzMzE5cuXERcXhx9//BGOjo6YN2+eQYJ1cXHBihUr0L17d9jY2ODIkSOYMmUK\noqKi8Nprr2ncLiUlRW8x6HNfpqxzhencnBzc1HDu1lAmFcsjIyMDxWWvTfxHy7t3VT5QupZNxeOk\npaWh1MVFp33URAeZDBWHYqzp9a1qe9GTJ6iYakxLS0NprVo1Oq6p0rY8xUVF6FhhXnp6Okr+/lv/\nQelRi6KiKp+By8zIQNE/g35ayu8P8d9/q16vtDSUPH6s8770VSZ+fn562Y+pWLduHQ4ePIjU1FTY\n2dmhc+fOWLZsGVq2bFnpdlevXsXChQvxyy+/wN3dHW+88QZfFUlERKQjjfW7uXPnYsqUKdixYwd2\n7tyJ77//XvFHViwWQy6XQ/7PtylyuRxt2rTB0qVLCRqpSgAAIABJREFUMX78eLgYqKFTp04dzJ07\nVzHdoUMHPHr0CBs2bKg0caCvylNKSor2+5LLYXPoEES5uSgZPRpwcNBLDEJxk0rVnrtOZWJBfHx8\nIGvaVGmevb29yno1LZumvr7AP4k6Y1D3iFFNzkGr+6OoSGVW06ZNAam02sc1VTp9XtQkCJo0aQJ5\nw4Z6jkq/7LX4Xeft7Q2Zn59l/f5QkyBo4uMDeYMGOu3GnMskMDAQ8+fPR7NmzXTa7saNG1i3bh02\nb95c6XpnzpzB1KlT0bFjR8jlcqxcuRIjRozA+fPnNY51lJeXh5EjR6Jnz544ceIEUlJSMHv2bDg5\nOSnVJ8h4ah89ilovvABRQcGzGWIxnkyZgqK1a4UNjIiIKlXpF0MuLi6YOXMmZs6ciczMTFy4cAE3\nbtzAo0ePADwba6B58+bo1q0bGjVqZJSAK+rYsSO+/vprQY5dGfuVK+GwejUA4Gl0NAr4SIVlsdRv\nqiz1vCyFOXR9N4cYjcXKyuLRo0fo0aMHevTogVdffRUvvPDCsySgGmlpaYiLi8P+/fuRmJiIF198\nscr979u3T2l6y5YtaNy4MRITEzFo0CC120RHR6OwsBBRUVFwdHREy5YtcePGDWzatAlz5sxhrwMj\ns42Ohs+yZRCX/2zIZLDbtg0AmDwgIjJhWr9VwdvbG97e3oaMpVquXLlikgMlliUNAMAmPh6ijAzI\nfXyEC4j0y1IbBEKcl6WWZU2xXMyflV3Db775BufPn8cnn3yC4OBglJaWolatWvD29oZUKoVcLkdO\nTg5u3ryJx48fw8bGBi+//DKOHj2KLl266Hy8/Px8yGQySCvpnXThwgX06NEDjo6Oinn9+/fHhx9+\niMzMTPjw77JROSxfrpw0+IcIgN2XXzJxQERkwgR9HWN+fj7S0tIAPHsTw61bt/Drr7/C3d0dXl5e\nCAsLQ1JSEmJiYgAAu3btgq2tLdq2bQuxWIzY2Fhs3boVoaGhAp6FdsT376PUnCsoVlYBrhaWEZF2\nrOWzYmWvYwSAbt26YefOnfjrr78QGxuLixcv4saNG7h//z6AZz0VX331VXTr1g0vvvgi6tSpU+1j\nLVq0CG3atEHXrl01rnP//n00qPC4SN26dRXLNCUOODaSYXS6dUvzwtJSqy0raz1vTVgeylgeylge\nqow1NpKgiYNLly5h2LBhiunw8HCEh4cjICAAUVFRuHv3LtLT05W2WbNmDbKysiCRSODr64uNGzdW\nOr6BybCWijKZN3bbJaoedb/jrfj3/nPPPYeJEydi4sSJBtn/4sWLkZiYiNjYWEgkFYd0rTlBxkay\nAvJGjSDKylK/UCKxyrLiPaKM5aGM5aGM5aHKmGUiaOKgT58+yMnJ0bg8KipKaXr8+PEYP368ocMi\nddigVMby0B8rblyRheO9bRAhISHYt28fDh48WOWjBh4eHsjOzlaaVzbt4eFhqBBJg6KlS+EwfbrK\n4wpyAE/eeEOQmIiISDuqQ6gTqcMKsDJ+u2hQIpalZd9jlnIeZHTBwcHYu3cvYmJitHp7Q9euXXHu\n3DkUlXt7S1xcHOrXr2+S4zZZupIxY5ARFgaZszPkeJYwkIvFeDJ1Ksc3ICIycUwcGAsrylQd7NlA\nZLaYANOvoKAg7Nq1C59//jmkUinu3buHe/fuIT8/X7FOWFgYXnnlFcX06NGj4ejoiFmzZuHatWuI\niYnB+vXrMWvWLL5RQSAPBw3C49u3kZeT8+zfw4dMGhARmQEmDoiqwXngQEhOnTL8gayh4WEN50iW\nz5J7iJiIrVu34vHjxxg+fDiaN2+u+BcZGalYp+LYSG5ubti/fz/u3LkDf39/LFy4ELNnz8acOXOE\nOAUiIiKzVa0xDvLz85GTkwO5mkqRl5dXjYMiMilq7nPxX3/BacoUPL5+HbARdKgQslRsdJo/XkO9\nqmxMpDIVx0YCgFatWuHo0aOGCImIiMhqaN3iKSoqQkREBHbs2IGHDx9qXK+yZVaNFUiLI37wAJLz\n51Haq5fhDmINXWn5Ta31sZbray3nSURERBZP68TBggULsHv3bgwZMgQ9evSAVCo1ZFxE5qG0VOgI\nyJqYQUOUz/WXI5MJHQERERGRXmidODh48CAmT56M9evXGzIeIpMhiY+H5LffUDJ0qOaVyjeS2GAi\nsl7sOaPW8ePHsWPHDmRkZKh9xFEkEuHy5csCRUdERETa0jpxIBKJ0K5dO0PGQmQybA4dgvPEiQAA\nh4gI4xzUFBoZQjwaYUkNLrkcNj/9BBQW4umQIYBEUqN9kZmz8mv4ySefIDQ0FB4eHujYsSNatmwp\ndEhERERUTVonDgYPHoyTJ09iypQphoyHyCQ4vf664mdRXp7mFcs3tC2hkWAJ5yAg+w8+gMM/rxV7\nMnIkCr/4QuCITJS13GfWcp4abN68GX379kV0dDRsbW2FDoeIiIhqQOvXMS5YsADp6el4++238fPP\nP+Pu3bvIzs5W+UcaWHkF0tyItB27wNDXlfeNWXEo9y5yu/37IeJgsdbNyj+/OTk5GD58OJMGRERE\nFkDrHgddunQBAFy5cgVff/21xvX4VgWiajKFRoapPKpgIUS5uZDXrl29jS3pEQ6ySp06dUJKSorQ\nYRAREZEeaJ04ePfddyGyhlfDkXpssKhnaY8qmIqqylIuf/ZPrHWnKTIl1vJZKbtPAet4tWoFa9as\nwZgxY9C+fXuMHTtW6HCIiIioBrROHISEhBgyDiLzpM8GkLV+w6zjOdrExsJp+nSgpASF69ej5LXX\nDBQYUc2IU1Lg9PrrkKSmonjWLBStXCl0SAbVrVs3lXlPnjxBYGAg/u///g/169eHpMKAoSKRCImJ\nicYKkYiIiKpJ68RBeXK5HA8ePAAA1KlThz0RtGHuDUBeYzIRjjNnKgasdJoxA7mjR9fs7QWkF5KT\nJ+E0bx7EmZlCh2IyHD74AJLUVACA/aZNeDJhAmStWgkcleE899xzKvWBunXr4vnnnxcoIiIiItIX\nnRIHaWlpWL58OY4fP46CggIAgLOzM1588UW8//77aNq0qUGCJBNg7okPQ9HnowosY62IHz1SmhY9\negT5c88JFI0BmVMPFLkcjgsWWHXSQKTm2pQlDcrY7dqFog8/NFZIRnf48GGhQyAiIiID0Tpx8N//\n/hcvvfQSioqKMGjQIDRr1gwAcOPGDRw+fBgnTpzA0aNH0aJFC4MFS2RyTLUhZ05q2kDmNTCMnBw4\nLl0KcWYmiufOxdMBAypdXfLHH9rvm9fMKuzevRs9e/aEt7e32uU3b95EQkICAgICjBwZERER6Urr\nxEFoaCicnJxw8uRJlZ4F6enpGDRoEMLCwrBnzx69B2kRzL2izEcVhGHu9011yWRCR2D1HD76CHbb\ntwMAJImJyEtJAVxd1a9srfcpVWr27NnYsmWLxsTBzz//jNmzZzNxQEREZAa0HpL83LlzmDZtmtrH\nEZo0aYKpU6fi7Nmzeg2OTAgbBurpM6FiAmUsN5EEke3332u/srHL7ckT2K1fD4fFiyHKyjLusY3I\nftMmxc+i4mLY7d6teWUTuHfJ9MiruC8KCwtVBkskIiIi06R1j4PS0lLY29trXO7g4IDS0lK9BEVk\nNspXjNl40hvH4GA8mTFDu5WNXO4Oy5bBPioKAGB78CAeJydbxWshRX//rb+dWeJnxRLPqRqysrJw\n8+ZNxfSNGzeQkJCgsl5OTg6++OILjb0RiIiIyLRonTho164dtm/fjkmTJkEqlSoty8nJwfbt29G+\nfXu9B2gxWKmkqvAeMQtlSQMAEGdlQZKQgNI+fQSMyATw3qV/7Ny5ExERERCJRBCJRFi7di3Wrl2r\nsp5cLodEIsEnn3wiQJRERESkK60TB4sXL8bIkSPRuXNnjB8/XvF6pZSUFOzZswe5ublYv369wQIl\nIsukbjR6cyLKyRE6BOGZ+TUk/Rk5cqRikOQ33ngDM2bMQI8ePZTWEYlEcHZ2Rtu2bVG3bl0hwiQi\nIiIdaZ046N27N/bu3Yv3338fkZGRSsvatWuH//znP+jVq5feAyQyG3wdozCELjcDHd/kEiqVxWNq\nsZoqKyin5s2bo3nz5gCATz/9FD179oSPj4+wQREREVGNaZ04AIC+ffvi1KlTuHfvHrL+GRTMy8sL\nnp6eBgmOiIzMRAZHJDI7VpAU0NX48eMVP+fm5irVG9zc3IQKi4iIiKpBp8RBGU9PTyYLiIxAJJfD\n4psj7KlhWkpLgcJC3bbhNdCOFSbmzp8/j9DQUJw/f15pfrdu3bBs2TJ0795doMiIiIhIFxoTB2Wj\nIJc9fqBuVGR1+LiCBqxYWz42gEmf1NwPhk4kie7fh9PYsbC5fFmreLRaRv9jZeV0/PhxjBs3Di4u\nLpg6dapibKTU1FR89913eOWVV7Br1y4MGDBA4EiJiIioKhoTB0OHDoVIJMLdu3dhZ2enmNZELpdD\nJBLh4cOHBgmUiMjsmFlD0W7TJvVJA6JqWLZsGZo0aYJjx47B3d1daVlISAgGDhyIsLAwJg6IiIjM\ngMbEwcGDBwEAdnZ2StNEZCDqGpkm1vCUnD0Lx6AgQCRC4dq1KNVHN2P21DAZDtV9M46O18DkBn7U\nB23OycoeVUhNTcWSJUtUkgYAULt2bbz++uv48MMPBYiMiIiIdKUxcdC7d+9Kp4moAktsDFXg+M47\nkNy48ezn+fORf/aswBHBtMvdkhqKfFSh5qysnHx8fFBQUKBxeUFBAby9vY0YEREREVWXWNsVhw0b\nhvj4eI3LT506hWHDhuklKItk7hVGc4/fHJh6GZeUKJIGACC5dk3AYKyAqd8P5ZlTrGQ0wcHB2Lx5\nM5KSklSWXbx4EZ9//jlCQkIEiIyIiIh0pfVbFc6cOYPJkydrXP7XX39pPYAiEWnJ2A0yIb4hN/dG\npzHjN/eyKlPxPGQy2G3eDMl//4snr7+O0s6dhYnL0CypB4oWzpw5g3r16uHFF19Ehw4d4OvrCwD4\n448/cOnSJbRo0QKnT5/G6dOnFduIRCKsWbNGqJCJiIhIg2q9jlGd27dvw9nZWV+7IyIhmGPD1Bxj\nNkcGfFTB7vPP4bh4MQDAds8ePL5+HfLatWu0T6PTpgys7F79z3/+o/j5l19+wS+//KK0/Nq1a7hW\noecSEwdERESmqdLEweHDh3HkyBHF9JdffomTJ0+qrJeTk4P4+Hh06tRJ7wFaEsmFC5AkJeHpSy9B\n1rSp0OGQvnGQv+qx1vOuiqmXi0wG2+hoiP7+GyU1fEzNMThY8bOopAR2n36K4iVLahohCezRo0dC\nh0BERER6Umni4Pr16zhw4ACAZ98CJCUlITk5WWkdkUgEJycn9OrVC+Hh4YaL1MzZnDkD+7VrIZLJ\nIP/wQzy+eBHy+vWFDotIWWVdqU21IWuqcVk4h5AQ2G/ZAuBZLwF9Et+5o9f9EREREVHNVDo44vz5\n83Hr1i3cunULcrkckZGRiumyf1lZWbh+/Tq++eYbNOW36Bo5rF4NkUwGABDl58P+o48EjohMjhm8\njpGsWIV7sSxpAAA258/XaF9k2U6ePIkVK1bg7bffxo1/BljNz89HQkICcnJydNpXQkICxo0bhxYt\nWkAqlWLnzp2Vrp+ZmQmpVKry76effqr2+RAREVkjrcc4YJdD/dK5ok2mz9IbQ4Ya2M3Sy626mEgy\nL7w2KgoLCzFx4kTExcUp5o0aNQrNmjWDnZ0dXn/9dbz11lsILveoSlUKCgrQsmVLBAQEIDAwUOvt\n9u7di9atWyum3d3dtd6WiIiIdHgdI5FWcnKA3FyhoxCEyFwbDn//DdGDB1WvZ6rnZ6pxWRqWM+lo\nxYoVOHPmDD777DNcuXIF8nL3kJ2dHUaMGIHY2Fid9jlw4EAsXboUw4cPh1isfRWmdu3a8PT0VPyz\ns7PT6bhERETWTqfEwfHjxzFy5Eg0adIEderUQe3atVX+kfWy/eoruD7/PFz9/GBbRfdRUkOAb5gl\nFy+iVtu2cPX1hcOCBbr3KjBgfHaRkYLHUGOmHBuRgX3//feYNm0aRo8eDUdHR5Xlfn5+yMjIMEos\nkyZNwvPPP4+XXnpJMXYTERERaU/rRxUOHz6MSZMm4V//+hdGjRqFbdu2YcyYMZDL5Th8+DD8/Pww\naNAgQ8ZKpkwmg9O8eYpJp9mzkTthgsbVxZcvw3niRIju3EHxe++heP58Y0RJFTgsXAjxX38BAOy3\nbat8ZUM1gjXs13HJEjyZOhVwcjLMcUkwLv3742m/fpCEhgodilHZb9oEmZcXnsycKXQoRvHgwQM0\nb95c43KRSISioiKDxuDi4oIVK1age/fusLGxwZEjRzBlyhRERUXhtdde07hdSkqK3mLQ574sBctE\nGctDGctDGctDGctDlb7KxM/Pr9LlWicO1q1bh/bt2+OHH35Abm4utm3bhgkTJqBfv37IyMjAgAED\n4OvrW+OAyURV1WgsKdFpd47vvQfxrVsAAIfly/FkwgTIPT2rG51lEODbaZvLl2u2A7nccGMfABDf\nvg1ZFb/ETFpNysbUeivoOR6b+HjUiYkBOnTQ634Fo2X5OIaE4ElAACCVGjgg4TVq1AjXr1/XuDwx\nMdHggyrXqVMHc+fOVUx36NABjx49woYNGypNHFRVedJWSkqK3vZlKVgmylgeylgeylgeylgeqoxZ\nJlo/qnDt2jWMHj0aNjY2kEgkAIDS0lIAgI+PD9588018/PHHhomSLI5NQoLy9I8/ChQJac3IPQ6M\ntr05sbBzbWylfzNsLl4UOgSjGDNmDL766iucO3dOMU/0TzJt27Zt+P777xEQEGD0uDp27Ii0tDSj\nH5eIiMicad3jwN7eHg4ODgAAZ2dniEQiZGdnK5Y3bNgQ6enp+o+QTIMBv1UGYBkNIkM0gC2hXIjI\nKs2fPx9JSUkYOnQonn/+eYhEIixatAgPHz7EvXv38PLLL2PWrFlGj+vKlSvwtPYebkRERDrSOnHQ\ntGlTpKamAgBsbW3RvHlzxMTEKLr6HTlyBPXq1TNMlCQ8NmBJnRrcF+Lr12H/8cfAPz2XqAJT+8yZ\nWjxk8uzs7BAdHY3o6Gh8//33EIlEePr0Kdq1a4eRI0fitddeU/RA0FZ+fr6it4BMJsOtW7fw66+/\nwt3dHV5eXggLC0NSUhJiYmIAALt27YKtrS3atm0LsViM2NhYbN26FaFWNr4GERFRTWmdOBgwYAC2\nb9+OsLAw2NraYubMmZg3bx46duwIAEhPT8fy5csNFiiRybP0hpWa87NfsQJP5s2DXNd3ostkcB4+\nHOK7d2sclkguh7mVvO3+/ZCcP4+SESMga94cdhs3AjY2KJ47F3BxETo8qg5L//zXwJgxYzBmzBi9\n7OvSpUsYNmyYYjo8PBzh4eEICAhAVFQU7t69q9L7cc2aNcjKyoJEIoGvry82btxY6fgGREREpErr\nxMHChQsRGBgIG5tnm0yePBkODg44cOAAJBIJFi5cKMizimbL3CqZhn5UgczyUQWH9ethc/48Co4e\n1Wk7SUKCXpIGJkHHa2Rz+DCcpkwBANh99hnk9epB/OefAADJ77/j76++0nuIRELJz8/HzZs3kZ+f\nDxcXF3h7e8PZ2bna++vTpw9ycnI0Lo+KilKaHj9+PMaPH1/t4xEREdEzWg+OaGtri9q1ayt1Kxw7\ndix27tyJ7du3VytpkJCQgHHjxqFFixaQSqXYuXNnldtcvXoVgwcPRr169dCiRQtERERAbuKNK4vA\nMiYN94DNuXNAJRV5dUT5+VquaHkJK6e33lL8LJLJFEkDALCt6v3yQn4O+TuAdPDTTz9h0KBB8PHx\nQe/evfHyyy+jd+/e8PHxwZAhQxAXFyd0iERERKQDrRMH7dq1w5EjRzQuj42NRbt27XQ6eEFBAVq2\nbIlVq1bB0dGxyvXz8vIwcuRIeHh44MSJE1i1ahUiIyOxceNGnY5rElgJtzxWfE1FT54Id3CBy13y\nxx86rS/6+2/tVrTi+4nM26effoqxY8fi559/Ru/evTFjxgzMnz8fM2bMQM+ePXHhwgWMHj0aW7Zs\nETpUIiIi0pLWjyrcvHkTBQUFGpcXFBQgKytLp4MPHDgQAwcOBACtRlaOjo5GYWEhoqKi4OjoiJYt\nW+LGjRvYtGkT5syZo/MgS2RgcrlFfmNstUyhIWuCj3M4rFiBpz16oLRnT0HjIIGZwufDBFy/fh2h\noaHo0qULtm3bhkaNGqmsk5WVhWnTpmHJkiXw9/dHs2bNBIiUiIiIdKF1jwMAlTbMU1NTUatWrRoH\nVJkLFy6gR48eSr0T+vfvjzt37iAzM9Ogx6YqsNJcYyITbBQbjD4TSiZQRo5z5qhfYAKx6Y0lnQsZ\nzBdffAFnZ2d88803apMGAODl5YU9e/bAyckJX3FMDyIiIrNQaY+DXbt2Yffu3YrpNWvWqP0jn5OT\ng2vXruHll1/Wf4Tl3L9/Hw0aNFCaV7duXcUyHx8ftdulpKToLQZt99W5iuVPiotV91VaCnFJCWT2\n9oJ/U18x/rzcXGRqOPeUlBSIiovRqeL8GzcAsfrcVMX937t/Hw/0eJ1qqqrrV+b27dt4/E/cbUpK\nYF9huS73niQ3Fx0qzEtPT0fJ48da70NX2pxn2TmICwvRUcM6aWlpeJqbW+n25bn9+Sf8tDh2RkYG\niss3WOVylZhV1jEwdWUmSUtDSkqK+th0fN1kWXnZZmej4sNfGRkZKNZpb7qp7H54+PAh/ix3LbX9\njFRFXbnl5eUhw4R+H2hD3fXS5Pbt28ir5Pz09TfLz0+bT5l+JSYmYsSIEZBKpZWu5+7ujhEjRiAh\nIcFIkREREVFNVJo4KCwsxIMHDxTT+fn5EKtpCDo7O+PNN99EcHCw/iPUA31VnlJSUvS2Lzt7e6V9\nibKz4fTaa7D55ReUDBiAv3fsALQY98FYXN3c1J67okyKilSW+fn5aUwcVOTp4YHaAlRya6phw4Yo\n/SduW1tbleW63C+iR49U5jVp0gTyevWqH6AeKM6hkgENmzZtCrmHh8p8TZ8ZGy3HBfBp0gQyX9//\nzZDJVNfx9obMBO4ddefp06QJZE2aKKa1aRCW7Uek5rWMQp5r7dq14WyAY6srN1dXV0EavTWh7npp\n0rBhQ3hqOD99/p0RQmZmJiZOnKjVuq1bt8aBqgYEJSIiIpNQaeJg6tSpmDp1KgCgbdu2WLVqFQYP\nHmyUwNTx8PBAdna20ryyaQ81jRaTVuEbUrvPPoPNL78AAGx/+gm2+/ahZMIEISKrHmvqZm8o5lyG\nQvaQMeUyMuXYdGVJ52IILB8AwOPHj+Hq6qrVurVq1cJjA/aoIiIiIv3ReoyDX3/9VdCkAQB07doV\n586dQ1G5b7fj4uJQv359eHt7CxhZzTmsXq08HR4uUCREGuizYWRhYxwQ0TOlpaVaD1QsEokgU9OL\niIiIiEyP1m9VMIT8/HykpaUBAGQyGW7duoVff/0V7u7u8PLyQlhYGJKSkhATEwMAGD16NCIiIjBr\n1iwEBQUhNTUV69evx7vvvmt5b1Qwt8aQNt+Wy+Ww27Sp6nfVm6uaXjNzu+ZCMLdeGTX5vWTK50U1\nY2l/ryrYuXMnEhMTq1zvDx1fZUpERETC0Zg4cHd3h1gsxp07d2BnZwd3d/cqG+cikUhpTISqXLp0\nCcOGDVNMh4eHIzw8HAEBAYiKisLdu3eRnp6uWO7m5ob9+/cjKCgI/v7+kEqlmD17NuZoGtHcnFng\ntzCSixfh+N57QodhXkyp8WhKsZD1KSyEw6pVEKemonj6dJT26yd0RKRBfHw84uPjtVrX4pL+RERE\nFkpj4qDsW3wbGxulaX3q06cPcnJyNC6PiopSmdeqVSscPXpUr3EIoqpGmI4jsZsDh0WLNC9ko5Rl\nUF0WVm6iP/+EvMLbYxQs7Fx1Yb9hA+w3bAAA2Pzww7O3ElQxcr9RWfG1Ke+RmkFeiYiIyPxpTByE\nhIRUOk0GZmI9DkRVVYq16EIuysvTY0QmiA0HwzO3RxWqwf7jj1G0erXFnVdNOaxapfhZVFIC+61b\nURwUJGBERERERNZD0DEOrFpVjQITSxwoycuD3bffQubpCTRvLnQ0+vf0qdARmCYhGrLa9HKysAa2\nSIfHvYzKxMpZVElvNSIiIiLSL50SBzk5Ofj0009x7Ngx3Lx5EwDQuHFjvPTSS5g9ezakptRt1NwZ\nIXEgTkuD5PJlPO3RA/L69StdV17WgJPL4TJ4MCS//QYAqDd3LrBihUV9E2xz6JD26545A9Hjx3j6\n8ss1P7AFlaFRsYyIiIiIiAxK69cxpqWloXfv3lizZg2ePn2KPn36oE+fPnj69CnWrFmDXr16cYRk\nPRIZOHEg/u03uPTpA6c334RLr14Q/fln5fH80ziTXLigSBoAQKPISIPGKQTx7dtar+uwejWcJ06E\n08SJlt+ANYXzM4UYdFGdeCvbxtzO35BMbVA9XhsiIiKyYFr3OFi4cCHy8vJw4MAB9O3bV2lZfHw8\nJk2ahODgYHz33Xd6D9IqGThx4Lh4MUQFBQAA8cOHsF+zBkXr1lW5nfjWLe0PYkUVadvY2JrvxIrK\nS9tGn/j33yHz9gYkEs0rmUq56TsOUzmvMoyHiIiIyGpp3ePg3LlzCAwMVEkaAEC/fv0wY8YMnD17\nVq/BWTSB36pgc+qU0rSttm+q0NTg06YSz4q+7qy8zJzHj4fziBGVfx6svIyIiIiIiAxN68SBm5tb\npWMYSKVSuLm56SUogukOjqhL92A26HTD8lLL5vRp/H97dx7eRLm2AfyeLG3SllK2tuyIlF0oFGQH\nBRTkQ1EEWWQRqOBh0e8gYP1cDnhQ5LAdBOSgleMCiqAgFQHZN9mUzbKV0kLZ20IpdKVtMt8fpZUh\nTZukk8wkuX/XxaWdzPLMM5PknSfvvKPbuLHwD3cbB0LNsRE5WdFYSEREROT+bC4cDB8+HCtWrEBG\nRobFa3fu3MGKFSswYsQIWYPzaiotHIjl6XGS0DhEAAAgAElEQVSgtnuSST62HNu7d+E3YAACK1eG\n36BBdq1e//PP1l8sx8W5ds8eVAgLQ2D16tCvWOHwesobh0vWR87F42WhZcuWaN68OcaOHYsvv/wS\n586dUzokIiIicpDNYxyEhYVBEAS0adMGQ4YMQf369QEACQkJWLVqFapVq4awsDCsW7dOstwLL7wg\nb8Tewsm3Kliw9aLeG3ocsMBRIsGB42mMj0fA6NEQbt+GuXZt6A4ccGzjTjqXjH//OzSpqQAAv4kT\ncWfAAMBgcMq2ZKHke8pd38+kmIULF+LgwYM4cOAA1qxZA0EQUKVKFbRv3x4dO3ZEhw4d0LJlSwj8\nzCUiIlI9mwsHY8eOLf7/hQsXWryekpKCsWPHQnygcSkIAgsHDnL2UxVkxzEOys/duuHboNbixdDG\nxgKwc2DNhxXlQeZ8aB96EowmIQHmZs0cW5kHHj8JT9oXcokRI0YU90S8fv06Dhw4UPxv06ZNMJvN\nqFChApKSkhSOlIiIiMpic+Hg59K6CnsR7f798J03D2K1asj98EOIVao4tiJ3bYR7eI8D4fp1GP/v\n/5QOQ50cOJ4VXTBgqiM9IayvjL98qoHPt98iZ8kSHg8PUr16dTz55JPw9fWFXq9HTk4OEhMTkXX/\n6T5ERESkbjYXDjp37uzMONyCJicHfsOHQ3PrVuEEsxk5n30my7pFjcY9ehmUpyHvBhcBxilTlNt4\nCRfAfuPGIXfGDJjatlUgIBUprTig8gKV9o8/4PPZZzA/+iiEZ5+1fUGV75ezaY8ehSkiAgCgOXFC\n4WhsYM/xKvosFEXoV62CbudOFPTsifyXXnJObAq5ceMG9u/fX/wvLi4Oer0erVq1wvPPP4+OHTvi\n8ccfVzpMIiIisoHNhQMCKm3f/lfRAIDP6tWOFw4ebmRqNKodEFFCYzmepubkSZhr1bKc1w0vfPS/\n/KJ0CBK6/fvh37cv7sbHA4GBygaj5MV7abcqOLHHgZCWBp8lSwCjEfcmTACMxrJjfHD5jAz49+0L\nITcXABCaliZfrC4ma88OGwT06IF7EyYg/+mn4T98uEu37SraPXvg97e/ASj8PsmsWRMIDlY4Kvk0\nadIEWq0WXbt2xYsvvogOHTogIiICvr6+SodGREREdrJaOJgwYQIEQcDChQuh1WoxYcKEMlcmCAIW\nL14sa4Bq4nvtmvNWXsIFuSqV0GsgoGdPZBw8qEAwHsbKhZlw7x58vvwSea+/7uKAVEShIpTfoEHQ\n/f47AEATF4eczz+3a3n9V18VFw0AoOayZeULyA2LceXhu2QJfJcsUToMp/GbNEnyt/HvfwdWrlQo\nGvmFhIQgOTkZx48fh8FggK+vL/z8/NCiRQto3OU7j4iIiACUUjjYs2cPNBoNzGYztFot9uzZU+bI\nxxwZ2Y2V46kKQm4uAnr2lDkgetCDPV1UyVU9Dpy97QfOb+H69eKiAQD4rFljd+FAc/Om47F4WZHA\nG2kuXZL+nZioUCTOcfbsWSQmJhbfqvDFF1/gvffeQ4UKFdC2bVt07Nix+OkKREREpG5WCwex90dC\nt/Y3eSkrBYYSL5B44eNZFDyegqtuVXhwm3fv2reAp5/vnr5/5cX8lKh+/fqoX78+hg0bBqDw6Qpb\nt27F4sWL8eGHH0IQBNxSe2GUiIiIOMaBYtytkSlHvO62z67mzvlxox4HwqVLENLTYX7ssRJefKAw\nlp9v13pLot+wodzrIHJ3d+/exYEDB4p7Hpw4cQL5+fnQ6/UIDw9XOjwiIiKygc03GR48eBDz58+3\n+vqCBQtw+PBhWYJSLXe+sJNLOR7HqE1IkDkYKg/DO+/Yt4CS579MhQPd+vWoEBGBCl27wvjaa6XP\nXFBg83pJAfw8Vr233noLXbp0Qf369TF48GB88cUXCAgIwJQpUxATE4OkpCRs2bLFrnX+9ttvGDx4\nMJo0aYKgoCCstGFMiFOnTqFPnz4IDQ1FkyZNMHv2bIg8f4iIiOxic4+D2bNnIygoyOrrJ0+exL59\n+/Djjz/KEhipFMexcJ5SGrKi3Hm/c8c9B50rZ2Pff+TI4v/3+f57yxkeHOPA3sKB3BciLrwtwyZO\n2rZh6lSnrJeU98MPP6B9+/YYNGgQOnbsiJYtW0Kr1ZZrnVlZWWjatCmGDBmC18oq/qGwt8MLL7yA\njh07YseOHYiPj8eECRPg5+eHSQ8NTklERETW2dzj4M8//yz1ectt27bFCXd41nZ5OPOi2V0uyO0Z\nCZu/6KiW5vp1+xdSw+MYXbltGW5VcIiXvW987RxwspjaPjO97LjZIiEhAStXrsTEiRPRunXrchcN\nAODpp5/G+++/j379+tn0ZIY1a9YgJycHS5cuRdOmTdGvXz+88cYb+PTTT1XT60C/Zg0qPPYYAitV\nQoXHHoN+zRqlQ3I6b9xnwHv3m4jKTw2fHzZfBWZnZ5f51ITMzMxyB0QKKcdTFWTxQAPO91//QmCd\nOvDv0QNCUpJztqdGKmnEqpF2377CD8tGjSxfdNJTFewuHPD4uRbz7TZMJhOOHj2Kn376CT/99BOO\nHj0Ks9nskm0fPnwYHTp0gNFoLJ7Wo0cPXL9+HUkq+H7Rr1kD4+uvQ3P5MgRRhObyZRhff92jLygr\nb9rkdfsMWD/WlTdtUjo0IlI5tXxX2Fw4aNCgAXbs2GH19W3btqF+/fqyBOUV3LXR6+gYBzbur+bC\nBRg++gjC3bvQHTkCQynjanga7bFjSofgOCefz5r09MIPy4wMl23b7lsV8vJk2a42Nhb+PXqgQufO\nsqxPNu76mUWKWrt2LZo3b46ePXti1KhRGDVqFHr27IlmzZph3bp1Tt9+SkoKqlWrJplW9HdKSorT\nt18WwwcfQMjJkUwTcnJg+OADhSJyvpqffup1+wxYP9Y1P/1UoYiIyF2o5bvC5jEORowYgWnTpmHa\ntGl4++23UalSJQBAWloaZs2ahR07duDDDz90WqCkEo72OLDx1yWf6Gjp3199hZyFCx3bppsxzJlj\n/UU1dMv2hgvHB/NsZ+FAv3WrLCFwEFHyFL/88gsiIyPRsGFDTJ48GQ0bNgQAnDt3DsuXL0dkZCR8\nfX3Rp08fhSO1FB8f75J1RVy5UuJ04coVWWNQk4jk5BKne/I+A9aPtU9yMmI9eL8d4cnngSOYDylv\nzEdp3xWAfDkJCwsr9XWbCwevvvoqYmNj8fnnnyM6OhrBwcEACiv2oihi6NCh+Nvf/la+aNVOxgsn\nwU0vwuwapO/BfXRRt1RSiKcM2vfg+W0y2bWo8dVX5YvDGjf93CDvNG/ePISHh2Pjxo0wGAzF07t1\n64YRI0agd+/emDt3rlMLB8HBwUhNTZVMK/q7qB1TkrIaT7aKj48vdV1irVoQLl8ucbpcMahNXkgI\nfG/csJjuyfsMWD/WeSEhHr3f9irrPeNtmA8pb81Had8VgHzfWWWxY6Q74JNPPkFMTAzGjBmDxx57\nDI899hjGjBmDn3/+GUvccYR2sp+TexyQizhyAarWi1ZnFQ7sXdTOQgORpztz5gxeeuklSdGgiK+v\nLwYNGoQzZ844NYbHH38cBw4cQG5ubvG0nTt3onr16qhbt65Tt22L3Pffh/jA+AsAIBqNyH3/fYUi\ncr6r48d73T4D1o/11fHjFYqIiNyFWr4rbO5xUKRLly7o0qWLM2IhD6P74w9oDx9GQefOMLVtq3Q4\n6qeG2xEc5Sk9Dsg65lk+1t7rHpZjo9GIW7duWX395s2bkkELbZGZmYnExEQAgNlsxpUrV/Dnn3+i\nUqVKqF27NmbMmIEjR44gJiYGADBgwADMnj0b48ePx5QpU3D+/Hn8+9//xrRp08oc8NkV8gcOBHD/\n/tUrVyDWqoXc998vnu6J0p55BqHVq3vVPgPWj3VaeDiqKBwbEalbqd8VLrx1w+7CQUJCAvbt24fU\n1FQMHDgQdevWRV5eHpKTkxESEgIfHx9nxElqYUdDy3/AAACAOGcOsteudVZE5CpqvahRSY8DIpLq\n1q0bli1bhieffBIdO3aUvHbw4EF89tln6Nmzp13rPHbsGJ599tniv2fNmoVZs2ZhyJAhWLp0KW7c\nuIELFy4Uv16xYkWsW7cOU6ZMwZNPPomgoCBMmDABEydOLN/OySh/4ECPv2h+mDfuM2Blv73wfm0i\nsp8aPjdtLhyYzWb8/e9/xzfffANRFCEIAtq2bVtcOOjUqROmTp2KSZMmOTNeZdl6UWHLhYzaLsLK\n2reieB24sBJEEUZPPi+IyOvZM26N9vBhFHTvXsJKPKtwNWPGDBw4cAB9+/ZFy5Yti+/BjI+Px4kT\nJxASEoLp06fbtc4uXbogPT3d6utLly61mNasWTNs4iPviIiIysXmMQ7mzZuHFStW4J133sHWrVsh\nPtBICggIwLPPPosNGzY4JUhSEQcbthoro4HavfmLF6Hdvx/Iz5dlfariyosGuQtXHnKrgl2DfyrB\nQ/Ls7Qwffwz9l18qHYbT1alTB/v27cNrr72GzMxMxMTEICYmBpmZmRg/fjz27t2LOnXqKB0mERER\n2cDmHgcrV67EsGHD8OabbyItLc3i9aZNm+LXX3+VNTjV8eaGc9EFlYIXVrodO+A3dCiE3FwUdOiA\nrI0bZY1He/SobOuSnRouaL35/Ccpngvl5ve//6t0CC5RtWpVfPTRR/joo4+UDoWIiIjKweYeB9eu\nXUNERITV141GIzIzM2UJyu050qhWw4Vhacpxq4JdSlm/cdw4CPdHxtYdOADdtm2ybtowebKs67OX\nS3/tZo8DIiIiIiKykc09DoKDg3Hp0iWrrx8/fhy1a9eWJShSMWdf3JZyEah56Fnc2v37UfDUU7Jt\nWnf8uGzr8kgqvUC3595y+1as8mKeqz2YZzWcC2qI4UFqi0cl4uLisHLlSly8eBHp6emS2xwBQBCE\n4icgEBERkXrZ3OPgueeew/Lly5GQkFA8rehRRlu3bsWqVavw/PPPyx+hp3K3RqbSF1F37lhMMixY\ngMDataFfvVqBgNyc3MfT3c5nK5xWhCDZ6X/+WekQqAyrVq1Cx44d8dlnnyExMRFmsxmiKEr+mc1m\npcMkIiIiG9jc4yAqKgr79u1D165d0b59ewiCgPnz5+ODDz7A0aNHER4ejskKd/VWDXe8+LD1qQoK\nxaGNiyt59owMGF97DfkvvADo9c6MzLM4cjzVel7LGZda97GI2uNzIc3ly4X5ULqoeZ/299+VDkF1\nPv74Y7Ro0QI//PADqlThk+qJiIjcmc09DgIDA7FlyxZMnjwZKSkpMBgMOHjwILKyshAVFYWNGzfC\naDQ6M1blydlAddcLAGfFXY71CmYzhKtXgfR0GMeORUDXrtCvWCFjcC6ikgsgt+Ou7yV3o7ZbFQAg\nJ0fpCIoZ3nqr/CtRS15lcuPGDQwbNoxFAyIiIg9gc48DADAYDHjzzTfx5ptvOisedfOARp1+7Vro\nV660f0E3uKj1XbwYPvdvW/CbOBF3u3eHWKOGwlHJRO23FnjK4IhlraugQL5tkUfRpKcrHYLqNGvW\nDNevX1c6DCIiIpKBTT0OsrOzUblyZcydO9fZ8XgGlRYYNBcuwG/0aOi3b7d87dIl6H/4wfrCRfuk\n1L6VsV1BFGF46Pz0XbzYmRF5H5We166MS79mjcu2pWpqORfUEodMBJNJ6RBk9eGHH2LFihU4ePCg\n0qEQERFROdnU48DPzw9Vq1ZFYGCgs+MhJ/L9+ONSX/eLjERmaChMnTu7KCIZlXQBkZfn+ji8ladc\nwJWxH35/+5uLAlEhTznGKlf0yFlPMG/ePFSoUAF9+vRBgwYNUKtWLWi1Wsk8giBgNQe4JSIiUj2b\nb1V4/vnnsW7dOkRGRkKjsXloBFIRIS2tzHmMr7+OzKNHrc+g1MWDWgZv9GZqzbFa43IGT7klRC5q\njKmcqmzaBDz2mNJhyOLs2bMQBAG1atVCbm4uzp8/bzGP4Aa3wREREZEdhYO+ffti79696N27N0aM\nGIF69eqVOBhiRESErAG6JTduzGoTE5UOwYJuyxbodu5UOgznYwPaMd70VIX7NPHx0P7xBwo6doRY\nt67rA1BLnsoRh+bCBWgPHoSpXTuY69eXMajy0ZXw6Fl3FRsbq3QIREREJBObCwf9+vUr/v/ff//d\n4lcCURQhCALSbPhV2yMVFEC3dy/M1arB3KiR0tGUTI7GvgIXDP4vveTybaqZ5swZaOLiYOrcGWLV\nqo6thI9jVG695aQ5dQoBPXtCyMmBGBiIjL17lSkeqIGDx0iTkICAbt0gZGZC9PdH5s6dMDdsKHNw\nRERERJ7D5sLB4sWL2aWwFH6DB0O/bRtEQUDOJ5/YvwK151bpwRHLota47GHDOaBbvx5+r7wCQRRh\nDg1F5q5dEENDXRBc6QRRhFJHwPDhh8js3VuelbnBeWR8+20I9x9DKNy9C8NHHyFn2TLnb1iNj2M0\nmx1azDB9OoTMTACAkJUFw/vvI3vVKjkjc5zavwvKYe/evVi9ejVu3LiBhg0b4rXXXkPt2rWVDouI\niIhsYHPh4OWXX3ZmHG5N8+ef0G/bBqDwAspv0iSblxVSUyHq9c4KzXuo5ULGWe5fTPguWQLh/r5q\nbtyA/ocfkDdxopKRKU7rrO7QKj2ndHv2SP/eskWhSNyX7pdfJH/rN29WKBLP8/HHH2PhwoWIjY1F\n1Qd6RK1cuRKTJk2CeP99tW3bNqxevRrbt29HnTp1lAqXiIiIbFTmKIe5ublYu3YtFixYgK+++go3\nbtyQNYDo6Gi0aNECISEh6NatG/bv32913qSkJAQFBVn823b/ol0p2lOn7F9IFOH78ccIDAtDYNOm\nxb8gqlbRL3tOupjSXL1avhWo9CJPbrrDh6V/Hzrkuo17Q47V+Kv6g0qKSY1xuojgifvu5j0O9u7d\ni+7du0uKBvfu3cPbb7+NwMBArF+/HleuXMHy5cuRmZmJ+fPnKxgtERER2arUHgfXr19Hnz59kJSU\nVPwrgZ+fH1atWoUuXbqUe+Nr165FVFQU5s2bh/bt2yM6OhoDBw7EwYMHS+2++OOPP6J58+bFf1eq\nVKncsTjKOH48Chx4fKGQng7D/ccjCtnZcodVsvI0sp3cQPdduBD33nnH8RV4wgWEIxcMju633Pny\nhPyT7dRyvB28VYGcJzExEaNHj5ZM2717NzIyMvDee++ha9euAIAXXngBu3btwq5duxSIkoiIiOxV\nao+DmTNn4tKlSxg/fjy+//57zJo1CwaDAW+99ZYsG1+yZAmGDh2KkSNHolGjRpgzZw5CQkKwfPny\nUperXLkyQkJCiv/5+PjIEo8jfL79FrpSeklYI2RlOSEaeQi3bllOdHIDXcjLK98K1HIh48EcOc/d\njtp7HJBUWcfo3j0Yx41DYN268BsxArg/roGaiW7e4+D27dsIfWjclb1790IQBPTq1UsyPTw8XPZe\njEREROQcpfY42LVrF4YMGYKZM2cWTwsODkZkZCSuXr2KmjVrOrzhvLw8HD9+HJMeGg+ge/fuOFRG\n9+vhw4cjNzcXjz76KMaPHy954oNTWWmk+qxY4Zrtu4jhH/+wnMiLKOfTlHnnkCUXHhfjtGmqiINU\nQC3Hu4yCpm7zZvh8/z0AQB8TA32PHsgfOdIVkXmtkJAQXL9+XTLtwIED8PPzQ+PGjSXTNRqNooV/\nIiIisl2phYPk5GS0a9dOMq19+/YQRRFXrlwpV+Hg1q1bMJlMqFatmmR6tWrVkJKSUuIyAQEB+Oc/\n/4n27dtDp9Nh48aNGDVqFJYuXYpBgwZZ3VZ8fLzDcT6ohixrsZ1ccRcJy8pCRRvmK6kQknH3Li7E\nx6PC5ctw1sMm4+PjUev2bTjyjICkpCQ0f2janfR0XLIjh20c2G5Z7DmGNVq2RI2HxjAokpaWhmvx\n8RYxZmZlIcGB88R46RKa2Thv0T60KeXX2osXL+LeQxdxwr17iLA7MsfYmueyjnHSxYvI1WoBAEHX\nrqFBOeOS2+VLl9D0oWlms1m2z4rS8vPg+8mVx7Y0iQkJKLhzx+rr4ePHS/72e+MN/NGxY4mxlzeH\ncn5+yHU8w8LCZFmPPSIiIvDdd99h7NixCAoKwsmTJ3Hs2DH07t0b2vvvrSJxcXHlakcQERGR65Ra\nODCZTDAYDJJpRX/n5uY6LyorqlSpIumh0KpVK9y+fRsLFy4stXAgV+PJ1TcXyN3o8/Pzc3jZChUq\nICwsDNryDmJYirCwMBgqV3Zo2boljMpdMShIkYbzg6xtX0hJgeHDD4HcXOS+/TbEevXgW7261fVU\nrlwZ/iWsK8Df36F91NgxGGdYgwZljr9Qr25dmBtIL7N9oqPtjstRch3nunXrwnx/XbozZ2RZp5xq\nl3CeawXBJee55P2kwOd/SerXqwexlPfNwxeqgPVzpVw5lLMHhouOp7O89dZb6NatGyIiItCoUSPE\nxsZCEAT87//+r2Q+URSxYcMGdO/eXaFIiYiIyB5lPo7x4sWLOHLkSPHfd+/eBVD4i0hAQIDF/BER\ntv0OVaVKFWi1WqSmpkqmp6amIjg42KZ1AEDr1q2xwlW3Crj5vadyDI7o9FHM1TLYn5MZJ0yAfutW\nAIDP99/j3pgx0J486boA7MmXKDp07hunTLF7GYc5GGOJ66GSqXH8B7XEISc3/55p1KgRYmJiMHfu\nXFy8eBHt2rXD66+/jrZt20rm27t3LwICAvDcc88pFCkRERHZo8zCwaxZszBr1iyL6dMeut9ZFEUI\ngoC0tDSbNuzj44Pw8HDs3LkTzz//fPH0nTt32tWQiI2NRUhIiM3zk4PUPnq5m11AFBUNivh+8YVj\nK3LFfrtDbuUqHDy8TlI3tRwjtcShEo8//jhWr15d6jxdu3Yt9fHLREREpC6lFg6WLFni1I1PmDAB\n48aNQ0REBNq1a4fly5fjxo0bGDVqFABgxowZOHLkCGJiYgAA3377LfR6PVq0aAGNRoPNmzcjOjoa\n06dPd2qchL8axs5uIDt68ceGu33s7XEg5/qcQa7tK70fjlAiZrXkSS0FTZlvVSAiIiJSm1ILB0OH\nDnXqxvv374+0tDTMmTMHycnJaNKkCVavXo069+/jvXHjBi5cuCBZZu7cubh8+TK0Wi0effRRLF68\nuNTxDWSllsayo2S4VcGZtPv3Q/frr07fjkdhjwOv4vRbhUqjxvNALTGpJQ4iIiIiJynzVgVni4yM\nRGRkZImvLV26VPL30KFDnV7MoJIJ+fmF/+PEBnJAnz6OL+zpDXclf4W0s8eBkJICTUKCEwMqffuy\nrcfTzylPoFCPA82ZMxByc2EKDy98b8p4rvCsIyIiIjVy4MHx5DIPjFwuXL0K4aFnY7uS/pdfeCHl\nrew47ppTpxDQrh0CnnnGiQGVwEvOTSE5WbmNs6gCAPD5z39QoUMHBDz5JAxRUYUTvTgfRERE5B1Y\nOFCxgCefhHDrFnwWLUKF5s1RoXlz+Cxfrlg82t9/V28DWa1xOZtablW4P4/hnXeguX3byQFZ376c\n61H0tgArFC0cqJCgQI8DY1GxAIDvsmXAnTsc44CIiIg8HgsHKqY9cwY+ixbB+N57EEQRgskE4+TJ\nDq+vvBdC2t9/L9fyTqXCizw1s+tcsGNe/a5d9gcjB2ccf55T6qeCY6S5eVMVcRARERE5EwsH9lDg\nlyDDv//t8m2WSq0NZLXGJRcbzj3fBQsQGBqKgLZtoTlzpvSZnVQ4UIy3PFVBJYMjCtnZysXxILUf\nL0ewx0GZoqOj0aJFC4SEhKBbt26lPtYxKSkJQUFBFv+2bdvmwoiJiIjcHwsH9nD3Rqq7x08W9Js3\nA6II4do1GGbMgJCbC218PAwzZ8q3EW99HKPS+6Ri+p9+UjqEQp74OEYq1dq1axEVFYU333wTe/bs\nweOPP46BAwfi8uXLpS73448/Ii4urvhf165dXRQxERGRZ2DhwA6VN29WOoTykaNxq9IGshL3OjtC\nt3kz/Hv0kHWdmhMnoF+7VjJN/8svpS/EHgfKrNMZFIjTOHWqy7dZIrUcI45x4DJLlizB0KFDMXLk\nSDRq1Ahz5sxBSEgIlpcx/k/lypUREhJS/M/Hx8dFERMREXkGFg5sJCQnw3D1qtJhkDVquYAoTXY2\n/EaPhu7IEVlXa/jgA/v33475/caMgVDWua90/r3lVgUlqTE3aigYiqK8j2Nk4cCqvLw8HD9+HN27\nd5dM7969Ow4dOlTqssOHD0eDBg3Qq1cvrF+/3plhEhEReSSd0gG4C5///EfpEKRyc+G7YAE0167h\n3vjxMDdp4prtqvHiAVDHBUQZ9Bs2OOfecEf23Z4BD3/9FZg0yf5tuJI39zjwZmo5RmqJw8PdunUL\nJpMJ1apVk0yvVq0aUlJSSlwmICAA//znP9G+fXvodDps3LgRo0aNwtKlSzFo0CCr24qPj5ctbjnX\n5SmYEynmQ4r5kGI+pJgPS3LlJCwsrNTXWTiwkWoGA7vP8OGH8F20CACg27ABGXFxgDd3vXSHhnte\nntIR/MXOfOl37JB1fbJTevsuosZHRCpJt3078po2lWdlZjOgcbATHo+LalWpUgWTHih8tmrVCrdv\n38bChQtLLRyU1XiyVXx8vGzr8hTMiRTzIcV8SDEfUsyHJVfmhLcq2EpNDUNRLC4aAIDm9u2y72m/\nv5wc21YltcblCo50bfa0fMm0P/rNm+GzeDGE5GTPy1E5qbFoYXzvPWgPH5ZlXZrTpwEA2mPH4LNo\nETSxsbYvzDEOXKJKlSrQarVITU2VTE9NTUVwcLDN62ndujUSExPlDo+IiMijsXDgjkpopAq3bysQ\niIq4wa0K5VLaxYTM91g7xEO2b/j4YxjffRcBTzyhrh4i92kSEiwnKp17hRknT4aQkgL9999Dc+qU\nw+vRJCVBc+IE/J96Csb33kNAjx7QnD0rY6RUXj4+PggPD8fOnTsl03fu3Il27drZvJ7Y2FiEhITI\nHR4REZFH460KtlJT41ypWARBXXl4UC5h8TYAACAASURBVElxqSBWTWwshNu3Yerc2bnxOHFwRLcg\n8/5orl+Hz5o1sq5TDr6LFysdgupoT55EQOfO0KSkQNTrkbV+PUwdO9q/IlGEMSoKQkEBAEDIy4Ph\nvfeQXdZ5IPfnInsclGrChAkYN24cIiIi0K5dOyxfvhw3btzAqFGjAAAzZszAkSNHEBMTAwD49ttv\nodfr0aJFC2g0GmzevBnR0dGYPn26gntBRETkflg4cEdKXvSp9YJTpT0OArp2hSCKyH/uOeQ/9ZRz\nNiII0J48ad8yaj2OKqLh4DtSKj5nNPcHxhPy8xHQpw/unjwJsVYtu9ejO3BA+vfevTYtJ6iwd4qn\n6t+/P9LS0jBnzhwkJyejSZMmWL16NerUqQMAuHHjBi5cuCBZZu7cubh8+TK0Wi0effRRLF68uNTx\nDYiIiMgSCwe2UtOFqaMNeBU3/MutpH1TwS93RfeF62NiYHLiwCV2/zou97mg8LkliCJkj8CT3y8e\nrkKrVshx1ZNwRBH61avlW58KPrfULjIyEpGRkSW+tnTpUsnfQ4cOxdChQ10RFhERkUfjGAfuqKQi\nhi0XOeUtfqjhXnpr1FTYsUJbnl+w5b6YUOtxdBQfx0gPEPLz4TdmjMu2p9u+XbZ1BRw7Jtu6iIiI\niOTCwoGt1HQR4UAs2kOHLLrhkoup6RySm9L75s2FA1fF6S75cHNVfv1V6RCIiIiILLBwYCs1NZod\niMU4dapi23YJtcb1IDXFKHMsQl4ejH/7m6zrtIuacktERERE5GFYOLCVii5MdA89isoW2j//dEIk\n6iG4wa0K5TqHTCb4zpxZ8muO3MYg8/ms27QJPt99J+s67cIeB0RERERETsPCgY0EFTXO/V57zXKi\nt3dXVmtcDypHcUN34AAMc+fKF4vM+ZI1Nkc8sD+6n36C///8DwxTpgCZmbKsk+D5+fD0/SMiIiIq\nBz5VwVYqalQKd+8qHYL6uEOPg/IUDvbtkzEQqOp8lsX9/RGuX4ffqFEQRBG6336DGByMe9OmlWud\n7kD300/Qr18PU/v2yHv1VUDDmrC9/MaNK3umks4JNzpPiIiIiBzF1iXZR6WNZMM77ygdAinp/nnp\nu2iRpHeQ4aOPyr1OtROys+H/yivwWbcOxrfegi4mxjkbKihwznpVQsjOLnsma4UDNzlXiIiIiBzF\nwoGt1N4wVHt8TqY9d07pEMrm5cfImXy+/hq+CxZAc+mSfCt10+Pl/8orTlmvz6pVTlmvWynhnPCd\nPx9CeroCwRARERG5Dm9VsJWbXkTIjnlwnJNyp9+xw/6FPOw4lqtnAZGtSnjfsKBCRERE3oA9Dmzl\nDvfQW+NhF4luS0XnkJoG+1Qt5siCfs0apUOQh6PHVkXvYSIiIiJXYuHAG5hM8qxHEKBNTJRnXd5I\nTRcdcp0TnoyFAwt+r76qdAh2sTaQrMOFM54TRERE5KV4q4Kt3LnBmJ8vy2r0q1dDd/y4LOvySmo6\nh9RUxCDnKCiA5vJlmKtVAwICyp5fTeenM5WnaOYtOSIiIiJ6CHsc2MqdG4wy/brMokH5qOr2APY4\nKJPgzsWV3Fz49+2LCq1aoUL79tAkJJS9jJrOTyfyf+45xxf2khwRERERPYyFA1u5c4PRnS+APIkT\nzyFzcLB9C7Bw4NGMEydCd/AgAEBz5Qp8Z84seyEv+ZzQ/fab48vu3i1jJERERETug4UDW6m9cFBa\nfF5yQaB6TrxYN9evb+cCPCc8mc8PP0j/Xreu7IVs/IwLDA11JCSPYJwyRekQiIiIiBTBwoGt3PgX\nWlV1kfdm2dlKR/AXFg7K5m3vGxvPCSE318mBqJfmyhWlQyAiIiJSBAsHNhIKCpQOwXG8SFQFayO8\nK8Gt7993FW8rHHjb/hIRERGRzVg4sJVMTyZwFuO0aRBu3iz5RV4kqoLWlgHq5JSXZ/01N+5BYy+r\n74uyeNuFtLftrw2E3Fz4LF7Mz1AiIiLyeiwc2MoNehwYpk0r/J/MTPjOmAHDm29CuHTJqy4SvZUm\nMdFimm7vXusLeNE54fPll44tmJMjaxyqx4vjEhnffRe6TZuUDoOIiIhIUSwc2Kq0X29VwmftWgCF\nA3gZFiyA7xdfwL9/f6+6SPRWmpQUy4mlXQh600ViVpZDi3nd7RzscWCV37hxSodAREREpCgWDmzk\nTmMc+KxaVfz/2vPnoT19WsFoSDFarfXXvKiYJHjRvpaLtxVK7CBkZiodAhEREZGiWDiwlRsVDh7G\nRq8NPPDXVrGEwoHm9Gno16wpuYeCh9KvWgXh4kWlw1A/D3wPEBEREZE8dEoH4DZUPjhiqfiLa9k8\nMUd6/V//bzbDd/58GGbOVC4ehWhSUlChSxdk7typdCjqJIrQ7t/PwgERERERWcXCgY0Edy4csAty\n2dz5+FrzQI8D47hx8FmzRsFglCVkZBSOjk8WjOPGwWf1aqXDICIiIiIV460KtnLjWxU88td0ubnz\n8bXmfsFIuHLFq4sGRXx+/FHpEFRHuHaNRQMiIiIiKhMLB7Zy51+k2eOgTO40+KXN7heMNBcuKByI\nOoiBgUqHoDqapCSlQyAiIiIiN8DCgY0EN3gco1UsHJTNnQtD1rCniYTo46N0CES24Wc2ERERqQwL\nB7Zy54swLx30TJOcbPvMHtjjQODFh5QgKB0BuTMXfo5q9+1z2baIiIiIbMHCga3c5RfpEi6APbIb\nvg30P/9s+8zucnzt4c7FLmdg4YDKw4WfEYY5c1y2LSIiIiJbKF44iI6ORosWLRASEoJu3bph//79\npc5/6tQp9OnTB6GhoWjSpAlmz54N0RW/BLnLheW9e5bT3Pk2CxcRPPEi2xP3qTy8vHDgN2gQ/IYO\nheb8eaVDcUu6X3913cYyMly3LTfkNu0GIiIiD6Lo4xjXrl2LqKgozJs3D+3bt0d0dDQGDhyIgwcP\nonbt2hbz3717Fy+88AI6duyIHTt2ID4+HhMmTICfnx8mTZrk3GA1itdYbFNCgcN3/nwFAlEv7e+/\nw3fRIgD373s3mVDwxBPKBuUMRYUDNpALeXnhQH//wle/cSNy330X915/ncUlOxinTXPZtnTHj7ts\nW+7GXdoN+jVrYBg7FhH8/LUQoXQAKsN8SDEfUsyHFPNhqSgneWPGIHfePKduS0hPT1fsW61Hjx5o\n1qwZPvnkk+JprVu3Rr9+/fCPf/zDYv4vvvgC06dPx7lz52A0GgEAc+bMwfLly3H69GkITrwwqPDI\nI9Dcvu209cslv3dv6DdvVjoM1ch7/nnJ30JWFvRbtyoUjWsVtGsHc/Xq0KSkQFfGL3JEpB6msDBk\n/v670mGokju0G/Rr1sD46qvw7lIlERG5kgjnFw8U+xk9Ly8Px48fR/fu3SXTu3fvjkOHDpW4zOHD\nh9GhQ4fiL3+gsBFx/fp1JDnzsWKiCFP79s5bv4xYNJDy+eknyT9vKRoAgO7QIfj89BOLBkRuJm/s\nWKVDUCV3aTcYPviARQMiInIpAYDPl186dRuK3apw69YtmEwmVKtWTTK9WrVqSElJKXGZlJQU1KhR\nw2L+otfq1atX4nLx8fHlC1YUIbz3HiI2bSrfeoiIiMqQULcucsv7vQUgLCxMhmjUw13aDRFXrji8\nLBERkcNMpnJ9f5XVblB0jANXkavxlFunDgyXLsmyLvIMYmAgct9916X3P3uS/B49oElNhfbPP5UO\nhdxA3ksvQf/zzxBycpQOxWlya9dG7aefVjoMr1eedoNYqxaEy5dljIaIiMgGWq1TfzRQrHBQpUoV\naLVapKamSqanpqYiODi4xGWCg4NLnL/oNWdLnDkTdUURYrVqhffKb9gA0WiEuUEDiP7+EP39IeTk\nQLh+HebmzSGkpkLIzobm7FkAwL2JEyGGhEC/bh2E9HSYq1eHYDZDt3s3cPcuxMqVIWRlQQwKglix\nIuDjA+HWLQg3b0Jz/TpMLVvCXLMmxMBAaI8cgZCfD1Pz5hBu3oQYGAixVq3iWIUrVwqXvXcPpkaN\ngIoVIaSnA1lZhdOaNYOpXTvoV62CkJwMIT0d+u3bgYwMmJs2hSkiAmKlSkB2NnS//QZT8+aAjw+0\nR45ArFQJpjZtYGrYEDdjYxEiiijo1QsoKID25ElAq4U5OBhi1arQHjgA3e+/Q3PmDMwNGyL/pZeQ\n/8wz0Jw/D82FC9BcuwbtoUMw161bmMcKFSCkpxfmougxktnZEO7dg2gwQHviBITkZIghIRAyMyHc\nulWY++BgaOLjoUlNhejnh/wBAyBWrmz1WGoSEyEaDIBWC82VKzC1alU4AOa9exCys4GsLECjQd7L\nL0MbGwtNQgLESpWgTUiAWLEi8nv2hOby5cKYQ0NR8NRT0H/7LS6HhKB6WBg0aWkAAHOdOkBmJnxW\nrgR0OhS0awfBZCrM7Z07hfPl5hbuR82ahcvUrAno9dBt3w4hJaXwWBiNEGvXhm7zZmiuX0d+795A\nQQE0KSkw16gBzcWLf50jjz5a4kCAmlOnCv97/TrM1avD3KwZkJlZuN/5+RDy8qA9ehSaCxeQP3Ag\nxCpVoDl9GmJoKMTKlaGJiyucVxSh27ULYtWqKOjcufBYpaVBrFYN2sOHoUlMhLlhQ2gSE3FHEGAc\nOhQQhMLjffUqhMxMmMLCYGrVCqZ27YD8fOgOHAAyM6G5eRMwm6FJSoL5kUcgarXwWbUKYnAwTE2b\nFsZerRrEgAAImZkQK1WCWKMGdNu2QfT1halLF0AUoYmNLTyP6tQBsrJgbtQI0BV+3Ak3bhReeIpi\n4WCiej00ly6hoFMnID8f+i1bUNCmDcSaNQv3v1o1aK5dg7lq1cL8JSTAXL8+8gcNgpCSAu2xY9Ce\nOwdT48YQg4NhDguD5s8/oT13DmLVqhA1GmgTE4E7d5B15Qr869UDBAFCdjZEf39Ap0P+889DuHsX\nPosXw9y0aeF+Va0KmM0Q0tJgfuQRICcHuoMHIWRkAJmZhduqVQuaCxdgfvRRCNnZhTEaDBBDQgrz\ncOYMzLVqAYGB0Jw7V9h76s6dwvO26P2Rnw/96tUwRUTA3LgxNKdOQXviBMyPPgpzvXrQnjwJMSAA\notEI7fHjQEAAzLVrw9S0KXTbtwN6Pcx16wJGI8w1ahQfl/w+faDbuRPaY8dgatUKBZ06QZOWBu3e\nvYXnU1ISbiYno2pICGA2w9SmTWF+k5IAjQairy+g00F77Bg0ly4hb9w4mCIikJucDN1vv0G/bl3h\n52+jRhD1ekCrLfz8yMiANi4O5tq1C88Bf3/o9u+HqWFDCJmZMIeFQbh7F9qjR4G8PBR06wZzs2YQ\n7n92aI8eLTxH27eHJja28Fa1xx8Hirq637lT+DlTqRKEtDToNm0q/Lz280NBz54wh4TA1KoVhJwc\n6Nevhzk4GNqjRyFWqQJz48YwNW4MIT0dQl4ehJQUQKuFNjYWyMpCQdeuONOoETyrn4B83KXdkPv+\n+xzjgIiIXEoEkPfKK07dhuKDIzZv3hwLFy4snhYREYHnnnuu1EGO4uPjYTAYAADz5s1DdHS00wdH\nBAq7Lnpa18/yYk6kmA8p5kOK+ZBiPiwxJ6Vzl3ZD0VMVBD5VgYiIXMAVT1VQ9FaFCRMmYNy4cYiI\niEC7du2wfPly3LhxA6NGjQIAzJgxA0eOHEFMTAwAYMCAAZg9ezbGjx+PKVOm4Pz58/j3v/+NadOm\nOb1oQERERMpyl3ZD/sCByB84kIWgEjAnUsyHFPMhxXxIMR+WXJkTRQsH/fv3R1paGubMmYPk5GQ0\nadIEq1evRp06dQAAN27cwIULF4rnr1ixItatW4cpU6bgySefRFBQECZMmICJEycqtQtERETkImw3\nEBERKUPxwREjIyMRGRlZ4mtLly61mNasWTNs4tMNiIiIvBLbDURERK6nUToAIiIiIiIiIlIvFg6I\niIiIiIiIyCoWDoiIiIiIiIjIKhYOiIiIiIiIiMgqIT09nQ8ZJiIiIiIiIqISsccBEREREREREVnF\nwgERERERERERWcXCARERERERERFZxcIBEREREREREVnFwgERERERERERWcXCARERERERERFZxcIB\nWRBFPqGTyFYmk0npEFQlPz8fAD9HHnTz5k1cuXJF6TCInIbvdyLbsd0gxXaDJbW2G1g4AGA2m5UO\nQRXy8vIAAFlZWQCYFwDIzMxEZmYmUlNTAfBD7fLlyzh79qzSYajG2bNnMXfu3OL3jLc7e/YsRo0a\nhYsXL0IQBKXDUYUzZ86gU6dO2LRpEwB+hngKfj8WYrvBEtsNUmw3SLHdIMV2gyU1txu8tnAQFxeH\n9evXAwA0Go2qDooSzp07h9dffx19+/bF8OHDcfDgQa/Py9mzZzF8+HA8++yz6NKlC7Zu3erVH2pX\nr15Fy5YtMXz4cJw+fVrpcBQXGxuLDh06QK/Xw9/fH4C6Ptxd7dSpU+jTpw+CgoJw584dpcNRhdjY\nWPTq1Qt5eXlYvnw5rl275tWfIe6O7QYpthsssd0gxXaDFNsNUmw3WFJ7u8ErCwcJCQno3r07Xnnl\nFXz99dcAAEEQvPbNe+bMGfTq1Qv+/v4IDw9HUFAQRo4cifPnz6vqZHWl06dPo1evXmjcuDEiIyPR\nq1cvTJo0Cenp6QC894O+cePGMJlMGDx4ME6dOqV0OIo5efIkevXqhTfeeAOTJ08unl7U/dDbzo/0\n9HSMHz8eAwYMwOLFi9GyZUtkZ2fj6tWrSoemmNjYWDz99NMYO3YsvvzyS6SlpRX/6sZuqu6H7QYp\nthsssd1QMrYbCrHdIMV2gyV3aDfolA7A1W7fvo0ZM2agR48eqFu3Lt544w2YTCaMGjWquBHgTV96\nKSkpmDhxIoYPH44PPvgAABAfH49Ro0bhxIkTaNCggdfl5PLlyxgzZgzGjBmD999/HwBQq1YtpKam\nwmw249q1a6hRo4bCUbqWyWSCVqtF1apVMW/ePEydOhXDhg3DmjVr0KBBAxw9ehStW7dWOkyXSExM\nRO/evdGvXz9Mnz4dALBw4ULExcUhOzsbL7/8Mp566illg3Sx9PR06HQ6REVFQRRFjBgxAikpKTh2\n7BgGDRqE559/Hj169FA6TJc5ceIEnnjiCbz55pt49913AQD16tXDggUL0L17d2i1WoUjJHuw3SDF\ndoMlthsssd3wF7YbLLHdIOUu7Qav63Fw584dVKhQAUOHDsXkyZMxbdo0TJ48Gf/9738BeN8vCHFx\ncfDz88OLL75YvN9hYWGoXLkyzpw5A8D7qqDJyclo0qQJRo4cWTxt79692L9/P/7nf/4HHTt2xAcf\nfIC7d+8qGKVrabVahIaGIjAwEOnp6Vi+fDlCQ0MxZMgQDBkyBLNmzfKafCQlJeHevXuoVq0aTp06\nhWeeeQbbtm1DWloa8vLy8NJLL2HRokUAvOe9k5WVhbS0NGRkZGDIkCHIycnBpEmT8Mknn+Ds2bNY\ntmwZjh8/rnSYLmEymRATE4PXX38d7777bvGvBJGRkbh48SJ27doFwHvODU/AdoMU2w2W2G6wxHbD\nX9husMR2w1/cqd3gdT0O6tWrh6lTp6JevXoAgLFjx0IURUyePBmiKGL06NEQBAEFBQUoKCiAwWBQ\nNmAnq1OnDsaMGYOWLVsCAAoKCqDT6WA0GlFQUACg8F5Ob9KmTRv885//RM2aNQEAX3/9NRYtWoQF\nCxagadOmiI+Px9ixY9GyZUv069dP4Whdo+jDKj8/H3v37kXbtm2xadMmNGrUCJs3b8by5csRGBio\ncJSu8eSTTyI6Ohpvv/02Vq1ahTZt2mD+/PkIDg6GRqPBp59+infffRedOnXyml9TAgICkJOTg+3b\nt6NSpUr4+9//joYNGwIAGjVqhJEjR+LAgQMIDw9XOFLn02q1mDx5cvH9q0W/EnTq1AkmkwlbtmzB\nE0884VW/xro7thuk2G6wxHaDJbYb/sJ2gyW2G/7iTu0GbVRU1HSlg3CVoq5zQUFBxX/7+fmhefPm\n0Ov1mD59OkJCQtCqVStERUXh0qVLaN26tSoOlLMEBQWhcePGAApHQy46WX/99VcYjcbibkIff/wx\nbt26VTyvpyo6R4q+zAoKChAbG4tJkybhmWeeQWhoKJo1a4ZffvkFALyma5kgCBAEATdu3IAoiujQ\noQPGjRuHixcvon79+ti9ezfatWuHkJAQpUN1KrPZDEEQ0LhxY9SpUwcJCQl466230Lhx4+LPiXr1\n6uH7779HWFgYWrVqpXDErhEUFIS0tDTMmDED58+fx+DBgxEaGgpRFFG9enUcOXIEly5dwgsvvKB0\nqE5V9Pnh4+MjmW42mxEYGAidTofPPvsM3bp1Q2hoqEJRkj3YbrDEdoMU2w0lY7uhENsNJWO7oZC7\ntRs8viScnJyMzMxMALD4Ii/6u1KlShg/fjymTp2KadOmoVevXvj888/Rvn17j6uaP5iPh7u8PLyv\nRa/PnDkTs2fPRt26dV0TpIuVdo7odDqMGDEC7dq1K552+/ZtBAUFeWwV9MF8PKxmzZr4448/MGrU\nKOzcuRPr1q3Dpk2boNVqMWnSJNy7d8/F0Trfg/nQaDTFjxt79tln8emnnxb/6lb0fsnJyUFISIhX\nvF+Av/Z7zJgxePnll5Gbm4tDhw6hoKCg+P1kMpnQoEEDReJ1haKcWLtYLPpsbdOmDYxGI/744w8A\n6hnsiKTYbpBiu8ES2w1SbDdIsd0gxXaDJXdtN3jWt9tD4uLi0LhxYwwbNgzZ2dmlzlupUiWMGTMG\nYWFhiI+Px759+4rf2J7i4XyUdLIWfbhlZGSgYsWK+M9//oNFixZh586dHvmFZ8s58nBDacmSJbh2\n7Ro6d+7sihBdylo+inJQs2ZNHDp0CKdOncLq1avRtGlT+Pv7Y9OmTfj666/h6+urVOhOUVI+Hnzc\n2COPPAKj0Qjgr8bjV199hYKCAjRt2lSZoJ2opHwU7XfNmjUxceJEDBw4EG+99RamT5+ORYsW4d13\n38Xu3bvx0ksvKRm609jzPRMREYGnnnoKc+bMQXZ2tmoGO6K/sN0gxXaDJbYbpNhukGK7QYrtBkvu\n3G4Q0tPTlR9pwQmSk5PxyiuvwGAwID4+Hg0bNsSKFSvg5+dX4vxmsxn/+Mc/8Omnn2LPnj1o1qyZ\niyN2LnvzMXbsWKxZswYBAQFYv369R95zZW9ODh48iB9//BFr1qzB+vXrPa6BaGs+oqOj0bFjR4/8\ngnuQvefHvn37sHbtWqxduxYxMTFo0aKFiyN2rtLyYTabi6vjOTk5+Prrr/HDDz+goKAAVatWxfvv\nv4/HHntM4T2Qnz3nSFGONm3ahJkzZ+KHH35A9erVFYiarGG7QYrtBktsN0ix3SDFdoMU2w2W3L3d\n4LE9Dv744w9Ur14dUVFR+OabbxAXF4fhw4dbrexcu3YN169fx86dOz3uyx+wPx+VK1dGQEAAfv31\nV4/88gfsy0lKSgpiY2Nx7tw5bNy40eO+/IGy81E06FVkZKTHf/kD9p0fqamp+PPPP3Hq1Cn88ssv\nHvflD5Sejwe7YhqNRowbNw5r1qzBtm3b8NVXX3nklz9g3zlS1EDq0aMH1q1bp/iXP1liu0GK7QZL\nbDdIsd0gxXaDFNsNlty93eCxPQ7S09Nx5MiR4kF6jh49iuHDh6Nhw4b45ptvEBAQAOCvQSmAwopX\nUfchT2NrPoqqW2fOnEFgYGDxCMGeyNacFMnKykJ+fn7xIFmexpH3jCez9/zIzMxEQUGB158fRc/u\n9gZ8z3gWthuk2G6wxHaDFD8DpdhukGK7wZK7v2c8tnBQkmPHjmHYsGFo1KgRvvnmG/j5+eGrr75C\n48aN0b59e9UeJGcpKR///e9/0bJlS0RERCgdniKs5eSxxx5D27ZtlQ7P5ay9Z5o2bYrHH39c6fBc\njueHFM8PS8yJZ2G7QYrtBkv8XpDiZ6AUzw8pnh+W3CknHlM4uHz5Mk6fPo3k5GQ8/fTTCAwMtLiH\nBvjr4DRp0gQ1atTAN998g2PHjhU/n9lTMB+WmBMp5kOK+ZBiPiwxJ56Fx1OK+bDEnEgxH1LMhxTz\nYcnTcuIRhYOTJ0+if//+CAkJQVJSEgICAvDiiy8iMjISdevWtTg4v//+O55++mkEBQVh3bp1Hjfq\nL/NhiTmRYj6kmA8p5sMSc+JZeDylmA9LzIkU8yHFfEgxH5Y8MSduPzhieno6Jk6ciMGDByMmJgaX\nLl3CsGHDcPjwYURFRSExMVHyGBSTyYRvv/0Wfn5+2LRpkyoPSnkwH5aYEynmQ4r5kGI+LDEnnoXH\nU4r5sMScSDEfUsyHFPNhyVNz4vaFg4yMDNy6dQvdunVDpUqVAAD/93//h+HDhyM9PR0fffQRkpOT\ni+9BPHDgAA4fPowNGzagcePGSobuFMyHJeZEivmQYj6kmA9LzIln4fGUYj4sMSdSzIcU8yHFfFjy\n1Jy4feFAq9XCaDTi6tWrAP569MuwYcMwYMAAnDp1Cjt27CiePzw8HOvXr0erVq0UidfZmA9LzIkU\n8yHFfEgxH5aYE8/C4ynFfFhiTqSYDynmQ4r5sOSpOfGIMQ6GDBmCS5cuISYmBlWqVJE81mP48OFI\nTk7Gli1bvGb0Y+bDEnMixXxIMR9SzIcl5sSz8HhKMR+WmBMp5kOK+ZBiPix5Yk7crsdBZmYm0tPT\ncfv27eJpixcvRlZWFkaPHo3s7GzJs0B79OgBs9mMvLw8tzko9mA+LDEnUsyHFPMhxXxYYk48C4+n\nFPNhiTmRYj6kmA8p5sOSt+TErQoHZ8+excsvv4y+ffuiTZs2+OKLL5CdnY0qVarg888/R0JCAl58\n8UWcOXMGOTk5AAofb1GhQoXiwSc8CfNhiTmRYj6kmA8p5sMSc+JZeDylmA9LzIkU8yHFfEgxH5a8\nKSduc6tCXFwc+vTpg8GDB6Ndu3b4888/sWDBAmzYsAEdOnQAAJw+fRqRkZHIyspCUFAQQkNDsX//\nfmzatAnNmzdXeA/kxXxYYk6knl08VAAAB/FJREFUmA8p5kOK+bDEnHgWHk8p5sMScyLFfEgxH1LM\nhyVvy4lbFA5u376NMWPGoEGDBvjXv/5VPP3FF19EaGgolixZIrk/JDo6GlevXoXBYED//v0RFham\nVOhOwXxYYk6kmA8p5kOK+bDEnHgWHk8p5sMScyLFfEgxH1LMhyVvzIlO6QBskZ+fj/T0dPTr1w8A\nigeXqF+/PpKTkwEAgiAUT4+MjFQyXKdjPiwxJ1LMhxTzIcV8WGJOPAuPpxTzYYk5kWI+pJgPKebD\nkjfmxC3GOAgODsZnn32GTp06AQDMZjMAIDQ0VDLQhFarxc2bN4v/drf7RmzFfFhiTqSYDynmQ4r5\nsMSceBYeTynmwxJzIsV8SDEfUsyHJW/MiVsUDgCgQYMGAAoPil6vBwDk5eVJDsTcuXMxd+5c3Lt3\nDwDcapRKezEflpgTKeZDivmQYj4sMSeehcdTivmwxJxIMR9SzIcU82HJ23LiFrcqPEijkdY6dLrC\nXfjwww8xd+5c7N69G76+vkqEpgjmwxJzIsV8SDEfUsyHJebEs/B4SjEflpgTKeZDivmQYj4seUtO\ntFFRUdOVDsJeZrMZgiDg4MGDEEUR58+fx9y5c7Ft2za0bNlS6fBcjvmwxJxIMR9SzIcU82GJOfEs\nPJ5SzIcl5kSK+ZBiPqSYD0vekBO363EA/FXV0Wg0WLFiBQIDA7F582aEh4crHJkymA9LzIkU8yHF\nfEgxH5aYE8/C4ynFfFhiTqSYDynmQ4r5sOQNOXGbMQ5K0r17dwDAr7/+ilatWikcjfKYD0vMiRTz\nIcV8SDEflpgTz8LjKcV8WGJOpJgPKeZDivmw5Mk5EdLT0913aEcAWVlZ8Pf3VzoM1WA+LDEnUsyH\nFPMhxXxYYk48C4+nFPNhiTmRYj6kmA8p5sOSp+bE7QsHREREREREROQ8bn2rAhERERERERE5FwsH\nRERERERERGQVCwdEREREREREZBULB0RERERERERkFQsHRERERERERGQVCwdEZGHlypUICgoq/hcS\nEoLGjRujf//++M9//oOMjAyH1nv27FnMmjULSUlJMkdMRERESmG7gcjz6ZQOgIjUKyoqCo888gjy\n8/ORkpKCffv24e2338aSJUvw3XffoXnz5natLy4uDrNnz0bnzp1Rt25dJ0VNRERESmC7gchzsXBA\nRFb16NEDbdu2Lf578uTJ2L17NwYPHowhQ4bg8OHDMBqNCkZIREREasF2A5Hn4q0KRGSXbt26YerU\nqbh8+TJWr14NADh58iTGjx+P8PBwhISEoH79+hg9ejQuX75cvNzKlSsxcuRIAMCzzz5b3J1x5cqV\nxfMcPXoUAwcORJ06dRAaGorevXtjz549rt1BIiIikg3bDUSegYUDIrLboEGDAAA7duwAAOzcuRPn\nz5/H4MGD8a9//QsjRozAtm3b0LdvX2RnZwMAOnXqhHHjxgEA3nzzTSxbtgzLli1Dp06dAAD79u3D\nM888g9u3b2Pq1KmYPn067t27h/79+2Pv3r0K7CURERHJge0GIvcnpKeni0oHQUTqsnLlSkyYMAFb\nt26VdDl8UJ06dVCvXj3s2bMH2dnZ8PPzk7x+6NAh9OrVC8uWLStuMKxfvx4jR47Ezz//jC5duhTP\nK4oiHn/8cdSoUQM//fQTBEEAAOTl5aFr164IDAzEli1bnLS3REREVB5sNxB5PvY4ICKHBAQEIDMz\nEwAkX/6ZmZlIS0tDgwYNULFiRRw/frzMdcXGxiI+Ph4DBgxAWloabt26hVu3biEjIwNPPPEE/vjj\nj+JfIIiIiMj9sN1A5N44OCIROSQzMxNVq1YFAKSnp2P69OlYv349bt++LZnv7t27Za4rISEBADBp\n0iRMmjSpxHnS0tIsfp0gIiIi98B2A5F7Y+GAiOx29epV3L17F/Xr1wcAvPLKKzh06BAmTJiAFi1a\noEKFChAEAaNHj4bZbC5zfUXzTJ8+HeHh4SXOU9TYICIiIvfCdgOR+2PhgIjs9v333wMAunfvjvT0\ndOzatQtRUVGIiooqnic3Nxfp6ek2re+RRx4BUNiN8YknnpA9XiIiIlIO2w1E7o9jHBCRXXbv3o05\nc+agbt26eOmll6DRFH6MiKJ0nNVPP/3U4lcDf39/ALBoGISHh6N+/fpYsmQJMjIyLLZ58+ZNOXeB\niIiIXITtBiLPwB4HRGTV9u3bkZiYiIKCAqSmpmLPnj3YuXMnateuje+++w4GgwEGgwGdO3fGJ598\ngvz8fNSuXRsHDhzA/v37UblyZcn6WrRoAa1WiwULFuDOnTswGo2IiIhAvXr1sGjRIgwYMADt27fH\nyy+/jJo1a+L69ev47bffIIoiNmzYoFAWiIiIyBZsNxB5LhYOiMiqjz/+GADg4+ODSpUqoWnTppg1\naxZefvllVKhQoXi+6OhoREVF4b///S8KCgrQsWNHxMTEoF+/fpL1BQcHY+HChZg/fz7eeOMNmEwm\nLFmyBPXq1UOnTp2wdetWzJkzB1988QUyMjIQHByM1q1bY8SIES7dbyIiIrIf2w1EnktIT08Xy56N\niIiIiIiIiLwRxzggIiIiIiIiIqtYOCAiIiIiIiIiq1g4ICIiIiIiIiKrWDggIiIiIiIiIqtYOCAi\nIiIiIiIiq1g4ICIiIiIiIiKrWDggIiIiIiIiIqtYOCAiIiIiIiIiq1g4ICIiIiIiIiKrWDggIiIi\nIiIiIqv+H3TqUxx/vSd2AAAAAElFTkSuQmCC\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Set up the plotting layout\n", - "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2, figsize = (15,10))\n", - "fig.autofmt_xdate(rotation = 45)\n", - "\n", - "# Historical Average Max Temp\n", - "ax1.plot(dates, features['average'])\n", - "ax1.set_xlabel(''); ax1.set_ylabel('Temperature (F)'); ax1.set_title('Historical Avg Max Temp')\n", - "\n", - "# Prior Avg Wind Speed \n", - "ax2.plot(dates, features['ws_1'], 'r-')\n", - "ax2.set_xlabel(''); ax2.set_ylabel('Wind Speed (mph)'); ax2.set_title('Prior Wind Speed')\n", - "\n", - "# Prior Precipitation\n", - "ax3.plot(dates, features['prcp_1'], 'r-')\n", - "ax3.set_xlabel('Date'); ax3.set_ylabel('Precipitation (in)'); ax3.set_title('Prior Precipitation')\n", - "\n", - "# Prior Snowdepth\n", - "ax4.plot(dates, features['snwd_1'], 'ro')\n", - "ax4.set_xlabel('Date'); ax4.set_ylabel('Snow Depth (in)'); ax4.set_title('Prior Snow Depth')\n", - "\n", - "plt.tight_layout(pad=2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Pairplots\n", - "\n", - "One of my favorite graphs to make is a pairplot which shows all the relationships between variables in a dataset. We can do this with the seaborn library and examine the plots to see which variables are highly correlated. We would suspect those that are more correlated with max temperature would be more useful for prediction." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Create columns of seasons for pair plotting colors\n", - "seasons = []\n", - "\n", - "for month in features['month']:\n", - " if month in [1, 2, 12]:\n", - " seasons.append('winter')\n", - " elif month in [3, 4, 5]:\n", - " seasons.append('spring')\n", - " elif month in [6, 7, 8]:\n", - " seasons.append('summer')\n", - " elif month in [9, 10, 11]:\n", - " seasons.append('fall')\n", - "\n", - "# Will only use six variables for plotting pairs\n", - "reduced_features = features[['temp_1', 'prcp_1', 'average', 'actual']]\n", - "reduced_features['season'] = seasons" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false, - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxIAAALECAYAAACc1AZNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Use seaborn for pair plots\n", - "import seaborn as sns\n", - "sns.set(style=\"ticks\", color_codes=True);\n", - "\n", - "# Create a custom color palete\n", - "palette = sns.xkcd_palette(['dark blue', 'dark green', 'gold', 'orange'])\n", - "\n", - "# Make the pair plot with a some aesthetic changes\n", - "sns.pairplot(reduced_features, hue = 'season', diag_kind = 'kde', palette= palette, plot_kws=dict(alpha = 0.7),\n", - " diag_kws=dict(shade=True)); " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data Preparation" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# One Hot Encoding\n", - "features = pd.get_dummies(features)\n", - "\n", - "# Extract features and labels\n", - "labels = features['actual']\n", - "features = features.drop('actual', axis = 1)\n", - "\n", - "# List of features for later use\n", - "feature_list = list(features.columns)\n", - "\n", - "# Convert to numpy arrays\n", - "import numpy as np\n", - "\n", - "features = np.array(features)\n", - "labels = np.array(labels)\n", - "\n", - "# Training and Testing Sets\n", - "from sklearn.model_selection import train_test_split\n", - "\n", - "train_features, test_features, train_labels, test_labels = train_test_split(features, labels, \n", - " test_size = 0.25, random_state = 42)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training Features Shape: (1643, 17)\n", - "Training Labels Shape: (1643,)\n", - "Testing Features Shape: (548, 17)\n", - "Testing Labels Shape: (548,)\n" - ] - } - ], - "source": [ - "print('Training Features Shape:', train_features.shape)\n", - "print('Training Labels Shape:', train_labels.shape)\n", - "print('Testing Features Shape:', test_features.shape)\n", - "print('Testing Labels Shape:', test_labels.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Establish New Baseline\n", - "\n", - "The new baseline will be the predictions of the model trained on only one year of data but tested on the expanded testing set. In order to make predictions, we will need to restrict the features to those in the original one year of data (exclude the wind speed, precipitation, and snow depth). Testing with the same test set allows us to assess the effect of using additional training data. " - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Metrics for Random Forest Trained on Original Data\n", - "Average absolute error: 4.3 degrees.\n", - "Accuracy: 92.49 %.\n" - ] - } - ], - "source": [ - "# Find the original feature indices \n", - "original_feature_indices = [feature_list.index(feature) for feature in\n", - " feature_list if feature not in\n", - " ['ws_1', 'prcp_1', 'snwd_1']]\n", - "\n", - "# Create a test set of the original features\n", - "original_test_features = test_features[:, original_feature_indices]\n", - "\n", - "# Make predictions on test data using the model trained on original data\n", - "baseline_predictions = rf.predict(original_test_features)\n", - "\n", - "# Performance metrics\n", - "baseline_errors = abs(baseline_predictions - test_labels)\n", - "\n", - "print('Metrics for Random Forest Trained on Original Data')\n", - "print('Average absolute error:', round(np.mean(baseline_errors), 2), 'degrees.')\n", - "\n", - "# Calculate mean absolute percentage error (MAPE)\n", - "baseline_mape = 100 * np.mean((baseline_errors / test_labels))\n", - "\n", - "# Calculate and display accuracy\n", - "baseline_accuracy = 100 - baseline_mape\n", - "print('Accuracy:', round(baseline_accuracy, 2), '%.')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Train on Expanded Data and Features\n", - "\n", - "The rf_exp uses the same number of decision trees (n_estimators) but is trained on the longer dataset with 3 additional features." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Instantiate random forest and train on new features\n", - "from sklearn.ensemble import RandomForestRegressor\n", - "\n", - "rf_exp = RandomForestRegressor(n_estimators= 1000, random_state=42)\n", - "rf_exp.fit(train_features, train_labels);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Metrics for Expanded Data and Features" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Metrics for Random Forest Trained on Expanded Data\n", - "Average absolute error: 3.7039 degrees.\n", - "Improvement over baseline: 16.67 %.\n", - "Accuracy: 93.74 %.\n" - ] - } - ], - "source": [ - "# Make predictions on test data\n", - "predictions = rf_exp.predict(test_features)\n", - "\n", - "# Performance metrics\n", - "errors = abs(predictions - test_labels)\n", - "\n", - "print('Metrics for Random Forest Trained on Expanded Data')\n", - "print('Average absolute error:', round(np.mean(errors), 4), 'degrees.')\n", - "\n", - "# Calculate mean absolute percentage error (MAPE)\n", - "mape = np.mean(100 * (errors / test_labels))\n", - "\n", - "# Compare to baseline\n", - "improvement_baseline = 100 * abs(mape - baseline_mape) / baseline_mape\n", - "print('Improvement over baseline:', round(improvement_baseline, 2), '%.')\n", - "\n", - "# Calculate and display accuracy\n", - "accuracy = 100 - mape\n", - "print('Accuracy:', round(accuracy, 2), '%.')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Using more data (more data points and more features) has decreased the absolute error, increased performance relative to the baseline, and increased accuracy.\n", - "Although the exact metrics will depend on the random state used, overall, we can be confident using additional high-quality data improves our model.\n", - "\n", - "At this point, our model can predict the maximum temperature for tomrrow with an average error of __3.7__ degrees resulting in an accuracy of __93.7%__." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Feature Reduction\n", - "\n", - "From previous experience and the graphs produced at the beginning, we know that some features are not useful for our temperature prediction problem. To reduce the number of features, which will reduce runtime, hopefully without significantly reducing performance, we can examine the feature importances from the random forest." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Feature Importances" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Variable: temp_1 Importance: 0.83\n", - "Variable: average Importance: 0.06\n", - "Variable: ws_1 Importance: 0.02\n", - "Variable: temp_2 Importance: 0.02\n", - "Variable: friend Importance: 0.02\n", - "Variable: year Importance: 0.01\n", - "Variable: month Importance: 0.01\n", - "Variable: day Importance: 0.01\n", - "Variable: prcp_1 Importance: 0.01\n", - "Variable: snwd_1 Importance: 0.0\n", - "Variable: weekday_Fri Importance: 0.0\n", - "Variable: weekday_Mon Importance: 0.0\n", - "Variable: weekday_Sat Importance: 0.0\n", - "Variable: weekday_Sun Importance: 0.0\n", - "Variable: weekday_Thurs Importance: 0.0\n", - "Variable: weekday_Tues Importance: 0.0\n", - "Variable: weekday_Wed Importance: 0.0\n" - ] - } - ], - "source": [ - "# Get numerical feature importances\n", - "importances = list(rf_exp.feature_importances_)\n", - "\n", - "# List of tuples with variable and importance\n", - "feature_importances = [(feature, round(importance, 2)) for feature, importance in zip(feature_list, importances)]\n", - "\n", - "# Sort the feature importances by most important first\n", - "feature_importances = sorted(feature_importances, key = lambda x: x[1], reverse = True)\n", - "\n", - "# Print out the feature and importances \n", - "[print('Variable: {:20} Importance: {}'.format(*pair)) for pair in feature_importances];" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Visualize Feature Importances" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAisAAAHFCAYAAAAzCLlHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Reset style \n", - "plt.style.use('fivethirtyeight')\n", - "\n", - "# list of x locations for plotting\n", - "x_values = list(range(len(importances)))\n", - "\n", - "# Make a bar chart\n", - "plt.bar(x_values, importances, orientation = 'vertical', color = 'r', edgecolor = 'k', linewidth = 1.2)\n", - "\n", - "# Tick labels for x axis\n", - "plt.xticks(x_values, feature_list, rotation='vertical')\n", - "\n", - "# Axis labels and title\n", - "plt.ylabel('Importance'); plt.xlabel('Variable'); plt.title('Variable Importances');" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjEAAAHFCAYAAAADhKhmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# List of features sorted from most to least important\n", - "sorted_importances = [importance[1] for importance in feature_importances]\n", - "sorted_features = [importance[0] for importance in feature_importances]\n", - "\n", - "# Cumulative importances\n", - "cumulative_importances = np.cumsum(sorted_importances)\n", - "\n", - "# Make a line graph\n", - "plt.plot(x_values, cumulative_importances, 'g-')\n", - "\n", - "# Draw line at 95% of importance retained\n", - "plt.hlines(y = 0.95, xmin=0, xmax=len(sorted_importances), color = 'r', linestyles = 'dashed')\n", - "\n", - "# Format x ticks and labels\n", - "plt.xticks(x_values, sorted_features, rotation = 'vertical')\n", - "\n", - "# Axis labels and title\n", - "plt.xlabel('Variable'); plt.ylabel('Cumulative Importance'); plt.title('Cumulative Importances');" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Limit Number of Features \n", - "\n", - "We will now reduce the number of features in use by the model to only those required to account for 95% of the importance. \n", - "The same number of features must be used in the training and testing sets." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of features for 95% importance: 6\n" - ] - } - ], - "source": [ - "# Find number of features for cumulative importance of 95%\n", - "# Add 1 because Python is zero-indexed\n", - "print('Number of features for 95% importance:', np.where(cumulative_importances > 0.95)[0][0] + 1)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Important train features shape: (1643, 6)\n", - "Important test features shape: (548, 6)\n" - ] - } - ], - "source": [ - "# Extract the names of the most important features\n", - "important_feature_names = [feature[0] for feature in feature_importances[0:6]]\n", - "# Find the columns of the most important features\n", - "important_indices = [feature_list.index(feature) for feature in important_feature_names]\n", - "\n", - "# Create training and testing sets with only the important features\n", - "important_train_features = train_features[:, important_indices]\n", - "important_test_features = test_features[:, important_indices]\n", - "\n", - "# Sanity check on operations\n", - "print('Important train features shape:', important_train_features.shape)\n", - "print('Important test features shape:', important_test_features.shape)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Training on Important Features" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Train the expanded model on only the important features\n", - "rf_exp.fit(important_train_features, train_labels);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Evaluate on Important features" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average absolute error: 3.821 degrees.\n", - "Accuracy: 93.56 %.\n" - ] - } - ], - "source": [ - "# Make predictions on test data\n", - "predictions = rf_exp.predict(important_test_features)\n", - "\n", - "# Performance metrics\n", - "errors = abs(predictions - test_labels)\n", - "\n", - "print('Average absolute error:', round(np.mean(errors), 4), 'degrees.')\n", - "\n", - "# Calculate mean absolute percentage error (MAPE)\n", - "mape = 100 * (errors / test_labels)\n", - "\n", - "# Calculate and display accuracy\n", - "accuracy = 100 - np.mean(mape)\n", - "print('Accuracy:', round(accuracy, 2), '%.')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Using only the eight most important features (instead of all 16) results in a minor decrease in accuracy by 0.17 degrees. For some models, decreasing the number of features can increase performance and therefore should be done. However, in other situations, performance will decrease but run time will also decrease. The final decision on how many features to retain will therefore be a trade-off between accuracy and run time. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Compare Trade-Offs" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "All features total training and testing time: 12.39 seconds.\n" - ] - } - ], - "source": [ - "# Use time library for run time evaluation\n", - "import time\n", - "\n", - "# All features training and testing time\n", - "all_features_time = []\n", - "\n", - "# Do 10 iterations and take average for all features\n", - "for _ in range(10):\n", - " start_time = time.time()\n", - " rf_exp.fit(train_features, train_labels)\n", - " all_features_predictions = rf_exp.predict(test_features)\n", - " end_time = time.time()\n", - " all_features_time.append(end_time - start_time)\n", - "\n", - "all_features_time = np.mean(all_features_time)\n", - "print('All features total training and testing time:', round(all_features_time, 2), 'seconds.')" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reduced features total training and testing time: 7.71 seconds.\n" - ] - } - ], - "source": [ - "# Time training and testing for reduced feature set\n", - "reduced_features_time = []\n", - "\n", - "# Do 10 iterations and take average\n", - "for _ in range(10):\n", - " start_time = time.time()\n", - " rf_exp.fit(important_train_features, train_labels)\n", - " reduced_features_predictions = rf_exp.predict(important_test_features)\n", - " end_time = time.time()\n", - " reduced_features_time.append(end_time - start_time)\n", - "\n", - "reduced_features_time = np.mean(reduced_features_time)\n", - "print('Reduced features total training and testing time:', round(reduced_features_time, 2), 'seconds.')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Accuracy vs Run-Time" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
featuresaccuracyrun_time
0all (17)93.7412.39
1reduced (5)93.567.71
\n", - "
" - ], - "text/plain": [ - " features accuracy run_time\n", - "0 all (17) 93.74 12.39\n", - "1 reduced (5) 93.56 7.71" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "all_accuracy = 100 * (1- np.mean(abs(all_features_predictions - test_labels) / test_labels))\n", - "reduced_accuracy = 100 * (1- np.mean(abs(reduced_features_predictions - test_labels) / test_labels))\n", - "\n", - "comparison = pd.DataFrame({'features': ['all (17)', 'reduced (5)'], \n", - " 'run_time': [round(all_features_time, 2), round(reduced_features_time, 2)],\n", - " 'accuracy': [round(all_accuracy, 2), round(reduced_accuracy, 2)]})\n", - "\n", - "comparison[['features', 'accuracy', 'run_time']]" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Relative decrease in accuracy: 0.198 %.\n", - "Relative decrease in run time: 37.748 %.\n" - ] - } - ], - "source": [ - "relative_accuracy_decrease = 100 * (all_accuracy - reduced_accuracy) / all_accuracy\n", - "print('Relative decrease in accuracy:', round(relative_accuracy_decrease, 3), '%.')\n", - "\n", - "relative_runtime_decrease = 100 * (all_features_time - reduced_features_time) / all_features_time\n", - "print('Relative decrease in run time:', round(relative_runtime_decrease, 3), '%.')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Concluding Graphs" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Find the original feature indices \n", - "original_feature_indices = [feature_list.index(feature) for feature in\n", - " feature_list if feature not in\n", - " ['ws_1', 'prcp_1', 'snwd_1']]\n", - "\n", - "# Create a test set of the original features\n", - "original_test_features = test_features[:, original_feature_indices]\n", - "\n", - "# Time to train on original data set (1 year)\n", - "original_features_time = []\n", - "\n", - "# Do 10 iterations and take average for all features\n", - "for _ in range(10):\n", - " start_time = time.time()\n", - " rf.fit(original_train_features, original_train_labels)\n", - " original_features_predictions = rf.predict(original_test_features)\n", - " end_time = time.time()\n", - " original_features_time.append(end_time - start_time)\n", - " \n", - "original_features_time = np.mean(original_features_time)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Calculate mean absolute error for each model\n", - "original_mae = np.mean(abs(original_features_predictions - test_labels))\n", - "exp_all_mae = np.mean(abs(all_features_predictions - test_labels))\n", - "exp_reduced_mae = np.mean(abs(reduced_features_predictions - test_labels))\n", - "\n", - "# Calculate accuracy for model trained on 1 year of data\n", - "original_accuracy = 100 * (1 - np.mean(abs(original_features_predictions - test_labels) / test_labels))\n", - "\n", - "# Create a dataframe for comparison\n", - "model_comparison = pd.DataFrame({'model': ['original', 'exp_all', 'exp_reduced'], \n", - " 'error (degrees)': [original_mae, exp_all_mae, exp_reduced_mae],\n", - " 'accuracy': [original_accuracy, all_accuracy, reduced_accuracy],\n", - " 'run_time (s)': [original_features_time, all_features_time, reduced_features_time]})\n", - "\n", - "# Order the dataframe\n", - "model_comparison = model_comparison[['model', 'error (degrees)', 'accuracy', 'run_time (s)']]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Comparison of Three Models\n", - "\n", - "There are three models to compare: the model trained on a single year of data and the original number of features, the model trained on six years of data with the full set of features, and the model trained on six years of data with only the 6 most important features" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
modelerror (degrees)accuracyrun_time (s)
0original4.29806292.4913582.528450
1exp_all3.70391693.74318812.392358
2exp_reduced3.82100093.5579347.714516
\n", - "
" - ], - "text/plain": [ - " model error (degrees) accuracy run_time (s)\n", - "0 original 4.298062 92.491358 2.528450\n", - "1 exp_all 3.703916 93.743188 12.392358\n", - "2 exp_reduced 3.821000 93.557934 7.714516" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model_comparison" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjUAAAPXCAYAAADT7rqlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Make plots \n", - "# Set up the plotting layout\n", - "fig, (ax1, ax2, ax3) = plt.subplots(nrows=3, ncols=1, figsize = (8,16), sharex = True)\n", - "\n", - "# Set up x-axis\n", - "x_values = [0, 1, 2]\n", - "labels = list(model_comparison['model'])\n", - "plt.xticks(x_values, labels)\n", - "\n", - "# Set up fonts\n", - "fontdict = {'fontsize': 18}\n", - "fontdict_yaxis = {'fontsize': 14}\n", - "\n", - "# Error Comparison\n", - "ax1.bar(x_values, model_comparison['error (degrees)'], color = ['b', 'r', 'g'], edgecolor = 'k', linewidth = 1.5)\n", - "ax1.set_ylim(bottom = 3.5, top = 4.5)\n", - "ax1.set_ylabel('Error (degrees) (F)', fontdict = fontdict_yaxis); \n", - "ax1.set_title('Model Error Comparison', fontdict= fontdict)\n", - "\n", - "# Accuracy Comparison\n", - "ax2.bar(x_values, model_comparison['accuracy'], color = ['b', 'r', 'g'], edgecolor = 'k', linewidth = 1.5)\n", - "ax2.set_ylim(bottom = 92, top = 94)\n", - "ax2.set_ylabel('Accuracy (%)', fontdict = fontdict_yaxis); \n", - "ax2.set_title('Model Accuracy Comparison', fontdict= fontdict)\n", - "\n", - "# Run Time Comparison\n", - "ax3.bar(x_values, model_comparison['run_time (s)'], color = ['b', 'r', 'g'], edgecolor = 'k', linewidth = 1.5)\n", - "ax3.set_ylim(bottom = 2, top = 12)\n", - "ax3.set_ylabel('Run Time (sec)', fontdict = fontdict_yaxis); \n", - "ax3.set_title('Model Run-Time Comparison', fontdict= fontdict);\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "Check in on [my blog](https://medium.com/@williamkoehrsen) for more data science/machine learning articles. Additional parts of this machine learning exercise will be out shortly. I appreciate any comments and constructive feedback. " - ] - } - ], - "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": "3.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Day_5/random_forest_explained/Improving Random Forest Part 2.ipynb b/Day_5/random_forest_explained/Improving Random Forest Part 2.ipynb deleted file mode 100644 index b781efa..0000000 --- a/Day_5/random_forest_explained/Improving Random Forest Part 2.ipynb +++ /dev/null @@ -1,716 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Load in Data" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Pandas is used for data manipulation\n", - "import pandas as pd\n", - "\n", - "# Read in data as a dataframe\n", - "features = pd.read_csv('data/temps_extended.csv')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Data Preparation" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# One Hot Encoding\n", - "features = pd.get_dummies(features)\n", - "\n", - "# Extract features and labels\n", - "labels = features['actual']\n", - "features = features.drop('actual', axis = 1)\n", - "\n", - "# List of features for later use\n", - "feature_list = list(features.columns)\n", - "\n", - "# Convert to numpy arrays\n", - "import numpy as np\n", - "\n", - "features = np.array(features)\n", - "labels = np.array(labels)\n", - "\n", - "# Training and Testing Sets\n", - "from sklearn.model_selection import train_test_split\n", - "\n", - "train_features, test_features, train_labels, test_labels = train_test_split(features, labels, \n", - " test_size = 0.25, random_state = 42)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training Features Shape: (1643, 17)\n", - "Training Labels Shape: (1643,)\n", - "Testing Features Shape: (548, 17)\n", - "Testing Labels Shape: (548,)\n" - ] - } - ], - "source": [ - "print('Training Features Shape:', train_features.shape)\n", - "print('Training Labels Shape:', train_labels.shape)\n", - "print('Testing Features Shape:', test_features.shape)\n", - "print('Testing Labels Shape:', test_labels.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "4.5 years of data in the training set\n", - "1.5 years of data in the test set\n" - ] - } - ], - "source": [ - "print('{:0.1f} years of data in the training set'.format(train_features.shape[0] / 365.))\n", - "print('{:0.1f} years of data in the test set'.format(test_features.shape[0] / 365.))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Restrict to the Most Important Features\n", - "\n", - "These were the six features required to reach a total feature importance of 95% in the first improving random forest notebook.\n", - "We will use only these features in order to speed up the model." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Important train features shape: (1643, 6)\n", - "Important test features shape: (548, 6)\n" - ] - } - ], - "source": [ - "# Names of five importances accounting for 95% of total importance\n", - "important_feature_names = ['temp_1', 'average', 'ws_1', 'temp_2', 'friend', 'year']\n", - "\n", - "# Find the columns of the most important features\n", - "important_indices = [feature_list.index(feature) for feature in important_feature_names]\n", - "\n", - "# Create training and testing sets with only the important features\n", - "important_train_features = train_features[:, important_indices]\n", - "important_test_features = test_features[:, important_indices]\n", - "\n", - "# Sanity check on operations\n", - "print('Important train features shape:', important_train_features.shape)\n", - "print('Important test features shape:', important_test_features.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Use only the most important features\n", - "train_features = important_train_features[:]\n", - "test_features = important_test_features[:]\n", - "\n", - "# Update feature list for visualizations\n", - "feature_list = important_feature_names[:]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Examine the Default Random Forest to Determine Parameters\n", - "\n", - "We will use these parameters as a starting point. I relied on the [sklearn random forest documentation](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html) to determine which features to change and the available options." - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Parameters currently in use:\n", - "\n", - "{'bootstrap': True,\n", - " 'criterion': 'mse',\n", - " 'max_depth': None,\n", - " 'max_features': 'auto',\n", - " 'max_leaf_nodes': None,\n", - " 'min_impurity_decrease': 0.0,\n", - " 'min_impurity_split': None,\n", - " 'min_samples_leaf': 1,\n", - " 'min_samples_split': 2,\n", - " 'min_weight_fraction_leaf': 0.0,\n", - " 'n_estimators': 10,\n", - " 'n_jobs': 1,\n", - " 'oob_score': False,\n", - " 'random_state': 42,\n", - " 'verbose': 0,\n", - " 'warm_start': False}\n" - ] - } - ], - "source": [ - "from sklearn.ensemble import RandomForestRegressor\n", - "\n", - "rf = RandomForestRegressor(random_state = 42)\n", - "\n", - "from pprint import pprint\n", - "\n", - "# Look at parameters used by our current forest\n", - "print('Parameters currently in use:\\n')\n", - "pprint(rf.get_params())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Random Search with Cross Validation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "from sklearn.model_selection import RandomizedSearchCV\n", - "\n", - "# Number of trees in random forest\n", - "n_estimators = [int(x) for x in np.linspace(start = 200, stop = 2000, num = 10)]\n", - "# Number of features to consider at every split\n", - "max_features = ['auto', 'sqrt']\n", - "# Maximum number of levels in tree\n", - "max_depth = [int(x) for x in np.linspace(10, 100, num = 10)]\n", - "max_depth.append(None)\n", - "# Minimum number of samples required to split a node\n", - "min_samples_split = [2, 5, 10]\n", - "# Minimum number of samples required at each leaf node\n", - "min_samples_leaf = [1, 2, 4]\n", - "# Method of selecting samples for training each tree\n", - "bootstrap = [True, False]\n", - "\n", - "# Create the random grid\n", - "random_grid = {'n_estimators': n_estimators,\n", - " 'max_features': max_features,\n", - " 'max_depth': max_depth,\n", - " 'min_samples_split': min_samples_split,\n", - " 'min_samples_leaf': min_samples_leaf,\n", - " 'bootstrap': bootstrap}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Use the random grid to search for best hyperparameters\n", - "# First create the base model to tune\n", - "rf = RandomForestRegressor()\n", - "# Random search of parameters, using 3 fold cross validation, \n", - "# search across 100 different combinations, and use all available cores\n", - "rf_random = RandomizedSearchCV(estimator=rf, param_distributions=random_grid,\n", - " n_iter = 100, scoring='neg_mean_absolute_error', \n", - " cv = 3, verbose=2, random_state=42, n_jobs=-1)\n", - "\n", - "# Fit the random search model\n", - "rf_random.fit(train_features, train_labels)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "rf_random.best_params_" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Evaluation Function" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "def evaluate(model, test_features, test_labels):\n", - " predictions = model.predict(test_features)\n", - " errors = abs(predictions - test_labels)\n", - " mape = 100 * np.mean(errors / test_labels)\n", - " accuracy = 100 - mape\n", - " print('Model Performance')\n", - " print('Average Error: {:0.4f} degrees.'.format(np.mean(errors)))\n", - " print('Accuracy = {:0.2f}%.'.format(accuracy))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Evaluate the Default Model" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model Performance\n", - "Average Error: 3.8210 degrees.\n", - "Accuracy = 93.56%.\n" - ] - } - ], - "source": [ - "base_model = RandomForestRegressor(n_estimators = 1000, random_state = 42)\n", - "base_model.fit(train_features, train_labels)\n", - "evaluate(base_model, test_features, test_labels)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Evaluate the Best Random Search Model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "best_random = rf_random.best_estimator_\n", - "evaluate(best_random, test_features, test_labels)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Grid Search \n", - "\n", - "We can now perform grid search building on the result from the random search. \n", - "We will test a range of hyperparameters around the best values returend by random search. " - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "from sklearn.model_selection import GridSearchCV\n", - "\n", - "# Create the parameter grid based on the results of random search \n", - "param_grid = {\n", - " 'bootstrap': [True],\n", - " 'max_depth': [80, 90, 100, 110],\n", - " 'max_features': [2, 3],\n", - " 'min_samples_leaf': [3, 4, 5],\n", - " 'min_samples_split': [8, 10, 12],\n", - " 'n_estimators': [100, 200, 300, 1000]\n", - "}\n", - "\n", - "# Create a based model\n", - "rf = RandomForestRegressor()\n", - "\n", - "# Instantiate the grid search model\n", - "grid_search = GridSearchCV(estimator = rf, param_grid = param_grid, \n", - " scoring = 'neg_mean_absolute_error', cv = 3, \n", - " n_jobs = -1, verbose = 2)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fitting 3 folds for each of 288 candidates, totalling 864 fits\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=42)]: Done 78 tasks | elapsed: 42.8s\n", - "[Parallel(n_jobs=42)]: Done 281 tasks | elapsed: 2.2min\n", - "[Parallel(n_jobs=42)]: Done 564 tasks | elapsed: 4.4min\n", - "[Parallel(n_jobs=42)]: Done 864 out of 864 | elapsed: 6.5min finished\n" - ] - }, - { - "data": { - "text/plain": [ - "GridSearchCV(cv=3, error_score='raise',\n", - " estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n", - " max_features='auto', max_leaf_nodes=None,\n", - " min_impurity_decrease=0.0, min_impurity_split=None,\n", - " min_samples_leaf=1, min_samples_split=2,\n", - " min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n", - " oob_score=False, random_state=None, verbose=0, warm_start=False),\n", - " fit_params=None, iid=True, n_jobs=42,\n", - " param_grid={'bootstrap': [True], 'max_depth': [80, 90, 100, 110], 'max_features': [2, 3], 'min_samples_leaf': [3, 4, 5], 'min_samples_split': [8, 10, 12], 'n_estimators': [100, 200, 300, 1000]},\n", - " pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n", - " scoring='neg_mean_absolute_error', verbose=2)" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Fit the grid search to the data\n", - "grid_search.fit(train_features, train_labels)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'bootstrap': True,\n", - " 'max_depth': 110,\n", - " 'max_features': 3,\n", - " 'min_samples_leaf': 5,\n", - " 'min_samples_split': 10,\n", - " 'n_estimators': 100}" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "grid_search.best_params_" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model Performance\n", - "Average Error: 3.6662 degrees.\n", - "Accuracy = 93.81%.\n" - ] - } - ], - "source": [ - "best_grid = grid_search.best_estimator_\n", - "evaluate(best_grid, test_features, test_labels)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Another Round of Grid Search" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fitting 3 folds for each of 54 candidates, totalling 162 fits\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=-1)]: Done 33 tasks | elapsed: 6.8s\n", - "[Parallel(n_jobs=-1)]: Done 154 tasks | elapsed: 25.3s\n", - "[Parallel(n_jobs=-1)]: Done 162 out of 162 | elapsed: 26.5s finished\n" - ] - }, - { - "data": { - "text/plain": [ - "GridSearchCV(cv=3, error_score='raise',\n", - " estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n", - " max_features='auto', max_leaf_nodes=None,\n", - " min_impurity_decrease=0.0, min_impurity_split=None,\n", - " min_samples_leaf=1, min_samples_split=2,\n", - " min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n", - " oob_score=False, random_state=None, verbose=0, warm_start=False),\n", - " fit_params=None, iid=True, n_jobs=-1,\n", - " param_grid={'bootstrap': [True], 'max_depth': [110, 120, None], 'max_features': [3, 4], 'min_samples_leaf': [5, 6, 7], 'min_samples_split': [10], 'n_estimators': [75, 100, 125]},\n", - " pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n", - " scoring='neg_mean_absolute_error', verbose=2)" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "param_grid = {\n", - " 'bootstrap': [True],\n", - " 'max_depth': [110, 120, None],\n", - " 'max_features': [3, 4],\n", - " 'min_samples_leaf': [5, 6, 7],\n", - " 'min_samples_split': [10],\n", - " 'n_estimators': [75, 100, 125]\n", - "}\n", - "\n", - "# Create a based model\n", - "rf = RandomForestRegressor()\n", - "\n", - "# Instantiate the grid search model\n", - "grid_search_ad = GridSearchCV(estimator = rf, param_grid = param_grid, \n", - " scoring = 'neg_mean_absolute_error', cv = 3, \n", - " n_jobs = -1, verbose = 2)\n", - "\n", - "grid_search_ad.fit(train_features, train_labels)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'bootstrap': True,\n", - " 'max_depth': 110,\n", - " 'max_features': 4,\n", - " 'min_samples_leaf': 7,\n", - " 'min_samples_split': 10,\n", - " 'n_estimators': 100}" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "grid_search_ad.best_params_" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model Performance\n", - "Average Error: 3.6809 degrees.\n", - "Accuracy = 93.79%.\n" - ] - } - ], - "source": [ - "best_grid_ad = grid_search_ad.best_estimator_\n", - "evaluate(best_grid_ad, test_features, test_labels)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This time our performance slightly decreased. Therefore, we will go back to the best model returned by the first grid search." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Final Model\n", - "\n", - "The final model from hyperparameter tuning is as follows." - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model Parameters:\n", - "\n", - "{'bootstrap': True,\n", - " 'criterion': 'mse',\n", - " 'max_depth': 110,\n", - " 'max_features': 3,\n", - " 'max_leaf_nodes': None,\n", - " 'min_impurity_decrease': 0.0,\n", - " 'min_impurity_split': None,\n", - " 'min_samples_leaf': 5,\n", - " 'min_samples_split': 10,\n", - " 'min_weight_fraction_leaf': 0.0,\n", - " 'n_estimators': 100,\n", - " 'n_jobs': 1,\n", - " 'oob_score': False,\n", - " 'random_state': None,\n", - " 'verbose': 0,\n", - " 'warm_start': False}\n", - "\n", - "\n", - "Model Performance\n", - "Average Error: 3.6662 degrees.\n", - "Accuracy = 93.81%.\n" - ] - } - ], - "source": [ - "print('Model Parameters:\\n')\n", - "pprint(best_grid.get_params())\n", - "print('\\n')\n", - "evaluate(best_grid, test_features, test_labels)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "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": "3.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Day_5/random_forest_explained/Random Forest Explained.ipynb b/Day_5/random_forest_explained/Random Forest Explained.ipynb deleted file mode 100644 index 985ef6d..0000000 --- a/Day_5/random_forest_explained/Random Forest Explained.ipynb +++ /dev/null @@ -1,1298 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Ask Question" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Can we predict the maximum temperature tomorrow for Seattle, Washington given one year of historical data? " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The data we have available is the maximum temperatures in Seattle from the National Oceanic and Atmospheric Administration's (NOAA) [Climate Data Online Tool](https://www.ncdc.noaa.gov/cdo-web/)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Data Acqusition" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false, - "hideCode": false, - "hidePrompt": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
yearmonthdayweektemp_2temp_1averageactualfriend
0201611Fri454545.64529
1201612Sat444545.74461
2201613Sun454445.84156
3201614Mon444145.94053
4201615Tues414046.04441
\n", - "
" - ], - "text/plain": [ - " year month day week temp_2 temp_1 average actual friend\n", - "0 2016 1 1 Fri 45 45 45.6 45 29\n", - "1 2016 1 2 Sat 44 45 45.7 44 61\n", - "2 2016 1 3 Sun 45 44 45.8 41 56\n", - "3 2016 1 4 Mon 44 41 45.9 40 53\n", - "4 2016 1 5 Tues 41 40 46.0 44 41" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Pandas is used for data manipulation\n", - "import pandas as pd\n", - "\n", - "# Read in data as pandas dataframe and display first 5 rows\n", - "features = pd.read_csv('data/temps.csv')\n", - "features.head(5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Identify Anomalies" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false, - "hideCode": false, - "hidePrompt": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The shape of our features is: (348, 9)\n" - ] - } - ], - "source": [ - "print('The shape of our features is:', features.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
yearmonthdaytemp_2temp_1averageactualfriend
count348.0348.000000348.000000348.000000348.000000348.000000348.000000348.000000
mean2016.06.47701115.51436862.51149462.56034559.76063262.54310360.034483
std0.03.4983808.77298211.81301911.76740610.52730611.79414615.626179
min2016.01.0000001.00000035.00000035.00000045.10000035.00000028.000000
25%2016.03.0000008.00000054.00000054.00000049.97500054.00000047.750000
50%2016.06.00000015.00000062.50000062.50000058.20000062.50000060.000000
75%2016.010.00000023.00000071.00000071.00000069.02500071.00000071.000000
max2016.012.00000031.00000092.00000092.00000077.40000092.00000095.000000
\n", - "
" - ], - "text/plain": [ - " year month day temp_2 temp_1 average \\\n", - "count 348.0 348.000000 348.000000 348.000000 348.000000 348.000000 \n", - "mean 2016.0 6.477011 15.514368 62.511494 62.560345 59.760632 \n", - "std 0.0 3.498380 8.772982 11.813019 11.767406 10.527306 \n", - "min 2016.0 1.000000 1.000000 35.000000 35.000000 45.100000 \n", - "25% 2016.0 3.000000 8.000000 54.000000 54.000000 49.975000 \n", - "50% 2016.0 6.000000 15.000000 62.500000 62.500000 58.200000 \n", - "75% 2016.0 10.000000 23.000000 71.000000 71.000000 69.025000 \n", - "max 2016.0 12.000000 31.000000 92.000000 92.000000 77.400000 \n", - "\n", - " actual friend \n", - "count 348.000000 348.000000 \n", - "mean 62.543103 60.034483 \n", - "std 11.794146 15.626179 \n", - "min 35.000000 28.000000 \n", - "25% 54.000000 47.750000 \n", - "50% 62.500000 60.000000 \n", - "75% 71.000000 71.000000 \n", - "max 92.000000 95.000000 " - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Descriptive statistics for each column\n", - "features.describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Use datetime for dealing with dates\n", - "import datetime\n", - "\n", - "# Get years, months, and days\n", - "years = features['year']\n", - "months = features['month']\n", - "days = features['day']\n", - "\n", - "# List and then convert to datetime object\n", - "dates = [str(int(year)) + '-' + str(int(month)) + '-' + str(int(day)) for year, month, day in zip(years, months, days)]\n", - "dates = [datetime.datetime.strptime(date, '%Y-%m-%d') for date in dates]" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Import matplotlib for plotting and use magic command for Jupyter Notebooks\n", - "import matplotlib.pyplot as plt\n", - "\n", - "%matplotlib inline\n", - "\n", - "# Set the style\n", - "plt.style.use('fivethirtyeight')" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAqYAAAKmCAYAAACfe1yfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAADAEBhMAQAAYAgMpgAAADCEn7TpnVRb6h2VAAAAAElFTkSuQmCC\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Set up the plotting layout\n", - "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2, figsize = (10,10))\n", - "fig.autofmt_xdate(rotation = 45)\n", - "\n", - "# Actual max temperature measurement\n", - "ax1.plot(dates, features['actual'])\n", - "ax1.set_xlabel(''); ax1.set_ylabel('Temperature'); ax1.set_title('Max Temp')\n", - "\n", - "# Temperature from 1 day ago\n", - "ax2.plot(dates, features['temp_1'])\n", - "ax2.set_xlabel(''); ax2.set_ylabel('Temperature'); ax2.set_title('Previous Max Temp')\n", - "\n", - "# Temperature from 2 days ago\n", - "ax3.plot(dates, features['temp_2'])\n", - "ax3.set_xlabel('Date'); ax3.set_ylabel('Temperature'); ax3.set_title('Two Days Prior Max Temp')\n", - "\n", - "# Friend Estimate\n", - "ax4.plot(dates, features['friend'])\n", - "ax4.set_xlabel('Date'); ax4.set_ylabel('Temperature'); ax4.set_title('Friend Estimate')\n", - "\n", - "plt.tight_layout(pad=2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Data Preparation" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### One-Hot Encoding" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "One hot encoding takes this:\n", - "\n", - "| week |\n", - "|------|\n", - "| Mon |\n", - "| Tue |\n", - "| Wed |\n", - "| Thu |\n", - "| Fri |\n", - "\n", - "and converts it into:\n", - "\n", - "| Mon | Tue | Wed | Thu | Fri |\n", - "|-----|-----|-----|-----|-----|\n", - "| 1 | 0 | 0 | 0 | 0 |\n", - "| 0 | 1 | 0 | 0 | 0 |\n", - "| 0 | 0 | 1 | 0 | 0 |\n", - "| 0 | 0 | 0 | 1 | 0 |\n", - "| 0 | 0 | 0 | 0 | 1 |" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false, - "hideCode": false, - "hidePrompt": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
yearmonthdaytemp_2temp_1averageactualfriendweek_Friweek_Monweek_Satweek_Sunweek_Thursweek_Tuesweek_Wed
0201611454545.645291000000
1201612444545.744610010000
2201613454445.841560001000
3201614444145.940530100000
4201615414046.044410000010
\n", - "
" - ], - "text/plain": [ - " year month day temp_2 temp_1 average actual friend week_Fri \\\n", - "0 2016 1 1 45 45 45.6 45 29 1 \n", - "1 2016 1 2 44 45 45.7 44 61 0 \n", - "2 2016 1 3 45 44 45.8 41 56 0 \n", - "3 2016 1 4 44 41 45.9 40 53 0 \n", - "4 2016 1 5 41 40 46.0 44 41 0 \n", - "\n", - " week_Mon week_Sat week_Sun week_Thurs week_Tues week_Wed \n", - "0 0 0 0 0 0 0 \n", - "1 0 1 0 0 0 0 \n", - "2 0 0 1 0 0 0 \n", - "3 1 0 0 0 0 0 \n", - "4 0 0 0 0 1 0 " - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# One-hot encode categorical features\n", - "features = pd.get_dummies(features)\n", - "features.head(5)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Shape of features after one-hot encoding: (348, 15)\n" - ] - } - ], - "source": [ - "print('Shape of features after one-hot encoding:', features.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Features and Labels and Convert Data to Arrays" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Use numpy to convert to arrays\n", - "import numpy as np\n", - "\n", - "# Labels are the values we want to predict\n", - "labels = np.array(features['actual'])\n", - "\n", - "# Remove the labels from the features\n", - "# axis 1 refers to the columns\n", - "features= features.drop('actual', axis = 1)\n", - "\n", - "# Saving feature names for later use\n", - "feature_list = list(features.columns)\n", - "\n", - "# Convert to numpy array\n", - "features = np.array(features)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Training and Testing Sets" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Using Skicit-learn to split data into training and testing sets\n", - "from sklearn.model_selection import train_test_split\n", - "\n", - "# Split the data into training and testing sets\n", - "train_features, test_features, train_labels, test_labels = train_test_split(features, labels, test_size = 0.25,\n", - " random_state = 42)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training Features Shape: (261, 14)\n", - "Training Labels Shape: (261,)\n", - "Testing Features Shape: (87, 14)\n", - "Testing Labels Shape: (87,)\n" - ] - } - ], - "source": [ - "print('Training Features Shape:', train_features.shape)\n", - "print('Training Labels Shape:', train_labels.shape)\n", - "print('Testing Features Shape:', test_features.shape)\n", - "print('Testing Labels Shape:', test_labels.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Establish Baseline" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average baseline error: 5.06 degrees.\n" - ] - } - ], - "source": [ - "# The baseline predictions are the historical averages\n", - "baseline_preds = test_features[:, feature_list.index('average')]\n", - "\n", - "# Baseline errors, and display average baseline error\n", - "baseline_errors = abs(baseline_preds - test_labels)\n", - "print('Average baseline error: ', round(np.mean(baseline_errors), 2), 'degrees.')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Train Model" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": true, - "hideCode": false, - "hidePrompt": false - }, - "outputs": [], - "source": [ - "# Import the model we are using\n", - "from sklearn.ensemble import RandomForestRegressor\n", - "\n", - "# Instantiate model \n", - "rf = RandomForestRegressor(n_estimators= 1000, random_state=42)\n", - "\n", - "# Train the model on training data\n", - "rf.fit(train_features, train_labels);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Make Predictions on Test Data" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mean Absolute Error: 3.83 degrees.\n" - ] - } - ], - "source": [ - "# Use the forest's predict method on the test data\n", - "predictions = rf.predict(test_features)\n", - "\n", - "# Calculate the absolute errors\n", - "errors = abs(predictions - test_labels)\n", - "\n", - "# Print out the mean absolute error (mae)\n", - "print('Mean Absolute Error:', round(np.mean(errors), 2), 'degrees.')\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Determine Performance Metrics" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy: 93.99 %.\n" - ] - } - ], - "source": [ - "# Calculate mean absolute percentage error (MAPE)\n", - "mape = 100 * (errors / test_labels)\n", - "\n", - "# Calculate and display accuracy\n", - "accuracy = 100 - np.mean(mape)\n", - "print('Accuracy:', round(accuracy, 2), '%.')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Improve Model if Necessary" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can create models with different hyperparameters to try and boost performance. The only way to find the best ones\n", - "are to try a few and evaluate them! " - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "rf_new = RandomForestRegressor(n_estimators = 100, criterion = 'mse', max_depth = None, \n", - " min_samples_split = 2, min_samples_leaf = 1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Interpret Model Results" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Visualizing a Single Decision Tree" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Import tools needed for visualization\n", - "from sklearn.tree import export_graphviz\n", - "import pydot\n", - "\n", - "# Pull out one tree from the forest\n", - "tree = rf.estimators_[5]\n", - "\n", - "# Export the image to a dot file\n", - "export_graphviz(tree, out_file = 'tree.dot', feature_names = feature_list, rounded = True, precision = 1)\n", - "\n", - "# Use dot file to create a graph\n", - "(graph, ) = pydot.graph_from_dot_file('tree.dot')\n", - "\n", - "# Write graph to a png file\n", - "graph.write_png('tree.png'); " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Decision Tree](tree.png)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The depth of this tree is: 15\n" - ] - } - ], - "source": [ - "print('The depth of this tree is:', tree.tree_.max_depth)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Smaller tree for visualization." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Limit depth of tree to 2 levels\n", - "rf_small = RandomForestRegressor(n_estimators=10, max_depth = 3, random_state=42)\n", - "rf_small.fit(train_features, train_labels)\n", - "\n", - "# Extract the small tree\n", - "tree_small = rf_small.estimators_[5]\n", - "\n", - "# Save the tree as a png image\n", - "export_graphviz(tree_small, out_file = 'small_tree.dot', feature_names = feature_list, rounded = True, precision = 1)\n", - "\n", - "(graph, ) = pydot.graph_from_dot_file('small_tree.dot')\n", - "\n", - "graph.write_png('small_tree.png');" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Small Decision Tree](images/small_tree.PNG)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Annotated Version of Tree" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Annotated Decision Tree](images/small_tree_annotated.PNG)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Variable Importances" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Variable: temp_1 Importance: 0.7\n", - "Variable: average Importance: 0.19\n", - "Variable: day Importance: 0.03\n", - "Variable: temp_2 Importance: 0.02\n", - "Variable: friend Importance: 0.02\n", - "Variable: month Importance: 0.01\n", - "Variable: year Importance: 0.0\n", - "Variable: week_Fri Importance: 0.0\n", - "Variable: week_Mon Importance: 0.0\n", - "Variable: week_Sat Importance: 0.0\n", - "Variable: week_Sun Importance: 0.0\n", - "Variable: week_Thurs Importance: 0.0\n", - "Variable: week_Tues Importance: 0.0\n", - "Variable: week_Wed Importance: 0.0\n" - ] - } - ], - "source": [ - "# Get numerical feature importances\n", - "importances = list(rf.feature_importances_)\n", - "\n", - "# List of tuples with variable and importance\n", - "feature_importances = [(feature, round(importance, 2)) for feature, importance in zip(feature_list, importances)]\n", - "\n", - "# Sort the feature importances by most important first\n", - "feature_importances = sorted(feature_importances, key = lambda x: x[1], reverse = True)\n", - "\n", - "# Print out the feature and importances \n", - "[print('Variable: {:20} Importance: {}'.format(*pair)) for pair in feature_importances];" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Model with Two Most Important Features" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mean Absolute Error: 3.9 degrees.\n", - "Accuracy: 93.8 %.\n" - ] - } - ], - "source": [ - "# New random forest with only the two most important variables\n", - "rf_most_important = RandomForestRegressor(n_estimators= 1000, random_state=42)\n", - "\n", - "# Extract the two most important features\n", - "important_indices = [feature_list.index('temp_1'), feature_list.index('average')]\n", - "train_important = train_features[:, important_indices]\n", - "test_important = test_features[:, important_indices]\n", - "\n", - "# Train the random forest\n", - "rf_most_important.fit(train_important, train_labels)\n", - "\n", - "# Make predictions and determine the error\n", - "predictions = rf_most_important.predict(test_important)\n", - "\n", - "errors = abs(predictions - test_labels)\n", - "\n", - "# Display the performance metrics\n", - "print('Mean Absolute Error:', round(np.mean(errors), 2), 'degrees.')\n", - "\n", - "mape = np.mean(100 * (errors / test_labels))\n", - "accuracy = 100 - mape\n", - "\n", - "print('Accuracy:', round(accuracy, 2), '%.')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "## Visualizations" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Variable Importances" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAbUAAAF5CAYAAAAYgLQSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XtYzvf/B/DnXUlyik5OJedyqAiZEDlts5JoKKTWhDD7\n2hyGUZOW5LjE1jKntjKZnOfQkNSEYohMyKHDOmA5pLp/f7i6f27dHe5Td+49H9fluvQ5vN+vz33X\n/bo/79NHUFhYKAQREZEa0FB1AERERIrCpEZERGqDSY2IiNQGkxoREakNJjUiIlIbTGpERKQ2mNTo\nnTFt2jTo6enhwYMHcpXz/vvvS11O165d0bNnT7nqJSLlY1KjSn366afQ09PD5s2bqz3Ww8MDenp6\n2LZtWy1Epn62b98OPT09zJ49W9WhKN0ff/zxn7lWqn1MalSpqVOnAnj9gVuV3NxcHDp0CI0bN8bY\nsWOVFo+/vz/+/PNPGBsbK60OInq3MalRpezs7NC5c2dcu3YNycnJlR73888/49WrVxg7diwaNWqk\ntHhatGiBzp07Q0tLS2l1ENG7jUmNquTh4QEAVTYrlt/Jld/ZAUBhYSHWr1+Pjz76CBYWFjA0NETH\njh0xceJE/PnnnxXKKCkpgZ6eHnr27InCwkLMnz8f3bt3h76+Pr7//nsAlfep7dixA5MmTYKVlRVa\ntGgBU1NTjBw5ElFRUVVeW2lpKdatWwcbGxsYGxuje/fuWLp0Kf79998avTblfvvtNzg5OaFt27Yw\nMjKCjY0N/P398fTpU6nKkWTFihXQ09NDVFQUjh8/jvfffx+tW7dGhw4dMGvWLDx+/BgAkJKSgo8/\n/hht27ZF69atMXHiRGRmZlYor7w/8d69e1Jde0pKCqZMmYKOHTvC0NAQ3bp1w+zZs3Hv3r0qYz5y\n5Ag++OADmJiYoEOHDlixYgWcnZ0BvH7f9PT0RP/K36+XL19iy5YtGDduHLp37w4jIyOYmZnB2dkZ\nx44dkxhf165doa+vj1evXiE4OBg9e/aEkZERunfvDj8/P7x69UrieTdv3oSvry969OgBIyMjdOjQ\nASNHjsSmTZsqHHvr1i34+vqie/fuMDQ0RIcOHTBp0iRcvny5wrFPnjxBUFAQ3nvvPZiYmKB169aw\ntLTE5MmTcfbsWYmxkGLwKy9Vyc3NDf7+/oiJicHKlSvRuHFjsf3x8fG4desWrKysYG1tLdp+/fp1\nrFixAnZ2dnj//ffRtGlTZGZm4vDhwzh27BiioqIwdOjQCvW9fPkSH330EZ49e4YRI0agfv36aNGi\nRZUxfv755+jRowfs7OxgbGyMvLw8HDt2DD4+Prh16xYWL14s8bz58+cjKSkJY8aMQaNGjXD8+HFs\n3LgRSUlJOHDgALS1tat9fT777DNs27YNbdq0gZOTE5o0aYLk5GSsWbMGv//+O44cOaKQu9f9+/fj\n2LFj+PDDD2FjY4M//vgDO3fuxIMHDzB//nyMHTsWgwYNwuTJk3H+/HkcPnwY9+7dQ3x8PAQCgVzX\nfvjwYXh4eEAoFIqS95UrV7Bjxw4cPHgQ+/fvR7du3SrUsWfPHpw4cQIjR46El5cX8vLyMHDgQGRm\nZiIqKgqWlpb44IMPRMeXl/HPP/9g0aJFsLW1xZAhQ2BgYICsrCwcPnwYrq6u2LBhA6ZMmSLxdfLy\n8sL58+cxdOhQNGrUCL///jvWrl2LvLw8bNiwQezYQ4cOwcvLC8XFxRg2bBjGjRuHJ0+e4OrVq1iz\nZg1mzpwpOjYuLg6TJk1CcXExRo4cifbt2+Phw4ei9yUqKgqDBw8GAAiFQri4uCA5ORl9+vTB5MmT\nUa9ePTx8+BAJCQk4ffo07Ozsav7mk1SY1KhKzZo1w+jRoxEdHY09e/aI3Y0B/38H9/Z2CwsL3Lhx\nA82bNxfbfu/ePQwbNgxLliyRmNQePnwICwsL7Ny5Ew0aNKhRjOfPn0e7du3Etr18+RIuLi5Yt24d\nPvnkE4mJMTk5GfHx8WjdujUAYNmyZXBzc8PRo0exefNmzJkzp8p6d+3ahW3btmH06NHYsmULdHR0\nRPsCAwMRFBSE4OBg+Pn51eg6qlKeIHv16iW6vkGDBiEuLg6XLl3CDz/8gFGjRgEAysrKMGbMGJw6\ndQq///47Ro4cKfO1P336FDNnzkRJSQn2798v9mG8detWfP7555g+fTrOnDlToY7jx48jJiZG9GFf\nTigUIioqClZWVli0aFGF8/T19fHXX3+hVatWYtsLCwsxYsQILF++HOPHj0f9+vXF9peWluLBgwdI\nTEyEnp4eAGDp0qWws7PDrl278PXXX8PAwAAAkJOTg2nTpqG0tBSxsbEYMGCAWFn3798X/b+goACe\nnp7Q0dFBXFwcOnfuLNp37do1DB8+HL6+vrh06RK0tbVx+fJlJCcnw8nJqUJ/tFAoREFBQYVrJsVh\n8yNVqzxhvd0EWVBQgNjYWDRq1Ajjxo0T26enp1choQGAqakpHB0dcf36dTx69EhifStWrKhxQgNQ\nIaEBQP369eHt7Y1Xr15J/MAFgJkzZ4o+1AFAU1NTlIB27txZbb2bNm1CvXr1sH79erGEBry+E9LT\n00N0dHSNr6Mq48ePFyU04PX1jR49GgBgZWUlSmgAoKGhIXo/rly5IrG8ml77/v37UVBQABcXlwp3\nF1OnTkX37t1x5coVXLx4sUIdjo6OFRJaTejo6FRIaMDr3yl3d3fk5+cjJSVF4rl+fn6ihAZA9LtZ\nWloqdk5kZCT+/fdfeHl5VUhoANCmTRuxYwsLC7Fo0SKxhAa8bvZ0d3fHgwcPEB8fX+E63iYQCCT+\nXZDi8E6NqtW/f3906dIFly5dwpUrV9CjRw8AwC+//IKXL19i/PjxFZolASAhIQFbtmxBcnIycnNz\nUVxcLLb/0aNHaNmypdi2hg0bwsLCQqr47t69iw0bNuDUqVN48OABnj9/XqEeSSQ1AZmbm0NfXx/p\n6el4/vx5pcn16dOnuHr1KgwMDBAWFibxmPr16+PRo0d4/PgxmjZtKtU1vc3S0rLCtvK7T0n7yl/X\nhw8fSiyvpteempoKABg0aFCF4wUCAezt7fHXX38hNTVVLOkCgI2NTTVXVbmrV69iw4YNSExMRFZW\nFl6+fCm2v7L3VNJcwvLkXVhYKNp2/vx5AMDw4cOrjSUpKQnA6y8IgYGBFfbfvn0bAHDjxg04ODig\nW7du6Nq1K6Kjo3H37l188MEH6Nu3L2xsbCrcXZLiMalRjXh4eOCrr77C9u3bERwcDEDyAJFyv/32\nG7y8vNCgQQMMHjwYZmZm0NXVhYaGBk6fPo1z585V+KACAENDQ6niun37NhwcHPDkyRP0798fDg4O\naNKkCTQ1NXHnzh1ERUVJrAcAjIyMKt2el5eHp0+fVprUypuQ/vnnHwQFBVUZY1FRkdxJrUmTJhW2\nlY8CrWpfZQMkanrtT548qfL48sRaPmClJnVUJzExEc7OzigrK4O9vT0+/PBDNGrUCBoaGkhNTcWR\nI0ckvqeampoS+y/LX4vS0lLRtvJ43/5SJUl+fj6AqgdLAa/f5/L6Dh48iFWrVmH//v1Yvnw5gNdf\n2FxcXODv749mzZpVWy/JhkmNamTixInw9/dHdHQ0/P39ceXKFVy/fh09evSo8A0dAAICAqCjo4M/\n/vgDnTp1Etv34MEDnDt3TmI9kgY1VGXjxo0oLCzEli1bMH78eLF9v/zyS5UjIHNyciQ2Xebk5EAg\nEEi8+yxXnkgsLS1x+vRpqWKuC2p67eXXmZOTI7GcrKwssePeJO17WS44OBgvXrzA4cOH8d5774nt\nW7VqFY4cOSJTuW8q/5Lx6NEjiYNc3lR+bfHx8ejevXuNym/WrBkCAwMRGBiIO3fu4OzZs9i5cyd2\n7NiB+/fvY+/evfJdAFWKfWpUI82aNYOTkxMeP36M3377rdIBIuUyMjJgYWFRIaGVlpaKmnMUobzp\nx8nJqcK+6oZOS9qflpaGvLw8dOrUqcp+PT09PXTu3BlpaWnvZMd/Ta/dysoKACrtlyxP6G+OfK2O\nhsbrj50375zedPv2bRgaGlZIaJXFLYs+ffoAQKVTBN7Ut29fAKj0i1h1zMzM4O7ujtjYWLRs2RJ/\n/PGH1NNGqOaY1KjGyhPY5s2bsXfvXjRs2BCurq4SjzUxMUF6ejqys7NF24RCIVauXIn09HSFxWRq\nagoAFTrpf//9d+zatavKczdt2iQ25620tBTLli0DALi7u1db96xZs1BcXAxfX1+x/ppyT548wYUL\nF6otRxVqeu2Ojo7Q09NDTEwMEhMTxcrYvn07rly5gm7dukm8W69M+UCJN0cYvsnU1BT//PMPrl+/\nLrZ969atOHXqVI3rqYq7uzsaNWqEiIgIiYnyzddm8uTJaNKkCYKCgiQuQiAUCnH27FlRU++dO3dw\n586dCsf9+++/ePbsGerVq8cFBJSIryzVWPmAkfLBA5MmTZLY7AS8Hl335ZdfYuDAgXBycoKmpibO\nnTuHv//+GyNHjsTRo0cVEpO3tzd++eUXTJo0CaNHj4axsTGuX7+OEydOYMyYMYiJian03N69e2PA\ngAFic7WuXbuGPn36YMaMGdXWPWXKFFy+fBnh4eGwtrbG0KFDYWJigsePH+Pu3btISEjAiBEjql1m\nTBVqeu2NGzdGaGgopk6dCkdHR4wePRqmpqa4evUqjh49WuO1Qd9kbm6Oli1b4syZM5g2bRo6dOgA\nDQ0NjBo1Cl27dsXMmTNx6tQpjBw5Es7OzmjcuDEuXryIP//8E05OToiNjZX7+g0NDfH999/Dy8sL\njo6OGDZsGLp164Z///0X165dw40bN3Dr1i0Ar6cYbN++HZMnT8bw4cMxaNAgmJubQ0tLC/fv38eF\nCxdw//593L9/H/Xq1UNqaiqmTp2Knj17okuXLmjZsiXy8/Nx9OhRPH78GHPnzpU4MpIUg0mNpDJ1\n6lTR3KLKmh6B14sh6+joYPPmzdi1axcaNGiA/v37Y8uWLdizZ4/CkpqVlRViY2MREBCAo0ePoqys\nDN27d8euXbugq6tbZVJbtWoVYmJisGPHDmRmZsLAwACzZs3CwoULazTxGgBWr16N4cOHIyIiAqdP\nn0ZBQQGaNWuGVq1aYdq0aZXeyaqaNNc+atQo/P777wgJCUFcXBweP34MIyMjuLu748svv4SZmZlU\ndWtpaSEyMhLLly/HkSNH8PTpUwiFQpiamqJr164YOXIkIiMjERISgpiYGGhqasLGxgYHDhxAenq6\nQpIaAHz44YeIi4vDunXrcObMGcTFxaFp06bo2LEjvvzyS7FjBw8ejPj4eISGhuLEiRNISkqClpYW\njI2NYWtrCz8/P+jq6gJ4Perz888/x9mzZ3HixAkUFBTAwMAAXbp0wbfffiuaikHKISgsLBSqOggi\nqh3vv/8+EhMTcfXqVbF5akTqgn1qRESkNpjUiIhIbTCpERGR2mCfGhERqQ3eqRERkdpgUiMiIrXB\npEZERGqDSU1BFLn0k6rqUIdrUJc61OEaWEfdKV+d6qgOkxoREakNJjUiIlIbTGpERKQ2VJ7UwsPD\nYWlpCWNjY9jb2yMhIaHSYwMDA6GnpyfxX25ubi1GTUREdZFKk1pMTAwWLlyIefPm4fTp0+jbty9c\nXV2RmZkp8fjZs2fjxo0bYv/s7OwwYMAAGBoa1nL0RERU16g0qYWGhsLNzQ0eHh7o0qULgoODYWxs\njIiICInHN2rUCMbGxqJ/r169wrlz5+Dh4VHLkRMRUV2ksqRWXFyMlJQUODg4iG13cHBAUlJSjcrY\nsWMH9PT04OTkpIwQiYjoHaOytR8fPXoECwsLHDx4EHZ2dqLtQUFB2L17t8THpr+ptLQUVlZWcHR0\nRGBgYJXH1oW5E0REpBidOnWqdN87++Tr48eP4/79+zVqeqzqBVCU9PR0pdej7Drq+jXobX2g0FgK\nPWV/SOZ//b1gHe9W+epUR3VU1vyor68PTU3NCqMWc3NzYWRkVO35P/30E2xtbWFubq6sEImI6B2j\nsqSmra0Na2trxMXFiW2Pi4uDra1tlec+evQIv//+O6ZMmaLMEImI6B2j0tGPvr6+iIyMxPbt23Hj\nxg0sWLAAWVlZ8PT0BAD4+flJHASyc+dONGzYEGPGjKntkImIqA5TaZ+ai4sL8vPzERwcjOzsbFhY\nWCA6OhqmpqYAgKysLGRkZIidIxQKsWPHDri6ukJXV1cVYRMRUR2l8oEi3t7e8Pb2lrgvLCyswjaB\nQIDLly8rOywiInoHqXyZLCIiIkVhUiMiIrXBpEZERGqDSY2IiNQGkxoREakNJjUiIlIbTGpERKQ2\nmNSIiEhtMKkREZHaYFIjIiK1waRGRERqg0mNiIjUBpMaERGpDSY1IiJSG0xqRESkNpjUiIhIbTCp\nERGR2mBSIyIitcGkRkREaoNJjYiI1AaTGhERqQ2VJ7Xw8HBYWlrC2NgY9vb2SEhIqPJ4oVCITZs2\noU+fPjAyMkKXLl2wfPny2gmWiIjqNC1VVh4TE4OFCxciJCQE/fr1Q3h4OFxdXZGYmAgTExOJ5yxe\nvBhHjx6Fv78/unXrhsePHyM7O7uWIyciorpIpUktNDQUbm5u8PDwAAAEBwfjxIkTiIiIwLJlyyoc\nn56eju+//x5nz55Fly5dajtcIiKq41TW/FhcXIyUlBQ4ODiIbXdwcEBSUpLEcw4dOgQzMzMcP34c\nVlZW6NGjB6ZPn47c3NzaCJmIiOo4QWFhoVAVFT969AgWFhY4ePAg7OzsRNuDgoKwe/duJCcnVzjn\n888/R2RkJLp37w5/f38IBAIsXboUAHDs2DFoaEjO0enp6cq5CKpVfeJ1FVre+QHPFFoeEdWOTp06\nVbpPpc2P0iorK8PLly+xZcsWdOzYEQCwZcsW9O7dGxcvXkTv3r0lnlfVC6Ao6enpSq9H2XXU+WuI\nf6DQWOS51v/8e8E63qny1amO6qis+VFfXx+ampoVmg5zc3NhZGQk8RxjY2NoaWmJEhoAdOjQAZqa\nmrh//75S4yUiorpPZUlNW1sb1tbWiIuLE9seFxcHW1tbief069cPJSUlyMjIEG27c+cOSktLKx0t\nSURE/x0qnafm6+uLyMhIbN++HTdu3MCCBQuQlZUFT09PAICfnx+cnJxExw8ePBhWVlbw9fVFamoq\nUlNT4evri969e6Nnz56qugwiIqojVNqn5uLigvz8fAQHByM7OxsWFhaIjo6GqakpACArK0vsrkxD\nQwNRUVFYsGABRo0aBR0dHQwZMgQBAQGVDhIhIqL/DpUPFPH29oa3t7fEfWFhYRW2tWjRAtu2bVN2\nWERE9A7i7Q0REakNJjUiIlIbTGpERKQ2mNSIiEhtMKkREZHaYFIjIiK1waRGRERqg0mNiIjUBpMa\nERGpDSY1IiJSG0xqRESkNpjUiIhIbTCpERGR2mBSIyIitcGkRkREaoNJjYiI1AaTGhERqQ0mNSIi\nUhtMakREpDaY1IiISG0wqRERkdpQeVILDw+HpaUljI2NYW9vj4SEhEqPvXv3LvT09Cr8O378eC1G\nTEREdZWWKiuPiYnBwoULERISgn79+iE8PByurq5ITEyEiYlJpeft2bMH3bt3F/3crFmz2giXiIjq\nOJXeqYWGhsLNzQ0eHh7o0qULgoODYWxsjIiIiCrPa968OYyNjUX/tLW1ayliIiKqy2RKasXFxdi+\nfTs+/fRTODs7IzU1FQBQWFiIn3/+GQ8ePKhRGSkpKXBwcBDb7uDggKSkpCrPnTx5Mjp27IiRI0di\n3759slwCERGpIambH/Pz8+Ho6Ihr167ByMgIubm5KCwsBAA0adIEAQEBSEtLg5+fX5Xl5OXlobS0\nFIaGhmLbDQ0NkZOTI/GcRo0a4ZtvvkG/fv2gpaWFQ4cOwdPTE2FhYRg/fnyldaWnp0t5lbKpjXqU\nXUfdvgbdOhKHYs5Xdfmso27VoQ7XUFt1dOrUqdJ9Uie1ZcuWITMzE0eOHEHHjh3RsWNH0T4NDQ04\nOTnh2LFj1SY1Wejr62P27Nmin3v27ImCggKsX7++yqRW1QugKOnp6UqvR9l11PlriK++BUAa8lzr\nf/69YB3vVPnqVEd1pG5+PHLkCHx8fGBrawuBQFBhf4cOHXD//v1qy9HX14empiZyc3PFtufm5sLI\nyKjG8fTq1Qu3b9+u8fFERKS+pE5qT58+RZs2bSrd//LlS5SWllZbjra2NqytrREXFye2PS4uDra2\ntjWO58qVKzA2Nq7x8UREpL6kbn5s3749Ll26BA8PD4n7T548CQsLixqV5evrCx8fH9jY2MDW1hYR\nERHIysqCp6cnAMDPzw8XLlxAbGwsACAyMhL16tWDpaUlNDQ0cOTIEYSHh2P58uXSXgYREakhqZOa\nh4cHli5div79+4tGLgoEAjx79gyrVq3CyZMnsXHjxhqV5eLigvz8fAQHByM7OxsWFhaIjo6Gqakp\nACArKwsZGRli56xevRqZmZnQ1NREhw4d8N1331XZn0ZERP8dUic1Hx8fpKWlwcfHB40bNwYAeHl5\nobCwEKWlpfD29oa7u3uNy/P29oa3t7fEfWFhYWI/u7m5wc3NTdqQiYjoP0KmFUXWrl2LCRMmYO/e\nvbh9+zbKysrQrl07jBkzBv3791d0jERERDUi8zJZtra2Ug3oICIiUjapRz/euHEDUVFRle6Pjo7G\nzZs35QqKiIhIFlInNT8/P+zZs6fS/Xv27IG/v79cQREREclC6qSWnJyMgQMHVrp/4MCBSE5Oliso\nIiIiWUid1B4/fgxd3crX4NPR0UFBQYFcQREREclC6qTWtm3bKh/kmZCQUOWKI0RERMoidVJzdXXF\n3r178d1336GkpES0vaSkBBs3bsRvv/2GcePGKTRIIiKimpB6SP/cuXNx7tw5LF26FGvWrBGt0n/r\n1i0UFBTA3t4e8+bNU3igRERE1ZE6qdWrVw979uxBZGQkYmNjcefOHQBAnz59MHr0aEyYMAEaGip9\noDYREf1HyTT5WiAQwN3dXarlsIiIiJSNt1RERKQ2ZLpTO3HiBHbs2IE7d+6gsLAQQqFQbL9AIEBK\nSopCAiQiIqopqZPahg0bsHz5chgZGaFXr17o2rWrMuIiIiKSmtRJbfPmzRg0aBB2796NevXqKSMm\nIiIimUjdp1ZYWIjRo0czoRERUZ0jdVKzsbFBenq6MmIhIiKSi9RJbfXq1Thw4ACio6OVEQ8REZHM\npO5TmzJlCoqLizF9+nR8/vnnaNmyJTQ1NcWOEQgESExMVFiQRERENSF1UjMwMIChoaFoeSwiIqK6\nQuqkdvDgQWXEQUREJDeVrygSHh4OS0tLGBsbw97evsrH2rzp77//Rps2bdC6dWslR0hERO8KmVYU\nAYBXr17h5s2bePLkCcrKyirst7Ozq7aMmJgYLFy4ECEhIejXrx/Cw8Ph6uqKxMREmJiYVHpecXEx\nvLy80L9/f5w9e1bWSyAiIjUjdVITCoX45ptv8MMPP6CoqKjS4/Lz86stKzQ0FG5ubvDw8AAABAcH\n48SJE4iIiMCyZcsqPW/ZsmXo1q0b7OzsmNSIiEhE6ubHdevWYe3atRg7diw2b94MoVCI5cuXY+3a\ntbCwsECPHj2wd+/easspLi5GSkoKHBwcxLY7ODggKSmp0vOOHj2Ko0ePYtWqVdKGTkREak7qO7Wd\nO3fCyckJ69atE92NWVlZwd7eHhMmTMDQoUMRHx8Pe3v7KsvJy8tDaWkpDA0NxbYbGhoiJydH4jmP\nHj3CZ599hp07d6JRo0Y1jrm2JovXRj3KrqNuX4NuHYlDMeerunzWUbfqUIdrqK06OnXqVOk+qZPa\n/fv34evrCwCih4G+fPkSAFC/fn2MHz8eW7ZsweLFi2WJtUo+Pj7w8vJC7969pTqvqhdAUdLT05Ve\nj7LrqPPXEP9AobHIc63/+feCdbxT5atTHdWRuvlRT08PL168AAA0adIE2traePDg/z9s6tevX6P+\nNH19fWhqaiI3N1dse25uLoyMjCSec/r0aQQFBUFfXx/6+vqYPXs2ioqKoK+vj59++knaSyEiIjUj\n9Z2ahYUFrly5AuD1nVqvXr3w448/YsSIESgrK8NPP/1Uo0ytra0Na2trxMXFwdnZWbQ9Li4OTk5O\nEs95e7j/oUOHEBISghMnTqBVq1bSXgoREakZqZOaq6srfvzxR7x48QI6Ojr4+uuvMWbMGPTo0QMA\nUK9ePURGRtaoLF9fX/j4+MDGxga2traIiIhAVlYWPD09AQB+fn64cOECYmNjAaDCs9suXboEDQ0N\nPtONiIgAyJDU3N3d4e7uLvr5vffeQ2JiIg4fPgxNTU0MHToUHTp0qFFZLi4uyM/PR3BwMLKzs2Fh\nYYHo6GiYmpoCALKyspCRkSFtiERE9B8ldVLLzMyEgYEBGjRoINpmZmaGGTNmAACeP3+OzMzMKidP\nv8nb2xve3t4S94WFhVV57tsJloiI/tukHihiZWWFAwcOVLr/8OHDsLKykisoIiIiWUid1IRCYZX7\nS0pKIBAIZA6IiIhIVjItaFxZ0nr8+DGOHz9eYUI1ERFRbahRUvv222/RvHlzNG/eHAKBANOmTRP9\n/Oa/du3aYffu3Rg7dqyy4yYiIqqgRgNFbGxs8MknnwB4/aiYIUOGVBjhKBAI0LBhQ1hbW1c6z4yI\niEiZapTUhg8fjuHDhwMAioqKZFqqioiISNmk6lN79uwZMjMzkZaWpqx4iIiIZCZVUtPV1UVqaipK\nS0uVFQ8REZHMpB792L9//wprMBIREdUFUie1VatW4cKFC1i6dCnu3LmDsrIyZcRFREQkNamXyerb\nty+EQiFCQ0MRGhoKDQ0N1KtXT+wYgUCAhw8fKixIIiKimpA6qY0ZM4YrhhARUZ0kdVKrbpFhIiIi\nVZFpmSwiIqK6SKakVlBQgK+//hr9+vVDq1at0KpVK/Tr1w/Lly9HQUGBomMkIiKqEamT2v379zFw\n4EBs3LgRDRo0gKOjIxwdHaGrq4v169dj4MCBuH//vjJiJSIiqpLUfWrLly/H48ePsX//fgwYMEBs\nX0JCAiaFz6+kAAAgAElEQVRMmAA/Pz/88MMPCguSiIioJqS+Uzt58iR8fHwqJDTg9cTsadOm4cSJ\nEwoJjoiISBpSJ7Xnz5/DwMCg0v0GBgZ4/vy5XEERERHJQuqkZm5ujt27d+Ply5cV9hUXFyM6OhoW\nFhYKCY6IiEgaUvepzZ07F1OnTsWQIUPg5eWFjh07AgDS09OxdetWpKWlYdu2bQoPlIiIqDpS36mN\nHj0amzdvRl5eHr788ku4uLjAxcUF8+fPR15eHsLCwuDo6Fjj8sLDw2FpaQljY2PY29tXuVhyWloa\nPvroI3Tq1AnGxsawsrKCv78/iouLpb0MIiJSQ1LfqQHA+PHjMXbsWFy6dAmZmZkAABMTE/Ts2RNa\nWjUvMiYmBgsXLkRISAj69euH8PBwuLq6IjExESYmJhWO19bWxsSJE2FpaYmmTZvir7/+wmeffYaS\nkhL4+/vLcilERKRGZEpqAKClpYU+ffqgT58+MlceGhoKNzc3eHh4AACCg4Nx4sQJREREYNmyZRWO\nb9++Pdq3by/62dTUFPHx8Th37pzMMRARkfoQFBYWCqU9qaSkBD///DOOHj2Ke/fuAXidYEaOHImJ\nEyfW6G6tuLgYLVu2xI8//ghnZ2fR9i+++ALXrl3DoUOHqi3j9u3bmDhxIj744AMsX7680uPS09Or\nvyiq8/rE6yq0vPMDnim0PCKqHZ06dap0n9R3arm5uXBxccFff/2Fpk2bom3btgCA+Ph4HDx4EFu2\nbEFMTAyMjIyqLCcvLw+lpaUwNDQU225oaIicnJwqzx0xYgRSU1Px8uVLeHh44Ouvv67y+KpeAEVJ\nT09Xej3KrqPOX0P8A4XGIs+1/uffC9bxTpWvTnVUR+qBIvPnz8eNGzewceNG/P333zh16hROnTqF\nv//+Gxs2bMDNmzexYMECZcQqEhERgVOnTiE8PBzHjh3DunXrlFofERG9G6S+Uzt+/Dh8fHwwadIk\nse2ampqYPHky0tLSsH379mrL0dfXh6amJnJzc8W25+bmVnuX16ZNGwCv58yVlpZizpw5mDNnjlSD\nVIiISP1Ifaemra0tcWRiubZt26J+/fo1Ksfa2hpxcXFi2+Pi4mBra1vjeMrKylBSUoLS0tIan0NE\nROpJ6lsbFxcX7NmzB56enqhXr57YvuLiYuzZswdjxoypUVm+vr7w8fGBjY0NbG1tERERgaysLHh6\negIA/Pz8cOHCBcTGxgIAfvnlF+jo6KBr167Q1tbGpUuX4O/vj9GjR9cokRIRkXqTOqk5OTkhISFB\ntKJI+RD7v//+G1u3bgXweoL2hQsXxM6zsbGpUJaLiwvy8/MRHByM7OxsWFhYIDo6GqampgCArKws\nZGRk/H+wWlpYs2YNbt++DaFQCBMTE3h7e2PmzJnSXgYREakhmZJauXnz5kEgEAAAhEKhxGOEQiEE\nAgHy8/Mlluft7Q1vb2+J+8LCwsR+HjduHMaNGydtyERE9B8hdVILDQ1VRhxERERykzqpubm5KSMO\nIiIiuUk9+pGIiKiukmli1507d7Br1y7cvXsXhYWFYv1pACAQCBAdHa2QAImIiGpK6qQWFRUFX19f\nlJaWomnTpmjSpEmFY8oHjxAREdUmqZPaN998g86dO2P79u2iB4QSERHVBVL3qRUUFIg98ZqIiKiu\nkDqp9e7dW/RgUCIiorpE6qT27bff4tdff8WePXuUEQ8REZHMpO5Ts7CwwFdffYVp06Zhzpw5aNmy\nJTQ1NcWOEQgESExMVFiQRERENSF1UtuyZQsWLVoEHR0ddOjQQeLoRyIiIlWQOqmtW7cOtra2+OWX\nX9C0aVNlxERERCQTqfvUnj59io8//pgJjYiI6hypk5qdnR2uXLmijFiIiIjkInVSCwkJQWJiIkJC\nQpCTk6OMmIiIiGQidZ9a7969IRQKERAQgICAANSrVw8aGuK5USAQ4OHDhwoLkoiIqCakTmpjxozh\n2o5ERFQnSZ3U3n4aNRERUV1Ro6R24cIFqQu2sbGR+hwiIiJ51CipDRs2rMZNjkKhEAKBAPn5+XIF\nRkREJK0aJbXQ0FClBRAeHo4NGzYgOzsb5ubmCAwMRP/+/SUee+bMGWzatAkXL17EkydP0K5dO8yY\nMQOTJ09WWnxERPTuqFFSc3NzU0rlMTExWLhwIUJCQtCvXz+Eh4fD1dUViYmJMDExqXD8n3/+iW7d\nuuGzzz5DixYtcOLECcydOxc6OjpwdXVVSoxERPTukHqgiCKFhobCzc0NHh4eAIDg4GCcOHECERER\nWLZsWYXj582bJ/bzJ598gjNnziA2NpZJjYiIpJ98rSjFxcVISUmBg4OD2HYHBwckJSXVuJynT59C\nT09P0eEREdE7SGV3anl5eSgtLYWhoaHYdkNDwxqvVHLkyBGcOnUKR48erfK49PR0meOURm3Uo+w6\n6vY16NaROBRzvqrLZx11qw51uIbaqqNTp06V7lNp86M8EhMT8emnnyIoKKja6QNVvQCKkp6ervR6\nlF1Hnb+G+AcKjUWea/3Pvxes450qX53qqI7Kkpq+vj40NTWRm5srtj03NxdGRkZVnnvu3Dl8/PHH\nWLRoET755BNlhkn/MXpba5o4dWuUZAs9W8sXEBFJRWV9atra2rC2tkZcXJzY9ri4ONja2lZ63tmz\nZ+Hq6ooFCxZg5syZyg6TiIjeISpLagDg6+uLyMhIbN++HTdu3MCCBQuQlZUFT09PAICfnx+cnJxE\nx585cwaurq7w9PSEq6srsrOzkZ2djX/++UdVl0BERHWISvvUXFxckJ+fj+DgYGRnZ8PCwgLR0dEw\nNTUFAGRlZSEjI0N0fGRkJJ49e4aNGzdi48aNou0mJiZ8xhsREal+oIi3tze8vb0l7nt78eSwsDAu\nqExERJVSafMjERGRIjGpERGR2mBSIyIitcGkRkREaoNJjYiI1AaTGhERqQ0mNSIiUhtMakREpDaY\n1IiISG0wqRERkdpgUiMiIrXBpEZERGqDSY2IiNQGkxoREakNJjUiIlIbTGpERKQ2mNSIiEhtMKkR\nEZHaYFIjIiK1waRGRERqg0mNiIjUhsqTWnh4OCwtLWFsbAx7e3skJCRUeuyLFy8wY8YM9O/fHwYG\nBhg1alQtRkpERHWdSpNaTEwMFi5ciHnz5uH06dPo27cvXF1dkZmZKfH40tJS6OjoYNq0aRgxYkQt\nR0tERHWdSpNaaGgo3Nzc4OHhgS5duiA4OBjGxsaIiIiQeHzDhg2xdu1aTJ06Fa1bt67laImIqK5T\nWVIrLi5GSkoKHBwcxLY7ODggKSlJRVEREdG7TEtVFefl5aG0tBSGhoZi2w0NDZGTk6PQutLT0xVa\nnirrUXYddfsadGshjtqoQ/nnso53rw51uIbaqqNTp06V7lNZUqtNVb0AipKenq70epRdR52/hvgH\nCo1FYhy1UUcN1Pn3gnW8U+WrUx3VUVnzo76+PjQ1NZGbmyu2PTc3F0ZGRiqKioiI3mUqS2ra2tqw\ntrZGXFyc2Pa4uDjY2tqqKCoiInqXqbT50dfXFz4+PrCxsYGtrS0iIiKQlZUFT09PAICfnx8uXLiA\n2NhY0TlpaWkoLi5GXl4eioqKcPnyZQCApaWlSq6BiIjqDpUmNRcXF+Tn5yM4OBjZ2dmwsLBAdHQ0\nTE1NAQBZWVnIyMgQO+fteWyDBg0CABQWFtZe4EREVCepfKCIt7c3vL29Je4LCwursO3KlSvKDomI\niN5RKl8mi4iISFGY1IiISG0wqRERkdpgUiMiIrXBpEZERGqDSY2IiNQGkxoREakNJjUiIlIbTGpE\nRKQ2mNSIiEhtMKkREZHaYFIjIiK1waRGRERqg0mNiIjUBpMaERGpDSY1IiJSG0xqRESkNlT+5Guq\nHXpbH9TgKF0gvvrjCj1by1i+fHWoC2W/F0T/ZbxTIyIitcGkRkREaoNJjYiI1IbK+9TCw8OxYcMG\nZGdnw9zcHIGBgejfv3+lx1+9ehVffvklLl68iGbNmmHq1KmYP38+BAJBLUatWOyPIiJSDJXeqcXE\nxGDhwoWYN28eTp8+jb59+8LV1RWZmZkSj3/y5AnGjBkDIyMjnDx5Et9++y02btyI7777rpYjJyKi\nukild2qhoaFwc3ODh4cHACA4OBgnTpxAREQEli1bVuH43bt34/nz5wgLC0ODBg3QtWtX3Lx5E5s2\nbcKsWbPe6bs1IkVRlzt/dbkOql2CwsJCoSoqLi4uRsuWLfHjjz/C2dlZtP2LL77AtWvXcOjQoQrn\n+Pj4oKCgANHR0aJtFy9ehIODA1JSUmBmZlYboRMRUR2lsubHvLw8lJaWwtDQUGy7oaEhcnJyJJ6T\nk5Mj8fjyfURE9N/G0Y9ERKQ2VJbU9PX1oampidzcXLHtubm5MDIykniOkZGRxOPL9xER0X+bypKa\ntrY2rK2tERcXJ7Y9Li4Otra2Es/p27cvzp07hxcvXogd37JlS7Rt21ap8RIRUd2n0uZHX19fREZG\nYvv27bhx4wYWLFiArKwseHp6AgD8/Pzg5OQkOn7cuHFo0KABZs6ciWvXriE2Nhbr1q3DzJkzOfKR\niIhUO6TfxcUF+fn5CA4ORnZ2NiwsLBAdHQ1TU1MAQFZWFjIyMkTHN23aFHv37sUXX3yBIUOGQE9P\nD76+vpg1a1atxVxSUoKTJ0+id+/eaN68ea3VS0RE1VPZkP53mbGxMf788893vsnz8uXLsLS0VEnd\nZWVlePDgAUxMTJRSflFREVJSUmBnZ6ewMnNychAVFYWMjAwsXrwY+vr6SExMRIsWLTid5C2PHj1C\nbm4uysrKxLZbW1vLVW5QUBBmz54NXV1dse3Pnz/Hhg0bsGDBArnKry3x8fHQ0dFB7969AQC7du3C\njh07YG5ujhUrVqBRo0Zy15GUlIT69euLXvNff/1VVMfy5cvRoEEDueuoi5jUZDB06FAsXboUgwcP\nrpX6lPUB0axZM1haWmLKlCkYN24cmjZtKld5b3rx4gUWLVqE2NhY6OnpwdvbGzNmzBDtz8nJgbm5\nOfLz8xVW55uuXLkCe3t7hZWfkpICJycntG3bFmlpaTh//jzMzMwQGBiIv//+G+Hh4VKXaWlpWeNm\n89TUVKnLB4BDhw5h+PDhqFevnsS5n2/68MMPZarjTampqfDx8cHNmzchFIp/tAgEArnfj+bNm+PG\njRsVpvbk5+ejY8eOCnu/Hzx4gISEBIl/d4poGRo4cCAWLlyIUaNGIT09HXZ2dpg8eTLOnTuHfv36\nYc2aNXLXYW9vjy+++AKOjo64ffs2+vXrB1dXV5w/fx729vYIDg6WusxmzZrV+HdWWX/b1VH52o/v\nooULF2Lx4sVYtGgRrK2t0bBhQ7H9zZo1U0g9yv6ASE5Oxs6dOxESEoKlS5fio48+wqRJkzBo0CC5\nygWAVatW4ejRo/jqq6/w5MkTrF69GpcuXcLmzZuhofG6K/fta6rLlixZgunTp+Orr75CmzZtRNuH\nDh2KXbt2yVTmp59+Kvp/UVERNm3ahF69eqFPnz4AgPPnz+PixYvw9fWVOW53d3fcvHkThoaGcHd3\nr/Q4Rfw+AcDcuXPRunVrrF+/Hi1atFB4X7dQKJRY5uXLlxX2dxcdHY1Zs2ZBS0sL+vr6YvUJBAKF\nJLWMjAx069YNABAbG4shQ4YgJCQEycnJmDJlikKS2u3bt9GjRw8AwL59+2Bvb4/Q0FAkJSXBy8tL\npqT2008/if6fm5uLlStX4qOPPhL7nT148CAWLVokd/yy4p2aDN7843nzF778D05R31CGDBmC5s2b\nY/78+RI/IMr7HuVVVlaGY8eOYdeuXThy5AhatWqFSZMmYeLEiWjdWralhaytrREcHIzhw4cDAO7d\nuwdXV1d069YN4eHh+Oeff+S6U6tpf6ai3gsTExOcOXMGZmZmaNOmDeLj42FmZoa7d++ib9++yM7O\nlqv8GTNmoGPHjpg3b57Y9jVr1iAtLQ3ff/+9TOWWlpZCU1NT9P+qlB8nj1atWuH06dPo2LGj3GW9\nqU2bNhAIBCgqKoKurq7Y30JpaSlevHgBLy8vrF69Wu66rK2t4eLigsWLFyvkNZHE1NQUcXFx6NCh\nA5ycnPDRRx9h2rRpuHfvHvr27YusrCy56zAxMcGpU6fQvn17jBkzBiNGjMCMGTOQmZmJPn36yF3H\nhAkT8MEHH4iWOSy3bds2HDx4UGzlp9rEOzUZ7N+/v1bquXHjhlI+IN6moaGBkSNHwt7eHj/++CP8\n/f0REBCAoKAgODo6YsWKFWjVqpVUZWZnZ6Nz586in01NTbF//344OTnBy8sLAQEBcsXcoEEDzJgx\nQ/RN9G337t2TuH6orHR0dFBYWFhhe3p6eoWmMFkcOHAAp06dqrDd2dkZ9vb2Mpdb/qH86tUrzJw5\nE1999RXatWsnc3nV6dq1K7KzsxX+O7tq1SoIhULMmjULS5YsQZMmTUT7tLW1YWpqir59+yqkrtzc\nXEyZMkVpCQ0AevbsiVWrVmHIkCE4d+4c1q9fD+D1762xsbFC6rCyssLatWsxdOhQxMfHixL+vXv3\nFPI7e+bMGaxcubLC9oEDB6r0To1JTQYDBgyolXqU9QHxtgsXLmDnzp2IiYlB48aNMXfuXEyaNAnZ\n2dlYuXIl3N3dK8wnrI6xsTEyMjLEBtMYGRlh3759cHR0xPTp0+WKuUePHmjWrBlGjx4tcf+VK1fk\nKv9tH374Ib799lts27ZNtO3u3btYtmwZHB0d5S5fV1cX8fHxaN++vdj2+Ph4hXTo16tXD0ePHsXi\nxYvlLqsqS5cuxbJly7BkyRJ07doV9erVE9svaxOhm5sbAKBt27awtbWtUK4iDR8+HMnJyUod/BMY\nGAhvb28cPnwY8+bNE33R2Ldvn8KS88qVK+Hp6Yndu3dj9uzZ6NChA4DXzZ2KqKN58+bYt28fPv/8\nc7Ht+/btg76+vtzly4rNj3J49OgR7t+/j+LiYrHt8oy4KygoEP3/8uXL+OabbxT+AVHuu+++Q2Rk\nJG7duoURI0ZgypQpGDZsmKjPC3jdYW5paYm8vDypyp49ezbKysoQGhpaYV9WVhZGjRqFjIwMmZsH\nQ0JCUFxcXOk3wvv372PlypXYtGmTTOW/7cmTJ/j4449x9epVFBUVwdjYGDk5ObC1tcXu3bsr9KtK\na/369QgICIC7u7toRFxycjJ+/vlnLFy4EHPnzpX7GmbOnImuXbsqdQpMbTXNA69bA97+21PEaNpt\n27YhODgYEyZMQNeuXaGlJf7d/825s7IoKyvDzZs30aZNmwqjHF+8eAFNTU2lJu0nT56gXr16cn9Z\n+vnnn+Hr64vBgweL+tSSk5Pxxx9/YOPGjaIvIrWNSU0Gjx49gre3NxISEiAQCCp0Xsvzh/v26KLy\nwRTK+IDo1asXJk+eDHd390qXGSsuLsavv/4q9S/ovXv3kJ6ejqFDh0rcn5WVhZMnT6rsF19Wp06d\nwuXLl1FWVgYrKyuFjoDdu3cvNm/ejBs3bgAAunTpgunTp2PMmDEKKT84OBjfffcd7O3tYW1tXWFY\nvLx3z8DrO8uqyNvK8eTJE8yfPx+//fZbhYQGKKYPtaovi4r4uxMKhTAyMkJSUlKFO3NluHbtGjIy\nMjBkyBDo6uqipKQEmpqaChnEk5ycjC1btoj9zvr4+Ii+mKkCk5oMpk6divz8fKxevRoODg749ddf\nkZOTg8DAQKxcuRJDhgyRuezqPhTeVFvNoLXh448/xoYNG9CiRYt3svx3QfloO0kEAgH++uuvWoxG\nNnPmzMHFixfh5+eHyZMn47vvvsPDhw+xefNmBAQEVNocXdf0798f69atU1hToyR5eXmiaQICgQAX\nL16EmZkZZs+ejUaNGiEwMFBpdasS+9RkcPbsWURHR6Nz584QCAQwMDBAv379UL9+fQQEBMiV1N5M\nVJmZmaJRX28SCoW4f/++zHW8TRnNqNJKSEgQW9OzrpUfFBQkcbtAIICOjg7atWuHYcOGKaT/q7Cw\nsMJ0B0UMV7969arcZdRETk4OfvjhB9y4cQMCgQDm5ub45JNPFLLo+PHjxxEeHo7+/ftDU1NTNFKx\nRYsW2Lp16zuT1Pz8/LB06VIEBwejR48eSlnmb9GiRWjYsCHS0tJgY2Mj2j5mzBiFDeQoX5Dgzp07\n+Oqrr+rEggRMajJ48eKFaEi5np4ecnNz0bFjR3Tp0kWhHxxWVlYSJ5oWFBTAyspK7maQR48e4ZNP\nPhF9k1NkM6q62bdvH+7fv4+ioiK0bNkSwOvXT1dXFwYGBnjw4AEMDQ1x8OBBmf6Y7927h//973+I\nj48X+3KhiKbmJ0+eiI0WVKbExESMGzcOhoaGon6W6OhobNq0CXv27JH7zuTx48eifrMmTZogPz8f\n7du3R58+fTBnzhy54wde9zVXRRF9kp6ennjx4gUGDx4MLS0t1K9fX2x/Zmam3HX88ccfiImJqTCa\nsn379gr5Uvz2ggSzZ8+Gvr4+4uLiZF6QQBGY1GTQqVMnpKeno23btujRowe2bt2K1q1bIzw8XPSB\npwiVTTT9999/oaOjI3f5ixYtgpaWFpKSkiQ2o9L/mzNnDn7++Wds2rRJNHfvwYMHmDVrFlxdXfH+\n++9j6tSp+OqrrxAZGSl1+b6+vnj8+DE2btyo8EnLZmZmYl+O5s2bJ/pWrWhLly7F2LFjsXbtWtGA\no7KyMnz++edYsmQJfv/9d7nKNzMzw507d2BiYoLOnTtjz549sLGxwf79+xU2+frtOYElJSXIyspC\ngwYNYGBgoJCktmrVKrnLqM6zZ88q9JsCr78UK2IgijIWJFAEJjUZTJ8+XTTZdv78+Rg3bhx+/fVX\n1K9fH2FhYXKXP3/+fACvm7b8/PzEmrTKyspw4cKFSudnSUOZzajqJjAwELt27RKbjN66dWv4+fnB\n3d0dbm5uWLp0qcwDXy5evIhjx46ha9euigpZ5O2mzOjoaNG3akW7cuUKNm3aJDaCVkNDA76+vgpZ\nqcbNzQ1Xr17FwIEDMXfuXEyYMAE//PADysrK8O2338pdPvB61PHbcnJy4OvriylTpiikjtoYINWv\nXz9ERUWJNTUKhUJs3LhRIf3xqampEu9qjY2NKzz3sjYxqcng448/Fv3f2toaly9fxs2bN2FiYqKQ\nD4pr164BeP0LePPmTbFvVdra2rCyssLs2bPlrqe2mlHVQU5ODl6+fFlhe3FxMf755x8AgKGhIZ4/\nfy5T+W3btpU4mk8ZlLk8WZMmTXD37l106tRJbPvdu3cVsrbom0uG2dvbIykpCSkpKejQoUOVA2Hk\nZWRkhCVLlsDT01Mh8xLfnLojiSLuOv38/DBq1CikpKSguLgY/v7+SEtLQ1ZWltx3zIDyFySQFZOa\nnHJycmBgYCD34sJvOnDgAIDX84q+/fZbpfWH1FYzqjqwt7fH3LlzsX79etF7nZKSgv/973+iYf3X\nrl2TeemywMBA+Pn5ISQkRCnDvGvreYMuLi6YPXs2/Pz8RP1nSUlJWL58OcaOHavw+kxNTRW2XFx1\nhEKhwu5A2rdvX+V7ooj+7G7duuHs2bP4/vvv8eLFC+Tm5sLBwQHTp08Xay6UlbIXJJAVh/TL4NWr\nV/jmm28QERGB58+f48KFCzAzM8OyZctgYmICb29vVYdYI9HR0Xj16hXc3d2RkpKCcePGIS8vD/Xr\n18fmzZvh7Oxca7GsWbMGXl5e0NPTq5Pl5+bmYvr06Th58qRo+aSysjI4ODggLCwMhoaGOH36NEpK\nSuDg4CB1+W3atMHLly9RWlqK+vXrV5jwK8/AgWbNmmHSpEmiZuyffvoJLi4uFb4sKaKfp7i4GEuX\nLsXWrVtRUlIC4PVqJl5eXvDz84O2trZM5V65cgUFBQViTZjR0dEICAhAUVERHB0dERQUJHP5b4qN\njRX7WSgUIjs7G+Hh4TAzM1PImoZvT90pKSnB5cuX8eOPP2LJkiVwdXWVuw5lU/aCBLJiUpPBihUr\nEBsbi2XLluHTTz9FQkICzMzMsG/fPqxfvx4nT55USD0vXrzA5s2bcerUKYmPwEhISFBIPeWePXum\n0GbUcikpKQgLCxNN0OzcuTNmzpypsLtbZZf/pvT0dKSnp4vqUdQSZtUNLpGnD2bUqFHV3qkJBAKF\nrmn67Nkz0QN+27VrJ3HAgjTGjx8PGxsbUX9zWloaBg4ciIEDB6JTp07YtWsX5syZI9ovj7eb/sr7\nmwcNGoQVK1Yoda7jvn37sGPHDvz6669yl5WWllblfnNzc7nrAJS7IIEsmNRkYG1tje+++w4DBgwQ\nW7G9fAWNe/fuKaQeX19fHDhwAM7OzhJHxC1cuFCmMmtK0hJX0oqOjsb06dMxaNAgsaV0Tp8+jU2b\nNmH8+PF1unyqGywsLLBjxw7RShUBAQE4fPiw6I5n+/btCAsLw7lz51QZptwyMjJgZ2eHhw8fyl1W\n+epEb/ahKmLKzp07d+r0Q3HZpyaDrKwsiWvMlZSUVPt4D2kcPHgQ27ZtU+g3n/JBDeXK56iVj7q7\nfv06ysrK0L9/f4XU980332Dx4sUSH6myYsUKuZOOsst/061bt0Tz1d4e1KGILwB15cnabz5mp6Zq\neockaxNnfn6+2B1SQkIC3n//fdHPAwcOVPpizcr277//ik0Zkdeff/4p9vOrV69w+fJlrF+/Hl9/\n/bXM5fbs2RMmJiYYNGgQBg4ciEGDBtWpPngmNRmYm5sjISFBbAV64PXafVZWVgqrR1dXV2G/4OWi\noqJE/1+zZg0aNGiA0NBQUft3UVERZs+erbCh5Xl5eRLXLnR2dpbpIYW1XX65o0ePYsqUKbC0tERK\nSgp69eqFjIwMvHz5Eu+9957c5b89kXXOnDkqm8gqy+jIH374AW3atIGZmVml58szWMXAwACPHj1C\nmzZtUFpaitTUVLERwMXFxXIPhqnNfru3VwoSCoV49uwZGjZsKPOz89729ghU4PWTP/T19bFmzRp8\n8OVOzUIAAB0VSURBVMEHMpW7d+9exMfH48yZM4iKikJpaSnat2+PQYMGiRKdKlfpZ1KTwYIFC+Dj\n44MHDx6gtLQUv/32G27evIlff/1VoQ/GmzNnDkJDQ7F27VqljF7bsmUL9u3bJ9ah27BhQ3z55ZcY\nPXo0vvjiC7nrGDhwYKWPVFHEMlzKLr/cypUrsWDBAvzvf/9DmzZtsGXLFrRo0QI+Pj6iZk951NWJ\nrDXl7OyMw4cPo23btpg8eTJGjx6tkAUCytnZ2SEoKAirV6/Gvn37ALx+78ulpaXJPQpyxYoVsLGx\nESW1tLQ0+Pr6ivXbtWzZUiH9dm/fsWpoaMDAwAC9e/dW2mCpch06dEBqaqrM5w8ePFjUelRUVIRz\n587hzJkzOHv2LLZv346ysjJYWFjg7NmzCopYOkxqMvjggw+wdetWhISEQENDA0FBQbCyssIvv/yi\n0KbCuLg4nDt3DsePH4e5uXmFEXG//PKLXOUXFRUhKyurQodxdna2zPOt3jZs2DD4+fnh0qVLYo9U\n2b9/PxYuXCg20kyWR3oou/xyt27dgouLCwBAS0sLz549g46ODubPn4/x48fLvcpEXZ3IWlNbt25F\nQUEBoqKisHHjRsyfPx9jx47F5MmT0bNnT7nLX7x4MZydndGzZ09oamoiKChI7MtYVFSUXA9TBV5P\nuv7yyy9FP+/ZswddunRBTEwMgNdD5MPCwhSS1Gpj8vWzZ8/Efi4fxbly5UqFPSi2YcOGGDZsGBwc\nHHDx4kUcOnQI4eHhuH79ukLKlwWTmgzc3NwwZcoUHDhwQGzlBEXT19fHRx99pLTyHR0d4evrC39/\nf7GEsGzZMoXVW/4B8NNPP+Gnn34S2/fmB4is6xsqu/xyjRo1Ei2I3KJFC9y+fRtdu3ZFSUmJxAmo\n0qqrE1ml0axZM0yfPh3Tp0/HpUuXsHPnTowZMwatW7fGsWPH5BoB2bZtW5w/fx7Xr1+HgYFBhT6c\nRYsWyd1Ur4p+u0ePHkkc2ayIkbutW7eW2MJjZGSEiIgIucoWCoVITU3FmTNncObMGSQmJqJRo0aw\ns7ODn5+fSp8gwqQmg4YNG8LLywtNmjSBm5sbJk2apJQJs4p6wGVl1qxZgyVLlmDmzJl49eoVgNd3\nIZMnT8Y333yjkDqqWzmhrpdfzsbGBomJiTA3N8eIESOwZMkS/PXXXzhw4IBCmh/r0kRWRTR1d+rU\nCT169EBycjL+/vtvhQyg0tLSqnR5uLe3yzLYpTb67cqlpqbCx8cHN2/erNAHqaiHqb49LUBDQwP6\n+vowNzeXq19wwoQJOHfuHBo3boz+/ftj1KhRCAwMFD1ZW9U4pF9GT548we7du7Fr1y5cunQJ/fr1\nw5QpU+Ds7KyQx4+86c6dO0hLS4NAIECXLl0UPhKuqKhIbE6RqiZN1mV37tzBv//+i+7du+PZs2dY\nsmQJEhMT0bFjRwQEBMj9xOW6NJH1zWkq0oqPj8eOHTtw4MAB9OjRA25ubnBxcanwhGdlk+Uapk2b\nJnpO4r59+xAcHIwbN26IXvvybdI887AyQ4YMQfPmzTF//nyJ03Xk6R8MCgrC7Nmz5Z4bWJlmzZqh\ndevWmDhxIgYMGABbW1uF9p/Ki0lNAa5fv47t27dj69at0NbWhouLC2bMmIEuXbrIVe6TJ08we/Zs\nxMbGipo5hUIhnJycsHHjRjRu3FgR4StdeTPFP//8U6GZxd/fv86XX1JSgpMnT6J3796itTKVRZkT\nWS9cuCD2XK03RUVFiaY/nDt3Dr169arwOJSqrF69GpGRkSgqKsKECRPg7u6Ozp07KyRuWciS1O7e\nvQtnZ2fcuXNH1G/3ySefiPa7ubmhXbt2CAgIkDu+Vq1a4fTp0wqbvP+m5s2bS3xklaI8fPhQ1Ox4\n5swZZGdno2fPnhgwYECdSHJManJ69OgRIiMjsWvXLuTk5MDZ2RnZ2f/X3r1HRVnmcQD/chOMtTNz\nvCJOjRtKKjokx0UFOhoqGmkgt7wU1k43UvOyB1kBCZSTHGkBo2N5UNCsXMHMVllpMxIHPbCJsrVe\nCAOhZEUxVi6iDsz+4ZlZhksy7/s+D+/g73OO58TM+DzvVMxvfu/v9zzPNRQWFiIhIUHUxsNRUVEo\nLS1Feno6vL29Adw/r2rdunXw9vZ+4LlPcpCRkYF33nkHKpUKI0aMMPtGamNjI3pjVdbjG40cORKl\npaXdlnFYEzc3N+Tn53cLNvv378e6detELfhVKpUYM2YM5s6d262hqTMeR64AwrNNvV7fa93u+++/\nh6urqyRfbIwNTiwO4lUqlaioqOBWi71y5Qp0Op2pzf/GjRuYOnUq8vPzuczfFQU1Ae7du4f8/Hzs\n27cPhYWFmDJlCiIjIxESEmK6zZKfn4833nhD1O4iY8eOxSeffNJtIXRxcTGWL19uumUoZ+7u7oiJ\nicHLL79sleMb+fv7Iz4+XtLMKTMzE1qtFk5OTlwOpkxPT0dWVhYKCgpMTRWfffYZ1q9fj927d5s1\nRViqP7bi+i1ibqH2laV1u87133/961/YvHkz4uLiMHHixG7nm4nZpV+pVOLHH3/EsGHDBI9hqdra\nWuh0OhQVFeHo0aNobm7ut0OGqVFEAHd3dxgMBoSGhuLbb7+Fh4dHt9fMnDlT9HqTzkfDdKZUKns8\nBkWOOjo6RLda9+f4RjExMYiNjcWf//xneHp6dqtxCfkQ2rlzJ5YuXQonJ6ffXHBrY2MjSVBbs2YN\nbty4gaCgIBw7dgwFBQVYv349cnJyEBAQIGrso0ePir4+KfE4lcDSRepdd+Y3GAwIDg7u9pgUjSKz\nZs0ybbzdGzFr1erq6sxuQdbU1GDQoEGYNm0aVq5cabaGkDfK1ATYv38/goKCmN83DgoKwpAhQ/DR\nRx+Zir4tLS1444030NTUhC+++ILp/FJ49913odfrER8fb5XjG3UOWiw+hHiKiorCqVOnUF9fj5yc\nHMybN4/7NQjpTrQEj0zN0jksaTAR0xKvVCoRHR39wOYcoaWRqVOnorq6Gvb29vDy8oKvry+efvpp\n/OEPf7CoDssKBTUZO3/+PEJCQtDa2mo6APH8+fMYPHgwPv/8c0yYMKGfr/DBDAYDwsLCcO3aNUyY\nMKHbbRaxeyayHt/oQR9IYj6E7t27h/nz5+PDDz/scWsjMboeowLcz27j4uIwe/ZszJ071/S4mMXp\nlhIadFg2u1hKyHtg3ZkIsK+pJSUlwc/PD97e3kzfh1AU1GSutbUVeXl5pmNV3N3dERYWJvmyAVaS\nkpKQnp4OjUaDESNGdHu+816UchyfFzc3Nxw7dkzybri+3hblnW0KDWosm10sJeQ9sO5M5DWHJVhn\n5V1RTU3mmpuboVAo8MQTT6CjowN379417QVoDYeRZmVlISsry7TFlLWN39m///1v5OTkoKqqCpmZ\nmRg1ahSOHDkClUoleiPrJUuWYM+ePZItejfitTidl5UrV2Lx4sW9NrvwJKRuJ2SzaDnOYQne10NB\nTcb++te/YvXq1TAYDFAoFN3a1a0hqA0ePBhTpkyx2vGNvvnmGyxZsgRz5sxBUVGRacusqqoqfPrp\npw885PNBWltbkZubi8LCQnh6ena7rcOrFV7uWDa7WErohzXrJpby8nKLOh95Z1KsUVCTsc2bN2P1\n6tXYsGHDb679kbOoqCjs2LEDqampTH6ZWY9vlJycjOTkZGi1WrNd9P38/ATX7YqLi+Ht7Q17e3tc\nunTJFJyrq6vNXifV+zIYDNi1axeysrJw5coVnD59Gmq1GmlpaVCr1T0e4cOKmPe0ZcsW3Lx5E/7+\n/qivr8eePXskbXbpa90uNzdX0DlirDsTLd2NRG6ZnVjW+Un5kGhqasKyZcusNqAB9zeFPX36NAoK\nCpicNMB6fKMLFy6YNVUYKRQKwbf4Fi5caKp91NbWorCwkOmOJTt27MD27dvx9ttvIzEx0fS4i4sL\ndu7cyTWoWfJB2lOzy7x581BUVISQkBC0tbWZXiNFs0tERMRv1u2MQU3oOXrLli3jvm3Yw8R6Py0f\nAuHh4SgoKMDrr7/e35ciGOuTBliPb6RUKlFXV9dtR5Hy8nKMHj1a0JgKhQJXrlzB8OHDUVNT022L\nL6llZ2cjIyMDAQEBZls9aTQaXLx4UZI5WGQ5kZGRvT63b98+7Nu3D4B0zS6s63ZarVY2TRw88Fgz\n2BkFNRlLTk7GsmXLcOLEiR53HdiwYUM/XVnfsT5pgPX4RqGhodi0aROys7NhY2MDvV4PnU6H+Ph4\nLFu2TNCYixYtQmBgIEaOHAkbG5vfvC0l5naUUW1tbY/LQBwcHEw1QrFYZDm8m11Y1u14f8DLATWK\nEJPs7Gx8/fXXGDp0KKqqqrr9QlhDUDM6e/YsqqqqEBAQAGdnZ7S0tMDR0VGyW6usxzce0TN58mQY\nDAZ4e3ubdpURekJ4WloaFixYgMuXLyM2Npb5bSm1Wo3y8vJuNZevvvpK9ObbRnLqThSDVd1OjvUr\noYGWde1RKFqnJmNubm5Yu3Yt3nrrrf6+FMHq6+uxdOlSnDlzBjY2NigrK4NarcaaNWvg6OiIlJQU\nWY/fVVVVlWkX/SlTpkh2hlRUVBRSUlKYnrywb98+JCcnIzExEWvXrkVaWhp++uknbN++HZmZmZIt\ni4iLi0NBQQGT7kRWzS68FqnX1NRApVL1OZDw6EwcCGsGO6NMTcba29vx7LPP9vdliLJx40YMHz4c\nVVVVZntkBgUFmU6tlvP4RkeOHEFAQADGjh2LsWPHSjauEY/bqMuXL0d7ezuSkpLQ2tqK119/HS4u\nLti6dauk6/xYdieyanbhVbfj2ZnIOpOSa1ZOmZqMxcXFYciQIVZ1m7GrcePG4fDhw5g4caLZN8Lq\n6mrMnDlT9Lc51uMbubi4wMnJCUFBQYiIiMD06dMlGbe/NDQ0oKOjQ5KGBZ5bcU2bNg1btmxBQECA\n2X/vCxcu4Nlnn7WKkyssIWYPSx6ZFMusXCjK1GTs9u3b2Lt3L7755htMmjSpW33IGhbktrW19Xh0\nfENDgyT78rEe36iiogKHDx9GXl4eAgMDMWbMGISFhSE8PLxfD8MUonP9EYDo+iPP7kQezS4DBY9M\nivWaQSEoqMlY5wW5FRUVZs9ZSxfVzJkz8emnn2LTpk2mx9rb25Geni7JkTGsxzcaMmQIli9fjuXL\nl6Ourg4HDx5EXl4e/vKXv0Cj0aCwsFCyuVjpqf7o7OyM2NhYUfVHnt2JPJpd5LRIXQwWXZy81wwK\nQUFNxo4cOdLflyBaYmIiAgMDUVZWhjt37iAuLg4XL17ErVu3UFBQIPvxe+Li4oLXXnsNKpUKqamp\nkrTb88Cr/sjSypUrER0djdu3b8NgMKC0tBT79+83NbtIQU6L1MV+eZU6k+K9ZlAICmqEKWdnZ+h0\nOmRnZ8PR0RF37txBUFAQtFot7t27J/vxuyoqKkJubq7p2+jChQvNFjLL2YkTJ3D48OFuh9eq1Wr8\n/PPPkszBOsvh0ezCY5F6X1naKMI6k7KGDbIpqBGmNBoNLl26hI0bN5o9fvPmTUycOFH0tznW4xvF\nx8fj888/x/Xr1+Hv74+MjAwsWLBAFoci9hWP+iOPLCcyMhKRkZGSNrt0xqNux6oz0RoyKdZs+/sC\nyMBmPBm6q+bmZklODmc9vlFpaSnWrVuHS5cu4bPPPkNQUJBVBTTg//XHzqSuPxqznDfffNOs8UTq\nLOfs2bM4ceKE2Ynwer1ekrGNdbuupKzbRUREdKuTA/c7E9euXWv6ecaMGRb9f/brr7/26Y8UAc1g\nMCArKwvTp0+Hi4uLaSPutLQ0HDp0SPT4QlGmRpgw1mhsbGyQmJhodqhpR0cHzpw5g8mTJ8t2/K4K\nCgqg1+tx5swZ/Pzzz7h7967Z80uWLJFsLlZ41B9ZZzmsml0641G3k+saL0vIqfbYGQU1wsT58+cB\n3P82V1FRYbZv5aBBg6DRaLBq1SrZjt/Vjz/+iBdeeAHV1dUwGAyws7ODXq+Hg4MDHB0drSKoPfnk\nkzh16hR27drVrf44atQoSeZg3Z3Io9mFR92Ox7lwrOubcqo9dkZBjTBh7NyMiorC1q1b8eijj1rV\n+F3FxMRAo9GgqKgI7u7uOHnyJP773/9i/fr1iIuLYzq3VIKDg+Hn5wd/f39ER0czOdKIdZbDo9kF\nYF+3A9iv8WKdScl1zSAFNcLUQNmlv6ysDEePHoWzszNsbW2h1+vh6emJxMREREdH49SpU1yuQwwv\nLy/84x//QEpKChwcHDBt2jT4+vrC19cXXl5ekgQ51lkOr8X2gPSL1Hmv8WKdSfFYMygEBTVC+sBg\nMJiaEoYOHYqrV69i3LhxcHV1tZqtmYwZ5e3bt1FaWoqTJ0/i66+/xtatW+Hk5ITa2lpJ5mGZ5fBY\nbM+qbse7M5F1JsWj9igEBTVC+mDChAn4/vvvoVar4eXlhYyMDNjZ2WHv3r1MNjhmqampCQ0NDbhx\n4wbq6+thb28PjUYj6RxSZzlGPJpdWNXteK/xYp1J8dog21IU1Ajpgz/96U9oaWkBcD/jCQ8Px8KF\nCzF06FBkZ2f389X1zfr166HT6VBbWwsvLy/4+PggIyMD06ZNk+zWHevuRB7NLrzqdqzxyKR41B4t\nRUGNkD7w9/c3/bNarUZpaSl+/fVXKBQKq9mHc/fu3Rg2bBjWrFmDuXPnwtPTU/JrZ92dyKPZhUfd\njsf+krwyKVZZuVC0+JoQgZRKpdUENOB+s0t8fDwqKyvx4osvQq1WIyIiApmZmTh37pwkc5w4cQLx\n8fHMshxjs8uiRYtMH/7vvfceSkpKJFt8zWOR+o4dO5CamorIyEizrbCMnYlSiYyMxA8//IDKykpU\nVFTg/PnzeOmllyQZu76+HnPmzMEzzzwDrVaL69evAwBiY2MRGxsryRxCUKZGyEPCeMCp8UOtoqIC\nGRkZSExMRHt7uyTNCayzHB7NLjzqdjzXeLHKpOS6QTYFNUIeEh0dHTh79ixOnjwJnU6HkpIStLW1\nwdPTE76+vpLMwesoIJbNLjzqdjzWeLGub8q19khBjZCHxOOPP447d+5Ao9HA19cXb775JqZPnw5n\nZ2fJ5mCd5fBoduFRt+Oxxot1JsVzzaAlKKgR8pDIycmRPIh1xTrL4dHswmOROo/ORNaZFK+s3FI2\njY2Nlh3YQwghvTBmOT4+PpIFgM6qqqpMt09PnTqFpqYmTJ8+HX5+fvD19YWnp6dkc3Wu2xUXF+O7\n776TdJH6nj17sG3bNvzyyy8A7jeJxMTESNbIoVKpUFhYCDc3N4wZMwY6nQ5qtRpnzpxBaGio6E0D\nLl68iMDAQEyePBnFxcUICAgwy8r7a/0mBTVCiGS2bNmC4uJilJWVMctyOjM2uxw4cECyZhej+vp6\n6HQ6FBUV4eTJk7h69Sq8vLwkP5Ge1RqviIgITJo0CZs2bTIFNZVKhRUrVsDOzg45OTmi57h27Rp2\n7dqF8vJydHR0QKPRSFp7FIKCGiFEcqyynAc1uyQkJIi+9p7qdr6+vpLW7Yw6dyY6OztLusaLdSbF\nOisXSh5XQQgZUFh1J/JoduFRt+NxLhzr+iaP2qMQlKkRQiTDOss5fvw482YXHnU7rVaLlpYW7Nix\nAx4eHqZ617fffovo6GiUlpaKnoNXJsW69mgpytQIIZJhneV03q6MFR6L1Hms8eKVSfHYINsSFNQI\nIZIpKyszZTl79+5l2p3ICo9F6jzWeLHefYXHmkEhKKgRQiTDI8thjUfdjucaL1aZFI/aoxBUUyOE\nSIZHdyJrPOp2PNZ4sa5v8lwzaAkKaoQQyahUKrMsx9fXl3mAsFas13gplUoMGzYMr776KpdMiuWa\nQUtQUCOESIZHljMQ8OhMZJ1JyTUrp6BGCCGc8d55BZA+k5JrVk5BjRBC+gnLNV6sMym5ZuXU/UgI\nIf2E5Rov1l2cPNYMCkGZGiGEcMZjf0m5ZlKsUVAjhBDOeHcmPkwoqBFCCGdyXeM1EFBQI4SQfiaX\nNV4DATWKEEIIZzz2l3xYUaZGCCGcyXWN10BAQY0QQjh7WDsTeaCgRgghZMCw7e8LIIQQQqRCQY0Q\nQsiAQUGNECvz2muv4amnnhL0d+fPn48ZM2Y88HV6vR4KhQLbtm0TNA8h/YWCGiEivPDCCxg5ciQa\nGxt7fU10dDQUCgUqKys5XhkhDycKaoSIEBERgTt37uDLL7/s8fn29nYcOnQIU6dOhZubmyRzfvDB\nBygpKZFkLEIGGgpqhIiwYMECPProo8jLy+vx+cLCQly/fh3h4eGi52ptbQUAODg4YNCgQaLHI2Qg\noqBGiAhOTk5YuHAhdDod6urquj1/4MAB2NnZISQkBACwd+9eLFq0COPHj8eIESPg5eWFjIwMdHR0\nmP09Y+3r3LlzCAwMxOjRo7FhwwYAPdfU+jquUXl5OebPnw8XFxd4eHggMzOzT++3sbERMTEx8PDw\nwPDhw+Hp6YnU1NRe5yGEN9omixCRwsPD8cknn+DgwYNYuXKl6fHW1lbk5+dj9uzZGD58OAAgKysL\nkyZNwrx58+Dk5ITCwkIkJCSgqakJcXFxZuPevHkToaGhCA4ORlhYGJRKZa/XYMm4jY2NCA0NxfPP\nP4/FixfjyJEjiIuLg8FgwKpVq3qdo7W1FYGBgbh69SpeeeUVqFQq/POf/0RycjJ++eUXpKWlCfnX\nR4ikKKgRIpKfnx9cXV2Rl5dnFtTy8/PR3Nxsduvx2LFjeOSRR0w/a7VavPXWW/joo4+wYcMGODg4\nmJ67du0aUlNTodVqH3gNloxbV1eHpKQkrF69GgDwxz/+Ec899xxSUlKwYsUKDBkypMc53n//fVRX\nV6OoqAhPPPEEAGDFihVQqVRISUnBqlWr8Pvf//6B10oIS3T7kRCRbG1tERISgnPnzpl1OObm5sLZ\n2RmBgYGmx4yBp729HY2NjWhoaICPjw+ampq6dUcOGjQIL730Up+uwZJx7e3t8corr5h+trOzg1ar\nRXNzM4qLi3ud44svvsCMGTOgUCjQ0NBg+jNr1iwYDAbodLo+XSshLFGmRogEwsPDsX37dhw4cAAb\nN25EQ0MDjh8/jsWLF5vt71dcXIwtW7bgu+++w71798zGuHXrltnPo0eP7nNDiCXjjho1Cr/73e/M\nHjNmXjU1Nb3OUVlZiQsXLphe29WNGzf6dK2EsERBjRAJeHh4YOLEiTh48CA2btyIQ4cOQa/Xm916\nvHz5MoKDgzF+/Hhs3boVY8aMgaOjI8rKypCUlNSt2WLw4MF9mtvScYUyGAyYPXs23n777R6fHzt2\nrCTzECIGBTVCJBIREYGEhASUlZUhNzcXI0aMwOzZs03P5+fn4+7duzhw4ABGjx5tevzy5cui5rV0\n3P/85z9obm42y9aMr33sscd6nUetVqOlpQWzZs0Sdb2EsEQ1NUIkEhoaCltbW2zbtg0lJSUIDg6G\nnZ2d6Xlb2/u/bgbD/w/GaGtrQ1ZWlqh5LR1Xr9dj9+7dpp/b29uRlZUFZ2dn+Pj49DpPcHAwSktL\ncfz48W7P3bp1C3fv3hX6FgiRDGVqhEjE1dUVPj4++Pvf/w7gfubW2Zw5c/DOO+8gLCwMK1asQFtb\nG/bv3w97e3G/hpaO6+Ligvfffx81NTVwd3fH3/72N5w+fRpJSUm9dj4CwNq1a/HVV18hIiICS5cu\nhaenJ1paWnDhwgV8+eWXKCkpgaurq6j3QohYlKkRIiFjDc3NzQ1Tp041e87d3R0ff/wxbG1tsWnT\nJuzcuROBgYFISEgQNael4yoUCuTm5uKHH35AfHw8fvrpJ2zevNnU4t+bRx55BEePHsXq1auh0+kQ\nExOD9PR0VFZWIjo6GsOGDRP1PgiRAh0SSgghZMCgTI0QQsiAQUGNEELIgEFBjRBCyIBBQY0QQsiA\nQUGNEELIgEFBjRBCyIBBQY0QQsiAQUGNEELIgEFBjRBCyIBBQY0QQsiA8T9PAa1fqFyhCAAAAABJ\nRU5ErkJggg==\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# list of x locations for plotting\n", - "x_values = list(range(len(importances)))\n", - "\n", - "# Make a bar chart\n", - "plt.bar(x_values, importances, orientation = 'vertical')\n", - "\n", - "# Tick labels for x axis\n", - "plt.xticks(x_values, feature_list, rotation='vertical')\n", - "\n", - "# Axis labels and title\n", - "plt.ylabel('Importance'); plt.xlabel('Variable'); plt.title('Variable Importances'); " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Predictions and Actual Values" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Dates of training values\n", - "months = features[:, feature_list.index('month')]\n", - "days = features[:, feature_list.index('day')]\n", - "years = features[:, feature_list.index('year')]\n", - "\n", - "# List and then convert to datetime object\n", - "dates = [str(int(year)) + '-' + str(int(month)) + '-' + str(int(day)) for year, month, day in zip(years, months, days)]\n", - "dates = [datetime.datetime.strptime(date, '%Y-%m-%d') for date in dates]\n", - "\n", - "# Dataframe with true values and dates\n", - "true_data = pd.DataFrame(data = {'date': dates, 'actual': labels})\n", - "\n", - "# Dates of predictions\n", - "months = test_features[:, feature_list.index('month')]\n", - "days = test_features[:, feature_list.index('day')]\n", - "years = test_features[:, feature_list.index('year')]\n", - "\n", - "# Column of dates\n", - "test_dates = [str(int(year)) + '-' + str(int(month)) + '-' + str(int(day)) for year, month, day in zip(years, months, days)]\n", - "\n", - "# Convert to datetime objects\n", - "test_dates = [datetime.datetime.strptime(date, '%Y-%m-%d') for date in test_dates]\n", - "\n", - "# Dataframe with predictions and dates\n", - "predictions_data = pd.DataFrame(data = {'date': test_dates, 'prediction': predictions}) " - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAbUAAAFfCAYAAADJdVI5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plot the actual values\n", - "plt.plot(true_data['date'], true_data['actual'], 'b-', label = 'actual')\n", - "\n", - "# Plot the predicted values\n", - "plt.plot(predictions_data['date'], predictions_data['prediction'], 'ro', label = 'prediction')\n", - "plt.xticks(rotation = '60'); \n", - "plt.legend()\n", - "\n", - "# Graph labels\n", - "plt.xlabel('Date'); plt.ylabel('Maximum Temperature (F)'); plt.title('Actual and Predicted Values');\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Targets and Data Visualization" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAbUAAAFfCAYAAADJdVI5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Make the data accessible for plotting\n", - "true_data['temp_1'] = features[:, feature_list.index('temp_1')]\n", - "true_data['average'] = features[:, feature_list.index('average')]\n", - "true_data['friend'] = features[:, feature_list.index('friend')]\n", - "\n", - "# Plot all the data as lines\n", - "plt.plot(true_data['date'], true_data['actual'], 'b-', label = 'actual', alpha = 1.0)\n", - "plt.plot(true_data['date'], true_data['temp_1'], 'y-', label = 'temp_1', alpha = 1.0)\n", - "plt.plot(true_data['date'], true_data['average'], 'k-', label = 'average', alpha = 0.8)\n", - "plt.plot(true_data['date'], true_data['friend'], 'r-', label = 'friend', alpha = 0.3)\n", - "\n", - "# Formatting plot\n", - "plt.legend(); plt.xticks(rotation = '60');\n", - "\n", - "# Lables and title\n", - "plt.xlabel('Date'); plt.ylabel('Maximum Temperature (F)'); plt.title('Actual Max Temp and Variables');" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "hide_code_all_hidden": false, - "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": "3.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Day_5/random_forest_explained/data/.RData b/Day_5/random_forest_explained/data/.RData deleted file mode 100644 index 9d48469..0000000 --- a/Day_5/random_forest_explained/data/.RData +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:26e03604f6895e6547964bc5cef6432001be4e3f37fa9275b94b70a14c1c1c09 -size 180158 diff --git a/Day_5/random_forest_explained/data/.Rhistory b/Day_5/random_forest_explained/data/.Rhistory deleted file mode 100644 index 3674410..0000000 --- a/Day_5/random_forest_explained/data/.Rhistory +++ /dev/null @@ -1,283 +0,0 @@ -library(gganimate) -library(ggthemes) -df <- read_csv('data_vis_challenge.csv') -library(tidyverse) -library(gganimate) -library(ggthemes) -df <- read_csv('data_vis_challenge.csv') -df <- gather(df, key = 'species', value = 'rate', -`Light Intensity`, -Temperature) -df <- dplyr::rename(df, intensity = `Light Intensity`, temp = Temperature) -ggplot(data = df, aes(x = temp, y = intensity, color = species, size = intensity)) + geom_point() + -xlab('Temperature') + ylab('Rate') -ggplot(df, aes(x = intensity, y = rate, color = species, frame = temp)) + -geom_point() + theme_classic(12) -library(tidyverse) -library(gganimate) -library(ggthemes) -df <- read_csv('data_vis_challenge.csv') -setwd("C:/Users/Will Koehrsen/Desktop") -load("C:/Users/Will Koehrsen/Desktop/.RData") -ggplot(data = df, aes(x = intensity, y = rate, color = species, frame = temp)) + -geom_point() + theme_classic(12) -table(df$intensity) -table(df$temp) -ggplot(data = df, aes(x = species, y = rate)) + geom_bar(fill = 'navy') + -facet_grid(temp, intensity) + coord_flip() -ggplot(data = df, aes(x = species, y = rate)) + geom_bar(fill = 'navy') + -facet_grid(temp ~ intensity) + coord_flip() -ggplot(data = df, aes(x = species, y = rate)) + geom_bar(stat = 'identity', fill = 'navy') + -facet_grid(temp ~ intensity) + coord_flip() -ggplot(data = df, aes(x = species, y = rate)) + geom_bar(stat = 'identity', fill = 'navy') + -facet_grid(intensity ~ temp) + coord_flip() + theme_hc(12) -ggplot(data = df, aes(x = species, y = rate)) + geom_bar(stat = 'identity', fill = 'navy') + -facet_grid(intensity ~ temp) + coord_flip() + theme_stata(12) -ggplot(data = df, aes(x = species, y = rate)) + geom_bar(stat = 'identity', fill = 'navy') + -facet_grid(intensity ~ temp) + coord_flip() + theme_economist(12) -ggplot(data = df, aes(x = species, y = rate)) + geom_bar(stat = 'identity', fill = 'navy') + -facet_grid(intensity ~ temp) + coord_flip() + theme_hc(12) -ggplot(data = df, aes(x = species, y = rate)) + geom_bar(stat = 'identity', fill = 'navy') + -facet_grid(intensity ~ temp) + coord_flip() + theme_classic(12) -setwd("~/Data-Analysis/random_forest_explained") -library(tidyverse) -library(lubridate) -setwd("~/Data-Analysis/random_forest_explained") -df <- read_csv('1169857.csv') -df <- dplyr::filter(df, NAME == 'SEATTLE TACOMA INTERNATIONAL AIRPORT WA US') -df <- read_csv('1169857.csv') -table(df$NAME) -df <- dplyr::filter(df, NAME == 'SEATTLE TACOMA INTERNATIONAL AIRPORT, WA US') -temps <- dplyr::filter(df, NAME == 'SEATTLE TACOMA INTERNATIONAL AIRPORT, WA US') -temps <- mutate(temps, month = lubridate::month(DATE), -day = lubridate::day(DATE), week = lubridate::wday(DATE, label = TRUE)) -temps$temp_1 <- c(45, temps$TMAX[1:{nrow(temps) - 1}]) -temps$temp_2 <- c(45, 44, temps$TMAX[1:{nrow(temps) - 2}]) -View(temps) -averages <- read_csv('averages.csv') -View(averages) -df <- read_csv('raw_temps.csv') -temps <- dplyr::filter(df, NAME == 'SEATTLE TACOMA INTERNATIONAL AIRPORT, WA US') -temps <- mutate(temps, month = lubridate::month(DATE), -day = lubridate::day(DATE), week = lubridate::wday(DATE, label = TRUE)) -temps$temp_1 <- c(45, temps$TMAX[1:{nrow(temps) - 1}]) -temps$temp_2 <- c(45, 44, temps$TMAX[1:{nrow(temps) - 2}]) -averages <- read_csv('averages.csv') -averages$month <- as.numeric(substr(averages$DATE, 5, 6)) -averages$day <- as.numeric(substr(averages$DATE, 7, 8)) -temps <- merge(temps, averages[, c('month', 'day', 'DLY-TMAX-NORMAL')], by = c('month', 'day'), -all.x = TRUE) -View(temps) -df <- read_csv('raw_temps.csv') -temps <- dplyr::filter(df, NAME == 'SEATTLE TACOMA INTERNATIONAL AIRPORT, WA US') -temps <- mutate(temps, month = lubridate::month(DATE), -day = lubridate::day(DATE), week = lubridate::wday(DATE, label = TRUE)) -temps$temp_1 <- c(45, temps$TMAX[1:{nrow(temps) - 1}]) -temps$temp_2 <- c(45, 44, temps$TMAX[1:{nrow(temps) - 2}]) -averages <- read_csv('averages.csv') -averages$month <- as.numeric(substr(averages$DATE, 5, 6)) -averages$day <- as.numeric(substr(averages$DATE, 7, 8)) -temps <- merge(temps, averages[, c('month', 'day', 'DLY-TMAX-NORMAL')], by = c('month', 'day'), -all.x = TRUE) -temps <- arrange(temps, DATE) -library(tidyverse) -library(lubridate) -df <- read_csv('raw_temps.csv') -setwd("~/Data-Analysis/random_forest_explained/data") -df <- read_csv('raw_temps.csv') -temps <- dplyr::filter(df, NAME == 'SEATTLE TACOMA INTERNATIONAL AIRPORT, WA US') -temps <- mutate(temps, month = lubridate::month(DATE), -day = lubridate::day(DATE), week = lubridate::wday(DATE, label = TRUE)) -temps$temp_1 <- c(temps$TMAX[1:{nrow(temps) - 1}]) -# Read in the data as a dataframe -df <- read_csv('raw_temps.csv') -# Make sure all readings are from same station -temps <- dplyr::filter(df, NAME == 'SEATTLE TACOMA INTERNATIONAL AIRPORT, WA US') -# Create month, day, and week columns -temps <- mutate(temps, month = lubridate::month(DATE), -day = lubridate::day(DATE), week = lubridate::wday(DATE, label = TRUE)) -# Create the past max temperature columns -temps$temp_1 <- c(NA, temps$TMAX[1:{nrow(temps) - 1}]) -temps$temp_2 <- c(NA, NA, temps$TMAX[1:{nrow(temps) - 2}]) -# Read in the averages as a dataframe -averages <- read_csv('hist_averages.csv') -# Create columns for the month and day -averages$month <- as.numeric(substr(averages$DATE, 5, 6)) -averages$day <- as.numeric(substr(averages$DATE, 7, 8)) -# Join the averages to the temperature measurements -temps <- merge(temps, averages[, c('month', 'day', 'DLY-TMAX-NORMAL')], -by = c('month', 'day'), all.x = TRUE) -View(temps) -temps <- merge(temps, averages[, c('month', 'day', 'DLY-TMAX-NORMAL')], -by = c('month', 'day'), all.x = TRUE) %>% arrange(DATE) -# Read in the data as a dataframe -df <- read_csv('raw_temps.csv') -# Make sure all readings are from same station -temps <- dplyr::filter(df, NAME == 'SEATTLE TACOMA INTERNATIONAL AIRPORT, WA US') -# Create month, day, and week columns -temps <- mutate(temps, month = lubridate::month(DATE), -day = lubridate::day(DATE), week = lubridate::wday(DATE, label = TRUE)) -# Create the past max temperature columns -temps$temp_1 <- c(NA, temps$TMAX[1:{nrow(temps) - 1}]) -temps$temp_2 <- c(NA, NA, temps$TMAX[1:{nrow(temps) - 2}]) -# Read in the averages as a dataframe -averages <- read_csv('hist_averages.csv') -# Create columns for the month and day -averages$month <- as.numeric(substr(averages$DATE, 5, 6)) -averages$day <- as.numeric(substr(averages$DATE, 7, 8)) -# Join the averages to the temperature measurements -temps <- merge(temps, averages[, c('month', 'day', 'DLY-TMAX-NORMAL')], -by = c('month', 'day'), all.x = TRUE) %>% arrange(DATE) -df <- read_csv('raw_temps.csv') -temps <- dplyr::filter(df, NAME == 'SEATTLE TACOMA INTERNATIONAL AIRPORT, WA US') -temps <- mutate(temps, month = lubridate::month(DATE), -day = lubridate::day(DATE), week = lubridate::wday(DATE, label = TRUE)) %>% -arrange(DATE) -temps$temp_1 <- c(NA, temps$TMAX[1:{nrow(temps) - 1}]) -temps$temp_2 <- c(NA, NA, temps$TMAX[1:{nrow(temps) - 2}]) -averages <- read_csv('hist_averages.csv') -averages$month <- as.numeric(substr(averages$DATE, 5, 6)) -averages$day <- as.numeric(substr(averages$DATE, 7, 8)) -temps <- merge(temps, averages[, c('month', 'day', 'DLY-TMAX-NORMAL')], -by = c('month', 'day'), all.x = TRUE) %>% arrange(DATE) -temps <- dplyr::select(temps, month, day, AWND, PRCP, TMAX, week, temp_1, temp_2, DLY-TMAX-NORMAL) -temps <- dplyr::select(temps, month, day, AWND, PRCP, TMAX, week, temp_1, temp_2, `DLY-TMAX-NORMAL`) -df <- read_csv('raw_temps.csv') -temps <- read_csv('raw_temps.csv') -temps <- dplyr::filter(temps, NAME == 'SEATTLE TACOMA INTERNATIONAL AIRPORT, WA US') -temps <- mutate(temps, month = lubridate::month(DATE), -day = lubridate::day(DATE), week = lubridate::wday(DATE, label = TRUE)) %>% -arrange(DATE) -temps$temp_1 <- c(NA, temps$TMAX[1:{nrow(temps) - 1}]) -temps$temp_2 <- c(NA, NA, temps$TMAX[1:{nrow(temps) - 2}]) -averages <- read_csv('hist_averages.csv') -averages$month <- as.numeric(substr(averages$DATE, 5, 6)) -averages$day <- as.numeric(substr(averages$DATE, 7, 8)) -temps <- merge(temps, averages[, c('month', 'day', 'DLY-TMAX-NORMAL')], -by = c('month', 'day'), all.x = TRUE) %>% arrange(DATE) -library(tidyverse) -library(lubridate) -temps <- read_csv('raw_temps.csv') -temps <- dplyr::filter(temps, NAME == 'SEATTLE TACOMA INTERNATIONAL AIRPORT, WA US') -temps <- mutate(temps, year = lubridate::year(DATE), month = lubridate::month(DATE), -day = lubridate::day(DATE), week = lubridate::wday(DATE, label = TRUE)) %>% -arrange(DATE) -temps$temp_1 <- c(NA, temps$TMAX[1:{nrow(temps) - 1}]) -temps$temp_2 <- c(NA, NA, temps$TMAX[1:{nrow(temps) - 2}]) -averages <- read_csv('hist_averages.csv') -averages$month <- as.numeric(substr(averages$DATE, 5, 6)) -averages$day <- as.numeric(substr(averages$DATE, 7, 8)) -temps <- merge(temps, averages[, c('month', 'day', 'DLY-TMAX-NORMAL')], -by = c('month', 'day'), all.x = TRUE) %>% arrange(DATE) %>% mutate(year = ) -temps <- dplyr::select(temps, year, month, day, week, AWND, PRCP, TMAX, temp_1, temp_2, `DLY-TMAX-NORMAL`, TMAX) -temps <- dplyr::select(temps, year, month, day, week, AWND, PRCP, temp_1, temp_2, `DLY-TMAX-NORMAL`, TMAX) -temps <- dplyr::select(temps, year, month, day, week, AWND, PRCP, temp_2, temp_1, `DLY-TMAX-NORMAL`, TMAX) -names(temps) <- c('year', 'month', 'day', 'weekday', 'ws', 'prcp', 'temp_2', -'temp_1', 'average', 'actual') -sapply(temps$average, function(x) (runif(1, min = x - 20, max = x + 20))) -sapply(temps$average, function(x) round(runif(1, min = x - 20, max = x + 20))) -temps$friend <- sapply(temps$average, function(x) round(runif(1, min = x - 20, max = x + 20))) -View(temps) -temps <- temps[-c(1,2), ] -ggplot(temps, aes(x = seq(1, nrow(temps)), y = ws)) + geom_point() -for (column in c('ws', 'prcp', 'temp_2', 'temp_1', 'average', 'actual', 'friend')) { -ggplot(temps, aes(x = seq(1, nrow(temps)), y = column)) + geom_point() -} -print(ggplot(temps, aes(x = seq(1, nrow(temps)), y = column)) + geom_point()) -for (column in c('ws', 'prcp', 'temp_2', 'temp_1', 'average', 'actual', 'friend')) { -print(ggplot(temps, aes(x = seq(1, nrow(temps)), y = temps[[column]])) + geom_point()) -} -temps <- temps[complete.cases(temps), ] -summary(temps) -write_csv(temps, 'temps_extended.csv') -table(temps$year) -library(tidyverse) -library(lubridate) -temps <- read_csv('raw_temps.csv') -temps <- dplyr::filter(temps, NAME == 'SEATTLE TACOMA INTERNATIONAL AIRPORT, WA US') -temps <- mutate(temps, year = lubridate::year(DATE), -month = lubridate::month(DATE), -day = lubridate::day(DATE), -week = lubridate::wday(DATE, label = TRUE)) %>% -arrange(DATE) -temps$temp_1 <- c(NA, temps$TMAX[1:{nrow(temps) - 1}]) -temps$temp_2 <- c(NA, NA, temps$TMAX[1:{nrow(temps) - 2}]) -library(tidyverse) -library(lubridate) -# Read in the data as a dataframe -temps <- read_csv('raw_temps.csv') -# Make sure all readings are from same station -temps <- dplyr::filter(temps, NAME == 'SEATTLE TACOMA INTERNATIONAL AIRPORT, WA US') -# Create month, day, and week columns -temps <- mutate(temps, year = lubridate::year(DATE), -month = lubridate::month(DATE), -day = lubridate::day(DATE), -week = lubridate::wday(DATE, label = TRUE)) %>% -arrange(DATE) -# Create the past max temperature columns -temps$temp_1 <- c(NA, temps$TMAX[1:{nrow(temps) - 1}]) -temps$temp_2 <- c(NA, NA, temps$TMAX[1:{nrow(temps) - 2}]) -# Shift the average wind speed, precipitation, and snow depth -temps$AWND <- c(NA, temps$AWND[1:{nrow(temps) - 1}]) -temps$PRCP <- c(NA, temps$PRCP[1:{nrow(temps) - 1}]) -temps$SNWD <- c(NA, temps$SNWD[1:{nrow(temps) - 1}]) -averages <- read_csv('hist_averages.csv') -averages$month <- as.numeric(substr(averages$DATE, 5, 6)) -averages$day <- as.numeric(substr(averages$DATE, 7, 8)) -temps <- merge(temps, averages[, c('month', 'day', 'DLY-TMAX-NORMAL')], -by = c('month', 'day'), all.x = TRUE) %>% arrange(DATE) -temps <- dplyr::select(temps, year, month, day, week, AWND, PRCP, SNWD, -temp_2, temp_1, `DLY-TMAX-NORMAL`, TMAX) -names(temps) <- c('year', 'month', 'day', 'weekday', 'ws', 'prcp', 'snwd', -'temp_2', 'temp_1', 'average', 'actual') -temps$friend <- sapply(temps$average, function(x) -round(runif(1, min = x - 20, max = x + 20))) -temps <- temps[-c(1,2), ] -temps <- temps[complete.cases(temps), ] -summary(temps) -View(temps) -# RF temperature modeling -# -# Read in data -library(tidyverse) -library(lubridate) -# Read in the data as a dataframe -temps <- read_csv('raw_temps.csv') -# Make sure all readings are from same station -temps <- dplyr::filter(temps, NAME == 'SEATTLE TACOMA INTERNATIONAL AIRPORT, WA US') -# Create month, day, and week columns -temps <- mutate(temps, year = lubridate::year(DATE), -month = lubridate::month(DATE), -day = lubridate::day(DATE), -week = lubridate::wday(DATE, label = TRUE)) %>% -arrange(DATE) -# Create the past max temperature columns -temps$temp_1 <- c(NA, temps$TMAX[1:{nrow(temps) - 1}]) -temps$temp_2 <- c(NA, NA, temps$TMAX[1:{nrow(temps) - 2}]) -# Shift the average wind speed, precipitation, and snow depth -temps$AWND <- c(NA, temps$AWND[1:{nrow(temps) - 1}]) -temps$PRCP <- c(NA, temps$PRCP[1:{nrow(temps) - 1}]) -temps$SNWD <- c(NA, temps$SNWD[1:{nrow(temps) - 1}]) -# Read in the averages as a dataframe -averages <- read_csv('hist_averages.csv') -# Create columns for the month and day -averages$month <- as.numeric(substr(averages$DATE, 5, 6)) -averages$day <- as.numeric(substr(averages$DATE, 7, 8)) -# Join the averages to the temperature measurements -temps <- merge(temps, averages[, c('month', 'day', 'DLY-TMAX-NORMAL')], -by = c('month', 'day'), all.x = TRUE) %>% arrange(DATE) -# Select and order relevant columns -temps <- dplyr::select(temps, year, month, day, week, AWND, PRCP, SNWD, -temp_2, temp_1, `DLY-TMAX-NORMAL`, TMAX) -# Rename columns -names(temps) <- c('year', 'month', 'day', 'weekday', 'ws_1', 'prcp_1', 'snwd_1', -'temp_2', 'temp_1', 'average', 'actual') -# Friend predictions -temps$friend <- sapply(temps$average, function(x) -round(runif(1, min = x - 20, max = x + 20))) -# Remove first two rows -temps <- temps[-c(1,2), ] -# Remove na -temps <- temps[complete.cases(temps), ] -# Summary of data -summary(temps) -# Write to csv file -write_csv(temps, 'temps_extended.csv') diff --git a/Day_5/random_forest_explained/data/hist_averages.csv b/Day_5/random_forest_explained/data/hist_averages.csv deleted file mode 100644 index 7815d05..0000000 --- a/Day_5/random_forest_explained/data/hist_averages.csv +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:02e5acdf9b9d5d0ee67e0f55b4db5759f68cda00b580c281d54f390323072c20 -size 38819 diff --git a/Day_5/random_forest_explained/data/munge_temps.R b/Day_5/random_forest_explained/data/munge_temps.R deleted file mode 100644 index df78f08..0000000 --- a/Day_5/random_forest_explained/data/munge_temps.R +++ /dev/null @@ -1,61 +0,0 @@ -# RF temperature modeling -# -# Read in data -library(tidyverse) -library(lubridate) - -# Read in the data as a dataframe -temps <- read_csv('raw_temps.csv') - -# Make sure all readings are from same station -temps <- dplyr::filter(temps, NAME == 'SEATTLE TACOMA INTERNATIONAL AIRPORT, WA US') - -# Create month, day, and week columns -temps <- mutate(temps, year = lubridate::year(DATE), - month = lubridate::month(DATE), - day = lubridate::day(DATE), - week = lubridate::wday(DATE, label = TRUE)) %>% - arrange(DATE) - -# Create the past max temperature columns -temps$temp_1 <- c(NA, temps$TMAX[1:{nrow(temps) - 1}]) -temps$temp_2 <- c(NA, NA, temps$TMAX[1:{nrow(temps) - 2}]) -# Shift the average wind speed, precipitation, and snow depth -temps$AWND <- c(NA, temps$AWND[1:{nrow(temps) - 1}]) -temps$PRCP <- c(NA, temps$PRCP[1:{nrow(temps) - 1}]) -temps$SNWD <- c(NA, temps$SNWD[1:{nrow(temps) - 1}]) - -# Read in the averages as a dataframe -averages <- read_csv('hist_averages.csv') - -# Create columns for the month and day -averages$month <- as.numeric(substr(averages$DATE, 5, 6)) -averages$day <- as.numeric(substr(averages$DATE, 7, 8)) - -# Join the averages to the temperature measurements -temps <- merge(temps, averages[, c('month', 'day', 'DLY-TMAX-NORMAL')], - by = c('month', 'day'), all.x = TRUE) %>% arrange(DATE) - -# Select and order relevant columns -temps <- dplyr::select(temps, year, month, day, week, AWND, PRCP, SNWD, - temp_2, temp_1, `DLY-TMAX-NORMAL`, TMAX) - -# Rename columns -names(temps) <- c('year', 'month', 'day', 'weekday', 'ws_1', 'prcp_1', 'snwd_1', - 'temp_2', 'temp_1', 'average', 'actual') - -# Friend predictions -temps$friend <- sapply(temps$average, function(x) - round(runif(1, min = x - 20, max = x + 20))) - -# Remove first two rows -temps <- temps[-c(1,2), ] - -# Remove na -temps <- temps[complete.cases(temps), ] - -# Summary of data -summary(temps) - -# Write to csv file -write_csv(temps, 'temps_extended.csv') diff --git a/Day_5/random_forest_explained/data/raw_temps.csv b/Day_5/random_forest_explained/data/raw_temps.csv deleted file mode 100644 index f9df2e9..0000000 --- a/Day_5/random_forest_explained/data/raw_temps.csv +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:1f786379e28bafe8cb4a4c60f3dfc888aa1bee3d71ee9fa5da5226a81066573b -size 362408 diff --git a/Day_5/random_forest_explained/data/temp_format.R b/Day_5/random_forest_explained/data/temp_format.R deleted file mode 100644 index f43062f..0000000 --- a/Day_5/random_forest_explained/data/temp_format.R +++ /dev/null @@ -1,27 +0,0 @@ -# RF temperature modeling -# -# Read in data -library(tidyverse) -library(lubridate) - -temps <- read_csv('1159640.csv') -temps <- mutate(temps, month = lubridate::month(DATE), - day = lubridate::day(DATE), week = lubridate::wday(DATE, label = TRUE)) -temps$temp_1 <- c(45, temps$TMAX[1:{nrow(temps) - 1}]) -temps$temp_2 <- c(45, 44, temps$TMAX[1:{nrow(temps) - 2}]) - -averages <- read_csv('1159653.csv') -averages <- dplyr::filter(averages, STATION_NAME == 'SEATTLE TACOMA INTERNATIONAL AIRPORT WA US') -averages$month <- as.numeric(substr(averages$DATE, 5, 6)) -averages$day <- as.numeric(substr(averages$DATE, 7, 8)) - -temps <- merge(temps, averages[, c('month', 'day', 'DLY-TMAX-NORMAL')], by = c('month', 'day'), - all.x = TRUE) - -temps <- temps[, c('month', 'week', 'day', 'DATE', 'TMAX', 'temp_1', 'temp_2', 'DLY-TMAX-NORMAL')] -temps <- dplyr::rename(temps, date = DATE, actual = TMAX) -temps <- dplyr::rename(temps, average = 'DLY-TMAX-NORMAL') -temps$year <- 2016 -temps <- temps[, -which(names(temps) == 'date')] - -write_csv(temps, 'mod_temps.csv') diff --git a/Day_5/random_forest_explained/data/temps.csv b/Day_5/random_forest_explained/data/temps.csv deleted file mode 100644 index 5425f12..0000000 --- a/Day_5/random_forest_explained/data/temps.csv +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:f5ba24c51570555bbe119b9bf0739785c65aceadcc8d01709b31ccc52afae335 -size 11260 diff --git a/Day_5/random_forest_explained/data/temps_extended.csv b/Day_5/random_forest_explained/data/temps_extended.csv deleted file mode 100644 index 0bf4ae2..0000000 --- a/Day_5/random_forest_explained/data/temps_extended.csv +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:45c030480125ea24f9cea7e87346858b9864f8e165a5fa6d8deae2856d15afef -size 91141 diff --git a/Day_5/random_forest_explained/images/Temperature Prediction Decision Tree - Page 1.png b/Day_5/random_forest_explained/images/Temperature Prediction Decision Tree - Page 1.png deleted file mode 100644 index 26379d2..0000000 Binary files a/Day_5/random_forest_explained/images/Temperature Prediction Decision Tree - Page 1.png and /dev/null differ diff --git a/Day_5/random_forest_explained/images/actual_and_variables.png b/Day_5/random_forest_explained/images/actual_and_variables.png deleted file mode 100644 index 43454a6..0000000 Binary files a/Day_5/random_forest_explained/images/actual_and_variables.png and /dev/null differ diff --git a/Day_5/random_forest_explained/images/basic_plots.png b/Day_5/random_forest_explained/images/basic_plots.png deleted file mode 100644 index 7d19831..0000000 Binary files a/Day_5/random_forest_explained/images/basic_plots.png and /dev/null differ diff --git a/Day_5/random_forest_explained/images/data_summary.PNG b/Day_5/random_forest_explained/images/data_summary.PNG deleted file mode 100644 index a085ba2..0000000 Binary files a/Day_5/random_forest_explained/images/data_summary.PNG and /dev/null differ diff --git a/Day_5/random_forest_explained/images/exp_additional_plots.png b/Day_5/random_forest_explained/images/exp_additional_plots.png deleted file mode 100644 index 1b8ca13..0000000 Binary files a/Day_5/random_forest_explained/images/exp_additional_plots.png and /dev/null differ diff --git a/Day_5/random_forest_explained/images/exp_cumulative_importances.png b/Day_5/random_forest_explained/images/exp_cumulative_importances.png deleted file mode 100644 index 0594033..0000000 Binary files a/Day_5/random_forest_explained/images/exp_cumulative_importances.png and /dev/null differ diff --git a/Day_5/random_forest_explained/images/exp_data.PNG b/Day_5/random_forest_explained/images/exp_data.PNG deleted file mode 100644 index e522746..0000000 Binary files a/Day_5/random_forest_explained/images/exp_data.PNG and /dev/null differ diff --git a/Day_5/random_forest_explained/images/exp_data_summary.PNG b/Day_5/random_forest_explained/images/exp_data_summary.PNG deleted file mode 100644 index b4e67f4..0000000 Binary files a/Day_5/random_forest_explained/images/exp_data_summary.PNG and /dev/null differ diff --git a/Day_5/random_forest_explained/images/exp_temp_plots.png b/Day_5/random_forest_explained/images/exp_temp_plots.png deleted file mode 100644 index ddb60b6..0000000 Binary files a/Day_5/random_forest_explained/images/exp_temp_plots.png and /dev/null differ diff --git a/Day_5/random_forest_explained/images/exp_variable_importances.png b/Day_5/random_forest_explained/images/exp_variable_importances.png deleted file mode 100644 index d799b89..0000000 Binary files a/Day_5/random_forest_explained/images/exp_variable_importances.png and /dev/null differ diff --git a/Day_5/random_forest_explained/images/feature_importances.png b/Day_5/random_forest_explained/images/feature_importances.png deleted file mode 100644 index d5c4712..0000000 Binary files a/Day_5/random_forest_explained/images/feature_importances.png and /dev/null differ diff --git a/Day_5/random_forest_explained/images/feature_tradeoffs.PNG b/Day_5/random_forest_explained/images/feature_tradeoffs.PNG deleted file mode 100644 index 9950f61..0000000 Binary files a/Day_5/random_forest_explained/images/feature_tradeoffs.PNG and /dev/null differ diff --git a/Day_5/random_forest_explained/images/human_decision_process.PNG b/Day_5/random_forest_explained/images/human_decision_process.PNG deleted file mode 100644 index 001cb4d..0000000 Binary files a/Day_5/random_forest_explained/images/human_decision_process.PNG and /dev/null differ diff --git a/Day_5/random_forest_explained/images/important_data_snapshot.PNG b/Day_5/random_forest_explained/images/important_data_snapshot.PNG deleted file mode 100644 index 50860b1..0000000 Binary files a/Day_5/random_forest_explained/images/important_data_snapshot.PNG and /dev/null differ diff --git a/Day_5/random_forest_explained/images/model_comparison.png b/Day_5/random_forest_explained/images/model_comparison.png deleted file mode 100644 index 5d64d97..0000000 Binary files a/Day_5/random_forest_explained/images/model_comparison.png and /dev/null differ diff --git a/Day_5/random_forest_explained/images/pair_plots.png b/Day_5/random_forest_explained/images/pair_plots.png deleted file mode 100644 index 8a3135c..0000000 Binary files a/Day_5/random_forest_explained/images/pair_plots.png and /dev/null differ diff --git a/Day_5/random_forest_explained/images/pair_plots_small.png b/Day_5/random_forest_explained/images/pair_plots_small.png deleted file mode 100644 index 033e5f6..0000000 Binary files a/Day_5/random_forest_explained/images/pair_plots_small.png and /dev/null differ diff --git a/Day_5/random_forest_explained/images/small_tree.dot b/Day_5/random_forest_explained/images/small_tree.dot deleted file mode 100644 index 6bb10dd..0000000 --- a/Day_5/random_forest_explained/images/small_tree.dot +++ /dev/null @@ -1,33 +0,0 @@ -digraph Tree { -node [shape=box, style="rounded", color="black", fontname=helvetica] ; -edge [fontname=helvetica] ; -0 [label="temp_1 <= 59.5\nmse = 145.7\nsamples = 162\nvalue = 62.7"] ; -1 [label="average <= 46.8\nmse = 42.6\nsamples = 63\nvalue = 51.2"] ; -0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ; -2 [label="temp_1 <= 44.5\nmse = 17.0\nsamples = 17\nvalue = 42.9"] ; -1 -> 2 ; -3 [label="mse = 4.4\nsamples = 8\nvalue = 41.0"] ; -2 -> 3 ; -4 [label="mse = 22.2\nsamples = 9\nvalue = 45.0"] ; -2 -> 4 ; -5 [label="temp_1 <= 55.5\nmse = 19.5\nsamples = 46\nvalue = 54.1"] ; -1 -> 5 ; -6 [label="mse = 7.7\nsamples = 29\nvalue = 51.9"] ; -5 -> 6 ; -7 [label="mse = 15.6\nsamples = 17\nvalue = 58.2"] ; -5 -> 7 ; -8 [label="temp_1 <= 67.5\nmse = 66.8\nsamples = 99\nvalue = 70.4"] ; -0 -> 8 [labeldistance=2.5, labelangle=-45, headlabel="False"] ; -9 [label="average <= 60.8\nmse = 23.5\nsamples = 42\nvalue = 63.9"] ; -8 -> 9 ; -10 [label="mse = 13.7\nsamples = 19\nvalue = 60.7"] ; -9 -> 10 ; -11 [label="mse = 17.3\nsamples = 23\nvalue = 66.3"] ; -9 -> 11 ; -12 [label="average <= 75.6\nmse = 44.2\nsamples = 57\nvalue = 75.3"] ; -8 -> 12 ; -13 [label="mse = 34.3\nsamples = 42\nvalue = 73.0"] ; -12 -> 13 ; -14 [label="mse = 27.1\nsamples = 15\nvalue = 80.6"] ; -12 -> 14 ; -} \ No newline at end of file diff --git a/Day_5/random_forest_explained/images/small_tree.png b/Day_5/random_forest_explained/images/small_tree.png deleted file mode 100644 index 9d4a436..0000000 Binary files a/Day_5/random_forest_explained/images/small_tree.png and /dev/null differ diff --git a/Day_5/random_forest_explained/images/small_tree_annotated.PNG b/Day_5/random_forest_explained/images/small_tree_annotated.PNG deleted file mode 100644 index 1feb96a..0000000 Binary files a/Day_5/random_forest_explained/images/small_tree_annotated.PNG and /dev/null differ diff --git a/Day_5/random_forest_explained/images/temp_actual_predicted.png b/Day_5/random_forest_explained/images/temp_actual_predicted.png deleted file mode 100644 index 5f413d0..0000000 Binary files a/Day_5/random_forest_explained/images/temp_actual_predicted.png and /dev/null differ diff --git a/Day_5/random_forest_explained/images/temperature_prediction_decision_tree.PNG b/Day_5/random_forest_explained/images/temperature_prediction_decision_tree.PNG deleted file mode 100644 index 40f0e7c..0000000 Binary files a/Day_5/random_forest_explained/images/temperature_prediction_decision_tree.PNG and /dev/null differ diff --git a/Day_5/random_forest_explained/images/tree.dot b/Day_5/random_forest_explained/images/tree.dot deleted file mode 100644 index e55400b..0000000 --- a/Day_5/random_forest_explained/images/tree.dot +++ /dev/null @@ -1,521 +0,0 @@ -digraph Tree { -node [shape=box, style="rounded", color="black", fontname=helvetica] ; -edge [fontname=helvetica] ; -0 [label="temp_1 <= 59.5\nmse = 145.7\nsamples = 162\nvalue = 62.7"] ; -1 [label="average <= 46.8\nmse = 42.6\nsamples = 63\nvalue = 51.2"] ; -0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ; -2 [label="temp_1 <= 44.5\nmse = 17.0\nsamples = 17\nvalue = 42.9"] ; -1 -> 2 ; -3 [label="week_Mon <= 0.5\nmse = 4.4\nsamples = 8\nvalue = 41.0"] ; -2 -> 3 ; -4 [label="friend <= 35.5\nmse = 2.7\nsamples = 7\nvalue = 40.6"] ; -3 -> 4 ; -5 [label="mse = 0.0\nsamples = 1\nvalue = 44.0"] ; -4 -> 5 ; -6 [label="temp_2 <= 44.5\nmse = 0.7\nsamples = 6\nvalue = 40.0"] ; -4 -> 6 ; -7 [label="average <= 45.5\nmse = 0.5\nsamples = 4\nvalue = 39.6"] ; -6 -> 7 ; -8 [label="mse = 0.0\nsamples = 2\nvalue = 39.0"] ; -7 -> 8 ; -9 [label="day <= 10.5\nmse = 0.2\nsamples = 2\nvalue = 40.2"] ; -7 -> 9 ; -10 [label="mse = 0.0\nsamples = 1\nvalue = 41.0"] ; -9 -> 10 ; -11 [label="mse = 0.0\nsamples = 1\nvalue = 40.0"] ; -9 -> 11 ; -12 [label="mse = 0.0\nsamples = 2\nvalue = 41.0"] ; -6 -> 12 ; -13 [label="mse = 0.0\nsamples = 1\nvalue = 46.0"] ; -3 -> 13 ; -14 [label="average <= 45.2\nmse = 22.2\nsamples = 9\nvalue = 45.0"] ; -2 -> 14 ; -15 [label="mse = 0.0\nsamples = 1\nvalue = 40.0"] ; -14 -> 15 ; -16 [label="average <= 45.5\nmse = 20.8\nsamples = 8\nvalue = 45.9"] ; -14 -> 16 ; -17 [label="mse = 0.0\nsamples = 1\nvalue = 57.0"] ; -16 -> 17 ; -18 [label="average <= 45.5\nmse = 9.4\nsamples = 7\nvalue = 44.8"] ; -16 -> 18 ; -19 [label="mse = 0.0\nsamples = 1\nvalue = 40.0"] ; -18 -> 19 ; -20 [label="average <= 46.1\nmse = 4.5\nsamples = 6\nvalue = 46.0"] ; -18 -> 20 ; -21 [label="day <= 1.5\nmse = 0.2\nsamples = 2\nvalue = 44.3"] ; -20 -> 21 ; -22 [label="mse = 0.0\nsamples = 1\nvalue = 45.0"] ; -21 -> 22 ; -23 [label="mse = 0.0\nsamples = 1\nvalue = 44.0"] ; -21 -> 23 ; -24 [label="temp_1 <= 48.5\nmse = 4.4\nsamples = 4\nvalue = 47.0"] ; -20 -> 24 ; -25 [label="average <= 46.6\nmse = 0.2\nsamples = 2\nvalue = 49.5"] ; -24 -> 25 ; -26 [label="mse = 0.0\nsamples = 1\nvalue = 50.0"] ; -25 -> 26 ; -27 [label="mse = 0.0\nsamples = 1\nvalue = 49.0"] ; -25 -> 27 ; -28 [label="week_Sun <= 0.5\nmse = 0.2\nsamples = 2\nvalue = 45.3"] ; -24 -> 28 ; -29 [label="mse = 0.0\nsamples = 1\nvalue = 45.0"] ; -28 -> 29 ; -30 [label="mse = 0.0\nsamples = 1\nvalue = 46.0"] ; -28 -> 30 ; -31 [label="temp_1 <= 55.5\nmse = 19.5\nsamples = 46\nvalue = 54.1"] ; -1 -> 31 ; -32 [label="day <= 4.0\nmse = 7.7\nsamples = 29\nvalue = 51.9"] ; -31 -> 32 ; -33 [label="week_Sat <= 0.5\nmse = 1.7\nsamples = 3\nvalue = 46.8"] ; -32 -> 33 ; -34 [label="mse = 0.0\nsamples = 2\nvalue = 46.0"] ; -33 -> 34 ; -35 [label="mse = 0.0\nsamples = 1\nvalue = 49.0"] ; -33 -> 35 ; -36 [label="friend <= 43.5\nmse = 5.8\nsamples = 26\nvalue = 52.3"] ; -32 -> 36 ; -37 [label="friend <= 41.5\nmse = 7.4\nsamples = 7\nvalue = 53.5"] ; -36 -> 37 ; -38 [label="average <= 50.5\nmse = 3.4\nsamples = 5\nvalue = 52.7"] ; -37 -> 38 ; -39 [label="day <= 21.5\nmse = 1.4\nsamples = 4\nvalue = 53.6"] ; -38 -> 39 ; -40 [label="friend <= 30.5\nmse = 0.2\nsamples = 3\nvalue = 54.3"] ; -39 -> 40 ; -41 [label="mse = 0.0\nsamples = 1\nvalue = 54.0"] ; -40 -> 41 ; -42 [label="mse = 0.0\nsamples = 2\nvalue = 55.0"] ; -40 -> 42 ; -43 [label="mse = 0.0\nsamples = 1\nvalue = 52.0"] ; -39 -> 43 ; -44 [label="mse = 0.0\nsamples = 1\nvalue = 50.0"] ; -38 -> 44 ; -45 [label="average <= 51.0\nmse = 2.2\nsamples = 2\nvalue = 58.5"] ; -37 -> 45 ; -46 [label="mse = 0.0\nsamples = 1\nvalue = 60.0"] ; -45 -> 46 ; -47 [label="mse = 0.0\nsamples = 1\nvalue = 57.0"] ; -45 -> 47 ; -48 [label="average <= 47.3\nmse = 4.3\nsamples = 19\nvalue = 51.8"] ; -36 -> 48 ; -49 [label="mse = 0.0\nsamples = 1\nvalue = 48.0"] ; -48 -> 49 ; -50 [label="friend <= 53.5\nmse = 3.6\nsamples = 18\nvalue = 52.1"] ; -48 -> 50 ; -51 [label="temp_2 <= 49.5\nmse = 3.1\nsamples = 5\nvalue = 50.7"] ; -50 -> 51 ; -52 [label="mse = 0.0\nsamples = 1\nvalue = 53.0"] ; -51 -> 52 ; -53 [label="week_Sun <= 0.5\nmse = 1.7\nsamples = 4\nvalue = 49.9"] ; -51 -> 53 ; -54 [label="day <= 16.5\nmse = 0.2\nsamples = 3\nvalue = 48.5"] ; -53 -> 54 ; -55 [label="mse = 0.0\nsamples = 1\nvalue = 49.0"] ; -54 -> 55 ; -56 [label="mse = 0.0\nsamples = 2\nvalue = 48.0"] ; -54 -> 56 ; -57 [label="mse = 0.0\nsamples = 1\nvalue = 51.0"] ; -53 -> 57 ; -58 [label="temp_1 <= 49.5\nmse = 1.8\nsamples = 13\nvalue = 53.0"] ; -50 -> 58 ; -59 [label="friend <= 62.0\nmse = 0.2\nsamples = 3\nvalue = 51.5"] ; -58 -> 59 ; -60 [label="mse = 0.0\nsamples = 1\nvalue = 52.0"] ; -59 -> 60 ; -61 [label="mse = 0.0\nsamples = 2\nvalue = 51.0"] ; -59 -> 61 ; -62 [label="month <= 2.5\nmse = 1.4\nsamples = 10\nvalue = 53.4"] ; -58 -> 62 ; -63 [label="month <= 1.5\nmse = 1.9\nsamples = 4\nvalue = 52.7"] ; -62 -> 63 ; -64 [label="temp_1 <= 51.5\nmse = 0.8\nsamples = 3\nvalue = 53.5"] ; -63 -> 64 ; -65 [label="mse = 0.0\nsamples = 2\nvalue = 54.0"] ; -64 -> 65 ; -66 [label="mse = 0.0\nsamples = 1\nvalue = 52.0"] ; -64 -> 66 ; -67 [label="mse = 0.0\nsamples = 1\nvalue = 51.0"] ; -63 -> 67 ; -68 [label="week_Sat <= 0.5\nmse = 0.5\nsamples = 6\nvalue = 53.9"] ; -62 -> 68 ; -69 [label="day <= 11.0\nmse = 0.1\nsamples = 5\nvalue = 54.1"] ; -68 -> 69 ; -70 [label="mse = 0.0\nsamples = 1\nvalue = 55.0"] ; -69 -> 70 ; -71 [label="mse = 0.0\nsamples = 4\nvalue = 54.0"] ; -69 -> 71 ; -72 [label="mse = 0.0\nsamples = 1\nvalue = 52.0"] ; -68 -> 72 ; -73 [label="week_Wed <= 0.5\nmse = 15.6\nsamples = 17\nvalue = 58.2"] ; -31 -> 73 ; -74 [label="average <= 51.8\nmse = 13.5\nsamples = 14\nvalue = 59.7"] ; -73 -> 74 ; -75 [label="mse = 0.0\nsamples = 4\nvalue = 55.0"] ; -74 -> 75 ; -76 [label="day <= 5.5\nmse = 7.7\nsamples = 10\nvalue = 61.4"] ; -74 -> 76 ; -77 [label="average <= 53.0\nmse = 0.2\nsamples = 2\nvalue = 64.7"] ; -76 -> 77 ; -78 [label="mse = 0.0\nsamples = 1\nvalue = 64.0"] ; -77 -> 78 ; -79 [label="mse = 0.0\nsamples = 1\nvalue = 65.0"] ; -77 -> 79 ; -80 [label="day <= 17.0\nmse = 5.9\nsamples = 8\nvalue = 60.5"] ; -76 -> 80 ; -81 [label="temp_1 <= 56.5\nmse = 3.1\nsamples = 5\nvalue = 59.0"] ; -80 -> 81 ; -82 [label="mse = 0.0\nsamples = 1\nvalue = 55.0"] ; -81 -> 82 ; -83 [label="week_Thurs <= 0.5\nmse = 0.6\nsamples = 4\nvalue = 59.7"] ; -81 -> 83 ; -84 [label="week_Sun <= 0.5\nmse = 0.2\nsamples = 3\nvalue = 60.3"] ; -83 -> 84 ; -85 [label="mse = 0.0\nsamples = 2\nvalue = 60.0"] ; -84 -> 85 ; -86 [label="mse = 0.0\nsamples = 1\nvalue = 61.0"] ; -84 -> 86 ; -87 [label="mse = 0.0\nsamples = 1\nvalue = 59.0"] ; -83 -> 87 ; -88 [label="month <= 6.5\nmse = 0.5\nsamples = 3\nvalue = 63.0"] ; -80 -> 88 ; -89 [label="temp_1 <= 57.0\nmse = 0.2\nsamples = 2\nvalue = 63.3"] ; -88 -> 89 ; -90 [label="mse = 0.0\nsamples = 1\nvalue = 64.0"] ; -89 -> 90 ; -91 [label="mse = 0.0\nsamples = 1\nvalue = 63.0"] ; -89 -> 91 ; -92 [label="mse = 0.0\nsamples = 1\nvalue = 62.0"] ; -88 -> 92 ; -93 [label="day <= 12.5\nmse = 3.4\nsamples = 3\nvalue = 54.8"] ; -73 -> 93 ; -94 [label="temp_2 <= 56.5\nmse = 0.8\nsamples = 2\nvalue = 56.5"] ; -93 -> 94 ; -95 [label="mse = 0.0\nsamples = 1\nvalue = 55.0"] ; -94 -> 95 ; -96 [label="mse = 0.0\nsamples = 1\nvalue = 57.0"] ; -94 -> 96 ; -97 [label="mse = 0.0\nsamples = 1\nvalue = 53.0"] ; -93 -> 97 ; -98 [label="temp_1 <= 67.5\nmse = 66.8\nsamples = 99\nvalue = 70.4"] ; -0 -> 98 [labeldistance=2.5, labelangle=-45, headlabel="False"] ; -99 [label="average <= 60.8\nmse = 23.5\nsamples = 42\nvalue = 63.9"] ; -98 -> 99 ; -100 [label="day <= 27.5\nmse = 13.7\nsamples = 19\nvalue = 60.7"] ; -99 -> 100 ; -101 [label="temp_2 <= 59.5\nmse = 8.2\nsamples = 17\nvalue = 59.8"] ; -100 -> 101 ; -102 [label="week_Sat <= 0.5\nmse = 7.7\nsamples = 7\nvalue = 62.4"] ; -101 -> 102 ; -103 [label="month <= 6.5\nmse = 3.7\nsamples = 6\nvalue = 61.6"] ; -102 -> 103 ; -104 [label="average <= 50.9\nmse = 0.2\nsamples = 3\nvalue = 59.7"] ; -103 -> 104 ; -105 [label="mse = 0.0\nsamples = 1\nvalue = 59.0"] ; -104 -> 105 ; -106 [label="mse = 0.0\nsamples = 2\nvalue = 60.0"] ; -104 -> 106 ; -107 [label="week_Fri <= 0.5\nmse = 1.5\nsamples = 3\nvalue = 63.0"] ; -103 -> 107 ; -108 [label="temp_1 <= 61.5\nmse = 0.2\nsamples = 2\nvalue = 62.3"] ; -107 -> 108 ; -109 [label="mse = 0.0\nsamples = 1\nvalue = 63.0"] ; -108 -> 109 ; -110 [label="mse = 0.0\nsamples = 1\nvalue = 62.0"] ; -108 -> 110 ; -111 [label="mse = 0.0\nsamples = 1\nvalue = 65.0"] ; -107 -> 111 ; -112 [label="mse = 0.0\nsamples = 1\nvalue = 68.0"] ; -102 -> 112 ; -113 [label="friend <= 47.0\nmse = 3.8\nsamples = 10\nvalue = 58.6"] ; -101 -> 113 ; -114 [label="average <= 59.2\nmse = 2.2\nsamples = 3\nvalue = 56.2"] ; -113 -> 114 ; -115 [label="mse = 0.0\nsamples = 2\nvalue = 58.0"] ; -114 -> 115 ; -116 [label="mse = 0.0\nsamples = 1\nvalue = 55.0"] ; -114 -> 116 ; -117 [label="day <= 12.5\nmse = 1.1\nsamples = 7\nvalue = 59.6"] ; -113 -> 117 ; -118 [label="temp_1 <= 61.5\nmse = 0.6\nsamples = 3\nvalue = 58.6"] ; -117 -> 118 ; -119 [label="mse = 0.0\nsamples = 1\nvalue = 57.0"] ; -118 -> 119 ; -120 [label="mse = 0.0\nsamples = 2\nvalue = 59.0"] ; -118 -> 120 ; -121 [label="average <= 59.5\nmse = 0.2\nsamples = 4\nvalue = 60.3"] ; -117 -> 121 ; -122 [label="mse = 0.0\nsamples = 2\nvalue = 61.0"] ; -121 -> 122 ; -123 [label="mse = 0.0\nsamples = 2\nvalue = 60.0"] ; -121 -> 123 ; -124 [label="mse = 0.0\nsamples = 2\nvalue = 68.0"] ; -100 -> 124 ; -125 [label="temp_2 <= 78.0\nmse = 17.3\nsamples = 23\nvalue = 66.3"] ; -99 -> 125 ; -126 [label="friend <= 54.5\nmse = 13.4\nsamples = 22\nvalue = 66.8"] ; -125 -> 126 ; -127 [label="month <= 7.5\nmse = 3.6\nsamples = 2\nvalue = 60.3"] ; -126 -> 127 ; -128 [label="mse = 0.0\nsamples = 1\nvalue = 59.0"] ; -127 -> 128 ; -129 [label="mse = 0.0\nsamples = 1\nvalue = 63.0"] ; -127 -> 129 ; -130 [label="temp_2 <= 64.5\nmse = 10.2\nsamples = 20\nvalue = 67.4"] ; -126 -> 130 ; -131 [label="friend <= 71.0\nmse = 9.0\nsamples = 8\nvalue = 68.9"] ; -130 -> 131 ; -132 [label="temp_1 <= 65.5\nmse = 2.0\nsamples = 3\nvalue = 71.6"] ; -131 -> 132 ; -133 [label="mse = 0.0\nsamples = 2\nvalue = 71.0"] ; -132 -> 133 ; -134 [label="mse = 0.0\nsamples = 1\nvalue = 75.0"] ; -132 -> 134 ; -135 [label="friend <= 77.0\nmse = 4.4\nsamples = 5\nvalue = 66.8"] ; -131 -> 135 ; -136 [label="day <= 24.0\nmse = 1.0\nsamples = 3\nvalue = 65.2"] ; -135 -> 136 ; -137 [label="mse = 0.0\nsamples = 1\nvalue = 66.0"] ; -136 -> 137 ; -138 [label="mse = 0.0\nsamples = 2\nvalue = 64.0"] ; -136 -> 138 ; -139 [label="week_Thurs <= 0.5\nmse = 1.7\nsamples = 2\nvalue = 68.8"] ; -135 -> 139 ; -140 [label="mse = 0.0\nsamples = 1\nvalue = 68.0"] ; -139 -> 140 ; -141 [label="mse = 0.0\nsamples = 1\nvalue = 71.0"] ; -139 -> 141 ; -142 [label="average <= 71.2\nmse = 7.4\nsamples = 12\nvalue = 66.0"] ; -130 -> 142 ; -143 [label="average <= 69.2\nmse = 5.1\nsamples = 10\nvalue = 64.9"] ; -142 -> 143 ; -144 [label="average <= 67.3\nmse = 3.4\nsamples = 9\nvalue = 65.3"] ; -143 -> 144 ; -145 [label="temp_2 <= 72.5\nmse = 3.0\nsamples = 6\nvalue = 64.6"] ; -144 -> 145 ; -146 [label="temp_1 <= 64.5\nmse = 1.6\nsamples = 5\nvalue = 64.1"] ; -145 -> 146 ; -147 [label="average <= 63.5\nmse = 0.2\nsamples = 3\nvalue = 63.2"] ; -146 -> 147 ; -148 [label="mse = 0.0\nsamples = 1\nvalue = 64.0"] ; -147 -> 148 ; -149 [label="mse = 0.0\nsamples = 2\nvalue = 63.0"] ; -147 -> 149 ; -150 [label="average <= 66.4\nmse = 0.9\nsamples = 2\nvalue = 65.3"] ; -146 -> 150 ; -151 [label="mse = 0.0\nsamples = 1\nvalue = 66.0"] ; -150 -> 151 ; -152 [label="mse = 0.0\nsamples = 1\nvalue = 64.0"] ; -150 -> 152 ; -153 [label="mse = 0.0\nsamples = 1\nvalue = 68.0"] ; -145 -> 153 ; -154 [label="day <= 16.0\nmse = 1.2\nsamples = 3\nvalue = 66.8"] ; -144 -> 154 ; -155 [label="mse = 0.0\nsamples = 1\nvalue = 65.0"] ; -154 -> 155 ; -156 [label="week_Sat <= 0.5\nmse = 0.2\nsamples = 2\nvalue = 67.3"] ; -154 -> 156 ; -157 [label="mse = 0.0\nsamples = 1\nvalue = 68.0"] ; -156 -> 157 ; -158 [label="mse = 0.0\nsamples = 1\nvalue = 67.0"] ; -156 -> 158 ; -159 [label="mse = 0.0\nsamples = 1\nvalue = 60.0"] ; -143 -> 159 ; -160 [label="average <= 73.2\nmse = 2.6\nsamples = 2\nvalue = 68.8"] ; -142 -> 160 ; -161 [label="mse = 0.0\nsamples = 1\nvalue = 72.0"] ; -160 -> 161 ; -162 [label="mse = 0.0\nsamples = 1\nvalue = 68.0"] ; -160 -> 162 ; -163 [label="mse = 0.0\nsamples = 1\nvalue = 57.0"] ; -125 -> 163 ; -164 [label="average <= 75.6\nmse = 44.2\nsamples = 57\nvalue = 75.3"] ; -98 -> 164 ; -165 [label="temp_1 <= 88.0\nmse = 34.3\nsamples = 42\nvalue = 73.0"] ; -164 -> 165 ; -166 [label="week_Sat <= 0.5\nmse = 28.9\nsamples = 40\nvalue = 72.4"] ; -165 -> 166 ; -167 [label="week_Mon <= 0.5\nmse = 27.0\nsamples = 34\nvalue = 73.5"] ; -166 -> 167 ; -168 [label="month <= 7.5\nmse = 22.7\nsamples = 30\nvalue = 74.3"] ; -167 -> 168 ; -169 [label="temp_1 <= 81.5\nmse = 22.1\nsamples = 22\nvalue = 75.1"] ; -168 -> 169 ; -170 [label="week_Thurs <= 0.5\nmse = 18.6\nsamples = 20\nvalue = 75.5"] ; -169 -> 170 ; -171 [label="day <= 10.5\nmse = 18.2\nsamples = 17\nvalue = 76.1"] ; -170 -> 171 ; -172 [label="temp_2 <= 81.5\nmse = 24.1\nsamples = 8\nvalue = 74.2"] ; -171 -> 172 ; -173 [label="average <= 61.7\nmse = 6.4\nsamples = 7\nvalue = 75.5"] ; -172 -> 173 ; -174 [label="mse = 0.0\nsamples = 1\nvalue = 71.0"] ; -173 -> 174 ; -175 [label="friend <= 56.5\nmse = 4.9\nsamples = 6\nvalue = 75.9"] ; -173 -> 175 ; -176 [label="mse = 0.0\nsamples = 1\nvalue = 80.0"] ; -175 -> 176 ; -177 [label="average <= 74.4\nmse = 0.9\nsamples = 5\nvalue = 74.9"] ; -175 -> 177 ; -178 [label="temp_1 <= 76.0\nmse = 0.2\nsamples = 3\nvalue = 75.8"] ; -177 -> 178 ; -179 [label="mse = 0.0\nsamples = 2\nvalue = 76.0"] ; -178 -> 179 ; -180 [label="mse = 0.0\nsamples = 1\nvalue = 75.0"] ; -178 -> 180 ; -181 [label="mse = 0.0\nsamples = 2\nvalue = 74.0"] ; -177 -> 181 ; -182 [label="mse = 0.0\nsamples = 1\nvalue = 60.0"] ; -172 -> 182 ; -183 [label="average <= 69.1\nmse = 8.2\nsamples = 9\nvalue = 77.6"] ; -171 -> 183 ; -184 [label="temp_2 <= 65.5\nmse = 2.4\nsamples = 5\nvalue = 79.9"] ; -183 -> 184 ; -185 [label="temp_2 <= 62.0\nmse = 0.8\nsamples = 2\nvalue = 78.5"] ; -184 -> 185 ; -186 [label="mse = 0.0\nsamples = 1\nvalue = 77.0"] ; -185 -> 186 ; -187 [label="mse = 0.0\nsamples = 1\nvalue = 79.0"] ; -185 -> 187 ; -188 [label="week_Fri <= 0.5\nmse = 0.2\nsamples = 3\nvalue = 81.2"] ; -184 -> 188 ; -189 [label="mse = 0.0\nsamples = 2\nvalue = 81.0"] ; -188 -> 189 ; -190 [label="mse = 0.0\nsamples = 1\nvalue = 82.0"] ; -188 -> 190 ; -191 [label="average <= 70.9\nmse = 2.3\nsamples = 4\nvalue = 75.0"] ; -183 -> 191 ; -192 [label="mse = 0.0\nsamples = 1\nvalue = 73.0"] ; -191 -> 192 ; -193 [label="week_Tues <= 0.5\nmse = 1.0\nsamples = 3\nvalue = 75.8"] ; -191 -> 193 ; -194 [label="mse = 0.0\nsamples = 2\nvalue = 75.0"] ; -193 -> 194 ; -195 [label="mse = 0.0\nsamples = 1\nvalue = 77.0"] ; -193 -> 195 ; -196 [label="day <= 26.5\nmse = 4.7\nsamples = 3\nvalue = 71.8"] ; -170 -> 196 ; -197 [label="mse = 0.0\nsamples = 1\nvalue = 68.0"] ; -196 -> 197 ; -198 [label="mse = 0.0\nsamples = 2\nvalue = 73.0"] ; -196 -> 198 ; -199 [label="week_Sun <= 0.5\nmse = 30.2\nsamples = 2\nvalue = 68.5"] ; -169 -> 199 ; -200 [label="mse = 0.0\nsamples = 1\nvalue = 74.0"] ; -199 -> 200 ; -201 [label="mse = 0.0\nsamples = 1\nvalue = 63.0"] ; -199 -> 201 ; -202 [label="temp_1 <= 71.5\nmse = 14.6\nsamples = 8\nvalue = 71.5"] ; -168 -> 202 ; -203 [label="temp_1 <= 69.5\nmse = 0.2\nsamples = 2\nvalue = 67.8"] ; -202 -> 203 ; -204 [label="mse = 0.0\nsamples = 1\nvalue = 68.0"] ; -203 -> 204 ; -205 [label="mse = 0.0\nsamples = 1\nvalue = 67.0"] ; -203 -> 205 ; -206 [label="friend <= 70.5\nmse = 8.7\nsamples = 6\nvalue = 74.0"] ; -202 -> 206 ; -207 [label="temp_1 <= 77.0\nmse = 0.2\nsamples = 2\nvalue = 70.5"] ; -206 -> 207 ; -208 [label="mse = 0.0\nsamples = 1\nvalue = 70.0"] ; -207 -> 208 ; -209 [label="mse = 0.0\nsamples = 1\nvalue = 71.0"] ; -207 -> 209 ; -210 [label="friend <= 77.5\nmse = 3.7\nsamples = 4\nvalue = 75.8"] ; -206 -> 210 ; -211 [label="mse = 0.0\nsamples = 1\nvalue = 79.0"] ; -210 -> 211 ; -212 [label="temp_2 <= 68.5\nmse = 0.2\nsamples = 3\nvalue = 74.7"] ; -210 -> 212 ; -213 [label="mse = 0.0\nsamples = 1\nvalue = 74.0"] ; -212 -> 213 ; -214 [label="mse = 0.0\nsamples = 2\nvalue = 75.0"] ; -212 -> 214 ; -215 [label="temp_2 <= 64.0\nmse = 20.2\nsamples = 4\nvalue = 67.2"] ; -167 -> 215 ; -216 [label="mse = 0.0\nsamples = 1\nvalue = 60.0"] ; -215 -> 216 ; -217 [label="average <= 74.6\nmse = 9.0\nsamples = 3\nvalue = 69.0"] ; -215 -> 217 ; -218 [label="day <= 8.5\nmse = 0.9\nsamples = 2\nvalue = 67.3"] ; -217 -> 218 ; -219 [label="mse = 0.0\nsamples = 1\nvalue = 68.0"] ; -218 -> 219 ; -220 [label="mse = 0.0\nsamples = 1\nvalue = 66.0"] ; -218 -> 220 ; -221 [label="mse = 0.0\nsamples = 1\nvalue = 74.0"] ; -217 -> 221 ; -222 [label="temp_2 <= 70.5\nmse = 7.9\nsamples = 6\nvalue = 67.5"] ; -166 -> 222 ; -223 [label="mse = 0.0\nsamples = 2\nvalue = 71.0"] ; -222 -> 223 ; -224 [label="friend <= 66.5\nmse = 1.7\nsamples = 4\nvalue = 65.6"] ; -222 -> 224 ; -225 [label="day <= 8.0\nmse = 0.9\nsamples = 2\nvalue = 64.3"] ; -224 -> 225 ; -226 [label="mse = 0.0\nsamples = 1\nvalue = 63.0"] ; -225 -> 226 ; -227 [label="mse = 0.0\nsamples = 1\nvalue = 65.0"] ; -225 -> 227 ; -228 [label="temp_1 <= 73.0\nmse = 0.2\nsamples = 2\nvalue = 66.5"] ; -224 -> 228 ; -229 [label="mse = 0.0\nsamples = 1\nvalue = 67.0"] ; -228 -> 229 ; -230 [label="mse = 0.0\nsamples = 1\nvalue = 66.0"] ; -228 -> 230 ; -231 [label="temp_2 <= 79.0\nmse = 5.6\nsamples = 2\nvalue = 84.3"] ; -165 -> 231 ; -232 [label="mse = 0.0\nsamples = 1\nvalue = 81.0"] ; -231 -> 232 ; -233 [label="mse = 0.0\nsamples = 1\nvalue = 86.0"] ; -231 -> 233 ; -234 [label="average <= 76.9\nmse = 27.1\nsamples = 15\nvalue = 80.6"] ; -164 -> 234 ; -235 [label="average <= 76.8\nmse = 22.5\nsamples = 6\nvalue = 83.7"] ; -234 -> 235 ; -236 [label="week_Mon <= 0.5\nmse = 8.8\nsamples = 4\nvalue = 79.6"] ; -235 -> 236 ; -237 [label="friend <= 77.0\nmse = 1.9\nsamples = 3\nvalue = 80.7"] ; -236 -> 237 ; -238 [label="mse = 0.0\nsamples = 1\nvalue = 82.0"] ; -237 -> 238 ; -239 [label="day <= 20.0\nmse = 0.2\nsamples = 2\nvalue = 79.3"] ; -237 -> 239 ; -240 [label="mse = 0.0\nsamples = 1\nvalue = 80.0"] ; -239 -> 240 ; -241 [label="mse = 0.0\nsamples = 1\nvalue = 79.0"] ; -239 -> 241 ; -242 [label="mse = 0.0\nsamples = 1\nvalue = 73.0"] ; -236 -> 242 ; -243 [label="temp_1 <= 83.5\nmse = 1.8\nsamples = 2\nvalue = 87.9"] ; -235 -> 243 ; -244 [label="mse = 0.0\nsamples = 1\nvalue = 87.0"] ; -243 -> 244 ; -245 [label="mse = 0.0\nsamples = 1\nvalue = 90.0"] ; -243 -> 245 ; -246 [label="temp_1 <= 77.5\nmse = 9.8\nsamples = 9\nvalue = 77.2"] ; -234 -> 246 ; -247 [label="temp_2 <= 71.5\nmse = 6.3\nsamples = 6\nvalue = 75.0"] ; -246 -> 247 ; -248 [label="mse = 0.0\nsamples = 1\nvalue = 80.0"] ; -247 -> 248 ; -249 [label="average <= 77.1\nmse = 2.5\nsamples = 5\nvalue = 74.2"] ; -247 -> 249 ; -250 [label="temp_1 <= 71.5\nmse = 0.2\nsamples = 2\nvalue = 75.7"] ; -249 -> 250 ; -251 [label="mse = 0.0\nsamples = 1\nvalue = 75.0"] ; -250 -> 251 ; -252 [label="mse = 0.0\nsamples = 1\nvalue = 76.0"] ; -250 -> 252 ; -253 [label="week_Tues <= 0.5\nmse = 0.2\nsamples = 3\nvalue = 72.7"] ; -249 -> 253 ; -254 [label="mse = 0.0\nsamples = 2\nvalue = 73.0"] ; -253 -> 254 ; -255 [label="mse = 0.0\nsamples = 1\nvalue = 72.0"] ; -253 -> 255 ; -256 [label="temp_1 <= 79.5\nmse = 2.2\nsamples = 3\nvalue = 79.7"] ; -246 -> 256 ; -257 [label="mse = 0.0\nsamples = 1\nvalue = 83.0"] ; -256 -> 257 ; -258 [label="mse = 0.0\nsamples = 2\nvalue = 79.0"] ; -256 -> 258 ; -} \ No newline at end of file diff --git a/Day_5/random_forest_explained/images/tree.png b/Day_5/random_forest_explained/images/tree.png deleted file mode 100644 index cd95b3d..0000000 Binary files a/Day_5/random_forest_explained/images/tree.png and /dev/null differ diff --git a/Day_5/random_forest_explained/rf_drawings.docx b/Day_5/random_forest_explained/rf_drawings.docx deleted file mode 100644 index ae6baff..0000000 Binary files a/Day_5/random_forest_explained/rf_drawings.docx and /dev/null differ diff --git a/Projects/ROOT/500ms_50000s.n0.root b/Projects/ROOT/500ms_50000s.n0.root deleted file mode 100644 index 0a016a8..0000000 Binary files a/Projects/ROOT/500ms_50000s.n0.root and /dev/null differ diff --git a/Projects/ROOT/Root_ex_1_readingFile.ipynb b/Projects/ROOT/Root_ex_1_readingFile.ipynb deleted file mode 100644 index dfa6cef..0000000 --- a/Projects/ROOT/Root_ex_1_readingFile.ipynb +++ /dev/null @@ -1,144 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# Method 1 ROOT version (Needs root installed)\n", - "#import ROOT\n", - "#from ROOT import TChain, TFile, TH1D, TCanvas\n", - "\n", - "# Method 2 pandas version (Needs root installed)\n", - "#import root_pandas\n", - "\n", - "# Method 3 uproot version ***NO ROOT NEEDED***\n", - "import uproot" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'TH1D' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m----------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n", - "\u001b[0;31mNameError\u001b[0m: name 'TH1D' is not defined" - ] - } - ], - "source": [ - "%%time\n", - "histogram = TH1D(\"h1\",\"h1\",50,450,700)\n", - "\n", - "f = TFile.Open(\"500ms_50000s.n0.root\")\n", - "for event in f.Sr90:\n", - " histogram.Fill(event.x1)\n", - " \n", - "c1 = TCanvas(\"c1\",\"c1\",800,600)\n", - "histogram.Draw()\n", - "c1.Draw()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 590 ms, sys: 46.8 ms, total: 637 ms\n", - "Wall time: 446 ms\n" - ] - } - ], - "source": [ - "%%time\n", - "df = root_pandas.read_root(\"500ms_50000s.n0.root\", \"Sr90\")\n", - "df.x1.hist(bins=50,figsize=(16,9),histtype ='stepfilled')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "Sr90 = uproot.open(\"500ms_50000s.n0.root\")[\"Sr90\"]\n", - "Sr90.arrays()[b'x1']\n", - "fig, ax = plt.subplots(figsize=(16,9))\n", - "ax.hist(Sr90.arrays()[b'x1'], bins=50, histtype ='stepfilled')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "\n" - ] - } - ], - "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": "3.8.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/README.md b/README.md index 80ffcee..a2345f3 100644 --- a/README.md +++ b/README.md @@ -1,122 +1,21 @@ -# STEM Python Course +# Python for STEM Course 2020 -University of South Carolina STEM Python Course Summer 2019 +Welcome to the University of South Carolina Python for STEM Course Summer 2020. Please proceed to the `01_Week1` folder if you are a participant in the Week 1-Beginner segment of the course. Please proceed to the `02_Week2` folder if you are a participant in the Week 2-Advanced segment of the course. -Taught by: +# Instructors -@blchavez : blchavez@email.sc.edu +Doug Adams: [@douglasquincyadams](https://github.com/douglasquincyadams) | [da2@email.sc.edu](da2@email.sc.edu) -@douglasquincyadams : da2@email.sc.edu +Ellie Davis: [@daviseleanorj](https://github.com/daviseleanorj) | [eleanord@email.sc.edu](eleanord@email.sc.edu) -@fcashman : fcashman@email.sc.edu +Justin Pierel: [@jpierel14](https://github.com/jpierel14) | [jr23@email.sc.edu](jr23@email.sc.edu) -@jpierel14 : jr23@email.sc.edu +Steve Rodney: [@srodney](https://github.com/srodney) | [srodney@sc.edu](srodney@sc.edu) -@srodney : steve.rodney@gmail.com +Nick Tyler: [@tylern4](https://github.com/tyler4) | [tylerns@email.sc.edu](tylerns@email.sc.edu) -@tylern4 : tylerns@email.sc.edu +Benedicth Ukhueduan: [ukhuedub@email.sc.edu](ukhuedub@email.sc.edu) -### Run with Binder +Yuhan Rao: [@geo-yrao](https://github.com/geo-yrao) | [yrao5@ncsu.edu](yrao5@ncsu.edu) -[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/uofscphysics/STEM_Python_Course/master) -# Mon. Jun 3 : Day 1 - Introduction to Python - -### Monday Morning (10am - noon) -* 9:45 Coffee and Light Breakfast Provided -* 10:00 Introductions, Overview of the week -* 10:10 Installation Check + Personal Goals for the week -* 10:25 Random group assignments -* 10:30 Basic Git -* _10:45 break_ -* 11:00 Python Basics - -#### 12:30 - 1:30 Go get lunch. -* there is space in the Astronomy Center to gather and eat together if you want. -* Basement floor of Jones, rooms 005, 007, 008. -* entering from Main St., go down the steps on the right, Astro Center is first door on the left. - -### Monday Afternoon (1:30 - 3:30pm) -* 1:30 More Python Basics -* _2:30 break (coffee + snacks)_ -* 2:45 Wrap up basic exercises -* 3:00 Git and Github -* 3:30 end -____ -# Tue. Jun 4 : Day 2 - Common Packages for Scientific Programming - -### Tuesday Morning (10am - noon) -* 9:45 Coffee and Light Breakfast Provided -* 10:00 Overview of the day, group assignments -* 10:15 Matplotlib, Numpy, Scipy -* _11:00 break (coffee + snacks)_ -* 11:15 Sympy, Pickle, os/sys/shutil/glob - -#### 12:30 - 1:30 Go get lunch. (eat in the Astro Center if you want) - -### Tuesday Afternoon (1:30 - 3:30pm) -* 1:30 Data I/O, Pandas, Fitting with scipy.optimize -* _2:30 break (coffee + snacks)_ -* 2:45 end of day data + fitting challenges -* 3:30 end - -____ -# Wed. Jun 5 : Day 3 - Optimization and Special Packages - -### Wednesday Morning (10am - noon) -* 9:45 Coffee and Light Breakfast Provided -* 10:00 Overview of the day, group assignments -* 10:15 Multiprocessing -* _11:00 coffee break (coffee + snacks)_ -* 11:15 Speeding up code -- cython -* 11:45 Importing C, C++, Fortran with Python - -#### 12:30 - 1:30 Go get lunch. -* (eat in the Astro Center if you want) - -### Wednesday Afternoon (1:30 - 3:30pm) -* 1:30 Workshop session -* 2:30 break -* 2:45 Workshop session -* 3:30 end - - -____ -# Thu. Jun 6 : Day 4 - Statistics and Fitting with Python - -### Thursday Morning (10am - noon) -* 9:45 Coffee and Light Breakfast Provided -* 10:00 Overview of the day, group assignments -* 10:15 Brief lecture and Q&A on Probability Theory -* 10:30 Model Fitting Activities: chi-squared minimization -* _11:00 coffee break (coffee + snacks)_ -* 11:15 Brief lecture and Q&A on Likelihood Analysis -* 11:30 Introduction to advanced fitting - -#### 12:30 - 1:30 Go get lunch. -* (eat in the Astro Center if you want) - -### Thursday Afternoon (1:30 - 3:30pm) -* 1:30 more Model Fitting Activities: MCMC -* _2:30 break (coffee + snacks)_ -* 2:45 end of day data + fitting challenges -* 3:30 end -____ -# Fri. Jun 7 : Day 5 - Scientific Programming in Python - -### Friday Morning (10am - noon) -* 9:45 Coffee and Light Breakfast Provided -* 10:00 Overview of the day, group assignments -* 10:35 carry on with data + fitting challenges -* _11:00 coffee break (coffee + snacks)_ -* 11:15 10:35 carry on with data + fitting challenges - -#### 12:30 - 1:30 Go get lunch. -* (eat in the Astro Center if you want) - -### Friday Afternoon (1:30 - 3:30pm) -* 1:30 Brief lecture and Q&A on Programming Practice -* 1:45 Setup of coding practice examples -* _2:00 break_ -* 2:15 Working through coding practice examples -* 3:30 end diff --git a/install_readme.pdf b/install_readme.pdf deleted file mode 100644 index cc5d3d7..0000000 Binary files a/install_readme.pdf and /dev/null differ diff --git a/participant_schedule.md b/participant_schedule.md deleted file mode 100644 index 45064e4..0000000 --- a/participant_schedule.md +++ /dev/null @@ -1,100 +0,0 @@ -# Mon. Jun 3 : Day 1 - Introduction to Python - -### Monday Morning (10am - noon) -* 9:45 Coffee and Light Breakfast Provided -* 10:00 Introductions, Overview of the week -* 10:10 Installation Check + Personal Goals for the week -* 10:25 Random group assignments -* 10:30 Basic Git -* _10:45 break_ -* 11:00 Python Basics - -#### 12:30 - 1:30 Go get lunch. -* there is space in the Astronomy Center to gather and eat together if you want. - * Basement floor of Jones, rooms 005, 007, 008. - * entering from Main St., go down the steps on the right, Astro Center is first door on the left. - -### Monday Afternoon (1:30 - 3:30pm) -* 1:30 More Python Basics -* _2:30 break (coffee + snacks)_ -* 2:45 Wrap up basic exercises -* 3:00 Git and Github -* 3:30 end -____ -# Tue. Jun 4 : Day 2 - Common Packages for Scientific Programming - -### Tuesday Morning (10am - noon) -* 9:45 Coffee and Light Breakfast Provided -* 10:00 Overview of the day, group assignments -* 10:15 Matplotlib, Numpy, Scipy -* _11:00 break (coffee + snacks)_ -* 11:15 Sympy, Pickle, os/sys/shutil/glob - -#### 12:30 - 1:30 Go get lunch. (eat in the Astro Center if you want) - -### Tuesday Afternoon (1:30 - 3:30pm) -* 1:30 Data I/O, Pandas, Fitting with scipy.optimize -* _2:30 break (coffee + snacks)_ -* 2:45 end of day data + fitting challenges -* 3:30 end - -____ -# Wed. Jun 5 : Day 3 - Optimization and Special Packages - -### Wednesday Morning (10am - noon) -* 9:45 Coffee and Light Breakfast Provided -* 10:00 Overview of the day, group assignments -* 10:15 Multiprocessing -* _11:00 coffee break (coffee + snacks)_ -* 11:15 Speeding up code -- cython -* 11:45 Importing C, C++, Fortran with Python - -#### 12:30 - 1:30 Go get lunch. -* (eat in the Astro Center if you want) - -### Wednesday Afternoon (1:30 - 3:30pm) -* 1:30 Workshop session -* 2:30 break -* 2:45 Workshop session -* 3:30 end - - -____ -# Thu. Jun 6 : Day 4 - Statistics and Fitting with Python - -### Thursday Morning (10am - noon) -* 9:45 Coffee and Light Breakfast Provided -* 10:00 Overview of the day, group assignments -* 10:15 Brief lecture and Q&A on Probability Theory -* 10:30 Model Fitting Activities: chi-squared minimization -* _11:00 coffee break (coffee + snacks)_ -* 11:15 Brief lecture and Q&A on Likelihood Analysis -* 11:30 Introduction to advanced fitting - -#### 12:30 - 1:30 Go get lunch. -* (eat in the Astro Center if you want) - -### Thursday Afternoon (1:30 - 3:30pm) -* 1:30 more Model Fitting Activities: MCMC -* _2:30 break (coffee + snacks)_ -* 2:45 end of day data + fitting challenges -* 3:30 end -____ -# Fri. Jun 7 : Day 5 - Scientific Programming in Python - -### Friday Morning (10am - noon) -* 9:45 Coffee and Light Breakfast Provided -* 10:00 Overview of the day, group assignments -* 10:35 carry on with data + fitting challenges -* _11:00 coffee break (coffee + snacks)_ -* 11:15 10:35 carry on with data + fitting challenges - -#### 12:30 - 1:30 Go get lunch. -* (eat in the Astro Center if you want) - -### Friday Afternoon (1:30 - 3:30pm) -* 1:30 Brief lecture and Q&A on Programming Practice -* 1:45 Setup of coding practice examples -* _2:00 break_ -* 2:15 Working through coding practice examples -* 3:30 end diff --git a/requirements.txt b/requirements.txt index d03da27..5a19b5b 100644 --- a/requirements.txt +++ b/requirements.txt @@ -6,4 +6,9 @@ matplotlib sympy scipy emcee -uproot \ No newline at end of file +uproot +nltk +geopandas +descartes +pillow +seaborn diff --git a/startup.sh b/startup.sh index adda44d..531aec8 100644 --- a/startup.sh +++ b/startup.sh @@ -1,5 +1,7 @@ #!/usr/bin/env bash +export PATH=${PATH}:${HOME}/.local/bin + pip install --upgrade pip pip install -r requirements.txt